Variables	Frequency(%)
Age
10-20	31 (20.7%)
21-30	20 (13.3%)
31-40	38 (25.3%)
41-50	27 (18.0%)
51-60	19 (12.7%)
61-70	12 (8.0%)
71-80	2 (1.3%)
81-90	1 (0.7%)
Sex
Male	46 (30.7%)
Female	104 (69.3%)
Weight
60-70	6 (4%)
71-80	41 (27.3%)
81-90	70 (46.7)
91-100	31 (20.7)
101-110	2 (1.3%)
ObeseClass
Over-Weight	4 (2.7%)
Obese Class I	136 (90.7%)
Obese Class II	10 (6.7%)

Table 1: Baseline characteristic of study participants (n=150)

Obese Classes	Fast Food					Total
	Zinger	Pizza	Chinese soup	Others	No
over-weight	1	0	0	0	3	4
obese class I	24	20	6	1	85	136
obese class II	1	0	0	0	9	10
Total	26	20	6	1	97	150

Table 2a: Use of fast food by different obese participants

Obese Classes	Rice consumption					Total
	1-2 times a week	2-3 times a week	4-5 times a week	4-8 times a month	No
over-weight	1	1	1	0	1	4
obese class I	47	47	14	7	21	136
obese class II	5	3	1	0	1	10
Total	53	51	16	7	23	150

Table 2b: Rice consumption by different obese class participants

Obese Classes	Diet			Total
	Veg	Non-Veg	Both
over-weight	1	0	3	4
obese class I	12	3	121	136
obese class II	0	0	10	10
Total	13	3	134	150

Table 3:Diet use among different obese class participants

Obese Classes	Routine daily exercise				Total
	walk / running	home workouts	strenuous activities	No
over-weight	0	1	1	2	4
obese class I	36	18	3	78	136
obese class II	1	2	0	7	10
Total	37	21	4	87	150

Table 4: Routine daily exercise by different obese class participants

Obese Classes	Other complications					Total
	Frequent infection	arthritis	back pain	others	No
over-weight	0	2	0	0	2	4
obese class I	10	21	27	17	61	136
obese class II	1	5	2	0	2	10
Total	11	28	29	17	65	150

Table 5: Complication associated with obese class participants

Obese Classes	Education					Total
	Illiterate	SSC	F. Sc	Bachelor	M.Phil./ PhD
over-weight	3	1	0	0	0	4
obese class I	77	23	24	8	4	136
obese class II	8	1	1	0	0	10
Total	88	25	25	8	4	150

Table 6: Education level of obese class participants

Obese Classes	Hypothyroidism		Total	P.Value
	Yes	No
over-weight	0	4	4	.817
obese class I	11	125	136
obese class II	1	9	10
Total	12	138	150

Table 7: Hypothyroidism among obese class participants

Obese Classes	Triglyceride			Total	P. Value
	100-150	151-200	201-250
over-weight	0	4	0	4	.144
obese class I	17	91	28	136
obese class II	1	4	5	10
Total	18	99	33	150

Table 8: Triglyceride among obese class participants

Obese Classes	Cholesterol			Total	P. Value
	151-200	201-250	251-300
over-weight	0	2	2	4	.204
obese class I	11	76	49	136
obese class II	3	4	3	10
Total	14	82	54	150

Table 9: Cholesterol level among obese class participants

Obese Classes	LDL			Total	P. Value
	50-100	101-150	151-200
over-weight	3	0	1	4	0.004
obese class I	100	34	2	136
obese class II	10	0	0	10
Total	113	34	3	150

Table 10: LDL level among obese class participants

Obese Classes	HDL				Total	P. Value
	20-30	31-50	51-70	71-90
over-weight	1	3	0	0	4	.565
obese class I	16	68	46	6	136
obese class II	1	3	5	1	10
Total	18	74	51	7	150

Table 11: HDL level among obese class participants

Figure 1: Prevalence of underweight, normal, overweight, and obese individuals based on BMI (A) Overall prevalence between different categories (p = 0.033); (B) Prevalence among males and females (p = 0.041); (C) Prevalence within age groups, 18-22, 23-27, and 28-32 years (p = 0.006)

