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Jarmo Saarelainen
Obesity and Bone
Mineral Measurements
in Postmenopausal Women
Publications of the University of Eastern Finland
Dissertations in Health Sciences
JARMO SAARELAINEN
Obesity and Bone Mineral Measurements in
Postmenopausal Women
To be presented by permission of the Faculty of Health Sciences, University of Eastern Finland for
public examination in the Auditorium MS301, Medistudia Building, University of Eastern Finland,
Kuopio, on Friday, December 19th 2014, at 12 noon
Number 250
Bone and Cartilage Research Unit, School of Medicine, University of Eastern Finland;
Department of Orthopaedics, Traumatology, and Hand Surgery, Kuopio University Hospital
Kuopio
2014
Kopijyvä OY
Kuopio, 2014
Series Editors:
Professor Veli-Matti Kosma, M.D., Ph.D.
Institute of Clinical Medicine, Pathology
Faculty of Health Sciences
Professor Hannele Turunen, Ph.D.
Department of Nursing Science
Professor Olli Gröhn, Ph.D.
A.I. Virtanen Institute for Molecular Sciences
Professor Kai Kaarniranta, M.D., Ph.D.
Institute of Clinical Medicine, Ophthalmology
Lecturer Veli-Pekka Ranta, Ph.D. (pharmacy)
School of Pharmacy
Distributor:
University of Eastern Finland
Kuopio Campus Library
P.O.Box 1627
FI-70211 Kuopio, Finland
http://www.uef.fi/kirjasto
ISBN (print): 978-952-61-1584-9
ISBN (pdf): 978-952-61-1585-6
ISSN (print): 1798-5706
ISSN (pdf): 1798-5714
ISSN-L: 1798-5706
III
Author’s address:
Bone and Cartilage Research Unit, Clinical Research Center
School of Medicine, Faculty of Health Sciences, University of Eastern Finland
P.O.Box 1627, 70211 KUOPIO
FINLAND
Supervisors:
Docent Leo Niskanen, M.D., Ph.D.
Finnish Medicines Agency Fimea, and
Institute of Clinical Medicine,
School of Medicine
KUOPIO
FINLAND
e-mail: [email protected]
Professor Heikki Kröger, M.D., Ph.D.
Department of Orthopaedics, Traumatology and
Hand Surgery
Kuopio University Hospital
KUOPIO
FINLAND
Professor Risto Honkanen, M.D., Ph.D.
Bone and Cartilage Research unit,
Clinical Research Center
Institute of Clinical Medicine,
School of Medicine
KUOPIO
FINLAND
Reviewers:
Docent Riku Kiviranta, M.D., Ph.D.
Department of Medical Biochemistry and Genetics
University of Turku
Turku
Finland
Professor Timo Jämsä, Ph.D
Department of Medical technology,
Institute of Biomedicine
University of Oulu
Oulu
Finland
IV
Opponent:
Docent Harri Sievänen, Sc.D.
The UKK Institute for Health Promotion Research
Tampere
Finland
V
Saarelainen, Jarmo
Obesity and and bone mineral measurements in postmenopausal women.
University of Eastern Finland, Faculty of Health Sciences, 2014.
Publications of the University of Eastern Finland. Dissertations in Health Sciences Number 250. 2014. 107 pp.
ISBN (print): 978-952-61-1584-9
ISBN (pdf): 978-952-61-1585-6
ISSN (print): 1798-5706
ISSN (pdf): 1798-5714
ISSN-L: 1798-5706
ABSTRACT
Measurement of bone mineral density (BMD, g/cm2) by dual-energy X-ray absorptiometry
(DXA) is the clinical golden standard technique with which to diagnose osteoporosis. In
general, overweight is considered to confer protection against from osteoporosis and
conversely underweight is considered as a major risk factor for osteoporosis and lowenergy fractures. Many biological mechanisms have been proposed to explain this positive
relationship between adiposity and bone tissue. However, the strong connection between
DXA-measured BMD and anthropometric parameters has been called into some question
due to the systematic errors inherent in planar DXA technology.
The present thesis evaluated the effects of adiposity on DXA and quantitative ultrasound
(QUS) measurements in postmenopausal women. This study population consisted of
stratified samples from both the Kuopio Osteoporosis Risk Factor and Prevention study
(OSTPRE) and its Fracture Prevention substudy (OSTPRE-FPS) study cohorts. Eighty-nine
women were measured twice with two different DXA devices in a cross-calibration study.
In addition 139 women were measured cross-sectionally with QUS, peripheral DXA
(pDXA) and central DXA devices. The effect of central obesity on BMD values was studied
on a sample of 198 women. The association between progress of bone loss and obesity was
studied in a sample of 300 women, who underwent BMD and body mass index (BMI)
measurements three times during a mean follow-up of 10.5 years.
There were systematic differences between the measurement values of two pencil-beam
DXA densitometers (DPX and DPX-IQ) from the same manufacturer, although their
correlation was high. The DPX measured BMD values were higher for lumbar spine and
Ward’s triangle, whereas femoral neck BMD results were lower compared to DPX-IQ. In all
regions of interests (ROI), the regression slope was significantly different from unity, thus
demonstrating the need for cross-calibration between the two densitometers. With most
ROI’s, except for lumbar spine, the performance of the two devices was also found to
depend on BMI or weight, thus affecting correction equations during cross-calibration.
A positive trend, although not always statistically significant, was found between the
body size (height) and most of the measured QUS and DXA bone parameters. DXA, pDXA
and QUS parameters were differently affected by adiposity. Thus, higher heel and lumbar
spine BMD values were observed with increasing adiposity, whereas femoral neck BMD
values and some of the QUS parameters were less affected by adiposity. There were
inconsistencies between the study population-based normalized values (z-scores) and the
manufacturer provided T-score values between the different devices.
DXA measured trunk fat and weight were the strongest determinants of postmenopausal
lumbar spine and femoral neck BMD, respectively. DXA-measured trunk fat was positively
VI
associated with spinal BMD, but not with femoral neck BMD. This positive association was
also present regardless of the use of hormone therapy (HT).
Obesity was associated with a higher BMD and it may delay the incidence of osteopenia
considerably. On the average, women with a BMI of 20 kg/m2 became osteopenic at the
spine 2 years and at the femoral neck 4 years after menopause, whereas women with a BMI
of 30 kg/m2 only became osteopenic approximately 5 (spine) and 9 (femoral neck) years
later.
The association between adiposity and DXA measurement values found here may be
explained by both biological and methodological factors. Adiposity may affect the crosscalibration results of DXA densitometers. Adiposity and different normative values may
explain at least in part the discrepancies between central DXA, pDXA and QUS
measurements. Trunk fat may be positively associated with spinal BMD values. Obesity
delayed the occurrence of osteopenia in postmenopausal women.
National Library of Medicine Classification: WB 286, WE 250, WE 200, WN 180, WP 580, QU 100
Medical Subject Headings: Absorptiometry, Photon; Adiposity; Body Mass Index; Body Weight; Bone
Density; Calibration; Female; Femur Neck; Heel; Hormone Replacement Therapy; Humans; Incidence;
Menopause; Obesity; Obesity, Abdominal; Osteoporosis; Overweight; Risk Factors; Thinness; Questionnaires;
Epidemiologic Studies; Follow-Up Studies; Cross-Sectional Studies
VII
Saarelainen Jarmo
Lihavuus ja luuntiheysmittaukset naisilla vaihdevuosien jälkeen.
Itä-Suomen yliopisto, terveystieteiden tiedekunta, 2014.
Publications of the University of Eastern Finland. Dissertations in Health Sciences Numero 250. 2014. 107 s.
ISBN (print): 978-952-61-1584-9
ISBN (pdf): 978-952-61-1585-6
ISSN (print): 1798-5706
ISSN (pdf): 1798-5714
ISSN-L: 1798-5706
TIIVISTELMÄ
Kaksienerginen röntgenabsorptiometria (DXA) on luun mineraalitiheyden (BMD)
mittaamisen kultainen standardi. Ylipaino suojelee yleensä luukadolta, kun taas alipaino on
merkittävä luukadon ja matalaenergisten murtumien riskitekijä. Monet biologiset tekijät
selittävät rasvakudoksen ja luun positiivista yhteyttä. Kuitenkin DXA-tekniikalla mitattujen
BMD-arvojen ja antropometristen muuttujien välinen vahva yhteys on osittain
kyseenalaistettu DXA-tekniikkaan luontaisesti liittyvien systemaattisten virheiden takia.
Tässä väitöskirjassa tutkittiin rasvakudoksen vaikutusta DXA ja kantaluun QUSultraäänimittauksiin naisilla vaihdevuosien jälkeen. Tutkimusaineisto pohjautuu Kuopion
Osteoporoosin vaaratekijät ja ehkäisy -tutkimuksen (OSTPRE) sekä sen alaisen
murtumanestotutkimuksen (OSTPRE-FPS) kohortteihin. 89 naiselta mitattiin luuntiheydet
kahdella eri luuntiheysmittarilla ristiinkalibrointitutkimuksessa. 139:ltä naiselta mitattiin
poikkileikkaustutkimuksessa luuntiheydet sekä kantaluusta ultraääni- ja perifeerisellä
DXA-tekniikalla että lannerangasta ja lonkasta DXA-menetelmällä. Keskivartalolihavuuden
vaikutusta luuntiheysarvoihin tutkittiin 198 naisen otoksella. Keskimäärin 10,5 vuoden
seuranta-aikana mitattiin luuntiheys, pituus ja paino kolmesti 300:n naisen otoksesta,
tarkoituksena tutkia luuntiheyden menetysnopeutta eri painoisilla naisilla.
Kahden kynäkeilatekniikkaa hyödyntävän DXA-laitteen (DPX ja DPX-IQ)
mittaustuloksissa havaittiin systemaattisia eroja, vaikka näiden saman valmistajan laitteilla
saatujen mittaustulosten välinen korrelaatio olikin korkea. Verrattuna DPX-IQ laitteeseen,
DPX-laitteen mittaamat arvot olivat korkeampia lannerangan ja Ward’n kolmion ja
matalampia reisiluunkaulan luuntiheyksien osalta. Kaikissa mittauskohteissa laitteiden
ristiinkalibrointi on välttämätöntä, koska regressiokäyrät poikkesivat tilastollisesti
merkittävästi toisistaan. Lähes kaikissa mittauskohdissa, paitsi lannerangassa, painon tai
painoindeksin vaikutus piti huomioida ristiinkalibroinnissa ja korjauskertoimia
laskettaessa.
Positiivinen yhteys havaittiin kehon koon (pituus) ja lähes kaikkien QUS- ja DXAparametrien välillä, vaikka positiivinen yhteys ei ollutkaan aina tilastollisesti merkitsevä.
Ainoastaan kantaluumittauksien äänennopeusarvoihin (SOS) tai lannerangan BMDarvoihin kehon koko ei vaikuttanut. Rasvakudos vaikutti eri tavoin sekä kantaluun
ultraääni (QUS) ja perifeeriseen DXA-mittaukseen (pDXA) että lannerangan ja lonkan DXA
mittaustuloksiin. Havaitsimme korkeampia luuntiheysarvoja etenkin kantaluussa ja
lannerangassa ylipainoisilla naisilla. Reisiluunkaulan luuntiheysarvot ja osa kantaluun
ultraäänimittausten tuloksista oli sen sijaan vähemmän riippuvaisia rasvakudoksen
määrästä. Myös eri laitteiden välillä havaittiin epäsuhta tutkimusväestöstä saatujen
normalisoitujen (z-score) ja laitevalmistajan (T-score) viitearvojen välillä.
VIII
DXA-laitteella mitattu ylävartalon rasvakudos selitti parhaiten lannerangan luuntiheyttä
ja paino parhaiten reisiluunkaulan luuntiheyttä naisilla vaihdevuosien jälkeen. DXAlaitteella mitatun ylävartalon rasvakudoksen ja lannerangan luuntiheyden välillä oli
merkittävä positiivinen yhteys, mutta keskivartalolihavuus ei vaikuttanut reisiluunkaulan
luuntiheysarvoihin. Positiivinen yhteys DXA-laitteella mitatun keskivartalolihavuuden ja
lannerangan luuntiheyden välillä säilyi vaihdevuosien hormonikorvaushoidon (HT)
käytöstä riippumatta.
Ylipainoisilla ja lihavilla naisilla havaittiin korkeampi luuntiheys vaihdevuosien jälkeen
kuin ali- ja normaalipainoisilla. Lihavuus hidasti osteopenian ilmaantumista vaihdevuosien
jälkeen: normaalipainoisten (painoindeksi, BMI=20 kg/m2) naisten luuntiheysarvot
heikentyivät keskimäärin osteopeeniselle tasolle lannerangassa 2 vuotta ja
reisiluunkaulassa 4 vuotta vaihdevuosien jälkeen, kun taas lihavien naisten (BMI=30 kg/m 2)
luuntiheyden heikkeneminen osteopeeniselle tasolle viivästyi keskimäärin 5 vuotta
lannerangassa ja 9 vuotta lonkassa.
Sekä biologiset että metodologiset syyt selittivät rasvakudoksen ja DXA-mittausten
yhteyttä. Rasvakudos voi vaikuttaa DXA-laitteiden ristiinkalibrointiin. Rasvakudos ja
erilaisista väestöistä saadut viitearvot selittävät osittain eroavaisuuksia kantaluun DXAmittausten, sentraalisten DXA-mittausten ja kantaluun ultraäänimittausten välillä. Vartalon
rasvakudoksen ja lannerangan luuntiheysarvojen välillä oli positiivinen yhteys.
Vaihdevuodet ohittaneilla naisilla lihavuus viivästyttää osteopenian ilmaantuvuutta.
Yleinen suomalainen asiasanasto: osteoporoosi; luuntiheys; reisiluu; lanneranka; lihavuus; paino;
painoindeksi; riskitekijät; ruumiinrakenne; vaihdevuodet; hormonihoito; epidemiologia
IX
Acknowledgements
This study was carried in the Bone and Cartilage Research Unit (BCRU) in the University of
Kuopio and the Eastern Finland (UEF) in years 2002-2014.
I wish to express my sincere thanks and deepest gratitude to:
Professor Heikki Kröger, M.D., P.h.D., Head of the Department of Orthopaedics,
Traumatology and Hand Surgery in the UEF and Kuopio University Hospital, for
providing me with the facilities to carry out research work in BCRU and for his
constructive criticism and stimulating comments during the study. I also admire his ability
to focus on what is essential.
Docent Leo Niskanen, M.D., P.h.D., formerly Professor of the Department of Endocrinology
in the UEF and Kuopio University Hospital, for his constructive criticism and important
comments during the study. I admire his ability to think scientifically and for his
encouragement while preparing this thesis.
Professor Risto Honkanen, M.D., P.h.D. for proposing the topic of the thesis and for
providing advice on epidemiological matters and statistics. I have been also impressed by
his genuine enthusiasm for osteoporosis research.
Professor Marjo Tuppurainen, M.D., P.h.D., Head of the Department of Gynaecology and
Obstetrics in the UEF and Kuopio University Hospital (KUH), for providing me with the
facilities to conduct research work in BCRU and for her constructive criticism and incisive
comments during the study.
Professor Jukka Jurvelin, P.h.D., for his expertise in DXA and QUS techniques in many
parts of the study. I am also grateful for his support in improving my understanding for the
essentials of scientific thinking.
Professor Seppo Saarikoski, M.D., Ph.D., Former Head of the Department of Gynaecology
and Obstetrics in the University of Kuopio, Professor Esko Alhava, M.D., Ph.D., Former
Head of The Department of Surgery and Professor of Clinical Physiology Esko Vanninen,
M.D., Ph.D., and Toni Rikkonen, Ph.D., for their constructive criticism during the study.
Professor Timo Jämsä and Docent Riku Kiviranta, the official reviewers of this thesis for
their valuable comments and important proposals to improve this thesis.
Pirjo Halonen, M.Sc., for skilful help in statistical analyses and Vesa Kiviniemi, Ph. Lic., for
his statistical knowledge and and help in preparation of one of the original articles of this
thesis.
X
Ewen MacDonald for revising the language of this thesis.
Ms. Seija Oinonen, the research secretary of the OSTPRE-study, for her rapid and very
skilful help in file management. Thank you very much!
Research nurses Eila Koski, Sirkka Harle, Marianne Elo, Kristiina Holopainen, AuneHelena Heikkinen and Riitta Toroi for their technical assistance in anthropometric, DXA
and QUS measurements.
My colleagues of the OSTPRE-project, friends and relatives for their encouragement and
friendship throughout the years.
All the subjects participating in this study.
KUH; EVO-grant, strategic funding of the UEF, Finnish Academy (grant No.: 203403) and
TULES graduate school for financial support of this work.
Finally, I dedicate this thesis to my precious family.
Kuopio, October, 2014
Jarmo Saarelainen
XI
List of the original publications
This dissertation is based on the following original publications:
I
Saarelainen J, Honkanen R, Vanninen E, Kröger H, Tuppurainen M, Niskanen L,
Jurvelin JS. Cross-calibration of Lunar DPX-IQ and DPX dual-energy X-ray
densitometers for bone mineral measurements in women: effect of body
anthropometry. J Clin Densitom. 8(3):320-329, 2005.
II
Saarelainen J, Rikkonen T, Honkanen R, Kröger H, Tuppurainen M, Niskanen L,
Jurvelin JS. Is discordance in bone measurements affected by body composition
or anthropometry? A comparative study between peripheral and central devices.
J Clin Densitom. 10(3):312-318, 2007.
III
Saarelainen J, Honkanen R, Kröger H, Tuppurainen M, Jurvelin JS and Niskanen
L. Body fat distribution is associated with lumbar spine bone density
independently of body weight in postmenopausal women. Maturitas. 69(1):86-90,
2011.
IV
Saarelainen J, Kiviniemi V, Honkanen R, Kröger H, Tuppurainen M, Jurvelin JS
and Niskanen L. Body mass index and bone loss among postmenopausal women:
the 10-year follow-up of the OSTPRE cohort. J Bone Miner Metab. 30(2):208-216,
2012.
The publications were adapted with the permission of the copyright owners.
XII
XIII
Contents
1 INTRODUCTION ......................................................................................................................................... 1
2 REVIEW OF THE LITERATURE .............................................................................................................. 3
2.1 Osteoporosis and bone loss ..................................................................................................................................... 3
2.1.1 Definition of osteoporosis ....................................................................................................................................... 3
2.1.2 Pathogenesis of osteoporosis ................................................................................................................................. 4
2.1.3 Menopause, hormone therapy (HT), body composition and osteoporosis ............................................................ 5
2.1.4 Peak bone mass and natural progress of bone loss ................................................................................................ 7
2.2 Bone densitometry .................................................................................................................................................. 8
2.2.1 Bone densitometric techniques .............................................................................................................................. 8
2.2.2 Dual-energy X-ray absorptiometry (DXA) ................................................................................................................ 9
2.2.3 Quantitative ultrasound (QUS) ................................................................................................................................ 9
2.3 Obesity .................................................................................................................................................................. 10
2.3.1 Definition of obesity .............................................................................................................................................. 10
2.3.2 Measurement of obesity ....................................................................................................................................... 11
2.3.3 Pathophysiology of obesity ................................................................................................................................... 13
2.4 Adiposity, BMD and fractures ................................................................................................................................ 13
2.4.1 Adiposity and BMD ................................................................................................................................................ 13
2.4.2 Body composition and BMD .................................................................................................................................. 18
2.4.3 Central obesity, bone marrow fat and BMD ......................................................................................................... 19
2.4.4 Weight changes and BMD ..................................................................................................................................... 20
2.4.5 Mechanisms of biological association between adiposity and bone tissue .......................................................... 21
2.4.6 Adiposity and biochemical markers of bone turnover .......................................................................................... 22
2.4.7 Adiposity and QUS ................................................................................................................................................. 23
2.4.8 Body weight and fractures .................................................................................................................................... 24
2.5 Accuracy, precision and cross-calibration of densitometry .................................................................................... 25
2.5.1 Accuracy and precision of densitometric measurements ..................................................................................... 25
2.5.2 Uncertainties in DXA ............................................................................................................................................. 29
2.5.3 Uncertainties associated with adiposity and DXA ................................................................................................. 29
2.5.4 Uncertainties in QUS technique ............................................................................................................................ 31
2.5.5 Cross-calibration of DXA machinery ...................................................................................................................... 32
3 AIMS OF THE STUDY .............................................................................................................................. 35
4 SUBJECTS AND METHODS .................................................................................................................... 37
4.1 Study design and subjects ...................................................................................................................................... 37
4.1.1 Cross-calibration study (Study I) ........................................................................................................................... 37
4.1.2 Study design and subjects (Studies I-IV) ................................................................................................................ 37
4.2 Methods ................................................................................................................................................................ 38
4.2.1 DXA (Studies I-IV) .................................................................................................................................................. 38
4.2.2 QUS (Study II) ........................................................................................................................................................ 39
4.2.3 Postal questionnaires ............................................................................................................................................ 39
4.2.4 Body weight and height (Studies I-IV) ................................................................................................................... 39
XIV
4.2.5 Grip strength measurements (Study IV) ................................................................................................................ 39
4.2.6 Menopausal status (Study IV) ................................................................................................................................ 40
4.2.7 HT use (Studies III / IV) ........................................................................................................................................... 40
4.3 Statistical methods ............................................................................................................................................... 40
5 RESULTS..................................................................................................................................................... 43
5.1 Cross-calibration study (Study I) ........................................................................................................................... 43
5.2 The effect of adiposity in DXA, pDXA, and QUS measurements (Study II) ............................................................. 46
5.3 The effect of central obesity and HT on BMD measurements (Study III) ............................................................... 48
5.4 Overweight and Progress of bone loss (Study IV) .................................................................................................. 50
6 DISCUSSION .............................................................................................................................................. 53
6.1 Study design and methods .................................................................................................................................... 53
6.2 Discussion of results.............................................................................................................................................. 55
6.2.1 The effect of adiposity on cross-calibration between two different DXA devices (I) ............................................ 55
6.2.2 The current role of adiposity in DXA, pDXA, and QUS measurements (II) ............................................................. 56
6.2.3 The effect of central obesity and HT on bone densitometry (III) .......................................................................... 57
6.2.4 Overweight and progress of bone loss (IV) ............................................................................................................ 60
7 SUGGESTIONS FOR FUTURE RESEARCH ......................................................................................... 63
8 CONCLUSIONS .......................................................................................................................................... 65
9 REFERENCES............................................................................................................................................. 67
10 APPENDIX ............................................................................................................................................. 109
XV
Abbreviations
ANOVA
AP
AUC
BMC
BMD
BMI
BUA
CI
CT
CTX
CV
DF
DXA
FEA
FM
FRAX
HT
IGF-1
ISCD
LBM
LSC
MRI
MRS
NHANES
NTX
OC
OPG
pDXA
PICP
Analysis of variance
Anteroposterior
Area under curve
Bone mineral content
Bone mineral density
Body mass index
Broadband ultrasound
attenuation
Confidence interval
Computed tomography
Urinary C-terminal crosslinking telopeptide of type 1
collagen
Coefficient of variation
Degrees of freedom
Dual-energy X-ray
absorptiometry
Finite element analysis
Fat mass
Fracture risk assessment tool
Hormone therapy
Insulin-like growth factor-1
International Society for
Clinical Densitometry
Lean body mass
Least significant change
Magnetic resonance imaging
Magnetic resonance
spectroscopy
National Health and
Nutrition Examination
Survey
Urinary N-terminal crosslinking telopeptide of type 1
collagen
Osteocalcin
Osteoprotegerin
Peripheral dual-energy X-ray
absorptiometry
Serum procollagen type I Cterminal peptide
PINP
PTH
QUS
RANK
RMSE
ROI
SAT
SD
SEE
SOS
TRACP
VAT
WHO
WHR
Serum procollagen type I Nterminal peptide
Parathyroid hormone
Quantitative ultrasound
Receptor activator of nuclear
factor kappa-B
Root mean square error
Region of interest
Subcutaneous adipose tissue
Standard deviation
Standard error of the estimate
Speed of ultrasound
Tartrate-resistant acid
phosphatase
Visceral adipose tissue
World Health Organization
Waist-to-hip ratio
XVI
1 Introduction
Two chronic disorders related to body composition, osteoporosis and obesity, are
increasing in prevalence and thus their interactions are of the utmost public health interest
(Rosen, Bouxsein 2006). The prevalence of osteoporosis has risen to 20% among Caucasian
postmenopausal women (Kanis et al. 2008c).
