Diabetes medications and cancer risk associations: a systematic review and meta-analysis of evidence over the past 10 years
Bibliographic record
Abstract
Abstract Diabetes medications may modify the risk of certain cancers. We systematically searched MEDLINE, Embase, Web of Science, and Cochrane CENTRAL from 2011 to March 2021 for studies evaluating associations between diabetes medications and the risk of breast, lung, colorectal, prostate, liver, and pancreatic cancers. A total of 92 studies (3 randomized controlled trials, 64 cohort, and 25 case-control studies) were identified, involving 171 million participants. Inverse relationships with colorectal (RR = 0.85; 95% CI = 0.78–0.92) and liver cancers (RR = 0.55; 95% CI = 0.46–0.66) were observed in biguanide users. Thiazolidinediones were associated with lower risks of breast (RR = 0.87; 95% CI = 0.80–0.95), lung (RR = 0.77; 95% CI = 0.61–0.96) and liver (RR = 0.83; 95% CI = 0.72–0.95) cancers. Insulins were negatively associated with breast (RR = 0.90; 95% CI = 0.82–0.98) and prostate cancer risks (RR = 0.74; 95% CI = 0.56–0.98). Positive associations were found between insulin secretagogues and pancreatic cancer (RR = 1.26; 95% CI = 1.01–1.57), and between insulins and liver (RR = 1.74; 95% CI = 1.08–2.80) and pancreatic cancers (RR = 2.41; 95% CI = 1.08–5.36). Overall, biguanide and thiazolidinedione use carried no risk, or potentially lower risk of some cancers, while insulin secretagogue and insulin use were associated with increased pancreatic cancer risk.
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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 | 0.014 | 0.034 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.017 | 0.030 |
| Bibliometrics | 0.009 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".