- Email: [email protected]
Metabolic syndrome and its association with obesity and lifestyle factors in Sudanese population
Accepted Manuscript Title: Metabolic Syndrome and its association with obesity and lifestyle factors in Sudanese population Author: H.E. Yasir O.A. Tahir B.M. Leena S.N. Imam PII: DOI: Reference:
S1871-4021(15)30033-3 http://dx.doi.org/doi:10.1016/j.dsx.2016.01.002 DSX 539
To appear in:
Diabetes & Metabolic Syndrome: Clinical Research & Reviews
Received date: Accepted date:
9-9-2015 4-1-2016
Please cite this article as: Yasir HE, Tahir OA, Leena BM, Imam SN, Metabolic Syndrome and its association with obesity and lifestyle factors in Sudanese population, Diabetes and Metabolic Syndrome: Clinical Research and Reviews (2016), http://dx.doi.org/10.1016/j.dsx.2016.01.002 This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.
Metabolic Syndrome and its association with obesity and lifestyle factors in Sudanese population
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Yasir HE1, Tahir OA2 , Leena BM 2, Imam SN1,*
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1. Department of Anatomy, faculty of medicine, Taibah University, Saudi Arabia.
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2. Department of physiology, faculty of medicine, National Ribat University, Khartoum, Sudan. * Corresponding author
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Email: [email protected]
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Yasir HE: Yasir Hassan Abd Alla Elhassan Tahir OA: Tahir Osman Ali
te
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Leena BM: Leena Babiker Mekki
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Imam SN: Syed Nazar Imam
Abstract
Although modern life style factors affecting health is a crucial problem globally, little information about metabolic syndrome (MetS) is available for the Sudanese population. With this consideration the study was planned to assess the prevalence of MetS among young people of Sudan and their association with obesity and lifestyle factors. Serum lipid profile, blood glucose and clinically established parameters for obesity were assessed in 179 young adult male and 201 females at National Ribat University, Sudan. Relevant statistical test were applied using
Page 1 of 16
SPSS software. Based on anthropometric measurements, 137 students were obese. Amongst the 243 non-obese students 5 were under weight, 135 normal weight and 103 were over weight. In the study population, 317 students were normal (83.4%) and 63 students had metabolic syndrome (16.6%) as defined by ATP III definition of MetS classification. MetS was found only
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in obese individuals (45.98%) and no case was detected in underweight, normal and overweight individual.. The mean of cholesterol level in subjects with metabolic syndrome was 159 as
cr
compared to those without it (149.93). Life style modification as healthy diet, regular exercise and preventive strategies may help reduce metabolic syndrome, thus improving general health
us
conditions in young individuals of Sudan.
an
Key words Metabolic syndrome, Obesity, Young Adult, Sudanese
M
Introduction
Metabolic syndrome (MetS) is a condition in which, set of risk factors increases the possibility of
d
developing heart disease, stroke, and type II diabetes. Other factors which influence the
te
susceptibility to this disorder include insulin resistance syndrome, dysmetabolic syndrome, as well as Syndrome X. Metabolic syndrome is a combination of physiological, biochemical,
Ac ce p
clinical and metabolic factors that directly increases the mortality of affected individuals [1,2]. Metabolic Syndrome is directly proportional with age as more than forty percent of individuals above sixty years suffer from this condition [3]. The criteria for diagnosis of MetS has been laid down by World Health Organization (WHO) [4], the European Group for the study of Insulin Resistance (EGIR) [5], the National Cholesterol Education programme Adult Treatment Panel III (NCEP ATP III) [6], American Association of Clinical Endocrinologists (AACE) [7], and the International Diabetes Federation (IDF) [8]. The present understanding of metabolic syndrome is limited as until now no exact cause is found and the extent of its spread is yet unknown in many countries in spite of its life threatening complications. There are limited studies that have reported the prevalence of metabolic syndrome from African countries and in particular for Sudan, there is lack of data for prevalence of metabolic syndrome. Therefore, this study was planned to analyze the prevalence of metabolic syndrome among medical students in Ribat
Page 2 of 16
University, Sudan. Further, we investigated the correlation between MetS and sedentary lifestyle, to draw a broader perspective of the problem. Met S was defined in this study according to the criteria laid by ATP III. Accordingly, a diagnosis of the metabolic syndrome is made when
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three or more of the risk factors shown in table.1.
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Methods
us
Study Population
380 medical students (179 male and 201 female) at National Ribat University, Khartoum, Sudan
an
participated in this study. Informed written consent was obtained from each participant and approval was obtained from institute ethical committee of National Ribat University, by No:
M
NRU/17/G/2/12", Khartoum, Sudan.
d
Determination of Sample Size
te
This is a descriptive cross sectional study done among medical students (males &females), in College of Medicine, National Ribat University, Khartoum, Sudan, from 2010 to 2012. Sample
Ac ce p
size was calculated by the following equation [11]
N =
Z1-a/22 p (1-p) D2
N: sample size
Z1-a/22: Standard normal variate (at 5% type 1 error it is (P<0.05) it is 1.96) P: prevalence, if it is unknown it will be 50% = 0.5 D = Absolute error of precision The total number of the students in College of Medicine were 1316, which represented 6 different batches. Representation of each batch and the number of males & females in the sample
Page 3 of 16
size is done by proportional allocation. The number of participating students from each batch is calculated by the following formula [11]. Number of the students in the batch X 384
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Total number of students
cr
After determining the size of the participating group from each batch, the percentage of males and females in each group was equal to the percentage of males & females in their original batch.
seriously ill, married and pregnant females were excluded.
an
Data Collection
us
The individuals were then chosen by computerized randomization. Those who were unwilling,
M
In every individual anthropometric parameters, blood pressure, lipid profile & fasting blood glucose level were measured. Participants were asked to fill a pre-drafted questionnaire (Table 1)
d
for their life style information.
