{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"institution":[{"name":"medRxiv"}],"indexed":{"date-parts":[[2026,10,6]],"date-time":"2026-10-06T17:52:52Z","timestamp":1791309172825,"version":"4.3.1"},"posted":{"date-parts":[[2026,10,2]]},"group-title":"Epidemiology","reference-count":68,"publisher":"openRxiv","license":[{"start":{"date-parts":[[2026,10,2]],"date-time":"2026-10-02T00:00:00Z","timestamp":1790899200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.medrxiv.org\/about\/FAQ#license"}],"funder":[{"name":"National Institute of Child Health and Human Development","award":["T32 HD113301"],"award-info":[{"award-number":["T32 HD113301"]}]},{"id":[{"id":"https:\/\/ror.org\/01cawbq05","id-type":"ROR","asserted-by":"publisher"}]},{"award":["R01AG069901"],"award-info":[{"award-number":["R01AG069901"]}],"id":[{"id":"https:\/\/ror.org\/049v75w11","id-type":"ROR","asserted-by":"publisher"}]},{"award":["U19AG063744"],"award-info":[{"award-number":["U19AG063744"]}],"id":[{"id":"https:\/\/ror.org\/049v75w11","id-type":"ROR","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"accepted":{"date-parts":[[2026,10,2]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                <jats:p>Metabolomic profiling predicts future disease in population cohorts, but its value in real-world healthcare remains uncertain. We profiled 250 nuclear magnetic resonance (NMR) biomarkers in 54,933 participants from a diverse integrated US healthcare system linked to longitudinal electronic health records, evaluating nine cardiovascular\u2013kidney\u2013metabolic disease endpoints. Adding NMR to standard-of-care predictors improved discrimination for nearly all endpoints, with the largest gains for type 2 diabetes, chronic kidney disease and metabolic dysfunction-associated steatotic liver disease. Beyond disease onset, NMR improved prediction of disease-specific complications and cross-organ outcomes. Gains persisted across demographic, anthropometric, and genetic-risk strata and were greatest where standard-of-care prediction was weakest. NMR-enhanced models generally preserved calibration, improved high-risk enrichment and increased decision-curve net benefit. Together, these findings support NMR metabolomics as a common approach to risk stratification across the cardiovascular\u2013kidney\u2013metabolic disease continuum and illustrate how NMR-enhanced risk estimates could be integrated with longitudinal clinical information.<\/jats:p>","DOI":"10.64898\/2026.10.01.26364322","type":"posted-content","created":{"date-parts":[[2026,10,3]],"date-time":"2026-10-03T00:05:39Z","timestamp":1790985939000},"source":"Crossref","is-referenced-by-count":0,"title":["NMR metabolomics for cardiovascular\u2013kidney\u2013metabolic risk stratification in an integrated healthcare system"],"prefix":"10.64898","author":[{"given":"Jonathan","family":"Chacon-Barahona","sequence":"first","affiliation":[{"name":"Englander Institute for Precision Medicine, Weill Cornell Medicine, New York, NY, USA"},{"name":"Department of Systems and Computational Biomedicine, Weill Cornell Medicine, New York, NY, 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