A hematology-based clock derived from the Study of Longitudinal Aging in Mice to estimate biological age.

Jorge Martinez-Romero, Maria Emilia Fernandez, Michel Bernier, Nathan L Price, William Mueller, Julián Candia, Simonetta Camandola, Osorio Meirelles, Yi-Han Hu, Zhiguang Li, Nigus Asefa, Andrew Deighan, Camila Vieira Ligo Teixeira, Dushani L Palliyaguru, Carlos Serrano, Nicolas Escobar-Velasquez, Stephanie Dickinson, Eric J Shiroma, Luigi Ferrucci, Gary A Churchill, David B Allison, Lenore J Launer, Rafael de Cabo

Translational Gerontology Branch, National Institute on Aging, Baltimore, MD, USA.Translational Gerontology Branch, National Institute on Aging, Baltimore, MD, USA. · pubmed

Paper

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.

The following analysis is generated by AI and may contain errors. It is separate from the authors’ abstract and is not peer review.

Research summary

The paper is relevant to longevity research as it develops a biological clock based on hematological markers to estimate biological age, which is directly related to understanding the aging process. The findings regarding the correlation between predicted blood age and mortality risk contribute to the field of aging research, but the impact is limited as it primarily focuses on a predictive model rather than addressing the underlying mechanisms of aging or interventions for lifespan extension.

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