Objective: To explore the association between the variant M235T locus of angiotensinogen (AGT) gene, 584C/T locus of Endothelial lipase (EL) gene, and coronary artery disease (CAD) by meta-analysis.Methods: The case–control studies on the association between AGT/EL gene polymorphism and CAD were collected through searching PubMed, Excerpta Medica Database (EMBASE), Web of Science, China National Knowledge Infrastructure (CNKI), and Wanfang databases up to 1 March 2020. Stata 15.0 software was used for analysis.Results: A total of 29 articles met the inclusion criteria. After analyzing, it was found that the M235T polymorphism of AGT gene was associated with the occurrence of CAD. In the allele model (T vs. M), OR = 1.38 (P<v0.05). In other heredity, there was also statistical significance. Subgroup analysis indicated that except the heterozygous genetic model of the Chinese population, other genetic models of the Caucasian and Chinese population were also statistically significant. The 584C/T polymorphism of EL gene was associated with the occurrence of CAD, with OR = 0.83 (P<0.05) in the allele model (T vs. C) and OR = 0.80 (P<0.05) in the dominant gene model. Also, in the allele model of Caucasian subgroup, OR = 0.83 (P<0.05), while in Asian subgroup, there was no statistically significant genetic model.Conclusion: AGT M235T and EL 584C/T polymorphisms are associated with CAD susceptibility. The genotype TT, TC or allele T of AGT M235T and genotype CC or allele C of EL 584C/T might be the genetic risk factors for the development of CAD. Coronary artery disease (CAD), the incidence of which is increasing year by year in both developed and developing countries, is the leading cause of deaths and seriously endangers human health ,2. With Renin is mainly secreted by renal juxtaglomerular cells, and it can catalyze the conversion of plasma pro-angiotensin (AGT) into Angiotensin I. Also, Angiotensin I can be further converted into angiotensin II, III, IV, to play the role of vasoconstriction. It has been found that AGT M235T polymorphism is closely related to the occurrence of cardiovascular disease. M235T refers to the substitution of nucleotide T at location 704 of the second exon by C, resulting in the conversion of amino acid at position 235 from methionine (M) into threonine (T). Some studies have confirmed that Angiotensinogen (AGT) M235T polymorphism is closely related to the severity of CAD . Van et Related studies have shown that low plasma high-density lipoprotein cholesterol (HDL-C) concentration is closely related to the incidence of coronary heart disease (CAD). In contrast, high HDL-C levels can reduce the risk of CAD . The conFurthermore, to compare different research results more scientifically and objectively, meta-analysis is used to conduct a comprehensive study on this issue. On this basis, a meta-analysis of the genotypic data from all eligible surveys in recent years was administered to more accurately evaluate the relationship between AGT (M235T), Endothelial (584C/T) polymorphism and susceptibility to CAD, thus providing evidence-based medicine for cardiovascular clinic.A combination of following terms: Angiotensinogen, Endothelial lipase, myocardial infarction, coronary artery disease, acute myocardial infarction, cardiovascular disease, ischemic heart disease, coronary heart disease, cute coronary syndrome and polymorphism. We systematically searched the relevant literatures of relationship between Angiotensinogen, Endothelial lipase Polymorphism and Coronary artery Disease on PubMed, Excerpta Medica Database (EMBASE), Web of Science, China National Knowledge Infrastructure (CNKI) and China Wanfang databases up to 1 March 2020. The retrieval strategy was as follows:  AND  AND (polymorphism OR Single nucleotide mutation). There were no language restrictions.(1) It was case–control study; (2) CAD was defined as coronary heart disease, coronary artery disease, myocardial infarction, acute Myocardial infarction, acute coronary syndrome, cardiovascular disease; (3) the number of genotypes should be provided in both the case group and the control group.(1) No specific amount of genotypes was provided, or the data of each genotype cannot be obtained by calculation, (2) repeated studies of the same ethnic group, (3) Newcastle–Ottawa Scale (NOS) score was less than 6.According to the NOS , the ful2I ≥ 50% or P≤0.05, heterogeneity was considered to exist, the random-effects model (REM) was selected; when 2I < 50% and P>0.05, heterogeneity was deemed to be low, and the fixed-effects model (FEM) was used for data integration. Z test was used to test the significance of the combined OR value. The funnel plot was performed to evaluate publication bias, and the criterion was whether the funnel plot was symmetric or not. If the funnel chart was asymmetric, there might be publication bias. Egger’s test was also used for checking publication bias. Subgroup analysis was carried out to assess whether Hardy–Weinberg equilibrium (HWE) was satisfied in the ethnicity and control groups. Finally, the sensitivity of the results was analyzed to determine the robustness of the research results.The odds ratio (OR) and its 95% confidence interval were used to evaluate the association between AGT/EL gene polymorphism and CAD. The data were merged and analyzed by statistical software Stata 15.0. Q test was applied to verify the heterogeneity of the included studies. When According to the inclusion and exclusion criteria, 29 articles were collected in this meta-analysis, including 16 studies on the relationship between AGT M235T polymorphism and CAD susceptibility , and 13 2I = 69.2%, P<0.05). Therefore, the REM was applied. The results indicated that OR = 1.38 (95% CI: 1.15–1.65), the difference was statistically significant. The same conclusion was obtained from the subgroup analysis of Chinese and Caucasian populations. The allele model was also statistically significant after the control group was removed from the study that did not meet HWE. The funnel plot . Therefore, the REM was used for data merging. The results showed that OR = 0.83 (95% CI: 0.73–0.95), the difference was statistically significant. The analysis of the Caucasian population subgroup and the subgroup without the control group that did not meet HWE showed statistically significant differences. In the Asian population, the allele model was not statistically significant. The funnel plot . Therefore, the FEM was used for data merging. The results showed that OR = 1.51 (95% CI: 1.28–1.80), the difference was statistically significant. The same conclusion was obtained in the subgroup analysis of Chinese and Caucasian populations. After excluding the study that the control group did not meet HWE, the dominant gene model was also statistically significant. The funnel diagram was symmetrical , so, the REM was used for data integration. The results showed that OR = 0.80 (95% CI: 0.68–0.95), the difference was statistically significant. Excluding the subgroup analysis of the control group that did not meet HWE, the difference was statistically significant. In the subgroup analysis of the Caucasian and Asian populations, the dominant gene model was not statistically significant. The symmetry of the funnel plot , then, the REM was used to integrate data. The results indicated that OR = 1.42 (95% CI: 1.11–1.83), the difference was statistically significant. The same conclusion was reached in the subgroup analysis of Chinese and Caucasian populations. The recessive gene model was also statistically significant after removing the control groups that did not satisfy HWE. The funnel plot was symmetrical, shown in P>0.05).The comparison of genotype TT with genotype MT+MM at M235T locus of AGT gene showed that the heterogeneity between the studies was significant , and the FEM was used. The results showed that there was no significant difference with OR = 0.87 (95% CI: 0.75–1.02). Subgroup analysis that removed the control group that did not meet HWE showed no statistically significant difference. The recessive gene model was also not statistically significant in the Caucasian and Asian subgroups. The funnel plot . Therefore, the FEM was applied. The results showed that OR = 1.56 (95% CI: 1.15–2.12), the difference was statistically significant. The same conclusion was obtained from the subgroup analysis of Chinese and Caucasian populations. After excluding the study that the control group did not meet HWE, the homozygous genetic model was statistically significant. The funnel plot was symmetrical (see in P>0.05).The comparison between genotype TT and genotype MM at M235T locus of the AGT gene showed no significant heterogeneity between studies , then, the REM was applied. The results showed that OR = 0.0.73 (95% CI: 0.55–0.98), the difference was statistically significant. Subgroup analysis that removed the control group that did not meet HWE showed no statistically significant difference. In the subgroup analysis of Caucasian and Asian populations, the heterogeneity was not significantly decreased, and the homozygous gene model was not statistically significant. The symmetry of the funnel chart shown in P-value of Egger’s test was slightly less than 0.05, indicating partial publication bias.Compared genotype CC with genotype TT at 584C/T locus of EL gene, there was significant heterogeneity . Therefore, the FEM was used for data merging. The results showed that OR = 1.32 (95% CI: 1.10–1.59), the difference was statistically significant. The same conclusion was obtained in the Caucasian population subgroup after removal of the control group that did not satisfy HWE. While, the heterozygous genetic model had no statistical significance in the Chinese population. The symmetry of the funnel plot was general , then the REM was applied for data merging. The results showed that OR = 0.84 (95% CI: 0.72–0.97), the difference was statistically significant. Subgroup analysis that removed the control group that did not meet HWE showed no statistically significant difference. In the subgroup analysis of the Caucasian and Asian populations, the heterozygous gene model was also not statistically significant. The funnel plot shown in P-value that was slightly less than 0.05.Compared genotype CC with genotype TC at 584C/T locus of EL gene, significant heterogeneity was found  there was heterogeneity in allele model, recessive gene model and homozygous model of AGT M235T gene, and the heterogeneity did not decrease significantly according to ethnic subgroup analysis, (2) the heterogeneity of the EL 584C/T gene was significant in all genetic models except the recessive gene model, and there was no significant decrease in the heterogeneity after analysis by ethnic subgroup, (3) except recessive gene model, EL 584C/T gene had publication bias in other genetic models, especially in Caucasian population, (4) the interactions between gene and gene, gene and environment were not analyzed.In conclusion, AGT M235T and EL 584C/T genes are associated with CAD susceptibility. Allele T, genotype TT and TC of AGT M235T gene and allele C, genotype CC of EL 584C/T gene could increase the risk of CAD. In particular, the relationship between the AGT M235T gene and CAD is robust, while the relationship between the EL 584C/T gene and CAD susceptibility is unstable. Furthermore, considering the limitations of the present study, further studies are required on a larger scale to explore the relationship between AGT and EL polymorphisms, and CAD susceptibility.Click here for additional data file.


