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Title: Computed tomography phenotyping of perivascular adipose tissue for cardiovascular disease diagnosis and risk stratification
Author: Oikonomou, Evangelos
ISNI:       0000 0004 9348 009X
Awarding Body: University of Oxford
Current Institution: University of Oxford
Date of Award: 2020
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Coronary inflammation is a major driver of cardiovascular disease and has been implicated in all stages of coronary atherosclerosis, from the early formation of coronary lesions to plaque rupture. Far from being an innocent bystander, perivascular adipose tissue (PVAT) is now recognised as a critical regulator of vascular biology. Further to its direct effects on the arterial wall through outside-to-inside paracrine signals mediated by a wide range of adipocytokines, recent evidence suggests that PVAT may also function as a sensor of vascular biology. More specifically, we have shown that coronary inflammation inhibits adipogenesis and triggers lipolysis in the adjacent perivascular adipocytes, resulting in smaller, fat-free adipocytes closer to the diseased artery. These phenotypic changes can now be captured as perivascular attenuation (radiodensity) gradients on coronary computed tomography angiography (CCTA) using a novel metric, the perivascular Fat Attenuation Index (FAI). My thesis focuses on the technological development of this method of PVAT characterisation (FAI mapping) and its value in improving the cardiac risk stratification of patients undergoing CCTA imaging. First, I demonstrate that perivascular FAI mapping not only detects coronary segments that have a higher likelihood of coronary atherosclerosis progression at five years but can also identify culprit lesions associated with recent acute myocardial infarction. Using longitudinal CCTA imaging, I also show that perivascular FAI exhibits dynamic changes, which reflect the resolution of vascular inflammation and injury around culprit coronary lesions. Next, using two independent cohorts of 3,912 CCTA scans with long-term follow-up, I show that higher perivascular FAI values at baseline are associated with a higher risk of future major adverse cardiac events. Of note, the prognostic value of FAI mapping is incremental to traditional cardiovascular risk factors and the standard interpretation of CCTA scans and remains significant in both patients with and without established coronary artery disease at baseline. Finally, using a radiotranscriptomic, machine learning-guided approach, I develop radiomic signatures that describe adverse fibrotic and microvascular remodelling changes in coronary PVAT, beyond inflammation. As opposed to FAI, these reflect persistent changes in the PVAT structure and offer incremental value for major adverse cardiac event prediction. Taken together, my findings propose that the radiomic phenotyping of coronary PVAT on standard CCTA can maximise the diagnostic and prognostic yield of one of the most commonly used non-invasive imaging modalities in cardiovascular medicine.
Supervisor: Antoniades, Charalambos ; Hopewell, Jemma Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID:  DOI: Not available
Keywords: Coronary arteries ; Imaging ; Coronary heart disease ; Medical sciences ; Cardiovascular disease