Association of estimated glucose disposition rate with aging acceleration and mortality risk in individuals with cardiovascular-kidney-metabolic syndrome: evidence from two large national population-based studies.

objectiveThis study investigated the relationship between estimated glucose disposal rate (eGDR), aging acceleration (AgeAccel), and mortality in adults diagnosed with cardiovascular-kidney-metabolic (CKM) stages 1 to 4.

methodsThe study utilized data from 4,826 adults with CKM syndrome stages 1 to 4, collected from the National Health and Nutrition Examination Survey (NHANES) conducted during the 2005-2010 survey cycles. The assessment of AgeAccel was performed using two complementary measures: phenotypic AgeAccel (PhenoAgeAccel) and biological AgeAccel (BioAgeAccel). Survey-weighted logistic regression and Cox proportional hazards models were used to assess the associations of eGDR with AgeAccel and mortality risk, respectively. To assess the prognostic value of eGDR for mortality risk, we implemented a suite of nine distinct machine learning models. Additionally, a nomogram was developed to enhance the clinical applicability of our findings. Furthermore, we performed causal mediation analysis to quantify the proportion of the total effect of eGDR on mortality that was mediated through AgeAccel. To ensure the robustness of the results, we replicated our primary analyses using data from the nationally representative China Health and Retirement Longitudinal Study (CHARLS) cohort.

resultsOur analysis included 4,826 NHANES participants, among whom we documented 831 all-cause mortality events and 208 cardiovascular disease (CVD)-specific deaths during follow-up. In multivariable-adjusted Cox regression models, each unit increase in eGDR was significantly associated with a 10% reduction in all-cause mortality risk (Hazard ratio [HR] = 0.90, 95% Confidence interval [CI] 0.86-0.93) and a 13% decrease in CVD mortality risk (HR = 0.87, 95% CI 0.81-0.93). Additionally, eGDR showed a negative association with AgeAccel, including both BioAgeAccel (odds ratio [OR] = 0.85, 95% CI 0.82-0.87) and PhenoAgeAccel (OR = 0.78, 95% CI 0.75-0.80). For predicting all-cause mortality from eGDR, the K-Nearest Neighbors (KNN) showed superior discrimination (Area Under the Curve [AUC]: 0.926), exceeding the performance of other machine learning algorithms in a comparative evaluation. Mediation analysis revealed that the protective effect of higher eGDR was partially explained by slower PhenoAgeAccel, with mediation effects accounting for 23.53% and 15.73% of the total impact on all-cause and CVD mortality, respectively.

conclusionsIn the CKM population, lower eGDR levels may be associated with both AgeAccel and an increased risk of mortality, with AgeAccel potentially mediating the relationship between eGDR and mortality. These findings suggested that eGDR could serve as a potential predictor and intervention target for delaying aging and reducing mortality risk.