Metabolic Syndrome and Clinical Markers in Obesity

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Obesity-Related Metabolic Syndrome:
Investigations into Novel Clinical
Markers and Possible Causes
Anne-Thea McGill
A thesis submitted in partial fulfilment of the requirements for the degree of Doctor of
Philosophy in Sciences/Medicine and Health Science, The University of Auckland, 2011
ABSTRACT
Obesity-related
metabolic
syndrome
(MetS)
increases
risks
for
degenerative
cardiovascular disease (CVD), and has become widespread. Efforts to stem the increase
of obesity are inadequate. New approaches to the problem may be required.
Novel CVD Risk and Prediction Markers were investigated for use in enhancing the
utility of MetS. Subsequently, a unifying hypothesis on causes of MetS was developed.
MetS comprises central obesity, hypertension, dyslipidaemia and hyperglycaemia, but
insufficiently reflects underlying oxidative, metabolic, inflammatory, and nutritional
stresses. Novel CVD laboratory (1) Risk Markers; consisting of clinical routine screening
haematology and
monocytes;
biochemistry (CRSHaem&Biochem):
lymphocytes;
eosinophils;
erythrocyte
Leukocytes:
sedimentation
neutrophils;
rate
(ESR);
haemoglobin A1c (HbA1c); urate; ferritin; liver function tests (LFT): alkaline phosphatase
(AlkPhos); alanine transferase (ALT); aspartate transferase (AST); gamma glutamyl
transferase (γGT); bilirubin (2) Protective Markers; (a)
serum fat cell-derived,
adiponectin (Adpn) with oligomers (high, medium, low molecular weight (HMW,
MMW, LMW) (b) serum fat soluble vitamins (sFSVitamins) beta carotene (sβCaro),
vitamins (sVit)D, sVitA, sVitE, and VitK(INR) were investigated.
Baseline, and mean over 6months (m), oxidants HbA1c and urate were strongly related to
MetS marker count, a MetS index. Over 6m, on wide-ranging multivariable mixed
modelling, γGT and ESR changed with MetS marker count.
Adpn showed anti- and pro-inflammatory correlations. sVitA and sVitE correlated with
MetS marker count, and dyslipidaemia. sβCaro and sVitD correlated with protective
markers. Thus 1) HbA1c, urate, γGT, ESR, and 2) sβCaro, sVitD may be acceptable
Novel CVD Risk and Protective Markers, respectively, for use with MetS.
The unifying hypothesis on obesity and MetS was predicated on humans evolving
proportionately large, energy demanding brains requiring co-adaptive mechanisms to: (1)
increase dietary energy by developing strong neural self-reward/motivation systems for
ii
the acquisition of energy dense food and (2) economise on body energy metabolism by
the co-option of many of antioxidant phytonutrients to confer long-lived cell protection.
The study indicated that strong risk markers in MetS are oxidant, and associate with
degenerative change, and antioxidant cytoprotection is augmented by plant food
micronutrients, such as food-derived or dietary βCaro (fβCaro). The unifying hypothesis
was compatible with the study results. Whole-food diets need studying for prevention and
reversal of MetS.
iii
ACKNOWLEDGEMENTS
I thank my supervisor, Associate Professor Sally Poppitt for her time and help with the
study, and especially for her perseverance in advice and constancy in attention to thesis
writing.
Thanks go to my co-supervisor Dr Alan Beedle for keeping a keen eye on the physiology
of energy metabolism and evolution, and Graeme Wake for guidance on systems theory
mathematical modelling. I am grateful to current and previous co-supervisors Associate
Professors Stephen Buetow and Raina Elley for their support of me and my ideas and
temporary supervisor Professor Bruce Arroll for his oversight.
I am very grateful to my statistician, Ms Joanna Stewart, for her good humour, and
patience in correcting my statistical thinking and running up-to-date analyses that
explained what I wanted.
Thanks to my proof readers Katy Wiessing and Angela Chong for their attention to detail.
My appreciation goes to all the team at the Human Nutrition Unit, and collaborators, for
all their work on the Diet and Health Weight Loss study (which as the randomised
placebo controlled study was named the Effects of Chitosan on Health and Obesity
(ECHO) trial and their continued support.
I thank my clinic patients who have given me ideas for improvement in obesity
management.
Lastly, my thanks, and appreciation, go to my family and friends for listening to my ideas,
and their years of general support.
iv
PUBLICATIONS ARISING FROM WORK DESCRIBED
IN THE THESIS
Papers
McGill A-T. Malnutritive Obesity ('Malnubesity') (2008) Is It Driven by Human Brain
Evolution? Metabolic Syndrome and Related Disorders; 6: (4):241-246
McGill A-T, Stewart JM, Lithander FE, Strik CM, Poppitt SD (2008) Relationships of Low
Serum Vitamin D3 with Anthropometry and Markers of the Metabolic Syndrome and
Diabetes in Overweight and Obesity. Nutrition Journal. 7 (4):
Ni-Mhurchu C, Poppitt SD, McGill A-T, Leahy FE, Bennett DA, Lin RB, Ormrod D,
Ward L, Strik C, Rodgers A (2004) The effect of the dietary supplement, Chitosan, on body
weight: a randomised controlled trial in 250 overweight and obese adults. International
Journal of Obesity; 28: 1149-56.
Abstracts
McGill A-T, Wake G Beedle A. Energy and Nutrient Modelling of Human Evolution.
(Poster, Abstract Number P56) Proceedings of the Australia and New Zealand Obesity
Society Annual Scientific Meeting (ANZOS ASM) Sydney, Australia. Oct 2010. Obesity
Research and Clinical Practice 4 (Suppl 1): S28,2010
McGill A-T, Stewart JM, Lithander FE and Poppitt SD. (Oral, Abstract Number S39) Interrelationships of changes in weight and metabolic syndrome. Proceedings of the Australia
and New Zealand Obesity Society Annual Scientific Meeting (ANZOS ASM) Sydney,
Australia. Oct 2010. Obesity Research and Clinical Practice 4 (Suppl 1): S28,2010
McGill A-T, Stewart JM, Lithander FE and Poppitt SD (Poster Abstract) Vitamin E in the
Metabolic Syndrome. Proceedings of the Australia and New Zealand Obesity Society
Annual Scientific Meeting (ANZOS ASM) part of the Health and Medical Research Council
Meeting. Brisbane, Australia. Nov 2008.
McGill A-T, Stewart JM, Lithander FE and Poppitt SD (Poster Abstract) Adiponectin
Oligomers, Gender and Beta-Carotene in the Metabolic Syndrome North American
Obesity Society Annual Scientific Meeting. Phoenix, USA Oct 2008. Obesity 16 (Suppl 1):
S272 2008
McGill A-T, Stewart JM, Lithander FE and Poppitt SD (Abstract Accepted) Fat Soluble
Vitamins as Metabolic Syndrome Markers. Central European Congress on Obesity: From
Nutrition to Metabolic Syndrome. Karlovy Vary, Czech Republic, Sep 2008
v
McGill A-T, Stewart JM, Leahy FE, Poppitt SD. (Oral Abstract) Adiponectin, Adiponectin
Oligomers and Fat Soluble Vitamins in the Metabolic Syndrome Proceedings of the
Australasian Society for the Study of Obesity (ASSO) Annual Scientific Meeting.2007
McGill A-T, Stewart JM, Leahy FE, Poppitt SD. (Oral Abstract), Adiponectin and Fat
Soluble Vitamins in the Metabolic Syndrome “From Genetics to Community. What is
Obesity?” Sept 2006, Auckland, New Zealand. Satellite to 10th International Congress on
Obesity 2006.
McGill A-T, Poppitt SD, Leahy FE, Wang Y, Cooper GJS & Xu A. (Oral Abstract), High
Molecular Weight Adiponectin is Inversely Associated with the Metabolic Syndrome in
Obesity and Changes During Weight Loss. 10th International Congress on Obesity Sep 2006,
Sydney, Australia. Obesity Reviews 7(Suppl. 2): 39-98 2006.
vi
TABLE OF CONTENTS
ABSTRACT ........................................................................................................................... II
ACKNOWLEDGEMENTS .....................................................................................................IV
PUBLICATIONS ARISING FROM WORK DESCRIBED IN THE THESIS ................................... V
LIST OF TABLES ............................................................................................................... XII
LIST OF FIGURES ............................................................................................................. XIII
LIST OF APPENDICES ....................................................................................................... XIV
LIST OF ABBREVIATIONS ................................................................................................ XIV
CHAPTER 1. INTRODUCTION AND BACKGROUND ............................................................... 1
1.1
Introduction .......................................................................................................... 1
1.1.1 Scope................................................................................................................ 1
1.1.1.1 Thesis Structure ......................................................................................... 2
1.1.2 General Introduction ........................................................................................ 2
1.2
Obesity.................................................................................................................. 6
1.2.1 Obesity - Definitions........................................................................................ 6
1.2.2 Obesity - Epidemiology and Natural History .................................................. 8
1.2.2.1 Obesity - Ethnicity and Socio-Economic Status ........................................ 9
1.2.3 Causes of Obesity .......................................................................................... 10
1.3
Central Obesity Related Metabolic Syndrome ................................................... 11
1.3.1 Definitions of the Metabolic Syndrome ........................................................ 13
1.3.2 Obesity and Type II Diabetes ........................................................................ 14
1.3.3 Obesity and Cardiovascular Disease ............................................................. 16
1.4
Physiology of Metabolic Syndrome – Adipose Tissue, Macro- and Micronutrients .............................................................................................................. 19
1.4.1 Adipose Tissue - Body Fat Distribution and Metabolism ............................. 19
1.4.2 Nutrients in Metabolic Syndrome .................................................................. 20
1.4.2.1 Lipids ....................................................................................................... 20
1.4.2.2 Protein ...................................................................................................... 23
1.4.2.3 Carbohydrate ............................................................................................ 24
1.4.2.4 Micronutrients .......................................................................................... 27
1.4.2.5 Diets: Palaeolithic to Westernised, and Metabolic Health ....................... 28
1.4.3 Pathophysiology of Metabolic Syndrome ..................................................... 31
1.5
Novel Cardiovascular Disease Risk and Protective Markers ............................. 32
1.5.1 Novel Metabolic Cardiovascular Disease Risk Markers ............................... 33
1.5.1.1 Clinical Routine Screening Haematology and Biochemistry .................. 33
1.5.1.2 Haematology ............................................................................................ 33
1.5.1.3 Biochemistry Long Term Plasma Glucose Indicator/Oxidant ................. 34
1.5.2 Novel Metabolic Cardiovascular Disease Protective Markers ...................... 37
1.5.2.1 Adiponectin .............................................................................................. 38
1.5.2.2 Serum Fat Soluble Vitamins .................................................................... 39
1.5.2.3 Vitamin D................................................................................................. 40
1.5.2.4 Carotenoretinoids – Beta-Carotene and Vitamin A ................................. 41
1.5.2.5 Tocopherols and Vitamin E ..................................................................... 43
vii
Theories on the Causes of Obesity and Metabolic Syndrome............................ 43
1.6
1.6.1 Rationale for studies ...................................................................................... 44
1.6.1.1 What is Known ........................................................................................ 44
1.6.1.2 What is Needed ........................................................................................ 45
1.7
Hypotheses ......................................................................................................... 46
1.7.1 Overarching Hypotheses ................................................................................ 46
1.7.2 The Clinically Tested Hypotheses ................................................................. 47
1.7.2.1 The Clinically Tested Primary Hypotheses ............................................. 47
1.7.2.2 The Clinically Tested Secondary Hypotheses ......................................... 47
1.7.3 The Unifying Hypotheses .............................................................................. 48
1.7.4 Aims and Objectives ...................................................................................... 48
CHAPTER 2. METHODS ...................................................................................................... 51
2.1
Introduction ........................................................................................................ 51
2.2
Study Designs: Rationale and Development ...................................................... 51
2.2.1 Study Concept Development ......................................................................... 51
2.2.1.1 Novel CVD Risk Markers – Concepts and Statistical Analyses Rationale
................................................................................................................. 52
2.2.1.2 Novel CVD Protective Markers – Concepts and Statistical Analyses
Rationale .................................................................................................. 52
2.2.1.3 Diet&Health-Weight Loss Trial............................................................... 53
2.3
Contributors to the Dietary Fibre and Lifestyle for Health Studies ................... 54
2.3.1 Author’s Contribution to the Studies ............................................................. 54
2.3.1.1 Collaborations .......................................................................................... 57
2.3.1.2 Study Site ................................................................................................. 58
2.4
Dietary Fibre and Lifestyle for Health – Weight Loss Trial .............................. 59
2.4.1 Study participants .......................................................................................... 59
2.4.1.1 Eligibility criteria ..................................................................................... 59
2.4.1.2 Ethical Approval and Study Registration ................................................ 60
2.4.2 Study Procedures ........................................................................................... 60
2.4.2.1 Study Visits and Procedures .................................................................... 60
2.4.3 Anthropometry............................................................................................... 64
2.4.3.1 Weight, Waist, Height, Bioelectrical Impedance and Blood Pressure..... 64
2.4.3.2 Randomisation, Blinding, Medication Dosing and Dispensing ............... 66
2.4.3.3 Trial Medication Details .......................................................................... 66
2.4.4 Laboratory...................................................................................................... 67
2.4.4.1 Laboratory Sample Analyses ................................................................... 68
2.4.4.2 Biochemical Analytes .............................................................................. 68
2.4.5 Fibre and Faecal Fat Loss Mechanism Substudy .......................................... 68
2.4.5.1 Method ..................................................................................................... 69
2.4.5.2 Procedures ................................................................................................ 69
2.4.5.3 Eligibility Criteria .................................................................................... 69
2.4.6 Demographic, Health Status and Lifestyle Questionnaire Analyses ............. 70
viii
2.4.6.1 Demographic, Lifestyle, Quality of Life and Eating Attitude
Questionnaires - Analytical Methods ...................................................... 70
2.4.6.2 Adverse Events Protocols ........................................................................ 72
2.5
Dietary Fibre and Lifestyle for Health – Novel Cardiovascular Disease Markers
72
2.5.1 Design, Population, Eligibility, Procedures, Anthropometry ........................ 73
2.5.2 Novel Cardiovascular Disease Risk Markers - Clinical Routine Screening
Biochemistry and Haematology .................................................................... 74
2.5.2.1 Laboratory Analyses ................................................................................ 74
2.6
Novel Cardiovascular Disease Protective Markers ............................................ 76
2.6.1 Adiponectin, Adiponectin and Oligomers: Novel Cardiovascular Disease
Protective Markers ........................................................................................ 76
2.6.2 Serum Vitamin D, Beta Carotene, Vitamin A and Vitamin E ....................... 78
CHAPTER 3. THE DIETARY FIBRE AND LIFESTYLE FOR HEALTH – WEIGHT LOSS TRIAL:
A RANDOMISED PLACEBO-CONTROLLED WEIGHT LOSS MODEL .................................. 81
3.1
Introduction ........................................................................................................ 81
3.1.1 Hypotheses ..................................................................................................... 82
3.1.2 Aims............................................................................................................... 82
3.2
Methods .............................................................................................................. 83
3.2.1 Method Rationale & Summary ...................................................................... 83
3.2.2 Statistical Analyses ........................................................................................ 84
3.3
Results ................................................................................................................ 86
3.3.1 Participant characteristics .............................................................................. 86
3.3.2 Primary Outcome ........................................................................................... 87
3.3.3 Secondary Outcomes ..................................................................................... 87
3.3.4 Other measures .............................................................................................. 93
3.4
Fibre and Faecal Fat Loss Mechanism Substudy ............................................... 95
3.4.1 Method ........................................................................................................... 95
3.4.2 Laboratory Work ........................................................................................... 95
3.4.2.1 Results ...................................................................................................... 95
3.5
Discussion .......................................................................................................... 97
3.5.1 General Comments ........................................................................................ 97
3.6
Conclusion ........................................................................................................ 103
CHAPTER 4. NOVEL CARDIOVASCULAR DISEASE RISK MARKERS IN THE METABOLIC
SYNDROME ...................................................................................................................... 104
4.1
Introduction ...................................................................................................... 104
4.1.1 Hypothesis ................................................................................................... 106
4.1.2 Aims............................................................................................................. 106
4.2
Methods ............................................................................................................ 107
4.2.1 Method Rationale & Summary .................................................................... 107
ix
4.2.2 Laboratory Procedures and Analyses .......................................................... 108
4.2.3 Statistical Analyses ...................................................................................... 108
4.2.3.1 General Comments................................................................................. 108
4.2.3.2 Demographic, Health Status and Lifestyle Questionnaires ................... 109
4.2.3.3 Anthropometry ....................................................................................... 109
4.2.3.4 Metabolic Syndrome Comparisons ........................................................ 109
4.2.3.5 Clinical Routine Screening Haematology and Biochemistry, and
Metabolic Syndrome ............................................................................. 111
4.3
Results .............................................................................................................. 113
4.3.1 Demographic, Health Status, Lifestyle and Quality of Life ........................ 113
4.3.1.1 Baseline Characteristics – Anthropometry and Questionnaire Data...... 113
4.3.2 Anthropometry Variability .......................................................................... 116
4.3.3 Metabolic Syndrome Comparisons.............................................................. 116
4.3.3.1 Metabolic Syndrome Comparisons ........................................................ 120
4.3.3.2 Clinical Routine Screening Haematology and Biochemistry and
Metabolic Syndrome ............................................................................. 127
4.4
Discussion ........................................................................................................ 135
CHAPTER 5. NOVEL CARDIOVASCULAR DISEASE PROTECTIVE MARKERS .................. 144
5.1
Introduction ...................................................................................................... 144
5.1.1 Hypothesis ................................................................................................... 146
5.1.2 Aims............................................................................................................. 146
5.2
Methods ............................................................................................................ 147
5.2.1 Method Rationale & Summary .................................................................... 147
5.2.2 Clinical Study Procedures............................................................................ 148
5.2.3 Laboratory Procedures and Analyses .......................................................... 148
5.2.4 Statistical Analyses ...................................................................................... 149
5.2.4.1 Adiponectin and Oligomers ................................................................... 149
5.2.4.2 The Fat Soluble Vitamins ...................................................................... 150
5.3
Results .............................................................................................................. 152
5.3.1 Adiponectin and Oligomers ......................................................................... 152
5.3.2 The Fat Soluble Vitamins ............................................................................ 158
5.4
Discussion ........................................................................................................ 170
CHAPTER 6. CAUSES OF OBESITY AND METABOLIC SYNDROME .................................. 181
6.1
Introduction ...................................................................................................... 181
6.1.1 Scope............................................................................................................ 181
6.1.2 The Need for a New Theory on Causes of Obesity ..................................... 181
6.1.3 Background to Theory Development .......................................................... 183
6.2
Aims ................................................................................................................. 185
6.3
Methods ............................................................................................................ 185
6.4
Influences from Thesis Study on the Unifying Hypothesis ............................. 187
6.4.1 The Unifying Hypothesis on Causes of Obesity and Metabolic Syndrome 187
6.4.1.1 Unifying Hypothesis Corollary: Evolutionary Adaptations Outwitted? 189
x
Current Theories on Causes of Obesity and Metabolic Syndrome .................. 191
6.5
6.5.1 Putative Contributors to Obesity ................................................................. 192
6.6
Evolution of the Human Brain ......................................................................... 196
6.6.1 Theories of Increased Energy Conservation Adaptations to Encephalisation
..................................................................................................................... 196
6.7
Increased Energy Uptake from Environment via Energy Dense Food Reward
System - First Part of the Unifying Hypothesis ............................................... 198
6.7.1.1 Evolution of Epigenetic and Genetic Brain Control Mechanisms ......... 198
6.7.1.2 The Mesolimbic System and Appetite Control ...................................... 199
6.7.1.3 Genetic and Epigenetic Dopamine Control ........................................... 201
6.8
Micronutrient Deficiency in Central Obesity – Second Part of the Unifying
Hypothesis ........................................................................................................ 208
6.8.1 Basic Principles in Energy Metabolism ....................................................... 208
6.8.1.1 Reactive Oxygen and Nitrogen Species ................................................. 208
6.8.1.2 Oxidative Stress and Central Obesity .................................................... 210
6.8.1.3 Methods of Dealing with Reactive Oxygen and Nitrogen Species ........ 212
6.8.1.4 Phytochemicals in Human Diets ............................................................ 217
6.8.1.5 NRF2-Mediated Indirect and Direct Homeostatic Control System ....... 222
6.8.2 The Unifying Hypothesis and the Current Westernised Environment ........ 226
6.8.2.1 A Whole-Food Diet Prescription Derived from the Unifying Hypothesis
and Consequences ................................................................................. 228
6.9
Future Directions .............................................................................................. 229
6.9.1 Sketch Plan for Whole-Food Diet Clinical Studies ..................................... 230
6.9.1.1 Introduction ............................................................................................ 230
6.9.1.2 Community Diet Study A) A 6m Randomised, Parallel, Adherence and
Health Change Study ............................................................................. 230
6.9.1.3 Residential Diet Study - Proof of Whole-Food Diet/ Health Concept ... 230
6.10 Modelling Energy Budgets with Incorporation of Food Micronutrients for Lean
and Obese Humans ........................................................................................... 231
6.10.1.1 Background ............................................................................................ 231
6.10.1.2 Hypothesis .............................................................................................. 233
6.10.1.3 Questions for review .............................................................................. 233
6.10.1.4 Aim ........................................................................................................ 233
6.11 Discussion ........................................................................................................ 243
CHAPTER 7. DISCUSSION AND CONCLUSION .................................................................. 246
7.1
7.2
Discussion ........................................................................................................ 246
Conclusion ........................................................................................................ 253
APPENDICES .................................................................................................................... 255
REFERENCES ................................................................................................................... 288
xi
LIST OF TABLES
Table 1-1. NCEP-Derived Metabolic Syndrome Definitions .......................................................... 4
Table 1-2. Classifications of Obesity ............................................................................................... 8
Table 1-3. Obesity in New Zealand Adults by Ethnic Group (Unadjusted) .................................. 10
Table 2-1. NHANES BMI Classification ....................................................................................... 60
Table 2-2. Laboratory Test Collection and Analytes ..................................................................... 68
Table 2-3. Faecal Collection Time Line......................................................................................... 69
Table 2-5. Criteria for Selecting the Adpn30 Subset for Oligomer Analysis .................................. 77
Table 2-7. Laboratory Test Collection and Analytes: Fat Soluble Vitamins ................................. 79
Table 3-1. Baseline Characteristics: All, Chitosan vs. Placebo ..................................................... 88
Table 3-2. Change in Weight Sensitivity Analyses: Chitosan vs. Placebo .................................... 89
Table 3-3. Change, Baseline to 6m BP & Lipids Sensitivity Analyses: Chitosan vs. Placebo ...... 91
Table 3-4. Secondary & other Outcomes - Anthropometry, BP & Laboratory Analytes over 6m:
Chitosan vs. Placebo ...................................................................................................................... 92
Table 3-5. Adverse Events by Treatment Group............................................................................ 94
Table 3-6. Baseline Characteristics of the Fibre&Fat Loss Substudy Completers: n=29 ............. 96
Table 3-7. Clinical Trials of Chitosan for Weight Loss or Cholesterol Lowering ......................... 99
Table 3-8. Summary of % Weight Loss and Cholesterol Lowering Trials .................................. 101
Table 4-1. Baseline Characteristics .............................................................................................. 114
Table 4-2. Baseline MetS Marker & CRSHaem&Biochem......................................................... 120
Table 4-3. Baseline, NCEP, NCEP/ADA & IDF/JIS MetS: Gender &Age Groups .................... 121
Table 4-4. Baseline, MetS Marker Frequency: NCEP, NCEP/ADA & IDF/JIS MetS ................ 122
Table 4-5. 6m Final Visit NCEP, NCEP/ADA & IDF/JIS MetS: Gender & Age Groups........... 123
Table 4-6. Change, Baseline to 6m. MetS, arker & MetS Marker Count: Women n=117, Men
n=30 ............................................................................................................................................. 123
Table 4-7. Baseline. MetS Marker Count, Excluding MetS Marker Waist by Gender ................ 124
Table 4-8. Baseline. MetS Marker Count, Excluding MetS Marker Waist by Ethnicity ............. 124
Table 4-9. Baseline. Clinical Routine Screening Haematology/Biochemistry vs. MetS Marker
Count, Simple and Partial Correlations ........................................................................................ 128
Table 4-10. Relationships of MetS Markers and MetS Marker Count with Demographic, Health
Status and Lifestyle Parameters ................................................................................................... 132
Table 4-11. Relationships of MetS Markers and MetS Marker Count vs. Clinical Routine
Screening Haematology/Biochemistry – Inflammatory ............................................................... 133
Table 4-12. Relationships of MetS Markers and MetS Marker Count vs. Clinical Routine
Screening Haematology/Biochemistry – Oxidant ........................................................................ 134
Table 5-1. Baseline, 6m & Change - Adpn250 & Adpn30: All, Women & Men............................ 152
Table 5-2. General Linear Model. Baseline Adpn250: Demography, Lifestyle & Quality of Life 154
Table 5-3. Baseline Adpn250 vs. Study Parameters - Correlations: All, Women & Men ............. 155
Table 5-4. Change in HMW30 vs. Study Parameters, Correlations: All, Women & Men ............ 157
Table 5-5. Baseline Adpn250 vs. Adpn30 - Correlations: All, Women & Men .............................. 158
Table 5-6. Serum Lipids vs Serum Fat Soluble Vitamins – Correlations .................................... 158
Table 5-7. Baseline, 6m & Change – Serum, Dietary, & Numbers Taking Supplemental
FSVitamins: All, Women & Men ................................................................................................ 161
xii
Table 5-8. Demographic, Health Status, Lifestyle, Quality of Life and Eating Attitudes with
Serum Fat Soluble Vitamins ........................................................................................................ 162
Table 5-9. Baseline Serum Fat Soluble Vitamins vs. Study Parameters ...................................... 164
Table 5-10. Change in sβCaro in All & Women, & Change in sVitE in All & Men vs. Study
Parameters .................................................................................................................................... 166
Table 5-11. Baseline Serum Fat Soluble Vitamin vs. Metabolic Syndrome Marker Count –
Ordinal Logistic Regression ......................................................................................................... 169
Table 6-1. Estimate of General Oxidant and Inflammatory Properties of Laboratory Markers in the
Diet&Health-Novel CVD Marker Study...................................................................................... 188
Table 6-2. Patterns of Association with CVD Risk or Protective Markers by Gender ................ 189
Table 6-3. Diet Comparisons: Forager and Westernised ............................................................. 225
Table 6-4. Energy Content Body Compartments of Female Hominoids ..................................... 236
LIST OF FIGURES
Figure 1-1. A Selection of Factors Involved in Hypertension ....................................................... 17
Figure 1-2. Structure of Chitin, Chitosan and Cellulose ................................................................ 26
Figure 2-1. Clinical Studies – Plan of Components ....................................................................... 55
Figure 2-2.Diet&Health–Weight Loss Trial and Fibre&Fat Loss Study Timeline ........................ 56
Figure 2-3. Diet&Health-Novel CVD Risk and Protective Marker Studies Timeline ................... 56
Figure 3-1. Study Recruitment ....................................................................................................... 86
Figure 3-2.Weight & Waist over 6m (A) Weight Observed (B) Weight LOCF, ITT & (C) Waist
LOCF, ITT: Chitosan vs. Placebo .................................................................................................. 89
Figure 3-3. BMI over 6m (A) Observed and (B) LOCF, ITT: Chitosan vs. Placebo..................... 90
Figure 3-4. BP over 6m ITT (A) SBP (B) DBP: Chitosan vs. Placebo.......................................... 90
Figure 3-5. Serum Lipids over 6m (A) TC (B) LDL-C: Chitosan vs. Placebo (LOCF ITT) ......... 92
Figure 3-6. Baseline, 6m & Change in Faecal Fat Excretion: Chitosan & Placebo, n=29............. 96
Figure 3-7. Change in sFSVitamins: Fibre&Fat Loss Substudy, n=29 .......................................... 96
Figure 3-8. Change in Serum Fat Soluble Vitamins vs. Faecal Fat: Fibre&Fat Loss Substudy,
n=29 ............................................................................................................................................... 97
Figure 4-1. Weight &Waist of Individuals at Baseline, 3m & 6m: Women, n=117 & Men, n=30
...................................................................................................................................................... 117
Figure 4-2. BMI & %Fat of Individuals at Baseline, 3m & 6m: Women, n=117 & Men, n=30 . 118
Figure 4-3. % Change Correlations Weight vs. Waist & %Fat: All, Women & Men ................. 119
Figure 4-4. Baseline (A) NCEP, NCEP/ADA & IDF/JIS MetSMCt & MetS ............................. 121
Figure 4-5. Baseline to 6m Change, Weight & Waist in MetS: Women & Men ......................... 125
Figure 4-6. Change Baseline to 6m Correlations: Weight vs. MetSM, MetSMCt & MetS ......... 126
Figure 5-1. Change in Adpn30 vs. Change in MetS Marker Count: Women & Men ................... 153
Figure 5-2. Baseline sFSVitamin vs. Waist ................................................................................. 165
Figure 6-1. A Version of Human Phylogeny ............................................................................... 183
Figure 6-2. Relative Brain Size & High Dietary Quality, or High Energy Diets, in Primates ..... 184
Figure 6-3. Reward Pathways - Hypothalamic Nuclei to Frontal Cortex .................................... 200
Figure 6-4. Excess Lipid in Adipocytes: Endoplasmic Reticulum & Mitochondrial Stress ........ 212
Figure 6-5. Antioxidant Vitamins in Central Obesity .................................................................. 214
Figure 6-6. Phytochemicals and Reactive Oxygen and Nitrogen Species: Antioxidant .............. 215
xiii
Figure 6-7. Phytoalexins .............................................................................................................. 218
Figure 6-8. Interrelations of Direct, Indirect and Bifunctional Antioxidants ............................... 223
Figure 6-9. Environment and Diet - Past and Present .................................................................. 227
Figure 6-11. Two Compartment Model – Growth or Increase in Biomass .................................. 238
Figure 6-10. An Engineering View of the Nrf2-Mediated Redox Homeostatic Control System 241
LIST OF APPENDICES
Appendix 1. Questionnaire Forms ............................................................................................... 255
Appendix 2. Adverse Events Form .............................................................................................. 273
Appendix 3. Evaluable Numbers and Normal Range for Laboratory Tests................................. 275
Appendix 4. Expanded Tables ..................................................................................................... 276
LIST OF ABBREVIATIONS
ACE
ADA
adipocyte
Adpn
Adpn30
Adpn250
ADHB
AE/SAE
AGE
AHA
Akt
Akt/PKB
ALE
AlkPhos
ALT
AMP
AMPK
ANCOVA
ANZFA
ANZOS
ARB
ASM
ASSO
AST
ATP
AUC
B6
B-cell
BIA
BMI
BMR
BP
C-C
CABG
Angiotensin converting enzyme
American Diabetes Association
adipose cell
adiponectin
the subgroup of participants in whom Adpn oligomers were analysed at baseline
(n=30) and 6 months (n=20)
the total study group of 250 participants
Auckland District Health Board
adverse event/serious adverse event
advanced glycation end-products
American Heart Association
serine/threonine kinase
serine/threonine protein kinase
advanced lipoxidation end-products
alkaline phosphatase
alanine transaminase or transferase
adenosine monophosphate
adenosine monophosphate activated protein kinase
analysis of covariance
Australia and New Zealand Food Standards Code – see FSANZ
Australia and New Zealand Obesity Society
angiotensin receptor blocker
Annual Scientific Meeting
Australasian Society for the Study of Obesity
aspartate transaminase or transferase
adenosine triphosphate
area under the curve
pyridoxine
bone-cell
bioimpedance analysis
body mass index
basal metabolic rate
blood pressure
carbon to carbon atom link
coronary artery bypass graft
xiv
CAM
CAT
CHD
CHO
CI
CKD
CMP-1
COX-2
CRP
CRSHaem&Biochem
CVA
CVD
d
DAT
DSMB
DBP
DEXA
DHA
DNA
DsbA-L
EAT-12
ECG
EDTA
EGCG
EGIR
ELISA
EPA
ESR
ER
f
FA/FFA
FBC
%Fat
FDA
FMHS
FPG
FSANZ
FSVitamin
FRAP
GCP
GLmM
GLUT4
GR
GRAS
GSH
GSK-3
GSX
H2O2
HbA1c
HDL-C
HMW
HNU
HO-1
HOMA-IR
HPA
HPLC
hr
HRC
IASSO
IB
complementary and alternative medicine
catalase
coronary heart disease
carbohydrate
confidence interval
chronic kidney disease
chemoattractant protein-1
cyclooxygenase-2 medications
C-reactive protein
clinical routine screening haematology and biochemistry
cerebrovascular accident
cardiovascular disease
day
dopamine transporter
data safety monitoring board
diastolic blood pressure
dual-energy X-ray absorptiometry
docosahexaenoic acid
deoxyribonucleic nucleic acid
disulfide bond-A oxidoreductase- like protein
Eating Attitudes Test – 12 question
epicatechin gallate
ethylenediamine tetra-acetic acid
(–)-epigallocatechin-3-gallate
European Group for the Study of Insulin Resistance
enzyme linked immunosorbent assay
ecosapentaenoic acid
erythrocyte sedimentation rate
endoplasmic reticulum (RER rough endoplasmic reticulum)
food derived/dietary
fatty acid/free fatty acid
full blood count
body fat percentage
Food and Drug Administration
Faculty of Medical and Health Science
fasting plasma glucose
Food Standards Australia New Zealand
fat soluble vitamin
ferric acid reducing potential
Good Clinical Practice
general linear mixed model
Glucose transporter type 4
glutathione reductase
generally regarded as safe
reduced glutathione
glycogen synthase kinase
glutathione peroxidase
hydrogen peroxide
haemoglobin A1c
high density lipoprotein-cholesterol
high molecular weight adiponectin
Human Nutrition Unit
haem oxygenase-1
homeostasis model analysis-insulin resistance
hypothalamic-pituitary-adrenal
high performance liquid chromatography
hour
Health Research Council
International Association for the Study of Obesity
investigational brochure
xv
ICF
IDL-C
IAS
IDF
IDFTFEP
IFG
Ig
IGT
IHD
IL-1 or 6
INR
IRS
IRS-1
ITT
JIS
JNK
Kcal
KeaP1/NRF2/ARE
kg
LDL-C
LFT
LINZ
LMW
LOCF
LOX
m
MCH
MCP-1
MCV
MetLife
MetS
MetSM
MetSMCt
MFBIA
MI
min
MMW
MnSOD
mToR
NAD(P)H
NAD+
NAFLD
NASH
NCEP
NEAT
NEFA
NFκB
NGSP
NHANES
NHF
NHLBI
NO
NQO1
NSAID
NRF2
NZ
OGTT
informed consent form
intermediate density lipoprotein-cholesterol
International Atherosclerosis Society
International Diabetes Federation
International Diabetes Federation Task Force on Epidemiology and Prevention
impaired fasting glucose
immunoglobulin
impaired glucose tolerance
ischaemic heart disease
Interleukin-1 or 6
international normalised ratio
insulin resistance syndrome
insulin receptor substrate-1
intention to treat
Joint Interim Statement - a joint interim statement [on MetS definitions] of the
IDFTFEP, NHLBI, AHA, WHF, IAS and IASSO, 2009
c-Jun N-terminal kinases
kilocalorie
Kelch ECH associating protein 1/nuclear factor-erythroid 2-related factor 2
/antioxidant response element
kilogram
low density lipoprotein-cholesterol
liver function tests
Life in New Zealand
low molecular weight adiponectin
last observation carried forward
lipoxygenases
month
mean cell haemoglobin
monocyte chemoattractant protein-1
mean cell volume
Metropolitan Life (insurance company)
metabolic syndrome
metabolic syndrome marker
metabolic syndrome marker count (the number of metabolic syndrome markers
added together, ranging from 0-5, where a count of 3 or more indicates MetS)
multi-frequency bioelectrical impedance analysis
myocardial infarction
minute
medium molecular weight
manganese-superoxide dismutase
mammalian target of rapamycin
nicotinamide adenine dinucleotide phosphate
nicotinamide adenine dinucleotide
non-alcoholic fatty liver disease
non-alcoholic steatoheptosis/hepatitis
National Cholesterol Education Prrogram
non-exercise activity thermogenesis
non-esterified fatty acids
nuclear factor kappa B
National Glycohaemoglobin Standardization Program
National Health and Nutrition Examination Survey
National Heart Foundation
National Heart, Lung and Blood Institute
nitric oxide
nicotinamide adenine dinucleotide phosphate: quinone oxidoreductase 1
non-steroidal anti-inflammatory
nuclear factor erythroid 2- related factor-2
New Zealand
oral glucose tolerance test
xvi
OSQoL
OTC
PA
PGC-1α
PDK1/Akt
phytochemical
phytonutrient
phytoalexin
PIS
POMC
PPAR-α, δ, or γ
PT
PTH
PUFA
PVD
QALY
QoL
r
RAAS
RBC
RCT
REE
RER
RIA
RNA
ROS
RONS
RXR
S/DBP
SBP
SAS
SCOTT
s
sd
sem
SF-36
SFB
Sir2
SIRT2
SOD
SOP
sp
TAS
T-cell
TG
TIA
TIDM
TIIDM
TNF-α
U
UCP
UGI
UK
USA
UV
VitA
VitC
VitD
VitDBP
VitE
obesity specific quality of life
over the counter (medication)
physical activity
peroxisome proliferator-activated receptor gamma coactivator alpha
phosphoinositide 3-kinase dependant / serine/threonine kinase
(food) plant chemical
(food) plant micronutrient
plant defence chemical (often antiyeast, antifungal or dehydration preventers)
participant information sheet
pro-opiomelanocortin
peroxisome proliferator-activated receptor alpha, delta or gamma
prothombin time
parathyroid hormone
polyunsaturated fatty acid
peripheral vascular disease
quality adjusted life year
quality of life
correlation coefficient
renin-angiotensin-aldosterone system
red blood cell
randomised controlled trial
resting energy expenditure
rough endoplasmic reticulum
radioimmunoassay
ribonucleic acid
reactive oxygen species
reactive oxygen and nitrogen species
retinoid X receptor
systolic/diastolic blood pressure
systolic blood pressure
Statistical Analysis Software™
standing committee on therapeutic trials
serum
standard deviation
standard error of the mean
short form – 36 -36 questions health related quality of life survey
Single frequency bioimpedance-3
S. cerevisiae silent information regulator 2, gave name to family of Sirtuins
silent mating type information regulation 2 homolog 1
superoxide dismutase
standard operating procedure
supplemental
total oxidant status
thymus-cell
triglyceride
transient ischaemic attack
type I diabetes mellitus
type II diabetes mellitus
tissue necrosis factor-alpha
unit
uncoupling proteins
upper gastrointestinal
United Kingdom
United States of America
ultraviolet (light radiation)
vitamin A, retinol
vitamin C, ascorbic acid
vitamin D3, 25 hydroxyvitamin D3 or calcifediol or calcidiol
VitD binding protein
vitamin E, α-tocopherol
xvii
VLDL-C
waist
weight
WBC
WHF
WHO
wk
XOR
y
α-MSH
βCaro
γGT
δ or Δ
ω-3
very low density lipoprotein-cholesterol
waist circumference
body weight
white blood cell
World Heart Federation
World Health Organisation
week
xanthine oxido-reductase
year
alpha melanocortin stimulating hormone
beta carotene
gamma glutamyl transferase
change
omega-3 or n-3, as in ω-3 fatty acid
xviii
Chapter 1. Introduction and Background
1.1
Introduction
1.1.1 Scope
This thesis addresses obesity, in particular central obesity, and central obesity-related
metabolic syndrome (MetS). MetS definitions have been devised to include a cluster of
cardiovascular disease (CVD) risk factors or risk markers that tended to occur together.
MetS was thought to have greater predictive power for disease than each of the factors
used alone. New research shows that MetS is associated with a wider range of chronic,
degenerative, non-communicable disease than CVD alone, and MetS has limitations in
predicting them all.
The Dietary Fibre and Lifestyle for Health (Diet&Health) study was designed and
conducted. It comprised two research themes. The randomised controlled trial (RCT), of
weight loss over 6 months (6m) in overweight and obese women and men using a natural
dietary fibre, provided the study model for the primary enquiry; the Novel Cardiovascular
Risk and Protective Marker (Novel CVD Markers) investigation. Thus the clinical weight
loss trial, comprising two research themes, was performed primarily to explore MetS,
MetS definitions and Novel CVD Markers and their change with weight change over
time. These markers could then be combined with MetS to increase prediction of obesityrelated degenerative disease in clinical practice.
The Novel CVD Risk Markers were drawn from clinical routine screening biochemistry
and haematology (CRSHaem/Biochem). The first Novel CVD Protective Marker study
concentrated on a key adipocytokine (adipo- adipose; cytokine signalling molecule),
adiponectin (Adpn), and the second group of Novel CVD Protective Markers were a
nutritional and antioxidant group, the serum fat soluble vitamins (sFSVitamins).
Consequently, an investigation of human obesity and MetS was undertaken by exploring
the multidisciplinary literature in order to perform a critical analysis of the causes of
obesity-related MetS. The enquiry proceeded by examining the evolution and effect of
human encephalisation and adaptive changes in energy metabolism pathways, energy
balance, nutrition, immune system, locomotion, behaviour, and technology. A unifying
1
hypothesis of the causes of obesity and MetS was formed. Research ideas on models to
further investigate the hypothesis and possible useful clinical dietary and energy
metabolism modelling studies are outlined.
1.1.1.1 Thesis Structure
The first Chapter presents the thesis Introduction and Background. The Methods are
covered in Chapter 2, although brief method summaries and statistical methods are found
in the relevant Chapters. Chapters 3, 4 and 5 describe the one clinical study; the
Diet&Health study. The Diet&Health study is further divided into the Diet&HealthWeight Loss trial detailed in Chapter 3, the Novel CVD Risk Markers study in Chapter 4
and the Novel CVD Protective markers studies in Chapter 5. Chapter 6 is the theoretical
work that follows the study sections, and Chapter 7 contains the thesis discussion and
conclusions.
1.1.2 General Introduction
Central obesity, a proxy marker for which is waist circumference (waist), has been shown
to increase risk for CVD1. Hypertension or raised systolic and diastolic blood pressure
(S/DBP), serum dyslipidaemia, namely decreased high density lipoprotein cholesterol
(HDL-C) and raised triglyceride (TG), and impaired fasting plasma glucose (FPG) or
FPG in the type II diabetes mellitus (TIIDM) range, are conditions which commonly
occur together.
The latter 4 markers are the traditional or conventional CVD risk factors. Waist has been
added as a 5th member to this list since the obesity epidemic became universally
recognised, in large westernised populations, in the mid 1990s2. All 5 of these conditions
cluster together and therefore form a syndrome.
A syndrome is a cluster of signs and symptoms or markers which occur together more
often than by chance alone. Diagnoses of medical syndromes are usually made on a
selection of markers that are outside a cut-point, or are outside the normal or a specified
range. The above-mentioned group of markers is referred to as MetS3-7.
There have been a number of definitions, as will be discussed, but MetS defined by the
National Cholesterol Education Program (NCEP), Adult Treatment Panel III, using the
above 5 markers, is the basis for MetS definitions used in this thesis (Table 1-1).
2
In order to be categorised as having MetS, individuals must have at least 3 of these 5
measures must be outside of the cut-points, the levels set where increased risk of CVD
has been shown by meta-analyses used to define MetS8. Note that central obesity need not
be present in the NCEP MetS, but must be included in the NCEP-derived International
Diabetes Federation (IDF) MetS, for which waist and 2 other of the MetS markers are
mandatory4 for diagnosis (Table 1-1). The IDF MetS also has ethnic specific waist cutpoints. More recently still the Joint Interim Statement (JIS) authors5 have modified the
IDF MetS by revoking the mandatory inclusion of waist. The inclusion criteria are that
any 3 markers (or more) outside the same cut points as for IDF defines the IDF/JIS MetS.
The IDF/JIS MetS is probably the most appropriate definition for populations which are
on average overweight, and is employed for the analyses in this thesis.
TIIDM is predicted by MetS, and both the risk of completed CVD events, such as
myocardial infarction (MI) or heart attack, and death, are greatly increased in individuals
who have MetS, using the NCEP-based and/or other definitions, compared with those
without MetS9. The importance of MetS is that 4 of its components; (1) raised blood
pressure (BP) (2) decreased HDL-C (3) raised TG, and (4) raised FPG, historically,
comprise the classical or traditional CVD risk factors. It is not clear, yet, whether the 5
MetS components detailed above are causative, contributing to, or are only indicators of,
CVD. All 5 will be referred to as MetS markers in this work (Table 1-1).
It is important to note that CVD is not the only non-communicable disease to occur at
higher rates in centrally overweight and obese individuals. Most of the noncommunicable diseases are degenerative, where degenerative is defined as ‘a disease
characterized by progressive deterioration and loss of function in organs or tissues’ and
are increased with central obesity10.
3
Table 1-1. NCEP-Derived Metabolic Syndrome Definitions
Metabolic
Syndrome
International Diabetes Federation /Joint Interim Statement 2009 (IDF/ JIS)
National Cholesterol Education Panel
(NCEP)1
Diagnosis
IDF requires waist plus any two of the disorders; JIS requires any 3 of the disorders, below.
Requires any 3 of the disorders below
Central Obesity
Population- & country-specific definitions. ‘It is recommended that the IDF cut points be used for nonEuropeans [for example Maori, Pacific peoples] & either the IDF or AHA/NHLBI cut points used for people
of European origin until more data are available’. IDF cut points: ‘Central obesity (defined as waist ≥ 94cm
for Europid men & ≥ 80cm for Europid women, with ethnicity specific values for other groups) i.e. Asian
(based on a Chinese, Malay & Indian Asian population) Male ≥90cm & Female ≥ 80cm’.
Waist >102 cm (40 in) men and >88 cm
(35 in) women
Hypertension
SBP ≥ 130 or DBP ≥ 85 mmHg. Medication for elevated BP is an alternate indicator.
S/DBP ≥ 130/85 mmHg
Dyslipidaemia
Hyperglycaemia
Raised TG ≥ 1.7mmol/L or specific treatment for this lipid abnormality or reduced HDL-C <1.03mmol/L in
men & <1.29mmol/L in women. Medications for decreased HDL-C or raised TG are alternate indicators.
‘The most commonly used medication for elevated TG and decreased HDL-C are fibrates and nicotinic acid.
A patient taking 1 of these can be presumed to have high TG & low HDL-C. High-dose ω-3 fatty acids
presumes high TG’. (Note: raised TG & decreased HDL-C are considered 2 separate factors counting toward
the 2 of 4 necessary secondary factors for diagnosis)
FPG ≥5.6mmol/L (100mg/dL) or previously diagnosed TIIDM .Medication for elevated plasma glucose is an
alternate indicator.
Plasma TG >1.7mmol/L (150 mg/dL),
HDL-C<1.03mmol/L(40 mg/dL) in
men and<1.29mmol/L (50 mg/dL) in
women
FPG >6.1 mmol /L (110mg/dL )
Joint Interim Statement (JIS) Harmonizing the Metabolic Syndrome. A Joint Interim Statement of the International Diabetes Federation (IDF) Task Force on
Epidemiology & Prevention; NCEP (ATPIII) National Cholesterol Education Program (Adult Treatment Panel III); American Heart Association (AHA); National
Heart, Lung, & Blood Institute (NHLBI); World Heart Federation; International Atherosclerosis Society; and International Association for the Study of Obesity 5 .
BP blood pressure; SBP systolic blood pressure; DBP diastolic blood pressure; TG triglyceride; HDL-C high-density lipoprotein cholesterol, IGT impaired glucose
tolerance; BMI body mass index; FPG fasting plasma glucose; TIIDM type II diabetes mellitus.
The NCEP/ADA MetS is the same as the JIS MetS except the waist cut point remains as for the NCEP MetS. The IDF advises the following are studied with MetS:“Abnormal body fat distribution; General body fat distribution; Duel Emission X-ray Absorptiometry (DEXA); Central fat distribution (CT/MRI); Adipose tissue
biomarkers: leptin; adiponectin; Liver fat content (MRI); Atherogenic dyslipidaemia (beyond elevated TG and low HDL-C); ApoB (or non-HDL-C); Small LDL
particles; Dysglycaemia; OGTT(oral glucose tolerance test); insulin resistance (other than elevated fasting glucose); Fasting insulin/proinsulin levels; HOMA-IR;
Homeostasis insulin resistance by Bergman Minimal Model; Elevated free fatty acids (fasting and during OGTT); M value from clamp vascular dysregulation
(beyond elevated blood pressure); Measurement of endothelial dysfunction; Microalbuminuria; Proinflammatory state; Elevated high sensitivity C-reactive protein
(hsCRP); Elevated inflammatory cytokines (e.g. TNF-α, IL-6); Decrease in adiponectin plasma levels; Prothrombotic state; Fibrinolytic factors (PAI-1 etc); Clotting
factors (fibrinogen etc); Hormonal factors; Pituitary-adrenal axis”. Adapted from NCEP 8 , IDF 1 1 , Peters 12 and JIS 5
.
4
The classical CVD risk and waist markers of the MetS are very useful concepts which
have made clinicians and researchers aware of the importance of central obesity.
However, it is clear that there is significant residual risk13,14 of CVD after medication
prescription and other standards of care are achieved. MetS does not fully predict
metabolic decompensation and risk for progression to irreversible degenerative disease.
Many CVD events occur in individuals who have had no risk factors suspected on
screening, or any previously diagnosed15.
In this thesis, it is contended that groups of novel risk and protective markers could be
employed in conjunction with MetS to strengthen disease prediction early enough in the
time course to effect illness prevention. Interestingly, the long definition of the IDF MetS
states that disease prediction marker development should focus on some of the newly
discovered biomarkers (Table 1-1). These include markers of a pro-inflammatory state,
such as CRP, and the anti-inflammatory marker, Adpn, both of which are investigated in
this thesis.
The first group, Novel CVD Risk Markers, is derived from well-known
CRSHaem&Biochem and includes markers often manifesting oxidant properties:
haemoglobin A1c (HbA1c), urate, ferritin, liver function tests (LFT); and inflammatory
markers: white blood cells (WBC) or leukocytes and serum and acute phase proteins,
albumin, globulin, C-reactive protein (CRP) and the erythrocyte sedimentation rate
(ESR).
Adpn and its different variants, or oligomers, are chosen as the first group of Novel CVD
Protective Markers (Table 1-1). Decreased plasma levels of Adpn oligomers, and altered
pathways appear to be involved in the profound metabolic disruption of obesity and MetS
(Table 1-1).
The second group of Novel CVD Protective Markers, the nutritional, ‘antioxidant’ and
obesity related serum fat soluble vitamins (sFSVitamins) are assessed.
Causative factors involved in cellular dysfunction in MetS are then more likely to be
identified, with the hope that endogenous, genetic and epigenetic, and exogenous,
environmental, influences can be modified.
Finally, as the fundamental metabolic defects and initial causes of obesity-related MetS
are not known, a new composite unifying hypothesis, predicated on new human
evolutionary and genetic/epigenetic findings, is proposed.
5
1.2
Obesity
Obesity is the accumulation of excess body fat to the extent that it is likely to adversely
affect health. Instances of obesity have been recorded for thousands of years, as
documented by Hippocrates16. In Great Britain (UK) pre 1900, obesity was present, but
not common. It was captured in paintings of the upper class, as well the merchant class,
and food and service industry individuals17. Obesity and associated ill health was well
recorded by physicians and others attending the affluent16. In other cultures obesity was
an induced condition for specific cultural or fertility practices18.
Obesity rates have increased over the last few decades probably in response to the rapid
increase in technologies for large-scale, cost- effective economy-of-size, food production
systems19. Food refining and energy concentration and preservation, has allowed large
quantities of energy-dense food to be transported to mass markets20. Television watching,
and screen-based computer and phone work and entertainment technologies all serve to
reduce physical activity (PA), compared to the past21. Concurrently, advances in public
and general medical health services have contributed to a reduction in the communicable
disease load, allowing individuals longer life spans, but also increasing time in which to
accumulate degenerative change and increase disease risk factors22. More accurate
clinical indicators to help improve prediction of chronic degenerative diseases associated
with central obesity are required and are the subject of this thesis.
1.2.1 Obesity - Definitions
Body Mass Index
An approximate measure of fatness or adiposity can be classified by a body mass index
(BMI, weight/height2, kilogram/metre2, kg/m2) where BMI ≥25kg/m2 denotes overweight,
and BMI ≥30kg/m2, denotes obesity (grade I), as shown in Table 1-2. Morbid obesity is
defined as BMI ≥40kg/m2 (grade II obesity), and a BMI of ≥45kg/m2 (grade III) is
classified as super obesity. This group has commensurate rates of disability and death23.
Most of the CVD risk studies had been in Europeans. BMI is very commonly recorded
but people of different ethnicities have different body sizes and composition so
adjustments are required for classification24. The World Health Organisation (WHO) has
suggested that researchers use narrower subcategories25 (Table 1-2). The shorter/slighter
less muscular groups tend to be the East Asian, South Asian/Indian, and also include
6
various current or recent hunter-gatherer groups, as for example Australian aborigine26-28.
Their overweight status may start at approximately 20-23kg/m2 and obesity at 25kg/m2.
Of the taller, larger, more muscular groups the African American, Hispanic, and Maori
and Pacific have been studied, and at this time are grouped with European29,30.
BMI proves to be a crude measure of different CVD risks31,32. Measures of central
adiposity may relate more closely to degenerative morbidity and mortality than BMI33,34.
Waist is a surrogate for, and gives an indication of unhealthy, visceral and, to a lesser
extent, subcutaneous, central adipose tissue depots35,36 and liver fat.
Waist and Waist Indices
These measures relate to degenerative disease, particularly TIIDM and CVD33,37. Waist,
and especially the related indices, waist/hip, waist/thigh and waist/height, may be more
appropriate than BMI as health indictors for certain ethnic groups34,38-42. Waist/hip and
waist/thigh give a ratio of the central adipose tissue to the healthy peripheral hip and thigh
fat43,44. This is an index that may be particularly appropriate for Asian ethnic groups
where modest appearing central abdominal adipose tissue is proportionately large
compared with minimal peripheral fat, in both women and men.
However, in mostly European populations, BMI and waist based indexes add no more to
the prediction of CVD if lipid measurements, history of diabetes and BP are available45.
Body Fat Percentage
Total body fat percentage (%Fat) measurements have been used to estimate morbidity and
mortality risk, although studies give contradictory results34,46. %Fat as measured
indirectly by bioimpedance analysis (BIA), the relative resistance of fat to lean tissue to a
small electric current, or dual-emission X-ray absorptiometry (DEXA) is frequently
performed.
Body composition varies widely across ethnic groups and the genders. Individuals of
some ethnic groups can accumulate massive peripheral fat stores, whereas some Asian
groups (East and South/Indian), and various hunter-gatherer group individuals gain
weight mainly by increasing central fat depots47. There are notable exceptions as seen
with sumo wrestlers and others. Thus, %Fat may be an adequate indicator of CVD in the
groups whose individuals have minimal peripheral adipose tissue. As noted above, these
same groups have a high central adipose tissue/height ratio and high rates of CVD at
lower BMI24,48.
7
Table 1-2. Classifications of Obesity
Underweight
Severe thinness
Moderate thinness
Mild thinness
Normal range
BMI(kg/m2)
Principal Cut-Points
<18.50
<16.00
16.00 - 16.99
17.00 - 18.49
18.50 - 24.99
Overweight
Pre-obese
≥25.00
25.00 - 29.99
Obese
Obese class I
≥30.00
30.00 - 34.99
Obese class II
35.00 - 39.99
Obese class III
Ethnic Group
Waist cut-points1
≥40.00
Taller/Larger (European,
African-American/black,
Maori/Pacific
> 80-88cm
> 94-102cm
Classification
Women
Men
Additional Cut-Points for Ethnic Differences
<18.50
<16.00
16.00 - 16.99
17.00 - 18.49
18.50 - 22.99
23.00 - 24.99
≥25.00
25.00 - 27.49
27.50 - 29.99
≥30.00
30.00 - 32.49
32.50 - 34.99
35.00 - 37.49
37.50 - 39.99
≥40.00
Smaller/Slighter (Asian/East, Asian/Indian,
Australian Aboriginal, Hunter-gatherer (forager))
> 80cm
> 90-94cm
Adapted from WHO, 1995, WHO, 2000, WHO-Western Pacific Region/International 4 9 WHO 2004 2 5
and 1JIS Metabolic Syndrome 5
1.2.2 Obesity - Epidemiology and Natural History
In many westernised countries, including relatively recently colonised countries such as
New Zealand (NZ) and the United States of America (USA), obesity rates have been
estimated to be approximately 3% at the turn of 20th Century, but no measured data was
retained50,51. Overweight began to be recognised as a health problem, perceptibly
reducing life-expectancy. In 1943 Metropolitan Life (MetLife), a life insurance company,
using actuarial data collected from 1943 European clients who could afford life insurance
since the 1930’s, published the first tables of Desirable Weights for Height and
Framesize51. These tables were updated in 198352,53. The tables indicated that above
certain weights for women and men, significant increases in morbidity and mortality
could be expected. A weight 20% greater than average for a medium frame person was
another definition for overweight. Introducing height into the equation was possible from
data collected, and weight-to-height tables gave an improved estimation of risk of death
for men and women54. Obesity was then defined as BMI, or Quetelet’s index55 >30kg/m2
from MetLife data. Although in the early 1900’s survival in those with tuberculosis was
8
higher in those who were fatter56, by the 1930’s links between obesity and premature
mortality were also becoming recognised by some clinicians and surgeons.
In 1923, studies from diabetes clinics were reported where obesity was linked with
degenerative CVD risk factors57 and atherosclerosis. The obesity rates increased rapidly
towards the latter third of the 20th Century and the WHO declared a ‘world epidemic of
obesity in 1997’53. Further, in 2008 the WHO noted that ‘globally, more than 1.5 billion
adults were overweight - and at least 500 million of them are clinically obese’58. USA
obesity rates measured in a 2007-2008 National Health and Nutrition Examination Survey
(NHANES) sample indicated that over 35% of women and 32% of men were obese59.
Combined overweight and obesity rates were 68%59. Morbid obesity, which had been
very rare, showed a prevalence of 4.8% in the USA in 200460.
In NZ, the reported obesity rates of women in 2006-2007 were 26% for women and 25%
for men, and for morbid obesity a prevalence of 5% in women and 2% in men61. The
number of obesity-related quality adjusted life years (QALYs) lost doubled over 16y, and
rose from an average of 0.0204 per person to 0.0464 during that time, an increase of
approximately 127%62.
1.2.2.1 Obesity - Ethnicity and Socio-Economic Status
In many countries either the colonized aboriginals/first nation peoples, or immigrant
worker groups, tend to be non-European groups, and often overlap with those who are
relegated to lower socio economic positions. Individuals and families from lower
socioeconomic groups have higher obesity rates than those in wealthier groups63. There is
often an ethnic gradient in BMI but disease may still be high in those with slight body
frames, for example Chinese and Japanese25, so BMI should be adjusted for ethnic
grouping64 or the subcategories in Table 1-2.
9
Table 1-3. Obesity in New Zealand Adults by Ethnic Group (Unadjusted)
Ethnic group
European/ Other
Maori
Pacific
Asian
Prevalence (95% CI)
24.3 (23.1−25.5)
41.7 (39.8−43.7)
63.7 (60.0−67.5)
11.0 (9.0−13.0)
Number of adults
619,200
148,300
104,900
30,800
From New Zealand Ministry of Health 6 1
Maori and Pacific in NZ had morbid obesity rates of 6.6% for women and 7.8% for men
in a 2008 report61 Overall morbidity and mortality increases proportionately. The
prevalence of obesity in the highest socioeconomic group in NZ was 20.4% for women
and 21.5% for men61 and in the lowest group, 39.7% for women and 35.4% for men61.
Obesity prevalence rates have risen over the last century, initially in the wealthier,
westernised, developed countries, but they are now epidemic in the poorer sections of
developing countries. A significant cause is likely to be the move from traditional diets to
the westernised processed, high saturated fat, high refined carbohydrate (CHO), and high
salt diet. This is denoted the nutrition transition65.
1.2.3 Causes of Obesity
The causes of obesity are likely to be the result of gene-environment interactions and
changes in this inter-action. Our environment has transformed over the last few hundred
years but most particularly in the last 100y. The first law of thermodynamics states that
energy cannot be created or destroyed so energy taken in by auxotrophs, organisms which
do not make energy from light energy, must be expended or stored.
In obesity, a relative excess of energy is consumed from the macronutrients compared
with energy expenditure. The macronutrients are composed of (1) CHO subgroups,
starches and sugars (2) lipids (fats), and (3) protein; of which the latter is not usually
ingested primarily for energy. Alcohol is metabolised immediately and unlike the other
macronutrients is not directly, or indirectly stored as fat. Diets throughout the world have
become westernised, which generally indicates they have a high proportion and levels of
processed, refined, energy dense and over-palatable CHO and fat, with many nonnutritive sweeteners and additives, and are low in fibre and micronutrients66.
Basal metabolic rate (BMR) is the energy used by all the processes that keep
homeotherms (warm-blooded animals), alive in the resting fasted state, and consists of
10
basal cell metabolism of maintaining electrical gradients. In the waking state, digestion
and PA result in further energy expenditure. The potential energy of food macronutrients
has been calculated from animal and human experiments since the mid 1900’s67 and
confirmed many times since68.
A decline in energy expended in PA may have occurred69 although not all researchers
agree. Modest PA alone may not be very efficacious for weight loss or weight loss
maintenance, but appears to prevent weight gain70-72. Estimates for the average energy
required for women and men to physically work efficiently was also estimated many
years ago, but the early researchers and authors knew that there were many conditions,
such as gender, age, temperature and other unknowns.
Techniques for the measurement of PA energy expenditure in individuals have become
more accurate73, and mechanisms and genetic influences for mobilising energy stores,
switching energy substrates74 and energy loss through uncoupling proteins (UCP)75 in the
obese and lean have been studied. Only recently, however, has the change in energy
expenditure with change in fat mass over a weight loss period been integrated in time, and
non-exercise activity thermogenesis (NEAT) been taken into account76. There are still
many unknown factors in the links between PA, energy expenditure and food intake77.
However PA will not be addressed in detail in this thesis.
Although there are many studies reviewing energy acquisition and expenditure, there are
other hard-to-determine factors in both. There are other probable evolutionary,
developmental78, inadequate sleep related79, psycho-behavioural80,81, socioeconomic82,83
and , physical factors, in addition to chemical additives/pollutants84-88, and other
environmental determinants89 which may affect obesity development and prevalence.
1.3
Central Obesity Related Metabolic Syndrome
MetS predicts pre-diabetes, specifically impaired fasting glucose (IFG) and glucose
intolerance, CVD risk markers, and other degenerative disease, including cancer90,91.
The prevalence of MetS is approximately proportional to obesity rates (34% in the USA,
2003-200692) but varies by definition and ethnicity from 25-50% in other countries93. In a
systematic review and meta-analysis, individuals with NCEP/American Diabetes
Association (ADA) MetS had a 2-fold increased risk of CVD, and all-cause mortality of
1.5 fold compared to those without94.
11
A few early TIIDM clinicians realised that the hyperglycaemia of adult onset diabetes and
the CVD-risk factor of hypertension were related to obesity. Harrick made ‘...the
observation that the association of high BP and increased concentration of glucose in the
blood... (in) the majority of the cases belong in a quite definite group characterized by the
four cardinal symptoms: hypertension, hyperglycaemia, obesity and arteriosclerosis
(demonstrable in the retina)’95. Furthermore he experimented and found that a CHO- but
expressly not a protein- restricted diet (1600 kilocalorie (Kcal)) resulted in weight loss,
and that this weight loss translated into decreases in hypertension and hyperglycaemia.
This was in the same year that Kylin, generally regarded as producing one of the earliest
descriptions of the clustering of CVD risk factors, published his work on the
‘Hypertension, Hyperglycaemia and Hyperuricaemia Syndrome’96. Hyperuricaemia has
frequently been associated with MetS and is often on verge of being selected to be part of
MetS97-107. Hyperuricaemia was present in 2/6 patients95. Gout was also recognised early
as being related to CVD108,109. Furthermore, Vague recognised that the central fat
distribution seen more often in men was associated with CVD110.
Over this time, obesity rates were gradually increasing and data continued to be collected
on the association of obesity with TIIDM, the other CVD risk markers, and CVD events.
Weight loss strategies, including prescribed medication111,112 to reduce weight in order to
lessen CVD risk were studied
113-115
before obesity rates began to accelerate, in the
1980’s. The realisation that central and/or upper body obesity was the adipose tissue
depot of risk was rather slow116-120, and there is still controversy about adipocyte size and
location121. As the rates of central obesity increased significantly, waist measurements
were factored into analyses of risk, and waist was shown to be a significant and valid
marker of CVD risk. Central obesity became factored into some indexes of
cardiometabolic and CVD risk, although the Framingham equations omitted central
obesity until as late as the 1990’s122.
Various CVD risk marker combinations were formulated in MetS. Kaplan coined the
idiom ‘deadly quartet’, an early MetS definition which included the upper body obesity
marker, waist, dyslipidaemia, hyperglycaemia and hypertension123, although he was keen
that hyperinsulinaemia was considered 124. Other descriptive terms for this heterogeneous
cluster of risk factors included, ‘the syndrome X’, ‘insulin resistance syndrome’,
12
‘plurimetabolic
syndrome’,
‘dysmetabolic
syndrome’,
‘cardiovascular
metabolic
syndrome’, ‘cardiometabolic syndrome’, and ‘hypertriglyceridaemic waist’125,126. Various
definitions were promulgated after the WHO MetS was published. A number of
definitions were proposed and MetS was accepted by most researchers as a useful clinical
index for a common clustering of risks factors. However, MetS greatest contribution may
have been to show in high relief that central adiposity or its causes are a serious risk to
health.
1.3.1 Definitions of the Metabolic Syndrome
It is instructive to review MetS definitions, as they reveal why there is controversy in
defining MetS, but also possibly a preference for insulin resistance as the core problem
rather than obesity as being a risk factor. Although the WHO Diabetes Group (1999)
made the first attempt to define the MetS in a formal manner, others were using ad-hoc
clusters of risk factors in trial reports127. The cut-points, except waist, have been similar
for all definitions.
The WHO MetS was primarily centred on the presence of insulin resistance. Diagnosis
comprised impaired glucose tolerance (IGT), as measured by a hyperinsulinaemic
euglycaemic clamp, where the insulin and glucose are infused intravenously at a level of
insulin that keeps the plasma glucose constant. Insulinaemia is measured, or TIIDM is
present, and at least 2 of the following risk factors: (1) obesity; waist-to-hip ratio and/or
BMI >30kg/m2 (2) dyslipidaemia; elevated plasma TG or decreased HDL-C levels
(different levels for men and women); or (3) hypertension and (4) microalbuminuria7.
Cut-points were derived from large pooled data sets or meta-analyses.
In 1999 the European Group for the Study of Insulin Resistance (EGIR)128,129 suggested
that the terminology insulin resistance syndrome (IRS) should be used and defined as the
upper quartile of a non-diabetic reference population. Their definition comprised waist,
fasting hyperglycaemia, hypertension, TG, HDL-C. Also included was medication for
hyperlipidaemia or hypertension.
The EGIR excluded TIIDM, and therefore the 3rd group of cardiometabolic medications;
those for hypoglycaemia or diabetes. Ironically, this IRS-based MetS already included the
5 markers, in addition to central obesity for the first time, with cut-points, that would later
13
become the IDF/JIS MetS, although to most clinicians the IDF/JIS MetS appears to be
NCEP
MetS-derived4,7,130.
Microalbuminuria
was
discarded.
In
2000,
the
‘hypertriglycerideamic waist’131 phenotype was proposed. It included 3 non-traditional
risk factors, which are not often clinically measured; small dense low density lipoprotein
(LDL) particles, elevated plasma apolipoprotein (apo) B levels, and elevated plasma
insulin levels. This definition would increase the 5y CVD risk 18-fold131.
The NCEP MetS related definitions have be discussed above, and outlined in Table 1-1.
In addition they have been widely publicised, debated and used132-141. In 2010, the WHO,
with the support of long-time proponents of MetS, surprisingly joined the North
American endocrinologists by announcing that it did not support the concept, nor use of
the term, MetS, and that newer biological markers derived from recent basic science
should be used133.
Articles still appear indicating that MetS is a useful concept and is likely to be hard to
erase7,132. Clinicians and researchers outside of the USA and Europe are much more
inclined to use the NCEP derived MetS definitions, as they are cheap and easy to use. In
some countries and ethnicities the ‘nutrition transition’, and consequent huge morbidity
from TIIDM and CVD, is occurring rapidly. Young individuals transition quickly through
MetS and develop TIIDM and CVD, as seen in South African and Fijian Indians142. The
IDF waist cut-point levels appear appropriate. With no provision to use new biological
markersl markers, clinicians in many situations have no reason for abandoning the
concept or clinical use of MetS133. Any index which gives prior warning of disease raises
the likelihood of TIIDM and CVD prevention.
1.3.2 Obesity and Type II Diabetes
Type II Diabetes Mellitus - Diagnosis and Natural History
TIIDM is part of MetS spectrum of diseases and is diagnosed by an abnormally high
plasma glucose measure. Hyperglycaemia, as determined by FPG >7mmol/L, random
plasma glucose, or an oral glucose tolerance test (OGTT) of >11mmol/L, and/or glycated
HbA1c >6.5% is used by the ADA143 to define this adult onset, degenerative condition.
TIIDM is a later stage of MetS where hyperglycaemic problems are to the fore. Those
with TIIDM are prone to coronary heart disease (CHD), and other CVD at double the rate
14
of those without TIIDM144. If hyperglycaemia is not corrected, very high blood glucose
can result in lethargy, dehydrating glyscosuria and hyperosmolar states and coma.
Whilst early testing and oral hypoglycaemic and insulin sensitising medication now
makes this presentation rare, chronic modest hyperglycaemia damages arterioles and
capillaries, particularly those supplying the kidneys, retinas and nerves. Excess
consumption of macronutrients (CHO, lipid and protein), and their subsequent
metabolism is linked to abnormal micronutrient metabolism as seen, for example, with
the transition metals145 in TIIDM.
Type II Diabetes Mellitus - Epidemiology
TIIDM accounts for approximately 90% of all diabetes. It has increased drastically in the
last 100y to a world prevalence calculated by the WHO at 220 million people in the early
21st Century and numbers are predicted to rise146. All cause- and vascular- mortality is
doubled in people with TIIDM and 50% of these die from CVD146,147. Of people with
TIIDM, 80% are overweight or obese, and most of this obesity is central. In 2005, 1.1
million people were estimated to have died from diabetes complications, of which 80%
are in developing countries and 55% are women. Projections show that by 2030 the death
rate from TIIDM will be double these figures146.
From NZ estimates the ‘most likely’ scenario reflects anticipated growth in the obesity
epidemic along with improvements in both diabetes and general health care. The model
estimates over 11,000 incident diagnoses, 180,000 prevalent diagnoses of NZ adults and
1900 deaths attributable to diagnosed TIIDM disease in 2011148. Note that estimates of
the prevalence of undiagnosed diabetes can be high, especially in obese groups and/or
non-European ethnicities.
In a NZ Maori group who were on average obese, age adjusted undiagnosed TIIDM rates
of approximately 5% (4.2% women and 6.5% men) were recorded, 2003-2006149.
Furthermore, as CHD, chronic kidney disease (CKD) and end-stage renal failure and
infection, gangrene, from peripheral vascular disease are common sequelae of TIIDM,
many patients are recorded as dying from these diseases, not diabetes itself.
15
1.3.3 Obesity and Cardiovascular Disease
Cardiovascular Disease – Diagnosis and Natural History
CVD is the major degenerative disease pattern to have overtaken infectious diseases in
the cause of morbidity and mortality, and is the main reason for the development for
MetS definition. CVD predominantly presents as ischaemic heart disease (IHD), the
symptoms and signs of which are 3 related IHD manifestations; (1) angina (2) MI (which
is often fatal150), and (3) chronic heart failure151. Cerebrovascular accidents (CVA),
transient ischaemic attacks (TIA) and multi-infarct dementia are other atherosclerotic
cerebrovascular manifestations. Lastly PVD, especially arterial atheroma, cause
claudication of lower limb muscle and severely reduced exercise tolerance. PVD
contributes to lower limb (diabetic) ulcers and gangrene, sometimes necessitating
amputation.
Historic physicians knew of atheromatous plaques and hardening of the aorta and
coronary arteries (arteriosclerosis) as described by Krell in 1740 (Stamler J, p21)152. Prior
to the 20th Century there is evidence that atherosclerotic IHD was rare compared with
infectious disease153.
Blood Pressure
Since late in the 19th Century BP has been able to be measured in patients, both as a
marker of CVD, and for other circulatory problems, such as shock. Its importance to CVD
was also appreciated since before the rapid increase in CHD. Raised BP is associated with
MI, but post infarct, and in heart failure, cardiac output and BP may fall to low,
inefficient levels. The rise in obesity has been associated with increases in, and
difficulties in control of, BP154. Outpatient S/DBP measurement is primarily used as a
marker of atherosclerotic CVD. Ultimately factors affecting the narrowness and reactivity
of the arteries in relation to the pump action of the heart control BP.
Various axes or systems are involved in BP control. The main function of
mineralocorticoids, particularly aldosterone, is to regulate kidney sodium resorption and
conservation, control fluid retention and hence maintain BP. Excess aldosterone and
cortisol also increase BP. Plasma cortisol is associated with central obesity, and adrenal
hypersecretion causes central obesity, as for example in Cushings Disease. However,
cortisone is converted into cortisol locally in adipose tissue by 11β-hydroxysteroid
16
dehydrogenase enzyme 1 (11β-HSD1), thus there may be much activity in the large
depots of an obese person, but no raised serum cortisol levels detectable. Some of the
complex relationships in BP are shown in (Figure 1-1).
Activation of the adrenals occurs through many mechanisms: the hypothalamic-pituitaryadrenal (HPA) axis, the renin-angiotensin-aldosterone system (RAAS), and the
sympathetic system. All of these processes seem to have systemic and tissue level
subsystems, and often the tissue involved is visceral adipose tissue155. It explains
phenomena such as the observation that angiontensin II blocking (ARB) agents reduce
CVD risk irrespective of BP lowering156.
Figure 1-1. A Selection of Factors Involved in Hypertension
NEFA
nonesterified
fatty
acids;
IL-6
interleukin-6;
TNFα
tissue
necrosis factoralpha. Diagram
from
Yanai
2008 1 5 5
Other biochemical factors are also involved. Hypoadiponectinaemia in obesity is
associated with reduced endothelium-dependent vasodilation and hypertension157. In
addition, perivascular adipose tissue and its cytokines affect BP with Adpn having a
hypotensive effect and leptin, hypertensive158. Endothelin modulates increased
endothelium-dependent
vasoconstriction
in
obesity159.
Other
obesity
induced
cardiomyopathies160 and non-obesity factors probably contribute in addition. In central
obesity, there appears to be a simultaneous loss of nitric oxide (NO)-mediated relaxation
in the large conduit arteries, loss of NO-independent, potassium-mediated relaxation in
the small arteries, and lastly a perpetration of insulin resistance by negative action on
metabolism and haemodynamic coupling161.
