Abstract

Backgroud: Pre-eclampsia (PE) is a common pregnancy-induced hypertension disease. Some case–control studies reported the association between vascular endothelial growth factor (VEGF) gene polymorphisms (rs3025039, rs2010963) and PE risk. However, these associations were inconsistent in several studies. Therefore, we conducted this meta-analysis to assess the role of VEGF gene polymorphisms in PE more precisely.

Methods: Eligible studies were searched in PubMed, Embase, Web of Science and Chinese (Chinese National Knowledge Infrastructure (CNKI) and WanFang) databases. Statistical analyses were performed by Stata 12.0 software. Odds ratio (OR) and 95% confidence interval (CI) were used to assess the strength of the association. In addition, subgroup analyses, sensitive analyses and publication bias analyses were performed to further assess this meta-analysis.

Results: In total, 21 studies were included in the meta-analysis covering 2018 cases and 2632 controls. There were significant associations between VEGF polymorphisms (rs3025039, rs2010963) and PE risk in the overall populations. In the subgroup analyses, we found that rs3025039 polymorphism was associated with the increased risk of PE among Chinese. As for rs2010963 polymorphism, a significant association was observed in subgroup of Caucasian.

Conclusion: The present study suggested that the two VEGF gene polymorphisms (rs3025039, rs2010963) are associated with increased risk of PE in different ethnic groups, which means that the targets may be useful genetic markers for early prediction of PE.

Introduction

Pre-eclampsia (PE), a common pregnancy disease diagnosed by hypertension and proteinuria, occurs in approximately 2–8% of pregnancies [1,2]. PE is an important reason for the maternal and fetal morbidity and mortality due to dysfunction of multiple systems and organs, such as liver, kidney and brain [3]. Although its etiology has not been well recognized, PE is now regarded as the result of the combined effect of multiple factors [4,5]. According to the results of the several epidemiological studies, PE has a substantial heritable component, which is estimated to be a major effect [4,6].

The vascular endothelial growth factor (VEGF) gene, located on chromosome 6p21.3, is a key regulator of angiogenesis and vascular function. Therefore, VEGF is vital for the formation of trophoblasts, embryonic vasculature and maternal and foetal blood cells in utero [7]. Abnormal vascular growth and endothelial dysfunction have been proposed to be the part of pathogenesis. Hence, VEGF has drawn the attention of many researchers [8,9].

The associations between polymorphisms of the VEGF gene and PE have been extensively studied [10–30]. However, the results were somewhat controversial. In 2013, two meta-analyses assessed the associations among four polymorphisms of the VEGF gene and the risk of PE [31,32]. But the retrieved datasets of these two meta-analyses were not sufficient, and several new studies have been published regarding this relationship between VEGF gene polymorphisms (rs3025039, rs2010963) and PE [10–15]. In addition, the results published recently remained inconsistent and conflicting, likely owing to heterogeneity of different researches or inadequate sample size. A comprehensive retrieval of the pertinent literature in multiple databases is likely to help assess disease risks more precisely. In view of the mortality of PE, more efficient biomarkers are required for early discovery and prevention in the clinical practice. Therefore, we performed an updated meta-analysis of all eligible studies including English and non-English journals to investigate the association between VEGF gene polymorphisms and the risk of PE. Moreover, we further divided the cases by ethnic groups, countries as well as genotyping methods and analyzed subgroup specific associations.

Methods

Identification of literature

This meta‐analysis was conducted in accordance with the guidance of the Preferred Reporting Items for Systematic Reviews and Meta‐Analyses (PRISMA) statement [33]. The PRISMA Checklist was presented in Supplementary Table S1. The literature search using the electronic databases PubMed, EMBASE, Web of Science, Chinese National Knowledge Infrastructure (CNKI) and WanFang was conducted by two study investigators. The comprehensive search strategies included the Mesh term and Keywords: (‘vascular endothelial growth factor’ or ‘VEGF’), (‘polymorphism’, ‘SNP’ ‘variant’, ‘genotype’ or ‘mutations’), (‘Pre-eclampsia’, ‘Preeclampsia’, ‘Pregnancy Toxemias’, ‘Pregnancy Toxemia’, ‘Edema-Proteinuria-Hypertension Gestosis’, ‘Edema Proteinuria Hypertension Gestosis’, ‘EPH Complex’, ‘EPH Toxemias’, ‘EPH Toxemia’, ‘Proteinuria-Edema-Hypertension Gestosis’ or ‘Proteinuria Edema Hypertension Gestosis’) through 3 January 2019. All eligible studies were retrieved and examined carefully. Review articles and references of other relevant researches were further searched to find additional eligible studies.

Inclusion and exclusion criteria

The inclusion criteria were as follows: (i) studies which estimated the associations between VEGF rs2010963 or rs3025039 and the susceptibility to PE; (ii) case–control studies or cohort studies of PE; (iii) patients must be clinically diagnosed for PE (blood pressure ≥ 140/90 mmHg on two measurements with ≥1+ proteinuria or 300 mg/24 h after the 20th week of pregnancy); (iv) reported the allele frequencies of both cases and controls for different genotypes; (v) genotype distribution in the control group confirmed by Hardy–Weinberg equilibrium (HWE). The exclusion criteria of the meta-analysis were: (i) non-human studies, meta-analysis, comments, letters, reviews, mechanism studies or studies without controls, (ii) studies with overlapping or incomplete data. When overlapped population between studies was identified, only the newest or most complete article was included in the analysis. According to the corresponding criteria, two independent authors screened the articles.

