sarah berry

zoe & king’s college london

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The Research and Applied AI Summit (RAAIS) is a community for entrepreneurs and researchers who accelerate the science and applications of AI technology. We’ve been running for 6 years now and have hosted over fifty entrepreneurs and academics who have built billion-dollar companies and published foundational papers that drive the AI field forward. 

In the lead up to our 6th annual event that will be broadcast live online on the 26th June 2020, we’re running a series of speaker profiles highlighting what you can expect to learn on the day!

Do you know what to eat in order to feel your healthiest self?

Nutrition is the bedrock of health, yet we know so little about our individual metabolism and which foods are best for us. 

Dr. Sarah Berry is a Senior Lecturer in the Department of Nutritional Sciences at King’s College London and Head of Nutrition Science at ZOE, the London-based AI-first nutritional science startup behind the PREDICT study, which is the world’s largest ongoing nutritional research project of its kind.

In her role, Sarah is a co-investigator of the study, which is run in collaboration with entrepreneurs and technologists at ZOE and scientists from Harvard, King's College London, Massachusetts General Hospital, Oxford and Stanford Universities. PREDICT assesses the genetic, metagenomic, metabolomic and meal-context drivers of postprandial metabolic responses (i.e. what happens after you’ve eaten a meal) to predict individual responses to food using machine learning techniques. 

The ongoing results from PREDICT now power a consumer-facing product built by ZOE and openly accessible on the iOS app store. The app helps users understand the nuances of their individual, changing metabolism and inform their food choices to live a healthier life. 

You can read more about her research here, ZOE’s science here, and follow her on Twitter here. Welcome, Sarah!

More about PREDICT

The PREDICT 1 multi-center postprandial study evaluated 1,000 individuals from the UK (unrelated, identical and non-identical twins) and 100 unrelated individuals from the US. Participants had their metabolic responses to sequential mixed-nutrient dietary challenges measured in a tightly controlled clinic setting. Glycemic (how carbs affect blood sugar levels) and lipaemic (levels of lipids in the blood) responses to multiple duplicate isocaloric meals of different macronutrient content and self-selected meals (>100,000), were tested at home using a continuous glucose monitor (CGM) and dry blood spots. Baseline factors included metabolomics, genomics, gut metagenomics and body composition. Dietary data was collected using the ZOE app and dashboard combining weighed food records, photographs, bar coding and live nutritional support. Sleep and activity were monitored using wearable devices.  The ‘Big Data’ collected in PREDICT included 2 million glucose responses, 28,000 triacylglycerol (TG) measurements (as a measure of lipaemia) and 132,000 weighed meal logs.