Skip to main content
Key Specialties
Cardiology Diabetology Endocrinology Gastroenterology Geriatrics and Gerontology Gynecology and Obstetrics Hematology Infectious Disease Internal Medicine Nephrology Neurology Oncology Pediatrics Respiratory Medicine Rheumatology Surgery
Congress Coverage
2024 ACC Congress 2023 ESMO Congress 2023 EASD Congress 2023 ERS Congress 2023 ESC Congress
Clinical Collections
Advances in Alzheimer’s

At a glance: The ONWARDS insulin icodec trials

A round-up of the ONWARDS phase 3 clinical trials evaluating the novel weekly insulin icodec in people with diabetes.
ADVANCED SEARCH
Log in
Top
Diabetologia
Published in: Diabetologia 9/2016

Open Access 01-09-2016 | Review

Proteomics for prediction of disease progression and response to therapy in diabetic kidney disease

Authors: Michelle J. Pena, Harald Mischak, Hiddo J. L. Heerspink

Published in: Diabetologia | Issue 9/2016

Login to get access

Abstract

The past decade has resulted in multiple new findings of potential proteomic biomarkers of diabetic kidney disease (DKD). Many of these biomarkers reflect an important role in the (patho)physiology and biological processes of DKD. Situations in which proteomics could be applied in clinical practice include the identification of individuals at risk of progressive kidney disease and those who would respond well to treatment, in order to tailor therapy for those at highest risk. However, while many proteomic biomarkers have been discovered, and even found to be predictive, most lack rigorous external validation in sufficiently powered studies with renal endpoints. Moreover, studies assessing short-term changes in the proteome for therapy-monitoring purposes are lacking. Collaborations between academia and industry and enhanced interactions with regulatory agencies are needed to design new, sufficiently powered studies to implement proteomics in clinical practice.
Literature
1.
2.
United States Renal Data System (2015) USRDS annual data report: epidemiology of kidney disease in the United States. National Institutes of Health, National Institute of Diabetes and Digestive and Kidney Diseases, Bethesda, MD
3.
Pippias M, Jager KJ, Kramer A et al (2016) The changing trends and outcomes in renal replacement therapy: data from the ERA-EDTA Registry. Nephrol Dial Transplant 31:831–841
4.
Liyanage T, Ninomiya T, Jha V et al (2015) Worldwide access to treatment for end-stage kidney disease: a systematic review. Lancet 385:1975–1982CrossRefPubMed
5.
National Kidney Foundation (2012) KDOQI clinical practice guideline for diabetes and CKD: 2012 update. Am J Kidney Dis 60:850–886
6.
Heerspink HJL, Ninomiya T, Persson F et al (2016) Is a reduction in albuminuria associated with renal and cardiovascular protection? A post-hoc analysis of the ALTITUDE trial. Diabetes Obes Metab 18:169–177CrossRefPubMed
7.
De Zeeuw D, Remuzzi G, Parving HH et al (2004) Proteinuria, a target for renoprotection in patients with type 2 diabetic nephropathy: lessons from RENAAL. Kidney Int 65:2309–2320CrossRefPubMed
8.
9.
Viazzi F, Leoncini G, Conti N et al (2010) Microalbuminuria is a predictor of chronic renal insufficiency in patients without diabetes and with hypertension: the MAGIC study. Clin J Am Soc Nephrol 5:1099–1106CrossRefPubMedPubMedCentral
10.
Ninomiya T, Perkovic V, de Galan BE et al (2009) Albuminuria and kidney function independently predict cardiovascular and renal outcomes in diabetes. J Am Soc Nephrol 20:1813–1821CrossRefPubMedPubMedCentral