Data extraction and assessment of methodological quality

Data were extracted by two authors independently from each study. The following information was collected: first author, publication year, participants’ country, ethnicity (categorized as Caucasian, Xanthoderm, Indo-European hybrid), sample size, study design (case–control or cohort), genotyping method, alleles and genotype frequency distribution in cases and controls, and the major conclusion of the study. When incomplete or apparent conflicting data were found in the article, we made an attempt to contact authors. Inconsistencies in data interpretation were resolved with discussion. The Newcastle–Ottawa Quality Assessment Scale (NOS) was employed to evaluate the methodological quality of the identified articles, and scores ranging from 0 (the worst) to 9 (the best) were assigned based on the quality of the studies. The studies with no less than 5 stars were considered to be of high quality.

Statistical analysis

First, deviation from HWE in the distribution of allele frequencies was estimated again by the chi-square test (determined by P<0.05). Stata 12.0 was used to perform quantitative meta-analysis. The association was estimated with four models: Allele comparison model, Dominant model, Recessive model and Homozygote model. The four models of the data analysis were conducted by the random-effects model to prevent exaggerated results. The association between the VEGF rs3025039 or rs2010963 and PE risk was assessed by the raw odds ratios (ORs) with 95% confidence intervals (CIs). The Student’s t test was used to determine the significance of the crude OR, and P<0.05 was considered statistically significant. In addition, heterogeneity assumption among the included researches was evaluated by the Chi-square and I2, which was regarded to be statistically significant if P<0.10. And I2 values of 25, 50 and 75% were nominally assigned as low, moderate and high estimates. To insure that any single study did not cause an obvious influence to the whole effects, sensitivity analysis was performed to estimate the validity and stability of the study. In addition, to further analyze the source of the heterogeneity and the specific association between the VEGF polymorphism and PE, studies were also divided into several subgroups on the basis of the country, the ethnicity of the related population and the genotyping method. Egger’s test was performed to estimate the potential publication bias.

Results

Study characteristics

As shown in Figure 1, the PRISMA flowchart demonstrated process of the literature retrieval. Four hundred and five studies were identified according to the result of the retrieval strategy and manual searches from PubMed, EMBASE, Web of Science, CNKI and WanFang database. On the basis of our inclusion/exclusion criteria, 142 studies were excluded for duplication and 239 studies were excluded as meta-analysis, reviews, mechanism studies or non-relevant research. Then, 24 studied were selected for full-text review. However, three studies were excluded, because two studies lacked genotype data and one study focused on placental polymorphism. Because most of the studies did not use the rs number to name the SNP, every SNP was manually confirmed by searching in the NCBI according to the sequence in the literature. Finally, 21 studies were included in the meta-analysis. Thereinto, 15 studies assessed the association between VEGF rs3025039 T/C polymorphism and the risk of PE, and 12 studies examined the association between VEGF rs2010963 C/G polymorphism and the risk of PE. The specific information about the included studies was exhibited in Table 1. The quality evaluation of each study following the NOS is presented in Table 2, which showed all of these studies can be regarded as high-quality studies.

