Abstract 516: Urinary Proteomic Biomarkers to Predict Cardiovascular Events

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 (CAD 238 ) 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 CAD 238 . 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.