Association Between MTHFR Polymorphisms and Congenital Heart Disease: A Meta-analysis based on 9,329 cases and 15,076 controls
Bibliographic record
Abstract
The aim of our study was to evaluate the association between polymorphisms in the methylenetetrahydrofolate reductase (MTHFR) gene and the risk for congenital heart disease (CHD). Electronic literature databases were searched to identify eligible studies published before Jun, 2014. The association was assessed by the odds ratio (OR) with a 95% confidence interval (CI). The publication bias was explored using Begg's test. Sensitivity analysis was performed to evaluate the stability of the crude results. A total of 35 studies were included in this meta-analysis. For the MTHFR C677T polymorphism, we detected significant association in all genetic models for Asian children and the maternal population. Significant association was also detected in T vs. C for a Caucasian paediatric population (OR = 1.163, 95% CI: 1.008-1.342) and in both T vs. C (OR = 1.125, 95% CI: 1.043-1.214) and the dominant model (OR = 1.216, 95% CI:b1.096-1.348) for a Caucasian maternal population. For the MTHFR A1298C polymorphism, the association was detected in CC vs. AC for the Caucasian paediatric population (OR = 1.484, 95% CI: 1.035-2.128). Our results support the MTHFR -677T allele as a susceptibility factor for CHD in the Asian maternal population and the -1298 C allele as a risk factor in the Caucasian paediatric population.
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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 | 0.011 | 0.015 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.011 | 0.036 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".