Extended Data Fig. 6: Feature importance for organ aging, Alzheimer’s disease, and mortality.

a, Scatterplot showing results from feature importance for biological aging (FIBA) algorithm to identify proteins in the brain aging model contributing to the brain age gap’s association with Alzheimer’s disease risk. FIBA score (y-axis) indicates Alzheimer’s disease risk effect size loss after permutation of protein values. X-axis indicates absolute protein weight in the brain aging model. Color indicates protein weight in the brain aging model. b, Mean gene expression of brain aging protein-encoding genes in Haney et al. 2024 human brain scRNA-seq data. c-f, Scatterplots showing results from feature importance for biological aging (FIBA) algorithm to identify proteins in the brain (c), conventional (d), artery (e), and immune (f) aging models that contribute to the model age gap’s association with future mortality risk. FIBA score (y-axis) indicates mortality risk effect size loss after permutation of protein values. X-axis indicates absolute protein weight in the aging model. Color indicates protein weight in the aging model. g, Forest plot showing results from Cox proportional hazards regression, testing the associations between extreme ager status and future all-cause mortality risk, controlling for age, sex. Points show extreme ager hazard ratios, error bars show 95% confidence intervals, and number on the right show number of events out of the total sample size. h, Protein levels of youthful brain agers versus normal agers (n = 12,696). The top ten (5 decrease with age, 5 increase with age) proteins based on mortality risk FIBA score are shown. Each protein was linearly adjusted for age, sex, and every other protein in the brain aging model before plotting. Proteins are ordered by the aging model coefficient. The box bounds are the Q1, median and Q3; the whiskers show Q1 − 1.5× the interquartile range (IQR) and Q3 + 1.5× the IQR. i, As in h, but for the immune aging model (n = 12,847).