Extracellular Vesicles for risk stratification in coronary artery disease: Trash or Treasure

Abstract

Coronary artery disease is caused by atherosclerosis and a typical disease of the elderly. Often the clinical situation is stable for a very long period but it can be disrupted by an acute myocardial infarction (obstructive coronary artery disease), especially if a patients in not on optimal (medical) treatment. All patient suspected of coronary artery disease undergo risk stratification to determine their current and future risk. Currently this stratification process is time consuming and expensive leading to a high burden on our healthcare system. Considering our aging population the need for diagnostic and prognostic risk stratification will increase even more. Extracellular vesicles (EV) are bilayer lipid membrane vesicles that contain bioactive material (RNA, DNA and proteins). With only a small amount of human blood we are able to isolate these vesicles and study their content. It is thought that EV content displays the condition of the mother cell of which they originate from and thereby enable a close look on cellular level. We investigated the protein content of extracellular vesicles as biomarker source to improve the risk stratification of patients suspected of coronary artery disease. Additionally to our projects regarding the EVs we studied another method to improve risk stratification. Coronary artery calcification (CAC) scores are known as one of the best prognostic factors for adequate risk stratification. For this a dedicated ECG triggered CT is made. The downside of coronary CT is that it is not possible to reliable determine perfusion defects, which is also an import factor in determining a patients current risk of having obstructive coronary artery disease. We know from studies that a combined approach results in an improved risk stratification, however this currently involves additional scanning and subsequently radiation exposure. In the second part of this thesis we studied a deep learning method to automatically determine the CAC score on CT images acquired for perfusion imaging