Vitamin D-Related Genetic Variants, Interactions with Vitamin D Exposure, and Breast Cancer Risk among Caucasian Women in Ontario
Bibliographic record
Abstract
BACKGROUND: Vitamin D, from diet and sunlight exposure, may be associated with reduced breast-cancer risk. This study investigated if candidate gene variants in vitamin D pathways are associated with breast cancer risk, or modify the associations between breast cancer and vitamin D exposure. METHODS: Breast cancer cases aged 25 to 74 years were identified from the Ontario Cancer Registry (histopathologically confirmed and diagnosed 2002-2003) and population-based controls were identified through random digit dialing of Ontario households. Saliva (DNA) was available for 1,777 cases and 1,839 controls. Multivariate logistic regression was used to evaluate associations between 19 single nucleotide polymorphisms (SNP) in vitamin D related genes, including vitamin D binding protein (GC), vitamin D receptor (VDR), and cytochrome P450 type 24A1 (CYP24A1). Statistical interactions were assessed using the likelihood ratio test. RESULTS: Some SNPs were found to be significantly associated with breast cancer risk. For example, breast cancer risk was associated with the GC rs7041 TT genotype (age-adjusted odds ratio (OR) = 1.23; 95% CI: 1.01, 1.51) and inversely with the VDR Fok1 (rs2228570) ff genotype (OR = 0.71; 95% CI: 0.57, 0.88). Few significant gene-environment interactions were observed between dietary vitamin D and genetic variants. CONCLUSION: Our study suggests certain vitamin D related genetic variants may influence breast-cancer risk and we found limited evidence that genetic variants modify the associations between vitamin D exposure and breast cancer risk. IMPACT: Variation in vitamin D-related genotypes may help to explain inconsistent results from previous epidemiologic studies and may lead to targeted prevention strategies.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".