11.
Susztak K, Bottinger EP (2006) Diabetic nephropathy: a frontier for personalized medicine. J Am Soc Nephrol 17:361–367CrossRefPubMed
12.
Schievink B, Kropelin T, Mulder S et al (2016) Early renin-angiotensin system intervention is more beneficial than late intervention in delaying end-stage renal disease in patients with type 2 diabetes. Diabetes Obes Metab 18:64–71CrossRefPubMed
13.
Inker LA, Lambers Heerspink HJ, Mondal H et al (2014) GFR decline as an alternative end point to kidney failure in clinical trials: a meta-analysis of treatment effects from 37 randomized trials. Am J Kidney Dis 64:848–859CrossRefPubMed
14.
Roscioni SS, Lambers Heerspink HJ, de Zeeuw D (2014) Microalbuminuria: target for renoprotective therapy PRO. Kidney Int 86:40–49CrossRefPubMed
15.
16.
17.
Gohda T, Niewczas MA, Ficociello LH et al (2012) Circulating TNF receptors 1 and 2 predict stage 3 CKD in type 1 diabetes. J Am Soc Nephrol 23:516–524CrossRefPubMedPubMedCentral
18.
Mischak H, Delles C, Vlahou A, Vanholder R (2015) Proteomic biomarkers in kidney disease: issues in development and implementation. Nat Rev Nephrol 11:221–232CrossRefPubMed
19.
Tam FW, Riser BL, Meeran K, Rambow J, Pusey CD, Frankel AH (2009) Urinary monocyte chemoattractant protein-1 (MCP-1) and connective tissue growth factor (CCN2) as prognostic markers for progression of diabetic nephropathy. Cytokine 47:37–42CrossRefPubMed
20.
Persson F, Rathcke CN, Gall MA, Parving HH, Vestergaard H, Rossing P (2012) High YKL-40 levels predict mortality in patients with type 2 diabetes. Diabetes Res Clin Pract 96:84–89CrossRefPubMed
21.
Hellemons ME, Mazagova M, Gansevoort RT et al (2012) Growth-differentiation factor 15 predicts worsening of albuminuria in patients with type 2 diabetes. Diabetes Care 35:2340–2346CrossRefPubMedPubMedCentral
22.
Conway BR, Manoharan D, Manoharan D et al (2012) Measuring urinary tubular biomarkers in type 2 diabetes does not add prognostic value beyond established risk factors. Kidney Int 82:812–818CrossRefPubMed
23.
Fufaa GD, Weil EJ, Nelson RG et al (2015) Association of urinary KIM-1, L-FABP, NAG and NGAL with incident end-stage renal disease and mortality in American Indians with type 2 diabetes mellitus. Diabetologia 58:188–198CrossRefPubMed
24.
Jorsal A, Tarnow L, Frystyk J et al (2008) Serum adiponectin predicts all-cause mortality and end stage renal disease in patients with type I diabetes and diabetic nephropathy. Kidney Int 74:649–654CrossRefPubMed
25.
26.
Hovind P, Rossing P, Tarnow L, Johnson RJ, Parving HH (2009) Serum uric acid as a predictor for development of diabetic nephropathy in type 1 diabetes: an inception cohort study. Diabetes 58:1668–1671CrossRefPubMedPubMedCentral
27.
Ficociello LH, Rosolowsky ET, Niewczas MA et al (2010) High-normal serum uric acid increases risk of early progressive renal function loss in type 1 diabetes: results of a 6-year follow-up. Diabetes Care 33:1337–1343CrossRefPubMedPubMedCentral
28.
Panduru NM, Forsblom C, Saraheimo M et al (2013) Urinary liver-type fatty acid-binding protein and progression of diabetic nephropathy in type 1 diabetes. Diabetes Care 36:2077–2083CrossRefPubMedPubMedCentral
29.
Morton J, Zoungas S, Li Q et al (2012) Low HDL cholesterol and the risk of diabetic nephropathy and retinopathy: results of the ADVANCE study. Diabetes Care 35:2201–2206CrossRefPubMedPubMedCentral
30.
