Diagnosis of Crohn's Disease and Ulcerative Colitis Using the Microbiome

Abstract

Abstract Background: Inflammatory bowel disease (IBD) is a multifactorial chronic inflammatory disease resulting from dysregulation of the mucosal immune response and gut microbiota. Crohn's disease (CD) and ulcerative colitis (UC) are difficult to distinguish, and differential diagnosis is essential for establishing a long-term treatment plan for patients. Furthermore, the abundance of mucosal bacteria is associated with disease severity. This study aimed to differentiate and diagnose these two diseases using the microbiome and identify specific biomarkers associated with disease activity. Results: We observed differences in the abundance and composition of the microbiome between patients with IBD and healthy controls (HC). Compared to HC, the diversity of the gut microbiome in patients with IBD decreased; the diversity of the gut microbiome in patients with CD was significantly lower. We identified 68 members of the microbiota (28 for CD and 40 for UC) associated with these diseases. Additionally, as the disease progressed through different stages, the diversity of the bacteria decreased. The abundances of Alistipes shahii and Pseudodesulfovibrio aespoeensis were negatively correlated with the severity of CD, whereas the abundance of Polynucleobacter wianus was positively correlated. The severity of UC was negatively correlated with the abundance of A. shahii, Porphyromonas asaccharolytica and Akkermansia muciniphilla, while it was positively correlated with the abundance of Pantoea candidatus pantoea carbekii. A regularized logistic regression model was used for the differential diagnosis of the two diseases. The area under the curve(AUC) was used to examine the model performance. The model discriminated between UC and CD at an AUC of 0.886 (training set) and 0.826 (test set) and an area under the precision-recall curve (AUCPR) of 0.871 (test set). Conclusions: Based on fecal whole-metagenome shotgun (WMS) sequencing, CD and UC were diagnosed using a machine-learning predictive model. Additionally, microbiome biomarkers associated with disease activity (UC and CD) have been proposed.