Associations of <i>Tumor Necrosis Factor Alpha</i> Gene Polymorphisms and Ankylosing Spondylitis Susceptibility: A Meta-analysis Based on 35 Case-control Studies
Bibliographic record
Abstract
Background: Scores of studies on tumor necrosis factor alpha (TNF-α) gene polymorphisms and AS have been performed with inconsistent results. The purpose of this study was to provide some more convincing evidence on the associations of TNF-a polymorphisms and AS by using a meta-analysis approach.Methods: Potentially relevant studies were identified from Web of Science, PubMed, EMBASE, Wanfang, and CNKI from inception to March 5, 2020. Newcastle–Ottawa Scale (NOS) was utilized to appraise the quality of included studies. Odds ratios (ORs) with 95% confidence intervals (95%CIs) were calculated to assess the strength of the associations under five genetic models.Results: Thirty-five studies with 37 independent cohorts in total were included in the meta-analysis. Based upon NOS, eligible studies were in moderate- to high quality. The merged data suggested rs1799724 polymorphisms were significantly correlated with a reduced risk of AS (C vs. T, OR = 0.55, 95%CI 0.38–0.79, P < .001, PBon = 0.005, PFDR = 0.003). Subgroup analysis by ethnicity indicated that rs1800629 polymorphism significantly increased the risk of AS in Caucasians and decreased the risk of AS in mixed populations. Besides, rs361525 and rs1800630 polymorphisms conferred to an elevated risk of AS, and rs1799724 conferred to a reduced risk of AS in Asians.Conclusions: This study suggests that rs1800629 polymorphism is associated with an increased AS risk in Caucasians, rs361525 and rs1800630 polymorphisms are linked to an elevated AS susceptibility in Asians.
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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 | 0.023 | 0.029 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.015 | 0.061 |
| Bibliometrics | 0.010 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.003 | 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".