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Record W4406183633 · doi:10.1089/thy.2024.0387

Glucagon-Like Peptide 1 Receptor Agonists and Risk of Thyroid Cancer: An International Multisite Cohort Study

2025· review· en· W4406183633 on OpenAlexafffundabout
Sarah A. Baxter, Lars Lund, Jacob Harbo Andersen, Thomas Heiberg Brix, Laszlo Hegedüs, Miyuki Hsing‐Chun Hsieh, Chunhai Su, Michael Chun‐Yuan Cheng, Edward Chia‐Cheng Lai, Swaleh Hussain, Cherry Chu, Tara Gomes, Tony Antoniou, Antoine Eskander, Zachary Bouck, Mina Tadrous, Sungho Bea, Eun‐Young Choi, Ju‐Young Shin, Karin Modig, Mats Talbäck, Rickard Ljung, Hanne Løvdal Gulseth, Øystein Karlstad, Blánaid Hicks, Anton Pottegård

Bibliographic record

VenueThyroid · 2025
Typereview
Languageen
FieldMedicine
TopicDiabetes Treatment and Management
Canadian institutionsSunnybrook Health Science CentreInstitute for Clinical Evaluative SciencesWomen's College HospitalUniversity of Toronto
FundersNational Health Research InstitutesOntario Ministry of Health and Long-Term Care
KeywordsMedicineThyroid cancerThyroidInternal medicineOncologyGlucagon-like peptide 1 receptorGlucagon-like peptide-1CohortEndocrinologyReceptorAgonistDiabetes mellitusType 2 diabetes

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Review · Consensus signal: none
Teacher disagreement score0.050
Threshold uncertainty score0.100

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.004
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.005
Bibliometrics0.0020.004
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.019
GPT teacher head0.337
Teacher spread0.318 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreReview

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".

Quick stats

Citations65
Published2025
Admission routes3
Has abstractyes

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