|Year : 2015 | Volume
| Issue : 2 | Page : 178-184
Prevalence of prediabetes and its associated risk factors among rural adults in Tamil Nadu
Logaraj Muthunarayanan1, Balaji Ramraj2, John Kamala Russel2
1 Department of Community Medicine, SRM Medical College Hospital and Research Centre, SRM University, Kancheepuram, Tamil Nadu, India
2 Department of Community Medicine, SRM Medical College, SRM University, Kancheepuram, Tamil Nadu, India
|Date of Web Publication||16-Dec-2015|
SRM Medical College Hospital and Research Centre, Kattankulathur - 603 203, Tamil Nadu
Source of Support: None, Conflict of Interest: None
Background: Considerable number of people in the prediabetic stage will go on to develop type 2 diabetes. Early diagnosis and intervention of prediabetic and their cluster of risk factors can prevent the cardiovascular events and other complications of diabetes. Objectives: To estimate the prevalence of prediabetes and associated factors among adults attending fixed mobile clinic in a rural block in Tamil Nadu. Materials and Methods: A cross-sectional study was carried out among 544 individuals over the age of 20 years through our fixed mobile clinic among 30 villages of a block in Tamil Nadu with a structured interview schedule. Results: A total of 544 participants above the age of 20 years were studied of which 72.6% were women and 27.4% were men. The prevalence of prediabetes was 8.5% and diabetes was 10.1%. Higher risk of being prediabetic and diabetic was noted above the age of 40 years (odds ratio [OR] = 7.79, 2.17), male gender (OR = 1.46, 2.34), body mass index of more than 23 kg/m 2 (OR = 1.52, 2.13), waist hip ratio of men >1 and women >0.8 (OR = 1.49, 2.28), alcohol intake (OR = 1.59, 2.45), and systolic blood pressure of more than 140 mm of Hg (OR = 2.23 and 2.15). Conclusion: Identifying people with prediabetes and creating awareness on the prevention of diabetes by lifestyle modification and development of cost-effective strategy to prevent or delay the progression of the prediabetic stage to diabetic stage is the need of the hour for the prevention of diabetes in country like India.
Keywords: Diabetic, prediabetic, prevalence, risk factors, rural adults
|How to cite this article:|
Muthunarayanan L, Ramraj B, Russel JK. Prevalence of prediabetes and its associated risk factors among rural adults in Tamil Nadu. Arch Med Health Sci 2015;3:178-84
|How to cite this URL:|
Muthunarayanan L, Ramraj B, Russel JK. Prevalence of prediabetes and its associated risk factors among rural adults in Tamil Nadu. Arch Med Health Sci [serial online] 2015 [cited 2019 Sep 16];3:178-84. Available from: http://www.amhsjournal.org/text.asp?2015/3/2/178/171899
| Introduction|| |
The term prediabetic is an intermediate stage used to describe a person with impaired blood glucose tolerance levels of fasting between 100 and 126 mg/dl of blood or whose 2-hour postprandial blood glucose was 140-200 mg/dl. Considerable number of these people in the prediabetic stage will go on to develop type 2 diabetes. Studies in India had shown that nearly 40-55% of the people with prediabetic stage will develop to type 2 diabetes mellitus over a period of 3-5 years. , It has been established by several studies that there is a clear link between type 2 diabetes mellitus and development of cardiovascular risk factors. This study was undertaken to diagnose patients in prediabetic stage and their clustering with the other risk factors for diabetic mellitus. The clustering of risk factors such as overweight and obesity, being older than 40 years, sedentary habits, smoking, alcoholism, hypertension, and intake of fruits and vegetables were studied. Prediabetic represents the tip of the iceberg. Early diagnosis and intervention of prediabetic and their cluster of risk factor can prevent the cardiovascular events and complications of diabetes such as diabetic retinopathy, neuropathy, and nephropathy. This study was carried out with an objective of estimating the prevalence of prediabetes and associated factors among adults attending fixed mobile clinic in a rural block in Tamil Nadu.
| Materials and Methods|| |
A cross-sectional study was carried out through our fixed mobile clinic among 30 villages of Kattankulathur block in Kancheepuram District in Tamil Nadu from March 2012 to February 2013. A total of 544 individuals over the age of 20 years who attended our fixed mobile clinic were interviewed in person with a structured questionnaire to elicit information on selected sociodemographic variables, tobacco and alcohol use, dietary intake, physical activity, and treatment history for diabetes and hypertension. The physical examination such as measurements of height, weight, and blood pressure (BP) and collection of blood samples for plasma glucose were carried out on all the participants. Of 2,112 adults participants above the age of 20 years, 544 participants who were willing to give blood for testing had not taken food in the past 2 h and given informed consent were include in the study. The survey was approved by Institutional Ethics Committee.