The anthropometric measurements: For every participant weight [10], height [11], body mass
te
index [12], waist circumference [13], hip circumference [14], mid upper arm circumference [15],
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and skin-fold thickness [16.17,18] were measured. For each anthropometric indicator, three consecutive readings were taken and their mean was considered. Further, to minimize interobserver variability, the anthropometric measurements in 12 males and 12 females were taken in duplicate, once by the investigator and once by a neutral observer. The inter- observer difference was tested by paired t-tests.
The body fat was assessed by the method of Parizkova and
Buzkova [19].
Lipid profile and fasting blood glucose: After overnight fasting (12-hour) venous 5 ml blood samples were collected for lipid profile and fasting blood glucose levels. For analysis of complete lipid profile, quantification of triglycerides (TG), total cholesterol (TC) and highdensity lipoprotein cholesterol (HDL-C) was done. Fasting Blood glucose was assayed using the (Memoram-2007) glucometer at National Ribat University hospital.
Page 4 of 16
Blood pressure measurement: Systolic and diastolic blood pressure was measured thrice each morning, for three consecutive days. A mean blood pressure was then calculated from these readings.
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Interview questionnaire: The information in the questionnaire was graded on a numeric scale of zero to one, where zero represents excellent lifestyle virtues while 1 represents worst lifestyle quality. In the questionnaire 0.5 represented the intermediate lifestyle. Therefore, for the 9
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parameters that were studied through questionnaire, best lifestyle would have a collective
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lifestyle score of zero, while the worst lifestyle would have a collective score of 9 for a
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participant.
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Statistical Analysis
Statistical analysis was done using SPSS software. Frequency tables were constructed to present proportion for categorical data, and average (mean + standard deviation) for continuous data.
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Comparison between groups was performed using Chi-square test for categorical data and
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student t-test and one way analysis of variants (ANOVA) test for continuous data. Pearson correlation between continuous data was done. P-values of less than 0.01 and 0.05 were
Results
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considered as significant at 0.99% and 0.95% confidence level respectively.
Among the 380 participating students (males and females), 317 students were normal (83.4%), And 63 students had metabolic syndrome (16.6%) (Table 3 and Fig 1). In the 179 males participating in the study, 29 have metabolic syndrome (16.2 %), and from 201 females, 34 have metabolic syndrome (16.9%) (Table 4 and Fig 2). Out of 380 participating students 137 were obese. Of 243 non-obese 5 students were under weight, 135 normal weight, 103 over weight, none of them had metabolic syndrome. While in obesity class I, 67 students were normal, 15 students with metabolic syndrome. With class II obesity, 7 students are normal, 32 students had metabolic syndrome. All the 16 students in the obesity class III, had metabolic syndrome. Table 4 illustrates that the mean of all skin- fold thickness measurements [triceps (TSF), biceps (BSF),
Page 5 of 16
subscapular (SSF), suprailaic (SISF)] were significantly higher in students who have metabolic syndrome as compared to normal students. It was observed that mid upper arm circumference (MUAC), hip circumference and waist-hip ratio, were higher in students who had metabolic syndrome than normal students, and this difference was highly significant (Table 4). Mean of
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weight and body mass index (BMI) was observed to be higher in students who had metabolic syndrome than normal students and this the difference was found to be highly significant. The
cr
difference in the mean of height between the two groups was non significant. Further, the mean of cholesterol blood level, low density lipoprotein cholesterol (LDLC), total body fat percent
us
(TBFP), and body fat weight(BFW), were significantly higher in students who have metabolic
Discussion and conclusion
an
syndrome than normal students (Table 5).
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The prevalence of MetS ranges from <10% to 84% globally, depending on the demography of the population, and the criteria used for MetS definition [21.22.23]. Cameron et al., have
d
concluded that the genetic makeup, life style, physical activity and education all influence the prevalence of the MetS [24]. The observed prevalence of the MetS in The National Health and
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Nutrition Examination Survey (NHANES) was 5% among the subjects of normal weight, 22%
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among the overweight, and 60% among the obese [25]. It further increases with age (10% in individuals aged 20–29, 20% in individuals aged 40–49, and 45% in individuals aged 60–69) [26]. Ponholzer et al. reported that there is high prevalence of MetS among postmenopausal women, which varies from 32.6% to 41.5% [27]. Peter and Wilson Study report indicated that a weight increase of ≥2.25 kg over a period of 16 yr was associated with an up to 45% increased risk of developing the MetS [28], and it has been shown by Palaniappan et al, that each 11 cm increase in waist circumference (WC) is associated with an adjusted 80% increased risk of developing the syndrome within 5 years [29]. There are very few studies, if any from African countries and none from Sudan. In this study there was no case of metabolic syndrome among the underweight, normal and overweight students. However most of the obese participants had metabolic syndrome. Generally, 16.6% of the particiapnts in this study met the criteria of metabolic syndrome according to Adults Treatment Panel III (ATP III) program. These results demonstrate that the metabolic syndrome is prevalent and it increases with increasing body mass
Page 6 of 16
index (BMI). These findings are in agreement with those by Ervin [30] who reported a positive correlation between metabolic syndrome and BMI in young adults (20 years of age) and also reported that in general the prevalence of each of the five risk factors of the metabolic syndrome increased as the BMI increased for both sexes. The present study is consistent with
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previous descriptions of the effects of fat distribution on risk factors for metabolic syndrome. A more central deposition of fat (android pattern) was associated with an elevation of triglyceride
cr
level, decreased high-density lipoprotein cholesterol level, increased systolic BP, and increased fasting blood glucose. These findings agreed with those of Abbasi et al [31] who emphasized that
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visceral obesity as measured by WHpR is an important risk factor for metabolic syndrome. Metabolic syndrome occurs only in obese participants and it increases dramatically with BMI.
