Metabolic Syndrome (MS) refers to a cluster of interrelated metabolic, physiological, biochemical, and clinical abnormalities that significantly elevate the risk for cardiovascular diseases (CVD), type 2 diabetes mellitus (T2DM), and all-cause mortality. It is characterized by a clustering of conditions, including central obesity, insulin resistance, dyslipidaemia (elevated triglycerides, low HDL cholesterol), and hypertension. The presence of MS identifies individuals at high risk for the development of atherosclerotic cardiovascular diseases and type 2 diabetes. Epidemiological studies indicate that individuals with MS have a two-fold increased risk of CVD and a five-fold increased risk of developing T2DM.¹

The etiopathogenesis of MS is primarily driven by excess adiposity and sedentary lifestyles, both of which promote insulin resistance and systemic inflammation.²  MS is often associated with a range of other clinical conditions, including non-alcoholic fatty liver disease (NAFLD), cholelithiasis (cholesterol gallstones), obstructive sleep apnoea, hyperuricemia, gout, polycystic ovarian syndrome (PCOS), and certain psychiatric disorders like depression.³ The prevalence of MS increases with advancing age, with a particularly sharp rise after 50 years, placing individuals at a significantly higher risk of adverse cardiovascular and metabolic outcomes, a trend observed in both rural and urban populations across southern India, including Kerala⁴.

 Review of previous literature on prevalence of metabolic syndrome revealed that most of the researches were hospital based studies  that assessed occurrence of diseases in high risk people who visit the hospital , exempting a significant proportion of unreported cases at community level. Our  aim was to  employ a multi stage systematic random sampling technique to study the prevalence of metabolic syndrome and its association with various sociodemographic, lifestyle, and behavioural factors among the adult population (aged above 18 years)  of rural area of South India.

METHOD

STUDY DESIGN-  Cross-sectional study

STUDY PERIOD-April 25^th 2024- September 30^th 2024

STUDY SETTING –   India is a federal parliamentary democratic republic, governed by the Central Government. It comprises of 28 states and 8 Union Territories, each having own State Government. Each state is further divided into districts. Each district is further divided into administrative divisions known as blocks.The Panchayati Raj  is the oldest system of local government followed  in rural areas. Each village or group of villages has a Gram Panchayat, which is the basic unit of local self-government .A Panchayat is further divided into wards, each represented by an elected member. These institutions ensure that governance and development reach the grassroots level. Kerala ,is one of states , located in the southwestern part of India. It has 14 districts, in which the most populated district located at the northern region is Malappuram . Setting of the study is Puzhakattiri panchayath located at Mankada block of Malappuram district which is also the field practice area of Medical college in Malappuram. Puzhakattiri Panchayath has total of 17 wards with total population of 40,512. Each ward has almost equal distribution of houses approximately 600.

Fig 1 .a).Map of India  ,b)Map of Kerala

INCLUSION CRITERIA

●        Participants aged 18 years and above.

●        Residents of Puzhakattiri panchayath for at least 6 months.

●        Individuals willing to provide informed consent and participate in the study.

EXCLUSION CRITERIA

●        Individuals diagnosed with PCOS, taking long-term steroids , psychiatric medications & on hormone replacement therapy.

●        Individuals with cognitive impairments that would hinder their ability to provide accurate data or participate in the study procedures.

SAMPLE SIZE

Sample size was calculated from the formulae n = Z²α*p*q/d² 

Prevalence of metabolic syndrome in Kerala was reported to be 61% in a study by Srinivasa et al.²⁸ (p = prevalence = 61; q = (100-p) = 39; d = absolute precision of 5%).

Zα = Z-value corresponding to the desired confidence level (e.g., 1.96 for 95% confidence)

Plugging the values into formulae, we got as 380.41 as the sample size.

Institutional ethics committee approval was obtained for the study and written informed consent was obtained by the participants. 

Sampling Technique:

A Multistage systematic random sampling was done. Study setting was Puzhakattiri panchayath which is the FAP field area of the Medical college. A line list of all the houses were available from the FAP register and was used as the sampling frame. Stage1 was selection of 4 wards out of total 17 wards of Puzhakattiri Panchayath using simple random sampling through lottery method. Stage 2 was selection of Participants from each of the four selected wards equally, ie 380/4 =95. Stage 3 was selecting Houses within Each Ward. Each ward has around 600 houses approximately. So Systematic random sampling (600/95≈6) by taking 6 as sampling interval was done. The first house was chosen randomly by lottery method. After selecting the first house, every 6th house was selected until 95 eligible participants were obtained from that ward.

