A machine learning-derived aging index for risk stratification and mortality prediction in cardiovascular-kidney-metabolic syndrome: A retrospective cohort study
- 1First Affiliated Hospital, Anhui University of Chinese Medicine, Hefei, Anhui Province, China
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. 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. Why was this study done?: What did the researchers do and find?: What do these findings mean?: In this cohort study, Zhengyang Zhu and colleagues developed a machine learning-derived cardiovascular-kidney-metabolic aging index that outperformed established biological age measures in identifying high-risk CKM profiles and predicting mortality, while revealing distinct aging-metabolic phenotypes and associations with depression that may inform risk stratification and prevention efforts.
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BibTeX
@article{zhu2026machine,
title = {{A machine learning-derived aging index for risk stratification and mortality prediction in cardiovascular-kidney-metabolic syndrome: A retrospective cohort study}},
author = {Zhengyang Zhu and Kejun Ren and Dong Wang and Yong Lv and Hua Jin and Lei Zhang and Yiping Wang},
journal = {PLOS Medicine},
year = {2026},
month = oct,
doi = {10.1371/journal.pmed.1005078},
url = {https://journals.plos.org/plosmedicine/article?id=10.1371/journal.pmed.1005078},
note = {CC BY 4.0},
}
RIS
TY - JOUR
TI - A machine learning-derived aging index for risk stratification and mortality prediction in cardiovascular-kidney-metabolic syndrome: A retrospective cohort study
AU - Zhengyang Zhu
AU - Kejun Ren
AU - Dong Wang
AU - Yong Lv
AU - Hua Jin
AU - Lei Zhang
AU - Yiping Wang
PY - 2026
DA - 2026/10/06
JO - PLOS Medicine
DO - 10.1371/journal.pmed.1005078
UR - https://journals.plos.org/plosmedicine/article?id=10.1371/journal.pmed.1005078
AB - 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. 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. Why was this study done?: What did the researchers do and find?: What do these findings mean?: In this cohort study, Zhengyang Zhu and colleagues developed a machine learning-derived cardiovascular-kidney-metabolic aging index that outperformed established biological age measures in identifying high-risk CKM profiles and predicting mortality, while revealing distinct aging-metabolic phenotypes and associations with depression that may inform risk stratification and prevention efforts.
SN - 1549-1676
ER -