|Year : 2018 | Volume
| Issue : 3 | Page : 95-101
Diabetes risk score for identifying cardiometabolic risk factors in adult Bangladeshi population
Bishwajit Bhowmik1, Tasnima Siddiquee2, Anindita Mujumder3, Tofail Ahmed4, Hajera Mahtab5, Abul Kalam Azad Khan2, Akhtar Hussain6, Gerd Holmboe-Ottesen7, Tone Kristin Omsland7
1 Department of Community Medicine and Global Health, Institute of Health and Society, University of Oslo, Centre for Global Health Research, Diabetic Association of Bangladesh, Dhaka, Bangladesh
2 Centre for Global Health Research, Diabetic Association of Bangladesh, Dhaka, Bangladesh
3 Department of Pathology, Ibrahim Medical College, Diabetic Association of Bangladesh, Dhaka, Bangladesh
4 Department of Endocrinology, Bangladesh Institute of Research and Rehabilitation in Diabetes, Endocrine and Metabolic Disorders, Dhaka, Bangladesh
5 Department of Medicine, Bangladesh Institutes of Health Sciences, Diabetic Association of Bangladesh, Dhaka, Bangladesh
6 Centre for Global Health Research, Diabetic Association of Bangladesh, Dhaka, Bangladesh; Faculty of Health Science, Nord University, Bodo, Norway; Department of Endocrinology, University of Cera, Fortaleza, Brazil
7 Department of Community Medicine and Global Health, Institute of Health and Society, Faculty of Medicine, University of Oslo, Oslo, Norway
|Date of Web Publication||22-Aug-2018|
Dr. Bishwajit Bhowmik
Institute of Health and Society, Faculty of Medicine, University of Oslo, Oslo
Source of Support: None, Conflict of Interest: None
Context: Simple non-invasive tools to identify high-risk individuals would facilitate screening of cardiometabolic diseases as well as diabetes. Aims: (1) To estimate variations in Bangladesh diabetes risk score (BDRS) according to stages of glucose intolerance, (2) to examine the usefulness of BDRS for identifying metabolic syndrome (MS), dyslipidaemia and 10-year risk of coronary artery disease (CAD) in people with normal glucose tolerance (NGT). Subjects and Methods: Data were taken from a randomised cross-sectional study of 2293 patients in a rural community of Bangladesh in 2009, based on questionnaire interviews, anthropometric measurements, fasting blood samples and oral glucose tolerance test. The BDRS includes age, sex, body mass index, waist-hip ratio and hypertension (HTN). Spearman correlation and logistic regression were done to assess the relationship between BDRS and cardiometabolic risk factors. Results: The mean BDRS increased significantly with higher glucose intolerance (P for trend < 0.001). Among NGT group, the prevalence of cardiometabolic risk factors increased progressively from low-to-medium-to-high-risk score groups; HTN: 7.8%, 12.3% and 19.8% (P for trend: <0.001), dyslipidaemia: 16.3%, 25.3% and 27.4% (P for trend: <0.001), MS: 10.2%, 22.4% and 30.9% (P for trend: <0.001) and CAD risk: 3.6%, 9.0% and 13.8% (P for trend: <0.001), respectively. BDRS was significantly associated with MS (odds ratio [OR]: 1.92, P < 0.001); dyslipidaemia (OR: 1.30, P = 0.018); and CAD risk (OR: 1.93, P < 0.001). Conclusions: BDRS can be used for identifying MS, dyslipidaemia and CAD risk even among people with NGT.
