Nasopharyngeal Carcinoma and Head and Neck Cancer in Patients with Type-2 Diabetes Mellitus receiving SGLT2I, DPP4I or GLP1a: A population-based cohort study
Bibliographic record
Abstract
Abstract Introduction Nasopharyngeal carcinoma (NPC) remains endemic in Asian regions, which risk factors were distinct from other head and neck (H&N) cancers. Anti-diabetic drugs has been proposed to reduce the risk of NPC. The associations between sodium glucose cotransporter 2 inhibitors (SGLT2I) versus dipeptidyl peptidase-4 inhibitors (DPP4I) and the risks of NPC and H&N cancer amongst type-2 diabetes mellitus (T2DM) patients remains unknown. Method This was a retrospective population-based cohort study including T2DM patients treated with either SGLT2I or DPP4I between 1 st January 2015 and 31 st December 2019 in Hong Kong. The primary outcome was new-onset NPC and other H&N cancer. The secondary outcome was cancer-related mortality. Propensity score matching (1:1 ratio) was performed using the nearest neighbour search. Multivariable Cox regression was applied to identify significant predictors. Results This cohort included 75,884 patients with T2DM, amongst whom 28,778 patients were on SGLT2I and 47,106 patients were on DPP4I. After matching (57556 patients), 106 patients developed NPC (0.18%), and 50 patients developed H&N cancer (0.08%). Compared to DPP4I, SGLT2I was associated with significantly lower risks of NPC (Hazard ratio [HR]: 0.41; 95% Confidence interval [CI]: 0.21-0.81) but not H&N cancer (HR: 1.00; 95% CI: 0.26-3.92) after adjustments. The result remained significant regardless of demographics and obesity. The association remained consistent in different risk models, matching approaches, and the sensitivity analysis. Conclusion This study provides real-world evidence that SGLT2I was associated with lower risks of NPC, but not H&N cancer compared to DPP4I after adjustments amongst T2DM patients.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".