Screening for osteoporosis is complicated by the dual nature of overweight; it is both a
major biological bone protective factor and a source of DXA measurement error (Bolotin
2007, Looker, Flegal & Melton 2007). Increasing tissue thickness as well as the presence of
non-homogenous and homogenous soft tissue adjacent to bone may alter X-ray attenuation,
resulting in a measurement artifact (i.e. there may not be any real change in bone density)
(Bolotin 2007).
However, there are biological reasons to explain why overweight can actually protect
from osteoporosis. First, excess body weight predisposes the body to mechanical loading,
which is crucial for bone health (Seeman, Delmas 2006, Greene et al. 2012). Second, in
postmenopausal women, the aromatization of androgens into estrogens occurs in lean and
fat tissue and is the major source of natural estrogen which may explain some of the
positive relationship between postmenopausal bone density and body weight (Douchi et al.
2000b, Liedtke et al. 2012). Third, adiposity regulating hormones have clear effects on BMD;
leptin is associated positively with BMD, whereas adiponectin shows a negative
relationship (van der Velde et al. 2012). Finally, obese postmenopausal women exhibit a
more androgenic hormone profile than their lean postmenopausal counterparts, which may
also link BMD with obesity (Clarke, Khosla 2009, Liedtke et al. 2012). Importantly, the
greater soft tissue padding around the greater trochanter in overweight women diminishes
impact forces experienced during a fall and thus this can reduce the incidence of pelvis and
hip fractures (Bouxsein et al. 2007, Beck et al. 2009).
The diversity of different techniques, devices and parameters complicates the selection of
optimal screening machinery. The results of DXA densitometry as measured with
equipment from different manufacturers or even with similar devices from the same
manufacturer, may differ significantly due to technical differences, unexpected changes in
the way equipment is calibrated and different normative values (Blake, Fogelman 2009,
Malouf et al. 2012).
It is not clear whether adiposity affects the cross-calibration of different DXA devices.
Furthermore, it is not known whether fat tissue is responsible for the differences
encountered between QUS and DXA devices. In addition, the number of long-term followup studies evaluating the association between obesity and bone mineral measurement is
limited. Finally, there is contradictory evidence between the association of central obesity
and bone mineral measurements. This study was conducted to examine the relationship
between adiposity and bone mineral measurements in postmenopausal women as a part of
the Kuopio Osteoporosis Risk Factor and Prevention (OSTPRE) and Fracture Prevention
(OSTPRE-FPS) studies.
2
3
2 Review of the Literature
2.1 OSTEOPOROSIS AND BONE LOSS
2.1.1 Definition of osteoporosis
Osteoporosis is defined as a skeletal disorder characterized by compromised bone strength
(density and quality) predisposing to an increased risk of fracture (NIH Consensus
Statement 2000). In 1994, World Health Organization (WHO) defined osteopenia according
to the T-score ((measured BMD – mean reference peak young adult BMD) / 1 standard
deviation (SD) of the peak young adult reference population) thresholds of between 2.5 and
1.0 SD below the healthy young adult mean at the spine, hip or radius measured with DXA
(Lewiecki et al. 2008). Osteoporosis was defined by a T-score threshold of more than 2.5 SD
below the healthy young adult mean. The established osteoporosis requires one or more
fragility fractures in addition to the normal definition of osteoporosis. One SD is about 10–
15% of the mean BMD and bone size differs by 50% in individuals at the 95th and 5th
percentiles. The variance (1 SD) in the rate of bone loss corresponds to a 1% bone loss of the
mean (Wang, Seeman 2008). However, in premenopausal women, the International Society
for Clinical Densitometry (ISCD) recommends the use of Z-scores (measured BMD – mean
age-matched reference BMD / age-matched population SD) rather than T-scores (Lewiecki
et al. 2008).
It may be advantageous to use the Z-score also in elderly individuals because a high
proportion of them are classified as osteoporotic according to T-score criteria, even when
BMD is normal for age (Steel, Peel 2011). The use of T- and Z-score normalizations reduce
bias, eliminate a constant calibration offset and reduce patient-dependent systematic errors,
such as fat errors (Engelke, Glüer 2006). However, the difficulty in determining reference
peaks, means and SDs is a significant weakness of the T-score approach (Lu et al. 2001). In
addition, the problem with clinically important T-score values is that the measurements are
performed with different devices from different manufacturers and/or ROI’s and thus
applying different reference populations for normalization (Leslie, Caetano & Roe 2005,
Blake, Fogelman 2009). National Health and Nutrition Examination Survey (NHANES) is
superior to manufacturers’ reference data due to the large size of the study and
representative cross-section of the population sampled (Binkley et al. 2005, Leslie, Caetano
& Roe 2005). However, the use of Swedish reference values as compared with
manufacturers’ and NHANES III reference populations has lead to a two-fold increase in
the prevalence of postmenopausal osteoporosis (Ribom, Ljunggren & Mallmin 2008). In
addition, different exclusion and inclusion criteria as well as inadequate sample size of
reference populations may lead to super healthy population and an artificially small
population variance (SD) (Bhandari et al. 2003). Furthermore, regional differences for the
same race and gender, racial differences, the onset of menopause (variation with age),
height and body weight (especially in extreme cases) should be taken into account when
assembling reference data (Engelke, Glüer 2006). When T-scores are used, any inaccuracy in
the measurement of SD is multiplied by 2.77 (√2 x 1.96, where 1.96 (95% confidence level) is
from the z-probability table) times in setting the threshold for the definition of osteoporosis
4
(Blake, Fogelman 2007, Badrick, Hickman 2008). T- and Z-scores may differ significantly in
women under the age of 50 years (Carey et al. 2007).
According to ISCD, the non-dominant femoral neck is considered the most suitable ROI
for predicting osteoporosis (Lewiecki et al. 2008), whereas lateral spine and Ward’s triangle
overestimate prevalence of osteoporosis (El Maghraoui, Roux 2008). Anteroposterior (AP)
spine is the optimal ROI with which to follow-up the response to osteoporosis treatment
(Blake, Fogelman 2009). The nondominant forearm ROI is recommended in very obese
patients (who exceed the body weight limitation of the DXA table) (Simonelli et al. 2008).
Each SD reduction at the hip and spine is associated with a 2.9-fold and 2.3-fold increased
risk of hip and spine fracture, respectively (Johnell et al. 2005, Kanis et al. 2008c). Thus, for
example a woman with a hip T-score of -3 would have a 24.4-fold (i.e. 2.93) increase in
probability of suffering hip fracture compared to a T-score of 0. The use of cumulative
clinical risk factors enhances the performance of BMD in the prediction of fractures
considerably (Kanis et al. 2007, Waris et al. 2011).
Thirty-five to 45 percent of 50 year old women, 49-52% of 60 year old women, 55-64% of
70 year old women and around 49% of 80 year old women may exhibit osteopenia,
respectively (Kanis et al. 2008c). The prevalence of osteoporosis is greater among the female
population and increases with advancing age. Thus, 2-6% of the 50 year old women, 6-11%
of the 60 year old women, 17-20% of the 70 year old women, 33-45% of the 80 year old
women and 49-73% of the 90 year old women may exhibit osteoporosis, respectively (Kanis
et al. 2008c). However, there have been concerns about the overdiagnosis and
overtreatment of osteoporosis (Moynihan, Doust & Henry 2012). Osteoporosis and
subsequent fragility fractures are associated with excess mortality, morbidity and extensive
medical care costs (Lim et al. 2009). According to, NHANES 2005-2006, 49% of women and
30% of men aged 50 years or older suffered from osteopenia and 10% of women and 2% of
men had osteoporosis at the femur neck, respectively (Looker et al. 2010). The lifetime risk
of any osteoporotic fracture for women is about 40 to 50% (Johnell, Kanis 2005). However,
non-osteoporotic women can also suffer a low trauma fracture (Stone et al. 2003, Schuit et
al. 2004, Wainwright et al. 2005). Thus, not all women with low BMD will actually sustain a
fracture (Kanis et al. 2012). For example, only 10 to 44% of women who suffered a fragility
fracture were osteoporotic, whereas more than half of fragility fractures occurred in nonosteoporotic women (Stone et al. 2003, Schuit et al. 2004, Wainwright et al. 2005). Thus,
most fragility fractures occur in osteopenic women due to their relatively higher
prevalence, whereas osteoporotic women have higher absolute fragility fracture risk. For
example, a tendency to fall should not be overlooked as a risk factor for causing fractures
(Merilainen et al. 2002).
2.1.2 Pathogenesis of osteoporosis
Cortical bone accounts for 70-80% of the mass of bone in the human body and 80-90% of
cortical bone is calcified. However, most of the bone surface area (80%) consists of
trabecular bone, but only 15-25% of trabecular bone is calcified and the remaining volume
contains bone marrow, connective tissue or adipose tissue. Bone consists of several
inorganic (60% of the bone dry weight is bone mineral, i.e. hydroxyapatite or
Ca10(PO4)6(OH2)) and organic (40% of the dry weight, mainly type 1 Collagen) molecules.
Bone mineral homeostasis is controlled by four hormones, i.e. parathyroid hormone (PTH),
5
1,25-dihydroxycholecalciferol, calcitonin, fibroblast growth factor-23 (Fleet, Schoch 2010,
Civitelli, Ziambaras 2011). In adult bones, the annual turnover rate of the whole skeleton is
about 10%, whereas the annual renewal rate of cortical and trabecular bone is 3% and 25%,
respectively. Bone remodeling regulates both mineral homeostasis (e.g., calcium and
phosphorus) and renews old bone and their mechanical properties. Bone remodeling units
work in four coupling phases: activation, resorption (osteoclasts, 1-3 weeks), reversal and
formation (osteoblasts, 3-4 months) (Seeman 2008a). Osteoporotic bone is characterized by
cortical and trabecular thinning as well as increased porosity (Armas, Recker 2012). There is
a decrease in normal bone formation during the low-turnover age-dependent bone loss
(type 2 osteoporosis), whereas, the high-turnover state of postmenopausal bone loss is
characterized by both abnormal bone formation and resorption (type 1 osteoporosis) (Syed,
Ng 2010). Furthermore, as the bone is removed from the inner endosteal surface, concurrent
new bone formation appears on the outer periosteal bone surface, thus increasing the
bone’s cross-sectional area and bone strength. This periosteal apposition is less in women
than men, thus increasing their susceptibility to fractures (Seeman 2008b). As compared to
women, men have a larger number of thicker trabeculae and thicker cortices in both tibia
and radius (Macdonald et al. 2011). Thus, a bone with a thicker cortical width will resist
fractures compared to bones with less thick cortical width (Webber 2006).
Fifteen percent of the life time cortical bone loss occurs before the age of 50, whereas the
corresponding trabecular bone loss is 40% (Riggs et al. 2008). Estrogen-depletion seems to
promote especially cortical bone loss, whereas the trabecular bone loss which begins in
young adults is partly estrogen-independent (Khosla, Melton & Riggs 2011). Thus,
trabecular bones may be less sensitive to estrogen, but also higher estrogen levels are
needed to preserve trabecular bone (Khosla, Melton & Riggs 2011). From an evolutionary
perspective, calcium reserves are liberated predominantly from trabecular bone, whereas
cortical bone is protected and reserved to carry skeletal load and support locomotion
(Khosla, Melton & Riggs 2011). Estrogen deficiency in postmenopausal women, but also
oxidative stress factors plays a role in the pathogenesis of osteoporosis (Manolagas 2010).
2.1.3 Menopause, hormone therapy (HT), body composition and osteoporosis
Menopause is the time of life when the menstrual cycles cease and it is classified as being
present after 12 months of amenorrhoea resulting from the permanent cessation of ovarian
function either naturally or surgically (ovariectomy). The perimenopause, a time of
deteriorating ovarian function, precedes the final menses by several years (Harlow,
Paramsothy 2011). The natural onset of perimenopause and menopause occurs on an
average at between 47.5 and 51.3 years in western societies, respectively (McKinlay,
Brambilla & Posner 2008). The menopausal transition is associated with increased adiposity
and central obesity as well as sarcopenia especially in the gluteal and abdominal areas
(Salpeter et al. 2006). Typically a woman will gain a few kilograms during the first
postmenopausal years; however it has been proposed that age better determines the
changes in body composition than menopausal transition (Sornay-Rendu et al. 2012).
Menopause is associated with a decrease of 65-75% in levels of estrone, 85-90% in
estradiol and 70% in adrenal androgen concentrations (Clarke, Khosla 2010, Yasui et al.
2012). HT, i.e. estrogen treatment with or without progestogen, is primarily used to treat
6
the symptoms of menopause such as hot flashes and urogenital atrophy (Santen, Allred et
al. 2010).
A Cochrane meta-analysis has indicated that estrogen with a progestogen increased the
risk of breast cancer, death from lung cancer, coronary heart disease and dementia, whereas
combined HT and estrogen-only therapy were associated with an increased risk of venous
thromboembolism, stroke and gallbladder disease (Marjoribanks et al. 2012). Estrogen use
alone for less than 5 years may even reduce breast cancer risk in patients starting therapy
many years after the onset of menopause, whereas estrogen use for over 5 years is believed
to increase the breast cancer risk, particularly in recently postmenopausal women (02.59/1000) (Santen, Allred et al. 2010). Combined HT was associated with an increased risk
of breast cancer 3-5 years after the initiation of therapy, but not after 5 years of combined
HT use (Santen, Allred et al. 2010). However, Santen et al. found no association between HT
and lung cancer risk (Santen, Allred et al. 2010). Estrogen alone may also increase the
ovarian cancer incidence (0.7/1000/5 year use), and furthermore estrogen alone may
increase endometrial cancer risk but this does not occur if sufficient doses of progestogen
are used (Zhou et al. 2008, Crosbie et al. 2010, Santen et al. 2010). The Cochrane metaanalysis found no association between HT, colorectal cancer, ovarian cancer and
endometrial cancer incidence (Marjoribanks et al. 2012).
HT may also reduce gastric cancer incidence and combined HT may also decrease the
risk of colorectal cancer (Camargo et al. 2012). Estrogen combined with drospirenone
(progestogen) is at least weight neutral or may be even associated with weight loss (Foidart,
Faustmann 2007). Santen’s group concluded that HT could reduce body weight, FM and
abdominal obesity, and thus it reduces the type II diabetes and degenerative arthritis risk
(Santen, Allred et al. 2010). Postmenopausal HT reduces abdominal obesity and preserves
muscle strength (Salpeter et al. 2006, Jacobsen et al. 2007). Fortunately, it is believed that the
breast cancer risk normalizes 5 years after withdrawal of estrogen alone and 3 years after
cessation of combined HT (Santen, Allred et al. 2010).
Estrogen affects both proliferation and apoptosis of osteoblasts and osteoclasts. Estrogen
suppresses tumor necrosis factor-α as well as decreasing receptor activator of nuclear factor
kappa-B-ligand (RANK-ligand) activation by elevating osteoprotegerin (OPG) levels
(Legiran, Brandi 2012). Postmenopausal estrogen and combined HT prevent early
postmenopausal bone loss and augments late menopausal bone mass as effectively as
bisphosphonates (Marjoribanks et al. 2012). There are reports that HT can also reduce the
incidence of vertebral fractures that occur after menopause by up to 34% and those of hip
fractures by 20-35% (Cranney et al. 2002, Nelson et al. 2010a). However, it seems that
withdrawal of HT treatment results in a rapid increase of bone turnover and a rate of bone
loss similar to early postmenopausal women and is associated with an increased risk of hip
fracture (Simon et al. 2006, Karim et al. 2011).
The Current Care guideline (Käypä hoito) of the Finnish Medical Society Duodecim
recommends HT for the treatment of osteoporosis in patients with primary and secondary
amenorrhea (over 1 year duration of amenorrhea) or in patients with early menopause
before the age of 45 years (Välimäki et al. 2006). Current Care guideline task force group
has suggested that HT may be continued up to 50 years of age (Välimäki et al. 2006).
According to the National Osteoporosis Society of the United Kingdom HT has a role to
play in the management of osteoporosis in women less than 60 years of age (Bowring,
Francis 2011). However, the risks and benefits as well as the duration of HT need to be
7
carefully assessed before initiation of prevention or treatment of osteoporosis (Staren, Omer
2004).
2.1.4 Peak bone mass and natural progress of bone loss
Bone mass is acquired during childhood and adolescence and the peak bone mass is
achieved in early adulthood (Winsloe et al. 2009). The accrual of spinal and femoral peak
bone mass varies but normally occurs between 16-36 and 29-40 years, respectively (Tamayo
et al. 2009, Berger et al. 2010). Once peak bone mass has been reached, there is a progressive
decrease in BMD with advancing age (Riggs et al. 2008). Heritability explains 50-85% of the
variability in peak BMD (Ralston, Uitterlinden 2010). Postmenopausal status and low body
weight are best at predicting low BMD and the risk of osteoporosis (Waugh et al. 2009).
During the first perimenopausal years, women may lose annually up to 2 to 4% of their
acquired spinal BMD (Mazzuoli et al. 2000, Finkelstein et al. 2008). Women lose around 15%
of total spinal BMD during the first 6 menopausal years (Mazzuoli et al. 2000). The most
rapid phase of spinal bone loss ceases around 2 years after menopause, but continues at a
considerable rate to 6-10 years after menopausal transition (Greendale et al. 2012). The
typical postmenopausal spinal bone loss rate varies from 0.5 to 2% per year (Finkelstein et
al. 2008, Ghebre et al. 2011). Spinal bone loss rate seems to slow down or even to plateau in
elderly women (Araneta, von Muhlen & Barrett-Connor 2009, Looker et al. 2012c). AP
lumbar spine BMD values may even increase in elderly women due to degenerative
changes (Liao et al. 2003). Femoral neck bone loss is instead rather more linear with an
annual bone loss rate varying from -0.4 to -1.4% (Araneta, von Muhlen & Barrett-Connor
2009, Looker et al. 2012b). In contrast trochanter bone loss rate is quite slow, ranging from 0.2 to -0.5%/year between 35 and 75 years of age (Kudlacek et al. 2003, Pedrazzoni et al.
2003). Furthermore, the trochanter bone loss rate is slower in Caucasian women than in
other races (Wu et al. 2003). However, it is not known if this reflects the true preservation of
the trabecular bone. Typical femoral neck, lumbar spine and trochanter BMD values in
different age groups are depicted in figure 1.
8
Figure 1. Femoral neck, lumbar spine and trochanter BMD values according to the Densitometric Italian Normative Study
(DINS, n=1622) and the NHANES (2005-2008, white women, n=1777-1928) using Hologic DXA devices in different age
groups (Pedrazzoni et al. 2003, Looker et al. 2012a). Lumbar spine ROI: L2-4 (DINS) and L1-4 (NHANES).
2.2 BONE DENSITOMETRY
2.2.1 Bone densitometric techniques
The diagnostic approach to osteoporosis includes a general examination, AP and lateral
radiographs of the thoracic and lumbar spine and of other sites during bone pain (crush
fractures), laboratory tests (secondary causes) and bone mineral measurements (Hudec,
Camacho 2013). The development of bone imaging techniques has made major advances
during the past few decades. Multidetector and high resolution quantitative computed
tomography (CT) and magnetic resonance imaging (MRI) provide information of the
macro-architecture of bone as well as of trabecular and cortical microarchitectural bone
structures (Griffith, Genant 2008). However, central DXA, pDXA as well as quantitative
ultrasound (QUS) are clinically the most relevant imaging modalities at the moment. Bone
imaging modalities can be used to diagnose reliably osteoporosis, to estimate the fracture
risk and to monitor the response to the therapeutic interventions (Cummings, Bates & Black
2002). However, in estimating an individual’s fracture risk, DXA should be utilized in
concordance with clinical risk factors such as the fracture risk assessment tool (FRAX)
(Kanis et al. 2010).
