|Year : 2018 | Volume
| Issue : 4 | Page : 709-716
Prevalence of metabolic syndrome and associated risk factors among geriatric population living in a high altitude region of rural Uttarakhand, India
Umesh Kapil1, Ritika Khandelwal1, Lakshmy Ramakrishnan2, Preetika Khenduja1, Aakriti Gupta1, Neha Sareen1, Ravindra Mohan Pandey3, Hem Chandra Sati3, Ravi Shankar Belwal1
1 Department of Human Nutrition, All India Institute of Medical Sciences, New Delhi, India
2 Department of Cardiac Biochemistry, All India Institute of Medical Sciences, New Delhi, India
3 Department of Biostatistics, All India Institute of Medical Sciences, New Delhi, India
|Date of Web Publication||4-Sep-2018|
Prof. Umesh Kapil
First Floor Room Number 118, Old Ot Block, Department of Human Nutrition, All India Institute of Medical Sciences, New Delhi - 110 029
Source of Support: None, Conflict of Interest: None
Introduction: Metabolic syndrome (MetS) is responsible for 2.5-fold increase in cardiovascular mortality and a 5-fold higher risk of developing diabetes. Materials and Methods: A community-based cross-sectional study was conducted during 2015–2016 in District Nainital. A list of all villages was developed. From this list, thirty villages were identified using population proportionate to size sampling method. From each village, thirty geriatric subjects (GSs) were selected. The study population included 979 GSs aged 60 years and above. The data were collected on anthropometry, blood pressure, blood glucose, and lipid profile from all the enrolled subjects. The prevalence of MetS was estimated using International Diabetes Federation criteria. Univariate and multivariate analysis was done to identify factors associated with MetS. Results: The prevalence of MetS was found to be 28.6%. Step-wise multivariate logistic regression analysis found that female gender, higher income, and body mass index ≥25 were significant and independent risk factors of MetS amongst GP. Conclusion: There is a need for screening of GP living in high altitude region so that efforts can be initiated to prevent complications of MetS.
Keywords: Geriatric, high altitude region, metabolic syndrome
|How to cite this article:|
Kapil U, Khandelwal R, Ramakrishnan L, Khenduja P, Gupta A, Sareen N, Pandey RM, Sati HC, Belwal RS. Prevalence of metabolic syndrome and associated risk factors among geriatric population living in a high altitude region of rural Uttarakhand, India. J Family Med Prim Care 2018;7:709-16
|How to cite this URL:|
Kapil U, Khandelwal R, Ramakrishnan L, Khenduja P, Gupta A, Sareen N, Pandey RM, Sati HC, Belwal RS. Prevalence of metabolic syndrome and associated risk factors among geriatric population living in a high altitude region of rural Uttarakhand, India. J Family Med Prim Care [serial online] 2018 [cited 2021 Jun 23];7:709-16. Available from: https://www.jfmpc.com/text.asp?2018/7/4/709/240402
| Introduction|| |
Metabolic syndrome (Mets) is a clustering of impaired glucose metabolism, dyslipidemia, hypertension, and central obesity. It is associated with the subsequent development of cardiovascular diseases and type 2 diabetes mellitus. Studies reported that MetS is responsible for 2.5-fold increase in cardiovascular mortality and a 5-fold higher risk of developing diabetes. Sociodemographic and lifestyle factors have found to be associated with MetS., Geriatric population (GP) considered as a vulnerable population for the development of MetS. In India, the prevalence of MetS among GP has been earlier reported as 42.1% in Hyderabad, 35.6% in Kolkata, and 29.9% in Karnataka. A strong association of the geographical region with the MetS has also been noted. The early detection of MetS is essential to prevent complication related to diabetes and cardiovascular disease among GP. There is a lack of data on the prevalence of MetS among GP living in high altitude region. Hence, to fill the gap in the existing knowledge, the present study was conducted to assess the prevalence of MetS and associated risk factors among GP living in high altitude region of rural Uttarakhand, India.
| Methodology|| |
A community-based cross-sectional study was conducted during 2015–2016 in District Nainital, Uttarakhand state, India. The district is situated at an altitude of 2084 m. A total of 1003 GP were enrolled from thirty clusters (villages) identified using population proportionate to size sampling methodology. After reaching the village, the village president member was contacted. From the selected village, one lane was selected randomly. From the selected lane, one household was selected randomly. The survey was initiated from the selected first household and contiguously covered all the required number subjects from that cluster. Thirty geriatric subjects (GS) in the age group of 60 and above were selected from each cluster by house-to-house visit. The GSs were identified with the help of village level health and nutrition functionaries such as Anganwadi workers. However, they did not participate in data collection. The objectives and procedure of data collection were explained to each subject. An informed written consent was obtained from each subject before data collection.
