Pericoronary fat: MACE risk from non-contrast CT and the role of iodine perfusion in contrast CT Summary Pericoronary adipose tissue (PCAT) inflammation is an important, emerging concept in coronary artery dis- ease, giving rise to the “outside-in” theory where inflammatory cells within PCAT, delivered by the vasa- vasorum, influence atherosclerosis plaque progression. Using cardiovascular CT images, we will use ad- vanced image processing and AI to better understand pericoronary fat appearance and to predict major ad- verse cardiovascular events (MACE). As cardiovascular disease remains the most common cause of death in the US, improved early detection, disease prediction, and patient management will positively impact health for many individuals. Using cardiac CT imaging (angiography, CCTA; perfusion, CCTP; and calcium score, CTCS) in elegant experiments and analyses, we will elucidate pericoronary fat assessments and create a new, inex- pensive CTCS assessment of pericoronary fat suitable for screening. As the principal pericoronary fat inflam- mation feature in CCTA is elevated HU, we will use CCTP to assess pericoronary fat perfusion and clarify the role of iodine on existing CCTA signatures, including confounds due to varying filling rates with obstructive dis- ease. Using paired images, we will associate CTCS pericoronary fat features to established ones from CCTA. Using appropriate pericoronary fat features from CTCS exams, we will predict major adverse cardiac events (MACE) without the iodine confound and combine with Agatston to get an even better prediction. Large CTCS cohorts enable interesting research studies. For example, using the serial Coronary Artery Risk Development in Young Adults (CARDIA) study, we will determine if pericoronary fat features precede the appearance of cal- cifications, giving credence to the “outside-in” theory. The CTCS exam is inexpensive (≤$99) at many institu- tions. At University Hospitals (UH), our nationally acknowledged free CTCS program currently servicing >13,000 patients/year with an archive of >65,000 cases, will provide an opportunity for big data, machine/deep learning analysis of PCAT. In addition, improved MACE prediction from PCAT plus Agatston will enable multi- ple future studies on health disparities, genes, cardiometabolic risk, co-morbidities (e.g., diabetes and psoria- sis), and cardio-oncology. To accomplish goals, we have assembled a world class team of biomedical engi- neers and physician scientists at University Hospitals/CWRU with deep knowledge of cardiovascular CT imag- ing and the biology of atherosclerosis. This proposal will ultimately advance the field of predictive medicine and propel new ways to pre-emptively detect at-risk patients so that they can be placed on evidence-based thera- pies.