Epigenetic and Metabolomic Biomarkers for Biological Age

A Comparative Analysis of Mortality and Frailty Risk

Journal Article (2023)
Author(s)

Lieke M. Kuiper (TU Delft - Electrical Engineering, Mathematics and Computer Science, Erasmus MC, Center for Nutrition)

Harmke A. Polinder-Bos (Erasmus MC)

Daniele Bizzarri (TU Delft - Electrical Engineering, Mathematics and Computer Science, Leiden University Medical Center)

Dina Vojinovic (Leiden University Medical Center, Erasmus MC)

Costanza L. Vallerga (Erasmus MC)

Marcel J.T. Reinders (TU Delft - Electrical Engineering, Mathematics and Computer Science, Leiden University Medical Center)

P. Eline Slagboom (Max Planck Institute for Biology of Ageing, Leiden University Medical Center)

Erik B. van den Akker (TU Delft - Electrical Engineering, Mathematics and Computer Science, Leiden University Medical Center)

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Research Group
Pattern Recognition and Bioinformatics
DOI related publication
https://doi.org/10.1093/gerona/glad137 Final published version
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Publication Year
2023
Language
English
Research Group
Pattern Recognition and Bioinformatics
Journal title
The journals of gerontology. Series A, Biological sciences and medical sciences
Issue number
10
Volume number
78
Pages (from-to)
1753-1762
Downloads counter
392
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Abstract

Biological age captures a person's age-related risk of unfavorable outcomes using biophysiological information. Multivariate biological age measures include frailty scores and molecular biomarkers. These measures are often studied in isolation, but here we present a large-scale study comparing them. In 2 prospective cohorts (n = 3 222), we compared epigenetic (DNAm Horvath, DNAm Hannum, DNAm Lin, DNAm epiTOC, DNAm PhenoAge, DNAm DunedinPoAm, DNAm GrimAge, and DNAm Zhang) and metabolomic-based (MetaboAge and MetaboHealth) biomarkers in reflection of biological age, as represented by 5 frailty measures and overall mortality. Biomarkers trained on outcomes with biophysiological and/or mortality information outperformed age-trained biomarkers in frailty reflection and mortality prediction. DNAm GrimAge and MetaboHealth, trained on mortality, showed the strongest association with these outcomes. The associations of DNAm GrimAge and MetaboHealth with frailty and mortality were independent of each other and of the frailty score mimicking clinical geriatric assessment. Epigenetic, metabolomic, and clinical biological age markers seem to capture different aspects of aging. These findings suggest that mortality-trained molecular markers may provide novel phenotype reflecting biological age and strengthen current clinical geriatric health and well-being assessment.