Abstract
Background
Coronary vascular dysfunction is a common cause of symptoms in patients with angina and no obstructed coronary arteries (ANOCA). Several endotypes have been defined but there are big gaps in our understanding of the underlying pathophysiology. Proteomic analyses may improve the understanding of the pathophysiology.
Purpose
Exploratory approach to 1) compare the proteomic biomarker profile across different types of vascular dysfunction in ANOCA and 2) assess the value of prediction models with protein biomarkers for vascular dysfunction in ANOCA.
Methods
We included 107 angina patients without previous coronary artery disease, left ventricular ejection fraction >45% and no obstructive coronary artery disease (CAD) (<50% stenosis of epicardial vessels) on coronary angiography. Three types of vascular dysfunction were assessed: 1) Vasomotor dysfunction (VMD) defined as epicardial or microvascular vasospasm on acetylcholine provocation, 2) Coronary microvascular dysfunction (CMD) defined as coronary flow velocity reserve (CFVR) ≤2.5 on echocardiography of the LAD on adenosine stimulation and 3) Reactive Hyperaemia Index (RHI) ≤1.67 as a measure of peripheral endothelial dysfunction. Blood samples were analysed for 184 protein biomarkers related to cardiovascular disease. Correlations between biomarkers and results of vascular function assessments were analysed with Pearson's correlation coefficient and visualized with volcano plots. Significantly correlated biomarkers (p<0.05) were tested in prediction models for their incremental value over age and gender with C-statistics.
Results
CFVR was correlated to 24 biomarkers before (figure 1a) and 2 biomarkers after adjustment for age and gender. The basic prediction model had AUC of 0.68 and was not significantly improved by adding biomarkers (figure 2a). RHI was correlated to 27 biomarkers before (figure 1b) and 10 biomarkers after adjustment for age and gender. The clinical prediction model was significantly improved (p=0.037) by adding TRAIL R2 and IL-18, in addition to age and gender, with an AUC of 84.4 (figure 2b). VMD was correlated to 14 biomarkers before (figure 1c) and 6 biomarkers after adjustment for age and gender. The prediction model was significantly improved (p=0.011) by adding HSP-27, RARRES-2 and SERPINA-12 in addition to age and gender in prediction of VMD with an AUC of 85.4 (figure 2c).
Conclusion
Several biomarkers were associated with vascular dysfunction in ANOCA patients with little overlap between different endotypes. We identified biomarkers that may contribute to the understanding of the underlying pathophysiology and have applications for screening. Results need to be confirmed in larger studies.
FUNDunding Acknowledgement
Type of funding sources: Public hospital(s). Main funding source(s): Department of Cardiology, Bispebjerg and Frederiksberg Hospital, University of Copenhagen, Denmark.Department of Cardiology and Angiology, Robert Bosch Krankenhaus, Stuttgart, Germany
A COMPARATIVE ANALYSIS OF CLINICAL PREDICTION MODELS VERSUS A CLINICIAN'S ASSESSMENT FOR THE PREDICTION OF OBSTRUCTIVE CORONARY ARTERY DISEASE IN PATIENTS WITH CHEST PAIN