Printer Friendly

General and abdominal obesity prevelances and their relations with metabolic syndrome components.

Byline: Olgun Goktas, Canan Ersoy, Ilker Ercan and Fatma Ezgi Can

KEYWORDS: Abdominal obesity, Obesity, Metabolic syndrome.

INTRODUCTION

Obesity is an important health problem affecting individual and society all over the world, as well as in our country, due to its association with increased morbidity and mortality. Other than dietary and physical activity habits, there are many factors affecting body weight increment like age, gender, marital status, education level, accompanying illnesses and drugs used.1,2 Assessment of obesity is mostly made by measuring body mass index (BMI) which is a parameter of general obesity. Beside this, obesity is also assessed by measurement of waist and hip circumferences (WaC, HC) and calculation of waist to hip ratio (WHR).3,4 Abdominal obesity has much more health risk according to the fat excess in other regions of the body. Evaluation of optimal WaC is made differently by organizations like International Diabetes Federation (IDF) and World Health Organization (WHO).

WaC and WHR are closely related to metabolic syndrome components which are abdominal obesity, insulin resistance and related disorders. Metabolic syndrome includes the components such as abdominal obesity, glucose intolerance, dyslipidemia, hypertension and atherosclerotic heart disease. Each component has harmful effects on human health, morbidity and mortality.4-8 There are different national studies conducted in our country evaluating obesity prevelance in adults from 1990 and 2010. In these studies obesity prevelance ranges between 18.8% to 39.7% with an increasing rate in recent years.2,9 In a previous study conductedby us and reported in 2005, obesity prevelance was found to be 35.5% in our city Bursa and we wanted to check this prevalence.10

METHODS

In the study, the population size of Bursa province (N=2901396) was accepted as the main population and the sample size was at least n=17729 at the level of significance of d=0.009 and at the level of significance of [alpha]=0.01 with reference to 36% obesity incidence rate.9 Sampling was based on size proportional stratified sampling. A total of 26356 individuals from 19 databases of family health centers identified by stratification were included in the study. When the subjects with missing data were excluded from the study, a total of 17812 participants were included. Age, gender, marital status, educational status, accompanying diseases, medications used, height, weight, WaC and HC were recorded from the files. BMI and WHR were calculated. BMI was computed by dividing body weight in kilograms to height in meters squared. The WHR was calculated by the division of WaC to HC as an index of abdominal obesity.3,4

The prevalences of obesity in our study were assessed according to BMI, WaC (IDF and WHO criterias) and WHR. Obesity classification according to BMI was [greater than or equal to]30 kg/mA2 for both genders, according to WaC defined by IDF was [greater than or equal to]94 cm for males and [greater than or equal to]80 cm for females and by WHO was [greater than or equal to]102 cm for males and [greater than or equal to]88 cm for females. According to WHR, subjects were considered as obese if >0.9 for males and >0.8 for females.11,12 The data of the consecutive adult residents of Bursa who were admitted to the family health centers due to routine control programmes from the 1st January to the 31st December 2016 were evaluated retrospectively. The data from the records of subjects older than 18 years of age were included in the study. The necessary physical examinations and hormonal evaluations were performed to exclude endocrinological causes for obesity.

Medication records were evaluated. Data of subjects with endocrine obesity and with usage of drugs that increase insulin resistance like steroids were not included in the study.

With reference to obesity evaluation according to BMI index, factors affecting the risk of obesity were investigated by backward binary logistic regression. Binary logistic regression analysis indicated regression coefficients, standard error of coefficients, odds ratio and 95% confidence intervals. The effect size was calculated for the mean values d=(u2-u1)/pooled for the ratios h = 1-2 and i = 2arcsinaPi. 13,14 Effect size value was evaluated according to 0.2 low, 0.5 moderate, 0.8 high, 1.3 very high. 15 P<0.05 was considered statistically significant. The analysis was done in the IBM SPSS v.20 program (Uludag University).

