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

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.

Fig. 1

Early identification with proteomics of patients at risk of kidney disease, prior to organ damage, and initiation of appropriate treatment is a strategy to interrupt disease progression to ESRD and death

Fig. 2

Overview of the CKD273 score for baseline risk prediction and drug response prediction. (a) Predictive ability of the CKD273 score in patients with diabetes and normoalbuminuria (n = 35 with 150 urine samples) at the time of urine sample collection up to 5 years prior to onset of diabetic nephropathy. The solid line shows the receiver operating characteristic (ROC) curve of the CKD273 score and the dashed line the ROC curve of the urinary albumin excretion rate (UAER) (p < 0.001 for difference in ROC curve between CKD273 score and UAER). Figure adapted from Zürbig et al [61]. (b) CKD273 score in patients with type 2 diabetes and normoalbuminuria (n = 48) or microalbuminuria (n = 40) at baseline. Patients transitioned during the albuminuria stage, whereas controls did not transition during follow-up. *p < 0.05, **p < 0.01 cases vs controls. White box, controls with normoalbuminuria; light grey box, patients with normo- to microalbuminuria; dark grey box, controls with microalbuminuria; black box, patients with micro- to macroalbuminuria. Figure adapted with permission from Roscioni et al [62]. (c) Box-and-whisker plots of the CKD273 score of patients from a nested case–control study in the IRMA-2 trial (n = 22) before (visit 2) and after 2 years (visit 9) of treatment with placebo (white boxes) or 300 mg irbesartan (grey boxes). *p < 0.05 baseline vs after 2 years. Figure adapted from Andersen et al [88]