Background: Prenatal intake of folic acid is important for prevention of NSCL/P . Associated genes in folate pathway are major enzymes of folic acid metabolism that is crucial for preventing birth defects. The present meta-analysis aims to investigate the association between four SNPs in folate pathway genes and the risk of NSCL/P.Methods: Comprehensive bioinformatics analysis was used to predict the functional pathogenicity of genetic variation. The PubMed, Embase database and Google Scholar were searched by two researchers. Stata 11.0 software was used to analyze the results. Subgroup analysis was carried out to assess the influence of genetic background. Sensitivity analysis, regression analysis and publication analysis were also conducted to enhance the strength of our results.Results: It is estimated that the probability of two missense mutation rs1801133 in MTHFR and rs1801394 in MTRR are more likely to be damaging by bioinformatics analysis. A significant association between rs1801133 and risk of NSCL/P in two genetic models: TT genotype vs CC genotype , and recessive model . A significant protective association between rs1801394 GG genotype and NSCL/P in Asian  was observed. Meta-regression, sensitivity analysis, and publication bias analysis confirmed that the results of the present study were statistically significant.Conclusions: The present study identified that rs1801133 in MTHFR is associated with the risk of NSCL/P, and rs1801394 GG genotype in MTRR play a protective role in Asian. Further, larger studies should be performed to confirm these findings. NSCL/P MTHFR (methylenetetrahydrofolate reductase), MTRR (Methionine synthase reductase), TCN2 , and BHMT (betaine-homocysteine methyltransferase) and their susceptibility to cleft lip [MTHFR mutations, while there are some reports on other genetic diseases. In 2017, Martinez Saguer et al. reported successful management of hereditary angioedema during pregnancy in a patient carried with heterozygous MTHFR mutation [MTHFR mutation [. also showed that MTHFR polymorphism was associated with treatment response in Jordanian population with epilepsy [MTHFR (rs1801133), 66A>G in MTRR (rs1801394), 776C>G in TCN2 (rs1801198), and 716 G>A in BHMT (rs3733890) have influence on protein function  original study of human participants; (2) an association study between rs1801133 and/or rs1801394 and/or rs1801198 and/or rs3733890 and NSCL/P; (3) case–control study or cohort study; (4) allele data were available; (5) the largest sample size or sufficient useful data were included in duplicate publications from the same population. Studies were excluded if they met the following criteria: (1) allele data were not available; (2) publications duplicate from the same population; and (3) review article and meta-analysis.The quality of research was evaluated to guarantee the strength of results and conclusions. The NOS  score was calculated to assess the quality of studies . A maximThe data were extracted independently by two researchers from all included studies using an integrated and standardized form. The following information was extracted: (1) first author name; (2) publication year; (3) population ethnicity; and (4) genotype distribution.P < 0.05. STATA  software was used to calculate the results of meta-analysis. Heterogeneity across individual studies was assessed by Cochran's Q test and 2I statistic (P < 0.10 and 2I > 50% indicated evidence of heterogeneity).The fixed-effects model (Mantel–Haenszel method) was used to estimate the pooled OR when there was no evidence of the heterogeneity; otherwise, the random-effects model analyzed by DerSimonian and Laird method was used. Using rs1801133 C>T as an example: (1) allele model, T allele vs. C allele; (2) dominant model, (CT+TT vs. CC); (3) recessive model, (TT vs. CT+CC); and (4) genotype model, . The same genetic models were performed for “rs1801394”, “rs1801198”, and “rs3733890”. A P value of P < 0.05 was established as the significant difference. Two subgroups, including Caucasian and Asian, based on ethnicity were analyzed to reduce the heterogeneity and influences from the genetic background. Meta-regression, and one-way sensitivity analysis, and Egger’s regression test were also performed [Polyphen2, SIFT, CADD, phyloP, and LRT were used for bioinformatics prediction. The HWE (Hardy–Weinberg equilibrium) test was calculated by the chi-square test. The distribution of allelic frequencies in controls were considered to deviate from HWE when erformed . The triAccording to the search strategy, 926 publications were identified in the initial search. After evaluating the titles and abstracts, 801 publications were excluded, and 125 full-text publications were further reviewed . By applP= 0.013) and recessive model    and 4. Tpulation –6.P= 0.008), but no associations in Caucasian  Asian and (ii) Caucasian. The results showed that there was a significant association between rs1801394 and NSCL/P risk in Asian . To avoid one single study affected the overall OR estimates, one-way sensitivity analysis was performed. The results showed that no study was found to exert an excessive influence on the pooled effect.Publication year, sample size and HWE were considered as covariates for meta-regression. The results showed that the above factors have no influence on the results  and recessive model . There were a significant protective association between rs1801394 GG genotype and NSCL/P in Asian .The present meta-analysis results demonstrated a significant association between rs1801133 and NSCL/P risk in two genetic models: TT genotype vs CC genotype (OR=1.333 95% CI=1.062–1.674, TCN2, encode transcobalamin2, transports vitamin B12 to cells, have been reported to be associated with multiple diseases, such as cancer, Alzheimer and other congenital abnormalities [. found that the C776G in TCN2 was associated with risk of cleft lip, but subsequent studies didn't get the significant results [malities . In 2006 results . Similar. first found that rs3733890 of the BHMT was associated with NSCL/P, and other studies also indicated its association with coronary artery disease and neural tube defects [BHMT, a zinc dependent cytosolic enzyme, is important for homocysteine metabolism and methionine synthesis. In 2010, Mostowska et al defects . In the  defects . We infe. in 2013 and Lei et al. in 2018 showed no association between rs1801394 and the risk of NSCL/P [MTRR plays a vital role in functional regeneration of methionine synthase, and it may be associated with increasing the congenital heart disease risk . But thef NSCL/P ,48. In tf NSCL/P ,14,25. HMTHFR rs1801133 in Asian are lower than that in European and American, so it is very valuable to summarize and analyze by systematic statistical methods. In 1998, Tolarava found TT genotype of rs1801133 increase the risk of CL/P, later on, several studies also found the associations between rs1801133 and NSCL/P in different population [MTHFR is an important enzyme in homocysteine metabolism and C677T rs1801133 is one of the most important functional polymorphisms. Prediction by bioinformatics tools showed that the change of genetic variant will influence the protein function and predispose to cause the disease . The allpulation ,34. Howepulation ,41. In tThe strength of this meta-analysis is that it expands to a large number of related studies, and the most updated publications were included. A strict procedure for search strategy, literature inclusion, data extraction, and quality assessment by two researchers was performed to guarantee the quality. Meta-regression and sensitivity analysis were also performed to strengthen the conclusions. We confirmed the previous investigation by summarizing a larger number of closely related studies.There are some limitations in the present meta-analysis. First, studies published only in English were included in the meta-analysis, and studies published in other languages were excluded. Second, environmental factors also contribute to NSCL/P, and in the present study, non-genetic factors and other potential interactions such as age, sex, folate level were not included in the analysis due to insufficient information.MTHFR is associated with the increasing risk of NSCL/P, and GG of rs1801394 in MTRR confers a protective role in Asian. Further well-designed studies are required to establish these findings.In the present study, we successfully identified rs1801133 in Click here for additional data file.


Joint stiffness is a common complication after anterior cruciate ligament (ACL) reconstruction, which seriously affects the efficacy of the operation and patient satisfaction. After ACL reconstruction, the identification of joint stiffness’ risk factors can help its prevention. This meta-analysis was conducted to evaluate joint stiffness’ risk factors and incidence after ACL reconstruction and provide guidance on its prevention.PubMed, Embase, and Cochrane Library were searched to obtain relevant studies. The odds ratios (ORs) with 95% confidence intervals (CIs) for all potential risk factors were analyzed using fixed or random-effects meta-analysis in RevMan 5.2.R = −0.62, P = 0.0094). After ACL reconstruction, the joint stiffness overall pooled incidence was 3% . Gender  was identified as a risk factor. Potential risk factors, such as trauma to surgery time interval, graft type, and concomitant surgery with meniscus injury, have no significant correlation with joint stiffness after ACL reconstruction.In total, there were 37 studies and 113,740 patients that were included in this study. After ACL reconstruction, joint stiffness’ incidence negatively correlated with the studies publication time (This study indicated that joint stiffness’ incidence after ACL reconstruction is 3% and that gender is a risk factor for joint stiffness after ACL reconstruction. Anterior cruciate ligament (ACL) injuries account for a large proportion of knee injuries and have a significant impact on knee joint stability . With thDue to the effect of joint stiffness on efficacy and patient satisfaction following ACL reconstruction, the identification and minimization of risk factors’ occurrence, are essential. Sanders et al.  reportedTherefore, we conducted this meta-analysis to investigate joint stiffness risk factors and incidence after ACL reconstruction and provide guidance on the joint stiffness’ prevention to improve ACL reconstruction efficacy and post-operative patients’ satisfaction.The systematic review and meta-analysis methods used in this study followed the recommendations of Moher et al . Using tThe studies should be randomized or non-randomized controlled studies or observational studies.The studies should contain sufficient information on joint stiffness risk factors and incidence after ACL reconstruction.The object of the study must be human participants.The language of the article must be English or Chinese.Studies that met the following inclusion criteria were included in our meta-analysis:Conference abstracts, letters, editorials, case reports, and reviews.Joint stiffness was not present in the clinical results of all study participants.Insufficient control information in the study which limits complete extraction.Studies that met the following exclusion criteria were removed from our meta-analysis:The following information was independently extracted by the two authors (WB and ZJL) using a standardized Excel table: (1) The baseline characteristics of the included literature comprised representative authors, publication time, nationality, study type, study period, number of included patients, time and number of patients who were followed up, and number of patients with joint stiffness and joint stiffness incidence; (2) Related risk factors mentioned in three or more studies.We evaluated the quality of included studies using the Newcastle-Ottawa quality assessment scale . StudiesI2 statistics. We considered I2 ≤ 50% and/or P ≥ 0.1 to be an insignificant heterogeneity. In the above heterogeneous outcome, we applied the fixed effect model in the analytic model for statistical processing. On the contrary, we used the random effect model. The above statistical analyses were performed using the Review Manager 5.2. The R software was used to fit the correlation between incidence and the studies’ publication time using Spearman analysis. P < 0.05 was considered statistically significant.Joint stiffness incidence after ACL reconstruction was determined using inverse variance in statistical methods and risk difference in effect, measured with 95% confidence intervals (CIs). The binary variables of potential risk factors were performed using Mantel-Haenszel in statistical methods and odds ratio in effect, measured with 95% CIs. To identify the heterogeneity of the included studies, we performed a chi-square test and calculation of Using the pre-designed search strategy, we identified a total of 1749 records from three databases. After removing duplicate results, 1005 potential results were screened for the follow-up study and via intensive reading of the article title and abstract; we further identified 168 studies to be included in the follow-up research process. Next, we downloaded and carefully screened the full text of the selected articles. As a result, 131 articles were excluded due to insufficient data identification. Finally, 37 studies were included in this meta-analysis and a detailed screening process was recorded in a flow diagram    Fig. .Fig. 4FP = 0.13)   (Fig. A total of 6 studies and 61,723 patients were included in this study group, and the results showed that there is no significant correlation between concomitant surgery with meniscus injury and joint stiffness after ACL reconstruction (OR, 0.73; 95% CI, 0.52-1.03; In this study, we found that the incidence of joint stiffness after ACL reconstruction varies from 0.1 to 71% with a relatively large fluctuation amplitude , 18–49. The pooled results indicated that gender was a risk factor for joint stiffness after ACL reconstruction. When ignoring other related risk factors, the incidence of joint stiffness was significantly higher in women than that in men. Previous studies have reported that the female athletes’ risk of ACL injury is 2 to 6 times higher than that in male athletes . The strOur pooled results showed that the time interval from trauma to surgery has no significant correlation with joint stiffness after ACL reconstruction. Our results were consistent with previous reports that indicated that early ACL reconstruction surgery, within 3 weeks or even 1 week after trauma, does not increase the risk of postoperative joint stiffness , 13. TheSome limitations existed in this meta-analysis. First, most of the included studies were retrospective, which may have affected the results’ credibility. Second, there is a clinical heterogeneity that cannot be eliminated through subgroup analysis, which may be caused by differences in patients’ standards, included in each study, and the surgeons’ surgical techniques. In addition, there were some potential risk factors, such as age, weight, rehabilitation training, and preoperative activity limitation, which were not included in our analysis due to insufficient data. Despite these limitations, we believe that this study deepens our understanding of joint stiffness and provides guidance for preventing joint stiffness after ACL reconstruction. In the future, further studies will be needed to investigate the risk factors of joint stiffness after ACL reconstruction.This study indicated that the incidence of joint stiffness after ACL reconstruction is 3%. Gender is a risk factor for joint stiffness after ACL reconstruction.


