Abstract 909: Polymorphisms In ABCA1 Predict Statin Mediated LDL Cholesterol Lowering And Suggest An Interaction With CETP
INTRO Low density lipoprotein cholesterol (LDL) lowering by statins is a complex trait with genetic and nongenetic influences. Predicting this lowering may improve statin therapy. Prior studies have studied various genes, often singly, and with contradictory results. None have systematically tested for interactions between single nucleotide polymorphisms (SNPs) in these genes.
METHOD The STRENGTH (Statin Response Examined by Genetic Haplotype Markers) study is a pharmacogenetic study of statin efficacy. Patients (n=509, 47% male, 84% white) with an LDL above goal (mean 172 mg/dl) were randomized to 8 weeks of atorvastatin 10mg, simvastatin 20mg, or pravastatin 10mg. We sequenced 34 genes involved in statin, cholesterol, and lipoprotein metabolism and tested for associations in 2,361 SNPs and percent reduction in LDL using linear regression. A within-gene false discovery rate <5% was used to adjust for multiple comparisons. In an exploratory analysis we used logic regression to search for interactions between SNPs.
RESULTS After adjustment for race, gender, and smoking we found 117 significant (p<.05) SNPs in 25 genes. Adjustment for within-gene multiple comparisons left 17 SNPs in 10 genes associated with LDL reduction. Of these, 4 were within ABCA1: a promoter SNP and three intronic SNPs (Table⇓). Logic regression suggested an interaction between a nonsynonymous exonic SNP in ABCA1 (rs9282541, R230C) and a novel promoter SNP in CETP. Neither SNP individually had a significant effect but those who were variant for both (n=90) had a reduced response (−28%±1.7 vs. −33.2% ±1.7, p=.02 for interaction).
CONCLUSION This study is the first to comprehensively investigate variation in lipid metabolism genes and LDL lowering by statins. SNPs in ABCA1 predict response to three statins and possibly interact with CETP. This study highlights reverse cholesterol transport as a determinant of LDL lowering and emphasizes the use of a multigene strategy in pharmacogenetic studies.