PRESENTATION NAME

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PRESENTATION NAME
Done by:
Mariam Hafiz
Sahar Bin Afif
Supervisrd by:
Mariam Al-Hilou
Eman AL-Omari
Mariam AL-Helou
Dr. Rania Hussien
1
Contents :
•
•
•
•
•
•
Introduction.
Methodology.
Results.
Discussion.
Conclusion.
Acknowledgement.
Mariam Al-Hilou
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Introduction
Mariam Al-Hilou
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Premenstrual Syndrome
(PMS)
• A common cyclic disorder of young and
middle-aged women.
• Characterized by emotional and physical
symptoms.
• Occurs during the luteal phase of the
menstrual cycle.
Mariam Al-Hilou
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premenstrual dysphoric
disorder
• Women with more severe affective symptoms are
classified as having premenstrual dysphoric
disorder. (PMDD)
Mariam Al-Hilou
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Common Symptoms of
Premenstrual Syndrome
• Behavioral symptoms.
• Psychologic symptoms.
• Physical symptoms.
• The length of symptom expression varies between a
few days and 2 weeks.
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The key criteria for a diagnosis of
PMS
1- Symptoms consistent with PMS.
2- Consistent occurrence of symptoms only during the
luteal phase of the menstrual cycle.
3- Negative impact of symptoms on some facts of the
woman’s life.
4- Exclusion of other diagnoses that may better explain the
symptoms.
Mariam Al-Hilou
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Etiology and Pathophysiology:
• The etiology of PMS is multifactorial.
• researchers suggest a variety of causes like:
1.
2.
3.
4.
Altered regulation of neurohormones and neurotransmitters.
Enhanced sensitivity to progesterone in women with underlying
serotonin deficiency.
Deficiencies in prostaglandins.
Genetic factors and Others.
Mariam Al-Hilou
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Management:
• Diagnosis is best achieved through daily rating
symptoms over at least one menstrual cycle; by:
- Ask patients to choose their worst symptoms and chart
the severity daily.
- Select a validated scale such as the Daily Record of
Severity of Problems.
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Glycemic Index
• The glycemic index is a numerical ranking of
carbohydrate-containing foods, based on
their potential to raise blood sugar levels.
• It allows an individual to indirectly estimate
both blood glucose and insulin levels.
• Glycemic index of white bread and white
sugar equal 100.
Mariam Al-Hilou
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Glycemic Load
• The glycemic load is calculated by
multiplying the amount of carbohydrate in a
given serving of food by the glycemic index
of that same food and then dividing that
number by 100.
Mariam Al-Hilou
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• Although books and magazine articles for the lay
public have touted the benefit of restricting sugar
and eating multiple small meals as management of
PMS, there is little support for these strategies.
•
At the same time, studies of diets that ↑relative
intake of carbohydrates suggest benefit, which might
be due to an enhanced transport of tryptophan into
the brain, leading to a transient increase in the
synthesis of the serotonin.
Mariam Al-Hilou
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Subjects and Method
Eman Al-Omari
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Goal
To study the relation between premenstrual
syndrome and dietary glycemic load in adolescent
girls, students at KAU, Jeddah.
Eman Al-Omari
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Objectives
1. To estimate the prevalence of premenstrual
syndrome in adolescent girls in KAU.
2. To identify the difference in MDQ
(menstrual distress questionnaire) total and
subscale scores according toDGL dietary
glycemic load and DGI(dietary glycemic
index)
3. To correlate between MDQ total and
subscale scores and different dietary and
non dietary variables.
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Methods
Study design:
Cross sectional study.
Study setting:
Faculty of Applied Medical Sciences, KAU, in
Muharram 1432
Target population:
adolescent girls , students at faculty of Applied
Medical Sciences , KAU, Jeddah.
Eman Al-Omari
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Sampling Design
•
Type of sample: Convenience sample
•
Sample size : 166 female students
Groups excluded:
5-Those taking vitamins or
1-Married people
mineral supplements.
2-Those who diagnosed as diabetic and
thyroid diseases patients
6-Those taking diuretics,
3-Those currently taking steroid hormones
analgesics, prostaglandin
inhibitors, and
4-Those who had few or no menstruations
antihistamines.
during the previous year
7-Those currently receiving
dietary counseling.
