Extended Data Fig. 3: Deeper investigation of the aging proteomic clock.

(a) Prediction of age in the 4 independent cohorts (n = 171) using the proteomic clock. Only 141 proteins out of the 373 constituting the clock were measured in these samples. (b) Prediction of age in the discovery cohort (n = 2,817) using the 373 plasma markers. (c) Feature reduction of the aging model in the Discovery and Validation cohorts to estimate whether a subset of the aging signature can provide similar results to the 373 aging proteins. Dashed lines represent a broken stick model and indicate the best compromise between number of variables and prediction accuracy. (d) Heatmap representing the associations between delta age and 334 clinical and functional variables. For quantitative traits, linear models adjusted for delta age, age and sex were used and significance was tested using F-test. For binary outcomes, binomial generalized linear models adjusted for delta age, age and sex were used and significance was tested using likelihood ratio chi-square test. As in (c) the analysis was performed for the top 2 to top 373 variables predicting age. The non-uniformity in the heatmaps suggests that specific subsets of proteins may best predict certain clinical and functional parameters.