Although those with high blood levels of antioxidants are known to be more protected
from degenerative disease, and it seemed sensible to give targeted antioxidant
supplements, most intervention studies have failed to decrease CVD162, BP and vascular
17
risk disorders such as pre-eclampsia163. Hypertension treatment guidelines may need to
change to accommodate MetS specific treatment of hypertension, in order to prevent
CVD and TIIDM158,164.
Cardiovascular disease - Epidemiology
CVD is the most common disease and cause of death in western post-industrial societies
and is fast becoming so in non-western countries165. “In 2005 CVD was the underlying
cause of death in 864,480 of the approximately 2.5 million total deaths in the United
States, and adults aged 65y or older accounted for 82% of all deaths attributable to
CVD”166. In the early 1920’s rates of CVD started to accelerate. CVD presumably
resulted from influences that had started some years before, perhaps in the 1880’s. From
this period, all cause death rates were starting to fall more rapidly in westernised
countries167. The rate of heart disease increased steadily from the 1920’s and
subsequently, so sharply between 1940-1967, that the WHO called this incline the world's
most serious epidemic51,167.
Environmental factors post the industrial revolution of the 19th Century may be
relevant153. During the early to mid 20th Century, various new environmental changes and
industries engendered lifestyles that were seen as increasing CVD risk. Highly processed,
hydrogenated, and heated fats and oils resulted in high saturated fat/trans fat diets168,
although there is still doubt about saturated fat169-172. Labour-saving devices and machines
were associated with decreased PA173. Environmental toxicants included tobacco smoke
and other new man-made chemicals and particles174-181.
The Framingham Heart Study was initiated in attempt to identify common factors that
contributed to CVD by following its development over a long period of time in a
modestly sized group of participants182. The study found that together with the classic
CVD risk factors, the postulated ‘genetic, ethnic, geographic differences, industrial
exposures/ food additives/ chemical factors and coronary-prone (Type A) behaviour
might be studied as risk factors for CHD’183. After 1965 rates of CVD started to drop,
particularly in reasonably affluent European westernised groups, and continued into the
present, but rather slower in the low socioeconomic groups. After the Surgeon General’s
Report on Smoking184, and public health campaigns, smoking slowly decreased in
prevalence amongst the well-educated, and is thought to have contributed to the reduction
in CVD prevalence.
18
Intensive commercial development of medication for the classical CVD risk markers
resulted in drugs that tend to normalise BP, LDL-C, and hyperglycaemia, and modestly
reduce post-event mortality. Further CVD rescue interventions such as coronary artery
bypass grafts (CABG) and stents were introduced. Public health campaigns were
developed to encourage low cholesterol- and low saturated fat-diets. Interestingly, the
above interventions may have only been responsible for approximately half of the decline
in CVD incidence185, and other reasons for change in CVD need investigating. However,
increasing central obesity may be threatening the decline in CVD186.
1.4
Physiology of Metabolic Syndrome – Adipose Tissue,
Macro- and Micro- nutrients
1.4.1 Adipose Tissue - Body Fat Distribution and Metabolism
It is important to discuss adipose tissue at this juncture in this thesis, as the type and
distribution of fat has very different effects on MetS, CVD and other degenerative
disease. Adipose tissue is necessary for many functions in mammals and is present in
almost all tissues of the body. Fat cell number appears to be inherited and set by the late
teenage years, and thereafter stays stable with about 8-10% turnover per year187.
Fat tissue is very important for structural and physical functions, including impact
buffering as seen under the skin, subcutaneous adipose tissue, around the kidneys, and in
the knee joint. It is therefore understandable that different depots perform different
metabolic functions. Storage of excess dietary energy as lipid is an important, specialised
function of most adipocytes.
The best adapted appear to be peripheral, subcutaneous adipocytes which can become
quite large without causing metabolic upset. Fat serves as an efficient continuous and
intermittent energy supply and store, as exemplified by bone marrow, as well as hip and
thigh, fat. Hip and thigh subcutaneous adipose tissue depots in women are probably
reserved for breast feeding or more severe energy shortages188. It has become clear that
some adipose tissue is able to store large amounts of lipid with neutral or even positive
metabolic effects189-193. Peripheral fat is less responsive to mobilisation than visceral fat
on sympathetic stimulation194.
19
In contradistinction to peripheral subcutaneous adipose tissue, excess central and/or upper
body adipose tissue depots are sites of metabolic risk195. Lipid deposited in visceral tissue
around the abdominal organs is thought to be temporary storage for rapid mobilisation.
Large visceral adipose tissue depots contain adipocytes that tend to be metabolically
active, but unlike peripheral adipocytes, appear unable to expand and function normally
when lipid reaches high levels. Overfull visceral adipocytes appear to suffer a decline in
the production of Adpn, an increase in secretion of the pro-inflammatory cytokines
tumour necrosis factor alpha (TNF-α) and interleukin 6 (IL-6), and are associated with
MetS. They appear to be adipocytes that suffer lipotoxicity196.
It was relatively recently that these upper body depots of fat were recognised as a
problem116-120 and there is still controversy about fat cell size and fat cell location121. It
also appears that there are other ectopic sites of adipose tissue which associate with
various organs and confer metabolic risk. Intra- and inter-muscular, and epi- or pericardial adipose tissue depots have been linked with adverse metabolic findings197-201.
Most men and some women appear to have limited peripheral subcutaneous adipocytes
with which to take up excess lipid metabolically safely. Consequently, in these
individuals the body is said to suffer from lipid overspill202,203. Excess lipid becomes
ectopic. Lipid is forced into the upper body skin layers, liver and muscle204, including
heart muscle or myocardium. Lipid overspill causes lipotoxicity in cells not designed to
function with large deposits of lipid in equilibrium between TG and non-esterified FA
(NEFA)205. Lipoxidation in lipoproteins, especially LDL-C, and other advanced
lipoxidation end-products (ALE) become common in MetS206.
1.4.2 Nutrients in Metabolic Syndrome
1.4.2.1 Lipids
Fat Absorption and Metabolism
Lipids have low miscibility in water which means that their absorption is specialised.
Chylomicrons are TG rich particles of dietary origin with minimal lipoprotein,
synthesised in the intestinal brush border and secreted through the cell junctions into the
lymph-stream, and via the thoracic duct into the blood circulation. Chylomicrons are
transported to the liver for processing into the non-HDL-C, Apo-B-associated
lipoproteins.
20
Apo A1 and Apo B are water soluble vehicles which transport TG and cholesterol. The
HDL-C particles contain Apo A1 and the non-HDL-C lipoproteins (intermediate density
lipoprotein-C (IDL-C), LDL-C and very low density lipoprotein (VLDL-C) and
associated chylomicrons) associate with Apo B. Following a fatty meal, TG within
chylomicrons rise, but are often also raised in obese persons with MetS and TIIDM when
fasting. LDL-C in the obese is not uniformly raised, whereas dyslipidaemia associated
with CVD in non-obese individuals has a high frequency of raised LDL-C.
The low HDL-C seen in MetS and TIIDM, and high TC/HDL-C ratio in CVD are often
all part of the same problem. The one lipoprotein that tends to be low in MetS is the
HDL-C. HDL-C and Apo A1 collect oxidised lipids from the artery lining endothelium
and ‘foam cell’ monocyctes, by reverse cholesterol transport using adenosine triphosphate
(ATP) binding cassette transporters, which carry lipids back to the liver for ‘antioxidant
treatment’ and recycling207. HDL-C influences adipocyte metabolism208. HDL-C has an
anti-inflammatory effect and is positively related to Adpn and sβCaro208-211. Raised serum
TG and low HDL-C are pivotal components of MetS.
Dietary Fats and Oils
Dietary lipids derive from various groups. Many are involved in energy metabolism and
relate to CVD risk. Glycerolipids, fatty acids plus glycerol are the main energy molecules
ingested from the diet. Lipids are the most energy dense of the 3 main macronutrients,
containing 38kJ/g of energy, as opposed to CHO and protein which only contain 17kJ/g.
Other fatty acids obligatorily required from the diet in the glycerolipid group are
arachadonic acid derivatives. The eicosanoids, obligatorily required from the diet, form
eicosapentanoeic acid (EPA) and are required for prostaglandins and thromboxane which
are involved in clotting and other vascular signalling. Docosahexanoeic acid (DHA), also
a dietary requirement, is long chain ω-3fatty acid employed in the brain.
Whilst humans synthesise many dietary sterol lipids, cholesterol derived hormones,
including VitD with sunlight exposure and signalling molecules, plant sterols
(phytosterols) such as β-sitosterol have serum cholesterol lowering properties. Prenol
lipids (carotenoids – βCaro, VitA, and VitE and VitK) are all required in the diet. Other
classes of lipids such as phospholipids, sphingomyelins can be assembled in the body.
Dietary fats and oils are mixes of lipids that occur in both animal and plant foods.
21
Homeotherms (warm-blooded animals) such as mammals and birds tend to have fats that
more or less solidify at room temperature, whereas most plant, usually seed, and marine
animals have fluid fat or oil that only becomes solid at very low temperatures.
Both fatty and oily foods can be processed to produce much more pure fats such as lard
and cooking oils. The dietary fats contain high proprtions of saturated carbon-carbon (CC) bonds. Oils usually contain mono- and/or poly-unsaturated C=C bonds. Saturated fat
has been thought to be a major contributor to the dyslipidaemia of MetS and TIIDM, and
dietary cholesterol to the LDL-C hypercholesterolaemia of CVD168. The monounsaturated
fatty acids (MUFA), present in olive oil appear to have an antiatherogenic profile and the
omega-3 (ω-3) long chain polyunsaturated marine FA, eEPA and DHA are
antiatherogenic, anti-inflammatory, antithrombotic and possibly antiarrythmogenic212,213
although may be diabetogenic214.
A number of semi-saturated fats and oils, such as palm oil, are further industrially
hydrogenated, and some polyunsaturated FA (PUFA) also, to form solid fats which are
less likely to become rancid, to leak from packaging on travel and can be added to oils to
form margarine-like spreads. This process tends to form trans-fats and research shows
that these industrial fats may increase atherosclerosis215-217. Dietary advisories such as
Food Standards Australia and New Zealand recommend that individuals now limit transfats levels in foods, but decline to regulate industrial use218. Interestingly, natural animal
trans-fats associated with saturated fats may be less atherogenic219.
As energy density is greater for lipids than for CHO, high fat diets have been thought to
be responsible for much of the obesity epidemic. As lipid metabolism is intimately
connected with insulin action and pyruvate derived from CHO in the Krebs cycle can be
used for the glycerol back-bone of TG, highly refined CHO diets can also lead to raised
TG. Low fat diets have therefore been advocated for weight loss220, although CVD risk
factors do not always improve221.
Up until approximately 10y ago, saturated fats diets were seen as atherogenic and
prothrombotic. In addition, there have always been anomalous findings about atherogenic
and thrombotic fats and diets214,222, and there is far from complete resolution of obesity
and improvement in CVD risk with low fat diets170. Subsequently, many studies have
22
shown that it is energy reduction, irrespective of diet composition which results in
negative energy balance and weight loss over 6-24m223-226.
1.4.2.2 Protein
Protein Metabolism and Functions
Protein is the emergency macronutrient or energy source in humans. Proteins, formed by
polymerisation of more than 20 amino acids into polypeptides, or amino acid polymers,
are the products of genetic transcription. They are used for structural purposes, as
enzymes and in many other functional roles as protein complexes, such as glycoproteins
and lipoproteins.
A part of protein function consists in organising energy production and being up- or
down- regulated depending on food type, balance and availability. However, the adverse
milieu found in MetS is not ideal for either structural or functional proteins. In the
hyperglycaemic context, protein is both glycated where glucose attaches to some proteins,
such as HbA1c, and glycosylated where by covalent or other permanent binding occurs
thus altering structure and function. These glycated proteins are oxidants in that they
easily react with oxygen, particularly in the circulation. Similarly, in hyperlipidaemia,
lipoproteins become oxidised, particularly in LDL-C where ALE form which exhibit
increased and abnormal activity227-229.
These altered proteins are involved in widespread oxidative stress and metaflammatory
activation, especially in the liver mitochondrion and endoplasmic reticulum. Cytokines,
which are messaging polypeptides, are involved in many energy metabolism processes,
especially inflammation, including that at the endothelium. Much inflammation is
mediated by acute phase proteins, such as CRP. The thrombosis cascade is another
protein based system related to MetS.
Dietary Protein
Dietary protein intake levels have been controversial. They often carry or contain many
nutrients, as for example iron in haemoglobin (Hb). Vegetable protein in human diets is
important, and is usually concentrated with other digestible energy sources, lipid and
CHO in seeds. Current protein intakes of 10-35% of total energy requirements are
advised230. There is evidence that pre-historic humans derived more energy from protein
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and animal fat than hitherto thought. Forager societies probably consumed 19-38%
protein from animal sources alone231. Dietary protein peptides and amino acids interact
with reducing CHO, leading to a train of reactions denoted as glycation, the Maillard
reaction or ‘browning’. Fatty meat, which may also contain glycogen, degrades or breaks
down to amino acids, sugars and oxydised lipids, which combine when left together
forming advanced glycation end-products (AGE) and ALE. Formation is accelerated with
high temperature frying or baking. Wheat protein and starch in baked breads and crackers
form such reactions. For those with MetS, or mild to major hyperglycaemia, ingesting
further preformed AGE/ALE’s may be ill-advised232-236.
1.4.2.3 Carbohydrate
Carbohydrate metabolism
Once CHO is digested and sugars are absorbed, insulin and non-insulin dependent
pathways allow glucose into the cell. Thereafter immediate oxidation, or glycogen, the
animal storage polysaccharide, synthesis commences in the liver and muscle. Plasma
glucose is the circulating form of CHO and control of its levels is complex, but insulin is
the key regulator. It is at the insulin receptor, in the organs which have episodic large
glucose uses (i.e. the liver and muscle), where insulin resistance is found. Initially the
pancreas is forced to produce compensatory insulin, or hyperinsulinaemia, but after time,
this fails and plasma glucose rises.
Abnormal insulin signalling, which involves many processes and controls, includes Adpn
and receptor dysfunction. The glucose-insulin interaction is one of the overt problems in
MetS but where the problem starts is not clear. Serum lipids are also affected by insulin
and plasma glucose levels.
Dietary Carbohydrate
CHO comprises the easily digested starch polysaccharides and sugar disaccharides and
monosaccharides. The starches are hydrolysed into absorbable monosaccharides, of which
glucose is the main plasma sugar. The disaccharide, sucrose, or ‘table sugar’ is
hydrolysed into glucose and fructose. The monosaccharide fructose is also a fruit sugar.
Unlike protein and dietary fats, CHO as starch or sugars can be refined to be an extremely
pure molecular type, such as amylose which is hydrolysed into glucose. In the past, CHO
mixtures, typical of unrefined grains, were usually ingested along with fibre (non-
24
digestible polysaccharides), and other nutrient complexes and vitamins, with fat and
protein present in variable ratios.
Dietary Fibre
The CHO group that is most helpful in prevention of obesity, MetS and TIIDM are the
non-digestible soluble and insoluble polysaccharide dietary fibres. A definition that was
arrived at for dietary fibre by the Australia NZ Food Authority (ANZFA) (now Food
Standards Australia New Zealand, FSANZ), proposed in 2000, is one of the more recent
listed by the Institute of Medicine of the National Academies of Science: ‘Dietary fibre is
that fraction of the edible part of plants or their extracts, or analogous CHO, that are
resistant to digestion and absorption in the human small intestine, usually with complete
or partial fermentation in the large intestine’237.
The term includes cell wall polysaccharides or polyglycans; cellulose (insoluble),
hemicellulose (non-glucose sugar links), hydrocolloids and sulphated polyglycans (many
from seaweed), gums and mucilages, gel-forming β-glucans, pectins, polydextrose,
resistant maltodextrins, the oligosaccharides (degrees of polymerization >2, usually
fructo-oligosaccharides derived from inulin238 or galacto-oligosaccharides), lignins
(polyphenol, phytoestrogens linked with cell wall polysaccharides), chitin β-pleated
sheets mostly derived from the arthropod, crustacean exoskeleton, and chitosan, a
partially deacetylated chitin, and non-CHO fibre.
A typical summary of dietary fibre functions is that fibre promotes one or more of these
‘beneficial physiological effects: laxation, reduction in blood cholesterol, and/or
modulation of blood glucose’237,239-241. Currently, prebiotic fibre, which promotes the
growth of useful gut bacteria, is under study. However, as all fibre has been a main part of
whole food diets, it is not surprising that the healthier microbe patterns are seen in those
who eat larger quantities of all fibre242. Other uses include bulking out of the diet and
adding satiety properties to meals243,244. Increased satiety may mean weight loss is
associated with increased fibre intake245, but more studies show improved MetS rather
than weight loss246-250.
Increased fibre specifically purified from food sources has been associated with some
CVD improvement (cholesterol-lowering) in some studies241, but not all251. In one recent
study psyllium at very high dose of >50g/d was successful for weight loss and lipid
25
lowering252. However, whilst some studies show positive effects with guar gum and βglucans, others have not253. The reduction in efficacy may have been due to changes
associated with processing during extraction, preparation and formulation to refine the
product for ease of administration, at least with β-glucans254.
Chitosan, derived from chitin, is the fibre to be used in the weight loss model of MetS
change employed in this thesis. It was chosen due to its unique properties in the intestine.
Chitosan occurs in nature, but it is chitin which is predominantly found. Chitin is
synthesised by fungi and invertebrates, including arthropods insects and crustaceans, and
molluscs, thus has always contributed fibre to human diets. Commercial chitin is
harvested from marine sources and in NZ from the squid fishery. The squid mollusc
forms a chitin pen, which is industrially deaminated and acetylated in order to control its
level of acetylation. The greater deactetylation gives chitosan its positive charge and
activity (Figure 1-2). Although relatively non-toxic and biodegradable, it does have
antimicrobial and immune enhancing effects and is used in agriculture and for industrial
films. Chitosan is soluble in gastric acid.
Figure 1-2. Structure of Chitin, Chitosan and Cellulose
The
polysaccharide
beta
pleated sheet structure of
chitin and derived chitosan,
and compared with cellulose.
The acetyl group RC(O)H 3 is
removed
from
amide,
leaving the
RC(O)NHR'
charged amine (R'NH 2 ) on
the
polysaccharide.
C
carbon, H hydrogen, N
nitrogen.
Graphic
from
Dalwoo 2009 2 5 5
If the level of deacetylation is at least 75%, putatively chitosan becomes sufficiently
positively charged to bind negatively charged FA in the stomach and non-esterified
cholesterol and bile acids in the duodenum256. It is reported to absorb >200% of its weight
26
in water to form a gel around non-micelle associated lipids257. The excreted gel is thought
to remove free fatty acid (FFA)/cholesterol from absorption/enterohepatic circulation,
thus decreasing serum cholesterol, %Fat and weight. Animal studies indicate that chitosan
can reduce the amount of fat absorbed from the diet258 and can increase fat eliminated in
the stool258,259. In addition, plasma glucose was decreased in experimentally induced
diabetes in rats260. Clinical trials have reported decreased weight257,261,262 or lipid
levels263,264, while others have reported no effect on either outcome265,266. In clinical
mechanistic studies minimal267, or no268, faecal fat loss has been shown, although faecal
cholesterol loss has only been recorded in rats269.
Fruit and Vegetable Fibre
High fruit and vegetable diets are composed of large amounts of most of the fibre types
referred to above. There have been numerous studies over more than 20y showing that the
traditional Mediterranean diet, with its high fruit and vegetable and fibre levels is
associated with decreased central fat and weight loss, maintaining a lower weight, lower
CVD and cancer risk, and longevity248,270-276. The fibre in these diets may be acting alone
as volume enhancing fibres, and/or with prebiotic effects, and/or as a matrix associated
with a large variety and volume of micronutrients and vitamins252. All mechanisms are
probable252,277.
1.4.2.4 Micronutrients
Most micronutrients function as cofactors. Cofactors are non-protein activators of
proteins. Many of these proteins are enzymes which catalyse biochemical reactions in
living organisms.
Cofactors include groups which are tightly or loosely bound to the protein. Members of a
group denoted as coenzymes are more loosely bound chemicals which bind with
enzymes. Vitamins, especially antioxidant vitamins, are an important part of the
coenzyme group, as are the metal ions and by definition all must be acquired from the
environment278-280.
Many of these vitamins and minerals are required in relatively small amounts, as they are
restored to initial conditions, being directly or indirectly recycled281. A number of the
vitamins fall into the food plant chemical (phytochemical) or more aptly, phytochemical
micronutrient (phytonutrient) groups. The are many other phytonutrients, which are not
27
essential for human life, but appear to be associated with significantly better health, less
central obesity, less degenerative disease and reduced rates of neoplastic malignant
disease281. Energy processing requires a complex array of micronutrients, most
particularly for efficient macronutrient oxidation282. Antioxidant micronutrients are those
that are thought to reduce free radical production in living systems. As discussed further,
many vitamins, including those examined in this thesis, are ‘antioxidant’ and are
employed in many biological systems mainly dealing with energy oxidation. This thesis
investigates this function. However, ‘antioxidant’ has become a marketing term to
indicate health . Recently, the European Food Safety Authority that deals with claims has
termed antioxidants as “food constituent(s) with antioxidant properties”283.
1.4.2.5 Diets: Palaeolithic to Westernised, and Metabolic Health
It is important to note at this juncture that initially in CVD risk studies dietary leafy green
vegetables and fruit were not particularly considered for dyslipidaemia or diabetes as
sugar and fat macronutrients were seen as the problem284. Somewhat later, ironically, high
fruit and vegetable intakes were promoted in hypertension studies in the form of the
Dietary Approaches to Stop Hypertension (DASH) diet285. The AHA Step 1 & 2 diets,
for dyslipidaemia and the many TIIDM dietary guidelines have changed over the years.
The AHA Dietary Guidelines now put more stress on high fruit and vegetable286.
Palaeolithic Diets
Palaeolithic diets287,288, typical of forager and pre-agricultural societies, include
hunted/scavenged/gathered animal and plant foods, some of which may have been
preserved by drying, such as nuts and fruit, and which may be cooked, but not
farmed/cultivated. Dairy, and often pulses, are excluded. Importantly, highly bred starchpacked grain and tuber foods that have developed in the last 10,000yr are not considered
Palaeolithic.
Mediterranean Diets
The Mediterranean diet studies for CVD amelioration appeared promising in the late
1980s289, but this dietary pattern was possibly difficult for nations where agribusiness and
food processing industries had firmly taken hold. Mediterranean diet studies have been
studied more recently for central obesity-related MetS, even though they do not
necessarily provide less energy240,248,273,290-301. Traditional Mediterranean diets were
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typified by high volumes and varieties of fruits and vegetables, including frequently used
phytonutrient (high polyphenolic)-containing olive oil. Low processed foods, such as
modest amounts of fermented dairy (cheese, yoghurt) and fruit (wine, beer and other
alcoholic drinks), seafood and meats were common. Most traditional fare comprised
whole-food items.
Whole-Food Diets
There does not appear to be a single any agreed upon, whole-food definition, however
most definitions indicate that it means ‘high in unprocessed fruit, vegetables and meats’
some of which is found in the traditional Mediterranean diet302-309. The inclusion of
farmed foods, including diary, pulses and modestly developed fruit and vegetables for
general nutrition, and low processed food preservation methods, differentiates a wholefood diet from a Paleolithic diet.
Thus whole-food diets include low processed foods, preferably local, sometimes organic,
which are still raw, lightly cooked and/or preserved in salt, vinegar or unrefined oils. Fats
and oils that are naturally present in nuts, seeds and animal products, or cold
pressed/skimmed, especially if combined with high proportions of protein and
phytonutrients are common whole-foods. Free range or wild animal products which tend
to be lean, with high proportions of ω-3 fatty acids are eaten in modest amounts. Fruit
(fresh, dried or preserved), which contain no added sugars and very modest amounts of
starch, including fermented products, are a good source of energy and nutrients.
Starch and Sugar Based Staples
Whole grain breads were one of the early processed foods but included high levels of
fibre and modest protein. However, these starch-based seeds, grains, which dry and store
well, and tuber starch storage organs, such as potato, have been bred to produce greatly
increased volumes and proportions of starch310.
Pre-agriculture starchy food was scarce. These food items have become very energy
dense and phytonutrient-depleted; even the germ and skin largely diminished. Baked
breads and crackers, even of the ‘whole grain’ type, contain much refined flour. White
potato based foods, even with skins on, contain very high levels of starch energy. Whilst
many studies include whole grain based foods as part of a ‘prudent diet’, and show health
29
benefits, when studied alone CVD risk markers did not improve on a whole grain
augmented diet311,312.
Whilst fruit may have been cultivated to be fleshier and contain more sugar they can still
be eaten whole, often with skins and seeds; even preserved they retain fibre. Non fruit
sources of mono- and disaccharides (fructose, sucrose) such as from sugar cane and
beet, have also been highly bred for their rich energy yield, and should be excluded from
a whole-food diet.
Potato, and ‘whole grain’, should be classified with refined starch and sugar food
ingredients, or foodstuffs, as producing too low a micronutrient/macronutrient ratio to be
fairly called whole-foods that will reliably aid in metabolic health and prevent excess
adipose tissue gain.
Industrialised Fat, Cholesterol and Protein
Oils, hydrogenated to prevent rancidity, some already saturated such as palm kernel, and
animal fats are refined, losing associated micronutrients.
Raised, and oxidised serum LDL-C is now know to largely be a function of an overall
metabolic problem, although some researchers still advise low cholesterol diets, even to
the extent of reducing egg yolk313,314.
Meat producing animals are often grown in small confines on grain and/or soy based
high-energy, processed diets, with varying degrees of antimicrobial and hormonal
support, producing protein with abnormally high levels and ratios of ω-6/ω3 fatty acids309,
which may have inflammatory effects315.
Westernised Food
In current westernised diets CHO, fats and oils, and proteins become foodstuffs, refined
and industrialised. Highly bred grains for CHO, and pulses and waste seafood for protein,
along with other additives are processed into feed for farmed meat/poultry/fish. Many
food extracts and non food items are added to these ingredients and formed into unusual
mixes for visual appeal, texture, taste, preservation and transport.
The above foodstuffs are manufactured into processed meat and textured vegetable
protein dishes, sauces, pies, breads, pasta, crackers, crisps, biscuits, cake and
30
confectionary. Highly processed foods which are energy rich, highly refined/ and
micronutrient depleted, with preservatives, colourants, artificial tastes for extra
palatability or ‘nutrient(s) fortification’ or other production additives. They have been
called ‘empty calorie’ or ‘junk’ food and have been shown to associate with MetS316.
1.4.3 Pathophysiology of Metabolic Syndrome
In central obesity excess macronutrients are ingested and energy is always in over-supply.
Not only are lipids unable to be accommodated and oxidised in a timely manner in the
body’s cells, but glucose uptake is also compromised. The pancreatic β-cells increase the
tonic insulin production to increase the transport of glucose into cells as glycogen storage
molecules in the liver. In MetS the normal, rapid first phase after meal or post-prandial
rise of insulin seen in healthy slim people, is lost.
The liver develops insulin resistance, becomes lipid and glycogen laden, and subsequently
malfunctions. Gluconeogenesis is no longer suppressed by insulin317-322. Liver insulin
resistance occurs initially, and post-prandial plasma glucose rises, but over time persistent
hyperglycaemia supervenes. Glycolipotoxicity becomes widespread in the large organs
that usually act as energy molecule processors namely liver and muscle tissue204,205,323,324.
As the liver is the powerhouse of energy metabolism and detoxification, pathologies in
this organ have wide ramifications. Non-alcoholic fatty liver disease (NAFLD) of central
obesity may or may not be pathologic, however non-alcoholic steatohepatitis (NASH)
involves probable glycolipotoxicity and inflammation which can lead to fibrosis, and
cirrhosis, which are unpredictable forms of progression of NAFLD in obesity. Such
oxidative stressors are the likely cause of the liver enzymes leaching out of the liver. As
the primary problem is with energy-yielding molecules, abnormal oxidation processes
occur, are poorly controlled and oxidative stress prevails. As many immune responses are
so closely related to oxidative signals, ‘inflammatory’ pathways are activated. It is
important to note that this form of inflammation does not follow the classic 4 steps of
inflammation (heat, redness, swelling and pain) arising from an insult such as injury or
infection. It has appropriately been denoted as ‘metaflammation’ (metabolically triggered
inflammation)325. Another part of the immune disruption to which obesity appears to
contribute involves the immune cells. Undifferentiated adipocytes and several of the
leukocytes share many features326. Large numbers of monocytes migrate to the central
adipose tissue. Possibly due to poor angiogenesis and relative hypoxia, monocytes
31
become macrophages327,328. Leukocytes and inflammatory processes are not fully
effectual in obesity. Infection329,330, auto-immune diseases, neoplastic and malignant
change, or cancers, are increased in MetS331-335.
1.5
Novel Cardiovascular Disease Risk and Protective
Markers
With the above background in mind, some specific comments will be made on the Novel
CVD Risk and Protective Markers that will be examined in this thesis.
There are many metabolic processes which appear to alter as individuals become obese
and acquire the typical MetS markers. Firstly, there are the Novel CVD Risk Markers,
which are novel in that they are a selection of commonly used CRSHaem&Biochem
which have not as yet specifically been employed to yield information which may help
with refining MetS prediction.
They are a diverse group of laboratory tests found to be useful for screening and tracking
inflammatory, nutritional and metabolic dysfunction, which frequently occur together and
almost always involve oxidative stress. They include markers of glucose metabolism,
FPG, HbA1c, serum lipids, urate, liver function tests and acute phase proteins, including
ferritin, leukocytes, ESR and are addressed below. The conditions of interest in this thesis
are central obesity, MetS, TIIDM, CVD and cancer; examples of multiple dysfunction,
hence the use of a battery of laboratory tests. How each CRSHaem&Biochem test fits into
this battery will be described.
Secondly, in this thesis, Novel CVD Protective Markers will be explored. They comprise
two groups.
Serum Adpn and its oligomers are peptides that are intimately related to obesity as they
are synthesised in the adipose tissue, and secreted into the blood. They are denoted as
experimental Novel CVD Protective Markers, as they are not used clinically at this stage.
The nutrition related serum fat soluble vitamins (sFSVitamins) are the second group of
Novel CVD Protective Markers to be examined. As vitamins they are indispensable, but
are altered in an environment of excess lipid.
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1.5.1 Novel Metabolic Cardiovascular Disease Risk Markers
1.5.1.1 Clinical Routine Screening Haematology and Biochemistry
1.5.1.2 Haematology
Leukocytes
It is known that overall WBC is increased in obesity336 and in other CVD risk settings337
but immune function, such as macrophage cell chemotaxis, can be altered or impaired in
these conditions338,339.
Neutrophils
Neutrophils can be stimulated by activated adipose tissue pro-inflammatory cytokines to
exocytose oxidant myeloperoxidase. Neutrophils from individuals with MetS have poorly
controlled cell release of vesicles or granules (exocytosis) containing reactive chemicals.
There is increased likelihood of exocytosis with their pro-oxidant and pro-inflammatory
effects, especially at the endothelium340.
Eosinophils
For many years the increase of eosinophils has been thought to be a signal of allergic
activation. Eosinophilia is a typical response in atopy, and is well documented in
asthma341. Eosinophils can be raised in obesity342, but high fat feeding may decrease the
pulmonary eosinophilic response but increase it in spleen cells, possibly as these cells are
not activating correctly343.
Basophils
Basophils probably contribute to providing mediators in order to initiate immune
responses. They are related to mast cells which classically, on noxious stimulation,
degranulate large quantities of histamine. Basophils can also act as antigen presenting
cells for certain T-helper cells on invasion by multi-cellular parasites and autoimmune
diseases of the skin344,345.
Monocytes
Overfull, large abdominal adipocytes appear to secrete monocyte chemoattractant protein1 (MCP-1) and large central adipose tissue depots can amass large numbers of immigrant
33
monocytes, transforming them to macrophages and increasing many-fold the leukocyte
numbers in this tissue.
Lymphocytes
Lymphocytes contribute to the innate part of the immune system as thymus (T)-cells and
the adaptive immune system as T-cells which are in the cellular immunity part of the
immune system and bone marrow (B)-cells which secrete immunoglobulins (Ig). The
balance of T- to B- cells and their activity may also play a role in the hygiene theory, for
example altering intestinal microbiota in obesity346. Adpn probably regulates lymphocytes
along with many other aspects of the immune system347.
Aggregation Factor
Erythrocyte Sedimentation Rate
The ESR is the time it takes for the red blood cells (RBC) or erythrocytes to settle through
the plasma to form stable stacks or rouloux. The rate is slowed by large molecules, such
as complex inflammatory and glyco-oxidised plasma proteins348. ESR is both an
inflammatory and a rheological measure348-351.
1.5.1.3 Biochemistry Long Term Plasma Glucose Indicator/Oxidant
Haemoglobin A1c
Hb is the protein responsible for blood oxygen carriage and delivery. A glycated form of
Hb is present in the plasma, HbA1c. HbA1c is related to raised post-prandial352 and FPG
levels. It indicates longer term plasma glucose levels over approximately 3-12wk
preceding testing.
Oxidant Markers in Metabolic Syndrome
Urate
Urate is the final product of purine metabolism in higher order primates353.
Hyperuricaemia has been shown to be associated with hypertension and hyperglycaemia
for eight decades,96 and has been proposed as a close fifth member354 to the deadly quartet
of CVD risk factors which are usually listed as abdominal obesity, glucose intolerance/
hyperinsulinaemia/ TIIDM, dyslipidaemia and hypertension355.
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Ferritin
Ferritin is an iron storage protein released from the liver probably related to the effect of
modest ‘oxidant iron’ overload. Ferritin is a pro-oxidant and inflammatory measure
possibly due to insulin resistance set against a background of a large, activated hepatic fat
depot356,357.
Liver Function tests; Oxidant and Antioxidant
Bilirubin
Bilirubin is the final excreted product of haem metabolism, is lipophilic and unlike the
other LFT is an effective antioxidant. The biliverdin reductase cycle, which serves to
amplify bilirubin levels 10,000-fold, provides a constant supply of intracellular bilirubin.
The liver conjugates bilirubin and secretes it in levels at which it is an effective
antioxidant. Higher bilirubin levels may help to delay atherosclerosis and aging, thus
acting as a cardioprotective biochemical 358-360
Alkaline Phosphatase
Alkaline Phosphatase (AlkPhos) is a universal animal enzyme known to de-phosphorylate
proteins and deoxyribonucleic acid (DNA) in alkaline media. Recently, tissue AlkPhos
has been isolated from pre-adipocytes and may form a significant proportion of total
serum AlkPhos361. Intestinal AlkPhos iso-enzymes are involved in chylomicron
formation, FA metabolism and influence CHO levels.
Transferases 1 – Alanine and Aspartate Transaminases
The liver serum transferases are alanine transaminase (ALT), aspartate transaminase
(AST), and gamma glutamyl transferase (γGT). The transferases have been extensively
reviewed in the literature with respect fatty liver in obesity, TIIDM and CVD, and have
been used to predict disease risk362-366. ALT and AST are involved in processing energy;
ALT in gluconeogenesis and AST in lipid and glucose oxidation367. A relationship exists
between the transaminases and the severity of MetS-driven atherosclerotic CVD with
ALT predicting TIIDM362. Importantly, raised AST may be an indicator of the more
serious oxidative stress- and liver fibrosis-related condition of NASH363. An index of
hepatic disease is a raised AST/ALT ratio, which is used in NASH, and may have CHD
predictive properties over and above MetS368.
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Transferases 2 - Gamma Glutamyl Transferase
γGT, the nearly ubiquitous cell membrane enzyme which maintains cellular
concentrations of glutathione, is an antioxidant, may be a sensitive measure of oxidative
stress and yet may itself produce oxidative stress in central obesity369,370. γGT
independently predicts changes in BP, TIIDM370-373 and atherosclerotic CVD374,
irrespective of alcohol intake, and even before NAFLD or central obesity
develops369,371,375.
Classic inflammatory protein (Acute phase protein)
CRP
Acute phase proteins have been used as predictors for TIIDM and atherosclerotic CVD in
previous studies376-378. CRP is synthesised in the liver but has additionally been found to
be secreted by adipocytes379-382, and is known to be released by stimulation of
inflammatory cytokines including TNF-α. CRP is known to be related to waist
circumference, MetS383,384, TIIDM381,382,385 and atherosclerotic CVD386-388 but the
relationship may be indirect, via the abdominal fat mass.
Variably inflammation sensitive serum proteins
Albumin
Albumin is the most abundant serum protein, existing in reduced, oxidised and glycated
forms in normal serum, and has multiple functions. These include being an oxidant
scavenger389 and a long chain FA transporter390. Serum albumin levels are usually
decreased in oxidative stress conditions, although they can initially be raised in young,
obese diabetics with near-normal kidney function, possibly due to hyperinsulinaemia
causing increased protein synthesis and viscosity391.
Globulin
Serum globulins encompass a vast array of proteins with many forms and functions. The
globulin class includes liver-derived sex and cortisol steroid binding globulins, which
may be suppressed by obesity and hyperinsulinaemia392,393. Plasma cell-secreted
inflammatory Ig’s appear to be activated in MetS 378,394, but they can also be consumed in
inflammation395. Thus Ig levels vary greatly with inflammatory stimuli.
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Insulin Resistance Marker
Insulin
Insulin is the classic and main hormone controlling entry of glucose into many cell types.
It is formed in the pancreatic β-cells, and secreted along with other molecules, including
amylin. The control of insulin action and signalling is very complex and depends on the
insulin receptor complex, and many downstream substances and pathways, including
Adpn. Reaven asserted that insulin resistance and compensatory hyperinsulinaemia was
the primary and unifying pathology driving syndrome X or MetS396. This theory
depended on tissue becoming less able to transport glucose into cells. Insulin resistant
tissues included tended to be muscle and liver which undergo large changes in glucose
and FFA flux.
Muscle has to contend with sudden large requirements with sudden bursts or long periods
of contractile activity during exercise. The liver drains the intestine and experiences large
portal vein monoglyceride and FFA loads after food is digested.
Homeostasis Model Assessment - Insulin Resistance
Insulin resistance can be assessed using a number of indices, but one frequently cited and
used in this thesis is the homeostasis model assessment-insulin resistance (HOMA-IR).
HOMA-IR is an index of FPG and fasting insulin originally described by Matthews et
al397. HOMA-IR closely correlates with the insulin sensitivity index by the standard
euglycemic hyperinsulinemic clamp. HOMA-IR is calculated using the following
formula: HOMA-IR (mmol/L. μU/ml) = fasting glucose (mmol/L) x fasting insulin
(μU/ml)/22.5. Most researchers now believe that metaflammation398 and acquired faulty
insulin signalling can be caused by diet-induced obesity399, and that this precedes IRS
rather than IRS being the initiating cause, and thus they no longer follow Reaven396,400
1.5.2 Novel Metabolic Cardiovascular Disease Protective Markers
There are many known CVD protective markers, but they may not be available or
economic for clinical use. In the present work Adpn, along with its oligomers, is a marker
which is synthesised in adipose tissue, and has deep seated insulin sensitizing properties
that modulate energy, lipid and glucose, metabolism. It also has anti-inflammatory
effects401. Adpn is still an experimental polypeptide, although clinical use has been
mooted. The sFSVitamins have been studied in CVD, as epidemiology shows that higher
37
levels of these are associated with lesser risk402-406. However, they have not been routinely
measured for nutritional diagnosis or tracking, and especially not with respect to MetS
status. The Novel CV Protective Markers are presumed to be protective factors but, as
with the risk markers, not enough is known to show clear causal effects.
1.5.2.1 Adiponectin
Adpn is an adiopocytokine, or an adipose tissue derived polypeptide signalling molecule,
secreted almost exclusively from adipose tissue407. There is growing evidence to indicate
that this bioenergetic homeostatic peptide may provide a causal link between obesity,
TIIDM and CVD408. The first description of the DNA encoding for Adpn was reported by
Scherer and co-workers in 1995409. The transcript encoded a hydrophilic protein with
predicted size of 29kDa and was called adipocyte complement-related protein of 30kDa
(Acrp30). Other groups subsequently reported both murine and humans forms407,410-412.
The Adpn gene is located in chromosome 3q27, a susceptibility locus for TIIDM and
related MetS413-415.
Low levels of total Adpn are correlated with increased adiposity416 407, excess muscle cell
lipid417, NASH418, dyslipidaemia (including low HDL-C419), impaired endotheliumdependent vasodilation420 and vascular inflammation421. Hence it is believed that this
adipocytokine has an important role in cardio-protection. Certainly higher levels of serum
Adpn have been associated with a reduced risk of MI, explained only partly by
differences in serum lipids422. The muddled physiology of MetS tends to be associated
with low levels of Adpn. Animal models have shown that exogenous administration of
Adpn can decrease atherogenesis and inflammation and hence improve CVD risk423, as
well as increasing fat oxidation and enhancing weight loss 424, decreasing hyperglycaemia
and reversing insulin resistance
425
. Modest to severe central obesity, CVD risk, and
CVD, are characterised by large, poorly functioning central adipocytes, dyslipidaemia,
insulin resistance and metaflammation426,427. This adipose tissue may also be hypoxic,
hence the widespread dysfunction of adipocytes327, and it is likely that cellular
dysfunction impedes Adpn synthesis.
On the other hand, Adpn may be pro-inflammatory in lean populations. Some chronic,
highly inflammatory, catabolic, autoimmune and tissue-destructive conditions such as
Crohns disease, rheumatoid arthritis, Type I Diabetes Mellitus (TIDM), liver disease
(including cirrhosis), and in fact a number of conditions in old age appear to elicit or
38
associate with high levels of Adpn428. It is possible that Adpn is involved in managing
energy release from adipose tissue in lean and catabolic states.
Adiponectin Oligomers
Data are accumulating on the relationships of total Adpn and more specifically the variant
forms of this peptide, the oligomers, with body weight and adiposity, or the established
metabolic risk factors indicative of CVD.
High-molecular weight (HMW) Adpn is composed of 18+ multimers, medium molecular
weight (MMW) hexamers and low molecular weight (LMW) trimers429. HMW Adpn is
more closely related to metabolic control than either trimer/ hexamer forms, or total
Adpn, hence it is important to investigate whether the isoform structure may change
during a dynamic phase such as weight loss430,431.
Sexual dimorphism of this hormone indicates that that females are more likely to have
high levels of the HMW heterodimers or multimers which appear cardio-protective,
whereas males have more hexamers which may be less cardio-protective432. LMW Adpn
may have protective anti-inflammatory properties, in addition433.
Adiponectin Receptors
Adpn has two main receptors AdipoR1 and AdipoR2, with both receptors being
prominent in muscle, but AdipoR2 being predominantly found in liver434. Both receptors
are found in the brain, but Adpn levels are 1/4000th that in serum, and HMW Adpn is not
present in cerebral spinal fluid(CSF)434. T-cadherin, which is expressed in endothelium
and smooth muscle, appears to be an Adpn-binding protein with preference for HMW
isomers434.
Adiponectin and Medication Effects
Thiozolidinediones, insulin sensitizers, increase circulating Adpn435 as does niacin436,437.
1.5.2.2
Serum Fat Soluble Vitamins
The second group of Novel CVD Protective Markers are the sFSVitamins. Those
examined in relation to the MetS for this thesis were serum vitamin D (sVitD), sβCaro,
vitamin A (sVitA), vitamin E (sVitE) and a surrogate for Vitamin K (VitK), the
international normalised ratio (INR).
39
VitK is a key molecule acting on the blood clotting cascade factors, thus one of the
factors prothrombin time (PT), or blood clotting time, and ratio is reported as the INR, a
surrogate for VitK. By definition the FSVitamins relate to lipid, and are essential in
humans. Part of the absorption, lymph and blood carriage of these vitamins is by the same
lipoproteins as those which transport cholesterol, namely β-Apo A1 & Apo B438,439,
although sVitD has its own plasma VitD binding protein (VitDBP)440. Often sVitE (and
other sFSVitamins) are corrected for lipids due to their solubility, but this may disguise
metabolic relationships as all the sFSVitamins have differing lipid affiliations and
solubilities441.
Healthy individuals, who have low risks of immune and oxidative-stress associated
disease, TIIDM, CVD and neoplastic diseases, tend to have higher sFSVitamin levels. In
people who are impoverished, or if general nutrition or environmental conditions are substandard, single or multiple sFSVitamin deficiencies manifest in a number of clinical
diseases.
1.5.2.3 Vitamin D
VitD is synthesised from sterol precursors in the skin upon ultraviolet (UV) B exposure
and is further metabolised to the circulating form, 25(OH) VitD3, mainly in the kidney,
but in many other tissues as well. Insufficiency is seen in those not exposed to sufficient
sunlight, as seen in many in office-based occupations and with the elderly, and is
especially common in high and low latitudes in combination with winter and dark skin
pigment442,443 443-446.
There is minimal VitD in food, with oily fish being a main supply in some regions. In
Europe, table spreads and milk are fortified. Definitions of sVitD insufficiency vary, but
at levels of less than 50nmol/L, parathyroid hormone (PTH) levels tend to rise, increases
calcium resorption from bone and bone turnover occur, and demineralisation increases447.
Recently, levels of 70-100nmol/L448 have been suggested as sufficient for humans to have
improved all round health requiring oral intakes of 50μg-100μg/day, if there is minimal
skin sun exposure.
Most studies449, although not all450 have shown sVitD insufficiency to be linked with
obesity and it has been hypothesised to be one of MetS abnormalities451. More recently
40
sVitD has been found to be important for growth452, immunologic modulating
functions444 cytokine inhibitory properties453 reducing adenocarcinoma
445
, hypertension
and CVD risk;454 conditions which in turn are linked to MetS. For many years TIIDM has
been related to sVitD insufficiency450. SVitD as 1,25(OH)VitD3 (calcitriol), influences
insulin sensitivity and secretion455. In addition, obesity has been associated with low
calcium intake456, and low calcium absorption is aggravated by low sVitD. In summary,
there is now extensive evidence of roles for sVitD in aiding the prevention of
degenerative disease with immune and macronutrient disturbances via gene regulation.
SVitD modulates proliferation, differentiation, and apoptosis of many cell types and has
effects on immune function, and insulin release457. In European women and men aged 5570y, after 8y of observation the hazard ratio of all cause death was 2.0, and for CVD
deaths 2.2 for the lowest quartile group with mean sVitD approximately 20nmol/L
compared with those in the top quartile whose sVitD was approximately 70nmol/L458.
1.5.2.4 Carotenoretinoids – Beta-Carotene and Vitamin A
Beta-Carotene
βCaro is the most well-known carotenoid. βCaro is a coloured prenol tetraterpenoid, and
can be measured in the clinical setting. Other carotenoids are the alpha and gamma
carotenes, lutein, xanthines, and lycopene. Although other food sources have been found
to contain βCaro in some form, they have usually been from a fruit or vegetable source459.
Good sources of βCaro are raw vegetables and fruits which usually provide many other
carotenoids that may all confer health benefits460,461.
βCaro is found in the cellular membranes of tissues462 and is an antioxidant. It is an
excellent free radical trapper, which has singlet oxygen-quenching effects in vitro. It is
thought to protect against both lipid peroxidation and DNA oxidation463. sβCaro is
associated with less CVD and less cancer404. For this reason it was thought that people at
high risk of carcinogenic change would benefit from high dose βCaro supplements.
However, a number of studies and meta-analyses have shown that lung cancer rates in
smokers were increased464. High dose (20-50mg) βCaro supplementation increased allcause mortality odds ratios (=1.07) and CVD mortality (= 1.1; p=0.003), and was higher
in smokers464. However, dietary carotenoids may help reduce smoking related lung
cancer465.
41
The calculated antioxidant content of a 3d food recall intake predicted levels of sβCaro,
rather than the converse, thus it may be the food type, including a mix of other
carotenoids, that confer metabolic advantages rather than βCaro466. The idea of nutrient
synergy, where the context and other co-ingested nutrients affect absorption and action
may apply to βCaro467. βCaro may split asymmetrically producing carotenals which have
profound effects on nuclear signalling, both inhibitory and stimulatory468.
βCaro is pro-VitA and often retinol equivalents are given when assessing VitA
sufficiency469. Whilst dietary fVitA and spVitA can cause toxicity, sβCaro only renders
the hairless skin yellow. sβCaro deficiency is not well characterised. sβCaro is transported
in the serum by HDL-C470.
Vitamin A
In affluent populations preformed VitA is usually plentiful in fatty animal foods. VitA
can be synthesised by the body from two symmetrical βCaro units, or ingested as one of
three biologically active molecules: retinol, retinal (retinaldehyde) and retinoic acid.
Although βCaro is required for formation of VitA, sβCaro and SVitA are virtually never
correlated even in populations marginal for intake of both471. Other carotenoids such as
lutein may be more highly correlated471.
VitA deficiency has received much attention as causing severe consequences, initially to
the eye and vision, as can be seen in populations with marginal food quantity and
quality472. Higher levels of sVitA and sVitE may be acquired from fortified, preserved
and processed food. The adverse associations of hypervitaminosis A have been well
reported, and include liver damage, failure of immune functions, growth deformities and
foetal malformation473. Retinol is thought to inhibit lipid peroxidation by scavenging
peroxyl radicals by electron transfer and chain breaking474. Retinaldehyde may repress
adipogenesis and diet-induced obesity via retinol-binding proteins and suppress the
retinoid X receptor (RXR)475. SVitA is transported by serum lipids, especially LDL-C.
42
1.5.2.5
Tocopherols and Vitamin E
Vitamin E
VitE is found in many foods such as meats and nuts and other fatty foods. The vitamer,
one form of the vitamin, α-tocopherol, is one of seven other fat soluble tocopherols; and
water soluble derivatives which appear in the blood, are less well studied. The
tocopherols have been found to have many functions, only some of which are
antioxidant476. α-tocopherol prevents AGE formation in native LDL-C and inhibits its
oxidation, but does may not
reconstitute oxidised LDL-C nor
glycated LDL-C
efficiently. VitC can regenerate α-tocopherol from its oxy free radical.
γ-tocopherol is highly added to the American diet as a fat antioxidant, but in fried food it
has negative health effects477. Some therapeutic trials have used the synthetic all-rac αtocopherol, not the d-form (and some in high dose) and have observed a possible increase
in haemorrhagic stroke478,479. Prevention and treatment trials (for heart failure and cancer
prevention) which have used α-tocopherol supplements show no benefits, or borderline
negative results478,479.
1.6
Theories on the Causes of Obesity and Metabolic
Syndrome
At least two authors of recent papers have called for a unifying theory on the causes of
obesity, and also for researchers in the area to cross disciplines to work on the root causes
of the widespread occurrence of obesity480,481. Part of the problem may have been that the
metabolic decompensation associated central obesity is pivotal, and the processes behind
MetS and central obesity and are inseparable, although differing in degree. Pure
peripheral subcutaneous obesity must be differentiated, and the locomotor and
psychological issues associated with this adipose tissue distribution, treated in a different
arena.
Obesity and MetS have always been related to a relative excess of macronutrient intake.
A question that has not been answered adequately is why do humans have such an acute
sense of taste, and motivation for very highly energy dense food? It is known that humans
have been going to extraordinary lengths for thousands of years to use technologies to set
up agricultural processes of horticulture and animal husbandry, and breeding
43
programmes, in order to yield increasingly concentrated starches, sugars and fatty or oily
foodstuffs.
Do humans eat enough protein if there is excess energy dense food, or does this refined
food effectively exclude adequate uptake of protein and other useful nutrients482? What
other micronutrients are required? Some excess energy is acquired from animal fat or
processed saturated and trans fats, has been associated with oxidative stress and LDL-C
lipoxidation.
Antioxidants have been of interest for many years and in epidemiological studies the
higher the levels of antioxidant sFSVitamins the lower the rate of the degenerative
diseases, CVD and cancer483. Intervention studies of the FSVitamins, as mentioned, have
not been successful. However, many medications are phytochemicals, and some have
anticancer effects as shown by paclitaxel, and can control neoplasia recurrence in breast
cancer484. Cancer is known to be associated with failure of the immune system to check
and control aberrant replication. Cancer is thought to arise in an environment of oxidative
stress as seen in those exposed to pollutants, tobacco products and other exogenous
stressors. Endogenous oxidative stress conditions include TIIDM, chronic autoimmune
inflammation as found in ulcerative colitis and chronic viral hepatic infection, and
inflammation485. It now appears that a similar constellation of influences is present in
central obesity, MetS and CVD to those of cancer. Multiple food micronutrients exert
beneficial preventative effects in all these conditions240,305,486.
There seem to be two processes at play. Humans seem to have a very high drive, and
possibly requirement for energy dense food, and yet they seem to have better health, and
less degenerative decline, with a high level of food micronutrients. Reasons for why
humans appear to have contrary drives and needs may be rooted in evolution, and may be
contained in those aspects of evolution which are unique to humans.
1.6.1 Rationale for studies
1.6.1.1 What is Known
Chronic, degenerative disease, particularly CVD, the most common manifestation of
which is atherosclerotic CHD, has been a major cause of death in westernised populations
since the beginning of the 20th Century. Central obesity rates have been increasing over
44
this period, in addition, but they have only accelerated in since the 1980’s. Central obesity
had been mainly associated with the non-insulin requiring type of diabetes, TIIDM prior
to this time. TIIDM, often precedes or is concurrent with CVD and they are both are
linked by common risk markers of hypertension, dyslipidaemia, hyperglycaemia, and
latterly central obesity. Having multiple moderately abnormal risk markers was found to
increase CVD risk, and it had been apparent for some years that they were clustering
together, and forming a syndrome, subsequently labelled MetS. Medical treatment for
significantly abnormal risk markers had been shown to normalise them, but not
necessarily reduce CVD events and death.
Categorising easily measured and well known clinical CVD risk markers into MetS was
certainly of interest for public health planners, but managing or preventing TIIDM and
CVD risk in individuals proved difficult in the clinical setting. Environmental and
behavioural contributions to MetS included energy dense, refined diets, lack of physical
exercise and pollutants, including smoking. The decrease in CVD after the 1960’s has
been attributed to dietary improvement, smoking cessation and medical and surgical
therapies, although combined they may have only contributed modestly. Central obesity
rates are still accelerating in many parts of the world, TIIDM rates are rising together with
the associated CVD risk markers, and CVD is expected to increase again. New research is
finding novel metabolic pathways that involve profound modulation of macronutrient
oxidation and immune system disturbance that may explain CVD.
1.6.1.2 What is Needed
Serum oxidant and metaflammatory markers can be used to detect metabolic stress.
Current clinical routine screening tests could be re-interpreted for useful information. Key
novel, yet well researched, metabolic, hormone and cytokine, or small molecule
signalling, peptides and markers could also be assessed for early screening of CVD risk.
Nutritional markers could be employed to assess metabolic health. All of the above could
be used together with MetS, and the most useful risk markers should be able to track
increases, in addition to decreases, in degenerative disease risk. Their timely introduction
into clinical care, in conjunction with MetS, for early detection of increased or decreased
decline in CVD risk may be especially important for the increasing number of young
centrally obese individuals. The importance to this group is a long lead time in which
there is the opportunity to effect change and prevention of permanent end organ damage
45
and completed CVD events and death. Furthermore, if protective CVD markers are found
to be associated with prudent diets, then rather than just preventing manifestations of
CVD risk, health could be realistically optimised by making much more of the wholefoods type diet.
There is a need for novel uses of clinical screening tests, new key metabolic pathway
markers and greater use of dietary indicators to be used with central obesity-related MetS
definitions. Other disciplines are revealing interesting findings in evolutionary, genetic/
epigenetic, nutritional and behavioural sciences, which, if coordinated and analysed, may
shed light on the root causes of obesity and related MetS.
An overview of the traditional CVD risk factors has been given in the introduction. The
importance of the energy macronutrients, CHOs, lipids and protein and their contribution
to oxidative stress and metaflammation to MetS has been outlined and limitations of MetS
definition
reviewed.
Properties
of
possible
CVD
risk
markers
taken
from
CRSHaem&Biochem are discussed. An experimental adipokine, Adpn and oligomers, the
sFSVitamin micronutrients, and the dietary fibre, chitosan, as CVD protective markers
were reviewed with respect to prevention and management of MetS. Various properties of
well-known dietary patterns have been discussed.
1.7
Hypotheses
1.7.1 Overarching Hypotheses
The overarching hypotheses of this thesis are that;
As human central obesity-related MetS, and its predicted diseases of TIIDM, CVD, and
probably cancer, are degenerative conditions, which have increased in the westernised
environment and for which we do not know the cause or have adequate treatments, reevaluation of previous assumptions on human evolution, energy balance and nutrition
together with re-interpreting current tests and introducing additional tests suggested by
new science, will illuminate some causal aspects of obesity and aid in clinical MetS
monitoring.
Results of the above clinical studies, when incorporated into a wide-ranging inquiry and
research of contemporary science from many different disciplines (human evolution,
human brain development, appetite and the mesolimbic system, nutrition, basic
46
biochemistry, epi/genetics, systems mathematical modelling), will indicate how humans
have diverged from other mammals with respect to metabolism and energy management.
Various co-adaptations that allowed development of the unique energy demanding human
brain, which functioned well in hunter-gather environments of the past, together with the
human intellect and use of dexterous prehensile forelimbs (hands). These new traits drove
technological development for producing, refining and adding appetisers, storing,
transporting and marketing energy dense food, in addition to myriads of other laboursaving devices. These all contribute to MetS.
The results of the clinical studies together with the literature search evidence from human
brain, energy and nutrition evolution and new basic bioscience, and their ramifications,
will result in strategies for obesity related MetS prevention and management strategies in
future.
1.7.2 The Clinically Tested Hypotheses
1.7.2.1 The Clinically Tested Primary Hypotheses
The clinically tested primary hypotheses are that:
In the Novel CVD Risk and Protective Marker study which assessed MetS change over
6m in overweight and obese participants who attempt to lose weight
1) Novel CVD Risk Markers, composed of a battery of CRSHaem&Biochem, which
exhibit inflammatory and oxidant properties, are independently and together, strongly
related to obesity-related MetS and degenerative disease risk and should be used to
enhance MetS disease prediction, and track CVD risk.
2) Novel CVD Protective Markers, comprising (a) Adpn and its 3 oligomers and (b) the
sFSVitamins, known for their anti-inflammatory and antioxidant attributes, are
strongly inversely related to obesity-related MetS and degenerative disease risk, and
should be used to enhance CVD prediction, and track protection from CVD.
1.7.2.2 The Clinically Tested Secondary Hypotheses
The clinically tested secondary hypotheses are that:
In the Diet&Health Weight Loss RCT used both as a model for Novel CVD Risk and
Protection studies and to test a natural non systemic weight loss agent
47
1) participants randomised to the fibre supplement chitosan and given best practice
dietary and moderate PA guidelines for 6m lose more weight and have a greater
decrease in TC and LDL-C than those randomised to placebo
2) chitosan’s putative mechanism of action consists in binding intestinal lipid, forming a
gel complex which is excreted and results in greater faecal lipid loss, and thus
decreases in circulating blood lipids and weight are observed in participants
randomised to chitosan rather than to placebo.
1.7.3 The Unifying Hypotheses
3) Causes of central obesity-related MetS stem from the evolution of the large human
brain, in proportion to the body, requiring very high levels of energy. Hypothesised
adaptations that developed to support this organ acted (a) to increase energy content
of the diet by developing a very strong neural self-reward/motivation system to drive
the search for, and acquisition of energy dense food and (b) to further economise on
general body energy metabolism by the co-option of a wide variety and volume of
antioxidant phytochemicals occurring in a whole-food diet, conferring energyefficient, long-lived cell protection.
Obesity and MetS develop with over-palatable, and for some individuals addictive,
refined, energy dense foodstuffs, without the modulating antioxidant/antitoxicant
phytochemicals required for efficient energy oxidation and cytoprotection.
1.7.4 Aims and Objectives
Primary Aims
To establish a model of weight loss in overweight and obese individuals in order to
investigate changes in MetS, and therefore CVD and degenerative disease risk
•
To investigate which markers from a battery of commonly used CRSHaem&Biochem,
especially markers with inflammatory or oxidant properties, associate the most highly
with MetS, and whether they as Novel CVD Risk markers could be used together with
changes in MetS to increase disease prediction and track disease changes
•
To investigate whether any of the adipocytokine, Adpn and oligomers, and the
sFSVitamins, strongly and consistently associate with various CVD protective
48
markers, and negatively with MetS, and can be used as Novel CVD Protective
markers together with changes in MetS to predict CVD protection and track
improvements in health
•
To interrogate the medical and scientific literature on composite theories of obesity
and MetS, then explore the multidisciplinary literature on human evolution and
nutritional and metabolic co-adaptations to the large human brain, and relate these to
humans in the current environment in order to formulate a unifying hypothesis, and
propose further research to test the hypothesis and report the above using narrative
method.
Other Aims
•
To assess the difference of serum lipids and weight, and mechanism of action, of
chitosan compared with placebo in an RCT
•
To compare the utility of MetS definitions as defined by NCEP, IDF and JIS.
•
To assess the variability of anthropometric change as individuals attempt to lose
weight in a community study with advice on diet and PA
•
To assess the quality of life and eating attitudes in a group of overweight and obese
individuals at the same time as investigating MetS markers
In order to test the first two hypotheses on Novel CVD Risk and Protective markers and
their relationship to MetS over time, random samples from a large community would be
ideal if equal numbers of adult individuals are weight stable, gaining or losing weight,
and there was no secular weight gain trend. As this pattern is not the case a weight loss
study model was required where there was minimal metabolic interference, and weight
loss counselling could be established as could occur in primary care. A Weight Loss RCT
as a parallel study investigating the difference of weight loss between the treatment and
placebo groups, using a mild natural non systemic agent over time would be appropriate.
This model would comprise best practice, dietary fat restriction/increased intake of fruit
and vegetables, and moderate PA, with the natural dietary fibre chitosan, hypothesised to
bind fat within the gastrointestinal tract and enhance weight loss. Increased dietary fibre
has been shown to improve MetS markers252,487. Chitosan, which may be either naturally
or industrially derived from chitin, is thought to achieve weight loss and cholesterol
lowering based on information from animal and preliminary short term human studies, as
detailed above488-493.
49
Demographics, medical history and lifestyle have significant impacts on MetS. However,
once established MetS has a significant effect on lifestyle and quality of life (QoL), thus
validated questionnaires assessing these parameters were required. A prudent lifestyle
study with dietary, PA and motivational counselling on a regular basis was established, to
emulate what may occur in community primary care settings. Since most weight is lost in
the first 3-6m, a 6m time frame was chosen for the intervention494. It was predicted that
modest weight change and improved diet seen in community weight loss studies would
produce a model by which anthropometry, MetS and novel CVD markers could be
assessed.
The detailed method rational and specific methods for the types of clinical studies needed
to answer the hypotheses and fulfil the aims, is developed in Chapter 2. Chapters 3-5
include the statistical analyses methods required for each data topic for the Chapter. The
methods used to develop the composite unifying theory of obesity are outlined in Chapter
6.
50
Chapter 2. Methods
Chapter 2. Methods
2.1
Introduction
This chapter outlines the thinking behind the study designs and then presents the common
general and specific methods employed in performing the Diet&Health-Weight Loss trial
and Diet&Health-Novel CVD Markers studies
The study nomenclature and relationships of the studies and topics in this thesis are
shown in Figure 2-1, Figure 2-2 & Figure 2-3. The study design rationales and
development comprise the first part of the current chapter. The common methods are
presented as the Diet&Health-Weight Loss trial, which is the model for the main
investigation; Novel CVD Risk and Protective Markers studies. The specific
Diet&Health-Novel CVD Marker study methods are presented separately and follow the
Diet&Health-Weight Loss trial (Figure 2-1).
The statistical methods details are discussed and included in the relevant chapters, unless
they are required for more than one chapter, as for example the Demographics, Health
Status, Lifestyle and QoL analyses section
2.2
Study Designs: Rationale and Development
The studies were designed from the outset to fulfil the requirements, concurrently, for 1)
the Novel CVD Risk and Protective Markers studies, whereby hypotheses were generated
or developed and 2) the Diet&Health-Weight Loss study, whereby a typical treatment
comparison RCT was performed.
2.2.1 Study Concept Development
As a primary care clinician, the author was routinely attempting to manage MetS or CVD
risk markers in patients, with or without obesity. The author’s supervisor had used an ad
hoc MetS definition in one of her previous studies221, prior to the NCEP MetS definition
publication, so an interest in MetS in the author’s team was already present. MetS was the
major outcome variable.
51
Chapter 2. Methods
2.2.1.1 Novel CVD Risk Markers – Concepts and Statistical Analyses
Rationale
The Novel CVD Risk Markers, or CRSHaem/Biochem in this case, were novel in the
sense that they are not typically used to track improvements or decline in metabolic status
or MetS. They were not novel in the fact that, although not included in current MetS
definitions, their associations with MetS are well enough known to possibly be included
in a MetS definition or used with MetS. Such use had been suggested with CRP, urate and
liver enzymes96,495.
Analyses were planned to ascertain whether singly or group relationships existed with
respect to MetS marker count change. However, some Novel CVD Risk Markers could be
more strongly related to individual MetS markers over and above others. It was unknown
which Novel CVD Risk Markers would perform better in relating to cross sectional data,
or, more importantly, which would be more effective as possible tracking markers of
MetS change.
Novel CVD Risk Markers - Statistical Analyses Rationale
Statistically, analysis of the Novel CVD Risk Markers were expected to correlate with
each MetS marker and with a MetS index. Partial correlations formed should rank the
most highly related Novel CVD Risk Markers to MetS over and above other Markers.
In addition, mixed modelling over time should allow both the assessment of the
independent relationship of within person changes in markers with changes in MetS, as
well as to the overall mean whilst also including other common and important
explanatory variables, such as age, gender and treatment group. The effect size is
important to record as well as the p value. Factor analysis was not appropriate as 1)
neither exploratory nor confirmatory analysis was required496,497 and 2) factor analysis
cannot be used longitudinally
2.2.1.2 Novel CVD Protective Markers – Concepts and Statistical Analyses
Rationale
With respect to the Novel CVD Protective Marker, Adpn and its oligomers the author
works in a team which has been studying Adpn in vitro and laboratory animals for many
years, and various clinical studies were planned as part of a programme.
52
Chapter 2. Methods
Novel CVD Protective Markers comprising the sFSVitamins, were familiar to the author
who had already worked as a clinician on a study of a non-systemic weight loss agent
where sFSVitamins analyses were performed to indicate whether faecal fat loss decreased
FSVitamin status. In using the natural food fibre, chitosan, for the current study, and its
possible faecal fat loss effect, correlational analyses of sFSVitamins was decided upon.
However, the more likely value of the precautionary analysis of the sFSVitamins was
soon perceived by the author as their being used as Novel CVD Protective Markers, and
factored into the study design.
Novel CVD Protective Markers - Statistical Analyses Rationale
The statistical analysis for the Novel CVD Protective Markers would differ slightly in
quality from the Novel CVD Risk Markers. This was to be a more hypothesis-generating
analysis than that performed for the CRSHaem/Biochem.
Thus, the analyses chosen were broad and wide-ranging correlational arrays of Adpn and
the sFSVitamins with known CVD protective and risk markers, including the MetS
markers and MetS marker count. Although this array was to be broad it was not random,
as all the correlation parameters related to CVD risk or protection. The effect size was as
even more important in the Adpn30 oligomer group owing to 1) the limitation of sample
number that was able to be analysed, and small sample number was also why participants
with extreme outcomes would be chosen, and 2) the very different gender groups’ size or
number of participants.
As the sFSVitamins are inter-correlated, mixed modelling would be fitted with the
sFSVitamins and common explanatory variables, to attempt to ascertain the relative
strength of each sFSVitamin, on an overall mean and intra-person basis, with the MetS
markers and MetS index. Extra sVitD analyses would be required due to the previously
established seasonal and skin pigmentation effects on dermal photosynthesis of this
vitamin.
2.2.1.3 Diet&Health-Weight Loss Trial
The RCT was to test a treatment, chitosan against placebo, for outcomes of weight loss
and traditional CVD marker improvement. Typical RCT statistical methods of
comparisons between outcomes, and various sensitivity tests on all randomised
participants as well as per protocol analyses would be run. As this was to be a treatment
53
Chapter 2. Methods
decision trial, probabilities for statistical relevance need to be set before hand and are
usually p<0.05 for the main outcomes However, effect size would influence decisions on
clinical use of the chitosan.
2.3
Contributors to the Dietary Fibre and Lifestyle for
Health Studies
There were a number of contributors to the Diet&Health study.
2.3.1 Author’s Contribution to the Studies
The author formulated the hypothesis, designed and wrote the draft protocol for the
Diet&Health- Weight Loss and Novel CVD Marker studies.
She researched the information on inflammatory and experimental markers such as CRP,
Adpn and, from pre-clinical and clinical research, and determined that the metabolic and
nutritional markers in clinical use needed specific study in obesity-related MetS. The
Diet&Health-Novel CVD Risk and Protective Markers studies were entirely designed,
written and undertaken by the author, with the supervisor particularly advising on the
Adpn section. The author designed and organised Adpn30the clinical and procedural
practical standard operating procedures (SOP).
The chitosan Investigational Brochure (IB) and Standing Committee on Therapeutic
Trials (SCOTT) applications were written by the author. She wrote the first draft of the
Ethics Committee approval application to the Auckland Ethics committee. The laboratory
and health status questionnaire coding was overseen by the author, and she set the safety
ranges of laboratory tests.
The author designed the participant interview protocol, and the examinations and
procedures standard operating procedures, performing many of them herself. She taught
these procedures to research assistants, and guided and monitored their performance.
54
Chapter 2. Methods
Figure 2-1. Clinical Studies – Plan of Components
The Clinical Study
Two Main Parts of Study
(Same participants)
Diet&Health -Weight Loss
A Model to Investigate
Modest Body Weight Loss
and CVD Risk Reduction
Component Studies
Chapter 3
Double Blind, Parallel, Randomised,
Placebo-Controlled Study: Dietary
Fibre Intervention
Fibre & Fecal Fat Loss Mechanism
(Fibre&Fat Loss) Substudy
Chaper 4
MetS & Novel CVD Risk Markers:
Clinical Routine Screening
Haemtology/Biochemistry
(CRSBiochem/Haem)
Dietary Fibre & Lifestyle for
Health
-1)Weight Loss
(Diet&Health -Weight Loss)
-2) Novel Cardiovascular
Disease Markers
(Diet&Health- Novel CVD
Markers)
Diet&Health- Novel CVD
Markers
A Study to Investigate
Metabolic Syndrome (MetS)
& Novel CVD Markers
Chapter 5
MetS & Novel CVD Protective
Markers:
(1) Adiponectin (Adpn250) & Adpn
Oligomers (Adpn30)
2) Fat Soluble Vitamins (FSVitamin)Vitamin (Vit)D (VitD),beta-carotene
(βCaro), VitA, VitE & VitK
Chapter 6
Theories: Causes of Obesity & MetS
Energy for Human Brain: Evolution
1) Energy Uptake: Energy Food
Reward & Motivation ...Addiction
2) Energy Economy: Phytonutrient
Antioxidants & Energy Modulation ...
Loss of Cytoprotection
Chapter 7
Discussion and Conclusion
55
Chapter 2. Methods
Figure 2-2.Diet&Health–Weight Loss Trial and Fibre&Fat Loss Study Timeline
Randomisation
6m Visit (n=164)
(n=250)
Recruitment
Phone or
Email Reply
Screening/
Registration
-2 wk Visit
Visit Baseline
Blood test
Anthropometry
Bioimpedance
1m Visit
2m Visit
Anthropometry
Anthropometry
3m Visit
Blood test
Anthropometry
Bioimpedance
Blood test
4m Visit
5m Visit
Anthropometry
Anthropometry
Anthropometry
Bioimpedance
Faecal Fat Collect
(n=29)
Collect
Questionnaires
Faecal Fat Collect
(n=51)
Figure 2-3. Diet&Health-Novel CVD Risk and Protective Marker Studies Timeline
Recruitment
(n=250)
6m Visit (n=164)
Visit Baseline
Recruitment
Phone or
Email Reply
Registration
-2wk Visit
Blood test
1m Visit
2m Visit
Anthropometry
Anthropometry
Anthropometry
Bioimpedance
Collect
Questionnaires
3m Visit
Anthropometry
Bioimpedance
4m Visit
5m Visit
Anthropometry
Anthropometry
Blood test
Anthropometry
Bioimpedance
Collect
Questionnnaires
56
Chapter 2. Methods
Simple statistical calculations and correlations were performed by the author for the
Fibre&Fat Loss substudy, and the Diet&Health-Novel CVD Risk and Protective Markers
studies. She discussed, with a Faculty of Medical & Health Sciences (FMHS)
biostatistician, Ms Joanna Stewart, the type of analyses appropriate for the questions
posed by the Diet&Health-Novel CVD Marker study, comprising CRSHaem&Biochem,
Adpn30 and the sFSVitamins sections. Ms Stewart ran the more complex analyses, as
detailed below in Sections 2.3.1.1, 4.2.3.5 and 5.2.5.
The theoretical Chapter was the author’s idea with the co-supervisors guiding on
scientific interpretation of the literature, ensuring energetics was addressed, and the
human side of addiction was acknowledged.
2.3.1.1 Collaborations
Collaboration with the Clinical Trials Research Unit
The Clinical Trials Research Unit (CTRU) at the University of Auckland became
collaborators. Their contribution encompassed editing the protocol and data management,
including setting up the databases and double data entry. General, anthropometric and
laboratory data entry and statistics were undertaken by the CTRU for the Diet&HealthWeight Loss trial only. CTRU staff chose the Obesity Specific Quality of Life (OSQoL)
QoL and Eating Attitudes 12 question (EAT 12) the questionnaires. The CTRU staff
contributed to the conception and design of the Fibre&Fat Loss substudy. The
manufacture, quality control and randomisation of the chitosan and placebo were the
responsibility of CTRU, and they had no contact with participants. Data monitoring and
audit was overseen by the CTRU.
Bioimpedance Collaboration
Dr Leigh Ward (University of Queensland) was responsible for the establishment of the
methods for the multifrequency bioelectrical impedance analysis (MFBIA, Impedimed,
Australia). He analysed the raw data in order to generate the calculated %Fat values.
Laboratory Analyses Collaborations
Two groups provided laboratory analyses.
57
Chapter 2. Methods
1) Laboratory staff and clinicians at the Auckland District Health Board (ADHB)
LabPlus were contracted to perform the CRSHaem&Biochem and specially adapted
sFSVitamin analyses. Faecal fat samples were analysed by this laboratory.
2) A School of Biological Sciences post-doctoral scientist, Dr Yu Wang, performed the
baseline Adpn analysis of the 250 samples from all participants, Adpn250, in
Auckland. She analysed the Adpn oligomers from the 30 subgroup participant
samples, Adpn30 (30 baseline, 20 6m) at her current workplace, the University of
Hong Kong.
Statistical Analyses Collaborations
CTRU
CTRU statisticians performed the Diet&Health-Weight Loss study repeated measures last
value carried forward (LOCF) and sensitivity tests. The statistical package used was SAS
v8.0 (SAS Institute Inc, Cary, NC, USA, 2001)
University of Auckland, Faculty of Medicine and Health Science Biostatistician
Ms Joanna Stewart collaborated with, advised and was directed by, the author to 1) run
the combined Pearson and partial correlations and the least square means analyses and 2)
fit the general linear models, ordinal logistic regressions, and general and generalised
mixed models. She used the statistical package SAS v9.1 (SAS Institute Inc, Cary, NC,
USA, 2006)
2.3.1.2 Study Site
The University of Auckland Human Nutrition Unit
The study was undertaken at the Human Nutrition Unit (HNU), a converted two-storey
wooden villa. The HNU is a community-based facility designed to carry out controlled
nutrition, supplement and medication interventional, publicly funded and commercial
trials. The HNU is located in a quiet inner Auckland suburb, with easy access to public
transport and private car parking. It provides a comfortable, non-clinical-appearing
environment
for
the
participants
of
research
trials.
