Responsiveness of epigenetic aging biomarkers to longevity interventions in humans
11 affiliations
- 1Department of Psychiatry, Yale University School of Medicine, New Haven, CT, USA. [email protected]
- 2Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT, USA. [email protected]
- 3Department of Psychiatry, Yale University School of Medicine, New Haven, CT, USA
- 4Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT, USA
- 5Program in Pathology, Yale University School of Medicine, New Haven, CT, USA
- 6TruDiagnostic, Lexington, KY, USA
- 7Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
- 8University of California, San Diego, La Jolla, CA, USA
- 9Department of Psychiatry, Yale University School of Medicine, New Haven, CT, USA. [email protected]
- 10Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT, USA. [email protected]
- 11Program in Pathology, Yale University School of Medicine, New Haven, CT, USA. [email protected]
Abstract
Aging biomarkers can potentially allow researchers to rapidly monitor the impact of an aging intervention, without the need for decade-spanning trials, by acting as surrogate endpoints. Prior to testing whether aging biomarkers may be useful as surrogate endpoints, it is first necessary to determine whether they are responsive to interventions that target aging. Epigenetic clocks are aging biomarkers based on DNA methylation with prognostic value for many aging outcomes. Many individual studies are beginning to explore whether epigenetic clocks are responsive to interventions. However, the diversity of both interventions and epigenetic clocks in different studies make them difficult to compare systematically. Here, we curate TranslAGE-Response, a harmonized database of 51 public and private longitudinal interventional studies and calculate a consistent set of 16 prominent epigenetic clocks for each study, along with 95 other DNAm biomarkers that help explain changes in each clock. With this database, we discover patterns of responsiveness across a variety of interventions and DNAm biomarkers. For example, clocks trained to predict mortality or pace of aging have the strongest response across all interventions and show consistent agreement with each other, pharmacological and lifestyle interventions drive the strongest response from DNAm biomarkers, and study population and study duration are key factors in driving responsiveness of DNAm biomarkers in an intervention. Some classes of interventions such as TNF-alpha inhibitors have strong, consistent effects across multiple studies, while others such as senolytic drugs have inconsistent effects. Clocks with multiple sub-scores (i.e. "explainable clocks") provide specificity and greater mechanistic insight into responsiveness of interventions than single-score clocks. Our work can help the geroscience field design future clinical trials, by guiding the choice of interventions, specific subsets of epigenetic clocks to minimize multiple testing, study duration, study population, and sample size, with the eventual aim of determining whether epigenetic clocks can be used as surrogate endpoints.
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BibTeX
@article{sehgal2026responsiveness,
title = {{Responsiveness of epigenetic aging biomarkers to longevity interventions in humans}},
author = {Raghav Sehgal and Daniel S. Borrus and Judith Armstrong and John T. González and Jessica Kasamoto and Yaroslav Markov and Ahana Priyanka and Ryan C. Smith and Natàlia Carreras-Gallo and Jessica Ann Lasky-Su and Varun Bhamidipati Dwaraka and Michael J. Corley and Albert Higgins‐Chen},
journal = {Nature Medicine},
year = {2026},
month = aug,
doi = {10.1038/s41591-026-04562-9},
url = {https://doi.org/10.1038/s41591-026-04562-9},
note = {CC BY},
}
RIS
TY - JOUR
TI - Responsiveness of epigenetic aging biomarkers to longevity interventions in humans
AU - Raghav Sehgal
AU - Daniel S. Borrus
AU - Judith Armstrong
AU - John T. González
AU - Jessica Kasamoto
AU - Yaroslav Markov
AU - Ahana Priyanka
AU - Ryan C. Smith
AU - Natàlia Carreras-Gallo
AU - Jessica Ann Lasky-Su
AU - Varun Bhamidipati Dwaraka
AU - Michael J. Corley
AU - Albert Higgins‐Chen
PY - 2026
DA - 2026/08/21
JO - Nature Medicine
DO - 10.1038/s41591-026-04562-9
UR - https://doi.org/10.1038/s41591-026-04562-9
AB - Aging biomarkers can potentially allow researchers to rapidly monitor the impact of an aging intervention, without the need for decade-spanning trials, by acting as surrogate endpoints. Prior to testing whether aging biomarkers may be useful as surrogate endpoints, it is first necessary to determine whether they are responsive to interventions that target aging. Epigenetic clocks are aging biomarkers based on DNA methylation with prognostic value for many aging outcomes. Many individual studies are beginning to explore whether epigenetic clocks are responsive to interventions. However, the diversity of both interventions and epigenetic clocks in different studies make them difficult to compare systematically. Here, we curate TranslAGE-Response, a harmonized database of 51 public and private longitudinal interventional studies and calculate a consistent set of 16 prominent epigenetic clocks for each study, along with 95 other DNAm biomarkers that help explain changes in each clock. With this database, we discover patterns of responsiveness across a variety of interventions and DNAm biomarkers. For example, clocks trained to predict mortality or pace of aging have the strongest response across all interventions and show consistent agreement with each other, pharmacological and lifestyle interventions drive the strongest response from DNAm biomarkers, and study population and study duration are key factors in driving responsiveness of DNAm biomarkers in an intervention. Some classes of interventions such as TNF-alpha inhibitors have strong, consistent effects across multiple studies, while others such as senolytic drugs have inconsistent effects. Clocks with multiple sub-scores (i.e. "explainable clocks") provide specificity and greater mechanistic insight into responsiveness of interventions than single-score clocks. Our work can help the geroscience field design future clinical trials, by guiding the choice of interventions, specific subsets of epigenetic clocks to minimize multiple testing, study duration, study population, and sample size, with the eventual aim of determining whether epigenetic clocks can be used as surrogate endpoints.
SN - 1078-8956
SN - 1546-170X
SN - 1744-7933
ER -