Associations of vitamin D pathway genes with circulating 25-hydroxyvitamin-D, 1,25-dihydroxyvitamin-D, and prostate cancer: a nested case–control study
Bibliographic record
Abstract
PURPOSE: Vitamin D pathway single nucleotide polymorphisms (SNPs) are potentially useful proxies for investigating whether circulating vitamin D metabolites [total 25-hydroxyvitamin-D, 25(OH)D; 1,25-dihydroxyvitamin, 1,25(OH)2D] are causally related to prostate cancer. We investigated associations of sixteen SNPs across seven genes with prostate-specific antigen-detected prostate cancer. METHODS: In a nested case-control study (within the ProtecT trial), we estimated odds ratios and 95 % confidence intervals (CIs) quantifying associations between SNPs and prostate cancer. Subgroup analyses investigated whether associations were stronger in men who had high/low sun exposure [a proxy for 25(OH)D]. We quantified associations of SNPs with stage (T1-T2/T3-T4) and grade (<7/≥7). Multiple variant scores included SNPs encoding proteins involved in 25(OH)D synthesis and metabolism. RESULTS: We included 1,275 prostate cancer cases (141 locally advanced, 385 high grades) and 2,062 healthy controls. Vitamin D-binding protein SNPs were associated with prostate cancer (rs4588-A: OR 1.20, CI 1.01, 1.41, p = 0.04; rs7041-T: OR 1.19, CI 1.02, 1.38, p = 0.03). Low 25(OH)D metabolism score was associated with high (vs low) grade (OR 0.76, CI 0.63, 0.93, p = 0.01); there was a similar association of its component variants: rs6013897-A in CYP24A1 (OR 0.78, CI 0.60, 1.01, p = 0.06) and rs10877012-T in CYP27B1 (OR 0.80, CI 0.63, 1.02, p = 0.07). There was no evidence that associations differed by level of sun exposure. CONCLUSION: We found some evidence that vitamin D pathway SNPs were associated with prostate cancer risk and grade, but not stage. There was no evidence of an association in men with deficient vitamin D (measured by having low sun exposure).
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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 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".