Abstract
Objective: Oxidative stress is thought to play an important role in the pathophysiology of pre-eclampsia. The glutathione S-transferases (GST) are a group of enzymes that protect cells from oxidative stress. Published data on the association between the GSTT1 and GSTM1 polymorphisms and pre-eclampsia risk are controversial. A meta-analysis was performed to assess whether the polymorphisms of GSTT1 and GSTM1 are associated with pre-eclampsia risk.
Methods: Medline, Embase, China National Knowledge Infrastructure, and Chinese Biomedicine Databases were searched to identify eligible studies. Pooled odds ratios (ORs) and 95% confidence intervals (CIs) for GSTT1 and GSTM1 polymorphisms and pre-eclampsia were appropriately derived from fixed-effects or random effects models.
Results: A total of 11 studies were enrolled in this meta-analysis. The pooled analyses revealed that polymorphisms of GSTT1 and GSTM1 was not associated with pre-eclampsia risk. Heterogeneity among studies was founded in GSTT1 polymorphism. Galbraith plot analyses were performed to assess the source of heterogeneity and one study was found to be contributor of heterogeneity. The heterogeneity decreased significantly after excluding that study.
Conclusion: Present meta-analysis reveals that GSTT1 and GSTM1 polymorphisms may be not correlated to pre-eclampsia risk.
1. Introduction
Preeclampsia (PE) is a leading cause of maternal and perinatal mortality worldwide [Citation1]. PE is characterized by de novo hypertension (two blood pressure measurements ≥140/90 mmHg) and proteinuria (>300 mg/24 h) that develops after 20 weeks of gestation in a formerly normotensive woman. The pathophysiology is characterized by an abnormal vascular response to placentation that is associated with increased systemic vascular resistance, enhanced platelet aggregation, activation of the coagulation system, and endothelial cell dysfunction. Various environmental factors, such as smoking status, hypertension, diabetes mellitus, advanced age and obesity have been reported to be associated with PE [Citation2, Citation3]. However, numerous PE patients are diagnosed in the absence of these risk factors, indicating that environmental factors cannot be solely responsible for the condition. Genetic factors are believed to play a crucial role in the onset and progression of PE [Citation4–6].
Oxidative stress a well-established contributor to the pathophysiological mechanism of PE [Citation7]. In this regard, superoxides and free radicals generated during pregnancy could attack lipids, proteins, and nucleic acids, resulting in damage to placental cells, tissues, and organs. In addition, maternal oxidative stress could initiate maternal vascular endothelial dysfunction and induce leukocyte activation. One of the most important systems involved in the metabolism and detoxification of reactive oxygen, xenobiotics, and carcinogens, is that of glutathione S-transferases (GSTs). GSTs conjugate reduced glutathione with reactive oxides substrates, defensing cells against reactive oxygen species. There are four classes of GSTs:alpha (GSTA), mu (GSTM), pi (GSTP)and theta(GSTT) [Citation8]. All classes have been reported to be genetically polymorphic. Deletion polymorphisms have been identified for GSTM1 and GSTT1, and having a null genotype for both genes results in virtual absence of enzyme activity [Citation9, Citation10].
Over the last 20 years, several studies have investigated the potential association between two polymorphisms (GSTT1 and GSTM1) and susceptibility to preeclampsia across different races. However, the results have been inconclusive and inconsistent [Citation11–15]. Anvar et al. performed a meta-analysis based on 5 studies and found no significant associations between both GSTT1 and GSTM1 polymorphisms and preeclampsia in 2011. Since the publication of the initial meta-analysis, a few new studies have been published, and we updated the systematic review and meta-analysis.
2. Materials and methods
2.1. Publication search
Embase, PubMed, CNKI (China National Knowledge Infrastructure) and Chinese Biomedicine databases were searched for all articles on the relationship between two polymorphisms (GSTT1 and GSTM1) and preeclampsia risk(last search update 10th July 2021).The following keywords were used: ‘‘GSTT1’’or “GSTM1” and ‘‘variant’’ or ‘‘polymorphism’’ and ‘‘preeclampsia’’. This meta-analysis adhered to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines.
2.2. Inclusion and exclusion criteria
To be considered for inclusion, all studies, regardless of sample size, must conform to the following criteria: (i) the study must assess the relationship between one or two polymorphisms (GSTT1 and GSTM1) and the risk of preeclampsia, (ii) case-control studies must be conducted, and (iii) adequate data must be available to calculate an odds ratio (OR) with a 95% confidence interval (95%CI). Studies were excluded if they met any of the following criteria: (i) they were conference abstracts, reported meetings, reviews or Meta-analyses, or (ii) they did not provide adequate data, (iii) they were laboratory studies.
2.3. Data extraction
Two reviewers(L. Li and A. Wang) independently extracted information from all eligible publications based on the inclusion criteria specified above. Disagreements were resolved by in consultation with an arbitrator(K. Yi).
