Finasteride Modifies the Relation between Serum C-Peptide and Prostate Cancer Risk: Results from the Prostate Cancer Prevention Trial
Bibliographic record
Abstract
Hyperinsulinemia and obesity-related metabolic disturbances are common and have been associated with increased cancer risk and poor prognosis. To investigate this issue in relation to prostate cancer, we conducted a nested case-control study within the Prostate Cancer Prevention Trial (PCPT), a randomized, placebo-controlled trial testing finasteride versus placebo for primary prevention of prostate cancer. Cases (n = 1,803) and controls (n = 1,797) were matched on age, PCPT treatment arm, and family history of prostate cancer; controls included all eligible non-whites. Baseline bloods were assayed for serum C-peptide (marker of insulin secretion) and leptin (an adipokine) using ELISA. All outcomes were biopsy determined. Logistic regression calculated odds ratios (OR) for total prostate cancer and polytomous logistic regression calculated ORs for low-grade (Gleason <7) and high-grade (Gleason >7) disease. Results were stratified by PCPT treatment arm for C-peptide. For men on placebo, higher versus lower serum C-peptide was associated with a nearly 2-fold increased risk of high-grade prostate cancer (Gleason >7; multivariate-adjusted OR, 1.88; 95% confidence interval, 1.19-2.97; P(trend) = 0.004). When C-peptide was modeled as a continuous variable, every unit increase in log(C-peptide) resulted in a 39% increased risk of high-grade disease (P = 0.01). In contrast, there was no significant relationship between C-peptide and high-grade prostate cancer among men receiving finasteride. Leptin was not independently associated with high-grade prostate cancer. In conclusion, these results support findings from other observational studies that high serum C-peptide and insulin resistance, but not leptin, are associated with increased risk of high-grade prostate cancer. Our novel finding is that the C-peptide-associated risk was attenuated by use of finasteride.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".