Figure 2: Assessment of abdominal obesity based on WC and W/H ratios (A) Proportion of individuals with normal and abnormal WCs across different categories based on BMI (p < 0.001); (B) Proportion of males and females with abnormal WC; (C) Proportion of individuals with normal and abnormal W/H ratios across different categories based on BMI (p < 0.001); (D) Proportion of males and females with abnormal W/H ratios

Figure 3: Hypertension prevalence and sleep duration (A) Proportion of individuals with hypertension across different categories (p < 0.001); (B) Proportion of individuals based on their sleep duration across different categories (p < 0.001)

Figure 4: High fat/sugar diet and smoking habit among different categories (A) Overall proportion of individuals consuming high fat/sugar diet across different categories (p = 0.246); (B and C) Proportion of males and females consuming high fat/sugar diet (p = 0.494 & p = 0.035, respectively); (D) Overall proportion of individuals based on their smoking habit (p = 0.001); (E and F) Proportion of males and females based on their smoking habit (p = 0.016 & p = 0.405, respectively).

Introduction

Non-communicable diseases (NCDs) are playing a significant role in clinical and public health problems worldwide. Although chronic energy deficiency and infectious diseases are highly prevalent, 41% of the disease burden and 52% of the total mortality are due to non-communicable diseases excluding injury [1-3]. Among the NCDs, cardiovascular diseases (CVDs) such as stroke, kidney failure, and heart disease are considered as major contributors of the NCD related deaths [4,5]. Hypertension and diabetes are two major metabolic risk factors of NCDs. Diabetes is an increasing epidemic which is projected to increase from 240 million in 2007 to 380 million in 2025, of which 80% patients belong to low and middle income countries [6]. More than 60% of the diabetic patients live in South Asia Pacific region, particularly in South Asia [7,8].The prevalence of hypertension has been rapidly increasing in developing countries such as India, China, Nepal and Bangladesh [9-12]. According to the World Health Organization, more than 35%of adult population in South Asia are affected by hypertension [7].

Bangladesh is one of the developing countries in South Asia, which has been experiencing an epidemiological transition from communicable diseases to NCDs [13-15] due to fast urbanization, unhealthy diet, sedentary life style, increased use of tobacco consumption, excess alcohol intake and so on [16,17]. Some recent studies and systematic reviews of studies between 1995 and 2000 reported a pooled estimate of 14% for hypertension prevalence [18]. Another systematic review and meta-analysis of the studies during 1995-2013 on diabetes prevalence [13] stated that the prevalence of diabetes ranges between 4.5% and 35.0% with the pooled prevalence of 7.4%.

A number of studies reported that overweight and obesity are well recognized predictors of cardiometabolic risks due to its close association with increased prevalence of hypertension, diabetes mellitus and dyslipidemia [19-21]. Central obesity, suggesting excess intra-abdominal fat deposition is also an important predictor of cardiometabolic risks [22]. Although there are several instruments to measure total body fat and its distribution, there is still no diagnostic definitions that would be regarded as accurate, precise, detailed and accepted worldwide determining obesity [21]. Body Mass index (BMI), waist circumference (WC), waist Hip Ratio (WHR), Waist Height Ratio (WHtR) are all useful anthropometric measurements that provide major information on obesity and therefore on cardiometabolic risks [23].

Due to lack of exact methods for assessing body composition directly, anthropometrics are often used as surrogates for assessing obesity and body fat distribution [24]. Body mass index (BMI) is the most commonly used anthropometric index and gives information on fat mass and lean mass [21]. Some studies have suggested waist-hip ratio (WHR) as an important anthropometric index to predict CVD risk and metabolic syndrome [20]. One prospective study reported that hip circumference (HC) is inversely associated with diabetes, CVD morbidity and mortality [25]. In another study, Onat et al. stated that both WC and WHR had strong association with BMI, age, diastolic blood pressure and plasma triglyceride (TG), and WHR was significantly associated with prevalence of CHD in Turkish women [26].

