|Year : 2016 | Volume
| Issue : 1 | Page : 16-22
Prevalence of metabolic syndrome in Mumbai City, India
Jagmeet G Madan, Ankita M Narsaria
Department of Food and Nutrition, SVT College of Home Science (Autonomous), SNDTWU, Mumbai, Maharashtra, India
|Date of Submission||12-Jun-2015|
|Date of Decision||10-Oct-2015|
|Date of Acceptance||08-Mar-2016|
|Date of Web Publication||16-Jun-2016|
Jagmeet G Madan
Department of Food and Nutrition, SVT College of Home Science (Autonomous), SNDTWU, Juhu Tara Road, Santacruz (West), Mumbai, Maharashtra
Source of Support: None, Conflict of Interest: None
Background: Metabolic syndrome (MetS) is a complex web of metabolic factors that are associated with a 2-fold risk of cardiovascular diseases and a 5-fold risk of diabetes. There are lacunae of Indian studies regarding its prevalence with special reference to metropolitan cities such as Mumbai, India. Aim: To determine the prevalence of MetS in apparently healthy adult male population from Mumbai city based on their anthropometric, biochemical, and clinical health markers. Materials and Methods: This study was a cross-sectional study comprising 313 apparently healthy adult males aged 18-65 years from upper-middle-income group from different locales of Mumbai. A standardized pretested questionnaire was used to collect data regarding demographic characteristics, anthropometric parameters, and biochemical and clinical health markers using standardized methods. The data were analyzed using SPSS statistical software. Any observed difference was considered statistically significant with P < 0.05. Results: The mean age of the subjects was 46 years. The prevalence of MetS was 40% with 82% of the population surveyed being overweight and obese and 70.3% of the population with waist circumference of 90 cm. It was observed that 36% of the subjects were prehypertensives and 23.4% had systolic and/or diastolic blood pressures 140/90 mmHg. Almost 40% of the subjects had dysglycemia with 34% of the subjects with high triglycerides, 26% with high total cholesterol, 64% with raised serum low-density lipoprotein cholesterol, and almost 66% with low serum high-density lipoprotein cholesterol levels. A significant positive correlation was observed between anthropometric and biochemical markers. Conclusion: In apparently healthy adult population of Mumbai, the prevalence of MetS was 40%. A significant positive correlation was observed between anthropometric, clinical, and biochemical markers. The study highlights the need for intervention to lower the risk markers predisposing the urban population to noncommunicable diseases.
Keywords: Metabolic syndrome, noncommunicable disease, prediabetes, prehypertensives
|How to cite this article:|
Madan JG, Narsaria AM. Prevalence of metabolic syndrome in Mumbai City, India. J Obes Metab Res 2016;3:16-22
| Introduction|| |
Metabolic syndrome (MetS) is a complex web of metabolic factors that are associated with a 2-fold risk of cardiovascular diseases (CVD) and a 5-fold risk of diabetes. MetS is a constellation of multiple cardiometabolic abnormalities including truncal (central) obesity, borderline and high blood pressure (BP), high fasting glucose, high triglycerides (TGs), and low high-density lipoprotein cholesterol (HDL-C). ,,,, Studies performed in India have reported the prevalence of MetS among adults as to be from 11% to 56%, depending on the definition used.  NCEP, ATP-III defines it with the presence of three out of five clinical and/or biochemical abnormalities; the International Diabetes Federation recommends abdominal obesity as an obligatory criterion and the presence of at least two other abnormal criteria. , The consensus statement for MetS in Asian-Indians [Table 1] recommends meeting any three out of the five criteria for diagnosing MetS. ,
Limited information exists regarding the burden of MetS among the apparently healthy population of Mumbai in India, particularly males. Therefore, we aimed to determine the prevalence of MetS in the apparently healthy male subjects in Mumbai and find the association between various anthropometric, biochemical, and clinical parameters.
| Materials and methods|| |
The present study was a cross-sectional, observational study in the metropolitan city Mumbai, India. A total of 313 adult males from different socioeconomic strata (middle to upper) were recruited from three reputed healthcare centers in different locales of the city. The subjects were recruited randomly from these centers during their general health checkup. Ethical permission was taken and informed consent was obtained from the patients.
The inclusion criteria included age group of 18-65 years, apparently healthy (not on any medications for diabetes, hypertension (HTN), other CVD), and willing to participate in the study. Children, adolescents, pregnant women, bedridden, physically challenged, and elderly patients above 65 years were excluded from the study. A detailed pretested and standardized questionnaire was designed to elicit information about the subject's demographic data, anthropometric measurements, biochemical details, and clinical parameters.
