Description of studies
Our literature search generated 351 studies, 274 of which remained when 77 duplications were removed. This number was reduced to 38 after screening of title and abstract (Figure 1). After reading the full text of these papers,18 studies were excluded, as they were review articles and the other 8 studies were excluded because the overlapping population was analyzed or the data were not related to the APOE polymorphism. Then 12 studies were involved in the meta-analysis, while two studies were removed because the data was incomplete. Finally, 10 eligible studies were identified, published from 1995 to 2019, that reported on genotypes of APOE and risk of CP, in which four were published in Chinese24-27 and the other six studies were published in English.16-23
Some studies have been put forward in this field in Brazil, China, the United States, Norway, Australia and Turkey. The combined participants included 1570 CP cases and 1982 subjects. The main features of the studies involved in the meta-analysis are provided in Table 1. We used the NOS rating scale to assess the quality score of each study as shown in Table 1. The data for the frequencies of APOE alleles and genotypes in the individual studies are shown in Table 1S. The deviation from HWE in the control population was found in three study. 17,21,22
Overall analyses of the association between APOE polymorphisms and CP Susceptibility
Firstly, the meta-analysis of the APOE alleles and the CP risk was analyzed. The overall 10 studies were used to evaluate the effect of APOE alleles on the CP risk.16,17,20-27 The comparison of the presence of ε2 vs. ε3 alleles within CP patients as well as the control group indicated heterogeneity between studies (p=0.01, χ2=21.37, I2 =58%, Figure 2A). The random-effects model was adopted. The findings showed that the existence of ε2 allele conferred CP a risk (p=0.04, OR 1.41, 95% CI 1.01 to 1.96, Figure 2A). What is more, the presence of ε4 vs. ε3 alleles between CP cases and control groups was estimated. Because of the heterogeneity within the studies (p<0.00001, χ2=41.01, I2 =78%, Figure 3A), the random-effects model was used. The meta-analysis showed that there was a significant positive correlation between ε4 allele and CP risk (p<0.001, OR 2.05, 95% CI 1.40 to 2.99, Figure 3A). Moreover, the pooled data supported the result that the E4 carriers showed significantly increased CP risk, contrasted with those with E3/3 genotype (p=0.004, OR 1.90, 95% CI 1.23 to 2.92, Figure 4A). The random-effect model was adopted due to heterogeneity in 10 studies (p<0.0001, χ2=34.68, I2 =74%, Figure 4A). The results of dominant and recessive models for contrasts of E4, E3, and E2 genotypes were shown in Table 2. To further exclude heterogeneity, we removed studies with substantial departure from HWE among controls. This time fixed-effects model was then applied, because the heterogeneity was not significant among the pooled 7 studies (I2 =44%, Figure 2B),16,20,23-27 and the meta-analysis showed that there was a significant positive correlation between ε2 allele and CP risk (p=0.001, OR 1.63, 95% CI 1.21 to 2.19, Figure 2B).
APOE Polymorphisms and CP Susceptibility in Chinese Subgroups
We also researched the subgroup of Chinese because we involved four Chinese studies that had never appeared in other meta-analyses. In this paper, four studies of the ε4 vs. ε3 alleles were carried out.24-27 The summary of the data supported a significant increase in the CP risk in individuals with ε4 alleles contrasted with those with ε3 alleles (p<0.00001, OR 3.70, 95% CI 2.37 to 5.78, Figure 3C). Because there was no heterogeneity between studies (I2 = 9%, Figure 3C), the fixed-effects model was then applied. We found that, comparison with those with ε4 alleles, individuals with ε2 alleles haven’t a risk for CP development in the Chinese population (p=0.69, OR 1.09, 95% CI 0.72 to 1.65, Figure 2C). In addition, the summary data showed that those with E4 carriers had a high risk of developing CP compared with the individuals with E3/3 genotype (p<0.00001, OR 3.95, 95% CI 2.38 to 6.53, Figure 4C). Because there was no heterogeneity between studies (I2 =13%, Figure 4C), the fixed-effects model was then used. What is more, Table 2 shows the results for comparing the dominant and recessive models of the E4, E3 and E2 genotypes.
Evaluation of Publication Bias
Firstly, Begg’s funnel plots were used to evaluate the publication bias. Asymmetry and publication bias showed on funnel plots were evaluated by Egger’s tests (Table 3). We found that both ε4 vs ε3 alleles and E4 carriers vs E3/3 genotypes has the evidence of publication bias (P<0.05 for both Begg’s test and Egger’s test). In contrast, there was a significant deviation both for ε2 vs ε3 alleles and E2 carriers vs E3/3 genotypes (P>0.05 for both Begg’s test and Egger’s test) (Figure S1A-D). Because of this, we used the trim and fill method for sensitivity analysis, which conservatively presupposes hypothetical negative unpublished studies to reflect the positive study that leads to the asymmetry of the funnel diagram.35 The collected analysis incorporating the hypothetical studies continued to suggest both APOE ε4 and E4 carriers acts as a risk factor for CP (Figure S1E-H).
Table 3 Publication bias of APOE polymorphisms and the risk of CP
Publication bias by Egger’s test
|
Variables
|
Coefficient
|
SE
|
z
|
p Value
|
95% CI
|
ε2 vs ε3 alleles
|
4.983226
|
1.93
|
2.575458
|
0.089
|
-.9557911 to 10.92224
|
E2 carriers vs E3/3
|
4.906202
|
3.225086
|
1.52
|
0.167
|
-2.530861 to 12.34326
|
ε4 vs ε3 alleles
|
8.115601
|
1.398786
|
5.80
|
0.000
|
4.889996 to 11.34121
|
E4 carriers vs E3/3
|
7.736085
|
1.921866
|
4.03
|
0.004
|
3.304254 to 12.16792
|