Keywords: Bangladeshi population, cardiometabolic risk, diabetes risk score
|How to cite this article:|
Bhowmik B, Siddiquee T, Mujumder A, Ahmed T, Mahtab H, Azad Khan AK, Hussain A, Holmboe-Ottesen G, Omsland TK. Diabetes risk score for identifying cardiometabolic risk factors in adult Bangladeshi population. J Diabetol 2018;9:95-101
|How to cite this URL:|
Bhowmik B, Siddiquee T, Mujumder A, Ahmed T, Mahtab H, Azad Khan AK, Hussain A, Holmboe-Ottesen G, Omsland TK. Diabetes risk score for identifying cardiometabolic risk factors in adult Bangladeshi population. J Diabetol [serial online] 2018 [cited 2020 Sep 28];9:95-101. Available from: http://www.journalofdiabetology.org/text.asp?2018/9/3/95/239563
| Introduction|| |
Diabetes and related cardiovascular diseases are increasing rapidly in both developed and developing countries. Most importantly, 80% of cardiovascular death occurs in low- and middle-income countries (LMICs)., At present, there is no systematic or structured policy for the screening of cardiometabolic diseases in most of the LMICs, though studies have shown the effectiveness of early detection and initiation of appropriate lifestyle intervention for prevention of cardiometabolic diseases.,,,
Like most of the developing nations, chronic diseases such as Type 2 diabetes mellitus (T2DM), coronary artery disease (CAD), hypertension (HTN) and dyslipidaemia are also increasing in Bangladesh. According to the WHO 2010 report, chronic diseases account for 62% of the total disease burden in Bangladesh. The International Diabetes Federation (IDF) has estimated that 6.9 million people living in Bangladesh had diabetes in 2017 and around half of them were unaware of their diabetes status. A systematic review and meta-analysis of studies from Bangladesh published between 1995 and 2010 reported a rising trend of T2DM (from 4 to 9%) and HTN (from 11% to 15.3%). Epidemiological studies at different time points have also reported an increased rate of T2DM, metabolic syndrome (MS) and CAD in the Bangladeshi population.,, The prevalence of prediabetes (an intermediate metabolic state between normoglycaemia and T2DM), a known risk factor for cardiovascular disease, is also high in Bangladesh., Therefore, early identification of the population at risk of cardiometabolic disease is essential.
In this context, we have recently developed a simple non-invasive Bangladeshi diabetes risk score (BDRS) using five simple parameters, namely, age, sex, body mass index (BMI) ≥25 kg/m2, waist-hip ratio (WHR) m ≥0.90; f ≥0.80 and presence of HTN.
The present article has the following aims: first, to assess the differences in BDRS according to different stages of glucose intolerance, namely, normal glucose tolerance (NGT), prediabetes (impaired glucose tolerance [IGT] and impaired fasting glucose [IFG]) and T2DM; second, to determine the usefulness of BDRS for identifying MS, dyslipidaemia and 10-year risk of CAD in people with NGT.
| Subjects and Methods|| |
Study site and population
Data were taken from a population-based cross-sectional dataset; the 2009 Chandra Rural Study. The Chandra Rural Study was carried out in rural areas of the “Gazipur” district of Bangladesh from March 2009 to December 2009. The area is situated approximately 40 km north of Dhaka city. Ten villages were randomly selected from 25 villages with a total population of approximately 20,000 aged ≥20 years. From a random selection of 3000 individuals, 2376 (79.2%) participated. A total of 2293 participants (842 men and 1451 women) had all the variables available. Data from 1915 participants with NGT were used for further analyses. A pre-tested questionnaire was used to collect socio-demographic, anthropometric and clinical information. The study protocol was approved by the Ethics Review Committee of the Diabetic Association of Bangladesh. Research participation, confidentiality and consent followed the Helsinki declaration. Informed verbal consent was taken from the patients before inclusion in the study. For the current paper, an anonymous data file was obtained from the 2009 Chandra Rural Study.
After a minimum of 8 h overnight fasting, a sample of 8 ml of venous blood was collected on arrival for fasting plasma glucose (FPG), lipid profiles, HbA1c and insulin measurements. Another 3 ml venous blood was taken 2 h after 75 g glucose (2hPG) drink. Plasma glucose was measured by the glucose oxidase method using Dimension RxL Max (Siemens AG, Erlangen, Germany). HbA1c and serum insulin were measured by high-performance liquid chromatography based ion exchange chromatography (Bio-Rad Laboratories, Hercules, CA, USA) and a two-site chemiluminescent immunoassay system (Diagnostic Products Co., Los Angeles, CA, USA), respectively. Serum lipid was estimated by standard enzymatic procedures (Dimension RxL Max; Siemens AG, Erlangen, Germany). High-density lipoprotein-cholesterol (HDL-C) was assessed by the direct assay method, and low-density lipoprotein-cholesterol (LDL-C) was estimated by Friedewald's formula.
