HLA-DRB1 Alleles as Genetic Risk Factors for the Development of Anti-MDA5 Antibodies in Patients with Dermatomyositis
Bibliographic record
Abstract
Objective. Patients with polymyositis/dermatomyositis (PM/DM) who express anti-melanoma differentiation associated protein 5 (anti-MDA5) antibodies frequently present with interstitial lung disease (ILD). The aim of this study was to investigate the association of HLA-DRB1 with anti-MDA5 expression in PM/DM. Methods. The frequency of DRB1 alleles was compared among 70 patients with PM, 104 patients with DM, and 400 healthy controls in a Han Chinese population. Results. Frequencies of DRB1*04:01 [17.0% vs 1.3%, corrected p value (pc) = 3.8 × 10−8, OR 16.2, 95% CI 6.6–39.7] and *12:02 (42.6% vs 19.3%, pc = 0.008, OR 3.1, 95% CI 1.7–5.7) were significantly higher in anti-MDA5–positive patients with PM/DM compared with the controls. The frequencies of DRB1*04:01 (p = 5.2 × 10−6, OR 17.1, 95% CI 5.3–54.9) and *12:02 (p = 3.8 × 10−4, OR 3.1, 95% CI 1.7–5.7) in anti-MDA5–positive patients with DM-ILD were higher than in the controls, whereas the frequencies of DRB1*04:01 and *12:02 did not differ between the anti-MDA5–negative patients with DM-ILD and controls. No difference in the frequency of DRB1 alleles, other than *04:01, carrying the “shared epitope” (SE), i.e., *01:01, *01:02, *04:05, and *10:01, was observed between the controls and patients with DM stratified by the presence of anti-MDA5 and ILD. Conclusion. DRB1*04:01 and *12:02 confer susceptibility to anti-MDA5 antibody production in DM, which cannot be explained by the SE hypothesis.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 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".