Exploring the Relationship Between Gut Health and Autoimmune Diseases: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
Autoimmune diseases (AIDs) are multifaceted, chronic illnesses characterized by immune dysregulation and systemic inflammation. Newer evidence has pointed a finger at the human gut microbiota, a trillion-fold population of microorganisms that inhabits the human GI tract, as a major influential modulator of immune reactivity and a significant contributor to autoimmune pathogenesis. This systematic review will seek to address how the literature correlates with systematic changes in the gut microbiota in AIDs as well as explore mechanistic associations with biological processes like intestinal permeability and modulation of the immune system, coupled with determining the effectiveness of microbiota-directed interventions. An extensive literature search was conducted in PubMed, Embase, Cochrane Central, and Web of Science, involving the availability of studies until May 2025. The eligible studies included observational studies, randomized controlled trials, and relevant mechanistic research regarding autoimmune diseases and alterations of the gut microbiome or administered interventions. Data extraction and risk of bias (ROB) assessments were performed by two independent reviewers, and a narrative synthesis with an illustrative meta-analysis was applied. Inclusion criteria were met by 10 studies, encompassing various autoimmune diseases, including systemic lupus erythematosus (SLE), rheumatoid arthritis (RA), multiple sclerosis (MS), type 1 diabetes mellitus (T1DM), autoimmune thyroid diseases (AITDs), and psoriasis. Familiar patterns of microbiome dysbiosis were identified, such as a reduction in microbial diversity, increased intestinal permeability, and the expansion of pro-inflammatory species like Ruminococcus gnavus. Dietary interventions, fecal microbiota transplantation, and probiotics demonstrated positive effects on clinical outcomes and immune measures across multiple studies. The meta-analysis revealed that microbiota-directed interventions significantly improved disease activity and immune response markers in AIDs, indicating a robust link between gut microbiota composition and autoimmune pathology. In autoimmune disorders, gut microbiota is a key factor in immunopathology. Gut biology as an adjunct interventional strategy provides potential in managing these diseases. Additional studies are required to help standardize methods and identify microbial targets specific to diseases that can then be addressed through therapeutic interventions.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.018 | 0.039 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.021 | 0.042 |
| Bibliometrics | 0.010 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".