Appraisal of risk factors for diabetes mellitus type 2 in central Indian population: a case control study

Transcription

1 Physical Anthropology Appraisal of risk factors for diabetes mellitus type 2 in central Indian population: a case control study Rama Lakshmi G. 1, Bandyopadhyay S.S. 1, Bhaskar L.V.K.S. 2, Sharma Madhubala 1, Rao Raghavendra V. 1,3 * Abstract Background: Diabetes mellitus is characterized by high levels of blood glucose, late onset of disease and associated with serious complications. Genetic and environmental risk factors are known to exist and the importance of elucidating these risk factors in different populations will be of importance in view of the ultimate goal of personalized medicine. The objective was to assess the impact of risk factors such as Body Mass Index (BMI), Waist Circumference (WC), and Waist to Hip Ratio (WHR) on diabetic and control subjects using statistical tools in a specific geographical category of Indian population. Methods: 92 diabetic patients and 123 controls living in urban areas of Nagpur city, Maharashtra, India, were selected for a case control study. BMI, WC, WHR, fasting glucose, blood pressure (systolic and diastolic) and skinfold thickness at four points were assessed. For logical interpretation, the data have been subjected to statistical analysis such as risk ratio, odds ratio and chi square. Multivariate regression analysis was carried out to adjust for age and sex. Results: The plasma glucose, HDL cholesterol and Waist to hip ratio are significant in between control and diabetes subjects even after adjusting to age and sex. Conclusion: Comparison of diabetic and control showed that the central obesity (WHR) and HDL were most important risk factors for type 2 diabetes in the studied population. Key Words Body Mass Index, Waist Circumference, Waist to Hip Ratio, diabetes mellitus type 2. Introduction Diabetes Mellitus is a major global health problem (Wild et al 2004). Based on the Diabetes atlas 2009 published by the International Diabetic Federation, the prevalence of type 2 diabetes in Indian population is estimated to be around 51 million and India is regarded as the diabetic capital of the world (Unwin et al 2010). Type 2 diabetes is a complex metabolic disease, primarily characterised by insulin resistance, relative insulin deficiency and hyperglycemia (Kraegen et al 2008) and leading to micro vascular diseases, blindness, nerve damage, 1 Anthropological Survey of India, Central Regional Centre, S. Hills, Nagpur, India 2 Department of Biomedical Sciences, Sri Ramachandra University, Chennai. India 3 Formerly, Director In-Charge, Anthropological Survey of India. Presently Professor, Department of Anthropology, Delhi University, North campus. Delhi, India * Corresponding Address: Prof. V. R. Rao, Department of Anthropology, Delhi University, North Campus Delhi Tel: ; Fax: ; Mobile: ; e.mail: drraovr@yahoo.com 103

