Multiomics: the intersection of personalized nutrition in cardiometabolic diseases

Background: Cardiometabolic diseases are among the leading causes of increasing morbidity and mortality worldwide. However, current population-based dietary recommendations do not sufficiently account for biological differences between individuals and therefore do not have the same effect on everyone. The multiomic approach, which incorporates genomic, epigenomic, transcriptomic, proteomic, metabolomic, and microbiome data, facilitates more accurate classification of disease risk and selection of appropriate nutritional interventions by mapping food-disease relationships across different biological layers. Methods: Through a narrative synthesis of the current literature, we focused on evidence from multiomic studies to assess their ability to guide personalized nutrition strategies based on individual genetic, metabolic, and microbiome characteristics in cardiometabolic diseases. Results: Recent evidence indicates that metabolomic markers have been reported to provide predictive value in addition to classic risk indicators and to increase the predictive power of models when combined with genetic data. Microbiome research shows that glycemic and lipemic responses can be predicted using algorithms based on gut microbiota. Recent clinical studies show that personalized nutrition plans, which evaluate the microbiome and clinical characteristics together, improve continuous glucose monitoring-based glycemic control, glycated hemoglobin levels, and triglycerides more than the classic Mediterranean diet. Conclusion: This review summarizes the current multiomic evidence, discusses the methodological and practical challenges in this field, and highlights future priorities. The integration of digital biomarkers obtained from wearable technologies with multiomic systems and artificial intelligence-supported models, when developed in accordance with ethical and equitable access principles, has the potential to support the transition from the discovery phase to patient-centered clinical applications. Graphical abstract: [Image: see text]