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Prevalence and risk factors for diabetes and impaired glucose tolerance in Asian Indians: A community survey from urban Eastern India
Diabetes & Metabolic Syndrome: Clinical Research & Reviews 6 (2012) 96–101
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Prevalence and risk factors for diabetes and impaired glucose tolerance in Asian Indians: A community survey from urban Eastern India D.S. Prasad a,*, Zubair Kabir b, A.K. Dash c, B.C. Das d a
Sudhir Heart Centre, Berhampur, Orissa, India Research Institute for a Tobacco Free Society, Dublin, Ireland c M.K.C.G. Medical College and Hospital, Berhampur, Orissa, India d Kalinga Institute of Medical Sciences, Bhubaneshwar, Orissa, India b
A R T I C L E I N F O
A B S T R A C T
Keywords: Diabetes IGT Prediabetes CHD Prevalence Predictors South Asians Asian Indians Urban
Objectives: To determine the prevalence of diabetes and impaired glucose tolerance (IGT) and to identify risk factors for the same specific to an underdeveloped urban locale of Eastern India. Methods: Study design. Population based cross-sectional study, with multistage random sampling technique. Setting. Urban city-dwellers in Orissa one of the poorest states of Eastern India bordering a prosperous state of Andhra Pradesh of Southern India. Participants. 1178 adults of 20–80 years age randomly selected from 37 electoral wards of urban populace. Definition and diagnosis of diabetes mellitus and IGT. These were based on a Report of a World Health Organiztion/International Diabetes Federation Consultation of 2006. Main outcome measure. Prevalence and significant risk factors for Diabetes and IGT. Statistical analysis. Both descriptive and multivariable logistic regression analyses. Results: The crude rates of diabetes and IGT in the study population were 15.7% and 8.8%, respectively. Similarly age-standardized rates of diabetes and IGT were 11.1% and 6.7%, respectively. Both diabetes and IGT had shown a male preponderance. Conclusion: Diabetes and IGT were very highly prevalent in this urban populace. Cardiometabolic risk factors like older age, central obesity, inadequate fruit intake, hypertension, hypertriglyceridemia and socio economic status were found to be significant predictors of diabetes in this study. ß 2012 Diabetes India. Published by Elsevier Ltd. All rights reserved.
1. Introduction The Diabetes Atlas of the International Diabetes Federation shows that India has the dubious distinction of leading the world in diabetes prevalence and is home to over 51 million diabetics [1]. Recent surveys indicate that the prevalence of Diabetes in urban Indian adults has increased from 1.2% in 1971 [2] to about 20% at present [3,4]. With its chronic course; we can expect diabetes to have a serious adverse impact on the life expectancy as well as the quality of life [5]. South Asians have an increased prevalence of coronary heart disease (CHD) and diabetes mellitus amongst all the ethnic groups irrespective of their religious affiliations, life style, diet or the country of residence [6–8]. This South Asian Paradox refers to the fact that high prevalence rates of diabetes are seen in
* Corresponding author at: Sudhir Heart Centre, Main road, Dharmanagar, Berhampur 760002, Orissa, India. Tel.: +91 0680 2224278; fax: +91 0680 2225080. E-mail address: [email protected] (D.S. Prasad).
people originating from South Asian nations of Bangladesh, India, Nepal, Pakistan and Sri Lanka despite low rates of obesity as defined by conventional body mass index criteria [9,10]. South Asians also seem to have a peculiar body phenotype known as South Asian phenotype, characterized by increased waist circumference, waist hip ratio, excessive body fat, increased plasma insulin levels, insulin resistance and an atherogenic dyslipidemias with low levels of HDL cholesterol and increased triglyceride levels [9,10]. All these predispose them not only to diabetes but also to premature CHD. There could also be unique genetic markers which make South Asians more susceptible to diabetes [9–11]. Unfortunately, accurate recent nationwide data are lacking in India [12,13], let alone data from specific states within India. Earlier we reported that the state of Orissa, one of the poorest states of Eastern India bordering a prosperous state of Andhra Pradesh of Southern India, showed interesting variations in classical coronary risk factors among an urban population [14]. Such a unique geographic location opens up to cultural and socioeconomic interactions. Diabetes is a lifestyle disease and factors
1871-4021/$ – see front matter ß 2012 Diabetes India. Published by Elsevier Ltd. All rights reserved. http://dx.doi.org/10.1016/j.dsx.2012.05.016
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contributing to changing patterns in Diabetes prevalence in recent years in this geographic region may provide interesting insights into tackling the ever-rising burden of Diabetes in South Asians in general. Interesting to note, one of the earliest studies for prevalence of diabetes in India was conducted from Orissa state [2,15]. The present study aims at updating on changing patterns of Diabetes in a unique urban Eastern Indian population and quantifying factors significantly contributing to any observed underlying pattern.
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2.5. Anthropometric profile
2. Methods
Body weight and height were measured with the subject barefoot and wearing light clothing and were used to calculate the body mass index (BMI). BMI was calculated as weight in kilograms over height in meters squared. Waist circumference was measured at the mid point between the lower limit of the rib cage and upper border of iliac crest. Blood pressure was recorded in a sitting position of the right arm to the nearest 2 mmHg using mercury sphygmomanometer. Two readings were taken 5 min apart and the mean was taken as the blood pressure.
2.1. Study design and setting
2.6. Biochemical analysis
The current study was a population-based survey of cohort under Berhampur Municipal Corporation with an estimated population of 307,724 in 2001, in Orissa one of the poorest states of Eastern India bordering a prosperous state of Andhra Pradesh of Southern India. So the inhabitants here are heterogeneous mix of language, religion, varied socioeconomic statuses and culture.
A fasting blood sample was collected after an overnight fast of at least 10 h for biochemical investigations. In addition, venous plasma glucose 2 h after ingestion of oral glucose load in all subjects except in known diabetics, who underwent 2 h postprandial plasma glucose estimation. All biochemical parameters were performed using enzymatic kits [22–26].
2.2. Sampling design and sample size
2.7. Ethical approval
The study population of 1178 adults of 20–80 years of age was selected using a multi-stage random sampling technique. The sampling frame constituted 37 electoral wards spread across the urban population of Berhampur city of Orissa state in Eastern India.
Institutional ethical committee approval was obtained prior to the start of study and informed consent was taken from all the study subjects [27].
2.3. Survey methods
Significant differences in proportions of potential lifestyle factors by diabetes status were estimated using Pearsons’ Chisquare. Univariate logistic regression and multivariable logistic regression analyses were performed using SAS software (9.1.2, Cary, NC, United States) to predict potential significant predictors of diabetes employing backward elimination modeling technique. Direct age-standardization was performed to calculate rates. Adjustments were done using a standard population from urban National health and family welfare survey 2005 of urban population of Orissa, based on the standard formula given below.
Demographic, socio-economic status as per modified Kuppuswamy scale [16], and self-reported behavioral information (smoking, alcohol, physical activity, fruit intake and diet), objective measures of anthropometry (height, weight, waist and hip circumferences), biochemical (plasma glucose, total cholesterol, triglycerides, HDL cholesterol levels), and Electrocardiographic data were collected from all study participants. Detailed interviews were performed through a previously validated questionnaire based on the guidelines of World Health Organization [17]. History of any chronic illness, in the participant as well as in the family, including diabetes mellitus, hypertension, cerebrovascular accident and coronary heart disease were recorded. Details of study methodology have been published elsewhere [14]. 2.4. Definitions of cardiovascular risk factors Definition and diagnosis of diabetes mellitus and intermediate hyperglycemia was based on a Report of a WHO/IDF Consultation, Geneva [18]. Diabetes was defined as individuals diagnosed by a physician and on glucose-lowering medications (self reported) and/or those who had a fasting plasma glucose level of 126 mg/dl (7.0 mmol/ l) or 2-h plasma glucose 200 mg/dl (11.1 mmol/l) [18]. Impaired glucose tolerance (IGT) was defined as fasting plasma glucose of <7.0 mmol/l (126 mg/dl) and 2–h plasma glucose of 7.8 and <11.1 mmol/l (140 mg/dl and 200 mg/dl) [18]. Obesity and overweight was based on the revised criteria specific for Asian Pacific populations [19]. Hypertension definition was based on the Seventh Report of the Joint National Committee on Prevention, Detection, Evaluation, and Treatment of High Blood Pressure [20]. Definition of Dyslipidemia was based on the Third Report of the National Cholesterol Education Program (NCEP) [21].
