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. 2020 Oct 19;15(10):e0240971. doi: 10.1371/journal.pone.0240971

Prevalence of metabolic syndrome among adult population in India: A systematic review and meta-analysis

Yuvaraj Krishnamoorthy 1, Sathish Rajaa 1, Sharan Murali 1, Tanveer Rehman 1, Jayaprakash Sahoo 2, Sitanshu Sekhar Kar 1,*
Editor: Jennifer A Hirst3
PMCID: PMC7571716  PMID: 33075086

Abstract

Objective

This review was done to determine the prevalence of metabolic syndrome (MS) among adult general population in India. We also wanted to find the gender, setting, and region-wide distribution of MS in India.

Methods

We conducted systematic searches in various databases including Medline, ScienceDirect, Cochrane library and Google Scholar from inception until August 2019. We included studies conducted in India reporting the prevalence of MS among adults aged 18 years or more. We used the Newcastle Ottawa scale to assess the quality of included studies. We carried out a meta-analysis with random-effects model and reported pooled prevalence with 95% confidence intervals (CIs). We used the Funnel plot to assess publication biases.

Results

In total, we analysed 113 data from 111 studies with 133,926 participants. Majority of the included studies (76 out of 111) had low risk of bias. We found significant heterogeneity among the included studies (p<0.001). We also found a symmetrical funnel plot indicating an absence of publication bias. The prevalence of MS among adult population in India was 30% (95%CI: 28%-33%). There was a steady increase in the burden across the age groups from 13% (18–29 years group) to 50% (50–59 years). We also found that people living in urban areas (32%; 95%CI: 29%-36%) had higher prevalence when compared to tribal (28%; 95%CI: 21%-36%) or rural adults (22%; 95%CI: 20%-25%). Gender distribution of MS showed that the females had higher prevalence (35%; 95%CI: 31%-38%) when compared to males 26% (95%CI: 22%-29%).

Conclusion

Almost one in three adults in India suffer from MS. Females, people living in urban areas and in northeast region had higher prevalence of MS. Development and implementation of policies and protocols for the screening of MS would enable us in early diagnosis and treatment with special focus towards the vulnerable and high-risk groups.

Introduction

Disease patterns around the globe are undergoing rapid structural changes over the last three decades, with a sudden increase in the burden of Non-Communicable Diseases (NCDs) and a decreasing trend of communicable diseases [1]. The Global Burden of Disease (GBD) study, brings to light this phenomenon of epidemiological transition in India, with a 62.7% of the total mortality in 2016 contributed by the NCDs. Key elements contributing to the development of these NCDs have been identified and are studied together under the heading of Metabolic Syndrome (MS) [2].

Metabolic syndrome is a constellation of interconnected physiological, biochemical, clinical and metabolic factors that directly increases the risk of cardiovascular diseases, type 2 diabetes mellitus (DM) and all-cause mortality. It is constituted by abdominal obesity, insulin resistance, hypertension, and hyperlipidemia [3]. Various diagnostic criteria have been proposed for quantifying MS. But the most widely used ones are from the International Diabetic Federation (IDF) and the National Cholesterol Education Program Adult Treatment Panel III (NCEP ATP III) [4, 5].

Metabolic syndrome increases the risk of developing type 2 DM and cardiovascular diseases over the next 5 to 10 years by five and two-fold respectively [3]. Furthermore the patients with MS have, an average four-fold increased risk of developing stroke & myocardial infarction and a two-fold risk of dying from a similar event compared with those without MS, regardless of previous history of cardiovascular events [6]. Identifying the individuals with (or) at risk of developing MS would help to inform the probabilities of worse outcomes and thus an urgent need for a promotive, preventive or curative action.

A recent study from the United States reported the prevalence of MS to be around 22.9% [7]. Various population-based studies were conducted in India too, to quantify the same and the results ranged from 10 to 30 percentage [8]. A study conducted in the eleven large urban cities of India during 2006–2010 reported the prevalence of MS as high as 35% [9]. Owing to the behavioural habits, urban population, seem to be the most vulnerable group for developing MS [10]. Prevalence of MS, thus, seems to vary greatly from region to region and from ethnicity to ethnicity.

Even though evidences are being generated on MS from different parts of the country, there is no nationwide pooled estimate to comment on the burden of MS in the Indian subcontinent, which could drive policy action [11]. With almost one third of the total population living in the urban areas, and with 40% of the population aged between 30 and 70 years, understanding the overall prevalence of MS in India becomes essential for predicting the future burden of type 2 DM and cardiovascular diseases [12].

While the NCD care in the country is segregated under the broad headings of Diabetes, and Cardiovascular diseases, MS informs us the need to look at the picture as a whole. Results from this study would, thus, help guide national policy making process to give due importance to prevention and treatment of MS in the community and clinical settings. This could thus be an essential key to keep a check on the ever-increasing burden of the NCD epidemic in the country. Hence, this review was conducted to generate a pooled prevalence estimate for MS in India.

Methods

Design and registration

We conducted a systematic review and meta-analysis of cross-sectional studies. We have obtained approval from the JIPMER Scientific Advisory Committee (JSAC). We have also obtained exemption from Institutional Ethics Committee (IEC). We have registered our protocol on the international prospective register of systematic review (PROSPERO). PROSPERO Registration Number is CRD42019147277. We followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) checklist for reporting systematic reviews incorporating meta-analyses for reporting our review.

Eligibility criteria

Type of studies

We included studies conducted in India reporting the prevalence of MS for the current review. There was no restriction related to study design, communities (rural/urban) or age groups. We included the studies irrespective of the setting in which the study was conducted (community or facility based or workplace based). Studies reported as full text were included while unpublished data were excluded from the review.

Type of participants

We included studies conducted among adults aged 18 years or more. We excluded the studies conducted among specific diseased population.

Type of outcome measure

Studies reporting the prevalence of MS and diagnosed it as per the NCEP ATP III or IDF guidelines were included to obtain the pooled prevalence estimate for MS in India.

