{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,23]],"date-time":"2026-10-23T02:59:08Z","timestamp":1792724348222,"version":"4.5.0"},"reference-count":39,"publisher":"Oxford University Press (OUP)","issue":"9","license":[{"start":{"date-parts":[[2026,7,13]],"date-time":"2026-07-13T00:00:00Z","timestamp":1783900800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0\/"}],"funder":[{"name":"Community Medicine Research Network of the University Medicine Greifswald"},{"name":"German Federal State of Mecklenburg\u2014West Pomerania"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2026,8,4]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>Biological age captures inter-individual heterogeneity in aging process arising from genetic and environmental influences. Metabolites, as the end-products of metabolism, integrate these factors and are therefore well suited for biological age estimation. Urinary metabolomics, in particular, provides a non-invasive and information-rich matrix for assessing systemic metabolic states. We applied different machine learning techniques to develop a biological age score from high-resolution 1H nuclear magnetic resonance metabolites measured in urine samples from a large population-based cohort. The derived metabolic age score was applied to evaluate longitudinal trajectories over more than a decade. Cross-sectional associations with age-related clinical phenotypes were examined, and prospective analyses assessed associations with incident diseases and all-cause mortality. Metabolic age progression over time varied between individuals, underscoring inter-individual heterogeneity in metabolic aging. In cross-sectional analyses, the metabolic age score showed biologically plausible associations with a range of age-related clinical phenotypes. Furthermore, metabolic age was predictive of multiple diseases and all-cause mortality independent of chronological age. Our findings highlight the utility of urinary metabolomics as a robust, non-invasive approach for biological age assessment. The characterization of long-term metabolic age trajectories provides novel insight into inter-individual differences in aging and establishes urinary metabolic age as a promising tool for risk stratification and aging research.<\/jats:p>","DOI":"10.1093\/gerona\/glag180","type":"journal-article","created":{"date-parts":[[2026,7,13]],"date-time":"2026-07-13T11:22:35Z","timestamp":1783941755000},"source":"Crossref","is-referenced-by-count":2,"title":["Urinary metabolic age from high-resolution nuclear magnetic resonance spectroscopy reveals longitudinal aging patterns"],"prefix":"10.1093","volume":"81","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2587-8587","authenticated-orcid":false,"given":"Karen Friederike","family":"Gau\u00df","sequence":"first","affiliation":[{"name":"Institute of Clinical Chemistry and Laboratory Medicine, University Medicine Greifswald , Greifswald,","place":["Germany"]},{"name":"Institute of Clinical Chemistry and Laboratory Medicine, University Medicine 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