Gut microbiota-based discriminative model for patients with ulcerative colitis: A meta-analysis and real-world study.

Abstract

Gut microbiota directly interacts with intestinal epithelium and is a significant factor in the pathogenesis of ulcerative colitis (UC). A meta-analysis was performed to investigate gut microbiota composition of patients with UC in the United States. We also collected fecal samples from Chinese patients with UC and healthy individuals. Gut microbiota was tested using 16S ribosomal RNA gene sequencing. Meta-analysis and 16S ribosomal RNA sequencing revealed significant differences in gut bacterial composition between UC patients and healthy subjects. The Chinese UC group had the highest scores for Firmicutes, Clostridia, Clostridiales, Streptococcaceae, and Blautia, while healthy cohort had the highest scores for P-Bacteroidetes, Bacteroidia, Bacteroidales, Prevotellaceae, and Prevotella_9. A gut microbiota-based discriminative model trained on an American cohort achieved a discrimination efficiency of 0.928 when applied to identify the Chinese UC cohort, resulting in a discrimination efficiency of 0.759. Additionally, a differentiation model was created based on gut microbiota of a Chinese cohort, resulting in an area under the receiver operating characteristic curve of 0.998. Next, we applied the model established for the Chinese UC cohort to analyze the American cohort. Our findings suggest that the diagnostic efficiency ranged from 0.8794 to 0.9497. Furthermore, a combined analysis using data from both the Chinese and US cohorts resulted in a model with a diagnostic efficacy of 0.896. In summary, we found significant differences in gut bacteria between UC individuals and healthy subjects. Notably, the model from the Chinese cohort performed better at diagnosing UC patients compared to healthy subjects. These results highlight the promise of personalized and region-specific approaches using gut microbiota data for UC diagnosis.