PRECISION MEDICINE in Diabetic Kidney Disease

Kidney News is pleased to present this edition’s special section on precision medicine. The ability to derive diagnosis and care, specific to the individual patient and the exact timing and nature of their disease process, is anticipated to result in a huge leap forward in the efficacy and safety of treatment. A PubMed search revealed more than 50,000 articles about this topic, including more than 1600 articles when restricted to “kidney,” and the rate of new articles is rapidly climbing. It thus seemed appropriate to review this approach with our audience. The first 4 articles were kindly curated and organized by our Europe editor, Professor Gert Mayer, and review new technology and approaches for the utilization of precision medicine in common kidney disease scenarios. The final 2 articles were kindly provided by the authors to round out this month’s section and make it more comprehensive, focusing on the efforts of the Kidney Precision Medicine project. We hope you find them of interest.

Richard Lafayette, MD, Editor-in-Chief, Kidney News

Approximately 415 million adults worldwide had diabetes mellitus (DM) in 2015, and even though over 650 billion USD were allocated for treatment, about 5 million individuals died. The impact of DM will continue to grow because the prevalence is expected to increase by more than 50% within the next 30 years (1). The rise in prevalence of diabetes will be accompanied by a significant rise in DM-associated complications such as diabetic kidney disease (DKD) (2).

/kidneynews/11_3/11/graphic/11f1.jpgTwo aspects of glomerular function, urinary albumin excretion and estimated GFR (eGFR), are used in clinical practice for defining stages of DKD (microalbuminuria, progressing to macroalbuminuria, followed by a loss of eGFR). This system is, however, sufficient only as long as the pathophysiology of the disease is simple, can be captured by easily accessible specific parameters, and most of all is similar in (at least most) patients. However, the concept of DKD being a “simple and uniform” disease has been challenged for a long time. On the basis of clinical observations, we have to conclude that contrary to our current uniform phenotypical categorization of patients with DKD using eGFR and urinary albumin excretion, the pathophysiology is multifactorial in nature.

Optimal glycemic control and renin-angiotensin-aldosterone system intervention is the cornerstone of treatment for slowing the progression of kidney function decline in patients with type 2 DM. Even though these strategies have contributed to a reduction in the risk for the development of ESRD, a substantial number of patients still continue to progress to ESRD. This can be explained in part by a large interindividual variation in treatment response. This induces uncertainty in optimal treatment selection and interferes with the development of novel drugs.

Identifying specific molecular processes associated with a specific phenotype of DKD and biomarkers associated with these processes based on molecular models of DKD can be used to characterize the progression of patients based on individual pathophysiology and may help to tailor treatment. A better understanding of deregulated DKD mechanisms in disease development and progression is therefore crucial, and the combination of molecular, clinical, and histologic data to decipher DKD pathophysiology and to unravel a drug’s mechanism of action at the molecular level might be the way forward to improve DKD therapy (3).

The large fraction of clinical trials in DKD still follow the classical one-size-fits-all approach with assigning a large (heterogeneous) population to either a treatment or a placebo arm. The use of biomarkers to enhance clinical trial design by either enriching the population for high-risk patients or patients who are more likely to respond to the drug under investigation is now quite common in oncology, but only a few trials are available in the field of nephrology. Two example trials in the context of DKD are the PRIORITY (Proteomic prediction and renin angiotensin aldosterone system inhibition prevention of early diabetic nephropathy in type 2 diabetic patients with normoalbuminuria) study and the SONAR (Study of Diabetic Nephropathy with Atrasentan) using biomarkers to enrich for high-risk patients and to identify treatment responders, respectively. The question, however, remains: what to do with patients not responding to the new drug in an enrichment trial?


The way forward might be to investigate multiple therapies in so-called platform trials (Figure 1). A platform trial signifies an experimental platform in which the effects of multiple interventions on one or more conditions can be tested, using modern adaptive designs and statistical approaches, including Bayesian analyses. The advantages of platform trials are 1) the availability of multiple compounds, thereby the ability to successively test patients until they show a biomarker response to a treatment, at which point they would be randomized to that treatment or to placebo plus standard of care; 2) the availability of a common master protocol to streamline clinical trial conduct; and 3) the possibility of optimization of treatment to improve efficacy and lower adverse events based on predictive and monitoring biomarkers (4, 5). Predictive biomarkers for the different treatment options for DKD patients are therefore key elements in the setup of platform trials (6).

Figure 1.

Simplified design of a platform trial. One of the possibilities of a platform is to use biomarkers to stratify a large heterogeneous cohort of DKD patients to the most promising treatment options


The BEAt-DKD project, which receives funding from the European Union within the Innovative Medicines Initiative framework, involves 31 partners from 11 countries. The major aim of this public–private partnership is to improve the prevention and management of DKD by molecular profiling and patient stratification of DKD patients. The first major project results include the validation of prognostic protein DKD biomarkers in the early stages of the disease and the identification of five major subgroups of diabetes that are associated with disease outcome (7, 8). One work package is specifically dedicated to the optimization of clinical trial design in DKD, with BEAt-DKD members actively engaging with representatives of the regulatory agencies in Europe and the United States regarding this issue. Future plans also foresee joining forces with other consortia such as another EU-funded research project entitled Rhapsody ( or the Kidney Precision Medicine Project (KPMP) ( in the United States to further increase the chances of success in enhancing the treatment options for DKD patients.

The identification of predictive and monitoring biomarkers for the DKD treatment regimens listed in Table 1 is one of the major research areas of the BEAt-DKD (Biomarker Enterprise to Attack Diabetic Kidney Disease) ( project.

March 2019 (Vol. 11, Number 3)


1. Ogurtsova K, et al. IDF diabetes atlas: Global estimates for the prevalence of diabetes for 2015 and 2040. Diabetes Res Clin Pract 2017; 128:40–50.

2. Thomas MC, Cooper ME, Zimmet P. Changing epidemiology of type 2 diabetes mellitus and associated chronic kidney disease. Nat Rev Nephrol 3026; 12:73–81.

3. Perco P, Mayer G. Molecular, histological, and clinical phenotyping of diabetic nephropathy: valuable complementary information? Kidney Int 2018; 93:308–310.

4. Heerspink HJL, Perkovic V. Trial design innovations to accelerate therapeutic advances in chronic kidney disease: Moving from single trials to an ongoing platform. Clin J Am Soc Nephrol 2018; 13:946–948.

5. Heerspink HJL, List J, Perkovic V. New clinical trial designs for establishing drug efficacy and safety in a precision medicine era. Diabetes Obes Metab 2018; 20[Suppl 3]:14–18.

6. Mayer B, et al. Predictive biomarkers for linking disease pathology and drug effect. Curr Pharm Des 2017; 23:29–54.

7. Heinzel A, et al. Validation of plasma biomarker candidates for the prediction of eGFR decline in patients with type 2 diabetes. Diabetes Care 2018; 41:1947–1954.

8. Ahlqvist E, et al. Novel subgroups of adult-onset diabetes and their association with outcomes: a data-driven cluster analysis of six variables. Lancet Diabetes Endocrinol.