8292519 Workplace exposure to endocrine-disrupting chemicals and circulating sex hormones: evidence from the UK biobank
Bibliographic record
Abstract
Objective Determine the relationship between occupational exposure to endocrine-disrupting chemicals (EDCs) and serum concentrations of total and free estradiol and testosterone. Material and Methods A cross-sectional study was nested in the UK Biobank (n=228,127). Total estradiol and testosterone concentrations (nmol/L) were measured at baseline, and free hormones were calculated. Occupational exposure to 10 EDC groups was estimated using a job-exposure matrix and classfied as ‘Unexposed’, ‘Possibly exposed’ or ‘Exposed.’ Multivariable linear regression was used to estimate β coefficients and 95%CI, stratified by sex and menopausal status. Results Occupational exposure to EDCs was not associated with total estradiol levels. Among pre-menopausal women, possible exposure to phthalates was associated with lower free estradiol. In men, free testosterone was lower with possible exposure to PAHs, pesticides, phthalates, organic solvents, alkylphenolic compounds, and metals, and with exposure to pesticides and organic solvents; while exposure to PAHs was associated with increased free testosterone concentration. Results for possible exposure to PAHs and metals were replicated in total testosterone among men. In pre-menopausal women, total testosterone was negatively associated with possible exposure to pesticides and exposure to metals, with free testosterone also decreasing with metal exposure. Among post-menopausal women, exposure to phthalates was associated with lower total testosterone, and possible exposure to organic solvents was associated with decreased free testosterone. Conclusion Occupational exposure to certain EDC groups was associated with a reduction in testosterone concentration. Acknowledgements: This research was funded by the CIHR Sex and Gender Science Chair in Cancer Research.
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 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 | 0.003 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.011 | 0.001 |
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".