Abstract
BACKGROUND: The glucose disposal rate (eGDR) and a body shape index (ABSI) are predictors strongly associated with cardiovascular disease (CVD) and outcomes. However, whether they have additive effects on CVD risk is unknown. This study aimed to investigate whether combined assessment of eGDR and ABSI could improve prediction of CVD risk. METHODS: The current study used data from NHANES from 1999 to 2018 and included 14,237 participants. Receiver operating characteristic (ROC) curve was used to evaluate the performance of each indicator in predicting CVD. Machine-learning algorithms were applied to screen variables to adjust the model. Finally, the ROC curve, net reclassification improvement (NRI), integrated discrimination improvement (IDI), calibration curve and decision curve analysis (DCA) were used to evaluate the predictive performance of the combination of eGDR and ABSI. RESULTS: The ROC curve showed that eGDR (C-statistics: 0.7255) and ABSI (0.7093) had the highest predictive performance. Among 14,237 participants, multivariate logistic regression showed that lower eGDR (≤6.448) and higher ABSI (≥0.086) significantly increased CVD risk (OR = 11.792, P < 0.05). The model adjusted by machine learning significantly improved CVD risk prediction (Model 3 vs. Model 1, C-statistics: 0.849 vs. 0.753). These findings were also consistent in the NRI (model 3 vs. model 1: 0.108), IDI (0.107), calibration curve, and DCA analyses. Subgroup analyses confirmed the robustness of these findings, with enhanced predictive performance particularly in younger populations. CONCLUSION: The eGDR and ABSI have potential additive effects on predicting CVD risk, and have excellent predictive performance, which can evaluate cardiovascular risk more comprehensively.