Abstract
Background
The cardiovascular‑kidney‑metabolic (CKM) syndrome shows substantial heterogeneity in progression, yet existing biological age indices are not tailored to CKM pathways. We developed an aging index (CKMAI) using machine learning and evaluated its predictive performance for mortality and high‑risk CKM status.
Methods and findings
We conducted a retrospective cohort study of 6,896 adults from the National Health and Nutrition Examination Survey (NHANES) 2005–2018, weighted for the US adult population. A two‑stage machine learning framework evaluated over 100 candidate survival models; the optimal model was selected via Pareto front optimization. Predictive performance was assessed using time‑dependent area under the curve (AUC), net reclassification improvement (NRI), integrated discrimination improvement (IDI), calibration, and decision curve analysis (DCA). Nonlinear relationships and threshold effects were examined with restricted cubic splines and two‑piecewise regression. Additive interactions were quantified by relative excess risk due to interaction (RERI) and attributable proportion (AP). Unsupervised clustering identified aging‑metabolic phenotypes. Weighted bootstrap mediation analysis assessed the mediating role of depression.
The Pareto‑optimal simple Cox model achieved a mean C‑index of 0.893. CKMAI consistently outperformed PhenoAge, KDM, and the cardiometabolic index (CMI) across all outcomes. For all‑cause mortality, CKMAI’s time‑dependent AUCs were 0.893 (95% CI [0.851, 0.925]) at 3 years, 0.907 (95% CI [0.884, 0.927]) at 5 years, and 0.890 (95% CI [0.868, 0.910]) at 10 years; for cardiovascular mortality, 0.904 (95% CI [0.840, 0.943]), 0.937 (95% CI [0.892, 0.960]), and 0.937 (95% CI [0.900, 0.959]); for high‑risk CKM status, 0.790 (95% CI [0.765, 0.816]). NRI and IDI were significant at all time points (
P
P
> 0.05), and DCA confirmed superior net benefit. Nonlinear associations were found for all three outcomes. Threshold analysis identified inflection points at CKMAI 61.587 (all‑cause mortality), 60.732 (cardiovascular mortality), and 34.092 (high‑risk CKM status), with steeper risk increases below each threshold. CKMAI exhibited super‑additive interactions with PhenoAge (RERI 15.06, 95% CI [8.14, 38.44]). Clustering revealed six distinct aging‑metabolic phenotypes; the frail elderly cluster had the highest mortality risk (hazard ratio [HR] 11.74, 95% CI [7.85, 17.57]). Depression partially mediated the CKMAI‑outcome associations (proportions mediated: 5.6% [95% CI 2.3%–9.0%] for all‑cause mortality, 8.2% [95% CI 2.8%–22.0%] for cardiovascular mortality, 11.1% [95% CI 6.7%–24.0%] for high‑risk CKM status). Findings were robust across prespecified subgroups and multiple sensitivity analyses. A key limitation is the lack of validation in geographically independent cohorts with complete mortality follow-up; however, temporal validation within NHANES and preliminary external validation in a hospital based Chinese cohort (
n
= 261) supported the generalizability of the CKMAI for identifying high risk CKM status.
Conclusions
CKMAI is a valid, CKM‑specific aging index that outperforms universal biological age measures in predicting mortality and identifying high‑risk CKM status. Its super‑additive interactions with PhenoAge and the partial mediation by depression offer new mechanistic insights into CKM heterogeneity. By integrating a diverse set of clinical laboratory measures, nutritional assessments, lifestyle factors, and social determinants of health, CKMAI provides a practical, implementable tool for risk stratification and targeted prevention in CKM syndrome.