RASSF1, TSHR, PTEN, SLC5A, DAPK, P16, RARβ2, and CDH1). The order of the pooled ORs for these eight TSGs from more to less significant was CDH1 (OR = 6.73), SLC5 (OR = 6.15), RASSF1 (OR = 4.16), PTEN (OR = 3.61), DAPK (OR = 3.51), P16 (OR = 3.31), TSHR (OR = 2.93), and RARβ2 (OR = 1.50). Analyses of publication bias and sensitivity confirmed that there was very little bias. Thus, our findings showed that CDH1 and SCL5A8 genes were associated with the risk of thyroid tumor genesis.Promoter methylation in a number of tumor-suppressor genes (TSGs) can play crucial roles in the development of thyroid carcinogenesis. The focus of the current meta-analysis was to determine the impact of promoter methylation of eight selected candidate TSGs on thyroid cancer and to identify the most important molecules in this carcinogenesis pathway. A comprehensive search was performed using Pub Med, Scopus, and ISI Web of Knowledge databases, and eligible studies were included. The methodological quality of the included studies was evaluated according to the Newcastle Ottawa scale table and pooled odds ratios (ORs); 95% confidence intervals (CIs) were used to estimate the strength of the associations with Stata 12.0 software. Egger’s and Begg’s tests were applied to detect publication bias, in addition to the “Metatrim” method. A total of 55 articles were selected, and 135 genes with altered promoter methylation were found. Finally, we included eight TSGs that were found in more than four studies ( Thyroid cancer is the most common endocrine malignancy, and the incidence of thyroid cancer is increasing rapidly worldwide , 2. ThyrBRAF , DAPK, P16, RARβ2, and cadherin 1 [CDH1/E-cadherin] that have been shown to be associated with thyroid cancer in more than four studies; accordingly, we selected these eight TSGs for the meta-analysis. The frequency of promoter methylation in these eight TSGs was evaluated 55 studies; 18 studies evaluated RASSF1 (855 cases versus 379 controls), 12 studies evaluated P16 (626 cases versus 268 controls), nine studies evaluated TSHR (501 cases versus 258 controls), nine studies evaluated SLC/NIS (340 cases versus 201 controls), six studies evaluated PTEN (398 cases versus 189 controls), six studies evaluated RARβ2 (400 cases versus 132 controls), six studies evaluated CDH1 (355 cases versus 120 controls), and five studies evaluated DAPK (354 cases versus 117 controls) The strategies and results of study selection are presented in p = 0.006). Subgroup analyses of NIS by the exact gene name indicated an OR of 7.33 for SLC5A8 and 4.31 for SLC5A5. Similar results were observed for the other seven genes, including RASSF1 , TSHR , P16 , PTEN , DAPK , CDH1, and RARβ2 , as shown in RASSF1 illustrated that studies with a quality of more than 7 yielded more specific thyroid cancer risk factors (OR: 2.67) in comparison with low-quality studies (OR: 7.39). Furthermore, subgroup meta-analyses of RASSF1 on the basis of the sampling method and tissue type showed that fresh-frozen tissues and paraffin-fixed embedded tissues were similar , in contrast to one study on blood samples .Our results revealed that the frequency of promoter methylation of all eight TSGs increased in patients with thyroid cancer compared with that in controls under the random-effects model. Meta-analysis of the association between NIS hyper methylation and thyroid cancer, among 340 cases of thyroid cancer and 201 controls indicated a statistically significant difference , funnel plots, Begg’s linear regression tests, and the trim-and-fill method were applied for analysis publication bias. However, for the other remaining six TSGs, funnel plots, Egger’s linear regression tests, and the trim-and-fill method were used for publication bias assessment because these TSGs were reported in less than 10 studies  and P16  and Egger’s graphs for TSHR, PTEN, CDH1, DAPK, SLC, and RARβ2 showed minimum bias. Sensitivity analysis by the trim-and-fill method specified that the results were stable in this meta-analysis, and removal of each study had no significant effect on the pooled ORs  was associated with an increased risk of thyroid cancer with an OR of 2 or more, and one gene (RARβ2) was associated with increased risk of thyroid cancer with an OR of 2 or less.In mammalian cells, epigenetic alternations play important role in regulation of gene expression. As the most common epigenetic alteration, DNA methylation usually occurs in CpG island regions of the gene promoter, causing activation or inactivation of gene function. The hypermethylation of TSG promoter suppresses genes expression by inhibiting transcription, thereby affecting cell signaling pathways. Two previous meta-analyses demonstrated the association between PTC risk , 41. In CDH1 gene is a protein-coding gene that encodes a trans membrane glycoprotein localized in the adherents junctions of epithelial cells [CDH1 gene