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Clinical Trial
. 2015 Aug;26(8):1999-2010.
doi: 10.1681/ASN.2014050423. Epub 2015 Jan 14.

Diagnosis and Prediction of CKD Progression by Assessment of Urinary Peptides

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Free PMC article
Clinical Trial

Diagnosis and Prediction of CKD Progression by Assessment of Urinary Peptides

Joost P Schanstra et al. J Am Soc Nephrol. .
Free PMC article

Abstract

Progressive CKD is generally detected at a late stage by a sustained decline in eGFR and/or the presence of significant albuminuria. With the aim of early and improved risk stratification of patients with CKD, we studied urinary peptides in a large cross-sectional multicenter cohort of 1990 individuals, including 522 with follow-up data, using proteome analysis. We validated that a previously established multipeptide urinary biomarker classifier performed significantly better in detecting and predicting progression of CKD than the current clinical standard, urinary albumin. The classifier was also more sensitive for identifying patients with rapidly progressing CKD. Compared with the combination of baseline eGFR and albuminuria (area under the curve [AUC]=0.758), the addition of the multipeptide biomarker classifier significantly improved CKD risk prediction (AUC=0.831) as assessed by the net reclassification index (0.303±-0.065; P<0.001) and integrated discrimination improvement (0.058±0.014; P<0.001). Correlation of individual urinary peptides with CKD stage and progression showed that the peptides that associated with CKD, irrespective of CKD stage or CKD progression, were either fragments of the major circulating proteins, suggesting failure of the glomerular filtration barrier sieving properties, or different collagen fragments, suggesting accumulation of intrarenal extracellular matrix. Furthermore, protein fragments associated with progression of CKD originated mostly from proteins related to inflammation and tissue repair. Results of this study suggest that urinary proteome analysis might significantly improve the current state of the art of CKD detection and outcome prediction and that identification of the urinary peptides allows insight into various ongoing pathophysiologic processes in CKD.

Keywords: CKD; albuminuria; biomarker; extracellular matrix; fibrosis; renal progression.

Figures

Figure 1.
Figure 1.
Correlation analysis. Scatter diagrams of regression analyses with baseline eGFR and log urinary albumin (A) or CKD273 classifier (B) or with the slope of eGFR per year and log urinary albumin (C) or CKD273 classifier (D).
Figure 2.
Figure 2.
Classification of fast progressors. (A and B) Regression analyses of patients with an eGFR slope decline of >−5% per year with log urinary albumin (A) or with CKD273-classifier scores (B). The cut-off (for microalbuminuria and the CKD273 classifier) is shown as a dotted line in each scatter diagram. (C) ROC analysis of the CKD273 classifier (black line) and albuminuria (dotted line) using patients with fast-progressing CKD (eGFR slope decline of >−5% per year).
Figure 3.
Figure 3.
Overlap of proteins. (A) Venn diagram of proteins corresponding to peptides significantly associated with eGFR in patients of the cross-sectional cohort and with eGFR slope in patients of the follow-up cohort. Overlap with the CKD273-classifier is shown as well. (B) Venn diagram of proteins corresponding to peptides significantly associated with baseline eGFR in patients of the cross-sectional cohort with either mild-moderate or advanced stage CKD. Overlap with the CKD273 classifier is shown as well.

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