2 arthrosclerosis and renal complications (McAlpine et al 2005, van Dieren et al 2010). The cause of Diabetes Mellitus is poorly understood. Changes in diet and life style due to rapid economic development are fore most among the principle drives of diabetes in developing and developed countries. Barring the environmental impact, genetic component plays a vital role for the development of diabetes (Mohan 2004). Though, obesity is a leading risk factor for late onset of diabetes, life style habits play a vital role to become obese (Gupta and Agrawal 2009). Obesity results from an imbalance between energy expenditure and intake which is modulated by genetic predisposition. International Diabetes Congress in Helsinki, Finland reported that obesity is the most preventable and important risk factor for Diabetes Mellitus Type 2 (WCPD 2008). Almost four out of five people who are newly diagnosed with diabetes are obese (Kissebah et al 1989). The risk for diabetes has been reported to be about 2-folds in the mildly obese, 5 fold in the moderately obese and 10 folds in morbidly obese persons (Scheen and Lefebvre 1998). Increasing general obesity or central obesity is known to predispose individuals for insulin resistance. The prevalence of diabetes is 2.9 times higher in over weight (BMI 27.8 in males and 27.3 in women) than in normal weight subjects of 20 to 75 years of age (Burton et al 1985). Abdominal fat deposition is hormonally controlled by secretion of adipokinase which may impair glucose tolerance. Abdominal obesity, measured by an elevated WHR is shown to be a strong risk factor for Diabetes Mellitus Type 2 (Bray et al 2008, Lahti-Koski et al 2000, Miljkovic-Gacic et al 2008). It has also been noted that for a given BMI, Asian Indian have higher fat percentage compared with Caucasian subjects (Banerji et al 1999). High proportion of upper-body fat or abdominal fat independent of overall obesity is recognized as an important component in the insulin resistance linked to obesity and Diabetes Mellitus Type 2 (Goodpaster et al 2000). Few studies have suggested less influence of fat distribution on carbohydrate and fat metabolism for African Americans where as other studies observed differences in the association among white people (Karter et al 1996). Asian Indians (living in the United States) are more susceptible of developing abdominal adiposity and insulin resistance, which might account for the excessive morbidity and mortality from diabetes in this population (Chandalia et al 1999). But studies on adiposity and hyperglycemic condition from Indian population are scanty. World Health Organization report predicted 79.4 million diabetic patients by the year 2030 (Wild et al 2004). Diabetes Mellitus Type 2 differs in Indians from that of Europeans in three aspects namely onset is at early age (Mohan et al 2007), obesity is less common (Joshi 2003) and genetic factors appears to be stronger (Ramprasad et al 2007, Rema and Pradeepa 2007). The standard epidemiological translations of BMI, WC and WHR have been shown to be associated with Type 2 Diabetes. From clinical perspective, central obesity (approximated by waist circumference or waist/hip ratio) is known to provide more information than general Body Mass Index (Vazquez et al 2007). The standard epidemiological translation of BMI, WC and WHR are important tools used for anthropometric measures. Waist circumference and waist to hip ratio have been used as measures of central obesity, whereas BMI has been used as a measure of general obesity (Molarius and Seidell 1998). Taking into consideration some of the said facts, this research study was planned to evaluate the influence of various socio biological parameters for the development of Diabetes, through a case control study.our objective is to test the hypothesis that increase of BMI, WHR and WC values enhance the chance of occurrence of diabetes in these subjects. To evaluate this hypothesis, we attempted to throw light on the role of distribution of fat measured through skin fold thickness at four sites and circumferences at two sites. Subjects and Method Subjects The criteria of world health organization (WHO 1999) (fasting plasma glucose 126 mg/dl) was followed to differentiate diabetic and control subjects. A total of 92 Diabetes Mellitus subjects (48 males and 44 females) and 123 controls (74 males and 49 females) were the subjects of the study. The diabetic patients were selected from the outpatient department of Central government polyclinic, Nagpur city, Maharashtra, India. The age, sex, ethnic group matched individuals who are willingly opted to become subjects of the study with normal fasting glucose values were considered as controls. The case history of diabetes was verified from the record of diabetic patients. The persons with communicable as well as non communicable diseases were excluded from the control 104