9
2.2.2 Dual-energy X-ray absorptiometry (DXA)
DXA is considered as the golden standard for densitometry. Central DXA is used to
measure BMD in hip (total, femoral neck, Ward’s triangle and trochanter), AP and lateral
spine as well as total body BMD. Femoral and lateral spine BMD explains about 70% of a
bone’s capability to resist fracture (Perilli et al. 2012). BMD varies between different sites
around the body and there is only a moderate inter-correlation between BMD at different
sites (Melton et al. 2005). Thus, BMD at a specific site is the best predictor of the fracture
risk at that particular site (Melton et al. 2005). DXA applies two different peak X-ray
energies, which are used to distinguish both mineralized (hydroxyapatite) bone and soft
tissue (Isales, McDonald 2007). In terms of physical interactions at a lower energy (~ 40 kV)
the photoelectric effect and Compton scattering (~80kV) predominate in both tissues (Blake,
Fogelman 2010). Compared to the older generations of pencil-beam DXA-densitometers
(e.g., DPX and DPX-IQ), newer narrow-angle fan-beam (e.g., Prodigy) and cone beam
machinery have reduced scanning times and improved the image quality (higher spatial
resolution), however, at the cost of requiring exposure of the subject to an increased
radiation dose (Blake, Knapp & Fogelman 2005).
pDXA devices are used to measure the BMD in heel, wrist or fingers. By using the same
WHO T-score criteria for osteoporosis as with central DXA, the risk of osteoporosis would
be underestimated (Fordham, Chinn & Kumar 2000). The National Osteoporosis Society
recommends T-score thresholds, which are specific for each type of peripheral device. This
approach identifies osteoporotic patients with 90% sensitivity and 90% specificity
compared to central DXA (Blake et al. 2005). According to the ISCD pharmacological
therapy may be started based on pDXA results however the validity for monitoring
treatment efficacy has not been demonstrated (Hans et al. 2008b). The Current Care
guideline of the Finnish Medical Society Duodecim recommends that pDXA devices may
be used as a screening test for osteoporosis in individuals at an increased risk for
osteoporosis (Välimäki et al. 2006). The Current Care guideline task force group
recommends that some of the patients with normal pDXA results should undergo central
DXA measurement because false negative results may occur using pDXA devices (i.e.
normal BMD with pDXA compared to low BMD with central DXA). Furthermore, task
force group recommends that patients with low BMD using pDXA are guided to central
DXA measurements. However, pharmacological treatment for osteoporosis may be started
based on pDXA measurements, if it is not possible to confirm the results adopted with
pDXA devices (Välimäki et al. 2006).
2.2.3 Quantitative ultrasound (QUS)
QUS instrumentation typically measures both the speed of ultrasound (SOS, m/s or
ultrasonic wave propagating velocity) and broadband ultrasound attenuation (BUA,
dB/MHz, attenuation/frequency). SOS is influenced not only by the elasticity of bone but
also by its density. The high attenuation of sound in BUA measurements of cancellous bone
in heel is due to diffraction, scattering and absorption of sound waves by the large number
of trabeculae, bone marrow and soft tissue (Malavolta, Mule & Frigato 2004, Langton, Njeh
2008). Thus, BUA can reflect porosity, connectivity and trabecular thickness (Malavolta,
Mule et al. 2004) and thus it can provide information about bone quality in terms of bone
architecture and its elasticity (Glüer 2008).
10
The heel compromises 90-95% of trabecular bone and exhibits a similar age-dependent
bone loss pattern to that occurring in the spine of women (Magkos et al. 2004). According to
ISCD, the heel is the recommended ROI for making QUS measurements (Krieg et al. 2008).
ISCD recommends that if central DXA is not available, and then pharmalogical treatment
may be initiated based on the QUS measurements in conjunction with recognized clinical
risk factors. However, QUS may not be used to monitor the effects of pharmalogical
treatment (Krieg et al. 2008) but further research is needed to confirm whether QUS can
reliably monitor the efficacy of treatment (Gonnelli, Caffarelli & Nuti 2007). The Current
Care guideline of the Finnish Medical Society Duodecim recommends that QUS devices
cannot replace central DXA equipment (Välimäki et al. 2006). Caution is also necessary
because of the false negative results associated with QUS measurements compared to
central DXA devices (Välimäki et al. 2006). The diagnostic osteoporosis thresholds for QUS
and pDXA measurement differ from the central DXA WHO criteria (Clowes, Peel & Eastell
2006). Unfortunately, one encounters significant intra- and intermanufacturer variation in
the values produced by different QUS devices (Krieg et al. 2003). As the availability of
central DXA equipment is limited, portable devices, such as QUS, may be considered as
relatively low cost screening tools for bone mass (Marin, Lopez-Bastida et al. 2004). Unlike
the more established central DXA, QUS requires no ionizing radiation, is cheaper, takes up
less space and is easier to use than densitometry-based equipment (Krieg et al. 2008). In
comparison to central DXA-measurements, the risk of false positive (sensitivity) and false
negative (specificity) results with QUS is around 7-21% and 10-72%, respectively (Krieg et
al. 2008, Floter et al. 2011). Further, other QUS parameters, stiffness index (Achilles, Lunar)
and the quantitative ultrasound index (Sahara, Hologic) are calculated as 0.28 x SOS + 0.67 x
BUA – 420 and 0.41 x SOS + 0.41 x BUA – 571, respectively (Hartl et al. 2002).
As both QUS and DXA predict independently future fragility fractures, neither of the
techniques should be preferred over the other (Krieg et al. 2008). The relative risk of hip
fracture for 1 SD decrease of heel BUA, SOS, stiffness index and quantitative ultrasound
index is 1.7, 2.0, 2.3 and 2.0, respectively (Moayyeri et al. 2012). Heel QUS and DXAassessed (central) BMD appear to be rather equally predictive for future fracture risk
(Marin et al. 2006). Validated QUS devices from different manufacturers predict fracture
risks with similar performances (Moayyeri et al. 2012). This suggests that an effective
screening and fracture risk assessment with portable equipment could be a realistic option
(Hans et al. 2008a, Navarro Mdel et al. 2012).
2.3 OBESITY
2.3.1 Definition of obesity
According to WHO, obesity is a medical condition in which excess body fat has
accumulated to such an extent that it may exert an adverse effect on health (WHO 2000).
Obesity is commonly defined by body mass index (BMI, kg/m2) (Table 1.) (WHO 1995,
WHO 2000). One-third of adult women in the United States were obese between the years
2009-2010 according to NHANES (Flegal et al. 2012). Several cardiovascular risk factors as
well as further measurement of anthropometry such as waist circumference or waist-to-hip
ratio (WHR) are important when assessing an obese patient in clinical medical practice
(Haslam, James 2005). Furthermore, obesity is associated with severe morbidity such as
11
type 2 diabetes, hypertension, dyslipidemia, stroke, coronary heart disease, obstructive
sleep apnea, certain types of cancer, degenerative joint disease, non-alcoholic fatty liver
disease, reflux esophagitis, venous stasis ulcers, cholelithiasis, erectile dysfunction and
polycystic ovary syndrome (Haslam, James 2005, Forte et al. 2012). Finally, obesity is one of
the leading preventable causes of premature death (Hjellvik et al. 2013). Therefore, in
overweight or obese individuals, a weight reduction of 10% or more is recommended since
this can achieve a significant reduction in co-morbid risk factors (Barte et al. 2010).
Table 1. Categorisation of BMI.
BMI category
BMI kg/m2
Severely underweight
<16.5
Underweight
16.5-18.4
Normal
18.5-24.9
Overweight
25-29
Obesity (Class 1)
30-34
Severe obesity (Class 2)
35-39
Morbid obesity (Class 3)
40-49
Super obesity (Class 4)
>50
2.3.2 Measurement of obesity
There are several methods available for estimating obesity in addition to the traditional
anthropometric tape and scale measurements. However, in elderly people, the weakness of
BMI is that it does not properly take into account of the height loss, LBM loss and
accumulation of visceral adipose tissue (VAT) (Han, Tajar & Lean 2011). BMI and waist
circumference may be an inaccurate measure of percentage body fat in an individual,
although they do correlate fairly well with DXA-measured percentage body fat in groups of
subjects. BMI is the best surrogate measurement of DXA-measured adiposity in women
(Flegal et al. 2009). Anthropometric studies are often clinically used as indirect
measurements of VAT. WHR, waist circumference and skinfold thickness measurements
are often used to further evaluate obesity and the risk of cardiovascular diseases. Of course
these indirect measurements cannot separate VAT and subcutaneous adipose tissue (SAT),
but especially WHR and waist circumference are more strongly correlated with VAT than
with SAT and therefore these measurements can be used as surrogates for VAT (Gradmark
et al. 2010). However, some have found that waist circumference and BMI correlate better
with FM and SAT than with VAT (Camhi et al. 2011).
MRI and CT can be considered as reference methods for quantitative measurement of
appendicular skeletal muscle as well as VAT and SAT (Meng, Lee & Saremi 2010). These
methods, however, are only used in scientific research and are not generally used for
clinical measurements due to their cost and poor availability. The radiation dose of CT
scans also limits its widespread use (Mancini, Ferrandino 2010). DXA can measure both
regional (head, trunk, arms and legs) and overall body composition parameters and thus
can separate lean body mass (LBM), fat mass (FM) and BMD from each other accurately,
precisely and with a minimal radiation dose (Toombs et al. 2012). The DXA measured
standard trunk or manually selected abdominal fat correlate well with VAT and show
12
minimal between- and within-examiner variation (Clasey et al. 1999, Kamel, McNeill & Van
Wijk 2000).
DXA assumes that there are three compartments (fat, lean and bone mass), whereas the
4-compartment model further separates lean tissue into water and protein (Toombs et al.
2012). As compared to the 4-compartment model, DXA seems to underestimate FM% in
lean subjects and overestimate it in the obese (Toombs et al. 2012). The disagreement of
DXA with the 4-compartment model ranges from 5% underestimation to 3% overestimation
of adiposity (Toombs et al. 2012). For example, Hologic QDR 4500A overestimated and
underestimated LBM and FM by 5%, respectively (Schoeller et al. 2005). Body composition
measurements with different DXA densitometers are sometimes in perfect agreement with
each other, whereas occasionally up to 5 kg mean differences in FM or LBM have been
reported between different devices (Toombs et al. 2012).
DXA is a reliable method for measuring abdominal fat (Glickman et al. 2004). When
compared to CT, DXA is also a good alternative for measuring abdominal fat in the elderly
(Snijder et al. 2002). The correlation between CT and DXA-measured overall abdominal fat
is relatively good (r = 0.8-0.9) but somewhat lower for predicting VAT(r=0.6-0.7) (Snijder et
al. 2002). There is controversy regarding measurement of abdominal FM by DXA and CT
devices. Snijder et al. reported that compared to CT, pencil beam DXA (Hologic QDR 1500)
underestimates total abdominal fat by about 10% with the underestimation being more
apparent in individuals with less abdominal fat (Snijder et al. 2002). In contrast, Bredella et
al. reported that compared to CT, fan beam DXA (Hologic Discovery A) overestimates total
abdominal fat by 58% in anorectic subjects and by 26% in overweight or obese subjects
(Bredella et al. 2013). Thus, compared to values obtained with CT, the measurement of total
abdominal fat with DXA devices seems to be less reliable especially in anorectic patients,
whereas the difference between DXA and CT measured abdominal fat decreases with
increasing adiposity (Snijder et al. 2002, Bredella et al. 2013). In comparison with CT, DXA
was reported to underestimate trunk and thigh fat and to overestimate thigh muscle mass
and this error increased with increasing body weight (Bredella et al. 2010). Some
investigators have proposed that DXA is not superior to waist circumference and BMI
when measuring VAT and SAT (r = 0.6-0.8 compared to CT) (Gradmark et al. 2010).
Furthermore, when compared to CT measurements, the values of changes in DXAmeasured abdominal fat correlated better with SAT (r = 0.9) than VAT (r= 0.7) after weight
loss compared to CT measurements (Doyon et al. 2011). The validity of DXA measured
trunk-leg fat ratio for measuring VAT has not been confirmed (Zillikens et al. 2010).
Older DXA technology cannot differentiate directly between VAT and SAT. However,
GE Healthcare Lunar iDXA and Prodigy as well as Hologic Discovery A fan beam
densitometers are able to quantify the amount of VAT using either the CoreScan (GE)
application of the enCORE software version 14 or the InnerCore (Hologic) application,
respectively (Kaul et al. 2012, Ergun et al. 2013, Bredella et al. 2013). It has been claimed that
DXA seems to be a reliable method for estimating VAT (Micklesfield et al. 2012). However,
controversy exists over the accuracy of DXA scanners’ abilities to measure VAT. As
compared to a CT scan, iDXA overestimates VAT by only 56 cm3 or 5.9% while the average
amount of VAT was 1 kg (Kaul et al. 2012). In addition, the correlation (r2) between waist
circumference and CT measured VAT was much lower (r2 = 0.69), as compared to the
correlation between VAT measured with iDXA or CT (r2 = 0.96) (Kaul et al. 2012). Bredella
et al. reported that when compared to CT, fan beam DXA (Hologic Discovery A)
13
overestimates VAT by 47% in anorectic subjects and by 18% in overweight and obese
subjects (Bredella et al. 2013). Furthermore, compared to CT, DXA overestimates SAT by
61% in anorectic subjects and by 17% in overweight or obese subjects (Bredella et al. 2013).
Thus, measurement of abdominal and VAT by DXA devices seems to be more reliable in
overweight subjects than in thin subjects. In addition, the accuracy of a DXA scanner to
measure different abdominal fat compartments may depend on the individual commercial
DXA device.
2.3.3 Pathophysiology of obesity
Every aspect of metabolism is under genetic control (Roth, Szulc & Danoff 2011). However,
a changing modern environment and lifestyle may override the physiological controls of
appetite and homeostatic body-weight regulation (Lenard, Berthoud 2008). Thus, from an
evolutionary perspective, human reproduction, immunity and having a constant
temperature are processes which require much energy. Gynoid fat depositions are less
lipolytic as compared to VAT and are meant to be mobilized during times of pregnancy
and nursing (Lev-Ran 2001). Previously, fat deposits were important in managing the wide
seasonal fluctuations in energy supply and demand (e.g. in hunter-gatherer societies).
Higher caloric intake and low energy expenditure lead inevitably to weight gain.
However, the regulation of weight occurs in a more complex manner. In the short term,
hunger and an empty stomach (ghrelin from stomach and pancreas) will increase food
intake. Conversely, the presence of food in the gastrointestinal tract cause the release of
cholecystokinin, glucagon like peptide-1 (GLP-1) and peptide YY signal short-term satiety.
Adiponectin and leptin (its amounts being proportional to adipose tissue) and insulin
(metabolism) signal long-term satiety. On the other hand, obesity is characterized by
hyperinsulinemia, insulin resistance and leptin resistance. Thus, hyperinsulinemia also
decreases dopamine clearance e.g. its uptake in the hedonic pathway, which elevates the
increase in the reward due to food and this can cause increased food intake and it maintains
hyperinsulinemic state in the long-term, insulin functions as an endogenous leptin
antagonist (Kamiji, Inui 2007). In women, testosterone and progesterone stimulate appetite,
whereas estradiol inhibits food intake through hypothalamic relays and by interacting with
gut hormones (Hirschberg 2012). The stimulation of endocannabinoid receptors enhances
food intake and sensations of hunger (Di Marzo, Ligresti & Cristino 2009). Finally, these
peripheral afferent signals of satiety and hunger are mediated through the brainstem to the
hunger and satiety centers in the hypothalamus (Kamiji, Inui 2007). This complex
communication between hormones, hypothalamic centers, cortex, brainstem, pituitary
gland and mesolimbic reward system regulates hormonal neuroendocrine functions, caloric
intake and energy expenditure.
2.4 ADIPOSITY, BMD AND FRACTURES
2.4.1 Adiposity and BMD
Body weight is one of the principal determinants of BMD with a typical correlation ranging
from 0.3 to 0.6 (Reid 2010). In contrast, the association between BMI and BMD is slightly
lower compared to the association between body composition parameters and BMD
(Akdeniz et al. 2009, Sheng et al. 2011), although there have been opposite findings have
14
been expressed (Leslie 2009). However, compared to BMI or body weight, the relationship
between BMD and resting energy expenditure (i.e. in women 655 + (9.56 x body weight) +
1.85 x body weight) – 4.68 x age)) maybe even stronger (Afghani, Barrett-Connor 2009).
Postmenopausal women have 1.5 and 3 times higher spinal BMD from an incremental body
weight increase as compared to premenopausal women and men, respectively (Puntus et
al. 2011). For example, in the European vertebral osteoporosis study of 19 European
countries, body weight explained 11.5%, 12.0% and 16.6% of the variance in the spinal,
femoral neck and trochanter BMD values, respectively (Lunt et al. 2001). When compared
to lean people, obese individuals have about a 20% higher total bone mineral content
(BMC) (Cifuentes et al. 2003). In general, a BMI of 30 kg/m2 is associated with BMD values
which are 8-10% greater in lumbar spine and 9-12% greater in hip as compared to an
individual with a BMI of 20 kg/m2 (Bjarnason, Christiansen 2000, Mendez et al. 2012). In
addition, the BMD of the non-weight-bearing wrist is associated positively with overweight
(Forsmo et al. 2006). Heavier body weight is positively associated with bone strength
parameters also in other non-weight bearing bones (Lorbergs et al. 2011). The odds ratio
(adjusted for medications, age and health status) for having spinal or femoral neck
osteoporosis was 1.8 (BMI <19 kg/m2), 0.5 (25-30 kg/m2) and 0.3 (≥30 kg/m2). Thus, the risk
of osteoporosis decreased by 12% for every unit increase in BMI (Asomaning et al. 2006).
Table 2 shows typical associations between anthropometry and bone densities.
Osteoporosis of the lumbar spine (L2-L4) was diagnosed in 37.5%, 23.1% and 14.1% Dutch
women (mean age 63, SD 7) with a BMI values of less than 24, 24-30 and over 30 kg/m2,
respectively (van der Voort, Geusens & Dinant 2001). The odds ratio for having spinal
osteoporosis was 3.1 (age<60) and 0.8 (age≥60) in women with a BMI of 27 kg/m 2 or less,
whereas women with a BMI of over 27 kg/m2 were less likely to suffer osteoporosis (0.3,
age<60 and 0.6 age≥60) (van der Voort et al. 2000). According to NHANES III, 33% of
women over the age of 50 years with a BMI <25 kg/m2 were osteoporotic compared to only
11% with BMI ≥25 kg/m2 (Looker, Flegal & Melton 2007). For example in women over 51
years of age, it is possible to predict mean femoral neck T-score as follows: T-score = -1.332 0.0404 x (age) + 0.0386 x (body weight) (Wildner et al. 2003). Ethnic and genetic factors can
modify the associations between body composition, obesity and BMD (Aloia et al. 1999,
Castro et al. 2005, Wallace et al. 2005). According to NHANES III, white women with a BMI
of over 25 kg/m2 had 13.9% higher BMD compared to women with a BMI of less than 25
kg/m2, whereas the difference was 14.5% and 19.7% with Mexican and black women,
respectively (Looker, Flegal & Melton 2007). The risk of osteoporosis is greatest in
underweight women. According to EPIDOS, 21.9% of women in the lowest weight centile
(mean body weight 43.4 kg) were osteopenic, 45.9% had T-scores from -3.5 to -2.5 and 30.6%
had femoral neck T-score below -3.5, respectively. In contrast, the respective percentages
were 57.6%, 19.4% and 1.4% in the highest body weight centile (mean 80.1 kg) and 48.5%,
41.5% and 5.2% in the 40-50th centile (mean 57.5 kg) (Dargent-Molina et al. 2000). Thus, the
odds ratio for having femoral neck T-score ≤ -3.5 in the EPIDOS study showed body weight
dependency i.e. 13.3 (<52.5 kg), 5.7 (52.5-59 kg) and 2.9 (59-66 kg), respectively (DargentMolina et al. 2000). Lower body weight has been associated with a greater bone loss
compared to women with higher body weight (Hannan et al. 2000, Macdonald et al. 2005).
Figure 2 shows the percentages of low T-score values according to age and anthropometry.
In contrast, obesity has been associated with an increased risk of osteoporosis according to
one small study (Greco et al. 2010). Finally body size, bone size and bone mineral values are
15
closely interrelated and this both affects and complicates the interpretation of the results
(Reid 2010).
Table 2. Anthropometry and bone density in selected studies from the 21 st century. Statistically
significant difference between BMI or weight groups (p-value): ** p ≤ 0.01, *** p ≤ 0.001.
Author
Publ. year
Country
BMI
(kg/m2)
or weight
(kg)
n
Age (years)
Years since
Lumbar spine
Femoral neck
mean (SD)
menopause
BMD (g/cm2)
or T-score
BMD (g/cm2)
or T-score
mean (SD)
1. Mendez
2. Bjarnarson
2012
20-24.9
123
59.6 (7.5)
10.7 (8.2)
0.80**
0.69**
Mexico
25-29.9
318
60.4 (7.8)
12.0 (8.5)
0.84
0.73
30-34.9
211
60.2 (7.1)
12.3 (8.2)
0.87
0.77
35-39.9
96
59.1 (6.9)
10.9 (8.8)
0.91
0.79
>40
65
58.5 (6.1)
11.1 (7.9)
0.95
0.80
2000
<23.48
51
52.8 (2.4)
3.3 (2.3)
0.916**
0.706***
Denmark
23.4826.75
51
54.1 (2.9)
3.2 (1.7)
0.961
0.741
51
53.5 (3.4)
3.3 (1.5)
1.009
0.776
50-80+
>26.75
3. Looker
2007
<25
1254
0.624***
USA
≥25
2045
2009
25.9/62.7
66
66.5 (9.9)***
18.9 (9.5)***
-3.6***
-2.9***
Turkey
27.3/68.1
193
61.2 (8.6)
14.1 (8.6)
-2.6
-1.6
30.5/77.8
281
56.4 (6.4)
8.51 (6.4)
-1.2
0.17
=59.3 (8.5)
=11.8 (8.5)
0.720
NHANES 3
4. Akdeniz
1. (Mendez et al. 2012), 2. (Bjarnason, Christiansen 2000), 3. (Looker, Flegal & Melton 2007),
4. (Akdeniz et al. 2009).
