# Apolipoprotein Eε4 Polymorphism as a Risk Factor for Ischemic Stroke: A Systematic Review and Meta-Analysis

Authors: Su-Ya Qiao; Ke Shang; Yun-Hui Chu; Hai-Han Yu; Xin Chen; Chuan Qin; Deng-Ji Pan; Dai-Shi Tian
Journal: Disease Markers
 (2022)

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## Abstract

Introduction. Rising studies indicate that the apolipoprotein E (APOE) gene is related to the susceptibility of ischemic stroke (IS). However, certain consensus is limited by the lack of a large sample size of researches. This meta-analysis was performed to explore the potential association between the APOE gene and IS. Methods. To identify relevant case control studies in English publications by October 2020, we searched PubMed, Embase, Web of Science, and the Cochrane Library. Pooled odds ratios (ORs) with fixed- or random-effect models and corresponding 95% confidence intervals (CIs) were calculated to analyze potential associations. Results. A total of 55 researches from 32 countries containing 12207 IS cases and 27742 controls were included. The association between APOE gene ε4 mutation and IS was confirmed (ε4 vs. ε3 allele: pooled OR=1.374, 95% CI, 1.214-1.556; ε2/ε4 vs. ε3/ε3: pooled OR=1.233, 95% CI, 1.056-1.440; ε3/ε4 vs. ε3/ε3: pooled OR=1.340, 95% CI, 1.165-1.542; ε4/ε4 vs. ε3/ε3: pooled OR=1.833, 95% CI, 1.542-2.179; and APOE ε4 carriers vs. non-ε4 carriers: pooled OR=1.377; 95% CI, 1.203-1.576). Interestingly, APOE ε4 mutation showed a dose-response correlation with IS risk (ε4/ε4 vs. ε2/ε4: pooled OR=1.625; 95% CI, 1.281-2.060; ε4/ε4 vs. ε3/ε4: pooled OR=1.301; 95% CI, 1.077-1.571). Similar conclusions were drawn in the small artery disease (SAD) subtype, but not in large artery atherosclerosis (LAA) or in cardioaortic embolism (CE), by subgroup analysis. Conclusions. These observations reveal that specific APOE ε4 mutation was significantly associated with the risk of IS in a dose-dependent manner, while APOE ε4 mutation was related to SAD subtype onset without a cumulative effect.


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## Body

## 1. Introduction

Ischemic stroke (IS) is a disturbing problem worldwide, which is attributable to its leading role in disability and mortality worldwide, regardless of age, ethnicity, or gender [1]. Uncovering the etiology of IS is crucial for recognition and prevention of this disorder. Genetic elements and environmental components positively contribute to this multifactorial disease [2, 3]. Genetic inheritance provides a guide to the identification of high-risk individual. It deserves to investigate candidate gene polymorphisms in IS pathophysiological pathways. The apolipoprotein E (APOE) gene locates on chromosome 19q13.2. Two single polymorphisms (rs7412 and rs729358), three common alleles (ε2, ε3, and ε4), and six genotypes (ε2/ε2, ε2/ε3, ε2/ε4, ε3/ε3, ε3/ε4, and ε4/ε4) generate in populations [4]. The product of the APOE gene is a polymorphic protein named apolipoprotein E, which modulates the translocation of the cholesterol and other lipids among highly diverse cells [5], involved with neuroinflammation [6] and myelin integrity maintenance [7]. A study indicated that the activated CypA–MMP9 pathway in APOE4 carriers facilitated pericyte injury, which caused blood vessel dysfunction [8]. APOE polymorphisms and its risk associations with coronary artery disease [9], hypertension [10], diabetes [11], and carotid arterial atherosclerosis [12] are widely debated. The abovementioned diseases place individuals at a potential serious risk of IS. Individual studies of the association between IS and APOE polymorphisms have been explored extensively. Clinical differences, ethnic diversities, and small sample sizes restricted the present finding to an inconsistent and controversial one. Previous meta-analyses concerning to this issue have been published several years ago [13] or limited to specific ethnicity [14, 15]. Accordingly, researches from 32 countries are qualified to form our meta-analysis to clarify how APOE genotypes are associated with IS. Moreover, we firstly revealed the correlation of the APOE gene and three IS subtypes (large artery atherosclerosis (LAA), small artery disease (SAD), and cardioaortic embolism (CE)).


## 2. Materials and Methods

We followed the rules of the preferred reporting items for systematic reviews and meta-analyses (PRISMA) statement to make this meta-analysis [16].


### 2.1. Data Availability

The data that contribute to the findings in our study are available and the corresponding authors can be contacted for data access.


### 2.2. Literature Search

Online databases (PubMed, Embase, Web of Science, and the Cochrane Library) were comprehensively searched for studies potentially involved and published in English publications and prior to October 30, 2020. We used a combination of some search terms relevant for IS (stroke, cerebral infarct, brain infarct, ischemic stroke, cerebral ischemia, transient ischemic attack, and cerebrovascular accident) and for the APOE gene (apolipoprotein E, APOE polymorphisms, apolipoprotein E polymorphisms, apolipoprotein E gene, rs429358, rs7412, apolipoprotein E epsilon 4, APOE e4, apolipoprotein E epsilon 2, and APOE e2). The detailed search strategies were showed next.


### 2.3. Selection Criteria

The selection of the studies was independently completed by two investigators, and any difference was resolved by discussion until an agreement was reached. We carefully selected case control studies that evaluated the relationship of the APOE gene and IS with definite IS diagnoses (using computed tomography, magnetic resonance, or autopsy) regardless of the ethnic background. The detailed inclusion criteria were (1) high-quality studies which explore the relationship between the APOE gene and IS, (2) explicit IS diagnostic criteria, (3) nonstroke individuals as the control group, and (4) original data including independent and sufficient APOE genotype data, to compute ORs and 95% CIs. The newest and largest studies were chosen to avoid duplicate or overlapped data information.


### 2.4. Data Extraction

Two investigators separately finished full-text reading to extract the needed information from each selected study and resolved the controversial items through serious discussion. The extracted information was (1) research characteristics, including the first author’s name, year of publication, and geographical location of the study; (2) participant details, such as the sex ratio, mean age, and the sample size of case and control groups; (3) diagnostic criteria for IS; (4) determination methods of the APOE gene; (5) each genotype frequency; (6) the sample sizes of IS subtypes according to TOAST norms and respective genotype frequency; and (7) HWE in controls.


### 2.5. Quality Assessment

We performed the quality assessment through the Newcastle-Ottawa Scale (NOS) score considering selection, comparability, and exposure. It ranged from 0 (worst) to 9 (best) and high-quality studies were known as with a NOSscore≥7.


### 2.6. Statistical Analysis

We performed Stata 14.0 to complete all data analyses. The chi-square test was used to examine the Hardy-Weinberg equilibrium (HWE) in control groups. An overt deviation from HWE was regarded asP<0.05. The compositive ORs and 95% CI were calculated. We explored five genetic models to generate the respective pooled ORs: (1) allele comparisons (ε2 allele vs. ε3 allele; ε4 allele vs. ε3 allele); (2) genotype comparisons (ε2/ε2 vs. ε3/ε3; ε2/ε3 vs. ε3/ε3; ε2/ε4 vs. ε3/ε3; ε3/ε4 vs. ε3/ε3; ε4/ε4 vs. ε3/ε3); (3) APOE ε4 carrier comparisons: we defined three ε4-containing genotypes (ε2/ε4+ε3/ε4+ε4/ε4) as APOE ε4 carriers and the other genotypes (ε2/ε2+ε2/ε3+ε3/ε3) as non-APOE ε4 carriers; (4) APOE ε2 carrier comparisons: similar comparisons of ε2-containing genotypes (ε2/ε2+ε2/ε3+ε2/ε4) vs. non-ε2-containing genotypes (ε3/ε3+ε3/ε4+ε4/ε4); and (5) comparisons between APOE ε4 homozygosis and ε4 heterozygote (ε4/ε4 vs. ε2/ε4; ε4/ε4 vs. ε3/ε4). The I2 statistic and Cochran’s Q test were applied to measure the heterogeneity between studies [17]. We selected the random effect model (DerSimonian-Laird method) when heterogeneity was found between studies (I2>50.0%) and fixed-effect model (Mantel-Haenszel method) when no heterogeneity existed (I2<50.0%). Subgroup analysis was conducted to confirm the relationship between the APOE polymorphisms and the risk of different IS subgroups. Sensitivity analysis was performed by successively removing a single study one by one to verify the stability and reliability of our conclusions. Meta-regression analysis was operated to recognize sources of heterogeneity. Funnel plots and quantified Egger’s tests were accomplished to test publication bias. Significant publication bias was considered as the P value of Egger’s test less than 0.10 or obvious asymmetric funnel plot.


### 2.7. The Result of Trial Sequential Analysis (TSA)

Insufficient sample size, continuous updating, and repeating “ significance testing” could increase the risk of type I errors. Therefore, traditional meta-analysis that focuses on the specific topic may suffer an increased risk of random error. Trial sequential analysis (TSA) was used to reduce the risk of type I error and obtain important information regarding the required sample size for such trials. Set the time sequence of a single study as the research node, and then, perform an interim analysis between the new study that will be included in meta-analysis and existing data accumulation. The required information size (RIS), trial sequential monitoring boundary, and futility boundary are estimated using the TSA. As the sample size of meta-analysis reaching the RIS or thez-curve crossing the trial sequential monitoring boundary, we can conclude that the results of meta-analysis are quite stable and further studies were not needed. We accomplished TSA following the guidelines of the user manual and previous article [18] by setting a significance of 5% for type I error, a relative risk reduction of 20%, and a statistical test power of 80% with TSA software (TSA, version 0.9 beta; Copenhagen Trial Unit, Copenhagen, Denmark).


