The Role of the Gut Microbiome in Immune Dysregulation and Pathogenesis of Inflammatory Bowel Disease
Bibliographic record
Abstract
Background: Inflammatory Bowel Disease (IBD), encompassing Crohn's Disease (CD) and Ulcerative Colitis (UC), is a chronic, debilitating disorder affecting the gastrointestinal tract. The gut microbiome is pivotal in maintaining intestinal homeostasis and regulating immune function. Dysbiosis, or microbial imbalance, has been increasingly recognized as a key factor in the pathogenesis of IBD, driving chronic inflammation and immune dysregulation. Objectives: This systematic review aims to explore the relationship between the gut microbiome and immune responses in IBD. Specifically, it investigates how dysbiosis contributes to disease pathogenesis and immune modulation, and evaluates the efficacy of microbiome-targeted therapies such as probiotics, prebiotics, and fecal microbiota transplantation (FMT). Methods: We conducted a comprehensive search of PubMed, Scopus, and Web of Science for studies published between 2000 and 2024. Studies included randomized controlled trials, observational studies, and systematic reviews focused on microbial alterations in IBD and the use of microbiome-targeted interventions. Quality was assessed using the Cochrane Risk of Bias Tool and Newcastle-Ottawa Scale. Data synthesis was performed using narrative analysis and descriptive statistics. Results: Key findings indicate that microbial dysbiosis in IBD is marked by a reduction in beneficial taxa such as Faecalibacterium prausnitzii and Akkermansia muciniphila, alongside the overgrowth of pathogenic microbes like Escherichia coli (AIEC). Microbiome-targeted therapies, including probiotics, prebiotics, and FMT, showed promising results in restoring microbial balance, though efficacy was variable, particularly between UC and CD. Conclusion: Dysbiosis is central to IBD pathogenesis. Microbiome-targeted therapies offer potential but require personalized approaches to improve treatment efficacy. Future research should integrate multi-omics technologies for better understanding and management of IBD.
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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 | 0.013 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".