Coronary Artery Disease
Coronary artery disease (CAD) are characterised by plaque formation or atherosclerosis in the arteries. Signatures of urinary polypeptides can contribute to the existing biomarkers for CAD. Mosaiques examined urine samples in a large cohort composed of patients with severe CAD and controls. Spot urine was analyzed using capillary electrophoresis online coupled to mass spectrometry (CE-MS) enabling characterization of more than 1000 polypeptides per sample. Multiple biomarker patterns clearly distinguished healthy controls from CAD patients and we extracted 238 peptides that define a characteristic CAD signature panel (CAD238). The signature panel showed a 79% sensitivity and an 88% specificity1. Furthermore, CAD238 was able to predict future CAD events independent of age in a prospective study2.
Figure 1: Polypeptide patterns distinguishing patients with coronary artery disease (CAD) from controls. This figure shows the compiled data sets of CAD samples (upper left panel) and control subjects (upper right panel) of the training set. Normalized molecular weight is plotted against normalized migration time. The mean signal intensity is given in 3D-depiction. The lower panel depicts 238 indicative polypeptides defining the specific pattern for CAD (lower left panel) and controls (lower right panel).
Acute coronary syndromes (ACS) including myocardial infarction are complications of CAD. A panel of biomarkers was developed for the prediction of ACS or the prediction of atherosclerotic plaques that will progress into ACS. Using the same proteomic platform, a biomarker panel of 75 urinary peptides (ACSP75) was identified in a cohort of patients who progressed to ACS and controls. The discriminative power of the classifier reached 83.3% sensitivity and 96.4% specificity2. A composite classifier taking into consideration the scores of ACSP75, CAD238 and the age showed a better performance than the 10-year Framingham risk scoring3 the current gold standard for the assessment of CAD. Therefore, urinary proteomics represents a pivotal tool in the management of CAD.
Figure 2: A. Frequency histogram of ACS cases during follow-up. B. Receiver operating characteristic (ROC) curve for the validation set (N = 168). ROC analyses for prediction of ACS using the urinary ACS biomarker pattern ACSP75 (blue solid line), urinary composite classification by ACSCP (red solid line) and Framingham risk score (FCVRS; black spotted line) are shown. C. Kaplan Meier survival curve showing the cumulative percentage with an ACS event based on an ACSP75 score above (red solid line) and below (black spotted line) the threshold of 0.041. D. Multi-variable adjusted Cox proportional-hazards regression analysis of the same data sets based on ACSP75 score above and below the threshold of 0.0413.
1. Delles C et al. J. Hypertens. 2010; 28: 2316-2322.
2. Brown et al. Proteomics Clin. Appl. 2015; 9: 610-617.
3. Htun et al. Manuscript in submission.