Interaction between smoking, the shared epitope, and anti–cyclic citrullinated peptide: A mixed picture in three large North American rheumatoid arthritis cohorts
Bibliographic record
Abstract
OBJECTIVE: Recently, Swedish members of the Epidemiological Investigation of Rheumatoid Arthritis (EIRA) provided evidence that smoking may trigger RA-specific immune reactions to citrullinated protein in carriers of HLA-DR shared epitope alleles. In an effort to confirm this interaction between smoking and shared epitope alleles, we performed a case-only analysis of 3 North American RA cohorts. METHODS: A total of 2,476 white patients with RA were studied, 1,105 from the North American Rheumatoid Arthritis Consortium (NARAC) family collection, 753 from the National Inception Cohort of Rheumatoid Arthritis Patients (Inception Cohort), and 618 from the Study of New Onset Rheumatoid Arthritis (SONORA). All patients were HLA-DRB1 typed, and tested for anti-cyclic citrullinated peptide (anti-CCP) and rheumatoid factor. Information about smoking history was obtained by questionnaire. RESULTS: A significant association was found between smoking and the presence of anti-CCP in the NARAC and the Inception Cohort, but not in the SONORA. The shared epitope alleles consistently correlated with anti-CCP in all 3 populations. Using multiple logistic regression analyses, shared epitope alleles were still the most significant risk factor for anti-CCP positivity. Weak evidence of gene-environment interaction between smoking and shared epitope alleles for anti-CCP formation was found only in the NARAC. CONCLUSION: Unlike the EIRA data, we could not confirm a major gene-environment interaction for anti-CCP formation between shared epitope alleles and smoking in 3 North American RA cohorts. Our data indicate a need for further studies to address the full range of environmental factors other than smoking that may be associated with citrullination and RA.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".