1. ¹Department of Epidemiology, School of Public Health, University of Michigan, Ann Arbor, MI, USA
2. ²Department of Epidemiology, School of Public Health, University of Alabama at Birmingham, Birmingham, AL, USA
3. ³Nutrition and Genomics Laboratory, Jean Mayer-United States Department of Agriculture Human Nutrition Research Center on Aging, Tufts University, Boston, MA, USA
4. ⁴Cardiovascular Genetics Research, University of Utah, Salt Lake City, UT, USA
5. ⁵Human Genetics Center, University of Texas Health Science, Houston, TX, USA
6. ⁶Department of Experimental and Clinical Pharmacology, College of Pharmacy, University of Minnesota, Minneapolis, MN, USA
7. ⁷Department of Epidemiology, School of Public Health, University of Minnesota, Minneapolis, MN, USA

Correspondence: JA Smith, Department of Epidemiology, School of Public Health, University of Michigan, 109 Observatory, Room 4605, Ann Arbor, MI 48109-2029, USA. Tel: +1 734 647 3721; Fax: +1 734 998 6837; E-mail: smjenn@umich.edu

Received 29 September 2007; Revised 12 December 2007; Accepted 13 December 2007; Published online 23 January 2008.

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–TG response to fenofibrate is critical for predicting individual fenofibrate response. As part of the Genetics of Lipid Lowering Drugs and Diet Network (GOLDN) study, 688 individuals from 161 families were genotyped for 91 single-nucleotide polymorphisms (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, single covariates, SNP–SNP interactions, and/or SNP–covariate interactions had a significant association with the change in total fasting TG and fasting VLDL–TG after 3 weeks of fenofibrate treatment. A 10-iteration fourfold cross-validation procedure was used to validate significant associations and quantify their predictive abilities. 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 models constructed using the top-ranked SNP--covariate interactions explained 11.9% more variation in the change in TG and 7.8% more variation in the change in VLDL than baseline TG alone. These results yield insight into the complex biology of fenofibrate response, which can be used to target fenofibrate therapy to individuals who are most likely to benefit from the drug.