Clinical Proteomics | |
Urinary proteomics in chronic kidney disease: diagnosis and risk of progression beyond albuminuria | |
Stein I Hallan2  Bjørn E Vikse1  Petra Zürbig3  Marius A Øvrehus4  | |
[1] Department of Medicine, Haugesund Hospital, Haugesund, Norway;Center of Renal Translational Medicine, University of California San Diego (UCSD), La Jolla, USA;Mosaiques Diagnostics GmbH, Hannover, Germany;Department of Cancer Research and Molecular Medicine, Faculty of Medicine, Norwegian University of Science and Technology, Trondheim, Norway | |
关键词: Disease progression; Proteomics; Albuminuria; Urine; Hypertensive nephropathy; Chronic kidney disease; | |
Others : 1222547 DOI : 10.1186/s12014-015-9092-7 |
|
received in 2015-01-15, accepted in 2015-07-18, 发布年份 2015 | |
【 摘 要 】
Background
The contrast between a high prevalence of chronic kidney disease (CKD) and the low incidence of end-stage renal disease highlights the need for new biomarkers of progression beyond albuminuria testing. Urinary proteomics is a promising method, but more studies focusing on progression rate and patients with hypertensive nephropathy are needed.
Results
We analyzed urine samples with capillary electrophoresis coupled to a mass-spectrometer from 18 well characterized patients with CKD stage 4–5 (of whom six with hypertensive nephropathy) and 17 healthy controls. Classification scores based on a previously developed panel of 273 urinary peptides were calculated and compared to urine albumin dipstick results. Urinary proteomics classified CKD with a sensitivity of 0.95 and specificity of 1.00. Overall diagnostic accuracy (area under ROC curve) was 0.98, which was better than for albuminuria (0.85, p = 0.02). Results for hypertensive nephropathy were similar to other CKD diagnoses. Adding the proteomic score to an albuminuria model improved detection of rapid kidney function decline (>4 ml/min/1.73 m 2per year) substantially: area under ROC curve increased from 0.762 to 0.909 (p = 0.042), and 38% of rapid progressors were correctly reclassified to a higher risk and 55% of slow progressors were correctly reclassified to a lower risk category. Reduced excretion of collagen types I–III, uromodulin, and other indicators of interstitial inflammation, fibrosis and tubular dysfunction were associated with CKD diagnosis and rapid progression. Patients with hypertensive nephropathy displayed the same findings as other types of CKD.
Conclusions
Urinary proteomic analyses had a high diagnostic accuracy for CKD, including hypertensive nephropathy, and strongly improved identification of patients with rapid kidney function decline beyond albuminuria testing.
【 授权许可】
2015 Øvrehus et al.
【 预 览 】
Files | Size | Format | View |
---|---|---|---|
20150823030301854.pdf | 888KB | download | |
Fig.3. | 25KB | Image | download |
Fig.2. | 13KB | Image | download |
Fig.1. | 13KB | Image | download |
【 图 表 】
Fig.1.
Fig.2.
Fig.3.
【 参考文献 】
- [1]Coresh J, Selvin E, Stevens LA, Manzi J, Kusek JW, Eggers P et al.. Prevalence of chronic kidney disease in the United States. JAMA J Am Med Assoc. 2007; 298(17):2038-2047.
- [2]Hallan SI, Coresh J, Astor BC, Asberg A, Powe NR, Romundstad S et al.. International comparison of the relationship of chronic kidney disease prevalence and ESRD risk. J Am Soc Nephrol. 2006; 17(8):2275-2284.
- [3]Levey AS, de Jong PE, Coresh J, El Nahas M, Astor BC, Matsushita K et al.. The definition, classification, and prognosis of chronic kidney disease: a KDIGO Controversies Conference report. Kidney Int. 2011; 80(1):17-28.
- [4]Halbesma N, Kuiken DS, Brantsma AH, Bakker SJ, Wetzels JF, De Zeeuw D et al.. Macroalbuminuria is a better risk marker than low estimated GFR to identify individuals at risk for accelerated GFR loss in population screening. J Am Soc Nephrol JASN. 2006; 17(9):2582-2590.
- [5]Berhane AM, Weil EJ, Knowler WC, Nelson RG, Hanson RL. Albuminuria and estimated glomerular filtration rate as predictors of diabetic end-stage renal disease and death. Clin J Am Soc Nephrol CJASN. 2011; 6(10):2444-2451.
- [6]Panduru N. Urinary liver type fatty acid binding protein and progression of diabetic nephropathy in type 1 diabetes. Diabetes Care. 2013; 36(7):2077-2083.
- [7]Hallan SI, Ritz E, Lydersen S, Romundstad S, Kvenild K, Orth SR. Combining GFR and albuminuria to classify CKD improves prediction of ESRD. J Am Soc Nephrol JASN. 2009; 20(5):1069-1077.