inactivation by promoter methylation in cancer contributes to the increase in the proliferation, invasion, and metastasis of tumor cells [The al cells . CDH1 geor cells –50. For or cells , 50, 51.SLC5A5 gene in humans [SLC5A5 plays a crucial role in thyroid hormone (TH) biosynthesis and uptake [SLC5A8 as an another variant of SLC5 gene, transports iodide through a passive mechanism [SLC5A8 gene is essential in human cancers [SLC5A5 and SLC5A8 in subgroup analysis using the exact SLC gene name. SLC5A8 showed the highest OR (7.33); therefore, SLC5A8 rather than CDH1 may be the most important gene showing hyper methylation in thyroid carcinogenesis.SLCs are a collection of membrane transport proteins comprising over 400 members , 53. NISn humans , 55. Thid uptake –58. Our d uptake –61 and id uptake –36, incld uptake . The SLCechanism  and monoechanism . SLC5A8  cancers , and the cancers . We obseRASSF1 has been reported in several studies [RASSF1 promoter inactivation has been detected in more than 30% of thyroid tumors, representing the most and frequent event in thyroid cancers [RASSF1 methylation and thyroid cancer risk was detected [RASSF1 promoter methylation and PTC risk [RASSF1 promoter methylation was significantly associated with increased risk of thyroid cancer, our study showed that the influence of RASSF1 promoter methylation was lower than those of CDH1 and SLC5A8 promoter methylation. Moreover, subgroup analysis demonstrated that RASSF1 was a risk factor with a higher OR in low-quality studies (OR: 7.39 versus 2.67). Additionally, subgroup analysis of the sampling source demonstrated major differences among blood samples, paraffin-fixed embedded tissues, and fresh-frozen tissues (OR: 5.65 versus 1.41 and 1.81). A significant association was also found for RASSF1 in both FTC and PTC [RASSF1 was 18 when conference presentations were excluded, whereas 12 studies, including two conference papers, were included in the previous study [RASSF1 was a risk factor for thyroid carcinogenesis, but that this gene was not as important as was previously thought. In contrast to our prediction, RASSF1 promoter hyper methylation was not the most important candidate for thyroid cancer association because higher ORs were observed for CDH1 and SLC5. The trim-and-fill method, which was not performed for RASSF1, implies that there was only very minor publication bias. Subgroup meta-analyses of RASSF1 according to the type of tissue showed that the fresh-frozen tissue and paraffin-fixed embedded tissue were similar, in contrast to blood samples. Due to the limited number of blood studies (only one), our investigation did not support the use of plasma or serum as the most reliable sample source for methylation analysis [Inactivation of the TSG  studies . RASSF1  studies –71. In s cancers , 73; howdetected , 74–78. PTC risk , 41. Alt and PTC . The numus study . Thus, wanalysis , 80.PTEN gene, encoding a phosphatase enzyme found in virtually all tissues, is located on chromosome 10q23.3 and has a major impact on the phosphatidylinositol 3-kinase (PI3K)/AKT pathway. In fact, PTEN functions to block this signaling pathway by converting PIP3 into PIP2, thereby regulating cell proliferation and differentiation [PTEN, which covered the zero point in the Egger graph, detected no publication bias. Moreover, visual inspection of the funnel plot indicated no asymmetry, suggested that there was no publication bias in the evaluation of PTEN methylation and thyroid cancer risk. Moreover, Egger’s test did not provide statistical evidence of the asymmetry. However, application of the trim-and-fill method and addition of two studies caused the estimated OR to be similar to the original estimate, indicating the reliability and strength of our analyses.The ntiation , 82. PTEntiation –87. Our DAPK gene was distinguished, and methylation-mediated silencing was verified in many human cancers [DAPK methylation and its association with thyroid cancer risk supported the involvement of this gene in thyroid tumor genesis, consistent with previous reports [DAPK is a large 160-kDa protein composed of various functional domains that interacts with cytoskeleton-associated serine/threonine kinase –90. The  cancers . Our fin reports , 85, 92.P16 (CDKN2a/INK4a) gene is an important TSG that is involved in the P16/cyclin-dependent kinase/retinoblastoma pathway and acts as a negative regulator of the cell cycle [P16, with the exclusion of Meta bias (which showed no publication bias), demonstrated that P16 was a thyroid cancer risk factor, consistent with previous studies [The ll cycle , 94. The studies , 95, 96, studies .TSHR gene, located on chromosome 14q31, is a G protein-coupled receptor that regulates signaling across the cell membrane of follicular cells in the thyroid tissue. After activation by TSH, TSHR generates corresponding effects in the cell using second messengers. Deregulation