An oral questionnaire was administered to obtain identification of data and sociodemographic profile such as gender, age, caste, religion, financial dependency, educational qualification, occupation, family monthly income, type of house, type of family, marital status, living arrangement, type of fuel used, and physical activity.
All the measurements were taken by an investigator trained by the first author.
Waist circumference was measured using SECA-203 fiberglass tape. Waist circumference was measured in standing position. A measurement was taken with abdomen relaxed and weight equally divided over both legs. The waist circumference was measured in the direction of the horizontal plane, midway between the inferior margin of the rib, and the superior border of the iliac crest. The reading was recorded to the nearest 0.1 cm. Precaution was taken to keep the head of the subject straight and arms relaxed.
Three readings were taken to minimize variation, and the average value was considered as WC of the subject.
Weight (in Kilograms) was measured with an electronic weighing scale SECA Model-813 to the nearest 100 g. The elderly subjects were asked to be barefoot and wear light clothing. They were asked to stand straight on a firm horizontal flat surface of scale, and weight on the screen was recorded.
Height (in centimeters) was measured using SECA-213 portable stadiometer to the nearest 0.1 cm. The individual was asked to stand upright without shoes with his/her back against the vertical backboard, heels together, and eyes directed forward.
Body mass index
Body mass index (BMI) was calculated using the formula: BMI (kg/m 2) = Weight (kg)/Height (m 2). BMI (kg/m 2) was classified as <18.5 (underweight), 18.5–24.9 (normal), 25–29.9 (overweight and preobese), and ≥30 (Obese [OB]) as per the World Health Organization classification.
Blood pressure (BP) was measured using the digital OMRON HEM– 7080 BP apparatus in the sitting position. Participants were restricted from alcoholic or caffeinated beverages and smoking at least 30 min before measurements. Two readings of BP were taken at 15 min intervals on the same arm; the mean of the two measures was taken as final reading.
Triglycerides and total cholesterol assessment
This was assessed using dried blood spot (DBS) methodology.,,
Collection of dried blood spot
Whatman filter paper number 3 was properly labeled with the unique identification, date, and center code. Each subject was requested to clean and rub his/her hands against each other to stimulate blood flow. Third (middle) or fourth (ring) finger of each subject was selected for the finger prick.
Fingertip of each subject was wiped with alcohol swab completely and was allowed to air dry.
Subject's hand was gently kneaded from palm toward fingertip. The fingertip was pricked using lancet device and a full drop of blood was allowed to form on finger. First drop of blood was wiped off using a sterile swab and waited for the next large drop of blood to form.
Blood drop was allowed to fall on one side of the filter paper without making any contact between finger and filter paper. The uniform distribution of blood on filter paper was ensured.
Five blood drops from each subject were collected on filter paper in the similar manner.
Drying of blood spots
Filter papers were kept at room temperature for 4 h to allow the blood spot air dry completely.
Drying process was considered to be completed when the blood spots had a uniformly dark brownish color, and no red areas were visible.
Storage and transportation of dried blood spot
The following steps were taken for the storage and transportation of DBS filter paper:
Each DBS filter paper was sealed in an autoseal plastic bag to protect from dust and moisture after drying out. A total of ten small pouches containing DBS filter paper were transferred into a larger ziplock bag. A desiccant was kept inside each ziplock bag before zipping it. The sealed filter papers were repacked into icebox containing ice packs and transported to central laboratory AIIMS, New Delhi. Filter papers were stored at 4°C before analysis for up to 6 months.