There
are
communal
lounges/sitting/waiting rooms for reading of ‘Participant Information Sheets’ (PIS). There
are separate private interview rooms. The HNU is Good Clinical Practice (GCP)
compliant. Private clinical rooms for anthropometry are available. These are provisioned
with electronic BP machines, body weight (weight) scales, stadiometers, non-stretch tape
58
Chapter 2. Methods
measures and other basic equipment. Portable electrical bioimpedance or other equipment
is procured and utilised as necessary. A basic laboratory is set up for study staff to process
human tissue, usually blood, and other specimens. Laboratory equipment includes -20ºC
freezers and centrifuges. The specimens are prepared for same day analysis and transport
or storage at other laboratories for specialist analyses. Staff include study managers and
research assistants who are contracted as needed, and research students in masters and
PhD programmes.
2.4
Dietary Fibre and Lifestyle for Health – Weight Loss
Trial
The Diet&Health-Weight Loss trial was a community based, 6m weight loss, parallel,
double-blind, randomised, placebo-controlled, intervention trial of a modified dietary
fibre, chitosan, as a example of a weight loss agent (Figure 2-1). This Diet&HealthWeight Loss was also the model used for the main investigation into MetS and
Diet&Health-Novel CVD Markers.
Fibre and Faecal Fat Loss Mechanism Substudy
Refer to Section 2.4.5 and Figure 2-2.
2.4.1 Study participants
Two hundred and fifty overweight and obese women and men who desired to lose weight,
and were living in the Auckland city area were invited to join the study.
2.4.1.1 Eligibility criteria
Inclusion
Participants were required to be over 18y, have a BMI between 28 and 50kg/m2 (Table
2-1), and not be actively trying to lose weight. The classification used to select the range
of BMI levels utilised one of the lower definitions of obesity at 28kg/m2 or above the
NHANES II definition of overweight498 (Table 2-1). An upper level of 50kg/m2 was
thought to encompass most ambulant participants.
59
Chapter 2. Methods
Exclusion
Participants were excluded if they: were on treatment with crustacean- or mollusc- marine
fibre chitosan-containing supplements; were on current or recent treatment with weight
loss medications; were on a current, or had recently attended a commercial weight loss
programme/clinic; had allergies to seafood; were pregnant or breastfeeding; had active
gastrointestinal disease or previous obesity surgery; had involvement in another clinical
trial and were individuals judged to be unlikely to comply with study treatment and
follow-up procedures.
Table 2-1. NHANES BMI Classification
Adult BMI kg/m2
Underweight
In normal range
Marginally overweight
Overweight
Very overweight or obese
Women
< 19.1
19.1 - 25.8
25.8 - 27.3
27.3 - 32.3
> 32.3
Men
< 20.7
20.7 - 26.4
26.4 - 27.8
27.8 - 31.1
> 31.1
From NHANES II (1976-1980) 49 8
2.4.1.2 Ethical Approval and Study Registration
Ethical approval for the study was obtained from the Auckland Ethics Committee
(currently Northern X Ethics Committee), Auckland, NZ499. All participants were given
detailed information on the studies. They provided written informed consent. The study
was registered as a clinical trial with the International Standard RCT Register #
96702062500
2.4.2 Study Procedures
2.4.2.1 Study Visits and Procedures
Participants were seen at 8 scheduled clinic visits during the study. Time periods were
measured in months as visits could vary by a week (wk) or two. These were held at
registration (-2wk), at baseline (0m and randomisation), and at 1m, 2m, 3m, 4m, 5m and
6m following randomisation. Eligible registrants were enrolled (-2wk, registration) and
randomised at baseline. Participants visited the HNU monthly for anthropometry and
advice on prudent diets and increasing PA.
60
Chapter 2. Methods
Written lifestyle advice on diet comprised standardised items: recipes, energy content
guides, fruit and vegetable information, PA (including a frequency x intensity x duration
counter) and stress management and motivation sheets were given at the standardised
visits. Fasting blood tests were drawn and body composition was performed at baseline,
3m and 6m. Food recall, PA and QoL questionnaires were administered at baseline and
6m501.
Questionnaires provided included (1) a validated 24Hour (Hr) Food Recall record
(Appendix 1) and instruction booklet (2) The Life in New Zealand (LINZ) validated PA
questionnaire502,503( Appendix 1) (3) the Short Form – 36 question QoL Health Survey
(SF-36) questionnaire (Appendix 1) (4) the OSQoL questionnaire (Appendix 1) and (5)
the EAT-12504 questionnaire (Appendix 1).
The questionnaires were given to participants at the screening/registration visit for
completion and return at the baseline visit. Questionnaires were reviewed with
participants to check for understanding prior to uplifting, and on return to check
completion. Participants were not permitted to have used any formal weight control
methods 2m prior to, or during the study. At each visit until 5m participants were given
systematic dietary and PA advice, and enough capsules in a large bottle for 12 treatment
or placebo capsules daily.
A personalised diary was distributed to all of the participants at the screening/recruitment
(-2wk) visit so they could book all their visits in advance, and be reminded forthcoming
visit times and procedures and record any AE. Additionally, visit specific instructions,
recorded in the participant diary, were annotated, with prompts to attend fasted for blood
tests. Study staff gave phone call reminders the day before visits, and rescheduled visits
within the pre-set limits. A travel allowance and gratuity was given at each visit.
Recruitment
Overweight and obese women and men were recruited through local city and suburban
newspaper advertisements. The advertisements were also posted around University
campuses, supermarkets and other public places. Email advertisements were sent around
tertiary institutions. Participants were invited to join a weight loss trial investigating the
use of chitosan, a dietary fibre, as an adjunct to weight loss. Refer to Section 2.4.2.1.
61
Chapter 2. Methods
Screening/Recruitment -2wk Visit
At the screening visit the prospective participants read the PIS and completed the
Informed Consent Form (ICF). The study protocol, questionnaires, and capsule regimen
were all explained in detail. Once the ICF was signed, body weight was measured, a
health status questionnaire administered and registration completed. Those registered
were given placebo capsules and instructed to take 4 capsules three times a day for the
following 2wk, and return the capsule bottles at the next visit to allow assessment of
compliance, whereby staff counted the capsules. The participants were also requested to
have the questionnaires completed, and with them, when they returned 2wk later. An
introduction was given on how to make changes towards prudent low fat diets and
increased PA. This 2wk period was the formal Diet&Health-Weight Loss trial singleblind placebo pre-randomisation run in.
Two Week Run-In
Only those participants who took greater than 85% of their study medication (4 capsules,
3 times a day, based upon capsule count), in the run-in phase were eligible to take part in
the 6m randomised intervention. A number of the registered participants either
discontinued, or were excluded as they did not complete the placebo capsule course
during the run in period.
Baseline Visit (0m)
Randomisation occurred at the baseline visit. Participants were asked whether they
thought they could continue the regimen of 12 x 250mg capsules/d, and visit monthly.
Adverse events (AE) were recorded. The run in capsules were emptied onto a pill counter
tray and counted. Those who had taken >85% of their capsules or who had returned <15%
and wished to continue were randomised to chitosan treatment or placebo. Computer
generated randomisation codes were supplied by the CTRU staff. A month’s supply of
capsules was dispensed.
All questionnaires (Refer to Section 2.4.2.1) were collected and checked for missed
questions by study staff. Any outstanding questions were completed with the participant,
where possible. One-on-one counselling was given on lifestyle changes for weight loss.
‘Best practice’ advice centred on low fat dietary change, increasing fruit and vegetable
intake, and increasing moderate intensity PA to at least 1/2hr a day. Motivational
62
Chapter 2. Methods
techniques were employed to assist modest lifestyle change. All information was
standardised so each participant received the same advice in written material, including
recipes, at the same stage of the trial. Pamphlets given at Visit 2 included: ‘National Heart
Foundation - I’m walking’, ‘Tips on exercise’ generated by study staff, ‘Step up to the
Challenge (healthy meals)’, ‘Healthy Lifestyles for a healthy weight’, and ‘Fat content of
foods (Weet-Bix™ comparison)’.
The baseline anthropometry was performed. Weight, height, waist and BP were measured
and %Fat was assessed indirectly by multi-frequency BIA (MFBIA, Impedimed,
Australia).
Blood was collected following a 12hr overnight fast using (1) an ethylene diamine tetra
acetic acid (EDTA) tube for a full blood count (FBC) and stored for later HbA1c batch
analysis (2) an ESR tube for ESR measurement (3) a serum tube for lipid: TC, HDL-C
and TG measurement and same day analysis (LDL-C and TC/HDL were calculated), and
(4) a serum tube wrapped in silver paper to prevent light exposure for batched
sFSVitamin analyses. All 1)-4) were sent to LabPlus. A fluoride oxalate tube for FPG and
two other serum tubes for CRSHaem&Biochem and cytokines were taken, spun and
pipetted into microtubes and frozen at -80ºC for later batch analyses.
1m Visit
Anthropometry (weight, waist and BP) was performed, a capsule count made, and any AE
recorded. Refer to Section 2.4.6.2. All participants were given standardised, visit-specific
low-fat dietary and prudent activity advice in the form of one-to-one sessions with
investigators throughout the trial. Written information provided at this visit to each
participant was ‘NZ National Heart Foundation Food Guide: ways to reduce your risk’,
‘Be Active Every Day’ (Exercise Pyramid)’, and ‘SPARK NZ, Push Play Information’.
The study staff did not give personalised lifestyle advice at any visit
2m Visit
The protocol for this visit was as for the 1m visit. The written lifestyle advice given was
‘Reading Food Labels’ (information taken from various sources) and ‘National Heart
Foundation Number Crunching’ pamphlets.
63
Chapter 2. Methods
3m Visit
The protocol for this visit was as for the baseline visit. Anthropometry and body
composition were taken as for the baseline visit. Blood was drawn for analyses. The
written lifestyle advice given was ‘Reasons for eating’ sheet and ‘Causes of Weight Gain’
sheets.
4m Visit
‘Motivation Wheel’ and ‘Motivation Explanation Sheet’ derived from Prochaska and
Diclemente505 and a Buttercup pumpkin recipe.
5m Visit
‘Satiety Index’ and an ‘Insulin Resistance model graphic’ which was derived from
various sources. Questionnaires were given to participants with instructions on
completion and return at the 6m visit.
6m Final Visit
The 6m visit was as for the baseline visit. No further lifestyle advice was given.
2.4.3 Anthropometry
2.4.3.1 Weight, Waist, Height, Bioelectrical Impedance and Blood Pressure
Participants were measured when lightly clad, without jackets/coats, head coverings, hair
clips or shoes and with empty pockets. The following measurements were taken and
recorded on the case report forms. All measurements were performed in duplicate.
Body Weight
Weight was measured on calibrated digital scales (Seca, Model 708, Germany) to the
nearest 0.1kg.
Height
Height was recorded at baseline in centimetres (cm) to the nearest millimetre (mm) using
a wall-mounted stadiometer (Seca, Model 222, Germany).
64
Chapter 2. Methods
Body Mass Index
BMI was calculated by using the standard equation BMI = weight/height2 (kg/m2)
Waist Circumference
Waist was recorded to the nearest mm. It was measured midway between the last rib and
the crest of the ileum at the natural point of waist narrowing using a non-stretch tape
measure. In the very obese if there was no waist narrowing, the point predicted as the
lower point of the ribs, laterally, was chosen and the tape passed horizontally around the
body by having participants anchor the tape against their bodies at the correct level and
slowly turn on the spot, until the tape was brought around to the start.
Bioelectrical Impedance
BIA was measured using a Seac bioimpedance multi-frequency meter MFBIA
(Impedimed, Australia). Participants were measured at the baseline, 3m and 6m visits.
They were fasted and had refrained from drinking for at least 2hr prior and from
strenuous exercise on the day of measurements. Women were asked if they were
pregnant, and if so, together with those with pacemakers, were excluded. The presence of
any metal implants such as pins or plates was noted. The arm and leg lengths, and sitting
height, were measured. Participants lay supine with limbs not touching the body.
Electrodes were placed on the right wrist, dorsum on the right hand, the right ankle and
the right foot. The electrodes of the MFBIA meter was positioned to perform separate
whole body, leg, arm, and trunk measurements and recorded on the CRF. The drive
electrodes were not moved in position during the measurement but the position of the
sensor electrodes was moved for each measurement.
The measurement was made directly after the participant’s characteristics were
completely entered. Characteristics measured were resistance R (ohms)=U (voltage drop
Watts)/I (electrical current Amp) and impedance Z=height (of body)2/resistance),
Z=(constant)k.h2/R. %Fat was calculated at the end of the study.
Blood Pressure
S/DBP was measured at every visit. Measurements were made after the participant had
been sitting for 5min with an appropriately sized cuff (pneumatic bag 20% wider than the
diameter of the upper arm – usually the large size) using a functional, calibrated blood
65
Chapter 2. Methods
pressure monitor Dinamap XL machine (model 9300 series, Palo Alto, USA). Each
measurement was made on the non-dominant arm. Readings were taken before blood was
drawn. The blood pressure monitor was set so that time between deflation steps depends
upon the frequency of these matched pulses or pulse rate of participant. If either of the
diastolic or systolic values differed by more than 10mmHg for each reading, additional
readings were obtained until values from two consecutive measurements were within
10mmHg.
2.4.3.2 Randomisation, Blinding, Medication Dosing and Dispensing
Study participants were randomised in a 1:1 ratio to receive chitosan or placebo capsules.
Staff at the HNU dispensed the study medication under blinded conditions using a
randomisation sequence generated by a computerised random-number generator with
mixed block sizes to prevent unblinding. There was no stratification by gender or other
demographic variables. Treatment assignment codes were not available to the
investigators, research staff or data entry staff at any point during the study and were held
centrally by the Diet&Health-Weight Loss trial statistician, at the CTRU, University of
Auckland.
2.4.3.3 Trial Medication Details
The chitosan used in the study was a β-chitosan (pleated sheet, poly-[1-- >4]beta-(Dglucosamine) derived from NZ squid pen chitin, and supplied by Healtheries, a food and
supplements company. Independent analysis verified that the level of deacetylation was
75.5%, which conformed to prior specifications.
The study medication was dispensed in identical capsules, each capsule containing either
250mg chitosan or 250mg placebo (maize corn flour). Participants were instructed to take
4 capsules with a glass of water 3 times daily before main meals such that a total of 12
capsules (3g/d) of either chitosan or placebo were consumed. Treatment allocation was
confirmed by independent assessment of capsule content in a sub-set of 25 participants
during the first 4wk of the trial. The CTRU statistician reviewed this data and confirmed
the treatment allocation.
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Chapter 2. Methods
2.4.4 Laboratory
Blood laboratory analyses were carried out at LabPlus, ADHB laboratory. LabPlus
ADHB is an accredited laboratory and observes the NZ Code of Laboratory Management
Practice incorporating ISO 9000 Guide 25:119 and ISO 9002:1987, issues(up-dates in
regulatory standards) 12-14 at the Greenlane/National Women’s Hospital site and issues
18-24 at the Grafton site in 2003. The Grafton site laboratory was used for immediate
routine and safety tests such as haematology, and primary outcome markers for the
Diet&Health-Weight Loss and Novel CVD Markers study including all biochemistry.
LabPlus also stored frozen batched laboratory samples, for later analysis with the followup 6m sample. FPG and HbA1c were stored in -80ºC freezers at the Greenlane Outpatients
Clinic and Hospital site, and urate, LFT, sβCaro sVitA and sVitE were stored at the
Grafton site for later analysis.
Sample Management
Sample Collection
All blood samples were collected by venous phlebotomy by staff at the HNU from fasted
participants. For a few (6) unsuccessful venepunctures, participants were sent to the
Auckland City Hospital blood collection rooms to have their blood drawn.
Sample Sorting
Samples for immediate transport to the analysing laboratory and/or same day analysis
were kept in their blood tubes at room temperature for up to 3hr until delivery to the
LabPlus Laboratory. These samples were FBC in the EDTA tubes, serum tubes for
immediate biochemistry and fluoride oxalate containing the FPG samples.
Sample Preparation; Centrifuging, Pipetting and Storage
Serum tubes of whole blood were left in racks on the bench to clot for 30-90min after
collection prior to being centrifuged. A Heræus Christ™ Labofuge GL (Hanau, Germany)
centrifuge was set at 3000rpm for 15min. Whole blood was separated into a supernatant
of serum, leaving the waste blood cells at the bottom. After centrifuging, the supernatant
was pipetted into 2mL screw cap tubes, double checked by other staff and put into
temporary storage, 4-6 wk, in a Westinghouse™ model FJ383, -20ºC freezer
(Westinghouse Electric Company, Risley, U.K.), then transferred into a -80ºC Sanyo™
freezer (Oosaka, Japan).
67
Chapter 2. Methods
2.4.4.1 Laboratory Sample Analyses
The analytes are shown in Table 2-2.
Table 2-2. Laboratory Test Collection and Analytes
Test group
1
Serum Full Lipid Profile
Serum Biochemistry
Test analytes
2
TC, 2HDL-C, 2TG, LDL-C (calculated), TC:HDL ratio (calculated).
3
FPG
4
sVitD, 3sβCaro, sVitA3VitA, 3sVitE, 2Prothrombin time (PT) International
Normalised Ratio (INR) surrogate for VitK. INR (VitK)
3
TG
Faecal Triglyceride
1
Safety tests 2 Tests were analysed immediately. 3 Sample stored frozen at -80ºC and analysed at a later
4
date. s VitD was batched analysed every two weeks. TC total cholesterol; HDL-C high density
lipoprotein cholesterol; TG triglyceride; LDL-C low density lipoprotein cholesterol; FPG fasting
plasma glucose; s serum; VitD vitamin D; βCaro beta-carotene; VitA/E/K vitamins A/E/K.
2.4.4.2 Biochemical Analytes
Lipids
Serum lipids (TC, HDL-C and TG) were measured using enzymatic colorimetric tests on
a Roche/Hitachi 917/MODULAR P (Mannheim, Germany). LDL-C was calculated using
the Friedewald equation506 at LabPlus, ADHB, Auckland.
Friedewald equation: In mmol/L: LDL-C=TC – HDL-C – (TG/2.2). Note: This method is
limited by <12hr fasting, and if TG >2.5-4.5mmol/L accuracy is limited. At >4.0mmol/L
the equation was not used in this study.)
Fasting Plasma Glucose
FPG
was
analysed
enzymatic
using
colorimetric
tests
on
a
Roche/Hitachi
917/MODULAR P autoanalyser (Mannheim, Germany).
Fat Soluble Vitamins
The methods of analysis are detailed in Section 2.6.2 below.
2.4.5 Fibre and Faecal Fat Loss Mechanism Substudy
The Fibre and Faecal Fat Loss Mechanism (Fibre&Fat Loss) substudy was part of the
Diet&Health-Weight Loss trial. The time line is shown in Table 2-3.
68
Chapter 2. Methods
2.4.5.1 Method
Fifty-one participants who were already recruited for the Diet&Health-Weight Loss trial
were asked to join the Fibre&Fat Loss substudy. They filled in the extra consent part of
the Participant ICF. Participants who entered Fibre&Fat Loss substudy were enrolled in a
serial manner until 25 were randomised to the chitosan treatment group and 26 into the
placebo group. Three day faecal collections were made by all participants for subsequent
fat content analysis.
Table 2-3. Faecal Collection Time Line
Baseline -2wk
Registration
Baseline minus
3d
Baseline
Baseline to 6m
6m minus 3d
3d
Faecal
Collection
Submit Faecal
Collection
Randomisation
6m
chitosan/placebo
Intervention
3d
Faecal
Collection
6m
Submit
Faecal
Collection
Finish
2.4.5.2 Procedures
On the screening visit (-2wk) and the second to last visit at 5m, the consented participants
were given a large thick paper bag with 6 plastic collection trays to collect all faecal
production for 3d. They were also given spatulae to direct each faecal collection into thick
double plastic bags.
2.4.5.3 Eligibility Criteria
Inclusion Criteria
The first 51 participants to agree to the faecal collection were randomised with 25 to
treatment code B, and 26 allocated to code A.
Exclusion Criteria
Participants could not take laxatives or cholestyramine/cholestipol 1, both of which could
affect lipid/sterol losses and hence interfere with measurements.
1
Cholestyramine/Cholestipol, prescribed for serum cholesterol lowering, are resins which bind bile acids
and hence effect enterohepatic circulation, prevent reabsorption and lead to an increased GI excretion
69
Chapter 2. Methods
Laboratory Work
Research staff thawed and combined the 3d faecal collect under a fume hood, and rapidly
sent the sample to LabPlus laboratory.
Analyses
A typical clinical procedure for analysis of faecal fat, as for documenting or monitoring
treatment for steatorrhoea, was performed. LabPlus Greenlane staff used a 3-step process
of saponification of fats, extraction of FFA, and determination of total FFA507.
2.4.6 Demographic, Health Status and Lifestyle Questionnaire Analyses
For the Novel CVD Marker study demographic, medical, medication, lifestyle (social
habits, diet, PA) and QoL, and eating attitudes questionnaires were administered at
baseline and 6m.
2.4.6.1 Demographic, Lifestyle, Quality of Life and Eating Attitude
Questionnaires - Analytical Methods
Demographics
Ethnicity grouping was performed as per the NZ Statistical Standard for Ethnicity
2005508.
Diet
A standard 24Hr Food Recall record was administered (Appendix 1, Questionnaires) at
baseline and 6m, with a detailed instruction booklet on food portion size estimates, recipe
recording and other qualitative information. Data was entered into FoodWorksTM v2.1
(2002 New Jersey, USA), a commercial computer programme that calculates the nutrient
content of the food items, modified for foods available in NZ.
Physical Activity
The validated questionnaire Life In NZ (LINZ)-PA502,503 assessed reported PA over the
previous month. Appended were 3 questions on sedentariness, modified from a nonvalidated sedentariness questionnaire published by Egger et al509. As the reported level of
PA was low with little sports activity, it was elected to selectively analyse general modest
70
Chapter 2. Methods
PA and intense PA indicators from the validated questionnaire, after scoring (Appendix 1
Questionnaires).
Short Form-36 Question Health Related Quality of Life Survey
The 36 item SF-36 was coded and analysed as per the validation articles504. The SF-36
was analysed with a specific macro-programme, adapted to NZ norms, and used on
licence from the SF-36 licence holder (QualityMetric, Ingenix UnitedHealth Group,
USA). Higher scores were the most normal, or least impaired. The normative value of the
component score was 50.
The scoring system for the SF-36 can be generated by two methods and both were used.
(1) Two summary scores can be derived from the SF-36: the physical component
summary and the mental component summary, and (2) subscale scores for physical
functioning, role limitations due to physical problems, bodily pain, general health
perceptions, vitality, social functioning, role-limitations due to emotional problems, and
mental health (Appendix 1. Questionnaires).
Of note, the questionnaires were inadvertently printed with an error in each of two
questions. Before the use of the SF-36 raw data, for question 8 (BP2), which had been
incorrectly coded into 6 categories, 1 was subtracted off the score for all those with a
score of 2 or more so that the first 2 options were combined and the remaining given the
score as on the questionnaire. For Q10 (SF2) one was subtracted off the score for those
coded as 2-6 to result in the score on the questionnaire. For those who had ticked the first
category (coded as 1) they were given a score of 5 if SF1 (Q6) was missing or 1, 4 if SF1
= 2 and 3 if SF1 = 3. This correction was made by the statistician used for the
multivariable analyses of the SF-36. The CTRU statisticians stated that they used a
similar method.
Obesity Specific Quality of Life
The scores were summed into 4 groups, as per the validation instructions510,511 then were
collapsed into 2 groups; (1) Physical state: ‘I have trouble squatting’ , ‘I cannot sit down
in a very low armchair’, ‘I walk as little as possible’, ‘I have to stop to catch my breath
after walking several hundred meters’, ‘I have trouble climbing’, ‘people say I am not
very athletic’, ‘people often say that I am not agile’ (2) Psychological State ‘I lack
energy’, ‘I don’t move around much’ ‘I feel attacked when people talk about my weight’,
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Chapter 2. Methods
‘I feel ill at ease’. Higher scores were the most normal, or least impaired. A percentage
score for each question, and then section was calculated. The reference for the use of this
tool was vague, and was not used for the RCT (Appendix 1, Questionnaires).
Eating Attitudes Test – 12 Question
The EAT-12501 was used. The question sets were divided into 3 subscale groups of 4
questions: (1) Diet: ‘I am preoccupied with a desire to be thinner’, ‘I engage in dieting
behaviour’, ‘I feel uncomfortable eating sweets’, ‘I think about burning up calories when
I exercise’ (2) Bulimia: ‘I vomit after I have eaten’, ‘I have gone on eating binges where I
feel that I might not be able to stop’, ‘I give too much thought to food’, ‘I feel that food
controls my life’ (3) Oral Control: ‘I take longer than others to eat meals’, ‘I cut my food
into small pieces’, ‘other people think I am too thin’, ‘I feel that others pressure me to
eat’. For each question, a score of 1 to 6 could be made, where 6 is ‘none’ or ‘never’. The
cut points for scoring were <15 for the Diet subscore, <20 for the Bulimia subscore and
<22 Oral Control subscore (Appendix 1, Questionnaires).
2.4.6.2 Adverse Events Protocols
Participants could be excluded for medical reasons including AE or protocol violations.
Good Clinical Practice and guidelines512, common terminology criteria for adverse
events513 and the Ethics Committee severe adverse events (SAE) forms514 were used for
grading and reporting AE and SAE as summarised the Adverse Events case report form
(CRF) (Appendix 2). The coding, severity and relationship to treatment for AE and SAE
were included on the AE form. AE included laboratory, physical or psychological
abnormalities. They were recorded at every visit, and all reviewed for regular audit. SAE
were faxed to the Auckland Ethics Committee514 and to the CTRU data safety monitoring
board (DSMB). A HNU mobile phone was available for participants to make contact with
research staff and the author was contactable via a mobile to take medical or emergency
calls from the research staff.
2.5
Dietary Fibre and Lifestyle for Health – Novel
Cardiovascular Disease Markers
The Diet&Health-Novel CVD Marker study involved all previously measured
anthropometry, BP, lipids, FPG, and the addition of the analyses of stored blood samples.
72
Chapter 2. Methods
These were the Novel CVD Markers, composed of CRSHaem&Biochem, and Adpn and
Adpn30, from the study participants at baseline and 6m. Refer to Section 2.4.
2.5.1 Design, Population, Eligibility, Procedures, Anthropometry
The design of the Novel CVD Markers study was as for the Diet&Health study.
Refer to Section 2.4.2.
Cardio-metabolic Medications
Cardio-metabolic medications were divided into: (1) BP lowering or hypotensive
medication: centrally acting beta-blockers, angiotensin I converting enzyme (ACE)
inhibitors, calcium channel blockers, thiazide diuretics, angiotensin II receptor blockers
(ARB); (2) serum lipid normalising hypolipideamic medications: statins, fibrates and
niacin; and (3) plasma glucose lowering or hypoglycaemic (diabetic) medications;
biguanide (metformin) insulin sensitisers and sulphonyl urea secretogogues.
Inflammatory Medications and Inflammatory-Effects Medications
Two groups were formed: anti-inflammatory classes and groups of medication and
medication with anti-inflammatory effects. The anti-inflammatory classes comprised the
non-steroidal anti-inflammatory (NSAID) medications and the Cyclooxygenase-2
(COX2) inhibitors, and steroidal anti-inflammatory medications.
Medication that was classed as having an anti-inflammatory effects included topical (skin,
eye, nasopharyngeal) and inhaled anti-inflammatory medication, for example: NSAID’s,
a leukotriene receptor antagonist, and steroids, were included. Other medication groups
with anti-inflammatory effects included were the anti-inflammatory tetracycline,
nitroimidazole and macrolide antibiotic groups. Statins and fibrates are also reported to
have anti-inflammatory effects and can reduce CRP515,516, so were added to the antiinflammatory effects group. The anti-inflammatory ω-3 FA’s, DHA and EPA (fish oil,
flaxseed oil) were included as was the VitA analogue (isotrentinoin).
Supplements
Supplement doses were not clear for all preparations as participants did not always know
what brand they were taking. It was not always possible to trace the dose after the study
was complete. Thus the types of vitamin were ascertained and whether it was taken at
73
Chapter 2. Methods
baseline and/or at 6m. Multivitamins were generally expected to contain the common
vitamins unless they were specified as some other. The percentage of participants who
were recorded as taking supplements at baseline and 6m was calculated.
Classification of Inflammatory Illnesses
Chronic inflammatory diseases recorded were: (1) Autoimmune: rheumatoid arthritis,
systemic lupus erythematosis (2) Allergic: chronic sinusitis/hay fever (3) Infective:
hepatitis, ulcers >1m; (4) Neoplastic: malignant tumours, metastases, or cancer not in
remission. Excluded as chronic diseases were: premalignant cervical and basal cell
dysplasias.
Infectious illnesses at the time of laboratory testing, at baseline or 6m, were included as
acute diseases that would increase inflammatory markers. The group comprised: viral
infections (colds and acute coughs that had associated fevers, influenza, upper respiratory
tract infection, gastroenteritides, diarrhoea and vomiting), bacterial (pneumonia, nasooro-pharyngeal, skin infections, acute toothache), wounds that were fresh or still
discharging and acute gout.
2.5.2 Novel Cardiovascular Disease Risk Markers - Clinical Routine
Screening Biochemistry and Haematology
Study Design
This Novel CVD Risk Markers-CRSHaem&Biochem study was part of the Diet&Health
study. For methods refer to Section 2.2.2.
Laboratory
2.5.2.1 Laboratory Analyses
The laboratory analytes for this study comprised the CRSHaem&Biochem. The tests are
shown in Table 2-4.
The metabolic and inflammatory, or metaflammatory, haematological markers were
leukocytes; neutrophils, lymphocytes, monocytes and eosinophils and the aggregation
factor; ESR. These laboratory tests were required to be measured on the same day as
being taken.
74
Chapter 2. Methods
Test group
Full Lipid Profile
2
Biochemistry
1
Haematology or
Full Blood Count
(FBC)
Test Analytes
1
TC; 1HDL-C; 1TG; LDL-C (calculated); TC/HDL (calculated).
2
FPG; 2LFT: bilirubin; AlkPhos; ALT; AST; γGT; total protein, albumin, globulin
(calculated=total protein-albumin). 2Ferritin, 2CRP, 3urate, 2HbA1c
FBC: red blood cells (RBC); Hb; Hct; MCV; MCHC; RBC width (RBCW)
Leukocytes or white blood cells (WBC): neutrophils; lymphocytes; monocytes;
eosinophils; basophils; ESR
1
Tests analysed immediately. 2 Stored frozen for later batch analysis. 3 Frozen, analysed within 2wk.
CRSHaem&Biochem Clinical Routine Screening Haematology and Biochemistry TC total cholesterol;
HDL-C high density lipoprotein cholesterol; TG triglyceride; LDL-C low density lipoprotein
cholesterol FPG fasted plasma glucose; LFT liver function tests; AlkPhos alkaline phosphatase; ALT
alanine transferase; AST aspartate transferase; γGT gamma glutamyl transferase; HbA 1 c haemoglobin
A 1 c ; FBC full blood count; RBC red blood cells; Hb haemoglobin; Hct haematocrit; MCV mean
corpuscular volume; MCHC mean cell Hb concentration; RBCW RBC width ; ESR erythrocyte
sedimentation rate
The metaflammatory biochemical markers were HbA1c, urate, ferritin, LFT; bilirubin,
AlkPhos, ALT, AST, γGT, acute phase serum or liver-derived proteins, total protein,
albumin, globulin and CRP. Serum for biochemistry, was spun and prepared for storage
as detailed above, Section 2.4.4.1. Frozen samples were sent to LabPlus.
Haemoglobin A1c
Blood was drawn into an EDTA blood collection tube and sent to LabPlus within 3hr. An
aliquot was removed by LabPlus, frozen and stored for later analysis of HbA1c. The
sample was later thawed and was analysed at by affinity chromatography with a Primus
CLC385 (Kansas City, USA) instrument calibrated to a National Glycohaemoglobin
Standardization Program (NGSP), based in the USA. NGSP results are standardized to
glycated haemoglobin test results so that clinical laboratory results are comparable to
those reported in the Diabetes Control and Complications Trial517.
Urate
Urate was
analysed
by enzymatic colorimetric testing on
a
Roche/Hitachi
917/MODULAR (Mannheim, Germany) analyser.
Ferritin
Ferritin was analysed by immune-turbidimetric test on Roche/Hitachi 917/MODULAR
(Mannheim, Germany) analysers.
75
Chapter 2. Methods
C-Reactive Protein
The Tina-Quant CRP (Latex) high sensitive immune-turbidimetric assay was used in the
in vitro quantitative determination of CRP in human serum on Roche Diagnostics TinaQuant (Mannheim, Germany) automated clinical chemistry analysers.
Full Blood Count
Blood was drawn into an EDTA tube and sent to LabPlus within 3hr of collection. The
FBC was performed on SYSMEX XE 2100 (Kobe, Japan) analysers.
Erythrocyte Sedimentation Rate
At LabPlus a modified Westergren method in addition to an automated method was used.
The modified Westergren uses 50ul saline mixed with 250ul well mixed blood at room
temperature of between 18- 25°C. A dilution was made in a small plastic test tube and a
dispette placed into blood/saline mixture and drawn up using an adapted syringe until
blood soaked into cotton plug. It was timed for 1hr and read.
2.6
Novel Cardiovascular Disease Protective Markers
2.6.1 Adiponectin, Adiponectin and Oligomers: Novel Cardiovascular
Disease Protective Markers
Study Design
The Novel CVD Protective Marker – Adpn250 and Adpn30 and Oligomer study was part of
the Diet&Health study. For Methods refer to Section 2.2.2 for study and Section 2.4.4
laboratory details.
Adiponectin Full Baseline Analysis – Adiponectin250
The blood samples were drawn as detailed previously, Section 2.4.4. Samples were stored
frozen. One microtube per person was reserved for baseline Adpn250. Dr Yu Wang
developed the following Adpn sandwich Enzyme-linked immunosorbent assay (ELISA)
method at the University of Auckland, School of Biological Sciences laboratory. A Histagged recombinant Adpn produced from an Eschericia coli antibody was mixed with
Freund complete adjuvant, and then injected subcutaneously into rabbits, boosted twice
with the same, and blood collected 1wk later. An ELISA was used for the evaluation of
the resultant antisera.
76
Chapter 2. Methods
Adiponectin and Adiponectin Oligomers – Adiponectin30
Fifty samples were able to be analysed for Adpn oligomers, Adpn30, as part of a
collaboration with the laboratory of Drs Y Wang and A Xu in Hong-Kong. These
collaborators had developed an assay to analyse the Adpn oligomers.
The procedure for selecting the 30 participants whose serum was to be used to analyse the
total Adpn30, HMW30, MMW30, and LMW30 was devised by the author and supervisor in
NZ. The method was based on selecting 30 participants post hoc for the extremes of
weight (high or low), and MetS marker count (high or low), and to include participants
with TIIDM. In addition 20 participants with baseline and 6m data were chosen from the
30 Adpn30 participants for extreme change in weight (gain or loss), and change in MetS
marker count (gain or loss) as shown in Table 2-5, to have their samples analysed. Thus
50 samples were selected for the Adpn oligomers (Adpn30) substudy. Refer for details to
Section 5.3.1.
Table 2-5. Criteria for Selecting the Adpn30 Subset for Oligomer Analysis
Parameter
High BMI + No MetSMCt
n, Adpn30
participants
3
Baseline
3W
6m
TIIDM
2W
0
IFG
0
High BMI + High MetSMCt
8
4 W, 4 M
2 W, 1 M
1
1
High BMI + Low MetSMCt
2
2M
2M
0
0
Low BMI + High MetSMCt
4
3 W, 1 M
1M
1M
0
Large Waist + Low MetSMCt
1
1M
1M
0
0
TIIDM (at Baseline)
2
2W
1W
2W
0
Large MetSMCt Decr./6m
3
2 W, 1 M
2 W, 1 M
0
0
Large Weight Loss/6m
4
4W
4W
0
0
MetSMCt Incr./6m
1
1W
1W
0
0
Large Weight Incr./6m
2
2W
2W
0
0
Total N
30
21 W, 9 M
14 W, 6 M
4 W, 2 M
1
2W 1M
1
1M
1
Baseline only. Adpn 30 subset of 30 participants for whom total Adpn was re-analysed with different
methods, time and place from Adpn 25 0 , with the main aim to simultaneously analyse the associated 3
HMW, MMW & LMW. W women. M men; TIIDM type II diabetes mellitus; IFG impaired fasting
glucose, Adpn Adiponectin, BMI body mass index, waist waist circumference HMW high molecular
weight Adpn, MMW medium molecular weight Adpn, LMW low molecular weight Adpn, decr.
decrease; incr. increase; MetSMCt metabolic syndrome marker count.
Ethics Approval and Regulation for International Transport
77
Chapter 2. Methods
Ethics Approval
Ethical permission was sought from the Northern X Committee499 and granted to have
these 50 samples analysed in Hong Kong.
Importation Approval
Arrangements were made and the samples were air freighted, on dry ice, to Dr Yu Wang
in Hong Kong.
Frozen stored serum samples were analysed for total Adpn (Adpn30) and 3, HMW30,
MMW30 and LMW30, oligomers (Table 2-6).
Test group
Serum Adipocytokine
Serum Adipocytokine oligomers
1
1
Test analytes
Adpn250
Adpn30, HMW30 MMW30 LMW30
Sample stored frozen at -80ºC and analysed at a later date.
The 3 oligomeric complexes of Adpn30 were separated using gel filtration
chromatography. In summary, 10-30µl of human serum was diluted to 1ml with PBS,
loaded onto an AKTA explorer fast protein liquid chromatography (FPLC) system,
fractionated through a Hiload 16/60 Superdex 200 column (GE Healthcare, UK), and
eluted with PBS at the flow rate of 1 ml/min. Each 1μml fraction was collected. The Adpn
concentrations in each fraction were measured by ELISA to determine the percentage
composition of each oligomeric isoform of Adpn per total Adpn.
The plasma levels of each oligomeric form of Adpn were calculated by multiplying the
percentage of each oligomeric form with the plasma level of total Adpn.
Results were reported by the laboratory as μg/ml and percentages.
2.6.2 Serum Vitamin D, Beta Carotene, Vitamin A and Vitamin E
Study Design
This study was also part of the Diet&Health -Novel CVD Protective Marker-sFSVitamin
study, and methods have been covered previously. Refer to Section 2.4.2.
78
Chapter 2. Methods
Laboratory
For laboratory collection details refer to Section 2.4.4 and Table 2-6.
Table 2-7. Laboratory Test Collection and Analytes: Fat Soluble Vitamins
Test group
Test Analytes
Serum Fat Soluble
Vitamins
1
sVitD, 2sβCaro, 2sVitA, 2sVitE, 3Prothrombin time (PT) International Normalised
Ratio (INR) surrogate for VitK. INR (VitK)
1
Short term batching; analysed within 2wk. 2 Stored frozen -80º and analysed at a later date. 3 Tests
were analysed immediately. sβCaro serum beta–carotene; PT prothrombin time; sVitD serum Vitamin
D; sVitA serum vitamin A; sVitE serum vitamin E The isocratic reversed-phase high performance
liquid chromatography (HPLC) method was augmented by Dr G Woollard 5 1 8 LabPlus, ADHB.
Serum Beta-Carotene, Vitamin A and Vitamin E
sβCaro, sVitA (as retinol) and sVitE (as α-tocopherol) were measured simultaneously by
isocratic reversed-phase HPLC using photodiode array detection, time-programmable
wavelength changes, vortex spun to precipitate proteins and release vitamins from their
binding proteins, extracted into twice the volume of n-hexane, evaporated to dryness with
nitrogen in a warm water bath and finally, the sample was reconstituted in ethanol and
injected into the chromatograph. Chromatography internal standards were retinyl acetate,
for retinol, tocopherol acetate, for α-tocopherol, and echinenone, for βCaro.
Vitamin D
VitD3 was analysed using VitD25 pre-extraction with acetonitrile, double antibody
radioimmunoassay (RIA) at LabPlus. The DiaSorin Inc Stillwater, MN, USA (formerly
Incstar) 25 hydroxy vitD assay consisted of a two-step procedure, involving a rapid
extraction of 25-OH-vitD and other hydroxylated metabolites from serum or plasma with
acetonitrile. They were then assayed by using an equilibrium RIA procedure based on an
antibody with specificity to 25 hydroxyvitD and tracer with antibody, were incubated for
90min at 20-25ºC. Phase separation was performed after a 20min incubation at 20-25ºC
with a second antibody-precipitating complex, an addition buffer complex was added
after incubation, and finally centrifugation performed to aid in reducing non-specific
binding519. Bio-pool controls were run from those expected extremes of sVitD.
79
Chapter 2. Methods
Vitamin K
VitK (menaquinone, VitK) was not measured directly. A surrogate of VitK, the PT and its
derived measure, the INR, which is the reported blood test, are measures of the extrinsic
pathway of coagulation. INR was only used in multivariable analyses.
Prothrombin Time
Blood was drawn into a vacuutainer containing liquid sodium citrate and aliquotted
correctly by filling to the mark on the tube. Citrate acts as an anticoagulant by binding the
calcium in a sample. The blood was mixed, then centrifuged to separate blood cells from
plasma which was analysed by LabPlus staff on an automated instrument, Dade Behring
Berichrom BCS coagulation analyser (Marburg, Germany)™ at 37°C. Thromborel S
(tissue factor from human placenta, ISI 1.09) and Pathrombin SL (Dade Behring,
Marburg, Germany) were added, and the time the sample takes to clot was measured
optically.
80
Chapter 3. The Dietary Fibre and Lifestyle for Health – Weight Loss Study
Chapter 3. The Dietary Fibre and Lifestyle for
Health – Weight Loss trial: A Randomised
Placebo-Controlled Weight Loss Model
3.1
Introduction
The Diet&Health-Weight Loss RCT was the model used to investigate the hypotheses of
interest in the Diet&Health-Novel CVD Risk and Protective Marker studies. This model
has been used throughout the thesis.
Weight loss is difficult to achieve using any method, but changing lifestyle to improve
diet and increase PA is the corner stone of any weight loss programme. Lifestyle changes
which include decreasing dietary fat, increasing fruit and vegetable intake and improving
physical fitness by increasing PA, have been shown to result in weight loss in highly
controlled trials520. Modest weight loss affords important risk marker reduction. In spite
of many weight loss agents and medications specifically designed to aid weight loss, there
continue to be significant safety and efficacy problems and few orthodox medications are
registered for prescription or over-the-counter (OTC) sale521.
Food supplements, herbal and ‘natural’ substances with ‘generally regarded as safe’
(GRAS) status have also been studied, although usually in trials with few participants for
short periods, and with very modest weight loss522,523. Chitosan is a dietary
polysaccharide fibre as described in Chapter 1. The positive charge arising from
deacetylation is thought to confer the added property of forming a gel by adsorbing
negatively charged, ‘polar’ lipids. Animal studies and short human trials have suggested
that weight loss and cholesterol lowering associated with chitosan ingestion, is caused by
faecal lipid loss257,261,262 including cholesterol263,264. Chitosan had not yet been studied
with the rigor of a pharmaceutical trial. In the current study the effect of chitosan on
weight loss and the underpinning mechanism of gastrointestinal lipid binding were
investigated.
81
3.1.1 Hypotheses
Dietary Fibre and Lifestyle for Health - Weight Loss trial
The hypothesis for the Diet&Health-Weight Loss trial is that a putative lipid binding
dietary fibre, chitosan, when compared with placebo, increases weight loss and improves
CVD risk markers in overweight and obese participants over 6m within a model of best
practice weight loss advice based on a low fat/high fruit and vegetable diet, and increased
PA.
The hypothesis for the Fibre&Fat Loss substudy is that the chitosan fibre, when compared
with placebo, binds intestinal lipid forming a complex or gel, which is excreted, thereby
resulting in greater faecal fat, and in turn greater weight loss and serum cholesterol
lowering.
3.1.2 Aims
The aims of this study in 250 overweight and obese participants, with best practice dietary
and PA advice, over 6m are:
Primary Aim
•
To assess the difference between the chitosan group and the placebo group in weight
loss
Secondary Aim
•
To assess the differences between the chitosan group and the placebo group in other
anthropometry: BMI, waist and %Fat, S/DBP, serum lipids: TC, LDL-C, TG, HDL-C
and TC/HDL, and FPG
Other Aims
•
To assess the differences between the chitosan group and the placebo group in dietary
energy intake, PA, health-related QoL, SF-36, and EAT-12
•
To establish a model of weight loss by employing dietary measures, including the
chitosan, in order to investigate the relationship of MetS and Novel CVD Risk and
Protective markers with waist change
82
The aims of this study, where 51 participants provided a 3d faecal sample for TG
analysis, are:
Primary Aim
•
To investigate the difference in faecal fat loss between the chitosan subgroup and the
placebo subgroup, thereby testing whether the mechanism of gastrointestinal lipid
binding by chitosan and thence driving weight loss is plausible
Secondary Aim
•
To investigate the difference between the chitosan subgroup and the placebo subgroup
sFSVitamins, which may decrease due to ingested FSVitamins dissolving in higher
amounts of faecal fat excreted in the chitosan group.
Outcomes
The outcomes for the Diet&Health-Weight Loss trial are differences between the chitosan
and placebo groups in:
Primary Outcome: Weight; Secondary Outcomes: (1) BMI, waist, %Fat (2) S/DBP (3)
TC, LDL-C, TG, HDL-C, TC/HDL, and (4) FPG; Other Outcomes: Energy intake, PA,
SF-36 and EAT-12 questionnaires scores
The outcomes for the Fibre&Fat Loss substudy are differences between the chitosan and
placebo groups in:
Primary outcome: Faecal fat loss; Secondary Outcomes: sFSVitamins
3.2
Methods
Methods for the Dietary Fibre and Lifestyle for Health - Weight Loss Study
For the methods of the Diet&Health-Weight Loss trial refer to Section 2.2.1
3.2.1 Method Rationale & Summary
In brief, 250 obese and ambulatory women and men were randomised to a 6m weight loss
double blind trial with a marine dietary fibre, chitosan or placebo, standardised advice on
lifestyle (a prudent diet, modest physical activity and motivation). Individuals who
answered the advertisements for the trial were screened by phone. Possible registrants
83
were interviewed, and if eligible gave informed written consent, were measured and
started the two week run of single blind placebo that required 85% of capsules (4 x 3
times/d) to be consumed by count prior to randomisation. This method of assessing
treatment or placebo capsule consumption compliance was observed at the 6 x monthly
visits, where weight and waist measurements were performed, and written and verbal
information given. Blood was drawn at for all tests at baseline and 6m, and for lipids at
3m. BIA body composition was measured at baseline, 3m and 6m. Questionnaires on
lifestyle and eating attitudes were collected from participants at baseline and 6m.
Methods for the Fibre and Faecal Fat Loss Mechanism Substudy
For the methods of the Fibre&Fat Loss substudy refer to Section 2.2.1.1
Method Rational & Summary
At registration for the Diet&Health-Weight Loss trial, all possible participants for the
Fibre&Fat Loss sub-study were screened for eligibility and consented to provide a
baseline and 6m faecal sample. Fifty one participants started this substudy and brought
their 3 day faecal sample for analysis on the day of randomisation, and participants
remaining, at the last 6m visit.
3.2.2 Statistical Analyses
Study Power
The study sample size of 250 comprised 125 participants in each of two groups
randomised to chitosan or placebo. For comparisons between 3g chitosan daily and
placebo, the sample size of 250 participants provided 90% power (at the 5% level of
significance) to detect a 2.5kg difference between each of the groups in weight loss from
baseline to 6m, assuming a standard deviation (sd) of 6kg524.
In a meta-analysis of randomised, controlled trials of chitosan, the mean difference in
weight loss between the chitosan and placebo groups was 3.3kg525. The sample size
calculations were based on the planned number of follow-up visits (6) and the size of the
correlation within the primary endpoint at each visit, and likely drop-out rate of 40%.
Since the trial had substantial statistical power overall, it had adequate power to reliably
assess whether effects differ according to baseline characteristics of age, gender or BMI.
84
Randomisation
Study participants were randomised in a 1:1 ratio to receive chitosan or placebo capsules.
Staff at the HNU dispensed the study medication under blinded conditions using a
randomisation sequence generated by a computerised random-number generator with
mixed block sizes to prevent discovery. There was no stratification by gender or other
demographic variables. Treatment assignment codes were not available to the
investigators, research staff or data entry staff at any point during the study and were held
centrally by the Diet&Health-Weight Loss trial statistician, at the CTRU, University of
Auckland.
Statistics
Observed data are untransformed recordings. Three analysis types were conducted: (1)
the area under the curve (AUC) summary measure was employed to assess the response
profile over time with the LOCF for any missing data, based on the intention to treat
(ITT) approach, to ensure participants were analysed in the groups to which they were
randomised (2) general linear mixed model, and (3) analysis of covariance (ANCOVA)
LOCF was used for data collected at least twice, adjusting for baseline imbalance and for
regression to the mean for normally distributed data, with Mann-Whitney tests used for
non-normally distributed data.
All randomised participants were analysed as ITT. The group who attended the last visit
were completers but may have missed visits (n=164), so data is only presented for the
completer group who ‘attended all visits’ (n=146). The group who complied with
attending all visits, returned capsule count and achieved a compliance of >85% were the
‘per protocol’ group (n=73). The AUC LOCF ITT was applied to all randomised
participants for weight, BMI, waist, %Fat, S/DBP and lipid change. AUC LOCF was
applied to the ‘attended all visits’ and ‘per protocol’ groups for weight, and ‘attended all
visits’ group for BMI, waist, %Fat, S/DBP and lipid change.
General linear mixed model sensitivity testing was applied to all participants, and
‘attended all visits’ groups for weight, S/DBP and lipid change. ANCOVA was applied to
weight, S/DBP and lipids, ‘attended all visits’ groups, as a sensitivity test and other
laboratory tests and questionnaire data. All analyses were carried out using SAS v8.0
85
(SAS Institute Inc, Cary, NC, USA 2001) and p≤0.05 was used to determine statistical
significance.
3.3
Results
All results are expressed as means and blood tests are levels or concentrations, unless
otherwise notated.
3.3.1 Participant characteristics
Recruitment, retention and completion data for the study are shown Figure 3-1
Figure 3-1. Study Recruitment
Received Enquiries ~ 1000
Registered (n=432)
Not randomised (n=182)
• Unwilling (n=90)
• Compliance<85%(n= 59)
• Ineligible (n=6)
• Unknown/Other (n=27)
Randomised (n=250)
Placebo (n=125)
Full follow-up data unavailable (n=44)
• Unwilling to continue (n=37)
• Lost to follow-up (n=2)
• Pregnancy (n=2)
• Death (n=1)
• Other (n=2)
Completed 6m follow-up (n=81)
Chitosan (n=125)
Full follow-up data unavailable (n=42)
• Unwilling to continue (n=38)
• Lost to follow-up (n=1)
• Pregnancy (n=2)
• Other (n=1)
Completed 6m follow-up (n=83)
Of the 432 individuals who registered to take part in the study, 182 withdrew or were
excluded prior to randomisation. Of the 250 individuals randomised, 125 received
chitosan and 125 received the placebo. Eighty-six participants dropped out during the
86
intervention period (42 in the chitosan group, 44 in the placebo group), and 164(65.6%)
completed the entire 6m (Figure 3-1).
Non-randomised individuals were similar to those randomised other than having a lower
mean(sd) age 42(11.5)y vs. 48(11.7)y and a higher proportion of current smokers (19%
vs. 9%) (p<0.05).
Baseline characteristics appear in Table 3-1, with all being the total group.
3.3.2 Primary Outcome
Body Weight
Observed data and LOCF ITT for weight is shown in Figure 3-2, with the number of
participants attending at each monthly visit. In the AUC LOCF ITT analysis for the
population, the chitosan group lost a mean(standard error of the mean(sem)) of
0.39(0.21)kg (p=0.03; 0.4%) and the placebo group experienced a net gain of
0.17(0.16)kg (p=1.36; 0.2%) with the difference being 0.56(0.26)kg (p=0.03) the during
the 6m intervention (Figure 3-2 & Table 3-2). General linear mixed model sensitivity
analyses performed on weight for the ITT group showed slightly greater changes, but at
the same level of significance (Table 3-2).
The AUC analyses of weight when restricted to the subset of individuals who attended all
study visits (n=146), and those who attended all visits and also maintained an average rate
of compliance with treatment or placebo of >85% throughout the trial (n=73, the per
protocol analysis participants), indicated that the mean (95% C.I.) difference between
groups remained small; 0.9 (0.1, 1.7)kg (p=0.03) and 0.9 (-0.5, 2.2)kg (p=0.20)
respectively. The general linear mixed model weight analysis of the completers gave a
difference of 1.10 (0.10, 40)kg (p=0.02) between the chitosan and placebo (Table 3-2).
3.3.3 Secondary Outcomes
Body Mass Index
Observed data for BMI is shown in Figure 3-3. Using an AUC, LOCF, ITT analysis the
difference in mean(sem) BMI between chitosan and placebo groups was 0.21(0.08)kg/m2
(p=0.07). A borderline decrease in the chitosan group over the 6m was observed (Figure
3-3).
87
Table 3-1. Baseline Characteristics: All, Chitosan vs. Placebo
Parameter Mean(sd) %
Age, y
Gender, n (%)
-Women
-Men
Ethnicity, n (%)
-Maori
-Samoan
-Rarotongan
-Tongan
-Maori/Pacific
-East Asian
-Indian
-African
-European
Reg. Cig. Smoker
Ever Smoked
Reg. Alc. Drinker
Weight, kg
BMI, kg/m2
Waist, cm
%Fat, %
SBP, mmHg
DBP, mmHg
TC, mmol/l
LDL-C, mmol/L
HDL-C, mmol/L
TC/HDL
TG, mmol/L
FPG, mmol/l
sVitD, nmol/L (n=243)
sβCaro, μmol/L (n=238)
sVitA, μmol/L(n=239)
sVitE, μg/L (n=239)
INR (VitK) (n=232)
Faecal fat, mmol/d (n=51)
SF-36 – Phys., subscore
SF-36 – Ment., subscore
All (n=250)
47.7(11.7)
Chitosan Group (n =125)
47.6(11.9)
Placebo Group (n=125)
48.8(11.5)
206(82.4)
44(17.6)
103(82.4)
22(17.6)
103(82.4)
22(17.6)
29(11.6)
5(3.2)
1(0.4)
2(0.8)
37(15.0)
1(0.4)
7(2.8)
1(0.4)
212(84.8)
20(8)
107(42.8)
119(48.0)
97.4(16.2)
35.4(5.2)
100.4(12.9)
38.3(6.6)
123.2(18.3)
69.7(9.5)
5.5(0.95)
3.4 (0.81)
1.3(0.34)
4.4(1.20)
1.6(0.83)
5.3 (1.38)
62.2 (22.66)
0.6 (0.50)
2.0(0.47)
31.4 (8.30)
1.0(0.18)
13.4 (7.38)
47.0 (8.9)
46.2 (10.9)
14(11.2)
3(2.4)
1(0.8)
0
18(14.4)
1(0.8)
3 (2.4)
1 (0.8)
104(83.2)
11(10.4)
56(45.0)
62(50.0)
95.9(15.2)
34.8(5.1)
99.6(11.9)
37.8(6.8)
122.6(17.7)
69.4(9.4)
5.6(1.0)
3.6(0.8)
1.3(0.3)
4.6(1.2)
1.6(0.8)
5.3(1.3)
64.9(25.2)
0.7(0.5)
2.0(0.5)
31.7(8.5)
1.0(0.2)
12.6(8.0)
47.9 (6.2)
46.7 (7.2)
15(12.0)
2(1.6)
0
2(1.6)
19(15.2)
0
4(3.2)
0
108(86.4)
9(8.0)
51(41.0)
57(46.0)
98.9(17.1)
36.0(5.1)
101.3(13.7)
38.9(6.5)
123.8(18.9)
70.1(9.7)
5.4(0.9)
3.3(0.8)
1.4(0.4)
4.2(1.2)
1.5(0.9)
5.4(1.4)
59.6(19.5)
0.6(0.5)
1.9(0.5)
31.2(8.1)
1.0(0.1)
14.3(6.8)
47.6 (6.2)
47.7 (7.2)
Faecal fat, normal range, 6-20 mmol or 0.5-5g/d (4 mmol=1g lipid) 2 6 8 , 5 2 6 , y year; Reg. regular; cig
cigarette; alc alcohol; BMI body mass index; %Fat body fat percentage; βCaro beta-carotene; S/DBP
systolic/diastolic blood pressure; %Fat body fat percentage; Waist waist circumference; HDL-C High
density lipoprotein-cholesterol; LDL-C Low density lipoprotein-cholesterol; TC/HDL Total/HDL
cholesterol; TC Total cholesterol; TG Triglyceride; FPG Fasting plasma glucose; s seurm; Vitamin
A; VitD Vitamin D; VitE Vitamin E; INR international normalised ratio; SF-36–Short Form 36
question Quality of Life ; SF-36-Phys physical component subscale (0 – 100); SF-36 Ment Short
Form 36 mental component subscale (0 – 100).
88
Table 3-2. Change in Weight Sensitivity Analyses: Chitosan vs. Placebo
Participants
All Included
7
8
Analysis of
Change
Mean Change(sem)
Mean Change(sem)
Mean Change(sem)
Weight, kg
Chitosan
(n=125)
Placebo
(n=125)
Treatment
Difference
pvalue
Chitosan
(n=78)
Placebo
(n=68)
Treatment
Difference
pvalue
Chitosan
(n=44)
Placebo
(n=29)
Treatment
Difference
pvalue
-0.39(0.21)
0.17(0.16)
0.56(0.26)
0.03
-0.94(0.29)
-0.05(0.26)
0.90(0.40)
0.03
-1.63 (0.44)
-0.74(0.49)
0.88(0.68)
0.20
0.77(0.33)
(0.10, 1.40)
0.02
AUC 2ITT
(LOCF)
4
GLmM,
5
C.I.
Attended All Visits Analysis
Per Protocol Analysis
1
3
1.10(0.45)
(0.10, 40)
1.07(0.41)
(0.27,1.87)
8
6
ANCOVA
(LOCF), 5C.I.
3
1
2
0.02
0.01
3
AUC Area under the curve; ITT Intention to Treat Analysis; LOCF Last observation carried forward. Missing values are assigned last value carried forward from
previous visit; 4 GLmM general linear mixed model; 5 95% C.I. confidence interval; 6 ANCOVA Analyses of Co-variance; 7 Includes only participants attending all
study visits, n=146. 8 Includes only participants who attended all study visits, maintaining >85% compliance throughout as measured by capsule counts, n=73.
Figure 3-2.Weight & Waist over 6m (A) Weight Observed (B) Weight LOCF, ITT & (C) Waist LOCF, ITT: Chitosan vs. Placebo
(A)
(B)
104
Placebo
125
125
125
Chitosan
Placebo
125
77
98
78
81
96
125
120
104
96
87
92
87
90
96
94
125
125
0
1
2
3
4
Months of Follow-up
5
6
7
125
125
125
125
100
99
98
125
97
125
125
125
125
125
125
125
96
125
125
125
125
95
Chitosan - Placebo = 0.56 (0.04, 1.08) kg (p = 0.03)
88
-1
125
102
98
90
88
125
101
92
83
Chitosan
Placebo
125
103
125
Waist, cm
90
Weight, kg
99
Weight, kg
125
100
115
94
125
102
125
100
104
104
Chitosan
102
(C)
94
-1
0
1
2
3
4
Months of Follow-up
5
6
7
-1
0
1
2
3
4
5
6
7
Months of Follow-up
89
Waist and Body %Fat
As waist was measured at all visits and %Fat at baseline, 3m and 6m; waist and %Fat
results are presented as AUC LOCF ITT analyses. Waist is shown in Figure 3-2.
Figure 3-3. BMI over 6m (A) Observed and (B) LOCF, ITT: Chitosan vs. Placebo
(A)
(B)
38
125
38
Chitosan
37
115
99
Placebo
90
78
77
Chitosan
Placebo
37
81
36
36
35
35
125
125
125
125
125
125
BMI, kg/m2
BMI, kg/m2
125
34
125
33
120
104
32
87
125
33
125
125
83
96
34
125
125
32
125
87
125
31
31
Chitosan - Placebo = 0.21(0.08) kg/m (p = 0.07)
2
30
30
-1
0
1
2
3
4
5
Months of Follow-up
6
7
-1
0
1
2
3
4
5
6
7
Months of Follow-up
Waist also tended to decrease over the 6m; but the difference between groups was not
significant as shown in Figure 3-2.
Systolic and Diastolic Blood Pressure
ITT analysis for S/DBP is depicted in Table 3–3. AUC changes in S/DBP are shown in
Figure 3-4, and sensitivity analysis in Table 3-4. There was no significant difference
between treatment groups for S/DBP in the AUC LOCF ITT analysis, although there was
a borderline difference in the general linear mixed model analysis of SBP in the
completers Table 3-4.
Figure 3-4. BP over 6m ITT (A) SBP (B) DBP: Chitosan vs. Placebo
(A)
(B)
72
130
Chitosan
126
SBP, mmHg
125
125
125
124
125
125
70
125
125
Chitosan
Placebo
125
71
Placebo
125
125
69
DBP, mmHg
128
125
122
125
125
120
125
125
68
125
125
125
125
67
125
66
65
125
118
125
125
125
64
125
125
116
125
125
63
Chitosan - Placebo = 1.16(10.6) mmHg (p = 0.39)
62
114
-1
0
1
2
3
4
Months of Follow-up
5
6
7
-1
0
1
2
3
4
5
6
7
Months of Follow-up
90
Table 3-3. Change, Baseline to 6m BP & Lipids Sensitivity Analyses: Chitosan vs. Placebo
1
Parameter
AUC (2LOCF 3ITT,)
Treatment Difference,
Chitosan – Placebo
95%
Mean(sem)
p-value
C.l.
All Participants
SBP, mm/Hg
1.16(10.6)
DBP, mm/Hg
0.03(5.86)
TC, mmol/L
0.14(0.32)
LDL-C, mmol/L
0.12(0.36)
HDL-C, mmol/L
0.01(0.1)
TC/HDL
0.15(0.36)
TG, mmol/L
0.06(0.37)
6
Completers, Attended all visits
SBP, mm/Hg
DBP, mm/Hg
TC, mmol/L
LDL-C, mmol/L
HDL-C, mmol/L
TC/HDL
TG, mmol/L
-1.49, 3.81
-1.43, 1.49
0.06, 0.22
0.05, 0.20
-0.03, 0.02
0.06, 0.24
-0.03, 0.15
0.39
0.97
<0.01
0.01
0.60
<0.01
0.21
4
5
GLmM
Chitosan – Placebo
Mean(sem)
2.27(1.31)
0.51(0.7)
0.20(0.07)
0.19(0.06)
-0.01(0.02)
0.18(0.08)
0.09(0.08)
2.58(1.85)
2.08(0.98)
0.18(0.06)
0.20(0.05)
-0.01(0.02)
0.13(0.06)
0.07(0.06)
ANCOVA (LOCF)
Chitosan – Placebo)
95% C.l.
p-value
Mean(sem)
95% C.l.
p-value
-0.30, 4.84
-0.87, 1.89
0.06, 0.34
0.06, 0.31
-0.05, 0.04
0.03, 0.33
-0.06, 0.24
0.08
0.47
<0.01
<0.01
0.69
0.02
0.24
2.36(1.64)
0.33(0.90)
0.15(0.06)
0.13(0.06)
-0.02(0.02)
0.15(0.07)
0.09(0.07)
-0.87, 5.59
-1.44, 2.10
0.019, 0.27
0.01, 0.24
-0.056, 0.02
0.021, 0.29
-0.047, 0.22
0.15
0.72
0.02
0.03
0.37
0.02
0.21
-1.073, 6.237
0.139, 4.01
0.059, 0.03
0.089, 0.03
-0.05, 0.03
0.014, 0.252
-0.046, 5.0
0.16
0.04
<0.01
<0.01
0.57
0.03
0.22
3.26(1.63)
1.70(0.84)
0.21(0.07)
0.22(0.07)
-0.01(0.02)
0.17(0.08)
0.07(0.07)
0.04, 6.49
0.04, 3.36
0.06, 0.36
0.08, 0.35
-0.06, 0.04
0.01, 0.32
-0.04, 0.25
0.05
0.05
0.01
<0.01
0.69
0.04
0.17
1
AUC Area under the curve; 2 LOCF Last observation carried forward; 3 ITT Intention to Treat Analysis; 4 GLmM general linear mixed model; 5 ANCOVA
Analyses of co-variance; S/DBP systolic/diastolic blood pressure; TC total cholesterol; LDL-C low density lipoprotein cholesterol; HDL-C high density
lipoprotein cholesterol; TG triglyceride; 6 Includes only participants who attended all study visits. Missing values are assigned last value carried forward from
previous visit.
91
Table 3-4. Secondary & other Outcomes - Anthropometry, BP & Laboratory Analytes over
6m: Chitosan vs. Placebo
Chitosan
Mean
N
change(sem)
1
AUC (2LOCF) Anthropometry
BMI, kg/m2
125
-0.17(0.09)
Waist, cm
125
-0.57(0.30)
Fat %, %
121
-0.85(0.27)
SBP, mmHg
125
-2.71(0.92)
DBP, mmHg
125
-2.70(0.55)
3
ANCOVA (2LOCF) Laboratory Tests
FPG, mmol/L
118
-0.14(0.05)
sVitD, μmol/L
122
-9.00(1.81)
sβCaro, μmol/L
120
-0.02(0.03)
sVitA, μmol/L
120
-0.04(0.02)
sVitE, μmol/L
120
-1.15(0.50)
INR(VitK), s
117
0.01(0.01)
Faecal fat, mmol/d
25
-0.08(1.53)
N
Placebo
Mean change
(sem)
Treatment Difference
125
125
118
125
125
0.05(0.07)
0.07(0.28)
-0.61(0.19)
-1.55(0.98)
-2.68(0.49)
0.21
0.64
0.24
1.16
0.03
-0.02, 0.44
-0.18, 1.46
-0.41, 0.89
-1.49, 3.81
-1.43, 1.49
0.07
0.13
0.46
0.39
0.97
116
121
118
119
119
115
26
0.06(0.05)
-8.64(1.81)
0.03(0.03)
0.00(0.02)
-0.08(0.50)
0.03(0.01)
0.12(1.50)
0.21
0.36
0.05
0.04
1.07
0.02
0.20
0.07, 0.34
-4.68, 5.40
-0.03, 0.14
-0.03, 0.11
-0.32, 2.47
-0.02, 0.05
-4.11, 4.51
<0.01
0.89
0.21
0.27
0.13
0.31
0.93
Mean
95% C.I.
p-value
1
AUC Area under the curve; 2 LOCF Last observation carried forwards AUC summary measure was
used to assess change for all endpoints measured at more than 2 time points; BMI body mass index;
Waist waist circumference; %Fat body fat %; S/DBP systolic/diastolic blood pressure; 3 ANCOVA
Analysis of Co-variance; FPG fasting plasma glucose; VitE vitamin E; VitA vitamin A; βCaro betacarotene; VitD vitamin D; INR international normalised ratio. The anthropometry was analysed using
AUC LOCF. Change in endpoints that were only measured at baseline and 6m (glucose, fat-soluble
vitamins, and faecal fat) was assessed using ANCOVA. ANCOVA was used for laboratory tests
Serum Lipids
In the AUC LOCF ITT analysis for TC, levels decreased by a mean(sem) of 0.13(0.03)
mmol/L (2.3%) in the chitosan group during the 6m period versus a net gain of 0.01(0.03)
mmol/l (0.2%) for the placebo group (Figure 3-5) and (Table 3-4), over the 6m
intervention.
Figure 3-5. Serum Lipids over 6m (A) TC (B) LDL-C: Chitosan vs. Placebo (LOCF ITT)
(A)
(B)
3.8
5.8
Placebo
3.7
Placebo
5.7
121
3.6
122
5.6
125
LDL-C, mmol/L
TC, mmol/L
Chitosan
Chitosan
125
5.5
5.4
5.3
124
122
124
124
125
3.5
3.4
3.3
5.2
3.2
5.1
3.1
125
122
Chitosan -Placebo = - 0.14 (0.05, 0.22) mmol/L (p < 0.01)
124
Chitosan - Placebo = 0.12 (0.05, 0.20) mmol/L, (p < 0.01)
5
3
-1
0
1
2
3
4
Months of Follow-up
5
6
7
-1
0
1
2
3
4
5
6
7
Months of Follow-up
92
The mean(sem) (95% C.I.) difference between treatment groups was therefore -0.14(0.32)
(0.05, 0.22) mmol/L (p<0.01). Using an AUC LOCF ITT analysis for LDL-C, levels
decreased with a mean(sem) (95% C.I.) difference between groups of 0.12(0.36) (0.05,
0.20) mmol/L (p=0.01), and TC/HDL decreased with mean(sem) (95% C.I.) of 0.15(0.36)
(0.06, 0.24) mmol/L (p<0.01), but there were no significant differences between groups in
HDL-C (p=0.5) or TG (p=0.2) (Table 3-3).
Fasting Plasma Glucose
FPG was measured at baseline and 6m. ANCOVA showed that the chitosan group had a
mean FPG -0.21 (0.07, 0.34) mmol/L lower than placebo (p<0.01) (Table 3-4).
Fat Soluble Vitamins
There were no significant changes between treatment and placebo groups for the
sFSVitamins over 6m (Figure 3-8).
3.3.4 Other measures
Energy intake
There were no differences between the groups in energy intake (p=0.79) over 6m.
Physical Activity
There were no differences between the groups in PA (p=0.60) over 6m
Short Form-36 Question Quality of Life
There were no differences between groups in the physical and mental component
subscales of the SF-36 throughout the period of the trial, mean(sem) difference of 1.3(1.7)
(p=0.5) and 1.3(1.4) (p=0.4) respectively, over 6m.
Eating Attitudes Test-12 Question
There were no differences in the EAT-12 in the dieting subscore, mean(sem) difference of
0.1(0.24) (p=0.7), bulimia subscore, -0.37(0.21) (p=0.1) and oral control subscore,
0.01(0.06) (p=0.9) over 6m
93
Compliance
Self-reported adherence as measured by capsule counts decreased only slightly over the
6m study period -4.5 (0.9)% in the chitosan group and –4.0 (0.8)% in the placebo group
(p=0.6).
Adverse Events
There were a total of 10 SAE recorded over the study period: 6 in the placebo group and 4
in the chitosan group (p=0.53) (Table 3-5).
Table 3-5. Adverse Events by Treatment Group
1
Adverse Event
Chitosan, n
Serious events
4
2
Non-serious events
109
Gastrointestinal events – non-infectious
Abdominal pain
6
Bloating
10
Constipation
17
Indigestion
3
Diarrhoea (presumed non3
infectious)
Other digestive disorder
7
Total
36
Gastrointestinal – infectious
Diarrhoea (presumed
9
infectious)
Nausea/vomiting
12
Total
17
Other non-serious events
105
1
Placebo, n
6
108
Relative Rate
0.67
1.01
95% C.I.
()0.19, 2.36
()0.77, 1.32
p-value
0.53
0.95
4
3
8
2
3
1.50
3.33
2.13
1.50
1.00
0.42, 5.32
0.92, 12.11
0.92, 4.92
0.25, 8.98
0.20, 4.95
0.53
0.07
0.08
0.66
1.00
2
19
3.50
1.89
0.73, 16.85
1.09, 3.30
0.12
0.02
9
1.00
0.40, 2.52
1.00
10
18
100
1.20
0.94
1.05
0.52, 2.78
0.49, 1.83
0.80, 1.38
0.67
0.87
0.73
1
Denotes number of people who experienced one or more adverse event. 2 Analyses compared number
of people who experienced one or more adverse events. Participants may have reported more than one
event and a total of 420 non-serious adverse events were reported in the chitosan group and 309 in the
placebo group (p<0.01).
The SAE were defined as hospitalisations, with 3 in the chitosan group and 4 in the
placebo group (p=0.71). Cancer incidence accounted for 1 event in the chitosan group and
3 events in the placebo group (p=0.34). There was 1 death in the placebo group. Of the
non-SAE, 36 participants in the chitosan group and 19 in the placebo group reported noninfectious gastrointestinal side effects, defined as abdominal pain, bloating, constipation,
indigestion, or non-infectious diarrhoea (p=0.02) (Table 3-5).
There was a significantly greater event number in the chitosan group. There were no
significant differences between intervention groups in any other category of non-SAE.
94
3.4
The Fibre&Fat Loss substudy was the part of the Diet&Health-Weight Loss trial designed
to test the hypothesis that chitosan adsorbs TG, and the excretion or loss of both fibre and
fat together, and prevention of cholesterol enterohepatic recycling, is the mechanism of
action.
3.4.1 Method
Refer to Section 2.2
3.4.2 Laboratory Work
Refer to Section 2.2
3.4.2.1 Results
Twenty-nine participants completed both baseline and 6m follow up faecal collections.
There were no significant differences in mean faecal fat excretion between the chitosan
group and the placebo group at baseline. On analyses of faecal fat excretion involving
only the 29 participants who provided both baseline and 6m samples, mean (C.I.)
difference between the chitosan and placebo groups was 0.3 (-7.5, 8.2) mmol/d (p=0.9)
(Figure 3-6).
There were no significant differences in sFSVitamin levels between the chitosan and
placebo subgroups in the Fibre&Fat Loss substudy participants (Figure 3-7).
There was no relationship between change in faecal fat and change in weight or waist and
each of the sFSVitamin changes in the 29 participants who provided samples at baseline
and 6m (Figure 3-8).
