Circulating Insulin-Like Growth Factor 1–Related Biomarkers and Risk of Lethal Prostate Cancer
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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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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 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 | 0.001 |
| 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 it