Background: Lipoprotein-related mechanisms have been associated with damage to the cardiovascular system in diabetic patients. Apolipoprotein E gene which affects the clearance of lipoproteins and consequently the lipid profile in our body is one of the most studied candidate genes and recently has been reported to be associated with T2DM and CAD. In this work, we studied the association of apoE gene polymorphism with T2DM and CVD and its effect on plasma lipids profile.
Methods: Our study was conducted on 284 subjects categorized into 100 patients with T2DM, 100 patients with T2DM complicated with CVD and 84 normal control subjects. ApoE gene polymorphism was genotyped by real-time PCR using TaqMan(®) SNP Genotyping Assay.
Results: ApoE E3/E3 genotype was the most common in our subjects. The frequencies of E3/E4 genotype and ε4 allele were increased in both T2DM patients and CVD patients as compared with controls, but were significant only in CVD patients (p = 0.004 and 0.007, respectively). Diabetic patients who carried E3/E4 genotype were at 2.4-fold increased risk to develop CVD (95 % CI 1.14-5.19, P = 0.02) and the ε4 allele associated with 2.23-fold higher CVD risk (95 % CI 1.09-4.59, P = 0.02). After adjustment for other established risk factors, E3/E4 genotype was an independent risk factor for CVD (OR = 2.3, p = 0.009) but not for T2DM (OR = 1.7, p = 0.28), while ε4 allele was an independent risk factor for both T2DM (OR = 2.2, p = 0.04) and CVD (OR = 3.0, p = 0.018) with 5.9-fold increased risk to develop CVD in T2DM patients (p = 0.019). E3/E4 genotype associated with significantly higher levels of TC and non HDL-C in all groups and with significantly higher levels of LDL-C in both T2DM and CVD patients.
Conclusions: ApoE gene polymorphisms associate with CVD and affect the lipid profile. The ε4 allele is an independent risk factor for both T2DM and CVD. Further genetic studies to add information beyond the traditional cardiovascular risk factors in T2DM and to identify risk genotypes will help in early prediction and identification of at risk patients.