Epigenetic and Metabolomic Biomarkers for Biological Age: A Comparative Analysis of Mortality and Frailty Risk

Author:

Kuiper Lieke M12ORCID,Polinder-Bos Harmke A1ORCID,Bizzarri Daniele34ORCID,Vojinovic Dina35ORCID,Vallerga Costanza L1ORCID,Beekman Marian3ORCID,Dollé E T6ORCID,Ghanbari Mohsen5ORCID,Voortman Trudy57ORCID,Reinders Marcel J T34ORCID,Verschuren W M Monique28ORCID,Slagboom P Eline39ORCID,van den Akker Erik B34ORCID,van Meurs Joyce B J110ORCID

Affiliation:

1. Department of Internal Medicine, Erasmus MC , Rotterdam , The Netherlands

2. Center for Nutrition, Prevention and Health Services , Bilthoven , The Netherlands

3. Molecular Epidemiology, Department of Biomedical Data Sciences, Leiden University Medical Centre , Leiden , The Netherlands

4. Pattern Recognition and Bioinformatics, Delft University of Technology , Delft , The Netherlands

5. Department of Epidemiology, Erasmus MC , Rotterdam , The Netherlands

6. Center for Health Protection, National Institute for Public Health and the Environment (RIVM) , Bilthoven , The Netherlands

7. Division of Human Nutrition and Health, Wageningen University & Research , Wageningen , The Netherlands

8. Julius Center for Health Sciences and Primary Care Utrecht, University Medical Center Utrecht, Utrecht University , Utrecht , The Netherlands

9. Max Planck Institute for the Biology of Ageing , Cologne , Germany

10. Department of Orthopaedics and Sports Medicine, Erasmus MC , Rotterdam , The Netherlands

Abstract

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.

Funder

Erasmus Medical Center

Erasmus University Rotterdam

Netherlands Organization for the Health Research and Development

Research Institute for Disease in the Elderly

Ministry of Education, Culture, and Science

Ministry of Health, Welfare, and Sports

European Commission

Municipality of Rotterdam

European Union

Innovation-Oriented Research Program on Genomics

Centre for Medical Systems Biology

Netherlands Consortium for Healthy Ageing

Netherlands Organization for Scientific Research

BBMRI-NL

VOILA

Medical Delta

Dutch Research Council

Ministry of Health, Welfare, and Sport

Publisher

Oxford University Press (OUP)

Subject

Geriatrics and Gerontology,Aging

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