Glucagon-Like Peptide 1 Receptor Agonists and Risk of Thyroid Cancer: An International Multisite Cohort Study
Bibliographic record
Abstract
Introduction: Concerns have been raised that glucagon-like peptide 1 receptor agonists (GLP1-RAs) may increase the risk of thyroid cancer, but evidence remains conflicting. We therefore investigated if GLP1-RA use, compared with use of dipeptidyl peptidase-4 inhibitors (DPP-4is), was associated with thyroid cancer risk in patients with type 2 diabetes. Methods: This multisite cohort study with subsequent meta-analysis included six population-based databases from Canada (Ontario), Denmark, Norway, South Korea, Sweden, and Taiwan. Study populations comprised patients with type 2 diabetes between 2007 and 2023. Cox regression models estimated hazard ratios (HR) and 95% confidence intervals (CIs) for thyroid cancer among GLP1-RA users compared with DPP-4is. Models were weighted using standardized mortality ratio weights generated from time-specific propensity scores. Site-specific HRs were pooled using a fixed-effects model. Results: We identified 98,147 users of GLP1-RA and 2,488,303 users of DPP-4i, with the median follow-up among users of GLP1-RA ranging from 1.8 to 3.0 years. Overall, use of GLP1-RA relative to use of DPP-4i was not associated with an increased risk of thyroid cancer (pooled weighted HR 0.81, CI 0.59–1.12). Similarly, we observed no increased risk in thyroid cancer with increasing cumulative dose of GLP1-RA among GLP1-RA ever-users. Subgroup analysis of types of thyroid cancer was not possible. Results remained consistent across a range of supplementary analyses. Discussion: In this large multisite study, utilizing data from six population-based databases, we found no evidence that GLP1-RA use is associated with an increased risk of thyroid cancer with follow-up ranging from 1.8 to 3.0 years, providing some reassurance to patients and clinicians about the short-term safety of these drugs. Nevertheless, evidence was insufficient to rule out excess risk with long-term use, due to the short follow-up.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.005 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".