Metabolomics and Clinical Assays Center

代谢组学和临床检测中心

• 批准号: 10549789
• 负责人: SUSAN J SUMNER
• 金额: $ 470.19万
• 依托单位: UNIV OF NORTH CAROLINA CHAPEL HILL
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-01-12 至 2026-12-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10549789
• 关键词: All of Us Research Program; Amino Acids; Artificial Intelligence; Automatic Data Processing; Award; Behavioral; Biogenic Amines; Bioinformatics; Biological; Biological Assay; Blood; Branched-Chain Amino Acids; CLIA certified; Cardiovascular Diseases; Catabolism; Ceramides; Chronic Disease; Classification; Clinical; Clinical Research; Cloud Computing; Collaborations; Collection; Communities; Companions; Consensus; Coupled; Data; Data Management Resources; Data Set; Deposition; Diabetes Mellitus; Dietary Assessment; Dietary Intervention; Dietary Practices; Dietary intake; Elements; Endocrine; Ensure; Environment; Faculty; Feces; Food; Funding; Future; Genetic; Health; Health Status; Heterogeneity; Human; Illicit Drugs; Individual; Inflammatory; Informatics; Ingestion; Intervention; Intervention Studies; Knowledge; Laboratories; Libraries; Link; Lipids; Malignant Neoplasms; Management Information Systems; Manuals; Mass Spectrum Analysis; Mediating; Metabolic; Metabolic Pathway; Metabolism; Metagenomics; Molecular; North Carolina; Nutrient; Nutrition Assessment; Nutritional Study; Nutritional status; Obesity; Ontology; Participant; Pathway interactions; Pharmaceutical Preparations; Physiology; Phytochemical; Policies; Population; Precision Health; Privacy; Procedures; Process; Protocols documentation; Quality Control; Quality of life; Reporting; Research; Research Design; Research Institute; Research Personnel; Resolution; Resources; Sampling; Scientist; Sphingomyelins; Structure; Study Subject; System; TRUST principles; Technology; Time; Tobacco use; Translating; United States National Institutes of Health; Universities; Urine; Vitamins; Work; acylcarnitine; algorithm development; analysis pipeline; biological systems; clinical center; clinical phenotype; computational pipelines; data ecosystem; data infrastructure; data interoperability; data modeling; demographics; design; dietary; disease phenotype; disorder risk; epidemiology study; evidence base; experience; feeding; improved; interoperability; knowledge base; lifestyle factors; metabolic phenotype; metabolic profile; metabolome; metabolomics; metatranscriptomics; microbial; microbiome; multimodal data; nutrition; personalized intervention; precision nutrition; prevent; programs; public database; quality assurance; response; social; success; timeline; tool; transcriptomics

Abstract (Metabolomics and Clinical Assay Center, MCAC) Determining how individuals differ in their metabolism, and in their response to dietary intake, is critical to developing personalized intervention strategies for preventing and delaying the onset of chronic diseases such as obesity, diabetes, cardiovascular disease, and cancer. The MCAC will a) acquire and process high quality targeted and untargeted metabolomics data, b) prioritize, predict, and confirm the identity of unknown peaks, c) provide CLIA certified clinical assays, d) collaborate with the Common Fund Data Ecosystem, e) construct a data infrastructure which ensures FAIRness and enables interoperability of the data with other Common Fund data sets, and f) collaboratively work with the NIH Common Fund Nutrition for Precision Health (NPH) Consortium. The MCAC brings an outstanding team of investigators from 3 UNC Systems Universities that are co-located on the North Carolina Research Campus (NCRC) and Duke University. Dr. Susan Sumner (UNC Chapel Hill, Nutrition Research Institute, NCRC, Untargeted Metabolomics) will serve as the PI with support from expert scientists who specialize in nutrition and targeted metabolomics of host metabolism (Dr. Christopher Newgard, Director, Sarah W. Stedman Nutrition and Metabolism Center and Duke Molecular Physiology Institute), dietary interventions and targeted phytochemical analysis (Dr. Colin Kay, North Carolina State University, NCRC), CLIA certified clinical assays (Dr. Steven Cotten, UNCCH), and Computational Metabolomics (Dr. Xiuxia Du, UNC Charlotte, NCRC). Our team provides a unique combination of long-standing expertise in metabolomics technologies, coupled with deep knowledge of nutrition, metabolic physiology, and chronic disease mechanisms. We are experienced with the application of targeted and untargeted metabolomics in large-scale clinical and epidemiology studies, including in other NIH Consortia. We have used metabolomics to define metabolic signatures and pathways associated with dietary intake, nutrition assessments, demographics, lifestyle factors, microbial populations, genetics, transcriptomics, clinical assays, and clinical phenotypes of health and wellness. We have developed comprehensive informatics capabilities for targeted and untargeted metabolomics and exposome research. We have developed an online mass spectral knowledge base resource for prioritizing and predicting unknown metabolites by leveraging publicly available data. Our high quality MCAC datasets produced under fine-tuned protocols with quality control and quality assurance metrics, will be essential for success of the NPH Consortium. The MCAC will provide data and expert biological interpretation in exploration of the heterogeneity in metabolism among study subjects, providing a roadmap that will help explain why individuals differ in their metabolic responses to dietary interventions, and what this portends for future disease risk. The MCAC will provide a robust data set to the Artificial Intelligence for Multimodal Data Modeling and Bioinformatics Center for use in development of algorithms to predict individual dietary responses that can ultimately be translated for design of targeted dietary interventions to improve health and quality of life.

