Prediagnostic Hormone Levels and Risk of Testicular Germ Cell Tumors: A Nested Case–Control Study in the Janus Serum Bank
Bibliographic record
Abstract
BACKGROUND: It has been hypothesized that poorly functioning Leydig and/or Sertoli cells of the testes, indicated by higher levels of serum gonadotropins and lower levels of androgens, are related to the development of testicular germ cell tumors (TGCT). To investigate this hypothesis, we conducted a nested case-control study within the Janus Serum Bank cohort. METHODS: Men who developed TGCT (n = 182) were matched to men who did not (n = 364). Sex steroid hormones were measured using LC/MS. Sex hormone binding globulin, follicle-stimulating hormone (FSH), and luteinizing hormone (LH) were quantified by direct immunoassay. Multivariable logistic regression was used to calculate ORs and 95% confidence intervals (CI) for associations between hormone levels and TGCT risk. RESULTS: Higher FSH levels [tertile (T) 3 vs. T2: OR = 2.89, 95% CI = 1.83-4.57] were associated with TGCT risk, but higher LH levels were not (OR = 1.26, 95% CI = 0.81-1.96). The only sex steroid hormone associated with risk was androstane-3α, 17β-diol-3G (3α-diol-3G; OR = 2.37, 95% CI = 1.46-3.83). Analysis by histology found that increased FSH levels were related to seminoma (OR = 3.55, 95% CI = 2.12-5.95) but not nonseminoma (OR = 1.19, 95% CI = 0.38-3.13). Increased levels of 3α-diol-3G were related to seminoma (OR = 2.29, 95% CI = 1.35-3.89) and nonsignificantly related to nonseminoma (OR = 2.71, 95% CI = 0.82-8.92). CONCLUSIONS: Higher FSH levels are consistent with the hypothesis that poorly functioning Sertoli cells are related to the development of TGCT. In contrast, higher levels of 3α-diol-3G do not support the hypothesis that insufficient androgenicity is related to risk of TGCT. IMPACT: Clarifying the role of sex hormones in the development of TGCT may stimulate new research hypotheses.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".