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. 2014 Aug 11;9(8):e104608. doi: 10.1371/journal.pone.0104608

Meta-Analysis of Apolipoprotein E Gene Polymorphism and Susceptibility of Myocardial Infarction

Hong Xu 1, Haiqing Li 1, Jun Liu 1, Dan Zhu 1, Zhe Wang 1, Anqing Chen 1,*, Qiang Zhao 1,*
Editor: Giuseppe D Norata2
PMCID: PMC4128680  PMID: 25111308

Abstract

A number of case-control studies have been conducted to clarify the association between ApoE polymorphisms and myocardial infarction (MI); however, the results are inconsistent. This meta-analysis was performed to clarify this issue using all the available evidence. Searching in PubMed retrieved all eligible articles. A total of 33 studies were included in this meta-analysis, including 18752 MI cases and 18963 controls. The pooled analysis based on all included studies showed that the MI patients had a decreased frequency of the ε2 allele (OR = 0.78, 95% CI = 0.70–0.87) and an increased frequency of the ε4 allele (OR = 1.15, 95% CI = 1.10–1.20); The results also showed a decreased susceptibility of MI in the ε2ε3 vs. ε3ε3 analysis (OR = 0.79, 95% CI = 0.68–0.90) and in the ε2 vs. ε3 analysis (OR = 0.78, 95% CI = 0.69–0.89), an increased susceptibility of MI in the ε3ε4 vs. ε3ε3 analysis (OR = 1.26, 95% CI = 1.12–1.41), in the ε4 vs. ε3 analysis (OR = 1.22, 95% CI = 1.12–1.32) and in the ε4ε4 vs. ε3ε3 analysis (OR = 1.59, 95% CI = 1.15–2.19). However, there were no significant associations among polymorphisms and MI for the following genetic models: frequency of the ε3 allele (OR = 0.99, 95% CI = 0.96–1.02); ε2ε2 vs. ε3ε3 analysis (OR = 0.73, 95% CI = 0.40–1.32); or ε2ε4 vs. ε3ε3 analysis (OR = 1.10, 95% CI = 0.99–1.21). Our results suggested that the ε4 allele of ApoE is a risk factor for the development of MI and the ε2 allele of ApoE is a protective factor in the development of MI.

Introduction

Myocardial infarction (MI) is a leading cause of death worldwide, and is a multifactorial disease, influenced by genetic and environmental factors [1]. The main risk factors for MI include hypertension, hypercholesterolemia, diabetes, obesity, and smoking. In addition, recent studies have also shown the importance of genetic factors caused by polymorphisms in the pathogenesis of MI [2]–[7].

Apolipoprotein E (Apo E) is a serum glycoprotein found in circulating chylomicrons (remnants), very low density lipoproteins, intermediate density lipoproteins and high-density lipoproteins [8]. ApoE is considered as an excellent candidate gene for studying the susceptibility to coronary heart disease (CHD) and MI because of its pivotal roles in the metabolisms of cholesterol and triglyceride [9]. The most extensively studied polymorphism in the ApoE gene codes for three variant alleles: ε2, ε3 and ε4, which yield six possible genotypes: ε2/ε2, ε2/ε3, ε2/ε4, ε3/ε3, ε3/ε4 and ε4/ε4 in general population [10]. The products of the three alleles differ in their properties such as their affinity for binding low density lipoprotein receptors and lipoprotein particles; therefore, this ApoE polymorphism could affect the serum levels of cholesterol and triglyceride, thus contributing to the progression of atherosclerosis. In fact, ApoE polymorphisms have been found to be associated with many lipid-related diseases and cardiovascular and cerebrovascular diseases [11]–[14].