Statistical adjustment is often considered to control confounding bias in observational studies, especially case–control studies. However, different adjustment strategies may affect the estimation of odds ratios (ORs), and in turn affect the results of their pooled analyses. Our study is aimed to investigate how to deal with the statistical adjustment in case–control studies to improve the validity of meta-analyses.Three types of adjustment strategies were evaluated including insufficient adjustment , full adjustment , and improper adjustment (covariates other than confounders were adjusted). We carried out a series of Monte Carlo simulation experiments based on predesigned scenarios, and assessed the accuracy of effect estimations from meta-analyses of case–control studies by combining ORs calculated according to different adjustment strategies. Then we used the data from an empirical review to illustrate the replicability of the simulation results.For all scenarios with different strength of causal relations, combining ORs that were comprehensively adjusted for confounders would get the most precise effect estimation. By contrast, combining ORs that were not sufficiently adjusted for confounders or improperly adjusted for mediators or colliders would easily introduce bias in causal interpretation, especially when the true effect of exposure on outcome was weak or none. The findings of the simulation experiments were further verified by the empirical research.Statistical adjustment guided by causal inference are recommended for effect estimation. Therefore, when conducting meta-analyses of case–control studies, the causal relationship formulated by exposure, outcome, and covariates should be firstly understood through a directed acyclic graph, and then reasonable original ORs could be extracted and combined by suitable methods. Meta-analysis is a well-developed statistical methodology to synthesize results of multiple original studies . Since iDuring the past few decades, a growing number of meta-analyses are conducted in observational settings and genetic areas , 8. CompIn our previous study, we have made a secondary data analysis based on all meta-analyses of passive smoking and breast cancer in non-smoking women published from 1966 to 2016, as well as all original studies included in these meta-analyses . We founTherefore, we designed this simulation study to assess the accuracy of effect estimations from meta-analyses by combining ORs of original case–control studies calculated according to different adjustment strategies. The strategies included fully adjustment of all preset confounders guided by causal inference, insufficiently adjustment of less confounders, and improperly adjustment of covariates other than confounders such as mediators or colliders. We set several scenarios and compared the performances of pooled ORs, and thereby provided recommendations on how to choose original ORs for meta-analyses under different circumstances. Then we used the data from an empirical review to give illustrations.We carried out a series of Monte Carlo simulation experiments to create original case–control studies and their meta-analyses. The design of the simulation study is displayed in Additional file A  on a dichotomous outcome variable Y . The causation from A to Y can be reached in four ways, i.e., the direct path A → Y, the indirect path through mediators A → M → Y (M denotes a set of mediators of A and Y), the backdoor path through common causes A ← L → Y or A ← L → R → Y , and the front-door path by conditioning on common effects (C denotes a set of colliders of A and Y). The simplified causal directed acyclic graph (DAG) between A and Y is shown in Fig. Suppose that we are interested in the causal effect of a dichotomous exposure variable L =  was sufficient to block all backdoor paths from A to Y. Only a dichotomous risk factor R, a dichotomous mediator M, a dichotomous collider C existed in the A-Y causal pathway. R was affected by L1, L2, L3, and L4. By specifying the positive probabilities of all variables and the association parameters, a target population with certain number of observations would be generated. Detailed method is shown in Additional file Without loss of generality, we assumed a vector of 6 dichotomous confounders A on Y was estimated by adjusting for 0 to 5 measured confounders, respectively. One kind of strategy was full adjustment, i.e., all confounders identified in Fig. L1 to L6) were controlled. The other kind of strategy was improper adjustment, i.e., covariates other than confounders were controlled in the logistic regression model. The effect of A on Y was estimated by adjusting for L1 to L6 plus R, M, C, and all, respectively. Detailed method is shown in Additional file From the target population, a series of case–control studies would be generated using random sampling method. For each case–control study, 11 original ORs were calculated by different adjustment strategies. One kind of strategy was insufficient adjustment, i.e., not all confounders were controlled in the logistic regression model. The effect of Meta-analyses were generated by combining several case–control studies, and pooled ORs were estimated under each adjustment strategy with fixed-effects model of the inverse variance method or two-stage random-effects model of the DerSimonian and Laird method as appropriate. We compared the 11 pooled ORs with the true effect specified in the target population, and thereby evaluated the performances of 11 adjustment strategies. Detailed method is shown in Additional file A and outcome Y in the target population, including the total effect of A on Y (ORAY), the independent associations of covariates U =  with A (ORUA) and Y (ORUY), and the correlations among different variables of U (rUU). Suppose that the positive probabilities of A, Y, and U were 20% in subjects unexposed to any parent variables, and the associations of U with A or Y were equal. We specified (1) ORAY, (2) ORUA, and (3) ORUY as 0.2, 0.5, 0.8, 1, 1.25, 2, or 5 in different scenarios to represent strong, medium, weak, and no associations with opposite directions. We also specified (4) rUU as 0, 0.2, 0.5, or 0.8, with rUU ≠ 0 indicating the nonindependence of covariates.Several factors may affect the performance of adjustment strategies. The first is the causation between exposure L6; 1:1, 1:2, or 1:4). Case–control studies with individual matching design should be analyzed using conditional logistic regression models.The second factor that may affect the performance of adjustment strategies is the sample size of the original case–control studies, which involves the number of cases and the matching approach (matching ratio). To reflect various scales of original studies, we specified (5) the number of cases as 20, 100, or 500, and (6) the matching approach as frequency matching or individual matching  the number of original studies as 5, 20, or 50, referring to real meta-analyses extracted by our previous research . We alsoA on outcome Y were estimated for certain adjustment strategies. The parameter A total of 32 scenarios were designed. In each scenario, 1000 meta-analyses of case–control studies were generated, and 1000 pooled ORs of exposure n = 1000) was decided by the equation The repetition times (α/2σ/δ)2 , where \research ), and \dAY = 1 . Publication bias was assessed by funnel plots. Moreover, sensitivity analyses were conducted to combine original ORs that seemed to underestimate and overestimate the true effect, respectively, through the guidance of causal inference. All meta-analyses were performed with Review Manager 5.3.A on outcome Y (ORAY = 2). The overestimation gradually decreased with the adjustment of more confounders. Combining original ORs that adjusted for all 6 confounders had a mean pooled OR of 2.01 (95% CI 1.72–2.32), which was the closest estimation to ORAY. Further adjusted for risk factor did not substantially change the estimation . However, further adjusted for mediator or collider in addition to confounders did underestimate the true effect. The underestimation was similar for mediator and collider, if they had an equal association strength with exposure and outcome. A more particular interpretation of the results is shown in Additional file Among all scenarios defined in the simulation study, set scenario Ref be the primary analysis. Figure Figure mentclass2pt{minimCoverage showed similar tendencies with MSE, i.e., CI estimations based on full adjustment strategy had the highest coverage rate to the true effect  was specified as 2. However, for scenarios 1–3 and 1–5 with weaker effects , power became insufficient and type II error rate exceeded 20% under inappropriate adjustment strategies. Moreover, for scenario 1–4 with null effect (ORAY = 1), type I error rate exceeded 5% if confounders were not adjusted under the guidance of causal inference. Error rates were acceptable only for full adjustment strategy when ORAY was around 1 . We supposed that the result of prospective studies might be more credible, since prospective studies generally exposed to less bias and provided relatively higher quality of evidence. However, without a background knowledge of the true effect, we could not definitely conclude whether the association existed or not. Therefore, we tried to re-analyze the data from the case–control studies and give a more decisive causal inference.The meta-analysis was focused on passive smoking and risk of breast cancer in nonsmoking women. Since the research question could not be fulfilled by experimental studies due to ethical reasons, all 47 original studies involved in the meta-analysis were observational studies. Among them, 30 were case–control studies, 15 were prospective studies, and 2 were case–control studies nested within prospective studies. In the principal analysis, 29 case–control studies and 16 prospective studies were included, with the pooled effect estimations as 1.26 (95% CI 1.13–1.41) and 1.02 (95% CI 0.97–1.08), respectively. A clear difference has been found between study types , and evaluated their impacts on pooled effect estimations of meta-analyses in different scenario settings. Full adjustment strategy is the recommended approach, because it can well eliminate the confounding bias while not cause other biases. Particularly when the effect size is weak or none, confounding bias may distort or even reverse the statistical inference. Full adjustment strategy can sufficiently control the bias in accordance with causal framework, and simultaneously avoid involving in mediation or colliding effect. The major disadvantage of full adjustment strategy is that, it is difficult to judge whether the confounders were sufficiently adjusted and whether the adjusted covariates were confounders without the help of a DAG. Therefore, the construction of DAG is very important for researchers to extract credible ORs, and should become a standard procedure of meta-analyses.Insufficient adjustment strategy cannot completely control the confounding bias. A typical example of insufficient adjustment strategy is univariate analyses, i.e., no covariates are adjusted for the calculation of original OR. Some meta-analyses combined these crude ORs to ensure statistical homogeneity , 20. HowImproper adjustment strategy may be interfered by the mediation or colliding effect that reduce the estimation accuracy. But it is commonly seen in practice, because researchers of original studies tend to include as many measured variables as possible in the regression model if the sample size allows. They may not carefully distinguish confounders from mediators or colliders, which have similar association patterns with exposure and outcome. Or they may provide several ORs calculated by different models, some adjusted for clear confounders, and some adjusted for all relative factors. Meta-analyses generally pooled most-adjusted