There are several osteoporosis risk-assessment instruments that take into account body
weight as a major risk factor. Some of these instruments identify women with low BMD
from moderate to well (area under curve, AUC, 0.70-0.89) (Table 3). However, sometimes
these instruments function only poorly or even fail to detect osteoporotic individuals from
the healthy population (AUC, 0.13-0.66) (Nelson et al. 2010a) (Table 3):
16
Table 3. Osteoporosis risk-assessment instruments and selection cut-points for referral to densitometry.
Instrument or
study
Studies,
n
Participants,
n
Range
of AUC
Risk factors (points)
1. ABONE
Age, BOdy size,
No Estrogen
2. Body weight
1
2365
0.72
6
9065
3. EPIDOS
EPIDemiologie
de
l'OStéoporose
(weight only)
4. ModAMS
Modified
AMSterdam
1
6958
0.130.79
0.74
Weight < 63.5 kg (1)
Age > 65 yrs. (1)
Never estrogen (1)
Weight < 70 kg
1
287
5. MOI
Mikkeli
Osteoporosis
Index
1
300
0.660.72
6. NOF
National
Osteoporosis
Foundation
3
3092
0.600.70
7. OPERA
Osteoporosis
Prescreening
Risk
Assessment
1
1522
0.81
(Femoral
neck)
0.87
(Lumbar
spine)
8. ORAI
10
11093
0.32-
Selection cutpoint:
recommendation
for densitometry
≥2
Weight < 59 kg
Weight < 60 kg (1)
Any previous steroid use
(1)
Immobility (1)
Age > 50 yrs. (1)
Rheumatoid
arthritis
activity (1)
Weight ≤ 57 kg (4)
58-63 kg (3)
64-70 kg (2)
71-79 kg (1)
Age ≥ 75 yrs. (6)
70-74 yrs. (4)
65-69 yrs. (3)
60-64 yrs. (2)
55-59 yrs. (1)
Fracture (2)
Family history of hip
fracture or spinal
osteoporosis (2)
Smoking (2)
Shortening of the stature:
3-4 cm (1)
5 cm (2)
Use of arms to rise from a
chair (2)
Weight < 57.6 kg (1)
Age ≥ 65 yrs. (1)
Minimal trauma fracture
after the age of 40 yrs.
(1)
Family history of fracture
(1)
Current smoking (1)
Weight < 57 kg (1)
Age ≥ 65 yrs. (1)
Early menopause < 45
yrs. (1)
Low trauma fracture after
the age of 45 yrs. (1)
Steroid use > 6 months >
5 mg/day (1)
Weight < 60 kg (9)
≥3
≥2
≥1
≥2
≥9
17
Osteoporosis
Risk
Assessment
Instrument
0.84
9. OSIRIS
Osteoporosis
Index of Risk
5
2657
0.630.80
10. OST
Osteoporosis
Selfassessment
Tool
11. SCORE
Simple
Calculated
Osteoporosis
Risk Estimation
10
13825
0.330.89
9
13710
0.660.87
12. SOFSURF
Study
of
Osteoporosis
Fractures
—
Study Utilizing
Risk Factors
1
208
0.72
60.0-69.9
kg
(3)
Age ≥ 75 yrs. (15)
65-74 yrs. (9)
55-64 yrs. (5)
Currently no estrogen (2)
+0.2 x Weight (kg)
-0.2 x Age (yrs.)
History of low impact
fracture (-2)
Estrogen therapy (+2)
0.2 x (Weight (kg) - Age
(yrs.))
(-1 x weight pounds) /
10
e.g. Pounds = 2.2 x kg
Age (yrs.) 3 x first digit
History of minimal fracture
after the age of 45 years
(4/fracture, max. 12)
Race non-black (5)
Rheumatoid arthritis (4)
Never user of estrogen (1)
Weight < 59 kg (2)
< 68 kg (1)
(Age (yrs.) - 65) x 0.2
Current smoker (1)
History of postmenopausal
fracture (1)
Risk:
< -3 high
-3 to +1 medium
> +1 low
<2
≥6
≥1
18
Figure 2. Percentage of subjects with low T-score values according to BMI, body weight or age. A) Femoral neck T-score ≤ 3.5 vs. body weight, EPIDOS, 2000, France, n=4638, over 75 years of age (Dargent-Molina et al. 2000). B) T-score ≤ -2.5 vs.
BMI, Manitoba, 2009, Canada, n=8254, age 52.7 (SD 4.9) (Morin, Tsang & Leslie 2009). C) Femoral neck T-score ≤ -2.5 vs.
BMI and age, NHANES III, 2007, USA, n=1877, over 50 years of age (Looker, Flegal & Melton 2007). D) T-score ≤ -2 vs. age,
fracture occurrence (Fx+/Fx-) and weight, Dubbo, 2004, Australia, n=846, age 70.6 (SD 7.3), Fx +=Prior fragility fracture
(Nguyen et al. 2004).
2.4.2 Body composition and BMD
In premenopausal and perimenopausal women, the bones adapt to prevalent muscle loads
and LBM is believed to be the main determinant of BMD values (Wang et al. 2005, Zagarins
et al. 2010). Thus, both ground-impact forces (gravitational loading, e.g. running) and
muscle contractions (i.e. muscle loading, e.g. weight lifting) may improve cortical thickness,
bone metabolism and reduce fracture risk (Kohrt, Barry & Schwartz 2009). There is still
some controversy concerning which one of the two DXA measured body composition
parameters (FM or LBM) is better related to BMD in postmenopausal women. Some studies
have indicated that FM is more important (Marques et al. 2012, Nur et al. 2013), whereas
some claim that both LBM and FM are positively associated with BMD values (Rikkonen et
al. 2012, Zhang et al. 2012). In sedentary women, FM and LBM were associated with BMD,
whereas in exercising women, only LBM could be linked with higher BMD values (Douchi
et al. 2003). A hip structure analysis revealed that femur BMD and indices of bone
geometry: axial (cross-sectional area) and bending strength (section modulus) were higher
in women with higher BMI, but they were proportional to LBM but not to FM (Beck et al.
2009).
In contrast, some studies have observed that increasing adiposity is deleterious for
bones. Interestingly, the initial positive association between FM and BMD changed into an
19
inverse association between FM and BMD after adjustment for body weight (Hsu et al.
2006). Trunk FM was inversely related to BMD values in a multiethnic cohort of women,
however they reported only multivariate results (adjusted for body weight) (Lu et al. 2011).
Thus, it has been reported that multicollinearity between FM, LBM and body weight may
lead to false conclusions about the association between soft tissues and BMD (Reid 2010).
The recent meta-analysis conducted by Ho-Pham et al. suggests that of the body
composition parameters (FM and LBM) LBM is the main determinant of femoral neck and
lumbar spine BMD values in both genders, although both body composition parameters are
associated with BMD values (Ho-Pham, Nguyen & Nguyen 2014). Especially in men LBM
is associated with femoral neck (r=0.43) and lumbar spine (r=0.36) BMD values, whereas FM
is less important with respect to femoral neck (r=0.18) and lumbar spine (r=0.23) BMD
values. Similarly, in premenopausal women, the association between LBM and femoral
neck (r=0.45) and lumbar spine (r=0.31) BMD values is stronger than the association
between FM and femoral neck (r=0.30) and lumbar spine (r=0.19) BMD values. Only in
postmenopausal women FM is as important as LBM to femoral neck (e.g. LBM, r=0.33 and
FM, r=0.31) and lumbar spine (e.g. LBM, r=0.34 and FM, r=0.31) BMD values. However,
individual studies in the meta-analysis showed also contradictory results and the effect of
FM and LBM on BMD values varied from slightly negative to strong positive correlations.
Thus, these heterogenous results in the meta-analysis may be explained by different study
designs with variabilities in age groups, gender, sample sizes and ethnicities (Ho-Pham,
Nguyen & Nguyen 2014).
2.4.3 Central obesity, bone marrow fat and BMD
The association between body fat distribution and postmenopausal central BMD has not
been as extensively investigated, as the association between BMI or body weight and BMD.
In one study conducted in postmenopausal women, the DXA-measured abdominal FM was
positively correlated with total body BMD measurements (Warming, Ravn & Christiansen
2003). Trunk FM was the main determinant of postmenopausal right leg BMD (Kawamura
et al. 2011). Trunk FM was positively associated with total body BMD in postmenopausal
women, whereas LBM was the main determinant of premenopausal BMD in Oriental
women (Kuwahata et al. 2008). A waist circumference of over 80 cm was positively
associated with spinal and femoral neck BMD values in postmenopausal women (Dytfeld
et al. 2011). The total abdominal FM as measured by MRI was positively associated with
spinal but not with hip BMD in postmenopausal women (Stewart et al. 2002). The WHR has
been found to be positively associated with femoral neck BMD in measurements conducted
with the older generation pencil beam (Lunar DPX) equipment in postmenopausal women
(Heiss et al. 1995). DXA-measured (Prodigy) trunk-leg fat ratio was associated with a
higher spinal BMD, but not with femoral neck BMD (Zillikens et al. 2010). Thus, the
redistribution of fat tissue does seem to affect BMD. However, the positive associations
between android fat distribution and BMD are largely explained by higher BMI or body
weight (Zillikens et al. 2010).
Some studies have not detected any relationship between abdominal fat and BMD
values. DXA-measured upper body fat distribution was not associated with hip BMD
(Kirchengast et al. 2001). The initial positive association between WHR and total body and
hip BMD was no longer apparent after adjustment for FM and LBM (Jurimae et al. 2008).
20
Many studies have concluded that adiposity and VAT have negative influences on
postmenopausal BMD. The initial positive association between FM and spinal BMD turned
into a negative association after adjustment for body weight in Korean women either under
or over 50 years of age, even though WHR was consistently inversely associated with spinal
BMD values (Kim et al. 2009). Abdominal and total FM was inversely associated with hip
and spinal BMD after adjustment for body weight in Puerto Rican women, however only
multivariate results were shown (Bhupathiraju et al. 2011). The DXA-measured android to
gynoid FM ratio was not associated with premenopausal BMD values or postmenopausal
BMD values after adjustment for FM. However, after adjustment for FM and LBM, the
DXA-measured android to gynoid FM ratio was inversely associated with total body BMD
in Chinese women (Fu et al. 2010). However, the inclusion of abdominal fat and
anthropometrical parameters (e.g. body weight, BMI, FM) into the same model can result in
multicollinearity, which may explain the negative association between abdominal fat and
BMD values (Reid 2010).
In younger women, SAT is beneficial for bone structure and strength, whereas VAT and
bone marrow fat have detrimental effects on the skeleton in both the general population
and in anorexic subjects (Bredella et al. 2013, Fazeli et al. 2013). However, the trunk-leg fat
ratio as measured by DXA has been positively associated with spinal BMD in
premenopausal women of Oriental origin (Douchi et al. 2000a). VAT measured by CT and
bone marrow fat measured by magnetic resonance spectroscopy (MRS) have been shown to
have negative effects on bone (Sheu, Cauley 2011). The amount of bone marrow fat may be
also associated with fragility fractures (Patsch et al. 2013). However, the association
between CT-measured VAT and BMD in postmenopausal women is less clear because
some investigators have included both pre- and postmenopausal Asian women in their
study populations (Choi et al. 2010). In addition the multicollinearity between VAT, body
weight and BMD may partly contribute to the above inverse relationship between VAT and
BMD in Asian women (Yamaguchi et al. 2009, Reid 2010). Race also markedly modifies the
body fat distribution as well as the relationship of anthropometry with BMD and thus these
results on Asian women cannot be extrapolated to Caucasian women (Nazare et al. 2012).
Thus it seems that adiposity has associations with bone that are age-, gender-, menopausal
status-, adipose depot-, and bone compartment-specific (Ng et al. 2013).
2.4.4 Weight changes and BMD
In general, weight gain leads to less bone loss (Park et al. 2007), whereas intentional or unintentional weight loss are associated with lower BMD values and enhanced bone loss
(Macdonald, New et al. 2005). A dietary-induced weight reduction of 10% has been said to
increase bone loss by 1-4% as compared to the situation in individuals with stable weight
(Shapses, Sukumar 2012). In one study, a 10 kilogram weight loss was associated with a 4%
BMD loss in the non-weight-bearing distal forearm (Wilsgaard et al. 2009). In
postmenopausal women, BMD loss during weight loss does not seem to fully recover with
weight regain (Villalon et al. 2011, Von Thun et al. 2013). However, there is a report that in
some women BMD may remain unaffected during weight gain (Hannan, Felson et al. 2000).
Some previous studies have also found discrepancies between the magnitude of the
changes in BMD and BMC when large weight changes occur (Fogelholm, Sievanen et al.
2001). Extreme weight loss due to dieting or surgical intervention has resulted in more
extreme bone loss (Coates, Fernstrom et al. 2004). After a Roux-en-Y gastric bypass (both
21
malabsorptive and restrictive procedure) for morbid obesity, a significant weight loss
occurs but there may also be malabsorption of calcium and vitamin D, resulting in
increased bone turnover (Fleischer, Stein et al. 2008). In addition, gastric banding
(restrictive procedure) not only induces weight loss but also exerts negative bone
remodeling effects (Giusti, Gasteyger et al. 2005). It has been claimed that there is a greater
bone loss (>1%) with weight loss in normal-weight subjects compared with overweight or
obese individuals (<1% bone loss) (Nguyen, Sambrook & Eisman 1998). Finally, the BMD
response to weight loss may differ significantly between instruments from different
manufacturers (Tothill 2005).
2.4.5 Mechanisms of biological association between adiposity and bone tissue
There are several biological factors that explain the apparently positive and negative
associations between bone and adipose tissue. Osteocytes are thought to sense mechanical
loading of the skeleton and this may account for some of the positive association between
body weight and BMD (Greene et al. 2012). In postmenopausal women, aromatization of
androgens into estrogens (the aromatization of androstenedione and testosterone to estrone
and estradiol, respectively) occurs in both peripheral lean (25-30%) and fat tissue (10-15%)
(Douchi, Yamamoto et al. 2000). Estradiol, testosterone, lutenizing hormone levels, follicle
stimulating hormone and sex hormone-binding globulin levels are positively associated
with increasing body weight (Stolzenberg-Solomon et al. 2012). Viscerally obese
postmenopausal women exhibit a more androgenic hormone profile (Janssen et al. 2010). In
overweight and obese postmenopausal women, the testosterone level was associated with
lumbar spine BMD, whereas levels of both estradiol and testosterone were associated with
femoral neck BMD (Filip, Raszewski 2009).
Obesity is associated with reduced plasma levels of both 1,25- and 25hydroxycholecalciferol due to its deposition in adipose tissue (Vimaleswaran et al. 2013).
Vitamin D deficiency and elevated PTH levels were encountered in 64% and 14% of
morbidly obese patients, respectively (DiGiorgi et al. 2008). PTH levels may be suppressed
at lower vitamin D levels in obese subjects compared to leaner people (Shapses et al. 2013a).
Malabsorptive procedures, but not gastric banding, for morbid obesity have been
associated with impairment of calcium absorption and vitamin deficiency, which in turn
can cause secondary parathyroidism and thus increased production of vitamin D and
release of calcium from bones (Toelle et al. 2012). Dieting also reduces the total amount of
calcium absorbed from the diet (Shapses et al. 2013b). There is a report that serum PTH and
estrogen could explain 36% of the variation in calcium absorption occurring during weight
loss in postmenopausal women (Cifuentes, Riedt et al. 2004). Serum cortisol levels may
increase with weight loss in postmenopausal women, and this might increase osteoclast
activity and decrease calcium absorption from the intestine (Riedt, Cifuentes et al. 2005).
Elevated levels of glucocorticoids in cachectic and anorexic patients may also contribute to
ongoing proteolysis and impaired protein synthesis (Morley, Thomas & Wilson 2006). The
bone trophic factors such as insulin-like growth factor-1 (IGF-1) and PTH exert synergistic
effects on bone (Lombardi et al. 2010). Obesity may also be associated with increased
cortisol levels (Alfonso, Araki & Zumoff 2013). In both anorectic and obese patients, IGF-1
levels are decreased, whereas growth hormone levels are elevated and this leads to an
acquired growth hormone resistant state (Galli et al. 2012, Chaves, Junior & Bertolini 2013).
22
Chronic inflammation may also explain the detrimental effect of VAT on bone health
(Shapses, Sukumar 2012). Obesity may be also associated with hyperinsulinemia, which in
turn exerts an anabolic effect on bones although, both type 1 and 2 diabetes may also be
predisposed to suffer impaired bone quality and an increased risk of fragility fractures
(Hamann et al. 2012).
The adipocytokines play a central role between adipose tissue and bone mineral
measurements. A recent meta-analysis summarized that adipose-derived adiponectin was
negatively associated with BMD and leptin was positively associated with BMD and even
with some fractures, whereas the effect of ghrelin, apelin, resistin and visfatin on bone
mineral measurements were negligible (Biver et al. 2011). The effects of leptin on bone are
mediated through several pathways. First, leptin reduces serotonin levels, and this in turn
leads to an activation of the β2-adrenergic receptors of the sympathetic nervous system as
well as the osteotesticular phosphatase-gene, which decreases the production of osteocalcin
(OC) in the osteoblasts and thus reduces bone formation. The reduced OC levels reduce
adiponectin levels, prevent the secretion of insulin and reduce insulin sensitivity. Secondly,
leptin inhibits bone resorption by activating the hypothalamic cocaine-amphetamineregulated transcript, by increasing OPG levels and, thus, OPG binds RANK-ligand directly
to block its interaction with RANK. Finally, leptin directly reduces insulin secretion. Insulin
triggers a decrease in OPG expression and enhances bone resorption and OC
decarboxylation. Furthermore, leptin, produced from bone marrow adipocytes, can directly
affect the proliferation and differentiation of osteoblasts, promoting bone nodule formation
and exert indirect influences through sympathetic signalling to osteoclasts (Schwetz, Pieber
& Obermayer-Pietsch 2012). Several gut peptides (e.g., glucagon-like peptide-2) may
explain why feeding reduces bone resorption and to some extent increases bone formation
(Dicembrini, Mannucci & Rotella 2012). Interestingly, the feedback system between bonederived osteokines (e.g., OC and osteopontin) and adiposity may maintain the homeostasis
between optimal body weight and adequate BMD levels (Gomez-Ambrosi et al. 2008).
The differentiation of mesenchymal stem cells into different kinds of specific cells such as
adipocytes, osteoblasts or myoblasts is regulated by several transcription factors with
peroxisome proliferator activator gamma playing a crucial role in this respect (Wei, Wan
2011). Similarly thiazolidinediones, orally active antidiabetic agents, affect stem cell
differentiation and consequently are associated with a shift towards lower body fat
distribution, but with the consequence of increased body weight (Schwartz, Sellmeyer et al.
2006). However, these agents decrease bone mass, and this is accompanied by an increased
risk of atypical fractures and increased bone marrow fat accumulation (Grey et al. 2012, Liu,
Aronson & Lecka-Czernik 2013). Interestingly, mesenchymal stem cells facilitate adipose
accretion in bone marrow at the expense of osteoblast formation in osteoporotics (Zhang,
Zhou & Kimondo 2012). However, it is not well known what genes link obesity to BMD
values (Fromm-Dornieden et al. 2012, Mendez et al. 2013). Fat mass and obesity associated
genes, have been proposed to link obesity to hip osteoporosis in an Asian population (Guo
et al. 2011).
2.4.6 Adiposity and biochemical markers of bone turnover
The levels of bone formation markers (such as bone alkaline phosphatase (mineralization)
and OC (mineralization, non-collagenous) and procollagen type I C and N-terminal
peptides (PICP and PINP, type 1 collagen synthesis in fibroblasts and osteoblasts)) have
23
been inversely associated with obesity (Papakitsou et al. 2004, Grethen et al. 2012). Thus,
obesity has been shown to suppress the formation of new collagen molecules (Papakitsou et
al. 2004). Serum PICP may be more related to BMI than to bone formation markers, which
are better associated with mineralization (Papakitsou, Margioris et al. 2004). The level of
OC was inversely associated with total and trunk FM in postmenopausal women
(Kanazawa et al. 2011). Bone resorption markers such as tartrate-resistant acid phosphatase
(TRACP) and markers of collagen hydrolysis (pyridinoline, deoxypyridinolin, urinary and
serum N- and C-terminal cross-linking telopeptide of type 1 collagen (NTX, CTX) inversely
associated with BMI (Cifuentes et al. 2003, Ardawi et al. 2010). However, not all studies
have detected an association between bone formation (alkaline phosphatase S-OC) and
resorption markers (s-TRACP-5b, NTX, CTX, pyridinoline and deoxypyridinolin) and
increasing adiposity (Papakitsou et al. 2004, Kumar et al. 2013). The disease, anorexia
nervosa, is associated with extensive bone resorption, OPG and RANK-ligand and low
bone formation levels but the changes in bone biochemical markers are reversed with
weight gain (Ostrowska et al. 2012). Compared to normal weighted women morbid obesity
was associated with lower vitamin D and higher PTH, bone formation (alkaline
phosphatase) and resorption (NTX) markers (Grethen et al. 2011, Grethen et al. 2012). The
weight loss occurring after morbid obesity is associated with an increase in both bone
formation and resorption markers (Tsiftsis et al. 2009). In conclusion, it seems that being
overweight is associated with lower bone formation and lower resorption, whereas women
with extreme BMI values display disturbances in bone balance, i.e. women with anorexia
nervosa as well as morbidly obese women have exhibited a relative increase in both bone
resorption and formation markers.
