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

Kuiper, Lieke M, Polinder-Bos, Harmke A, Bizzarri, Daniele, Vojinovic, Dina, Vallerga, Costanza L, Beekman, Marian, Dollé, Martijn E T, Ghanbari, Mohsen, Voortman, Trudy, Reinders, Marcel J T, Verschuren, W M Monique, Slagboom, P Eline, van den Akker, Erik B, van Meurs, Joyce B J·2023·The Journals of Gerontology: Series A
DOI 10.1093/gerona/glad137PMC PMC10562890

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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.

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Figure 1. Outline of the study and study population characteristics. *Smoking status was unknown for 5 Rotterdam Study participants (3 participants of the 450K-subcohort, 2 participants of the EPIC-subcohort) and for 17 LLS participants, of whom 4 belonged to the multiomics subcohort. (A) The Rotterdam Study overall study population with population characteristics; (B) the Rotterdam Study 2 subcohorts, 450K and EPIC, stratified by the DNA methylation array used and their population characteristics; (C) the external validation cohort, the Leiden Longevity Study with population characteristics; (D) the subcohort of the Leiden Longevity Study, where epigenetic information was available with population characteristics; and (E) we used both the overall Rotterdam Study population and its two subcohorts (i) to determine the correlations between each of the biological aging biomarkers, (ii) to perform a linear regression for the association between the biological aging biomarkers and frailty, and (iii) to determine the association between each of the biological aging biomarkers and all-cause mortality. We externally validated the correlations between the biological aging biomarkers and the association between the biological aging biomarkers and all-cause mortality in the Leiden Longevity Study and its subcohort. In A–D, BMI indicates body mass index; DNAm, DNA methylation; and n, size of the study population. Population characteristics in A–D are shown as a number for the population size; mean ± standard deviation (range) for age and BMI; and number (percentage) for the number of women and the number of participants currently smoking.
Figure 1. Outline of the study and study population characteristics. *Smoking status was unknown for 5 Rotterdam Study participants (3 participants of the 450K-subcohort, 2 participants of the EPIC-subcohort) and for 17 LLS participants, of whom 4 belonged to the multiomics subcohort. (A) The Rotterdam Study overall study population with population characteristics; (B) the Rotterdam Study 2 subcohorts, 450K and EPIC, stratified by the DNA methylation array used and their population characteristics; (C) the external validation cohort, the Leiden Longevity Study with population characteristics; (D) the subcohort of the Leiden Longevity Study, where epigenetic information was available with population characteristics; and (E) we used both the overall Rotterdam Study population and its two subcohorts (i) to determine the correlations between each of the biological aging biomarkers, (ii) to perform a linear regression for the association between the biological aging biomarkers and frailty, and (iii) to determine the association between each of the biological aging biomarkers and all-cause mortality. We externally validated the correlations between the biological aging biomarkers and the association between the biological aging biomarkers and all-cause mortality in the Leiden Longevity Study and its subcohort. In A–D, BMI indicates body mass index; DNAm, DNA methylation; and n, size of the study population. Population characteristics in A–D are shown as a number for the population size; mean ± standard deviation (range) for age and BMI; and number (percentage) for the number of women and the number of participants currently smoking.CC BY-NC-ND · PubMed Central
Figure 2. Correlations between biological age measures and the association between biomarkers of biological aging and frailty. (A) Spearman’s correlation of the different biological aging biomarkers in 1 424 Rotterdam Study participants with the histograms of epigenetic aging biomarkers in yellow and metabolomic-based aging biomarkers in blue. Labels in bold indicate aging biomarkers trained on outcomes including phenotypic and/or mortality information; the regular font, an aging biomarker trained on chronological age. Biomarkers are arranged by omics layer, ordered from fully age-trained to fully mortality-trained. Values after r = represent Spearman’s rank coefficient; values after p = represent the p value; the background color is darker for higher correlations. epiTOC = DNAm epiTOC; GrimAge = DNAm GrimAge; Hannum = DNAm Hannum; Horvath = DNAm Horvath; Lin = DNAm Lin; mHealth = MetaboHealth; Pheno = DNAm PhenoAge; PoAM = DNAm DunedinPoAm; Zhan = DNAm Zhang; mAge = MetaboAge. (B) Spearman’s correlation between the different Yeo–Johnson-transformed frailty measures in the 746 Rotterdam Study participants with information on all 5 frailty measures. Values represent Spearman’s rank coefficient; the background color is darker for higher correlations. CFP = continuous frailty phenotype; FI = frailty index; FP = frailty phenotype; MPI = Multidimensional Prognostic Index; TFI = Tilburg Frailty Indicator. (C) Risk of all-cause mortality per standard deviation increase of the Yeo–Johnson transformed FI (n cases = 130/n = 1 330), FP (n cases = 132/n = 1 328), CFP (n cases = 69/n = 743), TFI (n cases = 129/n = 1 328), MPI (n cases = 132/n = 1 333) in the RS overall study population. The figure represents the adjusted hazard ratios and 95%-confidence intervals. (D) Associations of standardized biological aging biomarkers with standardized FI (n = 1 341), FP (n = 1 339), CFP (n = 748), TFI (n = 1 339), and MPI (n = 1 344) based on linear regression analyses in all participants for whom data on biological aging biomarkers and frailty were available in the overall Rotterdam Study dataset. Analyses were adjusted for age, sex, BMI, cell counts, subcohort, and Rotterdam Study cohort and visit. The figure represents the adjusted betas and 95%-confidence intervals. Biomarkers are arranged by omics layer, ordered from fully age-trained to fully mortality-trained. DNAm Zhang is missing information on 2 out of 10 CpGs in the EPIC-subcohort (736 of the 1 347 participants). BMI = body mass index.
Figure 2. Correlations between biological age measures and the association between biomarkers of biological aging and frailty. (A) Spearman’s correlation of the different biological aging biomarkers in 1 424 Rotterdam Study participants with the histograms of epigenetic aging biomarkers in yellow and metabolomic-based aging biomarkers in blue. Labels in bold indicate aging biomarkers trained on outcomes including phenotypic and/or mortality information; the regular font, an aging biomarker trained on chronological age. Biomarkers are arranged by omics layer, ordered from fully age-trained to fully mortality-trained. Values after r = represent Spearman’s rank coefficient; values after p = represent the p value; the background color is darker for higher correlations. epiTOC = DNAm epiTOC; GrimAge = DNAm GrimAge; Hannum = DNAm Hannum; Horvath = DNAm Horvath; Lin = DNAm Lin; mHealth = MetaboHealth; Pheno = DNAm PhenoAge; PoAM = DNAm DunedinPoAm; Zhan = DNAm Zhang; mAge = MetaboAge. (B) Spearman’s correlation between the different Yeo–Johnson-transformed frailty measures in the 746 Rotterdam Study participants with information on all 5 frailty measures. Values represent Spearman’s rank coefficient; the background color is darker for higher correlations. CFP = continuous frailty phenotype; FI = frailty index; FP = frailty phenotype; MPI = Multidimensional Prognostic Index; TFI = Tilburg Frailty Indicator. (C) Risk of all-cause mortality per standard deviation increase of the Yeo–Johnson transformed FI (n cases = 130/n = 1 330), FP (n cases = 132/n = 1 328), CFP (n cases = 69/n = 743), TFI (n cases = 129/n = 1 328), MPI (n cases = 132/n = 1 333) in the RS overall study population. The figure represents the adjusted hazard ratios and 95%-confidence intervals. (D) Associations of standardized biological aging biomarkers with standardized FI (n = 1 341), FP (n = 1 339), CFP (n = 748), TFI (n = 1 339), and MPI (n = 1 344) based on linear regression analyses in all participants for whom data on biological aging biomarkers and frailty were available in the overall Rotterdam Study dataset. Analyses were adjusted for age, sex, BMI, cell counts, subcohort, and Rotterdam Study cohort and visit. The figure represents the adjusted betas and 95%-confidence intervals. Biomarkers are arranged by omics layer, ordered from fully age-trained to fully mortality-trained. DNAm Zhang is missing information on 2 out of 10 CpGs in the EPIC-subcohort (736 of the 1 347 participants). BMI = body mass index.CC BY-NC-ND · PubMed Central
Figure 3. Aging predictors and their univariable risk of all-cause mortality per SD. Risk of all-cause mortality per standard deviation increase of the aging biomarkers in (A) the overall Rotterdam Study population (n = 1 336). DNAm Zhang is missing information on 2 out of 10 CpGs in the EPIC-subcohort (727 of the 1 336 participants); and (B) the subcohort of the Leiden Longevity Study with information on the epigenetic aging predictors (n = 584). CpGs = methylation sites; BMI = body mass index; HR = hazard ratio; MPI= multidimensional prognostic index; SD = standard deviation. Biomarkers are arranged by omics layer, ordered from fully age trained to fully mortality trained.
Figure 3. Aging predictors and their univariable risk of all-cause mortality per SD. Risk of all-cause mortality per standard deviation increase of the aging biomarkers in (A) the overall Rotterdam Study population (n = 1 336). DNAm Zhang is missing information on 2 out of 10 CpGs in the EPIC-subcohort (727 of the 1 336 participants); and (B) the subcohort of the Leiden Longevity Study with information on the epigenetic aging predictors (n = 584). CpGs = methylation sites; BMI = body mass index; HR = hazard ratio; MPI= multidimensional prognostic index; SD = standard deviation. Biomarkers are arranged by omics layer, ordered from fully age trained to fully mortality trained.CC BY-NC-ND · PubMed Central

