# Project 3: The Virtual Human for Precision Nutrition

> **NIH NIH U54** · GRADUATE SCHOOL OF PUBLIC HEALTH AND HEALTH POLICY · 2022 · $242,501

## 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 t...

## Key facts

- **NIH application ID:** 10386501
- **Project number:** 1U54TR004279-01
- **Recipient organization:** GRADUATE SCHOOL OF PUBLIC HEALTH AND HEALTH POLICY
- **Principal Investigator:** Bruce Y Lee
- **Activity code:** U54 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $242,501
- **Award type:** 1
- **Project period:** 2022-01-19 → 2026-12-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10386501

## Citation

> US National Institutes of Health, RePORTER application 10386501, Project 3: The Virtual Human for Precision Nutrition (1U54TR004279-01). Retrieved via AI Analytics 2026-05-26 from https://api.ai-analytics.org/grant/nih/10386501. Licensed CC0.

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