Elution of dried blood spot
For the estimation of triglycerides (TG) and total cholesterol (TC), two 5 mm filter paper disc (10 μl of blood) was punched out. The punched blood spot was transferred into test tubes with Teflon screw cap. The 200 μl of methanol was added into test tubes. The tubes were incubated at 37°C for 1 h, with shaking at 100 rotations in an Environ Shaker.
Analysis of eluates
Biochemical estimation of TG was estimated by glycerophosphate oxidase–peroxidase method and TC was done by the cholesterol oxidase method using enzymatic kits from Randox Laboratories, Ltd., United Kingdom. For the measurement of TG and TC, 100 μl solution was extracted from the test tube. For TG, 50 μl of the extract was placed into test tubes and 50 μl of the extract was placed into Eppendorf for the analysis of TC. Commercially available enzymatic reagent (1 ml) was added in test tubes and eppendorf. The reaction mixture was stirred on a vortex mixer. Test tubes were incubated at 37°C for 15 min in water bath. Measurements of TG and TC were taken in spectrophotometer at 540 nm.
High-density lipoprotein assessment
Low-density lipoprotein (LDL) was derived using a new formula.
High-density lipoprotein (HDL) derived using a standard Friedewald formula.
Impaired fasting glucose Assessment
Blood sugar was measured using ACCU-CHEK active glucometer. Participants were instructed to be fasting for 12 h to assess blood sugar. Investigator inserted a test strip into a glucometer. Second drop of blood was used to fill up the blood glucose strip. Drop of blood put at the edge of the blood glucose test strip and blood entered in blood glucose strip. The blood sugar reading noted down after 5 s. All biohazardous waste (lancets, alcohol swabs, gauze, and gloves) collected into a plastic bag and burnt.
The study was approved by the Ethical Committee of All India Institute of Medical Sciences, New Delhi.
Metabolic syndrome criteria
The MetS classification by Indian Diabetic Federation was used. A subject was identified suffering from MetS if he/she had abnormal central obesity (≥90 cm in men and ≥80 cm in women) along with any of the following two metabolic indicators: (i) elevated TG (≥150 mg/dl), (ii) Low HDL cholesterol (<40 mg/dl in men and <50 mg/dl in women), (iii) HT (≥130/85 mm/Hg), and (iv) impaired fasting glucose (IFG) (≥100 mg/dl).
Hypercholesterolemia was defined as TC ≥200 mg/dL.
High LDL was defined as LDL ≥130 mg/dL.
The estimates are proposed to be generated at the district level in each state. Assuming the prevalence of malnutrition to be 50%, the desired sample size using the formula:
where z is standard normal variate corresponding to 5% level with 50% prevalence rate, 95% confidence level, 5% relative precision, design effect of 2, and 15% nonresponse; the total sample size was 883 and rounded up equivalent to 900 after considering. However, we included 979 GPs in the study.
Statistical Package for the Social Sciences (SPSS) version 20.0, IBM Corp., Armonk, NY, USA was utilized for conducting the statistical analysis of the data. Chi-square test was applied to analyze the association of various parameters with MetS and without MetS among the GP. Step-wise logistic regression analysis was applied to assess the independent contribution of different factors to the presence of MetS.
| Results|| |
A total of 1003 geriatrics were enrolled. Twenty-four subjects were excluded due to incomplete information. Hence, 979 subjects were analyzed.
The sociodemographic details of the GP are depicted in [Table 1]. Six hundred and forty (63.8%) females and 363 (36.2%) males were included in the study. The mean age of subjects was 69.5 ± 7.4 years for male and 67.8 ± 7.2 years for female. Majority of the subjects were living in pucca house (70.1%).
|Table 1: Distribution of elderly subjects according to sociodemographic profile (n=1003)|
Click here to view
Most common contributing factor for Mets was HDL (83.9%), whereas least common factor was hypertriglyceridemia (42.5%). The high BP was more prevalent in males (96.5%) than in females (77.0%); however, elevated level of HDL was found to be higher among females (97.7%) compared to males (31.0%) [Table 2].
|Table 2: Prevalence of metabolic syndrome and its components according to the International Diabetic Federation 2006 (280/979)|
Click here to view
[Table 3] depicts the distribution of GS having elevated levels of various components of MetS. It was found that majority (39.3%) of GS had derangement in four components followed by the subjects having abnormality in 5 (30.0%), 3 (19.3%), and 6 (11.4%) components.
|Table 3: Distribution of geriatric subjects according to the number of components of metabolic syndrome (280/979)|
Click here to view
The overall prevalence of MetS was found to be 28.6% (280/979).
