Optimizing metabolic health through deep-phenotyping-based personalized nutrition

With rising rates of chronic diseases, obesity and metabolic disorders, there is an urgent need for more effective lifestyle interventions. An increasing recognition of how an individual's diet affects their disease susceptibility has highlighted the limitations of traditional one-size-fits-all dietary guidelines, which often fail to account for the considerable metabolic heterogeneity among individuals. Precision nutrition tailors dietary advice and nutritional prescriptions by considering a broad range of factors, including genetics, age, sex, phenotype, lifestyle, preferences, clinical history and other personal characteristics. These data are used to stratify people into subgroups based on biomarkers of metabolic variation, enabling improved estimation of dietary requirements and more effective dietary advice and interventions. This Review explores the potential of utilizing deep human phenotype data from large cohort studies and advances in omics technologies and wearables to provide personalized nutrition guidance and improve the prevention and management of metabolic disorders. It also discusses how these approaches might be integrated into broader clinical practice and identifies the challenges that must be overcome before precision nutrition can be widely adopted in clinical and public health settings.