This project aims to identify and characterize distinct cardiometabolic risk types within the Swedish CArdioPulmonary bioImage Study (SCAPIS), a population-based cohort of 30,154 middle-aged individuals. SCAPIS offers a unique platform for comprehensive phenotyping, including state-of-the-art imaging to assess subclinical cardiovascular disease, detailed lifestyle and clinical data, as well as extensive molecular profiling. We will integrate imputed genotype data with multi-omics layers—proteomics, metabolomics, and gut metagenomics—to uncover how genetic and molecular risk factors converge into distinct biological processes that drive the development of coronary artery disease, stroke, and type 2 diabetes. First, we will derive polygenic risk scores (PRS) for key cardiometabolic outcomes and subdivide them by pathway, creating pathway-specific PRS that capture different molecular processes relevant to disease. We will then apply advanced clustering approaches to integrate these pathway-specific genetic risk profiles with proteomic, metabolomic, and metagenomic data to form multi-omics clusters. By adopting algorithms that allow soft clustering, we can account for the overlapping nature of biological pathways, thus permitting both variants and molecular features to participate in multiple clusters. Each cluster will provide insight into a biological pathway or process, such as inflammation or metabolic dysregulation, that may underlie cardiovascular risk. Building upon these clusters, we will calculate multi-omics risk scores (MORS) to quantify an individual’s risk load within each process. We will validate the clinical relevance of these MORS by examining their associations with subclinical imaging biomarkers (e.g., coronary artery calcification, carotid plaque burden) and established risk factors (e.g., blood lipids, blood pressure, glycemic markers). We will further evaluate their predictive utility for incident cardiovascular events and type 2 diabetes in prospective follow-up. In addition, we will investigate whether particular diet and lifestyle factors interact with elevated risk loads (e.g., in processes linked to insulin resistance, oxidative stress, or inflammation) to modify disease risk. These analyses will help pinpoint precision prevention strategies, identifying dietary or behavioral modifications that offer particular benefit for individuals at high risk in specific biological pathways.