The following information was collected from all eligible publications: the surname of the first author, the year of publication, country of participants, sample size of cases and controls, the source of control groups (whether they were population-based or hospital-based), the ethnicity of the study population, the single nucleotide polymorphisms(SNPs) studied, the genotyping methods, and the minor allele frequency(MAF). A hospital-based case-control study (HCC) was conducted using data obtained from hospitalized patients, while a population-based case-control study (PCC) was conducted using data obtained from healthy individuals. Ethnicity was classified as Asian, Caucasian or Mixed.
2.4. Quality assessment
The Newcastle–Ottawa Scale (NOS) tool for case–control studies, was used to assess the quality of all eligible studies. This standard assessed three sections (selection, comparability, exposure) and eight items. In the selection and exposure categories, a quality research item received 1 star, and a comparable category could receive at most 2 stars. The quality assessment values ranged from 0 to 9 stars. Generally, the study which scored at least 6 points was considered to be high-quality study in meta-analysis.
2.5. Statistical analysis
To assess the degree of correlation between the two polymorphisms (GSTT1 and GSTM1) and the risk of preeclampsia, we employed the odds ratio (OR) and its corresponding 95% confidence interval. Take the GSTT1 polymorphism as example, the OR for Present vs. Null was calculated. We also conducted subgroup analysis based on ethnicity. We employed the Cochran Q statistic and the I2 to verify and confirm the heterogeneity analysis. A P value >0.10 for the Q statistic ndicated a lack of heterogeneity across studies [Citation16]. To calculate the odds ratios (ORs), we utilized the fixed-effects model (the Mantel-Haenszel method) [Citation17]; and to pool the OR, we utilized the random-effects model (the DerSimonian and Laird method) [Citation18].
To investigate the publication bias, we utilized the Egger’s weighted regression method and the Begg’s rank correlation method. We visually inspected the funnel plot and considered a P-value < 0.05 as statistically significant [Citation19, Citation20]. We conducted the statistical analyses using STATA software, version 13.0 (STATA Corp., College Station, TX, United States) to process the statistical analyses.
3. Result
3.1. Characteristics of studies
After conducting a thorough literature search, we narrowed our focus to 14 articles that warranted a more detailed examination. Upon reviewing the titles and abstracts of these articles, we excluded 2 of them. We obtained the full texts of 12 articles and carefully reviewed them. Ultimately, we excluded 1 article as it was a literature review [Citation21]. In accordance with the MOOSE (Meta-analysis Of Observational Studies in Epidemiology) guidelines [Citation22], we ultimately collected 11 case-control studies [Citation11–15, Citation23–28] examining GSTT1 and 10 studies examining GSTM1 polymorphisms. The process of literature search and study selection is illustrated in .
Figure 1. Literature search and study selection procedures used for a meta-analysis of GSTT1 and GSTM1polymorphisms and risk of preeclampsia.
displays the characteristics of the selected studies. Out of the studies included, 6 involved subjects of Caucasian descent, 3 involved subjects of Asian descent, and 2 involved subjects of mixed descent. These studies were conducted in diverse locations, spanning the Netherlands, Iran, Mexico, Turkey, China, Korea, the USA, Serbia and Bangladesh. The control groups primarily consisted of healthy populations that were matched for age and/or geographical area. Of the included studies, 10 were population-based and 1 was hospital-based.
Table 1. Characteristics of studies included in this meta-analysis.
3.2. Quantitative synthesis
Connection between the GSTT1 polymorphism and preeclampsia susceptibility.
A total of 11 case-control studies, comprising 1226 cases and 1623 controls, were included to assess GSTT1 polymorphism [Citation11–15, Citation23–28]. Pooled odds ratios (ORs) and 95% confidence intervals (CIs) for GSTT1 polymorphism and pre-eclampsia were derived from fixed effects models. The results of the meta-analysis are presented in , while displays the forest plot demonstrating the associations between GSTT1 polymorphism and preeclampsia risk.
Figure 2. Forest plots of ORs with 95% CIs for GSTT1 (“) and GSTM1 (#) polymorphisms and risk of preeclampsia.
Table 2. Quantitative analyses of the GSTM1 and GSTT1 polymorphisms on the pre-eclampsia risk.
In summary, our findings indicated a lack of significant correlation between GSTT1 polymorphism and preeclampsia, with an OR of 1.10 and a 95%!C(MISSING)I of 0.94-1.30. Further stratification of the results based on ethnicity revealed no significant association, as presented in .
The connection between the GSTM1 polymorphism and preeclampsia susceptibility.
After thorough selection, a total of 10 case-control studies, consisting of 1166 cases and 15636 controls, were included in the analysis of GSTM1 polymorphism [Citation11–15, Citation23–25, Citation27, Citation28]. Pooled odds ratios (ORs) and 95% confidence intervals (CIs) for GSTM1 polymorphism and pre-eclampsia were derived from random effects models. and display the assessment of the connection between GSTM1 polymorphism and preeclampsia. Our results indicated no significant correlation between GSTM1l polymorphism and preeclampsia risk. The NOS scores of eligible studies are summarized in . The quality score of the included literature was between 6-8 points.
Table 3. Quality assessment of case–control studies included in this meta-analysisTable Footnotea.
