Metabolic syndrome (MetS) is a cluster of metabolic disorders, which are closely related to risk factors for noncommunicable diseases. This cluster includes indicators related to blood sugar, blood pressure, waist circumference (WC), and cholesterol. Several international agencies such as WHO, the European Group for the Study of Insulin Resistance, the National Cholesterol Education Program Expert Panel Adult Treatment Panel3 (NCEP ATP3) criteria, and the International Diabetes Federation have given different criteria for defining MetS. The pathologic mechanism leading to the effects of MetS acts through pro-inflammatory cytokines, such as tumor necrosis factor, leptin, adiponectin, plasminogen activator inhibitor, and resistin. It has been reported that MetS is associated with a 2-fold increased risk of cardiovascular disease mortality and stroke and a 1.5-fold increased risk of all-cause mortality. MetS has also been found to be associated with many different types of cancer.
Globally, the prevalence of MetS ranges from 5% to 7% in young adults, as reported in a pooled data analysis by Nolan etal. In the United States, in the adult population, it was estimated to be more than 25% using the WHO definition and ranged from 22% to 24% according to ATP III criteria.[6–8] In South Asian countries, national studies on the prevalence of MetS are generally not available but region-specific researches are ample. A study from neighboring country Pakistan showed a 49% prevalence of MetS according to NCEP ATP III criteria, which is conducted in Karachi. Another cross-sectional study conducted in seven of the nine provinces in Sri Lanka showed a 27.1% crude prevalence of MetS.
In India, the prevalence of MetS among urban adults ranges between 25% and 45%. A study conducted in the metropolitan city of Mumbai reported a prevalence of 19.5%. The prevalence of MetS in the urban area of Amritsar was 34.3%.
In Uttarakhand, limited studies are available on MetS. One hospital-based study reported a 21.1% prevalence of MetS and another study reported a 28.6% prevalence among the geriatric population in rural Uttarakhand who is living in the high altitude.[13,14] However, there is no published data available on community-based studies. True prevalence of MetS in Uttarakhand, especially in urban areas, where high prevalence of risk factors is expected, remains unexplored. Therefore, the current study was conducted in urban areas under the Municipal Corporation of Dehradun district to estimate the prevalence of MetS and its associated risk factors in people 19–60 years of age. Through this study, appropriate recommendations can be given for the formulation of effective strategies to prevent and control MetS. The aim and objectives of the study are to estimate the prevalence of MetS in adults in urban areas of Rishikesh, Uttarakhand, and to determine the association of sociodemographic variables and risk factors with MetS.
Individuals in the age group from 19 years to 60 years at the day of data collection who was residing for more than 6 months in the area were eligible for inclusion in the study. Diagnosed cases of cirrhosis of liver, chronic kidney disease, Cushing’s syndrome, and hypothyroidism, which cause an increase in weight due to pathologic water retention and may lead to false readings of weight and anthropometric measurements were excluded from the study. Furthermore, patients with type-1 diabetes mellitus (DM), secondary hypertension, and those on drugs causing overweight/obesity such as steroids, oral contraceptives, and antidepressants were also excluded. Pregnant females were also excluded due to physiologic gain in abdominal circumference and alteration of various parameters during pregnancy.
Materials and Methods
A community-based cross-sectional study was conducted for a duration of 1 year in the age group of more than 19–60 years residing in urban areas of Rishikesh. Assuming the proportion of MetS in young adults = 34.3%, the sample size was calculated to be 227 by keeping relative precision of 18%. Applying a design effect of 2.0, the sample size was calculated to be 454. Considering a dropout rate of 5%, the final sample size was calculated to be 478.
