Multiomic clocks to predict phenotypic age in mice.

Area of Science:

• Gerontology and aging research.
• Biomarker discovery and validation.
• Animal models in biomedical research.

Background:

• Biological age measures overall health during aging, distinct from chronological age.
• Existing "clocks" assess biological age and intervention efficacy in humans and animals.
• Most clocks predict chronological age; few predict composite biological age outcomes.

Purpose of the Study:

• To develop the first composite biological age measure for mice, termed Mouse PhenoAge.
• To create predictive models for Mouse PhenoAge using multi-omic data.
• To assess the utility of Mouse PhenoAge in predicting mortality and lifespan in mice.

Main Methods:

• Developed Mouse PhenoAge based on frailty, complete blood counts, and mortality risk in mice.
• Utilized multi-omic data, including metabolomics and DNA methylation, to build predictive clocks.
• Validated models by assessing the association of residuals with remaining lifespan.

Main Results:

• Successfully developed Mouse PhenoAge, a composite biological age measure for 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 age in mice.
• These predictive models can improve mortality prediction in laboratory mice.
• The approach may reduce the need for extensive and expensive survival studies in aging research.