3 subjects. Methods Anthropometric measurements were taken without heavy outdoor clothing. Stature was measured to the nearest millimeter using an Anthropometric rod. Weight was measured on a pre-standardized body weighing machine. Skinfold measurements were taken from subjects using Harpendent skinfold caliper with a jaw pressure of 10 gm/sq.mm. BMI was calculated using the formula, weight (kg) divided by height (meter square). The hip circumference was measured at the maximum circumference around the hips and the waist circumference was obtained at the level of the umbilicus using a steel tape. The general obesity was defined based on the revised standards for adult obesity in Asia and India, proposed by International ObesityTask Force (Low et al 2009). The cut off values for WHR and WC for men are 0.9 and 85cm and that for female 0.8 and 80 cm are considered as normal (Snehalatha et al 2003). Blood pressure was recorded in the sitting position in the right arm to the nearest 2 mm/ Hg with a mercury sphygmomanometer (Diamond Deluxe, Industrial Electronic products, Electronic Co. Pune, India). Two readings were taken at 5 minutes apart and the mean of the two was recorded as the final blood pressure reading of the individual. The blood sugar was determined by finger pricking method using a one touch glucose meter (Life Scan, Johnson and Johnson glucometer) after an overnight fast. Means± SD and proportions of baseline characteristics were compared using t-tests. Multivariate regression analysis was carried out to adjust for age and sex. Age and sex adjusted means of p-glucose, blood pressure, and lipids and skinfold thickness by category of diabetes, BMI, and waist circumference were calculated in general linear models. Diabetes status and obesity measurements (diabetes, waist circumference and BMI) were analysed as independent variables in general linear models with each of the metabolic risk factors as dependent variables. All statistical analyses were performed with SPSS statistical software version 16.0 (SPSS Inc, Chicago, IL, USA) for Windows. p 0.05 (two-tailed) was considered statistically significant. Results The basic demographic information of these subjects was summarised in Table 1. The mean age and standard deviation (SD) was 55.22±8.12yrs for the diabetes cases, 56.59±9.35yrs for the control subjects (p = 0.260). The age at onset of diabetes is ± 9.30 years for males and ± 7.64 years for females. Our study revealed that percent diabetic patients were non vegetarian. The enquiry of disease profile from diabetic patients showed that percent have Neuro and cardiopathic problems followed by percent who suffered from arthritis and spondylitis. In the present study there are no differences in age, SBP, DBP, fasting glucose, waist circumference and different forms cholesterol in between male and female subjects (Table 1). The BMI, waisthip ratio and skinfold variable have shown significant differences between male and female subjects. The plasma glucose, HDL cholesterol and Waist to hip ratio are significant in between control and diabetes subjects even after adjusting to age and sex. The mean values of the other parameters have not shown any significant difference between diabetes and control groups (Table 2). General obese and non-obese groups that defined by BMI were showing statistically significant differences in SBP, DBP, BMI, waist circumference and Skinfold thickness at triceps, biceps and Scapular even after adjusting to age and sex. LDL cholesterol in obese and non obese is statically significant, but the significance vanished after adjusting to age and sex. The mean waist circumference between the male (88.15±12.46) and females (87.28±9.08) is not statistically significant (p=0.555). Similarly the WC is not significant in between diabetics and control subjects (p= 0.741). The central obesity that defined by the WC has revealed statistically significant differences in the mean values of BMI, WC, waist to hip ratio and all skinfold thickness parameters (Triceps, biceps, abdomen and scapular) among obese and non-obese groups (Table 3). After categorization of BMI (>25 and <25), WHR (<.9 and >.9 for males, <.8 and >.8 females), WC (<85cm and >85 cm males, <80 cm and >80 cm females) in control and diabetics the risk ratio and odds ratios were also not supported the association of these variables with diabetes. For BMI the risk ratio is (95% CI is ) and odds ratio is 1.211(95% CI is ). The chi-square statistic indicates no significant relationship between general obesity and diabetes (χ2=0.48; p=0.488). For central obesity the risk ratio is (95% CI is ) and odds ratio is (95% CI is ) with chi-square of 0.09 demonstrated no significant relationship between central obesity and diabetes (p=0.764). Waist to hip ratio also has shown similar relationship with diabetes (χ2=2.26; p=0.133). The risk ratio and odds ratios are (95% CI is ) 105

4 and (95% CI is ) respectively. Discussion The present study demonstrated that the plasma glucose, HDL cholesterol and Waist to hip ratio are significant in between control and diabetes subjects even after adjusting to age and sex. The other parameters such as general obesity, central obesity and skinfold variables have not shown any significant difference between diabetes and control groups. Even though we tried to test for the effect obesity and other risk components in causing diabetes, we still failed to demonstrate such an association. Table 1 Demographic characteristics of study Subjects by sex. Total Subjects Female Male P value Number of samples Age (years) 56± ± ±9.05 <0.001 Systolic blood pressure (mm Hg) Diastolic blood pressure (mm Hg) Fasting plasma glucose ± ± ± ± ± ± ± ± Cholesterol ± ± HDL cholesterol LDL cholesterol ± ± ± ± ± ± ± Triglycerides (mg/dl) ± ± ± ± BMI (kg/m 2 ) 25.69± ± ±3.41 <0.001 Waist circumference (cm) 87.65± ± ± Waist/hip (ratio) 0.92± ±.09.93± Skin fold Triceps 12.04± ± ±3.42 <0.001 Skin fold biceps 7.91± ± ±2.60 <0.001 Skin fold abdomen 16.53± ± ± Skin fold scapular 17.97± ± ± Values are mean ±SD Body mass index, WC, WHR, hypertension, ethnicity, sedentary life style, genetic predisposition are the most easily detectable risk factors for Diabetes Mellitus. Adiposity leads to worsening of the metabolic syndrome namely insulin resistance, dyslipidemia and hypertension (McAlpine et al 2005). In the present study comparison of diabetic and control showed that the general obesity (BMI) was not effective; however, central obesity (WHR) was most important risk factor for type 2 diabetes. In recent times abdominal obesity has been correlated to 106

