Zacharias, Helena U. and Altenbuchinger, Michael and Schultheiss, Ulla T. and Samol, Claudia and Kotsis, Fruzsina and Poguntke, Inga and Sekula, Peggy and Krumsiek, Jan and Koettgen, Anna and Spang, Rainer and Oefner, Peter J. and Gronwald, Wolfram (2019) A Novel Metabolic Signature To Predict the Requirement of Dialysis or Renal Transplantation in Patients with Chronic Kidney Disease. JOURNAL OF PROTEOME RESEARCH, 18 (4). pp. 1796-1805. ISSN 1535-3893, 1535-3907
Full text not available from this repository. (Request a copy)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.
| Item Type: | Article |
|---|---|
| Uncontrolled Keywords: | RISK-FACTORS; PROGRESSION; FAILURE; MODEL; CKD; IDENTIFICATION; INSUFFICIENCY; SPECTROSCOPY; ASSOCIATION; BIOMARKERS; kidney failure risk equation; metabolomics; chronic kidney disease |
| Subjects: | 600 Technology > 610 Medical sciences Medicine |
| Divisions: | Medicine > Institut für Funktionelle Genomik > Lehrstuhl für Funktionelle Genomik (Prof. Oefner) Medicine > Institut für Funktionelle Genomik > Lehrstuhl für Statistische Bioinformatik (Prof. Spang) |
| Depositing User: | Dr. Gernot Deinzer |
| Date Deposited: | 14 Apr 2020 12:58 |
| Last Modified: | 14 Apr 2020 12:58 |
| URI: | https://pred.uni-regensburg.de/id/eprint/27253 |
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