Journal Article

DZNE-2026-00907

http://join2-wiki.gsi.de/foswiki/pub/Main/Artwork/join2_logo100x88.png Urinary metabolic age from high-resolution nuclear magnetic resonance spectroscopy reveals longitudinal aging patterns.

Gauß, K. F. ; Friedrich, N. ; Henning, A.-K. ; Hertel, J. ; Grabe, H. J.DZNE* ; Völzke, H. ; Nauck, M. ; Petersmann, A.

2026
Oxford Univ. Pr. Oxford [u.a.]

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Please use a persistent id in citations: doi:10.1093/gerona/glag180

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 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.

Keyword(s): Humans (MeSH) ; Aging: urine (MeSH) ; Aging: metabolism (MeSH) ; Metabolomics: methods (MeSH) ; Magnetic Resonance Spectroscopy: methods (MeSH) ; Female (MeSH) ; Male (MeSH) ; Aged (MeSH) ; Cross-Sectional Studies (MeSH) ; Machine Learning (MeSH) ; Longitudinal Studies (MeSH) ; Middle Aged (MeSH) ; Biomarkers: urine (MeSH) ; Prospective Studies (MeSH) ; Biological age ; High resolution 1H nuclear magnetic resonance (NMR) ; Machine learning ; Urinary metabolomics ; Biomarkers

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Contributing Institute(s):

1. Biomarkers of Dementia in the General Population (AG Grabe)

Research Program(s):

1. 353 - Clinical and Health Care Research (POF4-353) (POF4-353)

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Database coverage:
; Clarivate Analytics Master Journal List ; Current Contents - Life Sciences ; Current Contents - Social and Behavioral Sciences ; Ebsco Academic Search ; Essential Science Indicators ; Nationallizenz ; SCOPUS ; Science Citation Index Expanded ; Social Sciences Citation Index ; Web of Science Core Collection

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