Search strategy

We conducted extensive electronic search in the following databases: Medline, ScienceDirect, Cochrane library and Google Scholar. Combination of medical subject heading (MeSH) and free text terms were used for carrying out literature search. The detailed search strategy used to search the Medline database has been reported in S1 File. Similar strategies were used in Cochrane Library, ScienceDirect and Google scholar for literature search of published studies. Search was conducted in all the databases from inception of the database to August 2019 with English language restriction for publication. We also checked the reference list of primary studies obtained through electronic search and relevant articles were included in the review and analysis.

Selection of studies

Two independent investigators (YK and TR) independently performed the literature search and screened the title, abstract and keywords of all the studies identified for possible inclusion in the review. Full text article was obtained for those studies that are found to be relevant. Further screening of abstract and full text of the retrieved articles was done independently by two investigators (YK and TR) to select the studies which satisfy the eligibility criteria of the current review. Any disagreements during the entire selection process between two authors were resolved either through consensus or consultation with third investigator (SR). Quality of the overall review process was monitored by the third investigator (SR).

Data extraction and management

Primary investigator (YK) extracted the relevant study characteristics for the review from the included studies using the Cochrane Public Health group Data extraction and Assessment Template. Following data were extracted:

General information

Author, Study title, Publication year.

Methods section

Study design, study period, study setting (community/workplace/facility), community (urban/rural/tribal), geographical region, state, sample size, sampling technique, diagnostic criteria, outcome assessment and statistical tests employed.

Outcome section

Mean age, non-response rate and their characteristics, prevalence of MS.

Primary investigator (YK) transferred the obtained data into the software STATA version 14. Data entry was double checked for correct entry by another investigator (SR) through comparison of data presented in review and included study reports.

Risk of bias assessment in included studies

Two independent authors (SR and SM) assessed the quality of all the included studies using the Newcastle-Ottawa (NO) scale adapted for cross sectional studies [13]. Two criteria (selection and outcome) were used to assess the risk of bias. Following domains were used for assessing the risk of bias under selection criteria: representativeness of the sample, justification of sample size, rate of non-respondents and their characteristics and use of validated measurement tool. Under Outcome criteria, outcome assessment and statistical test employed were used to assess the risk of bias. Each of these outcomes was rated as high (1 point) or low (0 points) based on the quality of evidence and availability of information. Studies scoring more than or equal to 3 points were considered to have high risk of bias.

Statistical analysis

Meta-analysis was performed with the selected studies using STATA 14.2 (StataCorp, College Station, TX, USA). For each of the studies, standard error was calculated using the reported prevalence of MS and total sample size. “Metaprop” function was used for performing analysis of the current review [14]. To minimize the effect of extremely small or large prevalence on the overall estimate, Freeman Tukey double arc-sine transformation was done to stabilise the variance [14]. Final pooling of data was done with random effects model and reported as proportion with 95% confidence interval.

Evidence of between-study variance due to heterogeneity was assessed through following methods: Chi square test for heterogeneity and I2 statistics to quantify the inconsistency. I2 less than 25% was mild, 25–75% was moderate and more than 75% was considered as substantial heterogeneity [15]. Sub-group analysis was performed based on age group, gender, geographical region (North/South/West/East/Northeast/Central), study setting (community/workplace/facility), diagnostic criteria, waist circumference measurement, year of publication, quality of studies, representativeness of the sample and urban/rural/tribal communities. Potential covariates for meta-regression were study setting, region, diagnostic criteria, representativeness of the sample, year of publication, mean age and quality of evidence. We performed multivariable meta-regression analysis by including the study level factors with p value less than 0.2 in the univariate model. We performed sensitivity analysis to assess the robustness of the results by excluding the studies conducted exclusively on elderly (≥60 years), studies having high risk of bias or not conducted on a representative sample, and studies conducted before 2007.

Study specific prevalence estimates and pooled estimates were graphically represented through forest plot for both combined and subgroup analysis. Publication bias was assessed and graphically represented by funnel plot; asymmetry of the plot was tested using Egger’s test and p value less than 0.10 was considered as statistically significant publication bias [16].

Results

Study selection

We conducted a systematic search to find studies that report the prevalence of MS from the dates of database inception until August 2019. During the first stage of screening (title, abstract and keywords), we retrieved 310 studies from the following databases: Medline, ScienceDirect, Cochrane library and Google Scholar. After removal of duplicates, we reviewed 249 articles’ full texts against our eligibility criteria for the possible inclusion in the review. We reviewed the bibliographies of the retrieved articles and found four more relevant studies. Finally, we analysed 111 studies with 133,926 participants satisfying the inclusion criteria (Fig 1) [17–127].

Fig 1. Flow chart showing the search strategy and selection of studies.

Fig 1

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Characteristics of the included studies

Table 1 lists the characteristics of the studies analysed. Most of the studies (46) were conducted in Southern states like Andhra Pradesh, Karnataka, Kerala and Tamil Nadu. The mean age of study participants ranged from 19.6 to 69.4 years. Coming to the study setting, 83 were from community-based studies followed by 21 from facility-based studies and 9 from workplace-based studies. The sample sizes in the studies varied from 60 to 9,886. Majority of the studies (49 out of 111) have reported separate estimates for urban region, while 34 studies reported for rural and 4 studies for tribal region. Rest of the studies did not provide separate estimates for urban, rural or tribal regions because of the study setting (facility or workplace-based studies). Majority (82 out of 111) studies have reported estimates for female burden of MS while 76 studies have reported estimates for male adult population.

Table 1. Characteristics of the studies included (N = 111).