Table-I: The demographic features of both genders and all participants.

Variables###Females###Males###All participants

###(n=10939)###(n=6873)###(n=17812)

Age (years)###45.92+-18.17###46.45+-19.06###46.13+-18.52

Height (cm)###158.05+-18.17 172.63+-10.72 163.67+-17.25

Weight (kg)###72.61+-15.12###80.43+-14.47###75.63+-15.35

BMI (kg/m2)###28.75+-9.53###27.09+-9###28.10+-9.36

WaC (cm)###89.38+-15.21###94.37+-13.42###91.33+-14.74

HC (cm)###106.13+-12.52 102.46+-9.99###104.70+-11.74

WHR###0.84+-0.09###0.92+-0.09###0.87+-0.10

Table-II: Obesity rates according to BMI, WaC and WHR criterias in both genders and all participants.

Variables, % (n)###Females (n=10939)###Males (n=6873)###All participants (n=17812)

BMI (kg/m2)###37.8 (4090/10806)###23.3 (1597/6857)###32.2 (5687/17663)

WaC-IDF (cm)###69.7 (5860/8402)###52.9 (2842/5372)###63.2 (8702/13774)

WaC-WHO (cm)###53.6 (4507/8402)###29.8 (1602/5372)###44.4 (6109/13774)

WHR###67.8 (5690/8391)###58.4 (3134/5366)###64.1 (8824/13757)

Table-III: Investigation of risk factors affecting obesity (n=12505).

###[beta]###SE of ([beta])###Exp ([beta])###95% CI###p value

Age###0.003###0.001###1.003###1.001 1.006###0.014

Gender (Ref: Male)###0.681###0.049###1.976###1.795 2.174###p<0.001

Marital Status (Ref: Single)###0.476###0.051###1.609###1.455 1.780###p<0.001

Education (Ref: High school and university)###0.595###0.052###1.812###1.635 2.008###p<0.001

Previous obesity history (Ref: No)###1.845###0.132###6.326###4.882 8.197###p<0.001

Smoking (Ref: No)###0.214###0.066###1.238###1.088 1.409###p<0.001

Hypertension (Ref: No)###0.653###0.056###1.921###1.721 2.144###p<0.001

Dyslipidemia (Ref: No)###0.196###0.069###1.217###1.064 1.392###0.004

Hypothyroidism (Ref: No)###0.219###0.108###1.245###1.007 1.539###0.043

Antidiabetic drugs(Ref: No)###0.485###0.056###1.624###1.454 1.814###p<0.001

Table-IV: Assessments of obese and non-obese female (a) and male (b) subjects according to effect size of their risk factors as metabolic syndrome components on general and abdominal obesity indexes.

a)

###Female subjects

Variables###Indexes###Obesity present###Obesity absent###ES+-SD CI %95

Age###BMI###52.34+-16.13 (51.84;52.83)###42.24+-18.27 (41.81;42.68)###0.58+-0.02 (0.538;0.617)

###WHR###51.09+-17.825 (50.62;51.55)###37.12+-14.153 (36.58;37.65)###0.83+-0.024 (0.787;0.882)

###WaC-IDF###52.53+-16.408 (52.11;52.95)###32.91+-16.408 (32.39;33.42)###1.26+-0.026 (1.212;1.312)

###WaC-WHO###55.01+-15.59 (54.55;55.46)###36.86+-15.45 (36.37;37.34)###1.17+-0.024 (1.123;1.216)

Type 2###BMI###0.26+-0.007 (0.245;0.272)###0.12+-0.004 (0.115;0.130)###0.35+-0.02 (0.31;0.39)

diabetes###WHR###0.22+-0.005 (0.208;0.23)###0.06+-0.005 (0.05;0.067)###0.49+-0.024 (0.441;0.534)

