Comprehensive systematic review and meta-analysis of the TGF-β1 T869C gene polymorphism and autoimmune disease susceptibility
Bibliographic record
Abstract
Objective: Autoimmune diseases (ADs) result from an aberrant immune response, in which the body mistakenly targets its own tissues. The association between TGF-β1 gene polymorphisms and risk of developing autoimmune diseases remains to be established. This meta-analysis aimed to reassess the relationship between TGF-β1 T869C gene polymorphisms and susceptibility to autoimmune diseases. Methods: We conducted a comprehensive search of seven electronic databases for case-control studies investigating the TGF-β1 T869C polymorphism in relation to autoimmune diseases, including rheumatoid arthritis, systemic lupus erythematosus, systemic sclerosis, Sjögren's syndrome, and juvenile idiopathic arthritis. The search encompassed publications published up to June 2024. Studies were categorized by ethnicity into three groups: Asian, Caucasian, and mixed-ethnicity groups. Five different genetic models were assessed, and the quality of the included studies was evaluated using the Newcastle-Ottawa Scale (NOS). Statistical analyses were performed using Stata 14.0, by calculating the odds ratio (OR) and 95% confidence interval (CI). Results: A total of 32 case-control studies (31 articles), comprising 4,304 cases and 4,664 controls, were included in this meta-analysis. The overall analysis indicated no significant association between TGF-β1 T869C gene polymorphism and susceptibility to autoimmune diseases. However, subgroup analyses based on race and disease status revealed significant associations. Ethnic subgroup analysis showed that the TGF-β1 T869C allele model (T vs C: OR = 1.422, 95% CI = 1.109-1.824, P = 0.006), homozygous model (TT vs CC: OR = 1.923, 95% CI = 1.232-3.004, P = 0.004), and dominant model (TT + TC vs CC: OR = 1.599, 95% CI = 1.164-2.196, P = 0.004) were associated with autoimmune disease susceptibility in Asians. In the disease subgroup analysis, the results showed that the TGF-β1 T869C allele model (T vs C: OR = 1.468, 95% CI = 1.210-1.781, P = 0.000), recessive model (TT vs TC + CC: OR = 1.418, 95% CI = 1.097-1.832, P = 0.008), dominant model (TT + TC vs CC: OR = 1.747, 95% CI = 1.330-2.295, P = 0.000), homozygous model (TT vs CC: OR = 1.937, 95% CI = 1.373-2.734, P = 0.000), and heterozygous model (TC vs CC: OR = 1.555, 95% CI = 1.199-2.016, P = 0.001) were associated with rheumatoid arthritis susceptibility. Conclusion: The findings of this meta-analysis suggest that carrying the T allele of the TGF-β1 T869C polymorphism increases the risk of autoimmune diseases in Asian populations. Moreover, individuals carrying the T allele are at higher risk of developing rheumatoid arthritis.
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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 | 0.013 | 0.030 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.018 | 0.024 |
| Bibliometrics | 0.011 | 0.013 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".