I2 = 52.1%, Pfor heterogeneity = 0.008). Subgroup analyses according to characteristics including geographic location and study design showed consistent results with the overall result. In summary, findings from the present study indicated that dietary vitamin C intake is not associated with the risk of ovarian cancer.Changes in dietary vitamin C intake have been related to the risks of various cancers. However, the association between dietary vitamin C intake and the risk of ovarian cancer has not been fully determined. A meta-analysis was performed to evaluate the relationship between vitamin C intake and ovarian cancer risk. Observational studies that evaluated the association between vitamin C intake and ovarian cancer risk were identified via systematic search of PubMed and Embase databases. A random-effect model was used to combine relative risk (RR) with corresponding 95% confidence intervals (CIs). As a result, 16 studies (5 cohort studies and 11 case–control studies) with 4553 cases and 439,741 participants were included. Pooled results showed that dietary vitamin C intake had non-significant association on the risk of ovarian cancer (RR = 0.95, 95%CI = 0.81–1.11,  According to Globocan’s estimate in 2018, cancer is the second leading cause of death worldwide, with an estimated 9.6 million deaths . PreviouThe present study was performed and reported according to the Preferred Reporting Items for Systematic Reviews and Meta-Analysis Statement issued in 2009 . ElectroTwo authors independently conducted the literature search and selected the studies by reading the titles, abstracts, and full-text articles, and any disagreement was resolved by an additional author until consensus was reached.Studies were included if they met the following criteria: (1) Patients: patients diagnosed with ovarian cancer and ≥18 years of age; (2) Study design: all the observational studies were acceptable; (3) Interested and outcomes: the studies should assess the association about dietary vitamin C intake on the risk of ovarian cancer; (4) Data: the study should provide the available data of relative risk (RR) and 95% confidence intervals (CI). Furthermore, we only included studies that explore the relationship about dietary vitamin C intake only, but not vitamin C supplement, on the risk of ovarian cancer.The exclusion criteria were as follows: (1) case reports, conference abstracts, letters, editorials, reviews; (2) overlapping or duplicate studies; (3) irrelevant studies; (4) no available data of RR and 95%CI.One author conducted the data collection according to a standard flowchart, while another author checked it. If any disagreement was detected, they discussed the issue until consensus was reached. The data collected included the family name of the first author, publication year, country, cases and participants, age, category of vitamin C intake, the value of RR and 95%CI, adjustment for factors.I-square and P values for Q statistic, and significant heterogeneity was defined as an I-square >50.0% or P < 0.10 [The combined RR and 95%CI was pooled using STATA software  with a random-effects model . HeterogP < 0.10 ,15. The P < 0.10  and EggeP < 0.10  were useThe initial electronic searches produced 243 articles and one article was identified from the reference of reviews; of them, 205 were excluded due to irrelevant topics and duplication. The remaining 39 full articles were reviewed; of them, 16 articles  involvinI2 = 52.1%, Pfor heterogeneity = 0.008) . Subgroup analysis by study design got a consistent result both in case-control studies  and in cohort studies .Pooled RR suggested that highest category of dietary vitamin C intake was not associated with the risk of ovarian cancer  , when coP = 0.790). Sensitivity analysis showed that no single study had a potential influence on the pooled result  was found in the overall result about vitamin C intake on the risk of ovarian cancer. As far as we know, between-study heterogeneity is common in a meta-analysis, and it is an essential part to explore the sources of heterogeneity. We used meta-regression to explore the causes of heterogeneity for covariates of publication year, study design, geographic locations, and number of cases. We found that geographic locations (P = 0.017) may be a covariate that could influence this high heterogeneity. As seen in I2 = 2.0%, Pfor heterogeneity = 0.427). The I2 in Europe and Asia was not detected due to only one study in each group. Even though, the result in North American populations was consistent with the overall result.Significant heterogeneity , total energy intake, duration of oral contraception use, and so on were not fully adjusted in every study. Fourth, almost all the included studies come from North America; therefore, more studies conducted in other populations are warranted to further explore the association between geographic locations and ovarian cancer risk. Finally, since we did not get a positive association between dietary vitamin C intake and the risk of ovarian cancer, the dose–response analysis between them was not performed.In summary, findings from the present study indicated that dietary vitamin C intake is not associated with the risk of ovarian cancer. Further large-scale cohort studies should be conducted to explore the effect of dietary vitamin C intake on the risk of ovarian cancer due to some limitations existed in our research.


Polymorphisms of methylenetetrahydrofolate reductase (MTHFR) in hormone metabolism pathways might cause metabolic disturbances and contribute to the development of polycystic ovary syndrome (PCOS) and ovarian cancer, but the published studies were inconsistent. The aim of the present study was to evaluate the MTHFR C677T (rs1801133) and A1298C (rs1801131) gene polymorphisms in the risk of PCOS and ovarian cancer by meta-analysis. A comprehensive electronic search was conducted in databases for studies published from 1995 to 2020. The pooled ORs were calculated by Revman 5.2 software. Twenty-nine articles including 45 case–control studies were included. We found that MTHFR C677T polymorphisms were correlated with elevated PCOS risk , which were more obvious in Middle Eastern subgroup. MTHFR A1298C polymorphisms were also associated with overall PCOS susceptibility , which were mainly reflected in Asian subjects. For ovarian cancer, MTHFR C677T polymorphisms were only related with elevated ovarian cancer risk in Asian population, while no significant association was found for A1298C polymorphisms. This meta-analysis suggested that MTHFR C677T and MTHFR A1298C polymorphisms were correlated with elevated PCOS risk. MTHFR C667T only posed a higher risk for ovarian cancer in Asians instead of other populations, while MTHFR A1298C polymorphisms were not related to ovarian cancer risk. Further studies are needed to validate the conclusion. Polycystic ovary syndrome (PCOS) is one of the most common endocrine malfunctions, reportedly affecting 5–10% of reproductive age women . ClinicaWith emerging genetic findings such as homologous repair deficiency in gynecologic malignancies, more and more attention was focused on the genetic root of both diseases. Existing clinical studies suggest that both genetic background and environmental factors with a cluster of metabolic disturbances might contribute to the development of PCOS and ovarian cancer ,12. It wIn 1999, Gleuck first reported the association between MTHFR C677T polymorphisms and PCOS, ever since several similar researches have been conducted . In 2014A comprehensive electronic search was performed using PubMed, Embase, Medline (Ovid), Weipu, CNKI and Wanfang databases for studies published from March 1995 to February 2020. The following subject terms and keywords were used: “methylenetetrahydrofolate reductase”, “MTHFR”, “PCOS”, “polycystic ovarian syndrome”, “ovarian cancer” “polymorphism”, “variant” and “mutation”. The search was updated every week until February 20, 2020.Articles fulfilling the following criteria were included: (i) studied the MTHFR C677T and A1298C polymorphisms in PCOS or ovarian cancer patients, (ii) provided sufficient data in both case and control groups to calculate the odds ratios (ORs) and the corresponding 95% confidence intervals (95% CIs), (iii) mentioned specific diagnostic criteria for PCOS (the NIH criteria or the Rotterdam criteria) and described patients’ symptoms in details, (iv) case–control studies. When duplicate data were present in different articles, only the latest one would be taken into consideration. In addition, Newcastle–Ottawa Scale (NOS) was used to assess the quality of the observational studies included. Three aspects of selection, comparability, and exposure  were carefully assessed. Studies of moderate or high quality were included (score of 5 or higher). As such, articles that didn’t fulfill the criteria mentioned above were excluded.All potential studies were investigated by two independent viewers. The following items were extracted: first author, year of publication, ethnicity, matched parameters, target genotypes, genotyping methods, participant numbers and genotype distributions in cases and controls. Any discrepancies were resolved by discussion with a third reviewer until a consensus was reached.2Itest. When the 2Ivalue was less than 50%, a fixed-effects model was used otherwise a random-effects model was applied. The Z test was performed to determine the significance of the pooled ORs where P less than 0.05 was considered statistically significant. The presence of publication bias was evaluated by visually inspecting the asymmetry in funnel plots and Egger’s test. All statistical analyses were performed using Revman 5.2 software  except the Egger’s test, which was conducted using STATA 14.0 .Meta-analyses were undertaken using the Revman 5.2 softer ware  to calculate the pooled ORs and corresponding 95% CIs. SNPs of MTHFR C677T and A1298C were considered as binary variables. Different contrast models were judged: (i) homozygous mutants contrast, (ii) homozygous and heterozygous mutants contrast, (iii) heterozygous mutants contrast, (iv) homozygous mutants contrast in homozygotes, (v) mutant allelic contrast. Besides the overall comparisons, we also performed subgroup analyses stratified by ethnicities in consideration of population differences. Heterogeneity assumptions were tested using Higgins After primary search, 178 results were retrieved. In our further review, 129 articles were not related to MTHFR polymorphisms and PCOS risk by reading titles and abstracts and thus were excluded. 11 articles focused on MTHFR polymorphisms in different diseases or complication of PCOS like thrombophilia and pregnancy loss. Five studies estimated genetic variants other than C677T and A1298C. Four articles were excluded for non-case–control studies such as meta-analysis and lab research . Among t2 =I 64%) by pooling 9614 alleles together. In consistent with this, significant association was also found in T containing genotypes (TT+CT or TT) and TT genotypes alone  among 4807 participants  B. When sGenetic distributions and pooled ORs were shown in The results of MTHFR A1298C polymorphisms in ovarian cancer were straightforward D. The meP>0.05, data not shown).The shapes of the funnel plots appeared to be symmetrical in all genetic comparisons, indicating the lack of publication bias and the reliability of the meta-analysis in both overall and subgroups . We furtPolycystic ovary syndrome consists a heterogeneous endocrinological disorder, which is characterized by oligo- or anovulation, hyperandrogenism and polycystic ovaries . These pIn the present study, we performed an updated meta-analysis to explore MTHFR C667T and A1298C polymorphisms in the PCOS and ovarian cancer risk. Twenty-nine articles including 45 case–control studies were included. We found that MTHFR C677T polymorphisms were correlated with elevated PCOS risk, which were more obvious in Middle Eastern subgroups. Also, MTHFR A1298C polymorphisms were associated with overall PCOS risk, which were mainly reflected in Asians. For ovarian cancer, MTHFR C677T polymorphisms were only related with elevated ovarian cancer risk in Asians, while no significant association was found for A1298C polymorphisms. Several points could be noted based on current results. First, both MTHFR C677T and MTHFR A1298C polymorphisms were correlated with elevated PCOS risk. Although polymorphisms of A1298C seem to be less prevalent than C677T, the current findings suggest that any impairment on folate methylation could impact the development of PCOS, regardless of its frequency . As suchDespite our efforts to pool the results of currently published studies, some disadvantages of the present meta-analysis still existed. First, the number of enrolled studies was so far the largest among all relevant meta-analysis but was still limited. It is possible that the results of further investigations and unpublished articles might be different from the present conclusion, thus cautions should be paid to explain the results. Second, this meta-analysis was based on unadjusted estimations. Although matched parameters were carefully reviewed, it is known that some unreported risk factors like family history and genetic information  were also important in the development of PCOS or ovarian cancer . MeanwhiTo our knowledge, the present study was the most updated meta-analysis exploring the association between MTHFR C677T and A1298C polymorphisms and the PCOS and ovarian cancer. It was also the first study to explore the MTHFR polymorphisms in both diseases. Although it was hard to conclude a concrete association between PCOS and ovarian cancer under the mechanism of MTHFR gene polymorphisms, the present study suggested that MTHFR C677T and A1298C polymorphisms were correlated with elevated PCOS risk while MTHFR C677T polymorphisms only posed a higher risk for ovarian cancer in Asians. Further studies are needed to validate the conclusion.