S.N. Author and year State Geographical Region Setting Urban/Rural/Tribal Age group (years) Gender Sample size Diagnostic criteria Prevalence (95%CI)
1. Bal 2011 Chandigarh North Facility NA 25–50 Males & Females 440 NCEP ATP -III 37% (32%-42%)
2. Bandella 2017 Andhra Pradesh South Community Urban, rural and tribal 20–60 Males & Females 1032 NCEP ATP -III 32% (29%-35%)
3. Bansal 2009 New Delhi North Facility NA ≥18 Males & Females 1905 NCEP ATP -III 48% (45%-50%)
4. Bansal 2017 Uttar Pradesh Central Facility NA 30–70 Males & Females 350 NCEP ATP -III 17% (13%-21%)
5. Bansal A 2015 New Delhi North Community Urban 35–65 Males & Females 406 NCEP ATP -III 75% (71%-79%)
6. Barik A 2017 West Bengal East Community Rural ≥18 Males & Females 9886 NCEP ATP -III 16% (15%-17%)
7. Basha 2018 Andhra Pradesh South Community Urban ≥18 Males & Females 802 NCEP ATP -III 32% (29%-35%)
8. Bhagat 2017 Chandigarh North Community Urban 18–25 Males & Females 611 IDF 18% (15%-22%)
9. Bhat R 2010 Jammu & Kashmir North Facility NA ≥18 Males & Females 500 NCEP ATP -III 9% (6%-11%)
10. Bhattacharya 2016 Telangana South Facility NA ≥60 Males & Females 114 NCEP ATP -III 42% (33%-52%)
11. Bhutia 2017 Sikkim Northeast Facility NA ≥20 Males & Females 361 IDF 34% (29%-39%)
12. Chakraborty 2015 West Bengal East Community Urban ≥18 Males & Females 690 NCEP ATP -III 33% (29%-36%)
13. Chhabra 2014 Not mentioned North Community Urban & Rural 25–60 Females 200 NCEP ATP -III 33% (27%-40%)
14. Chinawale 2018 Gujarat West Community Not mentioned 20–80 Males & Females 473 IDF 41% (37%-46%)
15. Chow 2007 Andhra Pradesh South Community Rural ≥30 Males & Females 4535 NCEP ATP -III 25% (23%-26%)
16. Das 2011 West Bengal East Community Urban & Rural ≥30 Males & Females 350 NCEP ATP -III 48% (43%-54%)
17. Das 2017 West Bengal East Community Not mentioned 18–24 Males & Females 397 IDF 5% (3%-7%)
18. Das M 2011 West Bengal East Community Urban & Rural ≥30 Males & Females 448 NCEP ATP -III 29% (25%-34%)
19. Deedwania 2014 11 cities in India India Community Urban ≥20 Males & Females 6198 NCEP ATP -III 36% (35%-37%)
20. Deepa M 2006 Tamil Nadu South Community Urban ≥20 Males & Females 2350 NCEP ATP -III 18% (17%-20%)
21. Deshmukh 2012 Maharashtra Central Community Rural ≥18 Males & Females 300 NCEP ATP -III 17% (13%-22%)
22. Dhabriya 2015 Rajasthan North Community Urban ≥18 Males & Females 1130 IDF 23% (20–25%)
23. Goyal 2013 Uttarkhand North Facility NA ≥18 Males & Females 380 IDF 21% (17%-26%)
24. Gupta 2012 Rajasthan North Community Urban ≥20 Males & Females 6106 NCEP ATP -III 26% (25%-27%)
25. Gupta A 2004 Rajasthan North Community Urban ≥20 Males & Females 1091 NCEP ATP III 32% (29%-34%)
26. Gupta R 2007 Rajasthan North Community Urban ≥20 Males & Females 644 NCEP ATP III 47% (43%-51%)
27. Gupta R 2012 Rajasthan North Community Urban 20–60 Males & Females 739 NCEP ATP III 23% (20%-26%)
28. Harikrishnan 2018 Kerala South Community Urban & Rural ≥20 Males & Females 5063 NCEP ATP III 24% (23%-25%)
29. Ismail 2016 Kerala South Community Tribal ≥18 Males & Females 120 NCEP ATP III 28% (20%-37%)
30. Jain 2015 Maharashtra Central Facility NA 18–25 Males & Females 668 NCEP ATP III 11% (9%-14%)
31. Jeyasheela 2018 Tamil Nadu South Facility NA ≥45 Females 154 IDF 64% (56%-72%)
32. Jones 2016 Andhra Pradesh South Community Rural ≥18 Males & Females 6217 NCEP ATP III 14% (13%-15%)
33. Kamble 2010 Maharashtra Central Community Rural ≥18 Males & Females 300 NCEP ATP III 9% (6%-13%)
34. Kandpal 2016 Uttarkhand North Community Tribal 20–60 Males & Females 288 NCEP ATP III 39% (34%-45%)
35. Kanjilal 2008 Karnataka, Maharashtra South & Central Facility NA ≥20 Males & Females 2315 NCEP ATP III 58% (56%-60%)
36. Kapil 2018 Uttarkhand North Community Rural ≥60 Males & Females 979 IDF 29% (26%-32%)
37. Kaur J 2014 Punjab North Community Urban & Rural ≥20 Males & Females 351 NCEP ATP III 17% (14%-22%)
38. Kaur P 2010 Tamil Nadu South Workplace NA ≥20 Males 1077 IDF 41% (38%-44%)
39. Kaushal 2016 Uttar Pradesh Central Community Urban ≥20 Males & Females 127 NCEP ATP III 37% (29%-46%)
40. Kempegowda 2011 Karnataka South Community Rural ≥20 Males & Females 495 NCEP ATP III 20% (16%-23%)
41. Khan 2018 Uttar Pradesh Central Facility NA ≥20 Males & Females 420 NCEP ATP III 41% (36%-46%)
42. Kotokey 2013 Assam Northeast Community Urban ≥20 Males & Females 502 IDF 33% (29%-37%)
43. Kunti 2019 West Bengal East Facility NA ≥18 Males & Females 330 NCEP ATP III 64% (58%-69%)
44. Lakshmipriya 2012 Tamil Nadu South Community Urban ≥20 Males & Females 1875 IDF 28% (26%-30%)
45. Lateef 2007 Andhra Pradesh South Community Urban ≥20 Males & Females 1519 NCEP ATP III 24% (22%-27%)
46. Lavanya 2012 Andhra Pradesh South Community Urban ≥20 Males & Females 350 NCEP ATP III 23% (19%-28%)
47. Madan G 2016 Maharashtra West Community Urban & Rural 18–65 Males 313 IDF 40% (34%-46%)
48. Madhav 2013 Andhra Pradesh South Workplace NA ≥20 Males 180 IDF 22% (16%-28%)
49. Mahadik 2007 Maharashtra West Community Urban & Rural ≥20 Males & Females 1071 NCEP ATP III 24% (22%-27%)
50. Mahajan 2012 New Delhi North Community Urban & Rural ≥20 Males & Females 7174 NCEP ATP III 57% (56%-58%)
51. Mahanta 2013 Assam Northeast Community Urban & Rural ≥35 Males & Females 297 NCEP ATP III 27% (22%-32%)
52. Mahanta 2017 Assam Northeast Community Urban & Rural 20–60 Males & Females 3372 NCEP ATP III 48% (46%-49%)
53. Majumdar 2011 Karnataka South Community Not available 18–75 Males & Females 441 NCEP ATP III 28% (24%-32%)
54. Majumdar 2017 Andhra Pradesh South Community Urban ≥60 Males & Females 112 IDF 54% (45%-64%)
55. Mangat 2010 Chandigarh North Community Urban & Rural ≥18 Males & Females 605 NCEP ATP III 39% (35%-43%)
56. Manjunath 2014 Andhra Pradesh South Community Urban 18–25 Males & Females 473 NCEP ATP III 4% (2%-6%)
