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差异的原因还不清楚， 阻碍了为我们的人口提供最佳饮食的努力。迫切需要了解复杂的 人口统计学、遗传、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有关的慢性疾病。领域 “精确营养”对于阐明这些相互作用以最终预测最佳饮食有很大的希望 对于一个人或一群人来说。本申请的总体目标是证明 亚拉巴马大学伯明翰分校（UAB）具有独特的优势，能够加入该联盟，成为 Precision Health临床中心（RFA-RM-21-005）。研究小组将收集广泛的生理和 来自自由生活（模块1）和控制饮食（模块2和3）的个体的代谢数据， 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）将由研究小组雇用， 确定个性化饮食处方所需的综合表型。我们的目标是实现 具体目标1（模块1）：对2000名自由生活的人进行观察性研究， 吃了14天的日常饮食。对标准化试验餐的生理反应 将在他们食用其常规饮食时评估挑战。具体目标2（模块2）： 一项在400名受试者中进行的自由生活控制喂养研究，这些受试者喂养三种等热量饮食， 维持能源需求。节食的目的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餐的生理反应将是 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 压力，24小时体力活动，心肺功能和睡眠在每个饮食期间。具体目标3 （模块3）：对150名接受三种等热量饮食（相同）的受试者进行居家控制喂养研究 目标#1中的饮食）以维持能量需求喂养。除了模块2的成果外， 包括室内量热法，双标记水，心肺健康，肌肉和脂肪活检将是 完成模块3的参与者，而他们在湖滨基金会校园的小屋里 UAB附近实现这些目标将创建一个数据库，允许复杂的数据分析（例如，AI、机器 学习）来开发算法以使人们匹配最佳饮食。UAB，可访问> 16，000我们所有人 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，可以成为营养精准健康联盟的重要成员。