Plasma proteomics classifiers improve risk prediction for renal disease in patients with hypertension or type 2 diabetes

Journal of Hypertension

Volumn 33, Issue 10, 2015, Pages 2123-2132

  Pena, Michelle J ^a   Jankowski, Joachim ^b   Heinze, Georg ^c   Kohl, Maria ^d   Heinzel, Andreas ^e   Bakker, Stephan J L ^a   Gansevoort, Ron T ^a   Rossing, Peter ^f,g,h   De Zeeuw, Dick ^a   Heerspink, Hiddo J Lambers ^a   Jankowski, Vera ^b  

^a UNIVERSITY MEDICAL CENTER GRONINGEN   (Netherlands)

^b UNIVERSITY HOSPITAL RWTH AACHEN   (Germany)

^c MEDICAL UNIVERSITY OF VIENNA   (Austria)

^d UNIVERSITY OF ERLANGEN NUREMBERG   (Germany)

^e Emergentec Biodevelopment GmbH   (Austria)

^f STENO DIABETES CENTER   (Denmark)

^g AARHUS UNIVERSITY   (Denmark)

^h UNIVERSITY OF COPENHAGEN   (Denmark)

Author keywords

albuminuria; biomarkers; hypertension; nephropathy; proteomics; type 2 diabetes

Indexed keywords

ANTIHYPERTENSIVE AGENT; ANTILIPEMIC AGENT; CHOLESTEROL; GLUCOSE; MAMMALIAN TARGET OF RAPAMYCIN; MITOGEN ACTIVATED PROTEIN KINASE P38; PHOSPHATIDYLINOSITOL 3 KINASE; PROTEIN KINASE B; VASCULOTROPIN; WNT PROTEIN; BIOLOGICAL MARKER; PEPTIDE;

ADULT; AGED; ANGIOGENESIS; ARTICLE; CASE CONTROL STUDY; CHOLESTEROL BLOOD LEVEL; COHORT ANALYSIS; CONTROLLED STUDY; DIASTOLIC BLOOD PRESSURE; ELECTROSPRAY MASS SPECTROMETRY; FEMALE; FOLLOW UP; GLOMERULUS FILTRATION RATE; GLUCOSE BLOOD LEVEL; GROUPS BY AGE; HEMOGLOBIN BLOOD LEVEL; HUMAN; HYPERTENSION; INTRACELLULAR SIGNALING; KIDNEY FIBROSIS; MACROALBUMINURIA; MAJOR CLINICAL STUDY; MALE; MATRIX ASSISTED LASER DESORPTION IONIZATION TIME OF FLIGHT MASS SPECTROMETRY; MICROALBUMINURIA; MIDDLE AGED; MINERAL METABOLISM; NEPHRITIS; NON INSULIN DEPENDENT DIABETES MELLITUS; PRIORITY JOURNAL; PROTEIN PROTEIN INTERACTION; PROTEOMICS; RENIN ANGIOTENSIN ALDOSTERONE SYSTEM; REVERSED PHASE HIGH PERFORMANCE LIQUID CHROMATOGRAPHY; RISK ASSESSMENT; SEX DIFFERENCE; STATISTICAL SIGNIFICANCE; SYSTOLIC BLOOD PRESSURE; ALBUMINURIA; BLOOD; COMPLICATION; DISEASE COURSE; KIDNEY DISEASES; PATHOPHYSIOLOGY; PREDICTIVE VALUE; REPRODUCIBILITY; RISK FACTOR;

AGED; ALBUMINURIA; BIOMARKERS; CASE-CONTROL STUDIES; DIABETES MELLITUS, TYPE 2; DISEASE PROGRESSION; FEMALE; GLOMERULAR FILTRATION RATE; HUMANS; HYPERTENSION; KIDNEY DISEASES; MALE; MIDDLE AGED; PEPTIDES; PREDICTIVE VALUE OF TESTS; PROTEOMICS; RENIN-ANGIOTENSIN SYSTEM; REPRODUCIBILITY OF RESULTS; RISK FACTORS;

EID: 84939646955     PISSN: 02636352     EISSN: 14735598     Source Type: Journal    
DOI: 10.1097/HJH.0000000000000685     Document Type: Article

References (28)