57. Mini 2018 Kerala South Workplace NA 18–64 Males & Females 2287 NCEP ATP III 19% (18%-21%)
58. Misra 2011 Haryana North Community Rural ≥20 Males & Females 307 NCEP ATP III 12% (9%-16%)
59. Mittal 2018 Uttar Pradesh Central Community Urban & Rural ≥20 Males & Females 125 IDF 35% (27%-44%)
60. Mohan 2007 Tamil Nadu South Community Urban & Rural ≥20 Males & Females 1736 NCEP ATP III 17% (15%-19%)
61. Mohan 2009 Tamil Nadu South Community Urban & Rural ≥20 Males & Females 541 NCEP ATP III 37% (33%-41%)
62. Nag 2015 West Bengal East Community Rural ≥20 Males & Females 1007 IDF 26% (23%-29%)
63. Naik 2016 Andhra Pradesh South Community Urban ≥60 Males & Females 295 IDF 35% (29%-40%)
64. Nithya 2015 Tamil Nadu South Community Rural ≥60 Males & Females 514 NCEP ATP III 20% (17%-24%)
65. Pai 2019 Karnataka South Community Urban & Rural ≥20 Males & Females 420 NCEP ATP III 4% (2%-6%)
66. Pandey 2010 Maharashtra West Community Urban 35–65 Females 498 IDF 57% (52%-61%)
67. Parale 2008 Karnataka South Workplace NA ≥30 Males & Females 700 NCEP ATP III 27% (24%-30%)
68. Patel 2016 Gujarat West Facility NA 25–65 Males & Females 478 NCEP ATP III 26% (22%-30%)
69. Pathak 2018 Haryana North Community Rural ≥20 Males & Females 1200 IDF 9% (8%-11%)
70. Pemminati 2010 Karnataka South Community Urban ≥20 Males & Females 451 IDF 30% (26%-34%)
71. Prabhakaran 2005 New Delhi North Workplace NA 20–59 Males 2120 NCEP ATP III 35% (33%-37%)
72. Prabhakaran 2007 New Delhi North Community Urban & Rural 35–64 Males & Females 4044 NCEP ATP III 33% (32%-35%)
73. Pradeepa 2016 Tamil Nadu South Community Urban ≥60 Males & Females 222 IDF 37% (31%-44%)
74. Prakash 2013 Uttar Pradesh North Facility NA ≥18 Males & Females 1340 NCEP ATP III 32% (30%-35%)
75. Prasad 2012 Orissa East Community Urban 20–80 Males & Females 1178 IDF 43% (40%-46%)
76. Rajput 2014 Haryana North Community Urban & Rural ≥20 Males & Females 3042 IDF 29% (27%-31%)
77. Ramachandran 2003 Tamil Nadu South Community Not mentioned 20–75 Males & Females 475 NCEP ATP III 41% (37%-46%)
78. Ramachandran 2008 Tamil Nadu South Workplace NA ≥20 Males & Females 2499 NCEP ATP III 29% (27%-31%)
Ramachandran 2008 Tamil Nadu South Community Urban ≥20 Males & Females 3278 NCEP ATP III 41% (37%-46%)
79. Randhwa 2015 Punjab North Community Rural 25–55 Females 300 NCEP ATP III 26% (21%-31%)
80. Ravikiran 2010 Chandigarh North Community Urban & Rural ≥20 Males & Females 2225 NCEP ATP III 45% (43%-47%)
81. Roopa 2010 Tamil Nadu South Community Urban ≥20 Males & Females 358 NCEP ATP III 35% (30%-40%)
82. Sachdev 2009 New Delhi North Community Urban 26–32 Males & Females 1492 IDF 25% (23%-27%)
83. Sarkar P 2016 Karnataka South Community Rural ≥30 Males & Females 448 NCEP ATP III 26% (22%-30%)
84. Sarkar S 2006 West Bengal East Community Tribal ≥20 Males & Females 588 NCEP ATP III 25% (22%-29%)
85. Sarma 2015 Andhra Pradesh South Community Rural 30–50 Females 60 NCEP ATP III 20% (11%-32%)
86. Sawant 2011 Maharashtra West Community Urban ≥20 Males & Females 548 NCEP ATP III 20% (16%-23%)
87. Selvaraj 2012 Tamil Nadu South Community Rural 30–50 Females 150 NCEP ATP III 31% (23%-39%)
88. Selvaraj 2019 Tamil Nadu South Community Rural 20–40 Males 360 NCEP ATP III 17% (13%-21%)
89. Shalini 2013 Karnataka South Community Urban & Rural ≥18 Females 1023 NCEP ATP III 57% (54%-60%)
90. Sharma MK 2018 Uttar Pradesh Central Community Urban & Rural 20–55 Males & Females 290 NCEP ATP III 20% (16%-25%)
91. Sharma R 2019 Jammu & Kashmir North Community Not mentioned ≥18 Males & Females 210 NCEP ATP III 35% (29%-42%)
92. Sharma S 2016 Chandigarh North Facility NA 45–55 Females 350 NCEP ATP III 63% (57%-68%)
93. Sharma S 2016 Karnataka South Workplace NA 20–50 Males & Females 210 NCEP ATP III 12% (8%-17%)
94. Singh 2016 Haryana North Community Rural ≥20 Males & Females 1700 IDF 27% (25%-29%)
95. Singh 2017 Uttar Pradesh North Community Urban ≥25 Males & Females 2002 NCEP ATP III 19% (18%-21%)
96. Sinha N 2016 Telangana South Community Urban ≥60 Males & Females 114 IDF 42% (33%-52%)
97. Sinha S 2012 New Delhi North Community Urban ≥20 Females 226 NCEP ATP III 30% (24%-36%)
98. Srimani 2017 West Bengal East Community Rural 45–70 Females 222 IDF 46% (39%-53%)
99. Srimani 2018 West Bengal East Community Rural 45–70 Females 509 IDF 38% (34%-42%)
100. Srinivasan 2016 Kerala South Facility NA ≥20 Males & Females 432 NCEP ATP III 61% (56%-66%)
101. Subramani 2018 Madhya Pradesh Central Facility NA 20–79 Males & Females 1190 NCEP ATP III 50% (47%-53%)
102. Tandon 2010 Jammu & Kashmir North Community Rural ≥45 Females 500 NCEP ATP III 13% (10%-16%)
103. Tharkar 2010 Tamil Nadu South Community Urban ≥20 Males & Females 2021 NCEP ATP III 32% (30%-34%)
104. Tharkar S 2008 Tamil Nadu South Workplace NA ≥30 Males & Females 318 IDF 57% (52%-63%)
Tharkar S 2008 Tamil Nadu South Community Urban ≥30 Males & Females 410 IDF 28% (23%-32%)
105. Thayyil 2012 Kerala South Workplace NA ≥30 Males 823 NCEP ATP III 17% (14%-19%)
106. Thiruvagounder 2010 Tamil Nadu South Facility NA ≥20 Males & Females 1568 NCEP ATP III 29% (27%-31%)
107. Thyagi 2013 Uttar Pradesh Central Facility NA 25–65 Males & Females 405 NCEP ATP III 43% (38%-48%)
108. Vatakanchery 2019 Kerala South Facility NA 20–60 Males & Females 520 IDF 76% (72%-80%)
109. Venugopal 2019 Pondicherry South Community Rural ≥30 Males & Females 489 IDF 40% (35%-44%)
110. Wani 2014 Jammu & Kashmir North Facility NA 20–60 Males & Females 500 NCEP ATP III 9% (6%-11%)
111. Zafar 2017 Uttar Pradesh West Community Rural 18–55 Males & Females 2982 NCEP ATP III 12% (11%-13%)
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NCEP ATP III–National Cholesterol Education Program Adult Treatment Panel III.