- [8]Lambers Heerspink HJ, Gansevoort RT, Brenner BM, Cooper ME, Parving HH, Shahinfar S et al.. Comparison of different measures of urinary protein excretion for prediction of renal events. J Am Soc Nephrol JASN. 2010; 21(8):1355-1360.
- [9]Pepe MS, Janes H, Longton G, Leisenring W, Newcomb P. Limitations of the odds ratio in gauging the performance of a diagnostic, prognostic, or screening marker. Am J Epidemiol. 2004; 159(9):882-890.
- [10]Good DM, Zürbig P, Argilés À, Bauer HW, Behrens G, Coon JJ et al.. Naturally occurring human urinary peptides for use in diagnosis of chronic kidney disease. Mol Cell Proteomics. 2010; 9(11):2424-2437.
- [11]Urquidi V, Rosser CJ, Goodison S. Molecular diagnostic trends in urological cancer: biomarkers for non-invasive diagnosis. Curr Med Chem. 2012; 19(22):3653-3663.
- [12]Clarke W, Silverman BC, Zhang Z, Chan DW, Klein AS, Molmenti EP. Characterization of renal allograft rejection by urinary proteomic analysis. Ann Surg. 2003; 2237(5):660-664.
- [13]Neilson EG. Finding new sea legs for urine proteomics. J Am Soc Nephrol. 2009; 20(6):1162.
- [14]US Renal Data System. USRDS Annual Data Reports (2000–2009): Atlas of Chronic Kidney Disease and End-Stage Renal Disease in the United States, National Institutes of Health. (Accessed March 22, 2010, at http://www.usrdsorg/adrhtm). 2010.
- [15]Devarajan P. Emerging biomarkers of acute kidney injury. Contrib Nephrol. 2007; 156:203-212.
- [16]Stodkilde L, Madsen MG, Palmfeldt J, Topcu SO, Norregaard R, Olsen LH et al.. Urinary proteome analysis in congenital bilateral hydronephrosis. Scand J Urol Nephrol. 2012.
- [17]Julian BA, Wittke S, Haubitz M, Zurbig P, Schiffer E, McGuire BM et al.. Urinary biomarkers of IgA nephropathy and other IgA-associated renal diseases. World J Urol. 2007; 25(5):467-476.
- [18]González-Buitrago JM, Ferreira L, Lorenzo I. Urinary proteomics. Clin Chim Acta. 2007; 375(1–2):49-56.
- [19]Argiles A, Siwy J, Duranton F, Gayrard N, Dakna M, Lundin U et al.. CKD273, a new proteomics classifier assessing CKD and its prognosis. PLoS One. 2013; 8(5):e62837.
- [20]Siwy J, Schanstra JP, Argiles A, Bakker SJ, Beige J, Boucek P et al.. Multicentre prospective validation of a urinary peptidome-based classifier for the diagnosis of type 2 diabetic nephropathy. Nephrol Dial Transplant Off Publ Eur Dial Transplant Assoc Eur Ren Assoc. 2014; 29(8):1563-1570.
- [21]Kistler AD, Serra AL, Siwy J, Poster D, Krauer F, Torres VE et al.. Urinary proteomic biomarkers for diagnosis and risk stratification of autosomal dominant polycystic kidney disease: a multicentric study. PLoS One. 2013; 8(1):e53016.
- [22]Zurbig P, Jerums G, Hovind P, Macisaac R, Mischak H, Nielsen SE et al.. Urinary proteomics for early diagnosis in diabetic nephropathy. Diabetes. 2012.
- [23]Pencina MJ, D’Agostino RB, D’Agostino RB, Vasan RS. Evaluating the added predictive ability of a new marker: from area under the ROC curve to reclassification and beyond. Stat Med. 2008; 27(2):157-172.
- [24]Genovese F. The extracellular matrix in the kidney A source of novel non invasive biomarkers of kidney fibrosis. Fibrogenes Tissue Repair. 2014; 7(4):1-14.
- [25]Catania JM, Chen G, Parrish AR. Role of matrix metalloproteinases in renal pathophysiologies. Am J Physiol Renal Physiol. 2007; 292(3):F905-F911.
- [26]Ghoul BE, Squalli T, Servais A, Elie C, Meas-Yedid V, Trivint C et al.. Urinary procollagen III aminoterminal propeptide (PIIINP): a fibrotest for the nephrologist. Clin J Am Soc Nephrol CJASN. 2010; 5(2):205-210.
- [27]Altemtam N, Nahas ME, Johnson T. Urinary matrix metalloproteinase activity in diabetic kidney disease: a potential marker of disease progression. Nephron Extra. 2012; 2:219-232.