Fig 2.Multi stage systematic random sampling

From each selected house, one of  the available members who met the inclusion criteria (age 18 and above, residing in Puzhakattiri for at least 6 months) was chosen using Kish grid method. By following this method, we ensured that 380 participants were systematically selected from 4 wards, ensuring random representation from each area. The selected participants were asked to report at the medical camp set up at the Puzhakattiri Community health centre, where the data collection and results of the blood investigations were collected.

DATA COLLECTION

A pre-designed, pre-tested, semi-structured, and validated questionnaire was utilized to gather information on socio-demographic characteristics, lifestyle factors, personal habits, and existing co-morbidities. The questionnaire underwent content validation by three subject matter experts. Information regarding alcohol and tobacco use was collected.

Anthropometric measurements, including height, waist circumference, and hip circumference, were recorded using a non-stretchable fibre measuring tape. Body weight was measured in kilograms using a digital weighing scale (Health o Meter) with minimal clothing. Blood pressure was measured using a mercury sphygmomanometer (Elko meter Deluxe model), with the average of two readings taken at least one minute apart being considered. Blood investigations such as fasting blood sugar, fasting lipid profile including HDL and triglyceride levels were also carried out in the diagnostic laboratory Puzhakattiri Community health centre as part of their routine investigations.

The National Cholesterol Education Program Adult Treatment Panel III (NCEP ATP III) criteria were used in this study to diagnose METS. According to these criteria, the presence of any three or more of the following five conditions indicates a diagnosis of metabolic syndrome²⁰:

ANALYSIS

Data analysis was performed using the Statistical Package for the Social Sciences (SPSS) version 23.0 (IBM, New York, USA). Normality of data distribution was assessed using the Kolmogorov-Smirnov test (P value >0.05). Descriptive statistics were reported as mean ± standard deviation (SD) for normally distributed continuous variables. Chi square test was used to find the association between Metabolic syndrome and sociodemographic, lifestyle factors. Statistical significance was set at a P value < 0.05.

RESULTS

The study included 380 participants having mean age 49.2+13 years. Majority were female 68%, n=258), while males comprised 32% (n=121). The prevalence of metabolic syndrome was found to be 44.5%(Fig 3). Lifestyle factors and family history were assessed. Smoking prevalence was low at 2.1% (n=8), and alcohol consumption was reported by 9.5% (n=36). Family history of diabetes was present in 30.0% (n=114), and hypertension in 31.1% (n=118). Education up to high school level was reported by 46.3% (n=176), suggesting a diverse educational background (Table1).

Table 1: Lifestyle and Family History Distribution (n = 380)

On assessing the parameters of metabolic syndrome, Waist-to-hip ratio (WHR) indicates 51.3% to be normal and 48.7% with abdominal obesity. Raised blood pressure was seen in 52.9%. Low HDL was observed in 45.8%, whereas 54.2% normal. High triglycerides are present in 49.7%. Raised fasting plasma glucose affects 45.5%, with 54.5% normal(Table2).

Table 2: Health Indicators Distribution (n = 380)

Fig 3: Prevalence of Metabolic syndrome

Metabolic syndrome prevalence was found to be 44.5% (n = 169) versus 55.5% normal (n = 211)Fig1 .

Fig 4: Comparison of Parameters of NCEP ATP III  between Gender

The figure 4 compares NCEP ATP parameters between gender.It shows that male gender has slightly higher prevalence of abnormal parameters except fasting plasma glucose

ASSOCIATION OF SOCIO-DEMOGRAPHIC  VARIABLES WITH MS

Gender shows no significant association (p = 0.688), with 43.0% of males and 45.2% of females affected. Family history of diabetes (p = 0.457) and education level (p = 0.440) also lack significant associations.

