Longitudinal Evaluation of Lipoprotein Variables in Systemic Lupus Erythematosus Reveals Adverse Changes with Disease Activity and Prednisone and More Favorable Profiles with Hydroxychloroquine Therapy
Bibliographic record
Abstract
OBJECTIVE: Systemic lupus erythematosus (SLE) is associated with accelerated atherosclerotic cardiovascular disease. Patients with SLE have adverse lipoprotein variables, but little is known about how these change with treatment and disease activity. The nuclear magnetic resonance LipoProfile test contains a glycoprotein signal-termed GlycA, an inflammatory marker, which has not been evaluated in SLE. We assessed patients longitudinally to determine how lipoproteins and GlycA change with active SLE. METHODS: Sera from selected clinical visits of patients in the Hopkins Lupus Cohort were analyzed for lipoprotein and GlycA levels. Univariate and multivariate analyses were performed to evaluate lipoprotein variables and their relationship to ethnicity, disease activity, prednisone use, and hydroxychloroquine (HCQ) therapy. RESULTS: Fifty-two patients were included over 229 visits. Adverse changes in lipoprotein variables with disease activity were demonstrated. For each point increase in the Systemic Lupus Erythematosus Disease Activity Index, there was a decrease in high-density lipoprotein (HDL) even after adjusting for corticosteroid use. Prednisone was associated with higher very low-density lipoprotein, low-density lipoprotein, HDL, and triglycerides. HCQ was associated with more favorable variables. GlycA levels were higher than in normal populations and increased with disease activity. CONCLUSION: Adverse changes in lipoprotein profiles were associated with SLE activity and prednisone therapy. This gives insight into mechanisms of atherosclerosis in SLE. Favorable lipoprotein variables occurred in those taking HCQ. GlycA increased with disease activity and was higher than in healthy populations.
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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 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".