Eman Al-Omari
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Participants & Procedures
The questionnaire content:
1. PMS Assessment:
Menstrual cycle symptoms during the preceding
month were assessed using of the retrospective
version of the Moos Menstrual Distress Questionnaire
(MDQ) .
Eman Al-Omari
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1. PMS Assessment (CON.)
•
The % of subscale PMS relative to intermenstrual
phase = (The scores of subscale symptoms at the
week before the beginning of menstrual flow) /
(scores of subscale symptoms at the intermenstrual
phase ) ×100
•
The % of total PMS relative to intermenstrual
phase = (The total score at the week before the
beginning of menstrual flow) / (total score at the
intermenstrual phase ) ×100
Eman Al-Omari
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2- Assessment of other variable
Weight
Height
Age at menarche
date of the start of the most recent (or
current) menstrual flow
• usual length of the menstrual cycle
• usual number of days of bleeding
• The phase of the menstrual cycle
•
•
•
•
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3-Dietary Assessment
 Using food frequency questionnaire :
 Consumption frequency and number of portion
sizes of:
14 protein rich
foods and
beverages
5 added
fats
23 of carbohydrate
rich selected
food
 Consumption and frequency of caffeinated
beverages
 Calculating DGL= DGI × total daily carbohydrate
intake / 100
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Statistical Analysis
Statistical program:
1-Descriptive statistics
2-Analytical statistics:
One way anova
Pearson correlation
Multiple Linear regression
Eman Al-Omari
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Results
Mariam Hafiz
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Description of anthropometrical and
dietary characteristics of the sample
VARIABLE
RANGE
MEAN ± SD
Height in (m)
1.43-1.70 m
1.57 m ±6.08
Weight in (Kg)
40-100 kg
54.1 kg ± 10.7
Body Mass Index in (Kg/m²)
15.6-39.1
21.7± 3.8
Caffeine Intake in (mg)
0-342
67.8±65.3
Dietary glycemic load
36.4-346.3
90.5 ±47.01
Dietary glycemic index
44.2-85.9
59.9± 5.66
Number of carbohydrate servings
0.59- 5.73
1.51 ±0.8
Refined carbohydrate (g)
41-479.30
.9± 72.3129
Non refined carbohydrate (g)
0- 210
31.6± 34.1
Number of protein servings
0.14-14.8
3.3± 2.3
Number of added fat servings
0-4.3
1.3 ±1.1
Mariam Hafiz
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Description of menstrual
characteristics of the sample
VARIABLE
RANGE
MEAN ± SD
Age at menarche (years)
10-17
13.3±1.3
cycle length (days)
14-90
27 ±7.3
number of bleeding days
4-15
6.7±1.5
MDQ subscale scores:
Pain
Impaired Concentration
58.3-350
66.6-400
154±57.1
126.8±47.6
Behavioral changes
21.7-500
153.3±74.3
autonomic reaction
30.7-400
123±59.4
Water retention
61.5-400
148.2±55.5
Negative affect
45.8-500
161.2±68.5
Exciting
36.8-500
118.4±49.3
Undefined symptoms
47-280
109.2±26.4
MDQ total score
52.8-365.2
134.6±38.1
Mariam Hafiz
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Distribution of the whole sample
according to prevalence of PMS
manifestations
Mariam Hafiz
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Distribution of the whole sample
according to phase of menstrual
cycle during data collection period
Mariam Hafiz
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Average daily intake of refined
versus non refined carbohydrates
among the whole sample
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Difference between Menstrual Distress
Questionnaire total and Subscale Scores according
