Cigarette Smoking,<i>STAT4</i>and<i>TNFRSF1B</i>Polymorphisms, and Systemic Lupus Erythematosus in a Japanese Population
Bibliographic record
Abstract
OBJECTIVE: Recent studies have identified signal transducer and activator of transcription 4 (STAT4) as a susceptibility gene for systemic lupus erythematosus (SLE) in different populations. Similarly, tumor necrosis factor receptor superfamily, member 1B (TNFRSF1B) has been reported to be associated with SLE risk in Japanese populations. Along with environmental factors such as smoking, both polymorphisms may modulate an individual's susceptibility to SLE. We investigated these relationships in a case-control study to evaluate risk factors for SLE among Japanese women. METHODS: We investigated the relationship of the STAT4 rs7574865 and TNFRSF1B rs1061622 polymorphisms to SLE risk with special reference to their combination and interaction with cigarette smoking among 152 SLE cases and 427 controls. RESULTS: The TT genotype of STAT4 rs7574865 was significantly associated with increased risk of SLE (OR 2.21, 95% CI 1.10-4.68). Subjects with at least one G allele of TNFRSF1B rs1061622 had an increased risk of SLE (OR 1.56, 95% CI 0.99-2.47). The attributable proportion due to the interaction between the TNFRSF1B rs1061622 genotypes and smoking was estimated to be 0.49 (95% CI 0.07-0.92), indicating that 49% of the excess risk for SLE in smokers with at least one G allele was due to an additive interaction. A lack of significant associations of STAT4 with smoking was observed. No significant gene-gene interactions were found among polymorphisms of STAT4 and TNFRSF1B. CONCLUSION: Our findings suggest that the association between cigarette smoking and SLE could be differentiated by the TNFRSF1B rs1061622 T allele among female Japanese subjects. This preliminary exploratory result should be confirmed in a larger study.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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; 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".