Human fecal metabolomic profiling could inform Clostridioides difficile infection diagnosis and treatment
Casey M. Theriot and Joshua R. Fletcher
Department of Population Health and Pathobiology, College of Veterinary Medicine, North Carolina State University, Raleigh, North Carolina, USA.
Address correspondence to: Casey M. Theriot, Department of Population Health and Pathobiology, College of Veterinary Medicine, Research Building 406, North Carolina State University, 1060 William Moore Drive, Raleigh, North Carolina 27607, USA. Phone: 919.513.0711; Email: cmtherio@ncsu.edu.
Find articles by Theriot, C. in: JCI | PubMed | Google Scholar
Department of Population Health and Pathobiology, College of Veterinary Medicine, North Carolina State University, Raleigh, North Carolina, USA.
Address correspondence to: Casey M. Theriot, Department of Population Health and Pathobiology, College of Veterinary Medicine, Research Building 406, North Carolina State University, 1060 William Moore Drive, Raleigh, North Carolina 27607, USA. Phone: 919.513.0711; Email: cmtherio@ncsu.edu.
Find articles by Fletcher, J. in: JCI | PubMed | Google Scholar
First published August 12, 2019 - More info
J Clin Invest. 2019;129(9):3539–3541. https://doi.org/10.1172/JCI130008.
© 2019 American Society for Clinical Investigation
Clostridioides difficile is a significant public health threat, and diagnosis of this infection is challenging due to a lack of sensitivity in current diagnostic testing. In this issue of the JCI, Robinson et al. use a logistic regression model based on the fecal metabolome that is able to distinguish between patients with non–C. difficile diarrhea and C. difficile infection, and to some degree, patients who are asymptomatically colonized with C. difficile. The authors construct a metabolic definition of human C. difficile infection, which could improve diagnostic accuracy and aid in the development of targeted therapeutics against this pathogen.
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