Variants Within STAT Genes Reveal Association with Anticitrullinated Protein Antibody-negative Rheumatoid Arthritis in 2 European Populations
Bibliographic record
Abstract
OBJECTIVE: STAT3 and 4 are, among other factors, critical for the interleukin 12 (IL-12)-mediated Th1 response, for transfer of IL-23 signals, and for survival and expansion of Th17 cells. We investigated the association of STAT3 and STAT4 polymorphisms with serologically distinct subgroups of rheumatoid arthritis (RA). METHODS: A total of 41 single-nucleotide polymorphisms (SNP) within STAT3 and STAT1-STAT4 loci were investigated in a Swedish cohort of 2043 RA cases and 1115 controls. Nine of the associated SNP were tested in a Spanish cohort of 1223 RA cases and 1090 controls. RESULTS: Fourteen SNP in the STAT3 and STAT1-STAT4 loci were associated with anticitrullinated protein antibody (ACPA)-negative RA in the Swedish cohort. Three of the SNP in STAT4 and 2 SNP in STAT3 remained associated with ACPA-negative RA after considering the Spanish results. In addition, rs7574865 and rs10181656, in STAT4, were associated with ACPA-positive RA in the Swedish study. One of these SNP, rs7574865, showed a similar pattern of the association in serologically distinct subgroups of RA in a metaanalysis of all 7 published studies. CONCLUSION: Our findings suggest that variants in STAT genes may contribute differentially to susceptibility to RA in seropositive and in seronegative patients.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".