3.3. Heterogeneity analysis
For GSTM1 polymorphism, a substantial heterogeneity was detected among studies(Pheterogeneity = 0.021). We used random effects models to evaluate OR and 95% CI. Galbraith plot analyses were conducted to investigate potential sources of heterogeneity across the studies. It was observed that one study [Citation15] contributed to the heterogeneity for GSTM1 polymorphism, as shown in . Upon removal of the outlier study, a significant decrease in heterogeneity was noted After removing the outlier study, we used fix effects models to evaluate OR and 95% CI. The heterogeneity was significantly decreased (Pheterogeneity = 0.696) and the conclusion maintained unchanged after removal of the outlier study (OR = 1.17, 95% CI = 0.99–1.39).
Figure 3. Galbraith plots for heterogeneity test of GSTM1 polymorphism and risk of preeclampsia.
4. Sensitivity analysis
Sensitivity analyses were performed to evaluate the influence of each study on the overall pooled OR. The study of Akther et al. was considered to be the most influenced study on the pooled OR for the association of the GSTT1 and GSTM1 with preeclampsia risk (); however, the results of sensitivity analysis remained nonsignificant after the removal of that study.
Figure 4. Sensitivity analysis of association between GSTT1 polymorphism and preeclampsia risk.
4.1. Publication bias
Begg’s and Egger’s tests were conducted to evaluate the potential presence of publication bias in the literature. The Begg’s funnel plots suggested no evidence of asymmetry (). Besides, the statistical results indicated no publication bias [GSTT1: Begg’s test p = 0.76, Egger’s test p = 0.75; GSTM1: Begg’s test p = 0.47, Egger’s test p = 0.82].
Figure 5. Begg’s funnel plot for publication bias test.
5. Discussion
On the basis of 11 case–control studies, this meta-analysis reveals no significant associations between GSTT1 and GSTM1 polymorphisms and preeclampsia. Subgroup analysis was conducted based on ethnicity, and no significant associations were found among studies of all descents.
The findings regarding GSTT1 and GSTM1 polymorphisms are consistent with an earlier study by Anvar et al. who conducted a meta-analysis of 5 studies and similarly found no significant associations between both polymorphisms and preeclampsia [Citation25]. Elevated blood pressure during pregnancy is a common symptom of preeclampsia. Another meta-analysis, which included 14 studies examining the relationship between GSTT1 and GSTM1 null polymorphisms and the risk of hypertension, also found no significant associations [Citation29]. PE is characterized by high blood pressure in women after 20 gestational weeks, but GSTT1 and GSTM1 gene polymorphisms have been showed to be not associated with blood pressure in a general population.
Pre-eclampsia is proposed to be a disorder secondary to decreased placental perfusion interacting with maternal constitutional factors to result in oxidative stress, endothelial damage, and a multisystemic maternal disease. There is a high prevalence of null alleles of GSTM1 and GSTT1 genes in humans, but interestingly the presence of homozygous nonfunctional alleles appear to have no effect on corresponding activity, and thus, may not be related to GSTM1 and GSTT1 related placental oxidative stress or endothelial damage in preeclampsia pathogenesis. However, included studies did not measure the GST enzyme activity (total, and different GST isoenzymes); hence, there is a lack of direct biochemical/functional evidence of GSTT1 and GSTM1 polymorphisms with altered catalytic activities.
In conducting this meta-analysis, a critical issue to address is the degree of heterogeneity among the included studies, as non-homogeneous research can produce misleading results. Therefore, we utilized I2 statistics and Q-test to assess the significance of heterogeneity and confirmed that there was an apparent heterogeneity in GSTM1 polymorphism across the studies. We drew a Galbraith plot in order to identify the origins of heterogeneity, and confirmed that there was one study chiefly contributing to the heterogeneity. After excluding this outlier study, we observed a significant decrease in heterogeneity, and the conclusion remained unchanged.
Another major concern in the meta-analysis is publication bias caused by the potential selective publication of reports. To address this concern, we employed Egger’s and Begg’s tests to assess publication bias. Our analysis of the statistical results and funnel plots suggested that there was no publication bias present in this study.
There were some defects in this study: (i)due to the limited quantity of samples in this researches as well as the limited quantity of research covered by meta-analysis, the results would be insufficient to examine the actual connections statistically;(ii) this study was on the basis of unadjusted OR estimates because not all included trials provided with adjusted ORs. Furthermore, even if adjusted ORs were available, they may have been adjusted for different factors, such as race, age, or smoking status.9iii)there was significant heterogeneity among studies investigating the GSTM1 polymorphism, and one study was a hospital-based case-control study. (iv) due to a lack of unified data on the two-SNP, it was not possible to conduct a thorough pooled analysis investigating potential associations between these two single-SNPs.
In conclusion, our meta-analysis that there may be no association between the GSTT1 and GSTM1 polymorphisms and the risk of preeclampsia. However, due to the limited number of subjects and the inclusion of a limited number of ethnic populations in this study, it is essential to conduct well-designed and larger multicenter case-control studies to validate these findings and further expand on our present knowledge.
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