Abstract 516: Urinary Proteomic Biomarkers to Predict Cardiovascular Events

    loading  Checking for direct PDF access through Ovid


We have previously demonstrated associations between the urinary proteome profile and coronary artery disease (CAD) in cross-sectional studies. Here we evaluated the potential of urinary proteomics as a predictor of CAD in the Hypertension Associated Cardiovascular Disease (HACVD) sub-study population of the ASCOT study.

Thirty-seven cases with the primary endpoint CAD (fatal CAD, non-fatal myocardial infarction and coronary revascularisation) but without established cardiovascular disease at baseline were selected and matched for sex and age within ±2 years to controls who had not reached a CAD endpoint during the study (median observation time, 5 years). A spot urine sample collected at 1 to 1.5 years post randomisation was analysed using capillary electrophoresis (CE) on-line coupled to Micro-TOF mass spectrometry (MS). A previously developed 238-marker CE-MS model for diagnosis of CAD (CAD238) was assessed for its predictive potential.

Sixty urine samples (32 cases; 28 controls; 88% male, mean age 64±5 years) passed quality control for proteomic analysis. There was a trend towards lower ("healthier") values in controls for the CAD model classifier (-0.432±0.326 vs -0.587±0.297, P=0.062), and the CAD model showed statistical significance on Kaplan-Meier survival analysis (Log Rank (Mantel-Cox) P=0.021). After unblinding we found 190 individual markers out of 1501 urinary peptides that separated cases and controls with an AUC>0.6. Of these, 28 peptides including fragments of PGRC1, PTGDS, CO1A1, CO3A1, COGA1, PXDC2, B3GT6 and RET4 were also components of CAD238.

A urinary proteome panel that was originally developed in a cross-sectional study predicts CAD endpoints independent of age and sex in a well controlled prospective study. Proteomic analysis may have the potential to detect subclinical cardiovascular damage that is associated with increased cardiovascular risk.

Related Topics

    loading  Loading Related Articles