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 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 | 0.012 | 0.025 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.018 | 0.057 |
| Bibliometrics | 0.006 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.003 | 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".