Artificial Intelligence-Assisted Personalized Nutrition A Holistic Framework Linking Dietary Behaviour, Metabolic Health and Lifestyle Patterns

Abstract

SAI-driven personalized nutrition is revolutionizing nutrition advice by merging individual biological, behavioural, metabolic and contextual factors into adaptive nutrition systems. This study discusses the conceptual basis, the technology, clinical applications and implementation barriers to a comprehensive approach linking dietary habits, metabolic health and lifestyle. Data have shown that there is considerable inter-individual variability in glycemic responses, lipid metabolism, body composition, gut microbiome profiles, and nutrient requirements, making a one-size-fits-all approach to nutrition not very effective. The potential application of machine learning, deep learning, natural language processing, computer vision, recommender systems, wearable sensors, and mobile health platforms could enhance dietary assessment, food recognition, portion estimation, behavioral monitoring, metabolic-risk prediction, and meal planning. Combining data from genomic, metabolomic, proteomic, microbiome, physical-activity, sleep, stress, sedentary-behavior, and socioeconomic data provide more personalized, context-specific nutritional advice. In obesity, diabetes prevention, cardiovascular-risk reduction, and preventive healthcare, such systems have the potential for interesting applications. The success of their use, however, requires representative datasets, accurate and validated measurements, clinical interpretability, user engagement, affordability, digital access and integration of professional judgement. Multimodal data integration, long-term clinical trials, and diverse longitudinal datasets and standardized validation procedures should be emphasized in future research. In conclusion, an AI-driven holistic framework used to facilitate personalized nutrition, enhance metabolic outcomes and dietary adherence among populations, and is a supportive tool for responsive, evidence-based nutrition.