Numerous studies have been conducted to explore the association of this ApoE polymorphism and CHD; some of the studies found a significant association between the ApoE ε4 allele and CHD [15]–[17]. A meta-analysis conducted in 2004 provided evidence that the ε4 allele of ApoE was a risk factor for the development of CHD [18]. Another meta- analysis conducted in 2013 further confirmed this finding in a Chinese population [19]. However, no meta-analysis has been conducted to explore the association between this ApoE gene polymorphism and MI. In spite of the presence of advanced CHD, only a subset of patients develops MI during their life. The reasons for these individual differences in susceptibility to MI are poorly understood. Therefore, it is important to explore the association between ApoE gene polymorphisms and MI. In fact, a number of case-control studies have been conducted to clarify the association between ApoE gene polymorphisms and MI [20]–[52]; however, the results are inconsistent. Therefore, we conducted this meta-analysis including all of the evidence produced to date to explore this issue.

Materials and Methods

Search strategy

We searched all published studies in the Pubmed database (up to January 20, 2014) using the following combination of keywords: “Apolipoprotein E” OR “ApoE” AND “acute coronary syndrome” OR “myocardial infarction” AND “polymorphism” OR “polymorphisms” OR “variants” OR “variant”. In addition, manual searches for related articles were also performed to avoid missing any relevant studies.

Inclusion and exclusion criteria

The inclusion criteria for identified articles were as follows: 1) Case-control studies with full text articles on the relationship of ApoE polymorphisms and MI; 2) sufficient data for estimating an odds ratio (OR) with 95% confidence interval (CI). Those not designed as case-control studies, systemic reviews, those not written in English or Chinese, and those that provided no usable data, were excluded.

Data extraction

Two authors independently extracted the data from all included studies using a predesigned data extraction table. The following information was extracted from each included article: first author, year of publication, ethnicity and country, source of controls, total numbers of MI cases and controls, distribution of genotypes and alleles in MI cases and controls, and evidence of conforming to the Hardy-Weinberg equilibrium (HWE).

Statistical analysis

We firstly used chi-squared (χ2) test and and I2 statistic to assess heterogeneity across studies. A fixed effect model (Mantel–Haenszel) was used in the absence of heterogeneity. Otherwise, the random effect model (DerSimonian–Laird) was adopted. The strength of the association between the ApoE gene polymorphism and MI was assessed by odds ratios (ORs) with the corresponding 95% CI for each study. The ORs and their 95% CIs were assessed for the following seven genetic models: 1) ε2ε2 vs. ε3ε3; 2) ε2ε3 vs. ε3ε3; 3) ε2ε4 vs. ε3ε3; 4) ε3ε4 vs. ε3ε3; 5) ε4ε4 vs. ε3ε3; 6) ε2 vs. ε3; 7) ε4 vs. ε3. The allele frequencies of ε2, ε3 and ε4 were also assessed using the same method. Cumulative meta-analysis was also performed for the above genetic models. Subgroup analysis for ethnicity (Asian and Caucasian) was also performed. To find potential outliers, influence analysis was performed by omitting each study in turn. A funnel plot, calculated using Begg’s and Egger’s tests, was adopted for assessing potential publication bias. Statistical analysis was conducted using STATA statistical software (version 11; StataCorp, College Station, Texas, USA). A P value less than 0.05 was considered statistically significant.

Results

Literature selection and study characteristics

One hundred and thirty two articles were retrieved from PubMed, 79 of which were excluded after screening the titles and abstracts (58 were irrelevant studies, 13 were reviews and eight were not published in English or Chinese). Fifty-three articles were selected for detailed assessment, which excluded a further 20 articles (seven were not case-control studies, eight had no usable data (no case and control numbers according to the genotypes) and five were not about MI). Finally, 33 studies were included in this meta-analysis, which included 18752 MI cases and 18963 controls. The detailed selection procedure is shown in Figure 1 . There were three studies did not follow the HWE. The detailed characteristics of the included studies are shown in Table 1 . The present study met the PRISMA statement requirements ( Checklist S1 and Figure 1 ).

Figure 1. Flowchart of the study selection.

Figure 1

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Table 1. Detailed characteristics of studies included in this meta-analysis.

