Analysis pipeline. In the training cohort (FLEMENGHO), we first identified relevant metabolites to distinguish between atherosclerotic cardiovascular disease (ASCVD) cases and controls. The metabolites were selected from Partial Least Squares Discriminant Analysis (PLS-DA) and then used in eXtreme Gradient Boosting (XGBoost). Next, explainable machine learning of Shapley values (SHAP) with metabolites’ categorization was explored. After that, in an external cohort, we evaluated the same metabolites to distinguish between ischemic heart disease (IHD) cases and controls. In the figure, M stands for the number of metabolites.