of TSHR plays a crucial role in thyroid carcinogenesis [TSHR promoter is frequently found in thyroid carcinoma, although the promoter is un-methylated in normal and benign thyroid tumors [TSHR had an important role in thyroid cancer. Using Egger’s test results and the trim-and-fill method, we found that there was no publication bias.The ogenesis –99. Hyped tumors , 98–100.RARβ promoter methylation is significantly altered [RARβ and its association with BRAF mutations have revealed promoter methylation of this gene in thyroid tumor genesis [RARβ has been shown to be associated with thyroid cancer recurrence; however, in our meta-analysis, it was not possible to examine this concept by subgroup analyses due to the low sample size and lack of data related to recurrence in the identified studies. Our results for pooled ORs highlighted the accuracy of the previous studies. Funnel plots with pseudo 95% CIs for RARβ2 and covering the zero point in Egger graphs detected no publication bias. Moreover, visual inspection of the funnel plots showed no asymmetry; therefore, there was no publication bias in our evaluation of PTEN methylation and thyroid cancer risk. Egger’s test also did not display statistical evidence of asymmetry (P = 0.115). The trim-and-fill method was applied to check the precision of our results.In PTC, several methylation studies have shown that  altered , 46, 101 genesis , 92, 102In this meta-analysis, minimum publication bias was detected in the qualified studies for all eight TSGs. Begg’s test, Egger’s test, and funnel plots indicated that the data did not show a significant discrepancy among all studies. Furthermore, consistent results were found in the sensitivity analysis. However, there were several limitations to this analysis, including the influence of the small sample size in cases and controls and the non achievable cut-off point of methylation.CDH1 gene played an important role in thyroid cancer initiation and progression. Promoter methylation in this gene was found to be a promising biomarker for the early diagnosis of thyroid cancer. Other TSGs were also important, including SLC5A8.Taken together, our findings showed that promoter methylation of the S1 Fig(DOC)Click here for additional data file.S2 FigThe PRISMA  Checklist that is a 27 checklist items pertain to the content of a systematic review and meta-analysis. Discussion.(DOC)Click here for additional data file.S3 FigA 19 checklist items pertain to the content of a systematic review and meta-analysis on genetic association studies.(DOC)Click here for additional data file.S4 FigThe syntax and mesh terms that was used in this Meta-analysis.(DOC)Click here for additional data file.S1 TablesThe final candidate studies which were chosen for Meta-analysis.(DOC)Click here for additional data file.


Subgroup and stratification analyses have been widely applied in genetic association studies to compare the effects of different factors or control for the effects of the confounding variables associated with a disease. However, studies have not systematically provided application standards and computing methods for stratification analyses. Based on the Mantel-Haenszel and Inverse-Variant approaches and two practical computing methods described in previous studies, we propose a standard stratification method for meta-analyses that contains two sequential steps: factorial stratification analysis and confounder-controlling stratification analysis. Examples of genetic association meta-analyses are used to illustrate these points. The standard stratification analysis method identifies interacting effects on investigated factors and controls for confounding variables, and this method effectively reveals the real effects of these factors and confounding variables on a disease in an overall study population. We also discuss important issues concerning stratification for meta-analyses, such as conceptual confusion between subgroup and stratification analyses, and incorrect calculations previously used for factorial stratification analyses. This standard stratification method will have extensive applications in future research for increasing studies on the complicated relationships between genetics and disease. Meta-analysis is a powerful and effective tool in evidence-based medicine (EBM) for synthesizing results from multiple case-controlled studies into a single numerical estimate to provide more objective and quantitative evidence. This method has been widely applied over the past two decades to solve clinical problems in numerous genetic association studies –4. Meta-In the meta-analysis approach, subgroups are based on a characteristic of the included studies. In addition to exploring the source of heterogeneity between studies, subgroup analyses are conducted to determine whether differences occur among studies with two or more different levels of a