**Human fecal metabolomic profiling could informClostridioides difficileinfection diagnosis and treatment**

Theriot, Casey M.
Fletcher, Joshua R.

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: [email protected].

Conflict of interest: CMT is a scientific advisor to Locus Biosciences, a company engaged in the development of antimicrobial technologies.

Reference information:J Clin Invest. 2019;129(9):3539–3541. https://doi.org/10.1172/JCI130008.

Journal of Clinical Investigation 129(9):p 3539-3541, September 2019. | DOI: 10.1172/JCI130008

Clostridioides difficileis 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 theJCI, Robinson et al. use a logistic regression model based on the fecal metabolome that is able to distinguish between patients with non-C. difficilediarrhea andC. difficileinfection, and to some degree, patients who are asymptomatically colonized withC. difficile. The authors construct a metabolic definition of humanC. difficileinfection, which could improve diagnostic accuracy and aid in the development of targeted therapeutics against this pathogen.