2.8. Statistical analysis
2.9. Direct method
SR ¼
SUMðrio P i Þ SUM Pi
where SR is the age-standardized rate for the population being studied, rio is the age-group specific rate for age group i in the population being studied, Pi is the population of age group i in the standard population 3. Results This was one of the large community based surveys done from Eastern India for ascertaining the prevalence of cardiovascular risk factors with the aim of providing the baseline information on prevalence rates for intervention programmes to the policy planners. A total of eleven hundred seventy eight subjects participated in the study (185 had diabetes; 104 were having IGT and the remaining 889 individuals were considered normal). 3.1. Demographic and clinical profile of subjects The sex distribution in this study was equal. The age of the subjects ranged from 20–80 years, with a mean age of 47 years in
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Table 1 Prevalence of diabetes and impaired glucose tolerance in the study population. Diabetes
n
IGT a
No. of cases
Prevalence
15.7 (13.7–17.8) 17.8 (14.8–21.0) 13.8 (11.0–16.5)
Total
1178
185
Male
590
105
Female
588
80
Age-std
b
11.1 (9.6–12.7) 11.7 (9.5–14.0) 10.7 (8.4–12.9)
No. of cases
Prevalencea
Age-stdb
104
8.8 (7.3–10.5) 8.9 (6.8–11.5) 8.6 (6.5–11.1)
6.7 (5.4–8.0) 6.9 (4.8–8.8) 6.8 (4.9–8.7)
53 51
Age-std, age standardized; IGT, impaired glucose tolerance. a Estimated taking into account the stratified sampling procedure. b Age standardized using the urban population.
Table 2 Age and gender specific prevalence of diabetes and impaired glucose tolerance. Age group (years)
Total group
Diabetes IGT
Diabetes
20–30 31–40 41–50 51–60 61–70 71–80
IGT
Men
Women
Men
Women
n
%
n
%
n
%
n
%
n
%
n
%
4 19 35 69 43 15
2.2 6.6 12.2 28.8 33.1 27.8
3 13 33 29 16 10
1.7 4.5 11.5 12.1 12.3 18.5
2 9 15 42 26 11
2.4 7.8 10.9 30.4 33.8 28.2
2 10 20 27 17 4
2.1 5.8 13.4 26.5 32.1 26.7
2 5 16 14 9 7
2.4 4.3 11.7 10.1 11.7 17.9
1 8 17 15 7 3
1.0 4.7 11.4 14.7 13.2 20.0
IGT, impaired glucose tolerance.
males (SD = 14.46) and 44.21 years in females (SD = 13.26). The detailed age-distribution is shown in Table 2. 3.2. Prevalence of diabetes/IGT Table 1 shows both crude and age-standardized rates (with 95% CI) of both diabetes and IGT. The crude (unadjusted) rates of diabetes and IGT are 15.7% and 8.8%, respectively (significantly higher among males in both the disease conditions – 17.8% and 8.9%, respectively). Age-standardized rates are also similar: 11.1% had diabetes mellitus and 6.7% were having IGT, with preponderance among males (11.7% and 6.9%, respectively (Table 1). Fig. 1 shows the increase in both diabetes mellitus and IGT with increasing age. Detailed clinical and demographic characteristics of study population are shown in Table 3. Both diabetes mellitus and IGT were significantly more common in males, smokers, in obese individuals, in those with lower physical activity levels and having lower intake of fruits, and also in individuals with hypercholesterolemia, hypertriglyceridemia, with low HDL levels as well as in hypertensives (Table 3). 3.3. Significant risk factors for diabetes/IGT Detailed correlates of diabetes mellitus/IGT in Univariate Analysis for the base model (Table 4) and the final model of
Fig. 1. Age and gender specific distribution of diabetes and IGT subjects.
multivariate logistic regression method showing significant predictors of diabetes and IGT are summarized in Table 5. Older age, central obesity, inadequate fruit intake, being hypertensive, hypertriglyceridemia and middle socio economic status significantly contributed to an increased Diabetes risk among this urban population. Those aged 65 years and above are at five-fold increased risk of diabetes; individuals with Table 3 Clinical and demographic characteristics for diabetes, IGT and normal. Variable
Diabetes
IGT
Normal
N Mean age (years)
185 55.3 (11.4)**
104 53.0 (12.0)**
889 43.1 (13.5)
Gender Male Female
105 (56.8)* 80 (43.2)
Education Illiterate Elementary High school College Socio economic status Lower Middle Upper Smoking Physical in activity Low/no fruits intake Hypertension General obesity Central obesity Hypercholesterolemia Hypertriglyceridemia High LDL Low HDL
18 52 49 66
18 152 15 50 86 152 121 112 138 60 112 47 91
77 (51.0)y 65 (49.0)
432 (48.6) 457 (51.4)
(9.7)y (28.1) (26.5) (35.7)
15 22 27 40
(14.4)y (21.1) (26.0) (38.5)
100 220 235 334
(11.2) (24.7) (26.4) (37.6)
(9.7)* (82.2) (8.1) (27.0)y (46.5)** (82.2)** (65.4)** (60.7)** (74.6)** (32.4)* (60.7)** (25.4)y (49.2)y
15 74 15 32 45 70 48 66 75 34 64 28 55
(14.4)y (71.2) (14.4) (30.8)y (43.3)** (67.3)* (46.2)** (63.5)** (72.1)** (32.7)* (61.5)** (26.9)y (52.9)y
14 656 85 238 269 499 262 338 363 179 268 190 407
(16.6)y (73.8) (9.6) (26.8) (30.3) (56.1) (29.5) (38.0) (40.8) (20.1) (30.1) (21.4) (45.8)
IGT, impaired glucose tolerance. Number in parenthesis indicate percentage. * Significant at p < 0.05. ** Significant at p < 0.000. y Not significant.
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Table 4 Correlates of diabetes/impaired glucose tolerance: univariate analysis. Diabetes
Variable
IGT
Adjusted OR (95% C.I)
Sig
Adjusted OR (95% C.I)
Sig
Age 45–64 65
5.12 (3.36–7.79) 7.81 (4.68–13.02)
0.004* 0.00**
3.45 (2.12–5.61) 5.91 (3.18–10.97)
0.00** 0.00**
Socio economic status Middle High General obesity Central obesity Physical in activity Fruits intake Hypertension Hypercholesteromia Hypertrigleceridemia Low HDL High LDL
1.89 1.36 2.23 3.72 1.87 3.43 4.16 1.78 3.05 1.11 1.21
0.02* 0.97y 0.00** 0.00** 0.00** 0.00** 0.00** 0.001* 0.00** 0.505y 0.302y
1.11 1.74 2.83 3.74 1.75 1.60 2.05 1.92 3.70 1.32 1.35
0.72 0.15 0.00** 0.00** 0.007* 0.029* 0.001* 0.003* 0.00** 0.169y 0.196y
(1.12–3.16) (0.66–2.82) (1.62–3.0) (2.61–5.30) (1.36–2.58) (2.30–5.10) (2.99–5.80) (1.24–2.47) (2.21–4.21) (0.81–1.52) (0.84–1.74)
(0.62–1.99) (0.81–3.73) (1.85–4.31) (2.39–5.87) (1.16–2.65) (1.04–2.47) (1.35–3.09) (1.23–2.99) (2.43–5.64) (0.88–1.99) (0.85–2.15)
IGT, impaired glucose tolerance. * Significant at p < 0.05. ** Significant at p < 0.000. y Not significant.
inadequate fruit intake had three-fold increased risk of diabetes, centrally obese individuals were two-and half times more likely to have diabetes compared with those having normal waist and individuals with hypertension were almost twice as likely to be at risk of diabetes. Likewise individuals of middle socioeconomic status or with hypertriglyceridemia have a similar magnitude of diabetes risk. Similarly older age, central obesity, inadequate fruit intake and hypertriglyceridemia significantly contributed to an increased risk for IGT amongst this urban population.
