Background: Carbohydrates are one of the main sources of energy in the diet and have important health implications depending on their quality and the quantity in which they are consumed. Recent studies have identified OMICS such as metabolomics and proteomics as powerful tools to investigate the molecular signatures called metabotypes associated to the intake of specific foods and food patterns. To date, however, there is a scarcity of studies trying to identify metabotypes associated dietary carbohydrate quality patterns and to their impact on metabolic health. Identifying such metabotypes could help to better understand the biological processes that link carbohydrate quality to cardiovascular disease and health. Hypothesis: Prolonged intake of simple and complex carbohydrates will differentially modulate plasma metabolome and proteome producing OMIC signatures that can be further used to elucidate underlying mechanisms linking carbohydrate quality and cardiometabolic risk. Aim: To characterize molecular phenotype signatures reflecting the intake of different carbohydrate qualities, and subsequently to associate them with cardiometabolic risk factors, using data from the Swedish Mammography Cohort Clinical (SMC-C) and the Cohort of Swedish Men Clinical (COSM-C). Methods: We will use available dietary, sociodemographic, lifestyle, clinical, biochemistry, and OMICS (metabolome and proteome) data from adult individuals belonging to SMC-C and COSM-C. A carbohydrate quality index will be calculated considering fiber intake, glycemic index, whole grains/total grains ratio, and solid carbohydrates/total carbohydrates ratio intake among participants. OMICS data will be used as predictor variables in multivariate models to assess their association with the carbohydrate quality index and specific cardiometabolic health traits. 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 appropiate.