Data from Genetic Variation in the Vitamin D Receptor (<i>VDR</i>) and the Vitamin D–Binding Protein (<i>GC</i>) and Risk for Colorectal Cancer: Results from the Colon Cancer Family Registry
Bibliographic record
Abstract
Abstract Epidemiologic evidence supports a role for vitamin D in colorectal cancer (CRC) risk. Variants in vitamin D–related genes might modify the association between vitamin D levels and CRC risk. In this analysis, we did a comprehensive evaluation of common variants in the vitamin D receptor (VDR) and the vitamin D–binding protein (GC; group-specific component) genes using a population-based case–unaffected sibling control design that included 1,750 sibships recruited into the Colon Cancer Family Registry. We also evaluated whether any associations differed by calcium supplement use, family history of CRC, or tumor characteristics. Heterogeneity by calcium and vitamin D intake was evaluated for a subset of 585 cases and 837 sibling controls who completed a detailed food frequency questionnaire. Age- and sex-adjusted associations were estimated using conditional logistic regression. Overall, we did not find evidence for an association between any single-nucleotide polymorphism (SNP) in VDR or GC and risk for CRC (range of unadjusted P values 0.01-0.98 for VDR and 0.07-0.95 for GC). None of these associations was significant after adjustment for multiple comparisons. We also found no evidence that calcium or vitamin D intake (food and supplement) from the food frequency questionnaire modified the association estimates between VDR and GC SNPs and CRC. We did observe associations between SNPs in GC and microsatellite unstable CRC, although these results should be confirmed in additional studies. Overall, our results do not provide evidence for a role of common genetic variants in VDR or GC in susceptibility to CRC. Cancer Epidemiol Biomarkers Prev; 19(2); 525–36
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".