IDF–International Diabetes Federation.

NA–Not applicable.

Methodological quality of the included studies

We performed assessments of risk of bias for all the included studies using NO scale. First, selection bias domains were assessed and it was found that 64 out of 111 studies (58% of studies) had high risk of bias related to representativeness of the sample for our review, 80 (72%) studies did not report proper justification for sample size, 69 (62%) studies did not report non-response rate or their characteristics. All the studies reported use of validated measurement tool. Under the outcome domain, only one study did not independently assess the outcome; nine studies did not report the statistical methods used in the study. Almost one-third i.e. 35 (31.5%) of the included studies had high risk of bias as per NO scale.

Burden of metabolic syndrome in India

Pooled estimate was calculated after adjusting for population size weights and graphically depicted in Fig 2. The overall pooled prevalence of MS among adult population in India was 30% (95%CI: 28%-33%).

Fig 2. Forest plot showing the prevalence of metabolic syndrome among adult population in India.

Fig 2

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State wise analysis of MS showed that the maximum prevalence of MS was reported in Madhya Pradesh (50%) followed by New Delhi (43%), Odisha (43%) and Telangana (42%). Least pooled prevalence of MS was found in Jammu & Kashmir (15%) followed by Haryana (18%) and Punjab (21%). There was a significant heterogeneity found among the studies included in our review (I2 = 99.1%; p<0.001). Additional subgroup analysis was performed to explore the source of heterogeneity and obtain separate estimates based on age group, gender, study setting, geographical area and regions, year of publication, representativeness of sample, waist circumference measurement, quality of studies and diagnostic criteria (Table 2).

Table 2. Summary of findings and subgroup analysis of studies reporting prevalence of metabolic syndrome in India.