PRISMA flow chart of selection procedure

Figure 1
PRISMA flow chart of selection procedure
Figure 1
PRISMA flow chart of selection procedure
Table 1
Study characteristics of PE cases and controls in the analysis of VEGF polymorphisms
Author [Ref.]YearCountryEthnicitySources of controlsNumberPolymorphism(s)Genotyping methodAssociation findings
CaseCTR
Lu [102017 China Xanthoderm HB 156 286 rs3025039 Snapshot NS 
Amosco [112016 Philippines Xanthoderm HB 165 191 rs3025039
rs2010963 
MassARRAY system Supportive
NS 
Salimi [122015 Iran Caucasian HB 192 186 rs2010963 PCR-RFLP Supportive 
Silva [132015 Brazil Caucasian HB 79 210 rs2010963 PCR-RFLP NS 
Zhang Honghui [142014 China Xanthoderm HB 58 70 rs3025039
rs2010963 
Sequencing Supportive
NS 
Procopciuc [152014 Romania Caucasian HB 70 94 rs3025039 PCR-RFLP Supportive 
Chedraui [162013 Ecuador Indo-European hybrid HB 31 31 rs3025039
rs2010963 
Sequencing NS 
Andraweere [172013 Australia Caucasian HB 174 168 rs3025039 MassARRAY system NS 
Atis [182012 Turkey Caucasian HB 34 58 rs3025039 MassARRAY system NS 
Chen Baoli [192011 China Xanthoderm HB 84 71 rs3025039 PCR-RFLP Supportive 
He Yun [202011 China Xanthoderm HB 61 43 rs3025039
rs2010963 
Sequencing Supportive 
Garza-Veloz [212011 Mexico Indo-European hybrid HB 86 78 rs2010963 PCR-RFLP NS 
Cunha [222010 Brazil Caucasian HB 52 28 rs3025039 PCR-RFLP NS 
Liu Shifang [232010 China Xanthoderm HB 84 71 rs3025039 PCR-RFLP Supportive 
Huang Yuliang [242009 China Xanthoderm HB 128 231 rs3025039 PCR-RFLP Supportive 
Sandrim [252008 Brazil Caucasian HB 94 108 rs2010963 TaqMan-assays NS 
Nagy [262008 Hungary Caucasian HB 71 93 rs2010963 real-time PCR NS 
Shim [272007 Korea Xanthoderm HB 110 209 rs3025039 PCR-RFLP Supportive 
Kim [282007 Korea Xanthoderm HB 223 237 rs3025039
rs2010963 
Snapshot NS 
Banyasz [292006 Hungary Caucasian HB 84 96 rs2010963 PCR-RFLP Supportive 
Papazoglou [302004 Sweden Caucasian HB 42 73 rs3025039
rs2010963 
PCR-RFLP Supportive
NS 
Author [Ref.]YearCountryEthnicitySources of controlsNumberPolymorphism(s)Genotyping methodAssociation findings
CaseCTR
Lu [102017 China Xanthoderm HB 156 286 rs3025039 Snapshot NS 
Amosco [112016 Philippines Xanthoderm HB 165 191 rs3025039
rs2010963 
MassARRAY system Supportive
NS 
Salimi [122015 Iran Caucasian HB 192 186 rs2010963 PCR-RFLP Supportive 
Silva [132015 Brazil Caucasian HB 79 210 rs2010963 PCR-RFLP NS 
Zhang Honghui [142014 China Xanthoderm HB 58 70 rs3025039
rs2010963 
Sequencing Supportive
NS 
Procopciuc [152014 Romania Caucasian HB 70 94 rs3025039 PCR-RFLP Supportive 
Chedraui [162013 Ecuador Indo-European hybrid HB 31 31 rs3025039
rs2010963 
Sequencing NS 
Andraweere [172013 Australia Caucasian HB 174 168 rs3025039 MassARRAY system NS 
Atis [182012 Turkey Caucasian HB 34 58 rs3025039 MassARRAY system NS 
Chen Baoli [192011 China Xanthoderm HB 84 71 rs3025039 PCR-RFLP Supportive 
He Yun [202011 China Xanthoderm HB 61 43 rs3025039
rs2010963 
Sequencing Supportive 
Garza-Veloz [212011 Mexico Indo-European hybrid HB 86 78 rs2010963 PCR-RFLP NS 
Cunha [222010 Brazil Caucasian HB 52 28 rs3025039 PCR-RFLP NS 
Liu Shifang [232010 China Xanthoderm HB 84 71 rs3025039 PCR-RFLP Supportive 
Huang Yuliang [242009 China Xanthoderm HB 128 231 rs3025039 PCR-RFLP Supportive 
Sandrim [252008 Brazil Caucasian HB 94 108 rs2010963 TaqMan-assays NS 
Nagy [262008 Hungary Caucasian HB 71 93 rs2010963 real-time PCR NS 
Shim [272007 Korea Xanthoderm HB 110 209 rs3025039 PCR-RFLP Supportive 
Kim [282007 Korea Xanthoderm HB 223 237 rs3025039
rs2010963 
Snapshot NS 
Banyasz [292006 Hungary Caucasian HB 84 96 rs2010963 PCR-RFLP Supportive 
Papazoglou [302004 Sweden Caucasian HB 42 73 rs3025039
rs2010963 
PCR-RFLP Supportive
NS 

Abbreviations: CTR, control; HB: hospital-based study; NS: non-significant.

Table 2
Quality assessment conducted according to the Newcastle–Ottawa Scale for all the included studies
SelectionComparabilityExposure
AuthorAdequate definition of caseRepresentativeness of the casesSelection of controlsDefinition of controlsComparability of cases and controlsExposure assessmentSame method of ascertainment for cases and controlsNon-response rateTotal score
Lu [10  
Amosco [11 
Salimi [12 
Silva [13 
Zhang Honghui    
Procopciuc  
Chedraui  
Andraweere  
Atis  
Chen Baoli    
He Yun   
Garza-Veloz  ** 
Cunha  
Liu Shifang   
Huang Yuliang    
Sandrim  ** 
Nagy  
Shim  ** 
Kim  
Banyasz  ** 
Papazoglou  ** 
SelectionComparabilityExposure
AuthorAdequate definition of caseRepresentativeness of the casesSelection of controlsDefinition of controlsComparability of cases and controlsExposure assessmentSame method of ascertainment for cases and controlsNon-response rateTotal score
Lu [10  
Amosco [11 
Salimi [12 
Silva [13 
Zhang Honghui    
Procopciuc  
Chedraui  
Andraweere  
Atis  
Chen Baoli    
He Yun   
Garza-Veloz  ** 
Cunha  
Liu Shifang   
Huang Yuliang    
Sandrim  ** 
Nagy  
Shim  ** 
Kim  
Banyasz  ** 
Papazoglou  ** 

