Project 3: The Virtual Human for Precision Nutrition

Graduate School Of Public Health And Health Policy, New York NY

Investigators

Linked publications & trials

Abstract

Abstract-Project 3: The Virtual Human for Precision Nutrition The stated goal of the National Institutes of Health (NIH) Common Fund’s Nutrition for Precision Health (NPH), powered by the All of Us Research Program, is "to develop algorithms that predict individual responses to food and dietary patterns." This is because there's no such thing as a perfect, one-size-fits-all diet and understanding how different types/groups of people respond to different diets can help better tailor nutrition and dietary guidance. Emerging evidence demonstrates the potential value of precision nutrition but represent just a small piece of what it should encompass or can ultimately achieve; we are a long way off from being able to offer truly personalized nutrition. Beyond identifying and acting on specific gene-diet interactions, precision nutrition should connect these interactions with an individual’s broader genome, metabolic and digestive systems, microbiome, and their dietary behaviors, food preferences and habits, and other behaviors, in order to provide comprehensive, tailored nutritional information. Though "top down" approaches that perform traditional statistical analyses on large population cohort studies can show correlations between different factors and selected biomarkers or health outcomes, they can overlook the more complex mechanisms involved. Therefore, there is a need to use systems approaches and methods (which are “bottoms up”) to help better integrate different dimensions of data and understand the systems involved in nutrition for precision health. Agent-based models (ABMs) have served as computational "virtual laboratories" for a range of different issues, but their use to address nutrition issues is still nascent. Therefore, the goal of this proposed project is to develop and utilize The Virtual Human for Precision Nutrition, an ABM tool that can help better understand and predict an individual's response to food and dietary patterns, while bringing together and accounting for the interactions between genetic, physiological, and behavioral factors. Ultimately, researchers, clinicians, policymakers, and other decision makers may be able to use this ABM to help test the effects of different diets, determine the value of knowing particular parameters and mechanisms better to help guide data collection, and plan future studies. For over a deacde-and-a-half, our investigative team has been developing a wide range of mathematical and computational models, including ABMs, to address different health-related issues, including the impact of diet and physical activity on health. Aim 1 will develop an ABM from our existing ABM that represents a human and the human's hunger/satiety mechanisms, key dietary behaviors, and the effects on nutrient intake. Aim 2 will develop and integrate into the ABM representations of the human’s absorption and processing of key nutrients and translation into different biomarkers. Aim 3 will develop and integrate into the ABM representations of how the pathways from Aims 1 and 2 may result in longer-term changes in health such as the development of key chronic health conditions (e.g., cardiovascular disease, cancer, and diabetes) over time.