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Stool-Based Proteomic Signature for the Noninvasive Classification of Crohn's Disease and Ulcerative Colitis Using

Elmira Shajari1,2,3, David Gagné1,2,3,4, Francis Bourassa2,5

  • 1Laboratory of Intestinal Physiopathology, Faculty of Medicine and Health Sciences, Université de Sherbrooke, Sherbrooke, Quebec, Canada.

Clinical and Translational Gastroenterology
|October 2, 2025
PubMed
Summary

This study developed a noninvasive method using stool proteins and machine learning to accurately distinguish Crohn's disease (CD) from ulcerative colitis (UC). The predictive model achieved high accuracy, offering a potential alternative to invasive diagnostic procedures.

Keywords:
Crohn's diseaseDIA mass spectrometryinflammatory bowel disease subtypingmachine learningquantitative proteomicsstool biomarkersulcerative colitis

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Area of Science:

  • Gastroenterology
  • Biomarker Discovery
  • Proteomics

Background:

  • Crohn's disease (CD) and ulcerative colitis (UC) are distinct inflammatory bowel diseases with overlapping symptoms.
  • Current diagnosis relies on invasive procedures like colonoscopy and histopathology.
  • Noninvasive biomarkers from stool offer a promising alternative for disease differentiation.

Purpose of the Study:

  • To develop an accurate, noninvasive diagnostic model distinguishing CD from UC.
  • To identify a protein biomarker signature in stool samples.
  • To leverage machine learning for predictive modeling.

Main Methods:

  • High-throughput data-independent acquisition mass spectrometry analyzed stool proteomes from 69 patients.
  • Approximately 1,250 proteins were identified and quantified.
  • Feature selection and machine learning algorithms were employed to build a predictive model.

Main Results:

  • Sixteen proteins were identified as significant differentiators between CD and UC.
  • A Naive Bayes machine learning model was selected for its performance.
  • The model achieved an area under the curve of 0.96 on both training and independent test datasets, demonstrating robustness.

Conclusions:

  • Combining stool protein biomarkers with mass spectrometry and machine learning can create an effective predictive model.
  • This approach shows potential for noninvasively distinguishing between Crohn's disease and ulcerative colitis.
  • The developed model offers a stable and accurate diagnostic tool for inflammatory bowel disease subtyping.