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Urinary Podocalyxin as a Biomarker to Diagnose Membranous Nephropathy

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ABSTRACT

Background: A non-invasive diagnostic marker of membranous nephropathy (MN) is desirable. The urinary level of podocalyxin (PCX) is higher in various glomerular diseases, including MN. The aim of this study was to construct a diagnostic model of MN with the combination of urinary PCX and clinical parameters.

Methods: We performed this cross-sectional study to construct the diagnostic models for MN by using data and samples from the multicenter kidney biopsy registry of Nagoya University and its affiliated hospitals. Urinary (u-) PCX was measured by sandwich ELISA. We constructed 3 types of diagnostic models in 105 training samples: u-PCX univariate model, the combined model of clinical parameters other than u-PCX (clinical model), and the combined model of both u-PCX and clinical parameters (combined model). We assessed the clinical usefulness of the diagnostic models through the comparison of c-statistics and decision curve analysis (DCA) in 209 validation samples.

Results: The clinical model consisted of age, glomerular filtration rate, and diabetes mellitus. In the training cohort, the c-statistics were 0.868 [95% CI, 0.799–0.937] in the combined model. In the validation cohort, sensitivity was 80.5% and specificity was 73.5% on the cut-off value. The net benefit of the combined model was better between threshold probabilities of 40–80% in DCA.

Conclusions: In this study, we demonstrated the utility of u-PCX as a diagnostic marker for MN and the clinical usefulness of the diagnostic models, through the combination of u-PCX and clinical parameters including age, glomerular filtration rate, and diabetes mellitus.

No MeSH data available.


Decision curve analysis.(A) Training cohort. (B) Validation cohort.
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pone.0163507.g003: Decision curve analysis.(A) Training cohort. (B) Validation cohort.

Mentions: Fig 3 illustrates the decision curves for models A, B, and C to predict the correct diagnosis of MN in patients with NS. All models were useful between threshold probabilities of 40–60%, and the net benefit of model C was better than the other 2 models between threshold probabilities of 40–80%.


Urinary Podocalyxin as a Biomarker to Diagnose Membranous Nephropathy
Decision curve analysis.(A) Training cohort. (B) Validation cohort.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC5036798&req=5

pone.0163507.g003: Decision curve analysis.(A) Training cohort. (B) Validation cohort.
Mentions: Fig 3 illustrates the decision curves for models A, B, and C to predict the correct diagnosis of MN in patients with NS. All models were useful between threshold probabilities of 40–60%, and the net benefit of model C was better than the other 2 models between threshold probabilities of 40–80%.

View Article: PubMed Central - PubMed

ABSTRACT

Background: A non-invasive diagnostic marker of membranous nephropathy (MN) is desirable. The urinary level of podocalyxin (PCX) is higher in various glomerular diseases, including MN. The aim of this study was to construct a diagnostic model of MN with the combination of urinary PCX and clinical parameters.

Methods: We performed this cross-sectional study to construct the diagnostic models for MN by using data and samples from the multicenter kidney biopsy registry of Nagoya University and its affiliated hospitals. Urinary (u-) PCX was measured by sandwich ELISA. We constructed 3 types of diagnostic models in 105 training samples: u-PCX univariate model, the combined model of clinical parameters other than u-PCX (clinical model), and the combined model of both u-PCX and clinical parameters (combined model). We assessed the clinical usefulness of the diagnostic models through the comparison of c-statistics and decision curve analysis (DCA) in 209 validation samples.

Results: The clinical model consisted of age, glomerular filtration rate, and diabetes mellitus. In the training cohort, the c-statistics were 0.868 [95% CI, 0.799–0.937] in the combined model. In the validation cohort, sensitivity was 80.5% and specificity was 73.5% on the cut-off value. The net benefit of the combined model was better between threshold probabilities of 40–80% in DCA.

Conclusions: In this study, we demonstrated the utility of u-PCX as a diagnostic marker for MN and the clinical usefulness of the diagnostic models, through the combination of u-PCX and clinical parameters including age, glomerular filtration rate, and diabetes mellitus.

No MeSH data available.