Circulating Insulin-Like Growth Factor 1–Related Biomarkers and Risk of Lethal Prostate Cancer
Bibliographic record
Abstract
Abstract Background Experimental and epidemiologic evidence supports the role of circulating insulin-like growth factor-1 (IGF-1) levels with the risk of prostate cancer. Most circulating IGF-1 is bound to specific binding proteins, and only about 5% circulates in a free form. We explored the relation of free IGF-1 and other components of the IGF system with lethal prostate cancer. Methods Using prospectively collected samples, we undertook a nested case-only analysis among 434 men with lethal prostate cancer and 524 men with indolent, nonlethal prostate cancer in the Physicians’ Health Study and the Health Professionals Follow-up Study. Prediagnostic plasma samples were assayed for free IGF-1 and total IGF-1, acid labile subunit, pregnancy-associated plasma protein A (PAPP-A), and intact and total IGF binding protein 4. We estimated odds ratios (ORs) and corresponding 95% confidence intervals (CIs) for the associations between IGF-1–related biomarkers and lethal prostate cancer using unconditional logistic regression models adjusted for age, height, and body mass index. Results Men in the highest quartile of PAPP-A levels had 42% higher odds of lethal prostate cancer (pooled adjusted OR = 1.42, 95% CI = 1.04 to 1.92) compared with men in the lowest 3 quartiles. There were no statistically significant differences in the other plasma analytes. The positive association between PAPP-A and lethal prostate cancer was present among men with intact PTEN but not among those with tumor PTEN loss (2-sided Pinteraction = .001). Conclusions Our study provides suggestive evidence that among men who later develop prostate cancer, higher plasma PAPP-A levels measured prior to diagnosis are associated with increased risk of lethal compared with indolent disease.
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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 | 0.000 | 0.001 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".