Increased Cancer-Related Mortality for Patients With Type 2 Diabetes Who Use Sulfonylureas or Insulin
Bibliographic record
Abstract
OBJECTIVE: Numerous studies have identified an increased risk of cancer in type 2 diabetes. We explored the association between antidiabetic therapies and cancer-related mortality in patients with type 2 diabetes, postulating that agents that increase insulin levels might promote cancer. RESEARCH DESIGN AND METHODS: This was a population-based cohort study using administrative databases from Saskatchewan Health. Cancer-related mortality was compared among inception cohorts of metformin users and sulfonylurea monotherapy users. Multivariate Cox regression was used to estimate the hazard ratio (HR) of cancer-related mortality, after adjusting for age, sex, insulin use, and chronic disease score. All statistical tests were two-sided. RESULTS: We identified 10,309 new users of metformin or sulfonylureas with an average follow-up of 5.4 +/- 1.9 years (means +/- SD). The mean age for the cohort was 63.4 +/- 13.3 years, and 55% were men. Cancer mortality over follow-up was 4.9% (162 of 3,340) for sulfonylurea monotherapy users, 3.5% (245 of 6,969) for metformin users, and 5.8% (84 of 1,443) for subjects who used insulin. After multivariate adjustment, the sulfonylurea cohort had greater cancer-related mortality compared with the metformin cohort (adjusted HR 1.3 [95% CI 1.1-1.6]; P = 0.012). Insulin use was associated with an adjusted HR of cancer-related mortality of 1.9 (95% CI 1.5-2.4; P < 0.0001). CONCLUSIONS: Patients with type 2 diabetes exposed to sulfonylureas and exogenous insulin had a significantly increased risk of cancer-related mortality compared with patients exposed to metformin. It is uncertain whether this increased risk is related to a deleterious effect of sulfonylurea and insulin or a protective effect of metformin or due to some unmeasured effect related to both choice of therapy and 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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".