Heart disease is the leading cause of death in the western societies. The plasma level of high density lipoprotein cholesterol (HDL-C), the good form of cholesterol, is negatively correlated with the risk of heart disease, myocardial infarction, and coronary death. However, current composite risk score tests such as the Framingham tests identify only 1/3rd of individuals at risk, and HDL-C contributes very little to these composite risk scores. Phase I studies demonstrated that the protein composition of HDL measured by MALDI-MS differs markedly between patients with established CAD and age- and sex-matched healthy subjects. Phase I also established that CAD subjects could be distinguished from age- and sex- matched healthy subjects with a high sensitivity and specificity using HDL protein signals. The goal of the proposed work is to demonstrate that the HDL protein signals can be used to improve the accuracy of composite MI risk scores. This goal will be met through experiments that measure banked samples and new samples collected from 400 subjects who will be monitored for adverse events during the period of the project.
Relevance to public health: Our overall hypothesis is that pattern recognition MS analysis of HDL will be a powerful tool for detecting people at risk for myocardial infarctions (Ml). Each year, more than a million Americans have an Ml, of whom half die. However, our ability to identify subjects at increased risk for these events is severely limited. Therefore, the availability of diagnostics that could accurately predict risk in time to ward off an MI would have an enormous impact on health care costs and public health.
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