{
  "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.",
  "abstract_shown": true,
  "affiliations": [
    "First Affiliated Hospital, Anhui University of Chinese Medicine, Hefei, Anhui Province, China"
  ],
  "article_type": "Research Article",
  "arxiv": null,
  "author_total": 7,
  "authors": [
    {
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        0
      ],
      "name": "Zhengyang Zhu"
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    {
      "name": "Kejun Ren"
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    {
      "name": "Dong Wang"
    },
    {
      "name": "Yong Lv"
    },
    {
      "name": "Hua Jin"
    },
    {
      "name": "Lei Zhang"
    },
    {
      "name": "Yiping Wang"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/bpk93c0btyja3.bib",
  "cited_by": 0,
  "doi": "10.1371/journal.pmed.1005078",
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  "licence": "CC-BY-4.0",
  "licence_label": "CC BY 4.0",
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  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
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  "native_subjects": [
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    ],
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      "/Medicine and health sciences/Medical conditions/Metabolic disorders/Diabetes mellitus"
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      "/Medicine and health sciences/Mental health and psychiatry/Mood disorders/Depression"
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  "pdf_url": "https://journals.plos.org/plosmedicine/article/file?id=10.1371/journal.pmed.1005078&type=printable",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-06",
  "publisher": "Public Library of Science",
  "ris": "https://research.draconitic.com/paper/bpk93c0btyja3.ris",
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    {
      "first_seen": "2026-10-30T12:17:30Z",
      "native_id": "10.1371/journal.pmed.1005078",
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  "subjects": [
    {
      "field": "medicine",
      "origin": "plos:cardiovascular diseases",
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  "title": "A machine learning-derived aging index for risk stratification and mortality prediction in cardiovascular-kidney-metabolic syndrome: A retrospective cohort study",
  "title_text": "A machine learning-derived aging index for risk stratification and mortality prediction in cardiovascular-kidney-metabolic syndrome: A retrospective cohort study",
  "type": "peer-reviewed",
  "updated_at": "2026-10-30T12:17:30Z",
  "url": "https://research.draconitic.com/paper/bpk93c0btyja3",
  "venue": "PLOS Medicine",
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