Insulin-Like Growth Factors and Insulin-Like Growth Factor–Binding Proteins and Prostate Cancer Risk: Results from the Prostate Cancer Prevention Trial
Bibliographic record
Abstract
The role of the insulin-like growth factor (IGF) axis and whether IGFs interact with androgen-suppressing agents in relation to prostate carcinogenesis is unclear. This nested case-control study (n = 1,652 cases/1,543 controls) examined whether serum IGF1, IGF2, IGFBP2, IGFBP3, and the IGF1:IGFBP3 ratio were associated with prostate cancer in the Prostate Cancer Prevention Trial (PCPT), a randomized, placebo-controlled trial of finasteride for prostate cancer prevention. Presence or absence of cancer was determined by prostate biopsy. Baseline serum was assayed for IGF-axis analytes using ELISA. Logistic regression estimated ORs and 95% confidence intervals for risk of total, low-grade (Gleason 2-6) and high-grade (Gleason 7-10) cancers. Results were stratified by intervention assignment. In both the placebo and finasteride arms, serum IGF1, IGF2, IGFBP3, and the IGF1:IGFBP3 ratio were not associated with prostate cancer. However, men in the highest versus lowest quartile of serum IGFBP2 had a 48% (P(trend) = 0.02) and 55% (P(trend) = 0.01) increased risk for total and low-grade cancers, respectively. These IGFBP2 associations were attenuated and no longer statistically significant in the finasteride arm. Our results suggest that in general, serum IGF-axis analytes were not associated with prostate cancer risk in the PCPT in which presence or absence of all cancers was biopsy-determined. The exception was the finding that high serum IGFBP2 is a risk factor for low-grade disease, which was attenuated for men on finasteride. Further research is needed to understand better the risk incurred by high IGFBP2 and whether androgen-suppressing agents such as finasteride influence aspects of IGFBP2 physiology relevant to prostate carcinogenesis.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".