Regular Multivitamin Supplement Use, Single Nucleotide Polymorphisms in ATIC, SHMT2, and SLC46A1, and Risk of Ovarian Carcinoma
Bibliographic record
Abstract
ATIC, SHMT2, and SLC46A1 have essential roles in one-carbon (1-C) transfer. The authors examined whether associations between ovarian carcinoma and 15 variants in these genes are modified by regular multivitamin use, a source of 1-C donors, among Caucasian participants from two US case-control studies. Using a phased study design, variant-by-multivitamin interactions were tested, and associations between variants and ovarian carcinoma were reported stratified by multivitamin supplement use. Per-allele risk associations were modified by multivitamin use at six variants among 655 cases and 920 controls (Phase 1). In a larger sample of 968 cases and 1,265 controls (Phases 1 and 2), interactions were significant (P ≤ 0.03) for two variants, particularly among regular multivitamin users: ATIC rs7586969 [odds ratio (OR) = 0.7, 95% confidence interval (CI) = 0.6-0.9] and ATIC rs16853834 (OR = 1.5, 95% CI = 1.1-2.0). The two ATIC single nucleotide polymorphisms (SNPs) did not share the same haplotype; however, the haplotypes they comprised mirrored their SNP risk associations among regular multivitamin supplement users. A multi-variant analysis was also performed by comparing the observed likelihood ratio test statistic from adjusted models with and without the two ATIC variant-by-multivitamin interaction terms with a null distribution of test statistics generated by permuting case status 10,000 times. The corresponding observed P value of 0.001 was more extreme than the permutation-derived P value of 0.009, suggesting rejection of the null hypothesis of no association. In summary, there is little statistical evidence that the 15 variants are independently associated with risk of ovarian carcinoma. However, the statistical interaction of ATIC variants with regular multivitamin intake, when evaluated at both the SNP and gene level, may support these findings as relevant to ovarian health and disease processes.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".