Low prevalence of antibodies to glucose‐6‐phosphate isomerase in patients with rheumatoid arthritis and a spectrum of other chronic autoimmune disorders
Bibliographic record
Abstract
OBJECTIVE: Arthritis in the K/BxN mouse model results from pathogenic immunoglobulins that recognize glucose-6-phosphate isomerase (GPI), a glycolytic enzyme residing in the cytoplasm of all cells. Antibodies directed against GPI can, alone, transfer arthritis to healthy recipients. Previous experiments have revealed significant titers of anti-GPI antibodies in the serum of many patients with rheumatoid arthritis (RA). We evaluated the generality of these observations in cohorts of patients with 12 different arthritic and chronic autoimmune diseases and in population-matched healthy control subjects. METHODS: Anti-GPI antibodies were assayed in 811 individual serum samples by enzyme-linked immunosorbent assay with 2 forms of GPI, recombinant and native. Results were confirmed by immunoblotting. RESULTS: Several patients had significantly elevated anti-GPI antibody titers, but without the prevalence or the specificity reported previously. Only 15% of RA patients had anti-GPI antibodies (range 12-29% in different cohorts), with a higher prevalence in patients with active disease. Psoriatic arthritis, undifferentiated arthritis, and spondylarthropathy patients also displayed anti-GPI antibodies at similar frequencies (12-25%). Similar titers were detected in a proportion (5-10%) of control subjects or patients with Crohn's disease or sarcoidosis. Very high titers were found in rare cases of RA and systemic lupus erythematosus. CONCLUSION: No disease-specific pattern of antibody positivity to GPI was apparent. While the antibody-mediated mechanism at play in the mouse model may exemplify a generic mechanism for some forms of arthritis in humans, GPI itself does not appear to be a target common to the majority of RA patients.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".