Genotypes distribution (Cases/controls)
Study [Reference] Year Country Ethnicity Study HWE Total sample ε2/ε2 ε2/ε3 ε3/ε3 ε2/ε4 ε3/ε4 ε4/ε4 ε2 ε3 ε4
type Case/Control
Utermann1984[20] 1984 Germany Caucasion HCC Yes 523/1031 7/120 x68/124 333/617 11/15 92/236 12/29 86/359 493/977 115/280
Cumming1984[21] 1984 Scotland Caucasion PCC Yes 239/400 0/2 18/51 128/233 10/11 77/99 6/4 28/64 223/383 93/114
Lenzen1986[22] 1986 France Caucasion PCC Yes 570/624 1/6 50/67 360/393 10/20 137/125 12/13 61/93 547/585 159/158
Eichner 1993[23] 1993 USA Caucasion PCC Yes 114/412 0/2 16/35 67/276 0/4 30/85 1/10 16/41 113/396 31/99
Luc 1994[24] 1994 France Caucasion PCC Yes 574/680 3/6 54/92 352/428 14/14 133/126 18/14 71/112 539/646 165/154
Hergenc 1995'[25] 1995 Turkey Caucasion HCC Yes 50/60 0/0 7/6 41/47 0/2 2/5 0/0 7/8 50/58 2/7
Kim 1995[26] 1995 Korea Asian HCC Yes 97/137 2/1 17/25 57/95 0/4 20/12 1/0 19/30 94/132 21/16
Nakai 1998[27] 1998 Japan Asian PCC Yes 254/422 0/0 10/16 178/327 2/4 52/74 6/1 12/20 240/417 60/79
Scaglione1999[28] 1999 Italy Caucasion HCC No 98/98 NR NR NR NR NR NR 3/3 84/87 11/8
Lambert 2000[29] 2000 France Caucasion PCC Yes 567/678 3/4 67/100 332/420 0/3 152/138 18/13 70/107 551/658 170/154
Benes2000[30] 2000 Czech Caucasion PCC Yes 114/222 1/0 12/30 71/147 3/2 23/43 4/0 16/32 106/220 30/45
Batalla 2000[31] 2000 Spain Caucasion PCC Yes 220/200 0/0 9/18 174/151 1/1 32/28 4/2 10/19 215/197 37/31
Raslová 2001[32] 2001 Canada Caucasion PCC Yes 69/69 2/1 8/5 46/47 1/0 11/15 1/1 11/6 65/67 13/16
Bai 2001[33] 2001 China Asian PCC Yes 47/50 0/0 4/5 40/39 0/0 6/3 0/0 4/5 50/47 6/3
Freitas 2002[34] 2002 Australia Caucasion PCC Yes 411/624 3/4 24/67 254/372 9/15 111/147 10/19 36/86 389/586 130/181
Mamotte 2002[35] 2002 Australia Caucasion PCC Yes 359/639 4/4 24/68 217/383 7/16 96/149 11/19 35/88 337/600 114/184
Kolovou 2002[36] 2002 Greece Caucasion PCC Yes 124/240 0/0 3/34 94/159 0/5 27/40 0/2 3/39 124/233 27/47
Keavney 2003[37] 2003 UK Caucasion PCC Yes 4487/5757 NR 440/686 2566/3384 NR 1206/1376 NR 440/686 4212/5446 1206/1376
Kolovou 2003[38] 2003 Greece Caucasion PCC Yes 165/165 0/0 3/16 129/118 1/4 29/23 1/0 4/20 161/157 31/27
Kumar 2003[39] 2003 India Caucasion PCC Yes 35/45 0/2 6/9 12/32 1/0 6/0 10/2 7/9 24/41 17/2
Marques 2003[40] 2003 France Caucasion HCC Yes 400/338 NR NR 272/228 NR NR NR 37/40 272/228 91/70
Keavney 2004[41] 2004 UK Caucasion PCC Yes 4685/3460 NR 440/406 2566/1949 1206/810 NR NR 1646/1216 3006/2355 1206/810
Ranjith 2004[42] 2004 South Africa Caucasion PCC Yes 195/300 0/3 7/18 139/228 3/3 45/43 1/5 10/24 191/289 49/51
Baum 2006[43] 2006 China Asian PCC Yes 231/331 0/2 13/60 164/203 4/6 46/39 4/1 17/68 223/302 54/46
Aasvee 2006[44] 2006 Estonia Caucasion PCC Yes 71/85 1/1 4/13 45/52 2/3 16/16 3/0 7/17 65/81 21/19
Koch 2008[45] 2008 Germany Caucasion PCC Yes 3657/1211 26/7 402/164 2279/736 63/23 809/263 78/18 491/194 3490/1163 950/304
Kolovou 2009[46] 2009 Greece Caucasion PCC Yes 124/240 NR NR NR NR NR NR 5/19 106/197 13/24
Bahri 2008[47] 2008 Tunisia Caucasion PCC Yes 80/100 0/0 6/8 61/78 0/1 13/13 0/0 6/9 80/199 13/14
Martinelli 2009[48] 2009 Italy Caucasion HCC Yes 394/287 NR NR NR NR NR NR 34/25 285/220 76/42
Al-Bustan 2009[49] 2009 Kuwaiti Caucasion HCC No 88/122 4/9 2/2 72/98 2/3 8/9 0/1 6/11 90/33 16/5
Onrat 2012[50] 2012 Turkey Caucasion PCC Yes 36/100 0/0 12/4 72/27 0/0 16/4 0/1 12/4 100/35 16/5
Tanguturi 2013[51] 2013 USA Caucasion HCC Yes 202/210 0/0 8/14 142/167 4/3 37/23 11/3 12/17 187/204 52/29
Zende 2013[52] 2013 India Caucasion HCC No 150/150 6/7 13/16 59/85 7/4 22/14 43/24 26/27 94/115 72/42
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HWE, Hardy-Weinberg equilibrium; NR, not reported; Cases, MI patients; HCC, hospital based case-control study; PCC, population based case-control study.

