Increased frequency of DRB1*11:01 in anti–hydroxymethylglutaryl‐coenzyme A reductase–associated autoimmune myopathy
Bibliographic record
Abstract
OBJECTIVE: To investigate the association of anti-hydroxymethylglutaryl-coenzyme A reductase (anti-HMGCR) myopathy with HLA class I and II antigens. METHODS: HLA antigens were determined in 1) 20 white and 8 African American anti-HMGCR patients, 2) 487 white and 167 African American controls, and 3) 51 white subjects with mild self-limited statin intolerance. RESULTS: White anti-HMGCR patients had a higher frequency of the combination HLA-DR11, DQA5, and DQB7 than controls or statin-intolerant subjects (70% versus 17%; odds ratio [OR] 11.7 [95% confidence interval (95% CI) 4.0-35.3], P = 4.1 × 10(-7) and 70% versus 21%; OR 8.3 [95% CI 2.2-33.9], P = 5.4 × 10(-4) , respectively). This combination was not increased in African American anti-HMGCR subjects compared to controls (13% versus 3%; OR 4.6 [95% CI 0.2-53.3], P = 0.2). However, DR11 was increased in African American anti-HMGCR patients compared to controls (88% versus 21%; OR 26.4 [95% CI 3.1-590.3], P = 0.0002). High-resolution mapping showed that 95% with DR11 had DRB1*11:01. DQA1 and DQB6 were less frequent in white anti-HMGCR-positive patients compared to controls (25% versus 65%; OR 0.2 [95% CI 0.1-0.5], P = 5.5 × 10(-4) and 0% versus 45%; OR 0.0 [95% CI 0.0-0.3], P = 2.1 × 10(-5) , respectively). DRB11 was not associated with particular disease features. CONCLUSION: DRB1*11:01 is associated with an increased risk of anti-HMGCR myopathy in whites and African Americans. These findings suggest a mechanistic link between statin exposure, increased HMGCR expression, and the possible presentation of HMGCR-derived peptide(s) by DRB1*11:01.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".