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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 möglich sobald Postprint bei der ZB eingereicht worden ist.
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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Publikationstyp Artikel: Journalartikel
Dokumenttyp Wissenschaftlicher Artikel
Korrespondenzautor
Schlagwörter 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
Zeitschrift Journal of Proteome Research
Quellenangaben Band: 18, Heft: 4, Seiten: 1796–1805 Artikelnummer: , Supplement: ,
Verlag American Chemical Society (ACS)
Verlagsort 1155 16th St, Nw, Washington, Dc 20036 Usa
Nichtpatentliteratur Publikationen
Begutachtungsstatus Peer reviewed
Institut(e) Institute of Computational Biology (ICB)