Interrelationships between plasma testosterone, SHBG, IGF-I, insulin and leptin in prostate cancer cases and controls
Bibliographic record
Abstract
Despite strong indirect evidence that androgens stimulate prostate cancer development, data from most analytical studies on this association have been negative. To further investigate this issue, we studied the interrelationships between androgenicity and insulin-like growth factor I (IGF-I), insulin and leptin. Within a prospective cohort study, we measured testosterone, sex hormone-binding globulin (SHBG) and IGF-I, IGF-binding protein (IGFBP)-1, IGFBP-3, insulin and leptin, in plasma from 149 cases and 298 controls. Testosterone correlated positively with SHBG, whereas testosterone and SHBG correlated inversely with IGF-I, IGFBP-3, insulin, leptin and body mass index (BMI). Indices of free testosterone showed an inverse linear correlation with leptin (P<0.01), and a strong drop in the 5th quintile of BMI. However, levels of free testosterone showed non-linear relationships over quintiles of insulin and IGF-I, with a significant increase in the second quintile of IGF-I compared with other levels. The absence of an association between plasma levels of androgens and prostate cancer risk in analytical studies, despite the strong indirect evidence of their tumour-stimulating effects, may reflect the complex and mostly inverse associations of androgenicity to IGF-I, insulin and leptin which are hormones that have also been implicated as risk factors for prostate cancer.
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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 | 0.002 |
| 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 | 0.001 | 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".