Influence of the <i>IL6</i> Gene in Susceptibility to Systemic Sclerosis
Bibliographic record
Abstract
OBJECTIVE: Systemic sclerosis (SSc) is a genetically complex autoimmune disease; the genetic component has not been fully defined. Interleukin 6 (IL-6) plays a crucial role in immunity and fibrosis, both key aspects of SSc. We investigated the influence of IL6 gene in the susceptibility and phenotype expression of SSc. METHODS: We performed a large metaanalysis including a total of 2749 cases and 3189 controls from 6 white populations (Germany, The Netherlands, Norway, Spain, Sweden, and United Kingdom). Three IL6 single-nucleotide polymorphisms (SNP; rs2069827, rs1800795, and rs2069840) were selected by SNP tagging and genotyped using TaqMan(®) allele discrimination technology. RESULTS: Individual SNP metaanalysis showed no evidence of association of the 3 IL6 genetic variants with the global disease. Phenotype analyses revealed a significant association between the minor allele of rs2069840 and the limited cutaneous SSc clinical form (Bonferroni p = 0.036, OR 1.14, 95% CI 1.04-1.25). A trend of association between the minor allele of the rs1800795 and the diffuse cutaneous SSc clinical form was also evident (Bonferroni p = 0.072, OR 0.86, 95% CI 0.77-0.96). In the IL6 allelic combination analyses, the GGC allelic combination rs2069827-rs1800795-rs2069840 showed an association with overall SSc (Bonferroni p = 0.016, OR 1.13, 95% CI 1.04-1.23). CONCLUSION: Our results suggest that the IL6 gene may influence the development of SSc and its progression.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.005 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".