Grant Number:	5U24CA268153-03 Interpret this number
Primary Investigator:	Sumner, Susan
Organization:	Univ Of North Carolina Chapel Hill
Project Title:	Metabolomics and Clinical Assays Center
Fiscal Year:	2024

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.




Transforming Big Data into AI-ready data for nutrition and obesity research.
Authors: Thomas D.M. , Knight R. , Gilbert J.A. , Cornelis M.C. , Gantz M.G. , Burdekin K. , Cummiskey K. , Sumner S.C.J. , Pathmasiri W. , Sazonov E. , et al. .
Source: Obesity (Silver Spring, Md.), 2024 May; 32(5), p. 857-870.
EPub date: 2024-03-01.
PMID: 38426232
Related Citations

Psychedelics for Medicinal Use: How Will This Alter the Collective Laboratory Consciousness?
Authors: Cotten S.W. , Strathmann F.G. , Barrett F.S. , Labay L. , Mullally J. , Sherwood A.M. , Wiegand F. .
Source: Clinical chemistry, 2023-04-03; 69(4), p. 319-326.
PMID: 36881769
Related Citations

Concordance of Chronic Kidney Disease Stage and Metformin Management Using CKD-EPI 2021 Race-Free Equation vs CKD-EPI 2009 Equation to Estimate Glomerular Filtration Rate.
Authors: Maynard R.D. , Korpi-Steiner N. , Cotten S.W. .
Source: Clinical chemistry, 2023-02-01; 69(2), p. 202-204.
PMID: 36508321
Related Citations