AI Article Synopsis
- Researchers aimed to create a practical aging clock using common blood tests to predict "blood age" and compare it to actual chronological age in mice.
- They analyzed data from over 2,500 mice, utilizing deep neural networks to find a strong correlation between predicted blood age and actual age, with aging gaps linked to higher mortality risk and frailty.
- The study identified platelets as the key predictor and suggests this method could help understand aging variability in humans.
Article 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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| http://dx.doi.org/10.1038/s43587-024-00728-7 | DOI Listing |
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