95
Table 3-6. Baseline Characteristics of the Fibre&Fat Loss Substudy Completers: n=29
Mean(sd)
Gender, women:men
Age, y
Weight, kg
BMI, kg/m2
Waist, cm
Faecal fat, 1mmol/d
1
Chitosan, n=14
13:1
48(10)
98(14)
36(6)
102(16)
12.6(6.3)
Placebo, n=15
12:3
53(7)
100(20)
37(6)
103(12)
13.3(6.4)
Approximately 1g lipid=4mmol. sd standard deviation; y year; BMI body mass index
44
40
36
32
28
24
20
16
12
8
4
0
Baseline - Chitosan
6m - Chitosan
Normal Faecal Fat
Range 6-20 mmol/d
1 2 3 4 5 6 7 8 9 10 11 12 13 14
Participants
40
44
40
36
32
28
24
20
16
12
8
4
0
Baseline - Placebo
6m - Placebo
Faecal Fat Change, mmol/d
Faecal Fat , mmol/d
Figure 3-6. Baseline, 6m & Change in Faecal Fat Excretion: Chitosan & Placebo, n=29
Chitosan, n=14
Placebo, n=15
30
20
10
0
-10
-20
-30
Change in Chitosan - Change in Placebo =
0.2 (-4.1, 4.5) mmol/d, (p = 0.9)
-40
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15
Participants
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15
Participants
Figure 3-7. Change in sFSVitamins: Fibre&Fat Loss Substudy, n=29
5
0.28
Chitosan
Placebo
0.24
-10
-15
-20
-25
0.20
βCaro Change, umol/L
-5
0.16
0.12
0.08
0.04
0.00
-0.04
-0.08
-0.12
-30
-0.16
0.1
1.0
0.0
0.0
-0.1
-0.1
-0.2
-0.2
-1.0
VitE Change, umol/L
VitA Change, umol/L
VitD Change, nmol/L
0
-2.0
-3.0
-4.0
-5.0
-6.0
-7.0
-8.0
-0.3
-9.0
96
Figure 3-8. Change in Serum Fat Soluble Vitamins vs. Faecal Fat: Fibre&Fat Loss
Substudy, n=29
Placebo, n=15
-40 -30 -20 -10
0
10 20 30 40 50
1.2
0.8
0.4
0.0
-0.4
-0.8
-1.2
All r=-0.07, p>0.1
Chitosan r=-0.21, p>0.1
Placebo r=0.02, p>0.1
-1.6
-2.0
-40 -30 -20 -10
20
Chitosan, n=13
Placebo, n=14
Chitosan, n=13
Placebo, n=15
1.6
sVitE Change, µmol/L
sVitA Change, µmol/L
0.8
0.6
0.4
0.2
0.0
-0.2
-0.4
-0.6
-0.8
-1.0
-1.2
-1.4
-1.6
r, p-value
All r=-0.18, p>0.1
Chitsoan r=-0.03, p>0.1
Placebo r=-0.24, p>0.1
sβCaro Change, µmol/L
Chitosan, n=13
sVitD Change, nmol/L
70
60
50
40
30
20
10
0
-10
-20
-30
-40
-50
10
0
10 20 30 40 50
Chitosan, n=14
Placebo, n=15
0
-10
-20
All r=0.03, p>0.1
Chitosan r=0.17, p>0.1
Placebo r=-0.10, p>0.1
-40 -30 -20 -10 0 10 20 30 40 50
-30
-40
All r=-0.19, p>0.1
Chitosan r=0.39, p>0.1
Placebo r=0.09, p>0.1
-40 -30 -20 -10 0 10 20 30 40 50
s serum VitD vitamin D; βCaro beta-carotene; VitA vitamin A; VitE vitamin E;
3.5
Discussion
3.5.1 General Comments
This randomised placebo controlled trial showed that chitosan supplementation combined
with lifestyle and dietary advice produced marginal but significantly greater weight loss
than lifestyle and dietary advice alone, in overweight and obese individuals. In addition to
the main analyses, a number of sensitivity analyses were performed all of which gave
similar results.
The mean difference between groups in weight loss of just over half a kilogram achieved
over the 6m study period was also accompanied by a significant improvement in risk
factors associated with obesity, including fasting TC, LDL-C, and FPG in the chitosan
group compared to the placebo group. This may indicate that the effects of even very
modest negative energy balance may be under-appreciated and that weight increase
prevention is important. The secular trend is for weight gain in many populations, and the
overweight and obese tend to keep gaining weight or fat, until they are elderly and
become sarcopeanic527. Although a minor weight loss overall, the fibre nature of chitosan
may add to other studies where fibre generally confers health benefits, although studies
are not all in agreement252.
97
Excluded and Non Randomised Participants
The main reasons for the registrants, already a motivated group, failing to be randomised
or dropping out of the study included the selection criteria of taking >85% of capsules
daily, finding it difficult to get to appointments and disillusionment with the effort needed
for so little weight loss. Those to whom this applied were significantly more likely to be
younger and smokers, as shown in the results. A meta-interpretation of this finding was
that Maori smoke more, as we found in this study, and die younger. A propos of this
finding is that an even greater issue is that only two Maori men were even randomised,
and neither finished, so in fact Maori men were the least represented as coming forward
and this group has the greatest CVD risk and mortality528. They were effectively
unstudied. The perennial problem with lifestyle problems is the most in need are the least
likely to have the education, motivation and physical resources to attend trials or even
free health care. For these reasons a wider community responsibility for obesity related
MetS may have to be taken, particularly with overall, long term food safety and security –
which involves healthy food access, being made an easier choice529. Government
regulation of food processors and marketers may become important.
Comment on Results in View of the Previous Trial Results
A number of previous trials investigating the effect of chitosan on body weight and lipids
have been published but results are conflicting. A meta-analysis of 5 Italian trials
involving a total of 386 participants indicated a mean difference of 3.3kg weight loss
between intervention and placebo groups479. However, the trials included in the metaanalysis were not retrieved by searching electronic databases, but were obtained from a
single manufacturer and published in a single journal over a 2y period. They used a
similar study design of 28d trial duration, and an energy restricted diet. It is unclear if ITT
analyses were employed, and no description is given to the composition, or dose of
chitosan used. It is unclear if the individual patient populations may have overlapped. As
sustained fat loss is thought to be the main aim of weight loss studies, short studies of 1m
duration are not likely to be clinically important.
Trials examining the effect of chitosan have produced more variable results (Table 3-7).
Some trials have reported a positive effect on body weight257,261,262, or lipid levels263,264,
while others have reported no effect on either outcome265,266.
A Cochrane systematic review of research investigating the effect of chitosan on
overweight and obesity conducted, was performed by Jull et al. in 2005 and included co-
98
authors of the current study530. One other RCT report has appeared after the paper531 on
the current study and this showed chitosan was associated with significantly greater
weight loss than placebo and ‘self help’ methods, as assessed by weight and a DEXA
index. This was only a 60d study of 150 participants (134 completers) in a treatment,
placebo or ‘no treatment arm’ study design.
Table 3-7. Clinical Trials of Chitosan for Weight Loss or Cholesterol Lowering
Positive studies: RCT 14 (11 positive, 4 negative)
Either weight and/or cholesterol reduction
1
Abelin 1999532 (2-12 wk, not peer reviewed)
2
Ventura 1996491
3
Veneroni 1996490 (? Related to Ventura 1996, not peer reviewed)
4
Girola 1996 261(low dose chitosan, short period)
5
Wuolijoki 1999263
6
Zahorska-Markiewicz 2001262 (plus a very low energy diet (VLED))
7
Schiller 2001257 (plus VLED)
8
Gallaher 2002533 (low dose, mixture of chitosan + other ingredients)
9
Kaats 2006534 (mixture of chitosan + small amount other ingredients)
Cholesterol reduction
1
Maezaki 1993488 (2 wks)
2
Tai 2000256
Negative Studies: 4 (Incl. Diet&Health-Weight Loss)
1
Ho 2001266
2
Pittler 1999265 (low chitosan dose)
3
Gades & Stern 2003267 (clinically insignificant)
An ANCOVA, but not LOCF ITT, analysis was performed. The ratio of completers was
40:33 in the treatment: placebo groups, the self help group was mentioned but methods
and results not reported, and small amounts of other natural weight loss agents were
present in the preparation534.
One possible explanation for the variability in results obtained in these trials of chitosan is
that different types and compositions of chitosan were used in the various studies. The
composition of chitosan used has not been well described in many of the trials but in the
current study a β-chitosan derived from NZ squid pens was used, which was 75.5%
deacetylated. Few trials of dietary supplements and natural remedies are carried out
according to pharmaceutical industry standards. One report on a small trial did admit that
the sponsors found that there was only 42% not 71% of chitosan in the capsules265.
However, the current trial was conducted according to international Good Clinical
Research Practice guidelines and was the largest trial of chitosan to date with the largest
number of follow-up visits and the most outcome measures. Importantly, ITT analyses
99
and several sensitivity analyses were used thus limiting the bias that inevitably results
from restricting analysis to data from ‘completers’ only.
The minor effect of chitosan on clinical outcomes in the current trial is supported by the
results of the Fibre&Fat Loss substudy, which demonstrated that chitosan had no
detectable effect on faecal fat excretion. Other trials that have examined the effect of
chitosan on faecal fat excretion have also failed to find a significant effect of chitosan on
faecal fat excretion268, although in one study of men there was an increased loss267. It is
possible and plausible that chitosan might bind bile acids, as reported in animal studies267,
thus explaining some effect on serum lipid levels. Faecal cholesterol and bile acids have
not been measured in clinical trials. At the dose in the current study it seems unlikely that
chitosan could have a large effect on weight loss. There was no effect on the sFSVitamin
levels, and none would be expected if negligible fat was excreted.
Comparison with Other Weight loss and Cholesterol Lowering Agents
Reviews and meta-analyses show minimal efficacy of most OTC weight loss agents535
except ephedrine, now banned in most countries523. Medical or medication therapy of
obesity rarely results in on going large weight losses in communities, although some
individuals may lose significant amounts. The only registered long term weight loss
medication on the market in westernised countries is the non systemic lipase inhibitor,
orlistat. Compared to placebo, in a meta-analysis, orlistat reduced mean (95% C.I) weight
by 2.9 (2.5, 3.2)kg536, but in the present study, in chitosan mean weight loss was by only
0.56 (0.04, 1.08)kg.
The most efficient agents for reducing cholesterol are the medical therapies. The 3hydroxy-3-methyl-glutaryl-coA reductase (HMG coA reductase) inhibitors or statins are
widely used, and exert most of their considerable cholesterol lowering effects on LDL-C,
and lesser effects on TC (Table 3-8). Ezetimibe is specific cholesterol absorption
inhibitor. Niacin and fibrates (with a peroxisome proliferator-activated receptor gamma
(PPAR-γ) effect) are more useful for increasing HDL-C537. However, they all have
significant AE. Recently statins have been implicated in having an inappropriate
treatment target (LDL-C), increasing risk of TIIDM, and may not be effective for primary
prevention of CVD531,538-540
100
Table 3-8. Summary of % Weight Loss and Cholesterol Lowering Trials
Weight Loss
Current Study Chitosan: loss of ~ 0.9% body weight compared with gain of 0.2% on placebo
Orlistat: loss of ~ 9% body weight compared with 6% on placebo 541
Sibutramine: loss of ~ 5-8% body weight compared with 1-4% on placebo 542 (now deregistered)
Cholesterol Reduction (mainly via LDL-C)
Current Study Chitosan: 2.5% reduction in total cholesterol (current study)
Sterols/stanols: 10-20% reduction 543,544
Soluble fibre: 10 – 17% reduction 545
Bile acid sequestrants: 15-17% reduction 544
Statins: 23+% reduction 546
Non digested or non absorbed dietary plant fibre in food is GRAS and healthy. There are
claims that fibre is efficacious for weight gain prevention, weight loss and cholesterol
normalising240,547. Soluble fibre, β-glucans, found in oats and barley and active at 3g/d
have been allowed by the Food and Drug Administration (FDA) as a cholesterol lowering
agent. However, the evidence is derived mainly from observational study data, and the
clinical interventions, especially where studies used enriched β-glucans, do not clearly
show efficacy254. Soluble fibre including pectin, guar gum and psyllium, are reported to
have satiating properties in complementary and alternative medicine (CAM) studies548.
High dose fibre psyllium fibre, together with fruit and vegetable fibre may reduce weight
and MetS252.
Some non-absorbed bile acid sequestrants or ‘resins’ may have a positive charge and are
thought to bind to negatively charged intestinal lipids and cholesterol, thus preventing
absorption. The bile acid sequestrants, as exemplified by cholestyramine and cholostipol,
exchange anions, such as chloride ions, for bile acids. By doing so, they bind bile acids
and sequester them from enterohepatic circulation. They are an important, non systemic
medical therapy for cholesterol lowering544. The mechanisms of fibre and bile acid
sequestrants could possibly apply to chitosan but the current trial could not test for this
mechanism.
Toxicity: Chitosan and Other Weight Loss Agents
Chitosan is widely researched and utilized in both industrial and biomedical areas. In the
thousands of articles on chitosan use in the MEDLINE database of medical literature
(8114 in July 2011), chitosan has been GRAS. Chitosan complexes with carbon
nanotubes, hydrogels or film coatings are used for medication delivery to the intestine549101
554
, modifying haemostasis555, and many other uses. One very common area of research
and application of chitosan complexes is to enhance entry of substances through the
intestinal tight junctions, whereby polycations effect a reversible or irreversible loosening
of mucosal tight junctions556-559. As chitosan has been found to be a polycation that can
lead to irreversible opening of the intestinal cell tight junctions, toxic effects could result,
and some researchers have questioned the safety of long term uses of chitosan for weight
loss and cholesterol reduction560-562.
Obesity is often still seen as a failure to exert restraint in eating or increase PA563,564.
Treatment of obesity has been viewed as unnecessary for health, and optional, by some in
the health profession, and therefore any risks with pharmacotherapy are viewed as posing
more of a threat than the condition. However, obese individuals are often very distressed,
will resort to risky practices and in fact the centrally obese do have health risks, as
discussed in Chapter 1. Humans have complex energy balance and neurophysiology
(addiction and satiety issues) in the current ‘obesogenic’ environment536.
A brief review of toxicity in other weight loss medications is in order. Many older weight
loss medications were seen to be prescribed by fringe health practitioners, were poorly
regulated, often had serious metabolic and psycho-active adverse effects and were finally
banned565. There have been many weight loss studies on CAM and herbal medications
and supplements for weight loss. CAM in the USA do not have to prove efficacy or
safety, and as such are often marketed as natural, and GRAS566. However, in certain
circumstances it is clear that CAM are not safe at high doses567 including the well-studied
ephedra568-570.
Preparations may have accidental or purposeful toxic plant, microbe, biochemical, heavy
metal or orthodox medication contamination567,571,572. In contrast, more modern orthodox
weight loss drugs were expected to have very few, and no serious, side effects, and reduce
CVD risk. Even well trialled orthodox ‘designer’ medications such as dexfenfluramine in
1998, rimanobant in 2008, and sibutramine in 2010, all marketed for years, have been
removed from medication schedules after post-marketing surveillance confirmed
AE565,573-575.
In spite of many studies showing marginal efficacy for weight loss and cholesterol
reduction, and the above mentioned probable, but poorly demonstrated toxicity, chitosan
is widely marketed and sold in weight loss preparations.
102
Lastly, the Diet&Health-Weight Loss trial appears to be a realistic model with which to
test MetS in the community. In particular, the reality is that some individuals gain weight
and some lose, as occurred in this study, and this is expected to change MetS and MetS
markers.
3.6
Conclusion
In conclusion, this trial demonstrates that when chitosan was studied under rigorous
conditions and with thorough analysis it only had a minimal clinical effect on weight loss
or other measured outcomes in overweight and obese men and women taking a dose of
3g/d. No increase in faecal fat excretion was observed to support the putative mechanism
of action of chitosan and treatment is associated with some minor gastrointestinal side
effects. There may be intestinal toxicity with chitosan, so further work on this aspect, and
on bile acid secretion as a mechanism should be investigated. If low toxicity agents
improve weight loss at all, and particularly if other risk factors, including dyslipidaemia,
are reduced, cheap minimally-modified agents could be considered as part of a weight
loss strategy. In the interim, it seems appropriate to focus public attention on proven
effective means of weight loss, and improved health such as improved nutrition, with
large amounts of dietary fruit, vegetable and invertebrate fibre, including chitin and
chitosan, and increased PA.
103
Chapter 4. Novel Cardiovascular Disease Risk Markers in the Metabolic Syndrome
Chapter 4. Novel Cardiovascular Disease Risk
Markers in the Metabolic Syndrome
4.1
Introduction
MetS has been shown in meta-analyses and in population studies to significantly increase
the likelihood of CVD events in most ethnicities where it has been diagnosed9,94. The
definition and development of NCEP-derived MetS was addressed throughout Chapter 1.
Briefly, in the early 2000’s the simple NCEP6 MetS was welcomed by clinicians as being
more useable than previous definitions. However, over the last 5-7y the more central
obesity-oriented, and TIIDM and CVD risk preventative the definition became, the more
some westernised clinicians doubted the concept.
The IDF MetS includes increased numbers of overweight individuals from (1) many
different ethnicities, and (2) younger obese individuals with less immediate and more
reversible CVD risk, and not yet in need of cardiometabolic medication. The IDF MetS,
or the JIS MetS5, where any three of the markers outside the cut-points can be used, has
waist cut-points experimentally shown to be appropriate to many ethnicities. The IDF
MetS has become an important tool used by epidemiologists and researchers interested in
the world-wide rise of TIIDM and CVD. Much, but not all, of the non-European world
has embraced the IDF MetS132,133,146,576-581, especially in countries undergoing a rapid
‘nutrition transition’582. The IDF MetS populations were not meant to be the same as
those defined by NCEP/ADA, and a limited overlap should probably have been
expected583 136,137,584.
Recently the WHO issued an advisory against the use of MetS and its concept at all133.
These authors’ critiques included that lowered waist cut points were seen as including too
many individuals who did not need treatment whilst MetS does not appear to perform
better than the sum of its parts in older individuals who have established atherosclerosis
or have already experienced a CVD event585,586. The definitions have been widely
compared583
136,137,584
. In addition, these authors contend that MetS simplifies and
dichotomises risk markers that comprise MetS, and that there is too much CVD risk
104
marker variation. In defence of MetS, TIIDM and CVD are also beset by these latter
problems, and both conditions are just later stages of MetS133.
One way of reducing the dichotomisation of MetS or no MetS, is to form a MetS index, a
summary measure or count of the MetS markers; MetS marker count. Each MetS marker,
if outside the cut-point, qualifies so an individual can have a count of 0-5 MetS markers.
As with BMI, the various levels indicate categories of risk, and new studies, or reviewing
previous studies, with this in mind may be useful. The most important reason for using
the IDF MetS is as a starting point, or screen, that can classify CVD risk early, before end
organ disease has occurred. Diagnosis of MetS in young obese and non European groups,
who are at risk but are relatively unstudied, may be important. Thus, it is clear that no
definitions of MetS predict CVD perfectly, which might be expected in a definition of a
syndrome.
Sophisticated experimentation and modelling has shown that most historic ‘CVD risk
factors’, including those used in MetS, are only markers587. They do not appear to be
causes of the profound metabolic dysfunction of the many cross-linked biochemical and
nutrient systems588 seen in TIIDM, CVD and cancer. The basic causes are possibly
becoming clearer. The WHO authors133 state that MetS should be neither used, nor even
be further modified, and propose that new markers, such as those listed in the notes to the
IDF MetS definition4, be studied or used. Other markers derived from biological
pathways that are likely to be associated with root causes of degenerative disease should
be developed for use. However, none has been established as causative, and none is
available for clinical use at the present time. Thus, until the real causative factors are
found, it seems appropriate to use a modified IDF or JIS MetS as a screen, and other
extant or new tests which are related to profound disturbances in MetS.
What is known is that in the obesity-related MetS many biochemical and metabolic
processes are abnormal. As already indicated, there are classical CVD risk markers which
focus on circulating energy molecules, lipids/lipoproteins and plasma glucose. Wherever
there are energy nutrients in relative excess, especially in metabolically compromised
organisms, oxidative stress is likely. The glycated protein HbA1c, a plasma glucose
indicator and urate are established oxidant markers dyslipidaemia and endothelial stress.
Furthermore, there is overwhelming data now that the obesity-related MetS is associated
with metaflammation325, a concept introduced in Chapter 1.
105
The liver, the major general and energy metabolic organ involved in metaflammation,
releases enzymes and acute phase biomarkers into the circulation. Important markers are
thus the liver function tests, liver proteins, CPR and ferritin. Inflammatory white cells,
leukocytes, employ oxidative processes589. The ESR is a combination measure of
inflammatory plasma protein effects on the erythrocyte. Oxidative stress and
metaflammation are frequently linked and co-induce each other36, although inflammation
may not always occur in oxidative stress587. In MetS there are likely to be many
associated oxidative and inflammatory markers, although most investigations only
employ experimental markers.
A battery of laboratory CRSHaem&Biochem, as noted above, are often performed on
patients for metabolic disease screening, such as for diabetes and dyslipidaemia.
Screening and/or tracking infection, tumour growth and nutritional screening, such as for
anaemia and vitamin insufficiency, are further examples often also included in the same
group of tests. Often MetS patients will have had CRSHaem&Biochem testing, and
results reported in their files, but the results are not reviewed together with patients’ MetS
status. As MetS is so widespread, it is important that CRSHaem&Biochem are carefully
investigated, with a focus on refining estimates of metabolic decompensation and hence
improving CVD risk prediction in those with MetS.
4.1.1 Hypothesis
It was hypothesised that a battery of CRSHaem&Biochem, sensitive to inflammatory and
oxidant conditions, would provide strong markers of CVD risk, both individually and as a
group, and be related to a MetS index, MetS marker count. Furthermore, some of these
markers would be expected to worsen and/or improve in parallel with changes in MetS.
Thus CRSHaem&Biochem can be employed together with, and additional to, MetS, and
hence better predict CVD and track change in risk of CVD.
4.1.2 Aims
The aims of the Diet&Health-Novel CVD Risk Marker study in 250 overweight and
obese women and men enrolled in a 6m weight loss study are:
106
Primary Aims
•
To
investigate
the
strength
of
cross
sectional
relationships
between
CRSHaem&Biochem and MetS markers, and MetS marker count, in order to
determine the most useful markers to use either individually or together, to enhance
MetS and hence CVD prediction.
•
To
investigate
the
strength
of
change
between
baseline
and
6m
in
CRSHaem&Biochem and MetS markers, and MetS marker count, in order to
determine those most useful markers used either individually or together, to enhance
tracking change in MetS marker count and hence CVD prediction
Secondary Aims
•
To explore the variability of weight, BMI, waist and %Fat over the 6m intervention
and compare changes between 0-3m and 3-6m periods of the study
•
To compare the measures of body fat estimates – weight, BMI, waist and %Fat
•
To assess the utility of the various definitions of MetS
•
To assess the demographic, health status, lifestyle, and QoL measures
4.2
Methods
The methods for this Chapter are contained in the Diet&Health study. Refer to Section
2.2.2.
In brief, in the current study of 250 obese and overweight women and men who attended
a the Diet&Health-Novel CVD Risk markers and Diet&Health-Weight loss trial over 6m,
had their anthropometry weight, height, waist, BP and %Fat collected, with weight, waist
and BP collected monthly and %Fat at baseline, 3m and 6m. Anthropometry variability
from the two 3m periods could be compared.
The laboratory tests collected at baseline and 6m were used to provide the HDL, TG and
FPG, and together with waist and BP, which comprise the MetS and MetS marker count.
107
The utility of 3 NCEP related MetS definitions were assessed by cross-sectional and
longitudinal change comparisons in women and men. In addition, the MetS marker count
was the main outcome, in this study.
For this Diet&Health-Novel CVD Risk Markers study it was important to collect
demographic and medical history questionnaire details, especially CVD and risk. Data
from AE and medication report forms on change in medical status (illness and accident)
and treatments indicative of a metaflammatory condition were used for analyses.
The CRSHaem/Biochem laboratory samples were specifically, clinical routine screening,
diagnosing or tracking tests including, for 1) diabetes: plasma glucose, HbA1c; 2) gout
and renal dysfunction: urate; 3) dyslipidaemia: plasma TG and HDL-C; 4) liver
dysfunction such as steatohepatitis, cholestasis: liver function tests including the enzymes
ALT, AST, γGT and bilirubin 5) bone turnover and intestinal long chain FA transporter:
AlkPhos; 6) immune disorders (infection/infestation/parasitism, auto-immune disease,
cancer: leukocytes, acute phase proteins such as CRP, ferritin, immunoglobulins and
ESR were collected at baseline and 6m for the main Pearson r and mixed model analyses
analysis.
Clinical Study Procedures
Refer to Section 2.4.2
4.2.2 Laboratory Procedures and Analyses
4.2.3.1 General Comments
For the cross sectional data simple (Pearson) correlation coefficients (r) and partial r were
formed. For baseline multivariable analysis a general linear (regression) model was
employed where MetS markers and marker count were the outcomes, and the
CRSHaem&Biochem and demographic, health status, lifestyle and QoL data the
explanatory variables. For the longitudinal analyses a general linear mixed model was
fitted to the data. When interested in the effect of time dependent explanatory variables,
108
the effect of the variable was split into its mean over the 6m (overall mean) and the
deviation from the mean (change) at each time point. For the ordinal outcome an ordinal
logistic regression or generalised linear mixed model was fitted.
4.2.3.2 Demographic, Health Status and Lifestyle Questionnaires
4.2.3.3 Anthropometry
Untransformed anthropometric values are shown for 3 time points: baseline, 3m and 6m.
4.2.3.4 Metabolic Syndrome Comparisons
Metabolic Syndrome Definitions
The 5 markers of MetS were the same for all NCEP-derived definitions, NCEP,
NCEP/ADA and IDF/JIS. (Refer back to Table 1-1). Three or more of 5 markers were
required to be present, except for IDF MetS where waist is required to be one of the
markers. The markers were: waist, S/DBP, HDL-C, TG and FPG. If outside the cut
points, and fulfilling the definition, the parameters were denoted as MetS markers. In this
study, participant inclusion was identical in the IDF and JIS MetS definitions, and they
have been denoted IDF/JIS. (Refer back to Table 1-1).
Metabolic Syndrome Marker Count Definition
A MetS index was formed by calculation of a MetS marker count for which the minimum
was 0 (no abnormal markers), and the maximum was 5 (all markers abnormal). MetS
marker count was used in addition to MetS per se, to reduce the inefficiency that can be
introduced by using a dichotomised measure when it is in reality continuous590. The
individual markers remain dichotomised as they were required for the construction of a
MetS marker count.
Metabolic Syndrome Definition Comparisons
Three NCEP-derived MetS definitions were compared using simple statistics. Refer back
to Table 1-1.
IDF/JIS Metabolic Syndrome Relationships
The IDF/JIS MetS was the MetS definition used to explore relationships in this study.
109
Waist and IDF/JIS Metabolic Syndrome Relationships: Women and Men
To investigate the relationship of waist with the MetS marker count in women and men
separately, the MetS marker count was recalculated excluding the MetS marker of waist,
and the mean(sd) of waist(cm) within each category calculated.
Waist in Different Ethnicities: European/Other and Maori
To investigate the relationship of waist with the MetS marker count in European/Other
and Maori ethnicities separately, the same method as above was employed.
Relationship of (1) Weight to Metabolic Syndrome and (2) the Effect of Metabolic
Syndrome on Weight.
Effect of Weight on Metabolic Syndrome
In order to investigate whether MetS influenced the ability to lose weight, a general linear
mixed model was fitted with the log of the weight for an individual at each of the 7 visits
as the outcome and an autoregressive correlation structure assumed between time periods.
Age, gender, having ever smoked, number of cigarettes smoked/d, education level
(primary/secondary schooling or tertiary), treatment group (chitosan or placebo), and
whether or not participants completed the study, were included as explanatory variables
along with the baseline measures of the MetS markers. The interactions of treatment and
completion with time, were included as well as the interactions of the MetS markers, and
time as the question of interest was whether the change in weight over time differed
depending on the baseline values of the MetS markers. A separate analysis was performed
including the summary variable or MetS index, MetS marker count.
The effect of Metabolic Syndrome Markers and Count on Weight
In order to investigate whether the degree of weight change affected the degree of change
of any of the individual MetS markers, separate regression analyses were run. Each
analysis had a change in one of the MetS markers as the outcome, and age, gender, having
ever smoked, number of cigarettes smoked/d, education level, treatment group, weight
change, mean of baseline and final weight (to allow for a difference in amount of change
at different initial weights), as explanatory variables. A quadratic weight change term was
also included to see if there was a non linear relationship of weight change with change in
MetS variables, but this term was removed if not significant.
110
An ordinal logistic regression was also run with the change in the MetS marker count as
the outcome. The same explanatory variables were included as above, apart from age and
having ever smoked, as their inclusion caused the proportional odds assumption not to
hold.
Change in the Short Form-36 Question Quality of Life Scores with Metabolic
Syndrome
Two general linear mixed model analyses were used to investigate the relationship of the
SF-36 scores and their change over time with the MetS marker count at the 2 time points.
Age, gender, regular cigarette smoking, education level, and treatment group were
included as explanatory variables in both analyses. Along with these explanatory
variables, in the first analysis (1a) the mean, and (1b) the change of the combined the SF36 physical and mental component summary subscores, and in the second analyses (2a)
the mean, and (2b) the change of the individual subscales were included. Refer to Chapter
2, Section 2.4.6.1
For the MetS marker count the SF-36 subscores were measured at each time point, and
were split into their mean and their change. This enabled a separating out of the effect of
a mean relationship with MetS marker count and how the change was related to changes
in the MetS marker count.
4.2.3.5 Clinical Routine Screening Haematology and Biochemistry, and
Metabolic Syndrome
Classification
The haematology markers of inflammation were the WBC, or leukocytes, comprising
neutrophils, eosinophils, monocytes and lymphocytes. The aggregation factor of
inflammation, ESR, was also included. The inflammatory biochemistry markers, the acute
phase proteins, mostly synthesized in the liver, were the variably inflammation-sensitive
serum proteins, albumin and globulin and the classic CRP or high-sensitivity CRP
(hsCRP).
The biochemistry nominated for this study comprised an ‘oxidant’ group of markers, and
were composed of glycated HbA1c, urate, ferritin and LFT. The latter comprised the liver
111
enzymes, AlkPhos, the 3 liver transferases; ALT, AST and γGT. Bilirubin was included
as part of the LFT.
Laboratory Data
The CRSHaem&Biochem are presented using descriptive statistics.
Laboratory, gender specific reference ranges are given in Appendix 3. Evaluable
Numbers and Normal Ranges.
Cross Sectional Correlations
Pearson r coefficients were calculated to investigate the simple relationship between the
CRSHaem&Biochem, IDF/JIS MetS markers and MetS marker count. In order to
investigate independent associations, partial r coefficients of MetS marker variables and
MetS marker count were formed with each of the CRSHaem&Biochem, adjusting for all
other CRSHaem&Biochem. Further partial r analyses were run in the two groups,
‘oxidant’ and ‘inflammatory’ as detailed above. SAS v9.1 statistical software (Cary, NC,
USA, 2006) was used for these analyses.
Longitudinal Multivariable Relationships
In order to investigate the relationship of within-participant change over time of the
CRSHaem&Biochem with the individual MetS marker, a general linear mixed model was
fitted. Separate analyses were run for each MetS marker with its repeated measures as the
outcomes, and the CRSHaem&Biochem as explanatory variables. The effect of each
CRSHaem&Biochem was split into two parts (1) the mean of the 2 measures at the 2 time
points for the participant and (2) the change which was the deviation from this mean at
that time point.
The explanatory variables included for each outcome variable were age, gender,
education level, having ever smoked, regular cigarette smoker, number of cigarettes
smoked/d, treatment group, drinking >14 alcohol units/wk, acute or chronic inflammatory
illness, taking anti-inflammatory medication, and taking medication with an antiinflammatory effects. (Refer to Section 2.4.6.1). The CRSHaem&Biochem explanatory
variables were: neutrophils, eosinophils monocytes lymphocytes, ESR, HbA1c, urate,
ferritin, bilirubin, AlkPhos, ALT, AST, γGT, albumin, globulin, and hsCRP.
112
To investigate the relationship of within-participant change in the MetS marker count
with change in inflammatory markers, an ordinal logistic regression or generalised linear
mixed model was used, treating the MetS marker count variable as ordinal. The same
explanatory variables as above were used. One participant was removed from the analyses
as her extreme measure for AST prevented the generalised linear mixed model
converging. This person only had baseline measures and so was dropped from all
analyses for consistency.
4.3
Results
4.3.1 Demographic, Health Status, Lifestyle and Quality of Life
For this Chapter it is important to note that different numbers of participants qualified for
the various analyses. The full study had 250 participants (206 women; 44 men) enrolled,
but only 164 (132 women; 32 men), attended the final visit at 6m. For the anthropometry
analysis 147 participants (119 women; 28 men), had all data for weight, BMI, waist and
%Fat for 3 time points. For MetS analyses: at baseline there were 234 participants (194
women; 40 men), of whom all had waist, S/DBP, HDL-C, TG and FPG data recorded at
enrolment, and 147 (117 women; 30 men), who attended at 6m and whose data was
collected at both time points.
4.3.1.1 Baseline Characteristics – Anthropometry and Questionnaire Data
The data recorded at baseline are shown in Table 4–1. Eighty per cent of the participants
in this weight loss study were women, who in turn were, on average, younger than the
men. Approximately 80% of both women and men were of European ethnicity. The
women were more likely to be working full time, although educational level was
equivalent. Women were on average less likely to smoke currently, or drink alcohol.
There was a much higher rate of past smoking than current smoking in both women and
men. Men had a higher mean(sd) weight 105.5(13.0)kg and waist 111.1(11.5)cm than
women (weight 95.6(16.3)kg; waist 98.9(12.0)cm), but women had a higher BMI which
was 35.7(5.3)kg/m2, and %Fat 40.4(4.7), compared with the male participants whose BMI
was 33.8(3.9)kg/m2 and %Fat was 28.14 (5.0) (Table 4-1).
113
Table 4-1. Baseline Characteristics
Baseline measurements:
mean(sd) or N (%)
Age, y
Ethnicity
-Maori (%)
-Samoan (%)
-Rarotongan (%)
-Tongan (%)
-Chinese (%)
-Indian (%)
-Other (%)
European (%)
Education and Work
Edu. Level >Secondary (%)
Full Time Employment (%)
Lifestyle: Habits
Ever Drank Alcohol (%)
Reg. Alcohol Drinker (%)
1
Alcohol units/wk (LS)
≥ 14 alcohol/ U wk (%)
Ever Smoked (%)
Reg. cig. Smoker (%)
No. Cig. Smoked/d
Anthropometry
Height, cm
Weight, kg
BMI, kg/m2
Waist, cm
Waist/height, cm/cm
Body Fat, %
SBP, mmHg
DBP, mmHg
Medical Record
2
All Disease
Disease/Individual
2
Any Disease (%)
2
All CVD risk or CVD
CVD risk or CVD/Individual
2
Any CVD risk or CVD (%)
CV risk or CVD>1 (%)
Dyslipidaemia (%)
Hypertension (%)
TIIDM (%)
Shortness of Breath (%)
CHD (%)
Stroke (%)
Depression (%)
Sleep Apnoea (%)
Gallbladder disease (%)
All (N=250)
Women (N=206)
Men (N=44)
47.7(11.7)
46.9(11.5)
51.5(12.0)
29.0(11.6)
5.0(2.0)
1.0(0.5)
1.0(0.5)
1.0(0.5)
7.0(2.8)
2.0(0.8)
206.0(82.4)
27.0(13.0)
4.0(1.9)
1.0(0.5)
0
1.0(0.5)
4.0(2.0)
2.0(1.0)
169.0(82.0)
2.0(4.5)
1.0(2.3)
0
1.0(2.3)
0
3.0(6.8)
0.0
37.0(84.1)
116(46.4)
106(42.4)
95(46.1)
90(43.7)
21(47.7)
16(36.4)
154(61.8)
122(48.8)
6.4(5.8)
8(3.2)
107(42.8)
23(9.2)
9.9(5.9)
121(59.0)
92(44.7)
5.4(4.3)
3(1.5)
85(41.3)
18(8.7)
10.3(5.3)
33(75.0)
30(68.2)
9.7(8.4)
5(11.6)
22(50.0)
5(11.4)
8.3(8.8)
166.0(8.0)
97.4(16.2)
35.38(5.2)
100.4(12.9)
0.606(0.074)
38.3(6.6)
123.2(18.3)
69.7(9.5)
163.5(6.3)
95.6(16.3)
35.7(5.3)
98.9(12.0)
0.601(0.075)
40.4(4.7)
121.3(17.8)
68.5(8.9)
176.6(5.8)
105.5(13.0)
33.8(3.9)
111.1(11.5)
0.630(0.069)
28.14(5.0)
132.1(17.8)
75.3(10.5)
339
1.4
173(69.2)
128
0.5
89.0(35.7)
31(12.5)
27.0(10.9)
56.0(22.5)
14.0(5.6)
21.0(8.4)
7.0(2.8)
3.0(1.2)
25.0(10.0)
5.0(2.0)
2.0(0.8)
250
1.2
134(65.1)
83
0.4
61.0(29.8)
20.0(9.8)
19.0(9.3)
36.0(17.5)
5.0(2.4)
17.0(8.30)
4.0(2.0)
2.0(1.0)
17.0(8.3)
1.0(0.50)
2.0(1.0)
89
2.0
39(88.6)
45
1
28.0(63.6)
11.0(25.0)
8.0(18.2)
20.0(45.5)
9.0(20.5)
4.0(9.1)
3.0(6.8)
1(2.3)
8.0(18.2)
4.0(9.1) 5
0
114
Baseline measurements:
mean(sd) or N (%)
Hyperthyroid (%)
Hypothyroid (%)
Asthma (%)
Back problem (%)
Cancer (%)
Other Disease (%)
Tot. inflam. Disease (%)
Chronic inflam. Disease (%)
Acute inflam. Disease (%)
Medication
Anti-inflam. Med Effects (%)
Anti-inflam. Med (%)
Hypolipidaemic Med. (%)
Hypotensive Med. (%)
Hypoglycaemic Med. (%)
24Hr Food Recall Nutrients
Energy Intake, MJ/d
Calories, Kcal/d
Protein, g/d
Tot Fat, g/d
Saturated Fat, g/d
CHO, g/d
Tot. Sugars, g/d
Fibre, g/d
1
Alcohol, U/wk/ (from 24HrFR)
Physical Activity (PA)
TV Watching (h/wk)
Mild-Moderate PA (h/wk)
Vigorous PA (%)
Vigorous PA, mins/wk/ (n=94)
Quality of Life (QoL) and Attitudes
3
SF-36 Phys Subscore
3
SF-36 Ment Subscore
3,4
OSQoL Total Subscore
3,4
OSQoL % Phys Subscore
3,4
OSQoL % Psyc Subscore
3, 5,6
Eat-12 Dieting Subscore
3, 5,6
Eat-12 Bulimia Subscore
3, 5,6
Eat-12 Oral Control Subscore
All (N=250)
Women (N=206)
Men (N=44)
2.0(0.8)
5.0(2.0)
38.0(15.2)
54.0(21.6)
8.0(3.2)
72.0(28.8)
32.0(12.8)
22.0(8.8)
11.0(4.4)
2.0(1.0)
4.0(2.0)
34.0(16.5)
46.0(22.3)
6.0(2.9)
55.0(26.7)
26.0(12.6)
18.0(8.7)
8.0(3.9)
1
4.0(2.3)
4.0(9.1)
8.0(18.2)
2.0(4.5)
17.0(38.6)
6(13.6)
4(9.1)
3(6.8)
48.0(19.2)
40.0(16.0)
21.0(8.4)
56.0(22.4)
11.0(4.4)
32(15.5)
27(13.1)
12(5.8)
35(17.0)
3(1.5)
16(36.4)
13(29.6)
9(20.5)
21(47.7)
8(18.2)
8.657(3.522)
2068(841)
88.7(35.7)
85.8(46.8)
34.6(21.7)
226.9(102.7)
100.8(65.3)
20.4(9.0)
6.3(17.8)
8.278(3.293)
1978(786)
84.7(33.2)
81.2(43.3)
32.5(20.2)
222.8(98.5)
101.2(58.1)
20.3(8.8)
3.8(11.1)
10.428(4.030)
2491(963)
107.7(41.1)
106.9(56.3)
44.1(25.8)
246.4(120.0)
98.8(92.5)
20.9(10.0)
17.8( 32.6)
24.6(13.2)
9.8(7.4)
98(40)
123.9(117.0)
24.4(12.5)
9.9(7.3)
82(40)
125.9(118.2)
25.6(16.0)
9.6(8.0)
16(36)
113.44(114.0)
47.02(8.9)
46.18(10.9)
47.2(16.8)
42.8(19.5)
50.9(17.4)
14.9(4.1)
19.2(3.4)
21.1(2.4
47.04(8.9)
46.21(11.0)
47.4(17.0)
42.6(19.2)
51.4(17.7)
14.6(4.1)
18.7(3.4)
21.1(2.4)
46.94(8.9)
46.03(10.8)
46.5(15.8)
44.0(20.6)
48.6(16.1)
16.1(3.6)
21.2(2.5)
20.8(2.3)
1
1 unit Alcohol=10g in NZ. 2 CVD cardiovascular disease A single person may have >1. 3 For analyses
methods of SF-36 QoL, OSQol and EAT-12, refer to Section.2.4.6.1 and Error! Reference source
not found.. 4 OSQoL, Obesity Specific QoL higher score=most normal, 5 Eat-12 scoring. Low
scores=most normal, least impaired. 6 EAT-12 Eating Attitudes Test–12, received from n=148 -145,
w=201-204, m=44. Y year; wk week; waist waist circumference; S/DBP systolic diastolic blood
pressure; TIIDM type II diabetes mellitus; CHD coronary heart disease; inflam inflammatory; tot
total; OSQoL, Obesity Specific QoL PA physical activity; 24 FR 24 Food Recall; phys physical; ment
mental; psyc psychological Refer to Table 1-1.
115
Women had fewer CVD markers, and men averaged at least one CVD marker or had
diagnosed CVD, and 25% of all participants had more than 1 risk marker of CVD.
Hypertension was reported in >45% men, and 20% had TIIDM, but for women 18% were
hypertensive, and only 2% had TIIDM. The cardio-metabolic medications reflected the
spread of CVD. Men reported a higher intake of macronutrients and energy, except for
sugar, whilst fibre was similar in both women and men. PA levels were similar in both
women and men.
4.3.2 Anthropometry Variability
Anthropometry at baseline, 3m and 6m
Figure 4-1 & Figure 4-2 show the variation in anthropometric changes over 6m.In the
147 participants (117 women; 30 men) who had anthropometry recorded at 3 time points,
the change pattern was highly variable between- and within-individuals, with many
combinations of gain or loss over both 3m periods observed (Figure 4-1 & Figure 4-2).
The anthropometric changes for the 164 participants, who completed the study and had all
baseline and 6m measurements, were a mean(sd) weight loss of 1.2(3.9)kg, decrease in
BMI of 0.41(1.41)kg/m2, decrease in waist of 2.0(4.7)cm, decrease in waist/height ratio of
0.012(0.028), and %Fat of 0.14(3.0) (n=157).
Percentage weight and waist change correlated highly in men over the first 3m period
(r=0.63; p<0.001), and over the second 3m period, 3 to 6m (r=0.53; p<0.001) (Figure
4-3). In contrast, the percentage weight to waist change in women during the first 3m
showed a modest correlation (r=0.29; p=0.002), which increased greatly in the second 3m
period (r=0.75; p<0.001) (Figure 4-3).
In Figure 4-3 the percentage weight to %Fat change shown is borderline for the first 3m
in women and men, and for all (r=0.19; p=0.02), becoming significant for women and
men after the second 3m period (r=0.27; p=0.004) and (r=0.40; p=0.03), respectively.
4.3.3 Metabolic Syndrome Comparisons
Baseline Metabolic Syndrome Comparisons
Baseline laboratory data for this Chapter are shown in Table 4-2 and MetS data in Figure
4-3.
116
Figure 4-1. Weight &Waist of Individuals at Baseline, 3m & 6m: Women, n=117 & Men, n=30
Men
155
145
145
135
135
125
115
105
95
Weight, kg or Waist, cm
Weight, kg or Waist, cm
Women
155
125
115
105
95
85
85
75
75
65
65
Bl baseline, 3m 3 months, 6m 6 months
117
Figure 4-2. BMI & %Fat of Individuals at Baseline, 3m & 6m: Women, n=117 & Men, n=30
Men
55
50
50
45
45
40
40
35
30
25
BMI, kg/m2 or Fat, %
BMI, kg/m2 or Fat, %
Women
55
35
30
25
20
20
15
15
10
10
Bl baseline, 3m 3 months, 6m 6 months, BMI body mass index, %Fat body fat percentage.
118
There was a full set of baseline data for all MetS markers to calculate the baseline NCEP,
NCEP/ADA and IDF/JIS MetS comparisons in 234/250 participants (Table 4-1 & Table
4-2).
In contrast, the percentage weight to waist change in women during the first 3m showed a
modest correlation (r=0.29; p=0.002), which increased greatly in the second 3m period
(r=0.75; p<0.001) (Figure 4-3).
In Figure 4-3 the percentage weight to %Fat change shown is borderline for the first 3m
in women and men, and for all (r=0.19; p=0.02), becoming significant for women and
men after the second 3m period (r=0.27; p=0.004) and (r=0.40; p=0.03), respectively.
% Fat % Change, Baseline to
3m
-15
40
30
20
10
0
-10
-20
-30
-40
-50
-60
-70
-80
Women
Men
r, p-value
All, r=0.35, p<0.001
W, r=0.29, p=0.0017
M, r=0.63 p<0.001
-10
-5
0
5
% Waist Change, cm 3m to 6m
20
15
10
5
0
-5
-10
-15
-20
-25
-30
10
All, r=0.19, p=0.02
W, r=0.15, p=>0.1
M, r=0.32 p=0.08
-20
-10
0
10
% Weight Change, kg Baseline to 3m
% Fat % Change, cm 3m to 6m
% Waist Change, cm Baseline to
3m
Figure 4-3. % Change Correlations Weight vs. Waist & %Fat: All, Women & Men
20
15
10
5
0
-5
-10
-15
-20
-25
-30
-20
40
30
20
10
0
-10
-20
-30
-40
-50
-60
-70
-80
All, r=0.56, p<0.001
W, r=0.75, p<0.001
M, r=0.59 p<0.001
-15
-10
-5
0
5
10
All, r=0.29, p<0.001
W, r=0.27, p=0.004
M, r=0.40 p<0.03
-20
-15
-10
-5
0
5
10
% Weight Change, kg 3m to 6 m
Eight %Fat values were >25% different from baseline and 6m, and were not in keeping with the
weight, waist and BMI. These values were excluded for this analysis, but it made no difference to the
p-values whether these values were retained or not. W women; m men; weight body weight; Waist
circumference waist; %Fat body fat percentage; m month.
119
Table 4-2. Baseline MetS Marker & CRSHaem&Biochem
Parameter
RBC, x1012/L
Hb, g/L
Hct, %
MCV, fl
MCHC, pg
Leukocytes, x109/L
Neutrophils, x109/L
Basophils, x109/L
Eosinophils, x109/L
Monocytes, x109/L
Lymphocytes, x109/L
Platelets, x1012/L
ESR, mm/h
TC, mmol/l
LDL-C, mmol/l
HDL-C, mmol/l
TG, mmol/l
TC/HDL
FPG, mmol/l
HbA1c %
Urate, mmol/L
Ferritin, µg/L
Bilirubin, µmol/L
AlkPhos, U/L
ALT, U/L
AST, U/L
γGT, U/L
Albumin, g/L
Globulin, g/L
hsCRP, mg/L
All, mean(sd)
4.84(0.38)
142.1(11.5)
0.425(0.033)
87.9(4.2)
29.4(1.7)
7.00(2.21)
4.2(1.50)
0.071(0.021)
0.21(0.13)
0.34(0.10)
2.25(0.93)
329.2(70.5)
20.9(13.5)
5.46(0.95)
3.43(0.81)
1.33(0.34)
1.57(0.83)
4.36(1.20)
5.32(1.38)
5.3(0.8)
0.36(0.07)
227(211)
8.5(5.10)
77.0(24.4)
17.2(10.6)
23.5(12.0)
29.8(25.8)
42.9(2.0)
28.3(3.3)
5.3(5.53)
Women, mean(sd)
4.76(0.33)
139.3(9.9)
0.417(0.028)
87.8(4.3)
29.3(1.7)
6.98(2.31)
4.16(1.57)
0.070(0.020)
0.21(0.13)
0.33(0.10)
2.27(1.0)
336.1(65.4)
21.8(13.1)
5.50(0.92)
3.48(0.79)
1.37(0.34)
1.44(0.70)
4.24(1.13)
5.24(1.40)
5.2(0.8)
0.35(0.06)
83.2(77.6)
8.6(5.2)
76.6(23.4)
16.2(10.1)
23.0(13.0)
26.4(23.2)
42.6(2.2)
28.4(3.2)
5.4(5.45)
Men, mean(sd)
5.22(0.37)
155.2(9.6)
0.460(0.03)
88.4(4.1)
29.8(1.6)
7.10(1.67)
4.4(1.40)
0.075(0.022)
0.22(0.10)
0.39(0.09)
2.2(0.50)
296.2(84.3)
16.0(14.5)
5.29(1.09)
3.20(0.87)
1.1(0.26)
2.15(1.11)
4.94(1.35)
5.70(1.21)
5.5(0.7)
0.44(0.08)
182.6(119.0)
7.8(4.4)
78.98(28.6)
21.4(11. 6)
25.7(5.1)
45.6(30.9)
44.4(2.1)
28.1(3.9)
4.7(5.9)
MetS metabolic syndrome; CRSHaem&Biochem clinical routine screening haematology and
biochemistry; RBC red blood cells; Hb haemoglobin; Hct haematocrit; MCV mean cell volume;
MCHC MCHb concentration; ESR erythrocyte sedimentation rate; TC total cholesterol; L/HDL-C
low/high density lipoprotein-C; TG triglyceride; FPG fasting plasma glucose; AlkPhos alkaline
phosphatase; ALT/AST alanine/aspartate transferase; γGT Gamma glutamyl transferase; hsCRP high
sensitive C reactive protein.
4.3.3.1 Metabolic Syndrome Comparisons
Baseline Metabolic Syndrome Comparisons
Baseline laboratory data for this Chapter are shown in Table 4-2 and MetS data in Table
4-3.
There was a full set of baseline data for all MetS markers to calculate the baseline NCEP,
NCEP/ADA and IDF/JIS MetS comparisons in 234/250 participants (Table 4-1 & Table
4-2).
120
At baseline, using the NCEP definition, 31% (73/234) of participants had MetS, which
rose approximately 7 to 8% and 12% on using NCEP/ADA and IDF/JIS MetS definitions
respectively (Figure 4-4). Older men had higher rates of MetS than women, with the
lowest rates in young women, especially on using IDF/JIS MetS (Figure 4-4 & Table
4-4).
Hypoglycaemic or antidiabetic medication for raised plasma glucose and lipidnormalising medication for dyslipidaemia TG/HDL, also increased the IDF/JIS MetS
diagnosis, but BP treatment did not (Table 4-4).
Table 4-3. Baseline, NCEP, NCEP/ADA & IDF/JIS MetS: Gender &Age Groups
Baseline
Participants
MetS, %
2
NCEP, %
2
NCEP/ADA, %
2
IDF/JIS, %
All
N=23
4
Women
n=194
Women
<50y n=107
31.2
38.5
42.3
25.1
32.5
35.3
23.6
29.0
33.0
1
Women
≥50y n=87
Men
n=40
Men
<50y n=18
Men
≥50y n =22
38.9
44.4
55.6
77.3
86.4
86.4
% of each group
27.3
60.0
36.8
67.5
40.0
72.5
1
N total=234/250. Some blood tests were unable to be drawn. No MetS marker values were imputed.
Refer to Table 1-1 for MetS and MetS marker definitions. 2 For gender/age group, subtraction o f
NCEP and NCEP/ADA from IDF/JIS MetS gives an average effect size difference of mean(sd)
12.0(2.2) % and 7.4(3.6) % respectively. IDF International Diabetes Federation, JIS Joint Interim
Statement, NCEP National Cholesterol Education Panel, ADA American Diabetic Association, y year;
MetS marker count metabolic syndrome marker count.
Figure 4-4. Baseline (A) NCEP, NCEP/ADA & IDF/JIS MetSMCt & MetS
(B) IDF/JIS MetS in Age & Gender Groups, n=236
(A)
(B)
NCEP MetS
NCEP/ADA MetS
60
IDF/JIS MetS
55
50
40
Men ≥ 50 y n = 22
50
12%
4%
35
8%
30
25
20
15
40
25
15
0
10
M
et
S
+v
e
20
M
55% MetS +ve
30
5
M etabolic Syndrome M arker Count &
M etabolic Syndrome
86% MetS +ve
32% MetS +ve
35
10
et
S
M
M C
et t=
S 0
M
M C
et t=
S 1
M
M C
et t=
S 2
M
M C
et t=
S 3
M
M C
et t=
S 4
M
C
t=
5
40% MetS +ve
45
% Participants
% Participants, (n = 234)
45
Women < 50 y n = 107
Women ≥ 50 y n = 87
Men < 50 y n = 18
5
0
012345
012345
012345
012345
M etabolic Syndrome M arker Count
121
Table 4-4. Baseline, MetS Marker Frequency: NCEP, NCEP/ADA & IDF/JIS MetS
Frequency of Abnormal MetS Marker
Waist, cm
SBP, mmHg
DBP, mmHg
HDL-C, mmol/L
TG, mmol/L
FPG, mmol/L
NCEP MetS
NCEP/ADA MetS
IDF/JIS MetS
NCEP MetS
NCEP/ADA MetS
IDF/JIS MetS
NCEP MetS
NCEP/ADA MetS
IDF/JIS MetS
NCEP MetS
NCEP/ADA MetS
IDF/JIS MetS
NCEP MetS
NCEP/ADA MetS
IDF/JIS MetS
NCEP MetS
NCEP/ADA MetS
IDF/JIS MetS
All (%)
1
n=234
184 (78.6)
184 (78.6)
223 (95.3)
88 (37.6)
88 (37.6)
88 (37.6)
15 (6.4)
88 (37.6)
88 (37.6)
94 (40.2)
104 (44.4)
104 (44.4)
79 (33.8)
88 (37.6)
88 (37.6)
21 (9.0)
45 (19.2)
45 (19.2)
Women (%)
n=194
151.0 (77.8)
151.0 (77.8)
184.0 (94.8)
67.0 (34.5)
67.0 (34.5)
67.0 (34.5)
8.0 (4.1)
67.0 (34.5)
67.0 (34.5)
80.0 (41.2)
85.0 (43.8)
85.0 (43.8)
56.0 (28.9)
61.0 (31.4)
61.0 (31.4)
12.0 (6.2)
31.0 (16.0)
31.0 (16.0)
Men (%)
n=40
33.0 (82.5)
33.0 (82.5)
39.0 (97.5)
21.0 (52.5)
21.0 (52.5)
21.0 (52.5)
7.0 (17.5)
21.0 (52.5)
21.0 (52.5)
14.0 (35.0)
19.0 (47.5)
19.0 (47.5)
23.0 (57.5)
27.0 (67.5)
27.0 (67.5)
9.0 (22.5)
14.0 (35.0)
14.0 (35.0)
1
Baseline N total=234/250. Some blood tests were unable to be drawn, and samples were unsuitable
for testing. No MetS marker values were imputed. Refer to Table 1-1 for MetS and MetS marker
definitions. NCEP National Cholesterol Education Panel; ADA American Diabetic Association; IDF
International Diabetes Federation; JIS Joint Interim Statement; MetS Metabolic syndrome;; y years
Over the 6m study duration, decreased MetS marker count in men was nearly 10 times
more than that for women for the NCEP, but only 2 times more on using the IDF/JIS
MetS. As participants lost or gained weight, MetS marker count changed.
Table 4-5 shows the percentage of participants who lost and gained MetS overall was
highest for NCEP MetS.
There was a decrease of MetS marker count using all MetS definitions, but a gain in BP
in women, a slight loss in SBP in men, and gain in HDL-C in both women and men
(Table 4-6).
More variation of NCEP MetS loss or gain was shown when compared with IDF/JIS
MetS, but the NCEP MetS numbers were low. IDF/JIS showed participants losing MetS
more in line with 2/3rd of them losing weight and waist as shown in Figure 4-5.
122
Table 4-5. 6m Final Visit NCEP, NCEP/ADA & IDF/JIS MetS: Gender & Age Groups
Participants at 6m
All, N=147
Women, N=119
Women< 50y, N=57
Women≥ 50y, N=62
Men, N=28
Men< 50y, N=11
Men≥ 50yr N=17
MetS Disappearance/Baseline MetS
NCEP (%)
20/47(42.6)
11/31(35.5)
3/13(23.1)
8/18(44.4)
9/16(56.3)
3/4(75.0)
6/12(50.0)
NCEP/ADA (%)
17/56(30.4)
11/40(26.2)
6/16(37.5)
7/24(29.2)
6/19(31.6)
0
6/14(42.9)
IDF/JIS (%)
19/66(28.8)
13/45(28.9)
5/18(27.8)
8/27(29.6)
9/21(42.9)
1/7(14.3)
7/14(50.0)
MetS Acquisition/Baseline MetS
NCEP (%)
10/47(21.3)
8/31(25.8)
3/13(23.1)
5/18(27.8)
2/16(12.5)
1/4(25.0)
1/12(8.3)
NCEP/ADA (%)
6/56(10.7)
4/40(10.0)
2/16(12.5)
2/24( 8.3)
2/19(10.5)
1/5(20.0)
1/14(7.1)
IDF/JIS (%)
10/66(15.2)
7/45(15.6)
3/18(16.7)
4/27(14.8)
3/21(14.3)
1/7(14.3)
2/14(14.3)
6m 6 month; IDF International Diabetes Federation, 2009; NCEP National Cholesterol Education Panel; NCEP-ADA National Cholesterol Education PanelAmerican Diabetic Association; JIS Joint Interim Statement; y years.
Table 4-6. Change, Baseline to 6m. MetS, arker & MetS Marker Count: Women n=117, Men n=30
MetS Marker or MetS
Marker Count definition
Waist, cm
All: Baseline
Mean(sd)
98.66(11.82)
All: Change baseline to
6m Mean(sem)
-2.14(0.38)
Women: baseline,
Mean(sd)
96.0(10.6)
Women: Change baseline
to 6m, Mean(sem)
-2.11(0.42)
Men: baseline,
Mean(sd)
110.2(9.9)
Men: Change baseline
to 6m, Mean(sem)
-2.28(0.96)
SBP, mmHg
123.07(17.97)
0.96(1.17)
121.3(17.7)
1.37(1.23)
130.6(17.2)
-0.77(3.27)
DBP, mmHg
69.36(9.04)
0.55(0.67)
68.2(8.5)
0.54(0.69)
74.4(9.7)
0.59(1.93)
HDL-C, mmol/L
1.33(0.34)
0.06(0.02)
1.38(0.33)
0.07(0.02)
1.13(0.29)
0.04(0.03)
TG, mmol/L
1.57(0.82)
-0.06(0.05)
1.45(0.71)
-0.07(0.05)
2.08(1.06)
-0.02(0.13)
FPG, mmol/L
5.25(1.06)
-0.07(0.05)
5.16(1.06)
-0.07(0.06)
5.61(1.01)
-0.04(0.16)
NCEP MetSMCt
2.03(1.14)
-0.24(0.08)
1.91(1.15)
-0.23(0.08)
2.57(0.92)
-0.29(0.18)
NCEP/ADA MetSMCt
2.47(1.20)
-0.18(0.07)
2.08(1.26)
-0.20(0.08)
2.93(0.94)
-0.07(0.16)
IDF/JIS MetSMCt
2.24(1.25)
-0.23(0.07)
2.29 (1.14)
-0.24(0.08)
3.25(1.14)
-0.18(0.18)
NCEP MetS National Cholesterol Education Panel metabolic syndrome; NCEP/ADA MetS National Cholesterol Education Panel-Revised metabolic syndrome;
IDF/JIS International Diabetes Federation Joint Interim Statement; Waist waist circumference; S/DBP systolic/ diastolic blood pressure; HDL-C high density
lipoprotein cholesterol; TG triglyceride; FPG fasting plasma glucose; MetSMCt MetS marker count.
123
Waist in Women and Men.
When the waist MetS marker was excluded from the count, whereby the count drops by
one, the mean waist for those with no other MetS marker was 92cm in Women, and for
men the mean waist was approximately 101cm. (Table 4-7). Thus for a larger waist men
have fewer MetS marker.
Table 4-7. Baseline. MetS Marker Count, Excluding MetS Marker Waist by Gender
1
MetSMCt
Excluding Waist
0
1
2
3
4
Sum
Women, n=194
2
n (%)
49(25)
75(39)
43(22)
20(10)
7(4)
194(100)
Waist cm, mean(sd)
92.6(11.1)
97.5(11.9)
102.8(10.30)
100.8(9.1)
107.3(19.4)
Men, n=40
n (%)
4(10)
7(17)
13(33)
10(25 )
6(15)
40(100)
Waist cm, mean(sd)
101.4(8.3)
106.1(5.1)
113.2(11.7)
113.9(13.6)
111.2(10.7)
1
MetSMCt Metabolic syndrome marker count, number of metabolic syndrome markers outside th e
International Diabetes Federation–defined MetS marker cut-off level; 2 n total=234/250 as some blood
tests were unable to be drawn or samples were unsuitable for testing and no MetS Marker values were
imputed. Waist waist circumference; sd standard deviation
Waist in European and Maori.
In the same type of analysis, when the waist MetS marker was excluded from the count,
whereby the count drops by one, the mean waist for those with no other MetS marker was
92cm in European/Other, and for Maori the mean waist was approximately 99cm (Table
4-8). For a larger waist Maori had fewer MetS markers.
Table 4-8. Baseline. MetS Marker Count, Excluding MetS Marker Waist by Ethnicity
1
MetSMCt
Excluding Waist
0
1
2
3
4
Sum
n (%)
45(22)
71(35)
45(22)
28(14)
12(6)
201(100)
European 2n=201
Waist cm, mean(sd)
92.3(11.2)
98.5(11.5)
104.6(11.3)
104.6(11.9)
106.9(13.8)
n (%)
8(30)
10(37)
7(26)
1(4 )
1(4)
27(100)
Maori n=27
Waist cm, mean(sd)
98.9(8.9)
98.6(14.0)
113.7(8.3)
127.50
135.75
1
MetSMCt Metabolic syndrome marker count, number of metabolic syndrome markers outside th e
International Diabetes Federation–defined MetS marker cut-off level; 2 N total=234/250 as some blood
tests were unable to be drawn or samples were unsuitable for testing and no MetS marker values were
imputed. sd standard deviation; waist waist circumference.
Of the 119 women and 28 men with MetS who completed the study, 63.9% lost weight
during the intervention, with 14.8% and 3.4% losing ≥5% and ≥10% of body weight
124
respectively, and 61.2% of all participants losing waist, and 26.8% and 4.7% losing ≥5%
and ≥10 % of their waist respectively. Details for each gender are shown in Figure 4-5.
Figure 4-5. Baseline to 6m Change, Weight & Waist in MetS: Women & Men
(A)
(B)
10
10
Women
5
Men
0
Waist, cm
0
Weight, kg
Men
Women
5
-5
-10
-5
-10
-15
Women n=119, 66% Lost Weight Men n= 28, 57% Lost Weight
-20
Weight Lost
-25
1
16
31
Weight Lost
> 5% = 16%
> 10% = 4%
46
61
> 5% = 11%
> 10% = 0%
-15
-20
Women n= 119, 61% Lost Waist
Men n= 28, 57% Lost Waist
Waist Lost
Waist Lost
-25
76
91
106
121
136
151
1
16
31
> 5% = 29%
> 10% = 3%
46
61
Participants
76
91
106
> 5% = 18%
> 10% = 11%
121
136
151
Participants
In Figure 4-6 Pearson r are shown between change in weight and change in waist (r=0.59,
p <0.001), SBP (r=0.20, p=0.02) DBP (r=0.16, p=0.06), TG (r=0.21, p=0.01), FPG
(r=0.28, p=0.007), and MetS marker count (r=0.25, p=0.002). They were not significant
for HDL-C nor MetS. Gender differences are shown (Figure 4-6).
Effect of Weight on Metabolic Syndrome and Count, and the Effect of Metabolic
Syndrome and Count on Weight
Effect of Weight on Metabolic Syndrome
There was strong evidence of a linear relationship of weight change with changes in waist
(p<0.001), SBP (p=0.03), DBP (p=0.05) and FPG (p=0.001), but no indication that the
relationship was quadratic (p>0.45). Weight change was related to neither TG nor HDLC, on linear or quadratic analysis (p>0.45). (Note that one participant was excluded due to
an extremely large weight loss and TG change that was very influential in the test for a
quadratic effect. When this participant’s data were removed this effect was lost). A linear
relationship of weight change with change in MetS marker count was demonstrated
(p=0.03). However there was no evidence of a quadratic relationship (p=0.16).
125
r(p value)
All=0.59(<0.001)
W=0.56(<0.001)
M=0.72(<0.001)
All=0.20(p=0.02)
W=0.16(p=0.08)
M=0.37(p=0.05)
-20 -15 -10 -5 0
-20 -15 -10 -5 0 5 10 15 20 25
5 10 15 20 25
Weight Change, kg
3
0.8
0.6
0.4
0.2
0.0
All=-0.03(>0.1)
W=-0.05(>0.1)
M=0.12(>0.1)
-0.2
-20 -15 -10 -5 0
3
2
1
0
-1
-2
All=0.21(p=0.01)
W=0.23(p=0.01)
M=0.09(p>0.1)
-3
-20 -15 -10 -5 0
5 10 15 20 25
0
-1
-2
All=0.28(0.007)
W=0.26(0.005)
M=0.38(0.05)
-4
-20 -15 -10 -5 0
5 10 15 20 25
2
0
-4
1
5 10 15 20 25
2
-2
2
-3
4
-4
-20 -15 -10 -5 0
4
1.0
-0.4
IDF/JIS MetSMCt Change
All=0.16(p=0.06)
Wom=0.13(p>0.1)
Men=0.27(p>0.1)
1.2
5 10 15 20 25
4
50
40
30
20
10
0
-10
-20
-30
-40
-50
TG change, mmol/L
DBP Change, mmHg
50
40
30
20
10
0
-10
-20
-30
-40
-50
FPG Change, mmol/L
M
All=0.25(p=0.002)
W=0.21(p=0.02)
M=0.42(p=0.02)
-20 -15 -10 -5 0 5 10 15 20 25
Weight Change, kg
IDF/JIS MetS Change
W
HDL-C change, mmol/L
14
12
10
8
6
4
2
0
-2
-4
-6
-8
-10
-12
-14
SBP Change, mmHg
Waist Change, cm
Figure 4-6. Change Baseline to 6m Correlations: Weight vs. MetSM, MetSMCt & MetS
1
0
-1
All=0.11(p>0.1)
W=0.11(p>0.1)
M=0.11(p>0.1)
-2
-20 -15 -10 -5 0
5 10 15 20 25
Weight Change, kg
Weight Change, kg
Waist waist circumference; DBP diastolic blood pressure, SBP systolic blood pressure, TG
triglyceride, FPG fasting plasma glucose; MetS metabolic syndrome; MetSM metabolic syndrome
marker; IDF/JIS MetSMCt International Diabetes Federation/Joint Interim Statement metabolic
syndrome marker count; w women, m men
Effect of Metabolic Syndrome on Weight
The only baseline MetS marker for which there was any indication of a relationship with
the change in weight over time was SBP (p=0.06), in contrast to waist (p=0.16), DBP
(p=0.37), HDL-C (p=0.75), TG (p=0.73) and FPG (p=0.26). In case an influence of one
of these variables was being masked by their interdependence, the model was also run
with each MetS marker separately, however there was still no indication of a relationship
with weight change. The MetS marker count could not be shown to be related to weight
change (p=0.22).
Quality of Life Short Form-36 Scores and Change with the Metabolic Syndrome
The following results included the physical and mental summary subscores. There was
some weak indication that both the mean and the within-person change of the physical
summary subscore was related to the MetS marker count (with ordinal odds ratio 1.5;
p=0.08, and 1.2; p=0.10 respectively). There was no evidence of an effect of the mean, or
change of mental summary subscore (p=0.94 and p=0.60 respectively). The only subscale
that could be shown to be associated with MetS marker count was the mean of general
health (p<0.001; ordinal odds ratio 2.0).
126
4.3.3.2 Clinical Routine Screening Haematology and Biochemistry and
Metabolic Syndrome
The values for the laboratory metabolic, MetS marker and CRSHaem&Biochem tests
were presented as the mean(sd) in Table 4-2. Abnormal findings included TC, LDL-C
and TC/HDL-C being above their normal ranges. TG was outside the normal range, and
MetS cut point in men. FPG was in the impaired range and above MetS cut point in men.
Urate was borderline for women and above the normal range for men. HsCRP was above
the normal range for both women and men (Table 4-2). Ranges for both genders in the
normal population are shown in (Appendix 3).
Cross Sectional Correlations
The Pearson correlation coefficients (r) and the partial r which adjust for the other
CRSHaem&Biochem, and show an independent relationship, are set out in Table 4-9.
The table is configured with the Pearson r on the top of the three rows for each parameter.
In the next row are the partial r of that group, which is either oxidant (Ox) or
inflammatory (Infl), and the bottom row is the partial r correcting for all
CRSHaem&Biochem.
The major findings were that HbA1c and urate were much more strongly correlated with
the IDF MetS marker count on Pearson and partial r analysis (r=0.28-0.40; p<0.001), than
the other CRSHaem&Biochem. HbA1c and urate showed Pearson r with all MetS marker
except urate did not correlate with FPG (r=0.15-0.89; p=0.04-<0.001) and All, Partial
(Table 4-9).
Correlation adjustments for the other CRSHaem&Biochem with waist (r=0.26-0.41;
p=0.003-<0.001), and BP were significant (r=0.15-0.89; p=0.07-<0.001). AlkPhos
maintained independent modest relationships with TG and BP, and showed a possible
independent association with MetS marker count (r=0.14-0.15; p=0.06), after adjustment
for the other CRSHaem&Biochem (Table 4-9).
A similarly sized independent relationship was demonstrated by albumin (r=0.19;
p=0.04), with MetS marker count, while globulin (r=-0.14; p=0.07), showed a possible
negative independent relationship with MetS marker count.
127
Table 4-9. Baseline. Clinical Routine Screening Haematology/Biochemistry vs. MetS Marker Count, Simple and Partial Correlations
CRSHaem&
Biochem
Neutrophils,
x109/L
Eosinophils,
x109/L
Monocytes,
x109/L
Lymphocytes,
x109/L
ESR,
mm/h
Albumin,
g/L
Globulin,
g/L
hsCRP,
mg/L
Correlation
Type
Waist cm
r
Inflammatory measures
Pearson
0.14
Partial, Infl
-0.08
Partial, All
-0.12
Pearson
0.12
Partial, Infl
0.09
Partial, All
0.17
Pearson
0.13
Partial, Infl
0.10
Partial, All
0.10
Pearson
0.18
Partial, Infl
0.18
Partial, All
0.14
Pearson
0.10
Partial, Infl
-0.11
Partial, All
-0.13
Pearson
-0.05
Partial, Infl
-0.06
Partial, All
-0.14
Pearson
0.17
Partial, Infl
0.17
Partial, All
0.12
Pearson
0.25
Partial, Infl
0.27
Partial, All
0.15
SBP mmHg
DBP mmHg
HDL- C mmol/L
p-value
r
p-value
r
p-value
r
0.03
0.26
0.14
0.08
0.21
0.04
0.06
0.16
0.21
0.01
0.01
0.08
0.12
0.12
0.10
0.44
0.45
0.08
0.009
0.02
0.14
0.001
0.001
0.06
0.03
-0.01
-0.08
0.02
-0.01
-0.04
0.12
0.07
0.07
-0.06
-0.04
-0.06
-0.02
-0.05
-0.09
0.08
0.14
0.10
0.02
0.07
0.06
0.05
0.13
0.07
0.69
0.91
0.33
0.77
0.86
0.60
0.08
0.36
0.38
0.39
0.55
0.45
0.78
0.50
0.28
0.20
0.05
0.23
0.82
0.36
0.46
0.41
0.08
0.37
-0.01
0.00
-0.08
0.06
0.02
-0.03
0.03
-0.03
-0.04
-0.09
-0.08
-0.08
-0.03
-0.02
-0.06
0.14
0.22
0.16
0.03
0.11
0.11
-0.02
0.03
0.00
0.89
0.98
0.33
0.37
0.76
0.71
0.65
0.63
0.58
0.20
0.27
0.29
0.64
0.81
0.46
0.03
0.002
0.04
0.70
0.14
0.16
0.78
0.69
0.97
-0.25
-0.08
-0.09
0.00
-0.02
0.10
-0.17
-0.12
-0.12
-0.11
-0.02
-0.18
-0.20
-0.10
-0.10
-0.01
-0.07
-0.05
-0.17
-0.07
0.00
-0.14
-0.01
0.01
p-value
<0.001
0.27
0.24
0.21
0.80
0.21
0.01
0.09
0.12
0.10
0.75
0.02
0.002
0.15
0.21
0.86
0.30
0.52
0.008
0.36
0.99
0.03
0.84
0.91
TG mmol/L
pr
value
FPG mmol/L
pr
value
MetSMCt
pr
value
0.13
0.06
0.07
-0.01
-0.003
-0.09
0.07
0.08
0.07
0.03
-0.02
0.06
0.006
0.04
0.02
0.19
0.24
0.2
-0.007
0.01
-0.08
0.06
0.07
0.05
0.009
0.002
-0.05
-0.04
-0.04
-0.01
-0.05
-0.08
0.06
0.03
0.01
0.009
-0.001
-0.15
-0.05
-0.02
0.003
-0.01
0.17
0.25
-0.02
0.09
0.14
0.02
0.23
0.08
0.02
0.03
-0.02
-0.02
0.17
0.05
0.09
0.10
0.046
0.003
0.12
0.12
0.13
0.12
0.19
0.17
0.02
-0.04
-0.14
0.19
0.16
0.13
0.07
0.40
0.39
0.86
0.97
0.26
0.29
0.29
0.36
0.71
0.81
0.43
0.92
0.63
0.84
0.004
<0.001
0.01
0.92
0.90
0.32
0.33
0.36
0.55
0.90
0.98
0.57
0.61
0.59
0.86
0.47
0.27
0.43
0.72
0.85
0.92
0.99
0.04
0.57
0.81
0.97
0.86
0.009
<0.001
0.83
0.20
0.05
0.81
<0.001
0.26
0.77
0.69
0.81
0.85
0.01
0.47
0.24
0.14
0.53
0.97
0.07
0.10
0.12
0.06
0.007
0.03
0.79
0.60
0.07
0.004
0.02
0.12
128
CRSHaem&
Biochem
HbA1c,
%
Urate,
mmol/L
Ferritin,
μg/L
AlkPhos,
U/L
ALT,
U/L
AST,
U/L
γGT,
U/L
SBP mmHg
DBP mmHg
HDL- C mmol/L
p-value
r
p-value
r
p-value
r
p-value
Inflammatory measures
Oxidant Measures
Pearson
0.37
Partial, Oxi
0.32
Partial, All
0.26
Pearson
0.41
Partial, Oxi
0.37
Partial, All
0.41
Pearson
0.22
Partial, Oxi
-0.01
Partial, All
-0.07
Pearson
0.20
Partial, Oxi
0.16
Partial, All
0.12
Pearson
0.15
Partial Oxi
0.02
Partial, All
0.08
Pearson
0.16
Partial Oxi
-0.06
Partial, All
-0.09
Pearson
0.21
<0.001
<0.001
0.0013
<0.001
<0.001
<0.001
<0.001
0.88
0.39
0.002
0.03
0.13
0.03
0.82
0.35
0.02
0.40
0.26
0.001
0.22
0.17
0.15
0.38
0.30
0.30
0.17
0.02
-0.03
0.14
0.15
0.22
0.16
0.02
0.08
0.13
-0.04
-0.07
0.17
0.001
0.02
0.07
<0.001
<0.001
0.001
0.008
0.82
0.72
0.04
0.04
0.01
0.02
0.80
0.31
0.05
0.54
0.39
0.01
0.15
0.13
0.10
0.34
0.29
0.33
0.15
0.01
-0.08
0.11
0.11
0.19
0.12
0.06
0.11
0.05
-0.09
-0.12
0.15
0.03
0.07
0.20
<0.001
<0.001
<0.001
0.02
0.88
0.29
0.09
0.12
0.02
0.07
0.43
0.17
0.45
0.20
0.13
0.02
-0.17
-0.12
-0.12
-0.23
-0.22
-0.18
-0.03
0.09
0.06
-0.07
-0.08
-0.04
-0.14
-0.11
-0.13
-0.12
-0.03
-0.01
-0.02
Partial Oxi
0.05
0.46
0.00
0.98
-0.01
0.86
Partial, All
0.004
0.96
-0.08
0.30
-0.11
0.15
Correlation
Type
Waist cm
r
TG mmol/L
pr
value
FPG mmol/L
pr
value
MetSMCt
pr
value
0.01
0.09
0.14
<0.001
0.002
0.02
0.67
0.20
0.42
0.20
0.29
0.62
0.04
0.14
0.09
0.06
0.65
0.89
0.74
0.15
0.06
0.10
0.27
0.14
0.07
0.19
0.07
0.16
0.11
0.13
0.19
0.22
0.04
0.01
0.19
0.10
0.12
0.21
0.03
0.44
0.20
<0.001
0.05
0.37
0.004
0.31
0.05
0.09
0.08
0.02
0.003
0.54
0.86
0.004
0.16
0.13
<0.001
0.89
0.89
0.89
0.11
-0.12
-0.13
0.28
0.17
0.10
0.07
0.09
0.08
0.20
-0.06
-0.06
0.35
0.17
0.18
0.02
<0.001
<0.001
<0.001
0.12
0.09
0.11
<0.001
0.02
0.22
0.31
0.21
0.30
<0.001
0.43
0.46
<0.001
0.02
0.02
0.73
0.40
0.32
0.36
0.39
0.33
0.28
0.20
0.00
0.02
0.13
0.14
0.15
0.20
0.02
0.03
0.21
0.04
0.04
0.14
<0.001
<0.001
<0.001
<0.001
<0.001
<0.001
0.003
1.00
0.82
0.05
0.05
0.06
0.003
0.74
0.72
0.002
0.62
0.65
0.04
0.10
0.16
0.05
0.51
-0.20
0.01
-0.06
0.38
0.12
0.14
0.02
0.79
-0.17
0.04
-0.11
0.18
Partial r are for each measure over and above the others in that group. Eg Partial, All is for both oxidant and inflammatory measures. Oxi oxidant; Infl
inflammatory; MetSMCt metabolic syndrome marker count. r correlation coefficient; Waist waist circumference; HDL-C high density lipoprotein cholesterol; TG
triglyceride; FPG fasting plasma glucose; S/DBP systolic/diastolic blood pressure; HbA 1 c haemoglobinA 1 c ; AlkPhos alkaline phosphatase; ALT alanine transferase;
AST aspartate transferase; γGT gamma glutamyl transferase; ESR erythrocyte sedimentation rate; hsCRP high sensitive C reactive protein.
