Background: The aim of this study was to evaluate the time trend of seropositivity of Helicobacter pylori (H. pylori) over the period of 13 years in an asymptomatic Korean population, and investigate associated risk factors. Methods: This cross-sectional nationwide multicentre study surveyed anti-H. pylori IgG antibodies in 19,272 health check-up subjects (aged [greater than and equal to]16 years) in 2011. Risk factors for H. pylori infection were investigated using logistic regression. Seropositivity in asymptomatic subjects without H. pylori eradication was compared between the years 1998 and 2005. Birth cohort effects were also evaluated. Results: After exclusion of subjects with a history of H. pylori eradication therapy (n = 3,712, 19.3%) and gastric symptoms (n = 4,764, 24.7%), the seroprevalence of H. pylori infection was 54.4% in 10,796 subjects. This was significantly lower than the seroprevalence of 59.6% in 2005 and that of 66.9% in 1998, and this decrease of seropositivity of H. pylori became widespread across all ages and in most areas of the country. This decreasing trend could be explained by cohort analysis. All younger birth cohorts had a lower seroprevalence of H. pylori than older birth cohorts at the same age. Decreased seroprevalence within the same birth cohorts also accounted for this phenomenon. Clinical risk factors of H. pylori infection were higher cholesterol level ([greater than and equal to] 240 mg/dl) (OR = 1.33; 95% CI = 1.14-1.54), male gender, older age, low income, and residence in a rural area. Conclusions: A decreasing trend of H. pylori seroprevalence due to a birth cohort effect requires further studies on its related human host factors as well as socio-economic and hygienic factors. In addition, the relationship between H. pylori infection and high cholesterol level needs more investigation regarding underlying pathogenesis.

Fecal microbiota transplantation (FMT) is an accepted procedure for the management of recurrent Clostridioides difficile infections. FMT is generally considered safe and well-tolerated - even in high-risk patients. Most short-term risks are mild and known to be associated with delivery methods. Long-term side effects have not been established, and no signs of harm have been found to date. However, causality for several microbiome-associated diseases has to be established. Even though FMT is generally considered safe with strict donor screening, serious adverse events have been recently associated with the FMT product from the stool bank, where screening for multi-drug resistant organisms is not included in protocols. Here, we discuss the adverse events associated with FMT and safety issues.

Inflammatory bowel disease (IBD) is chronic inflammation of the gastrointestinal tract caused by high levels of pro-inflammatory cytokines and epithelial barrier dysfunction. Alnus japonica Steud. (Betulaceae) has been used in traditional Asian medicine. However, the potential of A. japonica for the treatment of intestinal inflammation has not been investigated. This study investigated the effects of ethanol extract from A. japonica bark (AJE) on colonic mucosa injury in mice with dextran sodium sulfate (DSS)-induced colitis. Treatment with AJE ameliorated pathological damage and the histopathologic features of DSS-induced colitis. The administration of AJE also inhibits DSS-induced pro-inflammatory cytokines expression, including interleukin (IL)-1β, IL-6, tumor necrosis factor (TNF)-α, and cyclooxygenase (COX)-2. Notably, AJE administration attenuated the reduction of tight junction proteins, zonula occludens (ZO)-1 and occludin, in DSS-induced colitis. In addition, AJE increased heme oxygenase (HO)-1 expression and prevented DSS-induced apoptosis in colonic epithelial cells. Furthermore, in vitro studies demonstrated that AJE inhibits TNF-α-induced IL-8, IL-1β, and COX-2 expression in human intestinal epithelial HT-29 cells and tert-butyl hydroperoxide-induced reduction of ZO-1 and occludin expression in human intestinal epithelial Caco-2 cells. AJE-induced HO-1 protein expression was also found in both HT-29 and Caco-2 cells. Taken together, our findings demonstrated that AJE inhibits intestinal inflammation and protects against intestinal barrier disruption in mice with DSS-induced colitis in vivo and human intestinal epithelial cells in vitro. These results suggest that AJE might have beneficial effects for the treatment of IBD.

Although gut microbiome dysbiosis has been associated with inflammatory bowel disease (IBD), the relationship between the oral microbiota and IBD remains poorly understood. This study aimed to identify unique microbiome patterns in saliva from IBD patients and explore potential oral microbial markers for differentiating Crohn’s disease (CD) and ulcerative colitis (UC). A prospective cohort study recruited IBD patients (UC: n = 175, CD: n = 127) and healthy controls (HC: n = 100) to analyze their oral microbiota using 16S rRNA gene sequencing. Machine learning models (sparse partial least squares discriminant analysis (sPLS-DA)) were trained with the sequencing data to classify CD and UC. Taxonomic classification resulted in 4041 phylotypes using Kraken2 and the SILVA reference database. After quality filtering, 398 samples (UC: n = 175, CD: n = 124, HC: n = 99) and 2711 phylotypes were included. Alpha diversity analysis revealed significantly reduced richness in the microbiome of IBD patients compared to healthy controls. The sPLS-DA model achieved high accuracy (mean accuracy: 0.908, and AUC: 0.966) in distinguishing IBD vs. HC, as well as good accuracy (0.846) and AUC (0.923) in differentiating CD vs. UC. These findings highlight distinct oral microbiome patterns in IBD and provide insights into potential diagnostic markers.

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.

Recent research has demonstrated the potential of fecal microbiome analysis using machine learning (ML) in the diagnosis of inflammatory bowel disease (IBD), mainly Crohn’s disease (CD) and ulcerative colitis (UC). This study employed the sparse partial least squares discriminant analysis (sPLS-DA) ML technique to develop a robust prediction model for distinguishing among CD, UC, and healthy controls (HCs) based on fecal microbiome data. Using data from multicenter cohorts, we conducted 16S rRNA gene sequencing of fecal samples from patients with CD (n = 671) and UC (n = 114) while forming an HC cohort of 1462 individuals from the Kangbuk Samsung Hospital Healthcare Screening Center. A streamlined pipeline based on HmmUFOTU was used. After a series of filtering steps, 1517 phylotypes and 1846 samples were retained for subsequent analysis. After 100 rounds of downsampling with age, sex, and sample size matching, and division into training and test sets, we constructed two binary prediction models to distinguish between IBD and HC and CD and UC using the training set. The binary prediction models exhibited high accuracy and area under the curve (for differentiating IBD from HC (mean accuracy, 0.950; AUC, 0.992) and CD from UC (mean accuracy, 0.945; AUC, 0.988)), respectively, in the test set. This study underscores the diagnostic potential of an ML model based on sPLS-DA, utilizing fecal microbiome analysis, highlighting its ability to differentiate between IBD and HC and distinguish CD from UC.