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Metadata

Authors
Kuiper, L. M., Polinder-Bos, H. A., Bizzarri, D., Vojinovic, D., Vallerga, C. L., Beekman, M., Dollé, M. E. T., Ghanbari, M., Voortman, T., Reinders, M. J. T., Verschuren, W. M. M., Slagboom, P. E., van den Akker, E. B., & van Meurs, J. B. J.
Venue
The Journals of Gerontology: Series A
Publisher
Oxford University Press (OUP)
Published
2023-06-12
Volume
78
Issue
10
Pages
1753-1762
ISSN
  • 1079-5006
  • 1758-535X
DOI
10.1093/gerona/glad137
PMC
PMC10562890
Cited by
92
References
10
Language
en
Licence
creativecommons.org/licenses/by-nc-nd/4.0/ (vor)
Funders
  • 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 (FP7/2007–2011, 259679)
  • Innovation-Oriented Research Program on Genomics (IGE05007)
  • Centre for Medical Systems Biology
  • Netherlands Consortium for Healthy Ageing (050-060-810)
  • Netherlands Organization for Scientific Research
  • BBMRI-NL (NWO 184.021.007, 184.033.111)
  • VOILA (ZonMW 457001001)
  • Medical Delta
  • Dutch Research Council (09150161810095)
  • Ministry of Health, Welfare, and Sport (S/010003)

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