Overall analysis

Overall results of this meta-analysis between the two SNP and PE are displayed in Tables 3 and 4. In total, we analyzed 1426 cases and 1872 controls for rs3025039 with the random-effect model, showing a significantly increased risk for the comparison of the T allele to the C allele (OR = 1.418, 95% CI = 1.060–1.898, P=0.019, Figure 2A). Also, the results of the three genotype models analysis all revealed a significant association between PE and the VEGF rs3025039 (Dominant model: OR = 1.637, 95% CI = 1.031–2.598, P=0.037, Figure 2B; Recessive model: OR = 1.501, 95% CI = 1.068–2.109, P=0.019, Figure 2C; Homozygote model: OR = 1.819, 95% CI = 1.021–3.240, P=0.042, Figure 2D), in which the result of the Recessive model exhibited high heterogeneity (I2 = 77.2%) and others were acceptable. An analysis of 1148 cases and 1388 controls for rs2010963 showed the C allele in allele comparison model, CC and CG genotype in the recessive model and CC genotype in the homozygous model increased the risk of PE significantly (Allele comparison model: OR = 1.207, 95% CI = 1.046–1.394, P=0.010, Figure 3A; Recessive model: OR = 1.310, 95% CI = 1.044–1.643, P=0.020 Figure 3C; Homozygote model: OR = 1.324, 95% CI = 1.024–1.713, P=0.032, Figure 3D), however the result of the dominant model did not indicate statistical significance (OR = 1.154, 95% CI = 0.912–1.460, P=0.232, Figure 3B).

Forest plot of PE risk associated with VEGF gene rs3025039 polymorphism

Figure 2
Forest plot of PE risk associated with VEGF gene rs3025039 polymorphism

(A) Allele comparison model. (B) Dominant model. (C) Recessive model. (D) Homozygote model.

Figure 2
Forest plot of PE risk associated with VEGF gene rs3025039 polymorphism

(A) Allele comparison model. (B) Dominant model. (C) Recessive model. (D) Homozygote model.

Forest plot of PE risk associated with VEGF gene rs2010963 polymorphism

Figure 3
Forest plot of PE risk associated with VEGF gene rs2010963 polymorphism

(A) Allele comparison model. (B) Dominant model. (C) Recessive model. (D) Homozygote model.

Figure 3
Forest plot of PE risk associated with VEGF gene rs2010963 polymorphism

(A) Allele comparison model. (B) Dominant model. (C) Recessive model. (D) Homozygote model.

