Prediagnostic plasma <scp>IGFBP</scp>‐1, <scp>IGF</scp>‐1 and risk of prostate cancer
Bibliographic record
Abstract
Insulin-like growth factor (IGF)-1 is associated with a higher risk of prostate cancer. IGF-binding protein (IGFBP)-1, a marker for insulin activity, also binds IGF-1 and inhibits its action. Data on IGFBP-1 and prostate cancer risk are sparse and whether the IGF and insulin axes interact to affect prostate cancer carcinogenesis is unknown. We evaluated the independent and joint influence of prediagnostic plasma levels of IGFBP-1 (fasting) and IGF-1 on risk of prostate cancer among 957 cases and 1,021 controls with fasting levels of IGFBP-1 and 1,709 cases and 1,778 controls with IGF-1 nested within the Health Professionals Follow-up Study. Unconditional logistic regression adjusting for matching factors was used to estimate the odds ratio (OR) and 95% confidence interval (CI). Higher prediagnostic fasting IGFBP-1 levels were associated with lower risk of prostate cancer (highest vs. lowest quartile OR = 0.67, 95% CI 0.52-0.86, p(trend) = 0.003), which remained similar after adjusting for IGF-1. Prediagnostic IGF-1 was associated with increased risk of prostate cancer (highest vs. lowest quartile OR = 1.28, 95% CI = 1.05-1.56, p(trend) = 0.01). The associations with each marker were primarily driven by lower-grade and non-advanced prostate cancer. Being low in IGFBP-1 and high in IGF-1 did not confer appreciable additional risk (p(interaction) = 0.42). In summary, prediagnostic fasting IGFBP-1 may influence prostate cancer carcinogenesis. Being low in IGFBP-1 or high in IGF-1 is sufficient to elevate the risk of prostate cancer.
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How this classification was reachedexpand
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".