BACKGROUND: Risk of kidney function decline in immunoglobulin A (IgA) nephropathy (IgAN) is significant and may not be predicted by available clinical and histological tools. To serve this unmet need, we aimed at developing a urinary biomarker-based algorithm that predicts rapid disease progression in IgAN, thus enabling a personalized risk stratification. METHODS: In this multicentre study, urine samples were collected in 209 patients with biopsy-proven IgAN. Progression was defined by tertiles of the annual change of estimated glomerular filtration rate (eGFR) during follow-up. Urine samples were analysed using capillary electrophoresis coupled mass spectrometry. The area under the receiver operating characteristic curve (AUC) was used to evaluate the risk prediction models. RESULTS: Of the 209 patients, 64% were male. Mean age was 42 years, mean eGFR was 63 mL/min/1.73 m2 and median proteinuria was 1.2 g/day. We identified 237 urine peptides showing significant difference in abundance according to the tertile of eGFR change. These included fragments of apolipoprotein C-III, alpha-1 antitrypsin, different collagens, fibrinogen alpha and beta, titin, haemoglobin subunits, sodium/potassium-transporting ATPase subunit gamma, uromodulin, mucin-2, fractalkine, polymeric Ig receptor and insulin. An algorithm based on these protein fragments (IgAN237) showed a significant added value for the prediction of IgAN progression [AUC 0.89; 95% confidence interval (CI) 0.83-0.95], as compared with the clinical parameters (age, gender, proteinuria, eGFR and mean arterial pressure) alone (0.72; 95% CI 0.64-0.81). CONCLUSIONS: A urinary peptide classifier predicts progressive loss of kidney function in patients with IgAN significantly better than clinical parameters alone.

Department of Internal Medicine 4 Nephrology and Hypertension Medical University Innsbruck Innsbruck Austria

Department of Medicine Division of Nephrology University Health Network University of Toronto Toronto Canada

Department of Nephrology 1st School of Medicine and General University Hospital Charles University Prague Czech Republic

Department of Nephrology and Rheumatology University Medical Centre Göttingen Göttingen Germany

Department of Nephrology and Transplantation Medicine Wroclaw Medical University Wroclaw Poland

Department of Nephrology Skaraborg Hospital Skövde Sweden

Department of Public Health and Clinical Medicine Umeå University Umeå Sweden

Division of Nephrology and KfH Renal Unit Hospital St Georg Leipzig Germany

Martin Luther University Halle Wittenberg Halle Saale Germany

Mosaiques Diagnostics GmbH Hannover Germany

Nephrology Research University of Toronto Toronto Ontario Canada

Research Health Institute Fundación Jiménez Díaz University Madrid Spain

See more in PubMed

McGrogan  A, Franssen  CF, de Vries  CS.  The incidence of primary glomerulonephritis worldwide: a systematic review of the literature. Nephrol Dial Transplant  2011; 26: 414–430 PubMed

Barbour  SJ, Coppo  R, Zhang  H  et al. ; for the International IgA Nephropathy Network . Evaluating a new international risk-prediction tool in IgA nephropathy. JAMA Intern Med  2019; 179: 942–952 PubMed PMC

Barbour  SJ, Espino-Hernandez  G, Reich  HN  et al. . The MEST score provides earlier risk prediction in lgA nephropathy. Kidney Int  2016; 89: 167–175 PubMed

Reich  HN, Troyanov  S, Scholey  JW  et al. . Remission of proteinuria improves prognosis in IgA nephropathy. J Am Soc Nephrol  2007; 18: 3177–3183 PubMed

Lv  J, Zhang  H, Wong  MG  et al. ; for the TESTING Study Group . Effect of oral methylprednisolone on clinical outcomes in patients with IgA nephropathy: the TESTING randomized clinical trial. JAMA  2017; 318: 432–442 PubMed PMC

