Urinary Metabolic Age from High-Resolution NMR Reveals Longitudinal Aging Patterns.

Authors

Karen Friederike Gauß, Nele Friedrich, Ann-Kristin Henning, Johannes Hertel, Hans Jörgen Grabe, Henry Völzke, Matthias Nauck, Astrid Petersmann

Published in

The journals of gerontology. Series A, Biological sciences and medical sciences. Jul 13, 2026. Epub Jul 13, 2026.

Abstract

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 (NMR) 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.

PMID:
42440338
Bibliographic data and abstract were imported from PubMed on 13 Jul 2026.

Read full publication at:
Please sign in to see all details.

Stats

1-terrible, 9-excellent. How would you rate this publication? Sign in in to submit your rating.

Post a comment

You need to be signed in to post comments. You can sign in here.

Comments

There are no comments yet.