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Multicenter Study
. 2024 Aug;84(2):205-214.e1.
doi: 10.1053/j.ajkd.2024.01.520. Epub 2024 Mar 5.

Urinary Plasminogen as a Marker of Disease Progression in Human Glomerular Disease

Marina de Cos  1 , Gohar Mosoyan  1 , Kinsuk Chauhan  1 , Jonathan P Troost  2 , Jenny S Wong  1 , Sean Lefferts  1 , Paul Morgan  1 , Kristin Meliambro  1 , Marc Egerman  1 , Justina Ray  1 , Tom Parker  3 , Daniel Levine  3 , Surya Seshan  4 , Yoni Bardash  5 , Benjamin Horowitz  6 , Candice A Kent  7 , Melissa M Shaw  7 , Alan Perlman  8 , Dennis G Moledina  7 , Steven G Coca  1 , Kirk N Campbell  9
Affiliations
Multicenter Study

Urinary Plasminogen as a Marker of Disease Progression in Human Glomerular Disease

Marina de Cos et al. Am J Kidney Dis. 2024 Aug.

Abstract

Rationale & objective: Glomerular disorders have a highly variable clinical course, and biomarkers that reflect the molecular mechanisms underlying their progression are needed. Based on our previous work identifying plasminogen as a direct cause of podocyte injury, we designed this study to test the association between urine plasmin(ogen) (ie, plasmin and its precursor plasminogen) and end-stage kidney disease (ESKD).

Study design: Multicenter cohort study.

Setting & participants: 1,010 patients enrolled in the CureGN Cohort with biopsy-proven glomerular disease (focal segmental glomerulosclerosis, membranous nephropathy, and immunoglobulin A nephropathy).

Predictors: The main predictor was urine plasmin(ogen) at baseline. Levels were measured by an electrochemiluminescent immunoassay developed de novo. Traditional clinical and analytical characteristics were used for adjustment. The ratio of urine plasmin(ogen)/expected plasmin(ogen) was evaluated as a predictor in a separate model.

Outcome: Progression to ESKD.

Analytical approach: Cox regression was used to examine the association between urinary plasmin(ogen) and time to ESKD. Urinary markers were log2 transformed to approximate normal distribution and normalized to urinary creatinine (Log2uPlasminogen/cr, Log2 urinary protein/cr [UPCR]). Expected plasmin(ogen) was calculated by multiple linear regression.

Results: Adjusted Log2uPlasminogen/cr was significantly associated with ESKD (HR per doubling Log2 uPlasminogen/cr 1.31 [95% CI, 1.22-1.40], P<0.001). Comparison of the predictive performance of the models including Log2 uPlasminogen/cr, Log2 UPCR, or both markers showed the plasmin(ogen) model superiority. The ratio of measured/expected urine plasmin(ogen) was independently associated with ESKD: HR, 0.41 (95% CI, 0.22-0.77) if ratio<0.8 and HR 2.42 (95% CI, 1.54-3.78) if ratio>1.1 (compared with ratio between 0.8 and 1.1).

Limitations: Single plasmin(ogen) determination does not allow for the study of changes over time. The use of a cohort of mostly white patients and the restriction to patients with 3 glomerular disorders limits the external validity of our analysis.

Conclusions: Urinary plasmin(ogen) and the ratio of measured/expected plasmin(ogen) are independently associated with ESKD in a cohort of patients with glomerular disease. Taken together with our previous experimental findings, urinary plasmin(ogen) could be a useful biomarker in prognostic decision making and a target for the development of novel therapies in patients with proteinuria and glomerular disease.

Plain-language summary: Glomerular diseases are an important cause of morbidity and mortality in patients of all ages. Knowing the individual risk of progression to dialysis or transplantation would help to plan the follow-up and treatment of these patients. Our work studies the usefulness of urinary plasminogen as a marker of progression in this context, since previous studies indicate that plasminogen may be involved in the mechanisms responsible for the progression of these disorders. Our work in a sample of 1,010 patients with glomerular disease demonstrates that urinary plasminogen (as well as the ratio of measured to expected plasminogen) is associated with the risk of progression to end-stage kidney disease. Urine plasminogen exhibited good performance and, if further validated, could enable risk stratification for timely interventions in patients with proteinuria and glomerular disease.

Keywords: Biomarkers; end-stage kidney disease; glomerular disease; urinary plasminogen.

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Conflict of interest statement

Financial Disclosure: DGM is named co-inventor on a pending patent, “Methods and Systems for Diagnosis of Acute Interstitial Nephritis,” and is a cofounder of the diagnostics company Predict AIN, LLC. SGC is an employee of Icahn School of Medicine at Mount Sinai, which owns part of Renalytix; is a consultant for Renalytix, Takeda, Nuwellis, and Vifor, Bayer, Boehringer-Ingelheim, Reprieve Cardiovascular, Axon, and 3ive; has ownership interests in Renalytix and pulseData; receives research funding from Renalytix, ProKidney, Renal Research Institute, and XORTX; receives payments for patents or royalties from Renalytix; has an advisory or leadership role in Renalytix; serves as Associate Editor for Kidney360; is a member of the editorial boards of JASN, CJASN, and Kidney International; and is an adjudicator for the Critical Path Institute’s Predictive Safety Testing Consortium Nephrotoxicity Working Group. KNC, GM, SC and MDC are named as co-inventors on a pending patent “Urinary plasminogen as a marker of disease progression in human glomerular disease”. All other authors declare that they have no relevant financial interests.

Figures

Figure 1.
Figure 1.
Scatterplots showing correlation between Log2uPlasminogen/Cr and Log2UPCR (A) and eGFR (B) in the CureGN cohort.
Figure 2.
Figure 2.
Unadjusted Kaplan-Meier curves of ESKD-free survival based on uPlasminogen/Cr quintiles. Log-rank test for equality of survivor functions: P < 0.001.
Figure 3.
Figure 3.
Hazard Ratio (HR) for the association between Log2uPlasminogen/Cr and risk of ESKD among all participants and stratified analysis in subgroups by UPCR levels, diagnosis and APOL1 genotype. APOL1 analysis was performed in the subcohort of patients whose genotype information was available (Non APOL1 high-risk genotype n = 850, APOL1 high-risk genotype n = 57).
Figure 4.
Figure 4.
Scatterplot showing measured Log2uPlasminogen/Cr and expected Log2uPlasminogen/Cr (based on eGFR and Log2UPCR). Color indicates measured/expected uPlasminogen tertiles.
See this image and copyright information in PMC

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