Individual and combined effects of the GSTM1, GSTT1, and GSTP1 polymorphisms on type 2 diabetes mellitus risk: A systematic review and meta-analysis

Backgrounds: Compared with previously published meta-analyses, this is the first study to investigate the combined effects of glutathione-S-transferase polymorphisms (GSTM1, GSTT1 and GSTP1 IIe105Val) and type 2 diabetes mellitus (T2DM) risk; moreover, the credibility of statistically significant associations was assessed; furthermore, many new original studies were published. Objectives: To determine the relationship between GSTM1, GSTT1, and GSTP1 polymorphisms with T2DM risk. Methods: PubMed, Embase, Wanfang, and China National Knowledge Infrastructure Databases were searched. We quantify the relationship using crude odds ratios and their 95% confidence intervals Moreover, the Venice criteria, false-positive report probability (FPRP), and Bayesian false discovery probability (BFDP) were used to validate the significance of the results. Results: Overall, significantly increased T2DM risk was found between individual and combined effects of GSTM1, GSTT1, and GSTP1 polymorphisms on T2DM risk, but, combined effects of the GSTT1 and GSTP1 polymorphisms was not statistically significant. GSTT1 gene polymorphism significantly increases the risk of T2DM complications, while GSTM1 and GSTP1 polymorphisms had no statistical significance. The GSTM1 null genotype was linked to a particularly increased risk of T2DM in Caucasians; the GSTT1 null genotype was connected to a significantly higher risk of T2DM in Asians and Indians; and the GSTP1 IIe105Val polymorphism was related to a substantially increased T2DM risk in Indians. Moreover, the GSTM1 and GSTT1 double null genotype was associated with substantially increased T2DM risk in Caucasians and Indians; the combined effects of GSTM1 and GSTP1 polymorphisms was associated with higher T2DM risk in Caucasians. However, all significant results were false when the Venice criteria, FPRP, and BFDP test were used (any FPRP >0.2 and BFDP value >0.8). Conclusion: The current analysis strongly suggests that the individual and combined effects of GSTM1, GSTT1 and GSTP1 polymorphisms might not be connected with elevated T2DM risk.

Results: Overall, significantly increased T2DM risk was found between individual and combined effects of GSTM1, GSTT1, and GSTP1 polymorphisms on T2DM risk, but, combined effects of the GSTT1 and GSTP1 polymorphisms was not statistically significant. GSTT1 gene polymorphism significantly increases the risk of T2DM complications, while GSTM1 and GSTP1 polymorphisms had no statistical significance. The GSTM1 null genotype was linked to a particularly increased risk of T2DM in Caucasians; the GSTT1 null genotype was connected to a significantly higher risk of T2DM in Asians and Indians; and the GSTP1 IIe105Val polymorphism was related to a substantially increased T2DM risk in Indians. Moreover, the GSTM1 and GSTT1 double null genotype was associated with substantially increased T2DM risk in Caucasians and Indians; the combined effects of GSTM1 and GSTP1 polymorphisms was associated with higher T2DM risk in Caucasians. However, all significant results were false when the Venice criteria, FPRP, and BFDP test were used (any FPRP >0.2 and BFDP value >0.8).