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All the students with class III obesity had metabolic syndrome. An increase in all skin-fold thickness measurements, Waist Circumference, Hip Circumference, WHpR, MUAC, TBFP and BFW, BMI, blood lipids [cholesterol, LDLC and TG], blood pressure (systolic and diastolic) and
M
fasting blood glucose were found to be risk factors of metabolic syndrome. A limiting factor of this study was the nature of the cross sectional study and so it is difficult to
d
make casual interference. However in this study we used a representative sample of the medical
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students, ensure the high quality of data and applied standard criteria for defining MetS.
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In conclusion our studies showed that an increase in all skin-fold thickness, Waist Circumference, Hip Circumference, WHpR, MUAC, TBFP and BFW, BMI, blood lipids [cholesterol, LDLC and TG], blood pressure (systolic and diastolic) and fasting blood glucose were found to be risk factors of metabolic syndrome. On the basis of the findings of this it is suggested to introduce intervention program for promotion of a healthy lifestyle. These should be started early in schools to reduce the prevalence of obesity and metabolic syndrome among young adults. Such methods may lead to simpler self-monitoring as part of a health education program and consequently may improve the health index of the nation as a whole.
Acknowledgements We would like to express our gratitude to all the participant of the study at National Ribat University and ministry of higher education Sudan for their funding of this study.
Page 7 of 16
Author contributions
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Conceived and designed the experimental study: Yasir H, Imam SN. Performed the experiment: Yasir H, Leena BM, Tahir OA. Analysis of data: Yasir H, Imam SN. Contributed reagents/
cr
materials/analysis tools: Yasir H. Wrote the paper: Yasir H, Imam SN. Contributed the
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discussion and data interpretation of manuscript: Yasir H, Imam SN
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References:
1. P.W. F.Wilson, R. B. D’Agostino,H. Parise, L. Sullivan, and J. B. Meigs: Metabolic syndrome as a precursor of cardiovascular disease and type 2 diabetes mellitus.
M
Circulation 2005; 112 (20): 3066–3072.
2. Grundy SM, J. I. Cleeman, S. R. Daniels: Diagnosis and management of the metabolic
d
syndrome. an American Heart Association/National Heart, Lung, and Blood Institute scientific statement. Circulation 2005; 112 (17): 2735–2752.
te
3. S. Desroches and B. Lamarche: The evolving definitions and increasing prevalence of the
Ac ce p
metabolic syndrome. Applied Physiology, Nutrition and Metabolism 2007; 32 (1): 23– 32.
4. K. G. Alberti and P. Z. Zimmet: Definition, diagnosis and classification of diabetes mellitus and its complications. Part 1: diagnosis and classification of diabetes mellitus
provisional report of a WHO consultation. Diabetic Medicine 1998; 15 (7): 539–553.
5. B. Balkau and M. A. Charles: Comment on the provisional report from the WHO consultation: European Group for the Study of Insulin Resistance (EGIR). Diabetic Medicine 1999; 16 (5): 442–443.
6. J. I. Cleeman: Executive summary of the third report of the National Cholesterol Education Program (NCEP) expert panel on detection, evaluation, and treatment of high blood cholesterol in adults (adult treatment panel III). Journal of the American Medical Association 2001; 285 (10): 2486–2497.
Page 8 of 16
7. D. Einhorn,G.M. Reaven, R. H. Cobin: American College of Endocrinology position statement on the insulin resistance syndrome. Endocrine Practice 2003; 9 (3): 237–252. 8. International Diabetes Federation: The IDF consensus worldwide definition of the metabolic syndrome, http://www.idf.org/metabolic-syndrome.
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9. World Health Organization, Health Research Methodology: a guide for training in research methodology. Manila, WHO Regional Office 1992; 75-76.
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10. Jaramillo P, Cubillos LA, Casas JP: Definition of metabolic syndrome. Ann Hum Biol 1998; 25(3): 263-70.
Martorell R (1988) Anthropometric Standardization, Reference
us
11. Timothy G, Alex F,
manual. Pennsylvania. Martorell: 7.
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12. World Health Organization: Obesity preventing and managing the global epidemic. Technical report series 2001; 8941.
Sci Sports Exerc 1984; 16: 92- 6.
M
13. Lohman TG: Methodological factors and prediction of body fat in female athletes. Med
14. Hartz A, Rupley DC, Rimm AA: The association of birth measurement with disease. Am
d
J Epidemiol 1984; 119: 71-80.
15. Gurny JM, Jelliffe DB: Arm anthropometry in nutritional assessment: nomogram for
te
rapid calculation of muscle circumference and cross –sectional muscles and fat areas.
Ac ce p
Am J Clin Nutr 1997; 26: 912- 5.
16. Lee MM, Ng CK: Postmortem studies of skin fold caliper measurementand actual thickness of skin and subcutaneous tissue. Hum Biol 1985; 37: 91-103.
17. Jelliffe DB: The assessment of the nutritional status of the community. Geneva: WHO 1996.
18. Jackson AS, Pollack ML: Practical assessment of body composition. Phys
Sport Med
1985; 76-90.
