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Zacharias, H.U. ; Altenbuchinger, M.* ; Schultheiss, U.T.* ; Samol, C.* ; Kotsis, F.* ; Poguntke, I.* ; Sekula, P.* ; Krumsiek, J. ; Köttgen, A.* ; Spang, R.* ; Oefner, P.J.* ; Gronwald, W.*

A novel metabolic signature to predict the requirement of dialysis or renal transplantation in patients with chronic kidney disease.

J. Proteome Res. 18, 1796–1805 (2019)
DOI PMC
Open Access Green as soon as Postprint is submitted to ZB.
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.
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Publication type Article: Journal article
Document type Scientific Article
Corresponding Author
Keywords Kidney Failure Risk Equation ; Metabolomics ; Chronic Kidney Disease; Risk-factors; Progression; Failure; Model; Ckd; Identification; Insufficiency; Spectroscopy; Association; Biomarkers
ISSN (print) / ISBN 1535-3893
e-ISSN 1535-3907
Journal Journal of Proteome Research
Quellenangaben Volume: 18, Issue: 4, Pages: 1796–1805 Article Number: , Supplement: ,
Publisher American Chemical Society (ACS)
Publishing Place 1155 16th St, Nw, Washington, Dc 20036 Usa
Non-patent literature Publications
Reviewing status Peer reviewed
Institute(s) Institute of Computational Biology (ICB)