CIRCULATING LEVELS OF PCBs AND SEX HORMONES IN A POPULATION-BASED SAMPLE
Bibliographic record
Abstract
Background: PCBs have been widely used in the environment and high levels are still found in humans. Although some of the PCBs are regarded as Ah-receptor agonists, effects on reproductive organs have been reported, suggesting effects of PCBs on the biosynthesis of steroids. Aims: We have now evaluated the relationships between circulating levels of PCBs and sex hormones in humans. Methods: Plasma samples from 1000 70-year-olds were analyzed as part of a large population-based study (Prospective Investigation of the Vasculature in Uppsala Seniors). Eleven sex steroids were quantified, using liquid chromatography-tandem mass spectrometry (LC-MS/MS) and PCBs (PCB118, 126, 156, 169, 170 and 206) were measured with high-resolution GC/MS. Women with current/previous menopausal hormone therapy were excluded from the data analysis. Results: Concentrations of two dioxin-like PCB (PCB118 and PCB156) and one non-dioxin-like PCBs (206) were inversely related to levels of testosterone (in women only, p<0.05). Two of the non-dioxin-like PCBs (PCB170 and 206) were furthermore inversely related to estradiol levels in women only (p<0.005). Conclusions: In samples of elderly, concentrations of circulating PCBs were associated with concentrations of sex hormones. Physiological effects of different PCBs are likely different and their association with steroidogenesis should be studied.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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 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".