Introduction
Insulin resistance, excessive or improper glucagon secretion, and insufficient insulin secretion are symptoms of type 2 diabetes mellitus (T2DM), a chronic endocrine metabolic disorder. According to the newly released International Diabetes Federation Diabetes Atlas (10th edition), there are an estimated 537 million people living with diabetes in the world now, with that number expected to rise to 784 million by 2,045 based on current trends. In recent years, the incidence of diabetic complications, such as diabetic nephropathy and diabetic cardiovascular disease, has gradually increased. With the increase of people's lifestyle such as poor diet, sedentary and inactive, the prevalence of T2DM will continue to rise, which will undoubtedly bring a huge burden on human health and medical expenditure (https://diabetesatlas.org/en/.). T2DM pathogenesis is complicated and diverse, involving both environmental and genetic risk factors. Currently, antioxidant and detoxification gene polymorphisms are believed to play a significant role in the risk of T2DM and related complications (Wu et al., 2014;Pahwa et al., 2017;Dendup et al., 2018).
Oxidative stress is the result of imbalance of reactive oxygen species generation and clearance, which is closely related to insulin resistance and functional impairment of islet β-cells. GST is a superfamily protease encoded by multiple genes with antioxidant and detoxification functions (Tangvarasittichai, 2015;Vats et al., 2015). It catalyzes the formation of thioether bonds between glutathione and electrophiles, thereby catalyzing the reduction of hydrogen peroxide, reducing fatty acids, phospholipids and DNA bases to corresponding alcohols, and scavenging lipid free radicals and hydrogen peroxide, thus reducing body damage (Hayes et al., 2005;Tabatabaei-Malazy et al., 2017). GSTM1 and GSTT1 gene locus has been mapped on chromosome 1p13.3 and 22q11.2, respectively, resulting in the reduced activity of a functional gene product (Hayes and Strange, 2000;Gonul et al., 2012). GSTP1 gene polymorphism is a single nucleotide polymorphism, a codon 105 A-G mutation at exon 5 in GSTP1 polymorphism leads to change in isoleucine (IIe) to valine (Val), is linked to a change in decreased enzymatic activity (Zimniak et al., 1994;Mastana et al., 2013). Thus, these three gene mutations may increase the risk of T2DM based on biological effects.
Independent and combined effects of GSTM1, GSTT1 and GSTP1 IIe105Val polymorphisms with the risk of T2DM have been described in a number of original investigations, and the association of GSTs gene polymorphisms with T2DM complications has also been reported. Of note, these results were contradictory. Five published meta-analyses (Tang et al., 2013;Yi et al., 2013;Zhang et al., 2013;Saadat, 2017;Nath et al., 2019) reported the GSTM1, GSTT1 and GSTP1 IIe105Val polymorphisms with T2DM risk. Three published metaanalyses have investigated the association between GSTs gene polymorphisms and T2DM complications (Orlewski and Orlewska, 2015;Sun et al., 2015;Nath et al., 2019). Yet, their studies had certain limitations. This could be due to the limited sample size or the lack of quality assessments of the literature. More importantly, past meta-analyses did not assess the reliability of positive results. As a result, we conducted a systematic review of the literature and a latest meta-analysis to assess whether GSTM1, GSTT1, and GSTP1 gene contribute to the risk of T2DM and related complications. We are also investigated the effect of gene-gene interactions on T2DM risk.

Search strategy
The present study followed the Preferred Reporting Items for Systematic Review and Meta-Analysis (PRISMA) group's guidelines. The PubMed, Embase, Wan-fang, and China National Knowledge Infrastructure (CNKI) databases were used to conduct the literature search (deadline to 16 May 2022). The following was utilized as a search strategy (polymorphism OR variant OR variation OR mutation OR SNP OR genome-wide association study OR genetic association study OR genotype OR allele) and (diabetes OR mellitus OR diabetes mellitus) AND (glutathione S-transferase M1 OR GSTM1 OR glutathione S-transferase T1 OR GSTT1 OR glutathione S-transferase P1 OR GSTP1). References of the retrieved articles and review articles on this topic were also carefully examined in additional suitable studies. If necessary, the respective authors were contacted by e-mail. Moreover, there were no restrictions or limitations on language.

Inclusion and exclusion criteria
The inclusion criteria were: 1) case-control or cohort design; 2) studies must examine the correlation between the individual and combined effects of GST polymorphisms with T2DM or T2DM complication risk; 3) studies with odds ratio (OR) and their 95% confidence interval (CI), or enough data to calculate these numbers. Exclusion criteria were: 1) case reports, reviews, commentaries and meta-analyses; 2) studies that did not give genotype frequencies or could not determine the number of genotypes and alleles; 3) overlapping data or incomplete data.

