Project 2: Causal Relationship Disentangler for Precision Nutrition

项目2：精准营养的因果关系解开器

• 批准号: 10552678
• 负责人: SAMANTHA KLEINBERG
• 金额: $ 18.72万
• 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/10552678
• 关键词: Address; Artificial Intelligence; Biometry; Chronic Disease; Computer software; Data; Data Science; Diet; Dietary Practices; Environment; Equilibrium; Face; Feedback; Food; Food Patterns; Funding; Genetic; Goals; Guidelines; Health; Individual; Informatics; Intervention; Knowledge; Learning; Link; Mental Health; Methods; Modeling; Outcome; Participant; Pathway interactions; Pattern; Persons; Physiology; Population; Precision Health; Risk Reduction; Social Characteristics; Specific qualifier value; Structure; System; Translating; United States National Institutes of Health; Work; causal model; cost; data preservation; dietary; dietary guidelines; emotional eating; improved; individual response; innovation; learning strategy; novel strategies; nutrition; physical conditioning; precision nutrition; preservation; programs; statistics; tool

Abstract-Project 2: Causal Relationship Disentangler for Precision Nutrition Predicting individual responses to food and dietary patterns, the stated goal of the National Institutes of Health (NIH) Common Fund’s Nutrition for Precision Health program, requires uncovering the causal connections between diet and health. Despite the importance of diet for treating and reducing risk of many chronic diseases, guidelines often rely on associations rather than causal relationships. Establishing a causal model (set of causal relationships) is vital to provide accurate dietary guidelines to individuals and help them balance priorities. The key obstacles to a comprehensive model of causes and effects of diet have been a lack of methods to translate findings to new populations and a lack of data suitable to learn about causes. The first major challenge is understanding to whom and under what conditions a finding applies. There are no existing methods that can identify causal relationships between diet and other factors and can determine when these findings apply. A second core obstacle is that dietary studies often capture different sets of variables due to the cost and challenge of collecting data on the many causes and effects of nutrition, and many studies rely on food logs kept by participants. This leads to missing variables and missing values, and both can confound causal inference. Many methods exist for imputing missing values but they may lead to unacceptable errors for individuals based on patterns of missingness in real-world data. Single imputation methods provide a single value for each missing instance. Thus, given the type of missingness we face in nutrition (both missing at random [MAR] and missing not at random [MNAR]) and the importance of establishing causal relationships rather than correlations, there is a significant need for new imputation methods. To address this, we introduce new approaches for handling missing data that preserve causal structure. In the Causal Relationship Disentangler for Precision Nutrition we propose new methods for causal generalizability that learn when and why causal relationships are true. Our methods are applicable to all health outcomes and timescales. Learning how to transfer causal knowledge and doing so with missing data is critically important for realizing the potential of nutrition for precision health. Precision health requires knowing what conclusions we can draw about both populations and individuals and being able to systematically predict what interventions will work for an individual. Our automated approaches to generalizing causal models will provide the critical link between data and actions, allowing the knowledge created to generalize beyond All of Us. Our investigative team has for over a decade developed new methods that learn causal models from observational data and provide automated causal explanations, as well as statistics, data science, and biostatistics. Aim 1 will develop methods for generalizing causal relationships and learning when they apply. Aim 2 will develop improved methods for reconstructing missing data that preserve causal structure. Aim 3 will develop individual and generalizable causal models of nutrition and health.

摘要项目 2：精准营养的因果关系解开剂 预测个人对食物和饮食模式的反应，这是国家研究所的既定目标 美国国立卫生研究院 (NIH) 共同基金的精准健康营养计划，需要揭示因果关系 尽管饮食对于治疗和降低许多疾病的风险很重要。 对于慢性病，指南通常依赖于关联而不是建立因果关系。 模型（一组因果关系）对于为个人提供准确的饮食指南并帮助他们至关重要 平衡优先事项的主要障碍是缺乏饮食因果关系的综合模型。 将研究结果转化为新人群的方法和缺乏适合了解原因的数据。 主要挑战是了解研究结果适用于谁以及在什么条件下适用。 可以识别饮食与其他因素之间因果关系的方法，并可以确定何时这些因素 研究结果适用的第二个核心障碍是，饮食研究经常捕获不同的变量集。 收集有关营养的多种原因和影响的数据的成本和挑战，并且许多研究依赖于 参与者保存的食物日志会导致变量缺失和值缺失，两者都会造成混淆。 存在许多用于估算缺失值的方法，但它们可能会导致不可接受的错误。 基于现实世界数据缺失模式的个体提供单一插补方法。 因此，考虑到我们在营养方面面临的缺失类型（两者都缺失）。 随机[MAR]和非随机缺失[MNAR]）以及建立因果关系的重要性 由于关系而不是相关性，因此非常需要新的插补方法。 为了解决这个问题，我们引入了处理丢失数据的新方法，以保留因果结构。 在精准营养的因果关系解开器中，我们提出了因果关系的新方法 普遍性，了解因果关系何时以及为何成立，我们的方法适用于。 学习如何传递因果知识并在缺失的情况下这样做。 数据对于实现精准健康所需的营养潜力至关重要。 知道我们可以对群体和个人得出什么结论，并能够 我们必然会预测哪些干预措施对个人有效。 因果模型将提供数据和行动之间的关键联系，使所创建的知识能够 十多年来，我们的研究团队开发了新的学习方法。 来自观察数据的因果模型并提供自动因果解释以及统计、数据 目标 1 将开发概括因果关系和学习的方法。 他们的目标 2 将开发改进的方法来重建保留因果关系的缺失数据。 目标 3 将开发个体的、可推广的营养与健康因果模型。