Anthropometric measurements and blood pressure
Trained field workers took anthropometric measurements with participants dressed in light clothing and bare feet. Body weight was measured to the nearest 0.1 kg using a digital scale. BMI was calculated using the height and weight measurement (kg/m2). Waist circumference (WC) was measured to the nearest 0.1 cm by placing a plastic tape at the midpoint between the lower rib margin and the iliac crest. Hip circumference was measured at the greatest protrusion of the buttocks. WHR was then calculated from WC (cm) and hip (cm). 10-min rest was assured before measurement of blood pressure and a standard adult cuff was used to minimize variation in measurement. Blood pressure was measured twice in the right arm in both sitting and standing position. Measurements were taken 5 min apart, and the mean of the two measurements was taken as the final blood pressure reading.
Definition of variables
According to the cut-off level of obesity for the Asian population, general obesity for both sexes was set at BMI ≥25 kg/m2; central obesity including WC for male and female being ≥90 and ≥80 cm, respectively, WHR for male and female ≥0.90 and ≥0.80., Diabetes was defined as FPG ≥7.0 mmol/L or 2hPG ≥11.1 mmol/l. Prediabetes was defined as FPG ≥6.1 mmol/L–<7.0 mmol/L (impaired fasting glycaemia) and 2hPG ≥7.8 mmol/L–<11.1 mmol/L (IGT). NGT was set at FPG <6.1 mmol/L and 2hPG <7.8 mmol/L. We calculated homeostatic model assessment for insulin resistance (HOMA-IR) using the method of Matthews et al. (fasting serum insulin in μU/ml × FPG in mmol/L)/22.5. HTN was defined as systolic blood pressure (SBP) ≥140 mmHg and/or diastolic blood pressure (DBP) at ≥90 mmHg, or on anti-hypertensive medication or have been diagnosed with HTN by a physician. Dyslipidaemia was defined as triglyceride (Tg) ≥1.7 mmol/l and HDL-C <1.04 mmol/l (for men) and <1.3 mmol/l (for women).
We used 2005 revised National Cholesterol Education Program Adult Treatment Panel III (ATP III) criteria to define MS. MS is identified by the presence of at least any three of the following factors: Abdominal obesity (Asian cut-offs for both men and women), hypertriglyceridemia (Tg ≥1.7 mmol/L); low HDL-C (HDL-C ≤1.04 mmol/l for male and ≤1.3 mmol/l for female); elevated blood pressure (SBP ≥130 mmHg and/or DBP ≥85 mmHg or current use of antihypertensive drugs); IFG (FPG ≥5.6 mmol/L).
Calculation of the Bangladesh diabetes risk score
BDRS was developed based on multiple logistic regression analysis using five simple parameters, namely, age (≤30 = 0, 31–40 = 3, ≥41 = 4), sex (female = 0, male = 2), BMI (<25 kg/m2 = 0, ≥25 kg/m2 = 2), WHR (m <0.90; f <0.80 = 0, m ≥0.90; f ≥0.80 = 5) and presence of HTN (no = 0, yes = 2). Patients with a BDRS of <5 was categorised as low risk, 5–9 as medium risk and >9 as high risk for diabetes.
Assessment of the coronary artery disease risk
Framingham risk score (FRS) was used to investigate the 10-year risk of CAD. Six cardiac risk factors including age, gender, total cholesterol (T-Chol), HDL-C, SBP and smoking habits used for calculation of the FRS. The cutoffs were as follows: TC <160, 160–199, 200–239, 240–279 and ≥280 mg/dL; for SBP: <120, 120–129, 130–139, 140–159 and ≥160 mmHg; and for HDL-C: <40, 40–49, 50–59 and ≥60 mg/dL. 10-year risk in percentage was calculated by total points (1 point 6%, 2 points 8%, 3 points 10%, 4 points 12%, 5 points 16%, 6 points 20%, 7 points 25%, 10 points or more >30%). Individuals with FRS at 10% or above were stated 10-year risk of CAD in our study.