Data extraction
Two investigators extracted and double-checked data. When their conclusions differed, through discussion were reached a consensus. The first author's name, year, country, source of control, match, race and other information were recorded, among which race was divided into "Asian," "African," "European," "Caucasian," "Indian" and "Mixed."

Quality score assessment
The quality assessment scale was independently assessed by two investigators (Supplementary Table S2). The total score was 18 points, studies scoring >12 were high, while scores of ≤12 were regarded as low quality in this meta-analysis. The scale was influenced by selection (two points), control and case source (four points), diagnostic criteria for T2DM (two points), Ascertainment of control (two points), matching (two points), Genotyping examination (two points), Association assessment (two points), and Hardy-Weinberg balance (HWE).

Statistical analysis
Stata 12.0 software (Stata Corporation, College Station, TX) was used to calculate all statistical analyses. The strength of the association of GSTM1, GSTT1, and GSTP1 polymorphisms with T2DM and T2DM complications risk were estimated by calculating the crude OR and their 95% CI. We used the Chisquare-based Q-test and I2 value to evaluate heterogeneity (Higgins et al., 2003). The random-effects model was used when p was less than 0.10 and/or I2 was larger than 50%. if not, the fixed-effects model was applied (DerSimonian and Laird, 2015). Furthermore, a meta-regression analysis was used to investigate sources of heterogeneity (Baker et al., 2009). Subgroups were carried out based on ethnicity, control source, control type, and matching. Sensitivity analysis was performed using the following methods: 1) excluded one study at a time; 2) removed low-quality or Hardy-Weinberg Disequilibrium (HWD) studies; 3) retained only high quality and HWE research. HWE was evaluated by Chi-square goodness-of-fit test, p < 0.05 was defined as HWE, otherwise as HWD. To determine publication bias, Begg's funnel plot (Begg and Mazumdar, 1994) and Egger's test (Egger et al., 1997) were conducted. If publication bias existed, the missing studies would be filled employing a nonparametric "trim and fill" method (Dual and Tweedie, 2000).

FIGURE 1
Flow diagram for searching studies in the current meta-analysis.
Combinations of GSTT1 and GSTP1 polymorphism were not associated with T2DM risk in overall analysis. However, when subgroup analysis for ethnicity was stratified, important association were found in Indians (model 6: OR = 1.75, 95% CI = 1.10-2.78, Table 6).

Heterogeneity and sensitivity analyses
We assessed heterogeneity source by applying a metaregression analysis. The source of heterogeneity for the GSTT1 null genotype and the combined effects of GSTT1 and GSTP1 was revealed to be the source of controls (p = 0.028 and model 1: p = 0.037). HWE (model 7: p = 0.045) was the source of heterogeneity between the combined effects of GSTM1, GSTT1, and GSTP1 gene and T2DM risk.

Publication bias
No publication bias was observed between GSTs polymorphisms with T2DM risk through the Begg's funnel plot and Egger's test (Figures not shown).

Discussion
Oxidative stress is considered to play an important role in the pathogenesis of diabetes mellitus and related complications. GST is a member of phase II metabolic isoenzyme group, which has the effect of regulating various cytotoxicity, genotoxicity and antioxidant. The risk genotypes expressed by each gene (GSTM1 null, GSTT1 null, and GSTP1 Val/Val) may decrease the antioxidant activity of the enzyme, thus increasing the susceptibility of T2DM and other diseases. There was much important evidence showing that GSTM1, GSTT1, and GSTP1 gene polymorphisms were potential genetic factors for T2DM. Yet, previous research remains contradictory. In addition, seven previously published

