{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,6]],"date-time":"2026-10-06T17:53:49Z","timestamp":1791309229894,"version":"4.3.1"},"reference-count":59,"publisher":"Public Library of Science (PLoS)","issue":"10","license":[{"start":{"date-parts":[[2026,10,6]],"date-time":"2026-10-06T00:00:00Z","timestamp":1791244800000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["82274307"],"award-info":[{"award-number":["82274307"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Natural Science Foundation of Anhui Province","award":["2208085MH269"],"award-info":[{"award-number":["2208085MH269"]}]},{"name":"Natural Science Foundation of Anhui Province","award":["2308085MH292"],"award-info":[{"award-number":["2308085MH292"]}]},{"DOI":"10.13039\/501100010814","name":"Anhui Provincial Department of Education","doi-asserted-by":"publisher","award":["2023AH050749"],"award-info":[{"award-number":["2023AH050749"]}],"id":[{"id":"10.13039\/501100010814","id-type":"DOI","asserted-by":"publisher"}]},{"name":"National Chinese Medicine Advantage Specialty Construction Project","award":["National Chinese Medicine Administration Letter [2024] No. 90"],"award-info":[{"award-number":["National Chinese Medicine Administration Letter [2024] No. 90"]}]},{"name":"Anhui Province Health and Wellness Research Projects in 2024","award":["AHWJ2024BAc20084"],"award-info":[{"award-number":["AHWJ2024BAc20084"]}]},{"name":"Key Teaching and Research Projects at the University Level of Anhui University of Chinese Medicine","award":["2020xjjy_zd026"],"award-info":[{"award-number":["2020xjjy_zd026"]}],"id":[{"id":"https:\/\/ror.org\/02my3bx32","id-type":"ROR","asserted-by":"crossref"}]},{"name":"Research Funds of Center for Xin\u2019an Medicine and Modernization of Traditional Chinese Medicine of IHM","award":["2023CXMMTCM018"],"award-info":[{"award-number":["2023CXMMTCM018"]}]}],"content-domain":{"domain":["www.plosmedicine.org"],"crossmark-restriction":false},"short-container-title":["PLoS Med"],"abstract":"<jats:sec id=\"sec001\">\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>The cardiovascular\u2011kidney\u2011metabolic (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\u2011risk CKM status.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec id=\"sec002\">\n                    <jats:title>Methods and findings<\/jats:title>\n                    <jats:p>We conducted a retrospective cohort study of 6,896 adults from the National Health and Nutrition Examination Survey (NHANES) 2005\u20132018, weighted for the US adult population. A two\u2011stage machine learning framework evaluated over 100 candidate survival models; the optimal model was selected via Pareto front optimization. Predictive performance was assessed using time\u2011dependent 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\u2011piecewise regression. Additive interactions were quantified by relative excess risk due to interaction (RERI) and attributable proportion (AP). Unsupervised clustering identified aging\u2011metabolic phenotypes. Weighted bootstrap mediation analysis assessed the mediating role of depression.<\/jats:p>\n                    <jats:p>\n                      The Pareto\u2011optimal simple Cox model achieved a mean C\u2011index of 0.893. CKMAI consistently outperformed PhenoAge, KDM, and the cardiometabolic index (CMI) across all outcomes. For all\u2011cause mortality, CKMAI\u2019s time\u2011dependent 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\u2011risk CKM status, 0.790 (95% CI [0.765, 0.816]). NRI and IDI were significant at all time points (\n                      <jats:italic>P<\/jats:italic>\n                      \u2009&lt;\u20090.05 for all), calibration was good (Greenwood\u2011Nam\u2011D\u2019Agostino test\n                      <jats:italic>P<\/jats:italic>\n                      \u2009&gt;\u20090.05), and DCA confirmed superior net benefit. Nonlinear associations were found for all three outcomes. Threshold analysis identified inflection points at CKMAI 61.587 (all\u2011cause mortality), 60.732 (cardiovascular mortality), and 34.092 (high\u2011risk CKM status), with steeper risk increases below each threshold. CKMAI exhibited super\u2011additive interactions with PhenoAge (RERI 15.06, 95% CI [8.14, 38.44]). Clustering revealed six distinct aging\u2011metabolic 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\u2011outcome associations (proportions mediated: 5.6% [95% CI 2.3%\u20139.0%] for all\u2011cause mortality, 8.2% [95% CI 2.8%\u201322.0%] for cardiovascular mortality, 11.1% [95% CI 6.7%\u201324.0%] for high\u2011risk 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                      <jats:italic>n<\/jats:italic>\n                      \u2009=\u2009261) supported the generalizability of the CKMAI for identifying high risk CKM status.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec id=\"sec003\">\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>CKMAI is a valid, CKM\u2011specific aging index that outperforms universal biological age measures in predicting mortality and identifying high\u2011risk CKM status. Its super\u2011additive 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.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1371\/journal.pmed.1005078","type":"journal-article","created":{"date-parts":[[2026,10,6]],"date-time":"2026-10-06T17:21:47Z","timestamp":1791307307000},"page":"e1005078","update-policy":"https:\/\/doi.org\/10.1371\/journal.pmed.corrections_policy","source":"Crossref","is-referenced-by-count":0,"title":["A machine learning-derived aging index for risk stratification and mortality prediction in cardiovascular-kidney-metabolic syndrome: A retrospective cohort study"],"prefix":"10.1371","volume":"23","author":[{"ORCID":"https:\/\/orcid.org\/0009-0002-7903-9743","authenticated-orcid":true,"given":"Zhengyang","family":"Zhu","sequence":"first","affiliation":[{"name":"Anhui University of 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Hospital"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hua","family":"Jin","sequence":"additional","affiliation":[{"name":"Anhui University of Chinese Medicine","place":["Hefei, Anhui Province, China"],"department":["First Affiliated Hospital"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lei","family":"Zhang","sequence":"additional","affiliation":[{"name":"Anhui University of Chinese Medicine","place":["Hefei, Anhui Province, China"],"department":["First Affiliated Hospital"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yiping","family":"Wang","sequence":"additional","affiliation":[{"name":"Anhui University of Chinese Medicine","place":["Hefei, Anhui Province, China"],"department":["First Affiliated 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