This meta-analysis based on prospective cohort studies aimed to evaluate the associations of lipid profiles with the risk of major adverse cardiovascular outcomes in patients with coronary heart disease (CHD).The PubMed, Embase, and Cochrane Library electronic databases were systematically searched for prospective cohort study published through December 2019, and the pooled results were calculated using the random-effects model.Twenty-one studies with a total of 76,221 patients with CHD met the inclusion criteria. The per standard deviation (SD) increase in triglyceride was associated with a reduced risk of major adverse cardiovascular events (MACE). Furthermore, the per SD increase in high-density lipoprotein cholesterol (HDL-C) was associated with a reduced risk of cardiac death, whereas patients with lower HDL-C were associated with an increased risk of MACE, all-cause mortality, and cardiac death. Finally, the risk of MACE was significantly increased in patients with CHD with high lipoprotein(a) levels.The results of this study suggested that lipid profile variables could predict major cardiovascular outcomes and all-cause mortality in patients with CHD. Cardiovascular disease (CVD) is the leading cause of morbidity and mortality worldwide, accounting for nearly 30% of the total deaths based on the World Health Organization (WHO) statistics. The WHO reported that about 17.3 million people have died of CVD in 2016 and that this number will reach up to 23.3 million by 2030 . HoweverNumerous studies have demonstrated the role of the lipid profile in the progression of CVD. Increases in triglyceride (TG) and total cholesterol (TC) levels could affect the constriction and abstraction of vessels in the heart, which are significantly correlated with the risk of CVD . MoreoveThe ACC/AHA guideline used the intensity of statin therapy as the goal of treatment and recommend the maximum appropriate intensity of statin without adverse effects should be applied . The ESCThis review was conducted and reported according to the Preferred Reporting Items for Systematic Reviews and Meta-analysis Statement issued in 2009 . The eliTwo authors independently performed the literature search and study selection following a standardized approach, and any inconsistencies between these 2 authors were resolved through a group discussion. The studies were judged for eligibility based on (1) study design (must be a prospective cohort study), (2) participants , (3) investigated variables ), (4) outcomes , and (5) the investigated outcomes needed reported ≥ 2 cohorts. Studies with a retrospective observational design were excluded because of various confounding factors that could affect the results.Data collection and quality assessment were conducted by 2 authors, and any disagreement was resolved by a third author by referring to the original works. The collected information from the retrieved studies included the first authors’ surname, publication year, country, sample size, age at baseline, percentage of men, disease status, follow-up duration, exposure, adjusted factors, and investigated outcomes. We selected the effect estimate that was maximally adjusted for potential confounders if a study reported several multivariable-adjusted effect estimates. Study quality was assessed using the Newcastle–Ottawa Scale, which was based on selection (4 items: 4 stars), comparability (1 item: 2 stars), and outcome (3 items: 3 stars). The “star system” for the assessment of each individual study ranged from 0 to 9 stars .2 and Q statistics, and P < 0.10 was considered to indicate a significant heterogeneity [P values are 2-sided, and P < 0.05 was considered to indicate statistical significance for all included studies. Statistical analyses were performed using STATA software .The role of the lipid profile on the prognosis of patients with CHD was assessed based on the effect estimates and corresponding 95% confidence intervals (CIs) in each individual study. The summary relative risks (RRs) with 95% confidence intervals (CIs) were calculated using the random-effects model , 13. Theogeneity , 15. Senogeneity . Publicaogeneity , 18. AllThe electronic searches of PubMed, Embase, and the Cochrane Library yielded 2318 records, of which 2231 were excluded for being duplicates and for having irrelevant topics. A total of 87 studies were selected for further evaluation, and 21 prospective cohort studies with a total of 76,221 patients with CHD were selected for the final meta-analysis , all-cause mortality , and cardiac death . The role of the per SD increase in TC on the risk of MACE in patients with CHD was altered when the study by Winter et al. [The number of studies (cohorts) available for the analysis of the association of each outcome with the per standard deviation (SD) increase in TC was 5, 2, and 3 for MACE, all-cause mortality, and cardiac death, respectively  and cardiac death .The number of studies (cohorts) available for the analysis of the association of each outcome with low versus high TC was 2, and 2 for MACE, and cardiac death, respectively . Moreover, high TG was not associated with the risk of MACE .Data for the association of per SD increase in TG with the risk of MACE was available in 4 studies (cohorts) Fig. . OverallP = 0.640; significant heterogeneity). The sensitivity analysis indicated that the risk of MACE was stable and not altered by the sequential exclusion of individual studies  Fig. . There wP = 0.512; significant heterogeneity), all-cause mortality , and cardiac death .The number of studies (cohorts) available for the analysis of the association of each outcome with high versus low LDL-C was 3, 2, and 2 for MACE, all-cause mortality and cardiac death, respectively , whereas it was not associated with the risk of MACE  and all-cause mortality . The sensitivity analysis indicated that the per SD increase in HDL-C might produce a protective effect against MACE  available for the analysis of the association of each outcome with the per SD increase in HDL-C was 5, 4, and 4 for MACE, all-cause mortality, and cardiac death, respectively Fig. . We obseP = 0.002; with no evidence of heterogeneity), all-cause mortality , and cardiac death . The pooled results for MACE, all-cause mortality, and cardiac death varied after excluding individual studies, owing to marginal 95% CI  available for the analysis of the association of each outcome with low versus high HDL-C was 7, 5, and 5 for MACE, all-cause mortality, and cardiac death, respectively Fig. . OverallP = 0.001; with no evidence of heterogeneity).Data for the association of high versus low lipoprotein(a) with the risk of MACE was available in 3 studies (cohorts) Fig. . The sumThis systematic review and meta-analysis of prospective studies evaluated the role of the lipid profile on the risk of MACE, all-cause mortality, and cardiac death in patients with CHD. This comprehensive quantitative study included a total of 76,221 patients with CHD from 21 prospective cohort studies with a wide range of patient characteristics. The results suggested that in patients with CHD, increased TG was associated with a reduced risk of MACE. Moreover, low HDL-C was associated with an increased risk of MACE, all-cause mortality, and cardiac death. Finally, high lipoprotein(a) was associated with an increased risk of MACE in patients with CHD.No previous systematic review and meta-analysis has focused on this topic, although numerous studies have illustrated the effects of lipid profile management in the secondary prevention of major cardiovascular outcomes. Gutierrez et al. conducted a meta-analysis of 11 randomized controlled trials and found that the use of statin for lipid profile management significantly reduced the risk of cardiovascular events in both sexes, whereas statin therapy had no significant effect on the risk of stroke and all-cause mortality in women . NavaresThe current study indicated that in patients with CHD, low TC was not associated with the risk of MACE and cardiac death. The potential reason for this result could be the twice higher prevalence of noncardiac death in the low TC group and cancer being the most frequent cause of noncardiac death. Moreover, we observed that patients with increased TG and LDL-C was not associated with the risk of MACE, all-cause mortality, and cardiac death. The result for TG was based on another study  that inc2, whereas an increased risk of all-cause mortality and cardiac death was noted with low HDL-C level when the body mass index was < 25.0 kg/m2 [The summary results indicated that CHD patients with low HDL-C have an excess risk of MACE, all-cause mortality, and cardiac death. Several included studies reported consistent results. Seo et al. found that patients with low HDL-C had a significantly increased risk of MACE after 832 days of follow-up, whereas low HDL-C had no significant impact on all-cause mortality and cardiac death . The pot.0 kg/m2 . Ding et.0 kg/m2 . They po.0 kg/m2 , 48.This study showed that the risk of MACE was significantly increased in CHD patients with high lipoprotein(a), which was consistent with the result of a previous study that found that the reduction in lipoprotein(a) was independently correlated with a reduced risk of MACE . FurtherThe limitations of this meta-analysis are as follows: (1) the cutoff values of lipid profile variables varied among the included studies, which could affect the effect size of the risk of MACE, all-cause mortality, and cardiac death in patients with CHD; (2) the dose–response analysis were not conducted owing to it requires that the distributions of cases and persons or person-years and effect estimate (RRs or HRs) with the variance estimates for at least 3 quantitative exposure categories; (3) several outcomes were reported in only a few studies, and stratified analyses according to patients’ characteristics were not described; (4) heterogeneity among included studies were substantial, which not fully interpret by using a sensitivity analyses. These results could introduce by various disease status, background therapies, cutoff value of lipid profiles, and adjusted factors; (5) the adjusted factors were different among the included studies, which could affect the prognosis of patients with CHD; (6) unpublished data were not identified, which might cause an overestimation of the summary effect estimate; and (7) the role of apolipoprotein in patients with CHD was not investigated in the included studies. Further studies investigating any potential role of apolipoprotein on the progression of major cardiovascular outcomes in patients with CHD are needed.The results of this study suggested that the lipid profile could affect the progression of MACE, all-cause mortality, and cardiac death in patients with CHD. Further large-scale prospective studies should be conducted with a focus on patients with specific characteristics to investigate the secondary prevention of major cardiovascular outcomes and mortality.Additional file 1. Search strategy in PubMed.Additional file 2. Sensitivity analysis.Additional file 3. Funnel plot.