Table 3:

Table 4 shows group Statistics for Age. Individuals with metabolic syndrome (n = 169) have a significantly higher mean age (59.43 ± 9.15 years) compared to those without (n = 211, 41.01 ± 9.2 years; p = 0.001), indicating age as the key factor in metabolic syndrome prevalence

Table 4: Group Statistics for Age

DISCUSSION

From our study ,the prevalence of Metabolic Syndrome (MS) was found to be 44.5%, which is higher than most global estimates. Giles et al. and Misra et al. reported global MS prevalence ranging from 7.1% to 41.6%^11,12, while Galassi et al.'s meta-analysis reported a range of 23%–46% using NCEP criteria, consistent with our findings¹³. Ford ES et al. noted a 35% prevalence in the USA , Marquez et al¹⁴. reported 24.9% in Latin America¹⁵, and Aryal et al. found 29.8% (IDF) and 26.1% (ATP III) in Southeast Asia¹⁶. Gulf countries showed prevalence rates between 20.7% and 37.2%¹⁷. These variability in prevalence may be due to sociodemographic factors, geographic differences, and differing diagnostic criteria.

India has seen a rapid shift in disease burden, with non-communicable diseases accounting for 60% of deaths¹⁸. A meta-analysis by Krishnamoorthy et al. reported a national prevalence of metabolic syndrome of  30%¹⁹, with state-wise rates highest in Madhya Pradesh⁴², followed by Odisha⁴², Delhi⁴³, and Telangana⁴³. Similar to our findings, Bansal et al. reported 48% in Delhi²⁰, Bhattacharya et al. found 42% in Telangana²¹, and Majumdal et al. noted 54% in Andhra Pradesh²². Das et al. found 48% in West Bengal²³, Tharkar et al. 57% in Tamil Nadu²⁴, Kanjilal et al. 58% in Karnataka²⁵, Kaur et al. 41% in Tamil Nadu ²⁶, and Madan et al. 40–43% in Maharashtra²⁷. These results reflect comparable sociodemographic profiles and shared diagnostic standards.

In Kerala, MS prevalence has varied widely. Srinivasa et al. reported 61% with a higher prevalence in females and older adults .²⁸Harikrishnan et al. found a lower prevalence of 24%²⁹, while Rosemary et al. reported a much higher rate of 76%, with males at 80.4% and females at 67.8%.³⁰Ismail et al. found a 28.3% prevalence in Kannur, with 32.5% among females and greater prevalence in those over 45 years³¹. While these studies noted higher prevalence in females, our study did not show a statistically significant gender association. However, the mean age of individuals with MS in our study was significantly higher (59.43 years) than those without MS (41.01 years), consistent with existing literature emphasizing age as a major risk factor.

Our study also. found a significant association between MS and family history of hypertension. Similar findings were observed by Chakraborty et al. and Bhagat et al.^33,34 Ranasinghe et al. reported a 1.19 times increased risk of MS with a family history of hypertension ³⁵, while Liu et al. in Japan found maternal hypertension linked to obesity and MS ³⁶. Kanchan et al. also identified familial hypertension as a key predictor of modifiable risk factors for MS³⁷. These findings support a genetic predisposition toward MS, especially through hypertensive pathways.

Regarding individual components of MS, 48.7% of participants in our study had abdominal obesity—more common among females. Elevated fasting glucose was found in 45.5%, elevated triglycerides in 49.7%, low HDL cholesterol in 45.8%, and elevated blood pressure in 52.9%, with hypertension more common in males. These patterns are well-documented. Hourfil et al., in a French cohort, emphasized abdominal obesity as a core MS component, linked with triglycerides, HDL, and blood pressure .³⁸Our finding of greater abdominal obesity in females is consistent with the CURES study from Chennai by Mohan et al., which reported central obesity in 54% of females³⁹. Similarly, Thankappan et al., in the Kerala Diabetes Prevention Program, and Azizi et al. from Iran, highlighted abdominal obesity as more prevalent among women and central to MS development. ⁴⁰

The prevalence of elevated blood pressure (52.9%) in our study, especially in males, also mirrors Harikrishnan et al.'s results in Kerala²⁹.In summary, our findings reflect the high burden of metabolic syndrome and its components in the adult population, consistent with national and some international data. Age and family history of hypertension were key factors, while gender showed mixed associations across different studies . As the limitation of the study we understand that cross-sectional design only captures a glimpse of the prevalence of MS at one specific moment, which limits the ability to infer causality between associated factors and MS. Hence we look forward to more longitudinal studies on the pattern over time about metabolic syndrome in future. Also, reliance on self-reported data may introduce bias, as participants might underreport unhealthy behaviours or overreport healthy ones.