摘要（MCAC代谢组学和临床测定中心） 确定个体如何在新陈代谢上以及对饮食摄入的反应时如何差异，对 制定个性化干预策略，以防止和延迟慢性病的发作 作为肥胖，糖尿病，心血管疾病和癌症。 MCAC将a）获取和处理高质量 靶向和未靶向的代谢组学数据，b）优先级，预测和确认未知峰的身份，c） 提供CLIA认证的临床测定，d）与普通基金数据生态系统合作，e）构建数据 确保公平性并实现数据互操作性与其他常见基金数据的互操作性 集合和f）与NIH普通基金精准健康（NPH）财团合作。 MCAC带来了来自3所UNC系统大学的一支杰出的调查员团队 北卡罗来纳州研究校园（NCRC）和杜克大学。苏珊·萨姆纳（Susan Sumner）博士（UNC教堂山， NCRC营养研究所，未靶向代谢组学）将在专家的支持下作为PI 专门从事营养和针对宿主代谢的代谢组学的科学家（克里斯托弗·纽格德博士， 莎拉·W·斯特德曼营养与代谢中心主任，杜克分子生理学研究所），饮食 干预和有针对性的植物化学分析（北卡罗来纳州立大学的Colin Kay博士，NCRC），CLIA 认证的临床测定法（UNCCH的Steven Cotten博士）和计算代谢组学（Xiuxia du博士，UNC 夏洛特，NCRC）。我们的团队提供了代谢组学长期专业知识的独特组合 技术，再加上对营养，代谢生理和慢性疾病机制的深入了解。 我们在大规模临床中应用有针对性和无靶向代谢组学的经验 流行病学研究，包括其他NIH联盟。我们已经使用代谢组学来定义代谢 与饮食摄入，营养评估，人口统计，生活方式因素相关的签名和途径， 微生物种群，遗传学，转录组学，临床测定和健康和保健的临床表型。 我们已经为有针对性和无靶的代谢组学开发了全面的信息能力， 外向研究。我们已经开发了一种在线质谱知识库资源，用于优先级和 通过利用公共可用数据来预测未知代谢产物。我们生产的高质量MCAC数据集 在具有质量控制和质量保证指标的微调协议下，对于成功的成功至关重要 NPH财团。 MCAC将提供数据和专家生物学解释，以探索 研究学科之间的代谢异质性，提供路线图，这将有助于解释为什么个人 他们对饮食干预措施的代谢反应不同，这对未来疾病风险预示了什么。这 MCAC将为多模式数据建模和生物信息学的人工智能提供强大的数据集 用于开发算法的使用中心，以预测最终可以成为的个体饮食反应 翻译以设计有针对性的饮食干预措施，以改善健康和生活质量。