Quantitative data synthesis

The meta-analysis of the included studies showed that there was significant association between the ApoE gene polymorphism and MI. The results showed that the MI patients had a decreased frequency of the ε2 allele (OR = 0.78, 95% CI = 0.70–0.87, Figure 2 ) and an increased frequency of the ε4 allele (OR = 1.15, 95% CI = 1.10–1.20, Figure 3 ). The results also showed a decreased susceptibility of MI in the ε2ε3 vs. ε3ε3 analysis (OR = 0.79, 95% CI = 0.68–0.90, Figure S1), and in the ε2 vs. ε3 analysis (OR = 0.78, 95% CI = 0.69–0.89, Figure S4), and an increased susceptibility of MI in the ε3ε4 vs. ε3ε3 analysis (OR = 1.26, 95% CI = 1.12–1.41, Figure S2) in the ε4ε4 vs. ε3ε3 analysis (OR = 1.59, 95% CI = 1.15–2.19, Figure S3) and in the ε4 vs. ε3 analysis (OR = 1.22, 95% CI = 1.12–1.32, Figure S5). However, there were no significant associations among polymorphisms and MI for the following genetic models: frequency of ε3 allele (OR = 0.99, 95% CI = 0.96–1.02); ε2ε2 vs. ε3ε3 analysis (OR = 0.73, 95% CI = 0.40–1.32); ε2ε4 vs. ε3ε3 analysis (OR = 1.10, 95% CI = 0.99–1.21). The detailed results are shown in Table 2 . Cumulative analysis further confirmed the results ( Figure 4 and Figure S6).

Figure 2. Forest plot for ApoE gene polymorphism and MI risk in the ε2 allele frequency analysis.

Figure 2

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Figure 3. Forest plot for ApoE gene polymorphism and MI risk in the ε4 allele frequency analysis.

Figure 3

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Table 2. Results of meta-analysis of ApoE polymorphism and MI.

