The dramatic rise in the incidence of obesity, metabolic syndrome, and diabetes has fueled interest to understand the role of interventions which affect elevated triglycerides (TGs), low HDL-C, and high non- HDL-C. Although LDL-C is a focus of NCEP-ATP-3 guidelines, HDL-C and TGs are also implicated as determinants of risk. Genetic variation influences lipid levels and their response to environmental factors, although the genetic basis of the variable response is not well described. To further characterize the genetic basis of lipids and lipoproteins and their response to environment, we propose a whole-genome association (GWA) study for the Genetics of Lipid Lowering and Diet Network (GOLDN), one of 4 networks in the NHLBI Programs in Gene-Environment Interaction (PROGENI) collaboration. GOLDN is a family-based intervention study designed to identify genomic regions that determine response of lipids (TGs, HDL-C, LDL-C, NMR-measured particle sizes) to 2 interventions, one to raise lipids (ingestion of an 83% fat, 700 kcals/m2 meal) and one to lower lipids (fenofibrate treatment 160 mg qd for 3 weeks). Additional phenotypes include adiponectin, glucose, and inflammatory markers (CRP, TNF1, MCP1, IL-2 soluble receptor, IL-6). Recruitment and follow up were completed in the fall of 2005 (n=1123 completed). During the high-fat meal intervention, fasting lipids and lipoproteins were collected at 0, 3.5 and 6 hours after the meal, and for the fenofibrate intervention, fasting lipid and lipoproteins were collected at days 0, 1, 20 and 21. Specifically, we propose to: (i) Genotype all participants using the Affymetrix 6.0 array. (ii) Test associations between genetic variants and intervention phenotypes (post-prandial lipids measured at 3.5 and 6 hours after ingestion of the meal, and post-fenofibrate lipids) using a mixed model controlling for population stratification using novel structured-association testing. False discovery rate methods will control for multiple testing. Confounding of genetic-lipid associations will be assessed for inflammatory and pharmacogenetic variables. (iii) Prepare for replication in external cohorts. We will identify 1,500 SNPs most significantly associated with lipid and lipoprotein baseline or intervention phenotypes and up to 1500 more variants by considering our findings in the context of evolving linkage evidence and candidate gene evidence within GOLDN, and findings from concomitant NHLBI GWA studies. Although the current proposal does not request funds for replication, we have already secured agreements from three other studies conveying intent to collaborate. Following replication, future research will extend the proposed work by examining the functional relevance of variants associated with gene-by-intervention interactions. Genotypic characterization of individuals who respond poorly to a high-fat diet or favorably to fenofibrate may enable targeted interventions to reduce dyslipidemia and identify effective treatments for clinical practice.
Health officials have long recognized the important role fat and cholesterol play in conditions and diseases such as obesity, diabetes, and heart disease. However, how people's genes interact with their consumption of dietary fat or their treatment with drugs to reduce blood fats is poorly understood. The proposed project aims to identify genetic variants that influence fat and cholesterol's response to diet and drugs; this knowledge may someday help doctors tailor prevention efforts and treatments based on individuals' genetic endowment. ? ? ? ? ? ? ?
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|Geng, Xin; Irvin, Marguerite R; Hidalgo, Bertha et al. (2018) An exome-wide sequencing study of lipid response to high-fat meal and fenofibrate in Caucasians from the GOLDN cohort. J Lipid Res 59:722-729|
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|Kind, Tobias; Fiehn, Oliver (2017) Strategies for dereplication of natural compounds using high-resolution tandem mass spectrometry. Phytochem Lett 21:313-319|
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|Kind, Tobias; Cho, Eunho; Park, Taeeun D et al. (2016) Interstitial Cystitis-Associated Urinary Metabolites Identified by Mass-Spectrometry Based Metabolomics Analysis. Sci Rep 6:39227|
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