The prevalence of MetS was significantly associated with female gender, higher income level, and BMI ≥25 (all P < 0.001).
The prevalence of MetS decreasing with ageing years (P = 0.013). The percentage of geriatric subjects with MetS in each age group was 60−<70 years (32.1%), 70−<80 years (24.4%) and ≥80 years.
MetS found to be higher among GS who were not doing regular physical activity 172 (61.4%) than those who were doing regular physical activity 108 (38.6%) (P = 0.004) [Table 4].
|Table 4: Factors associated with metabolic syndrome: Results of bivariate and stepwise multivariate logistic regression analysis|
Click here to view
It was found that decreasing age, female gender, higher income (13874 and above), irregular physical activity, and BMI ≥25 found to be significantly associated with MetS in univariate analysis.
Further, the female had a higher risk of developing MetS 2.77 (95% confidence interval [CI]: 2.0–3.8) as compared to their male counterparts.
The present study found that subjects with higher income (Rs. 13,874 and above) had 2.53 (95% CI: 1.5–4.0) times more risk of developing MetS compared to those with lower income ≤1865 [Table 4].
In stepwise multivariate logistic regression analysis, female gender, higher income (13874 and above), and BMI (≥25) were found significant and independent risk factors of MetS among GP.
Females subjects had 2.81 (95% CI: 1.8–4.4) times higher risk than males for developing Mets.
GS having BMI ≥25 had 34.34 (95% CI: 22.1–53.3) times higher risk of MetS than who had BMI ≤25.
In the present study, irregular physical activity was found to be an independent risk factor of MetS; however, this variable was not statistically significant for the risk factor of MetS [Table 4].
| Discussion|| |
The prevalence of MetS is increasing in both developed and developing countries. The number of GP is also growing in India. Many studies conducted on GP found that, with aging, the glucose tolerance deteriorates ,,, and CVD-related complications also increase., MetS is a premorbid condition that helps in identifying the risk of diabetes and cardiovascular disease. Therefore, this cross-sectional study was conducted for the first time among GP living in high altitude region of rural Uttarakhand, India, to see the prevalence of MetS.
The present study revealed the prevalence of MetS as 28.6%. Several studies in India have shown different rates of prevalence in different parts of the country ranging from 9.3% to 47.5%. The prevalence found in the present study was similar to earlier studies which documented 28.2% among rural population  and 28.1% among eastern GP. However, earlier studies conducted on GP documented the higher prevalence of MetS as 43.6% in rural Haryana, 35.2% in Punjab, 51.3% in Goa, and 65% in Kerala. The differences in the prevalence of MetS in different states of India may be due to different criteria used, different geographical area, different lifestyle, and different trend of prevalence of individual components of the MetS.
In the present study, low HDL was found to be most common abnormality among GP. Earlier study also documented low HDL as a most prevalent contributing factor for developing MetS., We observed that prevalence of low HDL was much higher among female than males. A similar finding has been noticed in other studies which attributed higher prevalence of low HDL cholesterol in females (70.4%) compared to males (37%).
On the other hand, elevation of the BP was the second most prevalent criterion, with a frequency of 81.0%. It is an important aspect of the present study because most of the studies reported that GS with high BP has high MetS prevalence and it is one of the most prevalent criteria.,,,,
The prevalence of MetS was found to be decreased with advancing age. Earlier studies have demonstrated that, with increasing age, the prevalence of Mets increased.,, However, various other studies conducted on GP have found no significant difference with aging.,
In the present study, the prevalence of MetS was higher in females (79.3%) as compared to males (20.7%). Majority of Indian studies reported similar results.,, An earlier study done by Srinivasan et al. showed the prevalence of 50% for males and 80% for females. Similarly, Prasad et al. in their study reported higher prevalence among females (52.4%) than males (47.6%). We observed higher prevalence among elderly female was probably due to a higher prevalence of impaired blood glucose and low HDL cholesterol levels [Table 2].
