Literature DB >> 25786980

Urinary proteomic biomarkers to predict cardiovascular events.

Catriona E Brown1, Nina S McCarthy2,3, Alun D Hughes4, Peter Sever4, Angelique Stalmach1, William Mullen1, Anna F Dominiczak1, Naveed Sattar1, Harald Mischak1,5, Simon Thom4, Jamil Mayet4, Alice V Stanton2, Christian Delles1.   

Abstract

PURPOSE: We have previously demonstrated associations between the urinary proteome profile and coronary artery disease (CAD) in cross-sectional studies. Here, we evaluate the potential of a urinary proteomic panel as a predictor of CAD in the hypertensive atherosclerotic cardiovascular disease (HACVD) substudy population of the Anglo-Scandinavian Cardiac Outcomes Trial study. EXPERIMENTAL
DESIGN: Thirty-seven cases with primary CAD endpoint were matched for sex and age to controls who had not reached a CAD endpoint during the study. Spot urine samples were analyzed using CE coupled to Micro-TOF MS. A previously developed 238-marker CE-MS model for diagnosis of CAD (CAD238 ) was assessed for its predictive potential.
RESULTS: Sixty urine samples (32 cases; 28 controls; 88% male, mean age 64 ± 5 years) were analyzed. There was a trend toward healthier values in controls for the CAD model classifier (-0.432 ± 0.326 versus -0.587 ± 0.297, p = 0.170), and the CAD model showed statistical significance on Kaplan-Meier survival analysis p = 0.021. We found 190 individual markers out of 1501 urinary peptides that separated cases and controls (AUC >0.6). Of these, 25 peptides were also components of CAD238 . CONCLUSION AND CLINICAL RELEVANCE: A urinary proteome panel originally developed in a cross-sectional study predicts CAD endpoints independent of age and sex in a well-controlled prospective study.
© 2015 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Entities:  

Keywords:  Biomarker; Cardiovascular risk; Urinary proteomics

Mesh:

Substances:

Year:  2015        PMID: 25786980     DOI: 10.1002/prca.201400195

Source DB:  PubMed          Journal:  Proteomics Clin Appl        ISSN: 1862-8346            Impact factor:   3.494


  12 in total

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Review 10.  Utilizing proteomics to understand and define hypertension: where are we and where do we go?

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