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TRUVACE RECORD VERSION
record: TRV-2026-0219
version: 1
kind: certified
reason: Certified into the record
timestamp: 2026-07-14T06:29:45.229522Z
status: published
lens: g_space
sector: health
headline: Urinary Metabolic Age from High-Resolution NMR Reveals Longitudinal Aging Patterns
dek: 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 resonan…
gain_title: Machine learning-derived metabolic age from urinary NMR metabolites tracks longitudinal aging trajectories and predicts incident diseases and mortality beyond chronological age.
problem_title: (none)
trace_subject: (none)
gain_reading: Machine learning-derived metabolic age from urinary NMR metabolites tracks longitudinal aging trajectories and predicts incident diseases and mortality beyond chronological age.
gain_evidence: metabolic age was predictive of multiple diseases and all-cause mortality independent of chronological age
problem_reading: (none)
problem_evidence: (none)
quick_read: Researchers developed a biological age score from urinary metabolites measured by high-resolution 1H NMR using machine learning in a large population cohort, then tracked its trajectory over more than a decade and tested its links to aging-related phenotypes and future health events.

The work matters because it suggests a non-invasive urine test could stratify risk for multiple diseases and death beyond chronological age, but the source does not report external validation, clinical implementation, or whether intervening on metabolic age changes outcomes.
limitation: 
tag: Evidence-backed gain
key_points: Researchers applied machine learning to high-resolution 1H NMR metabolites measured in urine from a large population-based cohort to build a biological age score. | Longitudinal analysis over more than a decade showed metabolic age progression varied between individuals. | Cross-sectional analysis found biologically plausible associations with age-related clinical phenotypes. | Prospective analysis found metabolic age predicted multiple incident diseases and all-cause mortality independent of chronological age.
rundown: The study used high-resolution 1H NMR metabolites measured in urine samples from a large population-based cohort and applied different machine learning techniques to develop a metabolic age score.

The score was evaluated for longitudinal trajectories over more than a decade, for cross-sectional associations with age-related clinical phenotypes, and prospectively for incident diseases and all-cause mortality.
sources:
- peer_reviewed | The Journals of Gerontology, Series A: Biological Sciences and Medical Sciences | https://doi.org/10.1093/gerona/glag180 | 2026-07-13
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