129
There were various modest Pearson r of the liver enzymes: AlkPhos, ALT, AST and γGT
(r=0.11-0.21; p=0.09-<0.001), with TG; and ALT, AST and γGT (r=0.14-0.20; p=0.0030.04) and acute phase proteins: CRP and ferritin (r=0.19-0.22; p=0.003-<0.001), and
leukocytes: neutrophils (r=0.14-0.23; p=0.03), lymphocytes and eosinophils (r=0.14-0.17;
p=0.01-0.08), with MetS marker, predominantly waist, and/or MetS marker count.
However, for these CRSHaem&Biochem, almost all relationships reduced considerably
or disappeared after adjusting for the other CRSHaem&Biochem. CRP showed a possible
weak independent relationship with waist (r=0.15; p=0.06). ALT and lymphocytes
showed a possible negative independent relationship with HDL-C. Other minor
relationships are shown in Table 4-9.
Longitudinal Multivariable Relationships
Demographic, Health Status, and Lifestyle Relationships
Age was highly significantly related to mean MetS marker count (p<0.001), and S/DBP
(p<0.001), lipids: HDL-C and TG (p=0.02), but not waist or FPG. Female gender was
negatively related to waist and HDL-C (p=0.001 for both) (Table 4-10).
A higher education level (p=0.06), indicated a borderline likelihood of a negative
relationship with MetS marker count, but not any MetS marker.
Alcohol intake of ≥14 units, or ≥280g/wk showed a significant negative relationship with
HDL-C (p=0.03), but a positive relationship with TG (p=0.01).
There was a likelihood that ever having smoked was directly related to waist (p=0.02).
Numbers of cigarettes/d, however, was significant negatively related to waist (p=0.004)
(Table 4-10). Whilst there was weak evidence that acute and/or chronic inflammatory
disease was related directly to HDL-C, there was also a weak relationship with TG
(p=0.07)No anti-inflammatory medication had effects on any MetS marker or MetS
marker count.
Medication with anti-inflammatory effects were positively related to HDL-C (p=0.03) and
negatively to FPG (p=0.01).
Chitosan was negatively related: to waist (p=0.03), to SPB (p=0.08, trend) and to FPG
(p=0.05)..
130
Mean and Change with Metabolic Syndrome Markers and Count
There was strong evidence of association of the overall mean HbA1c (p=0.001), and urate
(p=0.002), with waist, but less evidence for bilirubin (p=0.05), and evidence for overall
mean hsCRP, lymphocytes, and eosinophils, all three weak, associating with waist. There
was evidence for albumin, negative, associating with waist, but change in none of these
variables, could be shown to be related to waist (Table 4-11 & Table 4-12).
There was strong evidence of associations of overall mean urate (p=0.001), and ALT
(p=0.003), with SBP, and weaker evidence of mean ferritin (p=0.04), negatively and
HbA1c (p=0.06) relating to SBP. Change in AST (p=0.04), CRP (p=0.05), bilirubin
(p=0.001), negatively, urate (p=0.03), negatively and possibly γGT (p=0.07), were also
associated with SBP (Table 4-11 & Table 4-12).
There was strong evidence of overall mean urate (p=0.001), and ALT (p=0.001), being
related to DBP, and ferritin (p=0.003), and AST (p=0.04), both negatively being related to
DBP. Change in bilirubin (p=0.05), and ferritin (p=0.03), both negatively, were
associated with DPB, in addition (Table 4-11 & Table 4-12). There was evidence of
mean γGT (p=0.01), and ESR (p=0.03), negatively, and possibly urate (p=0.07),
negatively, relating to HDL-C, and also evidence of the change in albumin (p=0.04),
neutrophils (p=0.05), and AlkPhos (p=0.09), weakly, being associated with change in
HDL-C (Table 4-11 & Table 4-12).
There was evidence that the overall mean concentrations of AST (p=0.05), albumin
(p=0.03), lymphocytes (p=0.04), and bilirubin (p=0.002), strong and negative, and
AlkPhos (p=0.0), negative, were associated with TG level, while the change in γGT
(p=0.03), and globulin (p=0.02), was also associated with change in TG (Table 4-11 &
Table 4-12).
There was extremely strong evidence that HbA1c (p<0.001), was associated with FPG,
and there was some evidence of overall mean concentrations of ferritin (p=0.06), and
AST (p=0.09), weakly, and change in γGT (p=0.08), weakly and negatively, associated
with change of FPG.
131
Table 4-10. Relationships of MetS Markers and MetS Marker Count with Demographic, Health Status and Lifestyle Parameters
Explanatory variables
Demography, Lifestyle,
Illness, Treatment
Age, y
Gender (female)
Edu. Level > 2ndry school
Alcohol, ≥ 14 Units/wk
Ever smoked cig..
Number cig. smoked/d
Inflam. disease acute/chronic
Anti-inflam. Med.
Anti-inflam. Med. effects
Chitosan treatment,
pppSBP
DBP
HDL-C
value
value
value
Estimates (p value): Demographic, Health Status and Lifestyle
0.03
0.70
0.72
<0.001 0.27
<0.001 0.005
-9.34
<0.001 4.62
0.23
-0.34 0.86
0.30
-0.97
0.51
0.80
0.72
-0.06 0.96
-0.00
-3.32
0.45
-5.49 0.41
-6.14 0.06
-0.28
3.84
0.02
1.89
0.42
0.29
0.80
-0.01
-0.79
0.004
-0.41 0.31
-0.20 0.31
0.01
-0.96
0.46
2.02
0.46
2.01
0.16
0.08
1.00
0.56
1.24
0.67
0.28
0.85
0.09
-0.88
0.49
0.87
0.73
0.03
0.98
0.09
-3.28
0.03
-3.82 0.08
-0.88 0.40
-0.04
Waist
pvalue
pvalue
FPG
pvalue
1
TG
MetS
MCt
pvalue
0.02
0.001
0.96
0.03
0.88
0.51
0.08
0.10
0.03
0.32
0.01
-0.27
0.16
0.74
0.15
-0.02
-0.22
-0.22
-0.06
0.01
0.02
0.12
0.11
0.01
0.16
0.26
0.07
0.10
0.60
0.90
-0.003
0.02
0.06
0.23
0.10
-0.01
-0.02
-0.15
-0.31
-0.18
0.55
0.91
0.53
0.41
0.28
0.41
0.89
0.24
0.01
0.05
1.36
-0.52
-0.47
0.06
0.39
-0.17
-0.14
-0.18
-0.04
-0.33
<0.001
0.12
0.06
0.83
0.14
0.59
0.52
0.48
0.85
0.18
MetS metabolic syndrome; MetSMCt metabolic syndrome marker count; Waist waist circumference; S/DBP systolic/diastolic blood pressure; HDL-C high density
lipoprotein cholesterol; TG triglyceride; FPG fasting plasma glucose; y years; wk week; d day; edu education;.inflam inflammatory; med medication.
132
Table 4-11. Relationships of MetS Markers and MetS Marker Count vs. Clinical Routine Screening Haematology/Biochemistry – Inflammatory
Explanatory
variables
CRSHaem/Biochem
m Neutrophils
δ Neutrophils
m Eosinophils
δ Eosinophils
m Monocytes
δ Monocytes
m Lymphocytes
δ Lymphocytes
m ESR
δ ESR
m Albumin
δ Albumin
m Globulin
δ Globulin
m hsCRP
δ hsCRP
Waist
pvalue
SBP
pvalue
DBP
pvalue
HDL-C
pvalue
TG
pvalue
FPG
pvalue
1
MetS
MCt
pvalue
Estimates (p values) Mean (m) and Change (δ)
0.37
-0.50
12.85
10.35
1.91
-0.29
2.90
0.41
0.05
-0.01
-0.97
-0.06
0.22
-0.01
0.37
0.14
0.63
0.54
0.06
0.19
0.83
0.97
0.06
0.81
0.54
0.80
0.01
0.88
0.44
0.98
0.06
0.40
-0.05
-3.09
-5.75
5.20
9.88
10.70
-0.06
1.01
0.05
0.02
0.35
0.62
0.19
1.25
0.01
0.85
0.97
0.14
0.58
0.79
0.46
0.63
0.98
0.82
0.64
0.87
0.55
0.55
0.65
0.21
0.98
0.05
-0.03
-1.01
-0.45
-17.15
-4.83
-15.00
0.04
1.92
0.06
0.01
0.37
1.26
0.24
-0.26
-0.11
0.23
0.95
0.39
0.93
0.14
0.46
0.24
0.97
0.46
0.31
0.91
0.20
0.03
0.26
0.65
0.44
0.37
-0.03
0.06
0.05
-0.42
-0.05
0.03
-0.04
0.04
-0.005
<-0.001
-0.01
0.03
-0.004
-0.003
-0.003
-0.003
0.12
0.05
0.80
0.14
0.85
0.92
0.34
0.55
0.03
0.97
0.63
0.04
0.63
0.83
0.57
0.66
0.05
0.04
-0.62
-0.22
-0.20
-0.34
0.21
0.16
0.003
-0.004
0.06
-0.05
-0.02
0.11
0.01
0.03
0.32
0.68
0.19
0.80
0.75
0.73
0.04
0.42
0.54
0.41
0.03
0.23
0.28
0.02
0.56
0.13
0.004
0.06
-0.11
0.25
0.59
-0.49
-0.03
0.10
<-0.001
<0.001
-0.01
0.03
-0.01
-0.01
0.01
0.02
0.93
0.49
0.79
0.77
0.29
0.61
0.75
0.61
0.86
0.89
0.58
0.45
0.49
0.83
0.62
0.36
0.60
-0.32
0.25
0.28
-0.05
0.27
0.22
-0.14
0.93
0.24
-0.37
-0.19
-0.31
0.29
0.33
0.06
0.09
0.11
0.40
0.13
0.88
0.14
0.57
0.37
0.01
0.09
0.23
0.37
0.25
0.13
0.29
0.75
MetS metabolic syndrome; MetSM metabolic syndrome marker; MetSMCt metabolic syndrome marker count; Waist waist circumference; S/DBP systolic/diastolic
blood pressure; HDL-C high density lipoprotein cholesterol; TG triglyceride; FPG fasting plasma glucose; m mean (overall level), δ change (deviation from the
mean of the (2) repeated measures); ESR erythrocyte sedimentation rate; hsCRP high sensitive C reactive protein. 1 In both the general linear mixed model and
ordinal logistic regression (or generalised linear mixed mode) analysis method for MetSM and MetSMCt, the estimates were multiply transformed, so are not
intuitively interpretable. m mean (mean of baseline and 6m values), δ change (deviation from the mean of the (2) repeated measures).
133
Table 4-12. Relationships of MetS Markers and MetS Marker Count vs. Clinical Routine Screening Haematology/Biochemistry – Oxidant
Explanatory
variables
CRSHaem/Biochem
m HbA1c
δ HbA1c
m Urate
δ Urate
m Ferritin
δ Ferritin
m Bilirubin
δ Bilirubin
m AlkPhos
δ AlkPhos
m ALT
δ ALT
m AST
δ AST
m γGT
δ γGT
Waist
3.45
-1.43
39.93
-18.41
-0.02
0.02
0.35
0.17
0.03
-0.07
0.16
-0.04
-0.14
0.05
0.02
0.01
1
ppppppMetSM
SBP
DBP
HDL-C
TG
FPG
value
value
value
value
value
value
Ct
Estimates (p values) Mean (m) (overall level) and change (δ) (deviation from the mean of the (2) repeated measures)
0.001
2.90
0.06
0.89
0.26
-0.04
0.22
0.08
0.29
1.55
<0.001 1.47
0.40
-3.09
0.50
-2.27
0.40
0.01
0.81
-0.05
0.79
0.13
0.53
-0.21
0.002
77.65
<0.001 41.95
<0.001 -0.68
0.07
0.99
0.24
-0.54
0.49
0.92
0.20
-83.09
0.03
-6.80
0.75
0.40
0.44
0.73
0.66
-0.18
0.91
-0.17
0.11
-0.03
0.04
-0.02
0.003
<0.001
0.65
0.001
0.10
0.001
0.06
-0.44
0.28
-0.05
0.34
-0.06
0.03
<-0.001
0.27
0.001
0.53
<0.001
0.72
0.27
0.05
-0.15
0.58
-0.05
0.70
<-0.001
0.84
-0.04
0.002 -0.01
0.44
0.36
0.20
-1.14
0.001
-0.39
0.05
<0.001
0.98
-0.01
0.48
0.003
0.85
-0.17
0.36
0.04
0.38
0.02
0.30
<-0.001
0.34
0.004
0.09
<0.001
0.87
0.19
0.26
-0.14
0.38
0.12
0.20
0.004
0.09
-0.01
0.10
0.005
0.52
-0.23
0.20
0.54
0.003
0.30
<0.001 -0.004
0.23
-0.003
0.77
<-0.001
0.96
0.59
0.66
0.06
0.78
0.05
0.70
-0.002
0.57
<0.001
0.92
0.002
0.82
0.09
0.16
-0.23
0.11
-0.15
0.04
<-0.001
0.88
0.01
0.05
0.01
0.09
-0.30
0.67
0.64
0.04
-0.09
0.62
-0.005
0.29
0.02
0.14
0.01
0.70
0.34
0.61
-0.01
0.78
-0.03
0.25
0.003
0.01
<-0.001 0.97
-0.003
0.11
-0.14
0.86
0.36
0.07
0.05
0.67
-0.002
0.46
0.02
0.03
-0.02
0.08
0.38
pvalue
<0.001
0.17
0.003
0.32
0.12
0.12
0.19
0.33
0.46
0.15
0.12
0.56
0.58
0.11
0.63
0.06
δ change (deviation from the mean of the (2) repeated measures) MetS metabolic syndrome; MetSMCt metabolic syndrome marker count; waist waist circumference;
S/DBP systolic/diastolic blood pressure; HDL-C high density lipoprotein cholesterol; TG triglyceride; FPG fasting plasma glucose; m mean (overall level); HbA1c
Haemoglobin A1c; AlkPhos alkaline phosphatase; ALT alanine transferase; AST aspartate transferase; γGT gamma glutamyl transferase. 1 In the generalised linear
mixed model, ordinal logistic regression method for MetSMCt the estimates were multiply transformed, so were not intuitively interpretable.
134
There was evidence that the mean concentrations of HbA1c (p<0.001), urate (p=0.003),
and ESR (p=0.06), and neutrophils (p=0.09), weakly, were positively associated with
MetS marker count, while change in γGT (p=0.06), and ESR (p=0.09), both weak, were
positively associated with change in MetS marker count (Table 4-11 & Table 4-12).
4.4
Discussion
Main Findings: HbA1c and Urate, Cross-Sectionally; γGT and ESR,
Longitudinally
The most notable findings in the present study of overweight and obese participants were
that the oxidant markers, HbA1c and urate, were more highly related to MetS or MetS
marker count than the inflammatory markers, such as CRP and other acute phase proteins,
albumin and globulin. The diabetes monitoring marker, HbA1c, and gout marker, urate,
were highly related to IDF/JIS MetS marker count on Pearson and partial r, and overall
mean (baseline to 6m). The neutrophils were also highly correlated using Pearson r, but
exhibited no partial r, and had a borderline relationship of overall mean with IDF/JIS
MetS marker count.
With respect to the longitudinal change over the study, change in γGT and ESR showed a
relationship of borderline significance to change in MetS marker count, however this
change was over and above all the demographic and CRSHaem/Biochem relationships,
and thus could be interpreted as important. This mixed oxidant, γGT, and inflammatory,
ESR, result appears to show a merging of pathologies.
When the overall mean of HbA1c, and urate were examined with the other MetS marker
variables in the mixed model, they again proved to be highly significantly related to
waist, HbA1c to FPG and urate to BP, and modestly to most of the IDF/JIS MetS markers.
Other relationships with change in γGT is also related to change in SBP, and negatively to
change in TG and to FPG.
These relationships were much stronger than, and overwhelmed, the classic inflammatory
markers, notably CRP; and the neutrophil overall mean of the baseline and 6m was highly
significantly related to MetS marker count. In addition ESR, and neutrophils, the most
oxidant of the leukocytes591 due to their oxidative burst activity on pathogen marker
stimulation, also showed a relationship to MetS marker count. These results were
135
hypothesised but it was not certain whether inflammatory or oxidant markers would be
more highly related.
The most important overall relationship was that of the change in γGT and ESR being
related to the change over the study in MetS marker count. The results of the analysis of
change between baseline and 6m of the CRSHaem&Biochem and IDF/JIS MetS marker
and MetS marker count were also not predicted. With the same explanatory variables
included as for the mean, the change relationships shown were apparently weaker, but of
different in quality. A weaker relationship is to be expected with the added time variable.
These are important changes as they are tracking and changing with the altering
metabolism associated with either weight gain and positive energy balance or weight loss
and negative energy balance. γGT and ESR are related to MetS marker count index and
thus to MetS over and above the other CRSHaem&Biochem.
The implications are that these CRSHaem&Biochem are also likely markers that should
be studied in larger clinical trials as they may give valuable information on human health
over time, together with MetS or MetS marker count.
Other contenders that could be used to aid in the prediction of CVD risk may be the
highly significantly negative changes in bilirubin with changes in S/DBP which were
shown, and may relate to bilirubin being a potent antioxidant. The change in ferritin and
albumin, which were negatively related to change in DBP, could be expected from
albumin which can have antioxidant effects, but is more surprising from ferritin which is
usually seen as pro-oxidant. However, Ferritin can have significant indirect antioxidant
effects by absorbing large amounts of reactive iron281.
Variable Anthropometry
These results were produced in spite of the high variability of anthropometric
measurements in this study whereby weight and waist changed in both positive and
negative directions, and approximately 2/3 of participants lost a very modest amount of
weight and waist by 6m. There was no change relationship with waist and any of the
CRSHaem&Biochem
136
Demographic, Health Status, Lifestyle and Quality of Life
The above could be seen in the context of this population, which appears typical of NZ
adults, although it must be noted that the participants were not chosen at random, and
have particular characteristics that kept them in the study.
Age had by far the strongest effect on MetS marker count over and above all else, via BP
and lipids with HDL-C having a negative effect. Interestingly, age was not related to
either FPG or waist. Female gender, as was as expected, had a significant negative
relationship on waist and HDL-C. Low education level was borderline significantly
related to MetS marker count, but with no specific relationship to any MetS marker. It is
known that individuals from lower socioeconomic groups are more likely to have higher
intakes of processed food and less healthy diets592.
Current cigarette smoking related to a lower waist, and smoking is still used to keep
weight down. However, smoking cessation can lead to weight gain and may explain why
past smoking related to a large waist593. Greater than 2 standard drinks/d related to higher
TG, probably through alcoholic related fatty liver. The trend of lower DBP with higher
alcohol consumption was hard to explain, but BP did increase over the study. Metabolic
medication with the well known hypoglycaemic, hypolipidaemic and hypotensive and
possible anti-inflammatory and antioxidant effects were related negatively to FPG, with a
trend to being related to TG and HDL-C, negatively. This would be expected of a
medication designed to treat diabetes and dyslipidaemia. However, medication for
hypertension appeared to make no difference, and BP in MetS is hard to control594.
Anti-inflammatory drugs had no effects. Inflammatory disease was related to a positive
trend in HDL-C and negatively to TG, but whether this was a medication effect was not
known. Surprisingly, the chitosan treatment was negatively related to waist, SBP (trend)
and to FPG, and thus its efficacy for MetS may be greater than intimated in Chapter 3. In
the current study of weight loss, European, mid-aged, middle class women predominated.
The men who were enrolled had significantly more CVD risk factors such as higher
alcohol and saturated fat intake, reported and measured overweight, hypertension, TIIDM
and sleep apnoea and medication prescriptions for BP, lipids and TIIDM than the women.
The data show that the most common were obesity-related CVD or risk factors,
particularly TIIDM. Men who are centrally overweight are rarely healthy595,596.
Maoriwomen joined the study at rates that were roughly equivalent to the population
137
proportion (14-15% in 2006597), although Asians did not, probably due to entry criteria of
BMI excluding smaller sized ethnic groups42. PA may have been less than reported as
over-reporting is common598 and there may have been under-reporting of television
viewing599. In this overweight and obese group the mean energy intake recorded is close
to that predicted for a slim population at approximately 8MJ/d for women and 10MJ/d for
men600, as compared with the 10.5MJ/d and 14.2MJ/d in an overweight/obese group
respectively (mean BMI 29-30kg/m2), whose energy intake was observed and measured
by doubly labelled water68. Restrained obese women and men may under-report food
intake by up to a third
601-605
. Alcohol intake was notable in that it was infrequent and in
low volumes in the women, but may have been at a problematic level for the men’s CVD
risk profile.
IDF/JIS Metabolic Syndrome
The IDF/JIS MetS that was compared and finally chosen was shown to be appropriate. In
this study, as predicted from the literature review in Chapter 1, groups of less centrally
obese but older less healthy individuals were classified with MetS in addition to the
younger centrally obese. Interestingly, however, Maori could have a very large waist
before other MetS marker numbers increased. Maori are prone to raised urate, and early
TIIDM with obesity606. More work needs to be undertaken on ascertaining a waist cutpoint in Maori and Pacific peoples. MetS appears useful as a screen to which other
markers can be added.
Metabolic Syndrome Markers
Waist relationships with the CRSHaem&Biochem were mostly stronger with the oxidant
markers rather than inflammatory markers. CRP was highly correlated to waist and it has
been shown to be secreted by adipose tissue in obesity382. Surprisingly, in this population
there were no change relationships with waist and MetS marker count. It may be that
central obesity itself is a marker of a deeper issue, and this will be addressed in Chapter 6.
S/DBP were related to oxidant markers, overall more strongly to urate and ALT, and
negatively to ferritin, but with negative changes in bilirubin. Using this statistical mixed
model, some negative changes can be seen if there are strong interactions between
CRSHaem&Biochem, as are present with ALT, AST, γGT, and possibly ferritin. Change
in SBP was also related to CRP, the only inflammatory CRSHaem&Biochem.
138
With respect to the lipids, as HDL-C is related to inflammation, it could be expected that
HDL-C would relate negatively to ESR and directly to bilirubin, and direct changes in
albumin and neutrophils were seen. The direct changes in albumin and neutrophil
relationship with HDL-C was not expected, and may relate to alteration of HDL-C in
inflammation607. TG were strongly negatively related to bilirubin, compared to HDL-C,
but probably related by fat accumulation in the liver related to AST and negatively to
AlkPhos, again probably negatively due to the close relationship of AST and AlkPhos in
the liver, and the way the mixed model handles them. TG also related to albumin, on
which it is carried and lymphocytes, with changes in γGT and globulin. The latter two
CRSHaem&Biochem are probably changing with AST and albumin.
FPG was only related to oxidant liver proteins, ferritin and the transferases, in addition to
HbA1c, also an oxidant. Overall, relationships with MetS marker count were probably the
most important and again oxidants; HbA1c, urate, and neutrophils were related and ESR, a
mixed oxidant/inflammatory marker was directly related to MetS marker count.
Details on the Main Clinical Routine Screening Haematology and Biochemistry
HbA1c is related to raised FPG over approximately 3-12wk preceding testing352 and
AGE608 and their associated oxidative stress609. NHANES data shows that HbA1c predicts
FPG well into the normal range, showing a continuous relationship610. Interestingly,
beneficial mechanisms of the antidiabetic medication, metformin, may include serum
antioxidant preserver and antiLDL glycation activity rather than anti-inflammatory
action611,612.
The relationship of urate, hypertension and MetS has been controversial613 and often
ignored614. Whilst urate may act as a powerful antioxidant itself at normal levels in the
serum615, its synthesis requires catalysis by the potent pro-oxidant xanthine
oxidoreductase (XOR) 616-618. This enzyme is induced excessively in central obesity and is
also a regulator of adipogenesis619. XOR is known to lead to damaging per-oxynitrite
production at the arterial endothelium and increased BP463,620. Uric acid has a role in
innate immunity, and especially the activation of dendritic cells and antigen presenting
cells to endogenous antigens, and this includes tumour cells.
The mechanism of γGT becoming a sensitive oxidant marker may be that it catalyses
reduced glutathione (GSH) conjugation with xenobiotics, and high concentrations of γGT
139
deplete GSH87. Excess native oxidised and xenobiotic reactive oxygen species (ROS)
produce chemicals, examples of which are homocysteine621 and highly fat soluble
persistent organic pollutants87 which when ingested and/or inhaled, impose oxidative
stress. γGT concentrations can increase within the normal weight range, prior to obvious
central obesity, MetS and TIIDM, and are measureable using classic markers370,622-624.
Suggestions have already been made that liver enzymes, particularly γGT, be used to fine
tune CVD risk prediction374.
The acute phase proteins that impede erythrocyte sedimentation and increase ESR are
usually inflammatory and include Ig’s, CRP, fibrin and glycol-oxidised plasma proteins
which increase in number and activation378,625,626. Additionally, as “enhanced
inflammation-sensitive agents”627 such proteins may interact with the inflammatory cells,
various
leukocytes, and augment erythrocyte adhesion/ aggregation628-630. The RBC
deformability decreases and rheology increases in obesity and MetS and normalises with
weight loss349,351.
Recently, tissue AlkPhos has been isolated from pre-adipocytes and may form a
significant proportion of total serum AlkPhos361. Intestinal AlkPhos isoenzymes are
involved in chylomicron formation, FA metabolism, influence CHO levels, and thus are
more likely to have oxidant rather than inflammatory properties.
Ferritin predicts TIIDM development and was associated with γGT in the European
Prospective Investigation of Cancer (EPIC) study356. The liver in obesity appears to suffer
metabolic derangements of iron trafficking per se631. However, it may be a protective
protein, as a non enzymatic Phase II enzyme in healthy slim individuals281.
Bilirubin, reduced glutathione and possibly urate, have complementary antioxidant roles,
all conferring protection on cells from oxidative stress. Interestingly, Gilbert syndrome is
protective of CVD, and may be more protective than HDL-C632.
Leukocyte count is increased in MetS and other CVD risk settings633 The link between
CRP and leukocytes may be related to adipose tissue mass634. The relationship of
leukocytes to waist is thought by many to indicate that central adipose tissue drives the
inflammatory aspect of MetS635,636 although other researchers have favoured the theory
that insulin resistance drives weight gain637,638. Others do not discount that the innate
immune system itself could be a contributing, enabling or even a primary driving
factor639,640.
140
Neutrophils undergo reactions of an acute-phase immune response with increased
myeloperoxidase activity and myeloperoxidase is pro-oxidant641-643. Endothelial adhesion
can be initiated by oxidative stress associated with oxidised LDL-C and hyperglycaemia.
In turn Endothelial oxidative stress644 raises BP645, in conjunction with many other MetSrelated dysfunctional pathways.
Monocytes, circulating in the blood, can transform into macrophages in tissue646 and
activated, but functionally impaired, lipid-congested macrophages, foam cells and central
adipocytes share, and can interchange326 many functional and morphological traits338.
Additionally, this activated adipose tissue seems to provide an environment where
macrophages develop dynamic controlling actions over other immune cells.
Lymphocyte T-cells accumulate in adipose tissue, along with macrophages and upregulate cytokine production in obesity647. T-cells may contain levels of human urotensin
II a potent vasoconstrictor that may have a role in hypertension and atherosclerosis648
Oxidative Stress and Metaflammation
HbA1c, urate, γGT, ESR and the neutrophils the CRSHaem&Biochem most strongly
correlated with MetS in the current study, appear to be directly or indirectly associated
with oxidative stress. With a relative excess intake of energy, oxidative stress is thought
to arise from disordered macronutrient and micronutrient metabolism, and high, but
poorly controlled visceral energy flux, probably preceding insulin resistance and
obesity304,649,650.
Lipid-stuffed,
hypoxic651
and
dysfunctional
adipocytes652
may
accidentally ‘mal-activate’ the inflammatory system after priming by MetS-associated
oxidative stress.
Although the proportion of participants with IDF/JIS MetS was less than half in this
study, the increased numbers of participants with higher IDF/JIS MetS marker count,
compared with NCEP MetS marker count was due to the combination of lowered cut-off
levels for waist and FPG and the inclusion of those on cardiometabolic treatment.
These results need corroboration. The utility of having chosen these CRSHaem&Biochem
laboratory tests is that most of them are first line screening tests generally, as well as for
MetS, TIIDM, CVD, gout and cancer. They are routinely performed in primary care, and
if general practices belong to groups such as primary healthcare organisations, can be
pooled centrally. In countries such as NZ there is a free, annual ‘Diabetes Get Checked’
141
programme to monitor diabetes treatment. All clinical anthropometry, laboratory test
results and prescriptions are pooled from yearly checks. One region, Wellington, recently
has reported their results spanning 5y in TIIDM general practice patients using a mixed
model analysis653. The ADA advocates such practice654. All the tests reported in this
chapter, except the bioimpedance which is not routinely measured, could be pooled and
analysed. MetS index can easily be calculated from the 5 markers. In this manner the
current study could be corroborated using large data sets of patients with central obesity
and MetS.
Various methods could be considered for deducing which factor or index (formulated
from more than one marker) could be used to assess and weight markers to add, when
using MetS as a screening tool. Factor analysis has been used to assess new components
for MetS, including sleep and diet as well as laboratory markers655-657, however cannot be
used for longitudinal data. Receiver operating curves for sensitively and specificity could
also be considered for assessing the best markers to add to MetS, and have been used for
LFT, diabetes screening and waist-height markers in MetS374,658,659.
Limitations of this part of the study are that the participants were not randomly chosen to
undertake a weight loss study, so they may have special characteristics that make these
results not able to be extrapolated to the population at large, but this was understood from
the outset. Hip circumference was not measured. Hip measurement may have helped
assess who was in less metabolic danger. Comparisons of CRSHaem&Biochem with
some experimental oxidant markers would have been useful, and other studies have
looked at MetS and experimental oxidative stress markers463,660-664.
Conclusion
This study showed that firstly that the CRSHaem&Biochem comprise markers very
sensitive to the metabolic dysfunction in MetS, and secondly that these markers have
known oxidant (and in lean individuals, antioxidant) properties and associations.
The current study adds that IDF/JIS MetS does include individuals with a wide range of
CVD risk, and that MetS index devised, the MetS marker count, may extend the use of
MetS concept. In participants in the current 6m weight loss study whose weight changed,
the MetS marker count was related strongly, cross-sectionally, to a range of oxidant
markers, especially HbA1c, urate, and sufficiently well over the study period with γGT
142
and ESR. This study showed strong associations, and some of these markers are already
commonly casually used. HbA1c is now accepted as a screening test for TIIDM, and this
could be extended to those with IFG. Advisory interpretations on fatty liver for raised
LFT and ferritin, are already on laboratory test reports, in addition to tables of hsCRP
guides to be used in conjunction with dyslipidaemia665.
These markers should be researched formally in larger studies with hard CVD end points
in future with a view to making stronger, more accurate CVD prediction guides, for use
alongside the IDF/JIS MetS.
143
Chapter 5. Novel Cardiovascular Disease Protective Markers
Chapter 5. Novel Cardiovascular Disease
Protective Markers
5.1
Introduction
Modest central or upper body obesity is often the only sign of CVD risk, although this
risk may not be understood by individuals affected666. Obesity is often an early symptom
and sign, and is seen in younger adults667. Central obesity and CVD risk can be reversible,
especially if attended to early668. In populations where overweight and obesity are
increasing, primary preventative care could extend the concept of prevention, health
maintenance and longevity.
Clinical markers are usually tested to see if measurements are becoming abnormal, rather
than testing for beneficial markers to see where they lie in the normal range. There may
be a place for introducing protective CVD marker tracing to minimise CVD risk, but also
to optimise health. Patients’ health can proceed toward degenerative disease, remain the
same over the short term, or there can be regression of early signs of disease and
improved health and outlook. Appropriate markers need to be found to perform this
preventive medicine function. Hitherto, preventative primary care has often been
overtaken by OTC supplement, vitamin and nutraceutical products, with vigorous
marketing, but often with less than sound science behind the health claims.
Adpn, as previously introduced in Section 1.5.2.1, is a complex adipocytokine which is
almost exclusively synthesised in adipocytes, undergoes extensive post-translational
modification, and circulates multimeric forms669. Adpn appears to be associated with
protective markers, normal weight and fewer MetS associated problems434,670-673.
The gender dimorphism resulting in lower levels in men may reflect the generally greater
CVD risk in men428,430,674-676, which could make Adpn a gender specific protective
marker.
Adpn is one of few known cytokines whose levels decrease in central obesity, TIIDM,
CVD risk, and CVD itself. HMW becomes proportionately less than total Adpn in obesity
144
probably due to its complicated synthesis which appears to be dysfunctional in central
adipocytes in obesity related MetS677. Thus HMW may be linked more closely to weight
gain and loss and change in MetS than total Adpn. Although Adpn is complex, it is worth
investigating as a clinical marker, as it may be contributing to obesity and associate with
change in MetS.
The second group of adipose tissue-related Novel CVD Protective Markers are the
sFSVitamins, whose functions have been outlined in Chapter 1 Section 1.5.2.2.
As all 5 FSVitamins have been found to have profound cell organising and multiple
metabolic effects468,475,678-682, their testing in MetS is likely to be a very useful adjunct to
MetS screening. Although the vitamins are required biochemicals, and are antioxidants,
the sFSVitamins are not often tested in general medical care, either to check for
sufficiency or deficiency, or antioxidant status. Even sVitD testing in those in
institutionalised care, a high risk group, is discouraged due to cost, and supplements are
now becoming routine665. VitK is not usually tested directly in clinical practice, but a
prothrombin time (PT) is often measured, and reported as the international normalised
ratio (INR) in monitoring VitK blocking; an anticoagulation therapy.
It is not clearly known what aspects of CVD risk or protection testing the sFSVitamins
may reflect, or how wide ranging their effects in obesity and MetS are. Clinical testing
has been reserved for those thought to be deficient. As βCaro is only synthesised by
plants, serum carotenoids tend to reflect protective high fruit and vegetable diets309,683,684.
Furthermore, women have higher fruit and vegetable intakes in many westernised
populations684,685. In addition, in women, as HDL-C transports βCaro, higher HDL-C is
likely to reflect on sβCaro levels.
On the contrary, men in westernised countries, consume more energy and more of it from
saturated fat684 and processed meat686, and have higher rates of dyslipidaemia, CVD and
cancer. Furthermore, increased CVD rates have been detected in men with raised
sVitE687. VitE is abundant in healthy seed and olive oil products688 but also processed
meats where synthetic α and γ-tocopherols are added as antioxidants689,690. γ-tocopherol
has been linked with adverse health477,687.
VitA, which is classically sourced from liver, and lesser amounts in meat, high fat diary
and egg, is highly added to refined fried starch products such as grain and potato crisps.
145
Thus various analyses were run on Adpn, Adpn oligomers and the sFSVitamins to
ascertain their relationships with other CVD risk and protective markers which were
measured, and particularly with MetS markers and count.
5.1.1 Hypothesis
It is hypothesised that the Novel CVD Protective Markers comprising: (a) serum Adpn
and its 3 oligomers, and (b) the sFSVitamins, sVitD, sβCaro, sVitA and sVitE, are
strongly related to a number of other CVD protective markers drawn from an array of
anthropometry, laboratory, demographic, medical status, lifestyle (social habits, diet and
PA), QoL and eating attitude study parameters in women and men.
Change between baseline and 6m of HMW30, will change with similar array of study
markers, as above.
Change in sβCaro in all and women could indicate that women change their fruit and
vegetable intake over the study, and change in VitE in all and men could indicate that
men change their processed fatty food intake. Thus these Novel CVD Protective Markers,
together with MetS, could be used to predict or track protection from degenerative
disease.
5.1.2 Aims
The aims of the Diet&Health-Novel CVD Protective Marker study in 250 overweight and
obese women and men, and a substudy of 30 participants chosen on anthropometry and
MetS status to have their Adpn oligomers analysed, enrolled in a 6m weight loss study
are:
Primary Aims
•
To assess baseline Adpn250 and Adpn30, and 6m and change in Adpn30 oligomers
•
To explore relationships of Adpn250 with an array of demography, health status,
anthropometry, laboratory, lifestyle, QoL and eating attitude study parameters in all
participants and women and men separately.
•
To explore relationships of change between baseline and 6m of one of the oligomers,
HMW30, with an array of change in anthropometry and laboratory, in all participants,
and women and men separately.
•
To assess baseline, 6m and the change between baseline and 6m mean serum, dietary
and supplemental spFSVitamins: spVitD, spβCaro, spVitA and spVitE.
146
•
To explore relationships of each sFSVitamin with anthropometry, laboratory,
demographic, medical status, lifestyle, QoL and eating attitude study parameters in all
participants
•
To explore relationships of change between baseline and 6m of sβCaro in all
participants and sVitE in all participants with an array of change in anthropometry and
laboratory parameters.
Secondary Aims
•
To compare the magnitude of the effect of sFSVitamins combined.
•
To explore relationships of change between baseline and 6m of sβCaro in women and
sVitE in men with an array of change in anthropometry and laboratory parameters.
•
To assess the impact of ethnicity and season on sVitD
5.2
Methods
For the methods for the Diet&Health-Novel CV Protective Markers refer to Section 2.2.2.
Briefly, in order to explore the Novel CVD Protective Markers and their baseline and
change correlations with various CVD risk and protective markers and MetS markers, 250
obese and overweight women and men were enrolled in the Diet&Health-Weight loss
trial where they were encouraged to make lifestyle changes.
By attending monthly for anthropometry and for advice to make prudent dietary changes
to reduce fatty food and increase fruit and vegetables, increase moderate physical activity,
and use a paradigm to enhance motivation, taking daily chitosan or placebo, and
completing a 24HrFood Recall, validated Life in NZ Physical Activity questionnaire and
Eating Attitudes questionnaire and have blood tests at baseline and 6m, it was planned
that the participants would lose significant amounts of weight and show markers of
metabolic and nutritional improvement.
Baseline Adpn was taken in 250participants, a subset of 30 participants was chosen post
hoc for their extreme anthropometry and MetS, and change in these measurements. The
marked sexual dimorphism seen in Adpn meant separate analyses in women and men
were made.
147
Although only 24Hr Food Recalls were only performed twice, a detailed search through
participants’ baseline and 6m medication records for vitamin supplements gave some idea
of their FSVitamin intake.
Relationships of sβCaro with its HDL-C plasma carrier, and sVitE with the non-HDL-C
lipid carriers were important, and investigated. As sVitD, which is not carried on lipids,
but has its own plasma carrier VitD binding protein691, is highly seasonal, and ethnic
dependant regression analyses were performed for this vitamin, but not change
correlations.
The baseline analyses of the sFSVitamins were not separated into the 2 genders. As
treatment trials of change in sβCaro and sVitE have been controversial change
correlations of these 2 sFSVitamins with other laboratory and dietary CVD risk and
protective markers were formed, and also change correlations of sβCaro in the women,
who tend to have higher fruit and vegetable consumption patterns, were investigated. In a
similar manner, change correlations of sVitE in men who tend to have higher
consumption of processed fatty food with added tocopherol antioxidants were explored.
As sVitA derives preformed from various foods, is synthesised from fβCaro, and spVitA
limited to very low levels due to VitA toxicity, no specific change hypothesis was tested
for sVitA.
5.2.2 Clinical Study Procedures
5.2.3 Laboratory Procedures and Analyses
The procedure for selecting the 30 participants whose serum was to be used to analyse the
total Adpn30, HMW30, MMW30, and LMW30 is presented in Section 2.6.1, Table 2-5.
148
5.2.4.1 Adiponectin and Oligomers
Baseline, 6m and Change Over 6m for Adiponectin250 and Adiponectin30 &
Oligomers30
Baseline, 6m and change over the 6m of Adpn
30
and oligomers (HMW30, MMW30 and
LMW30) in all participants and women and men alone, and the percentage of each Adpn30
oligomer, %HMW30, %MMW30, and %LMW30, is tabulated with simple statistics.
General Linear Model for Adiponectin250 with Demography, Medical Status,
Lifestyle, QoL and Eating Attitude Study Parameters
A general linear (regression) model was fitted with Adpn as the outcome, and age,
gender, season, Maori/Pacific or other, regular cigarette smoker, alcoholic drinks/wk,
CVD, disease, EAT-12 diet subscore, EAT-12 bulimia subscore, EAT-12 oral control
subscore, OSQoL Mental subscore, OSQoL Physical subscore, moderate PA hr/wk,
intense PA hr/wk and TV watching hr/wk as the explanatory variables.
Least square means (LSM) were estimated to represent the effect size. The analysis from
which p values were produced was performed with the continuous variables entered in
their continuous form but for production of the LSM the continuous variables were split
in 2 with approximately 50% in each group.
Simple Correlation Arrays with Baseline Adiponectin250 in All, Women and Men
A simple (Pearson) correlation array was formed with serum Adpn250 in all participants,
and women and men alone with anthropometric and laboratory study parameters. Fuller
correlation arrays for Adpn250 serum concentrations with study parameters appear in
Appendix 4, Table A, where at least one out of the groups ‘all, women and men’ had a
statistically significant relationship and/or higher correlation than r>0.11.
Simple Correlation Arrays with Change in HMW30 in All, Women and Men
A simple (Pearson) correlation array was formed with change in HMW30 serum
concentrations in all participants, and women and men alone verses study parameters. For
this analysis as the participant numbers were small, the correlation coefficient levels were
important as they may be as high as for the full group, but not statistically significant.
149
Fuller correlation arrays for change in HMW30 serum concentrations verses study
parameters appear in Appendix 4, Table B. where at least one out of the groups ‘all
participants, and women and men alone’ has a statistically significant correlation and/or
higher correlation than r>0.20, then all are tabulated for comparison.
5.2.4.2 The Fat Soluble Vitamins
Note. All analysis are for sVitD, sβCaro, sVitA and sVitE, except one multivariable
analysis which includes the INR (VitK), which will be specified.
Baseline, 6m and Change Over 6m Vitamin D, Beta-Carotene, Vitamin A and
Vitamin E
Baseline, 6m and change over 6m in sFSVitamin concentrations, dietary intake, and
percentage of participants taking supplements of FSVitamins in all participants, and
women and men alone, is tabulated, with simple statistics.
General Linear Model for Vitamin D, Beta-Carotene, Vitamin A and Vitamin E
with Demography, Medical Status, Lifestyle, QoL and Eating Attitudes Study
Parameters
A general linear (regression) model was fitted for each sFSVitamin for the demographic,
medical, lifestyle, QoL and eating attitude data exactly as for Adpn. A log transformation
was used for the analysis with sβCaro as the outcome but the LSM are displayed back
transformed. Regular smoking was used instead of the number of cigarettes smoked
because of the large number of non smokers.
Simple Correlation Arrays with Vitamin D, Beta-Carotene, Vitamin A and
Vitamin E
A simple (Pearson) correlation array was formed with baseline concentrations of the 4
sFSVitamins in all verses study parameters. Fuller correlation arrays appear in Appendix
2. If at least one out of the groups ‘sVitD, sβCaro, sVitA and sVitE’ had a statistically
significant values or r > 0.2 then all are tabulated for comparison.
150
Simple Correlation Arrays with Change in Beta-Carotene in All & Women, and
Vitamin E in All & Men
A simple (Pearson) correlation array was formed with change in sβCaro in all participants
and women and men alone, and change in sVitE concentrations in all participants and
men, verses change in study parameters. Fuller correlation arrays appear in Appendix
2.Where at least one out of the groups ‘change in sβCaro in all and women, or change in
sVitE in all and men, verses a study parameter’ had a statistically significant correlation
and/or r>0.15 then all are tabulated for comparison.
Baseline: Investigation of Relationships of Metabolic Syndrome Marker Count
with Vitamin D, Beta-Carotene, Vitamin A, Vitamin E and INR (Vitamin K) and
Other Explanatory Variables
In order to investigate the relationship of vitamin levels with MetS marker count a
generalised linear (ordinal logistic regression) model was run using baseline data with
MetS marker count as the outcome and sVitD, sβCaro, sVitA, sVitE and INR (VitK)
together with age, gender, HbA1c, urate, albumin, globulin and AlkPhos as explanatory
variables.
Baseline to 6m: Investigation of Relationships of Metabolic Syndrome Marker
Count with Vitamin D, Beta-Carotene, Vitamin A, Vitamin E and INR (Vitamin
K) and other Explanatory Variables
In order to investigate the how changes in sFSVitamins are related to changes in the MetS
marker count, a generalised linear mixed model was fitted with MetS marker count
counted as the ordinal outcome, and sVitD, sβCaro, sVitA, sVitE and INR (VitK), split
into their overall mean (baseline to 6m) for a person and their change (difference from the
mean) at each time point included as explanatory variable along with the overall mean
and change of age, gender, chitosan treatment group, HbA1c, urate, albumin, globulin and
AlkPhos. Person was included as a random effect.
Baseline: General Linear Model Vitamin D
As sVitD has additional environmental effects of season, a general linear (regression)
model was used to investigate the baseline relationships of VitD with demographic data,
anthropometry and laboratory tests. Multivariable regression was performed with sVitD
as the outcome variable. Explanatory variables were age, gender, ethnicity, season, and
151
either BMI or waist as these were likely to be highly correlated. An analysis including
both BMI and waist was also carried out to investigate if one variable contributed over
and above the other.
Graphpad Prism 5 (V5.04 La Jolla, CA, USA) was used for the correlation arrays and
Microsoft Excel 7 for the T-Tests. SAS v9.1 statistical software (Cary, NC, USA, 2006)
was employed for general linear and generalised linear mixed model analyses.
5.3
Results
5.3.1 Adiponectin and Oligomers
Adpn is sexually dimorphic430. Results are presented for women and men separately
Table 5-1.
Table 5-1. Baseline, 6m & Change - Adpn250 & Adpn30: All, Women & Men
Adpn
Adpn250, ug/ml
Adpn30 & oligomers
Adpn30, μg/ml
All,
Baseline
(sd)
N=242
3.00(0.98)
Women,
Baseline
(sd)
n=199
3.08(1.02)
Men,
Baseline
(sd)
n=43
2.67(0.67)a3
All, 6m &
Change
(sd)
Women, 6m
& Change
(sd)
Men, 6m
& Change
(sd)
N=30
n=21
n=9
N=20
n=14
n=6
8.35(5.87)
9.47(6.50)
5.74(2.91)
a2
2.14(1.49)
a2
δ Adpn30, μg/ml
HMW30, μg/ml
3.80(3.18)
4.51(3.46)
δ HMW30, μg/ml
MMW30, μg/ml
3.48(2.29)
3.79(2.59)
2.77(1.22)
δ MMW30, μg/ml
LMW30, μg/ml
1.07(0.76)
1.18(0.87)
0.82(0.33)
44.2(10.2)
a2
δ LMW30, μg/ml
%HMW30
41.2 (11.0)
34.3(9.8)
δ %HMW30
%MMW30
45.1(8.8)
42.9(9.1)
50.1(5.9)
δ %MMW30
%LMW30
δ %LMW30
13.7(4.5)
12.9(4.2)
15.6(4.6)
a2
8.36(4.90)
9.84(5.05)
4.93(2.17)b1
0.37(2.45)
0.54(2.77)
-0.05(1.58)
4.33(3.27)
5.42(3.33)
1.80(0.82)b1
0.49(1.76)
0.65(1.99)
0.12(1.12)
3.00(1.51)
3.25(1.59)
2.40(1.23)
-0.15(0.92)
-0.16(1.10)
-0.12(0.29)
1.02(0.70)
1.16(0.79)
0.69(0.29)b3
0.01(0.30)
0.05(0.34)
-0.09(0.18)
48.30(11.57)
53.2(9.3)
36.9(7.8)b1
6.26(9.35)
6.7(9.3)2
5.3 (10.2)
39.2(10.0)
35.4 (9.0)
48.1(6.1)b1
-5.14(8.6)
-5.6(8.4)2
-4.17(9.7)
12.08(3.59)
11.1 (3.3)
14.4(3.4)b3
-1.5(3.6)
-1.4(4.2)
-1.8 (1.7)
1
p<0.01, 2 p<0.05, 3 p<0.1. a. baseline difference between women and men b. 6mth difference between
women and men, δ change; Adpn 25 0 Adiponectin in 250 participants, baseline only; Adpn 3 0
Adiponectin in 30 participants at baseline and 20 at 6m; HMW 3 0 high molecular weight Adpn;
MMW 30 medium molecular weight Adpn, LMW 3 0 low molecular weight Adpn; sd standard deviation.
Note the change in concentrations and percentages is only calculated for the 20 completers, but the
baseline values are from the thirty 30 enrolled participants.
152
The baseline mean serum concentrations in the full participant sample set, Adpn250, were
analysed and shown in Table 5-1. Women had significantly higher Adpn than men. In the
lower part of Table 5-1, the baseline data of a subset of 30 participants for the Adpn30
analysis is shown. Refer to the selection criteria for the Adpn30 subset in Section 2.6.1
Table 2-5. Twenty participants had data at 6m to calculate the change in Adpn30. The 3
MW Adpn30 oligomers are shown as concentrations and as percentages of Adpn30 and
their change over 6m is shown in Table 5-1.
Women had a borderline significantly higher Adpn at baseline and 6m (p<0.1) (Table
5-1). Adpn30 increased over 6m in women, but decreased slightly, together with LMW30,
in men. HMW30 increased but MMW30 decreased over 6m, but not significantly. The
%HMW increased, but both %MMW30 and %LMW30 decreased in women and men over
the study period. Women had significantly increased %HMW and decreased %MMW
over 6m (p<0.05). As shown in Figure 5-1, only in men did change in Adpn30, and
HMW30 (p=0.092 and p=0.056, respectively) show any evidence of negative correlation
with change in MetS marker count.
Analyses of the demographic, health status, lifestyle, QoL and eating attitudes
questionnaires are shown in Table 5-2.
Figure 5-1. Change in Adpn30 vs. Change in MetS Marker Count: Women & Men
10
10
Women, n=24
8
8
Adpn30 Change, µg/mL
6
4
2
0
-2
-4
r(p-value)
All, r=-0.17, p=0.49
W, r=-0.06, p=0.84
M, r=-0.74, p=0.09
-6
-8
-10
-4
-2
0
2
HMW30 Change, mg/dL
Men, n=6
6
4
2
0
-2
-4
-6
All r=-0.15, p=0.52
W r=-0.03, p=0.92
M r=-0.80, p=0.06
-8
-10
-4
4
5
-2
0
2
4
1.5
3
LMW30 Change, mg/dL
MMW30 Change, mg/dL
4
2
1
0
-1
-2
-3
-4
-5
-4
All r=-0.17, p=0.48
W r=-0.18, p=0.55
M r=-0.17, p=0.48
2
-2
0
M etS M arker Count Change
1.0
0.5
0.0
-0.5
-1.0
4
-1.5
-4
All r=0.24, p=0.32
W r=0.42, p=0.14
M r=-0.60, p=0.21
2
-2
0
M etS M arker Count Change
4
MetS metabolic syndrome; Adpn 30 adiponectin in the subgroup of 30 participants with extreme
weight, Mets or change in these measures. HMW 3 0 high molecular weight adiponectin, MMW 3 0
medium molecular weight adiponectin, LMW 3 0 low molecular weight adiponectin,
153
Table 5-2. General Linear Model. Baseline Adpn250: Demography, Lifestyle & Quality of
Life
Adpn250
Age y
Gender
Season
Maori/Pacific Ethnicity
Reg. Cig. Smoker
Alcohol, Unit/wk
CVD
Disease
Modest PA, hr/wk
Intense PA, min/wk, n=95
Watch TV, hr/wk
OSQoL Ment., Subscore
OSQol Phys., Subscore
EAT12 Diet, Subscore
EAT12 Bulimia, Subscore
EAT12 Oral, Subscore
Variable
<50
≥50
Women
Men
Summer
Winter
Yes
No
Yes
No
0
>0
Yes
No
Yes
No
<20
≥20
0
>0
≤20
>20
≤15
>15
≤10
>10
<15
≥15
<20
≥20
<22
≥22
LSM mg/dL
2.41
2.79
2.78
2.42
2.77
2.43
2.41
2.79
2.36
2.84
2.36
2.84
2.65
2.55
2.56
2.63
2.54
2.66
2.52
2.68
2.58
2.62
2.64
2.56
2.55
2.65
2.61
2.59
2.62
2.58
2.44
2.76
95% C.I.
(2.12, 2.71)
(2.45, 3.12)
(2.50, 3.05)
(2.05, 2.79)
(2.46, 3.08)
(2.12, 2.74)
(2.12, 2.71)
(2.45, 3.12)
(1.91, 2.81)
(2.61, 3.06)
(1.91, 2.81)
(2.61, 3.06)
(2.36, 2.94)
(2.19, 2.90)
(2.21, 2.92)
(2.35, 2.92)
(2.23, 2.84)
(2.34, 2.98)
(2.23, 2.81)
(2.35, 3.01)
(2.27, 2.89)
(2.31, 2.93)
(2.33, 2.95)
(2.24, 2.88)
(2.23, 2.86)
(2.33, 2.97)
(2.28, 2.94)
(2.29, 2.89)
(2.28, 2.95)
(2.28, 2.88)
(2.15, 2.72)
(2.43, 3.09)
p-value
0.008
0.04
0.01
0.21
0.008
0.58
0.34
0.77
0.98
0.67
0.96
0.56
0.81
0.36
0.94
0.01
LSM least square mean; C.I. confidence interval, serum s; VitD vitamin D; βCaro beta carotene; VitA
vitamin A; VitE vitamin E; reg. cig. smoker regular cigarette smoker; CVD cardiovascular disease; y
year; wk week; d day; hr hour, min minute; EAT-12 eating attitudes questionnaire – 12 question;
EAT-12 Diet/Bulimia/Oral diet/bulimia/oral control subgroups; OSQoL obesity specific quality of
life; PA physical activity; phys physical; ment. mental; TV television.
Adpn was significantly higher with age (p=0.008), higher with being a non-smoker
(p=0.008), being of the female gender (p=0.04), summer season (p=0.008), and EAT-12
oral control subscore (p=0.01) (Appendix 1. Questionnaire forms)
Shown in Table 5-3 are Adpn250 correlations with anthropometric and laboratory study
parameters.
154
Table 5-3. Baseline Adpn250 vs. Study Parameters - Correlations: All, Women & Men
Adpn250(n=250)
Parameters
Anthropometry
Weight, kg
Height, m
BMI, kg/m2
Waist, cm
Haematology
Hct, %
Leukocytes, x109
Neutrophils, x109
12
Platelets, x10
ESR, mm/hr
Biochemistry
TC, mmol/L
HDL-C, mmol/L
TC/HDL
Urate, mmol/L
hsCRP, mg/L
Hormones/Cytokines
Insulin, μU/L
HOMA-IR, mmol/uU/L
Adpn, mg/L
sVitD, ng/L
sβCaro, μg/L
sVitA, μg/L
sVitE, μg/L
Diet
fVitE, mg/d
Metabolic Syndrome
IDF/JIS MetSMCt
IDF/JIS MetS
All,
n=212-243
Pearson
p-value
r
Women,
n=176-199
Pearson
p-value
r
Men,
n=39-40
Pearson
r
-0.30
-0.32
-0.15
-0.20
<0.001
<0.001
0.02
0.01
-0.29
-0.28
-0.19
-0.17
<0.001
<0.001
0.007
0.02
-0.14
-0.37
-0.09
-0.07
0.38
0.01
0.55
0.63
0.18
-0.16
0.01
0.02
0.17
-0.13
0.02
0.06
0.38
-0.38
0.01
0.01
-0.19
0.01
-0.15
0.05
-0.39
0.02
-0.13
-0.13
0.05
0.04
-0.14
-0.16
0.06
0.03
-0.39
-0.26
0.01
0.11
0.23
0.40
-0.26
-0.26
-0.15
<0.001
<0.001
<0.001
<0.001
0.02
0.25
0.38
-0.23
-0.19
-0.14
<0.001
<0.001
0.001
0.007
0.06
0.04
0.36
-0.29
-0.32
-0.32
0.78
0.02
0.06
0.04
0.04
-0.25
-0.20
-
<0.001
0.001
-0.26
-0.24
<0.001
0.001
-0.27
-0.22
0.08
0.18
0.22
0.30
0.06
0.13
0.001
<0.001
0.33
0.05
0.25
0.28
0.14
0.18
<0.001
<0.001
0.05
0.01
0.13
0.17
0
0.06
0.42
0.29
0.99
0.72
0.14
0.03
0.15
0.04
0.10
0.53
-0.25
-0.23
<0.001
<0.001
-0.25
-0.21
0.001
0.003
-0.05
-0.09
0.77
0.60
p-value
Refer to Appendix 4, Table A for expanded Table 5.3. Weight body weight; BMI body mass index;
waist waist circumference; Hct haematcrit; ESR erythrocyte sedimentation rate; TC total cholesterol;
HDL-C high density lipoprotein cholesterol; TC/HDL total cholesterol/high density lipoprotein;
hsCRP high sensitive C reactive protein; HOMA-IR homeostasis model analysis – insulin resistance;
Adpn adiponectin; s serum; VitD vitamin D; βCaro beta carotene, VitA vitamin A; VitE vitamin E;
IDF International Diabetes Federation 4 ; JIS Joint interim statement 5 ; MetSMCt metabolic syndrome
marker count; d day.,
Both the r and p-values are of interest as there are different numbers of women and men.
The values are presented within the tables, with the significant correlations given in the
text.
155
For the correlations of Adpn250, the whole group, all, are shown for completeness in but in
the text the representative Adpn range for women and men alone are presented.
In women positive correlations of Adpn250 were shown with: TC, HDL-C, sVitD, and
sβCaro (all <p=0.006), haematocrit and fVitE (both p<0.04) (Table 5-3).
In men positive correlations of Adpn250 were shown with: haematocrit and HDL-C (both
p< 0.02) (Table 5-3).
In women, negative correlations of Adpn250 were shown with weight, height, BMI,
TC/HDL, urate, HOMA-IR, insulin, IDF/JIS MetS marker count IDF/JIS MetS (all
p<0.003) and waist, neutrophils and ESR (p<0.05) (Table 5-3).
In men, negative correlations of Adpn250 were shown with height, leukocytes, neutrophils,
platelets, ESR, urate, hsCRP (all, p<0.05) (Table 5-3).
There were few participants in the change in HMW30 part of the study, and men were
more influential in this analysis. Correlations were often high, so the cut off for reporting
in the text is r≥0.3 (Table 5-3). An expanded table appears in full in Appendix 4, Table
D.
In women, positive change correlations of HMW30 were shown with, bilirubin (p=0.01),
and note high correlations that are not quite significant in Table 5-4, such as niacin.
In men, positive change correlations of HMW30 that were significant or tended towards
significance were shown with niacin (p=0.01) eosinophils (p=0.09) but note the high
correlation levels with albumin, lymphocytes, sVitA, and bilirubin in (Table 5-4). In
women, negative change correlations of HMW30 were shown with BMI, %Fat, HbA1c and
weight (all, p<0.05), and note the high correlations with haematocrit, monocytes, and note
the correlation level with TC/HDL, leptin, and waist in (Table 5-4).
In men, negative change correlations of HMW30 were shown with weight, BMI, waist,
RBCW, leptin, thiamine (all; p<0.05) and note high correlation levels with TC/HDL,
VitD, niacin, iron and with neutrophils, ferritin and sβCaro in (Table 5-4).
156
Table 5-4. Change in HMW30 vs. Study Parameters, Correlations: All, Women & Men
Change in HMW30
δ Parameter
All, Change in
HMW30 n=15-20
Pearson r
p-value
Women, Change in
HMW30 n=9-14
Pearson r p-value
Men, Change in
HMW30 n=5-6
Pearson r
p-value
0.007
0.006
0.08
0.10
-0.53
-0.55
-0.27
-0.57
0.05
0.04
0.35
0.04
-0.90
-0.91
-0.85
0.13
0.02
0.01
0.03
0.83
0.18
0.42
0.46
0.80
0.06
0.11
-0.53
-0.11
-0.13
-0.14
-0.55
-0.73
0.06
0.73
0.69
0.66
0.07
0.01
0.13
-0.86
-0.74
0.74
-0.24
0.74
0.81
0.03
0.10
0.09
0.65
0.10
0.04
0.03
0.60
0.003
0.74
-0.43
-0.68
-0.03
0.71
-0.07
0.13
0.04
0.93
0.01
0.82
-0.76
0.25
-0.70
0.57
0.74
0.08
0.69
0.12
0.32
0.15
0.04
-0.45
0.11
-0.91
0.03
0.66
0.88
0.33
0.33
0.06
0.10
0.25
0.84
0.74
-0.77
-0.69
0.70
0.08
0.13
0.12
0.14
0.30
0.33
0.16
0.16
0.66
0.41
0.50
0.92
0.71
0.67
1.00
0.45
0.29
0.08
0.98
0.25
0.25
0.17
0.30
-0.24
0.00
0.10
-0.20
0.02
-0.11
0.54
0.52
0.32
0.42
0.42
0.59
0.33
0.42
0.99
0.75
0.51
0.94
0.71
0.06
0.07
0.29
0.63
0.05
0.63
0.53
-0.20
-0.28
0.04
-0.16
0.34
0.14
0.05
-0.82
-0.75
-0.49
-0.75
0.18
0.93
0.18
0.28
0.71
0.59
0.95
0.80
0.51
0.79
0.93
0.05
0.09
0.33
0.09
Anthropometry
δ Weight, kg
-0.58
2
δ BMI, kg/m
-0.59
δ Waist, cm
-0.40
δ %Fat, %
-0.41
Haematology
δ Hct, %
-0.32
δ RBCW, %
-0.20
δ Neutrophil, x109
-0.18
9
δ Eosinophils, x10
0.06
δ Monocytes, x109
-0.45
δ Lymphocytes, x109
-0.39
Biochemistry
δ TC/HDL
-0.47
δ HbA1c, %
-0.58
δ Ferritin, μg/L
-0.13
δ Bilirubin, μmol/L
0.68
δ Albumin, g/L
-0.08
Hormones/Cytokines
δ Leptin, pg/ml
-0.47
δ sVitD, ng/L
0.10
δ sβCaro, μg/L
-0.04
δ sVitA, μg/L
0.24
24Hr Food Recall Dietary Nutrients
δ Energy, J/d
0.35
δ Alcohol, mL/d
δ Protein, g/d
0.34
δ Tot Fat, g/d
0.34
δ CHO, g/d
0.11
δ Fibre, g/d
0.20
δ fVitD, μg/d
-0.17
δ fβCaro, μg/d
-0.03
δ Retinol Equivs., μg/d
0.09
δ fVitE, mg/d
-0.11
δ fVitA, μg/d
0.00
δ Thiamine, ug/d
-0.19
δ Niacin, μg/d
0.25
δ Niacin Equivs., μg/d
0.42
δ Iron, mg/d
-0.01
Refer to Appendix 4, Table B for expanded Table 5-4. δ delta or change; s serum; f food/dietary
weight body weight; BMI body mass index; waist waist circumference; %Fat body fat percentage; Hct
haematocrit; RBCW red blood cell width; TC/HDL total cholesterol / high density lipoprotein; HbA1c
haemoglobin A1c; VitD vitamin D; βCaro beta carotene; VitA vitamin A; tot total; CHO
carbohydrate; equivs equivalence; d day; g gram; J joule.
157
Table 5-5. Baseline Adpn250 vs. Adpn30 - Correlations: All, Women & Men
c
ccAdpn250(n=250)
cccccccccccBaseline
OligoAdpn
(n=30) Baseline
All
N=30
p-value
Women
N=21
p-value
Men N=9
p-value
Adpn30
0.59
0.001
0.54
0.011
0.87
0.002
%HMW30
0.45
0.014
0.29
0.210
0.79
0.012
%MMW30
-0.32
0.088
-0.17
0.474
-0.74
0.024
%LMW30
-0.46
0.010
-0.33
0.143
-0.71
0.031
HMW30
0.57
0.001
0.51
0.017
0.91
0.001
MMW30
0.59
0.001
0.55
0.010
0.81
0.008
LMW30
0.39
0.031
0.35
0.117
0.59
0.093
% percentage, Adpn Adiponectin, HMW high molecular weight Adpn, MMW medium molecular
weight Adpn, LMW low molecular weight Adpn, sd standard deviation.
5.3.2 The Fat Soluble Vitamins
The sFSVitamin results for this section have not been corrected for serum lipids as the
usual equation does not account for the different sFSVitamin carriers in obesity (Table
5-6).
Table 5-6. Serum Lipids vs Serum Fat Soluble Vitamins – Correlations
TC, mol/L
sVitD, nmol/L
n=243
Pearson
p-value
r
0.10
0.13
sβCaro, μmol/L,
n=239
Pearson
p-value
r
0.28
< 0.001
sVitA, μmol/L,
n=238
Pearson
p-value
r
0.30
< 0.001
sVitE, μmol/L,
n=236
Pearson
p-value
r
0.62
< 0.001
HDL-C, mol/L
0.07
0.25
0.27
< 0.001
0.08
0.25
0.09
0.15
LDL-C, mol/L
0.08
0.21
0.25
< 0.001
0.14
0.03
0.46
< 0.001
TG, mol/L
0.03
0.69
-0.08
0.24
0.41
< 0.001
0.51
< 0.001
TC/HDL
0.01
0.87
-0.09
0.18
0.12
0.07
0.31
< 0.001
sFSVitamin
Lipid
HDL-C high density lipoprotein-cholesterol, LDL-C low density lipoprotein-cholesterol, TC total
cholesterol, TG triglyceride; s serum; VitD vitamin D; βCaro beta carotene, VitA vitamin A; VitE
vitamin E;
sVitD related to none of the lipids (Table 5-6). SβCaro correlated with all lipid fractions
(r=0.25-0.28; p<0.001) except TG and TC/HDL. SVitA and sVitE correlated highly and
significantly with TG (r=0.41 & 0.45; p<0.001) and TC (r=0.30 & 0.62; p<0.001) (Table
5-6).
SVitE also correlated with LDL-C and TC/HDL (r=0.46 & 0.31; p<0.001). Neither sVitA
nor sVitE correlated with HDL-C (Table 5-6).
158
The baseline, 6m and change data is depicted in Table 5-7. SVitD was similar in women
and men, but decreased over the 6m, highly significantly in women (p<0.001) and
significantly in men (p<0.05), as shown in Table 5-7.SβCaro was significantly higher in
women at baseline and 6m (p<0.001).
SβCaro increased in women and decreased in men, non-significantly in both genders.
SVitA was significantly higher in men than women at baseline and 6m (p<0.001), and
decreased insignificantly in women but slightly increased in men. SVitE was borderline
higher in men than women at baseline and 6m (p<0.1), and decreased insignificantly in
women and men (Table 5-7).
Men consumed more fVitA & fVitE, and both vitamins were reported increased, but not
significantly, at 6m. In women, fVitA and fVitE consumed was less at 6m, but not
significantly (Table 5-7).
There was a borderline higher percentage of women taking spVitD and spVitE at baseline
than men (Table 5-7).
Demographic, health status, lifestyle, QoL and eating attitudes data is shown in Table
5-8.
Age was positively associated with sβCaro, sVitA and sVitE (all p<0.001). Female
gender was positively associated with sβCaro, and negatively with sVitA (both p<0.001).
Summer season was positively associated with sVitD (p<0.001), but negatively with
sβCaro (p=0.036).
Maori/Pacific ethnicity was negatively associated with sVitA (p=0.004). Regular cigarette
smoking was highly significantly negatively with sβCaro (p<0.001), with smokers having
approximately
Alcohol consumption was positively related significantly to sVitD (p=0.036). CVD and
disease were not significantly related to sFSVitamins, nor were the 3 EAT-12 subscores
159
except for a very weak possible positive relationship between the Eat-12 Oral Control and
sVitD (p=0.1) (Table 5-8).
The OSQoL Physical subscore was negatively related to sβCaro (p=0.013), but no other
OSQoL relationships were significant. Modest PA was negatively associated with sVitE
but intense PA (n=95), and television watching were not associated with the sFSVitamins
(Table 5-8).
half the sβCaro value (Table 5-8).
Baseline sVitD was positively correlated with haematocrit, sVitA, basophils and Adpn
(all p≤0.01), and with, albumin and sβCaro (p≤0.05) (Table 5-9).
Baseline sVitD was negatively correlated with weight, height, BMI, waist and leptin (all
p≤0.002) (Figure 5-2), HbA1c and INR (VitK) (both ≤0.02) (Table 5-9).
Baseline sβCaro was positively correlated with lipids as shown in Table 5-7 and with
Adpn, fβCaro, fVitE, fVitC, riboflavin, folate, potassium, magnesium and dietary fibre
(all p≤0.001), total sugars and fVitA equivalents (all ≤0.003), and sVitD and sVitE (both
p≤0.02) (Table 5-9).
Baseline sβCaro was negatively correlated with weight, height, leukocytes, neutrophils,
hsCRP and IDF/JIS MetS marker count (all p≤0.001) and with BMI, waist (), %Fat,
HbA1c, urate, NCEP MetS marker count (all p≤0.01) (Table 5-9).
Baseline sVitA was positively correlated with lipids as shown in Table 5-7and SBP, Hb,
haematocrit, MCV, MCHC, urate, ferritin, γGT, albumin, sVitD, sVitE and NCEP MetS
marker count (all p≤0.001) and with basophils, ALT, DBP, and IDF/JIS MetS marker
count (all p≤0.01). Baseline sVitA was negatively correlated with BMI (p<0.001) and
height (p=0.05), Table 5-9).
Baseline sVitE was positively correlated with lipids as shown in Table 5-7 and SBP,
DBP, ferritin, γGT, sVitA IDF/JIS MetS marker count, NCEP MetS marker count (all
p<0.001), height, Hb, urate, albumin, haematocrit (all p<0.01), MCHC and βCaro (all
p≤0.05) (Table 5-9). Baseline sVitE was negatively correlated with weight, BMI, Adpn30
(all p≤0.05) (Table 5-9).
160
Table 5-7. Baseline, 6m & Change – Serum, Dietary, & Numbers Taking Supplemental
FSVitamins: All, Women & Men
Fat
Soluble All,
Vitamin
Baseline
Serum
sVitD, nmol/L
Women,
Baseline
Men,
Baseline
All, 6m
or Change
Mean(sd)
n=238-243
n=197-200
n=41-43
62.2(22.7)
62.3(21.9)
61.7(26.4)
δ sVitD, nmol/L
sβCaro, μmol/L
δ sβCaro,
μmol/L
sVitA, μmol/L
0.63(0.50)
2.00(0.47)
0.67 (0.51)
1.9(0.44)
0.45(0.40) a1
2.27(0.50) a1
δ sVitA, μmol/L
sVitE, μmol/L
31.4(8.3)
30.9(7.9)
33.8(9.9)
a4
δ sVitE, μmol/L
Women, 6m
or Change
Men, 6m
or Change
n=154-162
n=124-131
n=30-32
47.16(16.40)
46.26 (16.7)
50.97(14.85)
1
-9.60(26.39)3
-13.66(23.4)
-14.61(22.7)
0.66(0.50)
0.73(0.53)
0.39(0.29)b1
0.01(0.40)
0.02(0.42)
-0.05(0.34)
1.970.53
1.88(0.51)
2.29(0.49) b1
-0.03(0.34)
-0.05(0.34)
0.05(0.33)
31.0(48.83)
30.4(79.1)
33.3(7.5) b4
-0.95(6.82)
-1.07(6.64)
-0.47(7.60)
Mean 24 Food Recall Nutrients(sd)
Dietary
fVitD, μg
n=249
n=205
n=44
n=158
n=127
n=31
2.4(2.6)
2.3(2.7)
2.5(2.4)
1.8(2.6)
1.7(2.4)
2.3(3.3)
-0.6(3.8)
-0.6(3.8)3
-0.5(3.7)
3661(4202)
3911(4232)
2637(3979)
-309(5066)
-351(5253)
-139(4281)
636(4022)
657(4459)
550(61053)
-79(5529)
-117(6146)
77.2(1120)
8.7(4.1)
8.3(3.8)
10.0(4.8)b4
0.3(5.7)
-0.1(5.4)1
1.4(7.0)
δ fVitD, μg
fβCaro, μg
4111(6989) 4420(7471)
2670(3785)
δ fβCaro, μg
fVitA, μg
587(3004)
614(3306)
461(422)
δ fVitA, μg
fVitE, μg
10.6(5.5)
10.6 (5.5)
10.6(5.6)
δ fVitE, μg
Supplemental
a3
n(%)
n=250
n=206
n=44
n=164
n=132
n=32
spVitD
33(13.2)
30(14.6)
3(6.8) a4
19(11.6)
15(11.4)
4(2.5)
spβCaro
26(10.4)
23(11.2)
3(6.8)
13(7.9)
9(6.8)
4(2.5)
23(14.0)
18(13.6)
5(5.6)
23(14.0)
15(11.4)
8(5.0)
a4
spVitA
33(13.2)
30(14.6)
3(6.8)
spVitE
37(15.8)
31(15.0)
6(13.6)
1
p<0.001, 2 p<0.01, 3 p<0.05, 4 p<0.1, a baseline difference between women and men b 6m difference
between women and men, δ change, sd standard deviation; s serum; f dietary/food; sp supplemental
VitD, vitamin D; βCaro beta-carotene; VitA, vitamin A; VitE vitamin E. c Note the problem with only
having 2 x 24hr Food recalls resulted in the wide sd for example in fβCaro intake reports ranging
from 58-95,000μg/d and fVitA 47-47,000 μg/d at baseline and fβCaro ranging from 43-22,000μg/d
and fVitA from 6-50,500μg/d at 6m, in different participants.
