Association of tumor necrosis factor α (rs1800629) and interleukin-10 (rs1800896) gene polymorphisms with systemic lupus erythematosus: a meta-analysis
Bibliographic record
Abstract
Introduction: Systemic lupus erythematosus (SLE) is a complex autoimmune disease influenced by genetic, environmental, and immunological factors. Variations in cytokine genes, including tumor necrosis factor α (TNF-α) and interleukin-10 (IL-10), have been implicated in SLE pathogenesis, but their associations remain uncertain owing to conflicting study results. Material and methods: A systematic search of the Google Scholar, PubMed, and Embase databases was conducted to examine TNF-α (rs1800629) and IL-10 (rs1800896) polymorphisms in SLE. Eligible studies were selected based on specific inclusion criteria, and data were independently extracted. Quality assessment was performed using the Newcastle-Ottawa Scale, and the Hardy-Weinberg equilibrium was evaluated. Meta-analyses were conducted using Cochrane Rob Tool 2 and Review Manager version 5.4 to determine odds ratios and 95% confidence intervals. Results: According to the meta-analysis, a significant association was found between SLE risk and TNF-α-308 G/A polymorphism in allelic, dominant, and heterozygote models. However, no association was found between homozygous and recessive models. Interleukin-10 polymorphisms were not significantly associated with SLE risk in any model. Ethnicity-specific analysis revealed a significant association between the TNF-α allele and SLE susceptibility in Asian populations but not in Caucasians. Conclusions: This meta-analysis identified a strong correlation between the TNF-α-308 G/A polymorphism and SLE susceptibility, particularly in Asian populations. However, no association was found between IL-10 polymorphisms and SLE. More extensive studies with diverse populations are required to validate and enhance these findings.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.027 | 0.005 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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; both teacher heads agree on what is shown here.
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".