Individuals with cardiovascular-kidney-metabolic (CKM) syndrome exhibit a substantially elevated risk of all-cause and cardiovascular-specific mortality. Although estimated glucose disposal rate (eGDR) and a body shape index (ABSI) are commonly used indicators for assessing insulin resistance and atherosclerotic risk, respectively, evidence regarding their combined effect on all-cause and cardiovascular-specific mortality in patients with CKM syndrome remains insufficient. Investigating this combined impact may help improve risk stratification in this population. This study utilized data from the National Health and Nutrition Examination Survey (NHANES, 1999–2018), including 18,186 individuals with stage 0–4 CKM syndrome. Cox proportional hazards models, Kaplan–Meier curves and subgroup analyses were used to evaluate the associations between eGDR and ABSI and mortality risk. The integrated discrimination improvement (IDI) and net reclassification index (NRI) were used to assess the incremental prognostic value of eGDR and ABSI. Finally, six machine learning algorithms were applied to develop predictive models. During the follow-up period, a total of 2536 all-cause mortality and 790 cardiovascular-specific mortality were documented. After multivariable adjustment, both low eGDR and high ABSI independently predicted mortality risk. Combined analysis revealed that individuals with both Low-eGDR and High-ABSI had the highest mortality risk: all-cause mortality hazard ratio (HR) = 2.79 (95% CI 2.30–3.38) and cardiovascular-specific mortality HR = 4.53 (95% CI 2.96–6.92). However, the interaction effect was not statistically significant. Among the six machine learning algorithms, XGBoost demonstrated the best performance, with areas under the curve (AUC) of 0.877 and 0.850 for predicting all-cause and cardiovascular-specific mortality, respectively. Both eGDR and ABSI are independent and combined predictors of mortality risk among individuals with CKM syndrome. Their combined use significantly improves risk stratification and machine learning models provide an effective tool for precise risk assessment in this population.
Fu et al. (Thu,) studied this question.