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ORIGINAL ARTICLE
Year : 2020  |  Volume : 64  |  Issue : 1  |  Page : 50-54  

Metabolic syndrome and its correlates: A cross-sectional study among adults aged 18–49 years in an Urban Area of West Bengal


1 Director-Professor, Department of Preventive and Social Medicine, All India Institute of Hygiene and Public Health, Kolkata, West Bengal, India
2 Junior Resident, Department of Preventive and Social Medicine, All India Institute of Hygiene and Public Health, Kolkata, West Bengal, India
3 Junior Resident, Department of Epidemiology, All India Institute of Hygiene and Public Health, Kolkata, West Bengal, India
4 Associate Professor, Department of Preventive and Social Medicine, All India Institute of Hygiene and Public Health, Kolkata, West Bengal, India

Date of Submission04-Feb-2019
Date of Decision02-May-2019
Date of Acceptance04-Feb-2019
Date of Web Publication16-Mar-2020

Correspondence Address:
Rajarshi Banerjee
143/15 Picnic Garden Road, Kolkata - 700 039, West Bengal
India
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Source of Support: None, Conflict of Interest: None


DOI: 10.4103/ijph.IJPH_50_19

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   Abstract 


Background: The prevalence of metabolic syndrome (MetS) is increasing dramatically of late, across all ages irrespective of gender, socioeconomic status, and ethnicity. People with MetS have twice the likelihood of developing and dying from cardiovascular disease and more than seven times the risk of developing diabetes. Objectives: This study was undertaken to determine the prevalence of MetS among adults who were in their first three decades of adulthood and to find out the risk factors of MetS among them. Methods: This was a community based cross-sectional study among 388 subjects aged 18–49 years selected by multistage random sampling in an area of Kolkata, India, from November 2016 to October 2018 over 2 years. Data collection was done using a structured questionnaire along with anthropometry, blood pressure measurement, and relevant blood tests. Physical activity was classified by the International Physical Activity Questionnaire Short-Form questionnaire. Data were analyzed using the Statistical Package for the Social Sciences (version 16.0), and descriptive statistics were calculated as frequency and percentage. Logistic regression was done to determine the strength of association between MetS and different risk factors. Results: The prevalence of MetS was 44.6% (35.4% in males and 55.6% in females), and female gender, poor economic status, sedentary lifestyle, poor diet, and addiction of tobacco were found to be the risk factors of MetS in the final model using multivariable logistic regression. Conclusion: This research revealed the high prevalence of MetS in the community. The effective primordial and primary level of prevention along with prevailing secondary or tertiary level of prevention should have been employed to curtail the epidemic of MetS.

Keywords: Metabolic syndrome, noncommunicable diseases, risk factors


How to cite this article:
Dasgupta A, Banerjee R, Pan T, Suman S, Basu U, Paul B. Metabolic syndrome and its correlates: A cross-sectional study among adults aged 18–49 years in an Urban Area of West Bengal. Indian J Public Health 2020;64:50-4

How to cite this URL:
Dasgupta A, Banerjee R, Pan T, Suman S, Basu U, Paul B. Metabolic syndrome and its correlates: A cross-sectional study among adults aged 18–49 years in an Urban Area of West Bengal. Indian J Public Health [serial online] 2020 [cited 2020 Dec 1];64:50-4. Available from: https://www.ijph.in/text.asp?2020/64/1/50/280777




   Introduction Top


Metabolic syndrome (MetS) is the disease of the new millennium; its prevalence is increasing dramatically of late. In 2015, 39.5 million of the 56.4 million deaths globally were due to noncommunicable diseases (NCDs);[1] among all these NCDs, MetS has been the real scourge globally.[2] It is prevalent across all ages starting from adolescent to the elderly irrespective of gender, socioeconomic status, ethnicity, and family history.[3],[4],[5] Therefore, it is a social, moral, medical, and economic responsibility to identify the risk factors of MetS as early as possible so that appropriate interventions can be planned to prevent the devastating sequelae of it in future. People in the early half of their adulthood have to take a maximum load in one's life due to various personal, familial, and social needs though their health is often neglected. It is worthwhile to mention that most of them are symptomless though they are harboring risk factors of various NCDs.

With this background in mind, this study was undertaken to determine the prevalence of MetS among adults who were in their first three decades of adulthood and to find out the risk factors of MetS among them.


   Materials and Methods Top


Study design and study area

This was a community-based observational cross-sectional study conducted over a period of 2 years from November 2016 to October 2018 in a ward (ward number 66) of Kolkata Municipal Corporation (KMC), West Bengal. Ward number 66 under KMC was selected purposively, and it had 71 distinctive electoral/geographical areas known as parts.