CONCLUSION

High prevalence of Metabolic syndrome driven by modifiable risks and genetic predisposition in Puzhakattiri panchayath, Malappuram, Kerala , highlights the urgent need for community health initiatives focused on lifestyle modifications and early detection . Routine Screening Program for NCDs especially for those in high risk group i.e. genetic predisposition has to be implemented systematically . Promotion of balanced diet with low refined sugars and fats, physical activity through village-based fitness initiatives has  to be strengthened at primary level.

REFERENCE

1. Pavithra H, Naik PR. Prevalence of Metabolic Syndrome and its Risk Factors among Adults in a Rural Area of Dakshina Kannada District. Indian J Community Med. 2023;48(6):861-866. doi:10.4103/ijcm.ijcm_743_22
2. Sharma M, Gaidhane A, Choudhari SG. A Comprehensive Review on Trends and Patterns of Non-communicable Disease Risk Factors in India. Cureus. 2024;16(3):e57027. doi:10.7759/cureus.57027
3. Achila OO, Araya M, Berhe AB, Haile NH, Tsige LK, Shifare BY, et al. Metabolic syndrome, associated factors and optimal waist circumference cut points: findings from a cross-sectional community-based study in the elderly population in Asmara, Eritrea. BMJ Open. 2022;12(2):e052296. doi:10.1136/bmjopen-2021-052296
4. Sundarakumar JS, Stezin A, Menesgere AL, Ravindranath V; SANSCOG and TLSA Collaborators. Rural-urban and gender differences in metabolic syndrome in the aging population from southern India: two parallel, prospective cohort studies. EClinicalMedicine. 2022;47:101395. doi:10.1016/j.eclinm.2022.101395
5. Khan MM, Sonkar VK, Singh R, Siddiqui SA, Gupta A, Singh SK. Prevalence of metabolic syndrome and its association with lifestyle factors among adults in rural India: a cross-sectional study. J Family Med Prim Care. 2023;12(6):1136-1142. doi:10.4103/jfmpc.jfmpc_1848_22
6. Krishnamoorthy Y, Rajaa S, Murali S, Sahoo J, Kar SS. Association between anthropometric risk factors and metabolic syndrome among adults in India: a systematic review and meta-analysis of observational studies. Prev Chronic Dis. 2022;19:E24. doi:10.5888/pcd19.210231
7. Krishna STR, Bahurupi Y, Kant R, Aggarwal P, Ajith AV. Prevalence of metabolic syndrome and its risk factors among newly diagnosed type 2 diabetes mellitus patients - a hospital-based cross-sectional study. J Family Med Prim Care. 2024;13(8):3325-3331. doi:10.4103/jfmpc.jfmpc_51_24
8. Jesmin S, Islam R, Islam S, Mia S, Sultana SN, Zaedi S, et al. Comprehensive assessment of metabolic syndrome among rural Bangladeshi women. BMC Public Health. 2022;22(1):49. doi:10.1186/s12889-021-12465-8
9. Gupta R, Sharma KK, Gupta A, Agrawal A, Mohan I, Gupta VP, et al. Prevalence of metabolic syndrome and its risk factors in urban India: a multisite study revisited in 2021. Diabetes Metab Syndr. 2021;15(4):102-109. doi:10.1016/j.dsx.2021.05.012
10. Niknam M, Olazadeh K, Azami M, Boroumandieh S, Yari-Boroujeni R, Izadi N, et al. Health-related quality of life in adults with metabolic syndrome: a multi-level analysis of family and individual level variation. BMJ Open. 2024;14(11):e087870. doi:10.1136/bmjopen-2024-087870
11. Giles WH, Ford ES, Dietz WH. Prevalence of the metabolic syndrome among US adults: findings from the third National Health and Nutrition Examination Survey. Jama. 2002;287:356–359.
12. Misra A, Khurana L. Obesity and the metabolic syndrome in developing countries. J Clin Endocrinol Metabol. 2008;93:s9–s30.