19. Parizkova J, Buzkova P: (1971) Relationship between skin-fold thickness measured by Harpenden Caliper and densitometric analysis of total body fat in men. Hum Biol 1971; 43: 16-21. 20. G. D. Kolovou, K. K. Anagnostopoulou, K. D. Salpea, and D. P.Mikhailidis: The prevalence of metabolic syndrome in various populations. The American Journal of the Medical Sciences 2007;333 (6) 362–371.
Page 9 of 16
21. A. J. Cameron, J. E. Shaw, and P. Z. Zimmet: (2004) The metabolic syndrome: prevalence in worldwide populations. Endocrinology and Metabolism Clinics of North America 2004; 33 (2): 375, 2004. 22. Y.-W. Park, S. Zhu, L. Palaniappan, S.Heshka,M. R. Carnethon, and S. B. Heymsfield:
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The metabolic syndrome: prevalence and associated risk factor findings in the US population from the Third National Health and Nutrition Examination Survey, 1988–
cr
1994. Archives of Internal Medicine 2003; 163 (4): 427–436.
23. E. S. Ford, W. H. Giles, and W. H. Dietz: (2002) Prevalence of the metabolic syndrome
us
among US adults: findings from the Third National Health and Nutrition Examination Survey 2002; Journal of the American Medical Association 287(3): 356–359.
an
24. A. Ponholzer, C. Temml, M. Rauchenwald, M. Marszalek, and S. Madersbacher: Is the metabolic syndrome a risk factor for female sexual dysfunction in sexually active women. International Journal of Impotence Research 2008; 20(1): 100–104.
M
25. P. W. F. Wilson, W. B. Kannel, H. Silbershatz, and R. B. D’Agostino: Clustering of metabolic factors and coronary heart disease. Archives of Internal Medicine 1999; 159
d
(10): 1104– 1109.
26. L. Palaniappan, M. R. Carnethon, Y. Wang: Predictors of the incident metabolic
Ac ce p
27 (3): 788–793.
te
syndrome in adults. The Insulin Resistance Atherosclerosis Study. Diabetes Care 2004;
27. Ervin RB: Prevalence of metabolic syndrome among adults of 20 years of age and over, by sex, age, race and ethnicity, and body mass index: USA. National Health Statistics Reports 2009; 13: 2003-2006.
28. Abbasi F, Malhotra D, Mathur A, Reaven GM: Body mass index and waist circumference associate to a comparable degree with insulin resistance and related metabolic abnormalities in South Asian women and men. Diab Vasc Dis Res 2012; 9 (4):296-300.
Page 10 of 16
Table.1: ATP III clinical identification of the metabolic syndrome.
Risk Factor Threshold Female
Waist circumference
>102cm
>88cm
Triglycerides
>150mg/dl
>150mg/dl
H.D.L Cholesterol
<40mg/dl
<50mg/dl
Blood Pressure
>135/>85mm Hg
Fasting Blood Glucose
>100mg/dl
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Threshold Male
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>135/>85mm Hg
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an
>100mg/dl
Page 11 of 16
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Table 2: Life style scoring questionnaire.
Good
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Score
Life Style Parameters
Moderat
Bad
e
0.5
1.0
M
0
<3
3
>3
Snacks /day
0
1
>1 >5
d
Number of meals/day
<5
Watching TV hours/day
1-2
3-7
>7
Computer, Net hours /day
1-2
3-7
>7
Sleeping hours/day
<7
8-9
>9
Exercise
regular
irregular 0
Smoking
0
irregular regular
Alcohol
0
irregular regular
0
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te
Soft drinks/week
Page 12 of 16
ip t Total
380
cr
d
63
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Metabolic
83.4
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317
Percent
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Frequency
Normal
us
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Table.3: Prevalence of metabolic syndrome.
16.6
100.0
Table 4: Prevalence of metabolic syndrome by gender.
Page 13 of 16
Males
Females
Metabolic
Total
Normal
Metabolic
Total
Frequency
150
29
179
167
34
201
Percent
83.8
16.2
100% 83.1
16.9
100%
cr
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Normal
Normal
Metabolic
SD
Mean
SD
TSF
24.355
7.107
40.019
6.378
.000
BSF
12.212
3.745
20.826
3.665
.000
SSF
23.744
6.431
37.918
5.771
.000
SISF
27.200
7.793
44.429
d
6.875
.000
4.158
39.325
3.781
.000
Body circumferances
27.816
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MUAC
M
Mean
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Skin Fold thickness
Sig
an
syndrome
us
Table 5: Relation between metabolic syndrome and various parameters.
Hip Circumference
101.941 8.218
118.269
9.815
.000
WHpR
0.815
0.088
1.000
.066
.000
Weight
73.552
14.567
109.214
14.234
.000
Height
167.086 8.484
169.619
5.916
.024
BMI
26.283
37.792
3.876
.000
BMI
4.270
Lipid profiles Cholesterol
144.938
42.356
159.000
33.557
.000
T.G
107.374
32.606
108.204
34.783
.811
H.D.L.C
41.416
9.166
42.224
8.675
.378
Page 14 of 16
L.D.L.C
82.677
40.582
94.960
31.466
.01
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P-value<0.05 is significant at 0.95 confidence level
Abbreviations: TSF, Triceps skin fold; BSF, Biceps skin fold; SSF, Subscapular skin fold;
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SISF, Suprailiac skin fold; MUAC, Mid upper arm circumference; WHpR, Waist hip ratio; BMI,
us
Body mass index; TG, Triglycerides; HDLC, High density lipoprotein cholesterol; LDLC, Low density lipoprotein cholesterol.
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Figure 1: Prevalence of metabolic syndrome
students had metabolic syndrome (16.6%)
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The 380 participating students (males and females), 317 students were normal (83.4%), And 63
d
Figure 2: Prevalence of metabolic syndrome by gender.
