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Urinary Podocalyxin as a Biomarker to Diagnose Membranous Nephropathy

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.


ROC curve.(A) Training cohort. AUC of each model is 0.777 [95% confidence interval (CI); 0.680–0.853] in Model A, 0.761 [95%CI; 0.652–0.848] in Model B, and 0.868 [95%CI; 0.781–0.931] in Model C. P value is 0.019 (A v.s. C), and 0.003 (B v.s. C). (B) Validation cohort. AUC of each model is 0.776 [95% confidence interval (CI); 0.717–0.841] in Model A, 0.690 [95%CI; 0.610–0.757] in Model B, and 0.846 [95%CI; 0.784–0.896] in Model C. P value is 0.003 (A v.s. C), and less than 0.001 (B v.s. C).
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pone.0163507.g002: ROC curve.(A) Training cohort. AUC of each model is 0.777 [95% confidence interval (CI); 0.680–0.853] in Model A, 0.761 [95%CI; 0.652–0.848] in Model B, and 0.868 [95%CI; 0.781–0.931] in Model C. P value is 0.019 (A v.s. C), and 0.003 (B v.s. C). (B) Validation cohort. AUC of each model is 0.776 [95% confidence interval (CI); 0.717–0.841] in Model A, 0.690 [95%CI; 0.610–0.757] in Model B, and 0.846 [95%CI; 0.784–0.896] in Model C. P value is 0.003 (A v.s. C), and less than 0.001 (B v.s. C).

Mentions: ROC curves for the training and validation cohorts are shown in Fig 2. In the training cohort, the AUC of model A, B, and C were 0.777 [95% confidence interval (CI), 0.689–0.864], 0.784 [0.691–0.878], and 0.868 [0.799–0.937], respectively. AUC of model C was significantly higher than that of models A and B (p = 0.019, 0.003, respectively). A similar result was shown in the validation cohort. The cut-off value of diagnostic score at the optimum point was 43.6 in model C. In the validation cohort, sensitivity was 80.5% and specificity was 73.5%. Cut-off points in each model are shown in S1 Dataset as a diagnostic score calculator. When you fill in the blank, diagnostic scores are calculated automatically. You can see whether the scores are higher than the cut-off points which were made in the training cohort.


Urinary Podocalyxin as a Biomarker to Diagnose Membranous Nephropathy
ROC curve.(A) Training cohort. AUC of each model is 0.777 [95% confidence interval (CI); 0.680–0.853] in Model A, 0.761 [95%CI; 0.652–0.848] in Model B, and 0.868 [95%CI; 0.781–0.931] in Model C. P value is 0.019 (A v.s. C), and 0.003 (B v.s. C). (B) Validation cohort. AUC of each model is 0.776 [95% confidence interval (CI); 0.717–0.841] in Model A, 0.690 [95%CI; 0.610–0.757] in Model B, and 0.846 [95%CI; 0.784–0.896] in Model C. P value is 0.003 (A v.s. C), and less than 0.001 (B v.s. C).
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pone.0163507.g002: ROC curve.(A) Training cohort. AUC of each model is 0.777 [95% confidence interval (CI); 0.680–0.853] in Model A, 0.761 [95%CI; 0.652–0.848] in Model B, and 0.868 [95%CI; 0.781–0.931] in Model C. P value is 0.019 (A v.s. C), and 0.003 (B v.s. C). (B) Validation cohort. AUC of each model is 0.776 [95% confidence interval (CI); 0.717–0.841] in Model A, 0.690 [95%CI; 0.610–0.757] in Model B, and 0.846 [95%CI; 0.784–0.896] in Model C. P value is 0.003 (A v.s. C), and less than 0.001 (B v.s. C).
Mentions: ROC curves for the training and validation cohorts are shown in Fig 2. In the training cohort, the AUC of model A, B, and C were 0.777 [95% confidence interval (CI), 0.689–0.864], 0.784 [0.691–0.878], and 0.868 [0.799–0.937], respectively. AUC of model C was significantly higher than that of models A and B (p = 0.019, 0.003, respectively). A similar result was shown in the validation cohort. The cut-off value of diagnostic score at the optimum point was 43.6 in model C. In the validation cohort, sensitivity was 80.5% and specificity was 73.5%. Cut-off points in each model are shown in S1 Dataset as a diagnostic score calculator. When you fill in the blank, diagnostic scores are calculated automatically. You can see whether the scores are higher than the cut-off points which were made in the training cohort.

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.