MTHFR C677T、MTHFR A1298C、MTRR A66G and MTR A2756G polymorphisms and male infertility risk: a systematic review and meta-analysis
Bibliographic record
Abstract
BACKGROUND: Epidemiological studies have reported that polymorphisms of folate-metabolizing genes have a significant impact on male infertility. However, the results of published studies have come to different conclusions. OBJECTIVE: To determine an association between folate-metabolizing gene polymorphisms and the risk of male infertility. METHODS: The meta-analysis was conducted according to the PRISMA 2020 statement. The protocol was registered with PROSPERO (CRD42023412251). Studies were searched from PubMed, Google Scholar, Embase, Scopus, and the Cochrane Library up to 24st October2023. Articles that satisfied the inclusion criteria were evaluated for their quality using the Newcastle-Ottawa Scale. Data were extracted from the eligible studies and were analyzed for pooled up odds ratio (OR) with 95% confidence interval (CI). Meta-analysis was conducted using STATA 12. RESULTS: Forty-six case-control studies were included in the meta-analysis which comprised 20,639 participants. The pooled analysis revealed that the MTHFR C677T polymorphism was significantly associated with male infertility and abnormospermia.Three-fifths of the model showed there was a significant association between the MTR A2756G polymorphism and male infertility. Both MTHFR A1298C and MTRR A66G polymorphisms were not significantly associated with male fertility. Furthermore, subgroup analysis revealed a significant association between the MTHFR C677T polymorphism and male fertility in Asian countries. CONCLUSION: This meta-analysis suggests that the MTHFR C677T and MTR A2756G polymorphisms may be a potential risk factor for male infertility.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.010 | 0.022 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.018 | 0.037 |
| Bibliometrics | 0.008 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".