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From 179 males participating in the study, 29 have metabolic syndrome (16.2 %), and from 201
Ac ce p
females, 34 have metabolic syndrome (16.9%)
Page 15 of 16
Page 16 of 16
d
te
Ac ce p us
an
M
cr
ip t
S1871-4021(15)30033-3 http://dx.doi.org/doi:10.1016/j.dsx.2016.01.002 DSX 539
To appear in:
Diabetes & Metabolic Syndrome: Clinical Research & Reviews
Received date: Accepted date:
9-9-2015 4-1-2016
Please cite this article as: Yasir HE, Tahir OA, Leena BM, Imam SN, Metabolic Syndrome and its association with obesity and lifestyle factors in Sudanese population, Diabetes and Metabolic Syndrome: Clinical Research and Reviews (2016), http://dx.doi.org/10.1016/j.dsx.2016.01.002 This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.
Metabolic Syndrome and its association with obesity and lifestyle factors in Sudanese population
ip t
Yasir HE1, Tahir OA2 , Leena BM 2, Imam SN1,*
cr
1. Department of Anatomy, faculty of medicine, Taibah University, Saudi Arabia.
us
2. Department of physiology, faculty of medicine, National Ribat University, Khartoum, Sudan. * Corresponding author
an
Email: [email protected]
M
Yasir HE: Yasir Hassan Abd Alla Elhassan Tahir OA: Tahir Osman Ali
te
d
Leena BM: Leena Babiker Mekki
Ac ce p
Imam SN: Syed Nazar Imam
Abstract
Although modern life style factors affecting health is a crucial problem globally, little information about metabolic syndrome (MetS) is available for the Sudanese population. With this consideration the study was planned to assess the prevalence of MetS among young people of Sudan and their association with obesity and lifestyle factors. Serum lipid profile, blood glucose and clinically established parameters for obesity were assessed in 179 young adult male and 201 females at National Ribat University, Sudan. Relevant statistical test were applied using
Page 1 of 16
SPSS software. Based on anthropometric measurements, 137 students were obese. Amongst the 243 non-obese students 5 were under weight, 135 normal weight and 103 were over weight. In the study population, 317 students were normal (83.4%) and 63 students had metabolic syndrome (16.6%) as defined by ATP III definition of MetS classification. MetS was found only
ip t
in obese individuals (45.98%) and no case was detected in underweight, normal and overweight individual.. The mean of cholesterol level in subjects with metabolic syndrome was 159 as
cr
compared to those without it (149.93). Life style modification as healthy diet, regular exercise and preventive strategies may help reduce metabolic syndrome, thus improving general health
us
conditions in young individuals of Sudan.
an
Key words Metabolic syndrome, Obesity, Young Adult, Sudanese
M
Introduction
Metabolic syndrome (MetS) is a condition in which, set of risk factors increases the possibility of
d
developing heart disease, stroke, and type II diabetes. Other factors which influence the
te
susceptibility to this disorder include insulin resistance syndrome, dysmetabolic syndrome, as well as Syndrome X. Metabolic syndrome is a combination of physiological, biochemical,
Ac ce p
clinical and metabolic factors that directly increases the mortality of affected individuals [1,2]. Metabolic Syndrome is directly proportional with age as more than forty percent of individuals above sixty years suffer from this condition [3]. The criteria for diagnosis of MetS has been laid down by World Health Organization (WHO) [4], the European Group for the study of Insulin Resistance (EGIR) [5], the National Cholesterol Education programme Adult Treatment Panel III (NCEP ATP III) [6], American Association of Clinical Endocrinologists (AACE) [7], and the International Diabetes Federation (IDF) [8]. The present understanding of metabolic syndrome is limited as until now no exact cause is found and the extent of its spread is yet unknown in many countries in spite of its life threatening complications. There are limited studies that have reported the prevalence of metabolic syndrome from African countries and in particular for Sudan, there is lack of data for prevalence of metabolic syndrome. Therefore, this study was planned to analyze the prevalence of metabolic syndrome among medical students in Ribat
Page 2 of 16
University, Sudan. Further, we investigated the correlation between MetS and sedentary lifestyle, to draw a broader perspective of the problem. Met S was defined in this study according to the criteria laid by ATP III. Accordingly, a diagnosis of the metabolic syndrome is made when
ip t
three or more of the risk factors shown in table.1.
cr
Methods
us
Study Population
380 medical students (179 male and 201 female) at National Ribat University, Khartoum, Sudan
an
participated in this study. Informed written consent was obtained from each participant and approval was obtained from institute ethical committee of National Ribat University, by No:
M
NRU/17/G/2/12", Khartoum, Sudan.
d
Determination of Sample Size
te
This is a descriptive cross sectional study done among medical students (males &females), in College of Medicine, National Ribat University, Khartoum, Sudan, from 2010 to 2012. Sample
Ac ce p
size was calculated by the following equation [11]
N =
Z1-a/22 p (1-p) D2
N: sample size
Z1-a/22: Standard normal variate (at 5% type 1 error it is (P<0.05) it is 1.96) P: prevalence, if it is unknown it will be 50% = 0.5 D = Absolute error of precision The total number of the students in College of Medicine were 1316, which represented 6 different batches. Representation of each batch and the number of males & females in the sample
Page 3 of 16
size is done by proportional allocation. The number of participating students from each batch is calculated by the following formula [11]. Number of the students in the batch X 384
ip t
Total number of students
cr
After determining the size of the participating group from each batch, the percentage of males and females in each group was equal to the percentage of males & females in their original batch.
seriously ill, married and pregnant females were excluded.
an
Data Collection
us
The individuals were then chosen by computerized randomization. Those who were unwilling,
M
In every individual anthropometric parameters, blood pressure, lipid profile & fasting blood glucose level were measured. Participants were asked to fill a pre-drafted questionnaire (Table 1)
d
for their life style information.
