ArticleNature aging2024
A hematology-based clock derived from the Study of Longitudinal Aging in Mice to estimate biological age.
Article in Nature aging, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- Hematopoietic stem cell aging: a review of transcriptional and multi-omics insights and potential paths for AI integration.Experimental & molecular medicine · 2026Review
- Time-restricted feeding extends healthspan in both sexes and lifespan in male C57BL/6 J mice.Nature aging · 2026Article
- Amino acid-based biological age clock and its implications for human health and aging.Nature communications · 2026Article
- Developing and internally validating AI-based aging resilience biomarkers in non-human primates.The journals of gerontology. Series A, Biological sciences and medical sciences · 2026Article
- DNA methylation signatures of frailty beyond age: a longitudinal study of female and male mice.bioRxiv : the preprint server for biology · 2025Article
- Accelerated Biological Aging and Genetic Pleiotropy in Age-Related Eye Diseases: A Population-Based Cohort and Integrative Genetic Analysis.Investigative ophthalmology & visual science · 2025Article
- Biomarker integration and biosensor technologies enabling AI-driven insights into biological aging.Frontiers in aging · 2025Review
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Authors and funding
24 authors.
Funding
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Abstract
Biological clocks and other molecular biomarkers of aging are difficult to implement widely in a clinical setting. In this study, we used routinely collected hematological markers to develop an aging clock to predict blood age and determine whether the difference between predicted age and chronologic age (aging gap) is associated with advanced aging in mice. Data from 2,562 mice of both sexes and three strains were drawn from two longitudinal studies of aging. Eight hematological variables and two metabolic indices were collected longitudinally (12,010 observations). Blood age was predicted using a deep neural network. Blood age was significantly correlated with chronological age, and aging gap was positively associated with mortality risk and frailty. Platelets were identified as the strongest age predictor by the deep neural network. An aging clock based on routinely collected blood measures has the potential to provide a practical clinical tool to better understand individual variability in the aging process.
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