## 2.1. Data Availability

The data that contribute to the findings in our study are available and the corresponding authors can be contacted for data access.


## 2.2. Literature Search

Online databases (PubMed, Embase, Web of Science, and the Cochrane Library) were comprehensively searched for studies potentially involved and published in English publications and prior to October 30, 2020. We used a combination of some search terms relevant for IS (stroke, cerebral infarct, brain infarct, ischemic stroke, cerebral ischemia, transient ischemic attack, and cerebrovascular accident) and for the APOE gene (apolipoprotein E, APOE polymorphisms, apolipoprotein E polymorphisms, apolipoprotein E gene, rs429358, rs7412, apolipoprotein E epsilon 4, APOE e4, apolipoprotein E epsilon 2, and APOE e2). The detailed search strategies were showed next.


## 2.3. Selection Criteria

The selection of the studies was independently completed by two investigators, and any difference was resolved by discussion until an agreement was reached. We carefully selected case control studies that evaluated the relationship of the APOE gene and IS with definite IS diagnoses (using computed tomography, magnetic resonance, or autopsy) regardless of the ethnic background. The detailed inclusion criteria were (1) high-quality studies which explore the relationship between the APOE gene and IS, (2) explicit IS diagnostic criteria, (3) nonstroke individuals as the control group, and (4) original data including independent and sufficient APOE genotype data, to compute ORs and 95% CIs. The newest and largest studies were chosen to avoid duplicate or overlapped data information.


## 2.4. Data Extraction

Two investigators separately finished full-text reading to extract the needed information from each selected study and resolved the controversial items through serious discussion. The extracted information was (1) research characteristics, including the first author’s name, year of publication, and geographical location of the study; (2) participant details, such as the sex ratio, mean age, and the sample size of case and control groups; (3) diagnostic criteria for IS; (4) determination methods of the APOE gene; (5) each genotype frequency; (6) the sample sizes of IS subtypes according to TOAST norms and respective genotype frequency; and (7) HWE in controls.


## 2.5. Quality Assessment

We performed the quality assessment through the Newcastle-Ottawa Scale (NOS) score considering selection, comparability, and exposure. It ranged from 0 (worst) to 9 (best) and high-quality studies were known as with a NOSscore≥7.


## 2.6. Statistical Analysis

We performed Stata 14.0 to complete all data analyses. The chi-square test was used to examine the Hardy-Weinberg equilibrium (HWE) in control groups. An overt deviation from HWE was regarded asP<0.05. The compositive ORs and 95% CI were calculated. We explored five genetic models to generate the respective pooled ORs: (1) allele comparisons (ε2 allele vs. ε3 allele; ε4 allele vs. ε3 allele); (2) genotype comparisons (ε2/ε2 vs. ε3/ε3; ε2/ε3 vs. ε3/ε3; ε2/ε4 vs. ε3/ε3; ε3/ε4 vs. ε3/ε3; ε4/ε4 vs. ε3/ε3); (3) APOE ε4 carrier comparisons: we defined three ε4-containing genotypes (ε2/ε4+ε3/ε4+ε4/ε4) as APOE ε4 carriers and the other genotypes (ε2/ε2+ε2/ε3+ε3/ε3) as non-APOE ε4 carriers; (4) APOE ε2 carrier comparisons: similar comparisons of ε2-containing genotypes (ε2/ε2+ε2/ε3+ε2/ε4) vs. non-ε2-containing genotypes (ε3/ε3+ε3/ε4+ε4/ε4); and (5) comparisons between APOE ε4 homozygosis and ε4 heterozygote (ε4/ε4 vs. ε2/ε4; ε4/ε4 vs. ε3/ε4). The I2 statistic and Cochran’s Q test were applied to measure the heterogeneity between studies [17]. We selected the random effect model (DerSimonian-Laird method) when heterogeneity was found between studies (I2>50.0%) and fixed-effect model (Mantel-Haenszel method) when no heterogeneity existed (I2<50.0%). Subgroup analysis was conducted to confirm the relationship between the APOE polymorphisms and the risk of different IS subgroups. Sensitivity analysis was performed by successively removing a single study one by one to verify the stability and reliability of our conclusions. Meta-regression analysis was operated to recognize sources of heterogeneity. Funnel plots and quantified Egger’s tests were accomplished to test publication bias. Significant publication bias was considered as the P value of Egger’s test less than 0.10 or obvious asymmetric funnel plot.


## 2.7. The Result of Trial Sequential Analysis (TSA)

Insufficient sample size, continuous updating, and repeating “ significance testing” could increase the risk of type I errors. Therefore, traditional meta-analysis that focuses on the specific topic may suffer an increased risk of random error. Trial sequential analysis (TSA) was used to reduce the risk of type I error and obtain important information regarding the required sample size for such trials. Set the time sequence of a single study as the research node, and then, perform an interim analysis between the new study that will be included in meta-analysis and existing data accumulation. The required information size (RIS), trial sequential monitoring boundary, and futility boundary are estimated using the TSA. As the sample size of meta-analysis reaching the RIS or thez-curve crossing the trial sequential monitoring boundary, we can conclude that the results of meta-analysis are quite stable and further studies were not needed. We accomplished TSA following the guidelines of the user manual and previous article [18] by setting a significance of 5% for type I error, a relative risk reduction of 20%, and a statistical test power of 80% with TSA software (TSA, version 0.9 beta; Copenhagen Trial Unit, Copenhagen, Denmark).


