Abdominal volume index is a better predictor of visceral fat in patients with type 2 diabetes: a cross-sectional study in Ho municipality, Ghana

ABSTRACT Background Visceral obesity is associated with increased risk of metabolic disorders and cardiovascular disease, hence, diagnosing visceral fat is indispensable in clinical practice. However, the diagnostic capacity of waist–hip ratio (WHR), conicity index (CI), and abdominal volume index (AVI) to predict visceral obesity in patients with type 2 diabetes remains unclear. This study was designed to evaluate the performance of WHR, CI, and AVI in predicting visceral fat among patients with type 2 diabetes in Ho municipality. Methods A hospital-based cross-sectional survey involved 221 patients with type 2 diabetes. A questionnaire was designed to collect data on demography and other relevant variables. Anthropometric measurements were obtained using standard methods. Visceral fat was measured using bioelectrical impedance analysis (BIA). The diagnostic performance of WHR, CI, and AVI in predicting visceral fat was evaluated based on receiver operating characteristics (ROC) curve analyses. Pearson correlation analysis was used to determine the relationship between adiposity indices and visceral fat. Results Among men, the optimal threshold for AVI, >15.56, demonstrated the highest sensitivity, 87.5% and specificity, 80.71% compared to CI and WHR while among women, the optimal cutoff value for AVI, >18.49, produced the highest sensitivity, 77.05% and specificity, 85.29%. Likewise, AVI showed a better discriminatory ability in the diagnosis of visceral fat (AUC: 0.89; p < 0.001) compared to CI (AUC: 0.68; p < 0.003), and WHR (AUC: 0.73; p < 0.001) in men and AUC: 0.89; p < 0.001 compared to CI (AUC: 0.62; p < 0.023), and WHR (AUC: 0.59; p < 0.066) in women. Similarly, the strongest positive correlation was observed between visceral fat and AVI after adjustment for age (male r = 0.787, p < 0.01; female r = 0.770, p < 0.01). Conclusion AVI appeared to have outperformed CI and WHR in the diagnosis of visceral fat. Therefore, it could be a better predictive tool for visceral obesity among patients with type 2 diabetes in low-resource settings.