Urinary proteomic biomarkers to predict cardiovascular events

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.

Keywords: Biomarker; Cardiovascular risk; Urinary proteomics.