5 various metabolic disturbances (DeNino et al 2001) and increased metabolic risk vis a vis diabetic and cardiovascular risk are in appraisal these days (Feller et al 2010). Metabolic profile worsens with the increase in BMI and WHR, however, abdominal obesity (WHR) causes more hazardous influence on the metabolic status of type 2 diabetic patients (Parikh et al 2002). As predicted earlier that by 2030, India s diabetic population will be increased to 79.4 million (Wild et al 2004). Preventive measures should be taken by identifying the risk factors for diabetes and accordingly modifying the life style activities through counseling. Early detection of risk factors of diabetes can be possible by screening of asymptomatic individuals and follow up counseling programmes as it not only helps in delaying the onset but also can reduce the burden of severity of the secondary complications such as stroke, heart diseases, micro vascular diseases, neuropathy, retinopathy and nephropathy. These in turn will help in reducing the morbid condition of health and economic burden on society. Table 2: Distribution of different risk components in diabetes and controls. Number of samples Control Diabetic p value P value a Age (years) 56.59± ±8.12 Systolic blood pressure (mm Hg) Diastolic blood pressure (mm Hg) Fasting plasma glucose Cholesterol HDL cholesterol LDL cholesterol Triglycerides (mg/dl) ± ± ± ± ± ± < ± ± ± ± ± ± ± ± BMI (kg/m 2 ) 25.71± ± Waist circumference (cm) 87.42± ± Waist/hip (ratio) 0.91± ± Skin fold Triceps Skin fold biceps Skin fold abdomen Skin fold scapular 11.84± ± ± ± ± ± ± ±

6 a p values adjusted for age and sex Table 3: Distribution of different risk components in central obesity and controls defined by the waist circumference. Number of samples Age (years) 56.42± ±8.02 Systolic blood pressure (mm Hg) Diastolic blood pressure (mm Hg) Fasting plasma glucose Control Obese p value P value a ± ± ± ± ± ± Cholesterol ± ± HDL cholesterol LDL cholesterol 42.28± ± ± ± Triglycerides (mg/dl) ± ± BMI (kg/m2) 23.00± ±3.34 <0.001 <0.001 Waist/hip (ratio) 0.89± ±0.09 <0.001 <0.001 Skin fold Triceps Skin fold biceps Skin fold abdomen Skin fold scapular 10.33± ±5.23 < ± ±4.07 <0.001 < ± ± ± ±7.57 <0.001 <0.001 a p values adjusted for age and sex While comparing differences between obesity indicators we found that the present study had several limitations for its meager sample size. Span length of BMI, WC and WHR character need to be evaluated in narrow range value for the assessment of relative risk for the causation of diabetes for subset of different variables. Further comparative studies on diabetes in varied ecozones through case control and identification of determinants of genetic and environmental factors of diabetic complications in different ethnic groups are the need of the hour of this vast country like India. The dissemination of knowledge acquired so through these processes if implemented, can diminish the burden on society to a great extent. 108