ORs, with the assumption that the more the variables are adjusted, the smaller the confounding bias –23. HoweNowadays, computing methods such as intelligent data analysis , 27, datOn the other hand, empirical research is also an important illustration of simulation results. Compared with the methodological representation, the practical application of the findings is always of value. By analyzing a motivating example, the reason for conducting the simulation study would be clarified, and the parameter settings of the scenarios would be justified. Moreover, a proper instance would help the technical paper popularize to non-technical audiences.To our knowledge, this is the first simulation study to evaluate how adjustment strategies of original case–control studies impact the pooled effect estimations of meta-analyses. From our previous case study on meta-analyses of passive smoking and breast cancer , we deteAlthough our findings provide important implications on how to choose original ORs of case–control studies for meta-analyses, caution is needed when the situation is more complex. First, we set a relatively fixed causal mode between exposure and outcome, and prespecified 6 confounders, a risk factor, a mediator, and a collider in the target population due to feasibility and availability considerations. While in real-world applications, the number of covariates and the inter-covariate causations were far more flexible. Since our study mainly focused on the causal attributes of the adjusted variables rather than the number of them, we made a general assumption without loss of generality. The potential impact of confounder numbers on the meta-results could be evaluated in future studies.Second, we assumed all generated case–control studies were conducted in an ideal circumstance without selection bias and information bias. However, in practice, it is difficult to ensure the quality of original studies. Therefore, the potential biases of original studies should be carefully evaluated , and theThird, we made all statistical adjustment in case–control studies based on logistic regression models. But in real cases, other adjustment methods such as propensity score and instrument variable are also used . How to Fourth, we combined case–control studies by fixed-effects model with the inverse variance method or two-stage random-effects model with the DerSimonian and Laird method, because they are the recommended methods of the Cochrane Collaboration . HoweverFifth, we focused on meta-analyses of case–control studies, while in most actual reviews, observational studies including case–control studies and cohort studies are combined together to calculate pooled ORs. Compared with case–control studies, cohort studies are exposed to more uncertain factors, such as different follow-up durations, different rates of loss to follow-up, etc. Whether the results of the present study are still valid for meta-analyses of cohort studies, is another important question to be answered in our future research.Statistical adjustment strategy guided by causal inference are recommended for effect estimations. Thus, when conducting meta-analyses of case–control studies, the causal relationship between exposure and outcome should be firstly understood through a DAG, and then reasonable original ORs should be extracted and combined by suitable methods to get accurate pooled ORs.Additional file 1: Supplementary materials.Additional file 2: Main SAS code of the simulation for scenario Ref.


Meta-analysis of genetic association studies is an efficient tool to gain a better understanding of multifactorial diseases and potentially to provide valuable insights into gene-disease interactions. The present study was focused on assessing the association between Pro12Ala variation in the PPARG and T2DM risk through a comprehensive meta-analysis. We searched PubMed, WoS, Embase, Scopus and ProQuest from 1990 to 2017. The fixed-effect or random-effect model was used to evaluate the pooled odds ratios (ORs) and 95% confidence intervals (CIs) depending on the heterogeneity among studies. The sources of heterogeneity and publication bias among the included studies were assessed using I2 statistics and Egger's tests. A total of 73 studies, involving 62,250 cases and 69,613 controls were included. The results showed that the minor allele (G) of the rs1801282 variant was associated with the decreased risk of T2DM under different genetic models. Moreover, the protective effect of minor allele was detected to be significantly more in some ethnicities including the European (18%), East Asian (20%), and South East Asian (18%). And the reduction of T2DM risk in Ala12 carriers was stronger in individuals from North Europe rather than Central and South Europe. Our findings indicated that the rs1801282 variant may contribute to decrease of T2DM susceptibility in different ancestries.Type 2 diabetes mellitus (T2DM) is a complex disease caused by the interaction between genetic and environmental factors. A growing number of evidence suggests that the  According to the recent statistics of the International Diabetes Federation (IDF), the global T2DM epidemic significantly grows at an alarming rate among populations and so it has become a common health problem worldwide2. Although T2DM usually affects older adults, it is also gradually seen in children, adolescents and younger adults due to increasing levels of obesity, low physical activity and poor diet3. T2DM is recognized as a major cause of morbidity and leads to premature coronary heart disease progression (CHD), stroke, peripheral vascular disease (PVD), renal failure, and amputation4. T2DM is characterized by hyperglycemia, impaired insulin secretion (IS) and insulin resistance (IR) that results from the interaction between numerous genes and environmental factors6. The genetic architecture of complex traits is now to be related to several numbers of causal variants. But, the most important common variants show small to modest effect sizes8.Type 2 diabetes mellitus (T2DM) is the most common form of diabetes and is described as a highly prevalent multifactorial disorder10.Single nucleotide polymorphisms (SNPs), the most common type of genetic variations between individuals, are the key players in precision medicine approach. SNPs are responsible for more than 80 percent of the variation between individuals which makes them ideal for genotype and phenotype association studies. Genetic association studies as powerful approach have identified several SNPs that are significantly associated with T2DM susceptibilityperoxisome proliferator-activated receptor gamma (PPARG), a member of the nuclear hormone receptor superfamily, as susceptible to T2DM loci in Finnish and British/Irish ancestries12. There is a lot of information about the genetic architecture of T2DM including the high degree of polygenicity and the tiny effect sizes of most genetic risk variants but several obstacles complicate translation process of these novel loci13. Therefore, there is a strong need to concentrate more on translational work to make sense of the hundreds of loci associated with common diseases such as T2DM.Many Genome-wide association studies (GWASs) identified several candidate genes, including PPARG (nuclear receptor) have an important role in controlling lipid and glucose metabolism15. Among them, the missense variant rs1801282  in the exon B leads to an amino acid change from Proline (P) to Alanine (A) have been extensively reviewed in epidemiologic studies.Several evidence have demonstrated that the SNPs of 17, Greater Middle Eastern18, and European ancestries such as Finnish19, Czech20, and White from Scottish21. Conversely, some another studies suggested that the PPARG Pro12Ala variant could be considered as a risk marker conferring susceptibility to T2DM in the Russian22, South Asian (Kashmiri)23 and mixed ancestry of south Africa24.Many case–control studies have reported that the Pro12Ala (Ala12) variant is associated with protection against T2DM risk in East Asian (Japanese)25. Based on these contradictory results from different independent studies, a comprehensive meta-analysis seemed to be a good approach to combine the results of various studies on the same topic and to further explain their findings.The results of these genetic association studies are not consistently reproducible and the majority of the initial positive genetic associations cannot be replicated in multiple studies. Furthermore, most of the candidate genes and their variants occasionally indicate only minor effects in genetic association studies. The lack of reproducibility may be due to several factors including variability in the ethnicities and small sample size. As some risk loci displayed significant ethnic differences in frequency and/or effect size. Therefore, meta-analysis is required to detect a small or moderate genetic effect of polymorphisms. The meta-analysis of the genetic association studies is considered to be decisive evidence when correctly performedPPARG gene and the risk of T2DM was reported in several meta-analysis29. Ludovico et al. (2007) demonstrated a heterogeneous effect of the Ala allele on lower development of T2DM in Europeans, Asians, and North Americans 27. The reduction effect of Ala allele in PPARG gene on T2DM risk was also reported in a meta-analysis by Huguenin et al. (2010) in Caucasians28. Two independent studies just conducted in the Chinese Han populations did not find any association of Pro12Ala and T2DM risk29. Moreover, a recent meta-analysis with 14 studies indicated the evidence for Pro12Ala as a susceptibility variant in Caucasian and Chinese populations30.The potential association of rs1801282 polymorphism in the entire coding region of the Despite the previous meta-analysis studies, we are trying to provide an updated and stronger result of evaluating the relationship between the Pro12Ala variant and the risk of T2DM by performing a comprehensive meta-analysis in a larger number of studies from different geographic regions and different ethnicity and interpret and compare data obtained.31.PPARs play important roles in various metabolic processes so they are considered good targets for the treatment of metabolic syndrome. But the use of PPAR agonists in diabetes treatment which mainly target PPARγ has been met with side effects. Accordingly, a better genomic understanding of the pathogenesis of diabetes may alleviate the side effects of these agents through targeting certain genetic variantsSNP biomarkers have potentially wide clinical application in precision medicine, including disease predisposition, screening, diagnosis, prognosis, monitoring, and pharmacogenetics. Precision medicine enables for early treatment and decreased morbidity and mortality. It is thus important in the precision medicine field to find the relevance of SNPs to medical practice and the extent of their impacts in healthcare practice. In type 2 diabetes as the known genetic associations explain only 5–10% of the inheritable basis of T2DM so the genetic variants prove useful in disease predisposition.PPARγ common genetic variant in the risk and pathogenesis of T2DM.Therefore, this systematic review and meta-analysis study could be essential for considering the importance of 32 and the published PROSPERO research protocol (CRD42017058832).The current review evaluated the association between rs1801282 (Pro12Ala) polymorphism and T2DM risk. This study was carried out following the criteria of the Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA)“Type 2 Diabetes Mellitus” OR “insulin independent diabetes mellitus” OR “Noninsulin-Dependent Diabetes Mellitus” OR …) and . The finalized search syntax in PubMed was adjusted in other databases for a comprehensive search , and ProQuest to collect relevant literature published from January 1990 to October 2017. We found the different synonyms of the related terms for all subjects by using thesauri systems such as Medical Subject Headings (MeSH) and EMBAS subject headings (Emtree). The combination of the following terms was used to design the search strategy: .Two reviewers (MR and MH) also reviewed the full-text articles to determine whether the selected articles adapted to the eligibility criteria and could be included/excluded in the final investigation. Conflicting opinions were resolved through further discussion to achieve a consensus. Moreover, the reference lists of all eligible studies were also checked to identify additional potentially relevant literature.Studies published in peer-reviewed journals.PPARG rs1801282 (Pro12Ala) polymorphism and risk of T2DM.All case–control studies just conducted on the human that assessed any association between The data about the allele or genotype frequencies should be sufficient to calculate the odds ratios (ORs) with the corresponding 95% confidence intervals (CIs) of the polymorphism in both the case and control groups.The control group included people without T2DM.Studies that just published in the English language.Studies that their full text was available.Short communication and brief genetic report with sufficient data.All selected studies had to fulfill the following inclusion criteria:Following studies were excluded:(1) Family based association studies, including case reports and case series.