<i>N</i>‐Linked Glycans in the Variable Domain of IgG Anti–Citrullinated Protein Antibodies Predict the Development of Rheumatoid Arthritis
Bibliographic record
Abstract
OBJECTIVE: Anti-citrullinated protein antibodies (ACPAs) are disease-specific biomarkers in rheumatoid arthritis (RA). More than 90% of IgG ACPAs harbor N-linked glycans in the antibody variable (V) domain. The corresponding N-glycosylation sites in ACPA V-region sequences result from somatic hypermutation, a T cell-dependent process. As ample evidence indicates that T cells drive the maturation of the ACPA response prior to arthritis onset, we undertook this study to investigate whether the presence of glycans in IgG ACPA V domains predicts the transition from predisease autoimmunity to overt RA. METHODS: We analyzed 2 independent sets of serum samples obtained from 126 ACPA-positive first-degree relatives (FDRs) of RA patients. Both sets originated from an Indigenous North American population and comprised cross-sectional and longitudinal samples of individuals who did or did not develop inflammatory arthritis. Serum IgG ACPAs were affinity-purified and subjected to ultra high-performance liquid chromatography-based glycan analysis. RESULTS: In both data sets, FDR-derived IgG ACPA displayed markedly lower levels of V domain glycans (<50%) compared to IgG ACPA from RA patients. Notably, FDRs who later developed RA showed extensive V-domain glycosylation before the onset of arthritis. Moreover, IgG ACPA V-domain glycosylation was strongly associated with future development of RA (hazard ratio 6.07 [95% confidence interval 1.46-25.2]; P = 0.013). CONCLUSION: Extensive glycosylation of the IgG ACPA V domain is present in a subset of predisposed FDRs of Indigenous North American RA patients. The presence of this feature substantially increases the risk of RA development. Based on these findings, we propose that glycosylation of the IgG ACPA V domain represents a predictive marker for RA development in ACPA-positive individuals and may serve to better target prevention measures.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".