7 Acknowledgements: This research work was supported by the Anthropological Survey of India, Ministry of Culture, Government of India. References Banerji MA, Faridi N, Atluri R, Chaiken RL, Lebovitz HE (1999). Body composition, visceral fat, leptin, and insulin resistance in Asian Indian men; in J. Clin. Endocrinol. Metab. 84: Bray GA, Jablonski KA, Fujimoto WY, Barrett-Connor E, Haffner S, Hanson RL et al (2008). Relation of central adiposity and body mass index to the development of diabetes in the Diabetes Prevention Program; in Am. J. Clin. Nutr. 87: Burton BT, Foster WR, Hirsch J, Van Itallie TB (1985). Health implications of obesity: an NIH Consensus Development Conference; in Int. J. Obes. 9: Chandalia M, Abate N, Garg A, Stray-Gundersen J, Grundy SM (1999). Relationship between generalized and upper body obesity to insulin resistance in Asian Indian men; in J. Clin. Endocrinol. Metab. 84: DeNino WF, Tchernof A, Dionne IJ, Toth MJ, Ades PA, Sites CK et al (2001). Contribution of abdominal adiposity to age-related differences in insulin sensitivity and plasma lipids in healthy nonobese women; in Diabetes Care 24: Feller S, Boeing H, Pischon T (2010). Body mass index, waist circumference, and the risk of type 2 diabetes mellitus: implications for routine clinical practice; in Dtsch. Arztebl. Int. 107: Goodpaster BH, Thaete FL, Kelley DE (2000). Thigh adipose tissue distribution is associated with insulin resistance in obesity and in type 2 diabetes mellitus; in Am. J. Clin. Nutr. 71: Gupta R, Agrawal M (2009). High cardiovascular risks in a North Indian Agarwal community: a case series; in Cases J. 2: Joshi SR (2003). Metabolic syndrome--emerging clusters of the Indian phenotype; in J. Assoc. Physicians. India. 51: Karter AJ, Mayer-Davis EJ, Selby JV, D'Agostino RB, Jr., Haffner SM, Sholinsky P et al (1996). Insulin sensitivity and abdominal obesity in African-American, Hispanic, and non-hispanic white men and women. The Insulin Resistance and Atherosclerosis Study; in Diabetes 45: Kissebah AH, Freedman DS, Peiris AN (1989). Health risks of obesity; in Med. Clin. North Am. 73: Kraegen EW, Cooney GJ, Turner N (2008). Muscle insulin resistance: a case of fat overconsumption, not mitochondrial dysfunction; in Proc. Natl. Acad. Sci. U S A 105: Lahti-Koski M, Pietinen P, Mannisto S, Vartiainen E (2000). Trends in waist-to-hip ratio and its determinants in adults in Finland from 1987 to 1997; in Am. J. Clin. Nutr. 72: Low S, Chin MC, Ma S, Heng D, Deurenberg-Yap M (2009). Rationale for redefining obesity in Asians; in Ann. Acad. Med. Singapore 38: McAlpine RR, Morris AD, Emslie-Smith A, James P, Evans JM (2005). The annual incidence of diabetic complications in a population of patients with Type 1 and Type 2 diabetes; in Diabet. Med. 22:

8 Miljkovic-Gacic I, Gordon CL, Goodpaster BH, Bunker CH, Patrick AL, Kuller LH et al (2008). Adipose tissue infiltration in skeletal muscle: age patterns and association with diabetes among men of African ancestry; in Am. J. Clin. Nutr. 87: Mohan V (2004). Why are Indians more prone to diabetes?, in J. Assoc Physicians India 52: Mohan V, Jaydip R, Deepa R (2007). Type 2 diabetes in Asian Indian Youth; in Pediatr. Diabetes 8 Suppl 9: Molarius A, Seidell JC (1998). Selection of anthropometric indicators for classification of abdominal fatness--a critical review, in Int. J. Obes. Relat. Metab. Disord. 22: Parikh P, Mani U, Iyer U (2002). Abdominal Adiposity and Metabolic Control in Patients with Type 2 Diabetes Mellitus; in Int. J. Diab. Dev. Ctries 22: Ramprasad S, Radha V, Mathias RA, Majumder PP, Rao MR, Rema M (2007). Rage gene promoter polymorphisms and diabetic retinopathy in a clinic-based population from South India; in Eye (Lond) 21: Rema M, Pradeepa R (2007). Diabetic retinopathy: an Indian perspective; in Indian J. Med. Res. 125: Scheen AJ, Lefebvre PJ (1998). Oral antidiabetic agents. A guide to selection; in Drugs 55: Snehalatha C, Viswanathan V, Ramachandran A (2003). Cutoff values for normal anthropometric variables in asian Indian adults; in Diabetes Care 26: Unwin N, Gan D, Whiting D (2010). The IDF Diabetes Atlas: providing evidence, raising awareness and promoting action; in Diabetes Res. Clin. Pract. 87: 2-3. van Dieren S, Beulens JW, van der Schouw YT, Grobbee DE, Neal B (2010). The global burden of diabetes and its complications: an emerging pandemic; in Eur. J. Cardiovasc. Prev. Rehabil. 17 Suppl 1: S3-8. Vazquez G, Duval S, Jacobs DR, Jr., Silventoinen K (2007). Comparison of body mass index, waist circumference, and waist/hip ratio in predicting incident diabetes: a meta-analysis; in Epidemiol. Rev. 29: WCPD (2008). International Diabetes Congress in Helsinki, Finland WHO (1999). Definition, Diagnosis and Classification of Diabetes Mellitus and its Complications: Report of a WHO Consultation. Part 1: Diagnosis and Classification of Diabetes Mellitus. Geneva, World Health Org. Wild S, Roglic G, Green A, Sicree R, King H (2004). Global prevalence of diabetes: estimates for the year 2000 and projections for 2030; in Diabetes Care 27: