UAB Precision Nutrition Clinical Center

UAB精准营养临床中心

• 批准号: 10540242
• 负责人: BARBARA A GOWER
• 金额: $ 257.19万
• 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/10540242
• 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; Hour; Hypertension; Individual; Infrastructure; Intervention; 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; Participant; Personal Satisfaction; Persons; Phenotype; Physical activity; Physiological; Population; Population Heterogeneity; Positioning Attribute; Precision Health; Psychosocial Factor; Randomized; Research; Resources; Site; Sleep; Standardization; Surveys; Testing; Translational Research; Universities; cardiorespiratory fitness; clinical center; data repository; design; dietary; doubly-labeled water; eHealth; experience; feeding; fitness; genetic makeup; individual variation; mathematical model; member; metabolomics; microbiome; multimodal data; nutrition; precision nutrition; predicting response; predictive tools; prevent; recruit; response; 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; Hour; Hypertension; Individual; Infrastructure; Intervention; 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; Participant; Personal Satisfaction; Persons; Phenotype; Physical activity; Physiological; Population; Population Heterogeneity; Positioning Attribute; Precision Health; Psychosocial Factor; Randomized; Research; Resources; Site; Sleep; Standardization; Surveys; Testing; Translational Research; Universities; cardiorespiratory fitness; clinical center; data repository; design; dietary; doubly-labeled water; eHealth; 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中个人变异性的原因尚不清楚，但 阻碍为我们的人群提供最佳饮食的努力。迫切需要理解综合大楼 影响影响影响的人口，遗传，代谢，行为，社会心理和环境因素的相互作用 对饮食模式的反应是为了预防和治疗与营养相关的慢性疾病。领域 “ Precision Nutrition”具有阐明这些相互作用以最终预测最佳饮食的巨大希望 对于个人或一群人。该应用程序的总体目的是证明 阿拉巴马大学伯明翰（UAB）的独特位置，可以加入该财团作为营养 精密健康临床中心（RFA-RM-21-005）。研究团队将收集广泛的生理和 来自个人的代谢数据，以响应自由生活（模块1）和受控饮食（模块2和3）， 将用于分析以确定对饮食反应的潜在预测指标。复杂的数据方法 （人工智能，机器学习，数学建模）然后，研究小组将雇用 确定个性化饮食处方所需的全面表型。我们旨在完成 以下三个特定目的：特定目标1（模块1）：进行2000自由生活的观察性研究 患有通常饮食的人14天。对标准化测试餐的生理反应 挑战将在消耗通常的饮食时评估。特定目标2（模块2）：进行 400名受试者的自由生活对照喂养研究喂养三种等分饮食，大量营养素有所不同 维护能量需求的组成。饮食旨在引起广泛的反应 在参与者中。对标准化测试餐和饮食特定餐食的生理反应将是 在每个14天饮食期结束时测量。我们还将收集24小时葡萄糖24小时血液的测量 压力，24小时的体育锻炼，心肺健康和在每个饮食期间睡眠。具体目标3 （3个模块）：在150名分泌饮食的150名受试者中进行定居的受控喂养研究（相同 饮食如AIM＃1）在维护能源需求下喂食。除了模块2结果，评估 包括房间量热法，双标记水，心肺健身以及肌肉和脂肪活检 在模块3的参与者居住在湖岸基金会校园的小屋中时完成 在UAB附近。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，AI，机器 学习）开发算法以使人们与最佳饮食相匹配。 UAB，可以访问> 16,000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密卫生财团营养的重要成员。