## 3. Results

### 3.1. Characteristics of Eligible Studies

We collect a total of 55 studies from 32 countries containing 12207 IS cases and 27742 controls to make the meta-analysis [19–73]. Figure 1 showed the detailed selection process. The selected studies and their main characteristics were exhibited in Table 1. Fifteen of the studies provided data about different subtypes (grouped by classification of cerebrovascular diseases III or TOAST classification) of IS: large artery atherosclerosis (LAA), small artery disease (SAD), and cardioaortic embolism (CE). We extracted them independently and specific information was showed in supplementary material table 1. There were seven studies (Koopal et al. 2016, Lai et al. 2007, Chowdhury et al. 2001, Kokubo et al. 2000, Ji et al. 1998, Couderc et al. 1993, Saidi et al. 2009) which deviated HWE obviously, and one study (Schneider et al. 2005) did not contain enough data to obtain HWE. Forty-eight studies used PCR-based method and seven researches (Slowik et al. 2003, Karttunen et al. 2002, Hachinski et al. 1996, Couderc et al. 1993, Brewin et al. 2020, Aalto-Setala et al. 1998, Schneider et al. 2005) used other methods to identify APOE genotypes. These studies used computed tomography or magnetic resonance to diagnose IS except that one research which used autopsy (Schneider et al. 2005). The NOS score mean value was 7.509, which suggested that the quality of included studies was reliable (supplementary material Table 2). PRISMA2020 checklist was provided to present our meta-analysis items (supplementary material Table 3).Figure 1
A flow diagram of identification and selection process of the included literatures in this meta-analysis.Table 1
Main characteristics of studies associated with APOE polymorphisms and IS stroke included in this meta-analysis.
Study IDRegionCriteria for ISGenotyping methodSource of controlCharacteristics and the counts of every genotypeHNGroupSample sizeMale/n (%)Age(years)ε2/ε2ε2/ε3ε2/ε4ε3/ε3ε3/ε4ε4/ε4ε2 alleleε3 alleleε4 alleleWu et al., 2020 [19]ChinaCT/MRIPCRH-BCase938581 (61.9%)65.6±10.62631868415615851587204Y8Control1028622 (60.5%)63.7±12.491311376310661621763131Zhao et al., 2017 [20]ChinaCT/MRIPCRH-BCase513294 (57.3%)62.3±12.2363734785876842108Y7Control514288 (56.0%)61.7±13.557083666418886674Coen Herak et al., 2017 [21]CroatiaCT/MRIPCRP-BCase7348 (65.8%)4.3±X0102501101212113Y8Control10063 (63.0%)6.5±X1110741311317215Das et al., 2016 [22]IndianCT/MRIPCR-RFLPP-BCase620434 (70.0%)49.4±17.4546643112012621028150Y8Control620428 (69.0%)49.1±16.9550443611312641035141Koopal et al., 2016 [23]NetherlandsCTPCRP-BCase278NANA33081606984441993N7Control4220NANA50389962422112713658563601495Luo et al., 2015 [24]ChinaCT/MRIPCRH-BCase712465 (65.3%)65.2±13.94931349410171141182128Y7Control774418 (54.0%)51.5±16.93107853511381211290137Wei et al., 2015 [25]MalaysiaCT/MRIPCRP-BCase29733 (11.1%)52.6±8.88682313754710739691Y8Control297119 (40.0%)51.8±8.74122716389247427120Yan et al., 2015 [26]ChinaCT/MRIPCR-RFLPH-BCase580387 (66.7%)59.8±13.7114133351826296825239Y8Control580379 (65.3%)59.4±13.16154493543329225795140Chatzistefanidis et al., 2014 [27]GreeceCT/MRIPCRH-BCase329225 (68.4%)59.7±11.633632275644554667Y7Control361205 (56.8%)60.4±13.722482784723662759Atadzhanov et al., 2013 [28]ZambianCTPCRP-BCase23NA54.0±16.004397072910Y9Control11650 (41.4%)NA0257383793213862Gelfand et al., 2013 [29]AmericaCT/MRIPCR-RFLPH-BCase1310 (77.0%)NA0125323149Y8Control8446 (55.0%)NA083551621113423Balcerzyk et al., 2010 [30]PolandCT/MRIPCRP-BCase7242 (58.3%)8.8±5.619052641111914Y7Control7141 (57.8%)8.2±5.408051111812113Tamam et al., 2009 [31]TurkeyCT/MRIPCRH-BCase65NANA072505191129Y7Control3010 (33.3%)61.9±14.701125212535Tascilar et al., 2009 [32]TurkeyCT/MRIPCRP-BCase8551 (60.0%)61.7±13.6318345972711726Y7Control7725 (32.5%)54.7±8.4316740922910520Wang et al., 2009 [33]ChinaCT/MRIPCRH-BCase396209 (52.8%)57.3±8.21698601248711190433169N7Control396202 (51.0%)57.3±8.1331164116439322348386Lai et al., 2007 [34]ChinaMRIPCRH-BCase257164 (63.8%)63.7±8.2117101626702940877N8Control11254 (48.2%)71.0±10.6455781911818026Parfenov et al., 2007 [35]YakutskCT/MRIPCRP-BCase10769 (64.5%)58.4±11.515163334816442Y8Control10161 (59.4%)57.6±11.61153582222015329Kang and Lee.2006 [36]KoreaMRIPCRH-BCase194116 (59.8%)62.0±9.502401264402432044Y8Control16894 (55.9%)62.3±6.321801281912229321Gao et al., 2006 [37]ChinaCT/MRIPCRH-BCase10071 (71.0%)61.1±10.81110751301317413Y8Control10071 (71.0%)61.0±10.611308060151796Baum et al., 2006 [38]ChinaCT/MRIPCRP-BCase243134 (54.5%)70.7±12.073961553245938146Y8Control311152 (45.2%)70.0±5.926062033917050547Pezzini et al., 2005 [39]ItalyCT/MRIPCRH-BCase16384 (51.5%)35.0±7.521211093811726841Y8Control15885 (53.8%)34.8±6.101611202101727722Cerrato et al., 2005 [40]ItalyCT/MRIPCRP-BCase302100 (33.1%)57.0±11.093102302844951936Y7Control228104 (33.1%)55.0±16.0325115837432378Jin et al., 2004 [41]ChinaCT/MRIPCR-RFLPP-BCase226129 (57.1%)48.5±3.421431525232137061Y8Control201109 (54.2%)47.1±2.421721562222335128Duzenli et al., 2004 [42]TurkeyCTPCRP-BCase62NANA081521091132Y8Control12661 (48.4%)58.0±1.92232801812920122Slowik et al., 2003 [43]PolandCT/MRIImmuno-blottingH-BCase7149 (69.0%)59.6±9.503053141312316Y7Control3019 (63.4%)63.1±8.801021801518Souza et al., 2003 [44]BrazilCTPCRP-BCase107NA68.8±9.20509381519910Y8Control100NA69.4±8.3082741601017218Karttunen et al., 2002 [45]FinlandCT/MRIImmuno-blottingP-BCase4427 (61.4%)15–600312713047014Y8Control10459 (56.7%)15–6014167283716635Morrison et al., 2002 [46]AmericaMRIPCRP-BCase400NANA148191991181569564167Y7Control1104NANA514839596288281971628383MacLeod et al., 2001 [47]ScotlandCTPCRP-BCase266150 (56.4%)65.7±12.212971705633842569Y7Control22594 (41.7%)77.0±1.002061336332634975Chowdhury et al., 2001 [48]BangladeshCTPCRH-BCase147116 (79.9%)57.9±11.1330113262925530N6Control190129 (67.7%)60.3±9.63611492921333334Frikke-Schmidt et al., 2001 [49]DenmarkCTPCRP-BCase738282 (61.8%)63.0±7.457723409207171101102264Y6Control89384022 (45.0%)57.2±0.2451126232505022442411448134702958Catto et al., 2000 [50]EnglandCTPCRP-BCase515259 (50.3%)73.0±X06183211151069818143Y7Control289151 (52.2%)72.5±X03771706964444688Kokubo et al., 2000 [51]JapanCT/MRIPCR-RFLPP-BCase201NA40–89121521383314132437N7Control1126333 (29.7%)64.3±10.511738819202131031913236Peng et al., 1999 [52]ChinaCTPCRH-BCase90NA62.6±8.90131551921414224Y7Control90NA63.1±8.3116163811915011Ji et al., 1998 [53]JapanCT/MRIPCR-RFLPP-BCase123NA70.2±7.2093792931219638N7Control117NA71.5±7.504495140820818Margaglione et al., 1998 [54]ItalyCT/MRIPCRP-BCase10051 (51.0%)66.2±10.01100592461215236Y8Control506NANA54773687816486187Kessler et al., 1997 [55]GermanyCT/MRIPCRH-BCase227108 (47.6%)62.3±14.223151325074034569Y8Control225108 (48.0%)62.6±14.012461494323236553Hachinski et al., 1996 [56]BritainCT/MRIIFP-BCase8961 (67.8%)64.6±8.71131472431613131Y8Control89NA64.5±8.62101571811514221Couderc et al., 1993 [57]FranceCTIFH-BCase6936 (52.2%)72.3±11.617050101911712N7Control566347 (61.3%)41.3±15.38605377109781923128Qian et al., 2012 [58]ChinaCT/MRIPCRH-BCase15287 (57.2%)66.8±5.50210952972124043Y9Control4013 (32.5%)64.0±12.605029605696Konialis et al., 2016 [59]GreeceCTPCRH-BCase200142 (72.0%)60.0±16.001031453931333948Y7Control15976 (47.5%)59.0±13.011601261601828416Fayed et al., 2009 [60]EgyptCT/MRIPCR-RFLPH-BCase40NANA03711118103634Y6Control20NANA03115104342Stankovic et al., 2004 [61]SerbianCT/MRIPCR-RFLPP-BCase65NANA06039182610222Y7Control330NANA125672054738751360Pedro-Botet et al., 1992 [62]SpainCTPCRP-BCase100NANA2120542661614638Y7Control100NANA0132691331516421Fekih-Mrissa et al., 2014 [63]TunisiaCT/MRIPCRP-BCase6NANA000051057Y7Control42NANA0801815185917Brewin et al., 2020 [64]LondonCT/MRIExome sequenc-ingP-BCase47NANA05814146134734Y7Control236NANA6411197711064306102Saidi et al., 2009 [65]TunisiaCT/MRIPCRP-BCase228114 (50.0%)61.5±12.10142574872839249168Y8Control323177 (54.8%)60.9±12.802728187711055472119Wen et al., 2006 [66]ChinaMRIPCRP-BCase67NA70.7±11.4472411121710017Y9Control134NANA2243891513121720Giassakis et al., 2007 [67]GreeceCT/MRIPCRP-BCase10070 (70.0%)60.7±9.8NANANANANANA1216622Y8Control9666 (68.8%)61.3±9.8NANANANANANA1016913Nakata et al., 1997 [68]JapanCT/MRIPCRP-BCase5525 (45.0%)66.0±14.0NANANANANANA29810Y7Control6130 (49.0%)67.0±8.0NANANANANANA71105Szolnoki et al., 2002 [69]HungaryMRIPCRH-BCase689356 (51.7%)59.8±17.7NANANANANANA104934340Y7Control652341 (52.3%)59.8±16.9NANANANANANA1181016170Aalto-Setala et al., 1998 [70]FinlandCT/MRIIFP-BCase231NA<60NANANANANANA1735095Y7Control615NA20–55NANANANANANA74861295Artieda et al., 2008 [71]SpainCT/MRIPCRP-BCase152NA61.7±6.8ε2/ε2+ε2/ε3=151110ε3/ε4+ε4/ε4=26NANANAY7Control215NANAε2/ε2+ε2/ε3=201164ε3/ε4 + ε4/ε4 = 30NANANASchneider et al., 2005 [72]AmericaCTPCRP-BCase76NANAε2/ε2=0; ε2/ε3+ε3/ε3=45; ε2/ε4+ε3/ε4+ε4/ε4=31NANANANA7Control138NANAε2/ε2=0; ε2/ε3+ε3/ε3=104; ε2/ε4+ε3/ε4+ε4/ε4=34NANANALi et al., 2016 [73]ChinaCT/MRIPCRP-BCase164113 (68.9%)60.8±11.9ε2/ε2+ε2/ε3+ε3/ε3=110; ε2/ε4+ε3/ε4=42; ε4/ε4=12NANANAY8Control10964 (58.7%)59.4±13.0ε2/ε2+ε2/ε3+ε3/ε3=85; ε2/ε4+ε3/ε4=22; ε4/ε4=2NANANA∗Age (years): different statistical patterns of age (mean and IQR, mean±SD, or range) were extracted. CT: computerized tomography; MRI: magnetic resonance imaging; PCR: polymerase chain reaction; RFLP: restriction fragment length polymorphism; IS: ischemic stroke; H: Hardy-Weinberg equilibrium; N: Newcastle-Ottawa Scale; NA: not available; IF: isoelectric focusing; H-B: hospital based; P-B: population based.


