Details

Autor(en) / Beteiligte

• Alkhalaf, Alaa
• Zürbig, Petra
• Bakker, Stephan J L
• Bilo, Henk J G
• Cerna, Marie
• Fischer, Christine
• Fuchs, Sebastian
• Janssen, Bart
• Medek, Karel
• Mischak, Harald
• Roob, Johannes M
• Rossing, Kasper
• Rossing, Peter
• Rychlík, Ivan
• Sourij, Harald
• Tiran, Beate
• Winklhofer-Roob, Brigitte M
• Navis, Gerjan J
• Federici, Massimo

Titel

Multicentric validation of proteomic biomarkers in urine specific for diabetic nephropathy

Ist Teil von

• PloS one, 2010-10, Vol.5 (10), p.e13421-e13421

Ort / Verlag

United States: Public Library of Science

Erscheinungsjahr

2010

Link zum Volltext

Quelle

Elektronische Zeitschriftenbibliothek - Frei zugängliche E-Journals

Beschreibungen/Notizen

• Urine proteome analysis is rapidly emerging as a tool for diagnosis and prognosis in disease states. For diagnosis of diabetic nephropathy (DN), urinary proteome analysis was successfully applied in a pilot study. The validity of the previously established proteomic biomarkers with respect to the diagnostic and prognostic potential was assessed on a separate set of patients recruited at three different European centers. In this case-control study of 148 Caucasian patients with diabetes mellitus type 2 and duration ≥5 years, cases of DN were defined as albuminuria >300 mg/d and diabetic retinopathy (n = 66). Controls were matched for gender and diabetes duration (n = 82). Proteome analysis was performed blinded using high-resolution capillary electrophoresis coupled with mass spectrometry (CE-MS). Data were evaluated employing the previously developed model for DN. Upon unblinding, the model for DN showed 93.8% sensitivity and 91.4% specificity, with an AUC of 0.948 (95% CI 0.898-0.978). Of 65 previously identified peptides, 60 were significantly different between cases and controls of this study. In <10% of cases and controls classification by proteome analysis not entirely resulted in the expected clinical outcome. Analysis of patient's subsequent clinical course revealed later progression to DN in some of the false positive classified DN control patients. These data provide the first independent confirmation that profiling of the urinary proteome by CE-MS can adequately identify subjects with DN, supporting the generalizability of this approach. The data further establish urinary collagen fragments as biomarkers for diabetes-induced renal damage that may serve as earlier and more specific biomarkers than the currently used urinary albumin.