Physiogenomic analysis of statin-treated patients: domain-specific counter effects within the ACACB gene on low-density lipoprotein cholesterol?

Aim: Administered at maximal dosages, the most common statins atorvastatin, simvastatin and rosuvastatin lower low-density lipoprotein cholesterol (LDLC) by an average of 37-57% in patients with primary hypercholesterolemia. We hypothesized novel genetic underpinnings for variation in LDLC levels in the context of statin therapy. Materials & methods: Genotyping of 384 SN Ps in 202 volunteers from a lipid outpatient clinic was accomplished and LDLC levels obtained from chart records. The SNPs were distributed across 222 genes representing physiological pathways such as general metabolism, cholesterol biochemistry, cardiovascular function, inflammation, neurobiology and cell proliferation. We discovered significant associations with LDLC levels for the rs34274 SNP (p <0.0002) and for rs2241220 (p < 0.008) in the acetyl-coenzyme A carboxylase 13 (ACACB) gene. When corrected for multiple testing, the falsediscovery rate associated with rs34274 was 0.076 (significance threshold: 0.10) and for rs2241220 the falsediscovery rate was 0.93 (not significant). The acetyl coenzyme A carboxylase p. enzyme synthesizes malonyl coenzyme A, an essential substrate for hepatic fatty acid synthesis and an inhibitor of fatty acid oxidation. Results: The SNPs were in weak linkage disequilibrium (EY -= 0.302). Minor alleles at these sites demonstrate opposing influences on LDLC; the C>T substitution at rs34724 is a risk marker and the C>T substitution at rs2241220 a protective marker for LDLC levels. These SNPs hypothetically influence enzymatic activity through different mechanisms, rs34274 through the Pll promoter and rs2241220 via alteration of the protein's responsiveness to allosteric influence. Conclusion: Physiogenomic evidence suggests a novel link between LDLC levels and the regulation of fatty acid metabolism. The findings complement previously discovered novel SNP relationships to myalgia (pain) and myositis (serum creatine kinase activity). By genotyping for myositis, myalgia and LDLC levels, a physiogenomic model may be developed to help clinicians maximize effectiveness and minimize side effects in prescribing statins.