Project 3: The Virtual Human for Precision Nutrition

项目3：精准营养的虚拟人 美国国立卫生研究院（NIH）共同基金的营养精准健康的既定目标 (NPH)由All of Us研究计划提供支持，是“开发预测个人行为的算法”。 对食物和饮食模式的反应。“这是因为没有一个完美的，一刀切的 饮食和了解不同类型/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。新出现的证据表明了精确营养的潜在价值， 它只代表了它应该包含或最终能够实现的一小部分;我们离实现目标还有很长的路要走。 能够提供真正个性化的营养。除了识别和作用于特定的基因-饮食相互作用之外， 精确营养应该将这些相互作用与个人更广泛的基因组，代谢和 消化系统，微生物组，以及他们的饮食行为，食物偏好和习惯，以及其他行为， 以提供全面的、量身定制的营养信息。虽然“自上而下”的方法， 对大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，他们可以忽略更复杂的机制 涉案因此，有必要使用系统的办法和方法（即“自下而上”的办法）， 更好地整合不同层面的数据，并了解营养所涉及的系统，以实现精确 健康基于主体的模型（ABM）已经作为一系列不同的计算“虚拟实验室”， 这些指标在解决营养问题方面仍处于起步阶段。因此，本项目的目标 是开发和利用精确营养虚拟人，这是一个ABM工具，可以帮助更好地 了解和预测个人对食物和饮食模式的反应，同时将 并解释遗传、生理和行为因素之间的相互作用。最后， 研究人员、临床医生、政策制定者和其他决策者可能能够使用这一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 指导数据收集，并规划未来的研究。一年半以来我们的调查小组 开发广泛的数学和计算模型，包括反弹道导弹，以解决不同的 与健康有关的问题，包括饮食和体育活动对健康的影响。目标1将发展反弹道导弹 从我们现有的代表人类和人类饥饿/饱足机制的ABM， 行为，以及对营养摄入的影响。目标2将发展并纳入反弹道导弹的表述， 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。目标3将 发展目标1和目标2的途径如何导致 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 疾病、癌症和糖尿病）。

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.