###WaC-IDF###0.23+-0.005 (0.217;0.239)###0.03+-0.003 (0.022;0.035)###0.65+-0.024 (0.607;0.703)

###WaC-WHO###0.27+-0.007 (0.255;0.281)###0.05+-0.004 (0.045;0.059)###0.63+-0.022 (0.586;0.674)

Hypertension###BMI###0.42+-0.008 (0.404;0.434)###0.20+-0.005 (0.191;0.21)###0.48+-0.02 (0.44;0.52)

###WHR###0.38+-0.006 (0.365;0.39)###0.14+-0.007 (0.131;0.158)###0.54+-0.024 (0.45;0.591)

###WaC-IDF###0.93+-0.003 (0.923;0.937)###0.07+-0.005 (0.061;0.081)###2.07+-0.029 (2.011;2.123)

###WaC-WHO###0.46+-0.007 (0.443;0.472)###0.12+-0.005 (0.113;0.134)###0.77+-0.023 (0.722;0.811)

Dyslipidemia###BMI###0.15+-0.006 (0.14;0.162)###0.07+-0.003 (0.064;0.076)###0.26+-0.02 (0.22;0.30)

###WHR###0.14+-0.005 (0.128;0.146)###0.041+-0.004 (0.034;0.049)###0.35+-0.024 (0.304;0.40)

###WaC-IDF###0.14+-0.005 (0.135;0.153)###0.02+-0.003 (0.014;0.025)###0.50+-0.024 (0.453;0.548)

###WaC-WHO###0.17+-0.006 (0.154;0.176)###0.04+-0.003 (0.032;0.044)###0.44+-0.022 (0.400;0.487)

Atherosclerotic###BMI###0.12+-0.005 (0.107;0.127)###0.06+-0.003 (0.05;0.061)###0.22+-0.02 (0.18;0.26)

heart disease###WHR###0.11+-0.004 (0.098;0.114)###0.03+-0.003 (0.026;0.039)###0.30+-0.023 (0.255;0.347)

###WaC-IDF###0.11+-0.004 (0.102;0.118)###0.02+-0.003 (0.012;0.022)###0.41+-0.024 (0.365;0.459)

###WaC-WHO###0.13+-0.005 (0.117;0.136)###0.03+-0.003 (0.025;0.035)###0.38+-0.022 (0.336;0.423)

b)

###Male subjects

Variables###Indexes###Obesity (+) present###Obesity (-) absent###ES+-SS SD CI %95

Age###BMI###53.24+-15.99 (52.45;54.02)###44.38+-19.42 (43.86;44.91)###0.47+-0.03 (0.42;0.53)

###WHR###53.02+-16.725 (52.42;53.60)###37.10+-18.356 (36.33;37.86)###0.91+-0.029 (0.857;0.971)

###WaC-IDF###53.78+-16.246 (53.18;54.37)###38.20+-18.712 (37.47;38.93)###0.89+-0.029 (0.837;0.949)

###WaC-WHO###55.12+-15.389 (54.37;55.88)###42.75+-19.334 (42.13;43.37)###0.68+-0.031 (0.618;0.738)

Type 2###BMI###0.23+-0.011 (0.209;0.250)###0.15+-0.005 (0.136;0.155)###0.22+-0.03 (0.16;0.27)

diabetes###WHR###0.22+-0.007 (0.203;0.232)###0.06+-0.005 (0.047;0.066)###0.49+-0.03 (0.44;0.55)

###WaC-IDF###0.22+-0.008 (0.209;0.24)###0.07+-0.005 (0.057;0.077)###0.46+-0.028 (0.409;0.517)

###WaC-WHO###0.26+-0.011 (0.24;0.283)###0.10+-0.005 (0.093;0.113)###0.42+-0.03 (0.36;0.479)

Hypertension###BMI###0.35+-0.012 (0.322;0.368)###0.23+-0.006 (0.220;0.243)###0.25+-0.03 (0.20;0.31)

