Meta-Analysis: Apolipoprotein E Genotypes and Risk for Coronary Heart Disease

Context Many studies investigate relationships between apolipoprotein E (apoE) polymorphisms and coronary heart disease (CHD) risk. What do these studies show? Contribution This comprehensive review of 48 studies found that carriers of the apoE4 allele had a higher risk for CHD than did persons with the 3/3 genotype. The reviewers found no consistent associations between the 2 allele and CHD risk. They also found that the results of studies sometimes varied because of differences in geographic settings, ethnicity of study samples, allele frequency, CHD phenotypes, and potential geneenvironment interactions. Implications The apoE4 allele is probably a risk factor for CHD. The Editors Apolipoprotein E (apoE) plays a key role in the metabolisms of cholesterol and triglyceride by serving as a receptor-binding ligand mediating the clearance of chylomicron and remnants of very-low-density lipoprotein cholesterol from plasma (1-4). A common polymorphism in the apoE gene that codes for the 3 common isoforms E2, E3, and E4 has been studied extensively. Compared with 3 homozygotes, carriers of the 2 allele, which has defective receptor-binding ability, have lower circulating cholesterol levels and higher triglyceride levels, whereas carriers of the 4 allele appear to have higher plasma levels of total and low-density lipoprotein cholesterol (1). It has been estimated that apoE polymorphism may account for 2% to 11% of the total variation in serum or plasma cholesterol levels in apparently healthy white persons (5). Recent evidence also indicates that apoE may play additional roles in the development of coronary heart disease (CHD) through macrophage cholesterol efflux, platelet aggregation, and allele-specific antioxidant and immune activities (2,6,7). Since Utermann and colleagues first described the effects of apoE polymorphism on dysbetalipoproteinemia (known as type III familial hyperlipoproteinemia) in 1977 (8), numerous epidemiologic studies have investigated the relation between apoE genotypes and CHD risk in the general population, especially in Europe (9-69). Apolipoprotein E polymorphism is believed to confer substantial susceptibility to CHD risk; however, the evidence from population studies remains controversial. In 1996, a meta-analysis of 14 published studies reported that compared with carriers of the apoE 3/3 genotype, carriers of the apoE 4 allele were at greater risk for CHD (odds ratio [OR], 1.26 [95% CI, 1.13 to 1.41]) while the 2 allele was not associated with CHD risk (OR, 0.98 [CI, 0.85 to 1.14]) (70). Since the publication of that review, more than 50 other studies have been conducted involving more than 10 000 additional patients with CHD. However, results have conflicted. Some studies have shown highly significant associations (22-47), while others have shown no association (48-69). Of note, differences in study design, end point validation, and sample, coupled with the fact that most individual studies have very limited statistical power, have led to difficulties in estimating the potentially true but modest effects of apoE genotypes on CHD risk in the general population. In an attempt to understand the cumulative evidence on this topic, we qualitatively and quantitatively examined sources of heterogeneity in previous studies regarding the direct relation of apoE genotypes to CHD risk. To reliably assess the hypothesized apoECHD relationship on the basis of these existing data, we also provided pooled estimates using both a classic random-effects model and a Bayesian hierarchical model. Methods Study Selection We conducted a comprehensive literature search of MEDLINE from 1966 through January 2004 using the following index terms: apolipoprotein E, apoE, APOE polymorphism, atherosclerosis, coronary heart disease, ischemic heart disease, coronary artery disease, and myocardial infarction. We also retrieved additional studies by hand searching the bibliographies of original research reports and review articles. All studies were considered potentially eligible if they aimed to investigate the relation between apoE genotypes and CHD risk. Our selection criteria were qualitatively predefined and required each included study to clearly describe study design, control selection, CHD end points, genotype frequency, and genotyping methods (71). Also, the included studies reported the relative risks or ORs and 95% CIs for CHD related to apoE polymorphism or provided raw data that allowed us to estimate these values. Figure 1 describes the study selection process that led to the final 48 studies in this meta-analysis. Figure 1. Literature search for selected studies. Two of the authors independently identified and reviewed each relevant paper. Disagreements were reconciled through group discussion. The final data set for our meta-analyses included 48 published studies comprising 15 492 case-patients and 32 965 controls. Data Extraction Following the Meta-analysis