Continuous variables were expressed by means and 95% confidence intervals (CIs) adjusted for age and percentages and 95% CIs expressed categorical variables. Skewed data (including Tg, HbA1c, fasting insulin, HOMA-IR) were log-transformed before analysis, and the results were transformed back to the original scale. Differences between the two groups of means adjusted for age were tested by analysis of covariance and logistic regression was used to examine the statistical difference of proportions. Trend analysis test was used to determine the differences in proportions and means across the groups. The Spearman correlation was used to determine the strength of the relationship between BDRS and cardiometabolic risk factors of patients with NGT, whereas the binary logistic regression was used to identify factors that were associated with BDRS. For the binary logistic regression, the low risk (<5) and medium risk (5–9) groups of BDRS risk score were combined as less risk, and BDRS score more than nine was stated as high risk for diabetes, MS and 10-year risk of CAD. Low-risk group was coded as 0 and high risk was coded as 1. Statistical inference was based on 95% CIs, and the significance level was set at 0.05. Both STATA 14 (StataCorp LP, TX, USA) and PASW statistics 21 for Windows (SPSS, Chicago, IL, USA) were used.
| Results|| |
Characteristics of the total sample according to glucose intolerance status are displayed in [Table 1]. Patients with prediabetes and diabetes were older than those with NGT. Adjusted mean BMI, WC, WHR, SBP, DBP, FPG, 2hPG, HbA1c, T-Chol, Tg, HDL-C, fasting insulin, HOMA-IR and 10 years CAD risk increased with the increasing severity of glucose intolerance. Significant linear trends were observed for these risk variables (P value for trend <0.001).
|Table 1: General characteristics of study subjects according to glucose intolerance status|
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The prevalence of general and central obesity, HTN, dyslipidaemia and MS increased with increasing glucose intolerance status (P value for trend <0.001). The prevalence of the following according to NGT, prediabetes and diabetes, respectively, were; general obesity: 22.3%, 24.3% and 24.4%, central obesity by WC: 36.1%, 56.4% and 62.1%, central obesity by WHR: 68.8%, 81.0% and 90.0%, HTN: 14.4%, 17.0% and 24.8%, dyslipidaemia: 24.6%, 40.0% and 58.7% and MS: 23.0%, 58.9%, 81.2%.
The mean BDRS increased significantly with higher glucose intolerance (NGT, 7.6; prediabetes, 8.9; and diabetes, 9.9%; P for trend <0.001). The percentage of individuals with BDRS >9 (the high-risk group) increased significantly with the increasing glucose intolerance, amounting to 31% of the NGT group, 48.7% of the prediabetics and 61.3% of those with diabetes, respectively.
Clinical and biochemical characteristics in the NGT group classified according to the BDRS scores are displayed in [Table 2]. In this group, 17.4% had a low score (BDRS <5), 51.5% had a medium score (BDRS 5–9) and 31.1 had a high score (BDRS >9). Except for the LDL-C, all clinical and biochemical characteristics of the NGT group differed significantly by BDRS category. With increasing risk scores, there was a corresponding increase in mean BMI (P for trend <0.001), WC (P for trend <0.001), WHR (P for trend <0.001), SBP (P for trend <0.001), DBP (P for trend <0.001), FPG (P for trend <0.001), 2hPG (P for trend 0.007), Tg (P for trend <0.001), fasting insulin (P for trend <0.001), HOMA-IR (P for trend <0.001) and 10 year CAD risk (P for trend <0.001).
|Table 2: Clinical and biochemical characteristics in the normal glucose tolerance group (n=1915) classified based on the Bangladesh Risk scores|
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Among individuals with BDRS scores <5, 5–9 and >9, the prevalence of the following parameters were; general obesity by BMI: 21.1%, 21.8% and 23.9%, central obesity by WC: 21.8%, 32.7% and 51.6%, central obesity by WHR: 50.2%, 60.4% and 92.9%, HTN: 7.8%, 12.3% and 19.8%, dyslipidaemia: 16.3%, 25.3% and 27.4% and MS: 10.2%, 22.4% and 30.9%.