Infections may play a role in the etiology of childhood cancer and immunizations may be protective because vaccinations stimulate the immune system. Observational studies reported inconsistent associations between vaccination and risk of childhood cancer. Since a synthesis of the evidence is lacking, we conducted a meta-analysis stratified by histological and site-specific cancer.We performed a systematic review (CRD42020148579) following PRISMA guidelines and searched for literature in MEDLINE, Embase, and the Science Citation Index databases. We identified in three literature databases 7,594 different articles of which 35 met the inclusion criteria allowing for 27 analyses of 11 cancer outcomes after exposure to nine different types of vaccinations. We calculated summary odds ratios (ORs) and 95% confidence intervals (CIs) using random effects models.We observed four inverse associations between childhood leukemia and certain vaccines as well as the number of vaccinations: OR 0.49 (95% CI = 0.32 to 0.74) for leukemia death after bacillus Calmette–Guérin vaccination; OR 0.76 (95% CI = 0.65 to 0.90) for acute lymphoblastic leukemia after Haemophilus influenzae type b vaccination; OR 0.57 (95% CI = 0.36 to 0.88) for leukemia; and OR 0.62 (95% CI = 0.46 to 0.85) for acute lymphoblastic leukemia after three or more vaccinations of any type. All other conducted analyses did not show any associations.The results are consistent with the hypothesis that vaccinations reduce the risk of childhood leukemia. However, the robustness and validity of these results is limited due to the small number, substantial heterogeneity, and methodological limitations of available studies. Childhood cancers include a broad spectrum of histological and site-specific cancers occurring before 18 years of age . An estiThe peaks in the incidence of acute leukemia in children aged 2 to 5 years that parallel the peaks in infection rates supports the hypothesis that immunological risk factors are involved in the etiology of leukemia –16. HoweTo summarize evidence on the association between vaccination and childhood cancer, only two meta-analyses have been conducted so far. One focusing on poliomyelitis vaccines, simian virus 40 and human cancer was published in 2004  and anotFollowing the meta-analysis of observational studies in epidemiology (MOOSE) guidelines  and the To identify studies on the association between vaccination and childhood cancer, we systematically searched the literature databases MEDLINE, Embase, and the Science Citation Index for relevant articles published before November 2020. We used subject headings and keywords in English depending on the search structure of the literature database to combine the references related to the population, the exposure, and the disease. The search was not restricted by language filters and no date limits or other filters were used. A detailed description of the search strategy is provided in Duplicates found by the three literature searches were deleted using EndNote. Two independent researchers performed the screening of titles and abstracts for relevant publications and conducted the full-text review of selected articles. Studies were considered for inclusion in the review if they met the following three criteria: They were 1) an original epidemiological study that examined the influence of vaccination on the risk of childhood cancer or cancer death; 2) a proper reference group without cancer or without the same cancer as the investigated outcome; and 3) the recommended first application of the studied vaccine should be before age 10 (e.g. exclusion of human papillomaviruses vaccination). Detailed exclusion criteria are shown in P value for measures of association and without the number of cases and controls, overlap with other study, less than three studies on an outcome or an exposure), statistical model, risk estimate , and number of subjects per group. A third reviewer adjudicated inconsistencies between the two original reviewers.Two reviewers extracted a predetermined set of data for each risk estimate on vaccinations and childhood cancer independently from each publication 2A–C. ThThe quality of each study was assessed by applying the Newcastle-Ottawa Scale (NOS) , which h2 was analyzed with the Q statistic [P value for heterogeneity (P)]. We explored whether heterogeneity could be reduced by omitting each study in turn from the meta-analysis . For analyses with five or more included studies, publication bias was evaluated using funnel plots and the tests described by Egger et al.    or ExcelOf the 9,590 identified articles, 1,996 were duplicate search results from the three literature databases and 7,349 articles were excluded on the basis of title and abstract screening, leaving 245 articles for full-text evaluation Figure 1–Characteristics of the 35 studies included in the meta-analysis are provided in –Among 27 specific analyses on 11 different childhood cancer outcomes after exposure to nine different types of vaccinations –4, we o2 = 36%; N = 4; P value = 0.20) for bacillus Calmette–Guérin (BCG) vaccination compared to children without this vaccination  vaccination was assessed based on five studies, the OR was 0.76  and one for ALL  after three or more vaccinations of any type. For both associations, all studies reported a risk estimate smaller than 1.0. Omitting each study in turn from the two analyses revealed obvious outlier studies for both associations. The heterogeneity across studies disappeared after exclusion of the German studies . However, trends required for the dose-response analysis could only be calculated for the studies of Kaatsch , for cancer after poliomyelitis vaccination , and for lymphoma after BCG vaccination . In each of these analyses, heterogeneity disappeared (I2 = 0%) after exclusion of one specific outlier study. However, the outlier studies differ in different analyses  and overall childhood cancer risk, cancer death, or site-specific cancers  are needed to assess the association between different types of vaccinations and specific childhood cancers.The original contributions presented in the study are included in the article/MM designed the meta-analysis, the analytic strategies and the detailed quality score, and directed its implementation, including quality assurance and control. She was in charge of creating the manuscript and conducting the literature review and the meta-analysis. LB and PK assisted in conducting the literature review and meta-analysis including acquisition of data, quality assessment, and preparation of figures and tables. LC assisted conducting the literature review including database searches, selections, and assessment of publications. IL, UH, and WA contributed to the analyses and interpretation of the results. WA assumes an overall scientific responsibility during the creation of the publication and thus assumes a particular responsibility for the quality and integrity of the publication. All authors revised the manuscript critically for important intellectual content and agreed to be accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved. All authors contributed to the article and approved the submitted version.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


PPAR-γ2 polymorphisms and DR risk by performing a systematic search and quantitative analysis. Overall, fourteen articles involving 10,527 subjects were included. The pooled results did not reveal an association between PPAR-γ2 rs1801282 C/G and DR susceptibility in the overall population . Furthermore, heterogeneity tests, cumulative analyses, sensitivity analyses, and publication bias analyses were conducted and showed that the results were robust. Similarly, race-based subgroup analyses and other subgroup analyses did not reveal an association between the rs1801282 C/G and DR susceptibility. In addition, no significant association was observed between PPAR-γ2 rs3856806 C/T polymorphism and DR risk . Overall, based on the current sample size and the level of evidence presented in the study, the results suggest that PPAR-γ2 gene polymorphisms are not associated with DR risk.A close association between peroxisome proliferator-activated receptor-γ2 (PPAR-γ2) and the development of diabetic retinopathy (DR) has been previously suggested. Herein, a meta-analysis was conducted to explore the association between  Diabetic retinopathy (DR) is the most common microvascular complication of diabetes mellitus (DM), which originates from the damage of capillary endothelial cells in DM, leads to the exudation of liquid components from the vessels into the tissues, and then causes retinopathy and dysfunction. DR is characterized by a series of typical pathological changes, including retinal detachment, vitreous hemorrhage, retinal neovascularization, neovascular glaucoma, and recurrent proliferative retinopathy, which are the major causes of sight loss and blindness in working-age adults worldwide , 2. BetwThe etiology and pathogenesis of DR have been studied extensively, but the underlying mechanisms remain unclear. Chronic exposure to hyperglycemia is one of the most common risk factors that ultimately results in microvascular damage and retinal dysfunction coupled with a series of biochemical and physiological changes . Other fin vivo. PPAR-γ comprises four functional isoforms with alternatively spliced mRNAs. PPAR-γ2 is highly expressed in adipocytes and is the key fat-selective PPAR subtype [Peroxisome proliferator-activated receptor-γ (PPAR-γ) belongs to the nuclear receptor superfamily, which mainly mediates ligand-dependent transcriptional regulation , 15. PPA subtype . Increas subtype . The ret subtype . PPAR re subtype –21. In f subtype .PPAR-γ2 gene is located on chromosome 3p25 and comprises over 100 kb DNA bases including 9 exons. Rs1801282 C/G and rs3856806 C/T polymorphisms are the most widely studied loci of PPAR-γ2. These polymorphisms have been extensively studied in different ethnic groups and have been found to be associated with several metabolic diseases, including diabetes and the associated complications. In 1999, Ringel et al. conducted the first case-control study on the association between rs1801282 C/G and DR risk in the German population, and did not report any significant relationship with DR risk [PPAR-γ2 polymorphisms and DR risk.The  DR risk . Malecki DR risk . MoreoveThe systematic search initially yielded 383 relevant studies. The selection process is shown in Thirteen case-control studies including 4,369 patients and 5,658 controls focused on the association between the rs1801282 C/G polymorphism and DR risk –34, 36. 