What Is the Impact of Glyphosate on the Thyroid? An Updated Review
Bibliographic record
Abstract
Background/Objectives: Thyroid dysfunction (hypo- and hyperthyroidism) and cancer incidence have increased over the past decades, possibly linked to environmental contributions from endocrine disrupting chemicals (EDCs). Glyphosate is one of the most widely used herbicides globally and has endocrine-disruptive properties. Because of the sensitivity of the thyroid gland to endocrine disruption and the increased glyphosate exposure worldwide, this comprehensive review aimed to summarize studies investigating the link between glyphosate/glyphosate-based herbicides (GBHs) and thyroid dysfunction in human, animal, and in vitro studies. Methods: PubMed, Scopus, and Embase were used to search for original studies assessing glyphosate or GBH exposure and thyroid-related outcomes through December 2024. Data were extracted on study design, population or model, exposure, and thyroid outcomes. A total of 28 studies, including 9 human, 3 in vitro, and 16 animal studies were included. Results: Human studies showed mixed findings with some suggesting associations between glyphosate exposure and altered thyroid hormone levels, while others found no significant effects. Animal studies, particularly in rodents and amphibians, showed thyroid hormone disruption and altered gene expression, especially after perinatal or developmental exposure. In vitro studies reported changes in thyroid-related gene transcription and cell viability, however at concentrations exceeding those seen in humans. Conclusions: While there is some evidence that glyphosate may disrupt thyroid function, differences in study populations, exposure assessment methods, species models, and exposure doses complicated the comparison and summarization of the results. Further mechanistic and longitudinal studies are needed to clarify the thyroid-specific risks of glyphosate exposure.
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 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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.008 | 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".