Table 3
Main results for the rs3025039 polymorphism with the risk of PE
ComparisonSubgroupNumberTest of associationTest of heterogeneity
OR95% CIP-valueI2P-value
T vs C Overall 15 1.418 1.060–1.898 0.019 76.6% <0.001 
 China 1.793 1.229–2.617 <0.001 69.5% 0.006 
 Korea 1.219 0.438–3.392 0.704 93.3% <0.001 
 Other Countries 1.189 0.738–1.917 0.476 73.6% <0.001 
 Xanthoderm 1.454 0.995–2.125 0.053 83.5% <0.001 
 Caucasian 1.361 0.758–2.444 0.301 73.1% 0.005 
 Indo-European hybrid 1.369 0.628–2.984 0.429 
 MassARRAY system 0.681 0.484–0.958 0.028 <0.1% 0.483 
 Sequencing 1.942 1.182–3.191 0.009 20.0% 0.264 
 PCR-RFLP 1.961 1.594–2.413 <0.001 14.1% 0.316 
 Snapshot 0.778 0.601–1.007 0.056 <0.1% 0.566 
TT vs CC+CT Overall 14 1.637 1.031–2.598 0.037 22.5% 0.210 
 China 2.420 1.400–4.180 0.002 <0.1% 0.436 
 Korea 1.514 0.334–6.873 0.591 73.6% 0.052 
 Other countries 1.062 0.502–2.246 0.911 <0.1% 0.465 
 Xanthoderm 1.795 0.979–3.291 0.059 42.1% 0.098 
 Caucasian 1.144 0.438–2.987 0.784 1.0% 0.400 
 Indo-European hybrid 2.148 0.364–12.693 0.399 
 MassARRAY system 0.338 0.091–1.256 0.105 <0.1% 0.555 
 Sequencing 4.378 1.525–12.572 0.006 <0.1% 0.328 
 PCR-RFLP 2.409 1.399–4.150 0.002 <0.1% 0.980 
 Snapshot 0.926 0.461–1.858 0.828 <0.1% 0.478 
TT +CT vs CC Overall 15 1.501 1.068–2.109 0.019 75.1% <0.001 
 China 1.933 1.205–3.102 0.006 71.6% <0.001 
 Korea 1.243 0.385–4.019 0.716 92.9% <0.001 
 Other countries 1.260 0.724–2.193 0.414 73.6% <0.001 
 Xanthoderm 1.520 0.977–2.365 0.063 82.8% <0.001 
 Caucasian 1.513 0.769–2.977 0.230 72.9% 0.005 
 Indo-European hybrid 1.295 0.477–3.515 0.612 
 MassARRAY system 0.700 0.481–1.019 0.063 <0.1% 0.317 
 Sequencing 1.976 1.035–3.772 0.039 12.1% 0.286 
 PCR-RFLP 2.186 1.678–2.848 <0.001 24.0% 0.230 
 Snapshot 0.726 0.540–0.978 0.035 <0.1% 0.697 
TT vs CC Overall 14 1.819 1.021–3.240 0.042 45.7% 0.032 
 China 3.009 1.403–6.453 0.005 32.3% 0.206 
 Korea 1.658 0.256–10.750 0.596 82.3% 0.017 
 Other countries 1.139 0.467–2.781 0.775 24.9% 0.239 
 Xanthoderm 2.041 0.958–4.346 0.064 60.6% 0.013 
 Caucasian 1.256 0.376–4.198 0.711 32.2% 0.207 
 Indo-European hybrid 2.267 0.362–14.185 0.382 
 MassARRAY system 0.316 0.085–1.177 0.086 <0.1% 0.605 
 Sequencing 5.284 1.322–21.116 0.019 33.0% 0.222 
 PCR-RFLP 3.120 1.793–5.429 <0.001 <0.1% 0.921 
 Snapshot 0.846 0.420–1.707 0.641 <0.1% 0.462 
ComparisonSubgroupNumberTest of associationTest of heterogeneity
OR95% CIP-valueI2P-value
T vs C Overall 15 1.418 1.060–1.898 0.019 76.6% <0.001 
 China 1.793 1.229–2.617 <0.001 69.5% 0.006 
 Korea 1.219 0.438–3.392 0.704 93.3% <0.001 
 Other Countries 1.189 0.738–1.917 0.476 73.6% <0.001 
 Xanthoderm 1.454 0.995–2.125 0.053 83.5% <0.001 
 Caucasian 1.361 0.758–2.444 0.301 73.1% 0.005 
 Indo-European hybrid 1.369 0.628–2.984 0.429 
 MassARRAY system 0.681 0.484–0.958 0.028 <0.1% 0.483 
 Sequencing 1.942 1.182–3.191 0.009 20.0% 0.264 
 PCR-RFLP 1.961 1.594–2.413 <0.001 14.1% 0.316 
 Snapshot 0.778 0.601–1.007 0.056 <0.1% 0.566 
TT vs CC+CT Overall 14 1.637 1.031–2.598 0.037 22.5% 0.210 
 China 2.420 1.400–4.180 0.002 <0.1% 0.436 
 Korea 1.514 0.334–6.873 0.591 73.6% 0.052 
 Other countries 1.062 0.502–2.246 0.911 <0.1% 0.465 
 Xanthoderm 1.795 0.979–3.291 0.059 42.1% 0.098 
 Caucasian 1.144 0.438–2.987 0.784 1.0% 0.400 
 Indo-European hybrid 2.148 0.364–12.693 0.399 
 MassARRAY system 0.338 0.091–1.256 0.105 <0.1% 0.555 
 Sequencing 4.378 1.525–12.572 0.006 <0.1% 0.328 
 PCR-RFLP 2.409 1.399–4.150 0.002 <0.1% 0.980 
 Snapshot 0.926 0.461–1.858 0.828 <0.1% 0.478 
TT +CT vs CC Overall 15 1.501 1.068–2.109 0.019 75.1% <0.001 
 China 1.933 1.205–3.102 0.006 71.6% <0.001 
 Korea 1.243 0.385–4.019 0.716 92.9% <0.001 
 Other countries 1.260 0.724–2.193 0.414 73.6% <0.001 
 Xanthoderm 1.520 0.977–2.365 0.063 82.8% <0.001 
 Caucasian 1.513 0.769–2.977 0.230 72.9% 0.005 
 Indo-European hybrid 1.295 0.477–3.515 0.612 
 MassARRAY system 0.700 0.481–1.019 0.063 <0.1% 0.317 
 Sequencing 1.976 1.035–3.772 0.039 12.1% 0.286 
 PCR-RFLP 2.186 1.678–2.848 <0.001 24.0% 0.230 
 Snapshot 0.726 0.540–0.978 0.035 <0.1% 0.697 
TT vs CC Overall 14 1.819 1.021–3.240 0.042 45.7% 0.032 
 China 3.009 1.403–6.453 0.005 32.3% 0.206 
 Korea 1.658 0.256–10.750 0.596 82.3% 0.017 
 Other countries 1.139 0.467–2.781 0.775 24.9% 0.239 
 Xanthoderm 2.041 0.958–4.346 0.064 60.6% 0.013 
 Caucasian 1.256 0.376–4.198 0.711 32.2% 0.207 
 Indo-European hybrid 2.267 0.362–14.185 0.382 
 MassARRAY system 0.316 0.085–1.177 0.086 <0.1% 0.605 
 Sequencing 5.284 1.322–21.116 0.019 33.0% 0.222 
 PCR-RFLP 3.120 1.793–5.429 <0.001 <0.1% 0.921 
 Snapshot 0.846 0.420–1.707 0.641 <0.1% 0.462 

Presentation with bold indicated a statistical significance.