(2) Reviews, meta-analysis, letters, editorial, comments, and conference abstracts.(3) In vitro, ex vivo or animal studies.(4) Studies lack sufficient data about allele frequency or data that could not be calculated.(5) Duplicate publications and redundant studies of duplicated data; for duplicate reports that published by the same authors using the same case series, only the most recent and the one with the largest sample size one was included.Two researchers (LH and KH) extracted the following items were selected from all eligible studies using a redesigned form according to the aforementioned inclusion and exclusion criteria:(1) First author’s names and year of publication(2) Country of setting or ethnicity of participants(3) The number of cases and controls(4) Mean age and body mass index (BMI) of participants(5) Genotyping methodPPARG gene variant in cases and controls(6) The genotype and allele frequencies of PPARG rs1801282 (Pro12Ala) gene polymorphisms in the control group.(7) Hardy–Weinberg equilibrium (HWE) was calculated based on genotype frequencies of certain The agreement between two researchers (LH and KH) was achieved by a discussion with a third expert person (MH) in the research team.If there was a lack of genotype information, the reviewers contacted the corresponding author to get the required data.33.The Newcastle–Ottawa Scale (NOS) was used to assess the quality of included studiesThe following items were selected for the inclusion of the study, including the selection of cases and controls , comparability between cases and controls  and exposure in cases and controls .The NOS has a score range of zero to nine, and studies with a rating of 7–9 were presumed to be of high quality, 4–6 as moderate quality, 4 or less was classified as low-quality studies. Quality assessment was also conducted by two investigators independently.First, any disagreement regarding the quality assessment was resolved by checking and discussion between the two reviewers. If the two authors could not achieve a consensus, then a third reviewer would resolve the cases of conflict.34. The odds ratio (OR) and 95% confidence interval (CI) were used to assess the association between rs1801282 polymorphism in PPARG gene and the risk of T2DM in seven genetic models as follow: allele model (G vs. C), homozygote model (GG vs. CC), heterozygote model (CG vs. CC), additive model (GG vs. CG), dominant model (GG + CG vs. CC), recessive model (GG vs. CC + CG), and co-dominant model (CG vs. CC + GG) which a ‘ ‘C’’ denotes a major allele; ‘ ‘G’’ denotes a minor allele.Within the study, the results were combined using RStudio (3.51). Genetic association studies do not follow a specific model and therefore multiple genetic models need to be investigated35 and I-square index (I2) determined the degree of the heterogeneity in meta-analysis based on I2 value of 25%, 50%, and 75% were nominally regarded as low, moderate, and high estimates, respectively36.The Cochrane Q-test index was used for detecting the existence of heterogeneity between the results of the primary studies2 > 50%). Otherwise, the fixed effects model (FEM) was occupied to assess the primary effect of the genotype37.We applied the random effect model (REM) for an inverse variance which was used to calculate the combining of primary results (the pooled OR estimate) if the heterogeneity was significant  and age (30–50 and ≥ 50 years) of participants, and year of publications  were carried out to achieve more specific results. Ancestries were categorized to European, Greater Middle Eastern, East Asian, South East Asian, South Asian, Asian unspecified, African American or Afro-Caribbean, Hispanic or Latin American, Native American, Other admixed ancestry, and Other) according to the classifications that is provided in a study by Morales et al. (2018)38.Subsequently, subgroup analysis by ancestry categories, BMI  were also excluded from the meta-analysis to getting possible stronger results. Moreover, the leave-one-out method in the sensitivity analysis was conducted through consecutive excluding only one study in each time to assess the cause of heterogeneity and to determine whether any individual study influences the stability of final results (pooled OR) in meta-analysis.39. If there was publication bias, the Duval and Tweedie trim-and-fill technique was accomplished to explore the impact of the publication bias on the results40.We also appraised the fundamental sources of potential publication bias in Egger’s linear regression test and visual inspection of the asymmetry of the Begg funnel plotDuring the first stage of our comprehensive search, 6,622 studies were identified through electronic databases and hand searches. As illustrated in Fig. Two articles due to the insufficient data were removed from further step . Finally, a total of 73 studies with 62,250 T2DM patients and 69,613 controls met our inclusion criteria for overall meta-analysis after reading the full texts. It should be noted that these 73 studies had been reported by 66 articles.41 that is available in Supplementary file Besides, other 13 studies data from four articles were lack genotypes or alleles frequency while only OR was reported for these. So we did not exclude from meta-analysis and analyzed using separately command from StataThe characteristics and genotype frequencies of included studies are listed in Table The NOS score of eligible articles ranged from two to eight stars. 11 of included studies were evaluated to be high quality, 33 were low quality, and 28 studies were considerate as moderate quality.There were 21 studies of Europeans, 17 studies of East Asians, 10 South Asians, 13 Greater Middle Eastern, five other, and two Hispanic or Latin American. Other ancestry groups such as South East Asian, Asian unspecified, other admixed ancestry, Native American, and African American or Afro-Caribbean included only one study.tetra-primer amplification refractory mutation system (TETRA-ARMS), restriction fragment length polymorphism (RFLP-PCR), mass spectrometry, direct sequencing, real-time PCR, and etc. which was listed in Table Different genotyping methods consist of TaqMan, The genotype frequency of the control group met to HWE in the included studies except for five case–control studies and one study that not reported the p-value of HWE.P < 0.01) with high between-study heterogeneity (I2 = 71%), homozygous, heterozygous, additive, dominant, recessive, and co-dominant genetic models. Further details on the genetic coding of the variant are provided in Table Combining the results of the primary studies showed a significant association between the Pro12Ala polymorphism and T2DM risk under REM in seven genetic models including Allelic , additive (4 studies), and dominant (4 studies) genetic models that were analyzed according to just OR  were consistent with the main results of the meta-analysis , homozygous , heterozygous , additive , dominant , recessive , and co-dominant  genetic models  and co-dominant  genetic models , homozygous , heterozygous , additive , recessive , and co-dominant  genetic models  genetic model , "Central European" , "South European" , and "not available subgroup data" (such as Caucasian) to confirm the previously reported findings. As presents in Fig. Same to the results of the study by Ludovico et al. 2 indicated a strong association with T2DM risk for homozygous (OR for participants with mean BMI ≥ 25 vs. BMI < 25 = 0.59 vs. 0.78), additive (OR for participants with mean BMI ≥ 25 vs. BMI < 25 = 0.68 vs. 1.00), and recessive (OR for participants with mean BMI ≥ 25 vs. BMI < 25 = 0.61 vs. 0.82) genetic models 42 in the additive model indicating that the results of these studies had the highest influence on the pooled estimate Furthermore, we excluded those HWE-violating studies for sensitivity analyses. However, the pooled ORs in overall was not statistically altered, indicating that the results were stable , heterozygote model (CG vs. CC), dominant model (GG + CG vs. CC), and co-dominant model (CG vs. CC + GG) genetic models.No evidence of asymmetry was observed among the primary studies by Begg funnel plots in any comparisons  and rare (G) alleles of rs1801282 encode the 'Pro' and 'Ala' amino acids, respectively. According to the previous GWAS, the Pro allele of this variant was reported to increase the risk of T2DM. But, the Ala allele has a protective effect on T2DM development48.Pro12Ala (rs1801282) is considered to be the most analyzed common variants in the PPAR-γ rs1801282 and T2DM risk. The findings of this meta-analysis showed that the G allele of Pro12Ala polymorphism could cause approximately an 18% reduction in the probability of developing T2DM. The reduction of the T2DM risk was also detected vary across different ethnicities; European (18%), East Asian (20%), and South Asian (18%) while no association was founded in the Greater Middle Eastern population.The result of the present systematic review and meta-analysis consists of 62,250 cases and 69,613 controls from 73 studies in order to achieve substantial evidence of any association between 27 the reduced risk in the Asian population more than European (35% vs. 14%) was reported. Consistent with the previous report, the reduced T2DM risk was stronger in North European populations in stratified Europe from Northern to Southern gradient27.As is obvious, the differences in the reduction of T2DM risk between those of European, South Asian, and East Asian ancestries are not really very different in the present study whereas in a study by Ludovico et al.49. It indicates that the stratification of the studies based on different ethnicities is very important in the present meta-analysis.The contradictory results from the different ethnic populations appear interesting that it can be partially attributed to the small sample size and the fact that different genetic backgrounds and various environmental factors might be lead to conflicting results from the same polymorphism among primary studies with different ethnicities29 which indicated the Pro12Ala variant of PPARG is not associated with T2D risk.The present result was different from previous studies exclusively in the Chinese Han populations30 which showing evidence of Pro12Ala as a susceptibility variant for the lowering development of T2DM.However, it was consistent with recent meta-analysis research results with 20,702 cases and 36,227 controls from 14 studies28. Also, a meta-analysis by Gouda in 2010 observed that the PPAR-γ Pro12Ala variant is positively associated with a reduction of T2DM risk50.It should be noticed that the results of our study could be closer to reality due to the number of cases, controls, and studies of different ancestries. Furthermore, a study by Huguenin et al. showed a significant effect of the Ala allele on reduction of T2DM risk in CaucasiansPPAR-γ has made an important contribution in providing convincing genetic evidence of a role in glucose homeostasis, lipid metabolism, and determination of fat mass. Such studies also provided data for the underlying mechanisms of insulin sensitivity, PPAR-γ action and, T2DM risk. However, neither environmental triggers nor genetics alone can explain T2D pathogenesis because of its multifactorial nature. Hence, more functional studies and large population-based validation surveys are needed to perform. To the best of our knowledge, this is one of the most comprehensive meta-analysis of the association of PPAR-γ rs1801282 (Pro12Ala) polymorphism and T2DM risk.The analysis of subjects harboring polymorphisms within Despite our promising findings, multiple limitations should also be addressed. Firstly, T2DM is a complex disorder and we only discussed individual genetic variant without having to consider the interaction with other genetic variants or environmental variables .. Therefore, a more precise association with sufficient data should be explored. These results should be interpreted with caution until further sequencing approaches verification and greater meta-analysis is required.Secondly, owing to the restriction of the accessibility of original research information, the study did not consider other appropriate variables such as gender, age, and genotype frequency data as the genotype frequency data was not available in some articles (11 from 73) and only the allele model was evaluated in order to assess the association among the overall populationThirdly, significant publication bias was observed in some T2DM comparisons including the allele model. This may be due to the fact that the ethnicity of the populations in the early studies is mostly European or Asian, or that there is a greater number of low or medium quality studies rather than the high-quality ones. And also, significant heterogeneity was detected in the primary study results, indicating that the inconsistent results of the included studies could not be fully explained by differences in ethnic background, BMI, age, and other unmeasured variables of participants that may also partially attribute heterogeneity to the inter-study.Fourthly, there are some gaps about particular ancestry groups including; Aboriginal Australian, African unspecified, Asian unspecified, Central Asian, Oceanian, and Sub-Saharan African that should be addressed.Finally, obesity is also a significant intermediate factor in the rise of T2DM and having BMI information would be important and useful in the association analysis. But the definitions of obesity were not the same or accessible in our included studies. Therefore, in our subgroup analysis with BMI, the mean BMI of populations was used which does not indicate the exact BMI individual level of the study. So, this may be causing the contradictory result of this stratification.Moreover, despite these limitations, our comprehensive research can still make a valid conclusion.10. The introduced genetic variants can be used to detect high-risk individuals for certain diseases. Thereby personalized medicine goals for improving patient outcomes will be achieved through such studies. A genetic variant that is associated with disease in one ethnic group but not in another may indicate ethnic differences in risk disease predisposition. So the result of genetic association studies represents only the tip of the iceberg and meta-analysis study shows great benefit for the personalized medicine approach10. The PPARG Pro12Ala variant in current meta‐analysis indicated enough evidence for the presence of a significant association of individuals carrying the PPARG Ala12 variant with a reduced risk of T2DM. Additionally, the results of analysis under diverse ancestries confirm the importance of SNPs association studies in different ethnicities. But this effect is not very different among European compared to other ancestries. And among Europeans, existence stronger in North European, and barely significant in South European, and not being in South European. The genetic architecture of diabetes, including polygenicity and most risk variants, has been discussed in previous studies with important implications for precision medicine51. But several obstacles complicate the translation of novel loci and variants into the clinical decision practice, overcoming these will lead to the development of new drugs to treat T2DM.Genomic association studies help in disease predispositions by using genomic variants which have been discovered by GWASsIt appears that the lack of efficacy in anti-diabetic drugs is returned to the preclinical models in clinical trials. Human genomic advancement provides a better condition for proper assessment of drug development efficacy in pharmaceutical R&D through combining a targeted pathway or genetic alteration to a desirable phenotype (T2DM).Decision-making through precision medicine needs therapeutic approaches which are obtained by the genetic association study of the common variants/loci.The identification of the right drugs that are most effective and safe for each patient and reducing the global economic impact will be possible when the genetic information of the diabetic patients will provide a valuable resource to predict T2DM progression. Genetic studies are one of the most important approach in order to predict and prevent T2DM in the near future. It is hoped, therefore, that the decades ahead will elucidate the extent to which the inherited variation and its interaction with the environmental factors help clinicians’ diagnostics.Supplementary Information 1.Supplementary Information 2.Supplementary Information 3.Supplementary Information 4.Supplementary Information 5.Supplementary Information 6.Supplementary Information 7.Supplementary Information 8.Supplementary Information 9.Supplementary Information 10.Supplementary Information 11.Supplementary Information 12.


MTHFR). However, the epidemiologic investigations described inconsistent results regarding MTHFR polymorphism and the risk of male infertility. For that reason, we carried out a meta-analysis of published case-control studies to re-examine the controversy.One of the possible male sterility risk factors are polymorphisms of Methylenetetrahydrofolate reductase  with a confidence interval of 95% (CI) was used to assess the relationship between MTHFR polymorphism and male infertility risk.Electronic searches of Cochrane, EMBASE, Google Scholar, and PubMed were conducted to select eligible studies for this meta-analysis (updated to May 2019). According to our exclusion and inclusion criteria, only high-quality studies that remarked the association between MTHFR polymorphisms had significant associations with male infertility risk ; . Further, when stratified by ethnicity, the significant association results were observed in Asians and Caucasians for 677C/T and just Asians for 1298A/C.Thirty-four case-control studies with 9662 cases and 9154 controls concerning 677C/T polymorphism and 22 case-control studies with 5893 cases and 6303 controls concerning 1298A/C polymorphism were recruited. Both MTHFR polymorphisms like MTHFR 677C > T are associated with an elevated male infertility risk. To confirm our conclusion and to provide more accurate and complete gene-environment communication with male infertility risk, more analytical studies are needed.Some of  Infertility is a global problem and according to the World Health Organization, almost one in seven couples are affected by fertility complications , 2. MaleThe catalytic enzyme- encoding gene MTHFR is located at the end of the short arm of chromosome 1 (1p36.3) and has 33 exons .Two C677T and A1298C polymorphisms that significantly alter MTHFR enzyme activity are recognized in this gene . The conThe adenine to cytosine conversion at nucleotide position 3289 in exon 3 of the MTHFR gene (A1298C), also leads to the replacement of glutamine by alanine . There aThe N. Gupta and colleagues studied the Indian population , Wiener’In this study, with the help of eligible findings, we carried out a meta-analysis to provide a comprehensive assessment of the association between C677T and A1298C MTHFR polymorphisms with male infertility.A comprehensive literature quest in Cochrane, EMBASE, Web of Science, PubMed, Scopus, as well as Google Scholar databases was conducted for all articles regarding the impact of C677T and A1298C polymorphisms on male infertility published up to May 2019“ Include from when the analysis was done. Articles were obtained with the following keywords: “methylenetetrahydrofolate reductase” or “MTHFR”, ‘polymorphism” or “variant”, “677C > T”, “1298A > C” and “male infertility”. The inclusion criteria were: 1) Well defined case-control study design; 2) sufficient data for examining an odds ratio (OR) with 95% confidence interval (CI). The exclusion criteria were: 1) non-human studies; 2) articles not available in English and Farsi languages; 3) duplicate study by the same group with lower sample number; 4) cases only studies; v) insufficient genotyping data.According to the inclusion and exclusion criteria, data extraction was achieved by two independent investigators. Any disagreements of the studies were resolved through a comprehensive reassessment by the other author and only high- quality studies can be included in our meta-analysis. Reference lists of all included full-text manuscripts were screened for additional articles. Authors of papers were contacted to ask clarification where inadequate information was provided. The following data were collected from studies: first author, year of publication, ethnicity, sample size, allele distribution in cases and controls and, a genotyping method used. The different ethnic groups were classified as Caucasian and Asian.P- values were calculated two-sided and P = 0.05 was statistically considered as significant. The Hardy–Weinberg equilibrium (HWE) was calculated by the Chi-square test in control groups, to verify the representation of the study population. The correlation between the polymorphisms and male infertility risk was calculated via assessment of odds ratios (ORS) and 95% confidence interval (CI). Pooled ORs and their 95% CIs for dominant, codominant, and recessive inheritance models were calculated.All analyses were performed using STATA 14.1 software . In this research, P < 0.05 (Forest plot). To indicate the presence of heterogeneity, the random effect model was selected; otherwise, the fixed-effects model was chosen.The significance of the pooled OR was assessed by Z-test and p-value less than 0.05 was introduced as a significant publication bias. Sensitivity analysis was conducted to measure the effect by ignoring a single study at a time.Of all the models available, a funnel plot was designed to assess the publication bias, and an asymmetrical plot was considered a sign of these impresses. The degree of the asymmetry in these plots was measured with the help of Egger’s test and a According to the present investigation, for both polymorphisms, 137 relevant articles were identified.The process of literature retrieval and selection is shown in Fig. P = 0.00). Also, the frequency of TC genotype to CC, the frequency of TT genotype to the combination of TC + CC genotypes, and the frequency of TT + TC combined genotype to CC showed a significant increase (P = 0.006)  Table , Fig. 2.P = 0.002), TC had significant increased frequency to CC (P = 0.038), TT frequency to the TC + CC genotypes (P = 0.007), and TT + TC showed