Role of Neutrophil Extracellular Traps Regarding Patients at Risk of Increased Disease Activity and Cardiovascular Comorbidity in Systemic Lupus Erythematosus
Bibliographic record
Abstract
OBJECTIVE: Neutrophil extracellular traps (NET) are essential in host defense, but are also linked to inflammation and autoimmunity, including in systemic lupus erythematosus (SLE). We recently described that immune complexes (IC) induce NET formation, promoting SLE-like disease in mice. In the current study, we investigated, for the first time to our knowledge, the role of NET in human SLE and their association with disease activity and severity. METHODS: Levels of NET (myeloperoxidase-DNA complexes) were analyzed in plasma from 4 cross-sectional SLE cohorts (n = 44-142), 1 longitudinal SLE cohort (n = 47), and healthy individuals (n = 100) using ELISA. Type I interferon activity was determined using a cell reporter system. RESULTS: Patients with SLE had elevated levels of NET in circulation compared to healthy controls (p < 0.01). NET levels identified patients with a severe disease phenotype characterized by IC-driven nephritis (p < 0.05). Though not associated with current disease activity (p = 0.20), levels of NET were associated with future increase in the Systemic Lupus Erythematosus Disease Activity Index (SLEDAI) within 3 months (OR 1.75, p = 0.01), as well as an overall heightened SLEDAI over 1 year (p < 0.01). Finally, levels of NET were associated with arterial events (OR 5.0, p = 0.02) and endothelial cell activation (p < 0.001). CONCLUSION: NET levels are elevated in patients with SLE, associated with IC-driven disease. NET levels provide significant clinical value in identifying patients at risk of active disease and/or severe disease, including nephritis and cardiovascular disease, and may allow for early interventions.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".