to DGL, and DGI
MDQ sub scale
DGL
DGI
pain
1.820
1.189
Impaired Concentration
4.605‫٭٭‬
.098
Behavioral change
1.927
.210
Autonomic reaction
4.234‫٭٭‬
.022
Fluid retention
3.324‫٭‬
.182
Negative affect
1.386
.043
exciting
2.362
.985
Undefined symptoms
3.488‫٭‬
1.938
MDQ total
3.603‫٭‬
.026
All reported P valus are 2 -tailed
*: correlation is significant at the 0.05 level
**. Correlation is significant at the 0.01 level
Mariam Hafiz
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Correlation coefficients of some dietary factors
with other anthropometric, dietary, and menstrual
variables
VARIABLES
CAFFEINE
Body Mass Index in (Kg/m²) 0.171*
DGL
DGI
PROTEIN
SERVINGS
FAT
SERVINGS
-0.081
-0.025
0.030
-0.078
Caffeine Intake in (mg)
1
0.102
0.118
0.025
0.019
Age at menarche (years)
-0.100
0.030
0.030
0.103
-0.055
cycle length (days)
-0.028
0.025
0.145
-0.041
0.018
number of bleeding days
-0.097
0.085
-0.082
-0.022
0.094
MDQ subscale scores:
Pain
0.051
-0.054
0.064
-0.100
0.021
Impaired Concentration
0.001
0.24**
0.030
0.107
0.065
Behavioral changes
-0.018
0.20*
0.001
0.003
-0.010
autonomic reaction
0.193*
0.334**
-0.019
0.073
-0.027
All reported P values are 2 -tailed
*: correlation is significant at the 0.05 level ( 2- tailed )
**: correlation is significant at the 0.01 level ( 2- tailed )
Mariam Hafiz
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Correlation coefficients of some dietary factors
with other anthropometric, dietary, and menstrual
variables
VARIABLES
CAFFEINE
DGL
DGI
FAT SERVINGS
-0.016
PROTEIN
SERVINGS
0.074
Water retention
0.027
0.159*
Negative affect
0.056
-0.003
0.019
-0.093
-0.087
Exciting
0.120
0.068
-0.002
0.036
-0.029
Undefined symptoms
0.035
0.108
-0.105
0.100
-0.121
MDQ total score
0.082
0.116
0.014
-0.011
-0.038
Dietary glycemic load
0.102
1
0.167*
0.569**
0.304**
Dietary glycemic index
0.118
0.167*
1
-0.044
0.009
Number of protein servings
0.025
0.569**
-0.044
1
0.362**
Number of fat servings
0.019
0.304**
0.009
0.362**
1
Mariam Hafiz
0.011
31
Regression of MDQ total score quintiles
with different subscale scores quintiles
QUINTILES OF MDQ TOTAL SCORE IN THE PREMENSTRUAL PHASE
MDQ percentile
total score
MDQ subscale
scores:
Pain
Impaired
Concentration
Behavioral changes
1 (n= 33)
2(n= 33)
3 (n= 33)
4(n= 34)
5(n= 33)
96.8 ± 12.4
111.5 ± 4.2
126.8 ± 4.2
144.2 ± 7.4
193 ± 38.8
111.2 ± 35.7
134.6 ± 32.4
151 .1 ±
36.3
172.4 ± 54.7
202 ± 70.2
0.243***
100.1 ± 12.4
100.8 ± 11.6
101.2 ± 28.4
117.1 ± 22.1 134.4 ± 32.5 154.9 ± 34.5
autonomic reaction
93 ± 22.7
104.4 ± 23
Water retention
101.5 ± 20.4
135 ± 36.3
Negative affect
100.8 ± 16.2
Exciting
Undefined
symptoms
119 ± 23.4
129.9 ± 28.8 169.5 ± 67.1
242.2 ±
102.8
110.9 ± 28.4 115.3 ± 27.9 184.1 ± 91.5
0.197***
0.161**
133 ± 34.6
145.2 ± 33.4 183.4 ± 40.6 248.4 ± 89.5
0.363***
96.5 ± 27.3
95.3 ± 19.6
110.1 ± 25.5 123.9 ± 37.3 166.2 ± 80.2
96.1 ± 18.5
100.6 ± 7.4
106.2 ± 17.6 117.5 ± 26.3
Mariam Hafiz
160 ± 41.2
0.212***
214.8 ± 70.8
***: correlation is significant at the 0.001 level ( 2- tailed )
149.4 ± 41
Regression
coefficient
(Beta)
130 ± 43
32
Discussion
Mariam Hafiz
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• In our study, age of girls ranged
between 19 and 22 years, the
prevalence of PMS among them was
89%.
• This is consistent with Pray W S, Pray J J
study, who found that approximately 40% to
90% of females report symptoms of PMS.
Mariam Hafiz
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Our study revealed that negative
affect had the highest score among
PMS subscales , with a mean± SD
equal to 161.2±68.5.