Study subjects, sample size, and sampling technique

Study subjects were adult population aged 18–49 years permanently residing in the selected ward number 66 of KMC for at least 6 months. Pregnant and lactating women, critically ill persons, and those who did not give informed written consent were excluded from the study.

Based on a 20.61% prevalence of MetS among adults aged between 20 and 40 years in a previous study,[6] 95% confidence interval, and relative error as 20%, the calculated sample size was 370. We assumed that the overall nonresponse rate would be 10%, so the final sample size calculated was = 370 + 37 = 407.

Multistage sampling technique was adopted to select the study subjects. Among 71 parts in the selected ward of KMC, 4 parts were included by simple random sampling (SRS). Selected parts (part 41, 56, 68, and 70) were renamed as Part I, II, III, and IV, respectively, for the study purpose. Voter list was collected for each part from the ward office and line listing was done for individuals aged 18–49 years in each part. A number of eligible individuals of the defined age group for this study, i.e., 18–49 years in Part I, II, III, and IV, were 678, 789, 837, and 992, respectively. Altogether, total adults in the age group of 18–49 years were 3296. On the basis of population proportionate to size from each part, 84, 97, 103, and 123 individuals were selected using SRS with a total sample of 407. Of them, 19 were nonresponders during the measurement process, and thus, the final analysis was done on 388 individuals.

Tools and technique: Data collection

A predesigned, pretested schedule in Bengali (local language) was used for data collection. Individuals were briefed about the purpose and procedure, and written informed consent was obtained before starting the data collection. At first, they were interviewed for necessary data. After the interview was over, the participants were requested to attend a camp organized in a nearby club on the following Sunday for a blood test in an empty stomach and for anthropometric measurement. Blood collection was done for fasting blood sugar and lipid profile following the standard procedure of 12 h of overnight fasting. Anthropometric and blood pressure (BP) measurements were done in accordance with standard operating procedures after allowing the participants to take rest for at least 10 min. Two days/week were spent for data collection, and 5–6 participants were interviewed each day.

Operational definitions

Some of the variables in this study were defined/described as follows:

Metabolic syndrome

MetS was diagnosed using the International Diabetes Federation (2005)[7] criteria which stated that to diagnose MetS, one must have central obesity: ≥90 cm (male) and ≥80 cm (female) (for Asian) along with any two of the following four factors:

  1. Triglyceride ≥150 mg/dL (1.7 mmol/L) or specific treatment for this lipid abnormality
  2. High-density lipoprotein cholesterol <40 mg/dL (1.03 mmol/L) in males or <50 mg/dL (1.29 mmol/L) in females or specific treatment for this lipid abnormality
  3. Systolic BP ≥130 or diastolic BP ≥85 mmHg or treatment of previously diagnosed hypertension
  4. Fasting plasma glucose ≥100 mg/dL (5.6 mmol/L) or previously diagnosed Type 2 diabetes.


Physical activity

Physical activity was classified by the International Physical Activity Questionnaire Short-Form.[8]

Diet score [Annexure 1]



It had 10 components as follows:

Each satisfactory option was given a score of “1” and unsatisfactory option was given “0”. Maximum attainable score was given 10 and minimum attainable score was given 0.

The diet score was kept linear during analysis. A higher score indicated satisfactory diet.

Smokers

Smokers were categorized into the following groups.

Current smoker: Individuals currently smoking cigarette or bidi daily.

Past smoker: Individuals did not smoke for more than 1-year.

Passive smoker: Individuals inhale the smoke of another smoker.

Current smokeless tobacco user: Individuals used smokeless tobacco (SLT) (Jorda/Guthka/Gudaku/Khaini/Snuff) daily.

Past smokeless tobacco user: Individuals did not consume SLT for more than 1-year.

Addiction score

Smoking tobacco (ST) score is obtained using the following formula: ST score = ({[score for number of cigarette and/or bidi/day] × [score for duration of use]} + score for duration of passive smoking) – score for duration of quit [Table 1].
Table 1: Frequency and duration of active and passive tobacco use and abstinence

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Where the “score for a number of cigarette and/or bidi per day” is multiplied by the “score for the duration of use” to which the “score for the duration of passive smoking” (if any) is added. Finally, the “score for the duration of quit” (if present) is deducted from the last value to have the ST score.

This ST score is formulated with the assumption that the adverse effect of smoking is increased proportionately with the increase of frequency and the duration of use. The adverse effect is even more for the sufferers of passive smoking, but the adverse effect of smoking is reversed with abstinence.

SLT score is obtained using the following formula where the “score for frequency of use per day” is multiplied by the “score for duration of use” and from the result, the score for duration of quit (if any) is deducted to have the final SLT score. Here, we also used the same assumption and principles like the ST score.

SLT score = ({[score for frequency of use/day] × [score for duration of use]} − score for duration of quit) [Table 1].

