HLA-DRB1 Haplotypes, Shared Epitope, and Disease Outcomes in US Veterans with Rheumatoid Arthritis
Bibliographic record
Abstract
OBJECTIVE: To evaluate associations of HLA-DRB1 haplotypes and shared epitope (SE) with rheumatoid arthritis (RA) severity and all-cause mortality in RA. METHODS: Patients with RA from the Veterans Affairs Rheumatoid Arthritis (VARA) registry were followed from enrollment until death or December 31, 2013. Clinical characteristics, DNA, and serum were collected at enrollment. Radiographic damage, the presence or absence of subcutaneous nodules, disease activity measures, and functional status were assessed at enrollment and updated during followup. Sixteen HLA-DRB1 haplotypes and SE status were determined from banked DNA. Associations between HLA-DRB1 haplotypes, RA disease characteristics, and mortality were assessed in multivariable regression models. RESULTS: Within VARA, 1443 participants had genotyping and accrued 6150 patient-years of followup. Haplotypes VKA, VRA, LRA, SRA, SRE, SKR, and SEA, and SE alleles were significantly associated with seropositivity for rheumatoid factor (RF) and/or anticyclic citrullinated peptide (anti-CCP). Haplotypes VKA and SKR were associated with higher RF concentrations, while VRA, DRE, and GRQ were associated with lower RF concentrations. Haplotypes VKA, VRA, and LRA were associated with higher concentrations of anti-CCP antibody, while haplotypes SRA, SRE, LEA, SKR, and SEA were significantly associated with lower anti-CCP concentrations. Haplotype VKA (OR 1.39, 95% CI 1.08-1.80) was associated with increased frequency of radiographic damage at enrollment but none of the haplotypes were associated with the presence of subcutaneous nodules. Haplotypes SKA (HR 1.52, 95% CI 1.26-1.83) was associated with higher mortality. CONCLUSION: HLA-DRB1 haplotypes are independently and variably associated with seropositivity, autoantibody concentrations, and outcomes in 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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".