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Record W4396999202 · doi:10.1681/asn.20203110s1543b

Urinary Biomarkers as a Tool for Monitoring Remissions and Predicting Relapses in Autoimmune Glomerulonephritis

2020· article· en· W4396999202 on OpenAlexaff
Suzanne Dominique Genest, Myriam Khalili, Jean‐Philippe Rioux, Jérémy Quadri, Arnaud Bonnefoy, Stéphan Troyanov

Bibliographic record

VenueJournal of the American Society of Nephrology · 2020
Typearticle
Languageen
FieldMedicine
TopicRenal Diseases and Glomerulopathies
Canadian institutionsCentre Hospitalier Universitaire Sainte-JustineUniversité de MontréalHôpital du Sacré-Cœur de Montréal
Fundersnot available
KeywordsMedicineGlomerulonephritisUrinary systemImmunologyAutoimmune diseaseInternal medicineIntensive care medicineKidneyAntibody

Abstract

fetched live from OpenAlex

Background: Complement-mediated injury, inflammation and fibrosis play central roles in the pathogenesis of autoimmune glomerulonephritis. The use of urinary biomarkers as a surrogate of these pathways of injury could assist clinicians during the clinical follow-up. We investigated the value of urinary biomarkers of complement activation, inflammation and fibrosis during periods of sustained remission among patients with autoimmune glomerulonephritis. Methods: We prospectively examined 100 patients with ANCA-associated vasculitis, focal segmental glomerulosclerosis, IgA nephropathy, lupus nephritis, membranoproliferative glomerulonephritis and membranous nephropathy. Proteinuria, urinary sC5b-9, monocyte chemoattractant protein-1 (MCP-1) and transforming growth factor-β1 (TGF-β1), expressed as creatinine ratios, were measured at presentation and during follow-up visits. We used standard definitions of remission and relapse for each type of glomerulonephritis. Wilcoxon signed-rank test was used to compare changes in urinary biomarkers during remissions and relapses. Results: We identified 95 periods of active disease and 82 episodes of sustained remission. Inactive periods lasted a median of 22 (11-32) months. Eighty percent (n=66) of these were not followed by a relapse. During these episodes of remission, urinary biomarkers continued to steadily decrease, achieving a reduction of 40% for proteinuria, 40% for urinary sC5b-9, 38% for MCP-1 and 40% for TGF-β1 (all p < 0.05). Twenty percent (n=16) of inactive periods reflected remissions with subsequent relapses. Biomarker levels during the inactive period preceding relapses did not significantly change for proteinuria (+8%), urinary sC5b-9 (+15%) and MCP-1 (4%), while they decreased for TGF-β1 (-30%, p=0.02). During relapses, we observed a 3.2-fold (1.9-8.3) increase in proteinuria and a significantly greater 8.5-fold (4.2-56.9) increase in urinary sC5b-9 (p=0.001). By contrast, urinary MCP-1 and TGF-β1 increased significantly less than proteinuria. Conclusions: Failure to achieve a sustained reduction in urinary biomarkers during remission was associated with a subsequent risk of relapse of autoimmune glomerulonephritis. Urinary sC5b-9 appears to be a more discerning marker of immunological relapse. Funding: Private Foundation Support

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)0.005
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 score0.002
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0010.001
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 head0.017
GPT teacher head0.290
Teacher spread0.273 · 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".

Quick stats

Citations0
Published2020
Admission routes1
Has abstractyes

Explore more

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