Extended Data Fig. 2: Supplementary analysis on the cross-sectional subsample.

(a) Feature importance, assessed via mean decrease in impurity (MDI), enabled the prediction of chronological age using biobehavioral factors. The sample size for the analyses reported in this figure included 102,725 individuals. Goodness-of-fit and feature importance metrics are provided. (b) MDI facilitated the characterization of groups with more delayed (left panel) and more accelerated (right panel) aging. Goodness-of-fit and feature importance metrics are provided. (c) Average BBAGs distribution by continent. The color bar indicates younger (blue) and older (red) BBAGs. (d) BBAGs comparisons by continent (left panel) and by European regions (right panel). (e) BBAGs comparisons between low- and high-income countries, based on gross national income (GNI) and gross domestic product (GDP) indicators. (f) Linear regression models were used to assess the relationship between BBAGs and all exposomes, as well as combined social, physical, and sociopolitical exposomes. (g) Linear regression models were also used to assess the associations between BBAGs and individual social (gender equality, migration, and structural equality) and physical exposomes (air quality). (h) Linear regression models were used to examine the association between BBAGs and individual sociopolitical exposomes (democracy indicators). All P-values reported in Panels f, g, and h were < 1e-15. The maps were created in Python using the Plotly library (https://plotly.com/python/maps/). All other illustrations and icons were designed using GIMP (https://www.gimp.org).