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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 teacher head, 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".