A Machine-Learning Model of Chronological Age Based on Routine Blood Biomarkers in a Central European Population: A Potential Biological Age Marker.

Pavel Borsky, Drahomira Holmannova, Tereza Maresova, Anabela Cizkova, Tereza Kempfova, Svatopluk Byma, Tom Philipp, Lenka Borska

Department of Preventive Medicine, Faculty of Medicine in Hradec Kralove, Charles University, Hradec Kralove, Czech Republic, cuni.cz.Department of Preventive Medicine, Faculty of Medicine in Hradec Kralove, Charles University, Hradec Kralove, Czech Republic, cuni.cz. · pubmed

Paper

Abstract

Aging is a gradual decline in physiological and functional capacities that leads to an exponentially increasing risk of death. Although aging is universal, the rate of aging differs substantially between individuals. Biomarkers of aging are being developed to improve the prediction of a person's susceptibility to disease onset, disease course, and complications, as well as to estimate lifespan and healthspan.

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Research summary

The paper proposes a machine-learning model that uses routine blood biomarkers to estimate biological age. This research is relevant as it aims to identify biological markers that could help in understanding and potentially mitigating the aging process.

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