Personalized nutrition: perspectives on challenges, opportunities, and guiding principles for data use and fusion.

Abstract

Personalized nutrition (PN) delivers tailored dietary guidance by integrating health, lifestyle, and behavioral data to improve individual health outcomes. Recent technological advances have enhanced access to diverse data sources, yet challenges remain in collecting, integrating, and analyzing complex datasets. To address these, the Personalized Nutrition Initiative at Illinois organized a workshop titled "Personalized Nutrition Data: Challenges & Opportunities," which gathered experts to explore three essential data domains in PN: 1) health and biological, 2) social, behavioral, and environmental, and 3) consumer purchasing data. Discussions underscored the importance of cross-disciplinary collaboration to standardize data collection, enable secure data sharing, and develop data fusion techniques that respect privacy and build trust. Participants emphasized the need for representative datasets that include underserved populations, ensuring that PN services are accessible and equitable. Key principles for responsible data integration were proposed, alongside strategies to overcome barriers to effective data use. By addressing these challenges, PN can enhance health outcomes through precise, personalized recommendations tailored to diverse population needs.