Data analysis

Data were analyzed using the Statistical Package for the Social Sciences (SPSS) (SPSS Inc., Chicago, IL, USA version 16.0 for Windows), and descriptive statistics were calculated as frequency and percentage. As the data were not distributed normally, median (interquartile range [IQR]) was calculated instead of mean (standard deviation). Univariate binary logistic regression analysis was performed to find out the strength of association between MetS and different risk factors. Multivariable binary logistic regression analysis (Hierarchical) was performed to construct different models for prediction of MetS, and P < 0.05 was considered significant throughout the analysis. Ethical clearance was obtained from the Institute Ethical Committee (IEC).


   Results Top


More than half of the study participants were males (54.6%) and most of the participants (43%) belonged to the age group of 29–39 years, followed by the age group of 40–49 years (39.3%). The median (IQR) age of the participants was 36 (32–44) years. A majority of the study participants were Hindus (86.9%) followed by Muslims (9.5%) and others (3.6%). A majority of the study participants were married (71.6%) and 41.8% had per capita income (PCI) more than 4000 rupees. Almost half of the participants were engaged in moderate physical activity and one-third in the low physical activity. Addiction of ST was seen among 22.7% and SLT among 14.4%, while 23.5% had a history of passive smoking at home. The median (IQR)X of diet score was 4.0 (3.0–5.0), and more than two-third of the participants had a diet score of ≤4. The prevalence of MetS was 44.6% (35.4% in males and 55.6% in females).

During univariate binary logistic regression [Table 2] to find out the predictors of MetS, it was found that females (odds ratio [OR] = 2.30) in comparison to males, individuals of 40–49 years of age (OR = 1.97) and 29–39 years of age (OR = 1.87) in comparison to those of 18–28 years, married individuals (OR = 1.99) in comparison to others, individuals with low (OR = 7.06) and moderate (OR = 6.66) physical activity in comparison to those with high physical activity, and individuals with poor diet (OR = 1.33), habit of smoking (OR = 1.06), or using SLT (OR = 1.14) were at higher odds of having MetS. Individuals having higher PCI (>4000 rupees) in comparison to those having PCI ≤4000 rupees had odds of 0.65 for having MetS though it was not statistically significant. During multivariable binary logistic regression [Table 3] to build the risk prediction model of MetS, risk factors were added stepwise in consecutive models according to the three relevant domains. In model 1, sociodemographic variables such as age, gender, marital status, and PCI were considered. In model 2, behavioral variables such as diet and physical activity were considered, whereas in model 3 variables on the addiction of tobacco (smoking and smokeless) were considered.
Table 2: Risk factors of metabolic syndrome: Univariate logistic regression (n=388)

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Table 3: Metabolic syndrome and its predictors: Multivariable logistic regression hierarchical analysis (Models I-III) (n=388)

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In the final model of MetS, there were augmentations of OR of few variables after adjusting for all other risk factors such as females (adjusted odds ratio [AOR] =3.67 vs. OR = 2.30), married (AOR = 2.39 vs. OR = 1.99), PCI of rupees 4000 or less (AOR = 1.89 vs. OR = 1.54), moderate physical activity (AOR = 9.45 vs. OR = 6.66) and low physical activity (AOR = 11.02 vs. OR = 7.06), decreasing diet score (AOR = 1.36 vs. OR = 1.33), and increasing ST score (AOR = 1.08 vs. OR = 1.06).

The goodness of fit of the models was judged by Hosmer and Lemeshow test which were not significant for all the models, and the Omnibus test of model coefficient was significant for all the models. Model 1 can define the outcome variables, i.e., MetS by 8.4% (Nagelkerke R2 = 0.084). Model 2 can define it by 28.7% (Nagelkerke R2 = 0.287), whereas model 3 can define the outcome variables by 31.7% (Nagelkerke R2 = 0.317).


   Discussion Top


In this study, during univariate logistic regression analysis, it was found that in comparison to male, female gender had 2.30 odds of having MetS, which was statistically significant. In comparison to those in the age group of 18–28 years, individuals of 29–39 years and 40–49 years had 1.87 and 1.97 odds of having MetS, respectively, which was statistically significant. Hence, it can be concluded from our study that with an increase in age, the risk of MetS is also increasing. Those who were currently married had 1.99 odds of MetS when compared with others, which was also statistically significant. Individuals with PCI of rupees 4000 or less had 1.89 odds of having MetS in reference to those having PCI more than 4000/- rupees, which was also statistically significant. This may be due to the fact that those with higher economic privilege can afford a better diet and healthy lifestyle than others. Individuals with low and moderate physical activity in reference to individuals with high physical activity had 7.06 and 6.66 odds of having MetS, which was statistically significant. During multivariable logistic regression analysis for determination of risk factors of MetS, gender, marital status, PCI, physical activity, diet, and addiction of tobacco predicted the occurrence of MetS.