With respect to income level, we found independent and significantly increased prevalence of MetS with increase in income. Similarly, prestudies documented that there are marked increases in the prevalence of MetS with increase in income., On the contrary, an earlier study reported the prevalence of MetS significantly higher in a lower income group.
In our study, odds ratio indicated that irregular physical activity independently increased the risk of MetS 1.51 times. In other parts of India, similar observations were noted among subjects who were physically inactive.,, This finding support that physical activity lowers the risk of MetS in GP. Epidemiological studies have also supported our findings which documented direct relation between physical inactivity and the presence of cardiovascular risk factors, insulin resistance, and diabetes.,,,
We found association between BMI ≥25 and MetS. The occurrence of MetS among overweight/OB was significant (OR = 34.34; 95% CI = 22.1–53.3). Previous studies reported a higher risk of developing MetS among overweight/OB people compared with those having normal weight.,,, Preventive lifestyle modification that can lower the BMI should be targeted among GP.
Strengths of the present study include a large population-based sample, representative sampling methodology, use of standardized data collection protocols, and first study among GP of Uttarakhand. This study had a very high response rate (96.6%).
| Conclusion|| |
The present study documented higher prevalence of MetS in a high altitude region of Uttarakhand. There is a need for regular screening of GP living in high altitude region for the presence of metabolic risk factors of MetS so that preventive action can be initiated to prevent complications of cardiovascular disease and diabetes mellitus.
Financial support and sponsorship
Conflicts of interest
There are no conflicts of interest.
| References|| |
Dragsbæk K, Neergaard JS, Laursen JM, Hansen HB, Christiansen C, Beck-Nielsen H, et al.
Metabolic syndrome and subsequent risk of type 2 diabetes and cardiovascular disease in elderly women: Challenging the current definition. Medicine (Baltimore) 2016;95:e4806.
Moreira GC, Cipullo JP, Ciorlia LA, Cesarino CB, Vilela-Martin JF. Prevalence of metabolic syndrome: Association with risk factors and cardiovascular complications in an urban population. PLoS One 2014;9:e105056.
Sinha N, Bhattacharya A, Deshmukh PR, Panja TK, Yasmin S, Arlappa N, et al.
Metabolic syndrome among elderly care-home residents in Southern India: A cross-sectional study. WHO South East Asia J Public Health 2016;5:62-9.
Xiao J, Wu CL, Gao YX, Wang SL, Wang L, Lu QY, et al.
Prevalence of metabolic syndrome and its risk factors among rural adults in Nantong, China. Sci Rep 2016;6:38089.
Blank K, Szarek BL, Goethe JW. Metabolic abnormalities in adult and geriatric major depression with and without comorbid dementia. J Clin Hypertens (Greenwich) 2010;12:456-61.
Sinha U, Mukhopadhyay B. Metabolic syndrome in the urban geriatric of Kolkata: Effects of sociodemographic and lifestyle factors. Asian J Gerontol Geriatrics 2015;10:1-6.
Sarkar P, Mahadeva SK, Raghunath H, Upadhya S, Hamsa M. Metabolic syndrome and its components among population of Holalu village, Karnataka. Int J Med Sci Public Health 2016;5:860-5.
Mohan V, Shanthirani S, Deepa R, Premalatha G, Sastry NG, Saroja R, et al.
Intra-urban differences in the prevalence of the metabolic syndrome in Southern India – The Chennai urban population study (CUPS no 4). Diabet Med 2001;18:280-7.
World Health Organization. Physical Status. The Use and Interpretation of Anthropometry. Report of WHO expert committee; Technical Report series No. 854. Geneva, Switzerland: World Health Organization; 1995. p. 424-38.
Nishida C, WHO Expert Consultation. Appropriate body-mass index for Asian populations and its implications for policy and intervention strategies. Lancet 2004;363:157-63.
Lakshmy R, Gupta R, Prabhakaran D, Snehi U, Reddy KS. Utility of dried blood spots for measurement of cholesterol and triglycerides in a surveillance study. J Diabetes Sci Technol 2010;4:258-62.
