GlycA, a novel marker of inflammation, is elevated in systemic lupus erythematosus
Bibliographic record
Abstract
BACKGROUND: GlycA is a novel marker of systemic inflammation detected by nuclear magnetic resonance (NMR) spectroscopy. In the general population, GlycA is correlated with inflammatory markers such as C-reactive protein (CRP) and associated with coronary heart disease and diabetes. The utility of GlycA in patients with systemic lupus erythematosus (SLE) has not been defined. Therefore, we tested the hypothesis that GlycA concentrations are elevated in patients with SLE and associated with other markers of inflammation and coronary atherosclerosis. METHODS: We compared concentrations of GlycA, detected by NMR, in 116 patients with SLE and 84 control subjects frequency-matched for age, sex, and race. SLE disease activity index (SLEDAI) and the SLE Collaborating Clinics damage index (SLICC) were calculated. Acute phase reactants, a panel of cytokines, and a lipid panel were measured. Electron beam computer tomography (EBCT) was used to quantify coronary artery calcification, a measure of coronary artery atherosclerosis. RESULTS: Patients with SLE had higher concentrations of GlycA (398 (350-445)) than control subjects (339 (299-391)) µmol/L, p < 0.001. In patients with SLE, concentrations of GlycA were significantly associated with sedimentation rate (rho = 0.43), C-reactive protein (rho = 0.59), e-selectin (rho = 0.28), intracellular adhesion molecule-1 (rho = 0.30), triglycerides (rho = 0.45), all p < 0.0023 to account for multiple comparisons, but not with creatinine, SLEDAI, SLICC, or coronary calcium scores. CONCLUSIONS: Concentrations of GlycA are higher in patients with SLE than control subjects and associated with markers of inflammation but not with SLE disease activity or chronicity scores or coronary artery calcification.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".