Ju W, Nair V, Smith S et al (2015) Tissue transcriptome-driven identification of epidermal growth factor as a chronic kidney disease biomarker. Sci Transl Med 7:316ra193CrossRefPubMedPubMedCentral
31.
Fechete R, Heinzel A, Perco P et al (2011) Mapping of molecular pathways, biomarkers and drug targets for diabetic nephropathy. Proteomics Clin Appl 5:354–366CrossRefPubMed
32.
Pena MJ, de Zeeuw D, Mischak H et al (2015) Prognostic clinical and molecular biomarkers of renal disease in type 2 diabetes. Nephrol Dial Transplant 30(Suppl 4):iv86–iv95CrossRefPubMed
33.
Persson F, Rossing P, Hovind P et al (2008) Endothelial dysfunction and inflammation predict development of diabetic nephropathy in the Irbesartan in Patients with Type 2 Diabetes and Microalbuminuria (IRMA 2) study. Scand J Clin Lab Invest 68:731–738CrossRefPubMed
34.
Astrup AS, Tarnow L, Pietraszek L et al (2008) Markers of endothelial dysfunction and inflammation in type 1 diabetic patients with or without diabetic nephropathy followed for 10 years: association with mortality and decline of glomerular filtration rate. Diabetes Care 31:1170–1176CrossRefPubMed
35.
Verhave JC, Bouchard J, Goupil R et al (2013) Clinical value of inflammatory urinary biomarkers in overt diabetic nephropathy: a prospective study. Diabetes Res Clin Pract 101:333–340CrossRefPubMed
36.
Agarwal R, Duffin KL, Laska DA, Voelker JR, Breyer MD, Mitchell PG (2014) A prospective study of multiple protein biomarkers to predict progression in diabetic chronic kidney disease. Nephrol Dial Transplant 29:2293–2302CrossRefPubMed
37.
Pena MJ, Heinzel A, Heinze G et al (2015) A panel of novel biomarkers representing different disease pathways improves prediction of renal function decline in type 2 diabetes. PLoS One 10:e0120995CrossRefPubMedPubMedCentral
38.
Avogaro A, Fadini GP, Gallo A, Pagnin E, de Kreutzenberg S (2006) Endothelial dysfunction in type 2 diabetes mellitus. Nutr Metab Cardiovasc Dis 16(Suppl 1):S39–45CrossRefPubMed
39.
Rabelink TJ, de Zeeuw D (2015) The glycocalyx-linking albuminuria with renal and cardiovascular disease. Nat Rev Nephrol 11:667–676CrossRefPubMed
40.
Broekhuizen LN, Lemkes BA, Mooij HL et al (2010) Effect of sulodexide on endothelial glycocalyx and vascular permeability in patients with type 2 diabetes mellitus. Diabetologia 53:2646–2655CrossRefPubMedPubMedCentral
41.
Garsen M, Rops AL, Rabelink TJ, Berden JH, van der Vlag J (2014) The role of heparanase and the endothelial glycocalyx in the development of proteinuria. Nephrol Dial Transplant 29:49–55CrossRefPubMed
42.
Banks RE, Dunn MJ, Hochstrasser DF et al (2000) Proteomics: new perspectives, new biomedical opportunities. Lancet 356:1749–1756CrossRefPubMed
43.
44.
Dihazi H, Muller GA, Lindner S et al (2007) Characterization of diabetic nephropathy by urinary proteomic analysis: identification of a processed ubiquitin form as a differentially excreted protein in diabetic nephropathy patients. Clin Chem 53:1636–1645CrossRefPubMed
45.
Papale M, Di Paolo S, Magistroni R et al (2010) Urine proteome analysis may allow noninvasive differential diagnosis of diabetic nephropathy. Diabetes Care 33:2409–2415CrossRefPubMedPubMedCentral
46.
Papale M, Di Paolo S, Vocino G, Rocchetti MT, Gesualdo L (2014) Proteomics and diabetic nephropathy: what have we learned from a decade of clinical proteomics studies? J Nephrol 27:221–228CrossRefPubMed
47.
Mischak H, Ioannidis JP, Argiles A et al (2012) Implementation of proteomic biomarkers: making it work. Eur J Clin Investig 42:1027–1036CrossRef