2.4.7 Adiposity and QUS
There is controversy regarding QUS measured parameters (i.e. BUA and SOS) and
anthropometrical measurements. Some have reported that anthropometrical parameters
may be associated with both BUA and SOS values using either the DTU-One (OSI
Osteometer Meditech) (Stewart et al. 2006) or the Achilles (GE Lunar) heel ultrasonometer
(Brunner, Pons-Kuhnemann & Neuhauser-Berthold 2011). Many studies have found that
anthropometrical values may be associated with BUA but not with SOS values (Hologic,
Sahara) (Schoffl et al. 2008, Kauppi et al. 2009). BUA parameters were positively associated
with body weight, whereas the association between SOS and body weight was inconsistent
and depended on the age of the subjects (Stewart et al. 2006). Thus, body weight was
positively associated with SOS values using both the Achilles (GE Lunar) and the Ubis 5000
(Diagnostic Medical Systems) in elderly subjects but body weight was not associated with
SOS values in younger subjects (Stewart et al. 2006). Stiffness index (GE Lunar, Achilles)
values were associated with body weight in both younger and older subjects (Stewart et al.
2006). In contrast, an increase of local heel fat thickness underestimated heel SOS, but not
BUA parameters (McCue Ultrasonics, CUBA and GE Lunar, Achilles) (Kotzki et al. 1994,
Hausler, Rich & Barry 1997). Thus, it seems that BUA values are associated with body
weight, but not with the local heel adipose tissue. Furthermore, the local heel fat affects
negatively SOS values, but the effect of overall adiposity on SOS parameters depends on
which QUS device is being used and the age of subjects. Furthermore, both body
composition parameters (FM and LBM) seem to influence BUA but not SOS values (McCue
24
ultrasonics, CUBA) (Tromp et al. 1999). There are also contradictory findings regarding the
association between LBM and QUS parameters. Brunner et al. suggested that it is LBM
rather than FM which can affect QUS parameters in elderly women (GE Lunar, Achilles)
(Brunner, Pons-Kuhnemann & Neuhauser-Berthold 2011). In contrast, LBM was associated
with QUS parameters (Hologic Sahara) in premenopausal women, but not in
postmenopausal women (Saadi et al. 2003).
In elderly lean (BMI ≤ 25.1 kg/m2) Finnish women, but not in heavier subjects, lifelong
recreational physical activity, low physical activity at work, type 2 diabetes and
hypertension were associated with increased BUA values, whereas high coffee intake was
associated with lower BUA values (Korpelainen et al. 2003).
2.4.8 Body weight and fractures
The incidence of fall injuries is higher in obese individuals as compared to the normal
weight population, which may be partly explained by the presence of comorbidities such as
diabetes and various arthropathies, relative immobility and requirement of greater
attentional resources to maintain postural stability (Mignardot et al. 2010). Although
traumatic forces increase with body weight, the greater soft tissue padding around greater
trochanter can diminish impact forces by up to 50% during a fall involving the greater
trochanter and thus this can reduce the incidence of pelvis and hip fractures (Bouxsein et al.
2007, Beck et al. 2009). According to a meta-analysis, the reduction of fracture risk ratio per
unit increase in BMI was 0.98 (0.97-0.99, 95% confidence interval, CI) for any fracture, 0.97
(0.96-0.98, 95% CI) for osteoporotic fracture and 0.93 (0.91-0.94, 95% CI) for hip fracture. The
effect of BMI on all osteoporotic fractures seems to be mediated mainly through its effect on
BMD, whereas the protective effect of BMI on hip fractures is independent of the effect of
BMD. The association between fractures and BMI is not linear because a BMI of 20 kg/m 2 as
compared with a BMI of 25 kg/m2 was associated with a near doubling of hip fractures,
whereas a BMI of 30 kg/m2 compared with a BMI of 25 kg/m2 was associated with only a
17% reduction in the hip fracture risk (De Laet, Oden et al. 2005). Overweight and obesity
reduced the risk of distal forearm fractures by 27% and by 56% compared to women with a
BMI less than 25 kg/m2, respectively (Honkanen et al. 2000) Obese women had 20% lower
risk of wrist fracture compared to non-obese women (Compston et al. 2011). The lowest
body weight or BMI quartile when compared to the highest quartile displayed a 40%
increase in vertebral fractures after adjustment for spinal BMD (van der Klift et al. 2004).
Higher body weight and FM were associated with an 85% and a 6-fold reduction in
vertebral fractures (Finigan et al. 2008). A 10% general weight loss (difference between
maximum self-reported body weight and measured baseline body weight) was associated
with a 2-fold increase in hip fracture rates, whereas in the women in the lowest tertile of
BMI who lost 10% of their body weight suffered a 2.4-fold increase in hip fractures
(Langlois, Mussolino et al. 2001). Measured general weight loss of 5% or more increased the
risk of distal forearm fractures by 33% (Omsland et al. 2009). It has been reported that the
FRAX predicts hip and major osteoporotic fractures in both obese and non-obese
postmenopausal women (Premaor et al. 2012). The 10 year hip fracture probability is 4.1%
without BMD (4.1% with a T-score of -2.8 SD) for women over the age of 65 in those with a
BMI of 20 kg/m2, 2.3% (4.4% with BMD) with a BMI of 25 kg/m2, 1.7% (4.2% with BMD)
with a BMI of 30 kg/m2, 1.4% (3.8% with BMD) with a BMI of 35 kg/m2 and 1.2% (3.5% with
BMD) with a BMI of 40 kg/m2 according to FRAX, respectively (Kanis et al. 2008b). Every 5
25
kg/m2 increase in BMI reduced hip fracture risk by 20%, clinical spine facture by 17% and
wrist fracture by 12% according to the Global Longitudinal study of Osteoporosis in
Women (Compston et al. 2014). Finally, obese postmenopausal women showed a 63%
lower risk of wrist fracture and 77% lower hip fracture risk compared to leaner women in
the Million Women Study (n=1,155,304) (Armstrong et al. 2012). Thus, prior fracture,
maternal fracture history, age and low body weight are the best predictors of fracture risk
(LaFleur et al. 2008).
According to the National Institute of Health and Clinical Excellence, obese and
morbidly obese women under 75 years of age may also suffer from osteoporosis with a
prevalence of 11.7% and they have a 4.5% prevalence of suffering low-trauma fractures
(Premaor et al. 2010). Obese postmenopausal women who sustain nonvertebral fractures
have a lower BMD on average compared to obese women without fracture (Premaor et al.
2011). The risk of osteoporotic fractures was lower (Hazard ratio 0.87 (0.85-0.90, 95% CI) in
women with a BMI of 35 kg/m2 as compared to women with a BMI of 25 kg/m2. However,
obese women exhibited a 16% higher risk of osteoporotic fractures compared to women
with a BMI of 25 kg/m2 after adjustment for BMD (Johansson et al. 2013). In particular, the
central body fat distribution has been associated with a higher hip fracture incidence (65%
difference between highest and lowest quintile after adjustment for BMI) because of an
increased falling tendency, a greater momentum developed during the fall and less soft
tissue covering the hip (Parker et al. 2008). Obesity has been shown to increase the risk for
proximal humerus fracture by almost 30% compared to normal- or underweight women
(Prieto-Alhambra et al. 2012). Compared to lean postmenopausal women (BMI<20 kg/m2),
obese postmenopausal women (BMI≥30 kg/m2) had a three-fold increased risk of suffering
an ankle fracture in the Million Women Study (Armstrong et al. 2012). Furthermore, there
are several reports that obesity increases the incidence of fractures in the upper leg and the
ankle with increases reported in the range from 50 to 69% (Valtola et al. 2002, Compston et
al. 2011). Furthermore, every 5 kg/m2 increase in BMI increased ankle fracture risk by 5%
(Compston et al. 2014). Interestingly, in postmenopausal women with osteoporosis, higher
BMI was associated with a 4.5% higher vertebral fracture risk in a cross-sectional study,
irrespective of the positive association between body weight and BMD (Pirro, Fabbriciani et
al. 2010). However, body weight was not associated with any increased risk of osteoporotic
fractures in perimenopausal women (Huopio, Kröger et al. 2000). Similarly, spine, wrist,
tibial, and multiple rib fracture rates were not associated with BMI (Prieto-Alhambra et al.
2012).
2.5 ACCURACY, PRECISION AND CROSS-CALIBRATION OF
DENSITOMETRY
2.5.1 Accuracy and precision of densitometric measurements
The accuracy (i.e. bias) of DXA reflects the degree to which the measured results of BMD
deviate from true values. The true values are obtained by ashing of the bone (BMC, grams)
under standard conditions and plain radiographs (area). The accuracy error of DXA devices
is said to vary from 5 to 10% (Blake, Fogelman 2008). This error maybe explained by factors
such as uncertainties in machine calibration, poor positioning, analysis, use of different
edge-detection algorithms and most importantly the algorithms used by the device for
26
handling intra- and extra-osseous fat. Accuracy is important when comparing density
values measured with different techniques or devices, as well as in determining an initial
diagnostic classification. However, provided that it remains constant, a small accuracy error
is of marginal clinical significance. In contrast, random errors (i.e. precision) that reflect the
repeatability of the DXA or QUS technique are clinically more relevant (Blake, Fogelman
2008). For example, as the population SD for some SOS measurements is relatively narrow,
a technique with poor precision may lead to misinterpretation of the diagnostic results.
Precision is affected by fluctuations in the operating characteristics of the device over time,
operator errors in positioning patients and data analysis. The precision error helps in the
decision making of whether a significant change has occurred in follow-up measurements.
The short-term coefficients of variation (CV% = (SD/Mean) x 100%), a measure for precision
error, and radiation doses for different regions of interests (ROIs) and imaging modalities
are shown in table 4 (Blake, Fogelman 2001, Blake, Fogelman 2007). The short-term in vitro
precision is achieved by scanning the manufacturer’s quality control phantom 10-20 times
(Patel et al. 2000). The short-term in vivo precision of repeated measurements (instrumental
errors) is measured after repositioning (positioning errors) the patient during the same visit
or during a maximal period of two weeks, during which time no true change can occur in
BMD. However, total hip and spine short term in vivo precision errors are greater when the
scan-pairs are acquired on different days as compared to repeated acquisitions made on the
same day (Leslie 2008). Thus, variability attributable to either the operator or the subjects
rather than machine performance can explain most of the error in measurements (Carbone
et al. 2005). According to ISCD, at least 30 degrees of freedom (df = (number of
measurements from each individual – 1) x number of individuals in the study) are needed
in order to be sure that the estimated short-term precision error is statistically accurate,
unbiased and has an adequate study power. Thus, 30 subjects should be measured twice or
15 individuals evaluated three times in order to be sure that the upper limit of precision
error (95% CI) is no more than 34% greater than the calculated value (Baim et al. 2005).
Table 4. Precision error and effective radiation dose of different bone imaging modalities.
Technique
ROI
Units
reported
Precision
error (CV%)
Effective
dose (μSv)
DXA
AP spine BMD
g/cm2
1
1-10
DXA
Proximal
BMD
g/cm2
1-2
1-10
pDXA
Heel
g/cm2
1-2
0.1
QUS
Heel BUA
dB/MHz
2-5
none
QUS
Heel SOS
m/s
0.1-1
none
hip
It is often more relevant clinically to know the long-term precision (in vitro CV% =
(standard error of the estimate i.e. SEE/Mean) x 100%) of the random measurements
conducted over time periods of months or years (Patel et al. 2000). Furthermore, root mean
square (RMS) is used to calculate the in vivo short-term precision error (CV% = (RMS
SD/Mean) x 100%) and the long-term precision error (CV% = (RMS SEE/Mean) x 100%),
27
respectively (Cawte et al. 1999, Diessel et al. 2000). Normally the long-term precision error
(df = n x (m - 2)) is larger than the short-term precision error, due to small drifts in
instrument performance, potential changes in patient weights and soft tissue composition,
variations in patient positioning or other differences in technique that arise over time or
between different personnel performing the test (Kiebzak, Morgan & Peace 2012). Obesity
or large changes in body weight may influence the precision error of measurements,
especially in individual patients (Patel et al. 2000, Nelson et al. 2010b). The relative (BMD in
elderly is lower than in younger people) and absolute imprecision (difficulties in
repositioning) increases when one deals with elderly subjects (Engelke, Glüer 2006).
Furthermore, the in vivo imprecision obtained solely in young healthy subjects may be
misleading (Engelke, Glüer 2006) as may direct comparisons of relative precision of
different modalities or techniques (Engelke, Glüer 2006). If two instruments have the same
CV%, then the device with a wider clinical range relative to the mean provides better
clinical precision. Therefore standardized coefficients of variation (sCV% = (SD/clinical
range) x 100) enable clinically more reliable comparisons of different DXA and QUS
techniques and parameters (Diessel et al. 2000). However, the definition of sCV% varies.
Some investigators use the 5th and 95th percentiles of the population under assessment
(Miller et al. 1993), whereas other report the difference between the mean for healthy young
adults and that for healthy individuals aged 70 and over (Blake, Wahner & Fogelman 1999).
The latter calculation has been used for estimating sCV% in table 5 (Blake 1999).
Standardized coefficients of variation show the inferior clinical precision (sCV%) of BUA
and SOS measurements compared to DXA measurements. However, stiffness index
improves the clinical precision over BUA and SOS alone, not only normalizing for heel
width but also for the temperature dependence of the QUS measurements because
temperature has opposite effects on BUA and SOS values (Nicholson, Bouxsein 2002).
Table 5. Comparison of coefficients of variation (CV%) and standardized coefficients of variation (sCV%) of different DXA
and QUS parameters.
DXA
DXA
QUS
QUS
QUS
Spine BMD
(g/cm2)
Hip BMD
(g/cm2)
BUA heel
(dB/MHz)
SOS heel
(m/s)
Stiffness
index
0.9 – 1.2
0.7 - 1.0
100 – 125
1500 – 1560
67 – 100
CV%
1.0%
1.2%
1.5%
0.3%
1.8%
sCV%
4.0%
4.0%
7.5%
7.8%
5.4%
Range of values
(Osteoporotic –normal)
Long-term in vivo precision error is typically about two times higher than the short-term
precision error (Tothill, Hannan 2007). The typical in vivo long-term precision error for
trimmed (untrimmed) data is obtained for lumbar spine BMD, 1.1% (1.7%); femoral neck
BMD, 2.2% (2.5%); and total hip BMD, 1.3% (1.6%) (Patel et al. 2000). The lateral vertebral
28
body contains predominantly trabecular bone, whereas AP spine evaluates mostly the
cortical posterior elements of the vertebrae. Therefore, lateral spine is less affected by the
spinal degeneration of spinous process and aortic calcification (Chun 2011). Although
lateral spine exhibits a stronger response to treatment compared to AP spine, this
advantage is cancelled by the poorer precision (treatment effect/precision ratio) especially
in elderly subjects (Blake, Herd & Fogelman 1996). The precision of DXA devices for
measurement of LBM and FM has been reported to vary from from 0.4 to 1.3% for LBM and
from 0.7 to 4.4% for FM (Toombs et al. 2012). The precision error of body composition
measurements has improved with the arrival of newer DXA modalities, those are better
than can be obtained with older DXA generations (Toombs et al. 2012).
There are a few situations in which serial measurements of bone densitometry are
routinely performed. The first is to monitor the effect of a therapeutic agent and to monitor
the response to therapy. The second is to determine the point when the risk of an individual
for low BMD warrants intervention or to undertake follow-up measurements in
osteoporosis research. Least significant change (LSC) can help to determine reliably the
appropriate interval of serial bone densitometry measurements for an individual in each
ROI. For example, if the long-term in vivo precision error for BMD at the spine is 1.5%, to
determine a LSC with 95% CI the bone density should change by 1.5 x 2.77, i.e. 4.2%, before
this change can be considered as significant (Bonnick et al. 2001). Thus, in reality, the range
of the 95% CI for a measured 6% change would be actually 6% ± 2.77, or 3.23-8.77%
(Bonnick et al. 2001). The time interval for serial measurements is equivalent to LSC
divided by the expected bone density change/year. If the precision error for AP spine DXA
is 0.015 g/cm2, the LSC is 0.015 g/cm2 x 2.77 = 0.042 g/cm2 and if the expected average annual
change in bone density with some agents is 0.03 g/cm2, then the time interval for repeated
measurements will be 1.4 years (=0.042 g/cm2 divided by 0.03 g/cm2). A generalized least
significant change can be used to compare results acquired with different densitometers
(Shepherd, Lu 2007). When compared to women of normal body weight, obesity increases
the precision error of spinal, femoral neck and total body BMD measurements significantly
(Knapp et al. 2012). Consequently, serial measurements in obese women need to be treated
with caution due to their larger than expected values of LSC (Knapp et al. 2012).
The measured BMD changes of <2–4% in the spine and 3-6% at the hip could be due to
an inherent measurement error in the DXA technology (NOF 2003). Thus, in clinical
practice involving treatments with bisphosphonates an interval of 1-2 year and for HT 2-4
years in measurements with central DXA from the first study can be recommended
(Writing Group for the ISCD Position Development Conference 2004). During a period of
rapid bone loss, such as that associated with prolonged glucocorticoid therapy, bone
density may be monitored as frequently as every 6 months (Writing Group for the ISCD
Position Development Conference 2004). Gourlay et al. have recommended rescreening
intervals based on DXA measured T-score values: 15 years (T-Score ≤ -1.49), 5 years (T-score
-1.50 to -1.99) and about 1 year (T-score -2.00 to -2.49) (Gourlay et al. 2012). However, to
adhere overly strictly to such T-score based intervals may delay initiation of appropriate
pharmacological therapy in some patients, especially if there are clear clinical risk factors
present (Lewiecki, Binkley 2012). The higher the BMD and the greater time elapsing from
menopause, the longer will be the time interval until a repeated measurement will be useful
(Writing Group for the ISCD Position Development Conference 2004).
29
2.5.2 Uncertainties in DXA
The role of DXA as a true golden standard for measuring bone health and fracture risk has
been criticized (Adams 2013). DXA provides an areal BMD (g/cm2) parameter, instead of
true volumetric bone density (g/cm3) acquired with CT devices (Adams 2009). Therefore,
DXA sums up the two properties that determine bone strength, i.e., its material
composition and its structural design (Currey 2009). Beam divergence produces a
magnification error in the medial-lateral direction near the X-ray source of fan-beam
densitometers. This affects the geometry of proximal femur and increases the bone area and
BMC. However, the effect of magnification on clinically relevant BMD values is negligible
(Toombs et al. 2012). The newer narrow fan-beam densitometers (e.g., GE Lunar Prodigy)
are however less affected by the beam magnification than their older fan-beam counterparts
(e.g., Hologic QDR-2000) (Toombs et al. 2012). Furthermore, DXA-measured BMD values
are inherently higher in taller or larger individuals compared to smaller or shorter people,
although their absolute bone density may be identical (Binkovitz, Henwood 2007).
However, larger bones in general have greater strength and are less vulnerable to fragility
fractures (Kanis et al. 2008a). DXA assumes that the hydration level of fat-free tissue is 73%.
However, the hydration can actually vary from 67 to 85%. If the assumed level of hydration
is exceeded, some DXA scanners will overestimate the fat content (Laskey 1996). Although
Kelly et al. proposed that a range of fat-free tissue hydration from 68.2 to 78.2% does not
significantly alter the total percentage of fat, (Kelly, Berger & Richardson 1998) severe
overhydration, such as ascites or oedema, may affect the resulting percentage of fat or BMD
(Guanabens et al. 2012). However, on some occasions, ascites has not interfered with
measurement values (Haderslev, Svendsen & Staun 1999). Hip positioning (e.g., hip
osteoarthrosis) can affect DXA measurements and manual selection of ROI may reduce the
precision of measurements (Cetin et al. 2008). The precision and BMD values of the ExpertXL device, but not those obtained with DXP-L or Prodigy devices, was found to be affected
by room temperature (Culton, Pocock 2003). The presence of osteomalacia will
underestimate total bone mass due to the decreased mineralization of the bone (Kanis et al.
2008a). Metal objects, stones, implants, calcium tablets, movement during scan acquisition,
contrast agents, scoliosis and aortic calcification can interfere with DXA measurements
(Gregson et al. 2013). Degenerative changes (such as osteophytes and intervertebral
arthrosis) as well as undetected/detected vertebral compression fractures incorrectly elevate
spinal BMD values especially in the AP direction of many people older than 65 (Gregson et
al. 2013). A manual data analysis, instead of one obtained via automation, may alter
significantly results obtained from body composition measurements (Libber, Binkley &
Krueger 2012). Antiresorptive treatment may reduce the risk of fracture by approximately
50% due to redistribution of mineral when BMD increases only by about 5% (Seeman 2007).
Thus, finite element analysis (FEA) of high-resolution micro CT device predicts fractures
better than DXA and is more sensitive at detecting changes during osteoporosis treatment
(Griffith, Genant 2011). Similarly FEA revealed that femoral bone strength decreased by
55% with aging, whereas BMD decreased by only 26% (Keaveny et al. 2010).
2.5.3 Uncertainties associated with adiposity and DXA
DXA assumes that there is a homogenous distribution of soft tissue around the edge of the
bone and uses this soft tissue density as its reference in the bone density measurements
30
(Bolotin et al. 2001). Unfortunately, alterations in tissue thickness and inhomogenous soft
tissue composition adjacent to the bone may alter the X-ray beam attenuation, affecting
adversely image information and potentially resulting in artificial changes in bone,
especially in the lumbar spine (Bolotin 2007). A non-homogenous adipose distribution may
confer a bias of 5% in the AP spine, 10% in the lateral spine and 6% in the femoral neck and
total hip at least when using older DXA equipment (Tothill, Pye 1992, Svendsen et al. 1995)
and 0.06 g/cm2 or 6% (hip) using modern DXA technology (Tothill, Weir & Loveland 2013).
