Precision medicine approaches for diabetic kidney disease: opportunities and challenges

Abstract

The prevalence of end-stage kidney disease (ESKD) continuously increases worldwide. The increasing prevalence parallels the growth in the number of people with diabetes, which is the leading cause of ESKD. Early diagnosis of chronic kidney disease (CKD) in patients with diabetes and appropriate intervention is important to delay the progression of kidney function decline and prevent ESKD. Rate of CKD progression and response to treatment varies among patients with diabetes, highlighting the need to tailor individual treatment. In this review, we describe recent advances and areas for future studies with respect to precision medicine in diabetic kidney disease (DKD). DKD is a multi-factorial disease that is subject in part to genetic heritability, but is also influenced by various exogenous mediators, such as environmental or dietary factors. Genetic testing so far has limited utility to facilitate early diagnosis, classify progression or evaluate response to therapy. Various biomarker-based approaches are currently explored to identify patients at high risk of ESKD and to facilitate decision-making for targeted therapy. These studies have led to discovery and validation of a couple of inflammatory proteins such as circulating tumour necrosis factor receptors, which are strong predictors of kidney disease progression. Moreover, risk and drug-response scores based on multiple biomarkers are developed to predict kidney disease progression and long-term drug efficacy. These findings, if implemented in clinical practice, will pave the way to move from a one-size-fits-all to a one-fit-for-everyone approach.

Keywords: chronic kidney disease; diabetic kidney disease; nephropathy; personalized medicine.

FIGURE 1

TNFR1, TNFR2 and KIM-1 were strongly associated with renal outcomes in the ACCORD (Action to Control Cardiovascular Risk in Diabetes) and VA NEPHRON-D (Veterans Affairs Nephropathy in Diabetes) trials involving patients with type 2 diabetes at early and advanced stages of CKD [24].

FIGURE 2

(A) A higher CKD273 score predicts the risk of microalbuminuria and development of stage 3 CKD. Patients with type 2 diabetes and normoalbuminuria were classified with the CKD273 score into a high risk or low risk group. Patients stratified into the high risk group showed a higher risk of developing microalbuminuria and CKD stage 3 during follow-up [26]. (B) High risk patients with type 2 diabetes and normoalbuminuria based on the CKD273 classifier in the PRIORITY trial were also randomly assigned to treatment with spironolactone or placebo. Spironolactone did not reduce the risk of development of microalbuminuria (primary endpoint). Among participants with baseline eGFR >60 mL/min/1.73 m², spironolactone increased the risk of stage 3 CKD [26].

FIGURE 3

Performance of a multiple PRE score in predicting the effect of various drugs on kidney outcomes using the short-term change in multiple biomarkers. The performance of the score was assessed in completed clinical trials with known outcomes (retrospective validation), and using data from phase 2 studies to predict the outcomes of ongoing phase 3 studies (prospective validation). The score was applied to trials with ARB, losartan (RENAAL trial) and irbesartan [IDNT (Irbesartan Diabetic Nephropathy Trial) trial]; the Glucagon like Petide-1 Receptor Agonists exenetide [EXSCEL (Exenatide Study of Cardiovascular Event Lowering) trial]; the endothelin receptor antagonist atrasentan (prediction of the SONAR trial) and the SGLT2i dapagliflozin [prediction of the DAPA-CKD (Dapagliflozin and Prevention of Adverse Outcomes in Chronic Kidney Disease) trial]. The predicted effect of the drug is shown in blue bars and the observed effect in grey bars.