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

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


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.

Publication types

  • Review
  • Research Support, Non-U.S. Gov't

MeSH terms

  • Animals
  • Biomarkers / metabolism
  • Diabetes Mellitus / metabolism
  • Diabetes Mellitus / pathology
  • Diabetic Nephropathies / metabolism*
  • Diabetic Nephropathies / pathology*
  • Disease Progression
  • Humans
  • Kidney Diseases / metabolism
  • Kidney Diseases / pathology
  • Proteome / metabolism
  • Proteomics / methods*


  • Biomarkers
  • Proteome