The anthropometric measurements: For every participant weight [10], height [11], body mass
te
index [12], waist circumference [13], hip circumference [14], mid upper arm circumference [15],
Ac ce p
and skin-fold thickness [16.17,18] were measured. For each anthropometric indicator, three consecutive readings were taken and their mean was considered. Further, to minimize interobserver variability, the anthropometric measurements in 12 males and 12 females were taken in duplicate, once by the investigator and once by a neutral observer. The inter- observer difference was tested by paired t-tests.
The body fat was assessed by the method of Parizkova and
Buzkova [19].
Lipid profile and fasting blood glucose: After overnight fasting (12-hour) venous 5 ml blood samples were collected for lipid profile and fasting blood glucose levels. For analysis of complete lipid profile, quantification of triglycerides (TG), total cholesterol (TC) and highdensity lipoprotein cholesterol (HDL-C) was done. Fasting Blood glucose was assayed using the (Memoram-2007) glucometer at National Ribat University hospital.
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Blood pressure measurement: Systolic and diastolic blood pressure was measured thrice each morning, for three consecutive days. A mean blood pressure was then calculated from these readings.
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Interview questionnaire: The information in the questionnaire was graded on a numeric scale of zero to one, where zero represents excellent lifestyle virtues while 1 represents worst lifestyle quality. In the questionnaire 0.5 represented the intermediate lifestyle. Therefore, for the 9
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parameters that were studied through questionnaire, best lifestyle would have a collective
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lifestyle score of zero, while the worst lifestyle would have a collective score of 9 for a
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participant.
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Statistical Analysis
Statistical analysis was done using SPSS software. Frequency tables were constructed to present proportion for categorical data, and average (mean + standard deviation) for continuous data.
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Comparison between groups was performed using Chi-square test for categorical data and
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student t-test and one way analysis of variants (ANOVA) test for continuous data. Pearson correlation between continuous data was done. P-values of less than 0.01 and 0.05 were
Results
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considered as significant at 0.99% and 0.95% confidence level respectively.
Among the 380 participating students (males and females), 317 students were normal (83.4%), And 63 students had metabolic syndrome (16.6%) (Table 3 and Fig 1). In the 179 males participating in the study, 29 have metabolic syndrome (16.2 %), and from 201 females, 34 have metabolic syndrome (16.9%) (Table 4 and Fig 2). Out of 380 participating students 137 were obese. Of 243 non-obese 5 students were under weight, 135 normal weight, 103 over weight, none of them had metabolic syndrome. While in obesity class I, 67 students were normal, 15 students with metabolic syndrome. With class II obesity, 7 students are normal, 32 students had metabolic syndrome. All the 16 students in the obesity class III, had metabolic syndrome. Table 4 illustrates that the mean of all skin- fold thickness measurements [triceps (TSF), biceps (BSF),
Page 5 of 16
subscapular (SSF), suprailaic (SISF)] were significantly higher in students who have metabolic syndrome as compared to normal students. It was observed that mid upper arm circumference (MUAC), hip circumference and waist-hip ratio, were higher in students who had metabolic syndrome than normal students, and this difference was highly significant (Table 4). Mean of
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weight and body mass index (BMI) was observed to be higher in students who had metabolic syndrome than normal students and this the difference was found to be highly significant. The
cr
difference in the mean of height between the two groups was non significant. Further, the mean of cholesterol blood level, low density lipoprotein cholesterol (LDLC), total body fat percent
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(TBFP), and body fat weight(BFW), were significantly higher in students who have metabolic
Discussion and conclusion
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syndrome than normal students (Table 5).
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The prevalence of MetS ranges from <10% to 84% globally, depending on the demography of the population, and the criteria used for MetS definition [21.22.23]. Cameron et al., have
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concluded that the genetic makeup, life style, physical activity and education all influence the prevalence of the MetS [24]. The observed prevalence of the MetS in The National Health and
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Nutrition Examination Survey (NHANES) was 5% among the subjects of normal weight, 22%
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among the overweight, and 60% among the obese [25]. It further increases with age (10% in individuals aged 20–29, 20% in individuals aged 40–49, and 45% in individuals aged 60–69) [26]. Ponholzer et al. reported that there is high prevalence of MetS among postmenopausal women, which varies from 32.6% to 41.5% [27]. Peter and Wilson Study report indicated that a weight increase of ≥2.25 kg over a period of 16 yr was associated with an up to 45% increased risk of developing the MetS [28], and it has been shown by Palaniappan et al, that each 11 cm increase in waist circumference (WC) is associated with an adjusted 80% increased risk of developing the syndrome within 5 years [29]. There are very few studies, if any from African countries and none from Sudan. In this study there was no case of metabolic syndrome among the underweight, normal and overweight students. However most of the obese participants had metabolic syndrome. Generally, 16.6% of the particiapnts in this study met the criteria of metabolic syndrome according to Adults Treatment Panel III (ATP III) program. These results demonstrate that the metabolic syndrome is prevalent and it increases with increasing body mass
Page 6 of 16
index (BMI). These findings are in agreement with those by Ervin [30] who reported a positive correlation between metabolic syndrome and BMI in young adults (20 years of age) and also reported that in general the prevalence of each of the five risk factors of the metabolic syndrome increased as the BMI increased for both sexes. The present study is consistent with
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previous descriptions of the effects of fat distribution on risk factors for metabolic syndrome. A more central deposition of fat (android pattern) was associated with an elevation of triglyceride
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level, decreased high-density lipoprotein cholesterol level, increased systolic BP, and increased fasting blood glucose. These findings agreed with those of Abbasi et al [31] who emphasized that
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visceral obesity as measured by WHpR is an important risk factor for metabolic syndrome. Metabolic syndrome occurs only in obese participants and it increases dramatically with BMI.