4. Discussion This cross-sectional study of adequate statistical power and representativeness (n = 1178) was conducted among an apparently urban healthy population in Eastern India, a region with unique lifestyles and culture. A very high age standardized prevalence of diabetes at 11.1% and 6.7% for IGT was reported in this study population. Older age, increased central obesity, inadequate fruit intake, being hypertensive, hypertriglyceridemia and middle socio economic status significantly contributed to
Table 5 Correlates of diabetes and impaired glucose tolerance: final model (backward elimination logistic regression modeling). Variables IGT Age 45–64 65 Central obesity Inadequate fruits intake Hypertriglyceridemia Diabetes Age 45–64 65 SES-middle Central obesity Inadequate fruits intake Hypertriglyceridemia Hypertension
Adjusted odds ratios (AOR)
95% confidence intervals (CI)
2.39 4.62 2.31 1.72 2.38
1.44–3.96 2.41–8.85 1.32–4.04 1.08–2.73 1.52–3.72
2.97 4.79 1.65 2.47 3.31 1.75 1.95
1.88–4.69 2.73–8.40 1.06–2.55 1.66–3.66 2.17–5.05 1.22–2.51 1.34–2.85
IGT, impaired glucose tolerance; SES, socioeconomic status.
increased diabetes risk among this urban population. The prevalence of diabetes is increasing exponentially in India, both in the urban and rural areas [4,28,29]. It has escalated in urban areas to figures now ranging up to 20% from 1.2% in 1971. Recent multicenter study by the Indian Council of Medical Research (ICMR) reported a weighted prevalence of diabetes [from 5.3% to 13.6%] and prediabetes [from 8.1 to 14.6%] across four states of India in 2010 [30]. Similarly an earlier study from urban south India documented a prevalence of 14.3% and 10.6% had IGT in 2006 [31]. Likewise an urban diabetes study from northern India revealed an age standardized prevalence of 11.1% for diabetes and 13.2% for prediabetes in 2009 [32]. Furthermore an earlier National Urban Diabetes Survey showed an overall age standardized prevalence of 12.1% for diabetes and 14.0% for IGT in six large metropolitan cities in 2001 [33]. Similarly a prevalence of 8.2% was reported in urban population in North Eastern India in 1999 [34]. But prevalence of diabetes in India study (PODIS) reported a prevalence of only 5.9% [35] in 2004 and on the contrary a study from Kerala state of South India reported very high prevalence of 19.5% in 2006 [3]. The present study shows that the proportion of diabetes and IGT increases with age. Similar trends were reported by various studies across the Indian subcontinent [36–39]. Present study reveals the prevalence of diabetes (11.7% vs. 10.7%) and IGT (8.9% vs. 8.6%) were higher amongst males than in female subjects, respectively. Similar observations of male preponderances were reported by other studies in India and in migrant Indians in other countries [40,41]. Generalized obesity in this study is significantly associated with diabetes/IGT in univariate analysis but failed to reach statistical significance in multivariable analysis. There is high prevalence of central obesity amongst the study subjects. This could indicate that South Asians have a predisposition to deposit abdominal fat which could be one of the risk factors for the high prevalence of diabetes. The susceptibility of South Asians to central adiposity was highlighted in this study. Studies in India had consistently shown that central obesity was more strongly associated with glucose intolerance than generalized adiposity. Another observation from the study reinforces that physical activity levels are decreasing in South Asians [42]. The present study reveals 46.5% of the subjects with diabetes are physically inactive. Moreover, hypertension in this study is significantly
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associated with diabetes. A similar association was reported by other studies from India/South Asia [30,32,43,44]. Furthermore, this study shows blood cholesterol level is a significant risk factor for diabetes mellitus. Similar findings were reported by few other studies from Asia Pacific [45,46]. Similarly hypertriglyceridemia emerged as a significant risk factor for diabetes and IGT in this population. There are similar observations elsewhere from other Asian populace [47,48]. Our study also revealed low fruit intake is a risk correlate for diabetes/IGT. Observations of inverse relation of fruit intake and Diabetes were noted from adult American population [49,50]. To our knowledge, the present study documents the most recent published prevalence rates for diabetes among an urban population from Eastern India aged 20–80 years. This study estimated very high prevalence of diabetes amongst the urban population in Eastern India, although the self-reported diabetes estimate is lower. All the known classical risk factors for diabetes were also found to be significant predictors of diabetes in the present study. No significant association with smoking was observed in this study following multivariable analyses. However, published reports suggest that predictive risk factors are associated with risk of diabetes mellitus in different combinations in different populations across the country. Similarly a recent multicenter study by Indian council of medical research revealed that age, male sex, family history of diabetes, urban residence, abdominal obesity, generalized obesity, hypertension and income status were significantly associated with diabetes [30]. Strengths of the present study include a large population-based sample, representative sampling methodology and the use of standardized data collection protocols. The survey had a high response rate (98.16%). Our study limitations are probability of a recall bias of the self-reported measures for behavioral risk factors, and possible biases from incomplete data due to non-respondents and missing item response data. Further the study is an observational study and therefore no causal inferences can be made. Longitudinal follow up studies are important to identify risk factors and mediators on the causal pathway of diabetes in this ethnically diverse population for a comprehensive control and prevention of diabetes among the South Asians in general. In conclusion, the observed high prevalence of diabetes and IGT in this urban populace of Eastern India reinforces the need for a comprehensive noncommunicable disease (NCD) prevention and control program reaching out to communities in high risk. This is the first study conducted in one of the poorest states within the fold of an emerging economy, clearly suggesting the ubiquitous nature of the diabetes. More importantly, this study is timely when the recently concluded UN high-level summit on NCD in New York acknowledged that diabetes is one of the four main NCDs contributing to the global mortality, morbidity and disability. Funding sources No funding received. Conflicts of interest None declared. Acknowledgments We thank Dr. K. Revathi Devi, Medical Officer, Sudhir Heart Centre, Berhampur, Orissa, India; Lt. Col (Retd.) Dr. M.S. Panda, Senior Medical Officer, Veterans Health Clinic, Berhampur, Orissa, India; Dr. U.S. Panigrahi, Professor of Psychiatry, Dr. Ram Manohar Lohiya Hospital, New Delhi; Mrs. Pearline Suganthy, Statistician,
Christian Medical College Hospital, Vellore, India; and Mrs. Mohini Sahu, Child Development Project Officer, Berhampur, Orissa, India.