### 3.2. Main Results of the Comparisons in the Abovementioned Five Genetic Models

#### 3.2.1. Allele Comparisons

In comparison with theε3 allele, the ε2 allele did not show association of the risk of IS (pooled OR=0.983, 95% CI, 0.867-1.115, P=0.79) (as showed in Table 2), while the ε4 allele contributed to an obviously increased risk of IS with the pooled OR=1.374 (95% CI, 1.214-1.556, P<0.0001) (Figure 2(d)).Table 2
The main results of the APOE gene associated with IS included in the meta-analysis.
Genetic model of APOE gene polymorphismsGroupNo. of included studiesResults of association with ISOR95% CIP value of ORsε2 allele vs. ε3 alleleAll510.983(0.867,1.115)0.79LAA130.962(0.712,1.299)0.80CE101.517(0.861,2.674)0.15SAD121.190(0.997,1.421)0.05ε4 allele vs. ε3 alleleAll511.374(1.214,1.556)<0.0001LAA131.149(0.898,1.469)0.27CE101.092(0.662,1.801)0.73SAD121.318(1.073,1.618)0.01ε2/ε2 vs. ε3/3All360.985(0.653,1.486)0.94LAA111.307(0.750,2.278)0.35CE104.290(1.917,9.600)<0.0001SAD111.803(1.037,3.134)0.04ε2/ε3 vs. ε3/3All460.980(0.900,1.066)0.63LAA130.869(0.705,1.071)0.19CE101.255(0.849,1.856)0.26SAD121.178(0.952,1.457)0.13ε2/ε4 vs. ε3/3All421.233(1.056,1.440)0.01LAA110.978(0.607,1.576)0.93CE101.458(0.534,3.980)0.46SAD100.932(0.526,1.652)0.81ε3/ε4 vs. ε3/3All471.340(1.165,1.542)<0.0001LAA141.154(0.841,1.584)0.38CE101.175(0.627,2.203)0.62SAD131.392(1.097,1.767)0.01ε4/ε4 vs. ε3/3All461.833(1.542,2.179)<0.0001LAA131.367(0.836,2.236)0.21CE101.543(0.591,4.029)0.38SAD111.809(1.030,3.175)0.04ε4 vs. non-ε4All501.377(1.203,1.576)<0.0001LAA141.149(0.876,1.506)0.32CE101.091(0.645,1.845)0.74SAD131.329(1.064,1.661)0.01ε2 vs. non-ε2All480.956(0.841,1.086)0.49LAA140.861(0.717,1.035)0.11CE101.358(0.966,1.910)0.08SAD131.117(0.926,1.347)0.25ε4/ε4 vs. ε2/4All401.625(1.281,2.060)<0.0001LAA111.551(0.791,3.043)0.20CE90.771(0.177,3.352)0.73SAD42.115(0.919,4.867)0.08ε4/ε4 vs. ε3/4All461.301(1.077,1.571)0.01LAA131.353(0.811,2.258)0.25CE61.077(0.402,2.887)0.88SAD111.332(0.739,2.400)0.34Figure 2
(a–g) Forest plots of the relationships between APOE gene polymorphisms in all studies included. (a) Forest plot ofε2/ε4 vs. ε3/ε3 comparison. (b) Forest plot of ε3/ε4 vs. ε3/ε3 comparison. (c) Forest plot of APOE ε4/ε4 vs. the ε3/ε3 genotype. (d) Forest plot of the APOE ε4 allele vs. ε3 allele. (e) Forest plot of APOE ε4 carriers vs. non-ε4 carriers. (f) Forest plot of APOE ε4/ε4 vs. ε2/ε4. (g) Forest plot of APOE ε4/ε4 vs. ε3/ε4.
(a)(b)(c)(d)(e)(f)(g)


#### 3.2.2. Genotype Comparisons

When compared with theε3/ε3 genotype, the pooled effects of the APOE genotype in the meta-analysis were as follows: for the ε2/ε2 genotype, pooled OR=0.985, 95% CI, 0.653-1.486, P=0.94, and for the ε2/ε3 genotype, pooled OR=0.980, 95% CI, 0.900-1.066, P=0.63; those two genotypes presented no association with the risk of IS (as showed in Table 2). Genotypes ε2/ε4, ε3/ε4, and ε4/ε4 were related to a higher risk of IS than ε3/ε3. The respective IS risk ORs were 1.233 (95% CI, 1.056-1.440, P=0.01) (Figure 2(a)), 1.340 (95% CI, 1.165-1.542, P<0.0001) (Figure 2(b)), and 1.833 (95% CI, 1.542-2.179, P<0.0001) (Figure 2(c)). The above results could be found in Table 2. A conclusion was drawn: every genotype which contained APOE ε4 mutation increased the risk of IS.


#### 3.2.3. APOEε4 Carrier Comparisons

Compared with the non-ε4 carriers, we confirmed that the ε4 carriers were associated with the increased risk of IS; the pooled outcome was pooled OR=1.377 (95% CI, 1.203-1.576, P<0.0001) (Figure 2(e)).


#### 3.2.4. APOEε2 Carrier Comparisons

In the genetic model ofε2 carriers vs. non-ε2 carriers, there was no association with the IS risk (pooled OR=0.956, 95% CI 0.841-1.086, P=0.49) (Table 2).


#### 3.2.5. APOEε4 Homozygosis versus APOE ε4 Heterozygote Comparisons

Given the above, the APOEε4 mutation was linked to IS risk. To identify whether there is a dose-response relationship between the ε4 allele and IS or not, we implemented the comparisons between the ε4/ε4 genotype and ε4 heterozygotes (ε2/ε4 or ε3/ε4 genotype). Compared with the ε2/ε4 and ε3/ε4 genotypes, the IS risk ORs for ε4/ε4 genotypes were 1.625 (95% CI, 1.281-2.060, P<0.0001) and 1.301 (95% CI, 1.077-1.571, P=0.01), respectively (Figures 2(f) and 2(g)); this part provided evidence that ε4 homozygosis might generate a higher risk of IS than ε4 heterozygotes.


### 3.3. Main Results of the Relationship between APOE Gene and Three IS Subtypes

We further investigated on the correlation of APOE gene polymorphisms and risks of IS subtypes by making comparisons in five genetic models, with a particular focus on the APOEε4 mutation. Subgroup analyses showed that APOE ε4 mutation significantly increased SAD risk (ε4 allele vs. ε3 allele: pooled OR=1.318, 95% CI, 1.073-1.618, P=0.01 (Figure 3(d)); ε3/ε4 vs.ε3/ε3: pooled OR=1.392, 95% CI, 1.097-1.767, P=0.01 (Figure 3(b)); ε4/ε4 vs. ε3/ε3: pooled OR=1.809, 95%, CI 1.030-3.175, P=0.04 (Figure 3(c)); and APOE ε4 carriers vs. non-APOE ε4 carriers: pooled OR=1.329, 95% CI, 1.064-1.661, P=0.01 (Figure 3(e))). But genotype ε2/ε4 did not increase the risk of SAD onset (Figure 3(a)). The result of APOE ε4 homozygosis versus ε4 heterozygote comparisons (ε4/ε4 vs. ε2/ε4 and ε4/ε4 vs. ε3/ε4) was a matter of concern: APOE ε4 mutation could not cause a cumulative effect in generating higher risk of SAD onset, as showed in Figures 3(f) and 3(g).Figure 3
(a–g) Forest plots of the relationships between APOE gene polymorphisms in subgroup analysis. (a) Forest plot ofε2/ε4 vs. ε3/ε3 comparison. (b) Forest plot of ε3/ε4 vs. ε3/ε3 comparison. (c) Forest plot of APOE ε4/ε4 vs. the ε3/ε3 genotype. (d) Forest plot of the APOE ε4 allele vs. ε3 allele. (e) Forest plot of APOE ε4 carriers vs. non-ε4 carriers. (f) Forest plot of APOE ε4/ε4 vs. ε2/ε4. (g) Forest plot of APOE ε4/ε4 vs. ε3/ε4.
(a)(b)(c)(d)(e)(f)(g)


### 3.4. Sensitivity Analysis

Sensitivity analysis was performed by removing studies one by one to check the effect of the individual study on overall ORs. No single study influenced on the pooled ORs and 95% CIs in all genetic model comparisons as our data showed (supplementary material table4).


### 3.5. Publication Bias

We carried out publication bias analysis by using funnel plots as qualitative description and Egger’s regression tests as quantitative outcome. Funnel plots of all genetic model comparisons did not exhibit apparent asymmetry (several funnel plots were showed in supplementary material figure1 and 2). In addition to subtype analysis of ε2/ε2 vs. ε3/3, all the Egger’s regression test outcomes indicated that there existed no evident publication bias with all P values exceeding 0.1 (supplementary material table 5). The above results showed that publication bias of our meta-analysis was not significant.


### 3.6. Regression Analysis

Meta-regression analysis was then performed to explore sources of heterogeneity as shown in supplementary material table5, considering the year of publication, region, sample size, genotyping method, HWE, NOS score, and source of control. However, the P value of each factor affecting overall heterogeneity was not statistically significant in comparisons of ε3/ε4 vs. ε3/3, ε4 vs. non-ε4, ε2 vs. non-ε2, ε4allele vs. ε3allele, and ε2allele vs. ε3allele (supplementary material figure 3). Heterogeneity sources were unascertainable.