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All the students with class III obesity had metabolic syndrome. An increase in all skin-fold thickness measurements, Waist Circumference, Hip Circumference, WHpR, MUAC, TBFP and BFW, BMI, blood lipids [cholesterol, LDLC and TG], blood pressure (systolic and diastolic) and
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fasting blood glucose were found to be risk factors of metabolic syndrome. A limiting factor of this study was the nature of the cross sectional study and so it is difficult to
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make casual interference. However in this study we used a representative sample of the medical
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students, ensure the high quality of data and applied standard criteria for defining MetS.
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In conclusion our studies showed that an increase in all skin-fold thickness, Waist Circumference, Hip Circumference, WHpR, MUAC, TBFP and BFW, BMI, blood lipids [cholesterol, LDLC and TG], blood pressure (systolic and diastolic) and fasting blood glucose were found to be risk factors of metabolic syndrome. On the basis of the findings of this it is suggested to introduce intervention program for promotion of a healthy lifestyle. These should be started early in schools to reduce the prevalence of obesity and metabolic syndrome among young adults. Such methods may lead to simpler self-monitoring as part of a health education program and consequently may improve the health index of the nation as a whole.
Acknowledgements We would like to express our gratitude to all the participant of the study at National Ribat University and ministry of higher education Sudan for their funding of this study.
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Author contributions
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Conceived and designed the experimental study: Yasir H, Imam SN. Performed the experiment: Yasir H, Leena BM, Tahir OA. Analysis of data: Yasir H, Imam SN. Contributed reagents/
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materials/analysis tools: Yasir H. Wrote the paper: Yasir H, Imam SN. Contributed the
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discussion and data interpretation of manuscript: Yasir H, Imam SN
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References:
1. P.W. F.Wilson, R. B. D’Agostino,H. Parise, L. Sullivan, and J. B. Meigs: Metabolic syndrome as a precursor of cardiovascular disease and type 2 diabetes mellitus.
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Circulation 2005; 112 (20): 3066–3072.
2. Grundy SM, J. I. Cleeman, S. R. Daniels: Diagnosis and management of the metabolic
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syndrome. an American Heart Association/National Heart, Lung, and Blood Institute scientific statement. Circulation 2005; 112 (17): 2735–2752.
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3. S. Desroches and B. Lamarche: The evolving definitions and increasing prevalence of the
Ac ce p
metabolic syndrome. Applied Physiology, Nutrition and Metabolism 2007; 32 (1): 23– 32.
4. K. G. Alberti and P. Z. Zimmet: Definition, diagnosis and classification of diabetes mellitus and its complications. Part 1: diagnosis and classification of diabetes mellitus
provisional report of a WHO consultation. Diabetic Medicine 1998; 15 (7): 539–553.
5. B. Balkau and M. A. Charles: Comment on the provisional report from the WHO consultation: European Group for the Study of Insulin Resistance (EGIR). Diabetic Medicine 1999; 16 (5): 442–443.
6. J. I. Cleeman: Executive summary of the third report of the National Cholesterol Education Program (NCEP) expert panel on detection, evaluation, and treatment of high blood cholesterol in adults (adult treatment panel III). Journal of the American Medical Association 2001; 285 (10): 2486–2497.
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7. D. Einhorn,G.M. Reaven, R. H. Cobin: American College of Endocrinology position statement on the insulin resistance syndrome. Endocrine Practice 2003; 9 (3): 237–252. 8. International Diabetes Federation: The IDF consensus worldwide definition of the metabolic syndrome, http://www.idf.org/metabolic-syndrome.
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9. World Health Organization, Health Research Methodology: a guide for training in research methodology. Manila, WHO Regional Office 1992; 75-76.
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10. Jaramillo P, Cubillos LA, Casas JP: Definition of metabolic syndrome. Ann Hum Biol 1998; 25(3): 263-70.
Martorell R (1988) Anthropometric Standardization, Reference
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11. Timothy G, Alex F,
manual. Pennsylvania. Martorell: 7.
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12. World Health Organization: Obesity preventing and managing the global epidemic. Technical report series 2001; 8941.
Sci Sports Exerc 1984; 16: 92- 6.
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13. Lohman TG: Methodological factors and prediction of body fat in female athletes. Med
14. Hartz A, Rupley DC, Rimm AA: The association of birth measurement with disease. Am
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J Epidemiol 1984; 119: 71-80.
15. Gurny JM, Jelliffe DB: Arm anthropometry in nutritional assessment: nomogram for
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rapid calculation of muscle circumference and cross –sectional muscles and fat areas.
Ac ce p
Am J Clin Nutr 1997; 26: 912- 5.
16. Lee MM, Ng CK: Postmortem studies of skin fold caliper measurementand actual thickness of skin and subcutaneous tissue. Hum Biol 1985; 37: 91-103.
17. Jelliffe DB: The assessment of the nutritional status of the community. Geneva: WHO 1996.
18. Jackson AS, Pollack ML: Practical assessment of body composition. Phys
Sport Med
1985; 76-90.
19. Parizkova J, Buzkova P: (1971) Relationship between skin-fold thickness measured by Harpenden Caliper and densitometric analysis of total body fat in men. Hum Biol 1971; 43: 16-21. 20. G. D. Kolovou, K. K. Anagnostopoulou, K. D. Salpea, and D. P.Mikhailidis: The prevalence of metabolic syndrome in various populations. The American Journal of the Medical Sciences 2007;333 (6) 362–371.
