The role of nutrigenomics and biotechnology in personalized nutrition : Trends, challenges, and future directions

Abstract

Personalized nutrition is redefining dietary science through the integration of genetic, metabolic, and microbiome data to enable targeted interventions. Nutrigenomics and nutrigenetics underpin this approach by elucidating gene–diet interactions and inter-individual variability in metabolic responses. Recent advances in high-throughput sequencing, polygenic risk modeling, and microbiome profiling, coupled with multi-omics integration and artificial intelligence, have improved the prediction of diet–health relationships and enabled data-driven nutritional strategies. However, clinical translation remains limited by inconsistent evidence, lack of standardized biomarkers, and insufficient external validation of predictive models, alongside challenges in cost and scalability. Ethical concerns, including data privacy, regulatory oversight, and equitable access, further constrain implementation. Future research should prioritize large, diverse cohorts, rigorous validation frameworks, and integration of AI-driven decision support with clinically actionable outcomes to advance personalized nutrition toward scalable and evidence-based practice.