Nutrigenomics meets multi-omics: integrating genetic, metabolic, and microbiome data for personalized nutrition...

The integration of multi-omics technologies with computational biology has had a profound impact on nutritional science, enabling the development of precision nutrition strategies tailored to individual biochemical profiles. This review synthesizes recent advances in integrating genomic, epigenetic, transcriptomic, proteomic, metabolomic, and microbiome data for personalized dietary interventions. The present study analyzed machine learning approaches, with a particular focus on transformer and graph neural networks, for the processing of multi-omics data and prediction of metabolic outcomes. Advanced computational models have demonstrated an accuracy rate of over 90% in predicting individual metabolic responses to dietary interventions. Large-scale clinical trials (PREDICT, FOOD4ME, and PRECISION-HEALTH) have demonstrated significant improvements in weight management, glycemic control, and dietary adherence compared with conventional approaches. Digital health technologies, including continuous glucose monitoring and artificial intelligence (AI)-powered applications, facilitate real-time physiological monitoring and enable dynamic nutritional adjustments in patients with diabetes. The paradigm shift from population-based dietary recommendations to individualized interventions is represented by multi-omics-driven precision nutrition. The integration of sophisticated computational methodologies with comprehensive biological profiling provides a unique opportunity to prevent and manage chronic diseases via targeted dietary interventions. However, the successful implementation of such a system necessitates interdisciplinary collaboration among biologists, computational scientists, clinicians, and policymakers to ensure equitable access and ethical deployment of the technology. Future research should focus on developing scalable implementation frameworks, establishing evidence-based clinical practice guidelines to standardize multi-omics applications in precision nutrition, and identifying strategies to address potential disparities in access to these applications.