Genomewide Association Study of Lipid Response to Fenofibrate and Dietary Fat

 描述(由申请人提供)：血脂异常仍然是心血管疾病风险的关键决定因素。有效的饮食和治疗干预措施可以降低血脂异常，包括升高的甘油三酯(TGS)和高非高密度脂蛋白-胆固醇，但这些干预措施对血脂和脂蛋白水平的反应存在很大差异。遗传学在一定程度上解释了这种变异。我们假设，通过使用改进的脂类表型技术，我们将能够进一步表征这种对饮食和药物反应的脂类的个体间差异。基于质谱学的技术的最新进展使代谢组学表征在大的人群中得以实现。对脂类分子种类的详细而精确的测量，这一领域被称为“脂类组学”，现在提供了一幅前所未有的脂类新陈代谢图景。降脂药物遗传学和饮食网络(GOLDN)研究为开展这项工作提供了一个理想的背景。GOLDN是迄今为止进行的最大规模的家庭研究，该研究试图确定两种短期干预措施的反应的遗传因素，一种是提高血脂(即，摄入高脂餐，“餐后血脂”干预)，另一种是降低血脂(即，非诺贝特(FFB)治疗)。“非诺贝特和膳食脂肪对血脂反应的全基因组关联研究”(Arnett，PI，R01-HL091357)的目标是确定解释干预反应的常见遗传变异。除此之外，GOLDN还通过NIH药物基因组研究网络(PGRN)进行全基因组表观基因组研究和外显子组测序。这项拟议的工作将在最先进的脂类表型的背景下利用这些丰富的基因组数据，特别是磷脂、中性脂和甾醇，来识别稀有和 决定对GOLDN饮食和药物干预反应的常见变异和表观遗传标记。具体地说，我们的目标是：(1)量化350种代谢物在FFB治疗前后/PPL的反应。GOLDN招募并检查了186个家庭的1048名高加索人。参与者被给予83%的脂肪饮食和FFB治疗三周，250 mg/d。在空腹和摄入后6小时，以及FFB治疗3周后再次抽血。来自这些时间点的血浆将被分析出350种脂类。将计算新脂体表型的PPL反应(估计为经基线调整的6小时TG反应)和非诺贝特反应(通过Tg后/Tg前的比值估计)。反应灵敏和可遗传的代谢物将前进到目标2。(2)测试这些小分子与(A)常见(Gwas)和稀有(Wes)变异(B)CpG位点甲基化(Ewas)的关联。(3)在Hapi心脏研究中从PPL反应极端中挑选的500名受试者中重复PPL的发现。(4)使用诱导多能干细胞衍生的肝细胞从功能上验证AIM 2中确定的非诺贝特反应变体。如果成功，GOLDN将识别新的脂体生物标记物，以预测高脂餐和非诺贝特治疗的疗效和不良反应，导致更个性化的饮食和药物治疗方法。

 DESCRIPTION (provided by applicant): Dyslipidemias remain a critical determinant of cardiovascular disease risk. Effective dietary and therapeutic interventions to reduce dyslipidemia, including elevated triglycerides (TGs) and high non-HDL-cholesterol exist, yet there is much variation in lipid and lipoprotein level's response to these interventions. Genetics partially explain this variation. We hypothesize that, by using improved lipid phenotyping techniques, we will be able further characterize this interindividual variation of lipids in respone to diet and drug. Recent advances in mass spectrometry-based techniques allow metabolomic characterization in large populations. Detailed and precise measurement of lipid molecular species, a field known as "lipidomics," now offers an unprecedented picture of lipid metabolism. The Genetics of Lipid-Lowering Drugs and Diet Network (GOLDN) Study provides an ideal context in which to pursue this work. GOLDN is the largest family study conducted to date that has sought to identify genetic factors that explain response to two short-term interventions, one to raise lipids (i.e., ingestion of a high-fat meal, the "post-prandial lipemia" (PPL) intervention and one to lower lipids (i.e., treatment with fenofibrate (FFB)). The goal of the "Genomewide Association Study (GWAS) of Lipid Response to Fenofibrate and Dietary Fat" (Arnett, PI, R01-HL091357) was to identify the common genetic variants that explain response to the interventions. In addition to this, GOLDN is also conducting a genome- wide epigenomics study and exome sequencing through the NIH Pharmacogenomics Research Network (PGRN). The proposed work will leverage this wealth of genomic data in the context of state-of-the-art lipidomic phenotypes, specifically phospholipids, neutral lipids, and sterols, to identify rare and common variants and epigenetic marks that determine response to GOLDN's diet and drug interventions. Specifically, we aim to: (1) Quantify pre- and post-FFB treatment/PPL response of 350 metabolites. GOLDN recruited and examined 1048 Caucasians from 186 families. Participants were given an 83% fat meal and three weeks of FFB treatment, 250 mg/d. Blood was drawn at fasting and 6-hr post ingestion and again after 3 wk of FFB treatment. Plasma from each of these time points will be analyzed with for 350 lipidomic species. PPL response (estimated as the 6-hr TG response adjusted for baseline), and fenofibrate response (estimated by the ratio of post/pre TG values) of the new lipidomic phenotypes will be calculated. Responsive and heritable metabolites will be moved forward to Aim 2. (2) Test the association of these small molecules with (a) common (GWAS) and rare (WES) variants (b) methylation at CpG sites (EWAS). (3) Replicate PPL findings in 500 subjects selected from extremes of the PPL response in the HAPI Heart Study. (4) Use induced pluripotent stem cell- derived hepatocytes to functionally validate fenofibrate response variants identified in Aim 2. If successful, GOLDN will identify novel lipidomic biomarkers to predict efficacy and adverse responses to a high-fat meal and fenofibrate treatment, leading to more personalized approaches to diet and pharmacologic treatment.