### 3.7. The Result of Trial Sequential Analysis (TSA)

The RIS was 8901 samples and the sample size of our meta-analysis reached it. Moreover, the cumulativez-curve crossed the trial sequential monitoring boundary before reaching the RIS as showed in Figure 4. The result of TSA guaranteed the stability of our meta-analysis results. Our sample size was proved to be enough for evaluating the relationship between APOE polymorphisms and IS risk.Figure 4
Trial sequential analysis of the association between APOE gene polymorphisms and ischemic stroke.


## 3.1. Characteristics of Eligible Studies

We collect a total of 55 studies from 32 countries containing 12207 IS cases and 27742 controls to make the meta-analysis [19–73]. Figure 1 showed the detailed selection process. The selected studies and their main characteristics were exhibited in Table 1. Fifteen of the studies provided data about different subtypes (grouped by classification of cerebrovascular diseases III or TOAST classification) of IS: large artery atherosclerosis (LAA), small artery disease (SAD), and cardioaortic embolism (CE). We extracted them independently and specific information was showed in supplementary material table 1. There were seven studies (Koopal et al. 2016, Lai et al. 2007, Chowdhury et al. 2001, Kokubo et al. 2000, Ji et al. 1998, Couderc et al. 1993, Saidi et al. 2009) which deviated HWE obviously, and one study (Schneider et al. 2005) did not contain enough data to obtain HWE. Forty-eight studies used PCR-based method and seven researches (Slowik et al. 2003, Karttunen et al. 2002, Hachinski et al. 1996, Couderc et al. 1993, Brewin et al. 2020, Aalto-Setala et al. 1998, Schneider et al. 2005) used other methods to identify APOE genotypes. These studies used computed tomography or magnetic resonance to diagnose IS except that one research which used autopsy (Schneider et al. 2005). The NOS score mean value was 7.509, which suggested that the quality of included studies was reliable (supplementary material Table 2). PRISMA2020 checklist was provided to present our meta-analysis items (supplementary material Table 3).Figure 1
A flow diagram of identification and selection process of the included literatures in this meta-analysis.Table 1
Main characteristics of studies associated with APOE polymorphisms and IS stroke included in this meta-analysis.
Study IDRegionCriteria for ISGenotyping methodSource of controlCharacteristics and the counts of every genotypeHNGroupSample sizeMale/n (%)Age(years)ε2/ε2ε2/ε3ε2/ε4ε3/ε3ε3/ε4ε4/ε4ε2 alleleε3 alleleε4 alleleWu et al., 2020 [19]ChinaCT/MRIPCRH-BCase938581 (61.9%)65.6±10.62631868415615851587204Y8Control1028622 (60.5%)63.7±12.491311376310661621763131Zhao et al., 2017 [20]ChinaCT/MRIPCRH-BCase513294 (57.3%)62.3±12.2363734785876842108Y7Control514288 (56.0%)61.7±13.557083666418886674Coen Herak et al., 2017 [21]CroatiaCT/MRIPCRP-BCase7348 (65.8%)4.3±X0102501101212113Y8Control10063 (63.0%)6.5±X1110741311317215Das et al., 2016 [22]IndianCT/MRIPCR-RFLPP-BCase620434 (70.0%)49.4±17.4546643112012621028150Y8Control620428 (69.0%)49.1±16.9550443611312641035141Koopal et al., 2016 [23]NetherlandsCTPCRP-BCase278NANA33081606984441993N7Control4220NANA50389962422112713658563601495Luo et al., 2015 [24]ChinaCT/MRIPCRH-BCase712465 (65.3%)65.2±13.94931349410171141182128Y7Control774418 (54.0%)51.5±16.93107853511381211290137Wei et al., 2015 [25]MalaysiaCT/MRIPCRP-BCase29733 (11.1%)52.6±8.88682313754710739691Y8Control297119 (40.0%)51.8±8.74122716389247427120Yan et al., 2015 [26]ChinaCT/MRIPCR-RFLPH-BCase580387 (66.7%)59.8±13.7114133351826296825239Y8Control580379 (65.3%)59.4±13.16154493543329225795140Chatzistefanidis et al., 2014 [27]GreeceCT/MRIPCRH-BCase329225 (68.4%)59.7±11.633632275644554667Y7Control361205 (56.8%)60.4±13.722482784723662759Atadzhanov et al., 2013 [28]ZambianCTPCRP-BCase23NA54.0±16.004397072910Y9Control11650 (41.4%)NA0257383793213862Gelfand et al., 2013 [29]AmericaCT/MRIPCR-RFLPH-BCase1310 (77.0%)NA0125323149Y8Control8446 (55.0%)NA083551621113423Balcerzyk et al., 2010 [30]PolandCT/MRIPCRP-BCase7242 (58.3%)8.8±5.619052641111914Y7Control7141 (57.8%)8.2±5.408051111812113Tamam et al., 2009 [31]TurkeyCT/MRIPCRH-BCase65NANA072505191129Y7Control3010 (33.3%)61.9±14.701125212535Tascilar et al., 2009 [32]TurkeyCT/MRIPCRP-BCase8551 (60.0%)61.7±13.6318345972711726Y7Control7725 (32.5%)54.7±8.4316740922910520Wang et al., 2009 [33]ChinaCT/MRIPCRH-BCase396209 (52.8%)57.3±8.21698601248711190433169N7Control396202 (51.0%)57.3±8.1331164116439322348386Lai et al., 2007 [34]ChinaMRIPCRH-BCase257164 (63.8%)63.7±8.2117101626702940877N8Control11254 (48.2%)71.0±10.6455781911818026Parfenov et al., 2007 [35]YakutskCT/MRIPCRP-BCase10769 (64.5%)58.4±11.515163334816442Y8Control10161 (59.4%)57.6±11.61153582222015329Kang and Lee.2006 [36]KoreaMRIPCRH-BCase194116 (59.8%)62.0±9.502401264402432044Y8Control16894 (55.9%)62.3±6.321801281912229321Gao et al., 2006 [37]ChinaCT/MRIPCRH-BCase10071 (71.0%)61.1±10.81110751301317413Y8Control10071 (71.0%)61.0±10.611308060151796Baum et al., 2006 [38]ChinaCT/MRIPCRP-BCase243134 (54.5%)70.7±12.073961553245938146Y8Control311152 (45.2%)70.0±5.926062033917050547Pezzini et al., 2005 [39]ItalyCT/MRIPCRH-BCase16384 (51.5%)35.0±7.521211093811726841Y8Control15885 (53.8%)34.8±6.101611202101727722Cerrato et al., 2005 [40]ItalyCT/MRIPCRP-BCase302100 (33.1%)57.0±11.093102302844951936Y7Control228104 (33.1%)55.0±16.0325115837432378Jin et al., 2004 [41]ChinaCT/MRIPCR-RFLPP-BCase226129 (57.1%)48.5±3.421431525232137061Y8Control201109 (54.2%)47.1±2.421721562222335128Duzenli et al., 2004 [42]TurkeyCTPCRP-BCase62NANA081521091132Y8Control12661 (48.4%)58.0±1.92232801812920122Slowik et al., 2003 [43]PolandCT/MRIImmuno-blottingH-BCase7149 (69.0%)59.6±9.503053141312316Y7Control3019 (63.4%)63.1±8.801021801518Souza et al., 2003 [44]BrazilCTPCRP-BCase107NA68.8±9.20509381519910Y8Control100NA69.4±8.3082741601017218Karttunen et al., 2002 [45]FinlandCT/MRIImmuno-blottingP-BCase4427 (61.4%)15–600312713047014Y8Control10459 (56.7%)15–6014167283716635Morrison et al., 2002 [46]AmericaMRIPCRP-BCase400NANA148191991181569564167Y7Control1104NANA514839596288281971628383MacLeod et al., 2001 [47]ScotlandCTPCRP-BCase266150 (56.4%)65.7±12.212971705633842569Y7Control22594 (41.7%)77.0±1.002061336332634975Chowdhury et al., 2001 [48]BangladeshCTPCRH-BCase147116 (79.9%)57.9±11.1330113262925530N6Control190129 (67.7%)60.3±9.63611492921333334Frikke-Schmidt et al., 2001 [49]DenmarkCTPCRP-BCase738282 (61.8%)63.0±7.457723409207171101102264Y6Control89384022 (45.0%)57.2±0.2451126232505022442411448134702958Catto et al., 2000 [50]EnglandCTPCRP-BCase515259 (50.3%)73.0±X06183211151069818143Y7Control289151 (52.2%)72.5±X03771706964444688Kokubo et al., 2000 [51]JapanCT/MRIPCR-RFLPP-BCase201NA40–89121521383314132437N7Control1126333 (29.7%)64.3±10.511738819202131031913236Peng et al., 1999 [52]ChinaCTPCRH-BCase90NA62.6±8.90131551921414224Y7Control90NA63.1±8.3116163811915011Ji et al., 1998 [53]JapanCT/MRIPCR-RFLPP-BCase123NA70.2±7.2093792931219638N7Control117NA71.5±7.504495140820818Margaglione et al., 1998 [54]ItalyCT/MRIPCRP-BCase10051 (51.0%)66.2±10.01100592461215236Y8Control506NANA54773687816486187Kessler et al., 1997 [55]GermanyCT/MRIPCRH-BCase227108 (47.6%)62.3±14.223151325074034569Y8Control225108 (48.0%)62.6±14.012461494323236553Hachinski et al., 1996 [56]BritainCT/MRIIFP-BCase8961 (67.8%)64.6±8.71131472431613131Y8Control89NA64.5±8.62101571811514221Couderc et al., 1993 [57]FranceCTIFH-BCase6936 (52.2%)72.3±11.617050101911712N7Control566347 (61.3%)41.3±15.38605377109781923128Qian et al., 2012 [58]ChinaCT/MRIPCRH-BCase15287 (57.2%)66.8±5.50210952972124043Y9Control4013 (32.5%)64.0±12.605029605696Konialis et al., 2016 [59]GreeceCTPCRH-BCase200142 (72.0%)60.0±16.001031453931333948Y7Control15976 (47.5%)59.0±13.011601261601828416Fayed et al., 2009 [60]EgyptCT/MRIPCR-RFLPH-BCase40NANA03711118103634Y6Control20NANA03115104342Stankovic et al., 2004 [61]SerbianCT/MRIPCR-RFLPP-BCase65NANA06039182610222Y7Control330NANA125672054738751360Pedro-Botet et al., 1992 [62]SpainCTPCRP-BCase100NANA2120542661614638Y7Control100NANA0132691331516421Fekih-Mrissa et al., 2014 [63]TunisiaCT/MRIPCRP-BCase6NANA000051057Y7Control42NANA0801815185917Brewin et al., 2020 [64]LondonCT/MRIExome sequenc-ingP-BCase47NANA05814146134734Y7Control236NANA6411197711064306102Saidi et al., 2009 [65]TunisiaCT/MRIPCRP-BCase228114 (50.0%)61.5±12.10142574872839249168Y8Control323177 (54.8%)60.9±12.802728187711055472119Wen et al., 2006 [66]ChinaMRIPCRP-BCase67NA70.7±11.4472411121710017Y9Control134NANA2243891513121720Giassakis et al., 2007 [67]GreeceCT/MRIPCRP-BCase10070 (70.0%)60.7±9.8NANANANANANA1216622Y8Control9666 (68.8%)61.3±9.8NANANANANANA1016913Nakata et al., 1997 [68]JapanCT/MRIPCRP-BCase5525 (45.0%)66.0±14.0NANANANANANA29810Y7Control6130 (49.0%)67.0±8.0NANANANANANA71105Szolnoki et al., 2002 [69]HungaryMRIPCRH-BCase689356 (51.7%)59.8±17.7NANANANANANA104934340Y7Control652341 (52.3%)59.8±16.9NANANANANANA1181016170Aalto-Setala et al., 1998 [70]FinlandCT/MRIIFP-BCase231NA<60NANANANANANA1735095Y7Control615NA20–55NANANANANANA74861295Artieda et al., 2008 [71]SpainCT/MRIPCRP-BCase152NA61.7±6.8ε2/ε2+ε2/ε3=151110ε3/ε4+ε4/ε4=26NANANAY7Control215NANAε2/ε2+ε2/ε3=201164ε3/ε4 + ε4/ε4 = 30NANANASchneider et al., 2005 [72]AmericaCTPCRP-BCase76NANAε2/ε2=0; ε2/ε3+ε3/ε3=45; ε2/ε4+ε3/ε4+ε4/ε4=31NANANANA7Control138NANAε2/ε2=0; ε2/ε3+ε3/ε3=104; ε2/ε4+ε3/ε4+ε4/ε4=34NANANALi et al., 2016 [73]ChinaCT/MRIPCRP-BCase164113 (68.9%)60.8±11.9ε2/ε2+ε2/ε3+ε3/ε3=110; ε2/ε4+ε3/ε4=42; ε4/ε4=12NANANAY8Control10964 (58.7%)59.4±13.0ε2/ε2+ε2/ε3+ε3/ε3=85; ε2/ε4+ε3/ε4=22; ε4/ε4=2NANANA∗Age (years): different statistical patterns of age (mean and IQR, mean±SD, or range) were extracted. CT: computerized tomography; MRI: magnetic resonance imaging; PCR: polymerase chain reaction; RFLP: restriction fragment length polymorphism; IS: ischemic stroke; H: Hardy-Weinberg equilibrium; N: Newcastle-Ottawa Scale; NA: not available; IF: isoelectric focusing; H-B: hospital based; P-B: population based.