Sampling technique for data collection
Rishikesh is a Nagar Palika Parishad city in the district of Dehradun, Uttarakhand. Rishikesh city is divided into 20 wards with a population of 70,189 as per Census India, 2011. Cluster sampling technique, which is a kind of two-stage sampling technique, was used to select the representative population of urban areas of Rishikesh. At the first stage, a list of urban areas (20 wards) from the District Urban Development Authority office was taken. The cumulative population was calculated and on dividing it by 10, i.e. the number of clusters, we obtained the sampling interval. A random number having same number of digits but less than or equal to the sampling interval was selected, which gave us the first cluster. The random number plus sampling interval gave the 2nd cluster (the cumulative population listed for that ward was equal to or more than the number we obtained by addition). Second cluster + sampling interval = 3rd cluster and so on. This procedure was continued until all 10 clusters were chosen at the second stage, and 48 study participants from each of the selected clusters were randomly selected. If all 48 study participants could not be found from a single cluster, then the contiguous cluster was taken until the desired number was completed. It was ensured to select only one study participant from each selected house. With a dropout of 2, the final analysis was done for 478 participants.
Written informed consent was obtained from the study participants before including them in the study. A pretested interview schedule was used for data collection regarding sociodemographic characteristics. WHO steps instrument and protocol were used for the assessment of risk factors and measurements, i.e., anthropometry and blood pressure.
Laboratory assessments included measurements of high-density lipoprotein (HDL), triglycerides (TGLs), and FBS obtained by venous blood samples in fasting state of 12 h, measured by fully automated chemistry analyzer. The blood samples were collected in a red vacutainer for measurement of fasting lipids (TGL, HDL cholesterol [HDL-C], and gray vacutainer were used for estimating fasting blood sugar. A total of 5 ml of blood samples was collected. All blood samples were properly labeled and transported to the laboratory at AIIMS, Rishikesh, for analysis.
Ethical considerations and confidentiality of data
Informed written consent from the participants was obtained after informing them that the participation was voluntary, and there was no harm to the participant due to or during our study. Study was started after getting approval from the Ethics Committee of the Institution, AIIMS Rishikesh. Confidentiality of the information obtained from the patient was maintained and the identity of the patient was not revealed.
Descriptive statistics were used for getting percentages, proportions, mean (standard deviation), and median (interquartile range). Chi-square test was used to examine the association between categorical variables, whereas the t-test was used for comparing the means. Binary logistic regression analysis was performed simultaneously to evaluate the effects of risk factors on MetS (dependent variable). A significance level of 5% was used for all of the statistical tests. The data were analyzed using SPSS Version 20.0 (IBM Corp., Armonk, NY).
Table 1 shows that only 6.7% of study participants did not have any of the components of MetS, whereas 2.9% of participants had all the components of MetS. About one-third of the participants (31%) had the presence of two criteria positive for MetS, whereas an equal number (24.1% and 23.8%) of people fulfilled one and three criteria, respectively.
Table 2 shows that the majority (32.8%) of the study participants were in the age group of 31–40 years. Females constituted 65.5% of the total sample. Twenty-nine percentage of the participants were high school pass and another 24.9% had completed education till higher secondary certificate. A little more than half of the participants (56.3%) were unemployed, whereas only 9 participants were engaged in professional or semi-professional type of occupations.
The odds of MetS were 1.3 times higher among females than males, however, statistical significance was not achieved. In reference to the age group 19–30 years, the odds of MetS were 2.7, 6.3, and 4.0 times in the age group of 31–40, 41–50, and 51–60 years, respectively. The odds of MetS were reduced in those with education less than middle school in reference to the illiterate participants. The odds increased with education levels more than middle school. However, this association did not achieve statistical significance. The odds of MetS increased in people involved in semi-skilled, skilled, or arithmetic skill jobs and it was observed to be reduced in those involved in unskilled work or semi-professional work as compared to those who were unemployed. However, these associations did not achieve statistical significance. It was observed that there was a significant association between age and MetS, however, no statistically significant association was observed between gender, education, occupation, and MetS. The odds of MetS were increased by 1.9 and 1.4 times in those with lower middle and upper middle socioeconomic status (SES) according to the revised modified Kuppuswamy classification as compared to those belonging to lower SES. However, the association did not achieve statistical significance [Table 2].
