Prediction of chronological and biological age from laboratory data.

ABSTRACT: Aging has pronounced effects on blood laboratory biomarkers used in the clinic. Prior studies have largely investigated one biomarker or population at a time, limiting a comprehensive view of biomarker variation and aging across different populations. Here we develop a supervised machine learning approach to study aging using 356 blood biomarkers measured in 67,563 individuals across diverse populations. Our model predicts age with a mean absolute error (MAE), or average magnitude of prediction errors, in held-out data of 4.76 years and an R² value of 0.92. Age prediction was highly accurate for the pediatric cohort (MAE = 0.87, R² = 0.94) but inaccurate for ages 65+ (MAE = 4.30, R² = 0.25). Variability was observed in which biomarkers carry predictive powe

SUBMITTER: Sagers L 

PROVIDER: S-EPMC7244024 | biostudies-literature | 2020 May

REPOSITORIES: biostudies-literature