aceA machine learning-derived aging index for risk stratification and mortality prediction in cardiovascular-kidney-metabolic syndrome: A retrospective cohort study
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…
A machine-learning-derived CKM-specific aging index predicted all-cause and cardiovascular mortality and high-risk CKM status better than PhenoAge, KDM and the cardiometabolic index in a weighted US adult cohort.
The authors acknowledge a lack of validation in geographically independent cohorts with complete mortality follow-up; external validation was only preliminary, in a hospital-based Chinese cohort of 261 people.
Evidence
- Peer-reviewedPLOS Medicine2026-10-06
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Truvace Impact Record TRV-2026-1817, v1: “A machine learning-derived aging index for risk stratification and mortality prediction in cardiovascular-kidney-metabolic syndrome: A retrospective cohort study.” Truvace, 2026-10-18. /record/TRV-2026-1817 (accessed at citation time). sha256 cb21f280124b01c6…
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