In individual cases the inaccuracy may be as large as 20-50% (Bolotin, Sievanen & Grashuis
2003). Furthermore, even a homogenous soft tissue distribution may result in errors,
especially with spinal BMD DXA-measurements (Bolotin 1998, Bolotin, Sievanen &
Grashuis 2003). The relatively lower quantity of soft tissue (<10 cm) around skull, arms, ribs
and legs may reduce the accuracy of soft tissue and bone measurement in these ROI’s
(Lands et al. 1996). Increases in body thickness causes beam hardening (i.e. lower energy
photons are attenuated from the radiation beam) and this reduces the accuracy of DXA
measurements (LaForgia et al. 2009). In particular, AP abdominal body thickness of over 25
cm (corresponds to a BMI of 45 kg/m2) overestimates BMD values (Blake, Fogelman 2008).
For example BMD was overestimated by 20.5% after addition of three layers of fat adjacent
to the bovine femur and the relative (%) overestimation of BMD increased with increasing
surrounding lard (Javed et al. 2009). Thus, BMD may not lie on a truly linear scale and
BMD may vary according to body thickness. Similarly densitometric inaccuracies may
complicate reliable BMD diagnostics during weight changes (Bolotin, Sievanen et al. 2003).
Fortunately, as compared to older DXA equipment, the newer imaging modalities (e.g., fanbeam densitometer Lunar Prodigy) are less prone to errors of excess soft-tissue thickness in
BMD measurements (Mazess et al. 2000).
In the elderly, the red hematopoietic marrow (approximately 40% fat) is replaced by the
yellow marrow (approximately 85% fat). In osteoporotic bone, the ratio of true bone tissue
to bone marrow, which is normally about 75%, can also decline to a value as low as 25%.
Thus, abnormal amounts of marrow fat and non-ossified bone tissue reduce falsely the
measured bone density (Bolotin 2007). Alterations in bone marrow composition, especially
during weight changes, may also diminish the reliability and precision of planar DXA
measurements (Bolotin, Sievanen & Grashuis 2003). However, correction for bone marrow
adiposity does not seem to improve spinal fracture prediction in Asian people (Blake et al.
2009).
The above adiposity-related DXA inaccuracies are similar to the typical changes
encountered in longitudinal intervention studies and are, therefore, clinically relevant.
Phantom studies have shown that spinal BMD inaccuracies can be as large as 25% for
normal BMD values, 35% for osteopenic values and 50% for osteoporotic values. The
inaccuracies in the values of distal radius and proximal femur BMD may be as great as 20%
for normal BMD values, 25% for osteopenic values and 35% for osteoporotic values. The
inaccuracy in BMD measurements conducted solely with cortical bone is less than 2%
(Bolotin 2004). Thus, generally in individuals who are either overweight and underweight,
the values of BMD will be overestimated and underestimated, respectively (Bolotin 2004),
although sometimes the presence of adiposity underestimates BMD values (Yu et al. 2011).
The association between the body fat distribution and BMD depends on both the selected
DXA device and on the method of estimating the body fat distribution (Blake, Harrison &
Adams 2004, Malouf et al. 2012).
31
According to Bredella et al., FM may be underestimated in large patients (Bredella et al.
2010). The difference in the attenuation characteristics and in the hydrogen content of fat
and lean tissues at the effective energy of the low energy beam in standard DXA can lead to
inaccuracies because DXA simplifies the body into two compartments: bone and soft tissue.
However, the attenuation coefficients of lean tissue and fat differ significantly, by about
22% and 15% for the X-ray energies of 42 keV and 68 keV, respectively (Hakulinen et al.
2003). Fat is less dense than lean tissue. Since bone free LBM is derived by subtracting the
FM from the total soft tissue mass, the presence of a relative error in the estimation of the
LBM (larger compartmental size) may cause a larger relative error in the FM (smaller
compartmental size) (Pietrobelli et al. 1996). For example, Lunar iDXA has overestimated
fat by up to 11.1%, but the relative overestimation of fat became less as the amount of fat
increased in an experimental study (Javed et al. 2009). In fact, the normal three component
DXA model of bone, LBM and FM (body composition parameters include also skin and
water), should ideally separate bone, fat tissue, muscle tissue, water and yellow or red bone
marrow tissue.
In contrast to DXA instruments, the heel dual energy X-ray laser technique is able to
minimize the effect of non-uniform soft tissue by taking the heel thickness into account
(Hakulinen et al. 2003). The effect of overlying adiposity on in vivo spinal CT BMD is less
prominent and more uniform as compared to spinal DXA measurements, although both
techniques are significantly affected by adiposity. When compared to CT, DXA
overestimated T-score values by 1-2 units in obese individuals (Weigert.J.M.:Cann.C.E.
1999). DXA (Hologic Discovery) BMD increased with increasing adiposity (+12 kg) around
the spine phantom, but the inclusion of lard did not affect CT BMD phantom
measurements. In contrast, fat (+7.5 kg) reduced in vivo spinal BMD measurements by 2.2%
but increased in vivo CT measurement values by 1.5%, respectively. The precision of DXA
hip measurements was reduced with increasing adiposity, leaving unaffected total hip and
femoral neck BMD values (Yu et al. 2011). Finally, there are reports that when using digital
X-ray radiogrammetry, metacarpal BMD is also minimally affected by the presence of
adjacent fat (Colt et al. 2010).
2.5.4 Uncertainties in QUS technique
Diffraction errors in BUA will be of the order of 0.6 and 10 dB/MHz for immersion (fixed
transducer separation, water immersion) and contact measurements (gel coupling,
transducer accommodating to heel width) on the heel, respectively (Langton, Njeh 2008).
The overlying cortical surfaces can induce frequency independent interface losses of up to
20 dB, as compared to pure cancellous bone (0.5 dB) (Langton, Njeh 2008). Phase-sensitive
single element piezoelectric transducers (diameter larger than the wavelength of sound)
limit the spread of ultrasound by diffraction. However, this leads to phase cancellation in
the heterogeneous cancellous bone. If signals are out of phase then this will underestimate
the true intensity of ultrasound and overestimate the attenuation, and this will result in an
artificial increase of 8-20% in BUA (Wear 2007, Cheng et al. 2011). The cortical plate itself
and the curved medial surface of the calcaneus also can induce phase cancellation (Xia, Lin
& Qin 2005). Calcaneal edema has been shown to reduce BUA values in contact (gel)
technique systems, but not in immersion (water surrounding limb) devices (Javaid et al.
2005). The temperature of water correlated strongly negatively and moderately positively
32
with SOS and BUA, respectively (Nicholson, Bouxsein 2002). Foot positioning is probably
the major cause of imprecision in clinical BUA measurement (Hans, Krieg 2009).
The thickness of the heel bone and surrounding soft tissue remains either unknown or
cannot be estimated reliably with the available QUS devices (Laugier 2008). Increase in heel
thickness will underestimate SOS, whereas BUA values may be even slightly overestimated
(Hausler, Rich & Barry 1997). Calcaneal length and heel width correlated significantly with
QUS (Hologic Sahara) SOS and BUA values, respectively. The heel width also correlated
significantly with Achilles + (Lunar) SOS, but not with BUA values (Cheng et al. 2002).
Mapping of bone surface topology may improve the assessment of os calcis width as
compared to assumptions about heel width incorporated into the conventional QUS
techniques (Xia, Lin & Qin 2007). Although BUA and SOS depend on thickness of the
calcaneus, the normalization for the width of calcaneus is not necessarily needed because
the interindividual variation in heel width is small (Laugier 2008). Acoustic impedance of
soft tissues is about 80% less than that of a typical bone (Chen et al. 2013). Measurement of
heel SOS with an unknown soft tissue thickness has been shown to introduce an error that
could be 3 - 20 times higher than the short-term precision error (CV% = 0.1%) (Chappard et
al. 2000). An increase of fat thickness in the heel was reported to lead to an underestimation
of SOS, but not of BUA parameters (Hausler, Rich & Barry 1997). Thus, BUA values are
negligibly affected by local adiposity as the relative thickness of soft tissues in heel is small
(Laugier 2008). Furthermore, the dual-frequency ultrasound technique may be applied to
minimize the errors introduced by soft tissue (Malo et al. 2010).
2.5.5 Cross-calibration of DXA machinery
Aging or deficient DXA technology reduces the reliability of BMD measurement,
necessitating a change of equipment. However, problems in maintaining the continuity of
the BMD calibration scale can arise when an old DXA system is replaced by a newer model.
Both body composition and bone mineral estimates are known to vary between DXA
instruments from different manufacturers (Pearson, Horton & Green 2006). Furthermore,
DXA densitometry results measured with equipment from different manufacturers may
vary significantly due to technical differences and different normative values (Blake,
Fogelman 2009). The mean BMD measured using Lunar Prodigy was 15% higher as
compared to the same measurements performed with Hologic QDR2000. Even when using
the standardized BMD, the mean difference was still 3% (Pearson, Horton & Green 2006). In
addition, different versions of densitometers from the same manufacturer may differ
significantly from each other (Blake, Harrison & Adams 2004). In the worst-case scenario as
much as 5% differences may exist between the same types of machines from the same
manufacturer (Gaither, Faulkner et al. 1996). However, typically less than 1% differences
are encountered between similar or different machines from the same manufacturer (Blake,
Harrison & Adams 2004, Shepherd, Morgan & Lu 2008). Although BMD values of two
different DXA devices could be similar, the way that the two pieces of equipment handle
different parameters of body composition may differ significantly (Covey et al. 2008).
Routinely, daily scanning of phantoms is performed in order to check the reliability of
the BMD calibration and to allow for correction of minor changes. However, a crosscalibration of two DXA devices using only phantom measurements can be inaccurate,
especially when performing hip ROI (Pearson, Horton & Green 2006). Nonetheless,
sometimes in vivo and in vitro cross-calibration results agree between different
33
densitometers (Pearson, Cawte & Green 2002). Therefore, according to ISCD, phantombased cross-calibration is considered to be adequate after hardware change or after
replacing the DXA system with an identical model from the same manufacturer, whereas in
vivo cross-calibration is necessary if an old DXA system is being replaced with a different
model from the same or a different manufacturer (Pearson, Horton & Green 2006).
Correction coefficients are needed if the difference between the values of the two
densitometers exceeds 1% (Shepherd et al. 2006).
Cross-calibration between machines is important because mean systematic differences
between instruments may exceed the annual biological changes in BMD values (Pearson,
Horton & Green 2006). Usually scans from a minimum of around 20-30 subjects will be
required during cross-calibration at each anatomical scan site to achieve an accuracy of ±
1% (95% CI) and an evaluation of 50-100 subjects will be required to achieve an accuracy of
± 0.5% (95% CI) (Shepherd et al. 2006). The statistical error of the intercept of each linear
regression line is examined and, if the 95% CI includes zero (i.e. intercept is not statistically
significant), the regression analysis is repeated with the intercept forced through the origin.
If the slope (95% CI) of the regression line is statistically significantly different from unity,
then the other device should be multiplied with this factor (Blake, Harrison & Adams 2004).
It is not acceptable to simply report the Pearson correlation coefficient between the
measurements with the two measuring devices (Bland, Altman 1986, Bland, Altman 2003).
The difference between the two machines on the y-axis is recommended to be plotted
against the average of the two measurements (x-axis) in order to detect any variation
between measurements (Bland, Altman 1986, Bland, Altman 2003). Scan speed and body
composition parameters need to be taken into account during a BMD cross-calibration
procedure (Blake, Harrison & Adams 2004). Although the cross-calibration reduces the
mean differences of two densitometers, systematic differences may remain between
individual subjects even after cross-calibration (Pearson, Horton & Green 2006).
34
35
3 Aims of the study
The aims of the study were:
1.
To evaluate the agreement between BMD measurements performed with Lunar
DPX and DPX-IQ bone densitometers as well as to compare cross-calibration
results acquired from human and phantom data. To calculate correction
coefficients to attain comparability between DPX and DPX-IQ measurements as
well as to evaluate and minimize the effect of BMI on this agreement.
2.
To assess the agreement between DXA and QUS techniques and to examine
whether bone size and body composition or different normative values account
for the discrepancies detected between different instruments.
3
To determine the association between body fat distribution
postmenopausal BMD and to study how this is modified by HT.
4.
To examine the effects of BMI on postmenopausal bone loss.
and
36
37
4 Subjects and methods
4.1 STUDY DESIGN AND SUBJECTS
4.1.1 Cross-calibration study (Study I)
Most of the study population consisted of women recruited from the OSTPRE study (n=69).
In addition, normal volunteers of younger age (n=20) were also included in order to cover a
larger spectrum of bone densities. Eighty-eight and 89 women underwent hip and spinal
BMD measurements, respectively.
4.1.2 Study design and subjects (Studies I-IV)
Figures 3 and 4 depict the selection process of the final study populations.
Figure 3. Flowchart depicts the selection process of the final study populations II and III as a part of the Kuopio
Osteoporosis-Fracture Prevention Study (OSTPRE-FPS) trial
38
Figure 4. Flowchart depicts the selection process of the final study population IV as a part of the population-based Kuopio
Osteoporosis Risk Factor and Prevention Study (OSTPRE).
4.2 METHODS
4.2.1 DXA (Studies I-IV)
In the OSTPRE study (Study IV), the baseline measurements of the AP lumbar spine (L2L4) and left hip (trochanter, ward and neck ROI’s) were carried out in 1989-91 and the
follow-up measurements in 1994-98 using DPX at the Kuopio University Hospital. The
third measurement was performed in 1999-2002 with DPX-IQ (Both of the above devices
were provided by Lunar Corporation, Madison, WI, USA). Both devices use pencil-beam
technology. The body weight limit recommendation (120 kg) set by the manufacturer was
not exceeded.
The agreement between DPX and DPX-IQ was measured during the same visit (Study I).
Correction equations between the densitometers are described in detail in study I. In vitro
short-term and long-term precision were calculated according to the methods of Glüer et al.
(Glüer et al. 1995) and Blake et al. (Blake, Wahner & Fogelman 1999).
In Study I, estimates for volumetric bone apparent mineral densities (vBMD; g/cm3) were
calculated from the areal BMD values using the following formulas (Kröger et al. 1993):
39
lumbar spine vBMD = BMD (g/cm2) × (4 / (π × width of measurement area in lumbar spine));
femoral neck vBMD = BMD (g/cm2) × (4 / π) × (height of measurement area / measurement
area of femoral neck).
In OSTPRE-FPS study, the subjects (Study III) were measured from hip and AP lumbar
spine as well as total and regional (head, chest, trunk, legs and arms) body composition
with the fan-beam DXA (Prodigy, GE, Lunar, Madison, WI, USA). The pDXA measurement
(Lunar PIXI Lunar Corporation, Madison, WI, USA), of the bare and clean left heel was
performed at the OSTPRE-FPS baseline between February 2002 and May 2004 at the same
time with central DXA measurements. PIXI uses a cone-beam technology with a chargecoupled device area detector.
Nonvalid bone (Studies II-IV, hip prosthesis, arthrosis, osteophytes, fractures,
operations, sclerosis, scoliosis or other spinal deformities) or body composition (Studies IIIII, measurement area too narrow for the detector, pacemaker or vascular prosthesis) were
excluded from the analyses. In addition, 9.3% of the measurement pairs of baseline lumbar
BMC and scan area (Study IV) were lost due to a computer hardware error.
4.2.2 QUS (Study II)
Left foot calcaneal ultrasound measurement (Lunar Achilles, Lunar Corporation, Madison,
WI, USA) was performed at the OSTPRE-FPS baseline at the same time with DXA
measurements. Achilles uses an immersion technique.
4.2.3 Postal questionnaires
The baseline OSTPRE postal enquiry was mailed in 1989 and included data about
medications, use of HT, gynecological history, history of fractures, weight, height, chronic
health disorders diagnosed by a physician, nutritional as well as lifestyle factors (Appendix
1).
The second baseline questionnaire was sent to those who underwent densitometry
during 1990-1991. It contained questions about the consumption of dairy products, alcohol
and coffee. Both questionnaires were reviewed by a trained nurse in the presence of the
participant at the baseline densitometry.
The OSTPRE 5-year enquiry was sent in 1994 and the 10-year enquiry in 1999. The
OSTPRE-FPS enquiry was sent in 2002 (Appendix 2).
4.2.4 Body weight and height (Studies I-IV)
Body weight (kg) and height (cm) were measured in light clothing without shoes at each
densitometry with a calibrated scale and a wall-mounted stadiometer, respectively.
4.2.5 Grip strength measurements (Study IV)
Right hand grip strength was measured by trained nurses at baseline with a hand-held
strain gauge dynamometer (Martin Vigorimeter, Tuttlingen, Germany) with the subject in a
seated position with elbow flexed at a 90 degree angle and was not allowed to touch any
part of the body. The resting time between subsequent measurements was 30 seconds and
was taken to be the mean of 3 successful measurements.
40
4.2.6 Menopausal status (Study IV)
A woman was regarded as postmenopausal if 12 months had elapsed since her last
menstruation or if she had undergone bilateral ovariectomy. Women were regarded as
perimenopausal if menopause occurred before the 2nd densitometry, early menopausal if
they were ≤5 years postmenopausal and late menopausal if they were >5 years
postmenopausal.
4.2.7 HT use (Studies III / IV)
In Study IV, users of HT were excluded and in Study III, women were divided into two
groups according to their use of HT. The use of HT was calculated based on estrogens,
progestagens, or their combination whether in tablets, transdermal gels or patches during
the follow-up or past taken for menopausal symptoms. The vaginal route of administration
(gel or pessary) was not considered as HT. Users of HT had been receiving HT either
occasionally or continuously during or before the study period, whereas HT non-users did
not use estrogen therapy either before or during the study. Information about the use of HT
was obtained from the questionnaires.
4.3 STATISTICAL METHODS
All statistical analyses were performed using the Statistical Package for Social Sciences
(SPSS, WI, USA, SPSS Inc.) for Windows software version 11.5.
In Study I, the Pearson's correlation analysis, the paired samples T-test and the linear
regression analysis were used to analyse the agreement between two densitometers.
Similarly, the Bland and Altman method (Bland, Altman 1986, Bland, Altman 2003) was
used to evaluate the bias in results between the devices. In the above method the mean of
the differences of the two DXA scanners were plotted against the mean values of
measurements. The limits of agreement were calculated with the formula: absolute mean
difference (d) ± 1.96 SD. Relative mean differences (d%) were obtained by using the
formula: [(DPX – DPX-IQ) × 100] / DPX-IQ. A test for significance of the slope coefficient's
equality to one was used to estimate adequacy of sample size. Calculations were based on
effect sizes with alpha=0.05 and power=80%. The accuracy of corrections, obtained with a
regression line, was expressed as the s.e.e. In mathematical terms the s.e.e. equals the root
mean square error (RMSE).
In Study II, the continuous data were analysed by using the Pearson's correlation
analysis and the linear regression analysis to establish the association and agreement
between DXA and QUS machines. Statistical z-score transformations [z-score = (ximean)/SD, where xi is the measured value for woman i] were used as a normalization
procedure for the measured values for BMD and QUS parameters. The differences (d)
between any two bone parameters were plotted against the anthropometric parameters and
linear regression analysis was utilized to evaluate any systematic differences in the results
between the measurements.
In Study III, the data was analysed by using the stepwise linear regression and the
Pearson's correlation analysis for continuous parameters as well as the T-test and univariate
analysis of variance (ANOVA) with the least significant difference post hoc test for
independent categorical parameters.
41
In Study IV, the repeated measures from the subjects were analysed applying mixed
models framework in the form of random coefficients regression (Brown, Prescott 1999).
DXA and body anthropometry measurements were modeled as a polynomial function of
time (years since menopause or age) taking into account the intra-subject correlation of the
measurements. The effect of continuous scale BMI was included into the model as a timevarying covariate. In practice, this means that the actual BMI at each time point is
considered as a predictor of the corresponding BMD.
In all analyses, a p-value below 0.05 was considered to be statistically significant.
42
43
5 Results
The main results of each study (I-IV) are presented in Table 6.
Table 6. The main results of the studies I-IV. Effect of adiposity on DXA and QUS parameters in postmenopausal women.
Study
Key variables
Number
study
subjects
of
Main result
I
Central BMD
88-89
BMI affects in vivo cross-calibration between DPX
and DPX-IQ DXA devices
II
Central BMD, heel
BMD and heel QUS
139
DXA, pDXA and QUS parameters are differently
affected by adiposity
III
Central BMD
198
Central obesity is associated with spinal BMD, but
not with hip BMD, irrespective of HT use
IV
Central BMD
300
Obesity delays the incidence of hip and spinal
osteopenia
5.1 CROSS-CALIBRATION STUDY (STUDY I)
There was a close linear relationship over the entire range of BMD values for the spinal
(r=0.989) and femoral neck (r=0.966) scans between DPX and QPX-IQ densitometers. In all
ROIs, the slopes of the regression lines were significantly different from unity, emphazing
the need for using correction equations between the two densitometers. DPX-IQ measured
lower BMD values than DPX in the lumbar spine (-0.012 g/cm2 i.e. -1.1%), in the Ward’s
triangle (-0.016 g/cm2 i.e. -2.1%) and in the trochanter (-0.001 g/cm2 i.e. -0.1%) ROIs’ (Table
7). In contrast, DPX-IQ measured higher BMD values than DPX only in the femoral neck
(+0.013 g/cm2 i.e. +1.4%) ROI. In comparison, a typical difference in BMD values between
GE Lunar Prodigy and iDXA devices ranged from 0.4% to 1.2% in the lumbar spine ROI
and from 0.7% to 2.0% in the femoral neck ROI (Choi et al. 2009, Hind et al. 2013). On
average women included in the NHANES 2005-2008 lost annually 0.0045 g/cm2 of the
acquired femoral neck BMD between the age of 50 and 80 years (Looker et al. 2012a). Thus,
a 0.013 g/cm2 difference in femoral neck BMD values between DPX and DPX-IQ devices in
femoral neck ROI corresponds to almost a typical mean femoral neck bone loss during a 3
year period. The difference between DPX and DPX-IQ scanners was minimal, after the
application of simple and multivariate (including body weight or BMI) correction
coefficients. Cross-calibration of lumbar spine BMD values was not affected by adiposity
(Table 7). In overweight and obese women, DPX measured higher BMD values than DPXIQ, although on average, DPX-IQ measured femoral neck BMD values were higher
compared to DPX (Figure 5A). There was practically no difference in mean femoral neck
BMD values between the densitometers after simple BMD-correction was applied, but even
44
after conventional correction coefficients were applied the difference between the DXA
devices was affected by adiposity (Figure 5B). The addition of BMI in the correction
equation for femoral neck BMD values minimized the difference between the two
densitometers (Figure 5C). However, the present results cannot be extrapolated to DXA
devices from different manufacturers or even to different devices from the same
manufacturer. The linear regression has been criticized because it assumes no random error
in the dependent variable and tends to underestimate the slope of the true linear
relationship (Pearson, Cawte & Green 2002). However, the two statistical methods (i.e. the
standardized principal components analysis (Feldmann et al. 1981) and the linear
regression analysis) yielded comparable results. The limits of agreement for lumbar spine
(i.e. from -0.035 to 0.060) and femoral neck (i.e. from -0.075 to 0.050) BMD values were
compatible in most ROIs or slightly wider in some ROIs compared to other reports (Choi et
al. 2009, Hind et al. 2013).
