Pattern Recognition Mass Spectrometry in the Diagnosis of Heart Disease

DESCRIPTION (provided by applicant): This proposal investigates a novel approach to high-throughput proteomics. Proteins bound to specific blood components, such as HDL (high-density lipoprotein), are examined via mass spectrometry (MS). The resulting data are processed with a novel pattern recognition technique with the aim of identifying abnormal protein patterns in HDL that predict the risk of heart disease. Abnormally low HDL is a well-established risk factor for premature coronary artery disease (CAD). Preliminary studies indicate that the protein composition of HDL differs markedly between patients with established CAD and age- and sex-matched healthy subjects. Several lines of evidence suggest that these changes in HDL are related to the formation of atherosclerotic lesions. In the proposed research, HDL will be extracted from blood and examined via MS. The resulting MS data will then be summarized and subjected to robust, accepted pattern recognition methods. Our initial studies demonstrate that these methods can readily classify samples as being from healthy or diseased subjects and can identify the proteins responsible for the classification. Our observations raise the possibility that we can use pattern recognition to establish a pattern of protein expression that distinguishes healthy from diseased subjects. 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 CAD. The general approach should also be applicable to a wide range of other diseases. About 13 million Americans suffer from CAD. Each year, more than a million Americans have a myocardial infarction (Ml), of whom half die. Surrogate biomarkers for the severity of atherosclerotic lesions may facilitate the selection of appropriate treatment options for CAD and heart attack, and hence produce better therapeutic outcomes.