Diagnosis of Clostridium difficile infection using an UPLC-MS based metabolomics method

Area of Science:

• Microbiology
• Metabolomics
• Clinical Diagnostics

Background:

• The fecal metabolome in Clostridium difficile (CD) infection is not well understood, especially non-volatile organic compounds.
• Current diagnostic tests for CD infection have limitations hindering their widespread application.

Purpose of the Study:

• To identify novel fecal metabolites characteristic of CD infection.
• To develop a metabolomics-based model for diagnosing CD infection.

Main Methods:

• Ultra-performance liquid chromatography-mass spectrometry (UPLC-MS) was employed to analyze fecal metabolomes.
• Samples included CD-positive diarrhea, CD-negative diarrhea, and healthy controls.
• Statistical models like Partial Least Squares Discriminant Analysis (PLS-DA) and Linear Discriminant Analysis (LDA) were utilized.

Main Results:

• Distinct fecal metabolomic profiles were observed between diarrhea and healthy controls, and between CD-positive and CD-negative groups.
• Specific metabolites such as sphingosine and chenodeoxycholic acid were elevated in CD-positive cases, while fatty amides and sphingomyelin were decreased.
• A diagnostic model based on capsiamide, dihydrosphingosine, and glycochenodeoxycholic acid achieved high accuracy (90.00% training, 78.57% validation).

Conclusions:

• Clostridium difficile diarrhea exhibits unique fecal metabolomic signatures compared to other hospital-onset diarrhea.
• The developed UPLC-MS metabolomics model shows potential as a valuable tool for diagnosing CD diarrhea.