higher frequency than CC genotype (P = 0.005). In addition, we found that 677C/T polymorphism was significantly associated with the male infertility risk in the Asian population where the TT to CC genotype (P = 0.01), TC to CC (P = 0.002), TT to TC + CC (P = 0.084), and combination of TT + TC genotype frequencies to the CC (P = 0.00) showed significant difference.When the results were stratified by ethnicity, the following positive associations were also observed in the Caucasian population where TT genotype had significantly elevated frequency to the CC genotype (P = 0.09), CA to the AA (P = 0.210), CC to the CA + AA (P = 0.194), and CC + CA to the AA (P = 0.084) genotype was not significantly different, Table P = 0.530), CA to AA genotype (P = 0.167), CC to CA + AA genotypes (P = 0.405), and CC + CA genotype to AA (P = 0.237) did not show significant increase in the frequency. Also, in Asian population, there was no significant difference between the examined genotypes as the frequency of CC to AA (P = 0.168); CA to AA (P = 0.071); CC to CA + AA (P = 0.305), and CC + CA to AA (P = 0.073) was observed in the population.We attained that the 1298A/C polymorphism was not associated with risk of male infertility where the frequency of CC to the AA genotype .Begg’s funnel plot and Egger’s test were conducted to assess the publication bias of the literature. The shape of funnel plots reveals a bit of evidence of funnel plot asymmetry Fig. . The staMale infertility is a disorder that is affected by environmental and genetic factors. MTHFR gene plays a crucial role in folate metabolism as a result of spermatogenesis. Studies in different populations have shown contradictory results for the association between MTHFR gene polymorphisms and male infertility , 53. TheOn the other hand, there was no significant association between the variant genotype of the MTHFR 1298A/C polymorphism and the risk of male infertility in Caucasian and Asian populations.P < 0.001) [In a meta-analysis study by H. Nikzad, et al. 2015, an MTHFR 677C/T polymorphism showed a significant association between allelic, dominant and codominant models and the risk of male infertility (< 0.001) . Concern< 0.001) , F. Tütt< 0.001) , N. Gupt< 0.001) , and W. < 0.001) , but in < 0.001)  and A.S.< 0.001) .In another study, B. Wei, et al.  showed tIn recent years, new genetics techniques such as GWAS have been used to evaluate the genetic variation of many diseases, including infertility, especially in idiopathic cases . ConcernSome of the limitations in this article were: lack of sufficient studies for the African and Latin American populations, unadjusted estimates, bias publication, and heterogeneities for MTHFR polymorphisms among all the studies. Although some modest bias could not be eliminated, this meta-analysis suggests that the MTHFR 677 T and 1298C alleles might be effective risk factors for male infertility, especially in the Asian population. We suggest screening genetic test for the MTHFR SNP’s as new male infertility biomarkers for Asian people.Also, nutritional management and folate administration under proper medical care could lessen the risk of infertility in such populations.In summary, this meta-analysis supports the hypothesis that both MTHFR polymorphisms, MTHFR 677C > T and MTHFR 1298A/C, might be markers of male infertility susceptibility, especially in the Asian population. However, more comprehensive studies are warranted to validate our findings.Our meta-analysis results showed that the MTHFR 677C > T polymorphism was associated with an enhanced risk of male infertility, and supporting the hypothesis that the most common MTHFR polymorphisms may be a potential cause of male infertility.However, there was no relation between MTHFR 1298A/C and male sterility. Subsequent investigations should use standardized unbiased genotyping methods, homogeneous infertility patients, well-matched controls, and subgroup analysis according to the sperm concentration to confirm our findings in the future.


The aim of this meta-analysis was to explore the rate of short-term exposure PM with aerodynamic diameters ≤2.5 μm  and 95% confidence intervals (95%CI) were also calculated to assess the association between the PM2.5 was associated with the risk of MI . The heterogeneity of the studies was assessed through a random-effects model with p < 0.0001 and the I2 was 69.52%, indicating a moderate degree of heterogeneity. We also conducted subgroup analyses including study quality, study design, and study period. Accordingly, it was found that subgroups time series study design and high study period could substantially decrease heterogeneity .Twenty-six published studies were ultimately identified as eligible candidates for the meta-analysis of MI until Jun 1, 2018. The results illustrated that a 10-μg/m 3 increase in PM2.5 and MI. It is vital decision makers implement effective strategies to help improve air pollution, especially in developing countries or prevent exposure to PM2.5 to protect human health.This meta-analysis indicated that exposure – response between PM Recent findings suggest that any exposure to PM2.5 can endanger lungs and blood stream more than other pollutants and can lead to adverse cardiovascular, respiratory, and neurological disorders  as well as premature birth [2.5 particles is higher than the defined standards, even higher than those set by the World Health Organization (WHO) (WHO) [2.5 [2.5.Air pollution (atmospheric pollution) is the release of harmful particles matter into air by one or more harmful gases. It is generally assumed that any exposure to outdoor particulate matter air pollution can pose a big challenge to both public health agencies and physicians in the world, especially in the developing countries . It is are birth –8. In moO) (WHO) , 10. It HO) [2.5 . Tehran HO) [2.5 . Cohen eMyocardial infarction (MI) or cardiac infarction is generally defined as detection of an elevated cardiac troponin (cTn) value which is above the 99th percentile upper reference limit . In rece2.5 were scrutinized to detect the risk of MI and it was found that the relative risk of overall PM2.5 ranked the second after the relative risk of overall carbon monoxide [2.5 can increase the risk of MI much more than the exposure to PM10. The findings also showed that there was a moderate heterogeneity in meta-analysis of the pooled estimates, but the subgroup analyses might not pinpoint the cause of this heterogeneity. Therefore, it is imperative to investigate the source of heterogeneity in a study with more details  [2.5 and the risk of MI are focused. It should be noted that PM2.5 was a subgroup in other meta-analysis studies. The time scope in this study is broader as the original studies conducted from January 2000 to June 2018 were attempted to be incorporated. This study has been performed in University of Medical Sciences, Iran in 2018.Therefore, incidence of MI with simultaneous concentration of fine particulate matter has been extensively studied all over the world , 21 but monoxide . Moreove., 2015) . In this2.5), including even a short-term exposure such as the same day or 7 days before the occurrence of MI. The excluded studies had the following traits: 1) not being case-crossover or time-series designs, 2) non-original studies, 3) patients with MI, (4) long-term exposure to particulates matter PM10, and 5) no reported relative risk (RR)/odds ratio (OR) and 95% Confidence Interval (CI 95%). Any disagreement in arbitration for the eligibility of the paper was discussed until a consensus was reached by the reviewers.This protocol was registered in PROSPERO, the International Prospective Register of Systematic Reviews, on 2 January, 2019 (registration number CRD42019118998). The findings of this study were based on the accommodation guidelines: “Preferred reporting items for a protocol for a meta-analysis (PRISMA-P) 2015” . In thisThe two researchers (H SH and M AD) extracted the data independently using a standardized form which was particularly prepared for studies based on the Cochrane guidelines . The stu2.5 (0 and 1) was based on the following criteria. Score 0 was recorded in case that the measurements were done under the condition that more than 25% of the data was missing and not taken daily, or showed that there was no description of pollutant measurements. On the other hand, score 1 was recorded in case that measurements were conducted at least once a day, or under the condition that less than 25% of the data was missing. The arrangement of confounders was based on 0 and 1. It is believed that there is a discrepancy between the time-series and the case-crossover studies in their research methods. As a result, the modalities for the arrangement of confounders would be different. Score 1 was recorded in case that the arrangement for covariates was accomplished for multiple main covariates, containing seasonality, temperature, pressure/moisture/day, and long-term processes of week for case-crossover studies which controlled fixed and stilly varying biases using the scheme itself and also for time-series studies. Score 0, however, was recorded for the original papers without modifying the above-mentioned important variables. Finally, if a research study obtained the highest score for all components, it was defined as a high quality one, whereas a study with a minimum score (0 point) for one of the three components was regarded as a low quality one.It is commonly assumed that the quality assessment report for all qualified papers is an indispensable requirement for all case-crossover and time-series studies. Nonetheless, there are currently no valid scales available for assessing the quality of the methodology. To this end, a quality rating scale was adopted and accepted according to the previous meta-analysis . The two reviewers (ST and H AG) managed to evaluate the quality of the study independently based on the following three components . The qua2.5 and MI with relative risk (RR) or odds ratio (OR) and 95% confidence intervals (95%CI) were included. The studies with the statistical estimation risk of MI, relationship with the exposure to PM2.5 as OR, and with 95% CI were converted to RR, with 95% CI by using the above formulaAll studies which examined the relationship between the exposure to PM2, and the I2 values of more than 50% offered a significant heterogeneity [p < 0.05 was statistically significant.Since, this meta-analysis aimed to was to explore the rate of short-term exposure on MI. Furthermore, in some original study there was no extended lag patterns of short-term effects of air pollution, thus the exposure lags of 0 or 1 day were selected for calculating the RR. The criteria for heterogeneity were determined through Iogeneity . It shouStudy characteristics, the risk of bias, and study selection for the included research studies. The selection procedure for the meta-analysis is shown in Fig. 2.5 pollutant was applied in the analysis [Totally, 13,468 papers were identified. Having excluded 2397 duplicated studies, 11,071 papers were obtained, out of which 10,918 were excluded by title and/or abstract. One hundred fifty-seven full-text papers were opted out and then thoroughly assessed. Finally, 131 unrelated studies were left out and 26 papers were identified eligible for the study. The total number of participants with hospitalization for myocardial infarction was 2,250,473. The largest number of participants was 452,343, which belonged to the study by Milojevic et al., (2014), and the smallest number of participants was 208, which was reported by Akbarzadeh et al., (2018). Considering Berglind et al. (2010), the research was conducted in three cities , the lag averaging time was 2 h, and the adjusted odds ratio for the PM2.5. The heterogeneity of the research studies was evaluated through random-effects with P < 0.0001, and I2 was 69.52%, showing a moderate