Anthropometric indicators of obesity for identifying cardiometabolic risk factors in a rural <scp>B</scp>angladeshi population

Bhowmik, Bishwajit; Munir, Sanjida Binte; Diep, Lien My; Siddiquee, Tasnima; Habib, Samira Humaira; Samad, Mohammad Abdus; Khan, Abul Kalam Azad; Hussain, Akhtar

doi:10.1111/jdi.12053

Cited by 30 publications

(47 citation statements)

References 27 publications

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“…On the other hand, the findings disclosed in this study approached to those found in studies carried out in Brazil (34%) and Bangladesh (32%), that estimated the %BF using predictive equations, involving age and anthropometric measurements. [47,48] …”

Section: Discussionmentioning

confidence: 99%

Waist-to-height ratio is the best anthropometric predictor of hypertension

Caminha¹,

Ferreira²,

Costa³

et al. 2017

Medicine

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The WHO recommends the use of some anthropometric parameters as a screening resource for individuals under cardiometabolic risk. However, in the validation of these indicators, Brazilian women were not included. These women have different anthropometric profile compared to women who integrated the samples of the validation studies. We aimed to verify the accuracy of anthropometric indicators as a resource for the screening of women with hypertension. A cross-sectional study, with a probability sample of 3143 women (20–49 years) from the state of Alagoas (northeast of Brazil), was carried out. Hypertension was identified by systolic blood pressure (SBP) ≥140 mm Hg and/or diastolic BP ≥90 mm Hg and/or regular use of antihypertensive drugs. The anthropometric indicators analyzed were BMI, waist circumference, waist-to-hip ratio, waist-to-height ratio (WHtR), body fat percentage, and conicity index. The accuracy definition of the indicators and the identification of best cut-off points were carried out on the basis of ROC curve analysis and Youden index, respectively. The prevalence of hypertension was 21.8%. All indicators used in hypertension identification had area under the ROC curve (AUC) >0.5. The WHtR with cut-off point of 0.54 was the best performance indicator (AUC = 0.72; P < 0.05; sensitivity = 67%, specificity = 66%). The WHtR with cut-off point of 0.54 has constituted the most accurate indicator in the screening of women with hypertension. In the absence of specific studies and considering the largest ethnic proximity and environmental/epidemiological similarity, the findings now obtained can be extended to women of other Brazilian states, especially those in the Northeastern region.

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Section: Discussionmentioning

confidence: 99%

Waist-to-height ratio is the best anthropometric predictor of hypertension

Caminha¹,

Ferreira²,

Costa³

et al. 2017

Medicine

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“…The recruitment methods and examination procedures of this rural study have been described before18–20. A total of 10 villages were randomly selected from 25 villages in the region.…”

Section: Methodsmentioning

confidence: 99%

“…The present cross-sectional study was carried out in 2009 between March and December in a rural community, 'Chandra', 40 km north of Dhaka, the capital city of Bangladesh. The recruitment methods and examination procedures of this rural study have been described before [18][19][20] . A total of 10 villages were randomly selected from 25 villages in the region.…”

Section: Participantsmentioning

confidence: 99%

Comparison of the prevalence of metabolic syndrome and its association with diabetes and cardiovascular disease in the rural population of Bangladesh using the modified National Cholesterol Education Program Expert Panel Adult Treatment Panel III and International Diabetes Federation definitions

Bhowmik

Afsana

Siddiquee

et al. 2014

J of Diabetes Invest

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Aims/IntroductionTo compare the prevalence of metabolic syndrome (MS) using the modified National Cholesterol Education Program Adult Treatment Plan III (NCEP) and the International Diabetes Federation (IDF) definitions and, using both definitions, determine and compare the association of MS, prediabetes, type 2 diabetes, hypertension (HTN) and cardiovascular disease risk (CVD).Materials and MethodsA total of 2,293 randomly selected participants (aged ≥20 years) in a rural community in Bangladesh were investigated in a population-based cross-sectional study. Sociodemographic and anthropometric characteristics, blood pressure, blood glucose, and lipid profiles were studied. Age-adjusted data for MS and cardiometabolic risk factors were assessed, and their relationships were examined.ResultsThe age-adjusted prevalence of MS was 30.7% (males 30.5%; females 30.5%) using the NCEP definition, and 24.5% (males 19.2%, females 27.5%) using the IDF definition. The prevalence of MS using the NCEP definition was also higher in study participants with prediabetes, type 2 diabetes, HTN and CVD risk. The agreement rate between both definitions was 92% (k = 0.80). The NCEP definition had a stronger association with type 2 diabetes and HTN (odds ratio 12.4 vs 5.2; odds ratio 7.0 vs 4.7, respectively) than the IDF definition. However, the odds ratios for prediabetes and CVD risk were not significantly different.ConclusionsThe prevalence of MS was higher using the NCEP definition, and was more strongly associated with prediabetes, type 2 diabetes, HTN and CVD in this Bangladeshi population.

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“…First of all, in the present study, both BMI and WC were strongly correlated (r = 0.85). Second, previous prospective and cross-sectional studies have found WHR to be a better predictor of undiagnosed diabetes in the Bangladeshi population 20,[29][30][31] . The usefulness of risk-prediction tools is generally assessed based on their sensitivity, specificity, and ROC curves.…”

Section: Discussionmentioning

confidence: 95%

Simple risk score to detect rural Asian Indian (Bangladeshi) adults at high risk for type 2 diabetes

Bhowmik

Akhter

Ali

et al. 2015

J of Diabetes Invest

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Aims/IntroductionTo develop and evaluate a simple, non-invasive, diabetes risk score for detecting individuals at high risk for type 2 diabetes in rural Bangladesh.Materials and MethodsData from 2,293 randomly selected individuals aged ≥20 years from a cross-sectional study in a rural community of Bangladesh (2009 Chandra Rural Study) was used for model development. The validity of the model was assessed in another rural cross-sectional study (2009 Thakurgaon Rural Study). The logistic regression model used included age, sex, body mass index, waist-to-hip ratio and hypertension status to predict individuals who were at high risk for type 2 diabetes.ResultsOn applying the developed model to both cohorts, the area under the receiver operating characteristic curve was 0.70 (95% confidence interval 0.68–0.72) for the Chandra cohort and 0.71 (95% confidence interval 0.68–0.74) for the Thakurgaon cohort. The risk score of >9 was shown to have the optimal cut-point to detect diabetes. This score had a sensitivity of 62.4 and 75.7%, and specificity of 67.4 and 61.6% in the two cohorts, respectively. This risk score was shown to have improved sensitivity and specificity to detect type 2 diabetes cases compared with the Thai, Indian, Omani, UK, Dutch, Portuguese and Pakistani diabetes risk scores.ConclusionsThis simple, non-invasive risk score can be used to detect individuals at high risk for type 2 diabetes in rural Bangladesh. Subjects with a score of 9 or above (out of 15) should undergo an oral glucose tolerance test for definitive diagnosis of diabetes.

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Anthropometric indicators of obesity for identifying cardiometabolic risk factors in a rural Bangladeshi population

Cited by 30 publications

References 27 publications

Waist-to-height ratio is the best anthropometric predictor of hypertension

Waist-to-height ratio is the best anthropometric predictor of hypertension

Comparison of the prevalence of metabolic syndrome and its association with diabetes and cardiovascular disease in the rural population of Bangladesh using the modified National Cholesterol Education Program Expert Panel Adult Treatment Panel III and International Diabetes Federation definitions

Simple risk score to detect rural Asian Indian (Bangladeshi) adults at high risk for type 2 diabetes

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