Review on Advancement of AI in Nutrigenomics

Abstract

Nutrigenomics, the study of how dietary components influence gene expression and how genetic variations affect individual responses to nutrition, has emerged as a cornerstone of personalized medicine. The integration of artificial intelligence (AI) with nutrigenomic research marks a significant advancement in our ability to understand and apply personalized nutrition principles. This chapter explores the transformative role of artificial intelligence in advancing nutrigenomic research and its practical applications in personalized nutrition. The convergence of AI with nutrigenomics has revolutionized our understanding of gene-diet interactions, enabling more sophisticated analysis of individual nutritional responses based on genetic profiles. Through advanced machine learning algorithms and deep learning approaches, researchers can now process vast amounts of genetic and dietary data to generate personalized nutritional recommendations with unprecedented precision. The emergence of smart wearable and mobile applications has further enhanced real-time nutrigenomic monitoring, particularly in areas such as continuous glucose monitoring (CGM) and metabolic profiling. While the field shows promising developments, especially in managing conditions like type 2 diabetes through precision nutrition, it faces several challenges including data privacy concerns and algorithmic biases. Despite these limitations, the integration of AI with nutrigenomic principles points toward a future where personalized nutrition becomes increasingly accessible and effective, though careful validation and implementation strategies remain crucial for its success.

Keywords: Artificial intelligence; Federated learning; Nutrigenetics; Nutrigenomics; Type 2 diabetes.