Project 3: The Virtual Human for Precision Nutrition

项目 3：精准营养虚拟人

• 批准号: 10386501
• 负责人: Bruce Y Lee
• 金额: $ 24.25万
• 依托单位: GRADUATE SCHOOL OF PUBLIC HEALTH AND HEALTH POLICY
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-01-19 至 2026-12-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10386501
• 关键词: Accounting; Adaptive Behaviors; Address; Affect; All of Us Research Program; Artificial Intelligence; Behavior; Behavioral; Biological Markers; Cardiovascular Diseases; Chronic; Cohort Studies; Complex; Computer Models; Consumption; Data; Data Collection; Data Set; Decision Making; Development; Diabetes Mellitus; Diet; Dietary Practices; Dimensions; Economics; Environment; Ethical Issues; Food Habits; Food Patterns; Food Preferences; Funding; Future; Genes; Genetic; Genome; Goals; Health; Human; Hunger; Individual; Informatics; Intake; Laboratories; Malignant Neoplasms; Metabolic; Methods; Modeling; Nutrient; Nutritional; Outcome; Pathway interactions; Persons; Physical activity; Physiological; Policy Maker; Population; Precision Health; Process; Public Health; Research Personnel; Satiation; Statistical Data Interpretation; System; Testing; Time; Translating; Translations; United States National Institutes of Health; Update; Work; absorption; base; computerized tools; dietary; gastrointestinal system; individual response; innovation; insight; mathematical model; microbiome; novel; nutrition; precision nutrition; prediction algorithm; programs; social; tool; virtual; virtual human; virtual laboratory

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.

抽象项目3：精确营养的虚拟人 美国国立卫生研究院（NIH）普通基金的精确健康营养的既定目标 （NPH）由美国所有研究计划提供支持，是“开发预测个人的算法 对食物和饮食方式的反应。 饮食和了解不同类型/人群如何应对不同饮食可以帮助更好地裁缝 营养和饮食指导。新兴证据证明了精确营养的潜在价值，但 仅表示应该包含或最终实现的一小部分；我们离我们还有很长的路要走 除了识别和对特定基因互动的作用外， 精确营养应将这些相互作用与个人的更广泛的基因组，代谢和 消化系统，微生物组及其饮食行为，食物偏好和习惯以及其他行为， 为了提供全面的，量身定制的营养信息。尽管执行的“自上而下”方法 大量人群研究研究的传统统计分析可以显示不同的相关性 因素和选择的生物标志物或健康结果，它们可以忽略更复杂的机制 涉及。因此，有必要使用系统方法和方法（“底层”）来帮助 更好地集成了数据的不同维度，并了解营养的系统以精确 健康。基于代理的模型（ABM）已成为一系列不同的计算“虚拟实验室” 问题，但是它们用于解决营养问题的用法仍然很新生。因此，这个拟议项目的目标 是开发和利用虚拟人来进行精确营养，这是一种可以帮助更好的ABM工具 理解并预测个人对食物和饮食方式的反应，同时结合在一起 并考虑遗传，身体和行为因素之间的相互作用。最终， 研究人员，临床医生，决策者和其他决策者可能能够使用此ABM来帮助测试 不同饮食的影响，确定更好地了解特定参数和机制的价值以帮助 指导数据收集，并计划未来的研究。为了超过一半，我们的调查团队一直在 开发包括ABM在内的广泛的数学和计算模型，以解决不同 与健康有关的问题，包括饮食和体育锻炼对健康的影响。 AIM 1将发展一个ABM 从代表人类和人类的饥饿/饱腹感机制的现有ABM中，关键的饮食 行为以及对养分摄入的影响。 AIM 2将发展并集成到ABM表示 人类对关键营养物质的滥用和加工，并将其转化为不同的生物标志物。目标3意志 开发并集成到ABM表示目标1和2可能导致的路径 健康的长期变化，例如关键慢性健康状况的发展（例如心血管 随着时间的流逝，疾病，癌症和糖尿病）。