[Table 3] shows the results of the correlation analysis of BDRS with CAD risk factors in patients with NGT. CAD risk factors including BMI, WC, WHR, SBP, DBP, Tg, fasting insulin and HOMA-IR showed a significant correlation (P < 0.001) with BDRS.
|Table 3: Spearman correlation analysis of Bangladeshi Diabetes Risk Score with cardiovascular risk factors in subjects with normal glucose tolerance|
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[Figure 1] shows the association of BDRS with MS, dyslipidaemia and CAD risk in patients with NGT. BDRS showed a significant association with MS (odds ratio [OR]: 1.92, P < 0.001); dyslipidaemia (OR: 1.30, P = 0.018); and CAD risk (OR: 1.93, P < 0.001).
|Figure 1: Association of Bangladesh diabetes risk score with metabolic syndrome, dyslipidaemia and 10-year risk of coronary artery disease in subjects with normal glucose tolerance|
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| Discussion|| |
To the best of our knowledge, this is one of few studies applying a diabetes risk score for identifying cardiometabolic risk factors, namely, MS, dyslipidaemia and CAD risk.
Studies have shown an excessive and premature burden of chronic diseases, especially T2DM and CAD in the South Asian populations., Both have a similar pathophysiological background and have common risk factors such as obesity, MS and HTN. An effective and cost-effective tool which can identify these cardiometabolic risk in a single sitting is warranted. Extensive laboratory screening procedures in Bangladesh are relatively expensive and time-consuming and in combination with budget constraints, lack of clinics and trained health-care professionals and infrastructure it becomes very difficult to conduct population-based screening programs which can include all clinical and laboratory parameters necessary for the widely used Framingham risk tool.
Several non-invasive risk scores have been developed worldwide.,,, Most of them are restricted to identify specific diseases, and often will not perform well for other populations and conditions.
We have already reported BDRS, a simple non-invasive risk score for identifying individuals at high risk of diabetes. The risk Score of >9 had a sensitivity of 62.4%, and specificity of 67.4%, respectively, while the area under the ROC curve was 0.70 (95% CI 0.68–0.72). BDRS has shown to be useful in detecting individuals at risk for T2DM. However, its effectiveness in detecting MS, dyslipidaemia and CAD risk in our population has not been examined so far.
In our study, the mean BDRS increased significantly with the increasing severity of glucose intolerance. Furthermore, the mean value of all the cardiometabolic risk factors including BMI, WC, WHR, SBP, DBP, FPG, 2hPG, HbA1c, T-Chol, Tg, HDL-C, fasting insulin, HOMA-IR and 10 years CAD risk also increased with higher glucose intolerance. Our findings are in line with Indian study. Therefore, considering both pathophysiological and causative risk factors, the usefulness of BDRS to identify MS, dyslipidaemia and CAD risk was examined in our population. Like the Indian study, most of our analyses were focused on patients with NGT as diabetes and prediabetes are the major risk factors for cardiovascular diseases in the Bangladeshi population at large. Our data revealed high levels of cardiovascular risk factors among the study participants with NGT. Our observations of higher prevalences of obesity, MS, dyslipidaemia, HTN and CAD risk in individuals with medium (BDRS 5–9) and high-risk score (BDRS >9) compared to low risk (BDRS <5) individuals are consistent with the Indian study. The Indian individuals with “medium (30–50)” and “high-risk (≥60)” Indian Diabetes Risk Score (IDRS) were also found to have a significantly higher prevalence of cardiovascular risk factors, although they used different variables and cut-off levels for their IDRS. The prevalence of MS and related CAD risk factors (including HTN, hypercholesterolaemia and hypertriglyceridaemia) increased significantly with increasing IDRS among individuals with NGT. Both study findings indicate that people with higher diabetes risk score values have a higher risk for cardiometabolic diseases.
