TNFA gene polymorphisms and the risk of endometriosis: An updated meta-analysis of genetic association studies
Bibliographic record
Abstract
The tumor necrosis factor-alpha (TNFA) gene plays a pivotal role in modulating inflammatory responses, and its variants have been hypothesized to influence susceptibility to endometriosis, a complex and multifactorial gynecological condition. Among the notable polymorphisms investigated are -238 G>A, -308 G>A, -850 C>T, -857 C>T, -863 C>A, and -1031 T>C. Despite substantial research, the evidence regarding their role as genetic risk factors for endometriosis remains inconclusive. To address this uncertainty, the present study conducted a systematic review of the literature to evaluate the association between TNFA variants and endometriosis risk. A comprehensive search of electronic databases was performed to identify relevant studies. Data extraction and quality assessment were carried out using the Newcastle-Ottawa Scale. Pooled odds ratios and 95% confidence intervals were calculated across various genetic models, with adjustments for multiple comparisons using the Bonferroni correction. Trial sequential analysis (TSA) was employed to determine the required sample size for conclusive results, and Egger’s test was used to assess publication bias. The analysis included 18 studies examining different TNFA polymorphisms, but no significant associations with endometriosis risk were identified. TSA revealed that the existing sample sizes were inadequate to detect definitive links. While the findings suggest that upstream variants of the TNFA gene are not associated with endometriosis risk, this does not conclusively rule out a role for TNFA in the disease pathogenesis. Further research involving larger, ethnically diverse populations is warranted to confirm these results and provide deeper insights into the genetic factors contributing to endometriosis.
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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 | 0.016 | 0.030 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.013 | 0.039 |
| Bibliometrics | 0.008 | 0.011 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.002 |
| 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".