From Table 3, it can be observed that people who were not achieving the recommended METS score per week had 1.13 times higher odds of MetS. However, the association was not found to be statistically significant. People with the habit of smoking and alcohol had 1.17 times and 1.19 times higher odds of MetS as compared to those who did not smoke. However, statistical significance was not achieved. It was observed that family history of obesity was significantly associated with MetS with an odds of 2.6. However, no association was observed between family history of hypertension, DM, cardiovascular diseases, cerebrovascular accident (CVA), or other diseases/conditions with MetS.
Significant differences were observed in the mean levels of fasting blood glucose (FBG), triglyceride (TG), and HDL between the two groups of study participants. The mean levels of FBG and TG were higher in those with MetS whereas HDL levels were found to be higher among those without MetS. These differences were also observed among males and females individually. Mean systolic and diastolic blood pressure was reported to be significantly higher among those study participants with MetS as compared to those without MetS [Table 4].
The prevalence of MetS in the urban area of Rishikesh is 38.2%. The prevalence of MetS in males and females is 33.9% and 40.5%, respectively. It was observed that the odds of MetS were 1.3 times higher among females than males, however, statistical significance was not achieved.
Binary logistic regression analysis was applied to determine the independent role played by important predictors of MetS. The Cox and Snell R Square and Nagelkerke R Square were found to be 0.222 and 0.302 indicating that the model could explain 69% of the variability in MetS after controlling the effect of confounders. It was observed that age, waist–hip ratio (WHR), and body mass index (BMI) were important in predicting the MetS status of the study participants in the absence of confounding factors.
The prevalence of MetS is increasing exponentially in India, especially in urban areas. The figures ranging from 11% to 41% in different parts of India. As the prevalence of diabetes and obesity is higher in India, Indians are having higher risk for MetS. Recent data show that about one-third of the urban population in India’s major cities have MetS. In our study, the proportion of females is higher than males, this could be because of number of reasons, for example, most of the males come back home late in the evening and go back to work early morning, and are reluctant to get included because of casual attitude, etc.
Singh etal. noted that the mean age was higher in subjects with MetS as compared with non-MetS and also observed that the mean values of BMI, WC, WHR, TG, and FBG were higher in study participants with MetS in both sexes. A study by Sawant etal. study revealed an increased prevalence of MetS in the age group of 41–60 years, suggesting that this group is at increased risk of developing coronary artery disease, CVA, etc. Our study also shows the prevalence is higher among the age group of 41–60 years.
In the current study, the prevalence of MetS by modified NCEP ATP III criteria came out to be 38.2%. Prevalence in males and females was found to be 33.9% and 40.5%, respectively. Banerjee etal., Bandela etal., Ramachandran etal., and Chinawale etal. reported a slightly higher prevalence of MetS in their study, which was 44.6%, 42.15%, 41.1%, and 40.01%, respectively.[21–24]
On the contrary, a lower prevalence of MetS was also observed by several studies by Singh etal., Singh etal., and Das etal., who reported MetS prevalence of 34.3%, 26.6%, and 31.4%, respectively.[12,20,25] Other studies which showed lower MetS prevalence was conducted in Sri Lanka (27.1%), Chennai, India (25.8%), and Nepal (22.5%).[10,26,27] Such variation in MetS prevalence might be either due to differences in lifestyle habits or environmental effects. This might also be due to the fact that among Asian Indians, the promoter polymorphisms − 482T and 455C of APOC3 genes are shown to be associated with the MetS.
The current study considered NCEP ATP3 criteria for defining MetS. Bandela etal. reported systolic blood pressure (mmHg) 122.3 ± 9.2, diastolic blood pressure (mmHg) 81.5 ± 4.3, FBG (mg/dl) 93.0 ± 16.86, serum TGL (mg/dl) 142.6 ± 29.1, and serum HDL-C (mg/dl) 34.3 ± 4.3.