Characteristic Number of studies pooled Pooled ES# (95% CI) I2 P for heterogeneity/trend
Pooled prevalence of Metabolic syndrome in India = 30% (95%CI: 28%-33%)
AGE GROUP
18–29 years 12 13% (8%-18%) 93.3% <0.001
30–39 years 11 32% (24%-41%) 96.7%
40–49 years 11 41% (33%-48%) 95.9%
50–59 years 10 50% (43%-57%) 94.3%
≥60 years 17 41% (34%-49%) 97.3%
GENDER
Male 76 26% (22%-29%) 98.6% 0.04
Female 82 35% (31%-38%) 98.8%
URBAN VS RURAL VS TRIBAL
Urban 49 32% (29%-36%) 98.9% <0.001
Rural 34 22% (20%-25%) 97.7%
Tribal 4 28% (21%-36%) 88.4%
STUDY SETTING
Community 83 29% (26%-32%) 99.1% 0.08
Facility 21 38% (30%-47%) 99.1%
Workplace 9 28% (22%-35%) 98.2%
GEOGRAPHICAL REGION
North 31 30% (25%-35%) 99.2% 0.83
South 46 30% (26%-33%) 98.5%
East 11 33% (23%-43%) 99.1%
West 7 30% (19%-44%) 99%
Central 10 27% (17%-39%) 98.4%
Northeast 4 35% (25%-46%) 96.7%
DIAGNOSTIC CRITERIA
NTEP-ATP III 81 29% (26%-32%) 99.2% 0.05
IDF 32 34% (30%-39%) 98.3%
WAIST CIRCUMFERENCE MEASUREMENT
Tip of iliac crest 34 29% (25%-33%) 98.6% 0.67
Midline of iliac crest & costal margin 22 32% (26%-39%) 99%
YEAR OF PUBLICATION
2003–2006 5 30% (22%-39%) 98.2% 0.62
2007–2010 26 32% (27%-36%) 98.9%
2011–2014 30 27% (23%-33%) 99.1%
2015–2019 52 31% (27%-36%) 99.1%
SAMPLING (REPRESENTATIVENESS)
Random 47 29% (25%-33%) 99.3 0.37
Non-random 64 31% (27%-35%) 98.8%
QUALITY OF STUDIES
Low 35 32% (28%-36%) 98.4% 0.40
High 76 29% (26%-33%) 99.2%
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Age wise distribution of metabolic syndrome in India

The pooled prevalence of MS differed significantly across the age groups (p<0.001). There was a steady increase in the burden across the age groups from 13% (18–29 years group) to 50% (50–59 years). There was significant heterogeneity among the studies reporting prevalence across all the age groups with I2>90% and p<0.001.

Gender distribution of metabolic syndrome in India

In total, 82 studies have reported prevalence of MS among female adult population while 76 studies have reported for males. The pooled prevalence of MS among adult females in India was 35% (95%CI: 31%-38%); while for males, the pooled prevalence was 26% (95%CI: 22%-29%). There was significant heterogeneity among the studies reporting prevalence in females and males (I2 = 98%, p<0.001).

Urban vs rural vs tribal

Overall, 49 studies have reported separate estimates for urban region, 34 for rural and 4 for tribal region. The pooled prevalence of MS among adult urban population in India was 32% (95%CI: 29%-36%); while in rural population, the pooled prevalence was 22% (95%CI: 20%-25%) and in tribal population, it was 28% (95%CI: 21%-36%). Significant heterogeneity was found among the studies reporting prevalence across urban, rural and tribal regions (p<0.001).

Study setting (community vs facility vs workplace)

Subgroup analysis based on study setting showed that the facility-based studies showed higher prevalence (38%; 95%CI: 30%-47%) followed by community based (29%; 95%CI: 26%-32%) and workplace-based studies (28% 95%CI: 22%-35%). However, this difference was not statistically significant (p = 0.08). There was significant heterogeneity among the studies reporting prevalence of MS irrespective of the study setting (p<0.001).

Geographical regions

North India. In total, 31 studies reporting the burden of MS were from Northern region. The pooled prevalence of MS in North India was 30% (95%CI: 25%-35%). There was a significant heterogeneity found among the studies conducted in North India (I2 = 99.2%; p<0.001). Among the 31 studies, 15 reported separate estimates for urban and 10 for rural regions. Prevalence of MS in urban North India was 35% (95%CI: 27%-44%; I2 = 99.3%; p<0.001) while for rural North India, the pooled prevalence was 21% (95%CI: 15%-27%; I2 = 97.4%, p<0.001). We also checked for gender wise distribution of MS in North India. The prevalence of MS among adult females in North India was 33% (95%CI: 26%-40%; I2 = 98.9%, p<0.001); while for adult male population, it was 26% (95%CI: 20%-34%; I2 = 99%, p<0.001) (S1 Fig).

Central India. Ten studies have reported prevalence of MS in Central India. The pooled estimate for prevalence of MS in Central India was 27% (95%CI: 17%-39%; I2 = 98.4%, p<0.001). Only one study was conducted in urban community of Central India. It reported a prevalence of 25% with 95%CI: 18%-33%. Three studies were conducted in the rural community of Central India, with which we found a pooled prevalence of 14% with 95%CI: 9%-20%. Gender wise distribution showed that the prevalence of MS was higher among adult females in Central India (30%; 95%CI: 17%-46%; I2 = 97.3%, p<0.001) when compared to males (21%; 95%CI: 12%-33%; I2 = 97.2%, p<0.001) (S2 Fig).

Western India. In total, 7 studies from Western India were included in our review. The pooled prevalence of MS in Western India was 30% (95%CI: 19%-44%; I2 = 99%, p<0.001). There was a significant urban-rural difference in the prevalence of MS in Western India. The prevalence of MS in urban Western India was 37% (95%CI: 21%-55%, I2 = 98.1%, p<0.001); while in rural Western India, it was 13% (95%CI: 12%-15%). Gender wise distribution of MS showed that the prevalence was higher among females (32%; 95%CI: 15%-51%; I2 = 98.3%, p<0.001) when compared to males (25%; 95%CI: 14%-35%; I2 = 95%, p<0.001) (S3 Fig).

Eastern India. Eleven studies have reported the prevalence of MS in Eastern India. The pooled prevalence of MS in Eastern India was 33% (95%CI: 23%-43%; I2 = 99.1%, p<0.001). The prevalence was higher among urban Eastern India (38%; 95%CI: 31%-45%; I2 = 90.3%, p<0.001) when compared to rural Eastern India (29%; 95%CI: 18%-40%; I2 = 98.5%, p<0.001). Gender distribution of MS showed that the burden was higher among the females (39%; 95%CI: 27%-53%; I2 = 98.8%, p<0.001) when compared to males (23%; 95%CI: 12%-36%, I2 = 98.5%, p<0.001) (S4 Fig).

