Precision Nutrition Powered by Artificial Intelligence, Nutrigenomics and Gut Microbiome Modulation: Future Directions in Personalized Healthcare

Abstract

Combining the best of AI, nutrigenomics, and the gut microbiome is redefining nutrition healthcare from a population-based approach to personalized nutrition strategies. This review summarizes the recent progress in multi-omics integration and discusses the hierarchy of the genomic, epigenomic, transcriptomic, proteomic, metabolomic and microbiome levels, which all impact metabolic phenotypes in a unique way and demonstrate the interrelationships between these layers. We will discuss the use of two recently developed machine learning algorithms, transformer and graph neural network, that have been shown to provide high accuracy, typically >90% in clinical applications, to process complex biological data and predict metabolic outcomes. Personalized treatment is already proven to be superior to conventional treatment in weight management, glycemic management, and dietary adherence in landmark clinical trials such as PREDICT, FOOD4ME and PRECISION-HEALTH. Digital health solutions like continuous glucose monitoring and AI-powered apps allow for continuous monitoring of the body and adaptive nutritional planning. With the adoption of digital health tools like continuous glucose monitoring and AI-powered apps, real-time monitoring and adaptive nutrition planning become possible. But to be successful in implementing solutions, issues such as data privacy, cost differences, clinical validation and equitable access must be dealt with. The review offers an in-depth view of the future of precision nutrition within health care systems globally and highlights the necessity to involve various fields of science, such as biologists, computational scientists, clinicians and policymakers.