DunedinPACE, a DNA methylation biomarker of the pace of aging
- Columbia University, United States
- Duke University, United States
- University of Otago, New Zealand
- King's College London, United Kingdom
- Peking University, China
- University of Exeter, United Kingdom
- Harvard TH Chan School of Public Health, United States
- VA Boston Healthcare System, United States
Abstract
Background: Measures to quantify changes in the pace of biological aging in response to intervention are needed to evaluate geroprotective interventions for humans. Previously we showed that quantification of the pace of biological aging from a DNA-methylation blood test was possible (Belsky et al. 2020). Here we report a next-generation DNA-methylation biomarker of Pace of Aging, DunedinPACE (for Pace of Aging Calculated from the Epigenome).
Methods: We used data from the Dunedin Study 1972-3 birth cohort tracking within-individual decline in 19 indicators of organ-system integrity across four time points spanning two decades to model Pace of Aging. We distilled this two-decade Pace of Aging into a single-time-point DNA-methylation blood-test using elastic-net regression and a DNA-methylation dataset restricted to exclude probes with low test-retest reliability. We evaluated the resulting measure, named DunedinPACE, in five additional datasets.
Results: DunedinPACE showed high test-retest reliability, was associated with morbidity, disability, and mortality, and indicated faster aging in young adults with childhood adversity. DunedinPACE effect-sizes were similar to GrimAge Clock effect-sizes. In analysis of incident morbidity, disability, and mortality, DunedinPACE and added incremental prediction beyond GrimAge.
Conclusions: DunedinPACE is a novel blood biomarker of the pace of aging for gerontology and geroscience.
Funding: This research was supported by US-National Institute on Aging grants AG032282, AG061378, AG066887, and UK Medical Research Council grant MR/P005918/1.
Data availability
DunedinPACE Data Availability StatementDatasets are available from the data owners. Data from the Dunedin and E-Risk Study can be accessed through agreement with the Study investigators. Instructions are available at https://sites.google.com/site/moffittcaspiprojects/. The data access application form can be downloaded here: https://sites.google.com/site/moffittcaspiprojects/forms-for-new-projects/concept-paper-template.Data from the Understanding Society Study is available through METADAC at https://www.metadac.ac.uk/ukhls/. All details are on the Metadac website (https://www.metadac.ac.uk/data-access-through-metadac/). The data access application form can be found here https://www.metadac.ac.uk/files/2019/02/v2.41-UKHLS-METADAC-application-form-2019-2hak8bv.docx.Data from the Normative Aging Study were obtained from the Study investigators. Data are accessible through dbGaP, accession phs000853.v1.p1.Data from the Framingham Heart Study were obtained from dbGaP, accession phs000007.v32.p13.GSE55763 is a publicly available dataset available from the Gene Expression Omnibus (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE55763).
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The Normative Aging StudydbGaP, accession phs000853.v1.p1.
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GSE55763Gene Expression Omnibus , GSE55763.
Article and author information
Author details
Funding
National Institute on Aging (AG032282,AG061378,AG066887)
- Daniel W Belsky
- Avshalom Caspi
- Terrie E Moffitt
Medical Research Council (MR/P005918/1)
- Terrie E Moffitt
The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.
Copyright
This is an open-access article, free of all copyright, and may be freely reproduced, distributed, transmitted, modified, built upon, or otherwise used by anyone for any lawful purpose. The work is made available under the Creative Commons CC0 public domain dedication.
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DunedinPACE, a DNA methylation biomarker of the pace of aging
eLife 11:e73420.
https://doi.org/10.7554/eLife.73420
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Methods: Untargeted metabolomics was performed on serum samples of 5,004 children aged 6-59 months, a subset of participants from the Brazilian National Survey on Child Nutrition (ENANI-2019). ECD was assessed using the Survey of Well-being of Young Children's milestones questionnaire. The graded response model was used to estimate developmental age. Developmental quotient (DQ) was calculated as the developmental age divided by chronological age. Partial least square regression selected metabolites with a variable importance projection ≥ 1. The interaction between significant metabolites and the child's age was tested.
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Conclusion: Serum biomarkers, including dietary and microbial-derived metabolites involved in the gut-brain axis, may potentially be used to track children at risk for developmental delays.
Funding: Supported by the Brazilian Ministry of Health and the Brazilian National Research Council.
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