48.
49.
Merchant ML, Perkins BA, Boratyn GM et al (2009) Urinary peptidome may predict renal function decline in type 1 diabetes and microalbuminuria. J Am Soc Nephrol 20:2065–2074CrossRefPubMedPubMedCentral
50.
Good DM, Zürbig P, Argiles A et al (2010) Naturally occurring human urinary peptides for use in diagnosis of chronic kidney disease. Mol Cell Proteomics 9:2424–2437CrossRefPubMedPubMedCentral
51.
Zürbig P, Decramer S, Dakna M et al (2009) The human urinary proteome reveals high similarity between kidney aging and chronic kidney disease. Proteomics 9:2108–2117CrossRefPubMedPubMedCentral
52.
Gansevoort RT, Ritz E (2009) Hermann Senator and albuminuria—forgotten pioneering work in the 19th century. Nephrol Dial Transplant 24:1057–1062CrossRefPubMed
53.
Mischak H (2015) Pro: urine proteomics as a liquid kidney biopsy: no more kidney punctures! Nephrol Dial Transplant 30:532–537CrossRefPubMed
54.
Thongboonkerd V, Malasit P (2005) Renal and urinary proteomics: current applications and challenges. Proteomics 5:1033–1042CrossRefPubMed
55.
Ben Ameur R, Molina L, Bolvin C et al (2010) Proteomic approaches for discovering biomarkers of diabetic nephropathy. Nephrol Dial Transplant 25:2866–2875CrossRefPubMed
56.
Jankowski J, Schanstra JP, Mischak H (2015) Body fluid peptide and protein signatures in diabetic kidney diseases. Nephrol Dial Transplant 30(Suppl 4):iv43–53CrossRefPubMed
57.
Critselis E, Lambers Heerspink H (2016) Utility of the CKD273 peptide classifier in predicting chronic kidney disease progression. Nephrol Dial Transplant 31:249–254
58.
Otu HH, Can H, Spentzos D et al (2007) Prediction of diabetic nephropathy using urine proteomic profiling 10 years prior to development of nephropathy. Diabetes Care 30:638–643CrossRefPubMed
59.
Schlatzer D, Maahs DM, Chance MR et al (2012) Novel urinary protein biomarkers predicting the development of microalbuminuria and renal function decline in type 1 diabetes. Diabetes Care 35:549–555CrossRefPubMedPubMedCentral
60.
Bhensdadia NM, Hunt KJ, Lopes-Virella MF et al (2013) Urine haptoglobin levels predict early renal functional decline in patients with type 2 diabetes. Kidney Int 83:1136–1143CrossRefPubMedPubMedCentral
61.
62.
Roscioni SS, de Zeeuw D, Hellemons ME et al (2013) A urinary peptide biomarker set predicts worsening of albuminuria in type 2 diabetes mellitus. Diabetologia 56:259–267CrossRefPubMed
63.
Schanstra JP, Zürbig P, Alkhalaf A et al (2015) Diagnosis and prediction of CKD progression by assessment of urinary peptides. J Am Soc Nephrol 26:1999–2010CrossRefPubMedPubMedCentral
64.
Lindhardt M, Persson F, Zürbig P et al (2016) Urinary proteomics predict onset of microalbminuria in normoalbuminuric type 2 diabetic patients, a sub-study of the DIRECT-Protect 2 study. Nephrol Dial Transpl (in press)
65.
Siwy J, Schanstra JP, Argiles A et al (2014) Multicentre prospective validation of a urinary peptidome-based classifier for the diagnosis of type 2 diabetic nephropathy. Nephrol Dial Transplant 29:1563–1570CrossRefPubMedPubMedCentral
66.
Rossing K, Mischak H, Parving HH et al (2005) Impact of diabetic nephropathy and angiotensin II receptor blockade on urinary polypeptide patterns. Kidney Int 68:193–205CrossRefPubMed
67.
Nkuipou-Kenfack E, Bhat A, Klein J et al (2015) Identification of ageing-associated naturally occurring peptides in human urine. Oncotarget 6:34106–34117PubMedPubMedCentral
68.