We have earlier shown from the same study population a significant association between MS and T2DM, prediabetes, HTN and CAD risk. Modified ATP III showed a better performance than the IDF definition. This indicates that early screening of MS has become essential for altering adverse outcomes, but the clinical diagnosis of the MS is time-consuming and invasive and is therefore difficult to implement in a population-based prevention program. In the current paper, we have shown a significant association between BDRS and MS, dyslipidaemia and CAD risk in NGT subjects. In line with the findings from the Bangladeshi and the Indian studies, a population-based cross-sectional study in Finland also found that the Finnish Diabetes Risk Score was a useful screening tool for undetected T2DM, prediabetes and MS in the general population. As BDRS is a non-invasive tool, it can be used by the primary health care workers for identifying individuals at risk for T2DM and other cardiometabolic diseases.
Strengths of this study are that it is a fairly large population-based study with an extensive set of known risk factors measured. One limitation is the cross-sectional design and thus does not allow for drawing any causal inferences. This implies that large prospective studies are needed to confirm the association between risk factors for cardiometabolic diseases and BDRS.
| Conclusions|| |
BDRS developed to detect individuals at high risk for T2DM in the Bangladeshi population, may also be useful for identifying MS, dyslipidaemia and CAD risk in people with NGT. Subjects with increased cardiometabolic risk should be offered intensive medical and lifestyle management to prevent or reduce the impact of further complications.
We acknowledge the contribution of our survey team members, participants, the village leaders, volunteers and laboratory technicians for their continuous effort in the collection of data. We also express our admiration to the authority of the Diabetic Association of Bangladesh for their support. The authors do not have any financial support or relationships that may pose a conflict of interest.
Financial support and sponsorship
This study was financially supported by Diabetic Association of Bangladesh.
Conflicts of interest
There are no conflicts of interest.
| References|| |
Murray CJ, Lopez AD. Alternative projections of mortality and disability by cause 1990-2020: Global burden of disease study. Lancet 1997;349:1498-504.
International Diabetes Federation. Diabetes Atlas. 8th
ed. Brussels: International Diabetes Federation; 2017.
Diabetes Prevention Program Research Group. Reduction in the incidence of Type 2 diabetes with life style intervention or metformin. N Engl J Med 2002;346:393-403.
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.
Tonstad S, Sundfør T, Seljeflot I. Effect of lifestyle changes on atherogenic lipids and endothelial cell adhesion molecules in young adults with familial premature coronary heart disease. Am J Cardiol 2005;95:1187-91.
Tenenbaum A, Motro M, Fisman EZ, Tanne D, Boyko V, Behar S, et al.
Bezafibrate for the secondary prevention of myocardial infarction in patients with metabolic syndrome. Arch Intern Med 2005;165:1154-60.
Saquib N, Saquib J, Ahmed T, Khanam MA, Cullen MR. Cardiovascular diseases and Type 2 diabetes in Bangladesh: A systematic review and meta-analysis of studies between 1995 and 2010. BMC Public Health 2012;12:434.
Bhowmik B, Afsana F, My Diep L, Binte Munir S, Wright E, Mahmood S, et al.
Increasing prevalence of Type 2 diabetes in a rural Bangladeshi population: A population based study for 10 years. Diabetes Metab J 2013;37:46-53.
Bhowmik B, Afsana F, Siddiquee T, Munir SB, Sheikh F, Wright E, et al
. Comparison of the prevalence of metabolic syndrome and its association with diabetes and cardiovascular disease in the rural population of Bangladesh using modified NCEP ATP III and IDF definitions. J Diabetes Invest 2015;6:280-8.
Bhowmik B, Binte Munir S, Ara Hossain I, Siddiquee T, Diep LM, Mahmood S, et al.
Prevalence of Type 2 diabetes and impaired glucose regulation with associated cardiometabolic risk factors and depression in an urbanizing rural community in Bangladesh: A population-based cross-sectional study. Diabetes Metab J 2012;36:422-32.
Bhowmik B, Akhter A, Ali L, Ahmed T, Pathan F, Mahtab H, et al
. A simple risk score for detecting undiagnosed Type 2 diabetes in rural Asian Indian (Bangladeshi) adults. J Diabetes Invest 2015;6:670-7.
