The Nutri-Exposome Intelligence Framework: Integrating Multi-Omics, Machine Learning, and Digital Nutrition for Precision Chronic Disease Prevention

Abstract

Background/Objectives: Precision nutrition is moving beyond population-based guidance and isolated gene–diet interactions toward integrative models of dietary response. However, current approaches remain fragmented across nutrigenomics, microbiome research, multi-omics profiling, digital health, and machine learning. This review proposes the Nutri-Exposome Intelligence Framework as a conceptual, data science-driven model for integrating cumulative dietary, environmental, microbial, molecular, clinical, and digital exposures for precision chronic disease prevention. Methods: This conceptual review synthesizes the literature on precision nutrition, nutrigenetics, nutrigenomics, exposomics, gut microbiome research, multi-omics integration, wearable and biomarker-based monitoring, and machine learning in nutrition studies. Evidence was organized into a framework linking exposure assessment, host susceptibility, microbiome-mediated biotransformation, molecular response profiling, computational modelling, personalized intervention, and longitudinal feedback. Results: The proposed framework consists of seven interconnected layers: diet, environment, and lifestyle exposures; host genome and microbiome; multi-omics molecular responses; machine learning-based integration; risk prediction and responder stratification; personalized dietary intervention; and wearable and biomarker-based feedback. It positions the nutri-exposome as a cumulative exposure–response system and highlights how machine learning can support data harmonization, feature engineering, predictive modelling, responder classification, explainable interpretation, and adaptive refinement of dietary recommendations. Key applications include obesity, type 2 diabetes, cardiovascular disease, metabolic dysfunction-associated steatotic liver disease, cardiovascular–kidney–metabolic syndrome, and broader cardiometabolic prevention. Conclusions: Nutri-exposome intelligence offers a structured pathway for transforming complex nutrition data into predictive, explainable, and adaptive precision nutrition strategies. Implementation will require longitudinal and multi-ethnic cohorts, standardized metadata, causal validation, interpretable machine learning, ethical governance, and equitable access to support responsible clinical and public health translation globally.