References [1] Sicree R, Shaw J, Zimmet P. Diabetes and impaired glucose tolerance. In: Gan D, editor. Diabetes atlas of International Diabetes Federation. 4th ed., Belgium: International Diabetes Federation; 2009. p. 1–105. [2] Tripathy BB, Panda NC, Tej SC, Sahoo GN, Kar BK. Survey for detection of glycosuria, hyperglycaemia and diabetes mellitus in urban and rural areas of Cuttack district. Journal of the Association of Physicians of India 1971;9: 681–92. [3] Menon VU, Kumar KV, Gilchrist A, Sugathan TN, Sundaram KR, Nair V, et al. Prevalence of known and undetected diabetes and associated risk factors in central Kerala-ADEPS. Diabetes Research and Clinical Practice 2006;74: 289–94. [4] Ramachandran A, Snehalatha C. Current scenario of diabetes in India. Journal of Diabetes 2009;1:18–28. [5] Mohan V, Venkataraman JV, Pradeepa R. Epidemiology of cardiovascular disease in type 2 diabetes: the Indian scenario. Journal of Diabetes Science and Technology 2010;4:158–70. [6] Rao GHR, Thanickachalam S. Coronary artery disease: risk promoters, pathophysiology and prevention, 1st ed., New Delhi: South Asian Society on Atherosclerosis and Thrombosis; 2005. [7] Prasad DS, Kabir Z, Dash AK, Das BC. Cardiovascular risk factors in developing countries: a review of clinico-epidemiological evidence. CVD Prevention and Control 2010;5:115–23. [8] Prasad DS, Kabir Z, Dash AK, Das BC. Childhood cardiovascular risk factors in South Asians: a cause of concern for adult cardiovascular disease epidemic. Annals of Pediatric Cardiology 2011;4:166–71. [9] Mohan V, Rao GHR. Type 2 diabetes in South Asians, 1st ed., New Delhi: South Asian Society on Atherosclerosis and Thrombosis; 2007. [10] Prasad DS, Kabir Z, Dash AK, Das BC. Abdominal obesity, an independent cardiovascular risk factor in Indian subcontinent: a clinico epidemiological evidence summary. Journal of Cardiovascular Disease Research 2011;2: 199–205. [11] Graham AH, McCarthy MI, Mohan V. The genetics of non insulin dependent diabetes mellitus in South India. An overview. Annals of Medicine 1992;24:491–7. [12] Anjana RM, Ali MK, Pradeepa R, Deepa M, Datta M, Unnikrishnan R, et al. The need for obtaining accurate nationwide estimates of diabetes prevalence in India—rationale for a national study on diabetes. Indian Journal of Medical Research 2011;133:369–80. [13] Shah B, Mathur P. Surveillance of cardiovascular disease risk factors in India: the need and scope. Indian Journal of Medical Research 2010;132:634–42. [14] Prasad DS, Kabir Z, Dash AK, Das BC. Coronary risk factors in South Asians: a prevalence study in an urban populace of Eastern India. CVD Prevention and Control 2010;5:125–32. [15] Tripathy BB, Panda NC, Tej SC, Patnaik DK, Samal KC. Prevalence of diabetes mellitus among tribal people of Orissa. Journal of the Association of Physicians of India 1979;27:497–503. [16] Kumar N, Shekhar C, Kumar P, Kundu AS. Kuppuswamy’s socioeconomic status scale-updating for 2007. Indian Journal of Pediatrics 2007;74:1131–2. [17] Luepker RV, Evans A, McKeigue P, Reddy KS. Cardiovascular survey methods, 3rd ed., Geneva: World Health Organization; 2004. [18] Definition and diagnosis of diabetes mellitus and intermediate hyperglycemia: report of a WHO/IDF Consultation. Geneva: World Health Organization; 2006. [19] The Asia Pacific perspective. Redefining obesity and its treatment. Melbourne: World Health Organization/International Association for the Study of Obesity and International Obesity Task Force/International Diabetes Institute World Health Organization, Western Pacific Region; 2000. [20] Chobanian AV, Bakris GL, Black HR, Cushman WC, Green LA, Izzo Jr JL, et al. Seventh report of the Joint National Committee on prevention, detection, evaluation, and treatment of high blood pressure. Hypertension 2003;42:1206–52. [21] 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). Final report. Circulation 2002;106: 3143–421. [22] Trinder P. Determination of glucose in blood using glucose oxidase as an alternative oxygen receptor. Annals of Clinical Biochemistry 1969;6:24–30. [23] Allain CC, Poon LS, Chan CS, Richmond W, Fu PC. Enzymatic determination of total serum cholesterol. Clinical Chemistry 1974;20:470. [24] Fossati P, Principe L. Serum triglycerides determined colorimetrically with an enzyme that produces hydrogen peroxide. Clinical Chemistry 1982;28:2077. [25] Donald WA, Jaysmith S. Six methods for isolating high density lipoprotein compared with the use of the reference for quantifying cholesterol in serum. Clinical Chemistry 1985;31:746. [26] Freidewald WT, Levy RJ, Fredrickson. Estimation of the concentration of LDL cholesterol in plasma without use of the preparative ultracentrifuge. Clinical Chemistry 1972;18:499. [27] Ethical guidelines for biomedical research on human subjects, 3rd ed, New Delhi: The Indian Council of Medical Research; 2006.
D.S. Prasad et al. / Diabetes & Metabolic Syndrome: Clinical Research & Reviews 6 (2012) 96–101 [28] Ramachandran A. Epidemiology of diabetes in India—three decades of research. Journal of the Association of Physicians of India 2005;53:34–8. [29] Mohan V. Why are Indians more prone to diabetes? Journal of the Association of Physicians of India 2004;52:468–74. [30] Anjana R, 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. [31] Mohan V, Deepa M, Deepa R, Shanthirani C, Farooq S, Ganesan A, et al. Secular trends in the prevalence of diabetes and impaired glucose tolerance in urban South Indian Cothe Chennai Urban Rural Epidemiology Study (CURES-17). Diabetologia 2006;49:1175–8. [32] Ravikumar P, Bhansali A, Ravikiran M, Bhansali S, Walia R, Shanmugasundar G, et al. Prevalence and risk factors of diabetes in a community-based study in North India: The Chandigarh Urban Diabetes Study (CUDS). Diabetes and Metabolism 2011;37:216–21. [33] Ramachandran A, Snehalatha C, Kapur A, Vijay V, Mohan V, Das AK, et al. High prevalence of diabetes and impaired glucose tolerance in India: National Urban Diabetes Survey. Diabetologia 2001;44:1094–101. [34] Shah SK, Saikia M, Burman NN, Snehalatha C, Ramachandran A. High prevalence of type 2 diabetes in urban population in north eastern India. International Journal of Diabetes in Developing Countries 1999;19:144–7. [35] Sadikot SM, Nigam A, Das S, Bajaj S, Zargar AH, Prasannakumar KM, et al. The burden of diabetes and impaired glucose tolerance in India using the WHO 1999 criteria: prevalence of diabetes in India study (PODIS). Diabetes Research and Clinical Practice 2004;66:301–7. [36] Ramaiya KL, Kodali VRR, Alberti KG. Epidemiology of diabetes in Asians of the Indian subcontinent. Diabetes/Metabolism Reviews 1991;6:125–46. [37] Abu Sayeed M, Ali L, Hussain MZ, Rumi MA, Banu A, Azad Khan AK. Effect of socioeconomic risk factors on the difference in prevalence of diabetes between rural and urban populations in Bangladesh. Diabetes Care 1997;20:551–5. [38] Shera AS, Rafique G, Khawaja IA, Baqai S, King H. Pakistan National Diabetes Survey: prevalence of glucose intolerance and associated factors in Baluchistan province. Diabetes Research and Clinical Practice 1999; 44:49–58.
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[39] Zargar AH, Khan AK, Masoodi SR, Laway BA, Wani AI, Bashir MI, et al. Prevalence of type 2 diabetes mellitus and impaired glucose tolerance in the Kashmir Valley of the Indian subcontinent. Diabetes Research and Clinical Practice 2000;47:135–6. [40] Dowse GK, Gareeboo H, Zimmet PZ, Alberti KG, Tuomilehto J, Fareed D, et al. High prevalence of NIDDM and impaired glucose tolerance in Indian, Creole, and Chinese Mauritians. Mauritius Noncommunicable Disease Study Group. Diabetes 1990;39:390–6. [41] Ramachandran A. Genetic epidemiology of NIDDM among Asian Indians. Annals of Medicine 1992;24:499–503. [42] Prasad DS, Das BC. Physical inactivity: a cardiovascular risk factor. Indian Journal of Medical Sciences 2009;63:33–42. [43] Shera AS, Rafique G, Khwaja IA, Ara J, Baqai S, King H. Pakistan national diabetes survey: prevalence of glucose intolerance and associated factors in Shikarpur, Sindh Province. Diabetic Medicine 1995;12:1116–21. [44] Mohan V, Shanthirani CS, Deepa R. Glucose intolerance (diabetes and IGT) in a selected South Indian population with special reference to family history, obesity and lifestyle factors—The Chennai Urban Population Study (CUPS 14). Journal of the Association of Physicians of India 2003;51:771–7. [45] Magliano DJ, Barr ELM, Zimmet PZ, Cameron AJ, Dunstan DW, Colagiuri S, et al. Glucose indices, health behaviors, and incidence of diabetes in Australia: the Australian diabetes, obesity and lifestyle study. Diabetes Care 2008;31:267–72. [46] Katulanda P, Constantine GR, Mahesh JG, Sheriff R, Seneviratne RD, Wijeratne S, et al. Prevalence and projections of diabetes and pre-diabetes in adults in Sri Lanka—Sri Lanka Diabetes, Cardiovascular Study (SLDCS). Diabetic Medicine 2008;25:1062–9. [47] Yang W, Xing X, Lin H. Baseline hypertriglyceridemia, a risk factor for noninsulin dependent diabetes mellitus: a 6-year follow-up study of 432 nondiabetics. Zhonghua Nei Ke Za Zhi 1995;34:583–6 [in Chinese]. [48] Tirosh A, Shai I, Bitzur R, Kochba I, Tekes-Manova D, Israeli E, et al. Changes in triglyceride levels over time and risk of type 2 diabetes in young men. Diabetes Care 2008;31:2032–7. [49] Ford ES, Mokdad AH. Fruit and vegetable consumption and diabetes mellitus incidence among U.S. adults. Preventive Medicine 2001;32:33–9. [50] Bazzano LA, Li TY, Joshipura KJ, Hu FB. Intake of fruit, vegetables, and fruit juices and risk of diabetes in women. Diabetes Care 2008;31:1311–7.