Of Observational Studies in Epidemiology (MOOSE) statement for reporting meta-analyses of observational studies (72), we used a standardized reporting form to independently abstract data from each included study. We collected information on the first author's name; country; year of publication; study design; sex studied; mean age or age range for case-patients; major CHD end point; selection of controls (population-based or other); allele frequencies of 2, 3, and 4 among controls; genotype distribution in case-patients and controls; confounding factors by matching or adjustment; and the relative risk or ORs for CHD associated with the apoE genotype. As was done in most previous studies, we defined carriers of the 4 allele as those who had the 3/4 and 4/4 genotypes, defined carriers of the 2 allele as those who had the 2/3 and 2/2 genotypes, and used carriers of the 3/3 genotype as a referent group. Definition of CHD end points included myocardial infarction, angina pectoris, percutaneous transluminal coronary angioplasty, coronary artery bypass graft surgery, and severe stenosis on coronary angiography. In most studies that included case-patients with coronary artery disease, diagnosis was based on angiographically documented luminal stenosis ( 50%) in at least 1 of the 3 major coronary arteries. Study design was classified as prospective if information about relevant variables, including blood sample, was collected before the onset of the CHD event and as retrospective if this information was collected after the event. Studies were also classified as population-based if the controls were selected from the same source population as the case-patients, including the community and the general population. Studies using hospital- or clinic-based patients with other illnesses as controls and studies that used an unidentified healthy control group were considered hospital-based. Data Synthesis Qualitative Analysis We first assessed all potential sources of heterogeneity across studies, following the established principles in evaluating the validity of genetic association studies (71, 73). All major features thought to contribute to between-study heterogeneity were examined a priori, including study design (prospective and retrospective studies; population-based controls and hospital-based controls), population origins (Europe, North America, Asia, and other continents), types of CHD end points (nonfatal myocardial infarction, fatal myocardial infarction, CHD death, and angiographically documented CHD), sex (men, women, and both), presence of HardyWeinberg equilibrium for apoE genotype frequencies among controls (yes or no), and genotyping methods (polymerase chain reactionbased methods vs. phenotype methods). Quantitative Analysis We used both the chi-square statistic and stratified analyses to formally examine between-study heterogeneity using features identified in our qualitative analysis. We synthesized the results from individual studies by using the DerSimonian and Laird random-effects model to incorporate both within-study and between-study variability (74). To further adjust for uncertainty due to between-study heterogeneity, we performed a Bayesian hierarchical (random-effects) model to assess the robustness of our pooled estimates (75, 76). Assuming a normal distribution for the natural logarithms of the OR from each study, we completed the Bayesian specification by assigning prior distributions to the overall mean effect () and between-study variation (2). Assuming that and are independent, we used the diffuse prior distribution for and a diffuse but proper (noninformative) prior distribution for . The posterior estimate for each study was determined through an application of Bayes theorem. The posterior mean for the pooled effect was estimated by combining the posterior mean from each study using the relative weights inversely in proportion to between-study variation. To examine the impact of the prespecified factors on the pooled estimates, we included them with the mean age of CHD case-patients and the allele frequency among controls from each study as study-level variables in the hierarchical model. Finally, we calculated the posterior OR and 95% credible interval (CrI), the Bayesian analogue to CI, for the effect of apoE genotypes on CHD risk. Our Bayesian hierarchical meta-analysis models were performed by using S-Plus programs (S-Plus, version 2000 for Windows, Mathsoft, Inc., Seattle, Washington) written by DuMouchel and colleagues (75, 76). For sensitivity analyses, we compared the pooled estimates from the classic random-effects model with those from Bayesian hierarchical models adjusted for study-level covariates. Furthermore, we examined whether excluding studies with substantial deviation from HardyWeinberg equilibrium among controls or different study-level covariates in the hierarchical model affected our pooled estimates of ORs. We also conducted a cumulative meta-analysis to assess the sufficiency of these accumulating data