Given the high risk of cardiovascular diseases in Bangladeshi adult population, very limited studies, to the best of our knowledge, were performed to examine the association of anthropometric measurements with cardiometabolic risk factors among mass population in Bangladesh. A few studies were performed for a specific group or region and hence findings from them cannot be generalized to general population of Bangladesh. This study focuses on general population of Bangladesh based on data collected in an outpatient department of the tertiary level government hospital under the Bangabandhu Sheikh Mujib Medical University (BSMMU) where people from diverse socio-economic groups, ethnic community and various regions of the country come, because of its cost-effective quality medical service. Therefore, findings of this study will reflect the true picture of the general population of Bangladesh and help the stakeholders to design urgent appropriate policies and programs for preventing and reducing the risks of CVDs.

Materials and Methods

Study population and Design

Data for this study obtained from a cross-sectional survey with structured questionnaire among the adult individuals attending outpatient department (OPD) of the BSMMU for providing blood sample for diagnostic test prescribed by a doctor from the same hospital or other different clinics during the period from August, 2017 to November, 2017. The diagnostic test services in OPD of the BSMMU is open for all patients either admitted in the same hospital of the BSMMU or from other clinics/private chamber run by a doctor and has been served by qualified technicians who were informed in advance about selection of patients for this study. The technicians selected those patients who reported about their fasting for 12 hours over the night and referred to the enumerators (a group of physicians who involved as co-researchers in this study). A total of 1250 adult subjects aged 20 to 65 years who did not reported to have any major illness (acute myocardial infarction and transient ischaemic attack in last 3 months, malignancy, pregnancy, or any condition of weight gain like ascites, hypothyroidism, nephrotic syndrome etc.) participated in the study. Of them, 50 subjects who had missing observation for some important variables were excluded from the analysis and hence data on a total of 1200 subjects (male:644 and female: 556) were used for analysis.

Data collection and measuring the variables

Information about lifestyle, habits, education, physical activity, monthly family income and past medical history were obtained through face to face interview with structured questionnaire conducted directly by a group of physicians who involved as co-researchers in this study. Body weight was measured with a standard scale while dressed in very light clothing without shoes, and height was measured by using a height bar fixed on the wall, with subjects standing without shoes with back, buttocks, and heals pressed together against the wall. Then BMI was calculated as weight (kilograms) divided by squared height (meters squared). Waist circumference (WC) was measured at the midpoint between the last rib and the superior iliac crest during mild expiration and hip circumference (HC) was measured at the level of the greater trochanter. In addition, both waist to hip ratio (WHR) and waist to height ratio (WHtR) were also used in the analysis. After at least 5 minutes of sitting, blood pressure (BP) was measured twice 5 min apart using a standard sphygmomanometer with the participants seated. Then mean value of two readings measured was used. For measuring diabetes, overnight fasting (9-12 hours) blood samples were collected from study subjects to estimate the serum glucose and lipid profile. Fasting serum glucose was measured by hexokinase method using the Architect system. Serum cholesterol was measured by enzymatic method on Beckman coulter AU analyser. Serum HDL-C was measured by enzymatic color- test for quantitative determination of HDL done by Beckman Coulter analyser. Serum triglyceride was measured by enzymatic Glycerol Phosphate Oxidase method by using the by Beckman Coulter analyser.

Diagnosis of Diabetes, Hypertension and Dyslipidemia

Diabetes was defined as fasting blood glucose (FBG) values of ≥ 7.0 mmol/L, or self-reported diabetes medication use prior to the interview date [27]. Hypertension was defined as if the blood pressure values were systolic blood pressure (SBP) ≥ 140 mmHg or diastolic blood pressure (DBP) ≥ 90 mmHg, or if they reported currently taking antihypertensive medication [28]. Dyslipidemia was defined as serum total cholesterol ≥ 200mg/ dL (5.17mmol/l), and/or triglyceride ≥ 150mg/dL (1.7mmol/l) and/or high density lipoprotein cholesterol < 40mg/dL (1.03 mmol/l).

