Comparative predictive performance of anthropometric indices and associated risk factors for obesity among Beninese adults: A cross-sectional study
Paper Details
Comparative predictive performance of anthropometric indices and associated risk factors for obesity among Beninese adults: A cross-sectional study
Abstract
Sub-Saharan Africa is undergoing an epidemiological transition marked by an alarming rise in obesity. This study compared the predictive performance of several anthropometric indices for obesity and identified independent factors associated with its occurrence among Beninese adults. A comparative cross-sectional study was conducted between February 2025 and February 2026 in two departments of Benin (Borgou and Littoral), enrolling 462 adults: 154 obese cases (Body Mass Index, BMI ≥ 30 kg/m²) and 308 age- and sex-matched non-obese controls. Discriminative performance was evaluated using binary logistic regression, receiver operating characteristic (ROC) curve analysis with Youden’s index, DeLong’s test for area under the curve (AUC) comparison, and classification and regression tree (CART) modeling. Participants were predominantly female (67.1%) with a mean age of 45.2 ± 12.8 years. After adjusting for confounders, obesity was significantly associated with employment status, family history of hypertension or type 2 diabetes, physical inactivity, and waist-to-height ratio (WHtR). The Ponderal Index (PI) demonstrated the highest discriminative capacity (AUC = 0.921), followed by the Abdominal Volume Index (AVI); AUC = 0.903), the Body Roundness Index (BRI); AUC = 0.896), and WHtR (AUC= 0.896). The CART decision tree utilizing AVI and Body Adiposity Index (BAI) achieved an accuracy of 84.1%, with 76.1% sensitivity and 88.0% specificity. This study identifies key risk factors for obesity and highlights the excellent predictive performance of weight-independent anthropometric indices, particularly PI, AVI, WHtR, and BRI. Given their simplicity and low cost, these indices represent highly valuable tools for early screening and monitoring in resource-limited settings.
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