Rauen  T, Eitner  F, Fitzner  C  et al. . Intensive supportive care plus immunosuppression in IgA nephropathy. N Engl J Med  2015; 373: 2225–2236 PubMed

Kidney Disease: Improving Global Outcomes (KDIGO) Glomerulonephritis Work Group. KDIGO clinical practice guideline for glomerulonephritis. Kidney Int Suppl  2012; 2: 139–274

Barbour  SJ, Reich  HN.  Risk stratification of patients with IgA nephropathy. Am J Kidney Dis  2012; 59: 865–873 PubMed

Haas  M, Verhave  JC, Liu  ZH  et al. . A multicenter study of the predictive value of crescents in IgA nephropathy. J Am Soc Nephrol  2017; 28: 691–701 PubMed PMC

Roberts  ISD, Cook  HT, Troyanov  S  et al. ; Working Group of the International Ig ANN, the Renal Pathology S . The Oxford classification of IgA nephropathy: pathology definitions, correlations, and reproducibility. Kidney Int  2009; 76: 546–556 PubMed

Cattran  DC, Coppo  R, Cook  HT  et al. ; Working Group of the International IgA Nephropathy Network and the Renal Pathology Society . The Oxford classification of IgA nephropathy: rationale, clinicopathological correlations, and classification. Kidney Int  2009; 76: 534–545 PubMed

Julian  BA, Wittke  S, Novak  J  et al. . Electrophoretic methods for analysis of urinary polypeptides in IgA-associated renal diseases. Electrophoresis  2007; 28: 4469–4483 PubMed

Haubitz  M, Wittke  S, Weissinger  EM  et al. . Urine protein patterns can serve as diagnostic tools in patients with IgA nephropathy. Kidney Int  2005; 67: 2313–2320 PubMed

Siwy  J, Zurbig  P, Argiles  A  et al. . Noninvasive diagnosis of chronic kidney diseases using urinary proteome analysis. Nephrol Dial Transplant  2017; 32: 2079–2089 PubMed PMC

Levey  AS, Stevens  LA, Schmid  CH  et al. ; for the CKD-EPI (Chronic Kidney Disease Epidemiology Collaboration) . A new equation to estimate glomerular filtration rate. Ann Intern Med  2009; 150: 604–612 PubMed PMC

Theodorescu  D, Schiffer  E, Bauer  HW  et al. . Discovery and validation of urinary biomarkers for prostate cancer. Prot Clin Appl  2008; 2: 556–570 PubMed PMC

Zimmerli  LU, Schiffer  E, Zurbig  P  et al. . Urinary proteomic biomarkers in coronary artery disease. Mol Cell Proteomics  2008; 7: 290–298 PubMed

Latosinska  A, Siwy  J, Mischak  H  et al. . Peptidomics and proteomics based on CE-MS as a robust tool in clinical application: the past, the present, and the future. Electrophoresis  2019; 40: 2294–2308 PubMed

Jantos-Siwy  J, Schiffer  E, Brand  K  et al. . Quantitative urinary proteome analysis for biomarker evaluation in chronic kidney disease. J Proteome Res  2009; 8: 268–281 PubMed

Klein  J, Papadopoulos  T, Mischak  H  et al. . Comparison of CE-MS/MS and LC-MS/MS sequencing demonstrates significant complementarity in natural peptide identification in human urine. Electrophoresis  2014; 35: 1060–1064 PubMed

Zurbig  P, Renfrow  MB, Schiffer  E  et al. . Biomarker discovery by CE-MS enables sequence analysis via MS/MS with platform-independent separation. Electrophoresis  2006; 27: 2111–2125 PubMed

Benjamini  Y, Hochberg  Y.  Controlling the false discovery rate: a practical and powerful approach to multiple testing. J R Statist Soc B  1995; 57: 289–300

Perez-Riverol  Y, Csordas  A, Bai  J  et al. . The PRIDE database and related tools and resources in 2019: improving support for quantification data. Nucleic Acids Res  2019; 47: D442–D450 PubMed PMC

