Plasma Levels of Acid-Labile Subunit, Free Insulin-Like Growth Factor-I, and Prostate Cancer Risk: A Prospective Study
Bibliographic record
Abstract
BACKGROUND: The acid-labile subunit (ALS) acts in the insulin-like growth (IGF) system by binding circulating IGF-I in a ternary complex with binding protein (IGFBP)-3 to prevent IGF-I from crossing the endothelial barrier. Given the role of the IGF system in prostate cancer, ALS may influence carcinogenesis by modulating IGF-I levels or bioavailability. METHODS: We undertook a prospective study nested in the Physicians' Health Study to examine ALS, free IGF-I, and prostate cancer. We assayed circulating levels of ALS and IGF components among 545 incident cases and 545 matched controls. We calculated relative risks (RR) and 95% confidence intervals (95% CI) adjusted for life-style factors, total IGF-I, and IGFBP3. RESULTS: ALS was positively correlated with total IGF-I (r = 0.58), IGFBP3 (r = 0.68), and free IGF-I (r = 0.36). Comparing highest versus lowest quartiles, we found no association between free IGF-I and prostate cancer risk (RR, 0.9; 95% CI, 0.6-1.3). In contrast, ALS was positively associated with risk among men in the 2nd (RR, 1.5; 94% CI, 1.0-2.3), 3rd (RR, 1.6; 94% CI, 1.1-2.5), and 4th quartiles (RR, 1.4; 94% CI, 0.9-2.1) compared with lowest quartile. The association was stronger for advanced stage tumors (RR, 2.0; 94% CI, 0.8-4.6). There was a suggestion of an interaction between ALS and total IGF-I, whereby high circulating IGF-I was associated with an increased risk of advanced prostate cancer among men with low but not higher ALS levels. DISCUSSION: Plasma ALS is positively associated with prostate cancer risk, and may interact biologically with IGF-I to affect carcinogenesis. These data provide further support for the role of the IGF axis in prostate cancer.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.002 | 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".