1. Hart PD, Bakris GL. Hypertensive nephropathy: prevention and treatment recommendations. Expert Opin Pharmacother 2010; 11:2675-2686.
2. Ninomiya T, Perkovic V, de Galan BE, Zoungas S, Pillai A, Jardine M, et al. Albuminuria and kidney function independently predict cardiovascular and renal outcomes in diabetes. J Am Soc Nephrol 2009; 20:1813-1821.
3. Viazzi F, Leoncini G, Conti N, Tomolillo C, Giachero G, Vercelli M, et al. Microalbuminuria is a predictor of chronic renal insufficiency in patients without diabetes and with hypertension: the MAGIC study. Clin J Am Soc Nephrol 2010; 5:1099-1106.
4. Brenner BM, Cooper ME, de Zeeuw D, Keane WF, Mitch WE, Parving HH, et al. Effects of losartan on renal and cardiovascular outcomes in patients with type 2 diabetes and nephropathy. N Engl J Med 2001; 345:861-869.
5. Lewis EJ, Hunsicker LG, Clarke WR, Berl T, Pohl MA, Lewis JB, et al. Renoprotective effect of the angiotensin-receptor antagonist irbesartan in patients with nephropathy due to type 2 diabetes. N Engl J Med 2001; 345:851-860.
6. Palmer AJ, Annemans L, Roze S, Lamotte M, Lapuerta P, Chen R, et al. Cost-Effectiveness of early irbesartan treatment versus control (standard antihypertensive medications excluding ACE inhibitors, other angiotensin-2 receptor antagonists, and dihydropyridine calcium channel blockers) or late irbesartan treatment in patients with type 2 diabetes, hypertension, and renal disease. Diabetes Care 2004; 27:1897-1903.
7. Hellemons ME, Kerschbaum J, Bakker SJ, Neuwirt H, Mayer B, Mayer G, et al. Validity of biomarkers predicting onset or progression of nephropathy in patients with Type 2 diabetes: a systematic review. Diabet Med 2012; 29:567-577.
8. Gerszten RE, Wang TJ. The search for new cardiovascular biomarkers. Nature 2008; 451:949-952.
9. Rossing K, Christensen PK, Hovind P, Tarnow L, Rossing P, Parving HH. Progression of nephropathy in type 2 diabetic patients. Kidney Int 2004; 66:1596-1605.
10. Merchant ML, Perkins BA, Boratyn GM, Ficociello LH, Wilkey DW, Barati MT, et al. Urinary peptidome may predict renal function decline in type 1 diabetes and microalbuminuria. J Am Soc Nephrol 2009; 20:2065-2074.
11. Pinto-Sietsma SJ, Janssen WM, Hillege HL, Navis G, De Zeeuw D, De Jong PE. Urinary albumin excretion is associated with renal functional abnormalities in a nondiabetic population. J Am Soc Nephrol 2000; 11:1882-1888.
12. Reinhard H, Hansen PR, Persson F, Tarnow L, Wiinberg N, Kjaer A, et al. Elevated NT-proBNP and coronary calcium score in relation to coronary artery disease in asymptomatic type 2 diabetic patients with elevated urinary albumin excretion rate. Nephrol Dial Transplant 2011; 26:3242-3249.
13. Abbasi A, Peelen LM, Corpeleijn E, van der Schouw YT, Stolk RP, Spijkerman AM, et al. Prediction models for risk of developing type 2 diabetes: systematic literature search and independent external validation study. BMJ 2012; 345:e5900.
14. Rohlfing CL, Little RR, Wiedmeyer HM, England JD, Madsen R, Harris MI, et al. Use of GHb (HbA1c) in screening for undiagnosed diabetes in the U.S. population. Diabetes Care 2000; 23:187-191.
15. Levey AS, Bosch JP, Lewis JB, Greene T, Rogers N, Roth D. A more accurate method to estimate glomerular filtration rate from serum creatinine: a new prediction equation. Modification of Diet in Renal Disease Study Group. Ann Intern Med 1999; 130:461-470.
16. Schulz A, Jankowski J, Zidek W, Jankowski V. Absolute quantification of endogenous angiotensin II levels in human plasma using ESI-LC-MS/ MS. Clin Proteomics 2014; 11:37-38.
17. Friedman J, Hastie T, Tibshirani R. Regularization paths for generalized linear models via coordinate descent. J Stat Softw 2010; 33:1-22.
18. Elashoff JD. NQuery Advisor Version 7. 0 User?s Guide 2007. Ireland Statistical Solutions, Cork; 2007.
19. Tibshirani R. Regression shrinkage and selection via the Lasso. J R Stat Soc Series B Stat Methodol 1996; 58:267-288.
20. Bhensdadia NM, Hunt KJ, Lopes-Virella MF, Michael Tucker J, Mataria MR, Alge JL, et al. Urine haptoglobin levels predict early renal functional decline in patients with type 2 diabetes. Kidney Int 2013; 83:1136-1143.
21. Zurbig P, Jerums G, Hovind P, Macisaac RJ, Mischak H, Nielsen SE, et al. Urinary proteomics for early diagnosis in diabetic nephropathy. Diabetes 2012; 61:3304-3313.
22. Good DM, Zurbig P, Argiles A, 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; 9:2424-2437.
23. Cho EH, Kim MR, Kim HJ, Lee DY, Kim PK, Choi KM, et al. The discovery of biomarkers for type 2 diabetic nephropathy by serum proteome analysis. Proteomics Clin Appl 2007; 1:352-361.
24. Kim HJ, Cho EH, Yoo JH, Kim PK, Shin JS, Kim MR, Kim CW. Proteome analysis of serum from type 2 diabetics with nephropathy. J Proteome Res 2007; 6:735-743.
25. Kolch W, Neususs C, Pelzing M, Mischak H. Capillary electrophoresismass spectrometry as a powerful tool in clinical diagnosis and biomarker discovery. Mass Spectrom Rev 2005; 24:959-977.
26. Lu CH, Lin ST, Chou HC, Lee YR, Chan HL. Proteomic analysis of retinopathy-related plasma biomarkers in diabetic patients. Arch Biochem Biophys 2013; 529:146-156.
27. Hansen HG, Overgaard J, Lajer M, Hubalek F, Hojrup P, Pedersen L, et al. Finding diabetic nephropathy biomarkers in the plasma peptidome by high-Throughput magnetic bead processing and MALDI-TOFMS analysis. Proteomics Clin Appl 2010; 4:697-705.
28. Moe SM, Chen NX. Mechanisms of vascular calcification in chronic kidney disease. J Am Soc Nephrol 2008; 19:213-216.