Precision Nutrition at Scale: Machine Learning Insights into Personal Metabolic and Microbiome Response

Does one diet fit all? Why do some people respond to low-fat diets, and others low-carb? Are our individual responses to food more variable than we have believed? Understanding these factors is key to predicting individual food responses, and at-scale machine learning can help the public access this science sooner. Speakers will analyze the results of the PREDICT studies, the largest intervention study in nutrition to date. They will discuss their findings in this precision nutrition intervention study seeking to understand and predict post-prandial glucose and fat responses as well as the role the microbiome plays in individual responses to foods and meals.

CPE Level: Level 2
CPEU: 1.0

Performance Indicators

• 6.2.3 Interprets data to make recommendations and to inform decisions.
• 12.3.1 Designs programs and/or interventions based on assessment and evidence-based literature
• 8.1.2 Applies knowledge of food and nutrition as well as the biological, physical and social sciences in practice.

Learning Objectives

• Upon completion, participants will be able to understand individual differences in post-prandial responses to food through analysis of blood glucose and blood fats.
• Learn cutting-edge insights on the role of the gut microbiome from the world's largest nutrition intervention study
• Upon completion, participants will be able to understand and consider how technology can positively scale the field of nutrition science, and metagenomic sequencing for public health

Speakers

• Sarah Berry
• Tim Spector
• Christopher Gardner