Women and men consumed similar amounts of fVitD with women significantly
decreasing fVitD over the study (Table 5-7). Women consumed more fβCaro at baseline,
but fβCaro intake dropped in both women and men, but not significantly.
161
Table 5-8. Demographic, Health Status, Lifestyle, Quality of Life and Eating Attitudes with Serum Fat Soluble Vitamins
sFSVitamins
<50
≥50
Women
Men
Summer
Winter
Yes
No
Yes
No
0
>0
Yes
No
Yes
No
<15
LSM,
nmol/L
56.9
56.4
55.0
58.3
64.8
48.5
54.2
59.1
52.6
60.7
52.5
60.9
55.1
58.2
56.8
56.5
57.5
≥15
55.9
(48.9, 62.8)
<20
58.4
(50.7, 66.1)
≥20
55.0
(48.0, 62. 4)
<22
54.3
(47.7, 61.0)
≥22
59.1
(51.4, 66.7)
≤15
54.4
(47.2, 61.6)
Parameter |Catergory
Age, y
Gender
Season
Maori/
Pacific
Reg. Cig.
Smoker
Alcohol,
U/wk
CVD
Disease
EAT12
Diet
Subscore
EAT12
Bulimia
Subscore
EAT12
Oral
Subscore
OSQoL
sVitD
95% C.I.
(50.1, 63.8)
(48.8, 64.1)
(48.6, 61.4)
(49.8, 66.9)
(57.6,72.0)
(41.4, 55.6)
(45.0, 63.4)
(52.9, 65.4)
(42.2,63.1)
(55.5, 66.0)
(45.6, 59.4)
(53.3, 68.4)
(48.4, 61.9)
(50.1, 66.4)
(48.6, 65.1)
(49.9, 63.2)
(49.9, 65.1)
sβCaro
pvalue
0.88
0.44
<0.001
0.11
0.19
0.04
0.69
0.85
0.73
0.19
0.10
0.89
LSM,
μmol/L
0.28
0.35
0.38
0.25
0.28
0.34
0.29
0.33
0.21
0.45
0.30
0.32
0.30
0.32
0.33
0.29
0.31
sVitA
95% C.I.
p-value
(0.22, 0.35)
(0.27, 0.44)
(0.31, 0.47)
(0.19, 0.33)
(0.27, 0.36)
(0.32, 0.43)
(0.21, 0.39)
(0.27, 0.41)
(0.15, 0.30)
(0.38, 0.54)
(0.24, 0.37)
(0.25, 0.41)
(0.24, 0.37)
(0.24, 0.42)
(0.25, 0.43)
(0.23, 0.36)
(0.24, 0.40)
<0.001
0.31
(0.25, 0.39)
0.33
(0.26, 0.43)
0.29
(0.23, 0.36)
0.29
(0.23, 0.36)
0.33
(0.26, 0.42)
0.34
(0.27, 0.42)
0.001
0.04
0.34
<0.001
0.75
0.93
0.16
0.67
0.14
0.24
0.97
LSM
μmol/L
1.81
2.00
1.74
2.06
1.88
1.92
1.77
2.04
1.83
1.98
1.87
1.93
1.85
1.95
1.87
1.93
1.92
sVitE
95% C.I.
p-value
(1.67, 1.95)
(1.84, 2.16)
(1.61, 1.88)
(1.88, 2.24)
(1.73, 2.03)
(1.78, 2.07)
(1.58, 1.95)
(1.91, 2.17)
(1.61, 2.04)
(1.87, 2.09)
(1.73, 2.02)
(1.77, 2.08)
(1.71, 1.99)
(1.78, 2.12)
(1.70, 2.04)
(1.79, 2.07)
(1.76, 2.08)
0.001
1.88
(1.74, 2.02)
1.93
(1.77, 2.09)
1.87
(1.73, 2.02)
1.88
(1.74, 2.01)
1.93
(1.77, 2.09)
1.89
(1.74, 2.04)
0.001
0.35
0.004
0.56
0.11
0.26
0.55
0.22
0.91
0.25
0.59
LSM,
μmol/L
28.7
32.3
30.0
31.0
30.4
30.6
29.3
31.7
29.8
31.2
30.1
30.9
29.4
31.5
30.0
31.0
30.4
95% C.I.
(26.1, 31.4)
(29.3, 35.3)
(27.5, 32.5
(27.6, 34.3)
(27.6, 33..2)
(27.8, 33.4)
(25.7, 32.8)
(29.3 34.1)
(25.7, 33.8)
(29.2 33.3
(27.4, 32.8)
(28.0,33.8)
(26.8, 32.1)
(28.4, 34.7)
(26.8, 33.2)
(28.4, 33.6)
(27.4, 33.3
30.6
(27.9, 33.3)
31.4
(28.3, 34.3)
29.6
(26.9, 32.3)
30.1
(27.5, 32.7)
30.9
(27.9, 33.9)
30.4
(27.6, 33.2)
pvalue
<0.001
0.63
0.98
0.18
0.64
0.62
0.19
0.66
0.63
0.66
0.56
0.16
162
sFSVitamins
LSM,
nmol/L
95% C.I.
>15
58.9
(51.5, 66.3)
≤10
61.4
(54.2, 68.7)
>10
51.9
(44.5, 59.3)
<20
≥20
0
55.6
57.8
55.7
(48.6, 62.5)
(50.5, 65.1)
(49.0, 62.3)
>0
57.7
(50.0, 65.4)
≤20
>20
55.8
57.6
(48.7, 62.9)
(50.4, 64.8)
Parameter |Catergory
Ment.
Subscore
OSQol
Phys.
Subscore
Mod. PA,
hr/ wk
Intense
PA min/
wk, n=95
Watch
TV, hr/wk
sVitD
sβCaro
pvalue
0.34
0.30
0.69
0.55
LSM,
μmol/L
95% C.I.
0.29
(0.22, 0.36)
0.33
(0.26, 0.42)
0.29
(0.23, 0.37)
0.30
0.32
0.31
(0.24, 0.38)
(0.25, 0.40)
(0.25, 0.38)
0.31
(0.24, 0.40)
0.30
0.32
(0.24, 0.38)
(0.25, 0.40)
sVitA
p-value
0.01
0.43
0.80
0.47
LSM
μmol/L
95% C.I.
1.91
(1.76, 2.07)
1.96
(1.81, 2.11)
1.85
(1.69, 2.00)
1.92
1.89
1.93
(1.77, 2.06)
(1.74, 2.04)
(1.79, 2.07)
1.87
(1.72, 2.03)
1.91
1.90
(1.76, 2.05)
(1.75, 2.05)
sVitE
p-value
0.12
0.71
0.44
0.99
LSM,
μmol/L
95% C.I.
30.6
(27.7, 33.4)
30.2
(27.4, 33.04)
30.8
(27.9, 33.6)
30.8
30.2
31.8
(28.1, 33.5)
(27.3, 33.0)
(29.2, 34.4)
29.2
(26.2, 32.2
30.2
30.8
(27.4, 32.9)
(28.0, 33.6)
pvalue
0.07
0.05
0.21
0.25
sd standard deviation; LSM least square mean; C.I. confidence interval, sVitD vitamin D; sβCaro beta carotene; sVitA vitamin A; sVitE vitamin E; s serum;
reg.cig.smoker regular cigarette smoker; CVD cardiovascular disease y year; wk week; d day; hr hour, min minute; EAT-12 eating attitudes questionnaire – 12
question; EAT-12 Diet/Bulimia/Oral diet/bulimia/oral control subgroups; OSQoL obesity specific quality of life; mod moderate; PA physical activity; TV television;
phys physical; ment mental;
163
Table 5-9. Baseline Serum Fat Soluble Vitamins vs. Study Parameters
sVitD,
n=238-243
…...sFSVitamin
sβCaro
n= 236-238
sVitA
n=236-238
sVitE,
n=237-239
Pearson
r
pvalue
Pearson
r
pvalue
Pearson
r
pvalue
Pearson
r
pvalue
Weight, kg
-0.22
0.001
-0.36
<0.001
-0.11
0.08
-0.15
0.02
Height, m
-0.26
<0.001
-0.18
0.006
-0.13
0.05
-0.18
0.006
BMI, kg/m2
-0.20
0.002
-0.29
<0.001
-0.24
<0.001
-0.13
0.05
Waist, cm
-0.14
0.03
-0.36
<0.001
0.03
0.62
0.08
0.21
%Fat, %
-0.09
0.27
-0.22
0.009
-0.08
0.33
-0.08
0.30
SBP, mmHg
-0.09
0.14
-0.09
0.16
0.22
0.001
0.30
<0.001
DBP, mmHg
0.03
0.66
-0.08
0.22
0.19
0.003
0.25
<0.001
Hb, g/L
0.04
0.58
-0.01
0.85
0.29
<0.001
0.18
0.007
Hct, %
0.23
<0.001
0.02
0.79
0.28
<0.001
0.17
0.01
MCV, fL
0.03
0.61
0.06
0.35
0.21
0.001
0.11
0.09
MCHC, L/L
0.12
0.07
0.07
0.27
0.27
< 0.001
0.15
0.02
Leukocytes, x109
0.00
1.00
-0.29
<0.001
0.01
0.86
-0.10
0.12
Neutrophil, x109
Basophils, x109
(n=164-166)
0.01
0.91
-0.28
<0.001
-0.03
0.68
-0.12
0.07
0.20
0.009
-0.11
0.16
0.23
0.004
0.11
0.16
HbA1c , %
-0.17
0.01
-0.19
0.006
0.01
0.88
0.01
0.83
Urate, mmol/L
0.02
0.79
-0.18
0.005
0.33
<0.001
0.19
0.004
Ferritin, μg/L
0.07
0.27
-0.01
0.84
0.34
<0.001
0.29
<0.001
ALT, U/L
0.01
0.94
-0.07
0.31
0.21
0.002
0.11
0.11
γGT, U/T
0.00
0.95
-0.04
0.59
0.25
<0.001
0.22
0.001
Albumin, g/L
0.15
0.02
0.02
0.81
0.34
<0.001
0.18
0.007
hsCRP, mg/L
Metabolic Syndrome
0.07
0.30
-0.28
<0.001
-0.03
0.63
-0.09
0.16
0.11
-0.23
0.001
0.16
0.01
0.24
<0.001
0.04
0.59
NCEP MetSMCt
sVitD, ng/L
Perfect line
-0.22
0.001
0.27
<0.001
0.25
<0.001
0.15
0.02
0.33
<0.001
0.06
0.36
sβCaro, μg /L
0.02
Perfect Line
0.12
0.08
0.16
0.02
0.33
sVitE, μg /L
0.06
INR (VitK)
-0.16
Hormones/Cytokines
<0.001
0.36
0.02
0.12
0.16
0.05
0.08
0.02
0.41
Perfect Line
0.46
<0.001
0.00
0.94
0.46
<0.001
Perfect line
0.00
0.97
Insulin, μU/L
-0.09
0.15
-0.22
0.001
0.03
0.63
0.05
0.40
Adpn250 ,mg/L
0.22
0.001
0.30
<0.001
0.06
0.33
0.13
0.05
LMW30, mg/mL
0.20
0.28
0.22
0.21
-0.34
0.07
0.16
0.41
%HMW30 %
-0.01
0.97
0.14
0.43
0.12
0.53
0.34
0.07
%MMW30 %
0.06
0.74
-0.21
0.24
-0.03
0.86
-0.34
0.07
Parameters
Anthropometry
Haematology
Biochemistry
IDF/JIS MetSMCt
-0.10
0.15
sVitA, μg /L
164
sVitD,
n=238-243
…...sFSVitamin
Pearson
r
Parameters
-0.27
Leptin, pg/ml
sβCaro
n= 236-238
sVitA
n=236-238
sVitE,
n=237-239
pvalue
Pearson
r
pvalue
Pearson
r
pvalue
Pearson
r
pvalue
<0.001
0.07
0.31
-0.20
0.002
0.05
0.49
Fibre g/d
0.02
0.72
0.25
<0.001
0.00
0.99
-0.04
0.58
Tot Sugars g/d
0.09
0.17
0.20
0.002
0.05
0.53
-0.01
0.94
fVitE, mg/d
0.05
0.46
0.21
0.001
0.02
0.83
0.04
0.54
fβ-Caro, mg/d
0.04
0.50
0.30
<0.001
0.07
0.37
-0.01
0.93
Tot fVitA, Equivs.
-0.01
0.91
0.20
0.003
0.08
0.28
0.11
0.12
VitC, mg/d
0.08
0.21
0.30
<0.001
-0.06
0.38
0.09
0.23
Riboflavin, mg/d
0.01
0.83
0.25
<0.001
0.01
0.89
0.05
0.51
Folate, mg/d
0.04
0.58
0.23
<0.001
0.04
0.54
0.06
0.45
Potassium, mg/d
0.08
0.24
0.32
<0.001
-0.01
0.92
-0.05
0.46
Magnesium, mg/d
0.09
0.16
0.24
<0.001
-0.01
0.02
0.75
0.92
Refer to Appendix 4 Table C for expanded Table 5-9Weight body weight; BMI body mass index;
waist waist circumference; %Fat body fat percentage; SBP systolic blood pressure; DBP diastolic
blood pressure; Hb haemoglobin; Hct haematocrit; MCV mean cell volume; MCHC mean cell
haemoglobin concentration; HbA1c haemoglobin; ALT alanine transferase; γGT gamma glutamyl
transferase; hsCRP high sensitive C reactive protein; IDF International Diabetes Federation8, JIS
Joint interim statement3; MetSMCt metabolic syndrome marker count; NCEP National Cholesterol
Education Programme; VitD vitamin D; βCaro beta carotene; s serum; VitA vitamin A; VitE vitamin
E; INR VitK Internationalised Normalised Ratio vitamin K; Adpn adiponectin; LMW low molecular
weight; HMW high molecular weight; MMW medium molecular weight; tot total; d day; g gram.
Figure 5-2. Baseline sFSVitamin vs. Waist
140
4
sβCaro, µmol/L
sVitD, nmol/L
120
100
80
60
40
r (p-value)
r=-0.144
p=0.03
20
0
60
80
100
120
140
160
180
3
2
1
0
60
r=-0.36
p<0.001
80
100
120
140
160
180
4
3
sVitE, µmol/L
sVitA, µmol/L
60
2
1
0
60
r=-0.025
p=0.61
80
100 120
Waist, cm
140
160
180
50
40
30
20
10
0
60
r=0.082
p=0.21
80
100 120 140
Waist, cm
160
180
s serum; sVitD vitamin D; sβCaro beta carotene; sVitA vitamin A; sVitE vitamin E
165
Change Correlations: Serum Beta Carotene in All and Women and Serum
Vitamin E in All and Men
As explained in the introduction change correlations were only formed for sβCaro, in all
and women, and for sVitE in all and men and are presented in Table 5-10.
Table 5-10. Change in sβCaro in All & Women, & Change in sVitE in All & Men vs. Study
Parameters
δ sβCaro
Change in
sFSVitamin
δ sVitE
All,
n=145-155
Pearson
pr
value
Women,
n=120-125
Pearson
pr
value
All,
n=144-154
Pearson
pr
value
Men,
n=28-30
Pearson
pr
value
-0.17
0.04
-0.19
0.04
0.09
0.28
0.15
0.42
δ BMI, kg/m
-0.17
0.03
-0.19
0.04
0.09
0.25
0.18
0.33
δ Waist, cm
-0.14
0.09
-0.16
0.07
0.13
0.10
-0.15
0.44
δ SBP mmHg
-0.11
0.20
-0.17
0.05
0.14
0.09
0.43
0.02
δ DBP mmHg
0.04
0.63
0.02
0.79
0.28
<0.001
0.56
0.001
δ RBC x 1012
0.07
0.41
0.10
0.27
0.18
0.03
-0.16
0.40
δ Hb g/L
0.15
0.08
0.21
0.02
0.17
0.05
-0.24
0.21
δ Hct, %
0.12
0.15
0.17
0.06
0.12
0.16
-0.21
0.26
0.15
0.07
0.18
0.05
-0.02
0.79
-0.26
0.18
-0.21
0.01
-0.15
0.11
-0.07
0.43
-0.05
0.80
-0.14
0.21
-0.12
0.35
-0.18
0.09
-0.24
0.37
-0.05
0.61
0.02
0.85
-0.22
0.01
-0.21
0.31
-0.30
0.001
-0.21
0.03
0.02
0.80
-0.03
0.87
δ TC, mmol/L
0.21
0.009
0.22
0.01
0.49
<0.001
0.52
0.003
δ LDL-C, mmol/L
0.21
0.01
0.22
0.01
0.41
<0.001
0.23
0.24
δ TG, mmol/L
0.04
0.59
0.02
0.86
0.37
<0.001
0.67
<0.001
δ TC/HDL
0.17
0.03
0.13
0.16
0.42
<0.001
0.69
<0.001
δ Bilirubin, μmol/L
0.15
0.08
0.13
0.15
0.14
0.10
0.06
0.77
δ ALT, U/L
0.06
0.43
0.03
0.76
0.19
0.02
0.41
0.03
δ γGT, U/L
-0.03
0.75
-0.04
0.69
0.22
0.008
0.29
0.13
δ Tot Protein, g/L
-0.14
0.08
-0.17
0.07
0.14
0.10
0.20
0.32
δ Albumin, g/L
-0.11
0.20
-0.14
0.12
0.14
0.09
0.33
0.08
δ Globulin, g/L
-0.18
0.03
-0.18
0.04
0.11
0.19
0.01
0.98
δ IDF/JIS MetSMCt
-0.11
0.18
-0.17
0.07
0.17
0.05
0.21
0.28
δ IDF MetS
-0.06
0.51
-0.12
0.18
0.14
0.10
0.02
0.91
0.21
0.008
0.27
0.15
0.30
<0.001
0.42
0.02
Change in
Parameter
Anthropometry
δ Weight, kg
2
Haematology
δ MCHC L/L
δ Leukocytes, x10
9
δ Eosinophils, x10
δ Monocytes, x10
9
9
δ Lymphocytes, x10
9
Biochemistry
Metabolic Syndrome
δ sβCaro, μg/L
-
δ sVitA, μg/L
0.12
0.16
0.16
0.07
166
Change in
sFSVitamin
Change in
Parameter
δ sVitE, μg/L
δ sβCaro
All,
n=145-155
Pearson
pr
value
0.21
0.008
δ sVitE
Women,
n=120-125
Pearson
pr
value
0.21
0.02
All,
n=144-154
Pearson
pr
value
-
Men,
n=28-30
Pearson
pr
value
-
Hormones/Cytokines
δ Energy, J/d
-0.02
0.82
0.00
0.96
0.17
0.03
0.39
0.03
δ Protein, g/d
0.06
0.49
0.08
0.38
0.22
0.006
0.53
0.003
δ Tot Fat, g/d
-0.11
0.20
-0.12
0.20
0.15
0.06
0.42
0.02
δ Sat Fat, g/d
-0.09
0.26
-0.10
0.28
0.13
0.11
0.47
0.01
δ Mono Fat, g/d
-0.08
0.31
-0.10
0.28
0.17
0.04
0.33
0.08
δ Fibre, g/d
0.12
0.13
0.16
0.08
0.15
0.08
0.40
0.03
δ Tot Sucrose, g/d
0.13
0.12
0.18
0.05
0.13
0.13
-0.07
0.73
δ VitC, mg/d
-0.08
0.33
-0.10
0.28
0.09
0.27
0.35
0.06
δ Riboflavin, μg/d
0.21
0.009
0.26
0.004
0.18
0.03
0.28
0.14
δ Niacin, μg/d
0.19
0.02
0.20
0.03
0.15
0.07
0.31
0.10
δ NiacinTrypto., μg/d
0.04
0.64
0.04
0.67
0.21
0.01
0.49
0.007
0.15
0.06
0.17
0.07
0.20
0.02
0.43
0.02
δ VitB6, μg/d
0.14
0.09
0.13
0.16
0.14
0.09
0.27
0.15
δ Potassium, mg/d
0.08
0.34
0.09
0.33
0.19
0.02
0.50
0.006
δ Magnesium, mg/d
0.10
0.24
0.11
0.24
0.16
0.06
0.48
0.008
δ Calcium, mg/d
0.11
0.20
0.13
0.15
0.21
0.01
0.46
0.01
δ Phosphorus, mg/d
0.09
0.29
0.14
0.14
0.23
0.005
0.49
0.007
δ Iron, mg/d
0.08
0.32
0.08
0.38
0.17
0.04
0.38
0.04
δ Zinc, mg/d
0.04
0.67
0.03
0.73
0.20
0.02
0.55
0.002
δ Copper, mg/d
0.20
0.02
0.22
0.02
0.06
0.50
-0.29
0.13
Refer to Appendix 4, Table D for expanded Table 5-10, δ change; weight body weight; BMI body
mass index; waist waist circumference; SBP systolic blood pressure; DBP diastolic blood pressure;
RBC red blood cell; Hb haemoglobin; Hct haematocrit; MCHC mean cell haemoglobin; TC total
cholesterol; LDL-C low density lipoprotein cholesterol; TG triglyceride; TC/HDL-C total cholesterol
/ high density lipoprotein cholesterol; U units; ALT alanine transferase; γGT Gamma glutamyl
transferase; tot total; IDF International Diabetes Federation 8 ; JIS Joint interim statement 3 ; MetSMCt
metabolic syndrome marker count, mono fat monounsaturated fat; sat fat saturated fat; MetS
metabolic syndrome; s serum, βCaro beta carotene; VitA vitamin A; VitE vitamin E; J joules; g
grams; VitC vitamin C; Trypto tryptophan; equivs equivalents; VitB6 Vitamin B6.
Previous diet and serum lipid relationships and intervention studies have led to
hypotheses relevant to the current enquiry.
Positive change correlations in all of sβCaro were seen with change of TC, sVitE,
Riboflavin, niacin and copper (all p≤0.009) and with TC/HDL and LDL-C (p≤0.05)
(Table 5–10).
167
Positive change correlations in women of sβCaro were seen with change of riboflavin
(p=0.004) and with change in TC, LDL-C, Hb, fVitE, niacin and copper (all p≤0.02)
(Table 5–10).
Change in βCaro in all negatively correlated with change in lymphocytes (p=0.001) and
in weight, BMI, leukocytes and globulin (all ≤0.03) (Table 5–10).
Change in βCaro in women negatively correlated with change in weight, BMI,
lymphocytes and in globulin (all p≤0.05) (Table 5–10).
Change in sVitE in all positively correlated with change in DPB, TC, LDL-C, TG,
TC/HDL (all p<0.001), and in γGT, dietary protein, dietary phosphorus and in sβCaro (all
p≤0.008) and in sVitA, RBC, ALT, Hb and in dietary: energy,
calcium, total fat,
riboflavin, niacin equivalents, MUFA, potassium, zinc and iron (all p≤0.05) (Table 5–
10).
Change in sVitE in men positively correlated with change in DPB, TG and in TC/HDL
(all p<0.001), and in TC, dietary: protein, potassium, zinc and phosphorus (all p≤0.007)
and in SBP, ALT, sVitA, and in dietary: energy, total fat, saturated fat, fibre, niacin
equivalents, calcium, and iron (all p≤0.05) (Table 5–10).
Change in sVitE in all negatively correlated with change in monocytes (p=0.01) alone,
and there were no negative change relationships in men (Table 5–10).
In the baseline ordinal regression, shown in Table 5-11, HbA1c (p=0.003), urate
(p<0.001), sVitE (p=0.03) and age (p=0.02), were significantly positively related to MetS
marker count. SβCaro (p<0.001), was highly significantly negatively related to MetS
marker count (Table 5-11).
168
Table 5-11. Baseline Serum Fat Soluble Vitamin vs. Metabolic Syndrome Marker Count – Ordinal Logistic Regression
Relationship to the outcome,
Metabolic Syndrome Marker Count
Analysis of Maximum Likelihood Estimates
Explanatory Parameter or Effect
Estimate
Standard Error
Wald Chi-Square
Pr>ChiSq
Point Estimate
Gender, F
-0.14
0.22
0.40
0.53
1.32
95% Wald
Confidence Limits
0.56
3.12
Age, y
0.03
0.01
5.32
0.02
0.97
0.95
1.00
sVitD, nmol/L
0.01
0.01
2.43
0.12
0.99
0.98
1.00
sβCaro, μmol/L
-0.90
0.28
10.23
0.001
2.46
1.42
4.28
sVitA, μmol/L
-0.00
0.38
<0.001
0.99
1.01
0.48
2.12
sVitE, μmol/L
0.04
0.02
4.71
0.03
0.96
0.92
1.00
INR (VitK)
0.67
0.74
0.82
0.37
0.51
0.12
2.19
HbA1c %
1.03
0.23
20.25
<0.001
0.36
0.23
0.56
Urate, mmol/L
6.31
2.12
8.84
0.003
0.002
<0.001
0.12
Albumin, IU
0.02
0.06
0.14
0.71
0.98
0.86
1.11
Globulin, IU
-0.04
0.04
1.01
0.32
1.05
0.96
1.14
AlkPhos, IU
0.0073
0.00549
1.78
0.18
0.99
0.98
1.00
Odds Ratio Estimates
The signs of the estimates indicate the direction of the relationship such that explanatory variable is negatively related to the outcome, MetSMCt. The odds ratio is
< 1 if there is a positive relationship of the explanatory variable to the outcome. βCaro beta carotene, AlkPhos alkaline phosphatase s serum; VitA vitamin A, VitD
vitamin D, VitE vitamin E, MetSMCt metabolic syndrome marker count.
169
Chapter 6. Causes of Obesity and Metabolic Syndrome
A generalised (ordinal regression) mixed model was fitted to the sFSVitamins with the
MetS marker count, over 2 time points, baseline and 6m. For sVitD there was no evidence
of a relationship with its mean for the two time points (p=0.64) and MetS marker count,
but there was evidence of a positive relationship of change (p=0.02) in sVitD with MetS
marker count.
There was evidence of a negative relationship of the overall mean (baseline to 6m) of
sβCaro (p=0.02) with MetS marker count and also possibly a negative relationship with
its change (p=0.08). No relationship could be demonstrated for the overall mean (baseline
to 6m) (p=0.99) of or change (p=0.32) in sVitA with MetS marker count. There was
strong evidence of a positive relationship in the mean of sVitE with MetS marker count
(p=.004) and a possible positive relationship of the change in sVitE (p=0.09).
With respect to sVitD a general linear (regression) modelling was formed to include
season and ethnicity. It showed an estimated decrease of 0.74nmol/L (p=0.002), in sVitD
per 1kg/m2 increase in BMI with the total model explaining 22% of the variation in sVitD
levels and BMI explaining 3% of the variation. On replacing BMI with waist, there was a
decrease of 0.29nmol/L (p=0.01), in sVitD per 1cm increase in waist, with the total model
explaining 21% of the variation in sVitD and waist explaining 3%. There was strong
evidence of the association of sVitD with ethnicity (p<0.001), and season (p<0.001).
When both BMI and waist were included neither could be demonstrated to contribute
over and above the other (p=0.25 and p=0.67 respectively), nor could an association of
sVitD with gender (p=0.52), or age (p=0.52), be shown.
5.4
Discussion
Overall, the findings in this part of the study were that the sFSVitamins had almost
opposite effects; sβCaro was a protective marker and sVitE acted as a strong risk marker.
The hypothesis that all FSVitamins, including sVitA and sVitE would be lower in the
disturbed metabolism of MetS, was true for sβCaro and sVitD, but not for sVitA and
sVitE, whose levels were higher in MetS.
The Adpn oligomers showed increased sexual dimorphism, with Adpn being more prooxidant, probably via MMW, were clearly shown to have very different and complex
relationships with obesity-related MetS, each other and other CVD risk and protective
170
markers. Not all were shown to have protective relationships, which meant that the
protective aspects of Adpn were not complete.
Firstly, and most importantly, sβCaro was shown in this analysis to perform better than
expected, in that it correlated with a wide range of parameters that were all beneficial, or
sβCaro correlated negatively with CVD risk markers or adverse markers, such as
leukocytes. The same relationships were maintained with change correlations of sβCaro
and when other strong CRSHaem/Biochem explanatory markers such as urate and HbA1c
were added in the mixed model sβCaro was still highly significantly negatively related to
MetS marker count.
In addition, sVitD had fewer numbers of associations but they were positive for protective
factors and negative for risk factors.
Surprisingly, sVitA and sVitE were the opposite; they could have functioned as potent
risk markers for MetS with multiple and overall positive relationships with MetS marker
count and many other MetS-related markers. On change correlations sVitE correlated
with dietary markers of a high fat, high meat diet. SVitE maintained a strong negative
change relationship with MetS marker count when the other sFSVitamins and other
strong CRSHaem/Biochem explanatory markers such as urate and HbA1c were included
in the mixed model. Both sVitA and sVitE tended to disprove part of the hypothesis, as
they showed no signs of being protective markers.
Adpn showed mixed relationships with MetS and other study parameters correlating with
some pro-oxidant appearing markers – such as age and AlkPhos, yet relationships with
female gender, HDL-C being antioxidant. Adpn in men was more pro-oxidant.
SβCaro was positively correlated with almost all major CVD health markers Adpn, HDLC, and many healthy food nutrients such as fibre. The adverse marker correlations were to
CVD risk. They fall into groups of inflammatory markers containing and oxidant or
metaflammatory markers and external oxidant stressors, namely cigarette smoking, and
lastly obese anthropometry. βCaro is mostly derived from vegetables and fruit and is a
marker of their dietary consumption692 Fruit and vegetable foods have extensive health
promotion properties, including mental health461,466,693 and Mediterranean diets have been
well shown to protect health240,300. Some dietary intake may be from βCaro as
171
red/orange/yellow food colouring, although phytoxanthines are highly used as yellow
colorants, since added βCaro might be considered a risk in some individuals694.
The only anomalies may appear to be the significant positive correlation of sβCaro to
lipids TC, and LDL-C in addition to HDL-C.
βCaro is carried on the latter two lipoproteins470, and notably βCaro was not related to
TG. Another negative appearing relationship was that sβCaro was related to age. Perhaps
as individual’s age they make an effort to eat more vegetables, or this older cohort always
ate more than the young obese695. Overall serum βCaro decreased in women over the
study, which may have been due to a decrease in sugar based fruit.
The βCaro-derived asymmetric carotenals, some of which can inhibit the nuclear
receptors Retinoid X (ROX) and PPARs, are interesting as they may antagonise βCaro
stimulatory actions468,475. Note that the PPARS are downstream signalling molecules of
Adpn. βCaro appears to be promising as a CVD protective marker to use for tracking
MetS and CVD risk.
In the current study there were fewer relationships of sVitD than the other sFSVitamins
with CVD protective markers. sVitD was negatively related to obesity-related
anthropometric markers of weight, height, BMI, waist, in addition to leptin and HbA1c.
The negative relationship of sVitD to HbA1c and waist, in the present study, adds weight
to sVitD as opposing MetS and insulin resistance. Central obesity is not healthy for
bone696. VitD, via its receptor which is present in insulin-producing beta-islet cells, is
known to be a potent regulator of cell proliferation and differentiation697. SVitD was
related to haematocrit, which relates to oxygen carriage, and physical fitness. Albumin, a
carrier of lipids390 is similar to VitDBP, and coincidental reductions in both are
detrimental to physical capability698. VitD has effects on muscle and the obese are often
relatively physically unfit, sarcopaenic and have excessive fat within muscle699,700.
Interestingly sVitD levels predict weight loss success701,702.
It is not known if low sVitD in obesity is a storage issue and the VitD is sequestered in
the large adipose tissue depots, or whether obese individuals are less likely to sunbathe or
be exposed to the sun. Finally, obesity may interfere with VitD metabolism, but
mechanisms are uncertain449,703.
172
The inter-relatedness of the sFSVitamins includes sVitD which was positively correlated
with sβCaro, sVitE in addition to Adpn. All have profound metabolic activities which
may link with obesity via the immune system and leukocytes704-706. Basophils are
probably involved as part of the skin immune system and skin barrier protection from
infection and autoimmune problems, as is sVitD678. The lack of dietary correlations of
sVitD possibly indicates its dependence on skin photosynthesis and hormone-type
actions. Alcohol was a surprise link to sVitD, but again both can be beneficial markers..
VitD may have a complex antioxidant effect on some chemicals associated with
smoking707.
Low sVitD in obesity appears to cause metabolic destabilisation451,708,709. Interestingly,
VitD action is usually assumed to be related to 1,25 hydroxyVitD, but the (serum 25
hydroxy) sVitD itself may be active in TIIDM and other CVD risk710. The relationship
between the two forms, taking into account parathyroid hormone, VitDBP and its
receptor, is complex.
In the current study, sVitD was highly influenced by season and ethnicity, likely via skin
pigment, as both factors interfere with skin solar photosynthesis of VitD711. These factors
overwhelmed the effects of obesity. Waist and weight explained only a few % of the
variability in this group. In fact, in the mixed model sVitD was positively correlated MetS
marker count. This may be explained by participants having more MetS at the study start
in late in summer after the greatest sun exposure of the year, and skin VitD
photosynthesis. There was a large drop in sVitD over the 6m as most participants enrolled
at the end of summer, thus mainly proceeding with the study through winter.
Therapeutic studies of spVitD have usually been positive, particularly on bone, although
recently both calcium with and without supplemental VitD have shown increased calcific
arteriosclerosis and myocardial infarction712. The current study and literature point to
sVitD having a positive influence in obesity and MetS. SVitD may be a useful protective
marker in MetS.
Historically, high blood levels of the antioxidant fat and water soluble vitamins have been
associated with health, and have been associated with a healthy diet684,713. In the current
study both sVitA and sVitE were negatively correlated to weight and BMI and the sVitA
and sVitE concentrations were higher in men.
173
Many researchers correct for lipids, but as shown in the present study all sFSVitamins
correlated differently and differentially with each fraction. That they are transported on
the CVD risk lipids may explain both the raised sVitA and sVitE, and their mimicking of
CVD risk. SVitE, especially, associates with both LDL-C and TG. Further their
correlations with TG and BP, and other CRSHaem/Biochem: urate, ferritin, and liver
enzymes, likely partly explain why SVitE and sVitA correlated MetS marker count and
MetS.
Another issue with sVitA and sVitE is that they are highly added to deep fried starches
such as potato crisps and processed meats respectively to prevent fat oxidisation and
rancidity. γ-tocopherol, a VitE vitamer, is probably the largest component of tocopherols
in the diet, and it has its own biology714,715. Thus, even without dietary supplements
higher levels of serum α- and γ- tocopherol are occurring716. γ-tocopherol is associated
with worse food choices and may be less healthy than α-tocopherol714.
There are an increasing number of reports that indicate that obesity is associated with
raised levels of sVitE and sVitA717,718. It has been suggested that high levels of sVitE may
be a CVD risk marker and diabetogenic716,719. As noted previously vitamin interventions
for CVD and cancer prevention have been disappointing162,720-722. High dose vitamin
intervention studies have been tested for disease prevention or amelioration, and generally
there has been no benefit, and where there has been benefit such as telomere preservation
has been shown, a concurrent healthy diet often cannot be excluded504,723,724.
Both VitA and VitE have antioxidant effects but they may be pro-oxidants in some
circumstances, and many small pro-oxidant molecules stimulate antioxidant response
systems, which will be addressed in Chapter 6.Oxidative stress may result in the excess
mobilisation and consumption of blood and stored antioxidant FSVitamins695, and the
related, low grade systemic metaflammation may impinge on functions and activities476.
The present study does not answer why sVitA and sVitE act as pro-oxidant markers, or
whether VitA and VitE action at the cell is normal and antioxidant. The hypothesis for
these two vitamins appears incorrect, and more work is required to resolve mechanisms
and other unknowns.
174
Adiponectin
There is still some doubt about the primary function of Adpn in health725-727. Adpn
appears to be associated with protective markers, normal weight and fewer MetS
associate problems434,670-673, and in the current study Adpn250 correlated with female
gender, HDL-C, and negatively with the markers of weight, BMI and waist, inflammation
and oxidant markers.
The sexual dimorphism was marked in this study as expected, with negative correlations
of Adpn250 with anthropometry were shown in women, but not men except for height.
Insulin and HOMA showed a negative correlation in women but this was lost in men.
Surprisingly, in men there was no negative correlation of Adpn250 with MetS.
Adpn30 responded as predicted to a large extent, and serum levels increased in women
over the study with weight loss. However, Adpn30 had a proportionately higher MMW30
and lower HMW30 levels, and overall Adpn30 decreased in men. Even so HMW30 and %
HMW30 increased in both women and men, and MMW30 and % MMW30 decreased in
both groups, with similar effect sizes. The other difference between the genders was that
change in MetS correlated with Adpn30 and oligomers in men, except for MMW30, but not
women in the small Adpn30 group.
Importantly, Adpn has a number of links with a high micronutrient diet, typified by large
volumes and variety of fruit, vegetables and nuts, low refined starches and modest meat
content, somewhat irrespective of energy content, and the Mediterranean diet728,729 Other
nutrient connections include resveratrol, from ground nuts, grapes and other seeds, which
affects multimerisation588,730-732. This may be the reason for the linkage of Adpn with the
sFSVitamins, especially which may be the marker of a high fruit and vegetable diet, and
some of these markers may interact with Adpn. However, only dietary fVitE, which can
also derive from nuts, correlated with Adpn in this study.
Adpn associates with other positive health behaviours including physical activity728,729,733737
.
Adpn was negatively correlated with raised %Fat in women, TG , hyperinsulinaemia and
HOMA in the current study, and prior studies show Adpn predicts lower %Fat, TG and
175
insulin738,739,740,741. Adpn release from adipocytes may be affected by large over full
central adipocytes. Mechanisms for low Adpn in obesity may involve mitochondrial and
ER stress, whose function is linked to Adpn synthesis in adipocytes742. As obesity
resolves Adpn synthesis and function return to normal327,743,744. 743
Recently Adpn has been reported to stimulate mitochondrial biogenesis, prevent apoptosis
and enhance longevity671,745. In this study Maori and Pacific individuals had consistently
lower levels of Adpn. They also typically smoked 1.6 x more than the European.
Socioeconomic factors of affluence, food quality and smoking rates affect obesity rates746748
rather than ethnic differences. More alcohol appeared to be healthier for men by Adpn
levels. Alcohol may compete with Adpn in the liver and may augment beneficial
effects418,749.
When there is a reason for inflammation such as in infection, in individuals with normal
fat mass, Adpn increases and becomes pro-inflammatory750. Chronic inflammatory
disease, although an immune disorder, is probably not a central adipocyte problem. As
such, Type I DM751 and Graves' disease752 in slim individuals is related to higher Adpn, as
presumably Adpn becomes proinflammatory.
Age correlated with Adpn, probably as more chronic inflammation is occurring due to
other senescence processes. The accumulation of inflammatory ‘oxidative stress’ in age,
especially as weight753 and even fat levels drop, may also explain the raised Adpn in
age754. Furthermore, those with ‘age-accelerated CVD’ appear less able to raise an
antioxidant response as ‘degenescence’ overtakes cellular functions587.
The HMW was shown to be higher in women than men in this study, and this is well
known, and MMW was lower. Increased testosterone suppresses HMW production in
men755 and may be the reason for both lower levels in men and the greater proportion of
MMW. The women in the study, who were over all healthier, had increased HMW and
lowered MMW & LMW, with HMW increasing, and MMW and LMW, decreasing over
time. In obesity, HMW formation is the most compromised and proportionately HMW is
reduced, so its peripheral effects are diminished, although the brain still receives input
into appetite controls from MMW and LMW740,741,743.
176
The disulfide bond-A oxidoreductase- like protein (DsbA-L) that multimerises the Adpn
monomers to the HMW is also decreased in obesity588. The 3 oligomers may be acting
with different functions. HMW may mediate some pro-inflammatory effects in TIIDM
and exercise, yet LMW may inhibit lipopolysaccaharide (LPS) and be negatively related
to proxidant myeloperoxidase750. The production of oligomers may also be differentially
affected by hyperinsulinaemia756.
Adpn receptors, Adipo R1& 2 also very complex, are increasingly found to have new
protective functions, including those of antiapoptosis, longevity, and have other
nutritional ligands745,757,758. The phytoalexins include osmotins (dehydration defence
chemicals) which stimulate the AdipR’s.
The consistently high relationship with HDL-C warrants comment. HDL-C and Adpn are
known to be related in normal health738 and confer protection from CVD208,672,759,760.
HDL-C is a collector/carrier of lipids470, taking them to the liver for oxidation or other
metabolic functions. Adpn controls the efflux of lipids from HDL-C and adipocytes and
sensitizes the liver and muscle to lipid uptake. At the arterial endothelium higher levels of
Adpn limit foam cell formation, by increasing lipid efflux761,762 as monocyte/macrophages
have the same lineage as adipocytes and share functions.
It appears that HMW can be the most pro-inflammatory oligomer, and is highly related to
HDL-C669. In intensive care hospital populations with high glucose and TC, HDL-C is
often low763. However, in some highly infected subsistence populations with low HDL-C,
there is little atherosclerosis764. There is no definitive research showing raising HDL-C
with pharmaceuticals, as opposed to diet and exercise, reduces CVD.
There were negative and positive correlations with leukocytes and Adpn, in the current
study, with men having higher leukocyte activity. Adipose tissue hypoxia may contribute
to inflammatory monocytes migrating to this tissue, and setting up inflammatory
communication with neutrophils and lymphocytes326,677. When the men’s HMW increased
over time there was a marked decrease in the white cells in the current study.
Urate negatively correlated with Adpn in both women and men indicating that Adpn is
sensitive to oxidant stress, but it did not change with HMW change. AlkPhos is usually
negatively related to obesity but had positive correlations with Adpn. CRP is now known
177
to be secreted by central adipose tissue and may be a product of adipose tissue mass380,382.
Positive correlations of Adpn with bilirubin should be helpful as this haem metabolite has
recently been shown to be an antioxidant, antiatherogenic and positively associated with
good health358,632.
Overall Adpn in women was correlated with other beneficial markers, except smoking
and then the relationship was negative when the number of cigarettes was correlated. The
changes in HMW in women were less beneficial.
As Adpn is such a complex molecule with complex functions it is hard to interpret how
useful it could be. However, obesity-related MetS is complex and it may be that once
more is known about Adpn’s functions, especially the separate oligomers, it may yet hold
potential as an adjuvant marker for MetS. At present, it is inadequately understood and its
use could well become confusing.
Although mentioned, change data for the sFSVitamins was limited to the two serum
vitamins where CVD treatment interventions have been performed. Added sampling of
the change in sVitE in men was performed for sVitE as the literature has shown
relationship with CVD in men with high levels of sVitE.
There were significant limitations to the current study.
As the Adpn and sFSVitamin studies were hypotheses generating, the usual limitation of
widespread correlations increasing the risk of false positives or type I errors still applies,
It is expected that the findings will be corroborated or not in further studies. Only the
Adpn250 baseline was available for assessment, and gave unexpectedly low mean values.
The Adpn30 oligomers analysed were selected or ‘hand picked’ post hoc from participants
who exhibited extremes in weight, MetS and in change in these parameters. The number
of participants was very small, especially in the men who completed the study. As the
range in the variables in this group was chosen to be high the correlations were high, as
expected and planned.
A major limitation was a 24Hr Food Recall instrument recording just one day of food
intake at baseline and 6m visit. Extremes are encountered, as there is no regression to the
178
mean, which makes their use limited. The range of βCaro reported was extremely wide at
40 to 95,000μg. In this type of study 3d Food Recall, recorded 3 times, as well as a Food
Frequency Questionnaire could have been administered765.
The physical activity questions were condensed, and the sedentary questions were not
validated and proved to be insensitive. Highly biased reporting was suspected in the food
energy. In the obese, under or misreporting of high energy food is more common than in
leaner individuals and over reporting of healthy food types, and physical activity which
are perceived as healthy can occur598,602,766-768. However, the findings of specific
correlations were expected and borne out by the literature.
The positive aspects of the study were that using a wide array allowed the separation of
general tendencies of the markers. The findings have shown that sβCaro is by far the most
widely correlated and predictable CVD protective marker and that sVitD was also
consistently associated with positive markers in CVD.
SVitA & sVitE remain important, as are all vitamins, but they appear to be trapped within
putative causes of obesity and MetS: (1) being carried on adversely raised serum lipids
and (2) consumed as modified fried industrial fat antioxidants in baked starch foods232-236.
Lastly, Adpn, reveals itself as being very complex multimeric adipokine with complex
receptor signalling in the literature. In this study its relationships appeared unpredictable.
The lack of complete knowledge about its normal function in health, and its abnormal
function in obesity render it an unreliable CVD protective marker in MetS at this time.
Conclusion
In conclusion, the study achieved its objectives of studying a number of putative
protective CVD markers. SVitA, sVitE, and Adpn were eliminated, for the time being as
Novel CVD Protective markers, owing to either having CVD risk properties or as having
an unpredictable combination of risk and protective correlations. Much more study of the
mechanisms of VitA, VitE and Adpn is needed.
The study was successful in indicating testing sβCaro and sVitD should be considered,
possibly as part of an index, as CVD protective markers for use with obesity-related
MetS.
179
Gaining adequate sun exposure for dermal VitD photosynthesis to suit latitude, skin
pigment and lifestyle, but minimise solar skin damage is also probably prudent advice,
but is controversial in pale skinned populations in some areas.
As βCaro has been available in food for as long as primates have been evolving, and is
well studied, the standard advice of eating a high fruit and vegetable and fish diet as part
of a healthy lifestyle can still be given.
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Chapter 6. Causes of Obesity and Metabolic
Syndrome
6.1
Introduction
6.1.1 Scope
In this chapter a unifying hypothesis on causes of obesity and related MetS is proposed,
with reference to a multidisciplinary literature. The hypothesis attempts to explain what is
occurring at the level of the individual, and also why MetS is developing in large numbers
in populations of humans, in the current era.
6.1.2 The Need for a New Theory on Causes of Obesity
There has been a failure to prevent up to nearly a third769 of some westernised populations
from developing obesity and its comorbidity of MetS. MetS contributes to the
degenerative diseases, as previously outlined248,770-772 and treatment has also largely been
unsuccessful. The need for a unifying hypothesis on causes is therefore reviewed.
In the existing main-stream paradigms on researching and managing obesity, certain
assumptions have been made that have set current working hypotheses on a trajectory that
has limited further interpretations of the problem and research needed. A more developed
understanding of deep seated causes may indicate new management approaches, or at
least an abandonment of unhelpful treatments, and a cautious approach to taking up new
treatments773.
The first assumption that may be problematic is the literal interpretation of the first law of
thermodynamics. The first law of thermodynamics states that energy can be neither
created nor destroyed so the assumption is that fat gain = energy in - energy out (PA) x
moderators774. The moderators in this scheme appear to be basal metabolic rate.
Individuals who are obese are assumed to eat excess of all foods. It is likely that this
group has the largest imbalance. Energy dense food is over consumed, by a large margin,
181
compared with fruit and vegetables, which are often severely neglected and under
consumed, along with their vitamins and micronutrients.
As all of the vitamins have been discovered, the types of foods where they are found are
well known, vitamin supplement use has become widespread in wealthy countries775,776,
food fortification is common777,778, thus it is often assumed that vitamin insufficiency
does not occur. Dietary studies relating to obesity and TIIDM are therefore performed
where food energy alone is adapted. Manipulating macronutrients in weight loss studies is
the main concern779 and controlling or lowering plasma glucose and HbA1c is the major
outcome in TIIDM trials780. Once the assumption that pure energy balance, rather than
nutrient balance, is the main issue, then future research follows along these lines, and
gradually filters into clinical management.
‘Pick the Tick’ is a Australasian dietary guide; ‘These foods have met the National Heart
Foundation’s criteria for lower fat or preferred fat choices, but may still be high in sugar
and energy’781. Food micronutrients are rarely considered by obesity researchers.
However, biology is about manipulation and control of energy reactions with catalysts
and antioxidants, and a myriad of other acquired or synthesised biochemical based
processes. In large mammals this is likely to be a complex process, and humans have
some extra issues to deal with, as will be discussed. This approach may explain why it
appears surprising to many obesity researchers that the Mediterranean diet can augment
health, decreasing MetS, stabilising or even decreasing weight and waist. This diet
includes a high content of plant food nutrients, but may contain 40% energy from fat and
oil, and even with added energy from nuts appears beneficial240,248,713,782,783.
Another assumption is that individuals just have to change their behaviours, and that they
are in control of their environment and food intake; that diet is just a matter of ‘personal
choice’784. Blame for lack of moral restraint, gluttony and sloth, is still common50,785.
Ridicule and stigma of obese individuals is profound, and associated with significant
psychological morbidity, not least of which are the eating disorders784,786,787. Obese
individuals themselves often feel guilt, distaste and despair, mirroring what others may
think788-790. Presently, as society has not been able to control the rise of obesity, either in
less affluent countries or in children791, the profound issues of the interaction of humans
and the westernised food pattern, is starting to be taken more seriously.
182
6.1.3 Background to Theory Development
As obesity appears to have influences arising from basic biology to socioeconomic
factors, it appears a new approach is needed. One new approach is to include an overview
from a more distant vantage point (evolution), that nevertheless incorporates concurrent,
congruent and plausible biological (biochemical), and sociological (behavioural) factors.
Thus there are two broad areas. The first is the biological aspect of obesity, including
energy balance and nutrition, and the other is the psychosocial area of why humans
produce and eat the food they do. Both of these areas are likely to be informed by studies
of human evolution (Figure 6-1).
Figure 6-1. A Version of Human Phylogeny
Slightly adapted from Carroll 2003 792 Myr million years
183
Of particular interest is the rapid evolution of humans (Figure 6-1), human brain
development, the effect on energy balance of encephalisation, and particularly other
associated co-adaptations. The current Chapter is about exploring the likely coadaptations proposed as arising in response to supplying a large brain with extra energy.
Hominins separated from a last common ancestor with the other hominoids, the great
apes, about 7 million y ago792 (Figure 6-1).
Humans have been consuming highly varied omnivorous diets, derived from a
frugivorous diet of a primate ancestor about 20 million y ago. Hominins had been
becoming omnivorous over the last 7 million y and by 2.5 million y ago, they were shown
to be relying on higher fat and energy animal-based food.
However, fruit and vegetable foods were always important for general nutrition. Even
before the shift to animal products, the diet, with fruit and oil seeds, has been described as
a high quality or a high energy diet793,794
795
as shown in Figure 6-2. The importance of
energy manipulation became especially relevant to hominins, as they developed a unique
metabolic state due to the development of a large, energy demanding brain.
Figure 6-2. Relative Brain Size & High Dietary Quality, or High Energy Diets, in Primates
Figure
from
Leonard
, 2007
793
184
Encephalisation in the early human, a medium-sized mammal, meant that the human
brain drew a relatively high proportion of the body’s energy. The human brain is
moderately, but constantly, metabolically active793,794,796-799.
6.2
Aims
In this theoretical Chapter the overall aims are to develop a unifying hypothesis of obesity
and MetS.
The specific aims are to:•
Review and critique past theories on the topic of obesity related MetS
•
Explore the multidisciplinary literature on the evolution of humans, including the
effect of brain enlargement on nutrition and health
•
Investigate
o 1) Appetite science, the modern mesolimbic functions of the human brain,
and energy dense food addiction, and
o 2) Basic metabolic biochemistry of human nutrition, relevant disease
pressures, and the employment of phytochemical modulation, or situation
specific and modest adjustments, in human metabolism.
The above are related to past and current living conditions and environments.
Further aims are to:•
Suggest further investigations on preparing an energy budget which takes
micronutrients into account.
•
Propose study designs for future clinical studies to test micronutrient dense wholefood diets in obesity and MetS.
6.3
Methods
This chapter does not have experimental data to present. A multitopic, multidisciplinary
broad-based literature search is undertaken whilst the composite unifying theory on
obesity related MetS is constructed. Importantly, the search cannot be a single topic,
multidatabase review800, or a systematic review, as the premises of much of what has
gone before, are questioned.
The search is presented in a narrative style, an accepted method for this purpose801.
185
The concept of proximal or distal is introduced. It is defined here because sociology,
philosophy, general biology and genetics produce different and contested meanings802-804.
Proximal causes contribute to what individuals actually do such as inventing technologies
when there is food scarcity, such a digging with tools for underground tubers to increase
consumption of energy dense foods, and which thence affect other factors, for example
body weight and shape. They often have behavioural aspects and individuals can differ in
the population. Distal causes are those whose influence, such as genetic endowment, has
already affected the population, and derives from the past804. Socioeconomic or physical
and environmental constraints, such as desertification and ice ages, are also distal causes.
Both causes overlap and leap-frog as genetic/epigenetic changes become embedded in
response to the environment.
Secondly, new technologies in archaeological investigation have become very
sophisticated. Genetic and epigenetic methods are now used, along with mathematical
modelling. Thus genomic and metabolomic high through-put microarray computer
assisted analysis and mathematical modelling have brought new knowledge into the two
major areas that are relevant; namely human evolutionary studies, and biochemistry.
Metabolomics is ‘the systematic study of the unique chemical fingerprints that specific
cellular processes leave behind’805. Nutrigenomics is the study of how nutrients influence
the genome.
Possibly surprisingly, the same areas of technology-assisted study also shed light on the
neurochemistry of food related behaviours. Human archaeology and biochemistry can
converge in genomic archaeology, both of which inform the new hypothesis.
This investigation, therefore, commences with an examination of initial conditions. In this
thesis initial conditions are taken as time periods in evolution where unique human
attributes that relate to energy metabolism were becoming established. As the brain
enlarged, new adaptations were required by allometry to support this organ’s increased
energy requirements. Allometry is a size to shape/metabolic rate/longevity relationship of
one part of the body as it grows with respect to the rest of the body. Various energy
expensive-tissue trade-off theories have been proposed by other researchers, which relate
to brain energy, will be considered.
186
The first principles of the biochemistry of energy metabolism are explored. These first
principles are examined to determine if humans were employing different pathways of
energy management from other animals.
6.4
Influences from Thesis Study on the Unifying Hypothesis
The study for this thesis confirmed reports in the literature that the pathology of oxidative
stress is highly likely to be present in obesity-related MetS, as seen in degenerative
disease351,806-810. Oxidative stress either antedates or co-induces inflammation36,463,661,772
or metaflammation325,811, of which inflammation may be of lesser influence (Table 6-1 &
Table 6-2) summarise an interpretation of the study results in the light of the association
with MetS of the strongly oxidant HbA1c812 and urate813 and γGT which predicts and
tracks MetS814.
To a lesser extent, ESR and neutrophil inflammatory stress markers, probably with
oxidant properties in addition, had both cross sectional and longitudinal relationships.
Bilirubin may protect from oxidant related hypertension. There was possible protection
from oxidant stress by sβCaro or associated dietary microconstituents or micronutrients,
as is also shown in the literature248,695,815, and sVitD, and the factors that increase sVitD.
6.4.1 The Unifying Hypothesis on Causes of Obesity and Metabolic
Syndrome
The hypothesis is expounded early in this Chapter. As it comprises two main parts, with
the modern day corollary, it is instructive to have it in mind as the evidence is
investigated in the following sections.
Firstly, appetite control, especially in hedonic mesolimbic pathways in the brain, which
relates to energy dense food acquisition, became very well developed.
The dopamine dependant reward/motivation system became a very strong mechanism to
ensure maximum energy uptake and intake, by driving smart energy dense food
procurement methods, and subsequently the invention of high energy food cultivating,
processing, storage technologies, transport, and marketing technologies.
187
Table 6-1. Estimate of General Oxidant and Inflammatory Properties of Laboratory
Markers in the Diet&Health-Novel CVD Marker Study
Parameter
Antioxidant
Pro-oxidant
+
Antiinflammatory
+
Proinflammatory
- (+)
Adpn
+
sVitD
Comment
?+
-
(-) ?
- (+)
sβCaro
+
-
+
-
Marker of high fruit and vegetable diet817
sVitA
-(+)
+
-
+/-
sVitE
-(+)
+
-
+/-
Neutrophils
-
+
-
+
Eosinophils
Basophils
Monocytes
?
-
+/+/+
(-)
(+)
-
+
Lymphocytes
-
+
-
+
ESR
Albumin
Globulin
-
+
+
-
+
+
HsCRP
HbA1c
Urate
Ferritin
?
-
?
++
++
+
(-)
+
+
++
(+)
Bilirubin,
AlkPhos
ALT
AST
γGT
+
-
+
+
+
+
?
?
?
?
?
+
+
?
Is an antioxidant, vital for vision, retinoid X
receptor. Recent studies show may be prooxidant716,817,818
Known antioxidant, recent studies show can be pro-oxidant. Intervention studies
failed716,817.
Pro-oxidant as secretes H2O2 to kill
pathogens739
Known allergy marker.
Related to histamine producing mast cells
Sensing and coordination of leukocytes in
inflammation, become foam cells819
T-cell natural killer cells, may be prooxidant
ESR an effect marker, not an item
Can be anti or pro-oxidant820
Known inflammatory marker, due to
immunoglobulins
Synthesised in adipose tissue and liver
Glycated protein, oxidant
Known antioxidant but oxidant in MetS
Known oxidant, but antioxidant effects in
healthy individuals281
Recently found to be antioxidant821,822
Weak oxidant, lipid relationships
Oxidant – in glucose metabolism
Oxidant – in glucose and lipid metabolism
Oxidant - can be antioxidant in lean healthy
individuals, but predicts obesity &
MetS371,373
Can be proinflammatory. Raised with age
and inflammatory conditions in the current
study739
Now thought to prevent infection,
autoimmune disease and cancer816,710
Key. A significant positive relationship with waist = +/- inflammatory or pro-oxidant. A negative
relationship with waist = +/- anti-inflammatory or antioxidant. A significant positive relationship
with Waist and negative relationship with HDL-C = + inflammatory and ? pro-oxidant. A significant
negative relationship with Waist and positive relationship with HDL-C = + anti-inflammatory and ?
antioxidant. A significant positive relationship with waist and TG and/or FPG and/or BP = + oxidant
and ? inflammatory. A significant negative relationship with Waist and/or FPG and/or BP = +
antioxidant and ? anti-inflammatory. No relationships is left blank. Well known properties not shown
in this study have (brackets). Adpn adiponectin; ESR erythrocytes sedimentation rate; HbA 1 c
haemoglobin A 1 c ; AlkPhos alkaline phosphatase; ALT alanine transferase; AST aspartate; γGT
Gamma glutamyl transferase; tot total; hsCRP high sensitive C-reactive protein; MetSMct metabolic
syndrome marker count; Mets metabolic syndrome; s serum; VitD vitamin D; βCaro beta carotene;
VitA vitamin A; VitE vitamin E
188
Table 6-2. Patterns of Association with CVD Risk or Protective Markers by Gender
Parameter
Adpn
All
Mostly
protective
Women
Almost all
protective
Change in HMW30
Many mostly
protective
Almost all
protective
sVitD
Fewer, All
protective
sβCaro
Many, and all
CVD protective
Many, and all
increase CVD
protection
Many, and most
increase CVD
risk
Many, and most
increase CVD
risk
Many, and most
increase CVD
risk
Change in sβCaro
sVitA
sVitE
Change in sVitE
Men
Equal balance
between
protective/risk
Balance
between risk/
protective
-
-
Many, and all
assoc. with CVD
protection
-
-
-
-
-
Increase in VitE
risk for CVD
-
Comment
With more research
Adpn and oligomers may
be able to predict risk in
women and men
separately
Good CVD protective
marker, but few
correlations
Good CVD protective
marker, many
correlations.
Antioxidants VitA and
VitE acted as oxidant
risk markers. More work
is needed to understand
why these Vitamins are
associated with CVD risk
not protection162,823
Adpn, adiponectin; HMW 30 high molecular weight Adpn; s serum, VitD vitamin D; βCaro beta
carotene; VitA vitamin A; VitE vitamin E
Secondly, the human body has become highly efficient in its energy metabolism by
exploiting a wide variety of whole-food, mainly phytochemical, micronutrients as energy
modulators. The postulated ‘super-efficient’ phytochemical antioxidant system, probably
the Kelch ECH associating protein 1-nuclear factor-erythroid 2-related factor 2antioxidant response element (Keap1/NRF2/ARE or NRF2) system based824, may protect
cells so efficiently that they potentially function for long periods of time. Longevity could
also allow slow brain growth and development in the infant, allowing ample energy for
daily use. In addition, a long lifespan for humans meant that culture and technologies
could be developed and passed to the next generations.
6.4.1.1 Unifying Hypothesis Corollary: Evolutionary Adaptations
Outwitted?
The corollary of the unifying hypothesis is that it evolved to serve forager ancestors well.
Evidence shows forager hominins to be taller, more muscular, more robust and with less
signs of infection. Note that there are few, if any foragers, currently living in arable land
with extant large game, although a few hardened subsistence survivors exist825,826.
189
The era of farming technologies dates from about 10,000y ago827. Energy dense, storable
and transportable, starch foodstuffs increased to 50-70% of the diet. The diminishment of
variety was extreme828. The diet has allowed adequate survival for humans to breed and
spread around the world. However, there was a health cost to the loss of a widely varied,
whole-food diet827. There is evidence that many, but not all, agricultural populations
became stunted; even brain size reduced829-831. Lack of micronutrients was the probable
cause.
City dwellers especially became more disease, particularly infection, prone827,831-833.
Examples include poor immunity to acute food-borne illnesses, wound, respiratory and
puerperal infection. The lack of sVitD predisposes humans to chronic infections of
tuberculosis, leprosy and syphilis834-836. Poor nutrition and infection probably also exerted
epigenetic, if not genetic, selection837-839.
The mean height of pre- 19th Century European women was approximately 155cm and of
men, 166cm, with men losing more highly nutritionally demanding, lean tissue831.
Improved sanitation and antibiotics has decreased infection and increased infant
survival835.
The ‘green revolution’ of chemical and machinery assisted energy food crop farming, last
century,
is
testament
to
the,
possibly
short
term,
success
of
agricultural
technology829,833,840-842. Human longevity, now a major research area, did increase843,844.
However non communicable degenerative CVD increased845, declined to a degree173, but
may be increasing again with the increase in obesity, the likely latest micronutrient
deficiency syndrome59,846,847. One public health publication warned of this scenario 60y
ago51. The current wave of industrial farming has surpassed itself, possibly as the energy
yield per capita has been so massive.
On the other hand, are the volumes of ever more addictive nature of the refined processed
energy dense foodstuffs. The addictive tendencies, excessive motivation and reward
seeking, that drive this type of eating pattern, leaves many individuals – populations dependant on the current westernised food type. Neglect of adequate, large amounts of
healthy whole-foods is currently the norm. The welldeveloped human reward/motivation
190
system has probably shaped current food production and trade environments. Overweight
and some metabolic problems in middle age are now typical of many populations59,846,847
in
many
countries.
Obesity
has
become
a
human
specific
public
health
problem53,59,769,784,846,847.
Whole-food micronutrients are no longer consumed in quantities enough to allow
efficient energy oxidation and protect cells from excess oxidants. Oxidative stress and
lipo-glycotoxicity203,204, including glycated protein and oxidised lipoprotein damage
results from non-metabolised toxic lipid, glycogen and glucose accumulating in the
circulation and ectopically in cells204,848.
Other environmental, fat soluble toxicants may also contribute to metabolic disruption in
cells into which excess lipid has spilled. Long lived human cells degenerate, and adipose
tissue may become hypoxic, apoptotic and metaflammation becomes chronic. Damage to
the endothelium occurs and ischaemic infarction is likely in the heart and brain. Infection
and cell dysplasia, and cancer risk increases.
6.5
Current Theories on Causes of Obesity and Metabolic
Syndrome
The literature in various disciplines revealed a number of theories on energy management
processes with respect to obesity and MetS in humans. Significant areas have not been
resolved. Obesity rates may be stabilising in some sectors in some countries but they are
rarely falling61.
Considerations on the Causes of Obesity
The causes of obesity discussed fall into both categories of proximal and distal, and many
current studies of gene-environment effects are looking at both concurrently.
By using BMI in many human epidemiology and interventional trials, adipose tissue
distribution is eclipsed. Favourable, but large, peripheral subcutaneous adiposity849,
sometimes seen in European women is not differentiated from, for example, thick-waisted
Asian men, who can be at considerable risk850.
It is important to give due weight to cohorts and sub populations studied in rapid
epidemics. The decline in CVD since the late 1950s was and is much less steep among
191
people from low socio-economic cohorts, ethnic minority women and those otherwise
disadvantaged173,851,852
6.5.1 Putative Contributors to Obesity
The paper by McAllister et al78 has 10 extra mainly proximal theories on obesity
causation, in addition to the usual two; excess dietary energy intake and lack of exercise.
These authors do not discount these two likely causes but expand on them. Some of the
more novel causes of obesity reviewed are ‘microorganisms, epigenetics, increasing
maternal age, greater fecundity among people with higher adiposity, assortative mating,
sleep debt, endocrine disruptors, pharmaceutical iatrogenesis, reduction in variability of
ambient temperatures, and intrauterine and intergenerational effects’. The current Chapter
will consider some of these theories.
The authors of the McAllister et al78
853
paper made an effort to look at in vitro work,
animal work and clinical studies for each topic.
Environmental Toxicants - Endocrine Disrupters and Psychotropic Medication
There is a large amount of data on man-made human toxicants in the environment. Many
pollutants and medications may cause metabolic disruption that increases risks of both
central obesity and MetS87,854,855.
With the unifying hypothesis in mind it is important to consider that environmental
pollution may have been impacting on CVD type diseases. Both CVD and environmental
pollution saw widespread increases from last few decades of the 19th Century, which
continued without abatement until the mid-20th Century. Obesity was increasing, but at a
lower rate. The huge uptake in tobacco smoking, a toxic, oxidant, certainly was shown to
be a health risk from the 1930s onwards184,856.
Persistent organic pollutants, endocrine disrupters 857,858, and iatrogenic causes have been
covered in the McAllister paper78. They have been, and are implicated in MetS, obesity
85,859-861
and, for many years, cancers861-865.
192
If there is a refined energy dense food environment present at the same time as an
increase in these persistent pollutants, detoxification may be impeded by deficient general
nutrition86,824.
Microbes and Central Obesity Related Metabolic Syndrome
Micro-organisms can contribute to both increases or decreases in obesity78,346. Early ideas
of true infection as a cause is now thought unlikely866-869 as CVD inflammation is
metaflammatiory. Some animals infected with AD-36 adenoviruses acquire increased fat
mass870 with metaflammation811. Infection of adipocytes by Chaga’s disease and
leukocytes by immunodeficiency virus (HIV) cause a peripheral lipodystrophy and
central obesity with variably severe MetS871. Leukocytes and adipocytes share a common
lineage, and cross functionality can be induced872.
Intestinal Microbes
The intestinal milieu changed over the last few thousand years as milk ferments, or
yogurt, from domesticated herbivores became part of some human diets873.
Probiotics containing certain exogenous, often dairy-derived, Lactobacilli microbes can
establish in the intestine and are credited with beneficial effects873-876. Plant fibre content
and type, and other food constituents have altered632, and with it the intestinal microbiota.
The genetic background of ethnic groups may alter the intestinal microbiota as some
groups have had significant gut pathogen stress, and high risks of infant mortality from
diarrhoea such as the Indian Asians and Europeans. Wells877 theorises that certain peoples
had different requirements for immediate energy release, thus central obesity allows large
and rapid energy outflows to immune cells and processes in the intestine.
The intestinal microbiota in obese and MetS individuals is different from that of the lean
and fit878. Firmicutes, depending on ethniticy879, are the more favourable groups, but less
numerous in obesity. The microbiota derive energy from CHO, especially fibre
(fermentation), protein (putrefaction), and lipids. The short chain lipid butyrate byproduct appears beneficial880. However, westernised food is typified by highly refined,
fried or baked protein, starch/sugars, and lipid mixes which can form AGE/ALES in
Mallaird reactions, which continue in the bowel.
193
Fermentation of these products by certain microbe groups to amines and phenols may be
variably toxic and/or carcinogenic to the colonic epithelium.881. If absorbed systemically,
the kidney may be negatively affected, in as seen in diabetes809,882. Interestingly, extra
nutrients may aid pre- and probiotic activity277,883. Invertebrate chitin and cellulose plant
fibre is usually ingested with other nutrients in addition to the fermentable moiety.
Lastly, the microbiota acquired as the infant passes through the cervico-vaginal canal and
the perineum during birth, and colostrum breast milk, including stem adipogenic cells884,
are ideal first exposures that help prevent autoimmune diseases346,885, which are more
common in those with MetS428,886.
Sleep deprivation
Sleep deprivation is one of many physiological/psychological stressors that induce
metabolic derangements that, as with sleep apnoea, are more MetS related than body fat
related887,888. Many genes including the newly understood clock genes889 participating in
circadian rhythm control are related to energy management, including appetite, digestion
and metabolism890. They also have high levels of genetic structural variations relating the
organism closely to the changes in the environment891. Sleep hygiene is now advised for
health.892.
Reproduction
In the McAllister et al paper78 the discussion on fecundity and age at first birth and
relationships to BMI-defined obesity was reviewed. In the past, more robust, wellnourished women would be expected to successfully bear children. Some constitutively
slim women, who do not have anorexia nervosa, may have bone and other fragilities893.
Currently, obesity-related PCOS, and consequent infection proneness, may increase rates
of gonorrhoea, chlamydia, herpes, syphilis and HIV, all contributing sub-fertility of obese
women, with increased foetal loss and neonatal infection835,894. First births are associated
with increased intrauterine pressures and smaller babies. Older mothers and their ova
accumulate oxidative damamge895-897.
Intergenerational Effects
The intrauterine and intergenerational effects section of McAllister et al’s paper78 was
wide-ranging. Bottle formula feeding, with poorly or never established breast feeding, in
194
human neonates is associated with later obesity, MetS, early degenerative disease853,898901
, and various other suboptimal outcomes such as possible cognitive deficits902.
Studies in omnivorous rat maternal diets, and their manipulations, may show some
comparable aspects with diet types of pregnant humans, producing various embryo and
foetal effects. One study of a true human cafeteria diet in rats showed worse metabolic
profiling than animals fed high fat but nutrient sufficient chow based diets903,904. The
proposed unifying hypothesis, however, indicates less extrapolation should be made from
laboratory animals to humans due to brain dynamics, and the adaptations discussed.
Maternal Capital
This theory is basically another way of saying that growth retarded babies skimp on lean
tissue – including the organs - but make sure that they have enough energy reserves to
survive, postnatally898.
Protein Leverage Hypothesis
The protein leverage hypothesis separates macronutrients into protein and the energy
macronutrients, CHO and fat482. These authors’ work shows that humans tend to eat
enough protein, if it is present in the food supply, but probably over eat CHO energy if it
is not well balanced482,905 906-909.
Macronutrients and Micronutrient Combinations
Various other authors have criticised the current dietary patterns as deficient in non
energy, nutrients248,270,910-912. Note that protein and fats and oils are often in combinations
of high micronutrient containing foods, as for example dairy and oil seeds. Oils can be
refined but still contain a mix of lipids913. Only CHO based foods such as sugar beet and
grains are commonly refined to pure starch and sugars, almost devoid of minerals and
other micronutrients. Refer to Section 1.4.2.
Critique of Theories
Above are various historical factors thought to contribute to obesity. None of them is
likely to explain why individuals and/or society set up systems to support unhealthy
eating patterns, and the discussion on excess dietary energy does not explain the extent of
degenerative change seen in MetS.
195
6.6
Evolution of the Human Brain
Humans have evolved rapidly, and became H. sapiens by approximately 200,000 y ago
(200 kilo years ago, 200kya) (Figure 6-1). The striking features were the development of
the combination of a proportionately very large brain and prehensile upper limbs, freed
from the task of locomotion, both of which allowed technological development. The
human palaeontology literature is vast.
Rather under-recognised in the nutrition and medical world are the significant energy
requirements of the human brain and the co-adaptations required to supply this over-large
organ with sufficient resources794,914. Maintaining a vast association cortex of expansive
dendritic arbours and long-range projecting axons is thought to be particularly energy
expensive915. Brain metabolism in a lean human adult requires ≈ 23% of the body’s basal
metabolic rate (BMR), as opposed to 3-8% in many other mammals830,915,916. The
allometry of brain to body was altering beyond sustainability917. Humans are required to
stay on the same metabolic to mass ratio line as other animals; commonly known as the
mouse to elephant curve or Kleiber curve. Although there is controversy about exact
equations and applicability918,919, this log-linear curve is similar for all animals. Small
mammals have small reservoirs of fat energy with high metabolic rates, and large
mammals the converse. More fuel had to be found to supply the brain by two possible
mechanisms: increased energy 1) conservation or 2) uptake from the environment.
6.6.1 Theories of Increased Energy Conservation Adaptations to
Encephalisation
Expensive Tissue Energy Trade-off Theories
Traditional theories of energy provision for the brain include the ‘expensive tissue energy
trade-off’ hypotheses914,920. The human intestine is the organ most commonly proposed as
the trade-off. This organ has become smaller and less energy demanding, compared to the
large complex foregut ruminants whose bacteria synthesise all required nutrients from
low energy grasses, but flexible in the types of food that it can digest. The omnivorous
pig has a smaller intestine, with similarities to the human; fermentation occurs in the
caeco-colon921. The intestinal-brain energy trade off theory however may be overrated as
other primate intestinal mass is nearly, but not quite, proportional795.
196
Humans are generally under-muscled compared to other mammals and primates795. It is
proposed that, in effect, this is a once removed ‘expensive tissue trade-off’ as in this case
high metabolic rate-muscle is traded for high energy content but low metabolic rateadipose tissue. Human babies are almost unique in being born with significant levels of
subcutaneous fat830,922. These adipose tissue depots are thought to be reserves which allow
the provision of continuous energy to supply the brain830,923.
Omnivory and Dietary Energy
Omnivory, arising at some stage in early H. erectus, the species before H. Sapiens, was
important, allowing greater variety in the diet. Archaeological evidence shows the postcanine molar grinding surface changed, tooth size decreased along with mandible and
maxillary robustness for supporting large masticator muscles795. Energy intake was
maximised from animal protein and fat, and there is evidence that diets derived up to 35%
protein, and >50% of energy from scavenged carrion and hunted animal231 brain and liver,
particularly. These sources supplied the long chain ω-3 fatty acids EPA and DHA, a good
supply of which is probably needed for optimal human brain development795,830,924.
As ω-3 fatty acids and other nutrients such as iodine were required for brain tissue, some
authors posit a period of rapid brain evolution using shore and water based (sea, lake or
river) resources, including fish830, but others do not support that postulate925. Most high
energy food sources were ripe fruit with fruit sugars, vegetables such as squash
containing starches, nuts and seed providing oils, and animal products including brain and
marrow which were high in fat. Of note, starches were a rare source of energy in the past
as they often occurred as very tough, stringy, dirty roots, that yielded a low proportion of
energy, especially if not cooked795.