heterogeneity. The meta-analysis showed a significant positive association between per 10 μg/m3 elevation in PM2.5 and MI risk  at lag exposure of 0 and 0–1 days.Figure P ≤ 0.0001) with a moderate degree of heterogeneity  and in low quality studies  with a moderate to high degree of heterogeneity , which was consistent with the results of the overall analyses , a significantly higher rate of MI risk was seen in high quality studies , which was basically consistent with the overall analyses. There was also statistical significance for 7 time series study subgroup   with a moderate degree of heterogeneity (I2 = 41.78 P ≤ 0.0001) and the short study period  with a moderate degree of heterogeneity , which was consistent with the overall analyses  revealed a significantly increased MI risk in the long study period , formed two subgroups based on study quality and lag exposure, and Luo et al. (2015), formed four subgroups based on study design, study quality, lag exposure, and geographic locations subgroup analyses. However, neither of them could successfully describe the sources of heterogeneity. In the same vein, three subgroup analyses  were performed in the present study. According to the results, there was a relatively little difference among the high quality subgroup of the study, the case-crossover study design, and the short study period, all of which contributed to the overall analyses in terms of statistical significance and evidence of heterogeneity. Except for the low quality subgroup of the study which was not statistically significance, the rest were statistically significant. It is assumed that subgroup time series study design and long study period could substantially decrease heterogeneity . Time-series analysis examined both pre-adjustment and co-adjustment. The pre-adjustment method picks up temporal trends from both the health and air pollution, whereas the co-adjustment approach considers air pollution forecasters and temporal trends [2.5) and did not measure the effect of PM2.5 on the risk of incident MI, which could affect the pooled estimates of the study. All these studies might be unintentionally entered the forest plot (PM2.5) and did not measure the effect of PM2.5 on the risk of Incident MI, which could affect the pooled estimates of the study. In addition, the number of studies identified in the work of Chowdhary et al. (2018), was higher than that of the two previous studies conducted by Mustafic et al. (2012) (13) and Luo et al. (2015) (16). Also, the two reviewers in this study conducted the data extraction phase independently, appraised the papers, investigated all the data, and removed the difference through a third person. Moreover, this study was extensive enough to lower the possibility of publication bias. Even the gray studies were enveloped without any language limitation. This study had some limitations. First, the included original research papers had a great variety and substantially differed from one another in case population, number of people from below 1000 to more than 400,000 people, the city examined, and the study period from under 6 months to over 300 months. Secondly, the assessment of the effect of air pollution on MI could not be well-established as MI is a multifactorial disease associated with diabetes, hypertension, smoking, alcohol, and obesity [This study aimed to assess the association between exposure to PMl trends . It is pl trends . Time-sel trends . It is rl trends . It is tl trends . We alsol trends , Xie et l trends , and Wicl trends . All the obesity . Thirdly obesity , 61.Finally, as the people who are exposed to a mix combination of air pollutants for longest periods are those at elevated risk of adverse health, outcomes related to individual exposure to a single pollutant cannot be obtained with a high degree of certainty .2.5 and MI with more accurate estimates than analysis presented by 26 studies alone, substantiating the notion that PM2.5 levels are key factors in the development of MI hospitalizations. It is highly imperative to conduct further investigations to determine all possible causal relationships and explore potential mechanisms affecting MI. The economic burden of air pollution health-related outcomes is very significant, especially for healthcare providers. Fiscal implications attributed to air pollution are calculated as 253 million to 2.9 billion USD in Asia. Policy makers adopt more effective strategies to help improve air pollution, especially in developing countries, or prevent exposure to PM2.5 so as to protect public health.The results of this Meta-analysis demonstrated the severity of the relationship between PM


The single nucleotide polymorphism (SNP) of the tumor necrosis factor‐α (TNF‐α) G‐308A gene is the most studied regarding susceptibility to major depressive disorder (MDD). However, results have been controversial perhaps due to the heterogeneous genetic backgrounds influenced by race as well as subtypes of depression.A systematic MEDLINE search was performed to retrieve all published studies that identified the connection between the TNF‐α G‐308A gene polymorphism and the risk of MDD.p values were more than .05, suggesting that the 9 articles included in the current analysis had no publication bias.There was no statistical difference between the allele frequencies or genotypes of TNF‐α G‐308A gene and the depressive patients or healthy subjects in the five models tested. Further, subgroup analyses showed that the TNF‐α G‐308A gene polymorphism also did not confer susceptibility to poststroke, late‐life, maternal, or major depression. Publication bias analysis showed Neither the allele frequencies nor genotypes of TNF‐α G‐308A gene could be served as an independent risk factor of depression. There was no statistical difference between the allele frequency or genotype of TNF‐α G‐308A gene and the depressive patients or healthy subjects in the five models tested. Further, subgroup analyses showed that the TNF‐αG‐308A gene polymorphism also did not confer susceptibility to post‐stroke, late‐life, maternal or major depression. It is accompanied by psychological and physiological symptoms, such as lack of motivation, changes in appetite, inability to feel pleasure, cognitive difficulties, and retraction from social interaction  The study utilized a case–control design, and all cases must be clearly diagnosed as depression as well as all controls must not with mental illness; (b) the study examined the associations between TNF‐α G‐308A gene polymorphism and depressive disorders; (c) the data are allowed to be calculated by ORs (odds ratios) and 95% CIs ; and (d) the articles were limited to English language. Research that does not meet the inclusion criteria will be excluded. The selection of studies was completed by two independent investigators (Wang and Cao), through reviewing titles, abstracts, and full text. When disagreement arose, it was decided by all researchers.2.3Data from selected studies were extracted independently by two researchers (Wang and Cao) and then were evaluated in terms of the methodological quality assessment scale. The following details were collected: first author's surname, publication year, type of depression, ethnicity of participants, sizes of case sample and control sample, genotyping methods, diagnostic criteria genotype distribution, HWE score, and NOS score. The methodological quality of the eligible researches was assessed by the NOS. Final scores ranged from 0 to 9 points; scores of 0–5 and 6–9 were deemed to be low and high quality, respectively. Divergences were handled by discussion among all researchers.2.4p > .05, the genotypes’ frequency of control groups was consistent with HWE. The following five genetic comparison models were evaluated: allele genetic model (G vs. A), homozygote genetic model (GG vs. AA), heterozygote genetic model (GA vs. GG), recessive genetic model (AA vs. GA + GG), and dominant genetic model (GG vs. AA + GA). The pooled effect size was estimated by ORs and its 95% CI. The heterogeneity between the included studies was based on chi‐square test and I2 statistics. If I2 < 50%, it was considered that substantial heterogeneity could be ignored, and then fixed‐effect mode was served to evaluate the pooled ORs and 95% CIs. Apart from this, we performed subgroup analyses by the type of depression. Begg's funnel plot was served to evaluate potential publication bias.The software Stata 12.0 and SPSS 21.0 were used to analyze the data. The genotypes’ frequencies of control groups were tested by HWE using the chi‐square test. If 33.1n = 3); they were not gene–disease association research about the role of TNF‐α G‐308A gene polymorphism played in depressive disorders (n = 14); or they were not randomized controlled trial (n = 3). Eventually, 9 reports satisfied the criteria were included in this meta‐analysis .For late‐life depression, there was no statistical difference under five genetic models: .For maternal depression, there was no statistical difference under five genetic models: . Unfortunately, TNF‐α G‐308A gene polymorphism also did not affect the susceptibility of different types of depression.3.5p value was greater than .05 .In this study, a total of nine case–control studies encompassing 717 cases and 2,414 controls were included in this meta‐analysis. We performed HWE test on the control groups, and the We also performed subgroup analyses on the type of depression. However, there were still no statistically significant associations in the subgroup analyses, which could first confirm that the connection between TNF‐α G‐308A gene polymorphisms and MDD was not affected by the type of depression. This discrepancy might be due to the small sample size of this meta‐analysis study, especially the small number of cases. Thus, the sampling error could not be ignored. What's more, it was a well‐known fact that there was a relationship between depression and the increased TNF‐α plasma level. However, evidence suggested that the genetic predisposition to a higher synthesis of this proinflammatory cytokine was associated with a risk of some physical illnesses rather than depression itself (Mihailova et al., However, there were still many shortcomings in this meta‐analysis. First, the number of researches we included and the sample sizes of subgroups were limited and the number of cases was less than the controls. Second, depression is influenced by multiple risk factors, such as race, age, sex, and interaction between environment and gene. They all may affect the susceptibility to depressive disorders. The studies we included mainly concerned Asian, elderly and women, which might result in a selective bias. Third, language bias, due to the English limitation in language, some potential researches may be omitted. Nonetheless, this meta‐analysis provides an evaluation about the potential effect that the TNF‐α G‐308A gene polymorphism has on depression.5In summary, neither the allele frequencies nor genotypes of TNF‐α G‐308A gene could be served as an independent risk factor of depression. What's more, this relationship was also not related to the type of depression. Considering that the incidence of depression is polygenic and it relates to many factors, such as race, environment, gender, and age, the connection between the SNP of TNF‐α G‐308A gene and depression is still required to be set forth by large sample case–control studies.All authors declare no competing interest. All funding sources supported only the execution of the experiments. We strictly disclosed that all funders had no role in the study design, data collection and analysis, decision to publish, or preparation of the manuscript.X Wang, HX Zhang, XY Zhou, Q Chen, and XL Cao contributed to literature search, data extraction, and data analysis. K MA and W Shi contributed to the project design and paper writing.Our study does not require ethical approval.