An autoantibody targeting glycated IgG is associated with elevated serum immune complexes in rheumatoid arthritis (RA)
Bibliographic record
Abstract
Advanced glycation end-products (AGE) play a role in diabetes complications and in RA. An autoantibody to IgG-AGE has been shown to correlate with RA disease activity. Thus we sought to analyse serum immune complexes (IC) and AGE-modified proteins in Caucasians and North American Indians to see if the presence of anti-IgG-AGE influenced their composition. Polyethylene glycol precipitation of IC from the serum of anti-IgG-AGE-positive or -negative RA patients, and healthy and diabetic controls were examined. Concentrations of circulating IC were highest in anti-IgG-AGE+ RA patients, followed by anti-IgG-AGE- RA patients, which were greater than healthy controls. IC amounts in the Ojibwe were consistently higher than in Caucasians. Affinity purification of AGE-modified proteins from IC and immunoblotting with antibodies against Ig gamma and mu heavy chains, kappa and lambda light chains, and AGE Nepsilon(carboxymethyl)lysine and imidazolone yielded similar results: anti-AGE+ RA patients had elevated levels relative to those without the autoantibody. Levels in both RA groups were higher than in controls. Glycated albumin amounts followed a similar distribution, but were not influenced by the presence of anti-AGE antibodies. A heavily glycated kappa-chain was present primarily in IC from anti-IgG-AGE+ patients. These studies indicate that anti-AGE antibodies have a direct impact on the accumulation of IgG-AGE but not glycated albumin, and may block the normal clearance of IgG-AGE through AGE receptors.
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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 | 0.000 |
| 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".