HLA class II haplotype and autoantibody associations in children with juvenile dermatomyositis and juvenile dermatomyositis–scleroderma overlap
Bibliographic record
Abstract
OBJECTIVES: To investigate a large cohort of children with juvenile dermatomyositis (JDM), and those with JDM-scleroderma (JDM-SSc) overlap, using detailed serological analysis, HLA class II genotyping and clinical characterization. METHODS: Children (114) with JDM were recruited, and clinical data collected, through the JDM National Registry and Repository (UK and Ireland). Sera were assayed for ANA using standard immunofluorescence techniques and specific antibodies characterized using ELISA, immunodiffusion and radioimmunoprecipitation. Patients and controls (n = 537) were genotyped at the HLA-DRB1 and DQB1 loci, and then the DQA1 locus data was derived. RESULTS: Over 70% of the patients were ANA-positive. Clear differences in serological and genetic data were demonstrated between JDM and JDM-SSc overlap groups. Strong associations were seen for HLA-DRB1*03 (all cases vs controls, P(corr) = 0.02; JDM-SSc vs controls, P(corr) = 0.001) and HLA-DQA1*05 (all cases vs controls, P(corr) = 0.01; JDM-SSc vs controls, P(corr) = 0.005). The frequency of the HLA-DRB1*03-DQA1*05-DQB1*02 haplotype was significantly increased in the JDM-SSc (P = 0.003) and anti-PM-Scl antibody (P = 0.002) positive groups. All anti-U1-RNP antibody-positive patients had at least one copy of HLA-DRB1*04-DQA1*03-DQB1*03 haplotype. Associations were observed between serology and specific clinical features. CONCLUSIONS: We present clinical data, HLA genotyping and serological profiling on a large cohort of JDM patients and a carefully characterized subset of patients with JDM-SSc overlap. The results confirm known HLA associations and extend the knowledge by stratification of data in serological and clinical subgroups. In the future, a combination of serological and genetic typing may allow for better prediction of clinical course and disease subtype in JDM.
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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".