Role of Regulatory T Cells and Transglutaminase 2 Inhibitors in Celiac Disease: A Systematic Review
Bibliographic record
Abstract
Celiac disease is an autoimmune disorder triggered by the ingestion of gluten in genetically predisposed individuals, leading to chronic intestinal inflammation and damage to the small intestinal lining (villus atrophy). While a strict gluten-free diet remains the primary treatment, emerging therapies targeting the immune response offer promising alternatives. This review focuses on the therapeutic potential of transglutaminase 2 (TG2) inhibitors, enzymes that, when overactive, contribute to immune system attacks on the gut, and regulatory T cells (Tregs), specialized immune cells that help calm down excessive immune reactions. This systematic review followed the Preferred Reporting Items for Systematic reviews and Meta-Analyses guidelines. Literature was searched across PubMed, Embase, and the Cochrane Library using both text terms and controlled vocabulary with Boolean operators ("AND," "OR"). We included full-text, open-access, English-language articles published between 2005 and 2025. The methodological quality of studies was assessed using the Mixed Methods Appraisal Tool. A total of 68 articles were initially identified. After screening and applying inclusion criteria, 14 studies were included in the final analysis. Among them, eight studies were rated as high quality (low risk of bias), while six were of moderate quality (uncertain risk of bias). TG2 inhibitors showed promising effects such as improved intestinal structure (villous architecture), reduced gastrointestinal symptoms, stabilized immune cell levels in the gut, and decreased activation of gluten-specific immune cells (CD4+ T cells). Treg therapies also demonstrated the ability to reduce inflammation by limiting the production of harmful immune signals such as interferon-gamma and interleukin-21. The findings highlight the therapeutic potential of TG2 inhibitors and Treg-based treatments in managing celiac disease by directly targeting the immune response. While preliminary results are promising, further clinical research is needed to confirm their effectiveness and safety for routine clinical use.
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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 | 0.009 | 0.034 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.013 | 0.011 |
| Bibliometrics | 0.009 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.001 |
| 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".