Urinary peptide biomarker panel associated with an improvement in estimated glomerular filtration rate in chronic kidney disease patients. Issue 5 (1st September 2017)

• Record Type: Journal Article
• Authors: Markoska, Katerina; Pejchinovski, Martin; Pontillo, Claudia; Zürbig, Petra; Jacobs, Lotte; Smith, Andrew; Masin-Spasovska, Jelka; Stojceva-Taneva, Olivera; Polenakovic, Momir; Magni, Fulvio; Mischak, Harald; Spasovski, Goce
• Is Part Of: Nephrology dialysis transplantation. Volume 33:Issue 5(2018)
• Page Start: 751
• Page End: 759
• Publication Date: 2017-09-01
• Subjects: biomarkers -- CKD -- diagnosis -- GFR -- urine peptide markers; Nephrology -- Periodicals; Hemodialysis -- Periodicals; Kidneys -- Transplantation -- Periodicals; Hemodialysis; Kidneys -- Transplantation; Nephrology; Periodicals; 616.61
• DOI: 10.1093/ndt/gfx263
• Languages: English
• ISSNs: 0931-0509

Abstract

ABSTRACT: Background: An improvement in the glomerular filtration rate (GFR) of chronic kidney disease patients has been an underestimated clinical outcome. Although this may be considered as an unexpected disease course, it may provide some insights into possible mechanisms underlying disease remission and/or regression. Therefore, our aim was to identify urinary peptide biomarkers associated with an improvement in estimated GFR (eGFR) over time and to improve patient stratification. Methods: Capillary electrophoresis coupled with mass spectrometry (CE-MS) was employed to evaluate the urine peptidome of patients with different types of renal diseases. In total, 376 patients with a slope/year between −1.5% and +1.5% were designated as non-progressors or stable, while 177 patients with a > 5% slope/year were designated as patients with an improved eGFR for state-of-art biomarker discovery and validation. Results: We detected 384 significant peptide fragments by comparing the CE-MS data of the stable patients and those with improved renal function in our development cohort. Of these 384, a set of 141 peptides with available amino acid sequence information were used to generate a support vector machine-based classification panel. The biomarker panel was applied to our validation cohort, achieving a moderate area under the curve (AUC) value of 0.85 (81% sensitivity and 81% specificity). The majority of the peptides (78%) from the diagnostic panel arose from different types of collagen. Conclusions: We have developed a panel of urinary peptide markers able to discriminate those patients predisposed to improve their kidney function over time and possibly be treated with more specific or less aggressive therapy.