Statistical analyses

Continuous variables were summarized in terms of mean and standard deviation while the categorical variables were summarized in terms of percentage as part of univariate analysis. To examine the association of each anthropometric variable with the cardiovascular risk factors such as diabetes, hypertension and dyslipidemia, two independent sample t-test was performed separately for each risk factor and ANOVA F-test was performed for the status of the presence of the risk factors (none, 1, 2+). Further, multivariable logistic regression model was fitted separately for each risk factor to examine the association of the anthropometric variables with the outcome (risk factors) after adjusting for the effect of demographic variables such as age and sex. Finally, the area under the receiver operating curve (AUC) was estimated to assess the performance of the anthropometric variables for predicting cardiovascular risks. All these analyses were conducted using statistical software Stata version 14.

Results

Table 1 shows summary statistics (mean and SD) of the anthropometric, clinical and demographic characteristics of the study participants. The mean age of participants is 39.64yrs (SD=12.67) with almost no difference between male and female. Male had greater mean of weight, height, WHR and TG value than female, while BMI, WC, HC, WHtR, total cholesterol, HDL, LDL and fasting blood glucose level were found to be less in male than in female. Table 2 shows participants having only hypertension, diabetes and dyslipidemia is 26.55%, 27.6% and 83% respectively. With respect to combination of these risk factors, 11.3%, subjects have had none of these risk factors, while it is 50.2% have had one risk factor and 38.5% had 2 or more risk factors.

Table 1

Table 2

The separate bivariate analysis (Table 3) for male and female showed that the variable age, weight, BMI, waist circumference, waisthipratioandwaistheightratiowererelatedtotheoccurrence of diabetes mellitus (p-value < 0.05). In case of combined data, all these indices were found to be significantly associated with the diabetes, except for height and hip circumference. Regarding hypertension, in both male and female group, except for height all other indices were found to be significantly associated (p value <.05). Similar findings were observed in the combined data (Table 4). In case of dyslipidemia, for male, weight, BMI, HC, WHR and WHtR showed significant association with the outcome (p < 0.01 in case of weight, BMI and HC and p< 0.05 in case of WH ratio). For female stratum, only WHR is significantly associated with dyslipidemia (p-value<.01). For combined data, except for age and WC, all indices are significantly associated the cardiovascular risk factors (Table 5). Similar results were observed (Table 6) when all these indices were summarized by the status of the presence of any of these risk factors (none, any one, 2 or more).

Table 3

Table 4

Table 5

Table 6

Table 7 summarizes the results of the multivariable logistic regression analysis separately for each risk factor. In case of age, the odds of having hypertension in people of age group 40-50 yrs is 3.98 times of the odds in subjects with 18-39 yrs (p-value< 0.01) and 9.27 times of the odds in subjects with 50+ yrs age (p-value< 0.05). Similar results can be observed for diabetes and dyslipidemia. Comparing with male, female participants are 14% less likely to have hypertension. BMI is positively associated with hypertension with 6% greater odds for every one unit increase of BMI (p-value< 0.01). Again, 1% greater odds of having diabetes and 5% greater odds of having dyslipidemia for every one unit increase in BMI. For WHR, 7% greater odds of having hypertension for one additional unit of WHR (p-value< 0.05), which is 11% in case of diabetes and 3% in case of dyslipidemia. For WHtR, the odds of having hypertension, diabetes and dyslipidemia are respectively 39%, 1% and 3% greater for addition one unit of WHtR. Table 8 shows the estimated AUCs for predicting the risk of hypertension, diabetes and dyslipidemia by BMI, WHR and WHtR for different age groups and gender. The estimated AUC for the combined effect of all these covariates for predicting the risk of hypertension is reported to 0.76 with 95% CI (0.73-0.79). Of these covariates, higher age group (50+ yrs) is reported to have greater predictive value, which is followed by WHtR and WHR. Similar pattern was observed in predicting the risk of diabetes. However, slightly different pattern was observed for dyslipidemia with relative lower predictive value by the combined effect of the covariates. Of them, WHR showed greatest predictive value, which is followed by sex. Table 9 shows adjusted relative risk ratio obtained from fitting multinomial logistic model for number of risk factors. All the indices and sex have significant association with one of the risk factors, and age along with all indices and sex has showed significantly associates with 2 or more cardiovascular risk factors. In particular, for every unit increase of BMI value there is 9% increase in relative risk of developing one of the risk factors (p-value <.01). In case of WHR, relative risk is found to be 3.11 times higher with each additional unit (p-value < 0.05). Similar findings can be observed for WHtR.