Anthropometric measurements included body weight (BW), waist circumference (WC), and hip circumference (HC), which were taken using standardized procedure. Body mass index (BMI) and waist to hip ratio were calculated. Body fat percentage was measured using Tanita body fat monitor. Biochemical and clinical parameters included blood sugar (fasting: Fetal bovine serum [FBS] and 2 h postprandial levels: Phosphate-buffered saline [PPBS]), blood lipid profile (total cholesterol [TC], low-density lipoprotein cholesterol [LDL-C], HDL-C, very low-density lipoprotein cholesterol [VLDL], TGs, TC: HDL-C, and LDL-C: HDL-C), and serum uric acid (SUA). Random BP reading was also recorded as a part of the health checkup program.
Recommended standard cutoffs were used for all the parameters.
Statistical analyses were performed using SPSS sotware version 16, Chicago, Illinois, USA. The following variables were analyzed: Demographic data, correlations between anthropometric and biochemical parameters, BP, and body fat percentage. These variables were analyzed using t-test, Chi-square test, and analysis of variance (ANOVA).
| Results|| |
The demographic and anthropometric details of the subjects are mentioned in [Table 2]. The mean age of the study population (n = 313) was 45.75 years. The mean BMI was 26.67 kg/m 2 and the mean WC was 95.34 cm. Almost 82% of the subjects were overweight and obese based on their BMI levels [Figure 1].
|Table 2: Mean Demographic and Anthropometric characteristics of the subjects |
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From the apparently healthy study population, 36% of the population was prehypertensive followed by 23.4% in Stage 1 HTN and 2.6% with Stage 2 HTN [Figure 2].
The mean levels of the biochemical profile of the subjects are discussed in [Table 3].
When subjects were subclassified based on the blood glucose levels and lipid profile, it was seen that almost 39% of the subjects had dysglycemia (FBS > 100 mg/dl) and 20% had deranged postprandial blood sugar levels [Figure 3]; 34% (n = 106) of the subjects had TGs levels >150 mg/dl and 25.6% had serum TC levels >200 mg/dl. LDL-C of >100 mg/dl was observed in 63.6% subjects and HDL-C of <40 mg/dl in 66.1% subjects [Figure 4].
Of the total subjects, 79% and 18% of the subjects had SUA levels of 3-7 mg/dl and >7 mg/dl, respectively. Clusters of 3 or more metabolic risk factors based on the recommendations for Asian-Indians showed that 39.9% (n = 125) of the subjects fulfill the criteria to be classified as having MetS.
Correlations between the parameters
A significant positive correlation between systolic blood pressure (SBP) with BW, BMI (P = 0.012), WC (P = 0.008), HC (P = 0.036), FBS (P = 0.001), PPBS (P = 0.003), TG (P = 0.025), and VLDL (P = 0.019) was seen [Table 4]. Diastolic blood pressure (DBP) was not correlated with any of the parameters. A significant positive correlation between WC and FBS (P = 0.044) and PPBS (P = 0.041) was observed. A positive correlation was observed between TGs and VLDL and BW (P = 0.004, P = 0.003, respectively), also between VLDL-C and BMI (P = 0.030); and LDL-C/HDL-C and HC (P = 0.035). SUA levels showed a significant positive correlation with BW (P = 0.0001), BMI (P = 0.002), WC (P = 0.009), and HC (P = 0.008).
An in-depth analytical ANOVA test was used to determine the trend of other health markers when one of the variables [for instance, WC in [Table 5] was categorized.
|Table 5: Mean Anthropometric and Biochemical variables of the population as per the Waist circumference categories |
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The stratified analysis of the WC categories with other parameters indicated that subjects with normal WC levels had high total body fat (TBF%) (26%), raised FBS (~111 mg/dl), and low HDL (<40 mg/dl). The analyzed data were further categorized into four major groups of different stages of HTN. Subjects with normal BP (both SBP and DBP) had their BMI, WC, TBF%, and LDL-C levels in the high-normal categories whereas HDL-C was below the normal range. In addition, raised FBS and SBP levels were seen in subjects with normal DBP which indicates that the development of metabolic abnormalities commences even in person with normal DBP levels.
Subjects were classified into three groups based on their FBS levels, and other parameters were statistically analyzed using ANOVA test for each category of the FBS [Figure 5]. Results also showed that subjects with normal FBS levels had a high mean BMI, WC, TBF%, LDL-C SBP, and low HDL-C levels.
|Figure 5. Mean Anthropometric and Biochemical variables of the population studied as per the FBS categories|
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| Discussion|| |
In the present study, almost 40% of the apparently healthy adult males (mean age 46 years, n = 313) were found to have MetS. About 82% of the subjects were overweight/obese with 70.3% of them had WC of >90 cm. Among these apparently healthy subjects, almost 36% of the study population was prehypertensive and 23.4% had Stage 1 HTN. They also had a deranged blood sugar levels and serum lipid profile, and their mean SUA level was also on a high-normal range.