Table 4
Main results for the rs2010963 polymorphism with the risk of PE
ComparisonSubgroupNumberTest of associationTest of heterogeneity
OR95% CIP-valueI2P-value
C vs G Overall 12 1.207 1.046–1.394 0.010 26.1% 0.188 
 Xanthoderm 1.178 0.879–1.581 0.273 46.1% 0.135 
 Caucasian 1.246 1.004–1.546 0.046 38.4% 0.150 
 Indo-European hybrid 1.199 0.817–1.760 0.353 <0.1% 0.417 
 Sequencing 1.620 1.044–2.516 0.032 <0.1% 0.940 
 PCR-RFLP 1.149 0.971–1.358 0.105 <0.1% 0.640 
 Other methods 1.224 0.866–1.731 0.253 68.1% 0.024 
CC vs GG+GC Overall 12 1.154 0.912–1.460 0.232 <0.1% 0.647 
 Xanthoderm 0.932 0.626–1.387 0.729 <0.1% 0.510 
 Caucasian 1.295 0.948–1.768 0.104 <0.1% 0.462 
 Indo-European hybrid 1.296 0.564–2.975 0.541 <0.1% 0.784 
 Sequencing 0.974 0.314–3.020 0.964 <0.1% 0.976 
 PCR-RFLP 1.231 0.896–1.690 0.200 <0.1% 0.689 
 Other methods 1.203 0.676–2.142 0.529 43.2% 0.152 
CC +GC vs GG Overall 12 1.310 1.044–1.643 0.020 42.6% 0.058 
 Xanthoderm 1.350 0.898–2.031 0.149 52.3% 0.098 
 Caucasian 1.278 0.894–1.826 0.178 56.0% 0.045 
 Indo-European hybrid 1.360 0.667–2.772 0.397 33.4% 0.221 
 Sequencing 2.328 1.294–4.189 0.005 <0.1% 0.901 
 PCR-RFLP 1.174 0.924–1.491 0.189 <0.1% 0.663 
 Other methods 1.322 0.804–2.175 0.272 73.8% 0.010 
CC vs GG Overall 12 1.324 1.024–1.713 0.032 <0.1% 0.622 
 Xanthoderm 1.134 0.734–1.753 0.570 <0.1% 0.433 
 Caucasian 1.461 1.019–2.094 0.039 8.9% 0.359 
 Indo-European hybrid 1.377 0.557–3.406 0.489 <0.1% 0.905 
 Sequencing 1.555 0.479–5.048 0.462 <0.1% 0.994 
 PCR-RFLP 1.287 0.907–1.827 0.157 <0.1% 0.699 
 Other methods 1.469 0.771–2.799 0.242 49.0% 0.117 
ComparisonSubgroupNumberTest of associationTest of heterogeneity
OR95% CIP-valueI2P-value
C vs G Overall 12 1.207 1.046–1.394 0.010 26.1% 0.188 
 Xanthoderm 1.178 0.879–1.581 0.273 46.1% 0.135 
 Caucasian 1.246 1.004–1.546 0.046 38.4% 0.150 
 Indo-European hybrid 1.199 0.817–1.760 0.353 <0.1% 0.417 
 Sequencing 1.620 1.044–2.516 0.032 <0.1% 0.940 
 PCR-RFLP 1.149 0.971–1.358 0.105 <0.1% 0.640 
 Other methods 1.224 0.866–1.731 0.253 68.1% 0.024 
CC vs GG+GC Overall 12 1.154 0.912–1.460 0.232 <0.1% 0.647 
 Xanthoderm 0.932 0.626–1.387 0.729 <0.1% 0.510 
 Caucasian 1.295 0.948–1.768 0.104 <0.1% 0.462 
 Indo-European hybrid 1.296 0.564–2.975 0.541 <0.1% 0.784 
 Sequencing 0.974 0.314–3.020 0.964 <0.1% 0.976 
 PCR-RFLP 1.231 0.896–1.690 0.200 <0.1% 0.689 
 Other methods 1.203 0.676–2.142 0.529 43.2% 0.152 
CC +GC vs GG Overall 12 1.310 1.044–1.643 0.020 42.6% 0.058 
 Xanthoderm 1.350 0.898–2.031 0.149 52.3% 0.098 
 Caucasian 1.278 0.894–1.826 0.178 56.0% 0.045 
 Indo-European hybrid 1.360 0.667–2.772 0.397 33.4% 0.221 
 Sequencing 2.328 1.294–4.189 0.005 <0.1% 0.901 
 PCR-RFLP 1.174 0.924–1.491 0.189 <0.1% 0.663 
 Other methods 1.322 0.804–2.175 0.272 73.8% 0.010 
CC vs GG Overall 12 1.324 1.024–1.713 0.032 <0.1% 0.622 
 Xanthoderm 1.134 0.734–1.753 0.570 <0.1% 0.433 
 Caucasian 1.461 1.019–2.094 0.039 8.9% 0.359 
 Indo-European hybrid 1.377 0.557–3.406 0.489 <0.1% 0.905 
 Sequencing 1.555 0.479–5.048 0.462 <0.1% 0.994 
 PCR-RFLP 1.287 0.907–1.827 0.157 <0.1% 0.699 
 Other methods 1.469 0.771–2.799 0.242 49.0% 0.117 

Presentation with bold indicated a statistical significance.