## 3.2. Main Results of the Comparisons in the Abovementioned Five Genetic Models

### 3.2.1. Allele Comparisons

In comparison with theε3 allele, the ε2 allele did not show association of the risk of IS (pooled OR=0.983, 95% CI, 0.867-1.115, P=0.79) (as showed in Table 2), while the ε4 allele contributed to an obviously increased risk of IS with the pooled OR=1.374 (95% CI, 1.214-1.556, P<0.0001) (Figure 2(d)).Table 2
The main results of the APOE gene associated with IS included in the meta-analysis.
Genetic model of APOE gene polymorphismsGroupNo. of included studiesResults of association with ISOR95% CIP value of ORsε2 allele vs. ε3 alleleAll510.983(0.867,1.115)0.79LAA130.962(0.712,1.299)0.80CE101.517(0.861,2.674)0.15SAD121.190(0.997,1.421)0.05ε4 allele vs. ε3 alleleAll511.374(1.214,1.556)<0.0001LAA131.149(0.898,1.469)0.27CE101.092(0.662,1.801)0.73SAD121.318(1.073,1.618)0.01ε2/ε2 vs. ε3/3All360.985(0.653,1.486)0.94LAA111.307(0.750,2.278)0.35CE104.290(1.917,9.600)<0.0001SAD111.803(1.037,3.134)0.04ε2/ε3 vs. ε3/3All460.980(0.900,1.066)0.63LAA130.869(0.705,1.071)0.19CE101.255(0.849,1.856)0.26SAD121.178(0.952,1.457)0.13ε2/ε4 vs. ε3/3All421.233(1.056,1.440)0.01LAA110.978(0.607,1.576)0.93CE101.458(0.534,3.980)0.46SAD100.932(0.526,1.652)0.81ε3/ε4 vs. ε3/3All471.340(1.165,1.542)<0.0001LAA141.154(0.841,1.584)0.38CE101.175(0.627,2.203)0.62SAD131.392(1.097,1.767)0.01ε4/ε4 vs. ε3/3All461.833(1.542,2.179)<0.0001LAA131.367(0.836,2.236)0.21CE101.543(0.591,4.029)0.38SAD111.809(1.030,3.175)0.04ε4 vs. non-ε4All501.377(1.203,1.576)<0.0001LAA141.149(0.876,1.506)0.32CE101.091(0.645,1.845)0.74SAD131.329(1.064,1.661)0.01ε2 vs. non-ε2All480.956(0.841,1.086)0.49LAA140.861(0.717,1.035)0.11CE101.358(0.966,1.910)0.08SAD131.117(0.926,1.347)0.25ε4/ε4 vs. ε2/4All401.625(1.281,2.060)<0.0001LAA111.551(0.791,3.043)0.20CE90.771(0.177,3.352)0.73SAD42.115(0.919,4.867)0.08ε4/ε4 vs. ε3/4All461.301(1.077,1.571)0.01LAA131.353(0.811,2.258)0.25CE61.077(0.402,2.887)0.88SAD111.332(0.739,2.400)0.34Figure 2
(a–g) Forest plots of the relationships between APOE gene polymorphisms in all studies included. (a) Forest plot ofε2/ε4 vs. ε3/ε3 comparison. (b) Forest plot of ε3/ε4 vs. ε3/ε3 comparison. (c) Forest plot of APOE ε4/ε4 vs. the ε3/ε3 genotype. (d) Forest plot of the APOE ε4 allele vs. ε3 allele. (e) Forest plot of APOE ε4 carriers vs. non-ε4 carriers. (f) Forest plot of APOE ε4/ε4 vs. ε2/ε4. (g) Forest plot of APOE ε4/ε4 vs. ε3/ε4.
(a)(b)(c)(d)(e)(f)(g)


### 3.2.2. Genotype Comparisons

When compared with theε3/ε3 genotype, the pooled effects of the APOE genotype in the meta-analysis were as follows: for the ε2/ε2 genotype, pooled OR=0.985, 95% CI, 0.653-1.486, P=0.94, and for the ε2/ε3 genotype, pooled OR=0.980, 95% CI, 0.900-1.066, P=0.63; those two genotypes presented no association with the risk of IS (as showed in Table 2). Genotypes ε2/ε4, ε3/ε4, and ε4/ε4 were related to a higher risk of IS than ε3/ε3. The respective IS risk ORs were 1.233 (95% CI, 1.056-1.440, P=0.01) (Figure 2(a)), 1.340 (95% CI, 1.165-1.542, P<0.0001) (Figure 2(b)), and 1.833 (95% CI, 1.542-2.179, P<0.0001) (Figure 2(c)). The above results could be found in Table 2. A conclusion was drawn: every genotype which contained APOE ε4 mutation increased the risk of IS.


### 3.2.3. APOEε4 Carrier Comparisons

Compared with the non-ε4 carriers, we confirmed that the ε4 carriers were associated with the increased risk of IS; the pooled outcome was pooled OR=1.377 (95% CI, 1.203-1.576, P<0.0001) (Figure 2(e)).


### 3.2.4. APOEε2 Carrier Comparisons

In the genetic model ofε2 carriers vs. non-ε2 carriers, there was no association with the IS risk (pooled OR=0.956, 95% CI 0.841-1.086, P=0.49) (Table 2).