- [28]Zeisberg M, Neilson EG. Mechanisms of tubulointerstitial fibrosis. J Am Soc Nephrol JASN. 2010; 21(11):1819-1834.
- [29]Scolari F, Izzi C, Ghiggeri GM. Uromodulin: from monogenic to multifactorial diseases. Nephrol Dial Transplant Off Publ Eur Dial Transplant Assoc Eur Ren Assoc. 2014.
- [30]El-Achkar TM, Wu XR. Uromodulin in kidney injury: an instigator, bystander, or protector? Am J Kidney Dis Off J Natl Kidney Found. 2012; 59(3):452-461.
- [31]Zhou J, Chen Y, Liu Y, Shi S, Wang S, Li X et al.. Urinary uromodulin excretion predicts progression of chronic kidney disease resulting from IgA nephropathy. PLoS One. 2013; 8(8):e71023.
- [32]Lou O, Alcaide P, Luscinskas FW, Muller WA. CD99 Is a Key Mediator of the transendothelial migration of neutrophils. J Immunol. 2007; 178(2):1136-1143.
- [33]Nangaku M. Complement membrane attack complex C5b9 mediates interstitial disease in experimental nephrotic syndrome. J Am Soc Nephrol JASN. 1999; 10:2323-2331.
- [34]Kazanecki CC, Uzwiak DJ, Denhardt DT. Control of osteopontin signaling and function by post-translational phosphorylation and protein folding. J Cell Biochem. 2007; 102(4):912-924.
- [35]Gang X, Ueki K, Kon S, Maeda M, Naruse T, Nojima Y. Reduced urinary excretion of intact osteopontin in patients with IgA nephropathy. Am J Kidney Dis Off J Natl Kidney Found. 2001; 37(2):374-379.
- [36]Hemmelgarn B. Relation between kidney function, proteinuria and adverse outcomes. JAMA. 2010; 303(5):423-429.
- [37]Wen CP, Yang YC, Tsai MK, Wen SF. Urine dipstick to detect trace proteinuria: an underused tool for an underappreciated risk marker. Am J Kidney Dis Off J Natl Kidney Found. 2011; 58(1):1-3.
- [38]Fliser D, Novak J, Thongboonkerd V, Argiles A, Jankowski V, Girolami MA et al.. Advances in urinary proteome analysis and biomarker discovery. J Am Soc Nephrol. 2007; 18(4):1057-1071.
- [39]Levey AS, Stevens LA, Schmid CH, Zhang YL, Castro AF, Feldman HI et al.. A new equation to estimate glomerular filtration rate. Ann Intern Med. 2009; 150(9):604-612.
- [40]Kolch W, Neususs C, Pelzing M, Mischak H. Capillary electrophoresis-mass spectrometry as a powerful tool in clinical diagnosis and biomarker discovery. Mass Spectrom Rev. 2005; 24(6):959-977.
- [41]Neuhoff N, Kaiser T, Wittke S, Krebs R, Pitt A, Burchard A et al.. Mass spectrometry for the detection of differentially expressed proteins: a comparison of surface-enhanced laser desorption/ionization and capillary electrophoresis/mass spectrometry. Rapid Commun Mass Spectrom RCM. 2004; 18(2):149-156.
- [42]Coon JJ, Zurbig P, Dakna M, Dominiczak AF, Decramer S, Fliser D et al.. CE-MS analysis of the human urinary proteome for biomarker discovery and disease diagnostics. Proteomics Clin Appl. 2008; 2(7–8):964.
- [43]Girolami M, Mischak H, Krebs R. Analysis of complex, multidimensional datasets. Drug Discov Today Technol. 2006; 3(1):13-19.
- [44]Yang Z. Biological applications of support vector machines. Brief Bioinform. 2004; 5:328-338.
- [45]Yang ZR, Chou KC. Bio-support vector machines for computational proteomics. Bioinformatics. 2004; 20(5):735-741.
- [46]Wittke S, Fliser D, Haubitz M, Bartel S, Krebs R, Hausadel F et al.. Determination of peptides and proteins in human urine with capillary electrophoresis—mass spectrometry, a suitable tool for the establishment of new diagnostic markers. J Chromatogr A. 2003; 1013(1–2):173-181.
- [47]Siwy J, Mullen W, Golovko I, Franke J, Zurbig P. Human urinary peptide database for multiple disease biomarker discovery. Proteomics Clin Appl. 2011; 5(5–6):367-374.
- [48]Kerr KF, Wang Z, Janes H, McClelland RL, Psaty BM, Pepe MS. Net reclassification indices for evaluating risk prediction instruments: a critical review. Epidemiology. 2014; 25(1):114-121.