2=57.8%; CG vs. CC: OR=0.90, 95% CI=0.71-1.15, P=0.40, I2=49.8%; GG vs. CC: OR=0.76, 95% CI=0.45-1.27, P=0.29, I2=17.6%; CG+GG vs. CC: OR=0.85, 95% CI=0.69-1.06, P=0.15, I2=62.9%, 2=0%)  . Moreove%, 2=0%) .Heterogeneity was observed in allele contrast, homozygote and dominant models. Meta-regression analysis was conducted with the above-mentioned factors, and no significant factors were identified as sources of the existing heterogeneity .A cumulative meta-analysis (CMA), which aims to aggregate accumulating evidence with additional studies based on their chronological order, was performed . No signPublication bias was investigated, and the results revealed an obvious asymmetry in the funnel plots of homozygote and recessive models   .DR is one of the most serious microvascular complications of DM. DR is also a major cause of visual impairment and loss of sight in working-age populations worldwide. According to the World Health Organization (WHO), the overall prevalence of DR is almost 34.6% in patients with diabetes, and DR accounts for 4.8% of all cases of blindness worldwide, which has become an important public health issue .PPAR-γ2 is an isoform of PPAR-γ with an additional NH2-terminal region composed of 30 amino acids [Although the mechanisms underlying DR progression have not yet been fully elucidated, hyperglycemia, hypertension, hyperlipidemia, obesity and duration of diabetes have been proven to be major risk factors for DR and have been investigated widely . Insulinno acids . This reno acids , 43. PPAno acids . Accumulno acids . PPAR-γ no acids . PPAR-γ no acids . Animal no acids . Rosiglino acids . Moreoveno acids .PPAR-γ2, the rs1801282 C/G and rs3856806 C/T polymorphisms have been the most widely investigated loci and have attracted increasing attention in the past few years. Many previous studies have suggested a significant association between rs1801282 C/G and various disorders of glycolipid metabolism, such as DM, coronary heart disease and atherosclerosis.SNPs are the most prevalent forms of gene mutations and can affect the regulation of gene expression and protein activity. For PPAR-γ2 genotype and DR in the German population [First, Ringel et al. conducted a case-control study in 1999 and found no significant relationship between the distributions of the pulation . Howeverpulation . Liu et pulation . HoweverPPAR-γ2 gene polymorphisms and DR susceptibility. To our knowledge, rs1801282 C/G polymorphism results in a missense mutation at codon 12 of the PPAR-γ2 gene, leading to the substitution of proline with alanine, which in turn leads to a conformational change in the protein structure, thereby altering the expression of genes involved in the regulation of insulin sensitivity and lipid metabolism [Therefore, we conducted a meta-analysis to explore the association between tabolism . The G atabolism .Our meta-analysis of 13 trials examining rs1801282 C/G polymorphisms and DR risk is the largest and most comprehensive meta-analysis on this topic to date. No significant association between the rs1801282 C/G polymorphism and DR risk was observed, which suggests that this single polymorphic locus may not be the determining factor for DR. To explore the potential association, subgroup analysis was conducted. Eight studies on Caucasian populations and four studies on Asian populations were analyzed, and no significant difference in genotype distributions was found between in DR cases and controls. This indicated that ethnicity may not be an independent factor contributing to a positive association between the rs1801282 C/G polymorphism and DR risk. Due to the different origins of the control groups, most controls were NDR individuals, and the pooled results indicated that neither NDR individuals nor healthy controls (HCs) had a significant association with DR risk. To our knowledge, non-proliferative and proliferative DR  are the two main subtypes of DR; the former is characterized by microaneurysms and retinal hemorrhages in the early stage followed by cotton wool spots, venous beading, and intraretinal microvascular abnormalities. PDR is a more advanced form of DR and always follows severe NPDR with the development of abnormal new retinal vessels . Here, wMoreover, in the early stages of cumulative analysis, temporarily significant associations were observed between the rs1801282 C/G polymorphism and DR risk, but the final result indicated a null association when the newly published studies were added. A sensitivity analysis was performed and individual studies affected the results in the primary analysis when studies were deleted one at a time. Moreover, an obvious asymmetry was detected with Begg’s funnel plots, indicating that there was some publication bias among the included studies. This may be because the selected studies were mainly from Asia and Europe, and the languages of the papers are limited (English and Chinese). In addition, the included information is mainly published data, and other unpublished data and gray literature have not been retrieved and found at present.PPAR-γ2 polymorphism and DR risk [PPAR-γ2 polymorphism on DR risk was observed in either the general population or stratified analysis.In 2012, Ma et al. conducted the first meta-analysis on the association between the  DR risk . The autCompared to previous studies, our meta-analysis provided more credible results for the following reasons: (1) a better, scientifically sound study retrieval strategy was employed; (2) larger sample sizes were collected; (3) more rigorous methodologies, such as cumulative and sensitivity analyses as well as subgroup and publication bias analyses and NOS evaluation, were conducted to ensure stability and accuracy; and (4) more subgroup analyses were conducted to explore potential associations. Despite these strengths, our study inevitably suffers from some limitations that should be addressed: (1) for each individual SNP, the number (sample size) of published studies was still limited to yield a definitive conclusion; (2) only unadjusted analyses were conducted without the original data, such as life habitat, environmental exposure, and gene-environment interactions, which limits the understanding of the underlying interaction mechanisms; (3) only studies published in English or Chinese languages were collected, which may lead to language and race biases; and (4) a moderate heterogeneity was found in some genetic models among the included studies. To our knowledge, clinical heterogeneity , methodological heterogeneity  and statistical heterogeneity were the three most common sources. In this meta-analysis, the HWE status, control design, and subtypes of DR may be the potential factors contributing to the existing heterogeneity. Fortunately, the heterogeneity was obviously alleviated in the stratified analysis of Caucasian populations and healthy-based controls in the rs1801282 C/G polymorphism loci.PPAR-γ2 polymorphism is not associated with DR susceptibility, despite the important role of PPAR-γ2 in metabolic diseases. More case-control studies with larger sample sizes of groups from different ethnicities are required to verify this conclusion.In summary, the current evidence indicates that the This meta-analysis was conducted in accordance with the guidance of the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) statement. All collected data were extracted from published studies, and there were no ethical issues.PPAR-γ2 polymorphisms and DR susceptibility. Only studies published in English and Chinese languages were selected. The bibliographies of all included studies were reviewed to identify any other relevant studies as well. The strategy was listed :The PubMed, Embase, Web of Science, CNKI and Wanfang online databases were searched for studies examining the association between #1 Peroxisome proliferator-activated receptor-γ#2 PPAR-γ#3 PPAR-γ2#4 #1 OR #2 OR #3#5 rs1801282#6 rs3856806#7 polymorphism#8 variant#9 mutation#10 #5 OR #6 OR #7 OR #8 OR #9#11 diabetic retinopathy#12 DR#13 #11 OR #12#14 #4 AND #10 AND #13PPAR-γ2 gene polymorphisms and DR; (2) studies with relevant genotype data in both case and control groups to evaluate the odds ratios (ORs) and 95% confidence intervals (CIs); (3) studies published only in English or Chinese; (4) the polymorphism locus was quantitatively analyzed in at least three studies;(5) when multiple publications or overlapping data were detected, the newest study or the study with the largest sample size was included. The exclusion criteria were as follows: 1) review articles, case reports and animal experiments; 2) fundamental biological research; 3) studies without enough data; and 4) duplicate or overlapping data on the same theme.The following criteria were used to identify relevant studies: (1) case-control studies on the association between Two authors independently reviewed the selected studies and extracted the following information: the name of the first authors, publication date, country in which the study was performed, sample sizes of cases and controls, control design, genotyping method, frequency data for the genotype distribution in case and control groups, assessment of Hardy-Weinberg equilibrium (HWE) in the control group, and the minor allele frequency (MAF) of case and control groups. The modified NOS evaluation was used to evaluate the quality of the included studies. The scores ranged from 0 (worst) to 11 (best) points . StudiesPPAR-γ2 polymorphisms and DR risk. For example, genetic models of the rs1801282 C/G locus were examined: allele contrast (G vs. C), co-dominant models (CG vs. CC and GG vs. CC), dominant model (CG+GG vs. CC), and recessive model (GG vs. CC+CG). Heterogeneity among the included studies was examined using Cochran’s Q and I2 tests. A fixed effects model was adopted when I2 ≤40%, and a random effects model was adopted otherwise. Subgroup analyses were performed according to the HWE status, ethnic differences, study country, control design, and disease subtype. Meta-regression was performed to identify the sources of heterogeneity. A cumulative meta-analysis was conducted to explore the tendency of change in the results in our meta-analysis. Sensitivity analysis was used to verify the stability of the results by gradually removing each study one by one. Potential publication bias was detected using Egger’s linear regression test and Begg’s funnel plots. All statistical analyses were performed using STATA version 14.0 . Statistical significance was set at p<0.05 (two-sided).The pooled ORs and 95% CIs were calculated to examine the statistical power of the association between Supplementary Table 1


Herein, we performed a comprehensive assessment using pooled analysis, FPRP , TSA , and eQTL (expression quantitative trait loci) analysis. Eighteen high-quality articles from China were enrolled. The pooled analysis of rs2295080 with 9502 cases and 10,965 controls showed a decreased risk of urinary system tumors and specific prostate cancers . FPRP and TSA data further confirmed these results. There was an increased risk of leukemia . The eQTL data showed a potential correlation between the rs2295080 and mTOR expression in whole blood samples. Nevertheless, FPRP and TSA data suggested that more evidence is required to confirm the potential role of rs2295080 in leukemia risk. The pooled analysis of rs2536 (6653 cases and 7025 controls) showed a significant association in the subgroup of “population-based” control source via the allele, heterozygote, dominant, and carrier comparisons . In conclusion, the TG genotype of mTOR rs2295080 may be linked to reduced susceptibility to urinary system tumors or specific prostate cancers in Chinese patients. The currently data do not strongly support a role of rs2295080 in leukemia susceptibility. Large sample sizes are needed to confirm the potential role of rs2536 in more types of cancer.There is still no conclusion on the potential effect of the rs2295080 and rs2536 polymorphisms of  VEGF  gene was associated with an increased risk of bladder cancer and renal cell carcinoma in Asians  but not in the others.An overall meta-analysis of rs2295080 with 16 case–control studies  from the Chinese population was first conducted. As shown in PAssociation<0.05) but not in the subgroups analysis by control source.Subgroup analyses according to three factors  were then conducted. As shown in PAssociation = 0.019, OR (95% CIs) = 0.86 (0.76–0.98)], heterozygote , dominant , and carrier  comparisons  = 0.80 (0.70–0.90)], dominant , and carrier  comparisons .Similarly, we observed a decreased risk of urinary system tumors via the allele , homozygote , and recessive  comparisons, suggesting a potential relationship between the GG genotype of mTOR rs2295080 and an increased leukemia risk in the Chinese population. We present the forest plot data of the subgroup analysis by disease type in mTOR rs2295080 by control source  and genotype method .Interestingly, we observed an increased risk for leukemia in cases in the allele , heterozygote , dominant , and carrier  comparisons. However, we observed negative results in other comparisons (PAssociation>0.05).A total of eight case–control studies with 6653 cases and 7025 controls were included in the pooled analysis of rs2536. As shown in We present the forest plot data of the subgroup analysis by control source according to the genotype comparisons in I2 value >50.0% or PHeterogeneity <0.05]. For rs2536, a random-effects model was used in the heterozygote  and dominant  comparisons.We used a random-effects model for the