Standard methods were used to measure weight and height.  Body mass index (BMI) was calculated, and standard cut-offs for Asian adults were used to define overweight and obesity.  According to which, overweight is defined as BMI of more than 23. Overweight is further classified as at-risk of obesity (BMI = 23-24.9), obesity grade 1 (BMI = 25-29.9), and obesity grade 2 (BMI ≥ 30).
BP was recorded in the sitting position in the left arm to the nearest 1 mm Hg using an electronic OMRON BP measuring device (Omron Corporation, Tokyo, Japan). Two readings were taken: First one before starting the interview and the second one at the end of the interview and the mean of the two readings was used for analysis. Hypertension was diagnosed using seventh report of the Joint National Committee on Prevention, Detection, Evaluation, and Treatment of High BP (JNC 7) criteria. 
All current smokers and those who had quit smoking <1-year before the assessment were considered smokers. Similarly, all current alcoholics and those who had quit alcohol <1-year before the assessment were considered alcoholics.
Based on the physical activity, the participants were classified into light work, moderate work, and heavy work. In case of women, homemakers with electrical and electronic appliances at home like grinder, mixer and oven were classified as light worker and homemakers without these were classified as moderate workers. Those who were involved in manual labor work were classified as heavy worker.
Postprandial blood sugar was measured using glucometer (Accutrend Plus). It was ensured before taking blood sample that the participants had not taken any food 2 hours before testing. A person was considered to have diabetes if postprandial plasma glucose concentration was over 200 mg/dl and prediabetic if the postprandial blood sugar was between 140 and 200 mg/dl.
Data were analyzed using the standard statistical software packages. Descriptive data were presented as percentages and unadjusted OR to measure the strength of association and 95% confidence intervals were calculated. Chi-square test was used to lend statistical support to prove associations between categorical variables.
| Results|| |
A total of 544 participants over the age of 20 years formed the study population. The study population comprised of 72.6% (395) women and 27.4% (149) men. Most of the participants belonged to nuclear families (77%/419) and the remaining belonged to joint families. The majority of the participants were Hindus (96.5%/525), 14 (2.6%) were Christians, and 19 (3.5%) were Muslims. Nearly, 36.2%/196 were illiterate, 53.8%/293 had school education and 10%/55 were graduates. Around 45% of the participants were homemakers and about 10% had income of rupees more than 24,000/month [Table 1].
[Table 2] depicts the mean of the study population. Means were found to be 46.43(standard deviation (SD) ± 13.31) for age, 24.47 (SD ± 5.45) for BMI, 2.65 (± 1.81) for fruit intake in days/week, 4.86 (± 2.14) for vegetable intake in days/week, 121.81 (± 18.60) for systolic BP, and 79.15 (± 11.79) for diastolic BP.
[Table 3] shows the prevalence of risk factors in categories of normal (81.4%), prediabetic (8.5%) and diabetic (10.1%). Among the prediabetic, 93.5% were above the age of 40 years whereas it was 80% for diabetic and 64.8% for normal individuals. This difference was found to be statistically significant (P < 0.001). Among prediabetic and diabetic, 67.4% and 56.4% were women, respectively. The prediabetic and diabetic conditions were higher in women compared to men, and it was found statistically significant. Compared to normal individuals (37.7%), the prediabetic (47.9%) and diabetic (56.4%) had BMI of more than 23 and it was found statistically significant (P = 0.009). The waist hip ratio (WHR) was higher in prediabetic (37%) and diabetic (47.3%) compared to normal individuals (28.2%) and it was found statically significant (P = 0.016). Alcoholics were higher in prediabetic (8.7%) and diabetic (12.7%) compared to normal individual (5.6%) but it was not statistically significant. Among diabetic and normal individuals, the prevalence of smoking was higher compared to prediabetic. Among prediabetic, the diastolic and systolic hypertensives were 17.4% and 39.1%. In diabetic, it was 34.5% and 38.2% and in normal individuals, it was 18.5% and 22.3%, respectively. The difference was found to significant both for diastolic and systolic hypertensives (P = 0.018, 0.003). Statically significant differences were not noted for prediabetic, diabetic, and normal individuals in case of light work, vegetable intake for more than 3 days/week, and fruit intake for more than 3 days/week.