Table 7

Table 8

Table 9

Discussion and Conclusion

This study investigated an association of anthropometric indices (BMI, WHR and WHtR) with cardiometabolic risk factors (DM, HTN and Dyslipidemia) among Bangladeshi adult population using data collected from patients visiting outpatient department of a tertial level government hospital which is accessed by general population of the country. Findings revealed that BMI, WHR and WHtR are significantly associated with cardiometabolic risk factors. In particular, all these indices are significantly associated with diabetes and hypertension for both male and female, which is also true in case of dyslipidemia in male while only WHR and WHtR are significantly associated with dyslipidemia in female. After adjusting for age and sex in the multivariable regression model, all these indices are found to be significantly associated with each of these cardiometabolic risk factors. Of these indices, the strongest association of WHtR with hypertension was found, while it is true for WHR in case of both diabetes and dyslipidemia.

The findings in another analysis performed considering the presence of one or more cardiometabolic risk factors revealed that all these indices are significantly associated with the presence of one or more risk factors after adjusting for age and sex. The degree of association is greater for having two/three risk factors compared to those for having only one risk factor. Of the indices, the magnitude of association of WHR and WHtR for having one or more risk factors are much greater than those with BMI.

All these findings are similar to the findings of the studies conducted in a similar settings, particularly in Asia [20,21,29,30]. For instance, a study in South China [31] revealed that cardiometabolic risk factors were significantly different between male and female and their anthropometric indices. Asians are known to be at an increased risk of cardiovascular disease (CVD) risk factors compared with those of European descent partly due to differences in body fat distribution and body build and frame size. Moreover, cardiometabolic risk factors associated with excess weight also vary by Asian sub-groups [9, 19]. Despite lower mean body mass index (BMI), Asian Americans are 30–50% more likely to have Type-2 Diabetes Mellitus (T2DM) than their White counterparts [32]. Asian Indians had the highest odds of prevalent type-2 diabetes, followed by Filipinos, other Asians, and Chinese. Insulin resistance has also shown to be higher in Asian Indians, and higher prevalence of cardiometabolic diseases are seen among Filipino and Japanese compared to other Asian groups [33].

The prevalence of obesity and cardiometabolic diseases is rapidly increasing in India and other South Asian countries, leading to increased mortality and morbidity [7,9]. Approximately about one third of urban South Asians have evidence of the cardio metabolic diseases [7]. Many of such manifestations are more severe and are seen at an early age in Asian Indians than Caucasians. Cardiometabolic diseases and cardiovascular risk in Asian Indians and South Asians are also heightened by their relative increase in body fat mass, truncal subcutaneous fat mass, intra-abdominal fat mass, and also in ectopic fat deposition. Cardiovascular risk cluster also manifests at a lower level of adiposity and abdominal obesity.

It is evident in different studies [20,34] that increasing body fat among the adult population make the anthropometric indices (BMI, WHR, WHtR) imbalanced particularly among the adult population in South Asia. Over the last two decades, the prevalence of overweight and obesity rapidly increases among the adult population in Bangladesh and other south Asian countries, particularly people who live in urban settings. This is due to change in overall lifestyle of the urban residents including their food habit and work pattern (often stressful) and work load [16]. The rapid growing of modern facilities, including telecommunication and information technology under country’s digitalization concept, in urban area make its resident to perform less physical work, which may influence the body to burn less body fat than the amount required [16]. In addition, due to unplanned urbanization, there is no spaces and facilities to spend leisure time during the weekend and holiday and hence people have hangout with friends and family in a food court, which may influence them to eat more fatty food than the amount required. This is another reason for a faster growing of different types of restaurants in urban settings of Bangladesh.

