Aberrant IgG galactosylation precedes disease onset, correlates with disease activity, and is prevalent in autoantibodies in rheumatoid arthritis
Bibliographic record
Abstract
OBJECTIVE: To examine the association between aberrant IgG galactosylation and disease parameters in rheumatoid arthritis (RA). METHODS: Analysis of N-glycan in serum samples from multiple cohorts was performed. The IgG N-glycan content and the timing of N-glycan aberrancy relative to disease onset were compared in healthy subjects and in patients with RA. Correlations between aberrant galactosylation and disease activity were assessed in the RA cohorts. The impact of disease activity, sex, age, anti-cyclic citrullinated peptide (anti-CCP) antibody titer, disease duration, and C-reactive protein level on aberrant galactosylation was determined using multivariate analysis. The N-glycan content was also compared between epitope affinity-purified autoantibodies and the remaining IgG repertoire in RA patients. RESULTS: Our results confirm the aberrant galactosylation of IgG in RA patients as compared with healthy controls (mean +/- SD 1.36 +/- 0.43 versus 1.01 +/- 0.23; P < 0.0001). We observed a significant correlation between levels of aberrant IgG galactosylation and disease activity (Spearman's rho = 0.37, P < 0.0001). This correlation was higher in women (Spearman's rho = 0.60, P < 0.0001) than in men (Spearman's rho = 0.16, P = 0.10). Further, aberrant IgG galactosylation substantially predated the onset of arthritis and the diagnosis of RA (3.5 years) and resided selectively in the anticitrullinated antigen fraction. CONCLUSION: Our findings identify aberrant IgG galactosylation as a dysregulated component of the humoral immune response in RA that begins prior to disease onset, associates with disease activity in a sex-specific manner, and resides preferentially in autoantibodies.
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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) | 0.001 | 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".