Effect modification of endocrine disruptors and testicular germ cell tumour risk by hormone‐metabolizing genes
Bibliographic record
Abstract
It has been hypothesized that the increased prevalence of testicular germ cell tumours (TGCT) may be attributable to endocrine disrupting chemicals, such as persistent organic pollutants (POPs); these may be modulated by hormone-metabolizing enzymes. Using data from 568 cases and 698 controls enrolled in the US Servicemen's Testicular Tumor Environmental and Endocrine Determinants Study, we examined associations between TGCT and POPs, including p,p'-dichlorodiphenyldichloroethylene, chlordane-related compounds and polychlorinated biphenyls (PCBs), modified by polymorphisms in five hormone-metabolizing genes (CYP17A1, CYP1A1, HSD17B1, HSD17B4 and AR). Odds ratios (OR) and 95% confidence intervals (CI) were estimated using logistic regression models that stratified associations of POP exposure and TGCT risk by genotype. Two polymorphisms in CYP1A1, rs1456432 and rs7495708, modified the association between trans-nonachlor and total chlordanes and TGCT risk. Among men with a minor allele for rs1456432, those with the highest quartiles had an increased risk of TGCT (OR = 1.90, 95% CI, 1.01-3.56) compared with those with the lowest; there was no increased risk among men with the homozygous major allele genotype (p-interactions = 0.024). Similar results were seen for rs7495708. HSD17B4 rs384346 modified the associations between TGCT risk and PCB-118 and PCB-138 concentrations: the 45-55% reductions in TGCT risk for men with the highest quartiles compared with the lowest quartiles were only present in those who had a major homozygous allele genotype (p-interactions < 0.04). Thus, there are suggestions that certain CYP1A1 and HSD17B4 polymorphisms may modify the associations between POPs and TGCT risk. With false discovery rate values >0.2, however, caution is advisable when interpreting the findings of this study.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".