DunedinPACE, a DNA methylation biomarker of the pace of aging

  1. Daniel W Belsky  Is a corresponding author
  2. Avshalom Caspi
  3. David L Corcoran
  4. Karen Sugden
  5. Richie Poulton
  6. Louise Arseneault
  7. Andrea Baccarelli
  8. Kartik Chamarti
  9. Xu Gao
  10. Eilis Hannon
  11. Hona Lee Harrington
  12. Renate Houts
  13. Meeraj Kothari
  14. Dayoon Kwon
  15. Jonathan Mill
  16. Joel Schwartz
  17. Pantel Vokonas
  18. Cuicui Wang
  19. Benjamin S Williams
  20. Terrie E Moffitt
  1. Columbia University, United States
  2. Duke University, United States
  3. University of Otago, New Zealand
  4. King's College London, United Kingdom
  5. Peking University, China
  6. University of Exeter, United Kingdom
  7. Harvard TH Chan School of Public Health, United States
  8. 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).

The following previously published data sets were used
    1. NHLBI
    (2021) The Framingham Heart Study
    dbGaP, accession phs000007.v32.p13.

Article and author information

Author details

  1. Daniel W Belsky

    Department of Epidemiology, Columbia University, New York, United States
    For correspondence
    db3275@cumc.columbia.edu
    Competing interests
    Daniel W Belsky, is listed as an inventor on a Duke University and University of Otago invention that was licensed to a commercial entity..
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0001-5463-2212
  2. Avshalom Caspi

    Center for Genomic and Computational Biology, Duke University, Durham, United States
    Competing interests
    Avshalom Caspi, is listed as an inventor on a Duke University and University of Otago invention that was licensed to a commercial entity..
  3. David L Corcoran

    Center for Genomic and Computational Biology, Duke University, Durham, United States
    Competing interests
    David L Corcoran, is listed as an inventor on a Duke University and University of Otago invention that was licensed to a commercial entity..
  4. Karen Sugden

    Department of Psychology and Neuroscience, Duke University, Durham, United States
    Competing interests
    Karen Sugden, is listed as an inventor on a Duke University and University of Otago invention that was licensed to a commercial entity..
  5. Richie Poulton

    Department of Psychology, University of Otago, Otago, New Zealand
    Competing interests
    Richie Poulton, is listed as an inventor on a Duke University and University of Otago invention that was licensed to a commercial entity..
  6. Louise Arseneault

    Social, Genetic, and Developmental Psychiatry Centre, King's College London, London, United Kingdom
    Competing interests
    No competing interests declared.
  7. Andrea Baccarelli

    Department of Environmental Health Sciences, Columbia University, New York, United States
    Competing interests
    No competing interests declared.
  8. Kartik Chamarti

    Department of Psychology and Neuroscience, Duke University, Durham, United States
    Competing interests
    No competing interests declared.
  9. Xu Gao

    Department of Occupational and Environmental Health, Peking University, Bejing, China
    Competing interests
    No competing interests declared.
  10. Eilis Hannon

    Complex Disease Epigenetics Group, University of Exeter, Exeter, United Kingdom
    Competing interests
    No competing interests declared.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0001-6840-072X
  11. Hona Lee Harrington

    Department of Psychology and Neuroscience, Duke University, Durham, United States
    Competing interests
    No competing interests declared.
  12. Renate Houts

    Department of Psychology and Neuroscience, Duke University, Durham, United States
    Competing interests
    No competing interests declared.
  13. Meeraj Kothari

    Robert N Butler Columbia Aging Center, Columbia University, Brooklyn, United States
    Competing interests
    No competing interests declared.
  14. Dayoon Kwon

    Robert N Butler Columbia Aging Center, Columbia University, New York, United States
    Competing interests
    No competing interests declared.
  15. Jonathan Mill

    Complex Disease Epigenetics Group, University of Exeter, Exeter, United Kingdom
    Competing interests
    No competing interests declared.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0003-1115-3224
  16. Joel Schwartz

    Department of Environmental Health Sciences, Harvard TH Chan School of Public Health, Boston, United States
    Competing interests
    No competing interests declared.
  17. Pantel Vokonas

    Department of Medicine, VA Boston Healthcare System, Boston, United States
    Competing interests
    No competing interests declared.
  18. Cuicui Wang

    Department of Environmental Health Sciences, Harvard TH Chan School of Public Health, Boston, United States
    Competing interests
    No competing interests declared.
  19. Benjamin S Williams

    Psychology, Duke University, Durham, United States
    Competing interests
    No competing interests declared.
  20. Terrie E Moffitt

    Department of Psychology and Neuroscience, Duke University, Durham, United States
    Competing interests
    Terrie E Moffitt, is listed as an inventors on a Duke University and University of Otago invention that was licensed to a commercial entity..

