Urinary metabolic age from NMR in a large cohort: longitudinal patterns and disease links
In this abstract, a metabolic age score built from urinary NMR metabolites in a large population cohort varied between individuals over more than a decade and was associated with age-related clinical phenotypes, later diseases and all-cause mortality independent of chronological age. [1]
Automatic summary · not editorially reviewed
The key findings
A biological age score was developed from high-resolution proton NMR metabolites in urine samples from a large population-based cohort using different machine learning techniques. [1]
Metabolic age progression over more than a decade varied between individuals, described as inter-individual heterogeneity in metabolic aging. [1]
In cross-sectional analyses the score was associated with age-related clinical phenotypes, and prospectively it predicted multiple diseases and all-cause mortality independent of chronological age. [1]
The source at a glance
- Study type / format
- Population-based cohort study with cross-sectional and prospective analyses (abstract).
- Population / context
- A large population-based cohort with urine samples and longitudinal follow-up over more than a decade.
- Summary based on
- Study abstract
- Original publication
- August 1, 2026
Urinary NMR metabolic age score in a population cohort
The researchers used different machine learning techniques to develop a biological age score from high-resolution proton nuclear magnetic resonance (NMR) metabolites measured in urine samples from a large population-based cohort. They describe urinary metabolomics as a non-invasive, information-rich way to assess systemic metabolic states. The abstract does not report cohort size, age range, which machine learning methods were compared or how the score was calibrated.
Supporting source [1]
Longitudinal trajectories and cross-sectional phenotype associations
The score was applied to evaluate longitudinal trajectories over more than a decade. Metabolic age progression over time varied between individuals, which the authors describe as inter-individual heterogeneity in metabolic aging. In cross-sectional analyses, the score showed biologically plausible associations with a range of age-related clinical phenotypes. These are associations, not evidence that metabolic age causes the phenotypes. The abstract gives no effect sizes or specific phenotypes.
Supporting source [1]
Prospective disease and mortality associations in the abstract
Prospective analyses assessed associations with incident diseases and all-cause mortality. The abstract states that metabolic age was predictive of multiple diseases and all-cause mortality independent of chronological age. It does not name the diseases, report follow-up length, hazard ratios or adjustment variables, so the strength and robustness of these predictions cannot be judged from the abstract. The authors present urinary metabolic age as a promising tool for risk stratification and aging research, not as a validated clinical test.
Supporting source [1]
How to interpret this
Abstract only; the full text was not reviewed. Cohort size, age range, follow-up length, specific phenotypes and diseases, effect sizes and adjustment variables are not reported here, so the strength and robustness of the associations cannot be assessed. Associations do not establish causation, and the score is not described as a validated clinical test.
Based on the study abstract; the full paper, tables and supplementary data were not reviewed.
Source & supporting passages
Gauß KF, Friedrich N, Henning AK, Hertel J, Grabe HJ, Völzke H, Nauck M, Petersmann A. · europepmc.org · August 1, 2026