A novel gas chromatography mass spectrometry-based serum diagnostic and assessment approach to ulcerative colitis

Abstract

Background & aims: To improve the clinical course of ulcerative colitis (UC), more accurate serum diagnostic and assessment methods are required. We used serum metabolomics to develop diagnostic and assessment methods for UC.

Methods: Sera from UC patients, Crohn's disease (CD) patients, and healthy volunteers (HV) were collected at multiple institutions. The UC and HV were randomly allocated to the training or validation set, and their serum metabolites were analyzed by gas chromatography mass spectrometry (GC/MS). Using the training set, diagnostic and assessment models for UC were established by multiple logistic regression analysis. Then, the models were assessed using the validation set. Additionally, to establish a diagnostic model for discriminating UC from CD, the CD patients' data were used.

Results: The diagnostic model for discriminating UC from HV demonstrated an AUC of 0.988, 93.33% sensitivity, and 95.00% specificity in the training set and 95.00% sensitivity and 98.33% specificity in the validation set. Another model for discriminating UC from CD exhibited an AUC of 0.965, 85.00% sensitivity, and 97.44% specificity in the training set and 83.33% sensitivity in the validation set. The model for assessing UC showed an AUC of 0.967, 84.62% sensitivity, and 88.23% specificity in the training set and 84.62% sensitivity, 91.18% specificity, and a significant correlation with the clinical activity index (rs=0.7371, P<0.0001) in the validation set.

Conclusions: Our models demonstrated high performance and might lead to the development of a novel treatment selection method based on UC condition.

Keywords: Biomarker; GC/MS; Metabolomics; Multiple logistic regression analysis; Ulcerative colitis.