Association Between HLA Alleles and Autoantibodies in Dermatomyositis Defined by Sarcoplasmic Expression of Myxovirus Resistance Protein A
Bibliographic record
Abstract
Objective The diagnosis in the studies analyzing HLA of dermatomyositis (DM) was based on a combined clinical category of polymyositis/DM. This retrospective study investigated the associations of HLA with 5 DM-specific autoantibodies in Japanese patients diagnosed by muscle pathology. Methods We diagnosed Japanese patients with DM based on sarcoplasmic expression of myxovirus resistance protein A. These patients underwent investigation for 5 DM-specific autoantibodies and HLA genotyping. Results Of 175 patients (83 males and 92 females; range 1-86 yrs; mean 46 yrs), 173 (98.9%) had 1 of the 5 autoantibodies. Seven alleles—A*02:07, B*46:01, DRB1*04:07, DRB1*07:01, DRB1*08:03, DQB1*06:01, andDPB1*02:02—were more frequently detected in the patients with DM than healthy controls, but these associations were not significant after multiple testing correction. Stratifying by DM-specific autoantibodies, we found the associations of 6 already known and 7 new alleles—B*48:01, B*52:01, C*12:02, DRB1*04:05, DRB1*15:02, DPB1*05:01, andDPB1*09:01—with subsets of DM. Moreover, significant associations of 5 alleles with antinucleosome remodeling deacetylase complex (Mi-2) remained after multiple testing correction. In particular, theDRB1*04:07(odds ratio [OR 28.9]; correctedP= 2.7 × 10−6) andDQB1*06:01(OR 4.0; correctedP= 1.6 × 10−4) alleles were significantly more prevalent in patients with anti-Mi-2 antibody than in controls. Conclusion This study demonstrates DM-specific autoantibodies defined immunogenetic subsets of DM.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.002 | 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".