Several studies are done till date to determine the prevalence of MetS in Indians. ,,,,,,,,,, Earlier studies used the standard ATP-III or modified ATP-III criteria for diagnosis and stated prevalence of 18-35% in urban subjects. ,,, Recent studies have used harmonized criteria and Asian-specific guidelines and have reported a higher prevalence, 25-50%, with large interstudy variations. ,, In a recently published study done by Sawant et al., in 2011, on 548 subjects in Mumbai, nearly 95% of the subjects had at least one abnormal biochemical parameter; the prevalence of MetS in the study population was found to be 19.52%. The prevalence of MetS in males was almost double than females (P = 0.008).  In our study where only male subjects were assessed, the prevalence studied was almost double, i.e., 39.9%. An investigation by the Indian Council of Medical Research in 2002, suggested that overweight, obesity, and physical inactivity have become important determinants of health in urban India as they lead to adverse metabolic changes, including increase in BP, unfavorable cholesterol levels, and increased resistance to insulin. 
The same observation was seen in our study where apparently healthy subjects had their BW, BMI, WC, and HC positively associated with SBP, VLDL, and SUA. Visceral adiposity as accessed by WC is linked with insulin resistance (IR) as suggested by various studies. ,, Recent research suggests that IR and the resulting hyperinsulinemia induce BP elevation by the activation of sympathetic nervous system and renin-angiotensin-aldosterone system with consequential sodium retention and volume expansion, endothelial dysfunction, and alteration in renal function. Visceral fat, in comparison to subcutaneous tissue, represents a metabolically active organ, strongly related to insulin sensitivity. One of the proposed mechanisms by which HTN is linked with central obesity includes sympathetic nervous system overactivation. ,,,,
WC was the only anthropometric parameter that was strongly associated with FBS and PPBS. The findings are similar to those reported by Kahn et al.  The mechanism linking obesity to IR and type II diabetes reported that in obese individuals, adipose tissue releases increased amounts of nonesterified fatty acids, glycerol, hormones, proinflammatory cytokines, and other factors that are involved in the development of IR. When IR is accompanied by dysfunction of pancreatic islet β-cells impaired, blood sugar control results. , Studies looked upon the merits of routine WC measurements for screening of impaired fasting glucose (IFG) and indicate the need for routine anthropometric measurements in clinical practice screening for IFG. ,
The present study also reported a significant positive association between SBP with FBS, PPBS, TGs, and VLDL. The plausible mechanism as suggested by Modan and Halkin is that HTN is an important risk factor for the development and worsening of many complications of diabetes and a major risk factor for CVD. In biological terms, the relationship between diabetes and high BP is a type of positive feedback loop, where one step causes a second step and that second step "feeds back" to cause more of the first step. Constant hyperglycemia causes damage to the blood vessels including that of renal nephrons causing compensatory increase BP, which in turn leads to lower sensitivity of the muscle for insulin-dependent glucose uptake (IR) and thus hyperglycemia. , The same fact was supported in earlier studies, which found that hyperinsulinemia due to IR was present in majority of the hypertensive obese subjects and was also associated with varied degree of glucose intolerance. , In the past decade, focus on uric acid as one of the determinants of MetS and CVD risk factor has gained a lot of interest. A study done by Dehghan et al. to investigate the association between SUA level and risk of type 2 diabetes suggested that SUA is a strong and independent risk factor for diabetes.  In our study, a significant positive correlation between SUA levels with weight, BMI, WC, and HC was observed.
The present study further analyzed the parameters and suggested that subjects with normal WC levels have high TBF%, increased FBS and SBP, and reduced HDL. Similarly, subjects with normal BMI had LDL-C and SBP/DBP levels on a higher than the normal side. The findings also show that along with increasing stages of SBP, there is a significant increase in the levels of weight, BMI, FBS, PPBS, and DBP, suggesting that the increase in SBP is strongly associated with these metabolically significant parameters. Subjects with normal BP (both SBP and DBP) had their BMI, WC, TBF%, and LDL-C levels in the high-normal categories whereas HDL-C was below the normal range. In addition, raised FBS and SBP levels were seen in some subjects with normal DBP which indicates that the development of metabolic abnormalities commences even in person with normal DBP levels. Results also showed that subjects with normal FBS levels had a high mean BMI, WC, TBF%, LDL-C, and SBP and low HDL-C levels.
