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Kidney Int Rep. 2017 Nov;2(6):1066-1075. doi: 10.1016/j.ekir.2017.06.004.

Prediction of Chronic Kidney Disease Stage 3 by CKD273, a Urinary Proteomic Biomarker.

Author information

1
Mosaiques Diagnostics GmbH, Hannover, Germany.
2
Charité-Universitätsmedizin, Berlin, Germany.
3
Studies Coordinating Centre, Research Unit Hypertension and Cardiovascular Epidemiology, KU Leuven Department of Cardiovascular Diseases, University of Leuven, Leuven, Belgium.
4
Institute of Cardiovascular and Metabolic Disease, Institut National de la Santé et de la Recherche Médicale (INSERM), Toulouse, France.
5
Université Toulouse III Paul-Sabatier, Toulouse, France.
6
Sanford Burnham Prebys Medical Discovery Institute, La Jolla, California, USA.
7
Department of Clinical Pharmacy and Pharmacology, University Medical Centre Groningen, University of Groningen, Groningen, The Netherlands.
8
Steno Diabetes Centre, Gentofte, Denmark.
9
Department of Ophthalmology and Visual Sciences, University of Wisconsin School of Medicine and Public Health, Madison Wisconsin, USA.
10
Department of Epidemiology, Graduate School of Public Health, University of Pittsburgh, Pittsburgh, Pennsylvania, USA.
11
Department of Medical Sciences, University of Turin, Torino, Italy.
12
Institute of Cellular Medicine, Newcastle University, Newcastle upon Tyne, UK.
13
Institute of Cardiovascular Sciences, University College London, London, UK.
14
Faculty of Health, University of Aarhus, Aarhus, Denmark.
15
Faculty of Health, University of Copenhagen, Copenhagen, Denmark.
16
Biotechnology Division, Biomedical Research Foundation, Academy of Athens, Athens, Greece.
17
Nephrology Section, Department of Internal Medicine, Ghent University Hospital, Ghent, Belgium.
18
Institute of Cardiovascular and Medical Sciences, University of Glasgow, Glasgow, UK.
19
Centre for Molecular and Vascular Biology, KU Leuven Department of Cardiovascular Sciences, University of Leuven, Leuven, Belgium.
20
R&D Group VitaK, Maastricht University, Maastricht, The Netherlands.
21
University Hospital, Rheinisch-Westfälische Technische Hochschule Aachen, Aachen, Germany.
22
Department of Pathology, Cardiovascular Research Institute Maastricht, Maastricht University, Maastricht, The Netherlands.

Abstract

Introduction:

CKD273 is a urinary biomarker, which in advanced chronic kidney disease predicts further deterioration. We investigated whether CKD273 can also predict a decline of estimated glomerular filtration rate (eGFR) to <60 ml/min per 1.73 m2.

Methods:

In analyses of 2087 individuals from 6 cohorts (46.4% women; 73.5% with diabetes; mean age, 46.1 years; eGFR ≥ 60 ml/min per 1.73 m2, 100%; urinary albumin excretion rate [UAE] ≥20 μg/min, 6.2%), we accounted for cohort, sex, age, mean arterial pressure, diabetes, and eGFR at baseline and expressed associations per 1-SD increment in urinary biomarkers.

Results:

Over 5 (median) follow-up visits, eGFR decreased more with higher baseline CKD273 than UAE (1.64 vs. 0.82 ml/min per 1.73 m2; P < 0.0001). Over 4.6 years (median), 390 participants experienced a first renal endpoint (eGFR decrease by ≥10 to <60 ml/min per 1.73 m2), and 172 experienced an endpoint sustained over follow-up. The risk of a first and sustained renal endpoint increased with UAE (hazard ratio ≥ 1.23; P ≤ 0.043) and CKD273 (≥ 1.20; P ≤ 0.031). UAE (≥20 μg/min) and CKD273 (≥0.154) thresholds yielded sensitivities of 30% and 33% and specificities of 82% and 83% (P ≤ 0.0001 for difference between UAE and CKD273 in proportion of correctly classified individuals). As continuous markers, CKD273 (P = 0.039), but not UAE (P = 0.065), increased the integrated discrimination improvement, while both UAE and CKD273 improved the net reclassification index (P ≤ 0.0003), except for UAE per threshold (P = 0.086).

Discussion:

In conclusion, while accounting for baseline eGFR, albuminuria, and covariables, CKD273 adds to the prediction of stage 3 chronic kidney disease, at which point intervention remains an achievable therapeutic target.

KEYWORDS:

biomarker; chronic kidney disease; clinical science; glomerular filtration rate; peptidomics; proteomics

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