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Updated: Aug 6, 2025
Proteomic Profile of EPS-Urine through FASP Digestion and Data-Independent Analysis
Published on: May 8, 2021
Prediction of coronary artery disease using urinary proteomics
Dongmei Wei1, Jesus D Melgarejo1, Lucas Van Aelst2
1Studies Coordinating Centre, Research Unit Hypertension and Cardiovascular Epidemiology, KU Leuven Department of Cardiovascular Sciences, University of Leuven, Campus Sint Rafaël, Kapucijnenvoer 7, Box 7001, BE-3000 Leuven, Belgium.
Insights
A new urinary proteomic classifier shows promise for predicting coronary artery disease (CAD) risk. This discovery offers a novel approach to personalized prevention strategies for cardiovascular health.
Area of Science:
- Urology
- Cardiology
- Biomarker Discovery
Background:
- Coronary artery disease (CAD) is a leading cause of global mortality.
- The multifactorial nature of CAD necessitates advanced diagnostic and predictive tools.
- Urinary proteomic analysis offers a potential avenue for identifying novel CAD biomarkers.
Purpose of the Study:
- To develop and validate a novel urinary proteomic classifier for predicting incident coronary artery disease (CAD).
- To assess the performance of the novel classifier against existing CAD risk prediction models.
- To explore the potential of urinary proteome in personalized CAD prevention.
Main Methods:
- Capillary electrophoresis coupled with mass spectrometry was used to analyze the urinary proteome of 965 participants.
- A support vector machine model identified a 160-peptide classifier in a discovery cohort.
- The classifier's predictive performance was validated in a separate cohort and compared with established risk scores (Framingham, SCORE2) and other classifiers (CAD238, ACSP75).
Main Results:
- The novel urinary proteomic classifier demonstrated strong predictive performance for CAD over 8 years (AUC: 0.82), outperforming existing classifiers (CAD238 AUC: 0.71, ACSP75 AUC: 0.53).
- Integration of the novel classifier with existing models significantly improved CAD prediction accuracy (AUC: 0.84).
- The classifier was independently associated with increased CAD risk (HR: 1.54) and improved risk reclassification when added to traditional risk scores.
Conclusions:
- A novel urinary proteomic classifier, linked to collagen turnover, lipid metabolism, and inflammation, shows significant potential for predicting CAD risk.
- This classifier offers a promising, non-invasive tool for personalized cardiovascular risk assessment.
- Urinary proteome analysis represents a valuable alternative approach for the personalized prevention of coronary artery disease.
Aims:
Coronary artery disease (CAD) is multifactorial, caused by complex pathophysiology, and contributes to a high burden of mortality worldwide. Urinary proteomic analyses may help to identify predictive biomarkers and provide insights into the pathogenesis of CAD.
Methods And Results:
Urinary proteome was analysed in 965 participants using capillary electrophoresis coupled with mass spectrometry. A proteomic classifier was developed in a discovery cohort with 36 individuals with CAD and 36 matched controls using the support vector machine. The classifier was tested in a validation cohort with 115 individuals who progressed to CAD and 778 controls and compared with two previously developed CAD-associated classifiers, CAD238 and ACSP75. The Framingham and SCORE2 risk scores were available in 737 participants. Bioinformatic analysis was performed based on the CAD-associated peptides. The novel proteomic classifier was comprised of 160 urinary peptides, mainly related to collagen turnover, lipid metabolism, and inflammation. In the validation cohort, the classifier provided an area under the receiver operating characteristic curve (AUC) of 0.82 [95% confidence interval (CI): 0.78-0.87] for the CAD prediction in 8 years, superior to CAD238 (AUC: 0.71, 95% CI: 0.66-0.77) and ACSP75 (AUC: 0.53 and 95% CI: 0.47-0.60). On top of CAD238 and ACSP75, the addition of the novel classifier improved the AUC to 0.84 (95% CI: 0.80-0.89). In a multivariable Cox model, a 1-SD increment in the novel classifier was associated with a higher risk of CAD (HR: 1.54, 95% CI: 1.26-1.89, P < 0.0001). The new classifier further improved the risk reclassification of CAD on top of the Framingham or SCORE2 risk scores (net reclassification index: 0.61, 95% CI: 0.25-0.95, P = 0.001; 0.64, 95% CI: 0.28-0.98, P = 0.001, correspondingly).
Conclusion:
A novel urinary proteomic classifier related to collagen metabolism, lipids, and inflammation showed potential for the risk prediction of CAD. Urinary proteome provides an alternative approach to personalized prevention.
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