Based on the findings, it can be recommended that people should think alternatively to make a balance in their anthropometric indices to reduce cardiovascular risk. The most recommended practice to make a balance in anthropometric indices is to increase routine exercise, change in food habit and make a balance between work load and leisure time. Therefore, it is necessary to take urgent policy to increase awareness on routine exercise, taking healthy food and making balance between work and family life.

This study has some limitations that needed to be mentioned. This study is based on the dataset from a sample of general patients visiting the BSMMU only and hence could not generalize the findings to all patients visiting other hospitals. Further study may be conducted in large scale considering patients from other tertiary level hospitals located in the other divisions of the country. In addition, this study excluded some patients based on self-reported known illness rather than any medical report.

Statements and Declarations

Competing Interest: The authors declared none.

Funding: This research is partially funded by the Bangabandhu Sheikh Mujib Medical University (BSMMU).

Data availability

Data are freely available upon request subject to the adherence to BSMMU data sharing policy. The request for the data can be sent to farah.sul.lail@gmail.com, the principal investigator of the project.

Ethics approval

Ethical clearance for this study was taken from the Institutional Review Board of the Bangabandhu Sheikh Mujib Medical University (BSMMU) in its 141th meeting held on July 15, 2017, with No. BSMMU/2017/7586.

Consent to Participate and Publication

All study subjects were thoroughly appraised about the nature, purpose and implications of the study as well as the spectrum of benefits and risk of the study. All study subjects were assured of adequate treatment of any risk developed in relation to study purpose and freedom to withdraw themselves from the study any time. Finally written consent of all the study subjects were taken without exploiting any weakness of subjects. Furthermore, all study participants were assured about their confidentiality by ensuring not to disclose their identity when sharing data for publication. With such assurance all the participants have given written permission for publication.

Acknowledgements

The authors acknowledge the authority of the BSMMU for providing necessary supports to conduct the study. The authors also acknowledge Professor M. Shafiqur Rahman, Institute of Statistical Research and Training, University of Dhaka for his kind supports while performing statistical analysis of data and providing valuable suggestions throughout the study.

Author Contributions

FSL and MH designed the study and developed the study protocol. FSL and SA undertook the clinical record of the data with help of technicians in the outpatient department of the BSMMU. FSL analyzed the data, interpreted the results and drafted the paper. MH made critical review and supervised the study. All authors approved the final version of the manuscript.

