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Diabetologia. 2016 Sep;59(9):1819-31. doi: 10.1007/s00125-016-4001-9. Epub 2016 Jun 25.

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

Author information

1
Department of Clinical Pharmacy and Pharmacology, University of Groningen, University Medical Center Groningen, P.O. Box 30.001, 9700 RB, Groningen, the Netherlands.
2
BHF Glasgow Cardiovascular Research Centre, University of Glasgow, Glasgow, UK.
3
Mosaiques Diagnostics GmbH, Hannover, Germany.
4
Department of Clinical Pharmacy and Pharmacology, University of Groningen, University Medical Center Groningen, P.O. Box 30.001, 9700 RB, Groningen, the Netherlands. h.j.lambers.heerspink@umcg.nl.

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.

KEYWORDS:

Diabetes mellitus; Kidney disease; Proteomics; Review

PMID:
27344310
PMCID:
PMC4969331
DOI:
10.1007/s00125-016-4001-9
[Indexed for MEDLINE]
Free PMC Article

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