|Table 3: Prevalence of risk factors for CVD among prediabetic and diabetic|
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[Table 4] shows the association between risk factors of cardiovascular disease and prediabetic and diabetic. Higher risk of being prediabetic and diabetic was noted above the age of 40 years (OR = 7.79, 2.17), male gender (OR = 1.46, 2.34) BMI of more than 23 kg/m 2 (OR = 1.52, 2.13), WHR of men >1, women >0.8 (OR = 1.49, 2.28), alcohol intake (OR = 1.59, 2.45), and systolic BP of more than 140 mm Hg (OR = 2.23, 2.15). But a statistically significant difference between normal and prediabetic was noted for gender (P < 0.001) and systolic BP (P = 0.01). Statistically significant differences between normal and diabetic for age (P = 0.02), sex (P = 0.003), BMI (0.007), WHR (P = 0.003), diastolic BP (P = 0.005), and systolic BP (P = 0.009) were noted. [Figure 1] shows 10.4% of the normal, 13% of the prediabetic, and 18.4% of the diabetic had 2 or more risk factors.
| Discussion|| |
In the present study of 544 participants, the prevalence of prediabetes was 8.5%. Similar findings were reported by Sajjadi et al. among adults over the age of 19 years of Isfahan city, Islamic Republic of Iran, Gu et al. among Chinese population of 35-74 years and Anjana RM et al. among adults over the age 20 years with prevalence of prediabetes of 6.2%, 7.3%, and 8.3%, respectively. ,, Slightly higher prevalence of prediabetic (13.5%) was noted by Padmini Balagopal et al. in rural India.  In the present study among the age of 20 years and above, it showed that the prevalence of prediabetes and diabetes was higher in women compared to men. Similar finding was reported by Sajjai et al. with higher prevalence of prediabetes and diabetes among women.  In contrast, higher prevalence of diabetes and diabetes was noted in men compared to women by Balagopal et al. and Anjana et al. , As the age advances the prevalence of prediabetes and diabetes also increases, and it was found to be statistically significant. Similar finding was reported by Shuqian Liu et al., Gu et al. and Anjana et al. ,,
Prediabetic condition was associated with several of the cardiovascular risk factors. Higher prevalence of both prediabetic and diabetic conditions was noted in respondents with BMI of more than 23 compared to respondents with BMI <23. Similar findings were reported by Anjana et al., Balagopal et al., Snehalatha et al. and Mayo Clinic. ,,, Higher prevalence of prediabetes and diabetes was noted among alcoholics compared to nonalcoholics. Similar findings were reported by Xianhui qin et al. as alcohol drinking being an important independent associated factor for impaired fasting glucose (IFG).  The prevalence of prediabetes was low among smokers compared to nonsmokers which was consistent with the finding of Xianhui qin et al.  But it was high for diabetes among smokers compared to nonsmokers. Similar finding of positive association of diabetes with smoking was observed by Willi et al.  Compared to prediabetes, the prevalence of diabetes was higher in respondents with diastolic BP of more than 90 mm Hg than in respondents with diastolic BP of <90 mm Hg. The prevalence of prediabetes and diabetes was higher among respondents with systolic BP of more than 140 mm Hg compared to respondents with systolic BP of <140 mm Hg and the difference was found statistically significant (P = 0.003). Anjana et al. had reported that prediabetic and diabetic conditions were significantly associated with hypertension.  Sushma and Raju reported having hypertension as one of the risk factors for the development of prediabetes.  Balagopal et al. had reported in her study with a linear (significant) increase in systolic and diastolic BP with the increase in respondents' blood glucose levels.  Mohan et al. had reported that subjects with hypertension at baseline had significantly higher prevalence of diabetes at follow-up.  In the present study, age, female sex, central obesity (higher WHR), general obesity (BMI of more than 23), and high BP were significantly associated with prediabetes. However, proportion of alcoholics and participants who consume vegetable <3 days was higher in prediabetics and the difference was not statistically significant.