Subgroup analyses

The subgroup analyses were carried out due to the heterogeneity of result and biases of the different subgroups. The results of subgroup analyses were shown in Tables 3 and 4. First of all, the different countries of population were divided into three parts including China, Korea and other countries in rs3025039 according to the source of the population in studies. For countries subgroup analyses in rs3025039 polymorphism, a significant correlation was found in the allele model and the three genotype models of the Chinese subgroup, in which the Allele comparison model showed moderate heterogeneity (I2 = 69.5%), the Dominant model showed low heterogeneity (I2 < 0.001%), the Recessive model showed high heterogeneity (I2 = 71.6%) and the Homozygote model showed moderate heterogeneity (I2 = 32.3%). However, no significant association was found in the Korea’s subgroup and other countries’ subgroup, and relatively high heterogeneity was observed in the almost all models of the two subgroups except in Dominant and Homozygote model of other countries’ subgroup, which indicated the differences of population countries were not the major cause of the heterogeneity for rs3025039 in this meta-analysis. Then, we performed an ethnic restriction including Xanthoderm, Caucasian and Indo-European hybrid for further subgroup analysis in rs3025039. No significant association was observed in the any model, and the heterogeneity of various models in different subgroups showed no significant reduction. Next, subgroup analysis was performed in rs3025039 according to the genotyping methods including MassARRAY system, Sequencing, PCR-RFLP and Snapshot. A significantly increased risk was found in the Sequencing and PCR-RFLP subgroup, but a protective effect was found in the MassARRAY system subgroup with Allele comparison model and Snapshot subgroup with Recessive model. The heterogeneity of all the subgroup in the four models were relatively low, which indicated that the genotyping methods might be the major source of the heterogeneity.

For rs2010963, we performed the subgroup analysis based on ethnicity and genotyping methods, because the overlapping countries were limited. The subgroups of ethnicity were divided as the same as the subgroups of rs3025039. A significant association was observed in the Caucasian subgroup with both Allele comparison model and Homozygote model (Allele comparison model: OR = 1.246, P=0.046; Homozygote model: OR = 1.461, P=0.039), and other subgroups showed no obvious difference. Moreover, the heterogeneity of allele comparison model and homozygote model in Caucasian subgroup was low (Allele comparison model: I2 = 38.4%, Homozygote model: I2 = 8.9%). However, the difference between the overall heterogeneity and subgroup heterogeneity was not apparent, illustrating that the ethnicity was not the important cause of heterogeneity in the meta-analysis. Regarding the genotyping methods subgroups, we divided into three groups: Sequencing, PCR-RFLP and other methods, owing to the duplicating number of the genotyping methods. The result of the Sequencing subgroup showed a statistically significant in Allele comparison model and Homozygote model with indistinctive heterogeneity (Allele comparison model: OR = 1.620, P=0.032, I2 < 0.1%; Recessive model: OR = 2.328, P=0.005, I2 < 0.1%). Besides, heterogeneity of the Sequencing and PCR-RFLP subgroups in all models was not significant (I2 < 0.1%), while heterogeneity of other methods subgroups was higher. The results suggested that the source of the heterogeneity might be the genotyping methods, consistent with the conclusion above.

Sensitivity analysis and publication bias

To confirm the reliability of our results, a sensitivity analysis was performed for the allele model, showing no apparent difference before and after the removal of each study shown in Figure 4. In addition, publication bias assessed by Egger’s regression test present no obvious evidence in statistics, which was displayed in Table 5.

Sensitive analyses of individual study for VEGF gene polymorphisms

Figure 4
Sensitive analyses of individual study for VEGF gene polymorphisms

(A) rs3025039 polymorphism. (B) rs2010963 polymorphism.

Figure 4
Sensitive analyses of individual study for VEGF gene polymorphisms

(A) rs3025039 polymorphism. (B) rs2010963 polymorphism.

Table 5
Egger’s regression test of the two VEGF polymorphisms
rs3025039rs2010963
T vs CTT vs CC+CTTT+CT vs CCTT vs CCC vs GCC vs GG+GCCC+GC vs GGCC vs GG
Egger’s test P-value 0.319 0.678 0.126 0.847 0.182 0.153 0.518 0.227 
 95% CI [−2.12, 6.02] [−2.05, 1.38] [−0.91, 6.60] [−2.26, 1.88] [−0.88, 4.05] [−0.43, 2.40] [−2.30 4.27] [−0.67 2.50] 
rs3025039rs2010963
T vs CTT vs CC+CTTT+CT vs CCTT vs CCC vs GCC vs GG+GCCC+GC vs GGCC vs GG
Egger’s test P-value 0.319 0.678 0.126 0.847 0.182 0.153 0.518 0.227 
 95% CI [−2.12, 6.02] [−2.05, 1.38] [−0.91, 6.60] [−2.26, 1.88] [−0.88, 4.05] [−0.43, 2.40] [−2.30 4.27] [−0.67 2.50] 

Discussion

Although the etiology of PE is considered to be multifactorial, genetic factors are thought to be strong determinants of this disease [4,6]. Early studies reported that VEGF genes were associated with vascular growth and endothelial dysfunction, which may somewhat interpret the development of PE. In recent decades, many researchers have been focusing on the role that VEGF gene may play in the cause of PE [34]. However, case–control studies have shown contradictory associations between VEGF gene polymorphisms and PE. The aim of this meta-analysis was to evaluate the association between VEGF rs3025039 and rs2010963 polymorphisms and PE for the use of the biomarkers in the clinical practice and the investigation of the concrete pathomachanism.