meta-analyses of rs2295080 via the allele, homozygote, heterozygote, dominant, and recessive genetic comparisons because substantial between-study heterogeneity was detected [PEgger>0.05, PBegg>0.05), except for the homozygote (PBegg=0.035) and recessive (PEgger=0.046) comparisons of rs2536. The funnel plots of Egger’s test are presented in Our sensitivity analysis suggested the stability of the above data. The detailed plots are displayed in mTOR rs2295080 polymorphism and the risk of urinary system tumors, prostate cancer, and leukemia, we performed FPRP analysis. As shown in Z-curve crossed the TSA monitoring boundary and did not contact the RIS line, suggesting a robust conclusion, although the enrolled study number did not reach the required information size. With regard to the TSA data for prostate cancer risk , suggesting the need for more evidence for the association between mTOR rs2295080 and the risk of leukemia.We only observed that the FPRP value was less than 0.2 for the allele comparison in the assessment of leukemia, at a prior probability of 0.1 . FurthermTOR gene expression. As shown in P=7.34e-23) but not in the prostate tissues or selected cells (EBV_transformed_lymphocytes and cultured_fibroblasts). With regard to rs2536, we did not observe a significant association between the SNPs and mTOR expression in most selected samples, except the cells in the cultured_fibroblasts dataset .Finally, we performed expression quantitative trait loci analysis of GTEx portal data to analyze the possible link between the rs2295080 (chr1_11262571_G_T_b38) and rs2536 (chr1_11106656_T_C_b38) SNPs and mTOR polymorphisms on cancer risk were retrieved. It was reported that mTOR rs2295080 may be associated with susceptibility to gastric cancer in the Chinese population [mTOR rs2295080 and the risk of gastric cancer in Chinese patients was also reported [mTOR rs2295080 and overall cancer susceptibility has not been comprehensively evaluated. Different study enrolment and analysis strategies were applied in this study compared with four prior meta-analyses [Publications with different conclusions on the effect of pulation ,36. Howereported . Therefoanalyses .mTOR rs2295080, Zhu and colleagues conducted a meta-analysis of seven case–control studies and showed that mTOR rs2295080 may be associated with reduced cancer susceptibility in homozygous, heterozygous and dominant models [mTOR rs2295080.With regard to t models . In our t models  and addet models ,40–42. Bt models , nine nemTOR rs2295080 containing five case–control studies [In 2014, Shao et al. carried out a meta-analysis of  studies ,31,32,39 studies . Herein, studies ,40–42. F studies ,30–32,43 studies ,42 for amTOR rs2295080 by Zining et al [mTOR rs2536 containing seven case–control studies [mTOR rs2536 and cancer risk [In total, 10 case–control studies from 9 articles ,38,39,41ng et al . It was ng et al . In addi studies ,43,45 ancer risk . In the cer risk  and addecer risk .mTOR rs2295080 and performed subsequent subgroup analysis [P<0.05, OR<1) [mTOR rs2295080 on the risk of urinary system tumors and digestive system tumors. However, an increased susceptibility to blood system tumors was observed only in the GG vs. TT comparison . In the present study, we removed one study [Zhang and colleagues enrolled 10 case–control studies from nine articles ,41,42,46analysis . They ob5, OR<1) , indicatne study ,35–37,40P<0.05, OR<1) and a decreased risk of specific prostate cancers in cases compared with controls via the TG vs. TT, TG+GG vs. TT, and carrier (G vs. T) comparisons . More importantly, we implemented FPRP analysis and TSA to confirm these associations. Nevertheless, we failed to detect a positive conclusion in the subgroups of studies related to digestive system tumors and specific gastric cancers. In addition, even though we also observed an increased risk of leukemia in cases compared with controls in the allele G vs. T, GG vs. TT, and GG vs. TT+TG comparisons , the FPRP and TSA data suggested a lack of association.Our findings showed a reduced susceptibility to urinary system tumors in cases compared with controls via the allele (G vs. T), TG vs. TT, TG+GG vs. TT, and carrier (G vs. T) comparisons , AG vs. AA, AG+GG vs. AA, and carrier (G vs. A) comparisons , suggesting a potential effect of the AG genotype of rs2536 on the susceptibility to cancer.Ten case-control studies from nine articles were enrolled in the meta-analysis of analyses . In our analyses ,46 with analyses ,34. We tThe following limitations should be noted. Owing to the very limited sample sizes, we failed to conduct subgroup analyses according to some specific cancer types, such as thyroid cancer and colorectal cancer. Additionally, all case–control studies were performed in the Chinese population. More data in the Caucasian population are needed. Several case–control studies did not utilize population-based controls. For example, we found that hospital-based controls were used in the subgroup of studies on “leukemia”. There was potential publication bias within the homozygote and recessive comparisons of rs2536. Genetic and environmental factors may contribute to this bias.mTOR rs2295080/rs2536 polymorphisms and the risk of different cancers. We highlight the positive association between the TG genotype within the mTOR rs2295080 polymorphism and a reduced risk of urinary system tumors, especially prostate cancer, in the Chinese population. This will help researchers conduct further experiments to determine the molecular mechanisms. Considering the less than sufficient sample size for the pooled analysis of leukemia and the potential genetic relationship between mTOR gene expression and the rs2295080 polymorphism, relevant population-based clinical investigations by clinicians and researchers are warranted.Taken together, our findings summarize currently published evidence comprehensive investigations regarding the genetic relationship between Click here for additional data file.


Epidemiological studies of the relationship between gallstone disease and circulating levels of bilirubin with risk of developing colorectal cancer (CRC) have been inconsistent. To address possible confounding and reverse causation, we examine the relationship between these potential risk factors and CRC using Mendelian randomisation (MR).SD) for CRC and tested for a non-zero causal effect of gallstones on CRC. Sensitivity analysis was applied to identify violations of estimator assumptions.We used two-sample MR to examine the relationship between genetic liability to gallstone disease and circulating levels of bilirubin with CRC in 26,397 patients and 41,481 controls. We calculated the odds ratio per genetically predicted SD unit increase in log bilirubin levels  or circulating levels of bilirubin  = 0.96–1.03, P value = 0.90) with CRC was shown.No association between either gallstone disease (Despite the large scale of this study, we found no evidence for a causal relationship between either circulating levels of bilirubin or gallstone disease with risk of developing CRC. While the magnitude of effect suggested by some observational studies can confidently be excluded, we cannot exclude the possibility of smaller effect sizes and non-linear relationships. It is especially noteworthy that many of the risk factors for gallstone disease include factors that are well-established as risk factors for CRC .4 Increased production of bilirubin, the metabolic by-product of haemoglobin degradation, is also associated with gallstone disease.10 This is intriguing as bilirubin has anti-oxidant and anti-inflammatory attributes, with experimental studies reporting mildly elevated levels being associated with decreased oxidative stress-related disease, including cancer.13Gallstone disease has been proposed to increase the risk of colorectal cancer (CRC); however, evidence for a causal relationship from epidemiological studies is lacking.19 Confounding can, however, be introduced to MR by population stratification, assortative mating and dynastic effects.20Mendelian randomisation (MR) is an analytic methodology in which germline genetic variants are used as proxies, or instrumental variables (IVs), for putative risk factors. We assume that these genetic variants follow Mendel’s laws of inheritance, where the variants are randomly assorted at conception and are not in linkage disequilibrium (LD) with one another. These genetic variants are not influenced by reverse causation. The variants can also provide unconfounded estimates of disease risk, provided they do not directly influence the disease without complete mediation of the risk factor and are not associated with confounders of the relationship between risk factor and disease.To address the shortcomings of conventional observational epidemiological studies, we investigated whether genetic liability to gallstone disease and circulating levels of bilirubin are causally associated with CRC using MR. Specifically, we used two-sample MR, in which genetic variants associated with relevant risk factors as instrumental variables were first identified from genome-wide association studies (GWAS). We then assessed the association between these instrumental variables and CRC in a large GWAS.22 comprising 27,174 cases and 736,838 controls;23 effect sizes were not adjusted for covariates. Summary statistics for log bilirubin levels were obtained from a meta-analysis of three GWAS comprising 9464 individuals of Europeans ancestry;24 SNP-exposure effect sizes were age- and sex-adjusted.The genetic instruments, i.e., single-nucleotide polymorphisms (SNPs) to be used as IVs, were identified from recent meta-analyses or the largest GWAS published to date. Details on each meta-analysis are provided in Supplementary Tables 26 After imputation, this meta-analysis related over 10 million genetic variants to CRC in individuals of European ancestry. One of the 15 GWAS was an analysis of UK BioBank data. Since the published gallstone disease meta-analysis included UK BioBank data, we recomputed CRC association statistics excluding UK BioBank data to avoid bias from sample overlap. The 14 studies provided data on 26,397 patients with CRC and 41,481 controls. Aside from principal component adjustment for residual population substructure, none of the contributing GWAS were adjusted for covariates  from a recent meta-analysis of 15 GWAS by Law et al.P value of less than 5 × 10−8 with minor allele frequency greater than 0.01 in Europeans as potential instruments. We excluded SNPs with poor imputation quality (info score <0.8). For each SNP, the chromosome position was recovered, and effect estimates harmonised according to their effect alleles and corresponding frequencies in both datasets . Effect estimates were expressed in standard deviations (SD) of the trait per allele, along with the corresponding SE. While Ferkingstad et al.23 used a ‘multiplicative allele’ model in their analysis of gallstone disease, we assumed association estimates equated to those that would have been obtained using an additive model (an assumption our MR estimator applies). To avoid co-linearity between SNPs for each trait, correlated SNPs were excluded  within each trait, with SNPs with the strongest effect size retained. Post filtering  and that the instrument strength independent of direct effect (InSIDE) and no measurement error (NoME) assumption is met.28 The results for the relationship between the levels of circulating bilirubin and CRC are reported as odds ratios per genetically predicted standard deviation unit increase (ORSD) and 95% confidence intervals (CIs). Two-sample MR analysis of the association between a binary risk factor  and a binary outcome  is problematic as estimates can be positively biased should there exist a true causal association, although bias becomes less of an issue as the true causal effect tends to unity.31 In view of this when testing the association between gallstone disease and CRC, we primarily consider whether there exists a significant non-zero effect, and only report ORs for completeness.33 We assessed evidence of