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Record W4410715747 · doi:10.3899/jrheum.2025-0390.o048

UTILITY OF URINARY BIOMARKERS TO PREDICT LONG-TERM RENAL OUTCOMES IN LUPUS NEPHRITIS

2025· article· en· W4410715747 on OpenAlexaffvenue
Laura Whittall-Garcia, R. Baker, Michael Kim, Dennisse Bonilla, Murray B. Urowitz, D. Gladman, Zahi Touma, Joan Wither

Bibliographic record

VenueThe Journal of Rheumatology · 2025
Typearticle
Languageen
FieldMedicine
TopicSystemic Lupus Erythematosus Research
Canadian institutionsUniversity Health NetworkUniversity of Toronto
Fundersnot available
KeywordsMedicineLupus nephritisUrinary systemUrinary sedimentNephritisIntensive care medicineInternal medicineDisease

Abstract

fetched live from OpenAlex

O048 / #626 Topic:AS15 - Lupus Nephritis-Clinical ABSTRACT CONCURRENT SESSION 08: RECENT ADVANCES IN LUPUS BIOMARKERS 23-05-2025 1:40 PM - 2:40 PM Background/Purpose Lupus nephritis (LN) affects up to 50% of patients with lupus, of whom 40% will experience a subsequent renal flare, and up to 20% will progress to end-stage renal disease. Repeat kidney biopsies (KB) performed 2 years after the last LN flare have been shown to predict subsequent renal flares and renal dysfunction. In this study, we assessed whether 5 urinary biomarkers (UB), including CD163, MCP-1, Adiponectin, sVCAM-1 and PF4 measured 2 years after a LN flare, predict long-term renal outcomes. Methods Patients who had a LN flare and stored urine 24±3 months after the LN flare were included in the study. The 5 UB levels were measured by ELISA 24±3 months after the LN flare. Examined renal outcomes: 1) Time to a subsequent LN flare (increase in proteinuria of at least 1000 mg/day if the baseline was <500 mg/day or doubling of proteinuria if the baseline was ≥500 mg/day, prompting a change in therapy) and 2) time to 30% decline in eGFR, after their 2-year urinary sample collection. Results 69 patients with LN were included. The median (IQR) follow-up time after their 2-year urinary sample collection was 129 (97.5-150) months. 50 patients achieved proteinuria of ≤700 mg at 2 years after the LN flare. This subcohort of patients had significantly lower UB levels 2 years after the LN flare compared to patients who persisted with proteinuria >700 mg (Figure 1). In this subcohort of patients, 27 (54%) experienced a subsequent LN flare with a median time to flare (IQR) of 3.5 (1.67-6.87) years, and 10 (20%) had a 30% decline in eGFR at a median time of 4.38 (3.73-5.33) years after their 2-year urinary sample collection. Elevated levels of MCP-1 (HR 1.13 (1.01-1.27), p=0.03) and CD163 (HR 1.48 (1.15-1.90), p=0.002) predicted a subsequent LN flare. While CD163 (HR 1.31 (1.10-1.57), p=0.002), Adiponectin (HR 1.53 (1.22-1.91), p=0.0002), sVCAM-1 (HR 1.11 (1.03-1.21), p=0.006), and PF4 (HR 1.14 (1.04-1.25), p=0.003) predicted a 30% decline in eGFR (Table 1). Figure 1. UB were significantly higher in patients who did not achieve an uPCR ≤700 mg (n=19) at 24±3 months after the LN flare as compared to those who did (n=50). Symbols represent the determination from a single individual, columns the median and the bars IQR. Table 1. Multivariable Cox Regression analysis. Predictors of adverse renal outcomes (Subcohort of patients who achieved a proteinuria of ≤700 mg at 24±3 months after the LN flare, N=50) Conclusions UB measured 2 years after an LN flare predicted long-term renal outcomes.

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 score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.021
GPT teacher head0.322
Teacher spread0.302 · 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
Published2025
Admission routes2
Has abstractyes

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