However, our study reported slightly lower systolic blood pressure (mmHg) (121.3 ± 20.7) and diastolic blood pressure (mmHg) (77.3 mmHg ± 9.4), whereas FBG (107.7 mg/dl ± 47.5), serum TGL (163.56 mg/dl ± 132), and serum HDL-C (41.79 mg/dl ± 9.4) were higher in our study.
Bandela etal. revealed that low HDL and high WC followed by raised TG are found to be potent risk factors for MetS. Our study has identified that elevated WC followed by decreased HDL-C and increased TGL are common cluster components of MetS in this population. The results were in accordance with the findings of Deedwania etal. and Kapil U et al.[14,28]
Singh etal. showed that only 7.7% of the participants showing zero components of MetS and only 1.8% showed all five components. Our study showed similar results where 6.7% of people showed zero components and 2.9% showed the presence of all five components. Singh etal. in their study observed that 34.5% of people showed the presence of atleast two components. Similarly, in our study, it is about 31%.
Singh J etal. (2016) showed waist–hip ratio was found to be the strongest predictor of MetS with adjusted odds ratio of (aOR) 32.4, followed by fasting plasma glucose (aOR, 4.5), triglyceride (aOR, 3.9), and hypertension (aOR, 3.6). Females had three times more chances of developing MetS as compared to males (aOR, 3.1), whereas overweight and obese subjects had two to three times more chances of having MetS compared to subjects with normal BMI.
The contribution of Mets components may oscillate with ethnic population, gender, and country. Obesity plays a significant role in the development of MetS and precedes the appearance of the other MetS components.
Conclusions and Recommendations
The prevalence of MetS was found to be high. The study showed a 38.2% prevalence of MetS in the urban population of Rishikesh. It was found that there were only 32 subjects out of 478 study participants, who did not show any abnormal values as per the criteria of MetS. The rest 442 subjects had either one or more abnormal components of the MetS as classified by the NCEP ATP 3 criteria. Health interventions required to prevent or reduce morbidity/mortality need to be addressed in the adult population starting from their childhood. In people with MetS, intervention strategies for the control of hypertension, hyperglycemia, and dyslipidemia would lead to reductions in subsequent cardiovascular mortality. Moreover, approaches such as weight reduction will be useful strategies for increasing insulin sensitivity. Implementation of these strategies is important in the elderly population as they are at increased risk of developing MetS due to age-associated physiology.
Financial support and sponsorship
Conflicts of interest
There are no conflicts of interest.
1. Saklayen MG. The global epidemic of the metabolic syndrome. Curr Hypertens Rep 2018;20:12.
2. Jung UJ, Choi MS. Obesity and its metabolic complications:The role of adipokines and the relationship between obesity, inflammation, insulin resistance, dyslipidemia and nonalcoholic fatty liver disease. Int J Mol Sci 2014;15:6184–223.
3. Mottillo S, Filion KB, Genest J, Joseph L, Pilote L, Poirier P, et al. The metabolic syndrome and cardiovascular risk a systematic review and meta-analysis. J Am Coll Cardiol 2010;56:1113–32.
4. Obesity and cancer fact sheet [Internet]. National Cancer Institute. Available from:https://www.cancer.gov/about-cancer/causes-prevention/risk/obesity/obesity-fact-sheet
. [Last accessed on 2023 Apr 03].
5. Nolan PB, Carrick-Ranson G, Stinear JW, Reading SA, Dalleck LC. Prevalence of metabolic syndrome and metabolic syndrome components in young adults:A pooled analysis. Prev Med Rep 2017;7:211–5.
6. Park YW, Zhu S, Palaniappan L, Heshka S, Carnethon MR, Heymsfield SB. The metabolic syndrome:Prevalence and associated risk factor findings in the US population from the third national health and nutrition examination survey, 1988-1994. Arch Intern Med 2003;163:427–36.
7. Ford ES, Giles WH. A comparison of the prevalence of the metabolic syndrome using two proposed definitions. Diabetes Care 2003;26:575–81.
