ANTI-NEUTROPHIL EXTRACELLULAR TRAP ANTIBODIES IN AUTOIMMUNE RHEUMATIC DISEASES: A SUITABLE BIOMARKER OF THROMBOSIS IN SYSTEMIC LUPUS ERYTHEMATOSUS
Bibliographic record
Abstract
PV029 / #143 Poster Topic: AS04 - Biomarkers Background/Purpose The release of intracellular material during neutrophil extracellular trap (NET) formation – NETosis – could have a role in breaking immunological tolerance to self-components. When NETs are formed in an uncontrolled manner or are not cleared properly, the immune system might recognize NETs and trigger an autoimmune reaction against NET components, giving rise to anti-NET antibodies. To date, limited information is available regarding the prevalence and the clinical significance of anti-NET antibodies in Systemic Lupus Erythematosus (SLE), Systemic Sclerosis (SSc) and Rheumatoid Arthritis (RA) patients. This study aimed to elucidate the prevalence and the potential role as biomarker of anti-NET antibodies in SLE, SSc and RA patients. Methods Serum samples from SLE, SSc, RA and healthy donors (HD) were evaluated for antiNETs IgG, using an ELISA home-made coated with phorbol myristate acetate (PMA)-induced NET. Based on a positivity threshold set at the 99th percentile for HD sera we calculated the positive samples. Results We enrolled 349 patients with autoimmune rheumatic diseases (ARD), founding a prevalence of anti-NETs of about 40% (Figure 1). Of the 136 SLE patients (Table 1) 50 (36.8%) were anti-NETs positive, revealing an association between anti-NETs and Antiphospholipid Syndrome (APS) (OR 3.37 [95% CI 1.22-9.36], p = 0.02) and with an history of arterial thrombosis regardless of coexisting Secondary APS (SAPS) (OR 5.52 [95% CI 1.07-28.52], p = 0.032). In RA patients 52 out 131 (39.7%) tested positive for anti-NETs and we found a significant difference in the anti-NETs OD between seronegative (33/131) and seropositive patients (median 0.079 OD [IQR 0.04] vs median 0.084 OD [IQR 0.05] respectively; p = 0.04). Almost all RA patients (88.6%) with a positive anti-NETs test were ACPA-positive. Indeed, our results revealed a significant association between testing positive for anti-NETs and the presence of ACPA (p = 0.049). In addition, the anti-NETs OD was significantly greater in RA patients than in those with SLE (p = 0.016). Of the 82 SSc patients enrolled 33 (40%) were anti-NETs positive, and we found a direct correlation with CCL-18 (r 0.289 [CI 95% 0.04-0.50], p = 0.02), a biomarker of worst prognosis and mortality in ILD- SSc patients. Figure 1. Levels of anti-NET in autoimmune rheumatic disease (ARD) patients and healthy donors (HD) Table 1. Clinical and demographic features of Systemic Lupus Erythematosus patients Conclusions Anti-NETs are highly prevalent in ARD patients. In SLE patients, they are associated with APS and arterial thrombosis, a major cause of mortality in SLE. Their association with ACPA further highlighted the possible role of NETosis in 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 | 0.001 | 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 | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".