The Nutri-Exposome Intelligence Framework: Integrating Multi-Omics, Machine Learning, and Digital Nutrition for...

Area of Science:

• Nutritional Science
• Computational Biology
• Public Health

Background:

• Precision nutrition is evolving towards integrative models beyond simple gene-diet interactions.
• Current approaches are fragmented across nutrigenomics, microbiome research, multi-omics, digital health, and machine learning.
• The Nutri-Exposome Intelligence Framework is proposed to unify these areas for chronic disease prevention.

Purpose of the Study:

• To present a conceptual, data science-driven framework for integrating cumulative exposures.
• To outline a model for precision chronic disease prevention by combining dietary, environmental, microbial, molecular, clinical, and digital data.
• To highlight the role of machine learning in this integrative approach.

Main Methods:

• Conceptual review synthesizing literature on precision nutrition, exposomics, microbiome, multi-omics, digital health, and machine learning.
• Organizing evidence into a framework linking exposure assessment to personalized intervention and feedback.
• Developing a seven-layered framework for data integration and analysis.

Main Results:

• The Nutri-Exposome Intelligence Framework comprises seven interconnected layers, from exposures to feedback.
• Machine learning is crucial for data harmonization, predictive modeling, and refining dietary recommendations.
• The framework is applicable to obesity, diabetes, cardiovascular, and liver diseases.

Conclusions:

• Nutri-exposome intelligence provides a structured approach for predictive, explainable, and adaptive precision nutrition.
• Successful implementation requires longitudinal, multi-ethnic data, standardized metadata, causal validation, and interpretable AI.
• Ethical governance and equitable access are vital for global clinical and public health translation.