Journal of Diabetology

ORIGINAL ARTICLE
Year
: 2018  |  Volume : 9  |  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

Correspondence Address:
Dr. Bishwajit Bhowmik
Institute of Health and Society, Faculty of Medicine, University of Oslo, Oslo
Bangladesh

Abstract

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.



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 2019 Jun 19 ];9:95-101
Available from: http://www.journalofdiabetology.org/text.asp?2018/9/3/95/239563


Full Text



 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).[1],[2] 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.[3],[4],[5],[6]

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.[7] 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.[2] 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%).[8] Epidemiological studies at different time points have also reported an increased rate of T2DM, metabolic syndrome (MS) and CAD in the Bangladeshi population.[9],[10],[11] The prevalence of prediabetes (an intermediate metabolic state between normoglycaemia and T2DM), a known risk factor for cardiovascular disease, is also high in Bangladesh.[9],[12] 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)[13] 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.[14] 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.

Blood samples

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.[15],[16] 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.[17] 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.[18] 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.[19] 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).[20]

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).[21]

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.[13]

Assessment of the coronary artery disease risk

Framingham risk score (FRS) was used to investigate the 10-year risk of CAD.[22] 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.

Statistical methods

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}

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}

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}

[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}

 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.[2],[23] Both have a similar pathophysiological background and have common risk factors such as obesity, MS and HTN.[24] 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[25] 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.[22]

Several non-invasive risk scores have been developed worldwide.[21],[26],[27],[28] Most of them are restricted to identify specific diseases, and often will not perform well for other populations and conditions.[12]

We have already reported BDRS,[13] 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.[29] 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,[29] 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.[29] 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.[10] Modified ATP III showed a better performance than the IDF definition.[10] 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.[30] 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.

Acknowledgement

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.

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