R&D Areas | Cardiology | Coronary Artery Disease

Signatures of urinary polypeptides can contribute to the existing biomarkers for coronary artery disease (CAD). Mosaiques examined a total of 359 urine samples from 88 patients with severe CAD and 282 controls. Spot urine was analyzed using capillary electrophoresis online coupled to electrospray ionization- time of flight mass spectrometry (CE-ESI-TOF-MS) enabling characterization of more than 1000 polypeptides per sample. In a first step a training set for biomarker definition was created. Multiple biomarker patterns clearly distinguished healthy controls from CAD patients and we extracted 15 peptides that define a characteristic CAD signature panel.

In a second step, the ability of the CAD specific panel to predict presence of CAD was evaluated in a blinded study using a test set. The signature panel showed sensitivity of 98% and 83% specificity. Further, the peptide pattern significantly changed towards the healthy signature correlating with the level of physical activity after therapeutic intervention. The results show that urinary proteomics can identify CAD patients with high confidence, and might also play a role in monitoring the effects of therapeutic interventions. The workflow is amenable to clinical routine testing suggesting that non-invasive proteomic analysis can become a valuable addition to other biomarkers used in cardiovascular risk assessment.

Figure 1: Polypeptide patterns distinguishing patients with coronary artery disease (CAD) from controls. This figure shows the compiled data sets of 30 CAD samples (upper left panel) and 20 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 the 15 indicative polypeptides defining the specific pattern for CAD (lower left panel) and controls (lower right panel).

Figure 2: (A) ROC curve of the proteomics panel diagnosis: Using the CAD specific polypeptide panel from Table 3 the classification factor F is used as variable in ROC analysis in the 50 samples of the training set (AUC=0.97) and (B) in the 59 samples of the test set (AUC=0.94). 95 % confidence intervals (95% CI) are indicated by thin lines. (C) Box-and-whisker plots of classification factor F obtained for classification of the test set. The boxes depict the quartiles Q1 and Q3 of each distribution; the statistical medians are shown as horizontal lines in the boxes. The whiskers indicate 3/2 times the interquartile range of Q1 and Q3. (D) CAD probabilities of the 59 urine samples of the test set are plotted against the classification factor F.