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

Area of Science:

• Metabolomics
• Gerontology
• Biomarkers

Background:

• Biological age reflects individual aging rates influenced by genetics and environment.
• Metabolites integrate genetic and environmental factors, making them suitable for biological age estimation.
• Urinary metabolomics offers a non-invasive method to assess systemic metabolic states.

Purpose of the Study:

• To develop a biological age score using urinary NMR metabolites and machine learning.
• To evaluate longitudinal metabolic age trajectories.
• To assess associations of metabolic age with clinical phenotypes, diseases, and mortality.

Main Methods:

• Applied machine learning to high-resolution 1H NMR urinary metabolite data from a large cohort.
• Calculated a metabolic age score.
• Examined cross-sectional and longitudinal associations with aging phenotypes and outcomes.

Main Results:

• Metabolic age progression varied significantly among individuals, highlighting aging heterogeneity.
• The metabolic age score showed plausible associations with age-related clinical phenotypes.
• Metabolic age predicted incident diseases and all-cause mortality independently of chronological age.

Conclusions:

• Urinary metabolomics provides a robust, non-invasive approach for biological age assessment.
• Metabolic age trajectories offer insights into inter-individual aging differences.
• Urinary metabolic age is a promising tool for risk stratification and aging research.