###WHR###0.38+-0.009 (0.361;0.395)###0.11+-0.007 (0.099;0.126)###0.64+-0.03 (0.58;0.70)

###WaC-IDF###0.38+-0.009 (0.358;0.394)###0.15+-0.007 (0.13;0.16)###0.54+-0.028 (0.481;0.590)

###WaC-WHO###0.42+-0.012 (0.396;0.444)###0.20+-0.007 (0.19;0.216)###0.48+-0.03 (0.416;0.534)

Dyslipidemia###BMI###0.16+-0.009 (0.138;0.174)###0.09+-0.004 (0.079;0.094)###0.21+-0.03 (0.16;0.27)

###WHR###0.16+-0.006 (0.143;0.168)###0.04+-0.004 (0.033;0.05)###0.40+-0.03 (0.35;0.46)

###WaC-IDF###0.16+-0.007 (0.144;0.17)###0.06+-0.005 (0.05;0.069)###0.32+-0.028 (0.267;0.375)

###WaC-WHO###0.18+-0.01 (0.157;0.195)###0.08+-0.004 (0.071;0.088)###0.29+-0.03 (0.236;0.354)

Atherosclerotic###BMI###0.14+-0.009 (0.122;0.156)###0.10+-0.004 (0.089;0.105)###0.13+-0.03 (0.08;0.19)

heart disease###WHR###0.16+-0.006 (0.143;0.168)###0.05+-0.005 (0.038;0.056)###0.37+-0.03 (0.32;0.43)

###WaC-IDF###0.16+-0.007 (0.143;0.170)###0.06+-0.005 (0.05;0.069)###0.32+-0.028 (0.265;0.373)

###WaC-WHO###0.17+-0.009 (0.154;0.191)###0.08+-0.005 (0.075;0.093)###0.27+-0.03 (0.21;0.33)

RESULTS

Data from the files of a total of 17812 subjects (10939 females; 61.41%, 6873 males; 38,59%) were included in the study. The mean age of all subjects was 46.13+-18.52 years, the mean BMI was 28.10+-9.36 kg/m2 with a mean WaC of 91.33+-14.74 cm, HC of 104.70+-11.74 cm, WHR of 0.87+-0.10 (Table-I). The prevalence of obesity in Bursa was found to be 32.2% (37.8% in females, 23.3% in males) according to BMI, 63.2% (69.7% in females, 52.9% in males) according to WaC-IDF criteria, 44.4% (53.6% in females, 29.8% in males) according to WaC--WHO criteria and 64.1% (67.8% in females, 58.4% in males) according to WHR (Table-II). The risk of obesity increased 1.003 fold with increasing age. Female subjects were 1.976 times more likely to be obese compared to male subjects, married ones 1.609 times compared to singles and those having lower education level 1.812 times compared to higher education level subjects.

The risk of obesity in those with previous obesity history was 6.326 times higher compared to those without, smokers 1.238 times compared to nonsmokers, hypertensives 1.921 fold compared to normotensives, dyslipidemics 1.217 fold compared to normolipidemics and hypothyroids 1.245 fold compared to euthyroids, diabetics with antidiabetic drug usage 1.624 times compared to nondiabetics (Table-III). When the effective size of age showed a high effect on WaC-WHO in females and moderate effect in males. When the effect size of type 2 diabetes was evaluated it had low effect on WaC-IDF and WaC--WHO in males and moderate effect in females. Dyslipidemia was evaluated accoprding to the WaC-IDF in females which had a moderate effect. Atherosclerotic heart disease was found to have a low effect on all obesity parameters in both genders (Table-IV).

