Causal pathways from exposures to health outcomes via metabolic regulations are not well understood. Metabolomics, representing an individual’s molecular phenotype, may provide novel insights into such pathways. Our aim is thus to investigate how environmental exposures (persistent organic pollutants (POPs), diet and microbiota) regulate metabolic pathways leading to cardiometabolic diseases (CMDs; type-2 diabetes, myocardial infarction (MI) and stroke) and intermediate risk markers, using metabolomics. In this study we plan to use 2 cohorts that form the basis of Swedish Infrastructure for Medical Population-based Life-course and Environmental Research (SIMPLER), i.e. the Swedish Mammography Cohort (SMC) and the Cohort of Swedish Men (COSM), more specifically focusing on their clinical subcohorts (SMCC and COSMC), with data on metabolomics, diet, risk markers, incidence of CMDs and epidemiological covariates, POPs (both per- and polyfluoroalkyl substances (PFAS) and organochlorine compounds (OCs)) (part of SMCC) and microbiota (COSMC). An in-house machine learning algorithm (MUVR) will be used to select metabolite features that reflect exposures and then these features will be further examined for their associations with CMDs and risk markers through logistic regression or survival analysis. An in-house tool (triplot) will be used to visualize how metabolites-of-interest associate upstream to exposures (via partial Spearman correlation) and downstream to intermediate risk markers and CMD (via linear models). All data analyses, including machine learning, logistic regression, survival analysis, correlation, and linear models will be adjusted for covariates (e.g., sex, age). The overarching aim is to explore metabolic regulations represented by untargeted metabolomics that link environmental exposures in SIMPLER (diet, POPs and microbiota) to CMD outcomes (i.e. T2D, MI, stroke and intermediate risk markers). Our hypothesis is that data-driven analysis of metabolomics data, through a combination of machine learning with a so-called “meet-in-the-middle” approach (Chadeau-Hyam et al. 2011) and mediation analysis (VanderWeele 2016), will allow identifying biological features and mechanisms relating environmental exposures to CMD outcomes. Our specific objectives are: i) To discover metabolite features that associate with environmental exposures. ii) To further investigate whether such exposure-related features also associate with the CMD outcomes. iii) To replicate exposure and outcome-related metabolite features in two other Nordic cohorts (the Northern Sweden Health and Disease Study - Västerbotten Intervention Programme - BioDiVa subcohort and the Danish Cancer and Health - Next Generations - MAX validation subcohort). iv) To replicate exposure and outcome-related metabolite features from the Northern Sweden Health and Disease Study - Västerbotten Intervention Programme - BioDiVa subcohort and the Danish Cancer and Health - Next Generations - MAX validation subcohort in SIMPLER. v) To further explore potential mechanisms from the metabolite features selected to link environmental exposures to CMD outcomes