### 3.2.5. APOEε4 Homozygosis versus APOE ε4 Heterozygote Comparisons

Given the above, the APOEε4 mutation was linked to IS risk. To identify whether there is a dose-response relationship between the ε4 allele and IS or not, we implemented the comparisons between the ε4/ε4 genotype and ε4 heterozygotes (ε2/ε4 or ε3/ε4 genotype). Compared with the ε2/ε4 and ε3/ε4 genotypes, the IS risk ORs for ε4/ε4 genotypes were 1.625 (95% CI, 1.281-2.060, P<0.0001) and 1.301 (95% CI, 1.077-1.571, P=0.01), respectively (Figures 2(f) and 2(g)); this part provided evidence that ε4 homozygosis might generate a higher risk of IS than ε4 heterozygotes.


## 3.2.1. Allele Comparisons

In comparison with theε3 allele, the ε2 allele did not show association of the risk of IS (pooled OR=0.983, 95% CI, 0.867-1.115, P=0.79) (as showed in Table 2), while the ε4 allele contributed to an obviously increased risk of IS with the pooled OR=1.374 (95% CI, 1.214-1.556, P<0.0001) (Figure 2(d)).Table 2
The main results of the APOE gene associated with IS included in the meta-analysis.
Genetic model of APOE gene polymorphismsGroupNo. of included studiesResults of association with ISOR95% CIP value of ORsε2 allele vs. ε3 alleleAll510.983(0.867,1.115)0.79LAA130.962(0.712,1.299)0.80CE101.517(0.861,2.674)0.15SAD121.190(0.997,1.421)0.05ε4 allele vs. ε3 alleleAll511.374(1.214,1.556)<0.0001LAA131.149(0.898,1.469)0.27CE101.092(0.662,1.801)0.73SAD121.318(1.073,1.618)0.01ε2/ε2 vs. ε3/3All360.985(0.653,1.486)0.94LAA111.307(0.750,2.278)0.35CE104.290(1.917,9.600)<0.0001SAD111.803(1.037,3.134)0.04ε2/ε3 vs. ε3/3All460.980(0.900,1.066)0.63LAA130.869(0.705,1.071)0.19CE101.255(0.849,1.856)0.26SAD121.178(0.952,1.457)0.13ε2/ε4 vs. ε3/3All421.233(1.056,1.440)0.01LAA110.978(0.607,1.576)0.93CE101.458(0.534,3.980)0.46SAD100.932(0.526,1.652)0.81ε3/ε4 vs. ε3/3All471.340(1.165,1.542)<0.0001LAA141.154(0.841,1.584)0.38CE101.175(0.627,2.203)0.62SAD131.392(1.097,1.767)0.01ε4/ε4 vs. ε3/3All461.833(1.542,2.179)<0.0001LAA131.367(0.836,2.236)0.21CE101.543(0.591,4.029)0.38SAD111.809(1.030,3.175)0.04ε4 vs. non-ε4All501.377(1.203,1.576)<0.0001LAA141.149(0.876,1.506)0.32CE101.091(0.645,1.845)0.74SAD131.329(1.064,1.661)0.01ε2 vs. non-ε2All480.956(0.841,1.086)0.49LAA140.861(0.717,1.035)0.11CE101.358(0.966,1.910)0.08SAD131.117(0.926,1.347)0.25ε4/ε4 vs. ε2/4All401.625(1.281,2.060)<0.0001LAA111.551(0.791,3.043)0.20CE90.771(0.177,3.352)0.73SAD42.115(0.919,4.867)0.08ε4/ε4 vs. ε3/4All461.301(1.077,1.571)0.01LAA131.353(0.811,2.258)0.25CE61.077(0.402,2.887)0.88SAD111.332(0.739,2.400)0.34Figure 2
(a–g) Forest plots of the relationships between APOE gene polymorphisms in all studies included. (a) Forest plot ofε2/ε4 vs. ε3/ε3 comparison. (b) Forest plot of ε3/ε4 vs. ε3/ε3 comparison. (c) Forest plot of APOE ε4/ε4 vs. the ε3/ε3 genotype. (d) Forest plot of the APOE ε4 allele vs. ε3 allele. (e) Forest plot of APOE ε4 carriers vs. non-ε4 carriers. (f) Forest plot of APOE ε4/ε4 vs. ε2/ε4. (g) Forest plot of APOE ε4/ε4 vs. ε3/ε4.
(a)(b)(c)(d)(e)(f)(g)


## 3.2.2. Genotype Comparisons

When compared with theε3/ε3 genotype, the pooled effects of the APOE genotype in the meta-analysis were as follows: for the ε2/ε2 genotype, pooled OR=0.985, 95% CI, 0.653-1.486, P=0.94, and for the ε2/ε3 genotype, pooled OR=0.980, 95% CI, 0.900-1.066, P=0.63; those two genotypes presented no association with the risk of IS (as showed in Table 2). Genotypes ε2/ε4, ε3/ε4, and ε4/ε4 were related to a higher risk of IS than ε3/ε3. The respective IS risk ORs were 1.233 (95% CI, 1.056-1.440, P=0.01) (Figure 2(a)), 1.340 (95% CI, 1.165-1.542, P<0.0001) (Figure 2(b)), and 1.833 (95% CI, 1.542-2.179, P<0.0001) (Figure 2(c)). The above results could be found in Table 2. A conclusion was drawn: every genotype which contained APOE ε4 mutation increased the risk of IS.


## 3.2.3. APOEε4 Carrier Comparisons

Compared with the non-ε4 carriers, we confirmed that the ε4 carriers were associated with the increased risk of IS; the pooled outcome was pooled OR=1.377 (95% CI, 1.203-1.576, P<0.0001) (Figure 2(e)).


## 3.2.4. APOEε2 Carrier Comparisons

In the genetic model ofε2 carriers vs. non-ε2 carriers, there was no association with the IS risk (pooled OR=0.956, 95% CI 0.841-1.086, P=0.49) (Table 2).


## 3.2.5. APOEε4 Homozygosis versus APOE ε4 Heterozygote Comparisons

Given the above, the APOEε4 mutation was linked to IS risk. To identify whether there is a dose-response relationship between the ε4 allele and IS or not, we implemented the comparisons between the ε4/ε4 genotype and ε4 heterozygotes (ε2/ε4 or ε3/ε4 genotype). Compared with the ε2/ε4 and ε3/ε4 genotypes, the IS risk ORs for ε4/ε4 genotypes were 1.625 (95% CI, 1.281-2.060, P<0.0001) and 1.301 (95% CI, 1.077-1.571, P=0.01), respectively (Figures 2(f) and 2(g)); this part provided evidence that ε4 homozygosis might generate a higher risk of IS than ε4 heterozygotes.


## 3.3. Main Results of the Relationship between APOE Gene and Three IS Subtypes

We further investigated on the correlation of APOE gene polymorphisms and risks of IS subtypes by making comparisons in five genetic models, with a particular focus on the APOEε4 mutation. Subgroup analyses showed that APOE ε4 mutation significantly increased SAD risk (ε4 allele vs. ε3 allele: pooled OR=1.318, 95% CI, 1.073-1.618, P=0.01 (Figure 3(d)); ε3/ε4 vs.ε3/ε3: pooled OR=1.392, 95% CI, 1.097-1.767, P=0.01 (Figure 3(b)); ε4/ε4 vs. ε3/ε3: pooled OR=1.809, 95%, CI 1.030-3.175, P=0.04 (Figure 3(c)); and APOE ε4 carriers vs. non-APOE ε4 carriers: pooled OR=1.329, 95% CI, 1.064-1.661, P=0.01 (Figure 3(e))). But genotype ε2/ε4 did not increase the risk of SAD onset (Figure 3(a)). The result of APOE ε4 homozygosis versus ε4 heterozygote comparisons (ε4/ε4 vs. ε2/ε4 and ε4/ε4 vs. ε3/ε4) was a matter of concern: APOE ε4 mutation could not cause a cumulative effect in generating higher risk of SAD onset, as showed in Figures 3(f) and 3(g).Figure 3
(a–g) Forest plots of the relationships between APOE gene polymorphisms in subgroup analysis. (a) Forest plot ofε2/ε4 vs. ε3/ε3 comparison. (b) Forest plot of ε3/ε4 vs. ε3/ε3 comparison. (c) Forest plot of APOE ε4/ε4 vs. the ε3/ε3 genotype. (d) Forest plot of the APOE ε4 allele vs. ε3 allele. (e) Forest plot of APOE ε4 carriers vs. non-ε4 carriers. (f) Forest plot of APOE ε4/ε4 vs. ε2/ε4. (g) Forest plot of APOE ε4/ε4 vs. ε3/ε4.
(a)(b)(c)(d)(e)(f)(g)


## 3.4. Sensitivity Analysis

Sensitivity analysis was performed by removing studies one by one to check the effect of the individual study on overall ORs. No single study influenced on the pooled ORs and 95% CIs in all genetic model comparisons as our data showed (supplementary material table4).


## 3.5. Publication Bias

We carried out publication bias analysis by using funnel plots as qualitative description and Egger’s regression tests as quantitative outcome. Funnel plots of all genetic model comparisons did not exhibit apparent asymmetry (several funnel plots were showed in supplementary material figure1 and 2). In addition to subtype analysis of ε2/ε2 vs. ε3/3, all the Egger’s regression test outcomes indicated that there existed no evident publication bias with all P values exceeding 0.1 (supplementary material table 5). The above results showed that publication bias of our meta-analysis was not significant.


