4d Predict

[Brian]

PREDICT activities supported emerging pandemic threats preparedness and the Global Health Security Agenda, primarily in Africa and Asia. A decade later, more than 30 countries around the world have stronger systems to safely detect, identify, prevent and respond to viral threats. PREDICT initiated One Health Surveillance, a transdisciplinary collaborative approach to understanding infectious disease risk at the animal-human interface. The PREDICT-trained workforce, including zoonotic disease specialists and laboratory scientists at more than 60 national, university and partner laboratories, is one of the best response resources to assist with safe and secure detection and response to COVID-19 and other emerging biological threats.

PREDICT established best practices in One Health surveillance and biosecurity, to identify viruses with the potential to spillover from animals into people and help prepare the world for more rapid detection in future epidemics and pandemics. PREDICT works in full compliance with US federal and international regulations and we have set the standard in optimizing collaborative transdisciplinary work needed for early detection of viral threats where these are most likely to emerge. Through our network of global and host country partners, our work adheres to national and international ethical, legal, and regulatory requirements and we go beyond these obligations to strive for optimal practices in transparency, cooperation, biosafety, and information sharing.

PREDICT is led by the UC Davis One Health Institute, and core partners include USAID, EcoHealth Alliance, Metabiota, Wildlife Conservation Society, and Smithsonian Institution. Contact us at pre...@ucdavis.edu.

These studies were designed to allow us to quantify and predict individual variations in postprandial responses to standardized meals in a real-world setting, while also gathering as much data about lifestyle factors as possible. This research has allowed us to explore many different features of the complex postprandial responses to better understand which factors influence them and how they subsequently impact health outcomes.

All of these studies are designed in such a way that their data can be seamlessly combined in our machine learning models to help us better understand individual responses to any food or meal and provide personalized food recommendations.

This groundbreaking nutrition research project has uniquely benefitted from being carried out largely on the twin population from the Twins UK Study (a 25-year investigation of health and lifestyle in over 14,000 twins). By studying twins, for the first time, we were able to disentangle the impact of genetics from other determinants of our responses to food and discovered that genes are not as important as previously believed for predicting our responses to food.

PREDICT 1 was specifically designed to quantify and predict individual variations in postprandial triglyceride, glucose, and insulin to 8 carefully designed standardized test meals. Additionally, we measured variations in metabolomic responses to the two standardized clinic-day test meals and variations in glucose responses to hundreds of thousands of meals eaten at home. The test meals, designed by nutritional experts with decades of experience of conducting postprandial intervention studies, consisted of isocaloric muffins with varying macronutrient composition and standard 75g oral glucose tolerance tests (OGTT).

Never before has a study combined postprandial data gathered in a clinical setting, free-living data for all meals consumed over two weeks, and shotgun sequenced microbiome data for this many individuals. This data, alongside the data from PREDICT 2, has provided core training data for the ZOE scores.

PREDICT 1 Plus (IRAS 236407; NCT03479866) (Figure 3) is the ongoing, second phase of the PREDICT 1 study that is recruiting a further 900 individuals from the Twins UK registry to undertake a similar protocol to PREDICT 1 to further enhance our prediction models. Building on the learnings from PREDICT 1, protocol modifications in PREDICT 1 Plus allows us to explore the lipemic dose-response and effects of meal order on glycemic and lipemic responses in more depth.

A subset of highly compliant PREDICT 1 participants (n=100) was recruited to take part in the entirely remote PREDICT-Carbs study (IRAS 236407; NCT03479866) (Jan-May 2019), which aimed to compare the glycemic responses elicited by different sources of carbohydrate within and between individuals (shown in Figure 4). Participants in this study received a standardized dietary intervention of breakfasts, lunches, and snacks based on various carbohydrate-rich staple foods.

The study also tested glycemic response to a high-carbohydrate snack when eaten after preloads of various non-nutritive sweeteners, to capture inter-individual and inter-sweetener differences. The breakfast-lunch intervention design also allowed us to explore and untangle the influence of meal sequence and time of day on the resulting glycemia.

