Abstract
Coronary artery disease (CAD) is a leading cause of morbidity and mortality globally, highlighting the need for improved risk assessment tools. Traditional measures like body mass index (BMI) do not fully capture the risk associated with body fat distribution. The weight-adjusted waist index (WWI) has emerged as a potential anthropometric measure that may provide a more precise evaluation of central adiposity and its cardiovascular risks. We analyzed data from 49,240 participants in the National Health and Nutrition Examination Survey (NHANES) from 1999 to 2020. Participants were stratified into quartiles based on WWI: Q1 (WWI < 10.43), Q2 (10.43 ≤ WWI < 10.99), Q3 (10.99 ≤ WWI < 11.56), and Q4 (WWI ≥ 11.56). The primary outcome was self-reported coronary artery disease (CAD). Multivariate logistic regression, generalized additive models, and stratified analyses were conducted. The predictive performance of WWI versus body mass index (BMI) for CAD was assessed using receiver operating characteristic (ROC) curves, with differences in area under the curve (AUC) evaluated by DeLong's test. Multivariate logistic regression analysis showed a positive association between WWI and CAD prevalence, which persisted after adjusting for confounding variables. Higher WWI was associated with increased risk of CAD, with Group 4 showing the highest risk compared to Group 1 (OR 2.06, 95% CI 1.37-3.09, P < 0.001). This relationship was not observed between BMI and CAD risk. Subgroup analysis revealed that the association between WWI and CAD prevalence was more pronounced in participants aged ≤ 60 years (P for interaction < 0.001). ROC curve analysis demonstrated that WWI had superior predictive ability for CAD compared to BMI (AUC 0.694 vs. 0.547, De-long test P < 0.001). WWI demonstrated a stronger association with CAD prevalence compared to BMI in the US population, with this relationship particularly pronounced among younger adults. Longitudinal studies are needed to establish this relationship.