FIGURE 3
Forest plot of the association between GSTM1 polymorphism and T2DM complications risk.
Frontiers in Genetics frontiersin.org meta-analyses also had certain limitations, therefore the association of the GSTs polymorphisms with T2DM risk needs to be further evaluated. Furthermore, to our knowledge, this research is the first large-scale metaanalysis to investigate the relationship between individual and combined GSTM1, GSTT1, and GSTP1 genes Frontiers in Genetics frontiersin.org 15 polymorphism of T2DM susceptibility, and utilizing FPRP and BFDP to explore the validity of positive results.
Overall, on the individual and combined effects of GSTM1, GSTT1, and GSTP1 polymorphisms, a statistically significantly increased T2DM risk was found. We found that GSTT1 null genotype increased the risk of T2DM complications.
However, GSTM1 and GSTP1 polymorphism were not associated with T2DM complications risk. The GSTM1 null genotype was correlated to a significantly increased risk of T2DM in Caucasians, Asia, hospital-based studies, and non-diabetic controls. The GSTT1 null genotype was connected with an increased risk of T2DM in Asians, Indians, Asia, and so on. GSTP1 IIe105Val polymorphism was correlated with an increased risk of T2DM in Indians, Asia, population-based studies, and healthy controls. The GSTM1-GSTT1 double null genotype significantly increased the risk of T2DM in both subgroup and sensitivity analysis. The effects of GSTM1-GSTP1 gene polymorphisms were related to the risk of T2DM in Caucasians and matching. However, no relationship was identified between GSTT1-GSTP1 polymorphisms and T2DM susceptibility, and, interestingly, Indians had a significantly increased risk. For the triple genotype combinations, we also discovered an association with T2DM. These findings showed that the same gene has variable susceptibility to T2DM in different race groups. It is worth considering that the obvious inconsistencies could be due to sample size, GSTs prevalence and environmental factors (lifestyle). Additionally, because T2DM is a chronic metabolic disease with multiple factors and genes, and different genetic backgrounds may lead to such differences. We found that GSTs gene polymorphism increased the risk of T2DM in Asia, but not in Europe. Most of the original research were conducted in Asia, which may lead to this difference. Therefore, a larger sample size is needed to study the relationship between GSTs gene polymorphism and T2DM in Europe, America, Africa and other geographic regions. The GSTM1-GSTT1, GSTM1-GSTP1 and GSTM1-GSTT1-GSTP1 gene polymorphisms combination are all associated with the increased susceptibility of T2DM. These results indicate that polygenes may have cumulative effects, but the potential gene-gene interactions still need to be studied with a larger sample size.
However, the present study used several subgroups and diverse genetic models, resulting in multiple comparisons, which necessitated adjusting the pooled p value (Attia et al., 2003). FPRP is regarded an acceptable tool for calculating the likelihood of positive findings in multiple hypothesis testing of molecular epidemiology investigations (Wacholder et al., 2004). Wakefield provided a much more reliable Bayesian metric of false positives in genetic epidemiology research (Wakefield, 2008). Potential errors and bias may be caused by genotyping errors or high inter-study heterogeneity (I 2 > 50%) (Page et al., 2003;Clayton et al., 2005;Pompanon et al., 2005;Balding, 2006;Zhang et al., 2013), while large volumes evidence (statistical power >80%) have stricter statistical significance levels or lower false-discovery rate (Benjamini and Hochberg, 1995). Therefore, we consider using FPRP tests, BFDP tests and the Venice criteria to evaluate the false findings in present studies. The current meta-analysis found that all substantial relationship were deemed as less credible during did the reliability analysis. According to the results of the meta-regression analysis, the HWD studies and sources of controls were the main causes of heterogeneity. Bias and errors are prevalent in some low-quality, small-sample HWD studies, making the conclusions of these original studies undependable, molecular biology and disease susceptibility study, in particular. Furthermore, research with a small sample size and substantial results may be likely to swallow than studies with negative outcomes. When they produce positive results, however, their research might not have been rigorous and typically of poor quality.
Therefore, we assessed the sensitivity analysis using matching, high-quality, and HWE.
