Nutritional epidemiology focuses on understanding the relationship between diet, health, and disease, playing a crucial role in identifying dietary risk factors and shaping public health recommendations. Traditional methods of analyzing dietary patterns have faced challenges due to the complexity of nutritional data and individual variability. However, recent advancements in digital technologies, such as artificial intelligence (AI), big data, and digital health tools, are transforming this field. These innovations allow for the integration and analysis of vast amounts of data from diverse sources, including genetic profiles, electronic health records, and wearable devices, enabling more accurate and personalized insights into dietary impacts on health. This evolution opens new opportunities to develop precision strategies for preventing and managing chronic diseases like diabetes, obesity, cardiovascular conditions, and cancer, advancing both research and public health interventions.

The goal of this Research Topic is to explore how emerging digital tools, including artificial intelligence (AI), big data, and digital health technologies, can revolutionize the field of nutritional epidemiology by enabling more personalized, precise, and real-time approaches to disease prevention and management. Despite advancements in nutrition science, challenges remain in understanding the complex relationship between diet, health, and disease due to the vast variability in individual responses to dietary patterns and the limitations of traditional methods. To address this, the integration of large-scale datasets—spanning genetic, metabolic, dietary, and lifestyle information—alongside innovative analytical tools like machine learning, can uncover patterns that were previously difficult to identify. This research aims to highlight the potential of these technologies to improve dietary recommendations, predict disease outcomes, and enhance public health interventions. By leveraging these digital innovations, we can move towards more targeted, individualized strategies for chronic disease prevention, ultimately leading to better health outcomes at both the individual and population levels.

This Research Topic invites contributions that explore the integration of digital health tools, artificial intelligence (AI), big data, and personalized nutrition in advancing nutritional epidemiology. We welcome manuscripts that address, but are not limited to, the following themes:

• The application of AI and machine learning in analyzing dietary patterns and disease risk.

• The use of big data to track and predict nutritional trends in populations.

• Integrating genetic, metabolic, and dietary data for personalized nutrition recommendations.

• The role of digital health technologies in real-time dietary monitoring and personalized health interventions.

• The impact of digital nutritional databases on public health policy formulation.

• Development of predictive algorithms for health outcomes based on dietary intake and lifestyle factors.

Are you interested in contributing a manuscript to this Research Topic?

Revolutionizing Nutritional Epidemiology: Harnessing Digital Health, AI, and Big Data for Population-Level Disease Prevention and Management