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Updated: Sep 15, 2025

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Multiomic clocks to predict phenotypic age in mice.

Daniel L Vera1,2, Patrick T Griffin3, David Leigh3

  • 1VoLo Foundation, Palm Beach, FL 33410 USA.

Biorxiv : the Preprint Server for Biology
|July 14, 2025
PubMed
Summary
This summary is machine-generated.

Researchers developed a new mouse phenotypic age model (Mouse PhenoAge) to assess biological aging. This model accurately predicts mortality and remaining lifespan in mice, reducing the need for extensive survival studies.

Keywords:
DNA methylationMetabolomicsbiological ageepigenetic clocksfrailty

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Area of Science:

  • Gerontology and aging research
  • Biomarker discovery
  • Animal models in aging

Background:

  • Biological age, distinct from chronological age, reflects overall health and aging.
  • Existing biological age measures ('clocks') primarily predict chronological age.
  • There is a need for composite biological age measures in animal models to assess interventions and mortality risk.

Purpose of the Study:

  • To develop the first composite biological age measure for mice, termed the mouse phenotypic age model (Mouse PhenoAge).
  • To create predictive clocks for Mouse PhenoAge using multi-omic data.
  • To validate the association of Mouse PhenoAge with remaining lifespan.

Main Methods:

  • Developed Mouse PhenoAge based on frailty, complete blood counts, and mortality risk in a longitudinal mouse cohort.
  • Utilized multi-omic data, including metabolomics and DNA methylation, to build predictive models.
  • Assessed the relationship between model residuals and remaining lifespan in mice.

Main Results:

  • Successfully developed and validated Mouse PhenoAge as a composite measure of biological age in mice.
  • Multi-omic models accurately predicted Mouse PhenoAge.
  • Model residuals correlated with remaining lifespan, even among mice of the same chronological age.

Conclusions:

  • Mouse PhenoAge provides a novel and accurate method for assessing biological aging and mortality risk in laboratory mice.
  • These predictive models can reduce the reliance on lengthy and resource-intensive survival studies.
  • This advancement facilitates more efficient aging research and intervention testing in mice.