Comparison of adiposity anthropometric indices and their associations with visceral fat levels determined by bioelectrical impedance analysis among diabetic patients.

Visceral fat (VF) and its effect on metabolic disorders have been extensively studied; nevertheless, there is a need for a simple and reliable index to equally assess VF in low-resource settings. This multihospital-based study was designed to compare the five adiposity anthropometric indices and their associations with VF levels determined by bioelectrical impedance analysis as the reference standard among diabetic patients. A pretested questionnaire was used to collect anthropometric, biochemical and hemodynamic data from 473 diabetic patients. Regression analysis was performed to determine the associations between the five adiposity anthropometric indices and VF levels. Receiver operating characteristic (ROC) curves were used to confirm the predictive capacities of the five adiposity anthropometric indices with VF levels. The waist-to-height ratio WHtR showed the greatest ROC value [area under the curve (AUC) = 0.745, p ˂0.001] in identifying diabetic patients with high VF levels compared to body mass index BMI [AUC = 0.584, p = 0.047], waist circumference WC [AUC = 0.723, p ˂0.001], hip circumference HC [AUC = 0.647, p ˂0.001] and waist-to-hip ratio WHR [AUC = 0.711, p ˂0.001]. Likewise, the regression analysis of WHtR and VF levels revealed the strongest association [unadjusted odds ratio (UOR) = 21.49, p < 0.001] compared to BMI [UOR = 6.77, p = 0.008], WC [UOR = 6.37, p < 0.001], HC [UOR = 5.93, p = 0.002] and WHR [UOR = 13.17, p < 0.001]. The optimal cut-off values to identify diabetic patients with high VF levels were > 0.5 for WHtR, > 25.7 kg/m2 for BMI, > 80.5 cm for WC, > 95.5 cm for HC and > 0.82 for WHR. WHtR was shown to have overpowered BMI, HC, WC and WHR in identifying diabetic patients with high VF levels. Therefore, the Ghana Health Service could recommend WHtR as a better diagnostic index for assessing VF levels due to its high predictive capacity.