Urinary proteomic biomarkers to predict cardiovascular events

Area of Science:

• Proteomics
• Cardiovascular Disease Research
• Biomarker Discovery

Background:

• Urinary proteome profiles have shown associations with coronary artery disease (CAD) in prior cross-sectional studies.
• Predictive biomarkers for CAD are crucial for early intervention and risk stratification.

Purpose of the Study:

• To evaluate a urinary proteomic panel's predictive potential for CAD endpoints.
• To assess the performance of a 238-marker CE-MS model (CAD238) in a prospective hypertensive atherosclerotic cardiovascular disease (HACVD) population.

Main Methods:

• Thirty-seven CAD cases were matched with controls from the HACVD substudy.
• Urine samples were analyzed using capillary electrophoresis coupled to Micro-TOF Mass Spectrometry (CE-MS).
• The predictive capability of the established CAD238 model was assessed.

Main Results:

• The CAD model classifier showed a trend toward healthier values in controls (p=0.170).
• Kaplan-Meier survival analysis indicated statistical significance for the CAD model (p=0.021).
• 190 individual urinary peptides differentiated cases from controls, with 25 peptides being part of the CAD238 model.

Conclusions:

• A urinary proteome panel, initially developed cross-sectionally, effectively predicts CAD endpoints prospectively.
• The panel's predictive ability is independent of age and sex.
• This highlights the potential of urinary proteomics for non-invasive CAD risk assessment.