Utility of a novel inflammatory marker, GlycA, for assessment of rheumatoid arthritis disease activity and coronary atherosclerosis
Bibliographic record
Abstract
INTRODUCTION: GlycA is a novel inflammatory biomarker measured using nuclear magnetic resonance (NMR). Its NMR signal primarily represents glycosylated acute phase proteins. GlycA was associated with inflammation and development of cardiovascular disease in initially healthy women. We hypothesized that GlycA is a biomarker of disease activity and is associated with coronary artery atherosclerosis in patients with rheumatoid arthritis (RA). METHODS: We conducted a cross-sectional study of 166 patients with RA and 90 control subjects. GlycA was measured from an NMR signal originating from N-acetylglucosamine residues on circulating glycoproteins. The relationship between GlycA and RA disease activity (Disease Activity Score based on 28 joints (DAS28)) and coronary artery calcium score was determined. RESULTS: GlycA concentrations were higher in patients with RA (median (interquartile range): 398 μmol/L (348 to 473 μmol/L)) than control subjects (344 μmol/L (314 to 403 μmol/L) (P < 0.001). In RA, GlycA was strongly correlated with DAS28 based on erythrocyte sedimentation rate (DAS28-ESR) and DAS28 based on C-reactive protein (DAS28-CRP) and their components, including tender and swollen joint counts, global health score, ESR and CRP (all P < 0.001). The area under the receiver operating characteristic curve for GlycA's ability to differentiate between patients with low versus moderate to high disease activity based on DAS28-CRP was 0.75 (95% confidence interval (CI): 0.68, 0.83). For each quartile increase in GlycA, the odds of having coronary artery calcium increased by 48% (95% CI: 4%, 111%), independent of age, race and sex (P = 0.03). CONCLUSION: GlycA is a novel inflammatory marker that may be useful for assessment of disease activity and is associated with coronary artery atherosclerosis in patients with RA.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".