Risk of Thyroid Tumors With GLP-1 Receptor Agonists: A Retrospective Cohort Study
Bibliographic record
Abstract
OBJECTIVE: To assess the association between glucagon-like peptide 1 receptor agonist (GLP-1RA) use and risk of incident thyroid tumors. RESEARCH DESIGN AND METHODS: The retrospective, active-comparator new-user cohort study used international administrative claims and electronic health record databases. Participants included patients with type 2 diabetes mellitus (T2DM) with prior metformin therapy initiating a GLP-1RA versus new users of sodium-glucose cotransporter 2 inhibitors (SGLT2is), dipeptidyl peptidase 4 inhibitors (DPP-4is), and sulfonylureas (SUs). The outcome was incident thyroid tumor and thyroid malignancy. Propensity score matching and stratification were used to adjust for confounders with an intention-to-treat and on-treatment strategy. Cox regression was used to estimate hazard ratios (HRs) pooled using a random-effects meta-analysis. Unmeasured confounding was evaluated using negative outcomes, with calibration of the HR. RESULTS: A total of 460,032 users of GLP-1RAs, 717,792 users of SGLT2is, 2,055,583 users of DPP-4is, and 1,119,868 users of SUs were included. Only U.S. cohorts passed study diagnostics. Thyroid tumor incidence ranged from 0.88 to 1.03 per 1,000 person-years in GLP-1RA cohorts. GLP-1RA exposure was not associated with an increased risk of thyroid tumors compared with SGLT2is, DPP-4is, or SUs (meta-analysis: GLP-1RA vs. SGLT2i HR range from 0.83 [95% CI 0.57-1.27] to 0.95 [0.85-1.06]; GLP-1RA vs. SU HR range from 0.95 [0.75-1.20] to 1.03 [0.87-1.23]; GLP-1RA vs. DPP-4i HR range from 0.78 [0.60-1.01] to 0.93 [0.83-1.04]). Analysis using thyroid malignancy and including a 1-year lag period produced similar conclusions. CONCLUSIONS: In patients with T2DM initiating second-line treatments, we observed no increased risk of thyroid tumors with GLP-1RA exposure.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".