DISCUSSION

Our study indicated high general and abdominal obesity in our population affecting the presence of components of metabolic syndrome. The prevelance of obesity increased as the cut off value for WaC was lowered as in WaC-IDF. Whatever criteria chosen (WHR, WaC-IDF and WaC-WHO) abdominal obesity seemed to be the prominent problem concerning metabolic syndrome components in both genders but especially in female gender. According to these results our hypothesis has been confirmed. According to Centers for Disease Control and Prevention National Center for Health Statistics (NCHS) the prevalence of obesity was 36.5% in United States among adults during 2011-2014. The prevalence of obesity among middle aged adults (40-59 years) was the highest (40.2%) among all age groups, in both genders (42.1% for females and 38.3% for males).16

In a study conducted in European countries, the prevalence of obesity in females ranged between 6.2 to 36.5% and males between 4 to 28.3% among different countries.1 The prevalence of obesity in our country was 36% according to TURDEP-II study. Between TURDEP-I and TURDEP-II surveys, average BMI increased from 26.6 to 28.6 kg/m2 and average waist from 87.2 to 94.5 cm over 12 years in Turkey. The rate of increase for abdominal obesity was 35% compared to TURDEP-I.9 In our present study, the mean age of all subjects was 46 years. Their mean BMI was 28.1 kg/m2 with a mean WaC of 91.3 cm, HC of 104.7 cm, WHR of 0.87. Similar to data reported in the literature, in our present study, we found a high prevalence of obesity in Bursa as 32.2% (37.8% in females and 23.3% in males) according to BMI. Abdominal obesity prevalences were high both with WaC-IDF and WaC-WHO criterias; 63.2% (69.7% in females, 52.9% in males) and 44.4% (53.6% for females, 29.8% for males) respectively.

In our present study, females were more obese compared to males both generally and abdominally. Gender, age, marital status, previous obesity history, education level, accompanying illnesses like diabetes, hypertension, dyslipidemia, atherosclerotic heart disease, hypothyroidism, medications and smoking are known to be the risk factors of obesity.1,2 In our previous study conducted in 2005, sedentary life style and dyslipidemia in males, being unemployed, having lower level of education and having hypertension in females and familial obesity in both genders were found to be related to increased obesity risk.10 In our present study, age, gender, marital status, previous obesity history, hypertension, dyslipidemia, smoking, hypothyroidism and antidiabetic drug usage in diabetics were found to be significant risk factors for obesity development. The underlying cause of the metabolic syndrome is still debatable.

Both general and abdominal obesities contribute to hypertension, dyslipidemia and hyperglycemia and are independently associated with higher cardiovascular disease risk.16 The risk of serious health consequences in the form of components of metabolic syndrome has been shown to rise with an increase in BMI, but an excess of body fat in the abdomen is more indicative of the metabolic syndrome profile than BMI.17,18 In the San Antonio Heart Study, the baseline WaC of the subjects who did not progress to type 2 diabetes was 88.7 cm while who developed type 2 diabetes in 7 years of follow up was 98 cms. In this study WaC was found to be the strongest predictor of type 2 diabetes. In the same study BMI, fasting insulin and triglyceride levels predicted the development of hypertension indicating the importance of overall adiposity19 In Nurses Health Study a WaC of 96.5 cm or more was associated with a relative risk of 3.06 for coronary heart disease.20

CONCLUSION

Our results indicated that, both general and abdominal obesities were much more prominent in female gender. All three criterias indicating abdominal obesity namely; WHR, WaC-IDF and WaC-WHO could be preferred for risk determination of metabolic syndrome components. Age, gender, marital status, previous obesity history, hypertension, dyslipidemia, smoking, hypothyroidism and antidiabetic drug usage in diabetic subjects were found to be significant risk factors for obesity development. Since aging and gender are unmodifyable risk factors, life style changes should be encouraged in the population and preventive measures should be taken by the health authorities to prevent obesity.

