EXPLORING ARTIFICIAL INTELLIGENCE AND GENE–DIET INTERACTION MAPPING TO DRIVE THE FORMULATION OF EVIDENCE-BASED FUNCTIONAL FOODS TAILORED TO INDIVIDUAL GENETIC PROFILES AND METABOLIC HEALTH FOR ADDRESSING AGE-OLD HEALTH CONDITIONS

The convergence of artificial intelligence (AI) with nutrigenomics has opened transformative possibilities in developing personalized functional foods aimed at improving metabolic health and managing chronic diseases. This paper explores how machine learning algorithms can be integrated with gene–diet interaction mapping to design dietary interventions aligned with individual genetic profiles. By identifying genotype-specific nutrient responses, AI can assist in crafting evidence-based nutritional strategies targeting longstanding health conditions such as obesity, diabetes, cardiovascular diseases, and inflammatory disorders. We review existing literature on gene–diet interactions, assess current AI-based nutrigenomic methodologies, and propose a framework for the rational formulation of personalized functional foods. Tables and diagrams illustrate data integration strategies and gene–nutrient relationships, emphasizing the potential of this approach for precision nutrition.