UAB Precision Nutrition Clinical Center

UAB精准营养临床中心

• 批准号: 10384253
• 负责人: BARBARA A GOWER
• 金额: $ 55.77万
• 依托单位: UNIVERSITY OF ALABAMA AT BIRMINGHAM
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-12-10 至 2026-11-30
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10384253
• 关键词: Affect; African American population; Alabama; Algorithms; Artificial Intelligence; Behavior; Behavioral; Bioinformatics; Biopsy; Blood Pressure; Body Composition; Calorimetry; Cardiovascular Diseases; Chronic Disease; Clinical Research; Clinical Sciences; Cognitive; Complex; Consumption; Crossover Design; Data; Data Analyses; Databases; Diabetes Mellitus; Diet; Dietary Intervention; Dietary Practices; Domiciles; Energy Metabolism; Environment; Environmental Exposure; Environmental Risk Factor; Exercise; Fatty acid glycerol esters; Foundations; Genetic; Genomics; Glucose; Goals; Health; Health Promotion; Health Surveys; Hour; Hypertension; Individual; Infrastructure; Intervention; Lead; Letters; Machine Learning; Macronutrients Nutrition; Maintenance; Malignant Neoplasms; Measurement; Measures; Medicine; Metabolic; Metabolism; Metadata; Methods; Modeling; Movement; Muscle; Non-Insulin-Dependent Diabetes Mellitus; Nutrient; Obesity; Observational Study; Outcome; Outcome Assessment; Participant; Personal Satisfaction; Persons; Phenotype; Physical activity; Physiological; Population; Population Heterogeneity; Positioning Attribute; Precision Health; Psychosocial Factor; Randomized; Research; Resources; Site; Sleep; Standardization; Testing; Translational Research; Universities; base; cardiorespiratory fitness; clinical center; data repository; design; dietary; doubly-labeled water; experience; feeding; fitness; genetic makeup; individual variation; mathematical model; member; metabolomics; microbiome; multimodal data; nutrition; precision nutrition; predicting response; predictive tools; prevent; recruit; response

ABSTRACT The reasons for individual variability in the physiologic response to dietary patterns are not well understood but hamper efforts to provide optimum diets to our population. There is an urgent need to understand the complex interaction of demographic, genetic, metabolic, behavioral, psychosocial, and environmental factors that affect the responses to dietary patterns in order to prevent and treat nutrition-related chronic diseases. The field of “precision nutrition” holds great promise for elucidating these interactions to eventually predict the optimal diet for an individual or groups of individuals. The overall objective of this application is to demonstrate that the University of Alabama at Birmingham (UAB) is uniquely positioned to join the consortium as a Nutrition for Precision Health Clinical Center (RFA-RM-21-005). The study team will collect a wide range of physiological and metabolic data from individuals in response to free-living (module 1) and controlled diets (modules 2 & 3), that will be used in analyses to determine potential predictors of response to diet. Sophisticated data methods (artificial intelligence, machine learning, mathematical modelling) will then be employed by the study group to identify the comprehensive phenotypes needed for individualizing diet prescriptions. We aim to accomplish the following three specific aims: Specific Aim 1 (module 1): Conduct an observational study of 2000 free-living individuals consuming their usual diet for 14 days. The physiologic responses to a standardized test meal challenge will be assessed while they are consuming their usual diet. Specific Aim 2 (module 2): Conduct a free-living controlled feeding study in 400 subjects fed three isocaloric diets varying in macronutrient composition at maintenance energy requirements. Diets are designed to elicit a wide range of responses among participants. The physiologic responses to standardized test meals and diet-specific meals will be measured at the end of each 14-day diet period. We will also collect measures of 24-hr glucose, 24-hr blood pressure, 24-hr physical activity, cardiorespiratory fitness, and sleep during each diet period. Specific Aim 3 (module 3): Conduct a domiciled controlled feeding study in 150 subjects of three isocaloric diets (same diets as in aim #1) fed at maintenance energy requirements. In addition to module 2 outcomes, assessments including room calorimetry, doubly labelled water, cardiorespiratory fitness, and muscle and fat biopsies will be completed in module 3 participants while they are domiciled in cottages at the Lakeshore Foundation Campus near UAB. Achieving these aims will create a database that allows sophisticated data analysis (e.g., AI, machine learning) to develop algorithms to match people to optimum diets. UAB, with access to >16,000 All of Us participants in Birmingham, outstanding facilities for conducting diet interventions, and an outstanding research team, can be a valued member of the Nutrition for Precision Health Consortium.

抽象的 对饮食模式的生理反应存在个体差异的原因尚不清楚，但 迫切需要了解这一复杂的情况。 影响人口、遗传、代谢、行为、社会心理和环境因素的相互作用 对饮食模式的反应，以预防和治疗与营养相关的慢性疾病。 “精准营养”有望阐明这些相互作用，最终预测最佳饮食 对于个人或个人团体来说，此应用程序的总体目标是证明 阿拉巴马大学伯明翰分校 (UAB) 拥有独特的优势，能够作为营养学联盟加入该联盟 Precision Health临床中心（RFA-RM-21-005）研究小组将收集广泛的生理和心理数据。 来自个体对自由生活（模块 1）和控制饮食（模块 2 和 3）的代谢数据， 将用于分析以确定对饮食反应的潜在预测因素。 （人工智能、机器学习、数学建模）将被研究小组用来 确定个性化饮食处方所需的综合表型，我们的目标是实现这一目标。 以下三个具体目标： 具体目标 1（模块 1）：对 2000 名自由生活者进行观察性研究 连续 14 天食用常规饮食的个体对标准化测试餐的生理反应。 将在他们日常饮食时评估挑战。 具体目标 2（模块 2）：进行一次挑战。 对 400 名受试者进行自由生活控制喂养研究，这些受试者喂食三种常量营养素不同的等热量饮食 饮食的组成旨在引起广泛的反应。 参与者对标准化测试餐和特定饮食餐的生理反应将是 我们还将收集 24 小时血糖、24 小时血液的测量结果。 每个节食期间的压力、24 小时体力活动、心肺健康和睡眠。 （模块 3）：对 150 名接受三种等热量饮食（相同 目标 #1 中的饮食）满足维持能量需求 除了模块 2 的结果外，还进行评估。 包括室内量热法、双标记水、心肺健康以及肌肉和脂肪活检 参加者在湖岸基金会校区的小屋中完成第 3 单元的学习 实现这些目标将创建一个允许复杂数据分析的数据库（例如人工智能、机器） 学习）开发算法来将人们与最佳饮食相匹配，并可访问超过 16,000 名我们所有人。 伯明翰的参与者、进行饮食干预的出色设施以及出色的 研究团队，可以成为精准健康营养联盟的重要成员。