Dose-Dependent Relationship Between Metformin and Colorectal Cancer Occurrence Among Patients with Type 2 Diabetes—A Nationwide Cohort Study
Bibliographic record
Abstract
BACKGROUND: Increasing bodies of evidence suggest that metformin may be beneficial in the primary prevention of colorectal cancer (CRC), and a dose-response relationship has been reported. However, long-term epidemiological observations between the treatment period, cumulative dose, and intensity of metformin and CRC are rarely reported. The aim of this study was to identify the association between the effect of metformin and CRC development in a nationwide cohort study. METHODS: This nationwide population-based study examined a cohort of 1,000,000 patients randomly sampled from individuals enrolled in the Taiwan National Health Insurance system. Patients with newly diagnosed type 2 diabetes mellitus (DM) between 1997 and 2007 were enrolled. A statistical variables, including the demographic data, treatment period, cumulative dose, and intensity of metformin use, was compared between patients developing CRC and those without CRC. RESULTS: This study included 47,597 patients. The mean follow-time was 7.17 ± 3.21 years. After adjustment, metformin use was an independent protective factor against CRC development (P < .001). Although the protective ability of metformin against CRC development was reduced during long-term therapy, the risk of CRC decreased progressively with a higher cumulative dose or higher intensity of metformin use (both P < .001). CONCLUSION: This study revealed that metformin use significantly reduced the risk of CRC in a dose-dependent manner in patients with type 2 DM in the Taiwanese population. However, a gradual decline in medication adherence may reduce the protective ability of metformin against CRC development during long-term therapy.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".