heterogeneity by Cochran’s Q,35I2 statistics36 and by examining funnel and forest plots of Wald ratio estimates for SNPs. To test the robustness of findings, we also derived weighted median37 and weighted mode38 MR estimators. Evidence of directional pleiotropy was assessed using MR-Egger regression.40 We also formally assessed the effect of SNP heterogeneity on the MRE-IVW estimate, by implementing MR-PRESSO outlier and distortion tests.41To examine for a causal relationship between genetic liability to circulating levels of bilirubin and gallstone disease with CRC, we used a multiplicative random effect inverse variance weighted estimator (MRE-IVW)P value of 0.025  was considered significant, with a 0.025 <P value < 0.05 being considered suggestive of a causal association. The power of our MR analysis to demonstrate causal effects was estimated considering the proportion of variance (PVE) in the risk factor explained by the genetic instruments,43 stipulating a P value of 0.05. We assumed that the PVE explained by all variants combined was approximately equal to the sum of the individual PVEs as per Shim et al..44 Since calculation of power for binary exposure and binary outcome in a two-sample setting is problematic, we did not estimate study power for gallstone disease. Bias from weak instruments, or violation of the strong variant–risk-factor association assumption, affecting bilirubin effect estimates, were analysed using the estimated total PVE, the mean F statistic and the Staiger–Stock rule.45 Analyses were conducted using R v3.4.046 and the TwoSampleMR R-package.17A Bonferroni-corrected P = 0.60). MR-Egger showed no evidence to suggest directional pleiotropy (P = 0.20). Following on from MRE-IVW, we examined the relationship between gallstone disease and CRC under weighted median, weighted mode and MR-Egger regression methodologies, all providing little evidence to support a causal association , and the outlier test from MR-PRESSO identified six SNPs being responsible for this heterogeneity (Supplementary Table P = 0.37). There was also no evidence to indicate that outliers were causing a large distortion in the causal association estimate  were stratified by sex, of CRC .2There has been interest in exploring a possible relationship between gallstone disease and risk of CRC since an association could be reflective of pathways linking bilirubin/bile acid metabolism, sex hormones and cholesterol metabolism. Previous epidemiological evidence for an association between gallstone disease and CRC has been inconclusive. Chen et al. reported that individuals with cholelithiasis had a higher risk of developing CRC (adjusted hazards ratio (AHR) = 1.36, 95% CI = 1.12–1.66). The risk increased for patients with cholelithiasis who went on to have a cholecystectomy .2 Moreover, the analysis was based solely on baseline data. Studies with more extensive data on potential confounders have typically been small, being based on fewer gallstone cases.47These epidemiological studies, however, differ substantially in design, sample size and their ability to adjust for key covariates. The large cohort studies with high numbers of gallstone cases having very limited covariate data and being heavily reliant on self-reported information. For example, in the EPIC cohort study, data on gallstones were missing from 17% of participants and cholecystectomy status was unknown.An important strength of our analysis is that by utilising the random allocation of genetic variants, we were able to overcome potential confounding, for example, from other interrelated traits. Furthermore, reverse causation and selection bias may have biased estimates from previously published observational studies. Exploiting data from large genetic consortia for circulating bilirubin levels, gallstone disease and CRC risk has enabled us to more precisely test study hypotheses than if we had been reliant on individual-level data from a small study. The use of summary test statistics in two-sample MR analyses does, however, require consideration of sample overlap, the winner’s curse and genotype uncertainty. We have sought to avoid sample overlap between the association studies of the exposure traits and outcome trait by excluding CRC data based on analysis of UK BioBank.In our MR, we have used genetic variants as proxies for circulating log levels of bilirubin and gallstone disease. Our findings suggest that neither trait is a major influence on the development of CRC. Our study has several advantages over those conducted previously. Firstly, we made use of summary GWAS data from a large published study of CRC. Secondly, provided the underlying assumptions of the MR estimator we use are met, the common sources of probable bias in previous conventional observational epidemiological studies, including reverse causation and residual confounding, will have been avoided, albeit with limitations and interpretation issues discussed below.48 for bilirubin, as SNP–CRC association estimates were not adjusted for by age and sex. For bilirubin, we were not able to model non-linear associations as this would have required us to have access to individual data.Firstly, as is the case with many MR analyses, despite our study being based on a large case–control data, we were not powered to detect small effects. For gallstone disease, we were unable to assess IV strength and power to detect causal effects as we are not aware of suitable methods for doing so when considering a binary exposure in a two-sample MR investigation. Moreover, we were not able to stratify by subgroups of interest, including sex and cancer site. This may be especially relevant, as some epidemiological studies have only reported significant associations for colon cancer in women. This lack of stratification or adjustment may also have led to bias in the causal estimates31 Simulations performed by Didelez et al. demonstrated that if standard IV assumptions were met, one-sample IV estimators do not misspecify a no-association effect as being false. Disney-Hogg et al.31 arrived at the same conclusion using two-sample IVW estimators. Both simulation studies did however report inflated causal effect magnitudes when causal associations were simulated. Based on such considerations, we believe that our gallstone-CRC causal effect estimates, and the corresponding P values, will only have been subjected to minimal bias. Nevertheless, in analysing the relationship between gallstone disease and CRC, we focused on testing whether there exists a causal association and not principally on the magnitude of any effect.33Analysing a binary exposure and a binary outcome in a two-sample MR setting is problematic with Wald-type ratio estimates potentially being positively biased should there exist a true causal association between a binary exposure and a binary outcome.In conclusion, our findings shed light on an area for which the evidence to date has been mixed. Specifically, they provide evidence against the levels of circulating bilirubin or gallstone disease as major risk factors for CRC development.Supplementary Figures, Legends and InfoSupplementary Tables


This package can fit both fixed- and random-effects meta-CART, and can handle dichotomous, categorical, ordinal and continuous moderators. In addition, a new look ahead procedure is presented. The application of the package is illustrated step-by-step using diverse examples.In meta-analysis, heterogeneity often exists between studies. Knowledge about study features  that can explain the heterogeneity in effect sizes can be useful for researchers to assess the effectiveness of existing interventions and design new potentially effective interventions. When there are multiple moderators, they may amplify or attenuate each other’s effect on treatment effectiveness. However, in most meta-analysis studies, interaction effects are neglected due to the lack of appropriate methods. The method meta-CART was recently proposed to identify interactions between multiple moderators. The analysis result is a tree model in which the studies are partitioned into more homogeneous subgroups by combinations of moderators. This paper describes the R-package The online version of this article (10.3758/s13428-020-01360-0) contains supplementary material, which is available to authorized users. Comprehensive Meta-Analysis . Most oIn practice, behavioral scientific meta-analyses often have multiple moderators to be examined. For example, interventions to change health-related behavior generally include various behavior change techniques (BCTs), and researchers are interested in investigating the influence of BCTs on the effectiveness of interventions , meta-CART has a good control of type I error rate (≤ 0.05), and can achieve satisfactory power and recovery rates . The meta-CART method has been acknowledged as a potential alternative statistical method for meta-regression to understand the combined effects of moderators , and includes various additional options such as tuning the pruning of the tree model, restricting the minimum number of studies in a subgroup, and so on. In addition, we developed a new option to apply a “look-ahead” strategy specifically focusing on interaction detection. This paper aims to provide a general overview of the capabilities of the metacart package for conducting multivariate moderator meta-analysis with R. “metacart package, and “In the present paper, we introduce the R package metacart. The root node  represents all the studies that are included in the analysis. From the root node, the studies are partitioned into two subgroups by applying a threshold on the values of a moderator. For example, the root node in Fig. The underlying goal of meta-CART analysis is to identify subgroups defined by the moderators that can explain the heterogeneity in the study effect sizes. Appropriate data for meta-CART include an outcome variable of interest , the within-study sample variance of the effect size, and the potential moderator variables that may influence the effect size. Depending on the type of study, there is a variety of different effect-size measures, including the odds ratio, the relative risk, the correlation coefficient, and (the standardized) mean difference. Meta-CART can deal with all these effect-size measures as long as all the studies use the same measureThe resulting subgroup memberships are defined by a hierarchically nested set of decision rules such as “does the intervention include certain treatment components?” or “is the average age of the patients smaller than 25?”