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.

Metrics

  • 40,758
    views
  • 2,733
    downloads
  • 395
    citations

Views, downloads and citations are aggregated across all versions of this paper published by eLife.

Download links

A two-part list of links to download the article, or parts of the article, in various formats.

Downloads (link to download the article as PDF)

Open citations (links to open the citations from this article in various online reference manager services)

Cite this article (links to download the citations from this article in formats compatible with various reference manager tools)

  1. Daniel W Belsky
  2. Avshalom Caspi
  3. David L Corcoran
  4. Karen Sugden
  5. Richie Poulton
  6. Louise Arseneault
  7. Andrea Baccarelli
  8. Kartik Chamarti
  9. Xu Gao
  10. Eilis Hannon
  11. Hona Lee Harrington
  12. Renate Houts
  13. Meeraj Kothari
  14. Dayoon Kwon
  15. Jonathan Mill
  16. Joel Schwartz
  17. Pantel Vokonas
  18. Cuicui Wang
  19. Benjamin S Williams
  20. Terrie E Moffitt
(2022)
DunedinPACE, a DNA methylation biomarker of the pace of aging
eLife 11:e73420.
https://doi.org/10.7554/eLife.73420

Share this article

https://doi.org/10.7554/eLife.73420

Further reading

    1. Epidemiology and Global Health
    Marina Padilha, Victor Nahuel Keller ... Gilberto Kac
    Research Article

    Background: The role of circulating metabolites on child development is understudied. We investigated associations between children's serum metabolome and early childhood development (ECD).

    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.

    Results: Twenty-eight top-ranked metabolites were included in linear regression models adjusted for the child's nutritional status, diet quality, and infant age. Cresol sulfate (β = -0.07; adjusted-p < 0.001), hippuric acid (β = -0.06; adjusted-p < 0.001), phenylacetylglutamine (β = -0.06; adjusted-p < 0.001), and trimethylamine-N-oxide (β = -0.05; adjusted-p = 0.002) showed inverse associations with DQ. We observed opposite directions in the association of DQ for creatinine (for children aged -1 SD: β = -0.05; p =0.01; +1 SD: β = 0.05; p =0.02) and methylhistidine (-1 SD: β = - 0.04; p =0.04; +1 SD: β = 0.04; p =0.03).

    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.

    1. Epidemiology and Global Health
    Riccardo Spott, Mathias W Pletz ... Christian Brandt
    Research Article

    Given the rapid cross-country spread of SARS-CoV-2 and the resulting difficulty in tracking lineage spread, we investigated the potential of combining mobile service data and fine-granular metadata (such as postal codes and genomic data) to advance integrated genomic surveillance of the pandemic in the federal state of Thuringia, Germany. We sequenced over 6500 SARS-CoV-2 Alpha genomes (B.1.1.7) across 7 months within Thuringia while collecting patients’ isolation dates and postal codes. Our dataset is complemented by over 66,000 publicly available German Alpha genomes and mobile service data for Thuringia. We identified the existence and spread of nine persistent mutation variants within the Alpha lineage, seven of which formed separate phylogenetic clusters with different spreading patterns in Thuringia. The remaining two are subclusters. Mobile service data can indicate these clusters’ spread and highlight a potential sampling bias, especially of low-prevalence variants. Thereby, mobile service data can be used either retrospectively to assess surveillance coverage and efficiency from already collected data or to actively guide part of a surveillance sampling process to districts where these variants are expected to emerge. The latter concept was successfully implemented as a proof-of-concept for a mobility-guided sampling strategy in response to the surveillance of Omicron sublineage BQ.1.1. The combination of mobile service data and SARS-CoV-2 surveillance by genome sequencing is a valuable tool for more targeted and responsive surveillance.