|Smith, Jennifer - UNIV OF MICH, ANN ARBOR|
|Arnett, Donna - UNIV OF ALABAMA|
|Kelly, Reagan - UNIV OF MICH, ANN ARBOR|
|Sun, Yan - UNIV OF MICH, ANN ARBOR|
|Hopkins, Paul - UNIV OF UTAH, SALT LAKE|
|Hixson, James - UNIV OF TEXAS, HOUSTON|
|Straka, Robert - UNIV OF MINNESOTA|
|Peacock, James - UNIV OF MINNESOTA|
|Kardia, Sharon - UNIV OF MICH, ANN ARBOR|
Submitted to: European Journal of Human Genetics
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: December 13, 2007
Publication Date: May 1, 2008
Citation: Ordovas, J.M., Smith, J., Arnett, D., Kelly, R., Sun, Y., Hopkins, P., Hixson, J., Straka, R., Peacock, J., Kardia, S. 2008. The genetic architecture of fasting plasma triglyceride response to fenofibrate treatment. European Journal of Human Genetics. 16(5):603-613. Interpretive Summary: Cardiovascular diseases are multifactorial with contributions from genetic and environmental factors. In addition to blood cholesterol concentrations, other circulating lipids, such as triglycerides, are also associated with increased disease risk. Therefore, dietary and pharmacological approaches to reducing triglyceride concentrations could be effective in decreasing the individual risk for developing cardiovascular diseases. However, the response to any intervention aimed to accomplish this goal is dramatically different from one individual to another which decreases the efficacy of recommendations and treatments. Response to treatment is complex and potentially includes multiple of genes. However, the current approach has been to study the genetic factors involved in response to diet and or drugs on an isolated fashion, one at a time. In this study, we have taken a more global approach and use advanced statistical approaches to identify clusters of genes that could be responsible for response to fenofibrate, a drug used for triglyceride lowering in humans. Ninety-one mutations in 25 genes were examined in about 800 subjects. Our data show that the several combinations of genes and polymorphisms significantly predict intervention response. These results yield insight into the complex biology of fenofibrate metabolism, which can be used to target fenofibrate therapy and other dietary interventions to individuals who will benefit most from them.
Technical Abstract: Metabolic response to the triglyceride (TG)-lowering drug, fenofibrate, is shaped by interactions between genetic and environmental factors, yet knowledge regarding the genetic determinants of this response is primarily limited to single gene effects. Since very low density lipoprotein (VLDL) is the central carrier of fasting TG, identifying factors that affect both total TG and VLDL response to fenofibrate is critical for predicting individual fenofibrate response. As part of the Genetics of Lipid Lowering Drugs and Diet Network (GOLDN) study, 792 individuals from 175 families were genotyped for 91 SNPs in 25 genes known to be involved in lipoprotein metabolism. Using generalized estimating equations to control for family structure, we performed linear modeling to investigate whether single SNPs, SNP-SNP interactions, and SNP-risk factor interactions had a significant association with the change in total fasting TG ('TG) and fasting VLDL ('VLDL) after 4 weeks of fenofibrate treatment. A ten-iteration four-fold cross-validation procedure was used to validate significant associations and quantify their predictive abilities in independent test samples. Multiple variable models were then constructed using the top-ranked SNP-SNP and SNP-risk factor interactions. The SNPs that showed the greatest number of interactions with risk factors that predicted 'TG are APOA4_M35, APOC3_3U386, ABCA1_I27943, and LIPC_T224T, which predicted 0.52% to 1.29% of the variation in independent test samples. The SNPs most involved in interactions with risk factors that predicted DeltaVLDL included ABCG8_C54Y, LIPC_i67180, FABP1_m2353, and FABP1_T94A, which predicted 0.56% to 1.47% of the variation in independent test samples. More than one-third of the significant, cross-validated SNP-SNP interactions predicting each outcome involved just five SNPs, showing that these SNPs are of key importance to fenofibrate response. Multiple variable modeling showed that the top-ranked SNP-risk factor interactions explained 11.9% more variation in DeltaTG and 7.8% more variation in 'eltaVLDL than baseline TG alone. These results yield insight into the complex biology of fenofibrate metabolism, which can be used to target fenofibrate therapy to individuals who will benefit most from the drug.