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Diabetes & Metabolic Syndrome: Clinical Research & Reviews journal homepage: www.elsevier.com/locate/dsx
Prevalence and risk factors for diabetes and impaired glucose tolerance in Asian Indians: A community survey from urban Eastern India D.S. Prasad a,*, Zubair Kabir b, A.K. Dash c, B.C. Das d a
Sudhir Heart Centre, Berhampur, Orissa, India Research Institute for a Tobacco Free Society, Dublin, Ireland c M.K.C.G. Medical College and Hospital, Berhampur, Orissa, India d Kalinga Institute of Medical Sciences, Bhubaneshwar, Orissa, India b
A R T I C L E I N F O
A B S T R A C T
Keywords: Diabetes IGT Prediabetes CHD Prevalence Predictors South Asians Asian Indians Urban
Objectives: To determine the prevalence of diabetes and impaired glucose tolerance (IGT) and to identify risk factors for the same specific to an underdeveloped urban locale of Eastern India. Methods: Study design. Population based cross-sectional study, with multistage random sampling technique. Setting. Urban city-dwellers in Orissa one of the poorest states of Eastern India bordering a prosperous state of Andhra Pradesh of Southern India. Participants. 1178 adults of 20–80 years age randomly selected from 37 electoral wards of urban populace. Definition and diagnosis of diabetes mellitus and IGT. These were based on a Report of a World Health Organiztion/International Diabetes Federation Consultation of 2006. Main outcome measure. Prevalence and significant risk factors for Diabetes and IGT. Statistical analysis. Both descriptive and multivariable logistic regression analyses. Results: The crude rates of diabetes and IGT in the study population were 15.7% and 8.8%, respectively. Similarly age-standardized rates of diabetes and IGT were 11.1% and 6.7%, respectively. Both diabetes and IGT had shown a male preponderance. Conclusion: Diabetes and IGT were very highly prevalent in this urban populace. Cardiometabolic risk factors like older age, central obesity, inadequate fruit intake, hypertension, hypertriglyceridemia and socio economic status were found to be significant predictors of diabetes in this study. ß 2012 Diabetes India. Published by Elsevier Ltd. All rights reserved.
1. Introduction The Diabetes Atlas of the International Diabetes Federation shows that India has the dubious distinction of leading the world in diabetes prevalence and is home to over 51 million diabetics [1]. Recent surveys indicate that the prevalence of Diabetes in urban Indian adults has increased from 1.2% in 1971 [2] to about 20% at present [3,4]. With its chronic course; we can expect diabetes to have a serious adverse impact on the life expectancy as well as the quality of life [5]. South Asians have an increased prevalence of coronary heart disease (CHD) and diabetes mellitus amongst all the ethnic groups irrespective of their religious affiliations, life style, diet or the country of residence [6–8]. This South Asian Paradox refers to the fact that high prevalence rates of diabetes are seen in
* Corresponding author at: Sudhir Heart Centre, Main road, Dharmanagar, Berhampur 760002, Orissa, India. Tel.: +91 0680 2224278; fax: +91 0680 2225080. E-mail address: [email protected] (D.S. Prasad).
people originating from South Asian nations of Bangladesh, India, Nepal, Pakistan and Sri Lanka despite low rates of obesity as defined by conventional body mass index criteria [9,10]. South Asians also seem to have a peculiar body phenotype known as South Asian phenotype, characterized by increased waist circumference, waist hip ratio, excessive body fat, increased plasma insulin levels, insulin resistance and an atherogenic dyslipidemias with low levels of HDL cholesterol and increased triglyceride levels [9,10]. All these predispose them not only to diabetes but also to premature CHD. There could also be unique genetic markers which make South Asians more susceptible to diabetes [9–11]. Unfortunately, accurate recent nationwide data are lacking in India [12,13], let alone data from specific states within India. Earlier we reported that the state of Orissa, one of the poorest states of Eastern India bordering a prosperous state of Andhra Pradesh of Southern India, showed interesting variations in classical coronary risk factors among an urban population [14]. Such a unique geographic location opens up to cultural and socioeconomic interactions. Diabetes is a lifestyle disease and factors
1871-4021/$ – see front matter ß 2012 Diabetes India. Published by Elsevier Ltd. All rights reserved. http://dx.doi.org/10.1016/j.dsx.2012.05.016
D.S. Prasad et al. / Diabetes & Metabolic Syndrome: Clinical Research & Reviews 6 (2012) 96–101
contributing to changing patterns in Diabetes prevalence in recent years in this geographic region may provide interesting insights into tackling the ever-rising burden of Diabetes in South Asians in general. Interesting to note, one of the earliest studies for prevalence of diabetes in India was conducted from Orissa state [2,15]. The present study aims at updating on changing patterns of Diabetes in a unique urban Eastern Indian population and quantifying factors significantly contributing to any observed underlying pattern.
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2.5. Anthropometric profile
2. Methods
Body weight and height were measured with the subject barefoot and wearing light clothing and were used to calculate the body mass index (BMI). BMI was calculated as weight in kilograms over height in meters squared. Waist circumference was measured at the mid point between the lower limit of the rib cage and upper border of iliac crest. Blood pressure was recorded in a sitting position of the right arm to the nearest 2 mmHg using mercury sphygmomanometer. Two readings were taken 5 min apart and the mean was taken as the blood pressure.
2.1. Study design and setting
2.6. Biochemical analysis
The current study was a population-based survey of cohort under Berhampur Municipal Corporation with an estimated population of 307,724 in 2001, in Orissa one of the poorest states of Eastern India bordering a prosperous state of Andhra Pradesh of Southern India. So the inhabitants here are heterogeneous mix of language, religion, varied socioeconomic statuses and culture.
A fasting blood sample was collected after an overnight fast of at least 10 h for biochemical investigations. In addition, venous plasma glucose 2 h after ingestion of oral glucose load in all subjects except in known diabetics, who underwent 2 h postprandial plasma glucose estimation. All biochemical parameters were performed using enzymatic kits [22–26].
2.2. Sampling design and sample size
2.7. Ethical approval
The study population of 1178 adults of 20–80 years of age was selected using a multi-stage random sampling technique. The sampling frame constituted 37 electoral wards spread across the urban population of Berhampur city of Orissa state in Eastern India.
Institutional ethical committee approval was obtained prior to the start of study and informed consent was taken from all the study subjects [27].
2.3. Survey methods
Significant differences in proportions of potential lifestyle factors by diabetes status were estimated using Pearsons’ Chisquare. Univariate logistic regression and multivariable logistic regression analyses were performed using SAS software (9.1.2, Cary, NC, United States) to predict potential significant predictors of diabetes employing backward elimination modeling technique. Direct age-standardization was performed to calculate rates. Adjustments were done using a standard population from urban National health and family welfare survey 2005 of urban population of Orissa, based on the standard formula given below.
Demographic, socio-economic status as per modified Kuppuswamy scale [16], and self-reported behavioral information (smoking, alcohol, physical activity, fruit intake and diet), objective measures of anthropometry (height, weight, waist and hip circumferences), biochemical (plasma glucose, total cholesterol, triglycerides, HDL cholesterol levels), and Electrocardiographic data were collected from all study participants. Detailed interviews were performed through a previously validated questionnaire based on the guidelines of World Health Organization [17]. History of any chronic illness, in the participant as well as in the family, including diabetes mellitus, hypertension, cerebrovascular accident and coronary heart disease were recorded. Details of study methodology have been published elsewhere [14]. 2.4. Definitions of cardiovascular risk factors Definition and diagnosis of diabetes mellitus and intermediate hyperglycemia was based on a Report of a WHO/IDF Consultation, Geneva [18]. Diabetes was defined as individuals diagnosed by a physician and on glucose-lowering medications (self reported) and/or those who had a fasting plasma glucose level of 126 mg/dl (7.0 mmol/ l) or 2-h plasma glucose 200 mg/dl (11.1 mmol/l) [18]. Impaired glucose tolerance (IGT) was defined as fasting plasma glucose of <7.0 mmol/l (126 mg/dl) and 2–h plasma glucose of 7.8 and <11.1 mmol/l (140 mg/dl and 200 mg/dl) [18]. Obesity and overweight was based on the revised criteria specific for Asian Pacific populations [19]. Hypertension definition was based on the Seventh Report of the Joint National Committee on Prevention, Detection, Evaluation, and Treatment of High Blood Pressure [20]. Definition of Dyslipidemia was based on the Third Report of the National Cholesterol Education Program (NCEP) [21].