Slow Growth and Development
Humans, usually singleton infants, grow and develop slowly for their size, possibly to
supply the brain with constant energy and to allow the acquisition of social and survival
skills. Theories abound on menopause in humans with explanations including the
grandmother hypothesis, the mother hypothesis and others926-929. Mathematical modelling
supports the development of the menopause to allow later and shorter breeding periods in
daughters, rather than longevity927-929.
197
Technologies
Technologies such as stone tools have been recorded as in use from at least 2.5 million y
ago930, and were used for acquiring hard-to-extract foods such as underground tubers931.
Cooking practices, probably commencing 800,000 y ago, released extra energy from food
by gelatinising starch, softening meat and killing pathogens828,932. Technologies slowly
advanced until, relatively rapidly, certain human groups started farming480,933.
6.7
Increased Energy Uptake from Environment via Energy
Dense Food Reward System - First Part of the Unifying
Hypothesis
In this section evidence and information will be presented that support the idea of
increased neural mechanisms to drive the acquisition of energy dense food.
Modern Food Energy Environments
Current human-modified food environments may relate to the human brain’s preference
for constant, large supplies of glucose798 and maladaptive feed-forward effects of chronic
hyperinsulinaemia and leptin resistance564. The salivary amylase copy number increased
starch digestion in the mouth for the populations who became dependant on tuber or grain
starch828
6.7.1.1 Evolution of Epigenetic and Genetic Brain Control Mechanisms
Of great interest to the current hypothesis generation are the many genetic and epigenetic
processes that occur in the brain, especially the frontal cortex. On reflection, a fast
evolving brain, with new energy requirements, but long lived cells, would be advantaged
to exploit DNA regulatory responses to the environment.
Epigenetic changes are molecular modifications to DNA and chromatin. DNA
methylation, chromatin packaging alterations of DNA by post-translational histone
modifications, regulation by non-coding ribonucleic acid (RNA), such as microRNAs,
and mechanisms that control the higher-level organization of chromatin within the
nucleus, are all called marks934. MicroRNAs can activate gene expression in non-dividing
cells such as neurons. Unlike in other tissues the human brain depends on active
198
epigenetic processes to allow for ‘adaptive plasticity’ but also well-coordinated extremely
complex patterning and this is a lifelong, continuous function841. Thus the brain can adjust
to and accommodate rapidly changing and variable environmental challenges. Any
disruption of epigenetic modifiers and/or epigenetic dysregulation may have severe
consequences as seen with mental retardation and complex psychiatric disease841.
In addition to the epigenetic variations depending on environmental influences, DNA
genetic variability between individuals is extremely wide. In the brain many genetic
structural or sequence variations935 and processes are employed as regulatory
mechanisms. DNA structural variations include many single nucleotide polymorphisms
(SNP), variable number tandem repeats (VNTR) of DNA sequence, and gene copy
number variations (CNV) where additional or missing chromosomal segments
occur841,889. VNTR are more common in the forebrain, a rapidly evolved enlarged section
of the human brain cortex841,891. SNP, VNTR and CNV often occur together in genes in
certain brain regions and can increase the information content, and variation of the
genome, in individuals.
Of note, in humans, CNVs are significantly overrepresented in telomere-proximal
regions841,889. Whilst these numbers and types of sequence variations in populations
usually confer plasticity to exploit new environments, they also risk dysfunctionality. Of
special interest to the current argument is that vast inter-individual behavioural
phenotypes will be manifest.
6.7.1.2 The Mesolimbic System and Appetite Control
The changes for the expansion of the physical brain included making provision for 1)
biochemical pathways for more energy to be readily available, 2) the development of the
neurotransmitter chemistry to enhance motivation of behaviours to procure more energy
and lastly 3) neuronal connections to plan and coordinate strategies for active
procurement in time and space.
These three attributes are those of the mesolimbic/mesocortical (mesolimbic) system, the
dopamine based reward and motivation system that spans the emotional midbrain and the
decision making frontal cortex (Figure 6-3).
199
The mesolimbic system involves the accumulation of expanded ‘coils’ of varicose axons
for dopamine, serotonin, and acetylcholine, and which may represent episodes of
plasticity and synaptic reorganisation923,936. These morphological features suggest
enhanced facilitation of intracortical processing in the prefrontal cortex by
neuromodulatory systems923. The large lateral cerebella hemisphere of hominoids is
thought to function in the planning of complex motor patterns, sensory discrimination,
attention shifting, and procedural learning923.
Figure 6-3. Reward Pathways - Hypothalamic Nuclei to Frontal Cortex
The
mesolimbic/
mesocortical dopamine
system. The ventral
tegmental area (VTA)
projects to the nucleus
acumbens, hippocampus and frontal cortex.
The substantia nigra
and striatum are also
dopaminergic,
and
involved in addiction.
The serotonin system
projects from the raphe
nuclei through-out the
cortex VTA Schematic
from US-NHI 9 3 7 .
This enlarged part of the cerebellum probably developed with pressure to perform and be
aware of the body in 3-dimentional space, and to coordinate visio-spatial mapping skills
when earlier primates were arboreal feeders, balancing fairly large bodies while collecting
food at the tips of branches. This area of the cerebellum also appears to relate to tracking
seasonal resource availability. In humans, it functions as a neuroanatomical base for
refining motor behaviour and then supporting other higher-order cognitive abilities.
Processing and coordination of spatial and temporal complex procedures are emphasised
in humans. Thus, this is evidence for the development of a magnified, highly organised,
neural ‘self-reward system’.
200
Energy dense foods also stimulate the release of ‘self-addictive’ opiates found in central
appetite control pathways, encouraging repeat acquisition938-940. Natural cannabinoids, the
endocannabinoids, are involved in the motivation for food seeking and hedonic behaviour
involving high-energy food941. In rats, orosensory stimulation by fat ingestion induces
intestinal endocannabinoids release, the neural effects of which are mediated by
dopamine942.
6.7.1.3 Genetic and Epigenetic Dopamine Control
Dopamine is the neurotransmitter involved in the mesolimbic system responsible for the
motivation to act, with or without pleasure, and for the planning and physical
coordination of that action. Pleasure requires other neurotransmitters, characteristically,
secreted opiates. Dopamine, may be under various rapidly evolving genomic and
epigenomic controls in humans. Patterns of genetic and epigenetic regulation change in
the face of environmental pressure. Such processes occur in rapidly evolving organisms,
and can result in response to altering environmental pressures throughout the individual’s
lifetime.
Dopamine, a highly conserved neurotransmitter itself, mediates core, yet seemingly
diverse, functions such as locomotion, cognition and motivation, and coordinates
behaviour, the reasons for which have been discussed. Any dysfunction in dopaminergic
signalling action can lead to neurological disease such as Parkinson’s, neuropsychiatric
disease including schizophrenia, attention deficit/hyperactivity disorders (ADHD) and
addiction841. Two receptors, D1 & D2, also carry polymorphisms943,944.
Dopamine Transporter
The control of dopamine resides largely within its transporter, dopamine transporter
(DAT). DAT recycles extracellular dopamine from the synaptic cleft, returning it to the
presynaptic terminal. DAT exerts control on dopamine expression. DAT expression is
highly dynamic so as the dopamine signalling increases, DAT increases and when
dopamine release is low, DAT decreases. The range and variation change over the
lifespan841.
Some of the genomic and epigenomic systems coding for, and controlling, DAT are
unique to humans. Influences on DAT include environmental factors, palatable food
reward motivation797,945, certain pharmaceuticals and other salient stimuli797,841,946,947.
201
Even in the same individual with region-specific DAT expression, in the same brain
region there is significant highly dynamic variability in DAT expression, and more so
than in serotonin. DAT control needs to be stable enough to perform routine tasks and
adaptable enough to function in an unpredictable environment841.
A comprehensive combination of modelling and computer programme analyses of
preconstructed databases was run by the authors of this paper to examine a large variety
of genomic and epigenomic mechanisms, which seem to regulate the activity of DAT841.
This one transporter employs nearly all the previously mentioned genetic and epigenetic
modes discussed above. Such processes endowed humans with such strong drives that
they have developed technologies to find, and later farm, process, refine, store and trade
energy dense food. Note also, that whilst dopamine control is crucial to the unifying
hypothesis, it is but one pathway in a complex organism.
Energy Uptake Enhancements in the Brain
Amino acid changes arose after some of the electron transport chain genes underwent
positive selection. These changes allowed greater functional efficiency of the
mitochondrial aerobic metabolism pathways to ensure that highly active cells such as
neurons were supplied with energy substrates948.
Gene copy number was doubled for glutamate dehydrogenase (GDH). The hominoid
isoform of this GDH gene, in the astrocyte, is activated during high glutamate flux949.
This increased capacity for glutamate metabolism, and thence energy throughput, was a
likely adaptation to enable relatively high levels of excitatory neurotransmission required
by hominoid brains923.
Of note, newly discovered orexin/hypocretin, or desire-to-eat hormones, facilitate
glutamate-mediated responses, and are necessary for glutamate dependent, long-term
potentiation in certain dopamine neurons. Thus orexin/hypocretin neurons play an
important role in reward processing950.
202
Appetite and Feeding
The type of neurons involved in appetite stimulatory (orexigenic) or inhibitory
(anorexigenic) pathways show nearly all can be involved in all substance addictions, via
the mesolimbic dopamine pathway. Highly refined energy dense food may have been the
first human addiction951. However, human appetite had often been classified into
homeostatic eating and hedonic eating, and addiction has not been seriously considered
until recently.
Appetite Mechanisms–Homeostasis to Addiction
Homeostatic mechanisms are those that sense basic hunger, coordinating brain memories
of nutritious, safe, non-toxic food objects and how to procure them, and ensure that the
body has the correct amount. Hedonic eating is where there is pleasure in the food. The
cortical part of the mesolimbic system is important for assessing decisions on what is
sensed. Energy sensing and awareness of levels of satiation are still present in hedonic
eating (Figure 6-3).
In the last few years the concepts of ‘liking’ and ‘wanting’ have been extended to the area
of food addiction952. Liking relates to pleasure, such as felt on exposure to opiates,
whether endogenous or exogenous. Wanting implies a motivation for something that
catches attention or has incentive salience, in the environment, such as pleasure and
acquisition which excites a feeling of reward.
Note that in addiction, often liking is minimally present, or has been lost. In addition, the
reward may not feel good, but just allows a sense of relief, in the same way as those with
obsessive disorders find relief but not pleasure on completing the compulsion to wash
hands, for example953. This is dopamine reward hypofunction797.
In the case of refined energy dense foodstuffs, for many individuals, especially those who
binge, either in obesity or bulimia, enjoyment is often no longer present. Whether
permanent or long term brain changes occur in binge eating and obesity, as in drug
addiction, is not known954-956.
Food becomes a problem in all ways – refined energy dense food is compulsively sought
and consumed, healthy food neglected and there is transient or minimal pleasure in eating.
203
Food addiction was proposed many years ago, but at that stage neuronal and dopamine
pathways were only suspected to exist, and clearly not all food was addictive953,957,958.
Refined Energy Dense Food Addiction - Psychology and Neuropsychiatry
It is instructive to review some of the characteristics of addictions. For those not addicted
to food or not obese, it is not easy to understand that most of the criteria of addictions
apply to obese individuals and binge eaters, and need to be taken into account when
treating. As neurotransmitter functional changes are profound in addictions,
neuropsychiatry developments have become important to the medical management.
Opiate blockers are increasingly being used for alcoholism, and their use in obesity is
being reviewed by the FDA959. Interestingly, bromocriptine, a dopamine agonist, is
licensed in some countries for prescription in TIIDM as it reduces plasma glucose, but the
mechanism is not known960.
There are many addiction definitions, and currently they cover gambling, computer games
and other similar past times, as well as substance abuse.
According to the Diagnostic and Statistical Manual Version 4961, for a person to be
considered dependent on, or addicted to, any given substance, at least three of the
following seven criteria must be met at any time within a given year: 1) tolerance; more
substance is needed for the same effect, 2) withdrawal, 3) taking a larger amount of the
substance or taking the substance for a longer period than was intended, 4) experiencing a
persistent desire [craving] for the substance or an inability to reduce or control its use, 5)
spending much time seeking or consuming the substance or recovering from its effects, 6)
use of the substance interfering with important activities [healthy eating], and 7) use of
the substance continuing despite known adverse consequences [obesity, MetS, TIIDM,
CVD and also in this section resorting to antisocial, guilt provoking or clandestine
behaviours]961. If either criterion 1 or 2 is met, then physiological dependence is
diagnosed.
These criteria are met for sugar, in animal studies
962
and as noted, the rat reward
orosensory effects of fat are also brain dopamine mediated via gut endocannabinoids942.
However, human substance dependence (or addiction) can be diagnosed using entirely
behavioural criteria. There are elegant studies performed in rats, which are omnivorous,
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showing that dopamine reward hypofunction or dysfunction953 develops only in those
animals which have had long term access to palatable, energy dense foods. Once obesity
develops, compulsive energy-dense food eating becomes established. However, if these
rats have been eating a continuous supply of energy dense cafeteria type food for many
wk (>40d) and subsequent access was restricted, only modest overeating occurred. They
ate as much as they did during the 40d cafeteria diet and were very hard to deter from this
monotonous, compulsive, behaviour, even with aversive training797.
Clinical addiction studies also show long lasting and obesity-related changes can occur in
the mesolimbic pathway. In a clinical study, the near-total mesolimbic pathway was
activated in neurological functional magnetic resonance imaging when obese women
were shown pictures of palatable energy-rich food. In contrast, in the normal weight
control women, only one part of the pathway, the dorsum striatum classically associated
with addiction, was activated. Interestingly, when normal weight women looked at low
energy food pictures their left lateral orbitofronal cortex activated more vigorously, and
they had less subcortical emotional activation963. The orbitofrontal cortex is part of the
emotion and reward limbic pathway, but it also functions as a centre of cognitive decision
making. One could speculate that the normal weight women were able to associate the
visual input via the orbitofrontal cortex and the left lateral cortex and together with the
prior memory/knowledge of safety and utility of the low energy food, make an analytical
assessment and informed decision less hampered by a contrary, emotional and hedonic
response963.
In another study normal weight restrained women and men could reduce ‘wanting’
perceived unhealthy, delicious food verses healthier less appetising food, more than
unrestrained participants963. Thus, learning before obesity develops may be able to occur
and reinforce efforts to prevent weight gain.
Craving and Cues
Opinions about whether only highly palatable, energy dense foods are addictive or
whether all foods can be, is still divided964,965. Humans can learn to crave behaviours,
events or unpalatable foods, of all types. Notably, humans can learn to crave punishment
and starvation in conditions such as anorexia966. Some authors maintain that humans can
be cued to crave any foods964,965.
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In many cultures, where food is perhaps limited or seasonal, seemingly strange items are
consumed, such as bird’s nest soup, and can become a delicacy. Repeated exposure to
certain foods, especially if there is very strong cueing and emotional investment, leads to
acceptability, which in time becomes a preference. As long as the taste organs, of which
some are in the intestine and do not have a clear taste sensation967, can sense nutrients
without toxins, humans can become accustomed to a wide variety of types of foods.
Unlike with addictive drugs, there is a solid homeostatic pathway for food, and energy
dense food and drug comparison experiments in rats show that they can opt for food over
cocaine954.
In contrast, there is a report stating that mammals have been seeking and procuring
psychoactive foods, as have hominins, for thousands of years. This behaviour has been
hypothesised to enhance positivity and fitness seeking968. However, the self- reward and
motivation to procure energy dense food appears to be a much more likely reason for a
well-developed mesolimbic system in humans, which, unfortunately, in the current
environment, can become dysfunctional.
Some authors state that care needs to be taken about labelling food as addictive, as all
organisms need to acquire nutrients964. Much of the CHO is particularly highly bred
grains and tubers for starches and sugars. Such foodstuffs, new in evolutionary terms, are
made even more attractive by adding extra well known, highly palatable flavours, eye
catching packaging, advertising of convenience and easy availability. Individuals are
‘cued in’ by advertising with other desirable items/lifestyle situations.
Thus, the current westernised food environment may be a result of these adaptations for
making plenty of energy available to the human brain. With the great power, adaptability
but variability of human motivation systems, it seems that when coupled with privation,
social or physical stress, and the tendency for positivity seeking in adversity or with
stress968, the limbic system overreacts and addiction can occur in many individuals.
The First Part of the Unifying Hypothesis in Light of Evidence Presented
Evidence has been presented to support the first part of the unifying hypothesis; that is a
highly-honed, robust, recently evolved neural self-reward/motivation system for
procuring and consuming energy dense food has developed in humans to augment energy
requirements of the brain.
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The mesolimbic/corticolimbic systems may have contributed to the current energy dense
food environment, but it may have over shot its design and become dysfunctional with the
extremely refined food, allowing addiction. Many individuals lose appetite control, and
binge or consume large amounts of mainly energy dense food, to the extent that they
become centrally obese with MetS. Other individuals make behavioural adaptations such
as bulimia, which is effectively defined in the EAT-12 in Section 2.4.6.1.
This is the ‘self-addiction hypotheses939. The self-addiction hypothesis is not new. What
is new is linking it clearly to the high brain requirement for energy. Strengthening the
hypothesis is the revelation, by genomic advances in the last 5y, that the human pattern of
addiction is related to rapid, and probably current, biochemical and physical brain
evolution.
Typical of addictions is the neglect of other parts of the individual’s life; in this case the
neglect or distaste for healthy or less stimulating types of foods, and the increasing
avoidance of them. These foods are the whole-foods, replete with fibre and essential and
useful micronutrients. This depletion leads to the second part of the hypothesis. Humans
preferentially seek high-energy food whilst at the same time requiring a wider variety and
volume of many preformed micronutrients than come from current processed foodstuffs.
In summary, the human brain expanded, and with is expansion and increased energy
requirement, the mesolimbic part of the brain also developed to serve itself. Homeostatic
food consumption for energy, as seen in all animals, expanded into hedonic eating, where
intense pleasure was associated with energy dense food. However, in humans the
mesolimbic/corticolimbic system developed further and the link between pleasure in
eating energy rich food, was reinforced with a reward system linked to a very high
motivation component, allowing a degree of perseveration that in a permissive
environment becomes addiction.
Thus human encephalisation is hypothesised to be a major driver in devising technologies
and engineering social systems to supply themselves with ever-increasing amounts of
refined energy dense foods or foodstuffs. In true addictive manner, self control is limited
and there is little regard for costs to health or the environment.
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6.8
Micronutrient Deficiency in Central Obesity – Second
Part of the Unifying Hypothesis
For this section, basic and comparative physiology is reviewed with respect to normal and
abnormal energy management. Oxidative stress and metaflammation will be related to
obesity and degenerative disease. Small oxygen or nitrogen molecules, termed reactive
oxygen and nitrogen species (RONS), or free radicals will be discussed. Examples of
energy controlling and modulating pathways and the use of micronutrients in the human
diet will be explored. Micronutrient influences related to reducing RONS and possibly
increasing longevity in humans will be addressed. A mathematical model proposal is
suggested to fit to the findings.
6.8.1 Basic Principles in Energy Metabolism
Energy Reactions
Basic reactions of glycogenolyis/glycolysis and fat oxidation, citric acid and fatty acid
oxidation cycle basic energy cycles are conserved from bacteria. However, as already
noted, increasing copy number of key pathway enzymes can increase enzyme activity828.
6.8.1.1 Reactive Oxygen and Nitrogen Species
One area of control in energy-related reactions is management of extra energy that is
transferred to molecules, which may or may not be part of the reaction, but whose atoms
become very unstable. Undirected or unchannelled energy transfer from RONS, as
defined above, can damage complex structures. These free radicals have an extra
energetic electron which is easily transferred to other molecules, disrupting their structure
and ability to perform their appropriate reactions. In normal physiology most animals
have both the production of RONS, and management, well controlled.
Sometimes controls are contained in the nature of the reaction. There is some evidence
that control of RONS lengthens survival662,843,969-973. That is not proven in humans, and is
part of the unifying hypothesis. Land tortoises, very low metabolic rate animals are long
lived, and birds decrease mitochondrial RONS production, living longer than mammals of
a similar size969,974,975.
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Reactive Oxygen and Nitrogen Species in Mammals - Signalling and Repair.
In most active mammals, many reactions are by definition variable in energy use and
energy supply. Normal oxidative processes can signal energy use and repair976-978.
Mitohormetic (mito = cell replication, hormesis = small useful amounts) RONS are
important for signalling that repairs and restitution are required. Post PA, upregulation of
repair to microdamage in muscle fibres and protein reconstitution are required977. The
endothelium, the largest body organ, relies on shear stress to activate endothelial nitric
oxide production and other cytoprotective mechanisms979,980.
It is likely that the whole body’s maintenance and repair systems are transiently increased
post-exercise, which is the great benefit of PA. Of interest, high dose antioxidant
supplements may abrogate this normal and healthy effect in athletes976. Note there are
also repair adaptive responses where small amounts of damage prepare the body for larger
insults978.
High RONS fluxes occur normally post-prandially, as CHO & protein are secreted into
the portal vein blood, or lipids into the aorta via the lymph thoracic duct. They are
transported to the liver which processes the energy and other nutrients for immediate
release into the blood stream and organ use, or stores excess for delayed use. For
example, during starvation, ketone bodies acetoacetate and β-hydroxybutyrate are
synthesised for use in obligate glucose-using organs, such as the brain922. The liver also
processes micronutrients for various reactions, including antioxidant and cytoprotective
functions281,824. Hepatic detoxification of xenobiotics, which are foreign man-made or
natural or native chemicals not normally expected to be found in the body, is another
energy requiring liver function281,824. These irregular processes produce variable amounts
of RONS and if there are excess free radicals oxidative stress ensues.
Immune activation requires high level oxidative processes. It is important that infection
and injury activate immediate and longer term innate and adaptive immune responses.
This is the classic inflammatory response of redness, swelling, heat and pain.
These processes often require intense energy mobilization, as in sepsis, and much energy
for tissue repair in injury. In managing infection leukocytes, especially neutrophils as
discussed, form, and release, oxidant molecules such as hydrogen peroxide, proteolytic
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biochemicals and cytotoxins, with which to destroy microbial pathogens. The body’s
thermostat is altered to increase the body’s temperature; another energy demanding
function. When the urgent need abates, high levels of oxidation are no longer required978
and resolution is complete.
An efficient return to normal lower metabolic rates, oxidation and inflammatory
deactivation, with high levels of immune readiness, should be effected.
6.8.1.2 Oxidative Stress and Central Obesity
It is surprising that obesity, characterised by a large depot of oxidisable lipid energy, has
not been the target of clinical studies of oxidative stress until recently. Publications on
inflammation in obesity increased after CRP was associated with CVD981. Mitochondrial
oxidation and stress has recently been shown to be increased in early stage obesity982.
Macronutrients, mainly as lipid stored in adipose tissue, but also as circulating lipid and
glucose, and liver and muscle lipid and glycogen, could be expected to exert secondary
oxidative stress. These macronutrients can spontaneously form glycoxidation/lipoxidation
products with protein and other biochemicals.
In contrast, researchers of treatments for the degenerative diseases of cancer and renal
failure have looked long and hard at the antioxidants, such as genistein and
epigallocatechin gallate484,983 sometimes to use with other plant derived agents such as
tamoxifen, a taxol antioestrogen anticancer plant derived chemotherapy984. In the last
decade or so CVD has been associated with oxidative stress. Unsuccessful antioxidant
vitamin intervention trials have been discussed.
In central obesity-related MetS, oxidative stress, whether a cause or an effect, is perhaps
the most basic pathology which occurs. The following is known but not usually framed
from an oxidative stress perspective. Note that oxidative stress, in western diet fed
animals, has been shown to precede insulin resistance and obesity203,650,985. Energy
overload in obesity also releases inflammatory cytokines that stimulate mitochondrial
RONS production via ceramide formation204.
An inflammatory link has been proposed whereby in central adipose tissue sufficient
increases in angiogenesis fail, impairing oxygen supply and causing hypoxia. With an
increased rate of cellular stress and damage, programmed cell death or apoptosis
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increases327,677. Monocyte chemoattractant protein-1 (MCO-1) is elaborated by
adipocytes. Circulating monocytes enter adipose tissue, becoming macrophages, and
neutrophils are also recruited; the metaflammatory reaction is initiated (Figure 6-4).
In addition to the mitochondria, the rough endoplasmic reticulum (RER) where proteins
are assembled, and smooth ER where steroid, lipids and CHO are processed, appear to
become involved. It is likely that oxidative stress activates metaflammation by a number
of pathways986. Insulin resistance is also linked, especially in hepatocytes and inactive
muscle.
Oxidative Stress-Induced Degenerative Change in Metabolic Syndrome
The processes in chronic oxidative stress, metaflammation, degeneration and organ
failure, form a well-known degenerative pattern. As soon as damage is inflicted, blood
vessels leak serum and clotting proteins, and leukocytes from the blood invade the site.
After pathogens are killed, debris is engulfed by leukocytes. Fibrin, fine temporary
resorbable strands that allow tissue regeneration, is secreted. As the swelling recedes,
replication of local stem cells of site-appropriate cells occurs. Restitution and/or repair
should occur and the fibrin reabsorbed, resulting in minimal permanent collagen scarring.
Chronic immune activation has a prolonged and intermittent destructive phase, with
damaged cells undergoing apoptosis and/or even unplanned cell death, necro(pto)sis987,
and cytokines and myeloperoxidase are constantly active, so ongoing repair is also
intermittent or often deferred.
Attempts at repair are abnormal. Fibrin is replaced with permanent collagen fibrosis
rather than restitution with tissue structure.
The liver is a clear example of stages of fibrinosis followed by fibrosis that proceeds to
cirrhosis, with tight bands of collagen strangling islands of hepatocytes, decreasing
function. Constant inflammatory stress increases rates of cell disruption, followed by
dysplasia and cancer. Degenerative change in and under the arterial endothelium and
vessel wall smooth muscle was described in Section 1.3.3. Chronic injury sites can also
calcify, as at the arterial endothelium, or even ossify.
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Figure 6-4. Excess Lipid in Adipocytes: Endoplasmic Reticulum & Mitochondrial Stress
‘Lean’ fat cells versus ‘Fat’ fat cells. Functional small metabolically active adipocytes are master
regulators of glucose homeostasis. They have high numbers of insulin and GLUT-4 receptors on their
cell surface. Normal central adipocytes are designed to work with a modest amount of TG droplets as
an energy source to be used as required. However, when surplus TG stored in central adipocytes
surpasses their critical mass of expansion, the cells become a source of adipocytokines such as TNFα, IL-6 and MCP-1. In addition, the lipid overload initiates ER and mitochondrial stress/RO(N)S
generation. More adipocytokines are produced and activate JNK, initiating insulin resistance and
decreased glucose uptake, in adipose tissue, and via adipocytokine cross talk, liver and muscle insulin
resistance. Large, inflamed adipocytes have higher rates of apoptosis, attract macrophages which
become adipophages. The eliciting of more cytokine release perpetuates a vicious cycle of cytokine
release. GLUT-4 Glucose transporter type 4; TNF-α tissue necrosis factor-α; IL-6 interleukin-6;
MCP-1 macrophage chemoattractant protein-1 ; RO(N)S reactive oxygen (and nitrogen) species; TG
triglyceride; ER endoplasmic reticulum; ser serine; tyr tyrosine; IRS-1 insulin receptor substrate1;JNK c-Jun N-terminal kinases. Graphic from Mitta 2008 2 0 4 .
ER stress can result in misfolded and oxidised proteins811,988. The kidney is a site of
oxidised proteins such as amylin that thicken the basement membrane and prevent
filtration989. Brain tissue degeneration is also associated with mitochondrial and ER
oxidative dysfunction and amyloid plaque complexes which may form the basis of
proteotoxic damage to neurons, glia and synapse maintanence990-992.
6.8.1.3 Methods of Dealing with Reactive Oxygen and Nitrogen Species
One of the major controls of oxidative stress is the buffering of reactions by antioxidant
cofactors. These cofactor complexes temporarily react with the RONS, allowing the
energy to be dissipated through the complex. These complexes are antioxidants, although
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probably they should be called oxidation control buffers or redox modulatory
agents993,994.
Cofactors and Vitamins
Many enzymes are catalysts, which allow reactions to proceed along routes, with less
energy required at each stage than without catalysis. These enzymes are proteins, but to
complete the task a vast array of cofactors is employed. These cofactors are of many and
varied types, some being formed in vivo but many being a required part of the diet. They
may be inorganic minerals, especially metals, salts or mixes or organic protein, lipid and
CHO complexes. Cofactor bonds vary from tight covalent links, ionic bonds and a large
number that depend on the spatial orientation and subtle ionic overall charges of a cleft
with its ligand.
There are loose associations where any of a certain group of molecules provides adequate
function. These cofactors are generally not consumed by reactions. They need constant
reconstitution rather than replacement. They or their constituents are often referred to as
the micronutrients. The constituents of these cofactors that are obligatorily derived from
the diet are the vitamins.
Antioxidants and Antioxidant Vitamins
Antioxidants are an amorphous group of biochemicals, with many being synthesised by
the organism. In humans particularly, a number are required in the diet, and thus
designated antioxidant vitamins. The balance of well-known vitamins, including those
which are fat soluble, alters with central obesity as shown in Figure 6-5.
Autophagy
Ideally, it is hypothesised, antioxidants maintain cells and there is minimal wear and tear.
Autophagy is a limited cell organelle recycling scheme. Old and damaged proteins and
organelles, including mitochondria and ER, are engulfed by autophagosomes, thence
fused with lysosomes to dismantle and reuse constituents995-997. As shown in the right part
of Figure 6-6, autophagy may help prevent cells dying through apoptosis or prevent them
replicating without adequate and normal controls, thus stalling neoplastia. However, as
with so many other processes, autophagy in obesity appears faulty998,999. Autophagy is
stimulated by starvation, as many cells, including adipocytes, appear able to economise
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on disused cell organelles by recycling and reusing nutrients when food is in short
supply1000.
Figure 6-5. Antioxidant Vitamins in Central Obesity
Figure 6-5 removed due to copyright reasons.
AOX antioxidant; SOD superoxide dismutase; GPX glutathione peroxidase; CAT catalase; TAS total
antioxidant status; FRAP ferric acid reducing potential; HMW high molecular weight; ROS reactive
oxygen species. Graphic adapted from Vincent 2005 4 6 3
Telomeres and Telomerase
Some short lived somatic cells have certain cell machinery called telomeres that allow
replication before they become too damaged to function. Telomeres are repeating parts of
DNA that stabilise and anchor the tip of the chromosome to the nuclear membrane.
Telomerase is an enzyme that maintains or even elongates the telomere, but in humans
most mature somatic cells have limited amounts of telomerase.
Stem cells frequently reform glandular epithelium that has high secretary activity as in
breast, prostate and intestinal cells. If there is plenty of telomerase the cells can replicate
faithfully for many cycles. These cells may or may not have performed repeated bouts of
autophagy first but finally apoptose and are sloughed off.
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Figure 6-6. Phytochemicals and Reactive Oxygen and Nitrogen Species: Antioxidant
It is hypothesised that the best protection for long-lived cells with minimal telomerase content is
constant oxidant sensing, signalling and prevention of excess reactive oxygen and nitrogen species
(RONS) 89 5 . Phytochemicals modulate antioxidant activity, providing continuous, cytoprotection,
damage prevention and repair mechanisms. If damage does slowly accumulate a periodic form of
repair is autophagy, a regulated process for the removal of damaged proteins and organelles.
Autophagy occurs under basal conditions and is stimulated by starvation, although it is not clear if it
is just energy, or total dietary deprivation. This process is mediated via sirtuins, but appears faulty in
obesity, a condition of relative lack of phytochemicals and of excess oxidative pressure. The removal
of damaged cellular components, especially damaged mitochondria, might decrease the level of
RONS, which in turn might reduce genomic instability (cancer initiation) or forestall cellular
senescence (apoptosis). Oncogenes in general block, and tumour suppressors stimulate, the process.
Adequate types and concentrations of phytochemicals to modulate oxidative stress may be the first
cell mechanism, and autophagy is a second tier process. Such mechanisms might allow moderate
increases in autophagy to reduce the incidence of cancer and prolong lifespan. Adapted from Virgili
2007 & Finkel 2007 9 94, 9 96 .
Short, aged telomeres may represent damage which autophagy should clear, and are more
common in obesity1001. Some normal somatic cells are not expected to replicate often and
are depleted in telomerase. Other long lived cells do not have telomerase at all996.
Longevity and aging
Longevity is how long-lived an organism is, whereas aging or senescence is the process
of cells becoming altered, usually with reduced reserve and function. Organs and organ
systems have many processes to maintain function including prevention of cells becoming
stressed and malfunctioning, repair, recycling cell parts, destruction and replacement, as
previously mentioned. Each species tends to have a life expectancy related to its
members’ interdependent mass and metabolic rate, and a dynamic energy budget model
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can be a useful framework to study this1002. Empirical evidence indicates a strong
relationship between the acceleration of embryo growth rate and the acceleration of
ageing-related mortality in mammals.
More rapid embryo growth produces lower-quality adult individuals, and consequently
reduced longevity1003. Development, including development of reproductive maturity, is
not the same as physical growth. From allosteric comparisons humans should have a
potential life of 37-40y, rather than the potentially 110+ years975. It appears there are
processes in human development and maintenance that allow a potentially much longer
lifespan.
Antioxidant Protection of Long-Lived Cells –Phytochemicals
Long lived cells are expected to have a high degree of antioxidant protection and if they
are part of a long lived homeotherm with a reasonably high metabolic rate the antioxidant
protection required may need to be particularly strong. A scheme is shown for these
combined roles, with phytochemicals modulating them (Figure 6-6).
It is hypothesised in this thesis that modulation of energy metabolism by phytochemicals
has a number of results. Keeping the cells in good condition, by cytoprotective
antioxidant mechanisms defers autophagy. Apoptosis in long lived cells is prevented.
Replication is carefully controlled in high turnover cells of skin, intestine and endocrine
and exocrine glands, and neoplastic change is abrogated. Functions of phytochemicals
appear to include antioxidant effects in addition to ‘signalling’ both in the glycolysis and
lipolysis pathways.
The vitamins are part of these phytochemical groups and are indispensable to normal
metabolism, but a multitude of phytochemicals from many different classes have variable
levels of control over basic biochemical pathways. The types of modulation are often in
inhibiting energy pathways, buffering oxidants, or having antioxidant effects, and
modulating the initiation or cessation of inflammatory processes (Figure 6-6).
Unusual Metabolic Pathways in Hominoids
It is worth mentioning a couple of unusual cases of antioxidant metabolism in humans, as
it would seem that both relate to energy metabolism and balance. Energetic cost or energy
saving has not been calculated.
216
Vitamin C Metabolism
VitC, ascorbate, is a vitamin, derived from c-glucuronate and L-gulonate sugars, and is an
antioxidant. Inactivation of the enzyme catalysing the last step prior to ascorbate
formation occurred at least twice during hominoid evolution. It appears that during
evolution there was plenty of vegetation supplying VitC in the hominoid diet obviating its
synthesis. VitC can be converted into a metabolisable sugar via the pentose pathway1004,
possibly as can ascorbate catabolism, via l-erythrulose1005. ‘Inefficient’ recycling of the
ascorbate conserves this vitamin. Furthermore ascobate may replace another major
intracellular antioxidant, glutathione, in some instances, or help its maintenance. VitC is
an important antioxidant that interacts with other antioxidants. The available
metabolisable sugars may save energy by not making VitC, but whether this is
energetically significant in saving energy for the brain, would need to be modelled.
Urate Metabolism
The loss of uricase in the higher order primates results in raised levels of urate. Urate may
aid survival during water stress, and raise blood pressure1005. Urate can be pro-oxidant in
obesity, infection or dysplasia, probably augmented by the action of the enzyme xanthine
oxido-reductase (XOR). Urate has antioxidant functions and aids in restoration of VitC. It
may have specific neurobiology effects, probably on foraging behaviour in some animals,
during the seasonal cycles of more or less food availability, promoting fat storage via
insulin resistance before winter1005
6.8.1.4 Phytochemicals in Human Diets
More work is being done on secondary phytochemicals which have evolved in plants,
some of which appear to be synthesized for specific effects on herbivores, although
probably not specifically for humans1006,1007.
Most human groups have had access to varieties and species of the main large plant food
genera on eating their typical fruits, vegetables, herbs and spices, so human metabolism
has developed uses for some, or probably many, and there is evidence of synergistic
action310,312,467 in phytochemical groups. The phytonutrients aid general health, including
immune modulation where cell replication is up-regulated in infection, and cytoprotection
in cardiovascular and brain metabolism1006,1008.
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Numerous groups of phytochemicals modulate reactions in humans and many of the
reactions are where large energy fluxes occur, as mentioned previously, post-prandially,
on physical activity or with acute infection. Many have been discovered to be antiinflammatory where experimental evidence shows antimicrobial and antidysplastic
properties1009.
Phytoalexin modulation of energy pathways
There are many microconstituents in fruits and seeds. Phytoalexins are pro- or reactive
plant defence chemicals that may be either constantly present or inducible in damaged
plants1010. Useful phytochemicals in humans include a large number of phytoalexins.
Older varieties of fruit and vegetables still synthesise large amounts of phytoalexins, as
they are without human applied or engineered defences. Apple juice coarsely squeezed
from old varieties may contain 4,000 useful phytochemicals1011.
Osmotins confer salt and dehydration tolerance to, and antimicrobial protection for, a
wide range of plants. Polyphenolic compounds, terpenes and other groups such as
chitinases keep yeasts at bay, especially in ripe damaged fruit such as grapes. Should the
yeasts proceed to ferment the fruit sugars, and an alcoholic beverage form, these
phytoalexins are additional useful phytochemicals to liquer. Also, being reactive defence
chemicals, phytoalexins could be apt phytochemicals to co-opt into energy modulatory
functions.
Figure 6-7. Phytoalexins
Diversity of beneficial actions of
plant defence chemical and proteins.
From Boue 2009 1 0 1 0
These chemicals are strongly represented in health claims as nutraceuticals but are also
heavily represented as anticancer adjuvants.
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The effect of these phytochemicals on key pathways that operate in obesity and MetS will
be addressed below.
Insulin Action and Phytochemical Modulation
There are a number of phytochemicals that modulate the insulin receptor complex.
Phytochemicals of groups from green tea, hops, and specifically phyto-oestrogens
resveratrol and quercetin1012-1015, stimulate glucose uptake via glycogen synthase kinase
and phosphatidylinositol-3303,1012-1014. The brain has non-insulin dependent glucose
uptake.
Oxidative and Inflammatory Processes with Nuclear Factor kappa-Light-ChainEnhancer of Activated B Cells(NFκB)
TNF-α and RONS are released from hypoxic adipose tissue tissue, within which the
adipocytes are overstuffed with lipid986, and both stimulate NFκB, a transcription
regulator of inflammatory processes. This NFκB proinflammatory pathway is one of a
number of systems set for maximal engagement but is inhibited, in this case by the
inhibitor kappa B kinase (IKK) part of the complex or by displacement of NFκB inhibitor
(IκB).
A number of diterpenoid phytochemicals are involved in modulating the inhibitors1016.
The failure of the innate and adaptive immune system is thought to be at the heart of
cancer initiation, ‘proliferation, deregulation of apoptosis, angiogenesis, invasion and
metastasis’1017. Metabolically influenced mutations as opposed to inherited mutations are
becoming more seriously investigated1018.
Adiponectin, Phytoalexins and Energy Management
Resveratrol, a berry/ground nut produced, antifungal, to be reviewed below, appears to
up-regulate HMW production via DsbA-L, suppressing the phosphoinositide-dependent
kinase-1/serine/threonine kinases (PDK1/Akt) signalling pathway, and activating another
signalling pathway adenosine monophosphate activated protein kinase (AMPK)588.
AdipoR1 and 2 have at least one phytoalexin ligand, osmotins, a member of the large
pathogenesis-related -5 protein family758. One could hypothesise that if native Adpn were
in low concentrations, a diet plentiful in osmotins may allow activation of liver insulin
sensing PPAR pathway, and/or the muscle AMPK signalling pathway.
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Sirtuins
Sirtuins, for example silent mating type information regulation 2 homologue 1 (SIR2),
proteins1019 are a highly conserved family of nicotinamide adenine dinucleotide (NAD+)dependent energy sensing, protein deacetylases. Sirtuins have various roles as providing
critical cellular responses for maintenance of homeostatic balance in metabolism, stress
resistance, and increasing life span1020.
SIRT2, a silencing agent, functions as a cell metabolic activity sensing agent that altered
gene transcription, initiated DNA repair and recombination and increased lifespan1019. Its
function was later clarified as linking chromatin silencing to cellular reduction-oxidation
(redox) status1020,1021. SIRT6 may be a master regulator of glucose metabolism1021.
Resveratrol, Other Polyphenols and Phytochemicals – Protective Effects
The sirtuins have been of interest as they are stimulated by some phytoalexin
polyphenols, of which resveratrol, in high concentrations in grapes and wine has been
well studied. Resveratrol is reported to have anti-inflammatory, antiatherogenic,
cardioprotective effects, in addition to exhibiting antiproliferative, chemopreventive and
anticancer properties1019,1020,1022-1024.
Resveratrol extends the lifespan of rodent animal models via sirtuin activation1025. Most,
but not all, of the studies show benefits of resveratrol, although it may be the combination
with other phytochemicals, the polyphenols; trans-resveratrol, pterostilbene and
quercetin, and the polyamine; spermidine, that confer a greater advantage to health588,10251029
. They synergistically restore levels of the endogenous antioxidant reduced glutathione
(GSH)1030 and stimulate autophagy1025.
Resveratrol mimics energy restriction1021,1031. Resveratrol has been shown to reverse
MetS and increase longevity, which agrees with microarray analysis revealing that
resveratrol opposed the effects of a high energy diet in 144 out of 153 significantly altered
genes, most of which are involved in the ageing process and metabolism1032. An increase
in mitochondrial biogenesis and enhanced oxidative phosphorylation were shown by
resveratrol1019. Human pre-adipocyte differentiation was shown to be augmented by
resveratrol in in vitro studies, but was dose dependent1033.
220
Much of the discussion above indicates that resveratrol activates sirtuins by low energy
sensing. This brings up an important point of whether it is pure energy restriction
engendering improvement in mitochondrial action. Most animal studies restrict energy in
healthy laboratory animals on healthy soya based diets. In restricting energy in animals
with oxidative stress, an amyotrophic lateral sclerosis model, lifespan was
shortened662,1034. It may be proportionate total nutrition restriction that stimulates sirtuins.
This same argument applies to autophagy.
Other Phytochemical effects
Different quantities of phytochemicals and their presence in different metabolic milieu
have different effects1006-1008.
Chitin and chitosan, although from insect and crustacean exoskeletons when used in this
study, are present in fungi. It is possible that even they are having more than just a stool
bulking effect, depending on concentrations and mixtures of other phytochemicals.
There is recent work on phytochemicals having direct and indirect effects on neuronal
activities and brain process control, via various mechanisms1007.
Cancer research particularly shows phytochemicals as frequently having hormone-like,
often oestrogen/breast, but also androgen/prostate modulation effects or having cell
replication regulation effects1035-1038.
Sometimes when phytochemicals act as ligands for hormone receptors, it is the
concentration, often very small, that determines the type of action, and in fact many
phytochemicals have low bioavailability; where higher blood or body concentrations are
toxic the gut purposely controls absorption and digestion1030,1039,1040.
Genistein, a flavanoid phyto-oestrogen derived from soya stimulates oestrogen receptor-β
which activates cell nuclear machinery via NF-κB subunit 50 to stimulate antioxidant
effects, and longer life in females843. Genistein (and daidzein) via PPARs can inhibit
adipogenesis1041, and TNFα, platelet aggregation843,1042. Thus Many phytochemicals are
not well absorbed but perform functions differently at different doses1015.
Aspergillus sojae, which is used for soya sauce fermentation903, induces the soya plant to
produce glyceollins. Glyceollins have been shown to have various biological activities
221
including antifungal, antiestrogenic, antidiabetic, and anticancer activities. Glyceollins
induced phase 2 antioxidants/antitoxicants nicotinamide adenine dinucleotide phosphate
NAD(P)H:quinone oxidoreductase (NQO1) activity in a dose-dependent manner in mouse
hepatoma cells and increased the expression of some other representative phase 2
antioxidant enzymes, such as haem oxygenase (in the bilirubin pathways) 1, γglutamylcysteine synthase, and glutathione reductase, by promoting nuclear translocation
of the nuclear factor erythroid 2- related factor-2 (NRF2)903.
The introduction of the NRF2 pathway is very apposite. Thus far in the discussion, plant
phytochemicals, especially the plant defence chemicals from a number of different
classes, appear to be active in rather non-specific modulatory ways. This does not answer
the question of why so many effects appear to be antioxidant.
6.8.1.5 NRF2-Mediated Indirect and Direct Homeostatic Control System
A ubiquitous complex antioxidant activator hub denoted NRF2, already introduced, is
present and active in humans. There are 3 other NFRs but they are developmental or
tissue specific, with specific subtypes NFR1 & 3 functioning in the liver antioxidant
system. NFR2 works in conjunction with an actin binding KeaP1 and antioxidant ARE, as
mentioned (Figure 6-8).
The cell is provided with interleaving levels of inducible defence and protection from a
number of groups. The inducible aspect suggests that this is a efficient system. There are
different groups of inducers which stimulate the NRF2 action and these are direct,
indirect and bifunctional, the latter a mix of both direct and indirect (Figure 6-8). Then
there are the types or Phases 1-III of reactors that perform the appropriate cytoprotective
functions, including antioxidation (Figure 6-8).
On entry to the direct, indirect and/or bifunctional small molecules inducers cause
KeaP1/NRF2/ARE to signal appropriate transcription of many Phase1-III reactors elciting
antioxidant, antixenobiotic or antiforeign particle, detoxifying, cell protection and repair
mechanisms (Figure 6-8)1043,1044. Phase III reactions are usually those through which
conjugated xenobiotics are transported out of the cell and excreted1045.
222
Figure 6-8. Interrelations of Direct, Indirect and Bifunctional Antioxidants
Interrelations of direct, indirect and bifunctional antioxidants. Cellular protection against oxidative
stress involves two types of small-molecule antioxidants: (i) direct, redox active, are consumed
during their antioxidant functions, need to be regenerated, are short-lived, and may evoke pro-oxidant
effects; and (ii) indirect, may or may not be redox active, function through induction o f
cytoprotective proteins that act catalytically, are not consumed, have long half-lives, are unlikely to
evoke pro-oxidant effects. Some antioxidants are both direct and indirect and can be designated as
bifunctional. Some direct antioxidants are also required for the catalytic functions of cytoprotective
proteins. Many cytoprotective proteins in turn participate in the synthesis and/or regeneration of
direct antioxidants. Note most are phytochemicals. Figure from Dinkova-Kostova & Talalay 2008 2 8 1
Phase I-II general antioxidant – type reactions may or may not be interrelated. The
cytoprotective phase II enzymes are comprised of the classical conjugating enzymes;
again the familiar glutathione transferases and uridine diphosphate-glucuronosyl
transferases. Other proteins which are usually classified as antioxidant enzymes,
examples of which are haem oxygenase 1 (bilirubin pathway enzyme) and catalase, are
included in this group. Interestingly, other significantly protective, non-enzyme proteins
also act as phase II cytoprotectors and include ferritin and thioredoxin. Phase II reactions
may not be related to those of phase I (Figure 6-8).
The direct antioxidants are usually low molecular-weight compounds, such as the wellrecognised vitamins: VitC, VitE, VitK in addition to bilirubin, some of which have been
the subject of the study in this thesis, and glutathione, lipoic acid and ubiquinol1046 824.
223
Direct antioxidants undergo the classic redox reactions and scavenge RONS (Figure 6-8).
They can be consumed intravascularly during their action and, of note, can become prooxidant824.
The indirect antioxidants are highly involved in deactivating xenobiotics and toxins.
Some quinone inducers are not adequately detoxified and are associated with
carcinogenesis824,1046. There is some overlap in these reactions and processes, and those
with dual functions have been termed bifunctional antioxidants as shown in Figure 6-8.
The bifunctional antioxidants can play a dual protective role behaving both as direct
antioxidants and as indirect antioxidants through induction of cytoprotective proteins.
These tend to be Michael acceptors. The Michael reaction concept is where organic
moieties with certain carbon, oxygen and hydrogen bond configurations have electrons
which are moved into different positions to enable C-C bonds, with a base temporarily
taking a proton and returning it once the electrons are stably repositioned1009,1047.
The most notable point of the powerful bifunctional antioxidants is that they are often
phytochemicals (Figure 6-8)1045. Examples are resveratrol from grape seeds, skins and
peanuts, isothiocyanate from cruciferous vegetables, catechins from green tea, and
curcumin from turmeric. Again note that they are usually mild pro-oxidant
inducers824,1048.
This bears crucially on previous antioxidant studies and brings to the fore the problems of
interventions of high dose pure synthetic antioxidant and or vitamin therapies to prevent
degenerative disease such as atherosclerosis, diabetes and cancer. Giving high dose
vitamin therapy or phytochemical monotherapy has been shown to worsen the prooxidant conditions just mentioned. This is likely to be due to these agents being mildly
pro-oxidant, thus in large doses they have an overall oxidant effect (Figure 6-8).
The biology of using small, useful but controlled amounts of an agent, that is toxic in
large amounts, to produce signals for remedial action is the concept of hormesis and
adaptive response976-978 as previously introduced, with respect to exercise and aging.
RONS stimulate signalling to initiate restitution. Thus small molecules that are mildly
oxidant stimulate antioxidant cytoprotection via the Keap1/NRF2/ARE system.
224
Currently, the Keap1/NRF2/ARE-dependent cytoprotective system is of interest in cancer
research. The whole system protects against degenerative change and may be pivotal in
obesity MetS and CVD (Figure 6-8). Already thousands of phytochemicals have been
studied for their benefits, which are almost all for degenerative disease prevention,
treatment or amelioration. Myriads of phyto- and some zoo- chemicals are part of most
traditional whole-food diets.
Second Part of Hypothesis - High Requirement for Phytochemicals to Modulate
Multiple Antioxidant/Cytoprotective Pathways
Humans have been ingesting a wide variety of phytochemicals in food, including herbs
and spices and herbal medicaments for thousands of years. They had learnt, by and large,
what foods were poisonous, with bitter taste playing a large part. The liver, which hosts 3
NRFs, is a powerhouse of detoxifying and antioxidant activity, and the omnivorous
human diet has been well supplied with many phytochemical groups (Table 6-3).
Table 6-3. Diet Comparisons: Forager and Westernised
Diet Attribute
1) Glycaemic load
2) Fatty acid composition
3) Macronutrient composition
4) Micronutrient density
5) Acid-base balance
6) Sodium-potassium ratio
7) Fibre content
Typical Forager
(past)
Low
Low ω -6/ ω -3, 1-2:1
Lower CHO/Higher Pro/Higher fat
High
Alkaline
Low
High
Typical Westernised (current)
High
High ω-6/ ω -3, 8-10:1
High CHO/Mod Pro/High fat
Low
Acidic
High
Low
Table adapted from Cordain 2005 933
It is theorised that this ample supply of, and experience with, phytochemicals has enabled
humans to have an extremely efficient inducible antioxidant cell protection system. This
is in addition to energy and inflammatory transcription phytochemical modulatory
systems, which have allowed energy sparing for the brain and produced an added bonus
of healthy, long lived cells and overall longevity. Limiting the energy dense, addictive
foods or foodstuffs and returning to a high volume, micronutrient dense diet of low
processed whole-foods may prevent or ameliorate MetS (Table 6-3).
225
6.8.2 The Unifying Hypothesis and the Current Westernised Environment
The unifying hypothesis then is summarised; as the human brain increased in size, coadaptations arose serving, to increase energy uptake from the environment, and to
enhance energy oxidation system efficiencies associated with phytochemical antioxidant
and cytoprotective systems (Figure 6-9).
Note that the unifying hypothesis is a composite of, and subsumes or depends on, many
others, but seeks to ensure that the two parts together are necessary complements.
Firstly, the newly expanded mesolimbic system of the human brain includes a welldeveloped self-reward and motivation system, both of which ensure the drive to attain
extra energy for the brain is maximised.
Note that this hypothesis also explains why humans have moulded their environment the
way they have. Farming practices, processing and storage of energy dense food, which is
then marketed with strong cues, is a product of this self- reward/motivation system.
At the individual level the dopamine-based limbic system can malfunction in the face of
greater chronic, excesses of the energy dense food, than it evolved to manage, and
become an addictive system.
This is likely when there is minimal tempering from homeostatic, and nutrient dense,
whole-food. The second part of the hypothesis is a response to economise on the body’s
use of energy. As large quantities and varieties of plant chemical comprised a major part
of the diet, it became energy efficient to utilise these phytochemicals as antioxidant
buffers/complex cytoprotectors via the NRF2 system, to maintain long lived cells.
If there is a failure in provision of these phytochemicals, in other words humans do not
consume enough, degenerative diseases develop much earlier in life, reducing
productivity and lifespan. The development and ramifications of MetS have been
described throughout this thesis.
226
Figure 6-9. Environment and Diet - Past and Present
Factors determining food intake and energy balance in the energy restrictive micronutrient replete (or
forager and modern environments. The availability of nutrients (internal milieu) is detected by a
plethora of distributed sensors and controls food intake directly through classical hypothalamicbrainstem pathways and indirectly through modulation of food reward processes in the meso- and
corticolimbic structures. Low macronutrient availability produces very strong sensitization o f
cognitive and hedonic mechanisms enabling procurement and ingestion of energy dense food as well
as generating high reward and satisfaction. In the past plenty of micronutrients always came with the
macronutrients. This system evolved to enhance adequate energy supply, partly for the brain, in
restrictive environments requiring a high physical activity level. The modern environment and
lifestyle are characterized by high macronutrient availability, abundant food cues and high food
palatability all enhancing food intake either directly or through the same meso- and cortico-limbic
systems. There is neglect of very low micronutrient variety and volume. In addition, the built
environment, sedentary lifestyle and low procurement costs lead to decreased physical activity and, in
turn, increased macronutrient availability. Obesity develops in prone individuals who either
efficiently translate exaggerated hedonic, cognitive and/or emotional pressure exerted by the modern
environment and lifestyle into increased eating, or in individuals in whom energy repletion signals
are not able to suppress hedonic eating, or both. As obesity is rarely welcomed by any sufferer it is
likely that most obese are out of control and have an addictive pattern of food craving and eating.
There is no data on micronutrient or vitamin craving. (NB. Micronutrient craving is unlikely to be the
(main) cause of pica 10 49 ). Schematic adapted from Zheng 2009 1 0 5 0
Both parts of the hypothesis would need addressing in any treatment paradigm. Current
patterns of food production and foodstuff processing will not change unless regulation for
healthy food is enacted – addicted individuals and addicted societies need help to change.
In addition and related, but only briefly touched on, is stress hormone release, prominent
in sleep deprivation.
227
6.8.2.1 A Whole-Food Diet Prescription Derived from the Unifying
Hypothesis and Consequences
Clinical and public health obesity management plans abound in diet prescriptions. Most
well-known are the Food Pyramids1051. Recently devised and published is a therapeutic
food plan for MetS with each point portrayed with an accompanying researched
rationale1052. It has many useful features such as advice to include ample fruit and
vegetables, eat mainly whole-foods, and decrease sugar and artificial sweeteners.
However, the advocating of other concepts such as controlling portions of whole grains,
choosing low glycaemic index foods, consuming almost no animal fat and mindfulness
eating attitudes, are not proven to work reliably in the population1053.
The current unifying hypothesis diet prescription needs more study as discussed below. It
is reminiscent of a Palaeolithic diet288, but humans have adapted since those times, and
the main thrust is to eat nutrient dense, low processed foods.
Whole-foods could include some dairy along with their enhanced fermented microbe
(probiotic) nutrients. Lightly cooked, free range unprocessed meats have reduced levels of
burnt
carbon,
heterocyclic
amines,
nitrite
additives
and
other
potential
carcinogens879,1054,1055. Other reasons for including the latter two foods is that dairy is a
highly nutritious food designed to support a young mammal, and free range or non-grain
fed meat/egg again is a high quality protein source with complex fats, and both have high
mineral levels231,1056. So saying bovine, nor other ruminant dairy, is not an ideal food for a
human infant.
The current hypothesis indicates that a prudent approach may be to nearly totally exclude
grain and potato tuber derived starches and sugars in most older individuals, and all obese
individuals with MetS. Modern wholegrain and potato skin products, as discussed, are
still too highly bred as dense starch foodstuff, too depleted in micronutrients, contain ω-6
oils and are too addictive to fit into a whole-food diet for weight maintenance and CVD
protection.
This strategy would strongly elevate the micronutrient/macronutrient ratio in these
groups. Restricting high starch and sugar and other processed foods, back to a very few
‘set date’ festivities per year tends to make these energy dense foods too occasional to
228
hold strong addictive cues. However, it is important, to help with restricting these foods
by long-term antiaddiction management techniques, possibly including medication.
The most controversial aspect, and the one that needs energetics modelling as will be
introduced in the next section, is that it is expected that whole-food raw fruit and
vegetables will be unrestricted or an increased volume encouraged. High energy wholefoods are permitted. There is no need for energy counting initially, and probably never, as
whole-foods tend to restrict themselves. Abstinence of processed food is expected to get
easier with time and with experience preparing and consuming a whole-food diet1057. The
higher fruit and vegetable fibre content also introduces food energy dilution. It is
unknown whether once the body is micronutrient-replete overall cravings reduce.
The unifying hypothesis proposes that, once the centrally obese body is absorbing plenty
of food micronutrients, its metabolism will mobilise fat stores and maximise energy use
to upkeep long lived cells, so extra energy will be gainfully employed in cell repair, and
weight loss should ensue.
A further corollary is that micronutrient dense, healthy plant and animal whole-foods,
which have vigorous microbe defence systems, are less likely to transmit food borne
pathogens, decreasing chances of
immunologically healthy consumers acquiring, or
succumbing to pathogens in prepared food346.
Finally, another consequence is the change required in food production. Although there is
much resistance to change in industrial farming practices, public health concerns may
force a review of current agribusinesses and industrial food processing practices1058-1061.
6.9
Future Directions
As the unifying hypothesis is theoretical thus far, a discussion follows on:1) performing clinical whole-food studies to test (A) proof of concept by change in
metabolic, oxidative, nutritional, genetic and metabolomic laboratory markers and
anthropometric markers and indexes, and (B) acceptability, adherence and response to
antiaddiction therapies and possibly medication, and
2) Initiating plans for mathematical modelling of the energetics in human models on
different diets in differing circumstances, including obesity.
229
6.9.1 Sketch Plan for Whole-Food Diet Clinical Studies
6.9.1.1 Introduction
Obese individuals generally eat a high energy diet and may not acquire an adequate
proportion of micronutrients in their diets, and thus many obese individuals develop
MetS. Whole-food diets may supply micronutrients which had hitherto been insufficient
in their diets. Palaeolithic diets have been studied in small, uncontrolled groups288,1062,1063.
The whole-food that is proposed to be studied requires that grain and potato based foods
be excluded, but dairy, pulses and access to olive oil remain. This is a main difference
with the modern Mediterranean diet, where bread (usually whole grain), pasta and potato
is eaten. The hypotheses have been introduced.
6.9.1.2 Community Diet Study A) A 6m Randomised, Parallel, Adherence
and Health Change Study
Aims A) The aims are to conduct a randomised parallel community weight and MetS
management study in overweight and obese individuals. Participants will be randomised
to join one of 3 x 6m options: a current best practice healthy diet, a guided Mediterranean
diet or a strict low refined CHO, whole-food only diet. An antiaddiction medication RCT
may be conducted at a later date to ascertain effects on reducing addiction to energy dense
food, in each diet.
Methods A) Overweight and obese participants will be randomised to one of three diet
groups. They will visit monthly for anthropometry, bio-impedance testing, and complete
3d Food Recall records and Food Frequency, PA, QoL and readiness to change, eating
disorders and attitudes questionnaires. Baseline, 3m and 6m blood tests will include
safety CRSHaem&Biochem, metabolic, nutritional42, various new oxidative stress295 &
inflammatory296 marker, genetic, epigenetic and metabolomic analyses.
6.9.1.3 Residential Diet Study - Proof of Whole-Food Diet/ Health Concept
Aim B) To conduct a randomised 12wk residential study with totally controlled and
measured whole food or a Mediterranean ad libitum diet, with a typical ad libitum NZ
diet as 3wk ‘wash in’ period before each test diet.
230
Methods B)
Overweight and obese participants will live in the HNU for 12 weeks on the test diets for
3wk each and both diets will be preceded by 3wk of a typical NZ western diet.
Participants will be randomised to either a modern Mediterranean diet or a whole-food
diet for 3wk, and each diet will be preceded by a 3wk ‘wash in ’period, to measure
various parameters that change with different diets, and treatment regimens.
A typical NZ ad libitum westernised diet to be defined from nutrition surveys
685
will be
food such as toast and jam or cereals for breakfast, ham and cheese sandwiches for lunch,
meat and three vegetables for dinner. Participants choose from a buffet-for-one breakfast
and dinner meal times. The buffet food is measured before and the remains after
consumption. Participants take packed measured lunches and snacks to work, which they
have chosen from a buffet for that particular diet. The food will be a totally controlled and
measured ad libitum diet, although the packed lunch consumption and the returned
remains recording will be taken on trust.
Weight and waist measurements will be taken daily. Blood testing, for similar tests as for
the Adherence study, will be performed before and after the 3wk ‘wash in’ and every two
days during the test diets. Daily visual analogue scale tests will be used for food effects
such as change in hunger, and food properties of palatability and acceptability. Eating
attitudes will be assessed at baseline, after the wash in and after the 3wk. Many other
details will need addressing.
6.10 Modelling Energy Budgets with Incorporation of Food
Micronutrients for Lean and Obese Humans
6.10.1.1 Background
The human body is generally over-fat compared with other terrestrial mammals, thus
keeping a brain energy buffer. One human body morphology, usually female, easily stores
extra fat, preferentially in the lower body subcutaneous adipose tissue depot. This may be
a buffer of energy for gestation and lactation, which is now a buffer for two brains. Men
tend to only have a central adipose, short term buffer, and they have a large muscle
energy reserve.
231
To recap in terms of energy management, the human body needs to organise enough
energy and micronutrient availability for its large demanding brain, and there are
probably two, at least, unusual processes for this function.
Humans have strong neural reward systems, the mesolimbic system, for making vigorous
efforts at succeeding in acquiring energy dense food.
Humans also employ a wide range and volume of environmentally available and
recognisable food micronutrients via the NRF2 system, as an energy modulator/toxin
manager for sparing use of energy in the cell and oxidative stress control. Slow growth
and development also spared energy for the demanding brain, but required some
irreplaceable cells to be maintained and conserved for decades, which in turn allowed
longevity and cultural transmission.
However, the large brain and prehensile forelimbs also gave rise to technology, and an
early and persevering technological aim has been to increase energy-dense food
acquisition.
Unfortunately, humans appear to have limited capacity for detecting micronutrient
deficiency, which has become a problem. The current westernised diet, designed to be
energy dense, does not supply enough micronutrients for normal energy (fat) oxidation,
let alone if there is a large surplus of energy, in addition.
Thus fat accumulates over and above adipocyte capacity, and is toxic when deposited
ectopically; in the liver, heart, pancreas and many other tissues. There is a coincident
general failing of protection of large organ systems prone to damage such as the
endothelium, and poor control of replication such as in immune cells and endocrine
tissue, which allows infection and tumour growth. All are part of the continuum of
degenerative change.
Mathematical modelling of nutraceuticals and biomarkers in health and disease is
occurring, but the energy component needs integrating1064-1067.
232
6.10.1.2 Hypothesis
The initial hypothesis, to be developed and tested in future studies, is that whole-food
micronutrient action can be mathematically modelled to show both economic cell energy
use and oxidative stress management by functional elements modulating energy
employment in cell maintenance. Thus, a high micronutrient, whole-food diet,
irrespective of modest excesses in energy intake, will prevent central adipose tissue
accretion and associated MetS.
To complete the model a total ecological human dynamic energy budget framework may
need to be put in place.
6.10.1.3 Questions for review
1) What is the utility of previous energy balance equations in humans using energy
containing mass relationship to food energy input and energy expenditure on a
number of levels with or without moderators?
2) What are the population groups (over evolution, in recent human technologically
advanced environments, over developmental periods) for which it is useful to
model body composition in order to develop an understanding of possible energy
metabolic processes and storage and fat distribution morphology in humans?
3) How can the energy compartments be handled?
4) Can a basic science, ‘bottom up’ energy use paradigm be constructed along the
line of micronutrients being rate-limiting or rate altering and thus introducing
energy inefficiency?
5) Can energy fluxes be modelled in antioxidant/energy management systems with
the micronutrients factored in?
6) Is an overall mathematically modelled energy budget framework is needed?
6.10.1.4 Aim
The aim is to put forward hypotheses and ideas in order to start planning for the
construction of human specific mathematical models of energy use, in the context of a
micronutrient-replete diet, in order to work toward understanding obesity related
metabolic syndrome in humans.
233
A very brief overview of problems with previous energy balance models for weight loss
will be made, showing the necessity for a review of the energetics in obese humans.
A basic science ‘bottom up’ approach could start with examining the actual metabolic
energy reactions, electing the micronutrient-replete scenario as optimal and making an
estimate of the reduced efficiency with micronutrient insufficiency.
An existing NRF2 model will be examined.
The relevant energy compartments in the modern obese human can be compared with
other hominoids/ancestral humans in order to understand environmental effects on body
energetics with respect to obesity.
Previous Energy Balance Equations in Obese Humans
Energy balance models have been developed for various reasons. Early examples,
previously mentioned, include energy estimates to sustain manual workers for the highest
labour output and, latterly, to estimate energy deficits required to for weight loss in
obesity.
The oft quoted first law of thermodynamics of energy conservation is rightly a starting
point: Energy Balance is expected to be equivalent to Energy Input – Energy Output.
The Energy (Fat) Equilibrium = [Energy in (fat) – Energy out (PA)] x physiological
adjustments or moderators774 does not factor in other nutrients and, of course, PA is a
small proportion of energy output even in fit, active humans. Some perceived problems
with the energy balance equations in humans have been addressed frequently, with ever
more adjustments made to accommodate the many variables, as has been thought
necessary1068. However, the models have not been very successful at predicting weight
loss, possibly due to issues such as tissue mass change and energy utilisation change
being mismatched1068, let alone knowing exactly what humans under study in the
community actually do with regard to food intake or physical activity1069. In much obesity
research this basic equation is firmly established and energy alone has become the
currency of weight loss, and by (questionable) extension, health774,1068-1072.
234
However, there have been hints, indirectly noted, that other food (micro) nutrient lack1073.
or presence1074 might be having some influence on fat gain. Furthermore, even large fat
losses do not necessarily lead to reduced mortality, or MetS reversal, especially if CVD or
risk factors are well established1075.
In summary, energy balance equations have struggled to explain weight or fat loss or gain
adequately. None has factored in micronutrient effects on energy flux.
Archetypal Primate to Obese Human
It would seem to be important to develop models which include a progression of
comparisons of energy balance leading to current day obese humans, the subject of this
thesis.
To aid in this project, a progression of comparisons of an obese modern human, a normal
weight modern (including growth and development of a human; neonate to adult), to a
human forager, to same body sized hominoid and archetypal primates in order to try to
ascertain as many human specific factors as possible will be suggested (Table 6-4).
The first set was the primate series of three hominoids; a small lesser ape, a larger great
ape and a forager human. The small frugivorous primate is to approximate the last
common ancestor (Table 6-4). An extant primate, Hylobagtes lar, the gibbon was chosen
for this model. The model to approximate human size was chosen from the great ape,
Pongo pygmeus, the orang-utan (Table 6-4).
The second set is all adult Homo sapiens; the early forager human, the current normal
weight human and the obese human. The forager human is leaner, with less fat, than a
current day adult.
The last set was to show an extreme aspect of human evolution, the normal fat baby and
the normal adult and the abnormal obese adult (Table 6-4).
The modern human is presumed to inhabit the current westernised lifestyle environment,
and the foragers and other primates are presumed to be eating diets as found during
evolutionary times, or in the wild habitat.
235
Table 6-4. Energy Content Body Compartments of Female Hominoids
Female Hominoids
Homo sapiens
Pongo
pymaeus
Hylobates
lar
Orangutan
Gibbon
23.00
20.00
Age, y
Modern
Obese
43.16
Modern
Slim
31.67
Past/ Current
Forager
25.00
Modern
Infant
0.25
Height, cm
164.96
161.67
150.00
60.00
127.00
66.00
Weight, kg
124.08
59.27
50.00
5.50
50.00
5.75
BMI, kg/m
45.60
22.68
22.22
15.28
31.00
13.20
Brain Weight, kg
1.360
1.360
1.360
0.480
0.370
0.102
Brain:Body Weight
1.10%
2.29%
2.72%
8.64%
0.67%
1.77%
Brain:Body Lipid
0.129%
0.930%
1.248%
4.711%
0.538%
1.419%
Est. Fat, kg
62.04
17.25
10.00
1.21
8.25
0.86
Est. Prot., kg
11.33
9.84
8.30
0.91
8.30
0.95
Est. CHO, kg
0.47
0.28
0.29
0.01
313.63
25.34
Fat/Weight, %
50.0
29.1
20.0
22.0
15.0
15.0
Protein/Weight, %
9.1
16.6
16.6
11.0
16.6
16.6
CHO/Weight, %
0.38
0.47
0.59
0.21
0.63
0.44
2364.14
637.68
380.00
45.98
313.50
32.78
192.59
167.25
141.10
10.285
141.10
16.23
Total CHO EN, MJ
7.94
4.74
4.98
0.19
5.33
0.43
Total EN in body, MJ
2564.67
809.67
526.08
56.46
459.93
49.43
REE, Kcal/day
1827.95
1180.23
1100.00
300.00
569.00
123.00
7.66
4.95
4.61
1.26
2.38
0.52
REE, x 1.33 MJ/day
10.19
6.58
6.13
1.67
3.17
0.69
REE, x 1.5 MJ/day
11.49
7.42
6.91
1.88
3.58
0.77
REE, x 2 MJ/day
15.32
9.89
9.22
2.52
4.77
1.03
Parameter
2
1
Total Fat EN, MJ
2
Total Pro EN, MJ
2
REE, MJ/day
The REE is given in three models to correlate with three extremes of dietary intake.
carbohydrate, EN energy MJ megajoule, Prot protein, REE resting energy expenditure
1
2
stringently defined than basal metabolic rate (BMR) and usually REE=BMR +10%. x38g,
Aiello 1995, Siconolfi,1995 Stewart1998, Hladik 1999, Wang 2003, Dixon 2007, Blundell
Jildmalm 2008, Sherwood 2008, Gron 2009, Kooijman 2010, Zhang 2010, and Plank L
perscom 7 93, 914, 92 3, 10 76- 10 85
CHO
(less
x17g
2008,
2010
Energy Content Body Compartment Modelling
Body and Energy Composition Table
To extend the evolutionary enquiry in this thesis, a preliminary estimate was made, from
the literature, of the body composition of three intersecting or overlapping sets, as
236
detailed above, and in Table 6-4. The body compartments were tissue type rather than
physical sites, and the brain indexes included to show the evolutionary changes. Females
have been chosen, as the data was more available. Data were sourced from many
references, as appended to the table in the subtitle (Table 6-4). Some values are best
estimates.
The human adult brain is very large compared with the other hominoids. To underline the
extreme physiology of normal humans, data presented show that the brain of the human
infant constitutes a very large proportion of its body, and requires an ever higher
proportion of its energy. At birth the human infant brain consumes 87% of its energy even
an active 5y old directs more than 2/5ths (44%) of its energy to its brain793. For the
compartment modelling the brain lipid was added to the fat compartment, even though
this lipid cannot be mobilised.
The energy contained in an obese person’s body can be 2.5 times that in a normal weight
person. The multiples of resting energy expenditure (REE) indicate possible excess
energy intake scenarios (Table 6-4). From this point mathematical modelling could be
commenced.
Energy Content Body Compartment Mathematical Systems Modelling
This part of the model could start with a simple energy per body mass as a concentration
such that Energy (J) / Biomass (Kg) = Concentration (J/Kg). The Concentration will be
subdivided into compartments. Numerous conditions apply but 3 will be addressed so that
a simple configuration can be modelled. They are outlined as follow.
1) Energy is spread between compartments in 2 forms a) basically structural
/functional which is usually conserved and, b) more or less mobilisable as a short running
energy source or a longer term reserve.
For the current argument each compartment will just be allocated a concentration, and
later constants can be used to allow for % mobilisable
2) The form of energy is in 3 compartments: CHO, fat, and protein.
The magnitude of CHO energy is small (Table 6-4) so will be subsumed into the lipid
compartment mobilisable concentration, as shown in Figure 6-11.
3) The compartments for this model are taken as discrete or independent, although
there are numerous structural and functional molecular overlaps. Lipoproteins,
glycoproteins, lipopolysaccharides will be allotted to Concentration Pro (Figure 6-11).
237
Thus, a two-compartment energy per mass model (Figure 6-11) could be a starting point.
Figure 6-10. Two Compartment Model – Growth or Increase in Biomass
Compartment Total Body
Compartment Lipid
Change over time (y)
in
Total Body Energy (J) /
Biomass (Kg)
= Concentration TotBod(t)
=
Compartment Protein
Change over time (y) in
Lipid Energy (J)/ Biomass
(Kg)
= Concentration Lip(t)
+
Change over time (y) in
Protein Energy (J)/
Biomass (Kg)
= Concentration Pro(t)
For total body growth, where there is a limit in adulthood, a logistic growth equation
could be used where G is specific growth, C concentration, t time and dC/dt is the rate of
change of C with respect to t
G
= 1/C TotBod.dC TotBod /dt
= r C TotBod
= r (1-C TotBod /K)
where r is the specific growth rate at low concentrations (ie. not yet limited by space,
resources etc.) and 1-C/K is the logistic term1086 which when C = K, growth stops.
Change over time (y) in Lipid
Energy (J) / Biomass (Kg)
r Lip
= Concentration Lip(t)
Change over time (y) in Total
Body Energy (J) / Biomass (Kg)
= Concentration TotBod(t)
r Pro
Change over time (y) in Protein
Energy (J) / Biomass (Kg)
= Concentration Pro(t)
Thus for this model of growth
238
dC TotBod/dt
= dC Lip/dt + dC Pro/dt
= r Lip (1-C Lip /K Lip) + r Pro (1-C Pro /K Pro)
where the r’s are the specific growth rate at low concentrations and are different for each
compartment.
This equation will not be investigated further at this stage, as it is immediately apparent
that many other factors need to be taken into account, and worked on in future. Normal
growth will have different rate limiting factors from expansion of extra lipid stores.
However, it is into this r term that the effect of the micronutrients will affect the system. It
is expected that the r (+micronutrients)
Pro
will increase where by more structural and
functional protein will be synthesised for lean tissue growth and that increased functional
molecules will employ (excess) energy for maintenance and repair. The corollary will be r
(micronutrients)
Lip
will maintain adequate reserves but excess energy will transfer to r
(+micronutrients) Pro.
Modelling Macronutrient Plus Micronutrient Oxidation Energy Expenditure
It would seem that the well-known examples of enzymes in biological systems which
catalyse reactions to manipulate energy pathways, in order to control and use energy in
smaller aliquots, are accepted in energy balance equations.
So one could hypothesize that a metabolic pathway could be modelled in terms of, for
example, setting a pathway at 75% of optimal because a micronutrient, which happens to
be a cofactor in a rate-limiting reaction, is deficient, and then examine likely energy flows
as a result.
