Increased Incidence or Detection Bias? Global Cohort Study Finds No Link between GLP1-RAs and Thyroid Cancer Risk
Bibliographic record
Abstract
BackgroundGlucagon-like peptide-1 receptor agonists (GLP1-RAs) are a class of glucose-lowering drugs commonly used for the management of type 2 diabetes and obesity given their favorable cardiovascular and weight management outcomes.1 The use of GLP1-RAs has rapidly increased in recent years. 2 However, in the midst of their increasing use, concerns have arisen regarding the risk of thyroid cancer that may be associated with these agents.Prior research has shown conflicting evidence regarding whether GLP1-RAs increase thyroid cancer risk, [3][4][5][6] with biologic plausibility grounded in the expression of GLP1 receptors in papillary thyroid cancer cells 7 and prior animal studies demonstrating an excess in C-cell malignancies in rodents.8 Given the growing use of GLP1-RAs in managing diabetes and obesity, this study aims to clarify the potential association between these drugs and thyroid cancer risk through a large multisite cohort study using data from six international databases.9 Methods This cohort study analyzed data from 2007 to 2023 from population-based databases in Canada, Denmark, Norway, South Korea, Sweden, and Taiwan that included patients with type 2 diabetes.Patients who began using GLP1-RAs were compared to those using dipeptidyl peptidase-4 (DPP-4) inhibitors, a commonly used alternative diabetes medication.By applying Cox regression models weighted with propensity scores, the researchers estimated hazard ratios (HRs) for thyroid cancer in GLP1-RA users compared to DPP-4 inhibitor users.The study followed patients for up to 10 years and pooled site-specific results using a fixed-effects model. ResultsThe study included 92,497 GLP1-RA users and 2,484,408 DPP-4 inhibitor users, with a median follow-up of 1.8 to 3.0 years for GLP1-RA users.Findings indicated no significant increase in thyroid cancer risk among GLP1-RA users as compared with DPP-4 inhibitor users (pooled weighted HR, 0.72; 95% CI, 0.52-1.00).Additionally, cumulative dose analysis did not show an increased risk of thyroid cancer with higher doses of GLP1-RAs.The results were consistent across multiple sensitivity analyses, including subgroup analyses based on sex and age, comparisons with different glucose-lowering med-
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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 | 0.052 | 0.079 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.005 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".