Precision Nutrition for Cardiometabolic Health: Biomarker Discovery, Personalized Metabolic Responses, and Data-Driven Dietary Optimization

Research Project, 2026 – 2029

This project aims to enhance precision in nutritional research by addressing two key challenges: improving dietary assessment accuracy and understanding inter-individual differences in metabolic responses to food and their relevance for long-term health. We will develop and validate novel objective metabolomics and metagenome informed metaproteomics biomarkers for dietary fiber and protein intake, respectively, and test their applicability in relation to cardiometabolic risk. Using comprehensive multi-OMICs analyses of individual’s metabolic responses from meal challenge tests and deep phenotype data from the SCAPIS cohort, we will identify personalized metabolic response patterns and their links to cardiometabolic disease (CMD) risk, including atherosclerosis, prediabetes, and liver fat accumulation. Machine learning models will be developed to predict postprandial metabolic responses and optimize personalized dietary recommendations. Algorithms developed will be made openly available. By integrating biomarker technology, microbiome profiling, and computational modeling, this project will advance precision nutrition strategies, facilitating more effective dietary interventions and e-health applications to prevent CMD and promote long-term metabolic health.