These three GSTs gene polymorphisms and their interactions were discovered to be strongly related to diabetes susceptibility in this investigation. From 2012 to 2019, a total of five related metastudies were published. Recent meta-studies by Nath et al. (2019) showed that the GSTM1, GSTT1 null genotype and GSTM1-GSTT1 double null genotype increased the risk of T2DM in both Asian and Caucasian populations, the results are consistent with this study. An analysis of eleven studies by Yi et al. (2013) revealed that the GSTM1 gene polymorphisms increases the susceptibility to T2DM in Asians, Caucasians, and Africans. However, Saadat (2017) selected 18 studies (2,595 patients and 2,888 controls) and suggested that GSTP1 IIe105Val polymorphism was not connected with the risk of T2DM, which was contrary to the results of this study. Tang et al. (2013) also analyzed the three genes and suggested that GSTM1 and GSTT1 null genotypes were correlated to an increased risk of T2DM in Asians, while GSTP1 gene polymorphism was not. For the GSTP1 gene, the opposite result was obtained. Three related meta-analysis were published from 2015 to 2019. Nath et al. showed that GSTT1 null genotype increased the risk of T2DM related complications, but GSTM1 null genotype did not, which was consistent with this study. On the contrary, Orlewski and Orlewska (2015) found that GSTM1 gene polymorphisms increased the risk of diabetic nephropathy, while GSTT1 and GSTP1 gene polymorphisms were not statistically significant. Sun et al. (2015) suggested that a strong association GSTM1 and GSTP1 polymorphisms and diabetic retinopathy susceptibility. Further investigate the effect of gene-gene interaction on T2DM, two previous studies reported the correlation between GSTM1-GSTT1 gene polymorphism and T2DM susceptibility, but did not study the combined effect between the remaining genes. In Frontiers in Genetics frontiersin.org this study, all except GSTT1-GSTP1 combination were found to be related. Moreover, these published meta-analyses are obviously inconsistent with current meta-analyses in terms of racial classification. The variability in results may be due to a variety of factors, such as small sample sizes, ethnic differences, failure to assess quality in most eligible studies and failure to develop a more complete genetic model. There are several advantages to the newer meta-analysis. First, The FPRP, BFDP test, and Venice criterion were used to assess the credibility with positive outcomes. Secondly, quality assessment of qualified research is carried out. Third, the sample size was much larger and more detailed data and information were collected. Fourth, quality assessment of qualified research. Fifthly, a relatively complete genetic model is established. Sixth, more subgroup analyses were performed. Seventh, to our knowledge, this is the first metaanalysis to explore the combined effects of GSTM1-GSTP1, GSTT1-GSTP1, and GSTM1-GSTT1-GSTP1 gene polymorphisms on the risk of T2DM. There are some limitations of the present meta-analysis. To begin with, latest meta-analyses only include published articles, and positive results are frequently more likely to be published than negative consequences. The zero effect of GSTM1 may be overestimated if negative results are included. Second, because we could not calculate the HWE of these two genes, we did not evaluate whether the genotype distributions of GSTM1 and GSTT1 polymorphisms in the control group was in HWE. Third, heterogeneity between studies was much large in overall and among several subgroups. According to the results of the meta-regression analysis, the HWD studies and sources of controls were the main causes of heterogeneity, and adequate statistical correction and analysis did not significantly improve the heterogeneity. The source of heterogeneity may also be due to some other factors, such as study area, lifestyle and so on. Fourth, we conducted a subgroup analysis of race and region, and population heterogeneity has been resolved. However, T2DM is closely associated with the environment, the original study did not provide data on lifestyle, so we failed to solve the heterogeneity caused by lifestyle, and did not explore the effect of the geneenvironment combination. Fifth, we failed to further investigate the effect of GSTs gene polymorphisms on the risk of T2DM related complications.
In conclusion, the current study revealed the relationship between GSTM1, GSTT1, and GSTP1 gene polymorphisms with the risk of T2DM, which may be attributed to false positive results rather than actual correlations or biological variables. Larger epidemiological studies should be carried out in the future to confirm or deny our findings.

Data availability statement
The original contributions presented in the study are included in the article/Supplementary Material, further inquiries can be directed to the corresponding authors.
Author contributions L-sL: designed research, performed research, collected data, analyzed data, wrote paper. DW and RT: collected data. QW, LZ and JW: check the data. YL and X-fH: designed research and revised article.