Development of Age‐ and Sex‐Specific Metabolomics‐Based Biological Ageing Clocks for 10‐Year Mortality Prediction

Metabolite concentrations vary by age and sex, yet age- and sex-specific metabolomic risk scores and biological ageing clocks for mortality prediction remain undeveloped. Nuclear magnetic resonance (NMR)-based metabolomic profiling is conducted in 209144 UK Biobank participants (12347 deaths) and 6820 from the German ESTHER study (804 deaths). Mortality risk scores are derived using least absolute shrinkage and selection operator (LASSO)-regularized Cox regression, and metabolomics-based mortality risk clocks (MetaboMR clocks) are constructed using elastic net regression in sex- and age-stratified subgroups (50-59 and 60-69 years). Models are trained in 70% of UK Biobank and validated internally (30%) and externally in ESTHER. 68 metabolites are significantly associated with 10-year all-cause mortality in both cohorts. 20, 18, 12, and 13 metabolites improved 10-year mortality prediction in younger and older men, younger and older women. Metabolite-augmented models improved c-statistics by 0.036-0.084 across subgroups. In the external validation set, each year of age acceleration is associated with an 8% and 9% higher 10-year mortality risk for MetaboMR clock1 and clock2. Sex- and age-specific metabolomic risk scores significantly enhance 10-year mortality prediction beyond traditional models. The MetaboMR clocks may serve as measures of biological ageing and support personalized risk stratification in clinical settings.

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Publication Details

Journal
Advanced Science
Published
2025-10-15
DOI
https://doi.org/10.1002/advs.202510189
Primary Topic
Metabolomics and Mass Spectrometry Studies
Type
article
Field-Weighted Citation Impact
0.00

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article

Development of Age‐ and Sex‐Specific Metabolomics‐Based Biological Ageing Clocks for 10‐Year Mortality Prediction

Ruijie Xie, Hermann Brenner, Lei Peng, Bernd Holleczek et al.
Advanced Science
Metabolomics and Mass Spectrometry Studies
article

Development of Age‐ and Sex‐Specific Metabolomics‐Based Biological Ageing Clocks for 10‐Year Mortality Prediction

Ruijie Xie, Hermann Brenner, Lei Peng, Bernd Holleczek, Ben Schöttker
article en

Abstract

Metabolite concentrations vary by age and sex, yet age- and sex-specific metabolomic risk scores and biological ageing clocks for mortality prediction remain undeveloped. Nuclear magnetic resonance (NMR)-based metabolomic profiling is conducted in 209144 UK Biobank participants (12347 deaths) and 6820 from the German ESTHER study (804 deaths). Mortality risk scores are derived using least absolute shrinkage and selection operator (LASSO)-regularized Cox regression, and metabolomics-based mortality risk clocks (MetaboMR clocks) are constructed using elastic net regression in sex- and age-stratified subgroups (50-59 and 60-69 years). Models are trained in 70% of UK Biobank and validated internally (30%) and externally in ESTHER. 68 metabolites are significantly associated with 10-year all-cause mortality in both cohorts. 20, 18, 12, and 13 metabolites improved 10-year mortality prediction in younger and older men, younger and older women. Metabolite-augmented models improved c-statistics by 0.036-0.084 across subgroups. In the external validation set, each year of age acceleration is associated with an 8% and 9% higher 10-year mortality risk for MetaboMR clock1 and clock2. Sex- and age-specific metabolomic risk scores significantly enhance 10-year mortality prediction beyond traditional models. The MetaboMR clocks may serve as measures of biological ageing and support personalized risk stratification in clinical settings.

Advanced ScienceVol. 13(1)
German Cancer Research Center (DE), Heidelberg University (DE), Krebsregister Saarland (DE)
Deutsches Krebsforschungszentrum, Wellcome Trust, Cancer Research UK, British Heart Foundation, Diabetes UK, Bundesministerium für Bildung und Forschung, Northwest Regional Development Agency, China Scholarship Council, Medical Research Council
Openalex Percentile: Top 74%
Metabolomics and Mass Spectrometry Studies
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