ACKNOWLEDGEMENTS

We would like to thank ARGEV (The Turkish Family Medicine Research Development and Education Foundation) providing education before the study for their cooperation and our colleagues and family physicians who have contributed to this study by participating in all the districts of Bursa province;

Murat Girginer:###Erturulgazi Family Health

###Center, Yildirim, Bursa,

###Turkey

Enes Mesutolu:###Ali Bakgor Family Health

###Center, Osmangazi, Bursa,

###Turkey

Erhan Kutsal:###Esenevler Family Health

###Center, Yildirim, Bursa,

###Turkey

Taner Odemi:###Aticilar Family Health Center,

###Osmangazi, Bursa, Turkey

Filiz Odemi:###Aticilar Family Health Center,

###Osmangazi, Bursa, Turkey

H. Burcu Bulunmaz:###Beevler Family Health

###Center, Nilufer, Bursa, Turkey

Bilgen Kucuk:###Kuplupinar Family Health

###Center, Osmangazi, Bursa,

###Turkey

Funda Arpaci:###Kuplupinar Family Health

###Center, Osmangazi, Bursa,

###Turkey

Tulay Gunduzcu:###Beevler Family Health

###Center, Nilufer, Bursa, Turkey

Fahri Ozaydin:###Eref Dincer Family Health

###Center, Gemlik, Bursa, Turkey

Alp Oktay:###Demirta Family Health

###Center, Osmangazi, Bursa,

###Turkey

Yusuf Karayurek:###Eref Dincer Family Health

###Center, Gemlik, Bursa, Turkey

Esad Uyanik:###Yeniehir Number 3 Family

###Health Center, Yeniehir,

###Bursa, Turkey

Olgun Gokta:###Uluda University Family

###Health Center, Nilufer, Bursa,

###Turkey

Nevin Ozdilek:###Orkent Family Health Center,

###Orhangazi, Bursa, Turkey

Atil Baar Bozkurt:###Zeytinbai Family Health

###Center, Mudanya, Bursa,

###Turkey

Aykut Ardic:###Siteler Family Health Center,

###Yeniehir, Bursa, Turkey

enay Karata:###S. Ust. A. Korolu Family

###Health Center, Osmangazi,

###Bursa, Turkey

Emre Yalcinta:###Buyukorhan Family Health

###Center, Buyukorhan, Bursa,

###Turkey

Disclosure: We authors, we are reporting that there is no conflict of interest.

Grant Support and Financial Disclosures: None.

REFERENCES

1. Berghofer A, Pischon T, Reinhold T, Apovian CM, Sharma AM, Willich SN. Obesity prevalence from a European perspective: a systematic review. BMC Public Health. 2008;8:200. doi: 10.1186/1471-2458-8-200.

2. Erem C. Prevalence of Overweight and Obesity in Turkey. IJC Metabolic Endocrine. 2015;8:38-41. doi: 10.1016/j. ijcme.2015.07.002.

3. Taskiran Tatar B, Ersoy C, Kacan T, Kirhan E, Sarandol E, Sigirli D, et al. Neck and Wrist Circumferences Propose a Reliable Approach to Qualify Obesity and Insulin Resistance. Med-Sci. 2014;3:1013-1025. doi: 10.5455/medscience.2013.02.8100.

4. Saqlain M, Saeed S, Fiaz M, Mahmood A, Ghani RA, Jabeen S, et al. Screening of cardiometabolic risks clustering in young Pakistani adults classified by anthropometric traits. J Pak Med Assoc. 2017;67:1825-1832.

5. Maffetone PB, Laursen PB. The Prevalence of Overfat Adults and Children in the US. Front Public Health. 2017;5:290 doi: 10.3389/fpubh.2017.00290.

6. Ramirez-Velez R, Correa-Bautista JE, Sanders-Tordecilla A, Ojeda-Pardo ML, Cobo-Mejia EA, Castellanos-VegaRDP, et al. Percentage of Body Fat and Fat Mass Index as a Screening Tool for Metabolic Syndrome Prediction in Colombian University Students. Nutrients. 2017;9:1009 doi: 10.3390/nu9091009.

