A Cross-sectional Study Evaluating the Relationship Between Emerging Adiposity Indicators in Predicting Cardiometabolic Risk - B, Ignatius.; Krishnan, Durga; Rao, V.R.Mohan; Gautham, S. (Journal of Neonatal Surgery, 2025)
Background: Obesity is a leading global health issue, significantly contributing to cardiometabolic diseases such as type 2 diabetes, hypertension, and coronary artery disease. Body Mass Index (BMI) is traditionally used to predict obesity-related health risks but does not account for variations in fat distribution, particularly visceral adiposity, which is strongly associated with cardiometabolic risk. Emerging adiposity indicators, including the Clínica Universidad de Navarra-Body Adiposity Estimator (CUN-BAE), Waist-to-Height Ratio (WHtR), and Waist Circumference (WC), have shown promise as better predictors of cardiometabolic risk, especially in diverse populations. Methods: A cross-sectional study was conducted on 100 participants aged ≥18 years, recruited from clinical and community health settings. Anthropometric data, including BMI, WC, WHtR, and CUN-BAE, were collected using standardized procedures. Cardiometabolic risk was assessed based on the presence of hypertension, diabetes mellitus, or dyslipidemia. Logistic regression models were used to assess associations between adiposity indicators and cardiometabolic risk, with results presented as odds ratios (ORs). Receiver operating characteristic (ROC) curves and the area under the curve (AUC) values were calculated to compare predictive performance. Results: CUN-BAE demonstrated the strongest association with cardiometabolic risk (adjusted OR: 1.55, 95% CI: 1.39–1.73, p < 0.001) and the highest predictive accuracy (AUC: 0.78). WHtR and WC also outperformed BMI in predicting cardiometabolic risk. Subgroup analyses revealed consistent performance across age, sex, and ethnicity, with slightly higher accuracy in females and older adults. Conclusion: CUN-BAE and WHtR are superior to BMI in predicting cardiometabolic risk. These indicators, by capturing central obesity and fat distribution, provide more precise risk stratification, emphasizing the need to incorporate them into routine clinical practice for better prevention and management of cardiometabolic diseases..