characteristic, and the characteristics are then compared. In this method, a study is considered a unit, and studies with the same features are considered a subset. For example, such analyses may be performed to determine whether a genetic polymorphism has different effects among different ethnicities. Then, all the included studies are divided into several groups, such as Asian, Caucasian and African. Odds ratios (ORs) with confidence intervals (CIs) are calculated within the different groups and then compared across the groups. The study by Borenstein M discusses the detailed application standards, computing methods and results interpretation of this method .The term “stratification” means that the study population is divided into several strata according to characteristics that may influence the clinical indexes. Stratified data are generally sorted as in Table As above, stratification is different from subgroups in two aspects: (a) in a single study, grouped indicators are generally interventions that must be pre-set, whereas stratified indicators are often considered potential confounding variables; (b) in a meta-analysis, an included study is considered a unit and assigned to one group in a subgroup analysis, whereas in a stratification analysis, an included study is divided into several parts, and studies with identical characteristics will form the same strata. This difference is shown in a schematic Figure . One of Two methods of calculating the ORs for stratified data are found in published meta-analyses: (a) with the first method, the unexposed group in a stratum is regarded as a reference and compared with the exposed group in the same stratum, the unexposed group in another stratum and the exposed group in the other stratum –11; (b) Although stratification analyses have been widely used by many investigators to study the association of genes with disease in meta-analyses –15, studet al. [PTGS2) polymorphism and nonsteroidal anti-inflammatory drug (NSAID) intake in the risk of developing cancer. In the second and third meta-analyses, we focused on the studies of Li L et al. [et al. [CYP46A1) rs754203 and methylenetetrahydrofolate reductase (MTHFR) rs1801133 polymorphisms with AD according to apolipoprotein E ε4 (ApoE ε4) status. The original data included in these three meta-analyses are displayed in The first study used here for illustrative purposes was conducted by Nagao M et al. , who invL et al.  and Zhan [et al. , who invIn this paper, we focus on the methodological aspects of statistical analyses rather than other issues, such as the source of the data and quality assessments of the studies. We will not discuss these substantive issues. Readers who are interested in the details of these three meta-analyses can refer to the original papers.PTGS2 rs5275 genotype carrier status (TC or CC carriers and TT carriers) or stratified by NSAID use history (NSAID users and non-NSAID users), as shown in PTGS2 rs5275 polymorphism and cancer risk and (b) to investigate the effect of NSAID use on preventing cancer.This meta-analysis included eight case-controlled studies containing a total of 3032 cancer patients and 5712 controls , and allCYP46A1 rs754203  or MTHFR rs1801133  polymorphisms and ApoEε4 carrier status (non-ApoE ε4 carriers and ApoE ε4 carriers) were determined by genotyping. The stratified data are shown in CYP46A1 rs754203 or MTHFR rs1801133 polymorphisms with the risk of developing AD and (b) the confounding or interaction effect of ApoE ε4.These two meta-analyses included eight and six case-controlled studies containing a total of 1700 AD patients and 1433 controls , respectIt would be difficult for a simple meta-analysis to fully utilize the stratified data of the case-controlled studies above to interpret the complicated relationship between gene-gene or gene-environment effects and disease risk. In the following sections, we will introduce the computing method and discuss the application of a standard stratification analysis to solve these issues in meta-analyses.For all selected examples in our paper, ORs with corresponding 95% CIs were selected as the effect sizes to assess the associations between the single nucleotide polymorphisms (SNPs) and the risks of the diseases. An OR with a 95% CI greater than 1 indicates that the investigated factor increased the risk of the disease, and an OR with a 95% CI less than 1 indicates that the investigated factor decreased the risk of the disease. When an OR with a 95% CI crosses 1, no significant association is indicated between the investigated factor and the disease.2-), the effect of the exposure factor within the same stratum (OR1+) and the effect of both the stratifying indicator and exposure factor (OR2+). We call this “factorial stratification analysis”. Appropriate effect models are selected to calculate the pooled ORs based on the heterogeneity test. Heterogeneity