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1117 Neutrophil extracellular traps as a biomarker to predict outcomes in lupus nephritis

2022· article· en· W4313532980 on OpenAlexaff
Laura Whittall-García, Farnoosh Naderinavi, Dafna D. Gladman, Murray B. Urowitz, Zahi Touma, Anna Konvalinka, Joan Wither

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldImmunology and Microbiology
TopicNeutrophil, Myeloperoxidase and Oxidative Mechanisms
Canadian institutionsTranslational Research in OncologyToronto General HospitalToronto Western HospitalUniversity of TorontoUniversity Health Network
Fundersnot available
KeywordsNeutrophil extracellular trapsLupus nephritisMedicineNeutrophil elastaseProteinuriaHMGB1CohortSystemic lupus erythematosusInternal medicineBiomarkerNephropathyCreatinineDipstickGastroenterologyImmunologyElastaseUrineFlareKidneyEndocrinologyReceptorInflammationBiology

Abstract

fetched live from OpenAlex

Background Neutrophil Extracellular Traps (NETs) have been implicated in Lupus Nephritis (LN) pathogenesis. SLE neutrophils release High Mobility Group Box-1 (HMGB1) protein, in turn, HMGB1 in NETs correlates with histologic findings of Active LN (ALN). The aim was to determine if the amount of NET complexes (Elastase-DNA and HMGB1-DNA) in serum at the time of a LN flare predicts renal outcomes in the following 24 months. Methods The study had a 2-staged approach. In an exploratory cohort composed of active SLE (clinical SLEDAI ≥ 1), inactive SLE and healthy controls (HC), we assessed the association between our in-house ELISA assays for Elastase-DNA and HMGB1-DNA complexes and ALN. A separate LN cohort was then used to determine the utility of NET complexes to predict renal outcomes over the subsequent 24 months. All patients had ALN, defined as a 24-hour urine protein >500mg with a subsequent modification in therapy by the treating physician, a baseline eGFR >30ml/min (3 months prior to the flare), stored serum sample ±3 months from the renal flare, and at least 2- years follow-up. The following outcomes were ascertained: Complete response (CR) at 12 and 24 months after flare (proteinuria <500mg/day and a serum creatinine within 15% of the baseline); severe renal impairment (eGFR≤30ml/min) at 12 and 24 months after flare; and the percentage decline in the eGFR over the 24 months after flare. Results Ninety-two individuals were included in the exploratory cohort (49 active SLE, 23 inactive SLE and 20 HC). NET complexes were significantly higher in SLE patients compared to HC and tended to be higher in active SLE compared to inactive patients. Patients with ALN (36.7%) had significantly higher levels of NET complexes compared to active SLE without LN. Furthermore, patients with proliferative LN had higher levels of NET complexes compared to non-proliferative LN (figure 1). The LN cohort included 109 ALN patients. The median (IQR) age was 29 (23-41) years, 84% were women, and disease duration was 6.4 (0.8-10.5) years. 37.9% were Caucasian, 22.2% Black and 17.5% Asian, the baseline eGFR was 112 (97-127) ml/min. 77.9% had a kidney biopsy at the time of the LN flare, of whom 55.9% had a proliferative or mixed class, 17.4% class V, and 4.5% class I or II. 39.4% and 50.5% of the ALN patients achieved CR at 12 and 24 months, respectively and 11% had an eGFR ≤ 30ml/min after 24 months. Similar to the results from the exploratory cohort, proliferative LN had higher levels of NET complexes compared to non-proliferative LN patients (Elastase-DNA: 111.7 vs 25.9, p=0.0003; HMGB1-DNA: 85.2 vs 25.4, p=0.002, proliferative vs non-proliferative, respectively). Patients with higher baseline levels of NET complexes had higher odds of not achieving CR and of having severe renal impairment after 24 months of the flare. NET complexes outperformed conventional biomarkers (table 1). There was a linear relationship between the amount of baseline Elastase-DNA and HMGB1-DNA complexes and the decline in renal function in the subsequent 24 months (figure 2). Conclusions Elastase-DNA and HMGB1-DNA complexes predicted renal outcomes, including response to therapy and decline in kidney function at 2 years after the LN flare.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.016
GPT teacher head0.241
Teacher spread0.226 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

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Citations1
Published2022
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