Human Leukocyte Antigen and Systemic Sclerosis in Japanese: The Sign of the Four Independent Protective Alleles, DRB1*13:02, DRB1*14:06, DQB1*03:01, and DPB1*02:01
Bibliographic record
Abstract
OBJECTIVE: Several studies on associations between human leukocyte antigen (HLA) allele frequencies and susceptibility to systemic sclerosis (SSc) have been reported. Anti-centromere antibodies (ACA) and anti-topoisomerase I antibodies (ATA) are found in SSc patients. Here, we sought to identify HLA alleles associated with SSc in Japanese, and explored their associations with SSc phenotypes including the presence of autoantibodies. METHODS: Associations of HLA-DRB1, DQB1, and DPB1 were analyzed in 463 Japanese SSc patients and 413 controls. RESULTS: We found that DRB1*13:02 (P = 0.0011, Pc = 0.0319, odds ratio [OR] 0.46, 95% confidence interval [CI] 0.29-0.73), DRB1*14:06 (P = 6.60X10-5, Pc = 0.0020, OR 0.05, 95%CI 0.01-0.41), DQB1*03:01 (P = 0.0009, Pc = 0.0150, OR 0.56, 95%CI 0.40-0.79), and DPB1*02:01 (P = 5.16X10-6, Pc = 8.77X10-5, OR 0.52, 95%CI 0.39-0.69) were protectively associated with SSc. In addition, these four alleles seemed to be independently associated with the protection against the susceptibility of SSc. On the other hand, we could not find predisposing alleles for overall SSc. With respect to SSc subsets, a tendency for these four alleles to be protectively associated was observed. However, there was a significant association between DRB1*01:01, DRB1*10:01, DQB1*05:01, and DPB1*04:02 and the susceptibility to SSc with ACA. On the other hand, the presence of DRB1*15:02, DQB1*06:01, DPB1*03:01, and DPB1*09:01 was associated with SSc with ATA. CONCLUSION: Thus, the present study has identified protective associations of the four HLA class II alleles with overall Japanese SSc and predisposing associations of HLA class II alleles with Japanese SSc subsets.
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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 | 0.001 |
| 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".