Metabolomics and Clinical Assays Center

代谢组学和临床检测中心

• 批准号: 10379509
• 负责人: SUSAN J SUMNER
• 金额: $ 39.89万
• 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/10379509
• 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; Clinical; Clinical Research; Cloud Computing; 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; Faculty; Feces; Food; Funding; Future; Health; Health Status; Heterogeneity; Human; Illicit Drugs; Individual; Inflammatory; Informatics; Ingestion; Institutes; 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 Genetics; Precision Health; Privacy; Procedures; Process; Protocols documentation; Quality Control; Quality of life; Reporting; Research; Research Design; Research Institute; Research Personnel; Resolution; Resources; Sampling; Scientist; Ships; Sphingomyelins; Structure; Study Subject; System; TRUST principles; Technology; Time; TimeLine; 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; 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 代谢组学和临床检测中心） 确定个体的新陈代谢以及对饮食摄入的反应有何不同，对于 制定个性化干预策略来预防和延缓慢性病的发作，例如 如肥胖、糖尿病、心血管疾病和癌症。 MCAC 将 a) 获取并处理高质量的 靶向和非靶向代谢组学数据，b) 优先考虑、预测和确认未知峰的身份，c) 提供 CLIA 认证的临床检测，d) 与共同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与其他共同基金数据的互操作性的基础设施 f) 与 NIH 精准健康营养共同基金 (NPH) 联盟合作。 MCAC 汇集了来自 3 所 UNC 系统大学的优秀研究人员团队，这些大学位于同一地点 北卡罗来纳研究园 (NCRC) 和杜克大学。 Susan Sumner 博士（北卡罗来纳大学教堂山分校， 营养研究所、NCRC、非靶向代谢组学）将在专家的支持下担任 PI 专门研究营养和宿主代谢的靶向代谢组学的科学家（Christopher Newgard 博士， 莎拉·W·斯特德曼营养与代谢中心和杜克分子生理学研究所主任），饮食 干预措施和有针对性的植物化学分析（Colin Kay 博士，北卡罗来纳州立大学，NCRC），CLIA 经认证的临床测定（Steven Cotten 博士，UNCCH）和计算代谢组学（Xiuxia Du 博士，UNC 夏洛特，NCRC）。我们的团队提供代谢组学方面长期专业知识的独特组合 技术，加上对营养、代谢生理学和慢性疾病机制的深入了解。 我们在靶向和非靶向代谢组学在大规模临床和研究中的应用方面拥有丰富的经验。 流行病学研究，包括其他 NIH 联盟的研究。我们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特征和途径， 微生物种群、遗传学、转录组学、临床分析以及健康和保健的临床表型。 我们开发了针对靶向和非靶向代谢组学的全面信息学能力， 暴露研究。我们开发了一个在线质谱知识库资源，用于确定优先级和 利用公开数据预测未知代谢物。我们生成的高质量 MCAC 数据集 根据具有质量控制和质量保证指标的微调协议，对于项目的成功至关重要 NPH 财团。 MCAC 将提供数据和专家生物学解释来探索 研究对象之间新陈代谢的异质性，提供了一个路线图，有助于解释为什么个体 他们对饮食干预的代谢反应有所不同，这预示着未来的疾病风险。这 MCAC 将为人工智能提供强大的数据集，用于多模式数据建模和生物信息学 用于开发算法来预测最终可预测的个体饮食反应的中心 翻译为设计有针对性的饮食干预措施，以改善健康和生活质量。