California Partnership for Personalized Nutrition

加州个性化营养合作伙伴关系

• 批准号: 10669429
• 负责人: Sean Harrison Adams
• 金额: $ 100万
• 依托单位: UNIVERSITY OF CALIFORNIA AT DAVIS
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-08-10 至 2023-08-09
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10669429
• 关键词: Address; Advisory Committees; All of Us Research Program; Artificial Intelligence; Behavior; Big Data; Big Data Methods; Biological Markers; Blood; California; Carbohydrates; Cessation of life; Characteristics; Chemistry; Chronobiology; Clinical Nutrition; Collaborations; Collection; Communication; Communities; Complex; Consent; Coupled; Crossover Design; Data; Data Collection; Diet; Diet Habits; Dietary Intervention; Dietary Practices; Domiciles; Eating; Eating Behavior; Ecology; Electronic Health Record; Emerging Technologies; Enrollment; Environment; Environmental Risk Factor; Epigenetic Process; Exposure to; Fatty acid glycerol esters; Food; Genetic; Goals; Health; Health Food; Health Personnel; Hormones; Human; Hunger; Individual; Intervention; Kinetics; Lipids; Marketing; Measurement; Measures; Medical center; Metabolic; Metabolism; Metagenomics; Methods; Microbe; Micronutrients; Modernization; National Health Programs; Nature; Non-Insulin-Dependent Diabetes Mellitus; Nutrient; Nutritional; Nutritional Science; Nutritionist; Outcome; Participant; Pattern; Persons; Phase; Phenotype; Physical activity; Physiological; Physiology; Population; Population Heterogeneity; Precision Health; Prediabetes syndrome; Protocols documentation; Public Health; Recommendation; Research Design; Risk; Rural Community; Satiation; Standardization; Subgroup; System; Technology; Testing; Time; Training; Type 2 diabetic; Unhealthy Diet; United States National Institutes of Health; Urban Community; Urine; biological systems; blood glucose regulation; clinical center; cohort; community based participatory research; community-level factor; data modeling; data sharing; design; diabetic; dietary; digital; expectation; experience; feeding; genomic data; gut microbiota; improved; indexing; individual response; individual variation; innovation; inter-individual variation; member; metabolomics; metatranscriptomics; microbiome; microbiota; multiple omics; non-diabetic; nutrition; organizational structure; precision medicine; precision nutrition; premature; programs; prototype; psychosocial; recruit; response; sedentary lifestyle; social factors; suburban communities; synergism; wearable device

Project Summary: Poor diet and sedentary behaviors are associated with some of the leading causes of premature death in the U.S. While nutritionists understand that there are a variety of eating behaviors that have positive benefits to health and function in general terms, it has been difficult to tailor dietary recommendations to the individual. Public health recommendations and marketing that aim to improve ‘diet quality’ (or complementary outcomes, such as physical activity) have been hampered by a broad brush ‘one-size fits all’ approach without distinguishing individualized needs. A big challenge to design of strategies for more personalized public health is the paucity of large-scale interventions that have tested person-to-person differences in physiological responses to standardized foods or diet patterns. The premise of the NIH Nutrition for Precision Health program and this application to establish the California Partnership for Personalized Nutrition Clinical Center is that variance in dietary patterns and physiological responses to patterns and specific foods are shaped by disparate factors including complex genetic, microbiome, psychosocial, human ecology, and metabolic variables. We aim to examine which factor or combination of factors influence responses to a test meal composed of strategic ingredients that interrogate multiple biological systems in parallel, population-wide but with a consideration of sub-groups that differ with respect to glucose control and microbiome (type 2 diabetic, pre-diabetic, and non- diabetic persons). In addition, the differential (intra- and interindividual) responses to three disparate diets (Mediterranean, low-carbohydrate, and high-carbohydrate) will be tested in free-living and domiciled conditions, to characterize microbiota shifts and to associate diet with changes in multi-system physiology and chronobiology. Finally, the study design also provides biospecimens for metabolomics, chemistry, and microbiome; advanced metabolic physiology data; innovative app and wearable derived data; in addition to core protocol EMR and genomic data suitable for high quality data modeling and advanced statistical/artificial intelligence “big data” methods for discovery of personalized nutritional optimization. The California Partnership for Personalized Nutrition established in this project grows out of the California Precision Medicine Consortium (CAPMC), the All of Us Health Provider Organization in California,. The CAPMC has established teamwork Statewide for recruitment and retention, with engagement of the great diversity of the population of the State of California. This team, combined with the considerable experience in clinical nutrition, feeding studies and micro- and macro-nutrient assessment, digital app and wearable technologies, modern human ecology, community alliances, and food for health and wellness provides a combination ideally suited to the goals of the Nutrition for Precision Health National program.

项目摘要：不良的饮食和久坐不动的行为与一些主要原因有关， 虽然营养学家知道，有各种各样的饮食行为， 一般来说，对健康和功能有积极的益处，很难根据需要调整饮食建议， 个人旨在改善“饮食质量”的公共健康建议和营销（或补充 结果，如身体活动）受到了广泛的“一刀切”方法的阻碍， 区分个性化需求。为更个性化的公共卫生设计战略的一大挑战 缺乏大规模的干预措施来测试人与人之间的生理差异， 对标准化食物或饮食模式的反应。NIH Nutrition for Precision Health计划的前提 这份申请建立加州个性化营养临床中心的合作伙伴关系的申请是， 饮食模式的差异以及对模式和特定食物的生理反应是由不同的 这些因素包括复杂的遗传、微生物组、社会心理、人类生态和代谢变量。我们的目标 检查哪些因素或因素的组合影响对由策略性食物组成的测试餐的反应， 成分，询问多个生物系统平行，人口范围内，但考虑到 在葡萄糖控制和微生物组方面不同的亚组（2型糖尿病、前驱糖尿病和非糖尿病） 糖尿病患者）。此外，对三种不同饮食的不同（个体内和个体间）反应 （地中海，低碳水化合物和高碳水化合物）将在自由生活和无菌条件下进行测试， 表征微生物群的变化，并将饮食与多系统生理学的变化联系起来， 时间生物学最后，研究设计还提供了用于代谢组学、化学和生物学的生物标本。 微生物组;先进的代谢生理学数据;创新的应用程序和可穿戴衍生数据;除了核心 协议EMR和基因组数据适用于高质量的数据建模和先进的统计/人工 智能“大数据”方法用于发现个性化营养优化。 在这个项目中建立的加州个性化营养伙伴关系源于加州 精准医学联盟（CAPMC），加州的全民健康提供者组织。CAPMC 已经建立了全州范围内的招聘和保留团队，与参与的巨大多样性， 加州的人口。该团队结合临床营养方面的丰富经验， 喂养研究和微观和宏观营养评估，数字应用程序和可穿戴技术，现代 人类生态学、社区联盟和健康食品提供了一个理想的组合， 精准健康营养国家计划的目标。