Metformin as a potential adjunct in the treatment of mild-to-moderate ulcerative colitis: a double-blind, randomized, placebo-controlled pilot study
Bibliographic record
Abstract
Abstract Managing relapsing ulcerative colitis (UC) remains a clinical challenge despite therapeutic advances. Metformin, primarily an antidiabetic agent with demonstrated anti-inflammatory properties, has been proposed as an adjunctive treatment for UC. This double-blind, randomized, placebo-controlled pilot trial evaluated the effectiveness of adjunctive Metformin in reducing disease activity among patients experiencing acute UC exacerbations. A total of 112 patients with UC were enrolled at Sabzevar, Iran, between 2017 and 2024. The mean age of participants was 34.52 ± 5.79 years, and 57 patients (50.1%) were male. Patients were randomly assigned to receive either 1000 mg Metformin tablets twice daily (after breakfast and dinner) in addition to routine treatment or placebo tablets with routine treatment for two months. The primary outcome was the change in UC Disease Activity Index (UCDAI) scores after the follow-up period. Ordinal logistic regression demonstrated that the odds of having a higher UCDAI score were approximately 54% lower in the Metformin group compared with the control group (OR: 0.46; 95% CI: 0.21–0.97; P = 0.044). Multiple linear regression further confirmed significantly lower mean UCDAI scores in the Metformin group (β = −1.11; 95% CI: −1.86 to − 0.37; P = 0.004). These findings suggest that adjunctive Metformin is associated with meaningful reductions in disease activity scores, supporting its potential therapeutic role in managing acute exacerbations of UC. While the results are promising, larger-scale clinical trials are needed to validate these outcomes and investigate the underlying mechanisms by which Metformin may exert its anti-inflammatory effects in UC management.
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".