Glucagon-like Peptide-1 Receptor Agonists and Thyroid Cancer: Myth or Reality?
Bibliographic record
Abstract
Glucagon-like peptide-1 receptor agonists (GLP-1RAs) are being used increasingly for the management of type 2 diabetes mellitus (T2DM) or obesity because of their association with robust glucose lowering, weight loss, and cardiorenal benefits. The association between GLP-1RA treatments and thyroid cancer has been a topic of discussion since their early development with the understanding that GLP-1 receptors are present on rodent thyroid parafollicular cells (C-cells), and that GLP-1RAs can cause an increase in calcitonin, and both C-cell hyperplasia and medullary thyroid carcinoma (MTC). This data from rodent studies has led to GLP-1RAs being contraindicated in patients with a personal or family history of MTC or with multiple endocrine neoplasia syndrome type 2. Despite this contraindication, the human relevance of GLP-1RA induced MTC in rodents has not been proven. Normal or hyperplastic C-cells in humans may not express the GLP-1 receptor, and studies of human MTCs have shown variable expression of the GLP-1 receptor. Studies have shown conflicting evidence regarding the expression of the GLP-1 receptor in human papillary thyroid cancer (PTC) cell lines: however, GLP-1RAs did not have significant effects on the proliferation of PTC cells. Because of the data that potentially links GLP-1RAs to an increased risk of thyroid cancer, clinical studies in humans are important in addressing this issue. I will review the relevant data from human studies that have analyzed the potential link between GLP-1RA treatment and thyroid cancer, including pharmacovigilance and observational studies as well as randomized controlled trials (RCTs).
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".