In our study, even though age above 40 years, male gender, higher BMI, high WHR, and systolic BP above 140 mm Hg were at higher risk of being prediabetes, statistically significant association was found only for age and systolic BP. Whereas statistically significant association was found for age, sex, BMI, WHR, diastolic BP, and systolic BP with diabetes. Shamima Akter et al. reported significant and positive association for prediabetes with older age and high body weight among Bangladesh adults.  Mohammed et al. reported statistically significant association with male gender, age above 45 years, and higher than average BMI.  Mohan et al. showed obesity, abdominal obesity, and hypertension to be significantly associated with incident diabetes.  Anjana et al. reported significant risk factors for prediabetes to be age, abdominal obesity, and hypertension.  Joji Ishikawa et al. reported that prediabetes was associated with masked hypertension.  Pramono and Pradana Soewondo reported sex, age, socioeconomic status, education level, obesity, central obesity, hypertension, and no smoking habit as predictive factors for prediabetes.  In our study, smoking habits were associated with diabetes but not for prediabetes. Mihardja et al. reported smoking habit as determinant factors on prediabetes/diabetes.  In our study, alcohol consumption increased risk of prediabetes and diabetes. Similar finding was reported by Cullmann et al. that total alcohol consumption and binge drinking increased the risk of prediabetes and Type 2 diabetes in men.  Ghorpade et al. reported significant association with sex, age group, educational status, per capita income, family history of T2DM, overweight/obesity, and alcohol use.  Shweta Sahai et al. reported that IFG increased with increasing waist circumference and showed a significant correlation with increasing WHR.  In our study, a negative association was found between physical activity and prediabetes and positive association for diabetes but it was not statistically significant. Farni et al. reported a negative association between measures of physical activity and the prevalence of prediabetes in middle-aged USA adults.  In our study, the proportion of prediabetic and diabetic having two or more risk factors were higher compared to normal individuals. Similar clustering of risk factors among prediabetic and diabetic was reported by other authors. , As the study was carried out among health seekers in a fixed mobile clinic and the proportion of female attendance out number male, the results cannot be extrapolated to the larger population.
| Conclusion|| |
In conclusion factors such as age above 40 years, males, higher WHR and BMI, systolic hypertension, and alcohol consumption, and less vegetable intake were higher among prediabetics. Thus, the target population for the intervention should be people with prediabetes and associated factors. Many studies had revealed that considerable number of patients in the prediabetic stage will develop diabetes. ,, Many studies had shown even the patients with prediabetes and newly diagnosed diabetes mellitus were presenting with complications of diabetes. ,, Identifying people with prediabetes and creating awareness on the prevention of diabetes by lifestyle modification and development of cost-effective strategy to prevent or delay, the progression of the prediabetic stage to diabetic stage is the need of the hour for prevention of diabetes in country like India, which experiencing the epidemiological transition of chronic noncommunicable diseases.
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| References|| |
Viswanathan V, Clementina M, Nair BM, Satyavani K. Risk of future diabetes is as high with abnormal intermediate post-glucose response as with impaired glucose tolerance. J Assoc Physicians India 2007;55:833-7.
Ramachandran A, Snehalatha C, Mary S, Mukesh B, Bhaskar AD, Vijay V, et al
. The Indian diabetes prevention programme shows that lifestyle modification and metformin prevent type 2 diabetes in Asian Indian subjects with impaired glucose tolerance (IDPP-1). Diabetologia 2006;49:289-97.
World Health Organization. The Asia Pacific Perspective: Redefining Obesity and its Treatment. Regional Office for the Western Pacific, International Association for the Study of Obesity & International Obesity Task Force; [Table 2].2, Feb, 2000. p. 18. Available from: http://www.wpro.who.int/nutrition/documents/docs/redefiningobesity.pdf
. [Last accessed on 2014 Feb 18].
United States Department of Health and Human Services. The Seventh Report of the Joint National Committee on Prevention, Detection, Evaluation, and Treatment of High Blood Pressure. National Institutes of Health, National Heart, Lung and Blood Institute & National High Blood Pressure Education Program. Classification of Blood Pressure; [Table 3], Aug, 2004. p. 12. Available from: http://www.nhlbi.nih.gov/guidelines/hypertension/jnc7full.pdf
. [Last accessed on 2014 Feb 18].
Sajjadi F, Mohammadifard N, Kelishadi R, Ghaderian N, Alikhasi H, Maghrun M. Clustering of coronary artery disease risk factors in patients with type 2 diabetes and impaired glucose tolerance. East Mediterr Health J 2008;14: 1080-9.
Gu D, Reynolds K, Duan X, Xin X, Chen J, Wu X, et al.
Prevalence of diabetes and impaired fasting glucose in the Chinese adult population: International Collaborative Study of Cardiovascular Disease in Asia (InterASIA). Diabetologia 2003;46:1190-8.
Anjana RM, Pradeepa R, Deepa M, Datta M, Sudha V, Unnikrishnan R, et al.
Prevalence of diabetes and prediabetes (impaired fasting glucose and/or impaired glucose tolerance) in urban and rural India: Phase I results of the Indian Council of Medical Research-India Diabetes (ICMR-INDIAB) study. Diabetologia 2011;54:3022-7.
Balagopal P, Kamalamma N, Patel TG, Misra R. A community-based diabetes prevention and management education program in a rural village in India. Diabetes Care 2008;31:1097-104.