Quraishi R, Lakshmy R, Prabhakaran D, Mukhopadhyay AK, Jailkhani B. Use of filter paper stored dried blood for measurement of triglycerides. Lipids Health Dis 2006;5:20.
Quraishi R, Lakshmy R, Prabhakaran D, Irshad M, Mukhopadhyay AK, Jailkhani BL, et al.
Effect of storage temperature on cholesterol measurement from dried blood. Indian J Med Res 2007;126:228-9.
] [Full text]
Anandaraja S, Narang R, Godeswar R, Laksmy R, Talwar KK. Low-density lipoprotein cholesterol estimation by a new formula in Indian population. Int J Cardiol 2005;102:117-20.
Friedewald WT, Levy RI, Fredrickson DS. Estimation of the concentration of low-density lipoprotein cholesterol in plasma, without use of the preparative ultracentrifuge. Clin Chem 1972;18:499-502.
International Diabetes Federation. The IDF Consensus Worldwide Definition of the Metabolic Syndrome. IDF Communications Edition. Brussels, Belgium: International Diabetes Federation; 2006. Available from: http://www.idf.org/webdata/docs/IDF_Meta_def_final.pdf
. [Last accessed on 2017 Jun 27].
Expert Panel on Detection, Evaluation, and Treatment of High Blood Cholesterol in Adults. Executive summary of the third report of the national cholesterol education program (NCEP) expert panel on detection, evaluation, and treatment of high blood cholesterol in adults (Adult treatment panel III). JAMA 2001;285:2486-97.
Mathew AC, Das D, Sampath S, Vijayakumar M, Ramakrishnan N, Ravishankar SL, et al.
Prevalence and correlates of malnutrition among elderly in an urban area in Coimbatore. Indian J Public Health 2016;60:112-7.
] [Full text]
Cornier MA, Dabelea D, Hernandez TL, Lindstrom RC, Steig AJ, Stob NR, et al.
The metabolic syndrome. Endocr Rev 2008;29:777-822.
Report- Elderly in India, Government of India Ministry of Statistics and Programme Implementation Central Statistic Office; 2016.
Shah C, Sheth NR, Solanki B, Shah N. To assess the prevalence of impaired glucose tolerance and impaired fasting glucose in Western Indian population. J Assoc Physicians India 2013;61:179-84.
Jain A, Paranjape S. Prevalence of type 2 diabetes mellitus in elderly in a primary care facility: An ideal facility. Indian J Endocrinol Metab 2013;17:S318-22.
Kalyani RR, Egan JM. Diabetes and altered glucose metabolism with aging. Endocrinol Metab Clin North Am 2013;42:333-47.
Halter JB, Musi N, McFarland Horne F, Crandall JP, Goldberg A, Harkless L, et al.
Diabetes and cardiovascular disease in older adults: Current status and future directions. Diabetes 2014;63:2578-89.
Peterson PN, Masoudi FA. Cardiovascular disease and the elderly: Can the evidence base avoid irrelevance? Eur Heart J 2007;28:1277-8.
Via-Sosa MA, Toro C, Travé P, March MA. Screening premorbid metabolic syndrome in community pharmacies: A cross-sectional descriptive study. BMC Public Health 2014;14:487.
Mangat C, Goel NK, Walia DK, Agarwal N, Sharma MK, Kaur J, et al.
Metabolic syndrome: A challenging health issue in highly urbanized union territory of North India. Diabetol Metab Syndr 2010;2:19.
Nag T, Ghosh A. Prevalence of metabolic syndrome in rural geriatric of Asian Indian origin. Am J Hum Biol 2015;27:724-7.
Prasad DS, Kabir Z, Dash AK, Das BC. Prevalence and risk factors for metabolic syndrome in Asian Indians: A community study from urban Eastern India. J Cardiovasc Dis Res 2012;3:204-11. [Full text]
Pathania D, Bunger R, Mishra P, Pathak R, Arora A. A study to assess prevalence of metabolic syndrome and its socio demographic risk factors in rural area of district Ambala, Haryana. J Community Med Health Educ 2013;3:1-4.
