Metabolic syndrome and its socio demographic and behavioral correlates: a cross sectional study among adult patients attending medicine outpatient department in a tertiary care hospital, West Bengal

Sanjoy Kumar Kunti, Santanu Ghosh, Amrita Samanta, Indranil Chakraborty


Background: Metabolic syndrome (MS) is a pre-condition for cardiovascular diseases and type 2 diabetes mellitus (T2DM) which are major contributors to morbidity and mortality worldwide.

Methods: The cross-sectional, observational study was conducted to estimate the proportion of MS and to explore crucial risk factors for MS among adult patients attending medicine OPD in a tertiary care hospital in West Bengal. The estimated final sample size was 315. Baseline socio demographic information and information on risk factors for MS, such as dietary habit, physical activity status, substance use, intake of related drugs, and presence of co-morbidities were collected by interviewing the patients with the help of a predesigned, pretested, structured schedule. Anthropometric measurements such as weight, height, waist circumference recordings were taken, and blood pressure was measured.

Results: About 64% of the final study population (210/330) suffered from MS. On bivariate analysis, significant association between female gender (df=1, Pearson chi-square=5.06, p=0.024), weekly frequency of consumption of junk foods (df=3, Pearson chi-square=10.40, p=0.015) and obesity according to BMI (independent samples Mann-Whitney U test, p=0.010) at 5% level of significance were observed. Performing binary logistic regression analysis, obesity according to BMI (AOR=1.388, 95% CI=1.064-1.810) was found to be significant.

Conclusions: Majority of the population suffered from MS who were mostly female, obese and consumers of junk foods. Appropriate interventional measures in terms of life style modification both at community and at tertiary care level are the need of the hour.


ATP-III, BMI, CVD, Diabetes, Obesity, Metabolic syndrome

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Alexander CM, Landsman PB, Teutsch SM, Haffner SM. Third national health and nutrition examination survey (NHANES III), national cholesterol education program (NCEP). NCEP-defined metabolic syndrome, diabetes, and prevalence of coronary heart disease among NHANES III participants age 50 years and older. Diabetes. 2003;52:1210-4.

Sattar N, Gaw A, Scherbakova O, Ford I, O′Reilly DS, Haffner SM, et al. Metabolic syndrome with and without C-reactive protein as a predictor of coronary heart disease and diabetes in the West of Scotland Coronary Prevention Study. Circulation. 2003;108:414-9.

Mathieu P, Poirier P, Pibarot P, Lemieux I, Després JP. Visceral obesity: The link among inflammation, hypertension, and cardiovascular disease. Hypertension. 2009;53:577-84.

Parikh RM, Mohan V. Changing definitions of metabolic syndrome. Indian J Endocrinol Metab. 2012;16:7–12.

Bloom DE, Rosenberg L. The Future of South Asia: Population Dynamics, Economic Prospects, and Regional Coherence. Available at: http://www. Accessed 12 December 2018.

Misra A, Khurana L, Ishwarlal S, Bharadwaj S. South Asian Diets and Insulin Resistance. Br J Nutr. 2009;101:465-73.

Gupta R, Gupta VP. Meta-analysis of coronary heart disease prevalence in India. Indian Heart J. 1996;48:241-5.

Mohan V, Shanthirani CS, Deepa R. Glucose intolerance (diabetes and IGT) in a selected South Indian population with special reference to family history, obesity and lifestyle factors-the Chennai Urban Population Study (CUPS 14). J Assoc Physicians India. 2003;51:771-7.

Pradeepa R, Mohan V. The changing scenario of the diabetes epidemic: Implications for India. Indian J Med Res. 2002;116:121-32.

Sarkar S, Das M, Mukhopadhyay B, Chakrabarti CS, Majumder PP. High prevalence of metabolic syndrome and its correlates in two tribal populations of India and the impact of urbanization. Indian J Med Res. 2006;123:679-86.

Kumar A, Kalra S, Unnikrishnan AG. Metabolic state of the nation: Results of the National Family Health Survey-4. Indian J Endocr Metab. 2016;20:429-31.