Southern India. In total, 46 studies reporting the burden of MS were from Southern region. The pooled prevalence of MS in South India was 30% (95%CI: 26%-33%). There was a significant heterogeneity found among the studies conducted in South India (I2 = 98.6%; p<0.001). Prevalence of MS in urban South India was 30% (95%CI: 25%-34%; I2 = 98.1%; p<0.001) while for rural South India, the pooled prevalence was 25% (95%CI: 20%-30%; I2 = 97.9%, p<0.001). Gender wise prevalence of MS in South India was also checked. The prevalence of MS among adult females in South India was 34% (95%CI: 28%-40%; I2 = 98.1%, p<0.001); while for adult male population, it was 26% (95%CI: 21%-31%; I2 = 97.9%, p<0.001) (S5 Fig).

Northeast India. Only 4 studies were conducted in Northeast India to determine the prevalence of MS. The pooled prevalence of MS in Northeast India was 35% (95%CI: 25%-46%). There was a significant heterogeneity found among the studies conducted in South India (I2 = 96.9%; p<0.001) (S6 Fig). There was no sufficient number of studies to see the urban-rural and gender-based differences in the burden of MS.

Additional subgroup analysis and sensitivity analysis

Subgroup analysis based on diagnostic criteria revealed a statistically significant difference in the pooled estimate between NCEP ATP-III and IDF criteria (29% vs 34%; p = 0.05). However, we did not see any statistically significant difference in the burden of MS based on year of publication, difference in measurement of waist circumference, quality of studies or representativeness of sample.

We performed sensitivity analysis by excluding the studies conducted exclusively on elderly (≥60 years), studies having high risk of bias or not conducted on a representative sample, and studies conducted before 2007. This is to provide robust and latest estimate on the burden of MS among adult population in India. In total, 47 out of the 111 studies satisfied these criteria and included in the sensitivity analysis. The pooled prevalence of MS obtained in sensitivity analysis was still almost similar to the overall findings (29%; 95%CI: 25%-33%) indicating that the estimate is robust to the changes in age group, year, and quality of studies.

Meta-regression

First, we conducted univariate meta-regression with individual study level factors such as study setting, geographical region, diagnostic criteria, year of publication, mean age, representativeness of the sample and quality of studies (Table 3). We found significant association with mean age and study setting. However, we performed multivariable meta-regression with factors having p value less than 0.2 such as study setting, mean age, and diagnostic criteria used. The adjusted model was able to explain 24.5% of the between-study variability and the model was statistically significant (p = 0.004).

Table 3. Univariate and multivariate meta-regression performed to explore the heterogeneity between the studies (N = 113).

Characteristics Unadjusted Co-efficient Unadjusted p-Value Adjusted Co-efficient Adjusted p-value
Mean age 0.0069 0.002 0.0068 0.002
Study setting
Community Ref - Ref -
Facility 0.10 0.006 0.10 0.04
Workplace -0.004 0.93 0.04 0.49
Diagnostic criteria
IDF Ref - Ref -
NCEP-ATP -0.05 0.10 -0.07 0.11
Year of publication {Not included in the multivariate model}
2003–2006 Ref -
2007–2010 0.02 0.75
2011–2014 -0.01 0.82
2015–2019 0.02 0.75
Geographical region
North Ref -
South 0.05 0.41
East 0.02 0.61
West 0.07 0.40
Central 0.02 0.63
Northeast 0.03 0.69
Representativeness of the sample
Representative Ref -
Non-representative 0.03 0.34
Quality of studies
Low risk Ref -
High risk -0.02 0.46
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Publication bias

Egger’s test was performed for the assessment of publication bias. There were no small study effects with non-significant coefficient value (Co-efficient: 0.78; 95%CI: -0.95 to 2.52; p = 0.372) which shows lack of evidence of publication bias. Graphical representation of the test of publication bias was depicted through funnel plot in Fig 3. Funnel plot also showed symmetric plot indicating the absence of publication bias.

Fig 3. Funnel plot checking the publication bias in the current review.

Fig 3

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Discussion

We have conducted this review to obtain a comprehensive estimate of burden of MS among adult population in India. We have also captured the gender distribution, urban-rural-tribal differences and geographical region wise estimates to find any significant difference in the estimates of MS. In total, we analysed data from 111 studies with 133,926 participants. Majority of the studies were conducted in Southern states followed by Northern states. Majority (83) were from community-based studies. Out of these, 49 have reported separate estimates for urban region, 34 for rural and 4 for tribal region. Majority of the included studies (76 out of 111) had low risk of bias.

The prevalence of MS among adult population in India was 30% (95%CI: 28%-33%). Similar reviews published from other low middle income countries in Middle East, South East Asia and Latin American region also reported that almost one third of general population have MS [128–130]. This recent upsurge in the prevalence of MS among low middle income countries including India might be directly linked with rapid economic development and urbanization in the country. This rapid industrialization can influence drastic changes in lifestyle patterns and nutrition [131–133].

We also found that people living in urban areas (32%; 95%CI: 29%-36%) were found to have higher prevalence of MS when compared to people living in tribal (28%; 95%CI: 21%-36%) or rural areas (22%; 95%CI: 20%-25%). These findings were in line with the studies conducted in neighbouring low middle-income countries [129]. Unhealthy lifestyles, better socio-economic status, decreased physical activity, stress, excessive salt and red meat consumption might be the contributory factors influencing the higher prevalence of MS in urban areas [134].