Singh A, Shweta S, Sandhu JS. Prevalence of metabolic syndrome and its risk factors among urban sikh population of Amritsar. J Postgrad Med Educ Res 2015;49:18-25.
Peixoto C, Shah HK. Prevalence of metabolic syndrome among adult population in a rural area of Goa. J Public Health Med Res 2014;2:34-7.
Srinivasan S, Lingegowda JB, Rajan C, Muddegowda PH, Kurpad R. Metabolic syndrome in rural Kerala: A hospital based study. Int J Adv Med 2016;3:898-904.
Ravikiran M, Bhansali A, Ravikumar P, Bhansali S, Dutta P, Thakur JS, et al.
Prevalence and risk factors of metabolic syndrome among Asian Indians: A community survey. Diabetes Res Clin Pract 2010;89:181-8.
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.
Kumar SV, Nagesh A, Leena M, Shravani G, Chandrasekar V. Incidence of metabolic syndrome and its characteristics of patients attending a diabetic outpatient clinic in a tertiary care hospital. J Nat Sci Biol Med 2013;4:57-62.
Lokanath DA, Chandrashekariah SA, Xaviour D, Rao J. The Incidence and alliance of metabolic syndrome with cardiovascular risk markers among kodavas. Open J Endocrine Metab Dis 2014;4:158-66.
Sharma M, Mahna R. Obesity, metabolic syndrome and physical activity in Indian adults. J Metab Synd 2012;1:1-4.
Rennie KL, McCarthy N, Yazdgerdi S, Marmot M, Brunner E. Association of the metabolic syndrome with both vigorous and moderate physical activity. Int J Epidemiol 2003;32:600-6.
Gustat J, Srinivasan SR, Elkasabany A, Berenson GS. Relation of self-rated measures of physical activity to multiple risk factors of insulin resistance syndrome in young adults: The Bogalusa heart study. J Clin Epidemiol 2002;55:997-1006.
Wareham NJ, Hennings SJ, Byrne CD, Hales CN, Prentice AM, Day NE, et al.
A quantitative analysis of the relationship between habitual energy expenditure, fitness and the metabolic cardiovascular syndrome. Br J Nutr 1998;80:235-41.
Lakka TA, Laaksonen DE, Lakka HM, Männikkö N, Niskanen LK, Rauramaa R, et al.
Sedentary lifestyle, poor cardiorespiratory fitness, and the metabolic syndrome. Med Sci Sports Exerc 2003;35:1279-86.
[Table 1], [Table 2], [Table 3], [Table 4]
|This article has been cited by|
infection in the manifestation of old age-related diseases
| ||Abolfazl Zendehdel,Maryam Roham |
| ||Molecular Genetics & Genomic Medicine. 2020; |
|[Pubmed] | [DOI]|
||Prevalence of metabolic syndrome among adult population in India: A systematic review and meta-analysis
| ||Yuvaraj Krishnamoorthy,Sathish Rajaa,Sharan Murali,Tanveer Rehman,Jayaprakash Sahoo,Sitanshu Sekhar Kar,Jennifer A. Hirst |
| ||PLOS ONE. 2020; 15(10): e0240971 |
|[Pubmed] | [DOI]|
||Association of tobacco and alcohol consumption with cardiovascular risk factors among elderly population in India
| ||Aakriti Gupta,Ravi Belwal,Lakshmy Ramakrishnan,Preetika Khenduja,Umesh Kapil |
| ||Journal of Family Medicine and Primary Care. 2020; 9(10): 5242 |
|[Pubmed] | [DOI]|
||The epidemiological profile of metabolic syndrome in Indian population: A comparative study between men and women
| ||Trupti Meher,Harihar Sahoo |
| ||Clinical Epidemiology and Global Health. 2020; |
|[Pubmed] | [DOI]|
||Metabolic syndrome: The Indian public health perspective
| ||Rajvir Bhalwar |
| ||Medical Journal Armed Forces India. 2020; |
|[Pubmed] | [DOI]|
||Diabetes in the Kokan region of India
| ||Patil Suvarna,Kadam Shruti,Desai Maruti,Joglekar Charudatta |
| ||World Journal of Diabetes. 2019; 10(1): 37 |
|[Pubmed] | [DOI]|