Table 7. Relative mean difference (d% (SD)) difference between DPX and DPX-IQ densitometers.
ROI
Lumbar spine L2-L4
Femoral neck
Ward’s triangle
Trochanter
Original
d% (SD)a
Simple
d% (SD)b
Multivariate
d% (SD)c
BMD
BMC
area
-1.1 (2.5)
-0.1 (3.8)
1.0 (3.0)
0.006 (1.046)
-0.164 (3.837)
-0.078 (3.015)
0.059 (3.452)
-0.019 (2.656)
BMD
BMC
area
1.4 (1.9)
0.5 (1.2)
-0.9 (2.8)
-0.008 (3.284)
-0.049 (1.743)
-0.001 (10.011)
-0.007 (3.205)
0.011 (3.511)
0.046 (9.442)
BMD
BMC
area
-2.1 (1.3)
-2.6 (11.7)
-1.9 (0.02)
-0.177 (0.595)
1.263 (23.821)
0.297 (17.434)
1.324 (23.122)
0.213 (16.559)
BMD -0.1 (1.6)
-0.009 (1.490)
-0.088 (1.508)
BMC 1.3 (7.4)
0.532 (8.009)
0.291 (3.627)
area 1.6 (6.7)
0.284 (7.838)
0.275 (6.150)
a Formula for relative mean difference (d%): [(DPX-IQ - DPX) × 100 / DPX].
b Formula for relative mean difference (d%): [(DPX simple corrected - DPXoriginal) × 100 / DPXoriginal].
c Formula for relative mean difference (d%): [(DPX multivariate corrected - DPXoriginal) × 100 / DPXoriginal].
45
Figure 5. Scatter plot for the difference between Lunar DPX and Lunar DPX-IQ against BMI at femoral neck BMD (I).
Comparison between (A) original BMD values; (B) corrected with simple linear regression (DPX BMD femoral neck =
0.9861 × IQ BMD); (C) corrected with multivariate linear regression (DPX BMD femoral neck = 0.9605 × IQ BMD + 0.0018 ×
BMI - 0.0252) values. Mean difference (d) = straight line, limits of agreements (d ± 1.96 × SD) = dashed lines. N=88 (A to C).
46
5.2 THE EFFECT OF ADIPOSITY IN DXA, PDXA, AND QUS
MEASUREMENTS (STUDY II)
Mostly, DXA, pDXA and QUS parameters were positively associated with body size
parameters. Only heel SOS and lumbar spine BMD values were not associated with body
height (p>0.05) (Table 8). Femoral neck BMD and heel SOS values were not associated with
adiposity (total body % fat, p>0.05, Figure 6 and Table 8). A moderate association was
observed between lumbar spine or heel BMD and adiposity, thus total body % fat explained
34% of the variation in the lumbar spine BMD and 27% in the heel BMD (p<0.05). Modest
correlation was observed between two QUS parameters (BUA and stiffness index) and total
body % fat (p<0.05) (Figure 6). Femoral neck T-score and Z-score values were not associated
with relative (%) total body fat (Figure 7). T-score and Z-score values of pDXA and QUS
were similarly affected by adiposity, whereas the association between lumbar spine T-score
values and adiposity was stronger than the association between Z-score values and fatness
(Figure 7). With respect to the anthropometrical parameters, body weight was the strongest
determinant of DXA, pDXA and QUS parameters (Table 8), whereas for the body
composition parameters (FM and LBM), higher LBM was also associated with higher
femoral neck and lumbar spine BMD values in the multivariate model but not with pDXA
or QUS values. The mean T-score value was -1.1 SD in three ROIs (i.e. lumbar spine,
femoral neck and heel QUS), whereas the mean T-score value measured with pDXA was
-0.3 SD in heel ROI.
However, of all anthropometrical parameters (i.e. height, body weight, BMI, FM (kg),
LBM (kg) and total body % fat), body weight was the strongest and the only determinant of
normalized bone parameters in all ROIs (p<0.05), except in heel SOS ROI, where none of the
anthropometrical variables were included in the final model (p>0.05) (method linear
regression, data not included in the original publication). Maximum grip strength value of
three successful measurements was included with body weight in the final multivariate
model only in normalized lumbar spine and heel pDXA ROIs. Furthermore, only heel SOS
was not associated with total body FM (kg) (p>0.05), whereas in femoral neck ROI BMD
values were positively associated with total body FM (kg) (Table 8). Of the body
composition parameters (FM (kg), LBM (kg) and total body % FM) FM (kg) was the
strongest and only determinant of normalized heel BUA, heel stiffness, heel BMD and
lumbar spine BMD values (p<0.05), whereas LBM (kg) was the only body composition
variable associated with normalized femoral neck BMD values (p<0.05) (method linear
regression, data not included in the original publication).
47
Figure 6. The association between relative (%) total body fat and normalized (z-score = (xi-mean)/SD, xi=measured value))
central DXA, heel pDXA and heel QUS measurement values (n=139).
Figure 7. The association between DXA, pDXA and QUS measured T-score (A) and Z-score (B) values with relative (%)
total body fat.
48
Table 8. Linear correlation coefficients (r) between anthropometric parameters and normalized results of QUS and DXA
measurements. *p<0.05, **p<0.01, ***p<0.001.
Device
Prodigy-DXA
ROI
PIXI-pDXA
Achilles-QUS
Lumbar Spine
Femoral Neck
Heel
Heel
Heel
Heel
BMD
BMD
BMD
SOS
BUA
Stiffness
Height (cm)
0.17
0.17*
0.20*
0.09
0.19*
0.18*
Weight (kg)
0.41***
0.29**
0.35***
0.08
0.25**
0.21*
2
BMI (kg/m )
0.34***
0.21*
0.27**
0.03
0.17*
0.13
FM (%)
0.34***
0.15
0.27**
0.08
0.18*
0.17
LBM (kg)
0.21*
0.23*
0.21*
0.03
0.05
0.12
FM (kg)
0.39***
0.23*
0.33***
0.07
0.23**
0.20*
5.3 THE EFFECT OF CENTRAL OBESITY AND HT ON BMD MEASUREMENTS
(STUDY III)
The ever users of HT were thinner, had less both trunk FM and total body LBM, and had
higher femoral neck and lumbar spine BMD values as compared to never users of HT
(Table 9). Lumbar spine T-score values, but not femoral neck T-score values, were affected
by trunk fat (Figure 8). Thus, trunk FM and use of HT were included in the final
multivariate model in the lumbar spine ROI, whereas body weight and use of HT best
explained femoral neck BMD values (p<0.05). There was no interaction between the use of
HT and trunk fat (linear regression, p = 0.408) implying that the effect of trunk fat on spinal
BMD values was independent of the use of HT. T-score values in lumbar spine ROI were
1.0 SD lower in the lowest trunk FM tertile compared to the highest tertile, whereas the
difference was 0.7 SD between the middle and the highest trunk FM tertiles (p<0.05).
49
Table 9. Mean values (SD) and range for the anthropometric and DXA parameters in the Kuopio Osteoporosis-Fracture
Prevention (OSTPRE-FPS) Study population (III, n=198). Differences (in variables) between never and ever users of HT are
marked with an asterisk. Student’s T test: *p<0.05, **p<0.01, ***p<0.001.
Never HT
Ever HT
Total population
(n=96)
(n=102)
(n=198)
Mean (SD)
Mean (SD)
Mean (SD)
Range
Age (yr)
67.6 (1.8)
67.4 (1.9)
67.5 (1.9)
65.3 – 71.7
Height (cm)
158.4 (4.9)
159.0 (4.7)
158.7 (4.8)
144.6 – 171.3
Weight (kg)
69.5 (11.1)
66.9 (8.7)
68.1 (10.0)
43.7 – 93.7
BMI (kg/m2)
27.7 (4.4)*
26.4 (3.2)
27.1(3.9)
19.2 – 40.9
Total body LBM (kg)
40.1 (3.8)*
39.0 (3.2)
39.6 (3.5)
31.3 – 50.1
Total body FM (kg)
26.6 (8.2)
24.8 (6.6)
25.7 (7.4)
7.7 – 47.8
Leg FM (kg)
9.3 (2.9)
9.1 (2.5)
9.2 (2.7)
3.8 – 18.3
Trunk FM (kg)
13.7 (4.9)*
12.4 (3.9)
13.1 (4.4)
2.5 – 24.9
Trunk-leg FM ratio
1.5 (0.5)
1.4 (0.4)
1.5 (0.4)
0.5 – 3.2
Lumbar Spine BMD (g/cm2)
1.020 (0.160)***
1.111 (0.164)
1.067 (0.168)
0.643 – 1.502
Femoral Neck BMD (g/cm2)
0.828 (0.101)***
0.884 (0.110)
0.857 (0.109)
0.610 – 1.189
Lumbar Spine T-score
-1.4 (1.3) ***
-0.7 (1.4)
-1.0 (1.4)
-4.6 – 2.6
Femoral Neck T-score
-1.3 (0.8) ***
-0.8 (0.9)
-1.0 (0.9)
-3.1 – 1.7
Anthropometry
Body composition
DXA
Figure 8. The association between the trunk FM (kg) tertiles (III) and A) postero-anterior lumbar spine (L2-L4) and B)
femoral neck T-score values. The results are adjusted for body weight. Method: Univariate Anova using least significant
difference post hoc tests with the rightmost (=lowest) tertile III as reference. P-value: *p<0.05, **p<0.01, ***p<0.001. Tertile
limits for the 1st (from 14.7 to 24.9 kg), the 2nd (from 11.1 to 14.7 kg) and the 3 rd (from 2.5 to 11.1 kg) trunk FM tertile.
Standard errors are shown with error bars.
50
5.4 OVERWEIGHT AND PROGRESS OF BONE LOSS (STUDY IV)
On average, women were overweight (BMI 27.3 kg/m2), although there was wide variation,
ranging from women who were underweight to morbidly obese women (BMI range from
18 kg/m2 to 44.9 kg/m2) in the present study population (n=300). The mean weight gain was
3.6 kg and mean height loss was 1.3 cm during the average 10.5-year follow-up. There was
considerable variation in weight ranging from a maximum weight loss of 17 kg to a
maximum weight gain of 29 kg. At baseline (Table 10), the prevalence of osteopenia was
37.7% and that of osteoporosis was 5.3% in the lumbar spine (L2-L4), and 29% and 0%
respectively in the femoral neck (based on manufacturer’s T-score values). At the third
DXA-measurement, the corresponding percentages were 44.7% and 10% for the spine and
57% and 1% for the femoral neck. The mean annual bone loss rate was -0.70% (SD 0.60) at
femoral neck and -0.37% (SD 0.62) at lumbar spine during the average 10.5 year follow-up.
A second-order model described best the age-related progress of bone loss in both femoral
neck and lumbar spine, but only in the lumbar spine menopause-related bone loss was
depicted best by the second-order model (Figure 9). Three clinically important BMI cut-off
values (20, 25 and 30 kg/m2) were selected from the continuous data. The actual follow-up
time (10.5 years) was only half of the 20 years of postmenopausal bone loss in figures 9 and
10 due to differences in menopausal status at baseline and variations in follow-up time.
Women with a BMI of 20 kg/m2 became osteopenic 2 years (spine) and 4 years (femoral
neck) after menopause on average, whereas women with a BMI of 30 kg/m2 became
osteopenic 7 years (spine i.e. 5 years later) and 13 years (femoral neck i.e. 9 years later) after
menopause on average (Figure 9). Women with a BMI of 20 kg/m2 became osteopenic at the
age of 51.5 years (spine) and 53.5 years (femoral neck) on average, whereas women with a
BMI of 30 kg/m2 became osteopenic at the age of 55.5 years (spine i.e. 4 years later) and 61.5
years (femoral neck i.e. 8 years later) on average (Figure 10).
51
Table 10. Baseline characteristics of the study population sample (IV, n=300) from the Kuopio Osteoporosis Risk Factor and
Prevention (OSTPRE) Study.
Characteristics
Mean (SD)
Range
53.6 (2.8)
48.4 – 59.0
Follow-up time (yr)
10.5 (0.5)
9.4 – 12.9
Years since menopause
2.9 (4.3)
-5.0 – 19.2
Height (cm)
161.1 (5.0)
150 – 174
Weight (kg)
70.7 (11.6)
48 – 115
BMI (kg/m )
27.3 (4.4)
18.0 – 44.9
Lumbar spine BMD (g/cm2)
1.117 (0.144)
0.625 - 1.646
Femoral neck BMD (g/cm2)
0.933 (0.116)
0.679 - 1.329
Trochanter BMD (g/cm2) (n=299)
0.853 (0.126)
0.571 - 1.298
n
%
<20 (underweight)
5
1.7
20–24.9 (normal weight)
98
32.3
25–29.9 (overweight)
132
44.0
30–39.9 (obese)
62
20.7
>40 (severely obese)
3
1.0
Peri: menopause before 2nd densitometry
93
31.0
Early: ≤ 5 years postmenopausal
125
41.7
Late: >5 years postmenopausal
82
27.3
Age (yr)
2
BMI kg/m
2
Menopausal status
52
Figure 9. The relationship between three selected cut off-values for BMI and T-score by years since menopause of the (IV)
A) AP lumbar spine (n=300) and B) femoral neck (n=300) with linear mixed model method. The horizontal straight line
indicates cut-off point (-1 SD) for osteopenia.
Figure 10. The relationship between three selected cut off-values for BMI and T-score by age of the (IV) A) AP lumbar
spine (n=300) and B) femoral neck (n=300) with linear mixed model method. The horizontal straight line indicates cut-off
point (-1 SD) for osteopenia.
53
6 Discussion
6.1 STUDY DESIGN AND METHODS
The OSTPRE cohort included all of the women born between 1932-1941, aged 47-56 years at
baseline in 1989 from a defined geographic area, Eastern Finland. The women of the
OSTPRE-FPS-study were aged 65 or over at baseline in fall 2002. The study populations are
Caucasian and homogenous. Thus, OSTPRE and OSTPRE-FPS cohorts are considered to
represent quite well the general Finnish-speaking majority of the Finnish population.
The original study cohorts consisted of a random sample as well as a stratified sample of
women belonging to the same population base. A selection bias cannot be ruled out as the
women were offered an opportunity to volunteer to undergo DXA measurements
(Bhandari et al. 2003). Overall, with respect to the OSTPRE and OSTPRE-FPS cohorts, 92%
and 77.4% responded to the baseline inquiry, and then 84% and 59.8% were willing to
participate in bone densitometry, respectively. With respect to the OSTPRE and OSTPREFPS populations, 67.5% (51.6% completed all three densitometric measurements) and from
14.4 to 20.3% underwent densitometric or body composition measurements, respectively.
Thus, especially in the OSTPRE-FPS a smaller proportion of women were selected for bone
densitometry. Finally, the loss of women during the several stages of selection procedure
for bone densitometry and those providing random samples was either small or moderate
at each individual stage. Nonetheless, the overall loss was considerable. This loss of
participants may have resulted in an overrepresentation of healthier women in the samples
(Leslie, Tsang & Lix 2008). Some of the body composition measurements were not available
at the time of the analysis of Study II data, thus reducing the size of that study’s final
population.
Responses to postal questionnaires were partially checked at the time of bone
densitometry. Incomplete or ambiguous data have thus been corrected over the telephone
during the coding phase. The information on postal inquiries could not be fully validated
and some errors may have remained in answering or coding.
Errors due to measurement technique were minimized by using specially trained
personnel and by the regular use of phantom measurements. The long-term precision
(coefficient of variation, CV%), based on regular measurements of an anatomical Hologic
spine phantom, was 0.88% and 0.62% with DPX (10/1994-4/1999) and DPX-IQ (4/199910/2003), respectively. The short-term in vivo repeatability has been shown to be 0.9% for
the spine and 1.5% for the femoral neck (Kröger et al. 1992, Komulainen et al. 1998).
Although the DXA device was changed during one study (Study IV), a comparison was
performed and correction coefficients were applied to minimize the bias (Study I).
However, measurement errors cannot be fully eliminated.
In the cross-calibration Study I, the study population of 88-89 women clearly exceeds the
recommended minimum of 20-30 subjects (Shepherd, Morgan & Lu 2008). Further, most of
the study population consisted of women recruited from the OSTPRE study (n=69),
however, normal volunteers of a younger age (n=20) were also included in order to gather a
larger spectrum of bone densities and measurement age in our OSTPRE study. Thus, the
54
generalizability of the cross-calibration results to the general Finnish population is quite
good (Kröger et al. 1992). The densitometers which were cross-calibrated are also in clinical
use in Kuopio University Hospital and are also used to monitor children’s bone densities.
However, although volumetric densities were calculated because volumetric densities are
used for the measurements of children, it cannot be ensured that these correction
coefficients (BMD, BMC, area and volumetric BMD) apply to men, children or other races.
The number of study subjects was small in Studies II, III and IV when compared to the
original OSTPRE cohort. However, incorrect DXA measurements were excluded because
they might spuriously elevate BMD values (Gregson et al. 2013). Users of HT, calcitonin
and bisphosphonates as well women who were not postmenopausal during the second
densitometry or whose menopausal status was not known were excluded in Study IV to
ensure that the examination was following the natural progress of bone loss. However,
women (n=66) with some other bone affecting medications or diseases (Kröger et al. 1994)
were included in the study and were treated as a confounding factor. These diseases were:
renal disease, liver disease, insulin-dependent diabetes, malignancies, rheumatoid arthritis,
endocrine abnormalities (parathyroid/thyroid glands, adrenals), malabsorption (including
lactose malabsorption), total/partial gastrectomy, alcoholism and long-term immobilization.
Bone-affecting medication included corticosteroids, diuretics, cytotoxic drugs,
anticonvulsive drugs, anabolic steroids and vitamin D. Selection of final study populations
(Studies II-IV) might affect adversely on the generalizability of all results and some kind of
selection bias cannot be ruled out (Bhandari et al. 2003).
The categorization of women according to HT status was straightforward in Study III.
However, the number of women would have been rather small to study the effect of
duration of HT on central obesity and BMD, and thus we avoided the use of arbitrary cutoff points. This would have reduced the study power considerably in the subgroup
analyses, leading perhaps to false conclusions. Thus, the dose-response of HT on the
relationship between central obesity and BMD could not be entirely verified. Furthermore,
3105 women (27.3%) from a total of 11377 women reported the use of HT from the year of
1996 to 1999. Of the self-reported users of HT, a total of 97.6% of the 3105 women were
confirmed as HT users. In contrast, 15.2% of the women who reported no use of HT had
been reimbursed and prescribed HT according to the Social Insurance Institution of Finland
data (Kansaneläkelaitos) (Sandini et al. 2008). Unfortunately, it is not possible to evaluate
how women actually used the prescribed and reimbursed HT. Thus, although some
uncertainty remains about the use of HT, Greendale et al. have reported that spinal BMD
values after short term use of HT (under 3 years) do not differ from the BMD values of
never users of HT (Greendale et al. 2002).
Study IV was based on prospective data, which avoids much of the inherent bias of the
cross-sectional and retrospective studies. BMI was measured on each occasion, which
reduces the risk bias. Repeated measures on the subjects were analysed by applying mixed
models framework in the form of random coefficients regression (Brown, Prescott 1999).
DXA and body anthropometry measurements were modeled as a polynomial function of
time (years since menopause or age) taking into account the intra-subject correlation of the
measurements. The effect of BMI as a continuous variable was included into the model as a
time-varying covariate. Thus, the statistical model implicitly accounts for the cyclical
changes of BMI. The alternative available techniques, such as repeated measures of
ANOVA, would have oversimplified the results. Some of the covariates (grip strength,
55
diseases and medications affecting bone, smoking and calcium intake) were adopted from
baseline data, which reduces their accuracy in the analyses. Maximum grip strength value
of the dominant hand may differ from measurement values adopted with the three
successful measurements of right hand grip strength values used in the present study.
However, both the maximum grip strength and the mean of three successful values are
considered as methodologically valid and reliable (Bohannon et al. 2007).
6.2 DISCUSSION OF RESULTS
6.2.1 The effect of adiposity on cross-calibration between two different DXA devices (I)
If adiposity is not taken into account during regular in vivo cross-calibration between two
different DXA devices then this could well lead to false conclusions (Blake, Harrison &
Adams 2004). In the present cross-calibration of the older DPX and the newer DPX-IQ
densitometers, obesity affected hip, but not spinal results. Interestingly, spine is generally
considered more sensitive to fat related inaccuracies (Bolotin 1998, Bolotin, Sievanen &
Grashuis 2003). The image resolution of newer DXA modalities has improved significantly
from that achieved with the older DXA densitometers (Mazess et al. 2000, Blake, Harrison
& Adams 2004). Compared to older pencil-beam DPX-L device the newer fan-beam
Prodigy overestimated the total body mass and total body BMD at high bone density
(Huffman et al. 2005). Importantly, 40% of the variance in the difference of measured spinal
BMD values by DPX-L and Prodigy devices could be explained by systematic differences in
body weight and body soft tissue composition measured with the devices (Blake, Harrison
& Adams 2004). In conclusion, significant differences may exist between different
densitometers even from the same manufacturer. A phantom-based cross-calibration does
not adequately correct for site-specific differences between the instruments. Thus, adiposity
needs to be taken into account during in vivo cross-calibration. In all ROIs, including the
trochanter BMD ROI, the slopes of the regression lines were significantly different from
unity, emphazing the need for the application of correction equations between the two
densitometers. According to ISCD correction coefficients are needed if the difference
between the values of the two densitometers exceeds 1% (Shepherd et al. 2006). Thus,
correction between DXA devices is not compulsory as the difference between DPX and
DPX-IQ densitometers in trochanter BMD values was only 0.1%. However, also in
trochanter ROI, closer agreement was observed in BMD values between DPX and DPX-IQ
devices after simple and multivariate correction coefficients were applied compared to
original values.