Harikrishnan et al.[9] in their study at Kerala during univariate and multivariable logistic regression analysis revealed that the risk of MetS was higher among women (OR = 1.84) and older individuals (OR = 1.16), which was in concordance to our study. The propensity for MetS was lower among current tobacco users (OR = 0.79) and those with poor consumption of fruits and vegetables, which were just opposite of the findings of our study. Chinawale et al.[10] in their study in Saurashtra, Gujarat, during logistic regression has found that individuals aged 40 years or more were 3.26 times more likely to have MetS as compared to individuals with lesser age, and the risk of MetS was increased by 1.67 times in individuals having a habit of chewing tobacco. These findings have a similarity with our study observations. Singh et al.[11] in their study opined that females had three times more chances of developing MetS compared to males (AOR = 3.1), which was true in the present study. Chakraborty et al.[12] have found that during binary logistic regression analysis, age below 30 years was protective with odds of 0.08, and females had greater chances of having MetS with odds of 2.43 like our observation. The higher socioeconomic class (Class IV and above) had 14.89 times chances of having MetS than those belonging to the lower socioeconomic class, but this finding did not match with our study finding where higher PCI was found to be protective for MetS and PCI is a proxy indicator of socioeconomic status. Again, the sedentary lifestyle had 17 times greater chance of having MetS similar to this study. Furthermore, positive family history and education level of graduate and above had odds of 11.84 and 7.96, respectively, for MetS, though in our study these two variables were not associated with MetS.

The temporal relationship between the risk factors and MetS could not be assessed by this study, as it was cross-sectional in design. Information regarding addiction, diet, physical activity, and income was collected using a schedule. Hence, a chance of recall bias was present in the study. Inspite of all precautions during data collection, there always remained a scope for deliberate fabrication by the respondents on certain information regarding diet, addiction, income, and physical activity.


   Conclusion Top


This research revealed the high prevalence of MetS and its risk factors in the community. Interventions aimed at reducing these factors can go a long way in alleviating this hidden problem in the society. Secondary or tertiary level of prevention which has been employed to curtail the overwhelming NCD epidemic will not suffice. Primordial and primary levels of prevention in the form of very strong, effective, and heart reaching IEC (information education communication) and behavioral change communication are required at individual, at risk, family, and community level to generate awareness regarding this grave problem along with periodic screening of all the risk factors as “The power of community to create health is far greater than any physician, clinic or hospital.”(Mark Hyman).

Financial support and sponsorship

Nil.

Conflicts of interest

There are no conflicts of interest.



 
   References Top

1.
Defronzo RA, Ferrannini E, Zimmet P, Alberti KGMM. International Textbook of Diabetes Mellitus. 4th ed, Vol.2. Oxford UK: Wiley-Blackwell; 2015.  Back to cited text no. 1
    
2.
Current Hypertension Reports; 2018: 20: 12 Available from: https://doi.org/10.1007/s11906-018-0812-z. [Last accessed on 2018 Jun 21].  Back to cited text no. 2
    
3.
Cornier MA, Dabelea D, Hernandez TL, Lindstrom RC, Steig AJ, Stob NR, et al. The metabolic syndrome. Endocr Rev 2008;29:777-822.  Back to cited text no. 3
    
4.
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.  Back to cited text no. 4
    
5.
King RA, Rotter JI, Motulsky AG. Approach to genetic basis of common diseases. Oxf Monogr Med Genet 2002;44:3-17.  Back to cited text no. 5
    
6.
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. Available from: https://doi: 10.1155/2011/920983. [Last accessed on 2018 Jun 25].  Back to cited text no. 6
    
7.
Balkau B, Charles MA. Comment on the provisional report from the WHO consultation. European Group for the Study of Insulin Resistance (EGIR) Diabet Med 1999;16:442-3.  Back to cited text no. 7
    
8.
International Physical Activity Questionnaire; 2002. Available from: https://evaluationframework.sportengland.org/media/1084/2015- ipaq-sf.pdf. [Last accessed on 2018 Jan 30].  Back to cited text no. 8
    
9.
Harikrishnan S, Sarma S, Sanjay G, Jeemon P, Krishnan MN, Venugopal K, et al. Prevalence of metabolic syndrome and its risk factors in Kerala, South India: Analysis of a community based cross-sectional study. PLoS One 2018;13:e0192372.  Back to cited text no. 9
    
10.
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.  Back to cited text no. 10
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11.
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 ClinMetab 2016;1:1-5.  Back to cited text no. 11
    
12.
Chakraborty SN, Roy SK, Rahaman MA. Epidemiological predictors of metabolic syndrome in urban West Bengal, India. J Family Med Prim Care 2015;4:535-8.  Back to cited text no. 12
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