Research from JAMA — Radiofrequency Ablation vs Endoscopic Surveillance for Patients With Barrett Esophagus and Low-Grade Dysplasia — A Randomized Clinical Trial

Objective: Type 1 diabetes (T1D) is characterised by islet autoimmunity and beta cell destruction. A gut microbiota–immunological interplay is involved in the pathophysiology of T1D. We studied microbiota-mediated effects on disease progression in patients with type 1 diabetes using faecal microbiota transplantation (FMT).Design: Patients with recent-onset (<6 weeks) T1D (18–30 years of age) were randomised into two groups to receive three autologous or allogenic (healthy donor) FMTs over a period of 4 months. Our primary endpoint was preservation of stimulated C peptide release assessed by mixed-meal tests during 12 months. Secondary outcome parameters were changes in glycaemic control, fasting plasma metabolites, T cell autoimmunity, small intestinal gene expression profile and intestinal microbiota composition.Results: Stimulated C peptide levels were significantly preserved in the autologous FMT group (n=10 subjects) compared with healthy donor FMT group (n=10 subjects) at 12 months. Small intestinalPrevotellawas inversely related to residual beta cell function (r=−0.55, p=0.02), whereas plasma metabolites 1-arachidonoyl-GPC and 1-myristoyl-2-arachidonoyl-GPC levels linearly correlated with residual beta cell preservation (rho=0.56, p=0.01 and rho=0.46, p=0.042, respectively). Finally, baseline CD4 +CXCR3+T cell counts, levels of small intestinalDesulfovibrio pigerand CCL22 and CCL5 gene expression in duodenal biopsies predicted preserved beta cell function following FMT irrespective of donor characteristics.Conclusion: FMT halts decline in endogenous insulin production in recently diagnosed patients with T1D in 12 months after disease onset. Several microbiota-derived plasma metabolites and bacterial strains were linked to preserved residual beta cell function. This study provides insight into the role of the intestinal gut microbiome in T1D.Trial registration number: NTR3697.

Background & Aims Radiofrequency ablation (RFA), with or without endoscopic resection effectively eradicates Barrett’s esophagus (BE) containing high-grade intraepithelial neoplasia and/or early-stage cancer. We followed patients who received RFA for BE containing high-grade intraepithelial neoplasia and/or early-stage cancer for 5 years to determine the durability of treatment response. Methods We followed 54 patients with BE (2−12 cm), previously enrolled in 4 consecutive cohort studies in which they underwent focal endoscopic resection in case of visible lesions (n = 40 [72%]), followed by serial RFA every 3 months. Patients underwent high-resolution endoscopy with narrow-band imaging at 6 and 12 months after treatment and then annually for 5 years (median, 61 months; interquartile range, 53−65 months); random biopsy samples were collected from neosquamous epithelium and gastric cardia. After 5 years, endoscopic ultrasound and endoscopic resection of neosquamous epithelium were performed. Outcomes included sustained complete remission of neoplasia or intestinal metaplasia (IM), IM in gastric cardia, or buried glands in neosquamous epithelium. Results After 5 years, Kaplan-Meier analysis showed sustained complete remission of neoplasia and intestinal metaplasia in 90% of patients; neoplasia recurred in 3 patients and was managed endoscopically. Focal IM in the cardia was found in 19 of 54 patients (35%), in 53 of 1143 gastric cardia biopsies (4.6%). The incidence of IM of the cardia did not increase over time; and IM was diagnosed based on only a single biopsy in 89% of patients. Buried glands were detected in 3 of 3543 neosquamous epithelium biopsies (0.08%, from 3 patients). No endoscopic resection samples had buried glands. Conclusions Among patients who have undergone RFA with or without endoscopic resection for neoplastic BE, 90% remain in remission at 5-year follow-up, with all recurrences managed endoscopically. This treatment approach is therefore an effective and durable alternative to esophagectomy; www.trialregister.nl number, NTR2938.

Background & Aims: The aim of this study was to compare magnified still images obtained with high-resolution white light endoscopy, indigo carmine chromoendoscopy, acetic acid chromoendoscopy, and narrow-band imaging to determine the best technique for use in Barrett’s esophagus. Methods: We obtained magnified images from 22 areas with the 4 aforementioned techniques. Seven endoscopists with no specific expertise in Barrett’s esophagus or advanced imaging techniques and 5 international experts in this field evaluated these 22 areas for overall image quality, mucosal image quality, and vascular image quality. In addition, the regularity of mucosal and vascular patterns and the presence of abnormal blood vessels were evaluated, and this was correlated with histology. Results: The interobserver agreement for the 3 features of mucosal morphology with white light images ranged from κ = 0.51 (95% confidence interval [CI]: 0.46–0.55) to κ = 0.53 (95% CI: 0.50–0.57) for all observers, from κ = 0.43 (95% CI: 0.33–0.54) to κ = 0.53 (95% CI: 0.41–0.64) for experts, and from κ = 0.51 (95% CI: 0.15–0.33) to κ = 0.64 (95% CI: 0.58–0.70) for nonexperts. The interobserver agreement in these groups did not improve by adding one of the enhancement techniques. The yield for identifying early neoplasia with white light images was 86% for all observers, 90% for experts, and 84% for nonexperts. The addition of enhancement techniques did not improve the yield neoplasia. Conclusions: The addition of indigo carmine chromoendoscopy, acetic acid chromoendoscopy, or narrow-band imaging to white light images did not improve interobserver agreement or yield identifying early neoplasia in Barrett’s esophagus.

There has been a vast increase in GI literature focused on the use of machine learning in endoscopy. The relative novelty of this field poses a challenge for reviewers and readers of GI journals. To appreciate scientific quality and novelty of machine learning studies, understanding of the technical basis and commonly used techniques is required. Clinicians often lack this technical background, while machine learning experts may be unfamiliar with clinical relevance and implications for daily practice. Therefore, there is an increasing need for a multidisciplinary, international evaluation on how to perform high-quality machine learning research in endoscopy. This review aims to provide guidance for readers and reviewers of peer-reviewed GI journals to allow critical appraisal of the most relevant quality requirements of machine learning studies. The paper provides an overview of common trends and their potential pitfalls and proposes comprehensive quality requirements in six overarching themes: terminology, data, algorithm description, experimental setup, interpretation of results and machine learning in clinical practice.