13. Galassi A, Reynolds K, He J. Metabolic syndrome and risk of cardiovascular disease: a meta-analysis. Am J Med. 2006;119:812–819.
14. Ford ES. Prevalence of the metabolic syndrome defined by the International Diabetes Federation among adults in the US. Diabetes Care. 2005;28:2745–2749.
15. Márquez-Sandoval F, Macedo-Ojeda G, Viramontes-Hörner D, Ballart JF, Salvadó JS, Vizmanos B. The prevalence of metabolic syndrome in Latin America: a systematic review. Publ Health Nutr. 2011;14:1702–1713.
16. Aryal N, Wasti SP. The prevalence of metabolic syndrome in South Asia: a systematic review. Int J Diabetes Dev Ctries. 2016;36:255–262.
17. Mabry RM, Reeves MM, Eakin EG, Owen N. Gender differences in prevalence of the metabolic syndrome in Gulf Cooperation Council Countries: a systematic review. Diabet Med. 2010;27:593–597.
18. Sharma M, Gaidhane A, Choudhari SG. A Comprehensive Review on Trends and Patterns of Non-communicable Disease Risk Factors in India. Cureus. 2024 Mar 27;16(3):e57027. doi: 10.7759/cureus.57027. PMID: 38681366; PMCID: PMC11046362.
19. Krishnamoorthy Y, Rajaa S, Murali S, Rehman T, Sahoo J, Kar SS. Prevalence of metabolic syndrome among adult population in India: A systematic review and meta-analysis. PLoS One. 2020 Oct 19;15(10):e0240971. doi: 10.1371/journal.pone.0240971. PMID: 33075086; PMCID: PMC7571716.
20. Bansal S, Paliwal A, Verma V, Chauhan J. A study on prevalence of metabolic syndrome in general population in Western Uttar Pradesh, India. Int J Res Med Sci. 2017; 5(6):2641.
21. Bhattacharyya A, Sinha N. Attributes of metabolic syndrome in geriatric institutional residents in Secunderabad, India. Int J Res Med Sci. 2016; 398–402.
22. Majumdar V, Nagaraja D, Christopher R. Vitamin D status and metabolic syndrome in Asian Indians. Int J Obesity. 2011. August;35(8):1131–4.
23. Das M, Pal S, Ghosh A. Prevalence of Cardiovascular Disease Risk Factors by Habitat: A Study on Adult Asian Indians in West Bengal, India. Anthropologischer Anzeiger. 2011; 68(3):253–64.
24. Tharkar S, Kumpatla S, Muthukumaran P, Viswanathan V. High prevalence of metabolic syndrome and cardiovascular risk among police personnel compared to general population in India. JAPI. 2008; 56:845
25. 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(1):189 10.2147/vhrm.2008.04.01.189 
26. Kaur P, Radhakrishnan E, Rao SR, Sankarasubbaiyan S, Rao TV, Gupte MD. The metabolic syndrome and associated risk factors in an urban industrial male population in South India. J Assoc Physicians India. 2010; 58(6):363–71.
27. Madan J, Narsaria A. Prevalence of metabolic syndrome in Mumbai City, India. J Obes Metab Res. 2016; 3(1):16. 
28. Srinivasan S, Lingegowda J, Rajan C, Muddegowda P, R. R. Metabolic syndrome in rural Kerala: a hospital based study. Int J Adv Med. 2016; 898–904. 
29. Harikrishnan S, Sarma S, Sanjay G, Jeemon P, Krishnan MN, Venugopal K, Mohanan PP, Jeyaseelan L, Thankappan KR, Zachariah G. Prevalence of metabolic syndrome and its risk factors in Kerala, South India: Analysis of a community based cross-sectional study. PLoS One. 2018 Mar 27;13(3):e0192372. doi: 10.1371/journal.pone.0192372. PMID: 29584725; PMCID: PMC5870937.
30. Vatakencherry RMJ, Saraswathy L. Prevalence of Metabolic syndrome among adults in a teaching hospital in Kochi, Central Kerala: A cross-sectional study. J Family Med Prim Care. 2019 Jun;8(6):2079-2083. doi: 10.4103/jfmpc.jfmpc_241_19. PMID: 31334183; PMCID: PMC6618204.
31. Ismail IM, Azeez K, Antomy A, Kunnummal SV. Metabolic syndrome and its associated factors among the adult population residing in Kannavam tribal area of Kannur District, Kerala. Trop J Med Res 2016;19:36-41.
