BACKGROUND:

Increased serum triglycerides are a significant independent risk factor for coronary artery disease and myocardial infarction. Besides typical lifestyle modification, the treatment with fibrates is standard in patients with hypertriglyceridemia. There is ample evidence that genetic factors influence triglyceride levels. Identifying genes, which affect triglyceride levels and moreover determining the response to fibrates is of great interest. Hispanics are among the populations with the highest prevalence of this disease. Therefore focusing on a high-risk population further adds to the overall significance of this study. Identifying genes and genetic mechanisms contributing to the treatment response can lead to new or improved treatment methods.

DESIGN NARRATIVE:

The study tests the hypothesis that differences in therapeutic effects of fibrates are related to variations in networks of genes regulating lipoprotein metabolism. An efficient approach for identifying and categorizing major gene variants is based on determination of sequence haplotypes. Only Hispanics will be studied to provide a homogenous population for this genetic study. Furthermore, Hispanics are the fastest growing ethnic group in the U.S. and have a higher prevalence of hypertriglyceridemia than other ethnic groups. The investigators will measure plasma triglycerides (TG), other lipids and lipoproteins, and lipoprotein particle size before and after treatment with fenofibrate in 800 Hispanic individuals with hypertriglyceridemia. They will reconstruct the haplotypes of 16 candidate genes known to regulate TG metabolism and mediate the effect of fibrates. The associations between phenotypes and major haplotypes will be analyzed in a two stage design, with the first half of the sample serving a hypothesis-generating function. Those candidate gene haplotypes that are found to be associated with quantitative traits and drug responses in the first half of the sample will then be tested for confirmation in the second half. In addition, data from the full cohort will be analyzed to identify additional pharmacogenetic associations that may require greater statistical power. Finally, direct fine mapping will be performed in the four most promising genes to identify the responsible variations.