2.8. Statistical analysis
2.9. Direct method
SR ¼
SUMðrio P i Þ SUM Pi
where SR is the age-standardized rate for the population being studied, rio is the age-group specific rate for age group i in the population being studied, Pi is the population of age group i in the standard population 3. Results This was one of the large community based surveys done from Eastern India for ascertaining the prevalence of cardiovascular risk factors with the aim of providing the baseline information on prevalence rates for intervention programmes to the policy planners. A total of eleven hundred seventy eight subjects participated in the study (185 had diabetes; 104 were having IGT and the remaining 889 individuals were considered normal). 3.1. Demographic and clinical profile of subjects The sex distribution in this study was equal. The age of the subjects ranged from 20–80 years, with a mean age of 47 years in
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Table 1 Prevalence of diabetes and impaired glucose tolerance in the study population. Diabetes
n
IGT a
No. of cases
Prevalence
15.7 (13.7–17.8) 17.8 (14.8–21.0) 13.8 (11.0–16.5)
Total
1178
185
Male
590
105
Female
588
80
Age-std
b
11.1 (9.6–12.7) 11.7 (9.5–14.0) 10.7 (8.4–12.9)
No. of cases
Prevalencea
Age-stdb
104
8.8 (7.3–10.5) 8.9 (6.8–11.5) 8.6 (6.5–11.1)
6.7 (5.4–8.0) 6.9 (4.8–8.8) 6.8 (4.9–8.7)
53 51
Age-std, age standardized; IGT, impaired glucose tolerance. a Estimated taking into account the stratified sampling procedure. b Age standardized using the urban population.
Table 2 Age and gender specific prevalence of diabetes and impaired glucose tolerance. Age group (years)
Total group
Diabetes IGT
Diabetes
20–30 31–40 41–50 51–60 61–70 71–80
IGT
Men
Women
Men
Women
n
%
n
%
n
%
n
%
n
%
n
%
4 19 35 69 43 15
2.2 6.6 12.2 28.8 33.1 27.8
3 13 33 29 16 10
1.7 4.5 11.5 12.1 12.3 18.5
2 9 15 42 26 11
2.4 7.8 10.9 30.4 33.8 28.2
2 10 20 27 17 4
2.1 5.8 13.4 26.5 32.1 26.7
2 5 16 14 9 7
2.4 4.3 11.7 10.1 11.7 17.9
1 8 17 15 7 3
1.0 4.7 11.4 14.7 13.2 20.0
IGT, impaired glucose tolerance.
males (SD = 14.46) and 44.21 years in females (SD = 13.26). The detailed age-distribution is shown in Table 2. 3.2. Prevalence of diabetes/IGT Table 1 shows both crude and age-standardized rates (with 95% CI) of both diabetes and IGT. The crude (unadjusted) rates of diabetes and IGT are 15.7% and 8.8%, respectively (significantly higher among males in both the disease conditions – 17.8% and 8.9%, respectively). Age-standardized rates are also similar: 11.1% had diabetes mellitus and 6.7% were having IGT, with preponderance among males (11.7% and 6.9%, respectively (Table 1). Fig. 1 shows the increase in both diabetes mellitus and IGT with increasing age. Detailed clinical and demographic characteristics of study population are shown in Table 3. Both diabetes mellitus and IGT were significantly more common in males, smokers, in obese individuals, in those with lower physical activity levels and having lower intake of fruits, and also in individuals with hypercholesterolemia, hypertriglyceridemia, with low HDL levels as well as in hypertensives (Table 3). 3.3. Significant risk factors for diabetes/IGT Detailed correlates of diabetes mellitus/IGT in Univariate Analysis for the base model (Table 4) and the final model of
Fig. 1. Age and gender specific distribution of diabetes and IGT subjects.
multivariate logistic regression method showing significant predictors of diabetes and IGT are summarized in Table 5. Older age, central obesity, inadequate fruit intake, being hypertensive, hypertriglyceridemia and middle socio economic status significantly contributed to an increased Diabetes risk among this urban population. Those aged 65 years and above are at five-fold increased risk of diabetes; individuals with Table 3 Clinical and demographic characteristics for diabetes, IGT and normal. Variable
Diabetes
IGT
Normal
N Mean age (years)
185 55.3 (11.4)**
104 53.0 (12.0)**
889 43.1 (13.5)
Gender Male Female
105 (56.8)* 80 (43.2)
Education Illiterate Elementary High school College Socio economic status Lower Middle Upper Smoking Physical in activity Low/no fruits intake Hypertension General obesity Central obesity Hypercholesterolemia Hypertriglyceridemia High LDL Low HDL
18 52 49 66
18 152 15 50 86 152 121 112 138 60 112 47 91
77 (51.0)y 65 (49.0)
432 (48.6) 457 (51.4)
(9.7)y (28.1) (26.5) (35.7)
15 22 27 40
(14.4)y (21.1) (26.0) (38.5)
100 220 235 334
(11.2) (24.7) (26.4) (37.6)
(9.7)* (82.2) (8.1) (27.0)y (46.5)** (82.2)** (65.4)** (60.7)** (74.6)** (32.4)* (60.7)** (25.4)y (49.2)y
15 74 15 32 45 70 48 66 75 34 64 28 55
(14.4)y (71.2) (14.4) (30.8)y (43.3)** (67.3)* (46.2)** (63.5)** (72.1)** (32.7)* (61.5)** (26.9)y (52.9)y
14 656 85 238 269 499 262 338 363 179 268 190 407
(16.6)y (73.8) (9.6) (26.8) (30.3) (56.1) (29.5) (38.0) (40.8) (20.1) (30.1) (21.4) (45.8)
IGT, impaired glucose tolerance. Number in parenthesis indicate percentage. * Significant at p < 0.05. ** Significant at p < 0.000. y Not significant.
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Table 4 Correlates of diabetes/impaired glucose tolerance: univariate analysis. Diabetes
Variable
IGT
Adjusted OR (95% C.I)
Sig
Adjusted OR (95% C.I)
Sig
Age 45–64 65
5.12 (3.36–7.79) 7.81 (4.68–13.02)
0.004* 0.00**
3.45 (2.12–5.61) 5.91 (3.18–10.97)
0.00** 0.00**
Socio economic status Middle High General obesity Central obesity Physical in activity Fruits intake Hypertension Hypercholesteromia Hypertrigleceridemia Low HDL High LDL
1.89 1.36 2.23 3.72 1.87 3.43 4.16 1.78 3.05 1.11 1.21
0.02* 0.97y 0.00** 0.00** 0.00** 0.00** 0.00** 0.001* 0.00** 0.505y 0.302y
1.11 1.74 2.83 3.74 1.75 1.60 2.05 1.92 3.70 1.32 1.35
0.72 0.15 0.00** 0.00** 0.007* 0.029* 0.001* 0.003* 0.00** 0.169y 0.196y
(1.12–3.16) (0.66–2.82) (1.62–3.0) (2.61–5.30) (1.36–2.58) (2.30–5.10) (2.99–5.80) (1.24–2.47) (2.21–4.21) (0.81–1.52) (0.84–1.74)
(0.62–1.99) (0.81–3.73) (1.85–4.31) (2.39–5.87) (1.16–2.65) (1.04–2.47) (1.35–3.09) (1.23–2.99) (2.43–5.64) (0.88–1.99) (0.85–2.15)
IGT, impaired glucose tolerance. * Significant at p < 0.05. ** Significant at p < 0.000. y Not significant.
inadequate fruit intake had three-fold increased risk of diabetes, centrally obese individuals were two-and half times more likely to have diabetes compared with those having normal waist and individuals with hypertension were almost twice as likely to be at risk of diabetes. Likewise individuals of middle socioeconomic status or with hypertriglyceridemia have a similar magnitude of diabetes risk. Similarly older age, central obesity, inadequate fruit intake and hypertriglyceridemia significantly contributed to an increased risk for IGT amongst this urban population.
