ACPA-IgG variable domain glycosylation increases before the onset of rheumatoid arthritis and stabilizes thereafter; a cross-sectional study encompassing over 1500 samples
Bibliographic record
Abstract
Abstract Objective The autoimmune response in rheumatoid arthritis (RA) is marked by anti-citrullinated protein antibodies (ACPA). A remarkable feature of ACPA-IgG is the abundant expression of N -linked glycans in the variable domain. Nonetheless, the presence of ACPA variable domain glycans (VDG) across disease stages and its’ response to therapy is poorly described. To understand its dynamics, we investigated the abundance of ACPA-IgG VDG in 1574 samples from individuals in different clinical disease stages. Methods Using liquid chromatography, we analyzed ACPA-IgG VDG profiles of 7 different cohorts from Japan, Canada, the Netherlands and Sweden. We assessed 184 healthy, 228 pre-symptomatic, 277 arthralgia, 305 patients at RA-onset and 117 RA-patients 4, 8 and 12 months after disease onset. Additionally, we measured VDG of 234 samples from RA-patients that did or did not achieve long-term drug-free remission (DFR) during up to 16 years follow-up. Results Our data show that ACPA-IgG VDG significantly increases (p<0.0001) towards disease-onset and associates with ACPA-levels and epitope spreading pre-diagnosis. A slight increase in VDG was observed in established RA and a moderate influence of treatment. Individuals who later achieved DFR displayed reduced ACPA-IgG VDG already at RA-onset. Conclusion The abundance of ACPA-IgG VDG rises towards RA-onset and correlates with maturation of the ACPA-response. Although, ACPA-IgG VDG levels are rather stable in established disease, a lower degree at RA-onset correlates with DFR. Even though the underlying biological mechanisms are still elusive, our data support the concept that VDG relates to an expansion of the ACPA-response pre-disease and contributes to disease-development.
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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 | 0.001 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".