GOLDN is an approved study funded by the National Institutes of Health through the University of Minnesota and in collaboration with the University of Utah, Washington University, Tufts University, University of Texas – Housten, University of Michigan, and Fairview-University Medical Center. GOLDN is part of the PROGENI Network, a group of family intervention studies focusing on gene-environment interactions.
Hypertriglyceridemia is emerging as an important predictor of atherosclerosis, and recent evidence suggests related phenotypes of triglycerides (TGs), such as TG remnant particles and small LDL particles, are particularly atherogenic. There is considerable variation in the response of TGs and related phenotypes to the environment. The aim of the proposed study is to characterize the genetic basis of the variable response of TGs to two environmental contexts, one that raises TGs (dietary fat), and one that lowers TGs (fenofibrate treatment).
We will recruit 1,200 family members from 3-generational pedigrees of the ongoing NHLBI Family Heart Study (FHS) in two genetically homogeneous centers (Minneapolis and Salt Lake City). We will collect measurements before and after a dietary fat challenge to assess postprandial TGs and related atherogenic phenotypes (VLDL TGs, chylomicron TGs, TG remnant particles, HDL and LDL particle sizes, total cholesterol, LDL-C, and HDL-C). In addition, every subject will participate in a three week unblinded clinical trial of treatment (micronized fenofibrate, 160 mg).
Many family members were genotyped by the Mammalian Genotyping Service (MGS; Marshfield WI) as part of NHLBI FHS; the remaining will be typed using the same marker set. We will conduct genome-wide linkage analyses using state-of-the-art methods to localize novel genetic loci contributing to TG response in the context of fat loading and fenofibrate treatment. We will type 15 single nucleotide polymorphisms (SNPs) in ten candidate genes known to contribute to the response of TGs to dietary fat and fenofibrate, and create haplotypes for association studies. We will use combinatorial partitioning methods and neural networks to test association of the individual SNPs and haplotypes with response to the two environmental interventions. The identification of genetic loci that predict TG response in the presence of two disparate contexts, fat loading and fibrate therapy, may provide insights into genetic pathways (a) predisposing to hypertriglyceridemia, ultimately leading to avenues for primary prevention, and (b) predicting response to TG lowering, leading to new drug targets for hypertriglyceridemia.