• In a study of adolescent girls, researchers
discovered that the most common
manifestation of PMS was negative affect
•
Mariam Hafiz
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• Excitement scored very low (mean±
SD= 118.4±49.3) , moreover showed an
absent correlation with any of dietary
variables (caffeine, DGL, DGI, protein,
fat).
• Parlee's study found the same observation on
a small group of women.
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• The etiology of PMS is a result of an interaction
between sex steroids and central
neurotransmitters.
• The levels of sex steroids, estrogen, progesterone,
and testosterone are normal
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Serotonergic dysregulation with
reduced serotonergic function in the
luteal phase is the most plausible
theory
1. carbohydrate ingestion → ↑tryptophan→↑
serotonin → Mood improvement
2. carbohydrate cravers select carbohydraterich foods to self-medicate negative mood
Mariam Hafiz
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Dietary Habits of Saudi
adolescent girls as regards
carbohydrate intake
Mariam Hafiz
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• A study conducted on students in Abha
revealed that diets of adolescent female
students were rich in carbohydrates, and
deficient in fiber
• Similarly, in our study, refined
sugars and carbohydrates
constituted about 80 % of total
carbohydrate intake of our sample.
Mariam Hafiz
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Refined sugars may have a harmful
effect
1. Several Studies showed that PMS
patients consume more refined sugar
2. consumption of refined sugar may
deplete the body of its reserves of B
vitamins
→ reduced glucose metabolism
3. Refined sugar triggers insulin release
→ hypoglycemia and sugar craving
4. Refined sugars lack fibers
Mariam Hafiz
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Effect of dietary fiber on estrogen
metabolism
1. alter enterohepatic circulation of estrogen
→↑fecal estrogen excretion
2. ↓estrogen bioavailability
3. Direct action on hypothalamic pituitary
gonadal system
Mariam Hafiz
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Effect of dietary fiber on estrogen
metabolism
luteal administration of
estrogen has been reported to
aggravate PMS
Mariam Hafiz
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So it was logic to find a positive
correlation between DGL (with
80% as refined sugars) and
impaired concentration,
behavioral changes, autonomic
reactions , and Water retention
Mariam Hafiz
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• Five subscales were highly significant
predictors of MDQ total score: negative
affect, impaired concentration, pain,
behavioral change, and fluid retention .
• They could explain 71.4 % of MDQ total
score variation.
Sahr Bin Afif
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Our sample consumed a low fiber diet ,
still their DGI failed to predict any of
total or subscale scores in PMS:
explanation:
1. Person specific variables ..
2. Food specific variables ..
3. Heterogeneity of glycemic response among
persons who are obese versus lean.
4. No universal recognized objective measure
of carbohydrate quality .
Sahr Bin Afif
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Age at menarche
• Age at menarche ranged between
10 and 17 years, with a mean of
13.3±1.3 SD . This is consistent with a
study done in Saudi Arabia which
showed the mean as 13.05 years .
Sahr Bin Afif
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Age at menarche
• Age at menarche in our study showed a
non significant negative association with
PMS .
– earlier menstruation →↑number of menstrual
cycles → ↑ fluctuation of sex steroids .
– responsibility at younger age → physical and
psychological stress → PMS .
Sahr Bin Afif
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In our study, Caffeine intake showed
a significant positive association with
autonomic reactions: explanation:
parasympathetic nerve activity is lower in the
late luteal phase in women with PMS.
1. Caffeine is a CNS stimulant → ↑imbalance
in the Sympathetic and parasympathetic
divisions
2. Caffeine → ↓ blood flow to brain .
Sahr Bin Afif
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Caffeine Intake and PMS
• caffeine effect was unclear on impaired
concentration, behavioral changes, Negative
affect, and exciting.
• The explanation is that caffeine kinetics are
nonlinear:
Regarding psychomotor effects after caffeine
intake:
– ↓doses produce a favorable effect.
– ↑doses produce unpleasant effect.
Sahr Bin Afif
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Caffeine Intake and BMI
• Significant positive correlation was found
between caffeine intake and BMI.
• Obese and overweight girls might consume
more coffee to control their body weight.
Sahr Bin Afif
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Strengths & Limitations
Sahr Bin Afif
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Strengths
1- Adjustment of potentially
confounding variables.
2- providing a valuable insight from a
prevention perspective.