Siddiquee T, Bhowmik B, Da Vale Moreira NC, Mujumder A, Mahtab H, Khan AK, et al.
Prevalence of obesity in a rural Asian Indian (Bangladeshi) population and its determinants. BMC Public Health 2015;15:860.
Choo V. WHO reassesses appropriate body-mass index for Asian populations. Lancet 2002;360:235.
World Health Organization, Western Pacific Region. The International Association for the Study of Obesity and the International Obesity Task Force. The Asia-Pacific Perspective: Redefining Obesity and its Treatment. Sydney, Australia: Health Communications Australia Pty Limited.; 2000. Available: http://www.diabetes.com.au/pdf/obesityreport.pdf
. [Last accessed on 2006 Aug 23].
World Health Organization. Definition, Diagnosis and Classification of Diabetes Mellitus and its Complications. Report of a WHO consultation. Geneva: World Health Organization; 1999.
Matthews DR, Hosker JP, Rudenski AS, Naylor BA, Treacher DF, Turner RC, et al.
Homeostasis model assessment: Insulin resistance and beta-cell function from fasting plasma glucose and insulin concentrations in man. Diabetologia 1985;28:412-9.
Guidelines Subcommittee. 1999 world health organization-international society of hypertension guidelines for the management of hypertension. J Hypertens 1999;17:151-83.
Expert Panel on Detection, Evaluation, and Treatment of High Blood Cholesterol in Adults Executive Summary of the Third Report of the National Cholesterol Education Program (NCEP) Expert Panel on Detection. Evaluation, and treatment of high blood cholesterol in adults (adult treatment panel III). JAMA 2001;285:2486-97.
Grundy SM, Cleeman JI, Daniels SR, Donato KA, Eckel RH, Franklin BA, et al.
Diagnosis and management of the metabolic syndrome: An american heart association/National heart, lung, and blood institute scientific statement. Circulation 2005;112:2735-52.
Sohn C, Kim J, Bae W. The Framingham risk score, diet, and inflammatory markers in Korean men with metabolic syndrome. Nutr Res Pract 2012;6:246-53.
Bhopal R, Unwin N, White M, Yallop J, Walker L, Alberti KG, et al.
Heterogeneity of coronary heart disease risk factors in indian, pakistani, bangladeshi, and european origin populations: Cross sectional study. BMJ 1999;319:215-20.
Ginsberg HN. Insulin resistance and cardiovascular disease. J Clin Invest 2000;106:453-8.
The Diabetes Prevention Program Research Group. Costs associated with the primary prevention of T2DM mellitus in the diabetes prevention program. Diabetes Care 2003;26:36-47.
Ramachandran A, Snehalatha C, Vijay V, Wareham NJ, Colagiuri S. Derivation and validation of diabetes risk score for urban Asian Indians. Diabetes Res Clin Pract 2005;70:63-70.
Gray LJ, Taub NA, Khunti K, Gardiner E, Hiles S, Webb DR, et al.
The Leicester risk assessment score for detecting undiagnosed Type 2 diabetes and impaired glucose regulation for use in a multiethnic UK setting. Diabet Med 2010;27:887-95.
Assmann G, Cullen P, Schulte H. Simple scoring scheme for calculating the risk of acute coronary events based on the 10-year follow-up of the prospective cardiovascular Munster (PROCAM) study. Circulation 2002;105:310-5.
Mohan V, Sandeep S, Deepa M, Gokulakrishnan K, Datta M, Deepa R, et al.
A diabetes risk score helps identify metabolic syndrome and cardiovascular risk in Indians-The Chennai urban rural epidemiology study (CURES-38). Diabetes Obes Metab 2007;9:337-43.
Saaristo T, Peltonen M, Lindström J, Saarikoski L, Sundvall J, Eriksson JG, et al.
Cross-sectional evaluation of the finnish diabetes risk score: A tool to identify undetected Type 2 diabetes, abnormal glucose tolerance and metabolic syndrome. Diab Vasc Dis Res 2005;2:67-72.
[Table 1], [Table 2], [Table 3]