Gupta R, Sharma KK, Gupta A, Agrawal A, Mohan I, Gupta VP, et al. Persistent High Prevalence of Cardiovascular Risk Factors in the Urban Middle Class in India: Jaipur Heart Watch-5. J Assoc Physicians India. 2012;60:11-6.

WHO STEPS Instrument (Core and Expanded). The WHO STEPwise approach to noncommunicable disease risk factor surveillance (STEPS). World Health Organization. Geneva. Available at Accessed 12 January 2019.

Trinder P. Quantitative determination of glucose using GOP-PAP method. Clin Biochemistry. 1969;6:24-7.

Trinder P. Triglyceride estimation by GPO-PAP method. Ann Clin Biochemistry. 1969;6:24-7.

Burstein M, Scholnick HR, Morfin R. Rapid method for the isolation of lipoproteins from human serum by precipitation with polyanions. J Lipid Res. 1970;11:583-95.

Expert Panel on Detection, Evaluation, and Treatment of High Blood Cholesterol in Adults. Executive Summary of The Third Report of The National Cholesterol Education Program (NCEP) (Adult Treatment Panel III). JAMA. 2001;285:2486-97.

Misra A, Chowbey P, Makkar BM, Vikram NK, Wasir JS, Chadha D, et al. Consensus Statement for Diagnosis of Obesity, Waste Obesity and the Metabolic Syndrome for Asian Indians and Recommendations for Physical Activity, Medical and Surgical Management. J Assoc Physicians India. 2009;57:163-70.

Guidelines for Data Processing and Analysis of the International Physical Activity Questionnaire (IPAQ) – Short and Long Forms, 2005. Available at: http// Accessed 21 December 2018.

Sodjinou R, Agueh V, Fayomi B, Delisle H. Obesity and Cardio-Metabolic Risk Factors in Urban Adults of Benin: Relationship with Socio-Economic Status, Urbanisation, and Lifestyle Patterns. BMC Public Health. 2008;8:84.

Gupta R, Deedwania PC, Sharma K, Gupta A, Guptha S, Achari V. Association of Educational, Occupational and Socioeconomic Status with Cardiovascular Risk Factors in Asian Indians: A Cross-Sectional Study. PLoS One. 2012;7(8):44098.

Misra A, Khurana L. Obesity and the metabolic syndrome in developing countries. J Clin Endocrinol Metab. 2008;93(1):9–30.

Batsis JA, Nieto-Martinez RE, Lopez-Jimenez F. Metabolic syndrome: from global epidemiology to individualized medicine. Clin Pharmacol Ther. 2007;82:509–24.

Sinha S, Misra P, Kant S, Krishnan A, Nongkynrih B, Vikram NK. Prevalence of metabolic syndrome and its selected determinants among urban adult women in South Delhi, India. Postgrad Med J. 2013;89:68-72.

Deshmukh PR, Kamble P, Goswami K, Garg N. Metabolic Syndrome in the Rural Population of Wardha, Central India: An Exploratory Factor Analysis. Indian J Community Med. 2013;38:33–8.

Dutra ES, Carvalho KMB, Miyazaki E, Merchán- Hamann E, Ito MK. Metabolic syndrome in central Brazil: prevalence and correlates in the adult population. Diabetol Metabolic Syndrome. 2012;4:20.

Kumar A, Kalra S, Unnikrishnan AG. Metabolic state of the nation: Results of the National Family Health Survey-4. Indian J Endocr Metab. 2016;20:429-31.

Das M, Pal S, Ghosh A. Interaction of physical activity level and metabolic syndrome among the adult Asian Indians living in Calcutta, India. J Nutr Health Aging. 2012;16:539-43.

Vaidya A, Krettek A. Physical activity level and its sociodemographic correlates in a peri-urban Nepalese population: a cross-sectional study from the Jhaukhel-Duwakot health demographic surveillance site. International J Behavioral Nutr Physical Activity. 2014;11:39.

Al-Kabba AF, Al-Hamdan NA, El Tahir A, Abdelshakour M. Prevalence and Correlates of Dyslipidaemia among Adults in Saudi Arabia: Results from a National Survey. Open J Endocr Metab Dis. 2012;2:89-97.