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21. A. J. Cameron, J. E. Shaw, and P. Z. Zimmet: (2004) The metabolic syndrome: prevalence in worldwide populations. Endocrinology and Metabolism Clinics of North America 2004; 33 (2): 375, 2004. 22. Y.-W. Park, S. Zhu, L. Palaniappan, S.Heshka,M. R. Carnethon, and S. B. Heymsfield:
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The metabolic syndrome: prevalence and associated risk factor findings in the US population from the Third National Health and Nutrition Examination Survey, 1988–
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1994. Archives of Internal Medicine 2003; 163 (4): 427–436.
23. E. S. Ford, W. H. Giles, and W. H. Dietz: (2002) Prevalence of the metabolic syndrome
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among US adults: findings from the Third National Health and Nutrition Examination Survey 2002; Journal of the American Medical Association 287(3): 356–359.
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24. A. Ponholzer, C. Temml, M. Rauchenwald, M. Marszalek, and S. Madersbacher: Is the metabolic syndrome a risk factor for female sexual dysfunction in sexually active women. International Journal of Impotence Research 2008; 20(1): 100–104.
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25. P. W. F. Wilson, W. B. Kannel, H. Silbershatz, and R. B. D’Agostino: Clustering of metabolic factors and coronary heart disease. Archives of Internal Medicine 1999; 159
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(10): 1104– 1109.
26. L. Palaniappan, M. R. Carnethon, Y. Wang: Predictors of the incident metabolic
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27 (3): 788–793.
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syndrome in adults. The Insulin Resistance Atherosclerosis Study. Diabetes Care 2004;
27. Ervin RB: Prevalence of metabolic syndrome among adults of 20 years of age and over, by sex, age, race and ethnicity, and body mass index: USA. National Health Statistics Reports 2009; 13: 2003-2006.
28. Abbasi F, Malhotra D, Mathur A, Reaven GM: Body mass index and waist circumference associate to a comparable degree with insulin resistance and related metabolic abnormalities in South Asian women and men. Diab Vasc Dis Res 2012; 9 (4):296-300.
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Table.1: ATP III clinical identification of the metabolic syndrome.
Risk Factor Threshold Female
Waist circumference
>102cm
>88cm
Triglycerides
>150mg/dl
>150mg/dl
H.D.L Cholesterol
<40mg/dl
<50mg/dl
Blood Pressure
>135/>85mm Hg
Fasting Blood Glucose
>100mg/dl
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Threshold Male
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>135/>85mm Hg
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>100mg/dl
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Table 2: Life style scoring questionnaire.
Good
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Score
Life Style Parameters
Moderat
Bad
e
0.5
1.0
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0
<3
3
>3
Snacks /day
0
1
>1 >5
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Number of meals/day
<5
Watching TV hours/day
1-2
3-7
>7
Computer, Net hours /day
1-2
3-7
>7
Sleeping hours/day
<7
8-9
>9
Exercise
regular
irregular 0
Smoking
0
irregular regular
Alcohol
0
irregular regular
0
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te
Soft drinks/week
Page 12 of 16
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380
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63
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Metabolic
83.4
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317
Percent
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Frequency
Normal
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Table.3: Prevalence of metabolic syndrome.
16.6
100.0
Table 4: Prevalence of metabolic syndrome by gender.
Page 13 of 16
Males
Females
Metabolic
Total
Normal
Metabolic
Total
Frequency
150
29
179
167
34
201
Percent
83.8
16.2
100% 83.1
16.9
100%
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Normal
Normal
Metabolic
SD
Mean
SD
TSF
24.355
7.107
40.019
6.378
.000
BSF
12.212
3.745
20.826
3.665
.000
SSF
23.744
6.431
37.918
5.771
.000
SISF
27.200
7.793
44.429
d
6.875
.000
4.158
39.325
3.781
.000
Body circumferances
27.816
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MUAC
M
Mean
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Skin Fold thickness
Sig
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syndrome
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Table 5: Relation between metabolic syndrome and various parameters.
Hip Circumference
101.941 8.218
118.269
9.815
.000
WHpR
0.815
0.088
1.000
.066
.000
Weight
73.552
14.567
109.214
14.234
.000
Height
167.086 8.484
169.619
5.916
.024
BMI
26.283
37.792
3.876
.000
BMI
4.270
Lipid profiles Cholesterol
144.938
42.356
159.000
33.557
.000
T.G
107.374
32.606
108.204
34.783
.811
H.D.L.C
41.416
9.166
42.224
8.675
.378
Page 14 of 16
L.D.L.C
82.677
40.582
94.960
31.466
.01
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P-value<0.05 is significant at 0.95 confidence level
Abbreviations: TSF, Triceps skin fold; BSF, Biceps skin fold; SSF, Subscapular skin fold;
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SISF, Suprailiac skin fold; MUAC, Mid upper arm circumference; WHpR, Waist hip ratio; BMI,
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Body mass index; TG, Triglycerides; HDLC, High density lipoprotein cholesterol; LDLC, Low density lipoprotein cholesterol.
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Figure 1: Prevalence of metabolic syndrome
students had metabolic syndrome (16.6%)
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The 380 participating students (males and females), 317 students were normal (83.4%), And 63
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Figure 2: Prevalence of metabolic syndrome by gender.
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From 179 males participating in the study, 29 have metabolic syndrome (16.2 %), and from 201
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females, 34 have metabolic syndrome (16.9%)
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Page 16 of 16
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