Immunogenetic Risks of Anti-Cyclical Citrullinated Peptide Antibodies in a North American Native Population with Rheumatoid Arthritis and Their First-degree Relatives
Bibliographic record
Abstract
OBJECTIVE: To determine the prevalence of anti-cyclic citrullinated peptide (anti-CCP) antibodies in unaffected relatives of North American Native probands with rheumatoid arthritis (RA); and the associations of the shared epitope (SE) and HLA-DRB1*0901 with RA and anti-CCP antibodies. METHODS: The subjects were RA probands, affected relatives, unaffected first-degree (FDR) and more distant relatives, and unaffected controls from the same population. HLA-DRB1 typing was determined by DNA sequencing and anti-CCP antibodies were determined by ELISA. RESULTS: DRB1*0901, SE, and SE/DRB1*0901 genotypes were all associated with RA. SE/DRB1*0901, but not other SE genotypes, was associated with disease onset at age<16 years. The frequency of anti-CCP antibodies was 82% in RA probands, 17% in FDR, 11% in more distant relatives, and 3% in controls. Among unaffected relatives, a significant increased risk of anti-CCP was associated with SE/DRB1*0901 genotype, but not with SE. CONCLUSION: An independent association of the non-SE allele DRB1*0901 with RA was confirmed in this population, and this allele in combination with a SE allele was associated with younger age at disease onset. FDR of RA probands have a higher prevalence of anti-CCP antibodies than more distant relatives and unrelated controls, suggesting a gradient of risk for disease development. Immunogenetic risks may act early in disease pathogenesis at the level of initiation of RA autoantibody formation; however, it is not clear what additional genetic and environmental risks are involved in progression to clinical disease.
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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 | 0.001 | 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".