32. Pavithra, H; Naik, Poonam R.. Prevalence of Metabolic Syndrome and its Risk Factors among Adults in a Rural Area of Dakshina Kannada District. Indian Journal of Community Medicine 48(6):p 861-866, Nov–Dec 2023. | DOI: 10.4103/ijcm.ijcm_743_22
33. Chakraborty S, Roy S, Rahaman M. Epidemiological predictors of metabolic syndrome in urban West Bengal, India. J Family Med Prim Care 2015;4:535–8
34. Bhagat A, Malhotra AS, Kaur G, Kapoor N. Metabolic syndrome:Not even the Urban Indian youth is spared. Indian J Physiol Pharmacol 2017;61:368–77.
35. Ranasinghe, P., Cooray, D.N., Jayawardena, R. et al. The influence of family history of Hypertension on disease prevalence and associated metabolic risk factors among Sri Lankan adults. BMC Public Health 15, 576 (2015). https://doi.org/10.1186/s12889-015-1927-7
36. Liu J, Sekine M, Tatsuse T, Hamanishi S, Fujimura Y, Zheng X. Family history of hypertension and the risk of overweight in Japanese children: results from the Toyama Birth Cohort Study. J Epidemiol. 2014;24(4):304–11.
37. KC, Kanchan; Katwal, Srijana; Yadav, Gopal K. MBBS Adhikari, Alisha; Thapa, Raj Kumar Jha, Saroj Kumar Sharma, Arun Rijal, Thaneshwar; Giri, Santoshi; Khadka, Sitaram. Family history of hypertension and its relation to other variables in hypertensive patients: a cross-sectional study from a tertiary care hospital. International Journal of Surgery: Global Health 6(5):e0235, September 2023. | DOI: 10.1097/GH9.0000000000000235
38. Ntougou Assoumou, HG., Pichot, V., Barthelemy, JC. et al. Obesity related to metabolic syndrome: comparison of obesity indicators in an older french population. Diabetol Metab Syndr 15, 98 (2023). https://doi.org/10.1186/s13098-023-01078-x
39. Mohan V, Deepa M, Deepa R, Shanthirani CS, Farooq S, Ganesan A, Datta M. Secular trends in the prevalence of diabetes and impaired glucose tolerance in urban South India--the Chennai Urban Rural Epidemiology Study (CURES-17). Diabetology. 2006 Jun;49(6):1175-8. doi: 10.1007/s00125-006-0219-2. Epub 2006 Mar 29. PMID: 16570158.
40. Thankappan KR, Sathish T, Tapp RJ, Shaw JE, Lotfaliany M, Wolfe R, Absetz P, Mathews E, Aziz Z, Williams ED, Fisher EB, Zimmet PZ, Mahal A, Balachandran S, D'Esposito F, Sajeev P, Thomas E, Oldenburg B. A peer-support lifestyle intervention for preventing type 2 diabetes in India: A cluster-randomized controlled trial of the Kerala Diabetes Prevention Program. PLoS Med. 2018 Jun 6;15(6):e1002575. doi: 10.1371/journal.pmed.1002575. PMID: 29874236; PMCID: PMC5991386.
41. Azizi F. Tehran Lipid and Glucose Study: A National Legacy. Int J Endocrinol Metab. 2018 Oct 9;16(4 Suppl):e84774. doi: 10.5812/ijem.84774. PMID: 30584440; PMCID: PMC6289307.
42. Misra A, Pandey RM, Devi JR, Sharma R, Vikram NK, et al. (2001) High prevalence of diabetes, obesity and dyslipidaemia in urban slum population in northern India. Int J Obes Relat Metab Disord 25: 1722–1729.
43. Prabhakaran D, Jeemon P, Ghosh S, Shivashankar R, Ajay VS, Kondal D, Gupta R, Ali MK, Mohan D, Mohan V, Kadir MM, Tandon N, Reddy KS, Narayan KMV. Prevalence and incidence of hypertension: Results from a representative cohort of over 16,000 adults in three cities of South Asia. Indian Heart J. 2017 Jul-Aug;69(4):434-441. doi: 10.1016/j.ihj.2017.05.021. Epub 2017 May 30. PMID: 28822507; PMCID: PMC5560901.
44. Ananth P. Panchayati raj in india. Journal of Education & Social Policy. 2014 Jun;1(1):1-9.