A Phase IIa Trial of Metformin for Colorectal Cancer Risk Reduction among Individuals with History of Colorectal Adenomas and Elevated Body Mass Index
Bibliographic record
Abstract
Abstract Obesity is associated with risk of colorectal adenoma (CRA) and colorectal cancer. The signaling pathway activated by metformin (LKB1/AMPK/mTOR) is implicated in tumor suppression in ApcMin/+ mice via metformin-induced reduction in polyp burden, increased ratio of pAMPK/AMPK, decreased pmTOR/mTOR ratio, and decreased pS6Ser235/S6Ser235 ratio in polyps. We hypothesized that metformin would affect colorectal tissue S6Ser235 among obese patients with recent history of CRA. A phase IIa clinical biomarker trial was conducted via the U.S. National Cancer Institute-Chemoprevention Consortium. Nondiabetic, obese subjects (BMI ≥30) ages 35 to 80 with recent history of CRA were included. Subjects received 12 weeks of oral metformin 1,000 mg twice every day. Rectal mucosa biopsies were obtained at baseline and end-of-treatment (EOT) endoscopy. Tissue S6Ser235 and Ki-67 immunostaining were analyzed in a blinded fashion using Histo score (Hscore) analysis. Among 32 eligible subjects, the mean baseline BMI was 34.9. Comparing EOT to baseline tissue S6Ser235 by IHC, no significant differences were observed. Mean (SD) Hscore at baseline was 1.1 (0.57) and 1.1 (0.51) at EOT; median Hscore change was 0.034 (P = 0.77). Similarly, Ki-67 levels were unaffected by the intervention. The adverse events were consistent with metformin's known side-effect profile. Among obese patients with CRA, 12 weeks of oral metformin does not reduce rectal mucosa pS6 or Ki-67 levels. Further research is needed to determine what effects metformin has on the target tissue of origin as metformin continues to be pursued as a colorectal cancer chemopreventive agent.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".