4. Discussion This cross-sectional study of adequate statistical power and representativeness (n = 1178) was conducted among an apparently urban healthy population in Eastern India, a region with unique lifestyles and culture. A very high age standardized prevalence of diabetes at 11.1% and 6.7% for IGT was reported in this study population. Older age, increased central obesity, inadequate fruit intake, being hypertensive, hypertriglyceridemia and middle socio economic status significantly contributed to
Table 5 Correlates of diabetes and impaired glucose tolerance: final model (backward elimination logistic regression modeling). Variables IGT Age 45–64 65 Central obesity Inadequate fruits intake Hypertriglyceridemia Diabetes Age 45–64 65 SES-middle Central obesity Inadequate fruits intake Hypertriglyceridemia Hypertension
Adjusted odds ratios (AOR)
95% confidence intervals (CI)
2.39 4.62 2.31 1.72 2.38
1.44–3.96 2.41–8.85 1.32–4.04 1.08–2.73 1.52–3.72
2.97 4.79 1.65 2.47 3.31 1.75 1.95
1.88–4.69 2.73–8.40 1.06–2.55 1.66–3.66 2.17–5.05 1.22–2.51 1.34–2.85
IGT, impaired glucose tolerance; SES, socioeconomic status.
increased diabetes risk among this urban population. The prevalence of diabetes is increasing exponentially in India, both in the urban and rural areas [4,28,29]. It has escalated in urban areas to figures now ranging up to 20% from 1.2% in 1971. Recent multicenter study by the Indian Council of Medical Research (ICMR) reported a weighted prevalence of diabetes [from 5.3% to 13.6%] and prediabetes [from 8.1 to 14.6%] across four states of India in 2010 [30]. Similarly an earlier study from urban south India documented a prevalence of 14.3% and 10.6% had IGT in 2006 [31]. Likewise an urban diabetes study from northern India revealed an age standardized prevalence of 11.1% for diabetes and 13.2% for prediabetes in 2009 [32]. Furthermore an earlier National Urban Diabetes Survey showed an overall age standardized prevalence of 12.1% for diabetes and 14.0% for IGT in six large metropolitan cities in 2001 [33]. Similarly a prevalence of 8.2% was reported in urban population in North Eastern India in 1999 [34]. But prevalence of diabetes in India study (PODIS) reported a prevalence of only 5.9% [35] in 2004 and on the contrary a study from Kerala state of South India reported very high prevalence of 19.5% in 2006 [3]. The present study shows that the proportion of diabetes and IGT increases with age. Similar trends were reported by various studies across the Indian subcontinent [36–39]. Present study reveals the prevalence of diabetes (11.7% vs. 10.7%) and IGT (8.9% vs. 8.6%) were higher amongst males than in female subjects, respectively. Similar observations of male preponderances were reported by other studies in India and in migrant Indians in other countries [40,41]. Generalized obesity in this study is significantly associated with diabetes/IGT in univariate analysis but failed to reach statistical significance in multivariable analysis. There is high prevalence of central obesity amongst the study subjects. This could indicate that South Asians have a predisposition to deposit abdominal fat which could be one of the risk factors for the high prevalence of diabetes. The susceptibility of South Asians to central adiposity was highlighted in this study. Studies in India had consistently shown that central obesity was more strongly associated with glucose intolerance than generalized adiposity. Another observation from the study reinforces that physical activity levels are decreasing in South Asians [42]. The present study reveals 46.5% of the subjects with diabetes are physically inactive. Moreover, hypertension in this study is significantly
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associated with diabetes. A similar association was reported by other studies from India/South Asia [30,32,43,44]. Furthermore, this study shows blood cholesterol level is a significant risk factor for diabetes mellitus. Similar findings were reported by few other studies from Asia Pacific [45,46]. Similarly hypertriglyceridemia emerged as a significant risk factor for diabetes and IGT in this population. There are similar observations elsewhere from other Asian populace [47,48]. Our study also revealed low fruit intake is a risk correlate for diabetes/IGT. Observations of inverse relation of fruit intake and Diabetes were noted from adult American population [49,50]. To our knowledge, the present study documents the most recent published prevalence rates for diabetes among an urban population from Eastern India aged 20–80 years. This study estimated very high prevalence of diabetes amongst the urban population in Eastern India, although the self-reported diabetes estimate is lower. All the known classical risk factors for diabetes were also found to be significant predictors of diabetes in the present study. No significant association with smoking was observed in this study following multivariable analyses. However, published reports suggest that predictive risk factors are associated with risk of diabetes mellitus in different combinations in different populations across the country. Similarly a recent multicenter study by Indian council of medical research revealed that age, male sex, family history of diabetes, urban residence, abdominal obesity, generalized obesity, hypertension and income status were significantly associated with diabetes [30]. Strengths of the present study include a large population-based sample, representative sampling methodology and the use of standardized data collection protocols. The survey had a high response rate (98.16%). Our study limitations are probability of a recall bias of the self-reported measures for behavioral risk factors, and possible biases from incomplete data due to non-respondents and missing item response data. Further the study is an observational study and therefore no causal inferences can be made. Longitudinal follow up studies are important to identify risk factors and mediators on the causal pathway of diabetes in this ethnically diverse population for a comprehensive control and prevention of diabetes among the South Asians in general. In conclusion, the observed high prevalence of diabetes and IGT in this urban populace of Eastern India reinforces the need for a comprehensive noncommunicable disease (NCD) prevention and control program reaching out to communities in high risk. This is the first study conducted in one of the poorest states within the fold of an emerging economy, clearly suggesting the ubiquitous nature of the diabetes. More importantly, this study is timely when the recently concluded UN high-level summit on NCD in New York acknowledged that diabetes is one of the four main NCDs contributing to the global mortality, morbidity and disability. Funding sources No funding received. Conflicts of interest None declared. Acknowledgments We thank Dr. K. Revathi Devi, Medical Officer, Sudhir Heart Centre, Berhampur, Orissa, India; Lt. Col (Retd.) Dr. M.S. Panda, Senior Medical Officer, Veterans Health Clinic, Berhampur, Orissa, India; Dr. U.S. Panigrahi, Professor of Psychiatry, Dr. Ram Manohar Lohiya Hospital, New Delhi; Mrs. Pearline Suganthy, Statistician,
Christian Medical College Hospital, Vellore, India; and Mrs. Mohini Sahu, Child Development Project Officer, Berhampur, Orissa, India.