Kolch W, Neususs C, Pelzing M, Mischak H (2005) Capillary electrophoresis-mass spectrometry as a powerful tool in clinical diagnosis and biomarker discovery. Mass Spectrom Rev 24:959–977CrossRefPubMed
69.
Jambunathan K, Galande AK (2014) Sample collection in clinical proteomics—proteolytic activity profile of serum and plasma. Proteomics Clin Appl 8:299–307CrossRefPubMed
70.
71.
Overgaard AJ, Thingholm TE, Larsen MR et al (2010) Quantitative iTRAQ-based proteomic identification of candidate biomarkers for diabetic nephropathy in plasma of type 1 diabetic patients. Clin Proteomics 6:105–114CrossRefPubMedPubMedCentral
72.
Hansen HG, Overgaard J, Lajer M et al (2010) Finding diabetic nephropathy biomarkers in the plasma peptidome by high-throughput magnetic bead processing and MALDI-TOF-MS analysis. Proteomics Clin Appl 4:697–705CrossRefPubMed
73.
Glorieux G, Mullen W, Duranton F et al (2015) New insights in molecular mechanisms involved in chronic kidney disease using high-resolution plasma proteome analysis. Nephrol Dial Transplant 30:1842–1852CrossRefPubMed
74.
Merchant ML, Niewczas MA, Ficociello LH et al (2013) Plasma kininogen and kininogen fragments are biomarkers of progressive renal decline in type 1 diabetes. Kidney Int 83:1177–1184CrossRefPubMedPubMedCentral
75.
Pena MJ, Jankowski J, Heinze G et al (2015) Plasma proteomics classifiers improve risk prediction for renal disease in patients with hypertension or type 2 diabetes. J Hypertens 33:2123–2132CrossRefPubMed
76.
Looker HC, Colombo M, Hess S et al (2015) Biomarkers of rapid chronic kidney disease progression in type 2 diabetes. Kidney Int 88:888–896CrossRefPubMed
77.
78.
Bringans S, Casey T, Davis T et al (2012) Biomarkers associated with pre-diabetes, diabetes and diabetes related conditions. Patent Application PCT/AU2011/001212
79.
Lollo B, Steele F, Gold L (2014) Beyond antibodies: new affinity reagents to unlock the proteome. Proteomics 14:638–644CrossRefPubMed
80.
81.
Schlafly A, Niewczas M, Pezzolesi M, Krolewski A (2015) Plasma proteins associated with declining renal function in patients with type 1 diabetes: results of a global proteomic analysis using SOMAscan platform. J Am Soc Nephrol 26:498A (Abstract)
82.
83.
Brenner BM, Cooper ME, de Zeeuw D et al (2001) Effects of losartan on renal and cardiovascular outcomes in patients with type 2 diabetes and nephropathy. N Engl J Med 345:861–869CrossRefPubMed
84.
Schievink B, de Zeeuw D, Parving HH, Rossing P, Lambers Heerspink HJ (2015) The renal protective effect of angiotensin receptor blockers depends on intra-individual response variation in multiple risk markers. Br J Clin Pharmacol 80(4):678–686CrossRefPubMed
85.
86.
Lambers Heerspink HJ, Oberbauer R, Perco P et al (2015) Drugs meeting the molecular basis of diabetic kidney disease: bridging from molecular mechanism to personalized medicine. Nephrol Dial Transplant 30(Suppl 4):iv105–112CrossRefPubMed
87.
Pena M, Mayer B, Heinzel A, Rossing P, Lambers Heerspink HJ (2015) A serum metabolite classifier predicts response to ARBs in diabetes. J Am Soc Nephrol 26:499A (Abstract)
88.
Andersen S, Mischak H, Zürbig P, Parving HH, Rossing P (2010) Urinary proteome analysis enables assessment of renoprotective treatment in type 2 diabetic patients with microalbuminuria. BMC Nephrol 11:29CrossRefPubMedPubMedCentral
89.
Silva S, Bronze MR, Figueira ME et al (2015) Impact of a 6-wk olive oil supplementation in healthy adults on urinary proteomic biomarkers of coronary artery disease, chronic kidney disease, and diabetes (types 1 and 2): a randomized, parallel, controlled, double-blind study. Am J Clin Nutr 101:44–54CrossRefPubMed
