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

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, …

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