Overall Caucasion Asian
Analysis OR (95% CI) P/Phet OR (95% CI) P/Phet OR (95% CI) P/Phet
ε2ε2 vs. ε3ε3 0.73 (0.40–1.32) 0.29/0.005 0.70 (0.38–1.31) 0.27/0.004 1.07 (0.08–13.78) 0.96/0.18
ε2ε3 vs. ε3ε3 0.79 (0.68–0.90) 0.001/0.001 0.80 (0.70–0.92) 0.001/0.008 0.70 (0.31–1.60) 0.84/0.007
ε2ε4 vs. ε3ε3 1.10 (0.99–1.21) 0.07/0.70 1.10 (1.00–1.21) 0.05/0.63 0.66 (0.26–1.70) 0.39/0.61
ε3ε4 vs. ε3ε3 1.26 (1.12–1.41) <0.001/0.001 1.23 (1.09–1.38) 0.001/0.001 1.51 (1.14–2.00) 0.004/0.39
ε4ε4 vs. ε3ε3 1.59 (1.15–2.19) 0.005/0.04 1.47 (1.07–2.02) 0.02/0.05 6.95 (1.75–27.65) 0.006/0.85
ε2 vs. ε3 0.78 (0.69–0.89) <0.001/0.04 0.80 (0.71–0.90) <0.001/0.04 0.67 (0.37–1.23) 0.20/0.22
ε4 vs. ε3 1.22 (1.12–1.32) <0.001/0.02 1.20 (1.10–1.30) <0.001/0.02 1.49 (1.15–1.93) 0.002/<0.001
ε2 allele frequency 0.78 (0.70–0.87) <0.001/0.001 0.79 (0.71–0.88) <0.001/0.005 0.65 (0.38–1.13) 0.13/0.09
ε3 allele frequency 0.99 (0.96–1.02) 0.38/1.00 0.99 (0.96–1.02) 0.39/1.00 0.99 (0.86–1.13) 0.22/0.94
ε4 allele frequency 1.15 (1.10–1.20) 0.001/0.17 1.14 (1.09–1.19) 0.001/0.18 1.47 (1.14–1.89) 0.003/0.70
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P, p value of the test on the association estimate; Phet, p value of the heterogeneity test.

Figure 4. Cumulative meta-analysis of ApoE gene polymorphism and MI risk: A) ε2 allele frequency analysis; B) ε4 allele frequency analysis.

Figure 4

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Tests of heterogeneity and subgroup analysis

Significant between-study heterogeneity existed in the analyses of seven genetic models: ε2 ε2 vs. ε3ε3 (p = 0.005); ε2ε3 vs. ε3ε3 (p = 0.001); ε3ε4 vs. ε3ε3 (p = 0.001); ε4ε4 vs. ε3ε3 (p = 0.04), ε2 vs. ε3 (p = 0.04), ε2 vs. ε3 (p = 0.02) and the ε2 allele frequency (p = 0.001). A random effects model was adopted for these analyses.

Furthermore, we performed subgroup analysis based on ethnicity and found a decreased susceptibility of MI in the ε2ε3 vs. ε3ε3 analysis (OR = 0.80, 95% CI = 0.70–0.92) and ε2 allele frequency (OR = 0.79, 95% CI = 0.71–0.88) among Caucasian populations. We also found an increased susceptibility of MI in the ε3ε4 vs. ε3ε3 analysis (OR = 1.23, 95% CI = 1.09–1.38), ε4ε4 vs. ε3ε3 analysis (OR = 1.47, 95% CI = 1.07–2.02) and the ε4 allele frequency (OR = 1.14, 95% CI = 1.09–1.19) among Caucasian populations. Among Asian populations, we also found an increased susceptibility of MI in the ε3ε4 vs. ε3ε3 analysis, ε4ε4 vs. ε3ε3 analysis and for the ε4 allele frequency; the detailed results are shown in Table 2 .

Sensitivity analysis

We conducted influence analysis to assess the sensitivity of each individual study on the pooled ORs by sequential omission of each individual study. The results suggested that no individual study significantly affected the pooled ORs in the ε2 allele and ε4 allele frequency analysis ( Figure 5 ), and in the ε2ε3 vs. ε3ε3 analysis, ε3ε4 vs. ε3ε3 analysis and ε4ε4 vs. ε3ε3 analysis (Figure S7).