. In the identified subgroups, the summary effect sizes and their confidence intervals are estimated. There are two types of meta-CART algorithm: fixed-effect (FE) meta-CART, a partitioning algorithm that ignores the residual heterogeneity unexplained by the moderators, and random-effects (RE) meta-CART, a partitioning algorithm that takes into account the residual heterogeneity. As in standard meta-analysis, the choice between FE and RE assumptions should be based on a researcher’s prior belief; in other words, a researcher should decide on which assumption to use before analyzing the data. In general, if a researcher believes that given the study characteristics the variance of the study effect sizes is merely due to the sampling variancemetacart package.In this section, we describe the FE and RE meta-CART algorithms, which underly the kth study by δk, and denote the observed effect size in the kth study by dk.FE meta-CART splits the studies into more homogeneous subgroups under the FE assumption; that is, moderator effects and the within-study sampling variance are the only two sources of the variation in study effect sizes. Denote the true effect size in the xmk  specify the values on the M moderators of the kth study, and the βs are the corresponding coefficients. The sampling error 𝜖k is assumed to be distributed as where g as the measure of effect size, the sampling variance of the kth study is estimated byThe sampling variance k th study, respectively  by dt+, the between-subgroups heterogeneity measure is computed asStarting from one group including all the studies , FE meta-CART partitions the root node into two subgroups , by searching through all possible splits and finds the moderator with corresponding split point that maximizes the between-subgroups heterogeneity. Denote the summary effect size of the T| is the total number of subgroupsd++ is the weighted grand mean of the parent node.where |QB between the left and right child nodes. After each split, the algorithm partitions a parent node into two child nodes. The tree-growing process is a recursive partitioning procedure since the same operation can be applied to any child node itself without taking the other nodes in the current tree into account. A flowchart of the tree growing process of FE meta-CART is shown in Fig. K0 studies, where K0 is a user-specified threshold  on study effect sizes is not significant, but a significant p value does not confirm the significance of the moderator effects. It is also worth to note that despite the over-optimism in the Q-test, the type I error rate of meta-CART  is not inflated, because the type I error of moderator effects is mainly controlled by the pruning procedure  that maximizes the between-subgroups heterogeneity. In contrast, a look-ahead strategy searches for the combination of two splits  that maximizes the partitioning criterion. As a result, the entire growing procedure that applies a look-ahead strategy consists of two sub-procedures. The first sub-procedure grows a tree with two splits, which searches for the optimal combination of a split of the root node and a split of one of its child nodes. The combination that maximizes It should be noted that such a strategy does not guarantee a globally optimal solution, and only partially addresses the local optimization issue. Although more steps to look ahead can be beneficial, we chose the number of steps as two because the computational burden of a look-ahead procedure grows exponentially as the number of steps increases  at https://cran.r-project.org/package=metacart, and can be directly installed within R by typing install.packages("metacart"). The current version is 2.0-0.The rpart package  and the predictor variables .The argument data specifies the name of the data set to be analyzed. The argument vi requires the column name of the sampling variance of the effect size.The argument subset is an optional expression to select a subset of the data to fit the model. The subset argument can either be a logical or a numeric vector indicating the indices of rows  to be included.c depends on the type of research at hand and the number of studies. In general, if a strict control of the type I error  is required, a pruning rule using c = 1 can be applied when the number of studies K < 80, and c = 0.5 when K ≥ 80  to make a split in the growing process; xval specifies the number of cross-validations in the pruning process. The default value of minbucket is chosen as 3, because a sample size of one or two is too small to produce reliable subgroup meta-analysis results. Consequently, the default value of minsplit is chosen as 6. The default value xval= 10 corresponds to the ten-fold cross-validation recommended by Breiman et al., . The results of each of these samples were given in terms of the number of left-handed left-eyed, left-handed right-eyed, right-handed left-eyed, and right-handed right-eyed individuals. We use the log odds-ratio as the measure of effect size, which is the same as in Bourassa et al.,  and variance ("vi") are added to the data set. The data set provides information about the following moderators: the publication year ("year"), whether the selection of subjects was based on eye-dominance or handedness , the type of investigator ” so that the analysis results described in this paper can be replicated.First, to answer the question “which moderators affect hand-eye association?”, we fit a meta-regression model with the main effects of the selected moderators.The output shows that three contrasts derived from the method to assess eye-dominance are significant: contrasted with the eye-dominance assessment method “E.1.a.i” , “E.1.a.ii”, “E.1.b”, and “E.1.c” result in significantly lower hand-eye association. The remaining two methods, “E.2.a” and “E.1.d” do not differ significantly from the reference method. Note that the interpretation of these contrasts depends highly on the choice of reference method.The plot function can be used to inspect the influence of eye-dominance assessment methods on the association between handedness and eye-dominance. The plot can be found in Fig. df = 1, p value < 0.0001). When the eye-dominance is assessed using the methods “E.1.a.ii”, “E.1.b”, or “E.1.c”, the observed association between handedness and eye-dominance is lower . When the eye-dominance is assessed using the methods “E.1.a.i”, “E.1.d”, or “E.2.a”, the observed association is generally higher . This is in accordance with the claims in Bourassa et al.,  moderators are influential?”. The combinations can be either combinations of multiple moderators, or combinations of multiple categories of one moderator. We fit a RE meta-CART model by et al.,  that theComparing to meta-regression, which estimates the coefficients and tests the significance for the contrasts derived from “eye_assess”, meta-CART partitions the multi-categorical moderator “eye_assess” into subgroups, and tests the heterogeneity between the resulting subgroups. The subgrouping membership can be used to verify prior hypothesesmetacart package provides the data object dat.BCT2009 as a subset of the meta-analytic data from Michie et al., , the sampling variance of the effect size, and whether specific BCTs were applied or not in a study (“0” for absent and “1” for present). In this example, we will re-analyze this data set focusing on the motivation-enhancing BCTs that may explain the heterogeneity in the effect sizes of the interventions. We use the pruning parameter c = 0.5 for both FE and RE meta-CART analyses. Four moderators “T1: Provide information about behavior-health link”, “T2: Provide information on consequences”, “T4: Prompt intention formation”, and “T25: Motivational interviewing” are included in the meta-CART analysis. The moderator “T3: Provide information about other’s approval” is excluded since none of the studies applied this BCT.In this example, we perform a meta-analysis to identify the most effective combination of treatment components by exploring the interactions between them. The If we have prior knowledge that the FE assumption is reasonable here, a FE meta-CART model can be fitted using the following codeThis output shows that a tree with three terminal nodes was detected. The studies are partitioned into three subgroups based on two influential moderators “T1” and “T4”. The between-subgroups heterogeneity is significant . The subgroup analysis results show the number of studies (K), the within-subgroup Q-statistic, the summary effect sizes (g) in each subgroup, the standard errors of the summary effect sizes (se), Z-test statistics of the summary effect sizes, and the confidence intervals of the summary effect sizes. The plot function can be used to inspect the final tree, in this case, the interaction between the two moderators “T1” and “T4”. The plot is shown in Fig. T1 = 0 is true), then the intervention ends up in the left terminal node (with K = 69). For those that include “T1” but not include “T4”, they end up in the middle terminal node (with K = 15). Interventions including both “T1” and “T4” end up in the right terminal node (with K = 22). Combined with the other results, we can see that the summary effect size is the highest when ”T1” and ”T4” are both present in the intervention . To obtain the summary output, we useWhen interpreting the FE meta-CART analysis results, it is important to realize that the FE assumption ignores the uncertainty introduced by the residual heterogeneity. As a result, the confidence intervals of the summary effect sizes are more narrow than those estimated by using the RE model.If a new intervention is designed with three BCTs “T1”, “T2” and “T25” (thus without “T4”), the prediction of its effect size based on this tree (modBCT.fe) can be obtained by the predict function.The message “no moderator effect was detected” indicates that the cross-validation procedure selected the best-sized subtree with no splits, which means that no influential moderators were identified. In this case, the summary method shows the standard RE meta-analysis results instead of the subgroup analysis results. The heterogeneity among the studies is significant . The estimated summary effect size for all studies (K = 106) is 0.270 (95% CI: 0.227 to 0.313).If we would like to take into account the residual heterogeneity, a RE meta-CART model can be fitted using the following commandsmetacart package provides a simulated data set dat.balanced to illustrate the look-ahead strategy for RE meta-CART analysis. The simulated data set contains K = 60 studies with four moderators: x1, x2, x3, x4, among which x1, x2, and x4 are randomly sampled dichotomous variables and x3 is sampled from a uniform distribution U. The sample size n was generated from a normal distribution The This model is similar to the simulated example used by Tibshirani and Knight . This moThe output shows that meta-CART failed to detect any moderator. The reason is that the algorithm got stuck at a local optimum in the splitting procedure. This can be verified by inspecting the initial tree, unpruned by cross-validation. This output shows the between-subgroups Q-statistic, the residual heterogeneity, the chosen split points , the chosen moderator, and the parent node to be partitioned at each split, with the first row presenting the root node when no split occurs. From the output we can see that the algorithm falsely chose the first splits at x3 and ended up with QB = 24.97 afterfive splits.First, we inspect the data and fit a RE meta-CART model without using the look-ahead strategy.Then we fit a RE meta-CART model using the look-ahead strategy.x1 = 0 and x2 = 1 (K = 17) and the terminal node with x1 = 1 and x2 = 0 (K = 15) have higher effect sizes with CIs (presented by the diamonds) covering 0.5, whereas the terminal node with x1 and x2 both equal to 0 (K = 16) and the terminal node with x1 and x2 both equal to 1 (K = 12) have lower effect sizes with CIs covering 0.The results show that the RE meta-CART model with look-ahead strategy successfully recovered the true model as in . A tree Comparing this tree to the initial tree obtained from the previous model without the look-ahead strategy, the look-ahead strategy correctly finds the first two splits, and obtains a solution with much larger between-subgroups Q-statistic (QB = 213.70 after five splits).The initial tree obtained with look-ahead strategy can be inspected bymetacart package written in R to perform meta-CART analysis both for fixed effect and random effects models. The algorithms and the main functions of the package metacart are described in “R-package metacart were illustrated through example analyses on two real-world data sets and one simulated data set in “This paper presents the metacart is that it can easily explore the interaction effects among multiple moderators using an interpretable tree model. Furthermore, for multi-categorical variables, it creates automatically the contrasts between (combinations of) categories that account for the highest amount of heterogeneity. Another strength is that metacart provides researchers various options for the tree growing and pruning processes. For example, researchers can choose the minimum number of studies in parent nodes and the minimum number of studies in terminal nodes based on the total sample size. In general, it is recommended to have at least three studies in each terminal nodes. The pruning parameter can be chosen based on the balance between power and type I error. A detailed guideline for choosing the pruning parameter can be found in Li et al., Below is the link to the electronic supplementary material.


