Cardiovascular disease (CVD) is the leading cause of morbidity and mortality in the United States and the developed world, and it will soon be the leading cause in the developing world. In the US alone, CVD accounts for almost 1 million deaths per year (38.5% of all deaths), nearly double that due to all cancers combined. Therefore, identification of population at risk at the disease's early stage has great implications in public heath. Whole genome gene expression profiles promise to enable better diagnosis in several human diseases. With advances in bioinformatics and biostatistics, a subset of genes combined from gene expression profiles can be identified that may provide clinically useful predictive value to refine atherosclerosis risk assessment. We plan to construct a multi-marker classifier based on expression profiles in peripheral blood leukocytes that can serve to identify individuals with substantial subclinical atherosclerosis (SA) despite having a low Framingham risk score. As such, this multi-marker classifier will be independent of traditional risk factors. We will perform a case-control study of 120 women from the Multi-Ethnic Study of Atherosclerosis (MESA), an ongoing, large, NIH-funded epidemiologic cohort study that is examining the presence and progression of SA. The unique combination of functional genomics, state-of-the-art statistical and bioinformatics methods, subclinical atherosclerosis imaging, excellent epidemiologic design and a well-phenotyped population available for this application may open up an new avenue to the early detection of atherosclerosis and prevention of CVD. This application may also provide a basis for the understanding of molecular mechanism of atherosclerosis in the population context, and hence increase our knowledge in the pathophyisology of this disease. [unreadable] [unreadable] [unreadable] [unreadable]