Project 3: The Virtual Human for Precision Nutrition

项目3：用于精确营养的虚拟人 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)是由我们所有人研究计划提供支持的，目的是开发预测个体的算法 对食物和饮食模式的反应。这是因为没有完美的、一刀切的东西 饮食和了解不同类型/群体的人对不同饮食的反应可以帮助更好地定制 营养和饮食指导。新出现的证据证明了精确营养的潜在价值，但 只是它应该包含的或最终能够实现的一小部分；我们距离 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， 以便提供全面的、量身定制的营养信息。尽管“自上而下”的方法 对大量人群队列研究的传统统计分析可以显示出不同的 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 牵涉其中。因此，有必要使用系统途径和方法(自下而上)来提供帮助 更好地集成不同维度的数据，并了解涉及营养的系统以实现精确度 健康。基于主体的模型(ABM)已经成为一系列不同领域的计算“虚拟实验室” 但是，它们在解决营养问题方面的应用仍处于起步阶段。因此，这个拟议项目的目标是 是开发和利用虚拟人for Precision Nutrition，这是一种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 并考虑了遗传、生理和行为因素之间的相互作用。最终， 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种ABM来帮助测试 不同饮食的影响，确定的价值，更好地了解特定的参数和机制 指导数据收集，并规划未来的研究。在超过一年半的时间里，我们的调查小组 开发广泛的数学和计算模型，包括ABM，以解决不同的 与健康有关的问题，包括饮食和体力活动对健康的影响。AIM 1将开发ABM 从我们现有的代表人类和人类饥饿/饱足机制的ABM中，关键的饮食 行为，以及对营养摄入量的影响。目标2将开发并集成到ABM表示法中 人体对关键营养物质的吸收和加工，并转化为不同的生物标志物。目标3将 开发并整合到ABM表示中，说明目标1和目标2中的路径如何导致 健康方面的较长期变化，例如发展关键的慢性健康状况(例如，心血管疾病 疾病、癌症和糖尿病)随着时间的推移。

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.