Project 3: The Virtual Human for Precision Nutrition

项目 3：精准营养虚拟人

• 批准号: 10552681
• 负责人: Bruce Y Lee
• 金额: $ 16.57万
• 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 2027-06-30
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10552681
• 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; biomarker selection; computerized tools; dietary; gastrointestinal system; individual response; innovation; insight; mathematical model; microbiome; novel; nutrition; precision nutrition; prediction algorithm; programs; social; tool; usability; 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.

摘要项目 3：精准营养的虚拟人 美国国立卫生研究院 (NIH) 精准健康营养共同基金的既定目标 （NPH）由“我们所有人研究计划”提供支持，旨在“开发预测个体的算法 对食物和饮食模式的反应。”这是因为不存在完美的、一刀切的东西。 饮食并了解不同类型/人群对不同饮食的反应可以帮助更好地定制 营养和饮食指导的新证据证明了精准营养的潜在价值，但 只是它应该包含或最终能够实现的一小部分；我们离它还有很长的路要走； 除了识别特定的基因-饮食相互作用并采取行动之外，能够提供真正个性化的营养。 精准营养应将这些相互作用与个体更广泛的基因组、代谢和 消化系统、微生物群及其饮食行为、食物偏好和习惯以及其他行为， 通过“自上而下”的方法提供全面的、量身定制的营养信息。 对大型人群队列研究的传统统计分析可以显示不同人群之间的相关性 因素和选定的生物标志物或健康结果，他们可能会忽略更复杂的机制 因此，需要使用系统方法和方法（“自下而上”）来提供帮助。 更好地整合不同维度的数据并了解营养所涉及的系统以实现精确 基于代理的模型（ABM）已成为一系列不同领域的计算“虚拟实验室”。 问题，但其用于解决营养问题的方法仍处于萌芽阶段。因此，该项目的目标是。 是开发和利用虚拟人进行精准营养，这是一种 ABM 工具，可以帮助更好地帮助 了解并预测个人对食物和饮食模式的反应，同时汇总 最终，解释遗传、生理和行为因素之间的相互作用。 研究人员、群体、政策制定者和其他决策者也许能够使用此 ABM 来帮助测试 不同饮食的影响，确定了解特定参数和机制的价值，以更好地帮助 十多年来，我们的调查团队一直致力于指导数据收集并规划未来的研究。 开发广泛的数学和计算模型，包括 ABM，以解决不同的问题 健康相关问题，包括饮食和身体活动对健康的影响，目标 1 将制定 ABM。 根据我们现有的代表人类和人类饥饿/饱足机制的 ABM，关键饮食 行为以及对营养摄入的影响将发展并整合到 ABM 表示中。 Aim 3 将研究人类对关键营养素的吸收和加工以及转化为不同的生物标志物。 开发目标 1 和 2 的路径如何导致的 ABM 表示并整合到其中 健康状况的长期变化，例如关键慢性健康状况的发展（例如心血管疾病） 疾病、癌症和糖尿病）随着时间的推移。