{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,28]],"date-time":"2026-08-28T11:31:27Z","timestamp":1787916687962,"version":"build-2784847793"},"reference-count":108,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2025,9,25]],"date-time":"2025-09-25T00:00:00Z","timestamp":1758758400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Microbiol."],"abstract":"<jats:sec><jats:title>Introduction<\/jats:title><jats:p>Human microbiota is a major factor contributing to the immune system, offering an opportunity to develop non-invasive methods for disease diagnosis. In some research on Autoimmune Diseases (AIDs), gut microbiota variation has been observed. However, there remains a paucity of research that explores the potential of gut microbiota as a microbial signature for the classification and diagnosis of multi-AIDs.<\/jats:p><\/jats:sec><jats:sec><jats:title>Methods<\/jats:title><jats:p>In this study, we analyzed 1,954 gut microbiota sequencing datasets from public databases collected from 1,043 patients with 10 AIDs to identify common or unique microbial signatures for AIDs through differential abundance testing and machine learning techniques. We evaluated five popular algorithms: Random Forest (RF), Support Vector Machine (SVM), K-Nearest Neighbors (KNN), Multilayer Perceptron (MLP), and eXtreme Gradient Boosting (XGBoost) models. Five-fold cross-validation and grid search were used to select the model parameters.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>After comparing the performance of five models, the XGBoost model showed superior performance and achieved an area under the receiver operating characteristic curve (AUROC) ranging from 0.75 to 0.99 when predicting different diseases in the test set. At a specificity of 0.7 to 0.96, the sensitivity ranged from 0.66 to 1. By correlating the top 77 microbiota genera with the disease phenotypes, 126 significant associations were identified [false discovery rate (FDR) &amp;lt; 0.05]. We improved the detection accuracy and disease specificity for AIDs and revealed microbiota features specific to 10 different AIDs. Moreover, we found changing trends in shared microbiota features across some AID phenotypes, such as Crohn's Disease (CD) and Ulcerative Colitis (UC). At the same time, opposite changing trends were observed in the shared microbial signatures, such as Psoriasis and Myasthenia Gravis (MG). These results suggest that specific gut microbiota genera may affect the host immunity and induce different AID phenotypes.<\/jats:p><\/jats:sec><jats:sec><jats:title>Discussion<\/jats:title><jats:p>This research holds potential for clinical application in the auxiliary diagnostic evaluation and monitoring of treatment responses. Simultaneously, it provides important clues for research on the characteristics of the intestinal immune microenvironment for different AIDs.<\/jats:p><\/jats:sec>","DOI":"10.3389\/fmicb.2025.1660775","type":"journal-article","created":{"date-parts":[[2025,9,25]],"date-time":"2025-09-25T05:28:01Z","timestamp":1758778081000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":6,"title":["Gut microbiota analysis reveals microbial signature for multi-autoimmune diseases based on machine learning model"],"prefix":"10.3389","volume":"16","author":[{"given":"Tianfeng","family":"An","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shuya","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jinjin","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hui","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Li","family":"Chen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yiran","family":"Shi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jingyi","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sirui","family":"Han","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ruoxi","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Linyuan","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zijing","family":"Huan","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ruiqi","family":"Yang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Desong","family":"Hao","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yanfang","family":"Liu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xuehua","family":"Liu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chao","family":"Yuan","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2025,9,25]]},"reference":[{"key":"B1","doi-asserted-by":"publisher","first-page":"E70266","DOI":"10.1111\/1756-185X.70266","article-title":"Impact of gut microbiota and probiotics on rheumatoid arthritis,: a., potential treatment challenge","volume":"28","author":"Ajith","year":"2025","journal-title":"Int. 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