Good  DM, Zurbig  P, Argiles  A  et al. . Naturally occurring human urinary peptides for use in diagnosis of chronic kidney disease. Mol Cell Proteomics  2010; 9: 2424–2437 PubMed PMC

KDIGO 2012 clinical practice guideline for the evaluation and management of chronic kidney disease. Kidney Int Suppl  2013; 3: 1–150 PubMed

Haubitz  M, Good  DM, Woywodt  A  et al. . Identification and validation of urinary biomarkers for differential diagnosis and evaluation of therapeutic intervention in anti-neutrophil cytoplasmic antibody-associated vasculitis. Mol Cell Proteomics  2009; 8: 2296–2307 PubMed PMC

Wendt  R, He  T, Latosinska  A  et al. . Proteomic characterization of obesity-related nephropathy. Clin Kidney J  2020; 13: 684–692 PubMed PMC

Rossing  K, Mischak  H, Dakna  M  et al. . Urinary proteomics in diabetes and CKD. J Am Soc Nephrol  2008; 19: 1283–1290 PubMed PMC

Magalhaes  P, Pejchinovski  M, Markoska  K  et al. . Association of kidney fibrosis with urinary peptides: a path towards non-invasive liquid biopsies?  Sci Rep  2017; 7: 16915. PubMed PMC

Decramer  S, Gonzalez de Peredo  A, Breuil  B  et al. . Urine in clinical proteomics. Mol Cell Proteomics  2008; 7: 1850–1862 PubMed

Eisen  AZ, Bauer  EA, Jeffrey  JJ.  Human skin collagenase. The role of serum alpha-globulins in the control of activity in vivo and in vitro. Proc Natl Acad Sci USA  1971; 68: 248–251 PubMed PMC

Prikryl  P, Vojtova  L, Maixnerova  D  et al. . Proteomic approach for identification of IgA nephropathy-related biomarkers in urine. Physiol Res  2017; 66: 621–632 PubMed

Smith  A, L’Imperio  V, De Sio  G  et al. . Antitrypsin detected by MALDI imaging in the study of glomerulonephritis: its relevance in chronic kidney disease progression. Proteomics  2016; 16: 1759–1766 PubMed

Siwy  J, Mischak  H, Zurbig  P.  Proteomics and personalized medicine: a focus on kidney disease. Expert Rev Proteomics  2019; 16: 773–782 PubMed

Pontillo  C, Zhang  ZY, Schanstra  JP  et al. . Prediction of chronic kidney disease stage 3 by CKD273, a urinary proteomic biomarker. Kidney Int Rep  2017; 2: 1066–1075 PubMed PMC

Pontillo  C, Jacobs  L, Staessen  JA  et al. . A urinary proteome-based classifier for the early detection of decline in glomerular filtration. Nephrol Dial Transplant  2017; 32: 1510–1516 PubMed

Schanstra  JP, Zurbig  P, Alkhalaf  A  et al. . Diagnosis and prediction of CKD progression by assessment of urinary peptides. J Am Soc Nephrol  2015; 26: 1999–2010 PubMed PMC

Zurbig  P, Mischak  H, Menne  J  et al. . CKD273 enables efficient prediction of diabetic nephropathy in nonalbuminuric patients. Diabetes Care  2019; 42: e4–e5 PubMed

Verbeke  F, Siwy  J, Van Biesen  W  et al. . The urinary proteomics classifier chronic kidney disease 273 predicts cardiovascular outcome in patients with chronic kidney disease. Nephrol Dial Transplant  2019; doi: 10.1093/ndt/gfz242 (14 December 2019, date last accessed) PubMed

Currie  GE, von Scholten  BJ, Mary  S  et al. . Urinary proteomics for prediction of mortality in patients with type 2 diabetes and microalbuminuria. Cardiovasc Diabetol  2018; 17: 50. PubMed PMC