At this point, it is envisaged, that food micronutrient factors should be considered with
these other factors. Here the equations become difficult, and extra interdisciplinary input
is required.
It is likely that with the non-essential micronutrients, the system is not so clear cut. For
example, insufficiency of various similar members of a phytonutrient group which tends
to work synergistically with others in other groups that commonly ‘travel’ together, have
239
important modulatory effects depending on a number of variables, may act through being
a Michael acceptor in the NRF2 system. Variable energy fluxes speculatively giving a
range of 75-90% of optimal are more likely. Thus, the lack of consuming certain plant
groups will be estimated. In these instances many combinations may need running in
large computer models, which by the time various genetic and epigenetic influences will
also be altering the model, render ball park-estimations of food micronutrient effects on
energy stores acceptable. As shown in Table 6-4, there is a large amount of fat mass
difference between an obese human and a fit slim one.
However, various parts of the system are being researched and modelled at very basic
energy flux levels. Following is work undertaken on energy of the actual micronutrient
reactions within the above mentioned NRF2 system.
Mathematical Modelling at the Chemical Reaction Level
The antioxidant/antitoxicant NRF2 management pathways and system, previously
discussed, has been modelled. This has been performed as a multicompartment ‘engine’
and could be a first step in calculating the energy flux through the system, as depicted in
Figure 6-101048.
The NFR2 system is mainly an antioxidant signalling and amplification, as well as a
detoxifying, system. The Michael and other similar reactions in organic chemistry are
generally well studied for their energy efficiencies, and have been studied in the NRF2
system, as shown in Figure 6-101048. These reactions mainly relate to slightly higher
energy unstable molecules or complexes having their electrons rearranged and the slight
difference in energy channelled to signalling transcription of high level antioxidants.
Once the mathematical modelling parameters are put in place computer programmes can
be run to follow the energy flux of the reactions. There are computer programmes for
calculating the energy of inducers, the xenosensor-controller interaction with the
Keap1/NRF2 as they dock1048. The actions of uncoupling protein activity may change
when individuals are well fed a whole-food diet.
240
Figure 6-11. An Engineering View of the Nrf2-Mediated Redox Homeostatic Control System
The system comprises several functional modules routinely found in man-made control devices–
transducer/xenosensor, controller, actuator, and plant – which are linked in tandem through feedback
and feedforward loops. The inner feedback loop contains two pathways which are not separately
illustrated here: a fast loop through increasing the activities of redox-sensitive antioxidant enzymes,
and a slow loop through increasing the stabilization of antioxidant messenger ribonucleic acids
(mRNAs). The two dashed loops represent positive autoregulation of nuclear factor erythroid 2related factor-2 (Nrf-2) and transcription factor V-musculoaponeurotic fibrosarcoma oncogene
homolog (Maf) genes. Constitutive and other signals represent inputs to the controller other than
NRF2, such as those mediated by activator protein 1 (AP-1), nuclear factor-kappaB (NF-κB), and
other redox-sensitive signals, etc. Nuclear receptors acting as xenosensors/inducers include aryl
hydrocarbon receptor (AhR) pregnane X receptor (PXR) constitutive androstane receptor (CAR).
Actuators: glutamate cysteine ligase (GCL), glutathione peroxidase (GPx), catalase (CAT),
glutathione peroxidase (GPx) superoxide dismutase (SOD), glutathione reductase (GR), glutathione
S-transferase (GST), UDP-glucuronyl transferase (UGT). reduced glutathione (GSH) /oxidized
glutathione (GSSG), nicotinamide adenine dinucleotide phosphate:H2
(NADPH2)/nicotinamide
adenine dinucleotide phosphate (NADP) ROS reactive oxygen species, ROOH; lipid peroxides, X
reactive electrophiles. Keap1/NRF2/ARE Kelch ECH associating protein 1/nuclear factor-E2-related
factor 2-/antioxidant response element. Graphic and adapted caption from Zhang 2010 1 0 4 8 .
Energy Flow Mathematical Modelling Framework - Dynamic Energy Budget
As alluded to there are a myriad extrinsic and intrinsic factors at work in energy
management of organisms. Humans have a number of special conditions, some
evolutionary and some brought about by their technologies, but also many in common
with other organisms that need consideration. In order to keep in mind these factors, the
241
above-considered aspects of human energy management could be worked into an energy
flow mathematical modelling framework, and a Dynamic Energy Budget may be
appropriate.
Mathematical modelling has been strongly developed in evolutionary biology and
ecology and should be able to be adapted for humans, human obesity and energy
metabolic dysfunction.
The Dynamic Energy Budget1087-1089 is both an individual and community energy budget
model that looks at life forms though life stages in a real environment, taking into account
diet, diet changes, environmental toxicants and many more factors. It has recently been
modified to deal with extra body reserves. The DEB depends on setting up the
mathematical systems analysis model appropriately. It is envisaged that this model would
provide the overall framework.
As yet this dynamic energy budget is not developed for a large homeotherm within an
unusual energy use pattern in ideal nutritional environment, let alone one in an
environment which is recently extensively altered from the period of most of its unusual
evolution, even if this organism was the activator of the change.
Summary
In summary, for this section, it is hoped that in future, an idea of the magnitude of cellular
energy processing in humans who consume plentiful food micronutrients can be
estimated. It will possibly start with the above energy compartment modelling, general
energy fluxes through known living systems, combined with the NRF2 micronutrientemploying system of energy processing, all within the broad DEB framework.
Models of smaller, then larger primates, and then examples of humans in various stages
of life, all with their known habitus and morphology in their different food environments,
will be employed.
It is hoped that the above modelling, probably bringing in other factors, will shed light on
the mismanagement of energy in a micronutrient depleted, energy-overloaded diet, and
will partly explain human obesity. A healthy, nutrient-replete whole food diet may help in
242
the prevention of excess central fat and/or its loss in the already obese, somewhat
independently of the pure energy content of the food supply.
6.11 Discussion
The reasons for formulating a new unifying hypothesis arose from observations that
obesity-related MetS has become epidemic, but also that only a few researchers or
clinicians appeared to be delving deeply into the causes, perhaps as relinquishing
accepted current practices is difficult773.
The new science of obesity management was making assumptions about obesity that may
or may not take into account CVD risk scenarios. Concurrently, historical research and
treatment of TIIDM and CVD was not really addressing the relationship to the newly
increasing rates of obesity. There were assumptions based on medical traditions and
societal attitudes to obesity.
Plentiful micronutrients and a high micro- /macronutrient ratio probably contributes
very significantly to human health, in terms of healthy longevity. However, humans are
resilient and manage to survive and breed on fairly meagre overall rations. However, if
the micronutrient deficiencies are severe, less successful reproduction and offspring
survival occurs, and humans suffer stunting, infection, and life tends to be short.
However, in the current westernised environment the massive energy overload and
micronutrient undersupply may be damaging health and also reducing fertility via central
obesity – TIIDM - polycystic ovary syndrome - dysmetabolism, and male hypogonadism
in the breeding years1090.
A sensible recourse was to initiate an inquiry from an initial conditions and basic
biochemistry. Happily, there was a rich, thorough resource of human archaeology using
modern techniques, and many theories of energetics and nutritional relationships to sift
through.
There was also an exhaustive set of research data on phytochemical properties and
relationships with cancer, which was very portable to MetS as MetS is a risk for cancer,
in addition to CVD.
243
Furthermore, there was also some very advanced biochemical and ‘omics’ work linking
behaviour, and neural studies on appetite control and addiction. Thus, reflections on the
unique development of the human brain and its energetics consequences precipitated
thoughts of adaptive sequelae.
The exploration proceeded, ideas were formulated, investigation continued to establish
likelihood, and more ideas were generated until the hypothesis appeared tenable.
Research is needed to increase the likelihood that the composite unifying hypothesis, on
causes and possible amelioration of central obesity-related metabolic syndrome, is a
working paradigm. It will need multidisciplinary teams that are open to new ideas.
The first new idea is that human metabolism has important differences from that of other
closely related animals – and that increased encephalisation is responsible for much that is
interesting, but brings some energetics challenges as well.
The clinical studies may need to abandon the RCT reductionist single item testing, and
instead work in a more ecological manner, which is more accommodating of less exact
dietary pattern analysis, and that food-effect testing uses systems that can analyse
synergistic effects of micronutrients1,1065,1066. Mathematically tested models of
epidemiological data are also helping to bring new data to the table482.
Modelling of energy use systems will require mathematical, dynamic modelling, with
large arrays of metabolites – metabolomics and an understanding that micronutrients can
act like hormones in some concentrations and cytokines at others, that they can alter
between anti- and pro- oxidant and inflammatory or metaflammatory in different
situations. In addition, genomics may need to deal with the idea psychological stressors
can push epigenetic/genetic selection. In addition, epigenetic environmental responses in
the still-evolving human brain should be expected, and not be a surprise.
Whether mathematical modelling of micronutrient effects will show large or easily
perceptible effects on human energy balance, central adipose tissue accretion and largely
reverse early MetS is yet to be seen.
244
There will be problems with the unifying hypothesis proposed. Not least will be that it
challenges beliefs and treatment practices, including medical and nutritional therapy. In
an obesogenic environment the hypothesised diet will still be hard to implement in the
current environment.
The research supported the main tenets of the hypothesis, which is borne out by
evolution, to a level that further study, as suggested, is warranted.
The unifying hypothesis dietary advice is congruent enough with current therapies to
consider using in some patient groups for whom other theoretical treatments have not
worked. The dietary advice is unlikely to do harm, but is likely be helpful. Various
antiaddiction drugs are in phase III trials of obesity, and the FDA is exercising due
caution in wanting longer term studies before registering them.
245
Chapter 7. Discussion and Conclusion
7.1
Discussion
The aim of this thesis was twofold: firstly to develop a model of best practice weight loss
through which to investigate MetS related Novel CVD Risk and Protective Markers in
overweight and obese women and men and, secondly to develop a unifying hypothesis on
causes of obesity-related MetS. Both aims were fulfilled although the study subhypotheses were disproven in some cases.
An over-riding theme in this thesis is an investigation into the most profound issue
affecting or causing obesity and MetS, both risks for the most common non
communicable causes of death165.
Initially, the concept of metaflammation was hypothesised to be the main issue to study,
and in the design of the CVD Risk Markers study the analysis was corrected for acute and
chronic inflammatory disease, inflammation modifying medication. ESR and CRP were
included as CRSHaem&Biochem.
Early in the analysis it became clear that, it was the oxidant markers, particularly HbA1c
and urate which were more highly correlated on Pearson and partial r analysis, and whose
mean in the mixed model analyses was related to the MetS marker count over and above
the other explanatory CRSHaem&Biochem variables, including CRP, for example.
Moreover, the oxidant markers were highly related to most of the other MetS markers,
with HbA1c most highly related to FPG, and urate to BP. Note also that the recently
discovered antioxidant marker, bilirubin, was negatively related to BP. HbA1c and urate
were quite complementary and they are both of interest.
Firstly, glycated proteins are oxidant, and such is HbA1c. Recently, HbA1c has been
recognised as a TIIDM screening and diagnostic marker in its own right by the ADA143,
which means that it will be frequently tested and on the medical records of millions of
obese persons. There have already been studies by NHANES looking at HbA1c TIIDM
screening and prediction610,1091. Prospective tracking using HbA1c for hard endpoints such
as CVD events and degenerative related disease mortality will be very important.
246
Urate is a cellular oxidant1092,1093 and although used for gout screening, was also very
strongly related to MetS. That Kylin96 in 1923 included hyperuricaemia in the first
version of MetS gives an indication of its prominence. Serum urate levels are raised in
obesity94,101,1005,1094 but in normal weight individuals serum urate has antioxidant effects.
The oxidant XO that catalyses urate’s high level in obesity has an effective antioxidant
inhibitor, allopurinol, which significantly lowers urate but, unfortunately, it is only used
for florid gout. Part of the reluctance to acknowledge urate as a marker may be that
treating oxidative stress has not been thought possible, although it is now known that
metformin, the insulin sensitiser, has antioxidant properties1095,1096.
Although, age was the most influential factor overall in the longitudinal study, that
γGT, the liver enzyme, was borderline significantly positively related to MetS marker
count in a general mixed model over and above the other the demographic, lifestyle and
the other CRSHaem&Biochem, makes γGT
potentially the most important marker.
HbA1c and urate, which were included as other explanatory variables of the 6m study
were strongly associated with MetS. This meant that γGT was able to act as marker over
time to track MetS change as weight was lost, or in fact gained, over the 6m.
γGT was also correlated with waist, BP, TG & MetS marker count at baseline. γGT is
becoming recognised as a strong predictor of MetS and CVD and even obesity in some
studies175,380,1097-1099. The biology of γGT probably explains why it is useful marker,
already in clinical terms. As a transferase, γGT catalyses reduced glutathione, depleting
glutathione depending on high cellular native, such as homocysteine, and xenobiotic,
toxicants87. Glutathione participates in the NRF2 system. In fact one research group
suggests that persistent organic pollutants via γGT actually precipitate TIIDM87. The
current study adds that γGT’s strong positive association with MetS marker count may
well be able to be used to track patients’ progress towards better health or CVD risk.
The other liver enzymes ALT and AST are usually positively associated with benign
NAFLD and NASH, respectively, but in this study they only had modest BP, TG and
FPG relationships, with ALT also associating with mean DBP. Interestingly, in this study,
their association with MetS marker count was significantly less than γGT. ESR, the
inflammatory marker, was the other border line CRSHaem&Biochem that positively
related to MetS marker count on mixed modelling over 6m.
247
The ESR may be affected by both inflammatory and oxidant plasma markers. The
autoimmune diseases, for which ESR is classically used as a monitoring tool, are in fact
pro-oxidant states1100. The most oxidant of the leukocytes, the neutrophils, were also
associated with the overall mean (baseline to 6m) in the mixed model MetS marker count.
The above CRSHaem&Biochem are tests for a generalised multi-metabolic condition.
Different markers are useful both cross-sectionally and longitudinally, thus the
CRSHaem&Biochem should be investigated further.
Centralised anonymously pooled results of some or all of these markers from many
patients belonging to large healthcare maintenance type organisations can be matched
with future health outcomes such as TIIDM incidence, CVD events (such as proven MI)
and CVD mortality. CVD events and CVD are examples of hard events which is
ultimately what MetS is used to try to predict and prevent. This could be effected by the
method mentioned previously, and used in the NZ ‘Diabetes Get Checked’ treatment
monitoring analysis653. Using this method key markers, and their values, are tracked over
time and entered into a mixed modelling analysis, as was used in the current study.
Mathematical modelling has been employed using metabolic markers1101. Results from
local community data can be compared with individual patient changes and predictions
made for future health.
When looking at the CVD Protective markers it is notable that a different method was
used from the CRSHaem&Biochem analysis. These markers do not have a history of use
as screening markers, although there is data relating them to CVD protection. Thus the
hypothesis for their study was that they were expected to be related to known protective
characteristics or protective markers with respect to CVD in the study group.
In spite of searching for clear information of the role of Adpn is in normal physiology this
information is not known. This is important as serum Adpn, the adipocytokine
investigated, is higher in normal weight individuals, but higher still in those who are lean
with autoimmune disease, and lowest in obesity750,1102. Studies show variable situationspecific Adpn levels, complicated by the oligomers with different functions, and sexual
dimorphism430,1103. Adpn and receptors have relationships with phytonutrients has been
shown434,728, and indicated in this study, with its relationship with sβCaro and fVitE.
248
A wide array of simple Pearson correlations was performed for the laboratory tests, which
were generally normally distributed data. The baseline Adpn250 data was mostly
correlated with healthy, non inflammatory markers in women, but in this group of older
men there were a number of positive correlations, as if the age of the men and their
increased number of CVD and inflammatory diseases were associating with
proinflammatory functions of Adpn.
HMW is the fraction that is most diminished in obesity due to problems with formation in
overfull adipocytes, and therefore should recover the most in weight loss743. It is also the
most insulin sensitising and anti-inflammatory740,741,743. The HMW was the only fraction
that increased in both genders, and appeared to be an appropriate choice for change
correlations.
The Adpn30 was the small study with both oligomer and follow up data available. The 30
participants were selected post hoc as having the most extreme baseline data with respect
to weight and waist and extreme changes over the 6m. The Adpn30 change data was only
performed in the HMW30 group of 14 women and 6 men completers. The change
correlations for the HMW30 were very high and with a number of positive study
parameters associated with health. As the HMW30 increased, weight and MetS, other
negative study parameter markers decreased, in women and men.
One could speculate that normally Adpn is present in adipose tissue to respond to sudden
increased energy requirements, such as during intense PA, and acute infections, and
releases itself and large quantities of lipid particularly for muscle and liver uptake. Once
the stimulus is gone Adpn again becomes anti-inflammatory and maintains insulin
sensitivity. This function is damaged in overlarge central adipocytes in obesity and may
contribute to MetS743. HMW should perhaps be investigated as a marker first rather than
total Adpn, although it function is most altered in central obesity. Thus Adpn is not a
useful clinical marker at this stage, as it is too complex and possibly malfunctioning. A
recent study showed that Adpn did not add further information on taking other clinical
factors into account1104.
With respect to the sFSVitamins, it is important to note that lipid standardization was not
performed on purpose441. Correction of lipid levels of the sFSVitamins may need
249
reviewing or another approach taken in obese populations. In addition, the community
laboratories do not report corrected levels. It is not clear whether the raw data indicate
vitamin activity or not. In the current study the pervasive adverse associations of sVitA
and sVitE with
CVD risk may need thorough testing in an obese, dyslipidaemia population, with good
diet and supplement data, with and without lipid standardisation.
Reporting on sβCaro was straight forward in comparison to the other CVD Protective
Markers. It was clearly negatively related to MetS marker count in women and men. The
adverse markers correlations were with CVD risk, inflammatory and oxidant markers, and
TC and LDL-C. LDL-C and HDL-C are its plasma carriers470 so it was positively related
to both. Age relationships were negative, too, which were thought to be that the older
participants were returning to the fruit and vegetable diets of their youth. As a marker of
probable whole-food diets, it is the best marker to use from the study. Interestingly, this
was the only novel marker to relate to a lot of food nutrients, mostly healthy plant
derived, including fibre. However, some nutrients were typically from animal foods, such
as iron, magnesium and phosphorous714-716.
SVitD did not relate to many study parameters, but any were positive markers, such as
Adpn, sβCaro and sVitE. SVitD was negatively related to HbA1c, leptin and waist.
Interestingly sVitD levels predict weight loss success701,702. It is possible that low sVitD
in obesity is partly a storage issue and the sVitD is sequestered in the large adipose tissue
depots703, as well as a serious metabolic issue449,451,708,709. SVitD reduces chronic
infection, especially of skin for example678, although this does not seem to be a proinflammatory effect. SVitD has effects on insulin action, and may have a cytoprotective
effect in the face of chemicals associated with smoking707.
Although, in this study, sVitD related far more to issues of skin sun exposure and skin
pigment and VitD photosynthesis, as NZ does not have VitD food fortification, increasing
amounts of evidence show VitD to have profound metabolic functions. This indicates that
VitD function is likely to be disrupted by MetS. Thus, hypovitaminosis D is likely to
impede metabolic recovery from MetS. As central obesity is not healthy for bone or
muscle, it was expected that there would be more negative correlations with low PA
250
parameters696,699,700
697
, and this may have been due to inadequate PA data collection.
Overall, however, sVitD should be a useful protective marker in MetS.
Historically, high blood levels of the antioxidant fat and water soluble vitamins have been
associated with health, and have been associated with a healthy diet684,713. In the current
study the sVitA and sVitE concentrations were higher in men. Both sVitA and sVitE were
negatively correlated to weight and BMI but otherwise they appear to be CVD risk
markers, not protective. VitE, especially, negatively correlated with both TG and LDL-C.
SVitE and sVitA correlated with urate, ferritin, and liver enzymes BP, CVD, MetS
marker count and MetS. There are an increasing number of reports that indicate that
obesity, as a CVD risk marker is associated with raised levels of sVitE and sVitA717,718,
and that these sFSVitamins diabetogenic716,719.
However, it may be the fat-soaked, starchy foods and fatty meats, which have VitA and
VitE, respectively, added as antioxidants may, in turn, become pro-oxidant, which is the
problem714-716. How and whether lipid standardization should occur with the
sFSVitamins, more work is required to ascertain if they are diabetogenic, and certainly
the issues need to be resolved before they can be markers.
All of the above FSVitamins have been problematic when given as mono or multivitamin
(high dose) supplements. VitA can be toxic at high dose, and high dose VitE and βCaro
have had probable pro-oxidant effects in cancer and heart failure studies, and VitD and
calcium has increased atherosclerosis162,720-722.
This brings the closing discussion to the unifying hypothesis of causes on obesity and
MetS, and the second part of the unifying hypothesis on micronutrients; however the
initial cause of desire to consume energy dense food will be addressed first.
The first part of the hypothesis on causes of obesity and MetS was a counter- or coadaptation for increasing energy for the expanding brain. The evidence has been reviewed
showing that an expanded mesolimbic reward/motivation system in humans has
developed. It responds rapidly, by genetic and epigenetic regulation, to cues to acquire
very energy dense food. It appears to be still evolving.
However, this same human reward/motivation system has also driven humans to develop
agriculture to farm and process a hitherto unrecognised rich sources of CHO energy. The
251
industry has developed so fast and been so complete that the reward/motivation system
has become overwhelmed, and become dysfunctional in many humans. This leads to a
refined food addiction, neglect of cell- and life- preserving food micronutrients, inability
to oxidise the excess macronutrient efficiently and safely, and oxidant and
metaflammatory stress; obesity and the beginning of degenerative disease or MetS.
A reversion to a whole-food diet would seem very prudent, but a great deal of effort will
need to be made to help individuals and populations overcome their addiction to
industrialised, over-palatable refined food. Two combination medications containing,
naloxone, an opiate blocker already used for alcohol addiction, are under review by the
FDA at present, but need more clinical testing959. Phentermine, an old, short term
medication for weight loss has monoamine, and probably, some dopaminergic effects1105.
Lastly bromocriptine, a dopamine agonist is on the market in some countries as a
hypoglycaemic960. The medications, if used, would need to accompany compassionate,
thorough, up-to-date addiction counselling.
The second part of the hypothesis explains how as part of an adaptation to provide more
energy to the expanding brain, the human body evolved to make particular use of many,
mainly reactive plant defence, chemicals as very efficient, direct antioxidants and/or
inducers of cell protection. This may be via the NRF2 system. Also these phytochemicals
are often modulators of energy or inflammatory processes.
This hypothesis of phytochemical induced antioxidant long lived cytoprotection also
explains why humans can live many decades longer than other mammals of comparable
size. It has since been found that many of these phytochemicals, of which few of the
possible millions have been studied, are often mild pro-oxidants that stimulate the
cytoprotective effects.
This does not bode well for mono or multivitamin or nutraceutical therapy. It does make
sense of why a high volume diet of fruit and vegetables, and low volume of refined
energy may be a very suitable diet for humans.
This returns us to the first part of the thesis study; the RCT of a natural fibre, chitosan.
With the demise of a number of orthodox weight loss medications in the last decade or so,
natural fibre based agents may be of some use.
252
The chitosan did show a clinically mild effect on both weight and MetS markers,
including waist in the mixed model. Although this fibre needs more mechanistic safety
studies, as it is a cation, before the dose could be raised, a recent study252 showed that a
mild natural fibre, psyllium at high dose together with a high fruit and vegetable diet
resulted in weight loss and metabolic improvement.
A high fruit and vegetable diet is already advised, but in order to use an ad libitum low
processed but medium energy whole diet prescription in obese individuals it is advisable
that 1) highly controlled proof of concept and longer adherence whole-food diet studies,
with ‘omics’ analyses, are conducted and 2) studies in dynamic energy budget
mathematical modelling are undertaken.
7.2
Conclusion
Finally, in obese individuals it is worth using the IDF/JIS MetS as a screen. From the
current study it was shown that MetS in central obesity, is related to important known
metabolic, pro-oxidant and coincident metaflammation markers.
The main new findings of the study were that the greatest issue in MetS is the pro-oxidant
state, partly metabolic and partly due to the large amount of non-oxidised lipid present,
and how extensive its degenerative effects are even early in the syndrome.
Indications from the widespread and profound effects of βCaro and VitD, or conditions
for which they are markers, such as high fruit and vegetable food, appropriate sun
exposure and probably PA, are that lifestyle change to these conditions are likely to bring
remedial change.
The unifying hypothesis investigation may be plausible. It brings together some new (and
ancient) information, unique to humans, to bear on the biological and psychosocial issue
of obesity and MetS. Thus, via: powerful new technologies, genetic archaeology, and
‘omics’ studies, together with other obesity-related MetS research, the current study, and
the further energy budget mathematical modelling and the clinical whole-food diet studies
suggested, a way forward is seen.
253
Clinically, for the normal weight, overweight and obese patient who wants to regain
and/or preserve their health, screening anthropometry and MetS marker laboratory tests
should be taken, and MetS status calculated. CRSHaem&Biochem, sβCaro and sVitD
testing should be performed to fine tune CVD degenerative risk prognosis or protective
diagnosis and for monitoring.
A simple plan for whole-food consumption and management should be discussed, and the
reasons why it is important for health, given. Consideration should be given to possible
high dose fibre and antiaddiction medication prescriptions, with ongoing support and
antiaddiction counselling, as needed.
254
Appendices
Appendices
Appendix 1. Questionnaire Forms
24-Hour Dietary Recall Record
255
Appendices
24-Hour Dietary Recall Record (Cont.)
256
Appendices
Appendix 1. Questionnaire Forms.
24-Hour Dietary Recall (Cont.)
257
Appendices
258
Appendices
Appendix 1. Questionnaire Forms.
259
Appendices
Life in NZ – Physical Activity
260
Appendices
Appendix 1. Questionnaire Forms. Life in NZ - Physical Activity (Cont.)
261
Appendices
262
Appendices
263
Appendices
264
Appendices
Appendix 1. Questionnaire Forms. Short Form -36 Question Quality of Life.
265
Appendices
Appendix 1. Questionnaire Forms. Short Form -36 Question Quality of Life. (Cont.)
266
Appendices
267
Appendices
268
Appendices
269
Appendices
270
Appendices
Appendix 1. Questionnaire Forms. Obesity Specific Quality of Life.
271
Appendices
Appendix 1. Questionnaire Forms. Eating Attitudes Test – 12 Question
272
Appendices
Appendix 2. Adverse Events Form
Form Includes Serious Adverse Event Grading and Relationship to Study Treatment
Appendix 2. Adverse Event Form (Cont.)
273
Appendices
274
Appendices
Appendix 3. Evaluable Numbers and Normal Range for Laboratory Tests
Laboratory test
Haematology
Hb, g/L
RBC, x1012/L
Hct, %
MCV, fl
MCH, pg
ESR, mm/h
Platelets, x1012/L
Leukocytes, x109/L
Neutrophils, x109/L
Eosinophils, x109/L
Basophils, x109/L
Monocytes, x109/L L
Lymphocytes, x109/L /L
Biochemistry
TC, mmol/L
LDL-C, mmol/L
HDL-C, mmol/L
TC/HDL
TG, mmol/L
FPG, mmol/L
HbA1c, %
Urate, mmol/L
AlkPhos, U/L
ALT, U/L
AST, U/L
γGT, U/L
Bilirubin, µmol/L
Albumin, g/L
Globulin, g/L
CRP, mg/L
Ferritin, µg/L
VitD, nmol/L
βCaro, μmol/L
sVitA, μmol/L
VitE, μg/L
Evaluable Number N
212-236
236
236
236
236
236
236
236
236
214212
166
212
212
217-244
243
244
243
244
244
236
217
236
236
230
233
236
236
236
236
236
235
243
238
239
239
Adpn, μg/m/L
242
Normal range
w 115-154, m 130-175
w 3.9-5.2, m 4.3-6.0
w 0.36 - 0.46, m 0.40 - 0.51
w 80-99, m 82-99
27-33
w 0 – 20, m 0 - 10
150-400
w 4.1-11.7, m, 4.1-11.2
1.9–7.9
0-0.2
0-0.5
w 0.3-1.0, m 0–0.9
1.0-4.0
<5
<3
w < 1.39, m < 1.0
< 4.5
<2
< 5.4
4.0-6.0
w 0.14-0.36, m 0.20-0.42
25-130 U/L
w 0-40, m 0-45
w 0-40, m 0-45
w 0-50, m 0-60
5-20
35-45
18-34
high sensitive range, 0-3
w 20-360, m 20-562
50- 50
0.2-1.5
0.7-2.8
12-46
5-30 (unpub data, our lab, ) mean(sd), w 8.8(3.2),
m 4.3(3.5),
275
Appendices
Appendix 4. Expanded Tables
Table A. Baseline Adpn250 vs. Study Parameters Correlations: All, Women & Men
Baseline Adpn
Parameters
Demographics
Gender: Female
Age, y
European
Maori/Pacific
Summer
Lifestyle
Alcohol >2 U/d
Alcohol U/d
Reg. Alcohol Drinker
Ever Smoked
Reg. Cig. Smoker
Edu. Level <Secondary
Number Cigs/d
Anthropometry
Weight, kg
Height, m
BMI, kg/m2
Waist, cm
%Fat, %
SBP, mmHg
DBP, mmHg
Haematology
RBC, x 1012
Hb, g/L
Hct, %
MCV, fL
MCHC, g/L
RBCW %
Leukocytes, x109
Neutrophil, x109
Eosinophils, x109
Basophils, x109
Monocytes, x109
Lymphocytes, x109
Platelets, x1012
ESR, mm/hr
Biochemistry
TC, mmol/L
HDL-C, mmol/L
LDL-C, mmol/L
TG, mmol/L
TC/HDL
FPG, mmol/l
HbA1c , %
Urate, mmol/L
Ferritin, ug/L
All, N=212-243
Women, n=176-199
Men, n=39-40
Pearson r
p-value
Pearson r
p-value
Pearson r
p-value
-0.17
0.23
0.25
-0.18
0.22
0.009
<0.001
<0.001
0.005
0.001
0.28
0.24
-0.17
0.20
<0.001
0.001
0.02
0.006
0.16
0.38
-0.44
0.19
0.31
0.013
0.003
0.22
-0.04
0
-0.12
0.01
0.23
-0.06
-0.13
0.52
0.10
0.06
0.91
<0.001
0.37
0.04
-0.04
0.02
-0.16
0.00
0.23
-0.05
-0.14
0.62
0.83
0.021
0.98
0.001
0.50
0.05
0.08
0.36
-0.09
-0.03
0.24
-0.15
-0.13
0.63
0.06
0.57
0.84
0.13
0.34
0.41
-0.30
-0.32
-0.15
-0.20
0.07
0.00
0.02
< 0.001
< 0.001
0.02
0.002
0.32
0.10
0.71
-0.29
-0.28
-0.19
-0.17
-0.08
0.02
0.07
< 0.001
< 0.001
0.007
0.02
0.29
0.82
0.32
-0.14
-0.37
-0.09
-0.07
-0.02
0.20
0.09
0.38
0.01
0.55
0.63
0.90
0.20
0.56
-0.13
-0.05
0.18
0.02
0.08
-0.07
-0.16
-0.19
0.08
0.04
0.03
-0.07
-0.13
-0.13
0.05
0.45
0.006
0.79
0.20
0.31
0.02
0.007
0.24
0.61
0.67
0.32
0.05
0.04
-0.06
0.03
0.17
0.03
0.10
-0.04
-0.13
-0.15
0.06
0.07
0.10
-0.07
-0.14
-0.16
0.38
0.64
0.02
0.68
0.16
0.54
0.06
0.05
0.41
0.41
0.17
0.35
0.06
0.03
-0.04
0.15
0.38
0.00
0.13
-0.17
-0.38
-0.39
0.15
-0.07
-0.13
-0.19
-0.39
-0.26
0.82
0.34
0.01
0.10
0.44
0.29
0.01
0.02
0.41
0.73
0.44
0.27
0.01
0.11
0.23
0.40
0.13
-0.12
-0.26
-0.12
-0.11
-0.26
-0.06
<0.001
< 0.001
0.05
0.07
< 0.001
0.08
0.10
< 0.001
0.36
0.25
0.38
0.12
-0.06
-0.23
-0.12
-0.12
-0.19
0.03
<0.001
< 0.001
0.08
0.37
0.001
0.11
0.12
0.007
0.73
0.04
0.36
0.00
-0.11
-0.29
0.03
0.11
-0.32
-0.02
0.78
0.02
0.98
0.46
0.06
0.86
0.55
0.04
0.88
276
Appendices
Baseline Adpn
Parameters
Bilirubin, umol/L
AlkPhos, U/L
ALT, U/L
AST, U/L
ALT,/AST
GGT, U/L
Tot Protein, g/L
Albumin, g/L
Globulin, g/L
hsCRP, mg/L
INR(VitK)
Hormones/Cytokines
Insulin, μU/L
HOMA-IR mmol/μU/L
Adpn mg/L
Leptinμ
sFSVitamin
sVitD, ng/L
sβCaro, μg/L
sVitA, μg/L
sVitE, μg/L
INR (VitK)
Diet
Water, mL
Energy, J/d
Alcohol ml/d
CHO g/d
Fibre g/d
Starch, g/d
Tot Sugars, g/d
Tot Sucrose, g/d
Glucose, g/d
Fructose, g/d
Protein, g/d
Tot Fat, g/d
Sat Fat, g/d
Poly Fat, g/d
Cholesterol, mg/d
fVitD, μg/d
fβCaro, mg/d
fVitA, mg/d
fVitE, mg/d
Tot fVitA Equivs., mg/d
Thiamin, mg/d
VitC, mg/d
Riboflav, mg/d
Niacin, mg/d
Niacin/Tryptophan
Niacin Equivs.
VitB6, mg/d
VitB12, ug/d
All, N=212-243
Women, n=176-199
Men, n=39-40
Pearson r
p-value
Pearson r
p-value
Pearson r
p-value
0.11
0.06
-0.08
-0.07
-0.05
-0.12
-0.20
-0.06
-0.17
-0.15
-0.04
0.10
0.40
0.24
0.27
0.42
0.07
0.00
0.34
0.01
0.02
0.60
0.10
0.03
-0.09
-0.06
-0.07
-0.08
-0.19
-0.03
-0.19
-0.14
-0.12
0.19
0.69
0.25
0.38
0.35
0.28
0.009
0.68
0.009
0.06
0.11
0.13
0.26
0.18
0.03
0.21
-0.06
-0.10
0.13
-0.14
-0.32
0.13
0.42
0.09
0.27
0.85
0.13
0.71
0.51
0.43
0.37
0.04
0.43
-0.25
-0.20
<0.001
0.002
-0.26
-0.24
<0.001
0.001
-0.27
-0.22
0.08
0.18
0.04
0.50
-0.02
0.82
-0.02
0.87
0.22
0.30
0.06
0.13
-0.04
0.001
< 0.001
0.33
0.05
0.60
0.25
0.28
0.14
0.18
-0.12
<0.001
< 0.001
0.05
0.01
0.11
0.13
0.17
0
0.06
0.13
0.42
0.29
0.99
0.72
0.43
0.02
-0.03
0.07
-0.01
0.08
-0.10
0.08
0.05
0.10
0.11
-0.08
-0.03
-0.06
0.02
-0.10
0.09
0.06
-0.07
0.14
-0.04
0.01
0.02
0.00
-0.02
-0.07
-0.05
0.08
-0.07
0.73
0.69
0.28
0.87
0.23
0.12
0.21
0.46
0.13
0.10
0.19
0.60
0.35
0.78
0.10
0.17
0.34
0.28
0.03
0.51
0.93
0.81
0.96
0.71
0.25
0.45
0.25
0.30
-0.03
0
0.13
0
0.12
-0.04
0.05
0.02
0.06
0.07
-0.05
-0.02
-0.05
0.03
-0.09
0.1
0.05
-0.08
0.15
-0.06
0.02
0
0.02
0
-0.03
-0.01
0.09
-0.06
0.68
0.97
0.08
0.99
0.08
0.54
0.48
0.82
0.40
0.32
0.50
0.83
0.48
0.63
0.21
0.17
0.52
0.24
0.04
0.39
0.79
0.98
0.81
0.94
0.68
0.90
0.18
0.37
0.43
0.13
0.24
0.06
-0.12
-0.23
0.23
0.18
0.26
0.26
-0.02
0.09
0.09
0.04
-0.03
0.05
0.03
0.29
0.10
0.20
-0.07
0.03
-0.01
-0.03
-0.03
-0.02
0.13
-0.10
0.004
0.42
0.13
0.7
0.46
0.14
0.14
0.24
0.09
0.10
0.89
0.58
0.56
0.79
0.84
0.74
0.85
0.06
0.53
0.20
0.64
0.83
0.95
0.86
0.86
0.89
0.42
0.53
-
277
Appendices
Baseline Adpn
Parameters
Folate, mg/d
Sodium, mg/d
Potassium, mg/d
Magnesium, mg/d
Calcium g/d
Phosphorous mg/d
Iron, mg
Zinc, mg
Manganese, mg
Copper, mg
Selenium, mg
Metabolic Syndrome
IDF/JIS MetSMct
NCEP/ADA MetSMCt
NCEP MetSMCt
All, N=212-243
Women, n=176-199
Pearson r
p-value
Men, n=39-40
Pearson r
p-value
Pearson r
p-value
0.00
-0.04
0.08
0.08
0.07
-0.02
-0.04
-0.08
0.02
0.00
0.06
0.95
0.49
0.21
0.21
0.31
0.77
0.52
0.22
0.78
0.94
0.37
0
-0.04
0.1
0.09
0.05
0.01
0
-0.03
0.04
-0.01
0.09
0.96
0.60
0.17
0.20
0.45
0.94
0.96
0.68
0.54
0.89
0.20
-0.04
0.05
0.06
0.13
0.25
0.05
-0.11
-0.07
-0.04
0.08
-0.07
0.79
0.77
0.68
0.41
0.11
0.76
0.50
0.66
0.82
0.60
0.64
-0.25
-0.22
-0.23
<0.001
0.001
0.001
-0.25
-0.21
-0.23
0.001
0.003
0.001
-0.05
0.00
0.01
0.77
0.99
0.95
y year; U unit; d day; reg regular; Cig cigarette; Edu education; weight body weight; BMI body mass
index; waist waist circumference; %Fat body fat percentage; SBP systolic blood pressure; DBP
diastolic blood pressure; RBC red blood cell; Hb haemoglobin; Hct haematocrit; MCV mean cell
volume; MCHC mean cell haemoglobin concentration; RBCW red blood cell width; ESR erythrocytes
sedimentation rate; TC total cholesterol; HDL-C high density lipoprotein cholesterol; LDL-C low
density lipoprotein cholesterol; TG triglyceride; TC/HDL total cholesterol / high density lipoprotein;
FPG fasting plasma glucose; HbA 1 c haemoglobin A 1 c ; AlkPhos alkaline phosphatise; ALT alanine
transferase; AST aspartate transferase; γGT Gamma glutamyl transferase; tot total; hsCRP high
sensitive C-reactive protein; INR(VitK) international normalised ration (vitamin K); HOMA-IR
homeostasis model analysis-insulin resistance; Adpn adiponectin; VitD vitamin D, βCaro beta
carotene, VitA vitamin A; VitE vitamin E; CHO carbohydrate; sat saturated; poly polyunsaturated;
VitC vitamin C; riboflav riboflavin; VitB6 Vitamin B6;VitB12 vitamin B12; g gram; J joule; IDF
International Diabetes Federation 4 ; JIS Joint interim statement 5 ; MetSMCt metabolic syndrome
marker count; NCEP National Cholesterol Education Panel; ADA American Diabetes Association.
278
Appendices
Appendix 4. Table B. Change in HMW30 vs. Study Parameters, Correlations: All, Women &
Men
Change in HMW
δ Parameter
All, Change in
HMW n=13-20
Pearson r p value
Women, Change in
HMW n=9-14
Pearson
p value
r
Men, Change in
HMW n=5-6
Pearson
p value
r
Anthropometry
δ Weight, kg
δ BMI, kg/m2
-0.58
-0.59
0.007
0.006
-0.53
-0.55
0.05
0.04
-0.90
-0.91
0.02
0.01
δ Waist, cm
-0.40
0.08
-0.27
0.35
-0.85
0.03
δ %Fat, %
-0.41
0.10
-0.57
0.04
0.13
0.83
δ SBP mmHg
-0.29
0.21
-0.42
0.13
-0.05
0.93
δ DBP mmHg
-0.14
0.55
-0.09
0.75
-0.32
0.54
δ RBC, x 10 11
-0.15
0.55
-0.26
0.39
0.15
0.78
δ Hb g/L
-0.13
0.60
-0.29
0.34
0.20
0.70
δ Hct, %
-0.32
0.18
-0.53
0.06
0.13
0.81
δ MCV, fl
-0.41
0.09
-0.48
0.10
-0.48
0.33
δ MCHC. L/L
-0.03
0.89
-0.09
0.77
0.36
0.48
-0.20
0.42
-0.11
0.73
-0.86
0.03
-0.24
0.32
-0.28
0.36
-0.02
0.97
-0.18
0.46
-0.13
0.69
-0.74
0.10
0.06
0.80
-0.14
0.66
0.74
0.09
δ Basophils, x108
-0.35
0.26
-0.41
0.28
-
0.36
δ Monocytes, x108
-0.45
0.06
-0.55
0.07
-0.24
0.65
δ Lymphocytes, x108
-0.39
0.11
-0.73
0.007
0.74
0.10
δ Platelets, x1011
-0.19
0.43
-0.36
0.23
0.25
0.64
δ ESR, mm/hr
Biochemistry
δ TC, mmol/L
δ HDL-C, mmol/L
δ LDL-C, mmol/L
δ TG, mmol/L
δ TC/HDL
δ FPG, mmol/l
δ HbA1c, %
δ Urate, mmol/L
δ Ferritin, µg /L
δ Bilirubin, umol/L
δ AlkPhos U/L
δ ALT U/L
δv AST U/L
δv ALT/AST
δ γGT U/T
δ Tot Protein g/L
δ Albumin g/L
0.37
0.12
0.37
0.21
0.26
0.62
-0.13
0.23
0.06
-0.24
-0.47
-0.23
-0.58
-0.18
-0.13
0.68
0.14
0.11
-0.18
0.15
-0.26
-0.14
-0.08
0.58
0.32
0.79
0.31
0.04
0.34
0.03
0.46
0.60
0.003
0.57
0.66
0.50
0.58
0.31
0.57
0.74
-0.13
0.24
0.09
-0.24
-0.43
-0.17
-0.68
-0.20
-0.03
0.71
0.16
0.46
-0.15
0.21
-0.27
-0.18
-0.07
0.65
0.41
0.77
0.41
0.13
0.56
0.04
0.52
0.93
0.00
0.61
0.13
0.66
0.53
0.37
0.560
0.817
0.09
0.35
-0.06
-0.01
-0.76
-0.30
0.25
-0.15
-0.70
0.57
0.10
-0.04
-0.11
0.03
0.19
0.32
0.74
0.87
0.49
0.92
0.99
0.08
0.56
0.69
0.81
0.12
0.32
0.88
0.95
0.86
0.96
0.76
0.61
0.15
-0.16
-0.15
0.54
0.55
-0.34
-0.08
0.253
0.807
0.12
-0.38
0.85
0.45
-0.02
0.94
-0.06
0.84
0.39
0.61
Haematology
δ RBCW%
δ Leukocytes, x10
9
δ Neutrophil, x109
δ Eosinophils, x10
9
δ Globulin, g/L
δ hsCRP, mg/L
Hormones/Cytokines
δ Insulin
279
Appendices
Change in HMW
δ Parameter
HOMA-IR mmol/L.mIU/L
δ Leptin, pg/ml
δ Adpn30, mg/L
δ HMW30, mg/L
δ MMW30, mg/L
δ LMW30, mg/L
All, Change in
HMW n=13-20
Pearson r p value
Women, Change in
HMW n=9-14
Pearson
p value
r
-0.09
0.77
-0.45
0.11
0.91
< 0.001
Men, Change in
HMW n=5-6
Pearson
p value
r
0.40
0.56
-0.91
0.03
0.99
<0.001
-0.03
-0.47
0.92
0.92
0.04
< 0.001
0.25
0.72
0.30
<0.001
0.23
0.69
0.44
0.007
0.75
0.93
0.09
0.007
0.66
0.88
0.33
0.72
0.01
0.33
0.06
0.10
-0.03
-0.68
0.25
0.84
0.74
0.91
0.01
-0.77
-0.69
0.70
-0.54
0.45
0.08
0.13
0.12
0.27
0.37
0.51
0.52
-0.09
-0.03
0.76
0.92
-0.52
-0.80
0.30
0.06
0.14
0.30
0.33
0.16
0.16
0.66
0.06
0.27
0.41
0.66
0.41
0.65
0.58
0.17
0.84
0.84
0.50
0.92
0.71
0.67
0.10
0.45
0.42
0.23
0.29
0.47
0.08
0.34
0.68
0.71
0.82
0.36
0.14
0.57
0.08
0.25
0.25
0.04
0.44
0.27
-0.01
0.17
0.30
-0.09
0.27
0.41
0.18
0.14
-0.24
0.00
0.10
-0.20
0.02
-0.11
0.23
0.31
0.54
0.08
0.52
0.38
0.06
0.10
0.02
0.29
0.50
0.18
0.38
0.42
0.42
0.91
0.14
0.37
0.98
0.59
0.33
0.76
0.37
0.17
0.56
0.66
0.42
0.99
0.75
0.51
0.94
0.71
0.45
0.31
0.06
0.80
0.07
0.20
0.85
0.75
0.95
0.34
0.08
0.55
0.20
0.63
0.05
0.63
0.53
0.48
0.39
0.11
0.60
-0.20
-0.28
-0.52
-0.19
0.56
-0.51
-0.41
0.04
-0.16
0.34
0.14
0.05
-0.82
-0.24
0.08
-0.75
0.40
-0.49
-0.07
0.14
0.34
-0.29
-0.08
-0.18
-0.04
0.60
0.18
0.93
0.18
0.28
0.34
0.44
0.84
0.21
0.71
0.59
0.30
0.72
0.25
0.30
0.42
0.95
0.80
0.51
0.79
0.93
0.05
0.65
0.89
0.09
0.44
0.33
0.90
0.79
0.51
0.58
0.88
0.73
0.93
0.21
-
Serum FSVitamins
δ VitD, ng/L
0.10
δ sβCaro, µg/L
-0.04
δ sVitA, µg/L
0.24
δ sVitE, µg/L
-0.09
δ INR(VitK)
-0.57
Metabolic Syndrome
δ NCEP MetSMCt
-0.16
δ IDF/JIS MetSMCt
-0.15
Dietary24Hr Food Recall Nutrients
δ Energy J/day
0.35
δ Alcohol
δ Protein g/day
0.34
δ Tot Fat, g/day
0.34
δ Sat Fat, g/day
0.11
δ Mono Fat, g/day
0.44
δ Poly Fat, g/day
0.27
δ Cholesterol, g/d
0.20
δ CHO g/day
0.11
δ Fibre g/day
0.20
δ Starch, g/day
-0.11
δ Tot Sugars g/day
0.14
δ Tot Sucrose g/day
0.33
δ Glucose g/day
-0.05
δ Fructose g/day
-0.05
δ fVitD, μg/d
-0.17
δ fβCaro, μg/d
-0.03
δ fRetinol Equivs., μg/d
0.09
δ fVitE
-0.11
δ fVitA, μg/d
0.00
δ Thiamine, μg/d
-0.19
δ VitC
0.19
0.29
δ Niacin, μg/d
0.25
δ NiacinTryptophan, μg/d
0.18
0.42
δ VitB6, μg/d
0.23
δ VitB12, μg/d
0.10
δ Tot Folate mg/d
0.09
δ Sodium g/d
0.06
δ Potassium, g/dy
0.22
δ Magnesium, mg/dy
0.35
δ Calcium, mg/dy
0.14
δ Phosphorus, mg/dy
0.42
280
Appendices
Change in HMW
δ Parameter
δ Iron, mg/dy
δ Zinc, mg/dy
δ Manganese, mg/dy
δ Copper, mg/dy
δ Selenium, mg/dy
All, Change in
HMW n=13-20
Pearson r p value
-0.01
0.36
0.98
0.14
0.23
0.33
0.13
0.35
0.16
0.60
Women, Change in
HMW n=9-14
Pearson
p value
r
0.32
0.29
0.54
0.06
0.48
0.27
-0.08
0.10
0.38
0.79
Men, Change in
HMW n=5-6
Pearson
p value
r
-0.75
0.09
0.14
0.79
-0.19
0.49
0.52
0.72
0.33
0.29
weight body weight; BMI body mass index; waist waist circumference; %Fat body fat percentage;
S/DBP systolic/diastolic blood pressure; DBP diastolic blood pressure; RBC red blood cell; MCV
mean cell volume; Hb haemoglobin; Hct haematocrit; MCV mean cell volume; MCHC mean cell
haemoglobin concentration; RBCW red blood cell width; ESR erythrocytes sedimentation rate; TC
total cholesterol; HDL-C high density lipoprotein cholesterol; LDL-C low density lipoprotein
cholesterol; TG triglyceride; TC/HDL total cholesterol / high density lipoprotein; FPG fasting plasma
glucose; HbA1c haemoglobin A1c; AlkPhos alkaline phosphatise; ALT alanine transferase; AST
aspartate transferase; γGT Gamma glutamyl transferase; tot total; hsCRP high sensitive C-reactive
protein; HOMA-IR homeostasis model analysis-insulin resistance; Adpn adiponectin; HMW 3 0 high
molecular weight adiponectin substudy; MMW medium molecular weight; LMW low molecular
weight; VitD vitamin D; βCaro beta carotene; f food/dietary; s serum; VitA vitamin A; VitE vitamin
E; INR(VitK) international normalised ration (vitamin K); NCEP National Cholesterol Education
Panel; MetSMCt metabolic syndrome marker count; IDF International Diabetes Federation 4 ; JIS Joint
interim statement 5 ; sat saturated;. mono monounsaturated; poly polyunsaturated; CHO carbohydrate;
equivs equivalence;
281
Appendices
Appendix 4. Table C. Base line Fat Soluble Vitamin Correlations
sFSVitamins
Parameters
sVitD
sβCaro
sVitA
sVitE
r
p-value
r
p-value
r
p-value
r
p-value
-0.22
0.001
-0.36
< 0.001
-0.11
0.08
-0.15
0.02
-0.26
<0.001
-0.18
0.006
-0.13
0.05
-0.18
0.006
BMI, kg/m
-0.20
0.00
-0.29
< 0.001
-0.24
<0.001
-0.13
0.05
Waist, cm
Fat %,
-0.14
-0.09
0.03
0.27
-0.36
-0.22
< 0.001
0.009
0.03
-0.08
0.62
0.33
0.08
-0.08
0.21
0.30
SBP, mmHg
-0.09
0.14
-0.09
0.16
0.22
0.001
0.30
<0.001
DBP, mmHg
0.03
0.66
-0.08
0.22
0.19
0.003
0.25
<0.001
RBC, x 1012
-0.07
0.31
-0.06
0.37
0.10
0.14
0.05
0.49
Hb, g/L
0.04
0.58
-0.01
0.85
0.29
< 0.001
0.18
0.007
Hct, %
0.23
<0.001
0.02
0.79
0.28
< 0.001
0.17
0.01
MCV, fL
0.03
0.61
0.06
0.35
0.21
0.001
0.11
0.09
MCHC, L/L
0.12
0.07
0.07
0.27
0.27
< 0.001
0.15
0.02
-0.10
0.13
-0.14
0.03
-0.07
0.27
-0.04
0.52
0.00
0.10
-0.29
< 0.001
0.01
0.86
-0.10
0.12
0.01
0.91
-0.28
< 0.001
-0.03
0.68
-0.12
0.07
0.01
0.85
-0.01
0.83
0.10
0.13
0.00
0.95
0.20
0.009
-0.11
0.16
0.23
0.00
0.11
0.16
0.09
0.18
-0.08
0.23
0.09
0.17
0.08
0.26
-0.03
0.63
-0.12
0.08
0.02
0.78
-0.07
0.34
0.04
0.49
-0.09
0.16
-0.10
0.11
-0.15
0.02
0.04
0.58
0.00
0.96
-0.18
0.007
-0.09
0.18
TC, mmol/L
0.09
0.14
0.26
< 0.001
0.30
< 0.001
0.62
< 0.001
HDL-C, mmol/L
0.08
0.21
0.28
< 0.001
0.10
0.13
0.10
0.10
LDL-C, mmol/L
0.06
0.32
0.23
<0.001
0.14
0.04
0.45
< 0.001
TG, mmol/L
0.01
0.90
-0.07
0.30
0.39
< 0.001
0.50
< 0.001
TC/HDL
0.00
0.96
-0.09
0.17
0.11
0.10
0.30
< 0.001
FPG, mmol/l
-0.06
0.34
-0.18
0.008
0.04
0.54
0.00
0.95
HbA1c , %
-0.17
0.01
-0.19
0.006
0.01
0.88
0.01
0.83
Urate, mmol/L
0.02
0.79
-0.18
0.005
0.33
< 0.001
0.19
0.004
Ferritin, µg/L
0.07
0.27
-0.01
0.84
0.34
< 0.001
0.29
< 0.001
Bilirubin, umol/L
0.12
0.063
0.05
0.43
0.05
0.46
0.06
0.40
AlkPhos U/L
0.10
0.13
-0.09
0.17
-0.05
0.49
-0.05
0.49
ALT U/L
0.01
0.94
-0.07
0.31
0.21
0.002
0.11
0.11
AST U/L
-0.02
0.82
-0.01
0.89
0.13
0.05
0.11
0.08
ALT/AST
0.02
0.79
0.14
0.04
-0.17
0.01
-0.08
0.23
GGT U/T
0.00
0.95
-0.04
0.59
0.25
< 0.001
0.22
0.001
Tot Protein g/L
-0.03
0.63
-0.14
0.04
0.05
0.46
0.05
0.44
Anthropometry
Weight, kg
Height, m
2
Haematology
RBCW %
Leukocytes, x10
Neutrophil, x10
9
9
Eosinophils, x10
Basophils, x109
(n=164-166)
Monocytes, x10
9
9
Lymphocytes, x10
Platelets, x10
12
ESR, mm/hr
9
Biochemistry
282
Appendices
sFSVitamins
sVitD
sβCaro
sVitA
sVitE
Parameters
r
p-value
r
p-value
r
p-value
r
p-value
Albumin g/L
0.15
0.02
0.02
0.81
0.34
< 0.001
0.18
0.007
Globulin, g/L
-0.14
0.04
-0.15
0.02
-0.19
0.004
-0.07
0.30
hsCRP, mg/L
0.07
0.30
-0.28
< 0.001
-0.03
0.63
-0.09
0.16
IDF/JIS MetSct
-0.10
0.11
-0.23
0.001
0.16
0.01
0.24
<0.001
IDF/JIS MetS
NCEP/ADA
MetSct
-0.04
0.58
-0.16
0.01
0.19
0.004
0.22
0.001
-0.02
0.75
-0.23
0.001
0.22
0.001
0.25
<0.001
NCEP/ADA MetS
0.06
0.36
-0.18
0.006
0.24
<0.001
0.21
0.001
NCEP MetSct
0.04
0.59
-0.22
0.001
0.27
< 0.001
0.25
<0.001
NCEP MetS
0.05
0.43
-0.19
0.004
0.21
0.001
0.17
0.01
Metabolic Syndrome
Fat Soluble Vitmins
VitD, ng/L
-
β-Caro, µg/L
0.15
0.02
-
VitA, µg/L
0.33
< 0.001
0.12
0.08
-
VitE, µg/L
INR (VitK)
0.06
-0.16
0.36
0.02
0.16
0.05
0.02
0.41
0.46
0.00
0.15
0.02
0.33
< 0.001
0.06
0.36
0.12
0.08
0.16
0.02
0.46
< 0.001
0.00
0.97
< 0.001
0.94
Hormones/Cytokines
Insulin, uU/L
HOMA
mmol/uU/L
-0.09
0.15
-0.22
0.001
0.03
0.63
0.05
0.40
-0.08
0.24
-0.19
0.003
0.08
0.24
0.04
0.56
Adpn mg/L
0.22
0.001
0.30
< 0.001
0.06
0.33
0.13
0.05
Adpn250, mg/mL
0.23
0.23
0.21
0.22
-0.25
0.19
0.17
0.37
HMW30, mg/mL
0.18
0.35
0.18
0.29
-0.21
0.27
0.23
0.23
MMW30, mg/mL
0.27
0.14
0.20
0.25
-0.21
0.27
0.00
0.99
LMW30, mg/mL
0.20
0.28
0.22
0.21
-0.34
0.07
0.16
0.41
%HMW30, %
-0.01
0.97
0.14
0.43
0.12
0.53
0.34
0.07
%MMW30, %
0.06
0.74
-0.21
0.24
-0.03
0.86
-0.34
0.07
%LMW30, %
-0.10
0.60
0.08
0.66
-0.30
0.12
-0.16
0.42
Leptin, pg/ml
-0.27
< 0.001
0.07
0.31
-0.20
0.002
0.05
0.49
Food Weight, g
0.06
0.33
0.09
0.18
0.04
0.55
0.11
0.12
Water, ml/d
0.06
0.36
0.08
0.23
0.05
0.47
0.13
0.08
Energy J/d
0.04
0.55
0.06
0.32
0.01
0.91
-0.02
0.74
Alcohol ml/d
0.09
0.16
0.05
0.47
0.09
0.23
0.12
0.10
Protein g/d
-0.06
0.39
0.01
0.85
-0.05
0.48
-0.02
0.74
Tot Fat, g/d
0.03
0.60
0.01
0.83
0.01
0.91
-0.01
0.92
Sat Fat, g/d
0.04
0.51
0.01
0.88
0.03
0.68
0.00
0.96
Mono Fat g/d
0.02
0.73
0.04
0.56
-0.01
0.87
-0.02
0.75
Poly Fat, g/d
0.04
0.50
0.01
0.85
-0.01
0.87
0.00
0.10
Cholesterol, mg/d
-0.09
0.18
-0.03
0.60
0.03
0.66
0.04
0.61
CHO, g/d
0.03
0.63
0.10
0.14
-0.01
0.91
-0.09
0.22
Fibre, g/d
0.02
0.72
0.25
< 0.001
0.00
0.99
-0.04
0.58
Starch, g/d
-0.05
0.48
-0.04
0.52
-0.07
0.36
-0.15
0.04
Tot Sugars, g/d
0.09
0.17
0.20
0.002
0.05
0.53
-0.01
0.94
283
Appendices
sFSVitamins
Parameters
sVitD
sβCaro
sVitA
sVitE
r
p-value
r
p-value
r
p-value
r
p-value
Tot Sucrose, g/d
0.08
0.21
0.17
0.009
0.05
0.46
-0.02
0.77
Glucose, g/day
0.04
0.53
0.16
0.01
0.06
0.40
0.05
0.51
Fructose, g/d
0.10
0.12
0.12
0.07
0.05
0.45
0.05
0.46
fVitD, µg/day
-0.02
0.73
-0.05
0.41
-0.04
0.55
-0.10
0.15
fVitE, mg/day
0.05
0.46
0.21
0.001
0.02
0.83
0.04
0.54
βCaro, μg/day
0.04
0.50
0.30
< 0.001
0.07
0.37
-0.01
0.93
fVitA, μg /day
fRetinol Equivs.,
μg/d
-0.02
0.71
0.10
0.13
0.07
0.35
0.12
0.11
-0.01
0.91
0.20
0.003
0.08
0.28
0.11
0.12
VitC, mg/d
0.08
0.21
0.30
< 0.001
-0.06
0.38
0.09
0.23
Thiamin, μg/d
-0.04
0.56
-0.08
0.23
-0.02
0.75
-0.08
0.28
Riboflavin, mg/d
0.01
0.83
0.25
<0.001
0.01
0.89
0.05
0.51
Niacin, μg/d
Niacin/Trypto.,
μg/d
Niacin
Equivs.,
μg/d
0.07
0.30
0.12
0.06
-0.01
0.85
0.01
0.91
-0.06
0.35
0.01
0.84
-0.07
0.35
-0.04
0.54
0.02
0.82
0.10
0.15
-0.04
0.60
-0.02
0.80
VitB6, μg/d
-0.01
0.93
0.17
0.009
-0.04
0.58
0.02
0.78
VitB12, μg/d
-0.01
0.89
0.12
0.08
0.07
0.35
0.10
0.15
Folate, mg/d
0.04
0.58
0.23
<0.001
0.04
0.54
0.06
0.45
Sodium, g/d
0.05
0.42
-0.05
0.42
-0.05
0.49
-0.12
0.10
Potassium, g/d
0.08
0.24
0.32
< 0.001
-0.01
0.92
-0.05
0.46
Magnesium, mg/d
0.09
0.16
0.24
<0.001
-0.01
0.92
0.02
0.75
Calcium, g/d
0.08
0.21
0.13
0.04
-0.05
0.51
0.02
0.78
Phosphorous, mg/d
0.03
0.67
0.13
0.05
-0.04
0.54
-0.02
0.76
Iron, mg/d
0.00
0.98
0.09
0.15
-0.03
0.70
-0.03
0.66
Zinc, mg/d
-0.03
0.62
0.09
0.19
0.03
0.64
0.00
0.95
Manganese, mg/d
0.06
0.33
0.09
0.19
0.00
0.95
0.00
0.96
Copper, mg/d
0.06
0.34
0.05
0.49
0.01
0.88
0.06
0.43
Selenium, mg/d
0.07
0.27
0.07
0.30
-0.06
0.40
-0.11
0.12
weight body weight; BMI body mass index; waist waist circumference; %Fat body fat percentage;
SBP systolic blood pressure; DBP diastolic blood pressure; RBC red blood cell; Hb haemoglobin; Hct
haematocrit; MCV mean cell volume; MCHC mean cell haemoglobin concentration; RBCW red blood
cell width; ESR erythrocytes sedimentation rate; TC total cholesterol; HDL-C high density
lipoprotein cholesterol; LDL-C low density lipoprotein cholesterol; TG triglyceride; TC/HDL total
cholesterol / high density lipoprotein cholesterol; FPG fasting plasma glucose; HbA 1 c haemoglobin
A 1 c ; AlkPhos alkaline phosphatase; ALT alanine transferase; AST aspartate transferase; tot total; d
day; g grams; s serum; VitD vitamin D; VitE vitamin E; βCaro beta carotene; VitA vitamin A; equivs
equivalents; VitC vitamin C; Trypt tryptophan; VitB6 Vitamin B6; VitB12 vitamin B12.
284
Appendices
Study Parameters
δ sβCaro
All, n=145-155
Women,
125
Pearson
pPearson
r
value
r
Change in
……….sβCaro &
….sVitE
Parameter
Anthropometry
n=120-
δ sVitE
All, n=144-154
Men, n=28-30
p-value
Pearson
r
pvalue
Pearson
r
p-value
δ Weight, kg
-0.17
0.04
-0.19
0.04
0.09
0.28
0.15
0.42
δ BMI, kg/m1
-0.17
0.03
-0.19
0.04
0.09
0.25
0.18
0.33
δ Waist, cm
-0.14
0.09
-0.16
0.07
0.13
0.10
-0.15
0.44
δ %Fat, %
0.00
0.97
0.05
0.58
0.11
0.19
0.17
0.38
δ SBP mmHg
-0.11
0.20
-0.17
0.05
0.14
0.09
0.43
0.02
δ DBP mmHg
0.04
0.63
0.02
0.79
0.28
<0.001
0.56
0.001
δ RBC, x 10 11
0.07
0.41
0.10
0.27
0.18
0.03
-0.16
0.40
δ Hb, g/L
0.15
0.08
0.21
0.02
0.17
0.05
-0.24
0.21
δ Hct, %
0.12
0.15
0.17
0.06
0.12
0.16
-0.21
0.26
δ MCV, fl
0.02
0.83
0.02
0.80
-0.06
0.49
-0.24
0.21
δ MCH, pg/L
0.15
0.07
0.18
0.05
-0.02
0.79
-0.26
0.18
δ RBC W %
-0.06
0.51
-0.05
0.57
-0.03
0.74
0.24
0.22
δ Leukocytes, x109
-0.21
0.01
-0.15
0.11
-0.07
0.43
-0.05
0.80
δ Neutrophil, x10
-0.13
0.14
-0.12
0.22
-0.07
0.45
-0.07
0.74
-0.14
0.21
-0.12
0.34
-0.18
0.09
-0.24
0.37
-0.05
0.55
-0.04
0.72
0.07
0.42
0.01
0.96
-0.05
0.61
0.02
0.85
-0.22
0.01
-0.21
0.31
-0.30
0.001
-0.21
0.03
0.02
0.80
-0.03
0.87
-0.10
0.23
-0.07
0.48
-0.01
0.91
-0.05
0.80
-0.06
0.48
-0.05
0.56
-0.03
0.74
-0.10
0.61
δ TC, mmol/L
0.21
0.009
0.22
0.01
0.49
< 0.001
0.52
0.003
δ HDL-C, mmol/L
0.00
0.99
0.03
0.76
0.01
0.93
-0.23
0.23
δ LDL-C, mmol/L
0.21
0.01
0.22
0.01
0.41
< 0.001
0.23
0.24
δ TG, mmol/L
0.04
0.59
0.02
0.86
0.37
<0.001
0.67
<0.001
δ TC/HDL
0.17
0.03
0.13
0.16
0.42
<0.001
0.69
<0.001
δ FPG, mmol/l
0.05
0.53
0.06
0.54
0.08
0.35
-0.08
0.70
δ HbA1c, %
0.04
0.66
0.06
0.54
-0.01
0.96
-0.19
0.40
δ Urate, mmol/L
-0.02
0.77
-0.05
0.60
0.11
0.17
0.33
0.08
δ Ferritin, µg/L
-0.06
0.46
-0.08
0.41
0.12
0.15
0.25
0.20
δ Bilirubin, umol/L
0.15
0.08
0.13
0.15
0.14
0.10
0.06
0.77
δ AlkPhos U/L
-0.08
0.32
-0.12
0.20
0.12
0.14
0.12
0.56
δ ALT U/L
0.06
0.43
0.03
0.76
0.19
0.02
0.41
0.03
δ AST U/L
0.04
0.62
0.03
0.73
0.19
0.02
0.10
0.63
δ ALT/AST
0.09
0.31
0.06
0.52
0.08
0.32
0.40
0.04
δ γGT U/L
-0.03
0.75
-0.04
0.69
0.22
0.008
0.29
0.13
δ Tot Protein g/L
-0.14
0.08
-0.17
0.07
0.14
0.10
0.20
0.32
δ Albumin g/L
-0.11
0.20
-0.14
0.12
0.14
0.09
0.33
0.08
Haematology
9
δ Eosinophils, x10
δ Basophils, x10
9
9
δ Monocytes, x10
9
δ Lymphocytes, x10
δ Platelets, x10
12
δ ESR, mm/hr
9
Biochemistry
285
Appendices
Parameter
δ Globulin, g/L
δ sβCaro
All, n=145-155
Women,
125
Pearson
pPearson
r
value
r
-0.18
0.03
-0.18
δ hsCRP, mg/L
-0.06
0.51
-0.08
δ NCEP MetSMCt
-0.08
0.34
δ NCEP MetS
-0.01
δ IDF/JIS MetSMCt
-0.11
δ IDF MetS
-0.06
Change in
……….sβCaro &
….sVitE
δ sVitE
All, n=144-154
Men, n=28-30
n=120p-value
0.04
Pearson
r
0.11
pvalue
0.19
Pearson
r
0.01
p-value
0.98
0.38
0.04
0.63
-0.01
0.98
-0.13
0.18
0.14
0.08
0.07
0.72
0.88
-0.08
0.38
0.18
0.03
-0.04
0.84
0.18
-0.17
0.07
0.17
0.05
0.21
0.28
0.51
-0.12
0.18
0.14
0.10
0.02
0.91
0.50
0.04
0.63
0.10
0.22
0.06
0.77
0.21
0.008
0.27
0.15
<0.001
0.42
0.02
Metabolic Syndrome
δ sVitD, ng/L
0.05
δ sβ-Carot, µg/L
-
δ sVitA, µg/L
0.12
0.16
0.16
0.07
0.30
δ sVitE, µg/L
0.21
0.008
0.21
0.02
-
δ INR (VitK)
-0.04
0.66
-0.04
0.67
-0.08
0.32
0.10
0.61
δ Leptin, pg/ml
-0.06
0.43
-0.10
0.28
0.08
0.35
0.09
0.66
δ Adpn30, mg/L
-0.11
0.65
-0.04
0.90
-0.04
0.86
-0.48
0.33
δ HMW30, mg/L
-0.04
0.88
0.06
0.84
-0.09
0.72
-0.54
0.27
δ MMW30, mg/L
-0.20
0.39
-0.20
0.49
0.10
0.69
-0.23
0.66
δ LMW30, mg/L
-0.05
0.85
0.03
0.91
0.07
0.78
-0.57
0.24
δ %HMW30
-0.21
0.38
-0.16
0.58
-0.06
0.80
-0.13
0.81
δ %HMW30
-0.11
0.64
-0.24
0.40
-0.01
0.95
0.41
0.43
δ %HMW30
-0.22
0.35
-0.26
0.37
0.14
0.56
-0.15
0.78
-
-
Hormones/Cytokines
δ Water
0.05
0.52
0.06
0.54
0.06
0.46
0.20
0.30
δ Energy J/day
-0.02
0.82
0.00
0.96
0.17
0.03
0.39
0.03
δ Alcohol
0.05
0.51
0.03
0.74
-0.07
0.43
-0.05
0.80
δ Protein g/day
0.06
0.49
0.08
0.38
0.22
0.006
0.53
0.00
δ Tot Fat, g/day
-0.11
0.20
-0.12
0.20
0.15
0.06
0.42
0.02
δ Sat Fat, g/day
-0.09
0.26
-0.10
0.28
0.13
0.11
0.47
0.01
δ Mono Fat, g/day
-0.08
0.31
-0.10
0.28
0.17
0.04
0.33
0.08
δ Poly Fat, g/day
-0.11
0.20
-0.11
0.21
0.03
0.74
0.17
0.38
δ C, g/d
-0.01
0.90
0.00
0.96
0.12
0.14
0.25
0.20
δ CHO g/day
0.05
0.56
0.09
0.35
0.11
0.18
0.11
0.57
δ Fibre g/day
0.12
0.13
0.16
0.08
0.15
0.08
0.40
0.03
δ Starch, g/day
-0.04
0.67
0.00
0.97
0.08
0.35
0.16
0.40
δ Tot Sugars, g/day
0.09
0.29
0.11
0.23
0.13
0.12
-0.03
0.89
δ Tot Sucrose, g/day
0.13
0.12
0.18
0.05
0.13
0.13
-0.07
0.73
δ Glucose, g/day
0.03
0.75
-0.03
0.77
0.06
0.47
0.15
0.43
δ Fructose, g/day
0.01
0.88
-0.02
0.81
-0.03
0.67
0.12
0.52
δ fVitD μg/d
-0.16
0.05
-0.11
0.22
-0.08
0.31
-0.03
0.88
δ fVitE, mg/d
-0.01
0.86
0.03
0.76
0.00
0.97
-0.13
0.50
δ fβCaro, μg/d
0.00
0.96
-0.01
0.88
0.06
0.44
0.10
0.61
286
Appendices
Change in
……….sβCaro &
….sVitE
δ sβCaro
All, n=145-155
Women,
125
Pearson
pPearson
r
value
r
0.04
0.60
0.05
n=120-
0.61
Pearson
pr
value
0.03
0.71
δ fRetinol Equivs,
μg/d
δ Thiamine, μg/d
0.04
0.60
0.04
0.63
0.04
0.63
0.05
0.78
-0.05
0.52
-0.05
0.58
0.28
0.001
0.26
0.17
δ VitC mg/d
-0.08
0.33
-0.10
0.28
0.09
0.27
0.35
0.06
0.21
0.009
0.26
0.004
0.18
0.03
0.28
0.14
δ Niacin, μg/d
0.19
0.02
0.20
0.03
0.15
0.07
0.31
0.10
δ NiacinTrypto
Equivs., μg/d
δ Niacin Equivs. .
0.04
0.64
0.04
0.67
0.21
0.01
0.49
0.007
0.15
0.06
0.17
0.07
0.20
0.02
0.43
0.02
δ VitB6, μg/d
0.14
0.09
0.13
0.16
0.14
0.09
0.27
0.15
δ VitB12, μg/d
0.07
0.41
0.08
0.38
0.07
0.41
0.37
0.05
δ Tot Folate, mg/d
0.17
0.04
0.23
0.01
0.20
0.01
0.25
0.19
δ Sodium g/d
0.08
0.35
0.10
0.29
0.02
0.80
-0.03
0.89
δ Potassium g/d
0.08
0.34
0.09
0.33
0.19
0.00
0.50
0.006
δ Magnesium mg/d
0.10
0.24
0.11
0.24
0.16
0.06
0.48
0.008
δ Calcium g/d
0.11
0.20
0.13
0.15
0.21
0.01
0.46
0.01
δ Phosphorus, mg/d
0.09
0.29
0.14
0.14
0.23
0.005
0.49
0.007
δ Iron mg/d
0.08
0.32
0.08
0.38
0.17
0.04
0.38
0.04
δ Zinc, mg/d
0.04
0.67
0.03
0.73
0.20
0.02
0.55
0.002
δ Manganese, mg/d
δ Copper, mg/d
0.02
0.20
0.81
0.02
0.02
0.22
0.85
0.02
0.15
0.06
0.08
0.50
0.17
-0.29
0.38
0.13
δ Selenium, mg/d
-0.08
0.35
-0.06
0.48
-0.05
0.58
0.18
0.35
Parameter
δ fRetinol, μg/d
p-value
δ sVitE
All, n=144-154
Men, n=28-30
Pearson
r
0.02
p-value
0.93
δ change; weight body weight; BMI body mass index; waist waist circumference; %fat body fat
percentage; SBP systolic blood pressure; DBP diastolic blood pressure; RBC red blood cell; Hb
haemoglobin; Hct haematocrit; MCV mean cell volume; MCH mean cell haemoglobin; RBC W red
blood cell width; ESR erythrocytes sedimentation rate; TC total cholesterol; HDL-C high density
lipoprotein cholesterol; LDL low density lipoprotein-cholesterol; TG triglyceride; TC/HDL total
cholesterol / high density lipoprotein; FPG fasting plasma glucose; HbA 1 c haemoglobin A 1 c ; AlkPhos
alkaline phosphatase; U unit; ALT alanine transferase; AST aspartate; ALT/AST alanine
transferase/aspartate; γGT Gamma glutamyl transferase; tot total; hsCRP high sensitive C-reactive
protein; NCEP National Cholesterol Education Programme; MetSMct metabolic syndrome marker
count;
Mets metabolic syndrome; IDF International Diabetes Federation 8 ; JIS Joint interim
statement 3 ; s serum; VitD vitamin D; βCaro beta carotene; VitA vitamin A; VitE vitamin E;
INR(VitK) internationalised normalised ratio vitamin K; Adpn adiponectin; HMW high molecular
weight; MMW medium molecular weight; LMW low molecular weight; %HMW percentage high
molecular weight; tot total; sat saturated; mono monounsaturated; poly polyunsaturated; C
cholesterol; CHO carbohydrate; equivs equivalents; VitC vitamin C; trypto tryptophan; VitB6 vitamin
B 6; VitB12
287
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Metabolic Syndrome and Clinical Markers in Obesity

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