P266 Potential oral microbial markers for differential diagnosis of Crohn's disease and ulcerative colitis using machine learning model. (30th January 2023)

• Record Type: Journal Article
• Authors: Park, S K; Kim, H; Kim, S; Lee, C W; Jung, Y; Choi, C H; Kang, S B; Kim, T O; Bang, K B; Chun, J; Cha, J M; Im, J P; Ahn, K S; Park, D I
• Is Part Of: Journal of Crohn's and colitis. Volume 17(2023)Supplement 1
• Page Start: i415
• Page End: i415
• Publication Date: 2023-01-30
• Subjects: Inflammatory bowel diseases -- Periodicals; 616.344005
• DOI: 10.1093/ecco-jcc/jjac190.0396
• Languages: English
• ISSNs: 1873-9946

Abstract

Abstract: Background: Although the gut microbiome dysbiosis have independently been shown to be associated with inflammatory bowel disease (IBD), less is known about the relationship between oral microbiota and IBD. This study aimed to elucidate unique microbiome patterns in saliva from patients with IBD and investigate potential oral microbial markers for differentiating Crohn's disease (CD) and ulcerative colitis (UC). Methods: A multicenter, prospective cohort study recruited patients with IBD (UC, n=175, CD, n=127) and unrelated healthy controls (HC, n=100) to examine microbiota within the oral microenvironments. We used 16S rRNA gene sequencing data as features in training machine learning models (sPLS-DA, Sparse Partial Least-Squares Discriminant Analysis) to classify CD and UC. Results: The V3-V4 amplicon reads of the saliva 16S rRNA sequencing data were taxonomically classified to a total of 2839 taxa (2270 genera) using Kraken2 based on Silva 138.1 reference. The sequences that were not classifiable down to family level were removed, and the samples having sequence depth less than 30000 were also removed, resulting in 2616 taxa for 390 samples (UC, n=168, CD, n=124, HC, n=98). The alpha diversity analysis revealed that the microbiome in IBD patients were significantly less rich than the healthy controls, while CD samples were slightly richer then UC samples (Figure 1, Observed, P = 0.01, Shannon index, p=0.02, Chao index, P=0.0001). An sPLS-DA model with 470 taxa as features was able to distinguish IBD vs control with high performance (AUROC=0.9774), while a separate sPLS-DA model with 130 features classified CD vs UC with an AUROC of 0.8755 (Figure 2, 3). Conclusion: Collectively, oral microbial profiles can serve as a diagnostic marker to discriminate patients with IBD from HC, and patients with CD from UC. As obtaining oral samples is relatively easier than obtaining stool or intestinal biopsies, an opportunity exists to perform oral microbiome-based studies in larger cohort sizes, preferentially in a longitudinal fashion.