## 3.6. Regression Analysis

Meta-regression analysis was then performed to explore sources of heterogeneity as shown in supplementary material table5, considering the year of publication, region, sample size, genotyping method, HWE, NOS score, and source of control. However, the P value of each factor affecting overall heterogeneity was not statistically significant in comparisons of ε3/ε4 vs. ε3/3, ε4 vs. non-ε4, ε2 vs. non-ε2, ε4allele vs. ε3allele, and ε2allele vs. ε3allele (supplementary material figure 3). Heterogeneity sources were unascertainable.


## 3.7. The Result of Trial Sequential Analysis (TSA)

The RIS was 8901 samples and the sample size of our meta-analysis reached it. Moreover, the cumulativez-curve crossed the trial sequential monitoring boundary before reaching the RIS as showed in Figure 4. The result of TSA guaranteed the stability of our meta-analysis results. Our sample size was proved to be enough for evaluating the relationship between APOE polymorphisms and IS risk.Figure 4
Trial sequential analysis of the association between APOE gene polymorphisms and ischemic stroke.


## 4. Discussion

Recently, scholars explored more how gene polymorphisms were contributing to the occurrence and prognosis of diseases. And several previous publications had well explored how gene polymorphisms related to diseases onset and potential mechanisms [74, 75]. As a heterogeneous multifactorial disorder, ischemic stroke could be regulated by certain gene synthesis and specific gene products. The genes involved in the pathological process of stroke are also worth of attention. Apolipoprotein E has been proven to affect atherosclerosis, neurodegeneration, and the process of nerve damage repair. That is why we explored the relationship between APOE gene polymorphisms and ischemic stroke risk.APOE is a 299-amino acid protein encoded by the APOE gene of three common polymorphisms,ε2, ε3, and ε4. The correlation of APOE gene polymorphisms and the risk of cerebral vascular and degenerative diseases have been investigated a lot, especially in Alzheimer’s disease (AD) and cerebral amyloid angiopathy (CAA) [76]. APOE ε4 is associated with increased risk for AD whereas APOE ε2 is associated with decreased risk [77]. Mirza et al. performed a meta-analysis to find that greater WMH volume was associated with worse performance on all cognitive domains in APOE ε4 carriers only in AD [78]. Charidimou et al. proved that the APOE ε2 allele might be associated with the pathophysiology and severity of cortical superficial siderosis in CAA [79]. As to IS, there existed quite many researches with inconsistent conclusions. Besides method differences, ethnic difference and unclarified pathophysiological mechanisms are probable reasons of the inconsistency.In a meta-analysis in 1999, McCarron et al. found that theε4 allele and carriers were more frequent among patients with ischemic cerebrovascular disease, compared with control subjects (27% versus 18%; odds ratio, 1.73; 95% CI, 1.34-2.23; P<0.0001) [13]. In another meta-analysis based on Chinese population, the ε4 allele is associated with an increased risk of developing cerebral infarction, in which the adjusted risk estimate for the ε4 allele versus ε3 allele was significant (OR=2.00, 95% CI 1.59-2.53, P<0.0001) [14]. Our estimates seemed to be coinciding with the above ones. Compared with the ε3 allele, the ε4 allele showed a higher risk of IS. Compared with ε3/ε3, both ε4 heterozygote (ε2/ε4, ε3/ε4) and ε4 homozygosis (ε4/ε4) exhibited a significant correlation with an increased risk of IS. Notably, OR in ε4 homozygosis (ε4/ε4 vs. ε3/3: 1.833 (95% CI 1.542-2.179)) was higher than those in ε4 heterozygotes (ε2/ε4 vs. ε3/3: 1.233 (95% CI 1.056-1.440) and ε3/ε4 vs. ε3/3: 1.340 (95% CI 1.165-1.542)), which implied that the ε4 allele might possess a cumulative effect. Then, we performed comparisons between ε4/ε4 and ε2/ε4 or ε3/ε4; there existed significant differences between ε4 homozygosis and ε4 heterozygote. The OR between ε4/ε4 and ε2/ε4 was 1.625 (95% CI 1.281-2.060, P<0.0001); the OR between ε4/ε4 and ε3/ε4 was 1.301 (95% CI 1.077-1.571, P=0.01), giving a hint that ε4 homozygosis might bring a higher risk of IS than ε4 heterozygotes.There are tremendous researches and discussions focusing on the pathogenicity ofε4. An Indian research reported that VLDL and triglycerides levels were found to be significantly associated with ε2/ε4 and ε3/ε4 genotypes; the ε4 allele exerted a higher influence than the ε3 allele in plasma cholesterol levels [22]. As a lipid transport protein, APOE3 and APOE2 preferentially bind to the smaller, more phospholipid-enriched high-density lipoproteins (HDL), while APOE4 preferentially binds to the larger, triglyceride-rich very low-density lipoproteins (VLDL). Miyata and Smith demonstrated an antioxidant activity in the order APOE2>E3>E4, and other researchers also reported similar results that APOE4 was associated with increased oxidative stress [25, 80], which might play a role in atherosclerosis and lead to increased risk of ischemic vascular diseases. Besides the above reasons, APOE4 was proved to be neurotoxic by assuming an abnormal conformation (the unique domain interaction between Arg-61 and Glu-255) which was highly susceptible to neuron specific proteolysis and generating neurotoxic fragments that escaped the secretory pathway and entered the cytosol [81]. Totally, from pathophysiological mechanisms to clinical research results, it seems that APOE4 is indeed related to a higher risk of IS, compared with other isoforms, both in ε4 heterozygote and homozygous. ε2 allele appears to be unclear and controversial in stroke [13]. In a meta-analysis of Martínez-González et al., compared with ε3/ε3, APOE ε2 was associated with intracerebral hemorrhage (OR=1.32; 95% CI, 1.01-1.74); meanwhile, APOE ε2 was more related to lobar hemorrhage than deep hemorrhage [82]. As to the association of IS with APOE based on previous investigation, it is uncertain. Our estimates showed that both ε2/ε2 and ε2/ε3 genotypes exhibited no significant effects on IS risk, compared with ε3/ε3. Also, no differences were found in comparisons of ε2 allele vs. ε3 allele and ε2 vs. non-ε2 carriers. This result remained consistent with another meta-analysis in 2013 [14]. Interestingly, in subtype analysis, ε2/ε2 displayed significances in the CE group (OR=4.290; 95% CI, 1.917-9.600; P<0.0001) and SAD group (OR=1.803; 95% CI, 1.037-3.134; P=0.04). The largest meta-analysis of the APOE genotype with IS showed a positive linear association of increasing risk when ordered from ε2/ε2, ε2/ε3, ε2/ε4, ε3/ε3, ε3/ε4, and ε4/ε4 in European ancestry population [83]. The conclusion might explain why APOE4 brings a higher risk of IS but could not clarify that the CE and SAD subgroups in comparison of ε2/ε2 with ε3/ε3 show significances. It is well known that all patients with type III hyperlipidemia (dysbetalipoproteinemia) were APOE ε2 homozygous, whereas most ε2/ε2 subjects (>90%) were normolipidemic or even hypolipidemic, owing to reductions in LDL or HDL or both. Therefore, the APOE ε2 allele has both increased and decreased risks for atherosclerosis, which induced a comprehensive and undetermined result [84].As to our subtype analyses, all LAA groups showed no significant difference among comparisons, which raised a question why isoforms of APOE, a lipid transport protein, seemed not to be related with IS caused by large artery atherosclerosis. Besides lipid metabolism and atherosclerosis, there might exist some other pathways underlying the relationships between APOE and risk of IS. Our estimates displayed that APOE isoforms were associated to risk of IS especially in the SAD subgroup. Hypertension was known to be an independent risk factor of SAD. Atherosclerosis, dyslipidemia, and hypertension have a complex interaction, and the causations with APOE need further investigation.Our meta-analysis has several limitations. First, just as the abovementioned, heterogeneity between studies remains undeterminable. Second, results of our meta-analysis based on case control studies cannot provide a causal relationship, but only an association. Third, age variable and ethnicity can influence APOE frequencies in a population; we cannot obtain sufficient related information to perform further subdivided subgroup analyses. Fourth, other pathogenic factors about IS, a multifactorial disease, such as plasma lipid levels, hypertension, life-style, BMI, and gene-environment interactions, were unachievable. Fifth, the controls in accessible studies were not strictly defined; some were selected from healthy populations and others were from nonstroke people. The expected genotype distribution in controls was not in accordance with HWE in seven studies. Population selection in control groups failed to avoid certain diseases which might have a relation with the APOE gene, such as dyslipidemia, hypertension, other vascular diseases, and diabetes. Sixth, the case groups were not selected by a prospective process and the design of case control studies often caused abnormal gene frequency.


## 5. Conclusions

In conclusion, our meta-analysis provides rational evidence that APOEε4 mutation is a genetic risk factor for IS. Prospective studies of a large sample size, which concerns gene-gene and gene-environment interactions, should be carried out in the future to reach a more comprehensive outcome about the association of APOE gene polymorphisms and IS. What is more, future researches should be designed to elucidate the mechanism by which APOE ε4 mutation adds the risk of IS.



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*Source: 1407183-2022-02-03.xml*