Hormone‐related diseases and prostate cancer: An English national record linkage study
Bibliographic record
Abstract
Insulin-like growth factor-I (IGF-I) and testosterone may be related to prostate cancer risk. Acromegaly is associated with clinically high IGF-I concentrations. Klinefelter's syndrome, testicular hypofunction and hypopituitarism are associated with clinically low testosterone concentrations. We aimed to investigate whether diagnosis with these conditions was associated with subsequent prostate cancer diagnosis and mortality. We used linked English national Hospital Episode Statistics and mortality data from 1999 to 2017 to construct and follow-up cohorts of men aged ≥35 years diagnosed with (i) acromegaly (n = 2,495) and (ii) hypogonadal-associated diseases (n = 18,763): Klinefelter's syndrome (n = 1,992), testicular hypofunction (n = 8,086) and hypopituitarism (n = 10,331). We estimated adjusted hazard ratios (HRs) and confidence intervals (CIs) for prostate cancer diagnosis and death using Cox regression in comparison with an unexposed reference cohort of 4.3 million men, who were admitted to hospital for a range of minor surgeries and conditions (n observed cases = 130,000, n prostate cancer deaths = 30,000). For men diagnosed with acromegaly, HR for prostate cancer diagnosis was 1.33 (95% CI 1.09-1.63; p = 0.005; n observed cases = 96), HR for prostate cancer death was 1.44 (95% CI 0.92-2.26; p = 0.11; n deaths = 19). Diagnosis with Klinefelter's syndrome was associated with a lower prostate cancer risk (HR = 0.58, 95% CI 0.37-0.91; p = 0.02; n observed cases = 19) and hypopituitarism was associated with a reduction in prostate cancer death (HR = 0.53, 95% CI 0.35-0.79; p = 0.002; n deaths = 23). These results support the hypothesised roles of IGF-I and testosterone in prostate cancer development and/or progression. These findings are important because they provide insight into prostate cancer aetiology.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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".