MTHFR single nucleotide polymorphism (SNP) and ASD susceptibilities, a meta-analysis was conducted to settle the inconsistencies.Autism spectrum disorder (ASD) is becoming increasingly prevalent of late. Methylenetetrahydrofolate reductase (MTHFR) has a significant role in folate metabolism. Owing to the inconsistencies and inconclusiveness on the association between For this meta-analysis, a total of 15 manuscripts published up to January 26, 2020, were selected from PubMed, Google Scholar, Medline, WangFang, and CNKI databases using search terms “MTHFR” OR “methylenetetrahydrofolate reductase” AND “ASD” OR “Autism Spectrum Disorders” OR “Autism” AND “polymorphism” OR “susceptibility” OR “C677T” OR “A1298C”.MTHFR C677T polymorphism is remarkably associated with ASD in the five genetic models, viz., allelic, dominant, recessive, heterozygote, and homozygote. However, the MTHFR A1298C polymorphism was not found to be significantly related to ASD in the five genetic models. Subgroup analyses revealed significant associations of ASD with the MTHFR (C677T and A1298C) polymorphism. Sensitivity analysis showed that this meta-analysis was stable and reliable. No publication bias was identified in the associations between MTHFRC677T polymorphisms and ASD in the five genetic models, except for the one with regard to the associations between MTHFRA1298C polymorphisms and ASD in the five genetic models.The findings of the meta-analysis indicated that MTHFR C677T polymorphism is a susceptibility factor for ASD, and MTHFR A1298C polymorphism is not associated with ASD susceptibility.This meta-analysis showed that  Autism spectrum disorder (ASD) is one of the complex neurodevelopmental disorders, which has been increasingly recognized as a public health issue . It affeThe distinguishing features of ASD include a set of behavioral phenotypes such as social communication deficits, restrictive and repetitive behaviors , 11, andMTHFR locus has been mapped to chromosome1 (1p36.3) [MTHFR [(1p36.3) . Convers(1p36.3) , 20. Sin(1p36.3) , 22, whi(1p36.3) , 24. Hom(1p36.3) . A reduc(1p36.3) –28. A de) [MTHFR . General) [MTHFR , 30.MTHFR C677T polymorphism and a higher susceptibility to ASD has been reported by Boris et al. [MTHFR C677T polymorphism is a risk factor for ASD among Chinese Han children [MTHFR C677T polymorphisms and ASD among Egyptian children [MTHFR C677T polymorphism and ASD [MTHFR A1298C polymorphism to represent a risk factor in correlation with ASD among Egyptian children. On the contrary, Mohammad et al. [MTHFR A1298C polymorphism variant allele has no link with any independent risk of ASD [MTHFR polymorphism (C677T/A1298C) and susceptibility to ASD.Correlations between single nucleotide polymorphisms (C677T and A1298C) and susceptibility to ASD are still debatable. A correlation between s et al.  among Cas et al. . Guo et s et al.  evidencechildren . El-baz children  recognizchildren . Nonethechildren  found no and ASD . Studies and ASD  and El-b and ASD , 34 descd et al.  evidencek of ASD . In thisk of ASD , 32, 36 Scientific literature published before January 26, 2020, in PubMed, Embase, Web of Science, Medline, WanFang datebase, and CNKI database were searched using specific search terms (Supplement file MTHFR polymorphism (C677T/A1298C) and ASD; (2) Cohort or case-control designs; (3) All genotype frequency information is available; (4) Diagnostic criteria of ASD described in the Diagnostic and Statistical Manual of Mental Disorders (4th or 5th edition) [Manual of Mental Disorders (3rd edition) [MTHFR polymorphism (C677T/A1298C) and ASD that are not original; (2) Studies that lack data and complete information; (3) Replicated studies; (4) Review studies.The following criteria had to be satisfied by the studies to be incorporated in this meta-analysis: (1) Original studies on the correlation between edition) , 38, andedition) . Certainedition) . The excTwo investigators, namely, Yan Li and Shuang Qiu, extracted all the relevant data with the help of a standardized protocol and data collection form. From every qualified study, data such as the name of the first author, year of publication, country, study population (ethnicity), study design, the definition of ASD, sample size of cases and controls, genotyping method, genotype information, and allele frequencies were gathered and documented. Disparities in the study selection were resolved through discussion or consensus with the third investigator (Yawen Liu). The corresponding authors of articles with missing data were emailed for the required data.OR) and 95% confidence intervals (CI) were deduced to analyse how strongly MTHFR (C677T/A1298C) polymorphism and the risk of ASD were correlated in the five genetic models, viz., allelic, dominant, recessive, heterozygote, and homozygote. Heterogeneity among studies was assessed through Q-test and I2. Random effects model (DerSimonian-Laird methods) [Ph < 0.05 and I2 > 50%); else, fixed effect model (Mantel-Haenszel methods) [p < 0.05 was considered to be statistically significant.Odds ratio (methods)  was selemethods)  was chosMTHFR C677T polymorphism and ASD susceptibility. Of those, only nine articles that included 1961 cases and 1652 controls qualified for the evaluation of the link between MTHFR A1298C and ASD as per the selection criteria. The characteristics of each primary study are summarized and presented in Tables Upon literature search and critical screening, about 15 studies from 125 articles were included in this meta-analysis, as already discussed in the Methods section  was used, and MTHFR C677T polymorphism was found to be remarkably linked to ASD susceptibility in allelic , heterozygote , homozygote , dominant , and recessive models , homozygote , and recessive models . Nonetheless, it was observed to be associated with ASD susceptibility among the states without mandatory fortification of folate: allelic , heterozygote , homozygote , dominant , and recessive models . Secondly, MTHFR C677T polymorphism was recorded to be correlated with ASD susceptibility in Caucasian population: allelic , heterozygote , homozygote , and dominant models . Nonetheless, MTHFR C677T polymorphism was not found to be linked to ASD susceptibility among Asians: homozygote model . Thirdly, a hospital-based and population-based sample was adopted for this study. MTHFR C677T polymorphism was found to be linked with ASD susceptibility under five genetic models in hospital- and population-based samples, respectively . Fourthly, our results showed that MTHFR C677T polymorphism was consistent/inconsistent with HWE; however, it was significantly associated with ASD susceptibility under five genetic models  (Table To further clarify the link between Ph < 0.05 or I2 ≥ 50%) was utilized, and no significant correlation between MTHFR A1298C polymorphism and ASD susceptibility in the five genetic models was identified  and dominant model . No significant correlation between MTHFR A1298C polymorphism and ASD susceptibility under the other genetic models in any subgroup was found  , heterozygote , homozygote , dominant: , and recessive models . However, publication bias was detected among the studies on the correlation between MTHFR A1298C polymorphisms and ASD risk in the following genetic models: allelic , heterozygote , homozygote , dominant , and recessive models   and ASD risk in this meta-analysis. The findings of this study exhibit that MTHFR C677T polymorphism is a susceptibility factor of ASD, but MTHFR A1298C polymorphism is not linked with ASD susceptibility.Relevant and up to date literature published prior to January 26, 2020 were selected for examining the correlation between MTHFR and ASD risk have been conducted. Frustaci et al. [MTHFR and ASD risk [MTHFR C677T in ASD [MTHFR is a risk factor for ASD susceptibility as well [MTHFR and ASD susceptibility.Several meta-analytic studies on the correlation between C677T polymorphism of i et al.  studied i et al. , 43, 44,ASD risk . Pu et aASD risk  investigASD risk , 35, 43 T in ASD . Rai et T in ASD  investigT in ASD , 48, 50  as well . Similar as well , 48, 50,MTHFR and ASD risk [MTHFR is remarkably linked to reduced ASD risk but only in the recessive model [A previous meta-analysis, conducted on the correlation between A1298C polymorphism of ASD risk   [MTHFR A1298C polymorphism represented a risk factor in association with ASD. This disagreement may be caused by small samples in the study.In the present meta-analysis, eight of the selected articles , 47, 50 ontrols)  and El-Bontrols)  revealedMTHFR A1298C polymorphisms and ASD risk.There are several limitations for this study. First, the subgroup analyses of environmental risk factors, sex, and gene-environment interactions were not performed owing to insufficient information. Second, this meta-analysis was mainly focused on Caucasians and Asians, thus limiting the generalization of the findings to other ethnicities. Third, in agreement with the findings of Frustaci et al. , Pu et aMTHFR is remarkably linked with ASD risk. Nevertheless, the findings agree that the A1298C polymorphism of MTHFR is not significantly correlated with ASD. Exploring gene-gene and gene-environment interactions could throw more light on the genetic link between MTHFR variants and ASD risk.To conclude, this meta-analysis confirms that C677T polymorphism of Additional file 1 : Supplement file 1. Search strategy: For this meta-analysis, a total of 15 manuscripts published up to January 26, 2020, were selected from PubMed, Google Scholar, Medline, WangFang, and CNKI databases using search terms “MTHFR” OR “methylenetetrahydrofolate reductase” AND “ASD” OR “Autism Spectrum Disorders” OR “Autism” AND “polymorphism” OR “susceptibility” OR “C677T” OR “A1298C”.