References

1. World Health Organization (2020) Global action plan for the prevention and control of no communicable diseases 2013–2020. 2013, Geneva: World Health Organization, Geneva, Switzerland.
2. World Health Organization (2010) Global status report on non-communicable diseases. 2010, World Health Organization: Geneva, Geneva, Switzerland.
3. Alwan A (2011) Global status report on no communicable diseases 2010: description of the global burden of NCDs, their risk factors and determinants. 2011, Geneva: World Health Organization, Geneva, Switzerland.
4. Boutayeb A (2006) The double burden of communicable and non-communicable diseases in developing countries. Trans R Soc Trop Med Hyg 100.
5. Kanno A (2012) Pre-hypertension as a significant predictor of chronic kidney disease in a general population: the Ohasama Study. Nephrol Dial Transplant 27.
6. King H, Aubert RE, Herman WH (1998) Global Burden of Diabetes, 1995–2025: Prevalence, numerical estimates, and projections. Diabetes Care 21: 1414.
7. Ghaffar A, Reddy KS, Singhi M (2004) Burden of non- communicable diseases in South Asia. BMJ 328.
8. Habib SH, Saha S (2010) Burden of non-communicable disease: Global overview. Diabetes and Metabolic Syndrome: Clinical Research and Reviews 4: 41-7.
9. Goyal A, Yusuf S (2006) The burden of cardiovascular disease in the Indian subcontinent. Indian J Med Res 124.
10. Gupta R (2005) Burden of coronary heart disease in India. Indian Heart J 57.
11. Sharma SK (2011) Prevalence of hypertension, obesity, diabetes, and metabolic syndrome in Nepal. Int J Hypertens 2011.
12. Wu S (2013) Cardiovascular events in a prehypertensive Chinese population: four-year follow-up study. Int J Cardiol 167.
13. Biswas T (2017) Bangladesh policy on prevention and control of non-communicable diseases: a policy analysis. BMC Public Health 17: 582.
14. Bleich SN (2010) Noncommunicable chronic disease in Bangladesh: Overview of existing programs and priorities going forward, in Health Policy. 2010.
15. El-Saharty SAA, Zunaid K, Koehlmoos A, Tarcey LP, Engelgau A, et al. (2013) Tackling Noncommunicable Diseases in Bangladesh: Now Is the Time. Tackling Noncommunicable Diseases in Bangladesh: Now Is the Time. 2013.
16. Laskar SI (1996) Urbanization in Bangladesh: some contemporary observations. Bangladesh Dev Stud 24.
17. Sayeed MA (2002) Prevalence of hypertension in Bangladesh: effect of socioeconomic risk factor on difference between rural and urban community. Bangladesh Med Res Counc Bull 28.
18. Khanam MA (2015) Prevalence and determinants of pre- hypertension and hypertension among the adults in rural Bangladesh: findings from a community-based study. BMC Public Health 15: 203.
19. Moni MA (2010) Blood pressure in relation to selected anthropometric measurements in senior citizens. Mymensingh Med J 19.
20. Tesfaye F (2007) Association between body mass index and blood pressure across three populations in Africa and Asia. J Hum Hypertens 21.
21. Brown CD (2000) Body Mass Index and the Prevalence of Hypertension and Dyslipidemia. Obesity Research 8: 605-19.
22. Bhowmik B (2013) Increasing prevalence of type 2 diabetes in a rural bangladeshi population: a population based study for 10 years. Diabetes Metab J 37: 46-53.
23. Sayeed MA (2003) Waist-to-height ratio is a better obesity index than body mass index and waist-to-hip ratio for predicting diabetes, hypertension and lipidemia. Bangladesh Med Res Counc Bull 29.
24. Chen Y (2006) Nutritional influence on risk of high blood pressure in Bangladesh: a population-based cross-sectional study. Am J Clin Nutr 84.
25. Lissner L (2001) Larger Hip Circumference Independently Predicts Health and Longevity in a Swedish Female Cohort. Obesity Research 9: 644-6.
26. Onat A (2001) Risk factors and cardiovascular disease in Turkey. Atherosclerosis 156: 1-10.
27. World Health organization (2006) I.D.F., Definition and diagnosis of diabetes mellitus and intermediate hyperglycemia :report of a WHO/IDF consultation, Geneva, Switzerland.
28. Chobanian AV (2003) The seventh report of the joint national committee on prevention,detection,evaluation,and treatment of high blood pressure :the JNC report. JAMA 289: 2560-72.
29. Srinivasan SR (1996) Adolescent overweight is associated with adult overweight and related multiple cardiovascular risk factors: The Bogalusa Heart Study. Metabolism 45: 235-40.
30. Roy PK (2019) Exploring socio-demographic-and geographical-variations in prevalence of diabetes and hypertension in Bangladesh: Bayesian spatial analysis of national health survey data. Spatial and spatio-temporal epidemiology 29: 71-83.
31. Li X (2013) The Prevalence and Awareness of Cardiometabolic Risk Factors in Southern Chinese Population with Coronary Artery Disease. The Scientific World Journal 2013: 416192.
32. Naghavi M (2015) Global, regional, and national age-sex specific all-cause and cause-specific mortality for 240 causes of death, 1990-2013: a systematic analysis for the global burden of disease study 2013. Lancet 385.
33. Mahar PS (2010) Prevalence of type-II diabetes mellitus and diabetic retinopathy: the Gaddap study. J Coll Physicians Surg Pak 2010: 20.
34. Zaman MM, Yoshiike N (2003) Prevalence of overweight defined by body mass index in a rural adult population of Bangladesh. J Health Popul Nutr 21.