Figure 5. Influence analysis of ApoE gene polymorphism and MI risk: A) ε2 allele frequency analysis; B) ε4 allele frequency analysis.

Figure 5

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Publication bias

Funnel plots examined potential publication bias qualitatively and no obvious asymmetry was observed in any genetic model, as shown in Figure 6 . Furthermore, the results from Begg’s and Egger’s tests did not provide any evidence of publication bias (Table S1).

Figure 6. Funnel plot of ApoE gene polymorphism and MI risk: A) ε2 allele frequency analysis; B) ε4 allele frequency analysis.

Figure 6

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Discussion

To the best of our knowledge, this is the first meta-analysis to evaluate the association between an ApoE polymorphism and susceptibility of MI. In this meta-analysis, we discovered an increased susceptibility of MI in the ε4 allele frequency analysis. Moreover, the individuals with ε2ε4 genotype, ε3ε4 genotype and ε4ε4 genotype had a significantly higher susceptibility of developing MI compared to those with the ε3ε3 genotype. Therefore, it is reasonable to assume that the ε4 allele of ApoE is an risk factor for the development of MI. These results were consistent with a previous meta-analysis, which showed that ε4 allele of ApoE is a risk factor for the development of CHD [11], [12]. In addition, we found a decreased susceptibility of MI in the ε2 allele frequency analysis and in the ε2ε3 vs. ε3ε3 analysis, which indicate that the ε2 allele is a protective factor in the development of MI. Cumulative meta-analysis also confirmed these findings. Considering the large sample size in the pooled analysis in this meta-analysis, we believe that our results are robust and reliable.

ApoE is a multifunctional protein that plays an important role in the metabolism of cholesterol and triglycerides, by binding to its receptors to help mediate clearance of chylomicron and remnant particles [53]. The three common isoforms, ε2, ε3 and ε4, have different receptor-binding abilities and could yield different circulating levels of cholesterol and triglycerides. Compared with ε3 homozygotes, carriers of the ε2 allele have lower circulating cholesterol levels, whereas carriers of the ε4 allele appear to have higher plasma levels of total and low-density lipoprotein cholesterol [54]. According to these mechanisms, our meta-analysis suggested that carrying the ε4 allele is a risk factor for MI and that the ε2 allele has a protective role in the development of MI. When stratifying the studies by ethnicity, the ε4 allele remained a risk factor and the ε2 allele was still protective in the development of MI among Caucasian populations; however, only the ε4 allele remained as a risk factor for MI among Asian population. This may be due to the small sample size in the analysis among Asian populations; in fact, there were only four studies that included Asian populations [19], [20], [26], [36]. Therefore, further studies are warranted among Asian populations. In addition, genotype distributions in the controls from Scaglione’s study [28], Bustan’s study [49] and Zende’s study [52] were not in agreement with HWE, therefore, the results may be biased. However, sensitivity analysis suggested that the pooled results were not significantly changed after excluding the three studies (data not shown). This may be due to the large sample size even though the three studies were excluded.

Although the primary results of this meta-analysis are suggestive, some limitations still exist. First, between-study heterogeneity existed in some of the genetic model analysis, which may have affected the results of the present meta-analysis, although a random effects model was adopted for these analyses. Second, publication bias may have occurred because our analyses were based wholly on published studies only in English and Chinese. Third, the results of this meta-analysis were based on unadjusted estimates because of the lack of adjusted estimates. Currently, some risk factors have been identified for MI, such as hypertension, hypercholesterolemia, diabetes, obesity and smoking. A more precise analysis should be performed if these data could be extracted from primary articles.

In conclusion, this comprehensive meta-analysis has evaluated all published data currently available on the association between the ApoE polymorphism and MI. Our meta-analysis suggested that the ε4 allele of ApoE is an risk factor for the development of MI and the ε2 allele of ApoE is a protective factor in the development of MI. This may be explained by the fact that ε4 allele of ApoE elevates the plasma levels of total and low-density lipoprotein cholesterol while the ε2 allele of ApoE lowers the circulating cholesterol levels. Further studies with larger sample sizes are warranted among Asian populations.