Furthermore, both body composition and bone mineral estimates by different DXA
devices are known to vary (Blake, Harrison & Adams 2004). Thus, the present results
cannot be extrapolated to DXA devices from different manufacturers or even to different
devices from the same manufacturer. Furthermore, the calculated correction equations
between DPX and DPX-IQ devices from the present study might not be equally
interchangeable when other DXA devices are in use. Indeed, there may be considerable
differences even between similar devices from the same manufacturer due to differences in
calibration, detector-problems or errors after maintenance (Gaither et al. 1996). Importantly,
56
regular phantom measurements can detect any drift in machine calibration (Shepherd et al.
2006).
6.2.2 The current role of adiposity in DXA, pDXA, and QUS measurements (II)
It is well known that both DXA and QUS techniques are affected by adiposity-related
artifacts (Chappard et al. 2000, Javed et al. 2009). Furthermore, overweight protects from
osteoporosis (Stewart et al. 2006, Reid 2010). According to the present study (II) there may
be considerable variation in how adiposity affects the results between different devices and
techniques. The effect of adiposity on central DXA, pDXA and QUS values has not
previously been examined. The association between anthropometrical measurement values
and heel QUS parameters (i.e. BUA and SOS) remains somewhat controversial. Some
studies indicate that both BUA and SOS values (including GE Lunar Achilles QUS) may be
associated with anthropometrical parameters (Stewart et al. 2006, Brunner, PonsKuhnemann & Neuhauser-Berthold 2011). In contrast, in the present study Achillesmeasured heel BUA values were associated with adiposity, whereas none of the
anthropometrical parameters were associated with heel SOS values. However, some studies
have reported that anthropometrical values are typically associated with BUA but not with
SOS values (Hologic Sahara QUS) (Schoffl et al. 2008, Kauppi et al. 2009). Furthermore, the
present results are in accordance with the results of Stewart et al. that stiffness index values
were associated with higher body weight and FM (kg), but in the present study (II) the
stiffness index was not associated with relative (%) FM (Stewart et al. 2006). Thus, it seems
that overall adiposity is associated with higher BUA values, whereas the association
between adiposity and SOS values is somewhat obscure and may partly depend on the
QUS device being used. In contrast, an increase in the local heel fat thickness may
underestimate heel SOS, but not BUA parameters (Hausler, Rich & Barry 1997).
Unfortunately, no measurements were available for either os calcis width or local heel soft
tissue thickness in the present study II. However, after adjustment for heel BMD (as a crude
surrogate estimate of bone size), heel BUA was no longer associated with the relative (%)
total-body FM, whereas an inverse relationship was found between the heel SOS and
adiposity. There are also inconsistent results regarding the effect of LBM on QUS
parameters. Brunner et al. suggested that it is LBM rather than FM which can affect QUS
parameters in elderly women (GE Lunar, Achilles) (Brunner, Pons-Kuhnemann &
Neuhauser-Berthold 2011). In contrast, LBM was associated with QUS parameters (Hologic
Sahara) in premenopausal women, but not in postmenopausal women (Saadi et al. 2003). In
the present study (II) LBM (kg) was not associated with any QUS parameters.
In the present study (II) increasing adiposity was associated with higher pDXAmeasured normalized heel BMD values similar to those measured by central DXA in
lumbar spine ROI. However, higher BMD values were observed with increasing adiposity
especially in lumbar spine ROI in the present study (II). Consequently, DXA technology
seems to overestimate especially spinal BMD values as obesity becomes more marked
(Bolotin 2004). However, the Prodigy-DXA device was less prone to BMD errors
attributable to excess soft-tissue thickness, being clearly superior to older DXA machines
(Mazess et al. 2000). According to the meta-analysis conducted by Ho-Pham et al. of the
body composition parameters LBM is the main determinant of femoral neck and lumbar
spine BMD values in both men and premenopausal women, whereas in postmenopausal
women both LBM and FM are positively associated with femoral neck (e.g. LBM, r=0.33 and
57
FM, r=0.31) and lumbar spine (e.g. LBM, r=0.34 and FM, r=0.31) BMD values (Ho-Pham,
Nguyen & Nguyen 2014). In the present study (II) both LBM (kg) and FM (kg) were
associated with femoral neck (e.g. LBM, r=0.23 and FM, r=0.23) and lumbar spine (e.g. LBM,
r=0.21 and FM, r=0.39) BMD values, whereas relative (%) FM was only associated with
lumbar spine (r=0.34), but not with femoral neck (r=0.15), BMD values. Furthermore, if both
body composition parameters (FM and LBM) were included in the same multivariate linear
regression model, then LBM (kg) was the only determinant of femoral neck BMD values
and FM (kg) was the only determinant of lumbar spine BMD values. Thus, especially
higher adiposity seems to be associated with higher DXA, pDXA and QUS values in the
present study, but it is important to prevent sarcopenia as higher LBM was positively
associated with clinically important femoral neck BMD values. It is difficult to assess the
extent to which the discrepancies between different ROI’s and adiposity are of biological
origin (Mendez et al. 2012). However, it is important to note that adiposity affects the
results and, consequently the same individual might be over- or underdiagnosed
depending on the equipment in use (Javed et al. 2009). Finally, according to present study
(II) weight seems to be the strongest determinant of DXA, pDXA and QUS parameters
(except heel SOS) of all anthropometrical parameters (i.e. FM, LBM, %FM, weight, BMI and
height).
In the present study, the devices were from the same manufacturer. However, there were
inconsistencies in T-score and normalized (z-score) values especially between the
peripheral and central devices when they were used in the present study population. These
inconsistencies cannot be explained by fat-related measurement errors or biological
differences. Therefore, there are also extra problems related to use of different reference
populations for normalization (Blake, Fogelman 2009). The same study sample should be
measured with different devices to ensure an optimal and unbiased comparison between
devices. In conclusion, the effects of adiposity on DXA, pDXA and QUS parameters may
differ significantly depending on the device and manufacturer selected.
6.2.3 The effect of central obesity and HT on bone densitometry (III)
In the present study, central distribution of body fat (i.e. trunk fat) was associated
especially with spinal, not with hip, BMD values. As described in the review of the
literature, there are several biological reasons that may link together central adiposity and
bone density positively. For example, centrally obese women have high estrogen,
testosterone, leptin and lower adiponectin levels as well as exposing their bones to higher
mechanical loading compared to leaner women (Biver et al. 2011, Liedtke et al. 2012).
Unfortunately, hormone levels were not measured in the present study. The relative
proportion of trabecular bone is much higher in the lumbar spine (60-95%) compared to
that in the greater trochanter (50%) and femoral neck (25%) (Riggs et al. 1982, Wells et al.
2002).
However, especially in the lumbar spine, increasing adiposity inside and adjacent to the
bone may reduce X-ray beam attenuation and thus overestimate the BMD (Bolotin 2007).
On the other hand, the Prodigy device, used in the present study, may be less prone to
errors attributable to excess soft-tissue on BMD measurements as compared to older
equipment (Mazess et al. 2000). It was not possible with the DXA technology used at the
time of the present study to separate VAT and SAT in the way now possible with iDXA or
58
Prodigy densitometers running enCORE software. Therefore, the results obtained in the
present study may not completely reveal the true relationship between VAT and BMD.
Furthermore the validity of the DXA-measured trunk-leg fat ratio for measuring central
obesity has not been adequately established (Zillikens et al. 2010). However, there is a
relatively good correlation between CT and DXA-measured abdominal fat (Snijder et al.
2002). Different surrogate measures for the relationship between central obesity and BMD
have shown inconsistent results. More sophisticated methods, such as CT or MRS, have
consistently revealed the detrimental effect of VAT on premenopausal BMD values (Sheu,
Cauley 2011). Moreover, obesity may be associated with a higher vertebral fracture risk in
postmenopausal women (Pirro et al. 2010). Thus, the positive association between central
obesity and BMD may at least partly reflect the methodological limitation of DXA
technology. However, trunk fat as measured by DXA contains both SAT and VAT, which
may explain also the positive association between spinal BMD and trunk fat (Bredella et al.
2010).
Is obesity acceptable from a medical perspective? Optimisation of peak bone mass
should be the cornerstone in efforts to prevent osteoporosis and subsequent fractures
(Mughal, Khadilkar 2011). Further research is needed to clarify the association between
childhood obesity and accrual of peak bone mass, however obesity and a sedentary lifestyle
cannot be recommended in children or early adulthood (Mughal, Khadilkar 2011). Even
though obesity and overweight are associated with higher BMD (Mendez et al. 2012), these
conditions also may also be linked associated with an increased risk of osteoporosis (Greco
et al. 2010). Similarly, the increasing prevalence of obesity in the population has not
necessarily been associated with a significant reduction in the number of osteoporotics
(Looker, Flegal & Melton 2007). Furthermore, a higher BMI and more fat do not
proportionally elevate bone strength (Sornay-Rendu et al. 2013). The relative bone strength
index of tibia was inversely associated with adiposity, but not with LBM, in three
generations of Finnish women (Xu et al. 2010). The ability of the tibia to support excess
weight of fat was worse in pre- and postmenopausal women as compared to the situation
in girls due to lower periosteal apposition which is needed to increase diaphyseal width
(Xu et al. 2010). Sarcopenia and osteoporosis are linked together (Sirola, Kröger 2011).
Obese adults are believed to have a higher falling tendency, lower physical activity and
function levels (Beck et al. 2009). Furthermore, obesity is sometimes associated with low
muscle mass, hence, the term sarcopenic obesity (Dupuy et al. 2013). In some individuals,
obesity may be regarded as a “high-caloric state of malnutrion” but also dieting may be
associated with nutritional deficiencies (Wells 2013). For example, obesity is often
associated with vitamin D deficiency (Vimaleswaran et al. 2013). However, overweight
subjects are less prone to osteoporotic fractures (De Laet et al. 2005b). On the other hand,
hip protectors and compliant flooring materials will also decrease the impact force and
fracture risk (Laing et al. 2011, Li, Tsushima & Tsushima 2013). Only about 30% of obese are
spared from metabolic complications (Bluher 2012). But even in those cases obesity is
associated with significant co-morbidity (Forte et al. 2012). Especially men as well as
postmenopausal and elderly women are at risk of accumulating VAT in comparison to
premenopausal women (Nedungadi, Clegg 2009, Shi, Clegg 2009). Central adiposity as
measured by DXA has been positively associated with BMD in postmenopausal women
(Zillikens et al. 2010). However, VAT and bone marrow fat have been shown to have
negative effects on bone when this is assessed with more sophisticated methods (Sheu,
59
Cauley 2011). Women are not spared from the other co-morbidities associated with VAT
(Shi, Clegg 2009). Thus, across the human lifespan obesity seems to be more detrimental
and is therefore not desirable in most cases. The increased risk of suffering the many other
co-morbid conditions outweighs the protective effects that being overweight confers on
avoiding osteoporosis and subsequent fractures (Forte et al. 2012).
Should we encourage people to be thin or underweight? Anorexia nervosa is associated
with lower BMD and severe medical complications (Misra, Klibanski 2011). Most hormonal
and metabolic disturbances return to normal values after renutrition in anorexic patients
(Howgate et al. 2013). However, especially the thinnest individuals are at risk of developing
osteoporosis and subsequent fragility fractures (Dargent-Molina et al. 2000, FernandezGarcia et al. 2009). For example, even a 5% weight loss especially in relatively thin middleaged individuals increases the fracture risk (Langlois, Mussolino et al. 2001). The
association between mortality and weight is j- or u-shaped, thus malnutrition and thinness
across the lifespan is a serious health concern (Wahlqvist, Chuang 2012) i.e. being
underweight is not recommendable.
Thus, ideally maintaining a healthy body weight is a good strategy for bone health
because huge swings in body weight and weight loss lead to enhanced bone loss (Villalon
et al. 2011). Should some people gain weight? Anorexia nervosa is associated with an
increased amount of VAT and bone marrow fat, whereas weight gain improves body
composition parameters (Fazeli et al. 2012). For the thinnest postmenopausal women, who
are at a greater risk of osteoporosis and fragility fractures, it may be advisable that they
should gain some weight, preferably by increasing muscle mass and secondly by increasing
SAT (De Laet et al. 2005a, Rikkonen et al. 2010). Should some people abstain from weight
loss? In the oldest (≥80 years) and in frail individuals, it may be advisable to abstain from
recommending weight loss (Darmon 2013). Who should lose weight? In overweight or
obese individuals, a weight reduction of 10% or more is recommended due to the reduction
this achieves in co-morbid risk factors (Barte et al. 2010). In the elderly, weight loss is
especially important in obese individuals with co-morbid conditions or functional
impairments (Mathus-Vliegen 2012).
What is the role of exercise? Physical activity is associated with less sarcopenia less bone
loss and conversely inactivity whether caused by immobilization or a sedentary lifestyle
has been shown to reduce LBM and BMD values (Sutton et al. 2009). The physical activity
level is inversely associated with BMI (Langsetmo et al. 2012). A greater muscle mass is
linked with better balance and this reduces falls associated with osteoporotic fractures
(Shapses, Sukumar 2012). Immobilization seems to be more detrimental in the elderly than
in younger subjects (Hvid et al. 2010). A vicious circle is evident because low BMD and
osteoporotic fractures increase the risk of immobility (Shiraki et al. 2010). Muscle work
causes dynamic loads, which promote bone formation more than a mere static load due to
overweight, although fat may indirectly affect the amplitude of the stimulus (Pfeifer et al.
2004). Exercise also improves physical function during weight loss in older obese adults
(Villareal et al. 2011). However, in anorectic patients, exercise does not improve BMD
(Waugh et al. 2011). Strength exercise, multi-component exercise (strength, aerobic, high
impact and/or weight-bearing training) and whole-body vibration have been found to
improve bone health (Gomez-Cabello et al. 2012). In contrast, walking is less effective for
preventing bone loss and during slippery weather it is recommended that the individuals
60
should wear to use anti-skid devices on their shoes to prevent falls and activity fractures
such as wrist fractures (Rikkonen et al. 2010, Gomez-Cabello et al. 2012). Especially in
elderly people, an evaluation of bone health before embarking on an exercise program
might be advisable, because heavy lifting is a risk factor for spinal compression fractures
(Wolfram, Wilke & Zysset 2011).
What should be taken into account during a weight loss program in osteoporosis and
sarcopenia point of view? For example, around 500 kCal deficiency/day is recommended in
elderly (Mathus-Vliegen, Obesity Management Task Force of the European Association for
the Study of Obesity 2012). Thus, if weight loss is necessary for improving cardiovascular
or general health, then adequate protein (1 g/kg/day) and calcium supplementation (1.7-1.8
g/day) as well as a regular exercise protocol should be included in the weight loss program
in order to attenuate bone loss secondary to weight loss (Shapses, Sukumar 2012). Vitamin
D, adequate protein intake and exercise can also prevent occurrence of sarcopenia (von
Haehling, Morley & Anker 2012). Obese individuals may need higher doses of vitamin D
supplements to achieve the same vitamin D level compared to leaner subjects (Gallagher,
Yalamanchili & Smith 2012). Vitamin D and calcium may also help in weight management
(Soares et al. 2012). HT is also known to attenuate weight loss-related bone loss; however
the risks and benefits of HT need to be carefully assessed before initiation of HT for
reducing bone loss during weight reduction program (Sirola et al. 2003, Howgate et al.
2013). Both estrogen therapy and bisphosphonate therapy may reverse the BMD loss
associated with anorexia nervosa (Howgate et al. 2013).
In the present study the effect of central obesity on BMD was not modified by the use of
HT. There are no previous studies investigating the interaction between HT, central obesity
and BMD. Users of HT are generally healthier than non-users (Grimes, Lobo 2002) and this
was also found in the present study. Users of HT were thinner and had less abdominal fat,
which is in accordance with most previous studies (Salpeter et al. 2006). However, due to
the cross-sectional nature of the present study, it is not possible to rule out a selection bias
as the effects of HT on body composition and weight were not followed up prospectively
(Bhandari et al. 2003).
6.2.4 Overweight and progress of bone loss (IV)
The observed protective effect of overweight, especially against lumbar spine osteopenia in
the present study, may not reflect the entire truth, as both conditions i.e. overweight and
underweight cause measurement inaccuracies in BMD i.e. overestimating and
underestimating BMD values, respectively (Bolotin 2004). Thus, the protective effect of
overweight against spinal bone loss would be reduced if DXA devices were not affected by
fat-related artefacts (Bjarnason, Christiansen 2000, Bolotin 2004, Mendez et al. 2012). In
contrast to some previous studies (Hannan et al. 2000, Macdonald et al. 2005), in the present
study the absolute bone loss rate was not affected by the presence of obesity. Of course one
must keep in mind that the relative bone loss rate of 3% would absolutely correspond to a
bone loss of -0.015 g/cm2 from an initial BMD level of 0.5 g/cm2 and -0.030 g/cm2 from a
value of 1.0 g/cm2. It is possible to estimate the effect of weight change on BMD results with
the help of the prediction models used in the present study. However, especially during
times when body weight is changing, the reliability and precision of planar DXA
measurements may be diminished (Bolotin, Sievanen & Grashuis 2003). Inherent errors
associated with body thickness and DXA technology as well as the relatively low number of
61
underweight and morbidly obese women in the present study limits the ability to estimate
the extent of bone loss in extreme cases. Furthermore, comparison of T-score results based
on manufacturers’ reference data between Lunar DPX and Hologic QDR-1000/W showed a
mean difference of -0.9 SD for femoral neck BMD (Faulkner, Roberts & McClung 1996). The
mixed models used in the present study IV are less prone to regression towards the mean
when compared to less sophisticated methods for analysing repeated measures of
longitudinal data (Peduzzi et al. 2002). The number of osteoporotics at the hip ROI might
differ significantly between manufacturer-based reference values compared to NHANES
normative database (Blake, Fogelman 2009).
The rapid perimenopausal spinal bone loss was confirmed also in the present study
(Mazzuoli et al. 2000, Finkelstein et al. 2008). Similarly, a cessation of rapid bone loss has
been observed during later menopause (Mazzuoli et al. 2000, Finkelstein et al. 2008). In the
present study, AP lumbar spine BMD values increased after 15 postmenopausal years
similarly to findings of Liao et al. (Liao et al. 2003). According to NHANES, spinal bone loss
seems to reach a plateau or even to increase in women over 70 years of age (Looker et al.
2012c). CT-derived spinal BMD also seems to plateau in the elderly similar to DXA
measurements (Blake, Fogelman 2009). In contrast, some studies have not detected any
increase in spinal BMD values in elderly women (Hammoudeh et al. 2005, Larijani et al.
2005). However, this may not accurately reflect the actual skeletal situation in the elderly
due to inherent errors of planar DXA technology that may result in artefactual minor
changes in the scan area (Gregson et al. 2013). Thus, there might have remained some
artifacts such as minor degenerative changes or vertebral compressive fractures unobserved
in the present study. It is not known whether improved algorithms and resolution of
modern DXA technology are less prone to artefacts attributable to degenerative processes
in the spine (El Maghraoui, Roux 2008). Thus, the risk for falsely elevated AP spinal BMD
values should be taken into account, especially in the elderly (Gregson et al. 2013). The
recommended site for osteoporosis diagnosis in late postmenopausal women is the
proximal femur (Lewiecki et al. 2008). In accordance with previous studies, a steady
progressive age-related cortical femoral neck bone loss was apparent in postmenopausal
and elderly women (Looker et al. 2012b). Indeed, plain radiographs and other techniques
such as visual semiquantitative technique assessment or digital computerized
morphometry of the spine may be helpful in revealing undetected spinal fractures (Difede
et al. 2010). Trabecular bone predominates at the spine whereas cortical is the major bone
form at the femoral neck, and this might explain the observed differences in the bone loss
rate between the femoral neck and the spine during the period of estrogen depletion in the
perimenopausal women (Riggs et al. 1982, Wells et al. 2002).
62
63
7 Suggestions for future research
Some future research plans have emerged on the basis of this thesis. Future experiments
could:
1.
Examine whether adiposity affects the cross-calibration of other commercial
DXA-instruments.
2.
Clarify the association between Lunar iDXA-measured VAT and BMD values.
3.
Quantify the relative importance between body composition and densitometric
parameters.
4.
Analyse the relationship between obesity, osteoporosis and fragility fractures.
5.
Study whether alternative osteoporosis medications other than HT could
reduce weight loss induced bone loss.
64
65
8 Conclusions
1.
Adiposity should be taken into account in in vivo cross-calibration between
different DXA devices.
2.
DXA, pDXA, and QUS parameters were differently affected by adiposity.
3.
Postmenopausal women with central body fat distribution are associated with
higher spinal BMD, but not with hip BMD, regardless of HT use.
4.
Overweight may delay the incidence of spinal and hip osteopenia considerably.
66
67
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Jarmo Saarelainen
Obesity and Bone
Mineral Measurements
in Postmenopausal Women
Dual-energy X-ray absorptiometry
(DXA) is commonly used to
diagnose osteoporosis. Overweight
is considered to confer protection
against from osteoporosis, but
DXA devices may be subject to
systematic errors when they are
measuring overweight individuals.
This study aimed to assess how
obesity can affect bone mineral
measurements.
isbn 978-952-61-1584-9

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