Coronary artery disease (CAD), the direct result of atherosclerosis, is the most common cause of death in people over the age of 65. Although, statins have been used to lower LDL and retard disease progression, these drugs have modest impact on lesion burden as reflected by the degree of regression seen in the REVERSAL and recent ASTEROID studies and the still substantial rates of heart attacks in large-scale clinical trials of these drugs. By understanding the factors that lead to plaque regression, better treatment options may be developed for many at risk, especially the older population, who already carry a heavy plaque burden. My supervisor, Dr. Edward Fisher, has led the development of in-vivo regression models using surgical and genetic approaches to the standard models of atherosclerosis progression. Using laser capture microscopy to capture foam cells, important mediators in plaque pathophysiology, we studied gene expression changes specifically in these cells in plaques. We showed that CCR7, an established maturation marker for dendritic cells that promotes their emigration from tissues to lymph nodes, was functionally required to promote regression in intermediate lesions, the type that most resembles the ones most prone to rupture and cause an acute myocardial infarction in people. Since members of the aging population with CAD have these lesions, we propose to 1) establish the functional requirement for CCR7 in regression by using a different in vivo approach and 2) determine the molecular mechanisms inducing CCR7 gene expression. Ultimately, the results obtained from the proposed studies may represent a novel path towards achieving regression of atherosclerosis in the aging, a population expanding due to the average life-span increasing. Coronary artery disease (CAD), the direct result of atherosclerosis, is an age-related disorder with a tremendous impact on public health. Although retarding the progression is important, this will not eliminate the huge plaque burden already present in the elderly. With regression models and the tools to analyze them on a molecular level, there is the exciting potential to identify factors that can be manipulated to accelerate regression in patients at risk for CAD, most of whom are members of the older population.
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