Celiac disease gut microbiome studies in the third millennium: reviewing the findings and gaps of available literature - Luz, Vanessa C. C.; Pereira, Sónia Gonçalves (Frontiers in Medical Technology, 2024)
Celiac disease is an autoimmune enteropathy caused by the ingestion of minute amounts of gluten in a subset of genetically predisposed individuals. Its onset occurs at different ages and with variable symptoms. The gut microbiome may contribute to this variability. This review aims to provide an overview of the available research on celiac disease gut microbiome and identify the knowledge gap that could guide future studies. Following the guidelines of the Preferred Reporting Items for Systematic Reviews and Meta-analysis extension for Scoping Reviews (PRISMA-ScR), four electronic databases were searched for literature from January 2000 to July 2023 addressing celiac disease gut microbiome characterization using next-generation sequencing (NGS) approaches. From the 489 publications retrieved, 48 publications were selected and analyzed, focusing on sample characterization (patients, controls, and tissues) and methodologies used for NGS microbiome analysis and characterization. The majority of the selected publications regarded children and adults, and four were randomized clinical trials. The number of participants per study greatly varied and was typically low. Feces were the most frequently tested sample matrix, and duodenal samples were analyzed in one-third of the studies. Incomplete and diverse information on the methodological approaches and gut microbiome results was broadly observed. While similar trends regarding the relative abundance of some phyla, such as Pseudomonadota (former Proteobacteria), were detected in some studies, others contradicted those results. The observed high variability of technical approaches and possibly low power and sample sizes may prevent reaching a consensus on celiac disease gut microbiome composition. Standardization of research protocols to allow reproducibility and comparability is required, as interdisciplinary collaborations to further data analysis, interpretation, and, more importantly, health outcome prediction or improvement.