| Conclusion|| |
Thus, all these findings emphasize the need for screening the apparently healthy population through regular health checkups so that the corrective and preventive dietary and lifestyle intervention can be initiated ahead of time to prevent further progression and increased risk to heart disease and diabetes. In conclusion, the present study contributes to the limited database on prevalence of MetS in apparently healthy adult males of Mumbai. The analysis highlighted the need to target the population in the age group of 30-50 years for maximizing the preventive effort. The data demonstrated that clustering of anthropometric clinical and biochemical parameters plays a significant synergistic role in the development of metabolic abnormalities. The study highlights the need for intervention to lower the risk markers predisposing the urban population to noncommunicable diseases.
We would like to acknowledge the Department of Food Science and Nutrition, SVT College of Home Science (Autonomous) SNDTWU, Mumbai; Asian Heart Institute, Bandra (East), Mumbai; and P. H. Medical Diagnostic Centre, Mumbai, and Spectrum Diagnostic Centre for granting permission to conduct and collect data with all due consents from their health centers.
Financial support and sponsorship
Conflicts of interest
There are no conflicts of interest.
| References|| |
Ghaffar A, Reddy KS, Singhi M. Burden of non-communicable diseases in South Asia. BMJ 2004;328:807-10.
Mohan V, et al. Epidemiology of type 2 diabetes: Indian scenario. Indian J Med Res 2007;3:217-230.
Bonora E, Targher G, Formentini G, Calcaterra F, Lombardi S, Marini F, et al. The metabolic syndrome is an independent predictor of cardiovascular disease in type 2 diabetic subjects. Prospective data from the Verona diabetes complications study. Diabet Med 2004;21:52-8.
Sawant A, Mankeshwar R, Shah S, Raghavan R, Dhongde G, Raje H, et al. Prevalence of metabolic syndrome in urban India. Cholesterol 2011;2011:920983.
Deedwania PC, Gupta R, Sharma KK, Achari V, Gupta B, Maheswari A, et al. High prevalence of metabolic syndrome among urban subjects in India: A multisite study. Diabetes Metab Syndr 2014;8:156-61.
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.
Alberti KG, Eckel RH, Grundy SM, Zimmet PZ, Cleeman JI, Donato KA, et al. Harmonizing the metabolic syndrome: A joint interim statement of the International Diabetes Federation Task Force on Epidemiology and Prevention; National Heart, Lung, and Blood Institute; American Heart Association; World Heart Federation; International Atherosclerosis Society; and International Association for the Study of Obesity. Circulation 2009;120:1640-5.
Misra A, Chowbey P, Makkar BM, Vikram NK, Wasir JS, Chadha D, et al. Consensus statement for diagnosis of obesity, abdominal 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.
Ramachandran A, Snehalatha C, Satyavani K, Sivasankari S, Vijay V. Metabolic syndrome in urban Asian Indian adults - A population study using modified ATP III criteria. Diabetes Res Clin Pract 2003;60:199-204.
Gupta R, Deedwania PC, Gupta A, Rastogi S, Panwar RB, Kothari K. Prevalence of metabolic syndrome in an Indian urban population. Int J Cardiol 2004;97:257-61.
Misra A, Wasir JS, Pandey RM. An evaluation of candidate definitions of the metabolic syndrome in adult Asian Indians. Diabetes Care 2005;28:398-403.
Deepa M, Farooq S, Datta M, Deepa R, Mohan V. Prevalence of metabolic syndrome using WHO, ATPIII and IDF definitions in Asian Indians: The Chennai urban rural epidemiology study (CURES-34). Diabetes Metab Res Rev 2007;23:127-34.
Mahadik SR, Deo SS, Mehtalia SD. Increased prevalence of metabolic syndrome in non-obese Asian Indian - An urban-rural comparison. Metab Syndr Relat Disord 2007;5:142-52.
Mangat C, Goel NK, Walia DK, Agarwal N, Sharma MK, Kaur J, et al. Metabolic syndrome: A challenging health issue in highly urbanized union territory of North India. Diabetol Metab Syndr 2010;2:19.
Wasir JS, Misra A, Vikram NK, Pandey RM, Gupta R. Comparison of definitions of the metabolic syndrome in adult Asian Indians. J Assoc Physicians India 2008;56:158-64.
Prabhakaran D, Chaturvedi V, Shah P, Manhapra A, Jeemon P, Shah B, et al. Differences in the prevalence of metabolic syndrome in urban and rural India: A problem of urbanization. Chronic Illn 2007;3:8-19.
