Interactions of the Insulin-Like Growth Factor Axis and Vitamin D in Prostate Cancer Risk in the Prostate Cancer Prevention Trial
Bibliographic record
Abstract
Some, but not all, epidemiologic studies report an association between vitamin D and prostate cancer risk. The inconsistent findings might be explained in the context of modification by members of the insulin-like growth factor (IGF) axis. Data and specimens for this nested case-control study (n = 1695 cases and n = 1682 controls) are from the Prostate Cancer Prevention Trial (PCPT). Baseline serum samples were assayed for 25(OH)D, IGF-1, IGF-2, IGFBP-2, IGFBP-3, and the ratio of IGF1:BP3, along with insulin-related markers c-peptide and leptin. The presence of prostate cancer was assessed by prostate biopsy. Multivariate logistic regression was used to estimate odds ratios (OR) and 95% confidence intervals (CIs) for prostate cancer risk. There were no interactions between serum 25(OH)D and IGF analytes in relation to prostate cancer risk when PCPT treatment arms were combined. In the placebo arm, above median serum 25(OH)D levels were associated with increased risk of prostate cancer among men with higher IGF-2 (OR:1.33, 95% CI: 1.00–1.65), with a significant interaction between 25(OH)D and treatment arm (Pinteraction = 0.04). Additionally, there was an interaction between treatment arm and serum IGFBP-3 (Pinteraction = 0.03). Higher serum 25(OH)D may increase risk of prostate cancer in the presence of higher circulating IGF-2.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".