Exposome-wide patterns predict brain health in aging

Figures

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  Fig. 1. Prediction of the grey matter health (BAG) from the expotype. Left panel: Machine learning algorithms were trained and tested in three subsets of participants. More than 200 variables were included in each subset, spanning biomedical, lifestyle, socioaffective, early life, and environmental domains. Right panel: grey matter health could be significantly predicted from the expotype. A full list of all exposome variables in the main, replication, and variables-restricted subsets is available in Supplementary Table 2, and detailed prediction performances are available in Supplementary Table 5. In this figure, BAG stands for Brain Age Gap.

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  Fig. 2. Thirty top contributing variables in the main subset (n = 3706, 261 exposome variables). Left Panel: Exposome variables contribution (as reflected by absolute SHAP value) for prediction of grey matter health based on random forest algorithm. Right panel: summary of the most contributing variables consistently across random forest and at least one other algorithm (see Supplementary Figs. 1 and 2). In this figure, SHAP stands for SHapley Additive exPlanations and SVR stands for Support Vector Regression. Source data are included in Supplementary Tables 6 and 7 and provided as a Source data file.

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  Fig. 3. SHAP value for the common most contributing variables when using random forest in the main subset (n = 3706, 261 exposome variables). The x axis represents the impact on the prediction: zero could be considered as the default prediction, while a shift to the right reflects a higher predicted BAG (i.e., poorer predicted brain health) and a shift to the left represents a lower predicted BAG (i.e., a better predicted grey matter health). Blue indicates a lower value for the variable, while pink/red indicates a higher value. The right panel summarizes which direction of the variable leads to poorer brain health (i.e., to higher BAG). In this figure, BAG stands for Brain Age Gap.

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  Fig. 4. General workflow in the UK Biobank. Two machine learning models were developed: a Brain Age Prediction Model was developed from a healthy set of UK Biobank participants (n = 5025, 2579 females), while a Grey Matter Health (BAG) Prediction Model based on exposome data was developed in participants who have not been included in the healthy set to avoid any data leakage. Continuous lines indicate features/predictors fed into the model, while dashed lines are used for target variables. Three subsets were further created from the population set to evaluate grey matter health (i.e., BAG) prediction models. The UK Biobank logo is used with permission. Cortical and subcortical atlas visualizations are based on the Schaefer cortical atlas (open-source license) and the Melbourne subcortex atlas, both cited in the Methods.

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  Fig. 5. Distribution of the brain age gap across the UK Biobank population and analysis subsets. Distribution of the brain age gap in the remaining UK Biobank population (i.e. excluding the healthy subset in which the Brain Age prediction model was developed; n = 34365, age range: 44–82 years, mean 63.86 ± 7.57 years, 18128 females, left panel) and in the specific subsets of participants (main subset, n = 3706; replication subset, n = 4202; variable-restricted subset, n = 7736, right panel) that were used for exposome-based prediction of grey matter health (see Supplementary Table 2).