<i>HLA-DRB1</i> Analysis Identified a Genetically Unique Subset within Rheumatoid Arthritis and Distinct Genetic Background of Rheumatoid Factor Levels from Anticyclic Citrullinated Peptide Antibodies
Bibliographic record
Abstract
Objective. HLA-DRB1 is the most important locus associated with rheumatoid arthritis (RA) and anticitrullinated protein antibodies (ACPA). However, fluctuations of rheumatoid factor (RF) over the disease course have made it difficult to define fine subgroups according to consistent RF positivity for the analyses of genetic background and the levels of RF. Methods. A total of 2873 patients with RA and 2008 healthy controls were recruited. We genotyped HLA-DRB1 alleles for the participants and collected consecutive data of RF in the case subjects. In addition to RF+ and RF− subsets, we classified the RF+ subjects into group 1 (constant RF+) and group 2 (seroconversion). We compared HLA-DRB1 alleles between the RA subsets and controls and performed linear regression analysis to identify HLA-DRB1 alleles associated with maximal RF levels. Omnibus tests were conducted to assess important amino acid positions. Results. RF positivity was 88%, and 1372 and 970 RF+ subjects were classified into groups 1 and 2, respectively. RF+ and RF− showed similar genetic associations to ACPA+ and ACPA− RA, respectively. We found that shared epitope (SE) was more enriched in group 2 than 1, p = 2.0 × 10−5, and that amino acid position 11 showed a significant association between 1 and 2, p = 2.7 × 10−5. These associations were independent of ACPA positivity. SE showed a tendency to be negatively correlated with RF titer (p = 0.012). HLA-DRB1*09:01, which reduces ACPA titer, was not associated with RF levels (p = 0.70). Conclusion. The seroconversion group was shown to have distinct genetic characteristics. The genetic architecture of RF levels is different from that of ACPA.
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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 | 0.000 |
| 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".