90.
Sleddering MA, Markvoort AJ, Dharuri HK et al (2014) Proteomic analysis in type 2 diabetes patients before and after a very low calorie diet reveals potential disease state and intervention specific biomarkers. PLoS One 9:e112835CrossRefPubMedPubMedCentral
91.
Hlatky MA, Greenland P, Arnett DK et al (2009) Criteria for evaluation of novel markers of cardiovascular risk: a scientific statement from the American Heart Association. Circulation 119:2408–2416CrossRefPubMedPubMedCentral
92.
Schutte E, Gansevoort RT, Benner J, Lutgers HL, Lambers Heerspink HJ (2015) Will the future lie in multitude? A critical appraisal of biomarker panel studies on prediction of diabetic kidney disease progression. Nephrol Dial Transplant 30(Suppl 4):iv96–104CrossRefPubMed
93.
Lathrop JT, Jeffery DA, Shea YR, Scholl PF, Chan MM (2016) US Food and Drug Administration perspectives on clinical mass spectrometry. Clin Chem 62:41–47CrossRefPubMed
94.
Conserva F, Gesualdo L, Papale M (2016) A systems biology overview on human diabetic nephropathy: from genetic susceptibility to post-transcriptional and post-translational modifications. J Diabetes Res 2016:7934504CrossRefPubMed
95.
96.
Domanski D, Percy AJ, Yang J et al (2012) MRM-based multiplexed quantitation of 67 putative cardiovascular disease biomarkers in human plasma. Proteomics 12:1222–1243CrossRefPubMed
97.
Mermelekas G, Vlahou A, Zoidakis J (2015) SRM/MRM targeted proteomics as a tool for biomarker validation and absolute quantification in human urine. Expert Rev Mol Diagn 15:1441–1454CrossRefPubMed
98.
Cisek K, Krochmal M, Klein J, Mischak H (2015) The application of multi-omics and systems biology to identify therapeutic targets in chronic kidney disease. Nephrol Dial Transplant doi:10.​1093/​ndt/​gfv364
Metadata
Title
Proteomics for prediction of disease progression and response to therapy in diabetic kidney disease
Authors
Michelle J. Pena
Harald Mischak
Hiddo J. L. Heerspink
Publication date
01-09-2016
Publisher
Springer Berlin Heidelberg
Published in
Diabetologia / Issue 9/2016
Print ISSN: 0012-186X
Electronic ISSN: 1432-0428
DOI
https://doi.org/10.1007/s00125-016-4001-9

Other articles of this Issue 9/2016

Diabetologia 9/2016 Go to the issue

Letter

Diabetes remission off medications is not a suitable endpoint for comparing bariatric/metabolic surgery with pharmacotherapy

Short Communication

Energy restriction and Roux-en-Y gastric bypass reduce postprandial α-dicarbonyl stress in obese women with type 2 diabetes

Review

Novel phenotypes of prediabetes?

Up front

Up front

Review

Coming of age: the artificial pancreas for type 1 diabetes

Commentary

Can genetic evidence help us to understand the fetal origins of type 2 diabetes?

ACC 2024 Congress Coverage

Heart Failure Video

Year in Review: Heart failure and cardiomyopathies

Watch now

Watch this official video from ACC.24. Dr. Biykem Bozkurt discuss last year's major advances in heart failure and cardiomyopathies.

Semaglutide benefits people with type 2 diabetes and obesity-related heart failure

16-04-2024 Type 2 Diabetes News

Incentivised to exercise

11-04-2024 Lifestyle Modifications News

New intervention for STEMI-related cardiogenic shock

10-04-2024 Myocardial Infarction News

» View more highlights on the ACC 2024 congress hub page

Image Credits