Sahr Bin Afif
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Limitations
1- The cross-sectional nature of the study did
not permit assessment of causality due to
uncertain temporal association .
Sahr Bin Afif
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Limitations (cont.)
2- Small sample size is not representative of the
Saudi adolescent population as a whole.
– However , It afforded sufficient statistical
power to detect differences in PMS with
DGL .
Sahr Bin Afif
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Limitations (cont.)
3- Subjectivity of the questions in the reference
to the intensity of symptoms , and in self
administered dietary assessment
questionnaire .
– However , the instrument was present for
all the girls and thus be considered a nondifferential source of error .
Sahr Bin Afif
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Limitations (cont.)
4- we assessed premenstrual symptoms using a
retrospective questionnaire (i.e., MDQ)
– providing an over estimation of symptom
severity
– rely on subjects’ memory of past
menstrual-related symptoms.
• However , The MDQ is the most widely
recognized and used questionnaire .
Sahr Bin Afif
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Conclusion
•In our study we found a significant positive association
between DGL and several MDQ subscale scores, including
impaired concentration, behavioral change, autonomic
reactions, and fluid retention.
•Caffeine intake correlated positively with autonomic
reactions in the premenstrual phase.
Eman Al-Omari
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Conclusion
The correlation between CHO quality with PMS
total and subscale scores need more
prospective studies to confirm whether
nutrition treatment can be used for women with
mild to moderate PMS .
Eman Al-Omari
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Acknowledgment
• We praise and thank Allah, who help us and drove us to
this scientific stage
• We would like to express our deepest thanks and
gratitude to our supervisor, Dr . Rania Hussein
for supporting this project and for the special effort she
exerted.
Eman Al-Omari
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References
•
•
•
•
•
•
•
Moos RH . The development of a menstrual distress questionnaire. Psychosomatic
medicine 1968;30(6):853-67.
Silva CM, Gigante DP, Minten GC. Premenstrual symptoms and syndrome according to age at
menarche in a 1982 birth cohort in southern Brazil. Cadernos de saúde pública / Ministério
da Saúde, Fundação Oswaldo Cruz, Escola Nacional de Saúde Pública 2008; 24(4):835-44 .
Neuhouser M L, Tinker LF, Thomson C , Caan B ,Horn L V, Snetselaar L , et al. Development of
a glycemic index database for food frequency questionnaires used in epidemiologic studies.
The journal of nutrition 2006; 136(6):1604-9
Babay Z A, Addar M H , Shahid K , Meriki N. Age at menarche and the reproductive
performance of Saudi women. Annals of saudi medicine 2004:354-6.
Matsumoto T, Ushiroyama T, Kimura T, Hayashi T, Moritani T. Altered autonomic nervous
system activity as a potential etiological factor of premenstrual syndrome and premenstrual
dysphoric disorder. Biopsychosocial medicine 2007 1:24
Kondo M, Hirano T, Okamura Y. Changes in autonomic nerve function during the normal
menstrual cycle measured by the coefficient of variation of R-R intervals. Nippon Sanka
Fujinka Gakkai Zasshi 1989; 41:513- 8.
Nehlig A, Daval JL, Debry G. Caffeine and the central nervous system: mechanisms of action,
biochemical, metabolic and psychostimulant effects. Brain research review 1992; 17(2):13970
Eman Al-Omari
61
•
•
•
•
•
Dickerson LM, Mazyck PJ , Hunter MH. Premenstrual syndrome.
American Family Physician 2003 ; 67(8) :1743-52.
Yonkers KA, O’Brien PM, Eriksson E. Premenstrual syndrome. Lancet
2008; 371: 1200–10.
Taylor D. Perimenstrual symptoms and syndromes: guidelines for
symptoms management and self care. Obstetrics and gynecology
2005;5(5):228-41.
Freeman EW, Sondheimer SJ, Rickels K. Gonadotropin-releasing
hormone agonist in the treatment of premenstrual symptoms with
and without ongoing dysphoria: a controlled study.
Psychopharmacology bulletin 1997;33:303-9.
Campbell B. Glycemic load versus glycemic index. National strength
and conditioning association. Available at http://www.nscalift.org/HotTopic/download/Glycemic%20Load.pdf. Accessed 2/1/
2011.
Eman Al-Omari
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