To investigate the factors affecting diagnostic delay and outcomes of diagnostic delay in inflammatory bowel disease (IBD) We retrospectively studied 165 patients with Crohn’s disease (CD) and 130 patients with ulcerative colitis (UC) who were diagnosed and had follow up durations > 6 mo at Korea University Ansan Hospital from January 2000 to December 2015. A diagnostic delay was defined as the time interval between the first symptom onset and IBD diagnosis in which the 76^th to 100^th percentiles of patients were diagnosed. The median diagnostic time interval was 6.2 and 2.4 mo in the patients with CD and UC, respectively. Among the initial symptoms, perianal discomfort before di-agnosis (OR = 10.2, 95%CI: 1.93-54.3, P = 0.006) was associated with diagnostic delays in patients with CD; however, no clinical factor was associated with diagnostic delays in patients with UC. Diagnostic delays, stricturing type, and penetrating type were associated with increased intestinal surgery risks in CD (OR = 2.54, 95%CI: 1.06-6.09; OR = 4.44, 95%CI: 1.67-11.8; OR = 3.79, 95%CI: 1.14-12.6, respectively). In UC, a diagnostic delay was the only factor associated increased intestinal surgery risks (OR = 6.81, 95%CI: 1.12-41.4). A diagnostic delay was associated with poor outcomes, such as increased intestinal surgery risks in patients with CD and UC.

Gastrointestinal lymphoma is the most common type of extranodal lymphoma, and most commonly affects the stomach. Marginal zone B-cell lymphoma of the mucosa-associated lymphoid tissue (MALT) and diffuse large B-cell lymphoma are the most common histologic types of gastric lymphoma. Despite its increasing incidence, diagnosis of gastric lymphoma is difficult at an earlier stage due to its nonspecific symptoms and endoscopic findings, and, thus, a high index of suspicion, and multiple, deep, repeated biopsies at abnormally and normally appearing sites in the stomach are needed. In addition, testing for Helicobacter pylori (H. pylori) infection and endoscopic ultrasonography to determine the depth of tumor invasion and involvement of regional lymph nodes is essential for predicting response to H. pylori eradication and for assessment of disease progression. In addition, H. pylori infection and MALT lymphoma development are associated, and complete regression of low-grade MALT lymphomas after H. pylori eradication has been demonstrated. Radiotherapy and/or chemotherapy can be used in cases that show poor response to H. pylori eradication, negativity for H. pylori infection, or high-grade lymphoma.

Inadequate bowel preparation is observed in more than 25% of all colonoscopies. Identification of predictive factors for inadequate colon cleaning is helpful and more detailed preparation methods should be used for patients at high risk. Age, male sex, inpatient status, and comorbidities were identified as independent risk factors in several previous studies. In patients with insufficient colon preparation, colon irrigation with endoscopic pumps or next-day colonoscopy following further bowel cleaning should be performed. In order to improve the efficacy and safety of both bowel preparation and colonoscopy, the endoscopic team should identify the patient's medical conditions and choose the optimal bowel preparation agent and regimen.

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.

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.