We conducted a thorough literature retrieve and review to identify as many relevant studies as possible in our meta-analysis. Compared with previous meta-analyses, we made an effort to gain some improvements in our analysis: first, several studies were not included in previous meta-analyses (Lum (2017), Amosco et al. (2016), Salimi et al. (2015), Silva et al. (2014), Zhang Honghui et al. (2014), Procopciuc et al. (2014), Atis et al. (2012), Chen Baoli et al. (2011), Liu Shifang (2010)); second, multiple subgroups were divided to be analyzed; thereby a more adequate statistical power was gained in our study. Similar to the published researches, we found significant associations between the two VEGF gene polymorphisms (rs3025039, rs2010963) and PE, suggesting VEGF gene variants in rs3025039 and rs2010963 loci might be involved in the development of PE. Our results provide evidence of a significantly increased risk about rs3025039 polymorphisms for PE with the four models. Compared with the previous meta-analyses, a significantly increased risk for PE was observed in rs2010963 polymorphisms with less heterogeneity except for the Dominant model. In the stratified analysis by ethnicity and countries for rs3025039, a significantly increased risk of pre-eclampsia was observed in studies conducted among Chinese population. As for subgroup analyses of ethnicity in rs2010963, a statistically association was found in the Allele comparison and the Homozygote models of Caucasian.

In addition, the heterogeneity could be accounted for by the subgroup analysis of genotyping methods. For rs3025039 polymorphism, the subgroup analyses of four genotyping methods including MassARRAY system, Sequencing, PCR-RFLP and Snapshot all showed low levels of heterogeneity (I2 < 40%, P>0.10), where the results of the PCR-RFLP and Sequencing were consistent with the total result (OR > 1, P<0.05), but different from the result of the other genotyping methods. Similarly, the heterogeneity of the three subgroups covering PCR-RFLP and Sequencing and other methods was different for rs2010963 polymorphism. The heterogeneity of the PCR-RFLP and Sequencing subgroup was quite low (I2 < 10%, P>0.1), whereas the heterogeneity of the other methods subgroup was extensive (I2 > 40%). The reason could be that studies in each subgroup are relatively few or different genotyping methods may influence the genotyping result. This observation is similar to previous studies, in which differences in genotyping methods might contribute to heterogeneity [35,36]. The results would be more reliable and accurate if the same appropriate genotyping method was applied in different studies, because different genotyping methods have specialty in different aspects. Genotyping results with new genotyping technologies need to be confirmed using direct sequencing. Furthermore, we have made efforts to seek out the potential sources of heterogeneity via sensitivity analysis assess and publication biases assessment through Egger’s test, demonstrating impact of the individual literature and the publication biases were not obvious. Although the exact pathogenesis of how the SNPs change VEGF and PE susceptibility are not fully understood, a significant correlation between VEGF SNPs (rs3025039 and rs2010963) and PE have been confirmed by our present meta-analysis. At present, several biomarkers have been associated with PE, including soluble endoglin, Flt-1, MAP, PlGF and so on [37–39]. Integration of more reliable biomarkers and figuring out the feasibility in different ethnical groups will increase the accuracy the prediction of the PE, which is quite important for the early prevention of PE. In the present study, our results provide the evidence that the status of the VEGF is close to occurrence of PE and the two SNPs of the VEGF could be applied in prediction of PE, particularly different ethnical groups.

Several limitations of our meta-analysis should be acknowledged. First, unpublished reports or studies published in other non-international journals could not be included in the analysis. These problems may have affected the stability of the meta-analysis data. Secondthe pooled sample sizes for the subgroup analyses among Xanthoderm and Caucasian for both rs2010963, rs3025039 were relatively small (<2000 for cases), which may limit the statistical power. Third, the recruitment criteria of patients and controls varied in different studies. Finally, gene–gene or gene–environment interactions were not considered in the present study—such as age, smoking, alcohol status and mental state—which may have influenced the associations between VEGF gene polymorphisms and PE risk. Nevertheless, this meta-analysis improves our understanding of the associations between two polymorphisms of VEGF gene and the risk of PE.

In conclusion, the two VEGF gene polymorphisms are associated with an increased risk of pre-eclampsia in different ethnic groups, respectively. A large number of and high-quality studies are required to establish more precise evidence and minimize the bias in meta-analysis.

Competing Interests

The authors declare that there are no competing interests associated with the manuscript.

Funding

This work was supported, in part, by the National Natural Science Foundation of China [grant number 81671118].

Author Contribution

Weicheng Duan: project design, result interpretation, data collection, data validation and manuscript writing. Chenlu Xia: data collection, data validation, writing and revise of the manuscript. Weicheng Duan and Chenlu Xia contributed equally to this paper. Kang Wang: result interpretation and data collection. Yijie Duan: data collection and project design. Ping Cheng: data validation. Bo Xiong: project design, result interpretation and manuscript writing.

Abbreviations

     
  • CI

    confidence interval

  •  
  • CNKI

    Chinese National Knowledge Infrastructure

  •  
  • Flt-1

    fms-related tyrosine kinase 1

  •  
  • HWE

    Hardy–Weinberg equilibrium

  •  
  • MAP

    mitogen-activated protein

  •  
  • NOS

    Newcastle–Ottawa Quality Assessment Scale

  •  
  • OR

    odds ratio

  •  
  • PE

    pre-eclampsia

  •  
  • PIGF

    placental growth factor

  •  
  • PRISMA

    Preferred Reporting Items for Systematic Reviews and Meta‐Analysis

  •  
  • SNP

    single nucleotide polymorphism

  •  
  • VEGF

    vascular endothelial growth factor

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Supplementary data