Ulcerative colitis (UC), a relapsing-remitting chronic inflammatory bowel disease (IBD), has a variable natural course but potentially severe disease course. Since the development of anti-tumor necrosis factor (TNF) agents has changed the natural disease course of moderate-to-severe UC, therapeutic options for patients who failed conventional treatments are expanding rapidly. IBD clinical trials have demonstrated the potential efficacy and safety of novel biologics such as anti-integrin α4β7 and anti-interleukin-12/23 monoclonal antibodies and small molecules such as a Janus kinase inhibitor. Anti-TNF biosimilars also have been approved and are widely used in IBD patients. Wise drug choices should be made considering evidence-based efficacy and safety. However, the best position of these drugs remains several questions, with limited data from direct comparative trials. In addition, there are still concerns to be elucidated on the effect of therapeutic drug monitoring and combination therapy with immunomodulators. The appropriate treatment regimens in acute severe UC and the risk of perioperative use of biologics are unclear. As novel biologics and small molecules have been approved in Korea, we present the Korean guidelines for medical management of adult outpatients with moderate-to-severe UC and adult hospitalized patients with acute severe UC, focusing on biologics and small molecules.

Crohn’s disease (CD) and ulcerative colitis (UC) can be difficult to differentiate. As differential diagnosis is important in establishing a long-term treatment plan for patients, we aimed to develop a machine learning model for the differential diagnosis of the two diseases using RNA sequencing (RNA-seq) data from endoscopic biopsy tissue from patients with inflammatory bowel disease (n = 127; CD, 94; UC, 33). Biopsy samples were taken from inflammatory lesions or normal tissues. The RNA-seq dataset was processed via mapping to the human reference genome (GRCh38) and quantifying the corresponding gene models that comprised 19,596 protein-coding genes. An unsupervised learning model showed distinct clusters of four classes: CD inflammatory, CD normal, UC inflammatory, and UC normal. A supervised learning model based on partial least squares discriminant analysis was able to distinguish inflammatory CD from inflammatory UC after pruning the strong classifiers of normal CD vs. normal UC. The error rate was minimal and affected only two components: 20 and 50 genes for the first and second components, respectively. The corresponding overall error rate was 0.147. RNA-seq analysis of tissue and the two components revealed in this study may be helpful for distinguishing CD from UC.

Rescue antiviral treatment for patients with resistance to preexisting nucleos(t)ide analogues remains a clinical challenge. The correct choice of a first-line treatment of high potency and with a high genetic barrier to achieve sustained long-term suppression of viral replication provides the best chance of preventing treatment failure and the emergence of drug resistance. The management of treatment failure and drug resistance requires a precise and accurate clinical and virologic monitoring. Combination treatment with antiviral drugs that belong to different groups is associated with a lower chance of developing resistance to rescue drugs. To guarantee better control of viral replication in patients with drug resistance, the addition of another drug without a cross resistance profile should be given as early as possible, preferably at the time when genotypic resistance emerges. Long-term surveillance for treatment efficacy and possible emergence of drug resistance should be continued to prevent the emergence of multidrug-resistant strains.

Early intestinal resection in patients with Crohn’s disease (CD) is necessary due to a severe and complicating disease course. Herein, we aim to predict which patients with CD need early intestinal resection within 3 years of diagnosis, according to a tree-based machine learning technique. The single-nucleotide polymorphism (SNP) genotype data for 337 CD patients recruited from 15 hospitals were typed using the Korea Biobank Array. For external validation, an additional 126 CD patients were genotyped. The predictive model was trained using the 102 candidate SNPs and seven sets of clinical information (age, sex, cigarette smoking, disease location, disease behavior, upper gastrointestinal involvement, and perianal disease) by employing a tree-based machine learning method (CatBoost). The importance of each feature was measured using the Shapley Additive Explanations (SHAP) model. The final model comprised two clinical parameters (age and disease behavior) and four SNPs (rs28785174, rs60532570, rs13056955, and rs7660164). The combined clinical–genetic model predicted early surgery more accurately than a clinical-only model in both internal (area under the receiver operating characteristic (AUROC), 0.878 vs. 0.782; n = 51; p < 0.001) and external validation (AUROC, 0.836 vs. 0.805; n = 126; p < 0.001). Identification of genetic polymorphisms and clinical features enhanced the prediction of early intestinal resection in patients with CD.

Limited studies have examined the intestinal microbiota composition in relation to Crohn’s disease (CD) prognosis. We analyzed the differences in microbial communities and relevant metabolic pathways associated with prognostic variables in patients with CD. We applied 16S rRNA gene sequencing to analyze a cohort of 1110 CD and healthy control (HC) fecal samples. We categorized patients with CD into good (CD-G), intermediate (CD-I) and poor (CD-P) prognosis groups, according to the history of using biologics and intestinal resection. Microbiota α-diversity decreased more in CD-P than CD-G and CD-I. Microbiota ß-diversity in CD-P differed from that in CD-G and CD-I. Thirteen genera and 10 species showed differential abundance between CD-G and CD-P groups. Escherichia coli (p = 0.001) and species Producta (p = 0.01) and genera Lactobacillus (p = 0.003) and Coprococcus (p = 0.01) consistently showed differences between CD-G and CD-P groups after adjusting for confounding variables. Functional profiling suggested that the microbial catabolic pathways and pathways related to enterobacterial common antigen and lipopolysaccharide biosynthesis were better represented in the CD-P group than in the CD-G group, and E. coli were the top contributors to these pathways. CD prognosis is associated with altered microbiota composition and decreased diversity, and E. coli might be causally involved in CD progression, and may have adapted to live in inflammatory environments.