Kamble P, Deshmukh PR, Garg N. Metabolic syndrome in adult population of rural Wardha, central India. Indian J Med Res 2010;132:701-5.
Misra P, Upadhyay RP, Krishnan A, Vikram NK, Sinha S. A community-based study of metabolic syndrome and its components among women of rural community in Ballabgarh, Haryana. Metab Syndr Relat Disord 2011;9:461-7.
Das M, Pal S, Ghosh A. Association of metabolic syndrome with obesity measures, metabolic profiles, and intake of dietary fatty acids in people of Asian Indian origin. J Cardiovasc Dis Res 2010;1:130-5.
Ravikiran M, Bhansali A, Ravikumar P, Bhansali S, Dutta P, Thakur JS, et al. Prevalence and risk factors of metabolic syndrome among Asian Indians: A community survey. Diabetes Res Clin Pract 2010;89:181-8.
Shah B. Overview of Noncommunicable Diseases Research at ICMR. ICMR-MRC Workshop Building Indo - UK Collaboration in Chronic Diseases; 2002.
Lee S, Bacha F, Gungor N, Arslanian SA. Waist circumference is an independent predictor of insulin resistance in black and white youths. J Pediatr 2006;148:188-94.
Carey DG, Jenkins AB, Campbell LV, Freund J, Chisholm DJ. Abdominal fat and insulin resistance in normal and overweight women: Direct measurements reveal a strong relationship in subjects at both low and high risk of NIDDM. Diabetes 1996;45:633-8.
Després JP. Excess visceral adipose tissue/ectopic fat the missing link in the obesity paradox? J Am Coll Cardiol 2011;57:1887-9.
Dandona P, Aljada A, Bandyopadhyay A. Inflammation: The link between insulin resistance, obesity and diabetes. Trends Immunol 2004;25:4-7.
Gundogdu Z. Relationship between BMI and blood pressure in girls and boys. Public Health Nutr 2008;11:1085-8.
Kotani K, Adachi S, Tsuzaki K, Sakane N. Relationship between the abdominal wall fat index and blood pressure in elderly women: A comparison with the body mass index. Aging Clin Exp Res 2009;21:349-52.
Sakurai M, Miura K, Takamura T, Ota T, Ishizaki M, Morikawa Y, et al. Gender differences in the association between anthropometric indices of obesity and blood pressure in Japanese. Hypertens Res 2006;29:75-80.
Bruce KD, Byrne CD. The metabolic syndrome: Common origins of a multifactorial disorder. Postgrad Med J 2009;85:614-21.
Kahn SE, Hull RL, Utzschneider KM. Mechanisms linking obesity to insulin resistance and type 2 diabetes. Nature 2006;444:840-6.
Bogers RP, Bemelmans WJ, Hoogenveen RT, Boshuizen HC, Woodward M, Knekt P, et al. Association of overweight with increased risk of coronary heart disease partly independent of blood pressure and cholesterol levels: A meta-analysis of 21 cohort studies including more than 300 000 persons. Arch Intern Med 2007;167:1720-8.
St-Pierre J, Lemieux I, Vohl MC, Perron P, Tremblay G, Després JP, et al. Contribution of abdominal obesity and hypertriglyceridemia to impaired fasting glucose and coronary artery disease. Am J Cardiol 2002;90:15-8.
Gautier A, Roussel R, Ducluzeau PH, Lange C, Vol S, Balkau B, et al. Increases in waist circumference and weight as predictors of type 2 diabetes in individuals with impaired fasting glucose: Influence of baseline BMI: Data from the DESIR study. Diabetes Care 2010;33:1850-2.
Modan M, Halkin H. Hyperinsulinemia. A link between hypertension obesity and glucose intolerance. Nature 2006;444:840-6.
Henry P, Thomas F, Benetos A, Guize L. Impaired fasting glucose, blood pressure and cardiovascular disease mortality. Hypertension 2002;40:458-63.
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 European Association for the Study of Diabetes. Diabetes Care 2005;28:2289-304.
Zhang L, Qiao Q, Tuomilehto J, Hammar N, Alberti KG, Eliasson M, et al. Blood lipid levels in relation to glucose status in European men and women without a prior history of diabetes: The DECODE study. Diabetes Res Clin Pract 2008;82:364-77.
Dehghan A, van Hoek M, Sijbrands EJ, Hofman A, Witteman JC. High serum uric acid as a novel risk factor for type 2 diabetes. Diabetes Care 2008;31:361-2.
[Figure 1], [Figure 2], [Figure 3], [Figure 4], [Figure 5]
[Table 1], [Table 2], [Table 3], [Table 4], [Table 5]