Background: Overweight and obesity are major risk factors for and contributors to increase morbidity and mortality from cardiovascular disease, cancer and other chronic diseases. Conversely, maintenance and loss of weight and fat mass are crucial to ameliorate health issues related to overweight and obesity. Several OMICS techniques have emerged as powerful tools to investigate the molecular signatures associated to changes in weight and body composition, namely gain or loss of weight and fat mass. However, there is still a need for more studies to identify molecular profiles associated to changes in weight and body composition and their relationship with different metabolic health traits. Defining OMICS-based signatures that reflect differential metabotypes associated with weight and body composition change over time could help to better understand the biological processes underlying weight and fat mass gain and loss, as well as their repercussion on cardiovascular health. Hypothesis: The proteome and metabolome profiles are important determinants of weight and fat mass trajectories and cardiometabolic risk factors, in response, or not, to different lifestyle behaviors. Aim: To characterize molecular phenotype signatures reflecting changes of weight and fat mass, and the association of these with cardiometabolic traits, using data from the Swedish Mammography Cohort Clinical (SMC-C) and the Cohort of Swedish Men Clinical (COSM-C). Methods: We will use available data on diet, sociodemographic, lifestyle, clinical history, blood biochemistry, and OMICS from men and women participating in the SMC-C and COSM-C. Changes in weight and body composition will be using data from the previous and/or next time-point to the OMICS analyses. OMICS data, including plasma metabolome and proteome will be used as predictor variables for changes in weight and body composition in multivariate models, as well as for their association with specific cardiometabolic health parameters. Importantly, further analysis including microbiome data will be conducted when these are available. Diagnoses of chronic diseases, as the aforementioned in the CD10 codes will be used as exclusion criteria or controlling variables in multivariate models, as appropriate.