Translating cellular aging clocks into disease risk prediction.
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- Record sourced from PubMed, PMID 42612620.
- Also identified by DOI 10.1016/j.xcrm.2026.102996.
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Abstract
Ding et al. mapped over 7,000 plasma proteins to more than 40 cell types and developed machine learning aging clocks across 60,000 individuals, demonstrating that cell-type-specific biological aging is heterogeneous, measurable from blood alone, and powerfully predictive of neurodegenerative disease, cancer, and mortality up to 15 years before clinical onset.<sup>1</sup>.
Medical subject headings
- Cellular Senescence
- Neurodegenerative Diseases
- Neoplasms