Apolipoprotein E polymorphisms contribute to statin response in Chinese ASCVD patients with dyslipidemia

Abstract

Background: Apolipoprotein E (ApoE) plays an important role in lipid metabolism and clearance. Statins are the most common drugs used to modulate the lipid profile in the clinic therapy; the associations between ApoE polymorphisms and statin response to lipids were inconsistent in previous studies among different ethnicities. Our study aimed to demonstrate the relationships among the statins response and the ApoE gene common polymorphisms and lifestyle risk factors in Chinese arteriosclerotic cardiovascular disease (ASCVD) patients with dyslipidemia.

Methods: A total of 1002 dyslipidemia ASCVD patients were recruited in this study, including 311 patients with a history of type 2 diabetes mellitus (T2DM). These patients were all treated with drugs atorvastatin (10 mg/d) or rosuvastatin (5 mg/d) for at least 4 weeks and genotyped for ApoE e2/e3/e4 alleles, using Kompetitive Allele Specific PCR (KASP) and Sanger sequencing. The plasma lipids levels were determined before and after statins treatment.

Results: The results of ApoE genotyping with KASP method were consistent with the sequencing analysis. In the total 1002 patients, the E2 phenotypes (e2/e3, e2/e2) had significant lower low-density lipoprotein cholesterol (LDL-C) baseline levels than subjects with E3 (e3/e3, e2/e4) and E4 (e3/e4, e4/e4) phenotypes (P = 0.007, 0.005, respectively), and E2 phenotypes had the highest triglyceride (TG) baseline levels. To statins treatment, E2 phenotypes had a better response in TG, Total cholesterol (TC) and LDL-C reduction percentage compared with other phenotypes, and smoking/alcohol drinking status also had a significant influence on statins response of LDL-C lowering. No significant difference was found in the effects of lipids decreasing between atorvastatin and rosuvastatin drugs in all patients.

Conclusions: We developed the KASP technique for the ApoE genotyping, and demonstrated ApoE polymorphisms interacted with smoking/drinking to influence the declining extent of TG, TC and LDL-C levels after statins therapy in Chinese dyslipidemia ASCVD patients. These discoveries developed our cognition with the genetic polymorphisms effects on statin response, which should be taken more seriously in smoking/drinking E4 amino acid isoform carriers.

Keywords: Apolipoprotein E; KASP; Polymorphism; Statins response.

Fig. 1

Plots of rs429358 and rs7412 by KASP method and sequencing KASP method for rs429358 (a) and rs7412 (e), and sequencing results of rs429358 (b, c, d) and rs7412 (f, g, h); d: rs429358 T/T (wild type, homozygous); g: rs7412 T/T (homozygous); c: rs429358 C/C (homozygous); h: rs7412 C/C (wild type, homozygous); b: rs429358 T/C (heterozygous); f: rs7412 T/C (heterozygous).

Fig. 2

Changes of lipids profiles with statin treatment The difference between male and female of statin therapy in TC (a) and LDL-C (b) levels. The difference between T2DM and non-T2DM of statin therapy in TG (c) and HDL-C (d) levels.

Fig. 3

Associations between lifestyles and statins responses The difference between smoking patients and non-smoking patients in LDL-C (a) and HDL-C (b) baseline levels. The difference in he HDL-C response to statins in smoking and non-smoking groups (c). Associations between lifestyles and statins responses to LDL-C. (d: smoking status, e: alcohol drinking status). “validness” means the LDL-C reduction percentage ≥ 25%, and “voidness” means the LDL-C reduction percentage < 25%.

Fig. 4

Lipids baseline levels among ApoE genotypes The association of ApoE polymorphisms and baseline levels in TG (a) and LDL-C (b).

Fig. 5

Associations between statin response and ApoE variations The relationships between ApoE polymorphisms and lipids reduction with statin therapy in TG (a), TC (b), HDL-C (c) and LDL-C (d).

Fig. 6

Distribution of high-risk and low-risk genotypes in the best four-locus model Distribution of high-effective and low-effective models associated with statin response to LDL-C among rs7412, smoking status, alcohol drinking, and T2DM. The numbers of “effective” patients (left) and “non-effective” (right) are shown in cells. Dark gray indicates high-effective models, gray cells indicate low-effective models. Blank cells indicate no subjects. “1” means have the habit of smoking/drinking or the underlying disease of T2DM, “2” means negative of them.