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.
- Upon completion, participants will be able to understand individual differences in post-prandial responses to food through analysis of blood glucose and blood fats.
- Upon completion, participants will 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
- 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
- 8.1.2 Applies knowledge of food and nutrition as
well as the biological, physical and social sciences in
Meaghan Reardon, MS, RD
Sarah Berry, PhD
King’s College London, UK / PREDICT Research
Tim Spector, MD, FRCP, FRSB FMEDSC
King’s College London, UK
Christopher Gardner, PhD
Professor of Medicine