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

Yiqing Song, MD; Meir J. Stampfer, MD, DrPH; and Simin Liu, MD, ScD
[+] Article and Author Information

From Brigham and Women's Hospital, Harvard Medical School, and Harvard School of Public Health, Boston, Massachusetts.


Acknowledgments: The authors thank Sharon-Lise Normand, PhD, from the Department of Biostatistics, Harvard School of Public Health, and Jun S. Liu, PhD, from the Department of Statistics, Harvard University, for their assistance in choosing a proper prior distribution and building the Bayesian hierarchical meta-analysis model.

Grant Support: By the National Institutes of Health (grants DK62290 and HL26490).

Potential Financial Conflicts of Interest: None disclosed.

Requests for Single Reprints: Simin Liu, MD, ScD, Division of Preventive Medicine, Harvard Medical School and Brigham and Women's Hospital, 900 Commonwealth Avenue East, Boston, MA 02215; e-mail, simin.liu@channing.harvard.edu.

Current Author Addresses: Drs. Song and Liu: Division of Preventive Medicine, Harvard Medical School and Brigham and Women's Hospital, 900 Commonwealth Avenue East, Boston, MA 02215.

Dr. Stampfer: Department of Epidemiology, Harvard School of Public Health, Kresge Building, 677 Huntington Avenue, Boston, MA 02115.


Ann Intern Med. 2004;141(2):137-147. doi:10.7326/0003-4819-141-2-200407200-00013
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Background: Apolipoprotein E (apoE) genotypes play critical roles in lipid metabolism and are believed to influence risk for coronary heart disease (CHD). Despite many population studies, however, the impact of apoE polymorphism on risk for CHD remains uncertain.

Purpose: To qualitatively and quantitatively assess the evidence regarding the relation of apoE polymorphism to CHD risk.

Data Sources: All relevant reports and references from original and review papers published from 1966 to January 2004.

Study Selection: Predefined criteria were used to identify 48 relevant studies.

Data Extraction: A summary database that contained variables of study design, study sample and ethnicity, sex, apoE genotypes, CHD end points, plasma lipid levels, and other CHD risk factors was developed.

Data Synthesis: The authors qualitatively evaluated many potential sources of heterogeneity. To quantify the extent of heterogeneity and assess the consistency of apoE–CHD associations, stratified analyses were conducted using the classic random-effects model. To further incorporate uncertainty due to between-study variation, the pooled odds ratios (ORs) and 95% credible intervals (CrIs) were estimated by using a Bayesian hierarchical model. Finally, the robustness of the pooled estimates was tested in multiple sensitivity analyses. Compared with individuals with the ε3/3 genotype, carriers of the apoE ε4 allele had a 42% higher risk for CHD (OR, 1.42 [95% CrI, 1.26 to 1.61]). The ε2 allele had no significant association with CHD risk (OR, 0.98 [CrI, 0.66 to 1.46]).

Limitations: This meta-analysis did not include unpublished data or studies published in languages other than English.

Conclusions: Inadequate statistical power, differences in geographic and ethnic background, allele frequency, sex, CHD phenotypes, study design, and potential gene–environment interactions may have contributed to the conflicting results of previous studies. The apoE ε4 allele is a significant risk factor for CHD.

Figures

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Figure 1.
Literature search for selected studies.

apoE = apolipoprotein E; CHD = coronary heart disease; IMT = intima–media thickness.

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Figure 2.
Odds ratios for coronary heart disease in ε2 carriers versus persons with the ε3/3 genotype.

Black circles indicate the odds ratio in each study; horizontal lines represent the 95% CI. Black diamonds show the pooled estimates from the random-effects models (with 95% CI) and the Bayesian hierarchical model (with 95% credible interval). The hierarchical model adjusted for study-level variables, including the mean age of case-patients, sex, continent of origin, study design, control selection, genotype frequency among controls, the presence or absence of Hardy–Weinberg equilibrium among controls, and genotyping methods. The study by Stengard and colleagues represents 2 populations from eastern and southwestern Finland.

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Figure 3.
Odds ratios for coronary heart disease in ε4 carriers versus persons with the ε3/3 genotype.

Black circles indicate the odds ratio in each study; horizontal lines represent the 95% CI. Black diamonds show the pooled estimates from the random-effects models (with 95% CI) and the Bayesian hierarchical model (with 95% credible interval). The hierarchical model adjusted for study-level variables, including the mean age of case-patients, sex, continent of origin, study design, control selection, genotype frequency among controls, the presence or absence of Hardy–Weinberg equilibrium among controls, and genotyping methods. The study by Stengard and colleagues represents 2 populations from eastern and southwestern Finland.

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Appendix Figure 1.
Cumulative meta-analysis of risk for coronary heart disease in ε2 carriers versus persons with the ε3/3 genotype.

Circles indicate the cumulative odds ratio over time (publication year) in individual studies; horizontal lines represent the corresponding 95% CI.

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Appendix Figure 2.
Cumulative meta-analysis of risk for coronary heart disease in ε4 carriers versus persons with the ε3/3 genotype.

Circles indicate the cumulative odds ratio over time (publication year) in individual studies; horizontal lines represent the corresponding 95% CI.

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