Appropriate nutritional intervention is essential for growth, development, and resilience, preventing diseases and their impacts. Ingested nutrients transit through metabolic pathways influenced by genetics, epigenetics, the microbiome and environmental factors, contributing to heterogeneity in individual needs and responses. These factors also underlie differences in disease susceptibility and response to therapy. Thus, they have the potential to inform recommendations for different segments of populations in precision health approaches. Precision health combined with spatially resolved data could help the target populations of interventions where resources are limited. Integration of big data related to genetic, epigenetic, microbiome, lifestyle, and environmental factors is required to expand our understanding of the diversity of human metabolism in response to diet. Based on the need of individuals, agriculture policy could focus on the production of the required foods, and food industry could provide the necessary food supply. Nutraceuticals and functional foods could play a critical role for the population. Precision health encompasses utilizing a group or an individual’s characteristics to optimize interventions. It is rapidly gaining traction, largely because of new ‘omics’ tools and consequent insights into the roles of the intestinal microbiome in metabolic and hormonal processes, and their association with diet. Differential responses to diet due to genetic and epigenetic factors (‘nutrigenetics’) are increasingly recognized. For example, malnutrition in all its forms (undernutrition, micronutrient deficiencies, overweight or obesity) influences DNA methylation, affecting host metabolism. Metabolites of the microbiota, such as short-chain fatty acids, can also alter DNA methylation and histone acetylation, potentially affecting epigenetic regulation of metabolic processes. Clinical status and social factors also offer the potential to target approaches. For example, integrating anthropometry, diet, physical activity, and microbiome can accurately predict glycemic response to a meal. Speaker: Chin-Kung Wang, PhD