Peter J Prendergast, Haig V Bishop, Craig W Herbold, Elena F Verdu, Renwick C J Dobson, Andrew S Day, Olivia J Ogilvie
{"title":"Comprehensive cross-cohort analysis reveals global gut microbiome signatures of celiac disease.","authors":"Peter J Prendergast, Haig V Bishop, Craig W Herbold, Elena F Verdu, Renwick C J Dobson, Andrew S Day, Olivia J Ogilvie","doi":"10.1038/s43856-026-01627-1","DOIUrl":null,"url":null,"abstract":"<p><strong>Background: </strong>Celiac disease affects ~1-2% of people and remains incurable, requiring lifelong dietary restriction. The gut microbiome is thought to contribute to the development and progression of celiac disease. However, findings across previous studies are fragmented, making it difficult to understand exactly how the gut microbiome is involved.</p><p><strong>Methods: </strong>We integrate over 900 samples from global datasets spanning different disease stages (before onset, during active disease, and after treatment), body sites, and research methods. Datasets produced using both 16S rRNA gene sequencing and shotgun metagenomics profile the gut microbiome. Alpha and beta diversity analyses and differential abundance testing identify consistent changes in bacterial communities linked to celiac disease. Machine learning tests how well microbiome data predicts disease status.</p><p><strong>Results: </strong>Here, we show that celiac disease is not marked by large changes in gut microbiome diversity. Instead, there are subtle, consistent changes in specific bacteria, including a reduction in beneficial butyrate producers (Faecalibacterium, Prevotella, Agathobacter, Gemmiger), changes in mucin-associated microbes (Akkermansia muciniphila), and an increase in potentially harmful bacteria (Helicobacter, Campylobacter, Haemophilus parainfluenzae). These changes are seen before and during active disease and persist on a gluten-free diet. Microbiome-based disease prediction is moderately accurate for active disease and weaker for prospective performance, likely constrained by training data.</p><p><strong>Conclusions: </strong>Our findings suggest that celiac disease is linked to specific changes in gut bacteria that are not fully resolved by diet alone. Future treatments may need to focus on restoring healthy gut bacteria, not just avoiding gluten, to better manage the disease.</p>","PeriodicalId":72646,"journal":{"name":"Communications medicine","volume":" ","pages":""},"PeriodicalIF":5.4000,"publicationDate":"2026-05-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Communications medicine","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1038/s43856-026-01627-1","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"MEDICINE, RESEARCH & EXPERIMENTAL","Score":null,"Total":0}
引用次数: 0
Abstract
Background: Celiac disease affects ~1-2% of people and remains incurable, requiring lifelong dietary restriction. The gut microbiome is thought to contribute to the development and progression of celiac disease. However, findings across previous studies are fragmented, making it difficult to understand exactly how the gut microbiome is involved.
Methods: We integrate over 900 samples from global datasets spanning different disease stages (before onset, during active disease, and after treatment), body sites, and research methods. Datasets produced using both 16S rRNA gene sequencing and shotgun metagenomics profile the gut microbiome. Alpha and beta diversity analyses and differential abundance testing identify consistent changes in bacterial communities linked to celiac disease. Machine learning tests how well microbiome data predicts disease status.
Results: Here, we show that celiac disease is not marked by large changes in gut microbiome diversity. Instead, there are subtle, consistent changes in specific bacteria, including a reduction in beneficial butyrate producers (Faecalibacterium, Prevotella, Agathobacter, Gemmiger), changes in mucin-associated microbes (Akkermansia muciniphila), and an increase in potentially harmful bacteria (Helicobacter, Campylobacter, Haemophilus parainfluenzae). These changes are seen before and during active disease and persist on a gluten-free diet. Microbiome-based disease prediction is moderately accurate for active disease and weaker for prospective performance, likely constrained by training data.
Conclusions: Our findings suggest that celiac disease is linked to specific changes in gut bacteria that are not fully resolved by diet alone. Future treatments may need to focus on restoring healthy gut bacteria, not just avoiding gluten, to better manage the disease.