Precision Nutrition & Healthy Aging

Various fruits and vegetables arranged in a chain formation

The HNRCA has been at the forefront of exploring the relationship of nutrition, genetics and other environmental factors influencing health for more than 40 years. Precision Nutrition, as it is known, is defined as a personalized approach to dietary recommendations that considers an individual's unique genetic, metabolic, and environmental factors. This approach to nutrition seeks to understand the complex interrelations to optimize responses to diet and to provide tailored dietary recommendations.  By integrating rich datasets with state-of-the-art artificial intelligence, machine learning, and complex system modeling, we aim to gain a deep understanding of the intricate interactions between diet, genetic predisposition, and a spectrum of environmental, social, and behavioral factors.

By leveraging advances in omics, bioinformatics, artificial intelligence, and machine learning, Precision Nutrition seeks to optimize health outcomes and prevent or manage chronic diseases more effectively than traditional, one-size-fits-all dietary guidelines. However, for our purpose, we emphasize a sub-group-based approach that focuses on shared characteristics within specific populations, aiming to understand their complex interrelations and provide more targeted dietary guidance accordingly. Ultimately, our approach aims to translate scientific insights into actionable, equitable nutrition guidelines that can be adopted by all, thus promoting better health outcomes across the lifespan. 
 

Objectives

  • Identify genomic, epigenomic, metabolomic, microbiome, physiological, bio-behavioral, lifestyle, environmental, and social determinants of healthspan and aging and evaluate their individual or interactive influence.
  • Evaluate determinants of healthy, sustainable, and equitable access to food to improve the nutritional status of population subgroups.
  • Develop models and algorithms to advance nutrition and lifestyle strategies for population subgroups.