Human leucocyte antigens and Japanese patients with polymyalgia rheumatica: the protective effect of <i>DRB1*09:01</i>
Bibliographic record
Abstract
Objective The hallmarks of the chronic inflammatory disease polymyalgia rheumatica (PMR) include pain, and morning stiffness in areas of the neck, shoulder and pelvic girdle. The human leucocyte antigen (HLA) gene was reported to be an important risk factor for PMR, but it has not been analysed precisely, especially in populations other than Europeans. Methods Genotyping of DRB1 and DQB1 was performed in Japanese PMR patients (n=270) and controls (n=413). Associations between allele carrier and genotype frequencies were determined for PMR. Results DRB1*04:05 was associated with a predisposition to PMR (p=0.0006, Pc=0.0193, OR 1.85, 95% CI 1.31 to 2.62). DRB1*09:01 was associated with protection against PMR (p=1.46×10−5, Pc=0.0004, OR 0.40, 95% CI 0.26 to 0.61). A shared epitope (SE) associated with PMR (p=3.07×10−6, OR 2.11, 95% CI 1.54 to 2.88). DQB1*03:03 (p=0.0010, Pc=0.0140, OR 0.52, 95% CI 0.35 to 0.77) was associated with protection against PMR and DQB1*04:01 (p=0.0009, Pc=0.0140, OR 1.82, 95% CI 1.28 to 2.58) was associated with predisposition to PMR. A gene dosage effect was observed for DRB1*09:01 and DQB1*03:03, but not for DRB1*04:05, SE or DQB1*04:01. Haplotype and logistic regression analyses suggested a protective effect for DRB1*09:01. Conclusion This study is the first to demonstrate predisposing associations of DRB1*04:05, SE, and DQB1*04:01, and protective associations of DRB1*09:01 and DQB1*03:03 with PMR in Japanese patients. Our data indicate HLA has predisposing and protective effects on the pathogenesis of PMR.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.002 | 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".