Elevated Serum Glucose-6-Phosphate Isomerase Correlates with Histological Disease Activity and Clinical Improvement After Initiation of Therapy in Patients with Rheumatoid Arthritis
Bibliographic record
Abstract
OBJECTIVE: To determine serum glucose-6-phosphate isomerase (GPI) concentrations in patients with rheumatoid arthritis (RA), and to test whether they correlate with objective measures of disease activity. METHODS: Sera from 116 patients with RA, 69 patients with non-RA rheumatic diseases, and 101 healthy controls were analyzed. Levels of soluble serum GPI were measured by ELISA. Histological disease activity was determined with the synovitis score in synovial needle biopsies from 58 of the 116 patients with RA. Thirty-one of the 58 synovium samples were stained for CD68, CD3, CD20, CD38, CD79a, and CD34 by immunohistochemistry. Demographic data were collected, as well as serological and clinical variables that indicate RA disease activity, for Spearman correlation analysis. RESULTS: Serum GPI level correlated positively with the synovitis score (r = 0.278, p = 0.034). Significantly higher soluble GPI levels were detected in the RA sera compared with sera from healthy controls and the non-RA disease controls (2.25 ± 2.82 vs 0.03 ± 0.05 and 0.19 ± 0.57 μg/ml, respectively; p < 0.0001). The rate of serum GPI positivity was significantly higher in the RA patients than in the non-RA disease controls (64.7% vs 10.1%; p < 0.0001). Spearman analysis showed no significant correlation between serum GPI level and Disease Activity Score in 28 joints at baseline. After initiation of antirheumatic treatments, GPI levels decreased significantly (2.81 ± 3.12 vs 1.44 ± 2.09 μg/ml; p = 0.016), paralleling improvement of the disease activity indices. CONCLUSION: Elevated serum GPI may be involved in the synovitis of RA and may prove useful as a serum marker for disease activity of RA.
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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 | 0.002 |
| 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".