Folate Pathway Gene Polymorphisms and Risk of Childhood Brain Tumors: Results from an Australian Case–Control Study
Bibliographic record
Abstract
BACKGROUND: Recent research suggests that maternal folic acid supplementation is associated with a reduced risk of childhood brain tumors (CBT); polymorphisms in folate pathway genes could modify this association or directly influence CBT risk. METHODS: Associations between risk of CBT and folate pathway polymorphisms were investigated in a population-based case-control study in Australia (2005-2010). Cases were recruited through all Australian pediatric oncology centers and controls by national random digit dialing. Data were available from 321 cases and 552 controls. Six polymorphisms were genotyped in children and parents (MTHFR 677C>T, MTHFR 1298A>C, MTRR 66A>G, MTR 2756A>G, MTR 5049C>A, and CBS 2199 T>C). Maternal folic acid use was ascertained via questionnaire. ORs were estimated using unconditional logistic regression. Case-parent trio analyses were also undertaken. RESULTS: There was weak evidence of a reduced risk of CBT for the MTRR 66GG genotype in the child or father: ORs 0.71 [95% confidence interval (CI), 0.48-1.07]; 0.54 (95% CI, 0.34-0.87), respectively. Maternal prepregnancy folic acid supplementation showed a stronger negative association with CBT risk where the child, mother, or father had the MTRR 66GG genotype (Pinteraction = 0.07, 0.10, and 0.18, respectively). CONCLUSIONS: Evidence for an association between folate pathway genotypes and CBT is limited in this study. There was possible protection by the MTRR 66GG genotype, particularly when combined with maternal prepregnancy folic acid supplementation; these results are novel and require replication. IMPACT: The possible interaction between folic acid supplementation and MTRR 66A>G, if confirmed, would strengthen evidence for prepregnancy folate protection against CBT.
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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) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".