Liu S, Wang W, Zhang J, He Y, Yao C, Zeng Z, et al
. Prevalence of diabetes and impaired fasting glucose in Chinese adults, china national nutrition and health survey, 2002. Prev Chronic Dis Public Health Res Pract Policy 2008;8:1-9.
Snehalatha C, Ramachandran A, Satyavani K, Sivasankari S, Vijay V. Clustering of cardiovascular risk factors in impaired fasting glucose and impaired glucose tolerance. Int J Diabetes Dev Ctries 2003;23:58-60.
Qin X, Li J, Zhang Y, Ma W, Fan F, Wang B, et al.
Prevalence and associated factors of diabetes and impaired fasting glucose in Chinese hypertensive adults aged 45 to 75 years. PLoS One 2012;7:e42538.
Willi C, Bodenmann P, Ghali WA, Faris PD, Cornuz J. Active smoking and the risk of type 2 diabetes: A systematic review and meta-analysis. JAMA 2007;298:2654-64.
Sushma N, Raju AB. Pre-diabetes: A review. Int J Biomed Res 2011;2:161-70.
Mohan V, Deepa M, Anjana RM, Lanthorn H, Deepa R. Incidence of diabetes and pre-diabetes in a selected urban south Indian population (CUPS-19). J Assoc Physicians India 2008;56:152-7.
Akter S, Rahman MM, Abe SK, Sultana P. Prevalence of diabetes and prediabetes and their risk factors among Bangladeshi adults: A nationwide survey. Bull World Health Organ 2014;92:204-213A.
Al-Shafaee MA, Bhargava K, Al-Farsi YM, Mcilvenny S, Al-Mandhari A, Al-Adawi S, et al
. Prevalence of pre-diabetes and associated risk factors in a n adult Omani population. Int J Diabetes Dev Ctries 2011;31:166-74.
Ishikawa J, Schwartz JE, Pickering TG. Hypertension: Ambulatory and vascular risk assessment. Circulation 2009;120:S1060.
Soewondo P, Pramono LA. Prevalence, characteristics, and predictors of pre-diabetes in Indonesia.Med J Indonesia 2011;20:283-94.
Mihardja L, Delima, Manz HS, Ghani L, Soegondo S. Prevalence and determinants of diabetes mellitus and impaired glucose tolerance in Indonesia (a part of basic health research/Riskesdas). Acta Med Indones 2009;41: 169-74.
Cullmann M, Hilding A, Östenson CG. Alcohol consumption and risk of pre-diabetes and type 2 diabetes development in a Swedish population. Diabet Med 2012;29:441-52.
Ghorpade AG, Majgi SM, Sarkar S, Kar SS, Roy G, Ananthanarayanan PH, et al
. Diabetes in rural Pondicherry, India: A population-based study of the incidence and risk factors. WHO South East Asia J Public Health 2013;2:149-55.
Sahai S, Vyas D, Sharma S. Impaired fasting glucose: A study of its prevalence documented at a tertiary care centre of central India and its association with anthropometric variables. J Indian Acad Clin Med 2011;12:187-92.
Farni K, Shoham DA, Cao G, Luke AH, Layden J, Cooper RS, et al.
Physical activity and pre-diabetes-an unacknowledged mid-life crisis: Findings from NHANES 2003-2006. PeerJ 2014;2:e499.
Jagannathan R, Nanditha A, Sundaram S, Simon M, Shetty AS, Snehalatha C, et al.
Screening among male industrial workers in India shows high prevalence of impaired glucose tolerance, undetected diabetes and cardiovascular risk clustering. J Assoc Physicians India 2014;62:312-5.
Mohan V, Deepa M. The metabolic syndrome in developing countries. Diabetes Voice 2006;51:15-7.
Meigs JB, Muller DC, Nathan DM, Blake DR, Andres R, Baltimore Longitudinal Study of Aging. The natural history of progression from normal glucose tolerance to type 2 diabetes in the Baltimore Longitudinal Study of Aging. Diabetes 2003;52:1475-84.
Qian Q, Joushilahti P, Erikson J, Tuomilehto J. Predictive propertiesof impaired glucose tolerance for cardiovascular risks are not explained by the development of overt diabetes during follow up. Diabetes Care 2003;26:1910-4.
Ruigómez A, García Rodríguez LA. Presence of diabetes related complication at the time of NIDDM diagnosis: An important prognostic factor. Eur J Epidemiol 1998;14:439-45.
Maji D, Maji T. Neuropathy is the commonest long term complication of Type 2 diabetic individuals at diagnosis. Diabet Metab 2003;2373.
[Table 1], [Table 2], [Table 3], [Table 4]
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