Dipeptidyl peptidase-4 inhibitors and the risk of skin cancer among patients with type 2 diabetes: a UK population-based cohort study
Bibliographic record
Abstract
INTRODUCTION: The dipeptidyl peptidase-4 (DPP-4) enzyme significantly influences carcinogenic pathways in the skin. The objective of this study was to determine whether DPP-4 inhibitors are associated with the incidence of melanoma and nonmelanoma skin cancer, compared with sulfonylureas. RESEARCH DESIGN AND METHODS: Using the United Kingdom Clinical Practice Research Datalink, we assembled two new-user active comparator cohorts for each skin cancer outcome from 2007 to 2019. For melanoma, the cohort included 96 739 DPP-4 inhibitor users and 209 341 sulfonylurea users, and 96 411 DPP-4 inhibitor users and 208 626 sulfonylurea users for non-melanoma skin cancer. Propensity score fine stratification weighted Cox proportional hazards models were used to estimate hazard ratios (HRs) with 95% confidence intervals (CIs of melanoma and non-melanoma skin cancer, separately. RESULTS: Overall, DPP-4 inhibitors were associated with a 23% decreased risk of melanoma compared with sulfonylureas (49.7 vs 65.3 per 100 000 person-years, respectively; HR 0.77, 95% CI 0.61 to 0.96). The HR progressively reduced with increasing cumulative duration of use (0-2 years HR 1.14, 95% CI 0.84 to 1.54; 2.1-5 years HR 0.44, 95% CI 0.29 to 0.66; >5 years HR 0.33, 95% CI 0.14 to 0.74). In contrast, these drugs were not associated with the incidence of non-melanoma skin cancer, compared with sulfonylureas (448.1 vs 426.1 per 100 000 person-years, respectively; HR 1.06, 95% CI 0.98 to 1.15). CONCLUSIONS: In this large, population-based cohort study, DPP-4 inhibitors were associated with a reduced risk of melanoma but not non-melanoma skin cancer, compared with sulfonylureas.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".