The ability to identify individuals at risk for cardiovascular disease (CVD) is of paramount importance with respect to both early detection and control. The 'quality'of lipoprotein particles circulating in the blood exerts a significant influence on CVD. One measure of this 'quality'is its density, the gold standard parameter for characterizing the major lipoprotein classes and their subclasses. The long-term goal is to have in place a general method for characterizing lipoprotein subclasses to accurately distinguish between subject groups and controls in a clinical study. The first specific aim is to develop a comprehensive, accurate and reproducible method for measuring a lipoprotein density profile based on sedimentation equilibrium density-gradient ultracentfiugation. Development of an array of secondary lipoprotein density profiling methods is the focus of the second specific aim. Coupled with this project is a method to recover specific lipoprotein subclasses for a more detailed characterization of structural and functional features. The third specific aim has a statistical analysis and a clinical component. Data derived from the lipoprotein density profile are used to classify subjects and controls using modern statistical methods for classification analysis. The analysis will be developed and applied to a clinical study on atherogenic HDL to determine which statistically-significant parameters are contained in the lipoprotein density profile. The methodology developed from the first three specific aims will be applied to the fourth specific aim, a clinical study to identify lipoprotein subclasses associated with in-stent restenosis following implantation of a bare metal stent. Lipoproteins are active participants in cardiovascular disease and health. They are also internal probes of the circulatory system at the molecular level. The delicate in vivo interplay between lipoprotein subclasses is reflected in features of the lipoprotein density profile, the focus of this proposal.
This project is a contribution to identifying those individuals with normal HDL and LDL cholesterol levels who are actually at risk for developing for cardiovascular disease. Using the same methodology, individuals at risk for premature restenosis following coronary stenting will be identified.
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