Evidence map›Paper›PMID 40875557›Full record

ArticleThe journals of gerontology. Series A, Biological sciences and medical sciences2025

Multiomic clocks to predict phenotypic age in mice.

Daniel L Vera, Patrick T Griffin, David Leigh, Jason Kras, Enrique Ramos, Isaac Bishof, Anderson Butler, Karolina Chwalek, David S Vogel, Alice E Kane and 1 more

Abstract read
In one paragraph

Article in The journals of gerontology. Series A, Biological sciences and medical sciences, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

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2 · The registry

The trial behind it

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3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

11 authors.

Daniel L VeraVoLo Foundation, Palm Beach, Florida, United States.
Patrick T GriffinPaul F. Glenn Center for Biology of Aging Research, Department of Genetics, Blavatnik Institute, Harvard Medical School, Boston, Massachusetts, United States.
David LeighPaul F. Glenn Center for Biology of Aging Research, Department of Genetics, Blavatnik Institute, Harvard Medical School, Boston, Massachusetts, United States.
Jason KrasVoLo Foundation, Palm Beach, Florida, United States.
Enrique RamosVoLo Foundation, Palm Beach, Florida, United States.
Isaac BishofVoLo Foundation, Palm Beach, Florida, United States.
Anderson ButlerVoLo Foundation, Palm Beach, Florida, United States.
Karolina ChwalekVoLo Foundation, Palm Beach, Florida, United States.
David S VogelVoLo Foundation, Palm Beach, Florida, United States.
Alice E KanePaul F. Glenn Center for Biology of Aging Research, Department of Genetics, Blavatnik Institute, Harvard Medical School, Boston, Massachusetts, United States.
David A SinclairPaul F. Glenn Center for Biology of Aging Research, Department of Genetics, Blavatnik Institute, Harvard Medical School, Boston, Massachusetts, United States.

Funding

Role of Sgs1 DNA helicase in telomere maintenanceR01AG019719 · NIA · HARVARD UNIVERSITY (MEDICAL SCHOOL) · PI SINCLAIR, DAVID A. · 2001 to 2022
$5.3M
Elucidating mechanisms of SIRT1 activationR01DK100263 · NIDDK · HARVARD MEDICAL SCHOOL · PI SINCLAIR, DAVID A. · 2016 to 2020
$2.5M
Development of a Predictive Frailty Clock & Longitudinal Investigation of its Epigenetic DeterminantsR00AG070102 · NIA · INSTITUTE FOR SYSTEMS BIOLOGY · PI KANE, ALICE E · 2023 to 2025
$1.0M
A novel method to identify regulators of biological aging 
based on high-throughput sequencing of epigenetic clocks.K00AG073499 · NIA · HARVARD UNIVERSITY · PI Patrick Griffin · 2023 to 2026
$304k
NIA/NIH R01AG019719NIA/NIH R01DK100263NIA NIH HHS 4K00AG073499NIA NIH HHS K00 AG073499NIA NIH HHS R00 AG070102NIA NIH HHS R00AG070102NIA NIH HHS R01 AG019719NIDDK NIH HHS R01 DK100263Paul F. Glenn Foundation for Medical ResearchVoLo Foundation
6 · The paper itself

Abstract

Biological age refers to a person's overall health in aging, as distinct from their chronological age. Diverse measures of biological age, referred to as "clocks," have been developed in recent years and enable risk assessments and an estimation of the efficacy of longevity interventions in animals and humans. Although most clocks are trained to predict chronological age, clocks have been developed to predict more complex composite biological age outcomes, at least in humans. These composite outcomes can be made up of a combination of phenotypic data, chronological age, and disease or mortality risk. Here, we develop the first such composite biological age measure for mice: the mouse phenotypic age model (Mouse PhenoAge). This outcome is based on frailty measures, complete blood counts, and mortality risk in a longitudinally assessed cohort of male and female C57BL/6 mice. We then develop clocks to predict Mouse PhenoAge, based on multiomic models using metabolomic and DNA methylation data. Our models accurately predict Mouse PhenoAge, and residuals of the models are associated with remaining lifespan, even for mice of the same chronological age. These methods offer novel ways to accurately predict mortality in laboratory mice, thus reducing the need for lengthy and costly survival studies.

Indexed as

AgingAnimalsDNA MethylationFemaleFrailtyLongevityMaleMiceMice, Inbred C57BLPhenotypeBiological ageDNA methylationEpigenetic clocksFrailtyMetabolomics

Identifiers

PMID40875557
PMCPMC12510163

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.