Age wise distribution showed that there is a steady rise in the burden of MS with increase in age groups and it differed significantly (p<0.001). Gender distribution of MS showed that the females had higher prevalence (35%; 95%CI: 31%-38%) when compared to males 26% (95%CI: 22%-29%). Similar reviews in Eastern Mediterranean [135], South East Asian [136], and Western pacific [137] regions abide by our findings. Major reason for this finding could be the gender specific risk factors such as menopause, polycystic ovarian syndrome and use of hormonal contraceptives among women [138]. Other probable reasons could be the excess risk women carries in terms of elevated body weight, increased waist girth, and low high density lipoprotein cholesterol when compared to men [139–142].

Highest prevalence of MS was reported in Madhya Pradesh (50%) followed by New Delhi (43%), Orissa (43%) and Telangana (42%). Stratified analysis across the geographical regions showed that people living in Northeast India (35%) have highest prevalence of MS followed by Eastern India (33%), regions with unique lifestyle and culture [142]. This highlights the need of understanding the influence of sociocultural, ethno-geographical factors in determining the risk of MS. This high prevalence among the north eastern states could also be due to lesser number of studies (n = 4) from the region. Hence, the current review may not be representative of the entire north eastern population. Further studies should be carried out in this region to have conclusive evidence on the burden of MS.

The major strength of the study is that we have tried to provide the first comprehensive review on burden of MS among adult general population in India. We also provided estimates based on gender, study setting, community and geographical regions. Test for publication bias have found that there was no significant bias in the current review. However, our review has certain limitations. Summarizing and concluding the burden of MS as 30% among adults in India with demographic and socio-economic differences is difficult because of the inherent heterogeneity. We have tried to overcome this limitation by conducting subgroup analysis based on study setting, geographical regions and provided individual prevalence estimates. The chi square test for heterogeneity also revealed significant variability across the national and region wise included studies. Hence, we tried to explain the between-study variability using meta-regression and found the potential sources of heterogeneity.

In spite of these limitations, current review provides important baseline information on the burden of MS among adult population in India. Our review has showed a higher prevalence among women, people living in urban areas and in specific geographical pockets. The findings of our review highlight that MS is a major public health problem in India. It is necessary for the government to allocate adequate resources and establish appropriate cost-effective interventions to tackle the burden of MS. Development and implementation of policies and protocols for the screening (focusing on population level screening strategies) would enable us in early diagnosis and treatment. Special focus should be given towards the vulnerable and high-risk groups. A wholesome comprehensive health care approach encompassing various levels of health care is necessary to achieve better management of MS.

This crucial step would not only enable us to reduce the mortality imposed by its individual components but also to curtail the out-of-pocket expenditure incurred by these conditions. Further comprehensive meta-analysis on population-based studies is necessary to find the factors responsible for MS which will help the policy makers, especially in the low and low middle-income regions to devise region specific interventions.

Supporting information

S1 Checklist. PRISMA 2009 checklist.

(DOC)

Click here for additional data file. (64KB, doc)
S1 Fig

Forest plot showing the setting wise and gender wise distribution of metabolic syndrome in North India a) North India–Male b) North India–Female c) North India–Urban d) North India–Rural.

(TIFF)

Click here for additional data file. (6.6MB, tiff)
S2 Fig

Forest plot showing the setting wise and gender wise distribution of metabolic syndrome in Central India a) Central India–Male b) Central India–Female c) Central India–Rural.

(TIFF)

Click here for additional data file. (2MB, tiff)
S3 Fig

Forest plot showing the setting wise and gender wise distribution of metabolic syndrome in Western India a) Western India–Male b) Western India–Female c) Western India–Urban d) Western India–Rural.

(TIFF)

Click here for additional data file. (2MB, tiff)
S4 Fig

Forest plot showing the setting wise and gender wise distribution of metabolic syndrome in Eastern India a) Eastern India–Male b) Eastern India–Female c) Eastern India–Urban d) Eastern India–Rural.

(TIFF)

Click here for additional data file. (2.4MB, tiff)
S5 Fig

Forest plot showing the setting wise and gender wise distribution of metabolic syndrome in South India a) South India–Male b) South India–Female c) South India–Urban d) South India–Rural.

(TIFF)

Click here for additional data file. (8.3MB, tiff)
S6 Fig

Forest plot showing the setting wise and gender wise distribution of metabolic syndrome in Northeast India a) Northeast India–Male b) Northeast India–Female.

(TIFF)

Click here for additional data file. (735.8KB, tiff)
S1 File. Search strategy.

(PDF)

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S2 File

(DTA)

Click here for additional data file. (67.5KB, dta)

Data Availability

All relevant data are within the paper and its Supporting Information files.

Funding Statement

The authors received no specific funding for this work.

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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

S1 Checklist. PRISMA 2009 checklist.

(DOC)

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S1 Fig

Forest plot showing the setting wise and gender wise distribution of metabolic syndrome in North India a) North India–Male b) North India–Female c) North India–Urban d) North India–Rural.

(TIFF)

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S2 Fig

Forest plot showing the setting wise and gender wise distribution of metabolic syndrome in Central India a) Central India–Male b) Central India–Female c) Central India–Rural.

(TIFF)

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S3 Fig

Forest plot showing the setting wise and gender wise distribution of metabolic syndrome in Western India a) Western India–Male b) Western India–Female c) Western India–Urban d) Western India–Rural.

(TIFF)

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S4 Fig

Forest plot showing the setting wise and gender wise distribution of metabolic syndrome in Eastern India a) Eastern India–Male b) Eastern India–Female c) Eastern India–Urban d) Eastern India–Rural.

(TIFF)

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S5 Fig

Forest plot showing the setting wise and gender wise distribution of metabolic syndrome in South India a) South India–Male b) South India–Female c) South India–Urban d) South India–Rural.

(TIFF)

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S6 Fig

Forest plot showing the setting wise and gender wise distribution of metabolic syndrome in Northeast India a) Northeast India–Male b) Northeast India–Female.

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S1 File. Search strategy.

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S2 File

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Data Availability Statement

All relevant data are within the paper and its Supporting Information files.

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