7. Parikh RM, MohanV. Changing definitions of metabolic syndrome. Indian J Endocrinol Metab. 2012;16:7-12. doi: 10.4103/2230-8210.91175.

8. Kahn R, Buse J, Ferrannini E, Stern M. American Diabetes Association; European Association for the Study of Diabetes. The metabolic syndrome: time for a critical appraisal: Joint statement from the American Diabetes Association and the Eur Assoc Stud Diabet Care. 2005;28:2289-2304.

9. Satman I, Omer B, Tutuncu Y, Kalaca S, Gedik S, Dinccag N, et al. TURDEP-II Study Group. Twelve-year trends in the prevalence and risk factors of diabetes and prediabetes in Turkish Adults. Eur J Epidemiol. 2013;28:169-180. doi: 10.1007/s10654-013-9771-5.

10. Ersoy C, Imamoglu S, Tuncel E, Erturk E, Ercan I. Comparison of the factors that influence obesity prevalence in three district municipalities of the same city with different socioeconomical status: a survey analysis in an urban Turkish population. Prev Med. 2005;40:181-188 doi: 10.1016/j.ypmed.2004.05.018.

11. Waist circumference and waist-hip ratio: Report of a WHO expert consultation 2008,27. Geneva, Switzerland: WHO Press.

12. Baik I. Optimal cutoff points of waist circumference for the criteria of abdominal obesity: comparison with the criteria of the International Diabetes Federation. Circ J. 2009;73:2068-2075 https://doi.org/10.1253/circj.CJ-09-0303.

13. Huedo-Medina TB, Sanchez-Meca J, Marin-Martinez F, Botella J. Assessing heterogeneity in meta-analysis: Q statistic or I2 index? Psychol Methods. 2006;11:193-206 doi: 10.1037/1082-989X.11.2.193.

14. Cohen J. Statistical power analysis for behavioral sciences. 2nd edition. Lawrence Erlbaum associates, publishers: New York. 1988.

15. Sullivan GM, Feinn R. Using effect size-or why the p value is not enough. J Grad Med Edu. September 2012. doi: 10.4300/JGME-D-12-00156.1.

16 Ogden CL, Carroll MD, Kit BK, Flegal KM. Prevalence of Obesity Among Adults: United States, 2011-2012 NCHS Data Brief, No. 131, 2013: https://www.cdc.gov/nchs/data/databriefs/db131.pdf.

17. Swarup S, Zeltser R. Metabolic Syndrome. Treasure Island (FL): StatPearls Publishing; 2017.

18. Pouliot MC, Despres JP, Lemieux S, Moorjani S, Bouchard C, Tremblay A, et al. Waist circumference and abdominal sagittal diameter: best simple anthropometric indexes of abdominal visceral adipose tissue xaccumulation and related cardiovascular risk in men and women. Am J Cardiol. 1994;73:460-468. doi: 10.1016/0002-9149(94)90676-9.

19. Haffner SM. Obesity and the metabolic syndrome: the San Antonio Heart Study. Br J Nutr. 2000;83(Suppl 1):S67-S70.

20. Lebovitz HE. Clinician's Manual on Insulin resistance. Science press Ltd. London UK; 2002.
COPYRIGHT 2019 Knowledge Bylanes
No portion of this article can be reproduced without the express written permission from the copyright holder.
Copyright 2019 Gale, Cengage Learning. All rights reserved.

Article Details
Printer friendly Cite/link Email Feedback
Publication:Pakistan Journal of Medical Sciences
Date:Aug 31, 2019
Words:4474
Previous Article:Injection of immunoglobulin in the treatment process of children with severe pneumonia.
Next Article:Assessment of knowledge about childhood autism spectrum disorder among healthcare workers in Makkah-Saudi Arabia.
Topics:

Terms of use | Privacy policy | Copyright © 2020 Farlex, Inc. | Feedback | For webmasters