Interaction of HLA-DRB1*09:01 and *04:05 with Smoking Suggests Distinctive Mechanisms of Rheumatoid Arthritis Susceptibility Beyond the Shared Epitope
Bibliographic record
Abstract
OBJECTIVE: Although HLA-DRB1 shared epitope (SE) alleles and HLA-DRB1*09:01 have repeatedly been shown to be associated with susceptibility to rheumatoid arthritis (RA), the effect of each allele on levels of anticyclic citrullinated peptide autoantibodies (anti-CCP) and interaction with cigarette smoking in RA remains to be fully defined. We investigated whether HLA-DRB1 risk alleles influence anti-CCP levels and whether each allele interacts with smoking in anti-CCP-positive or -negative RA. METHODS: All patients with RA (n = 1924) and controls (n = 1119) were Korean. The HLA-DRB1 4-digit genotyping was performed by standard PCR-sequencing based typing method. OR and biologic interactions as departures from additivity or multiplicity were analyzed by logistic regression. RESULTS: SE alleles were significantly associated with increased anti-CCP levels. Conversely, HLA-DRB1*09:01 was associated with reduced levels, in both SE-positive and SE-negative patients. Each of SE alleles interacted significantly with smoking, whereas HLA-DRB1*09:01 did not. Interactions between the 2 most significant risk alleles, HLA-DRB1*04:05 and HLA-DRB1*09:01, (attributable proportion = 0.68, 95% CI 0.46-0.89, multiplicity p = 0.012) significantly increased RA susceptibility regardless of anti-CCP and smoking status. Smoking increased the risk for RA by significant interaction with the heterozygote HLA-DRB1*04:05/*09:01. CONCLUSION: HLA-DRB1*09:01 differs from SE alleles with regard to anti-CCP levels and interaction with smoking, suggesting a distinct mechanism of HLA-DRB1*09:01 in the pathogenesis of RA that may bypass anti-CCP formation. Also, a significant increase of the HLA-DRB1*04:05/ *09:01 heterozygote in RA susceptibility may be attributable to the synergistic contribution of 2 different pathways in which 2 alleles participate independently.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".