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J Proteome Res. 2019 Apr 5;18(4):1796-1805. doi: 10.1021/acs.jproteome.8b00983. Epub 2019 Mar 12.

A Novel Metabolic Signature To Predict the Requirement of Dialysis or Renal Transplantation in Patients with Chronic Kidney Disease.

Author information

1
Institute of Computational Biology, Helmholtz Zentrum München , Neuherberg 85764 , Germany.
2
Institute of Genetic Epidemiology, Department of Biometry, Epidemiology, and Medical Bioinformatics, Faculty of Medicine and Medical Center , University of Freiburg , Freiburg 79106 , Germany.
3
Renal Division, Department of Medicine IV, Faculty of Medicine and Medical Center , University of Freiburg , Freiburg 79106 , Germany.
4
Institute for Computational Biomedicine, Englander Institute for Precision Medicine, Department of Physiology and Biophysics , Weill Cornell Medicine , New York , New York 10065 , United States.

Abstract

Identification of chronic kidney disease patients at risk of progressing to end-stage renal disease (ESRD) is essential for treatment decision-making and clinical trial design. Here, we explored whether proton nuclear magnetic resonance (NMR) spectroscopy of blood plasma improves the currently best performing kidney failure risk equation, the so-called Tangri score. Our study cohort comprised 4640 participants from the German Chronic Kidney Disease (GCKD) study, of whom 185 (3.99%) progressed over a mean observation time of 3.70 ± 0.88 years to ESRD requiring either dialysis or transplantation. The original four-variable Tangri risk equation yielded a C statistic of 0.863 (95% CI, 0.831-0.900). Upon inclusion of NMR features by state-of-the-art machine learning methods, the C statistic improved to 0.875 (95% CI, 0.850-0.911), thereby outperforming the Tangri score in 94 out of 100 subsampling rounds. Of the 24 NMR features included in the model, creatinine, high-density lipoprotein, valine, acetyl groups of glycoproteins, and Ca2+-EDTA carried the highest weights. In conclusion, proton NMR-based plasma fingerprinting improved markedly the detection of patients at risk of developing ESRD, thus enabling enhanced patient treatment.

KEYWORDS:

chronic kidney disease; kidney failure risk equation; metabolomics

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