Urinary Proteomics for Early Diagnosis in Diabetic Nephropathy

mosaiques diagnostics GmbH, Hannover, Germany.
Diabetes (Impact Factor: 8.47). 08/2012; 61(12). DOI: 10.2337/db12-0348
Source: PubMed

ABSTRACT Diabetic nephropathy (DN) is a progressive kidney disease, a well-known complication of long-standing diabetes. DN is the most frequent reason for dialysis in many Western countries. Early detection may enable development of specific drugs and early initiation of therapy, thereby postponing/preventing the need for renal replacement therapy. We evaluated urinary proteome analysis as a tool for prediction of DN. Capillary electrophoresis-coupled mass spectrometry was used to profile the low-molecular weight proteome in urine. We examined urine samples from a longitudinal cohort of type 1 and 2 diabetic patients (n = 35) using a previously generated chronic kidney disease (CKD) biomarker classifier to assess peptides of collected urines for signs of DN. The application of this classifier to samples of normoalbuminuric subjects up to 5 years prior to development of macroalbuminuria enabled early detection of subsequent progression to macroalbuminuria (area under the curve [AUC] 0.93) compared with urinary albumin routinely used to determine the diagnosis (AUC 0.67). Statistical analysis of each urinary CKD biomarker depicted its regulation with respect to diagnosis of DN over time. Collagen fragments were prominent biomarkers 3-5 years before onset of macroalbuminuria. Before albumin excretion starts to increase, there is a decrease in collagen fragments. Urinary proteomics enables noninvasive assessment of DN risk at an early stage via determination of specific collagen fragments.

Download full-text


Available from: Petra Zürbig, Aug 24, 2015
  • Source
    • "In consequence, when aiming at a comprehensive assessment of progressive breast cancer phenotypes multimarker panels are needed, e.g., implemented by a multiplexed assay holding 70 individual molecular features (Buyse et al., 2006). Such multimarker panels have generally become a promising strategy for characterizing complex clinical presentations, e.g., utilizing a serum marker panel for predicting coronary artery disease in symptomatic patients, or a urinary proteomics profile for early diagnosis of diabetic kidney disease (LaFramboise et al., 2012; Zürbig et al., 2012). Failure for identifying a single causative factor as proxy for determining progression of a complex clinical phenotype becomes apparent when comparing the performance of marker panels with single markers, with the latter e.g., reviewed by Hellemons et al. for onset and progression of diabetic kidney disease (Hellemons et al., 2012). "
    [Show abstract] [Hide abstract]
    ABSTRACT: Omics profiling significantly expanded the molecular landscape describing clinical phenotypes. Association analysis resulted in first diagnostic and prognostic biomarker signatures entering clinical utility. However, utilizing Omics for deepening our understanding of disease pathophysiology, and further including specific interference with drug mechanism of action on a molecular process level still sees limited added value in the clinical setting. We exemplify a computational workflow for expanding from statistics-based association analysis toward deriving molecular pathway and process models for characterizing phenotypes and drug mechanism of action. Interference analysis on the molecular model level allows identification of predictive biomarker candidates for testing drug response. We discuss this strategy on diabetic nephropathy (DN), a complex clinical phenotype triggered by diabetes and presenting with renal as well as cardiovascular endpoints. A molecular pathway map indicates involvement of multiple molecular mechanisms, and selected biomarker candidates reported as associated with disease progression are identified for specific molecular processes. Selective interference of drug mechanism of action and disease-associated processes is identified for drug classes in clinical use, in turn providing precision medicine hypotheses utilizing predictive biomarkers.
    Frontiers in Cell and Developmental Biology 08/2014; 2:37. DOI:10.3389/fcell.2014.00037
  • [Show abstract] [Hide abstract]
    ABSTRACT: Diabetic patients show a high susceptibility to oral diseases of inflammatory, catabolic and chronic nature with potential impact on saliva composition. In this study, our purpose was to characterize type 1 diabetes-induced alterations in the salivary peptidome aiming to find prospective biomarkers for type 1 diabetes oral health evaluation. Peptidomic analysis of saliva from controls (n = 5) and type 1 diabetic patients (n = 5) were performed by liquid chromatography followed by mass spectrometry. The proteolytic activity and metalloproteinases expression was accessed by zymography and slot blot analysis, respectively. Data evidenced a significant increase in the percentage of peptides in diabetic patients paralleled by a higher proteolytic activity, compared with healthy individuals. The nonsalivary gland protein fragments identified in saliva were mainly derived from collagen and extracellular matrix proteins, namely collagen type I. The cleavage site frequency analysis showed significant differences between healthy and type 1 diabetic individuals, highlighting the activity of proteases such as matrix metalloproteinase-9 and cathepsin D. Our results highlight salivary collagen fragments as potential biomarkers to follow up diabetes-related oral damage.
    Biomedical Chromatography 05/2012; 26(5):571-82. DOI:10.1002/bmc.1677 · 1.66 Impact Factor
  • Source
    Diabetes 12/2012; 61(12):3072-3. DOI:10.2337/db12-1299 · 8.47 Impact Factor
Show more