Folic acid supplementation use and the MTHFR C677T polymorphism in orofacial clefts etiology: An individual participant data pooled‐analysis
Bibliographic record
Abstract
BACKGROUND: This study examines gene-environment interaction between the MTHFR C667T polymorphism and folic acid in the etiology of orofacial clefts (OFC). We used a pooled-analytical approach on four studies that used similar methods. METHODS: We used logistic regression to analyze the pooled sample of 1149 isolated cases and 1161 controls. Fetal and maternal MTHFR C677T genotypes, and maternal periconceptional exposure to smoking, alcohol, vitamin containing folic acid and folic acid supplements were contrasted between the cleft types [non-syndromic clefts lip or without cleft palate (CL(P)) and non-syndromic cleft palate (CP)] and control groups. RESULTS: There was a reduced risk of CL(P) with maternal folic acid use (p = 0.008; OR = 0.70, 95% CI: 0.65-0.94) and with supplements containing folic acid (p = 0.028, OR = 0.80, 95% CI: 0.65-0.94). Maternal smoking increased the risk of both CL(P) (p < 10 e-3; OR = 1.62, 95% CI: 1.35-1.95) and CP (p = 0.028; OR = 1.38, 95% CI: 1.04-1.83). No significant risk was observed with either maternal or fetal MTHFR C677T genotypes. CONCLUSION: This individual participant data (IPD) meta-analysis affords greater statistical power and can help alleviate the problems associated with aggregate-level data-sharing. The result of this IPD meta-analysis is consistent with previous reports suggesting that folic acid and smoking influence OFC outcomes.
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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 | 0.025 | 0.028 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.024 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".