8. Ford ES, Giles WH, Dietz WH. Prevalence of the metabolic syndrome among US adults:Findings from the third national health and nutrition examination survey. JAMA 2002;287:356–9.
9. Hydrie MZ, Shera AS, Fawwad A, Basit A, Hussain A. Prevalence of metabolic syndrome in urban Pakistan (Karachi):Comparison of newly proposed international diabetes federation and modified adult treatment panel III criteria. Metab Syndr Relat Disord 2009;7:119–24.
10. Katulanda P, Ranasinghe P, Jayawardana R, Sheriff R, Matthews DR. Metabolic syndrome among Sri Lankan adults:Prevalence, patterns and correlates. Diabetol Metab Syndr 2012;4:24.
11. 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.
12. Singh A, Shenoy S, Sandhu JS. Prevalence of metabolic syndrome and its risk factors among urban Sikh population of Amritsar. J Postgrad Med Educ Res 2015;49:18.
13. Goyal R, Kumar A, Singhai M. Study of metabolic syndrome and its risk components in patients attending tertiary care Center of Uttarakhand. Indian J Clin Biochem 2014;29:362–6.
14. Kapil U, Khandelwal R, Ramakrishnan L, Khenduja P, Gupta A, Sareen N, et al. Prevalence of metabolic syndrome and associated risk factors among geriatric population living in a high altitude region of rural Uttarakhand, India. J Family Med Prim Care 2018;7:709–16.
15. Rishikesh Nagar Palika Parishad City Population Census 2011-2019 | Uttarakhand; 2019. Available from: https://www.census2011.co.in/data/town/800313-rishikesh-uttarakhand.html
. [Last accessed on 2019 Oct 26].
16. STEPwise approach to NCD risk factor surveillance (STEPS) [Internet]. Available from: https://www.who.int/teams/noncommunicable-diseases/surveillance/systems-tools/steps
. [Last accessed on 2023 Apr 03].
17. Misra A, Khurana L. Obesity and the metabolic syndrome in developing countries. J Clin Endocrinol Metab 2008;93:S9–30.
18. Kanjilal S, Shanker J, Rao VS, Khadrinarasimhaih NB, Mukherjee M, Iyengar SS, et al. Prevalence and component analysis of metabolic syndrome:An Indian atherosclerosis research study perspective. Vasc Health Risk Manag 2008;4:189–97.
19. Misra A, Khurana L. The metabolic syndrome in South Asians:Epidemiology, clinical correlates and possible Solutions. Int Diabetes Monit 2009;21:92–101.
20. Singh J, Rajput M, Rajput R, Bairwa M. Prevalence and predictors of metabolic syndrome in a North Indian rural population:A community based study. J Glob Diabetes Clin Metab 2016;5:2.
21. Banerjee R, Dasgupta A, Naskar NN, Kundu PK, Pan T, Burman J. A study on the prevalence of metabolic syndrome and its components among adults aged 18-49 years in an Urban Area of West Bengal. Indian J Community Med 2019;44:261–4.
22. Bandela PV, Dongre NN, Ambekar JG, Prasad KD. Metabolic syndrome risk factors in rural population of Kurnool District. Age 2016;32:41–26.
23. 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.
24. Chinawale CG, Parmar DV, Kavathia P, Rangnani T, Thakkar J, Kartha G. Metabolic syndrome among adults of Surendranagar District of Saurashtra, Gujarat:A cross-sectional study. Indian J Community Med 2018;43:24–8.
25. 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.
26. 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.
27. Sharma SK, Ghimire A, Radhakrishnan J, Thapa L, Shrestha NR, Paudel N, et al. Prevalence of hypertension, obesity, diabetes, and metabolic syndrome in Nepal. Int J Hypertens 2011;2011:821971.
28. Deedwania PC, Gupta R, Sharma KK, Achari V, Gupta B, Maheshwari A, et al. High prevalence of metabolic syndrome among urban subjects in India:A multisite study. Diabetes Metab Syndr 2014;8:156–61.