Supporting Information

Figure S1

Forest plot for ApoE gene polymorphism and MI risk in the genetic model of ε2ε3 vs. ε3ε3 analysis.

(TIF)

Click here for additional data file. (723.5KB, tif)
Figure S2

Forest plot for ApoE gene polymorphism and MI risk in the genetic model of ε3ε4 vs. ε3ε3 analysis.

(TIF)

Click here for additional data file. (724.9KB, tif)
Figure S3

Forest plot for ApoE gene polymorphism and MI risk in the genetic model of ε4ε4 vs. ε3ε3 analysis.

(TIF)

Click here for additional data file. (712.1KB, tif)
Figure S4

Forest plot for ApoE gene polymorphism and MI risk in the genetic model of ε2 vs. ε3 analysis.

(TIF)

Click here for additional data file. (656.6KB, tif)
Figure S5

Forest plot for ApoE gene polymorphism and MI risk in the genetic model of ε4 vs. ε3 analysis.

(TIF)

Click here for additional data file. (679.1KB, tif)
Figure S6

Cumulative meta-analysis of ApoE gene polymorphism and MI risk: A) ε2ε3 vs. ε3ε3 analysis; B) ε3ε4 vs. ε3ε3 analysi; C) ε4ε4 vs. ε3ε3 analysis.

(TIF)

Click here for additional data file. (1.2MB, tif)
Figure S7

Influence analysis of ApoE gene polymorphism and MI risk: A) ε2ε3 vs. ε3ε3 analysis; B) ε3ε4 vs. ε3ε3 analysi; C) ε4ε4 vs. ε3ε3 analysis.

(TIF)

Click here for additional data file. (475.7KB, tif)
Table S1

Results of Egger’s and Begger’s test.

(XLS)

Click here for additional data file. (20KB, xls)
Checklist S1

PRISMA Checklist.

(DOC)

Click here for additional data file. (73KB, doc)

Acknowledgments

We thank Dr Weifeng Qu for her excellent editorial work.

Funding Statement

This study was supported by the National Natural Science Foundation of China (NO.81200093) and the 2012 Shanghai College Special Research Funding for Outstanding Young Teachers (NO.82013011900002). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

Figure S1

Forest plot for ApoE gene polymorphism and MI risk in the genetic model of ε2ε3 vs. ε3ε3 analysis.

(TIF)

Click here for additional data file. (723.5KB, tif)
Figure S2

Forest plot for ApoE gene polymorphism and MI risk in the genetic model of ε3ε4 vs. ε3ε3 analysis.

(TIF)

Click here for additional data file. (724.9KB, tif)
Figure S3

Forest plot for ApoE gene polymorphism and MI risk in the genetic model of ε4ε4 vs. ε3ε3 analysis.

(TIF)

Click here for additional data file. (712.1KB, tif)
Figure S4

Forest plot for ApoE gene polymorphism and MI risk in the genetic model of ε2 vs. ε3 analysis.

(TIF)

Click here for additional data file. (656.6KB, tif)
Figure S5

Forest plot for ApoE gene polymorphism and MI risk in the genetic model of ε4 vs. ε3 analysis.

(TIF)

Click here for additional data file. (679.1KB, tif)
Figure S6

Cumulative meta-analysis of ApoE gene polymorphism and MI risk: A) ε2ε3 vs. ε3ε3 analysis; B) ε3ε4 vs. ε3ε3 analysi; C) ε4ε4 vs. ε3ε3 analysis.

(TIF)

Click here for additional data file. (1.2MB, tif)
Figure S7

Influence analysis of ApoE gene polymorphism and MI risk: A) ε2ε3 vs. ε3ε3 analysis; B) ε3ε4 vs. ε3ε3 analysi; C) ε4ε4 vs. ε3ε3 analysis.

(TIF)

Click here for additional data file. (475.7KB, tif)
Table S1

Results of Egger’s and Begger’s test.

(XLS)

Click here for additional data file. (20KB, xls)
Checklist S1

PRISMA Checklist.

(DOC)

Click here for additional data file. (73KB, doc)

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