Abdominal Obesity and Coronary Heart Disease in Thai Men

Abstract

Objective: To determine the association of four simple anthropometric indices with coronary heart disease (CHD) in Thai men, and to determine the optimal cut-off points for each index in the prediction of CHD. Research Methods and Procedures:This is a cohort study with 17 years of follow-up. A total of 2536 male employees from the Electricity Generating Authority of Thailand 35 to 59 years of age at baseline were included in the study. Height, weight, waist circumference, and hip circumference were measured to generate BMI, waist circumference (WC), waist-to-hip ratio (WHR), and waist-to-height ratio (WHtR). Cox regression models were used to estimate hazard ratios by thirds of each index. Receiver operating characteristic curves were used to assess discrimination of CHD. Results: WHtR was most strongly associated with CHD events in Thai men. The age-adjusted hazard ratio for those in the highest, compared with the lowest, third was 2.89 (1.37, 6.11). Although WHtR had the largest area under the receiver operating characteristic curve (AUC) with the optimal cut-off estimated to be 0.51 (sensitivity, 55%; specificity, 61%), no statistically significant difference (p Ͼ 0.10) was found between the AUC for WHtR and that for the other three indices. Conclusion: WHtR is, marginally, the best of the four indices considered to predict CHD events in Thai men.

Prevention of Overweight/Obesity as a Strategy to Optimize Cardiovascular Health

“…20 Markers of central obesity such as waist circumference and waist-to-hip ratio have been shown to be independently associated with CVD risk. [21][22][23][24] Furthermore, obesity has been shown to be associated with the development of T2DM, with excess adiposity being a key contributor to the development of insulin resistance. 25,26 In fact, the significant rise in the prevalence of diabetes mellitus is thought to be primarily due to the obesity epidemic.…”

A systematic review of waist-to-height ratio as a screening tool for the prediction of cardiovascular disease and diabetes: 0·5 could be a suitable global boundary value

“…In men (29,32,36) and women (29,31,34,36) Not in whole population or non-hypertensive men and women (33) , for obstructive CAD, all adverse events, major adverse events or all-cause mortality (37) All stroke 2/2 In women (38) and in women before and after adjustment for BMI (30) 2/2…”

“…In men (29,32,36) , women (29,31,34,36) and men and women (35) In non-hypertensive men, but not whole population (33) , for all adverse events but not for obstructive CAD, major adverse events or all-cause mortality (37) 7/9…”

“…This information was used to further compare the usefulness of WHtR and WC as practical diagnostic tools. At the stage of searching full papers, thirty-one papers with AUROC analysis were identified, twenty of which (1,23,24,26,28,32,34,36,48,51,54,56,60,61,63,65,66,72,73,77) also met criteria for the review and eleven (97 -107) of which were excluded from the systematic review at that stage (as they did not report the required analysis type and usually instead focused on AUROC analysis). Of these thirty-one papers with AUROC analysis, four did not report AUROC values (48,63,98,107) , but only reported the optimum cut-off values and one did not report values for WC (100) , leaving twenty-six studies reporting full ROC information for different ethnic groups and age ranges.…”

Effectiveness of Various Anthropometric Indices in Prediction of Cardiovascular Risk Among Adult Jains

“…Waist height ratio demonstrated the best model fit and strongest associations with cardiovascular diseases [43]. Aekplakorn et al also reported that WHtR to be associated with adverse cardiovascular risk in men [44]. Cox et al reported WHtR to be having stronger linear associations with log odds of CVD development than BMI in men as compared to women [45].…”