Predicting physiological aging rates from a range of quantitative traits using machine learning

Eric D. Sun; Yong Qian; Richard Oppong; Thomas J. Butler; Jesse Zhao; Brian H. Chen; Toshiko Tanaka; Jian Kang; Carlo Sidore; Francesco Cucca; Stefania Bandinelli; Gon&#xE7;alo R. Abecasis; Myriam Gorospe; Luigi Ferrucci; David Schlessinger; Ilya Goldberg; Jun Ding

doi:doi:10.18632/aging.203660

← Back

Research Paper Volume 13, Issue 20 pp 23471—23516

Predicting physiological aging rates from a range of quantitative traits using machine learning

Figure 3. Correlation between epigenetic aging rate and physiological aging rate. Correlation between PAR and other aging rate measures. (A) Mean physiological aging rates (PARs) obtained from physiological age measurements were correlated (R² = 0.362, r = 0.601) with the mean epigenetic aging rates (EARs) calculated for the same individuals across the baseline and latest follow-up InCHIANTI studies using the Horvath DNAm age. (B) Pearson correlation values between different pairings of aging rate measures including PAR, Horvath DNAm age, the Hannum DNAm age, GrimAge, rescaled PhenoAge, and the corrected KDM biological age with eight covariates.