We also measured liver fat (measured by MRI), which is central to metabolic dysregulation and is emerging as an important measure of metabolic health that is modulated by diet. This study involved a sub-cohort (n=50) of PREDICT 1 Plus participants who were females, aged >55 years and predicted to be either low or high postprandial responders to dietary fat.

A total of 987 volunteers took part in this home-based study from almost every state in the US, in which we demonstrated the efficacy of remote study delivery at an unprecedented depth and scale. This study was able to collect much of the phenotype data that was collected during PREDICT 1, but entirely remotely. The successful delivery of this study has put us at the forefront of remote nutritional research and enabled us to design an at-home testing product for ZOE that is comparable to these cutting edge research studies. Many of the tests in the ZOE product have never previously been available commercially, including the lipid response tests and the detailed output from the microbiome analysis.

The ZOE PREDICT 3 study (NCT04735835) is an ongoing single-arm mechanistic intervention study which commenced in July 2020. The PREDICT 3 study will build on previous research in over 2,000 individuals (PREDICT 1 and 2) to further refine machine learning models that predict individual responses to foods, with the aim of advancing precision nutrition science and individualized dietary advice.

The study incorporates both standardized and controlled dietary intervention, for the purpose of testing postprandial responses to specific mixed meals, in addition to a free-living period with a dietary record for measuring responses to a large variety of meals consumed in a realistic context, where the role of external factors (e.g. exercise, sleep, time of day) on postprandial responses may be determined.

For the first time this PREDICT study is built on top of a commercial product which will allow access to a much larger group of participants who are already collecting large amounts of data through digital and biochemical devices that can contribute to science.

To date more than 45,000 participants have completed PREDICT 3 program.

Through the various studies that form part of our PREDICT Program, we have been able to gather more in-depth data than has previously been possible in nutrition studies. This is a result of using novel technologies and high-quality dietary assessment methods, allowing for both scale and precision.

For precision nutrition to be a success, high precision data must be obtained. This enables the discrimination of subtle differences in biomarkers between individuals, using comparable methods so all this data can be fed into machine learning algorithms. To achieve this, we have worked with leading analytical laboratories to refine collection and analysis methodologies, thus improving the precision of data from samples collected remotely and ensuring results are comparable to those collected in controlled, clinical settings.

Our founders - Tim Spector, Jonathan Wolf and George Hadjigeorgiou - have long believed that food and health are deeply personal, and that science is the key to unlocking the connection between the two.

The journey to PREDICT started 25 years ago, when Tim set up TwinsUK - a groundbreaking study of thousands of pairs of identical and non-identical twins, aiming to answer important questions about health, genetics and lifestyle.

ZOE was founded in 2018 with the aim of combining the latest nutritional science with cutting edge machine learning technology to help everyone understand their personal responses to food and eat in the way that suits your body best. But humans are complicated - and so is nutrition - so we needed lots of data from lots of people, which is where PREDICT comes in.

More than 1000 people volunteered to take part in PREDICT 1, including 660 identical and non-identical twins from the TwinsUK cohort, providing detailed measurements covering a wide range of markers from blood glucose, fat and insulin levels to exercise, sleep and gut bacteria (microbiome).

Each participant spent a full day in our clinic at the start of the study where they gave blood, urine and poop samples. We also made detailed measurements of their responses to standardized muffin-based meals with carefully controlled calorie, fat, protein, carbohydrate and fiber content.

Throughout the duration of the study they wore a stick-on continuous glucose monitor that tracked their blood sugar 24/7, showing us how their body responded to each meal throughout the day. They also took regular fingerprick blood samples to measure blood fat and other markers, and wore activity trackers to keep tabs on their sleep and exercise.

It all added up to millions of data points - and 32,000 muffins - which our researchers fed into their hungry computers, building a machine learning algorithm that can predict individual responses to any food.

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