Association of Interleukin 23 Receptor Polymorphisms with Anti-Topoisomerase-I Positivity and Pulmonary Hypertension in Systemic Sclerosis
Bibliographic record
Abstract
OBJECTIVE: IL23R has been identified as a susceptibility gene for development of multiple autoimmune diseases. We investigated the possible association of IL23R with systemic sclerosis (SSc), an autoimmune disease that leads to the development of cutaneous and visceral fibrosis. METHODS: We tested 9 single-nucleotide polymorphisms (SNP) in IL23R for association with SSc in a cohort of 1402 SSc cases and 1038 controls. IL23R SNP tested were previously identified as SNP showing associations with inflammatory bowel disease. RESULTS: Case-control comparisons revealed no statistically significant differences between patients and healthy controls with any of the IL23R polymorphisms. Analyses of subsets of SSc patients showed that rs11209026 (Arg381Gln variant) was associated with anti-topoisomerase I antibody (ATA)-positive SSc (p = 0.001)) and rs11465804 SNP was associated with diffuse and ATA-positive SSc (p = 0.0001, p = 0.0026, respectively). These associations remained significant after accounting for multiple comparisons using the false discovery rate method. Wild-type genotype at both rs11209026 and rs11465804 showed significant protection against the presence of pulmonary hypertension (PHT). (p = 3x10(-5), p = 1x10(-5), respectively). CONCLUSION: Polymorphisms in IL23R are associated with susceptibility to ATA-positive SSc and protective against development of PHT in patients with SSc.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".