References [1] Sicree R, Shaw J, Zimmet P. Diabetes and impaired glucose tolerance. In: Gan D, editor. Diabetes atlas of International Diabetes Federation. 4th ed., Belgium: International Diabetes Federation; 2009. p. 1–105. [2] Tripathy BB, Panda NC, Tej SC, Sahoo GN, Kar BK. Survey for detection of glycosuria, hyperglycaemia and diabetes mellitus in urban and rural areas of Cuttack district. Journal of the Association of Physicians of India 1971;9: 681–92. [3] Menon VU, Kumar KV, Gilchrist A, Sugathan TN, Sundaram KR, Nair V, et al. Prevalence of known and undetected diabetes and associated risk factors in central Kerala-ADEPS. Diabetes Research and Clinical Practice 2006;74: 289–94. [4] Ramachandran A, Snehalatha C. Current scenario of diabetes in India. Journal of Diabetes 2009;1:18–28. [5] Mohan V, Venkataraman JV, Pradeepa R. Epidemiology of cardiovascular disease in type 2 diabetes: the Indian scenario. Journal of Diabetes Science and Technology 2010;4:158–70. [6] Rao GHR, Thanickachalam S. Coronary artery disease: risk promoters, pathophysiology and prevention, 1st ed., New Delhi: South Asian Society on Atherosclerosis and Thrombosis; 2005. [7] Prasad DS, Kabir Z, Dash AK, Das BC. Cardiovascular risk factors in developing countries: a review of clinico-epidemiological evidence. CVD Prevention and Control 2010;5:115–23. [8] Prasad DS, Kabir Z, Dash AK, Das BC. Childhood cardiovascular risk factors in South Asians: a cause of concern for adult cardiovascular disease epidemic. Annals of Pediatric Cardiology 2011;4:166–71. [9] Mohan V, Rao GHR. Type 2 diabetes in South Asians, 1st ed., New Delhi: South Asian Society on Atherosclerosis and Thrombosis; 2007. [10] Prasad DS, Kabir Z, Dash AK, Das BC. Abdominal obesity, an independent cardiovascular risk factor in Indian subcontinent: a clinico epidemiological evidence summary. Journal of Cardiovascular Disease Research 2011;2: 199–205. [11] Graham AH, McCarthy MI, Mohan V. The genetics of non insulin dependent diabetes mellitus in South India. An overview. Annals of Medicine 1992;24:491–7. [12] Anjana RM, Ali MK, Pradeepa R, Deepa M, Datta M, Unnikrishnan R, et al. The need for obtaining accurate nationwide estimates of diabetes prevalence in India—rationale for a national study on diabetes. Indian Journal of Medical Research 2011;133:369–80. [13] Shah B, Mathur P. Surveillance of cardiovascular disease risk factors in India: the need and scope. Indian Journal of Medical Research 2010;132:634–42. [14] Prasad DS, Kabir Z, Dash AK, Das BC. Coronary risk factors in South Asians: a prevalence study in an urban populace of Eastern India. CVD Prevention and Control 2010;5:125–32. [15] Tripathy BB, Panda NC, Tej SC, Patnaik DK, Samal KC. Prevalence of diabetes mellitus among tribal people of Orissa. Journal of the Association of Physicians of India 1979;27:497–503. [16] Kumar N, Shekhar C, Kumar P, Kundu AS. Kuppuswamy’s socioeconomic status scale-updating for 2007. Indian Journal of Pediatrics 2007;74:1131–2. [17] Luepker RV, Evans A, McKeigue P, Reddy KS. Cardiovascular survey methods, 3rd ed., Geneva: World Health Organization; 2004. [18] Definition and diagnosis of diabetes mellitus and intermediate hyperglycemia: report of a WHO/IDF Consultation. Geneva: World Health Organization; 2006. [19] The Asia Pacific perspective. Redefining obesity and its treatment. Melbourne: World Health Organization/International Association for the Study of Obesity and International Obesity Task Force/International Diabetes Institute World Health Organization, Western Pacific Region; 2000. [20] Chobanian AV, Bakris GL, Black HR, Cushman WC, Green LA, Izzo Jr JL, et al. Seventh report of the Joint National Committee on prevention, detection, evaluation, and treatment of high blood pressure. Hypertension 2003;42:1206–52. [21] 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). Final report. Circulation 2002;106: 3143–421. [22] Trinder P. Determination of glucose in blood using glucose oxidase as an alternative oxygen receptor. Annals of Clinical Biochemistry 1969;6:24–30. [23] Allain CC, Poon LS, Chan CS, Richmond W, Fu PC. Enzymatic determination of total serum cholesterol. Clinical Chemistry 1974;20:470. [24] Fossati P, Principe L. Serum triglycerides determined colorimetrically with an enzyme that produces hydrogen peroxide. Clinical Chemistry 1982;28:2077. [25] Donald WA, Jaysmith S. Six methods for isolating high density lipoprotein compared with the use of the reference for quantifying cholesterol in serum. Clinical Chemistry 1985;31:746. [26] Freidewald WT, Levy RJ, Fredrickson. Estimation of the concentration of LDL cholesterol in plasma without use of the preparative ultracentrifuge. Clinical Chemistry 1972;18:499. [27] Ethical guidelines for biomedical research on human subjects, 3rd ed, New Delhi: The Indian Council of Medical Research; 2006.
D.S. Prasad et al. / Diabetes & Metabolic Syndrome: Clinical Research & Reviews 6 (2012) 96–101 [28] Ramachandran A. Epidemiology of diabetes in India—three decades of research. Journal of the Association of Physicians of India 2005;53:34–8. [29] Mohan V. Why are Indians more prone to diabetes? Journal of the Association of Physicians of India 2004;52:468–74. [30] Anjana R, 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. [31] Mohan V, Deepa M, Deepa R, Shanthirani C, Farooq S, Ganesan A, et al. Secular trends in the prevalence of diabetes and impaired glucose tolerance in urban South Indian Cothe Chennai Urban Rural Epidemiology Study (CURES-17). Diabetologia 2006;49:1175–8. [32] Ravikumar P, Bhansali A, Ravikiran M, Bhansali S, Walia R, Shanmugasundar G, et al. Prevalence and risk factors of diabetes in a community-based study in North India: The Chandigarh Urban Diabetes Study (CUDS). Diabetes and Metabolism 2011;37:216–21. [33] Ramachandran A, Snehalatha C, Kapur A, Vijay V, Mohan V, Das AK, et al. High prevalence of diabetes and impaired glucose tolerance in India: National Urban Diabetes Survey. Diabetologia 2001;44:1094–101. [34] Shah SK, Saikia M, Burman NN, Snehalatha C, Ramachandran A. High prevalence of type 2 diabetes in urban population in north eastern India. International Journal of Diabetes in Developing Countries 1999;19:144–7. [35] Sadikot SM, Nigam A, Das S, Bajaj S, Zargar AH, Prasannakumar KM, et al. The burden of diabetes and impaired glucose tolerance in India using the WHO 1999 criteria: prevalence of diabetes in India study (PODIS). Diabetes Research and Clinical Practice 2004;66:301–7. [36] Ramaiya KL, Kodali VRR, Alberti KG. Epidemiology of diabetes in Asians of the Indian subcontinent. Diabetes/Metabolism Reviews 1991;6:125–46. [37] Abu Sayeed M, Ali L, Hussain MZ, Rumi MA, Banu A, Azad Khan AK. Effect of socioeconomic risk factors on the difference in prevalence of diabetes between rural and urban populations in Bangladesh. Diabetes Care 1997;20:551–5. [38] Shera AS, Rafique G, Khawaja IA, Baqai S, King H. Pakistan National Diabetes Survey: prevalence of glucose intolerance and associated factors in Baluchistan province. Diabetes Research and Clinical Practice 1999; 44:49–58.
101
[39] Zargar AH, Khan AK, Masoodi SR, Laway BA, Wani AI, Bashir MI, et al. Prevalence of type 2 diabetes mellitus and impaired glucose tolerance in the Kashmir Valley of the Indian subcontinent. Diabetes Research and Clinical Practice 2000;47:135–6. [40] Dowse GK, Gareeboo H, Zimmet PZ, Alberti KG, Tuomilehto J, Fareed D, et al. High prevalence of NIDDM and impaired glucose tolerance in Indian, Creole, and Chinese Mauritians. Mauritius Noncommunicable Disease Study Group. Diabetes 1990;39:390–6. [41] Ramachandran A. Genetic epidemiology of NIDDM among Asian Indians. Annals of Medicine 1992;24:499–503. [42] Prasad DS, Das BC. Physical inactivity: a cardiovascular risk factor. Indian Journal of Medical Sciences 2009;63:33–42. [43] Shera AS, Rafique G, Khwaja IA, Ara J, Baqai S, King H. Pakistan national diabetes survey: prevalence of glucose intolerance and associated factors in Shikarpur, Sindh Province. Diabetic Medicine 1995;12:1116–21. [44] Mohan V, Shanthirani CS, Deepa R. Glucose intolerance (diabetes and IGT) in a selected South Indian population with special reference to family history, obesity and lifestyle factors—The Chennai Urban Population Study (CUPS 14). Journal of the Association of Physicians of India 2003;51:771–7. [45] Magliano DJ, Barr ELM, Zimmet PZ, Cameron AJ, Dunstan DW, Colagiuri S, et al. Glucose indices, health behaviors, and incidence of diabetes in Australia: the Australian diabetes, obesity and lifestyle study. Diabetes Care 2008;31:267–72. [46] Katulanda P, Constantine GR, Mahesh JG, Sheriff R, Seneviratne RD, Wijeratne S, et al. Prevalence and projections of diabetes and pre-diabetes in adults in Sri Lanka—Sri Lanka Diabetes, Cardiovascular Study (SLDCS). Diabetic Medicine 2008;25:1062–9. [47] Yang W, Xing X, Lin H. Baseline hypertriglyceridemia, a risk factor for noninsulin dependent diabetes mellitus: a 6-year follow-up study of 432 nondiabetics. Zhonghua Nei Ke Za Zhi 1995;34:583–6 [in Chinese]. [48] Tirosh A, Shai I, Bitzur R, Kochba I, Tekes-Manova D, Israeli E, et al. Changes in triglyceride levels over time and risk of type 2 diabetes in young men. Diabetes Care 2008;31:2032–7. [49] Ford ES, Mokdad AH. Fruit and vegetable consumption and diabetes mellitus incidence among U.S. adults. Preventive Medicine 2001;32:33–9. [50] Bazzano LA, Li TY, Joshipura KJ, Hu FB. Intake of fruit, vegetables, and fruit juices and risk of diabetes in women. Diabetes Care 2008;31:1311–7.