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.

摘要 个体对饮食模式的生理反应不同的原因还不是很清楚，但 阻碍了为我们的人口提供最佳饮食的努力。迫切需要了解这种复杂的情况 人口、遗传、新陈代谢、行为、心理社会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病。这一领域 “精确营养”对阐明这些相互作用最终预测最佳饮食有着很大的希望。 对于个人或个人群体而言。本应用程序的总体目标是演示 阿拉巴马大学伯明翰分校(UAB)作为营养公司加入该联盟具有独特的地位 精准健康临床中心(RFA-RM-21-005)。研究小组将收集广泛的生理和 来自个人对自由生活(模块1)和受控饮食(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。复杂的数据方法 (人工智能、机器学习、数学建模)然后将被研究组用来 确定个性化饮食处方所需的综合表型。我们的目标是实现 以下三个具体目标：具体目标1(单元1)：对2000年自由生活进行观察性研究 连续14天食用正常饮食的人。对标准化测试餐的生理反应 挑战将在他们正常饮食的同时进行评估。具体目标2(单元2)：开展 三种不同常量营养素等热量饮食的400名受试者自由生活控制喂养研究 在保持能量要求的情况下组成。饮食的设计是为了引起广泛的反应 在参与者中。对标准化测试餐和特定饮食的生理反应将是 在每个14天的节食期结束时测量。我们还将收集24小时血糖、24小时血液的测量 压力，24小时体力活动，心肺健康，以及在每个节食期的睡眠。具体目标3 (模块3)：对150名受试者进行家庭控制喂养研究，包括三种等卡路里饮食(相同 按照目标1)饲喂维持能量需求的饲料。除了模块2的结果外，评估 包括室内量热法、双标签水、心肺健康、肌肉和脂肪活检 在模块3中完成，参与者居住在湖滨基金会园区的小屋中 在UAB附近。实现这些目标将创建一个允许进行复杂数据分析(例如，人工智能、机器)的数据库 学习)开发将人们与最佳饮食相匹配的算法。UAB，可以访问我们所有人&gt；16,000人 伯明翰的参与者，进行饮食干预的杰出设施，以及杰出的 研究团队，可以成为精准营养健康联盟的重要成员。