New omics based biomarkers for early prediction of diabetic kidney disease
Diabetic kidney disease is a frequent, costly and lethal complication of diabetes. There is a need for a better understanding of the underlying pathophysiology through discovery of new pathways behind the development and progression of diabetic kidney disease as well as of early biomarkers which can identify subjects at risk of progression to renal complications.
A specific risk classifier based on urinary proteomics (CKD273) has been shown to identify normoalbuminuric diabetic patients who later progressed to overt kidney disease. We conduct a clinical trial that combines the ability of CKD273 to identify patients with highest risk of progression and reserve preventive treatment with aldosterone blockade.
Metabolomics is another powerful tool to investigate changes of metabolites and metabolic pathway fluxes in biological systems. Metabolomics can monitor, classify and evaluate the complex biochemical alterations during disease states. Metabolomics provide a platform for the discovery of biomarker patterns for use in improved risk stratification and the design of individualized interventions. The clinical application and breakthrough has yet to be established though.
Aim: To test if the urinary proteomics based classifier CKD273 can predict microalbuminuria prospectively, and to test whether mineralocorticoid receptor antagonism delays progression to microalbuminuria.
Collaborators: Bethesda Diabetes Research Center, Hoogeven, Groningen, Netherlands; Instituto de Investigacion Sanitaria de la Fundacion Jimenez D¡az (IIS-FJD UAM), Madrid, Spain; Clinical Study Centre, GWT TU-Dresden GmbH, Dresden, Germany; Istituto di Richerche Farmacologiche Mario Negri, Bergamo, Italy; Institute of Cardiovascular and Medical Sciences, University of Glasgow, Glasgow, UK; VU University Medical Center, Hoorn, Netherlands; Division of Nephrology, Department of Internal Medicine, University Medical Center Groningen, Groningen, Netherlands; Department of Nephrology, Cyril and Methodius University in Skopje, Skopje, Former Yugoslav Republic of Macedonia; Mosaiques Diagnostics, Hannover, Germany; Hannover Clinical Trial Center, Hannover, Germany; 2nd Department of Medicine, 3rd Faculty of Medicine, Universita Karlova v Praze, Prague, Czech Republic; Klinikum St. Georg, Nephrology and KfH Renal Unit, Leipzig, Martin-Luther University Halle, Wittenberg, Germany; Universitair Ziekenhuis, Dienst Nefrologie, Gent, Belgium; Diabetes Center, Geniko Nosokomeio Athinas Ippokrateio, Athens, Greece; Institut Klinické a Experimentálni Mediciny, Prague, Czech Republic; Diabetologische Schwerpunktpraxis, Diabetologen Hessen, Marburg, Germany
2: PROFIL metabolomics
Aim: The aim of this study is to evaluate the relationship between plasma metabolomics and diabetic kidney disease in patients with type 1 diabetes and to assess the predictive value of this metabolomics risk pattern in relation to kidney disease.
Collaborators: Systems medicine, SDCC
3: BBMRI metabolomics
Aim: The aim of this study is to evaluate the relationship between plasma metabolomics and diabetic kidney disease in patients with type 2 diabetes and to assess the predictive value of this metabolomics risk pattern in relation to kidney disease.
Collaborators: VU University Medical Center, Netherlands.