is evaluated according to the χ2-based Q (Cochran’s Q test) and I2 statistic tests [I2 < 50%, then the fixed-effect model (the Mantel-Haenszel method) [In the first step, the computing method for ORs refers to the first type of variants of stratification analysis Table  found inic tests . If I2 < method)  is appli method) . This mo1+ vs. OR2-). The true role of the weaker factor is more difficult to detect under the confounding influence of the stronger one. Figure In the second step, the computing method for ORs refers to the second type of variants of stratification analysis Table  found inIn each meta-analysis, the ORs were calculated for stratified data in two sequential steps for a standard stratification analysis Figure :In the f2- based Q test is also conducted for this evaluation.Traditional subgroup-typed stratification analysis without combining the sizes of effects across strata can show only the investigated effect in a particular subpopulation, which may not reveal the real effect among all study populations. In addition, although observed effects may be different across strata due to the confounding factors, their true values may be consistent. To solve this problem, we use the test of homogeneity across strata to determine whether to combine the effects of different strata. The variation across strata is then assessed. The χP value of the χ2 statistic ≥0.1, no significant variation is indicated as shown in the test of homogeneity. An overall estimation is determined for the effect of the investigated factor. The fixed-effect model (the Inverse-Variance method) is used to combine the effect size with the upper and lower CIs of each strata because the fixed-effect model assumes a common true effect size (fixed-effect) or different but countable true effect sizes (fixed-effects) across strata [If the s strata , 6, 18.Then, the adjusted OR by stratification, or the combined OR, is compared with the crude OR calculated from the overall study populations in a simple meta-analysis without stratification. Any change in significance between these values indicates that the stratified moderator is a confounding variable.P value of the χ2 statistic <0.1, significant variation across strata is indicated. In this case, we do not use a random-effects model to combine the ORs from different strata. The random-effects model assumes that true effect sizes are distributed across strata and that the number of strata is countless [2+ to report the unilateral effect of the investigated factor in each stratum as well as the interacting effect between this factor and the stratified moderator on diseases, respectively.If the ountless , 17, 18.(I) When working within strata, the process of effect model selection is the same as that of normal meta-analysis and factorial stratification analysis mentioned above. Proper effect models are used to pool the stratified data of every included study within each stratum. Because this sub-step is similar to the subgroup analysis, we call this “subgroup-type stratification analysis”.A factorial stratification analysis can be used to determine whether exposure or confounding factors have a multilevel perspective, especially the simple effect of a confounding factor in a study population, which cannot be provided by the subgroup-typed calculation method. In confounder-controlling stratification analysis, we can control for the confounding variable via stratification, determine the real effects of exposure factors, or discover whether interactions occur between exposure and confounding factors. This interaction is referred to as a gene-gene or gene-environment interaction. In a standard stratification analysis, these two sub-types of stratification methods are included as the first step and the second step.metan command [Q test and the fixed-effect and random-effects models refer to the Cochrane Handbook for Systematic Reviews of Interventions: Version 5.1.0 [et al. [All analyses were performed using STATA version 14.0 software  with the  command . The aboon 5.1.0  and the  [et al. . The codPTGS2 rs5275 among NSAID users ; (b) the minor allele carriers compared with the carriers of the homozygous major allele PTGS2 rs5275 among non-NSAID users ; (c) the NSAID users compared with the non-NSAID users among the homozygous major allele carriers of PTGS2 rs5275 ; and (d) the NSAID users compared with the non-NSAID users among the minor allele carriers of PTGS2 rs5275 . The data are shown in PTGS2 rs5275 polymorphism did not show any significant effect on cancer development among NSAID users or non-NSAID users [PTGS2 rs5275 polymorphism or NSAID use in the overall study population was not explored in this study; and (b) repeated pairwise comparisons increased the potential rate of type I errors in the meta-analysis, which may lead to false-positive results.In the original publication, the researchers calculated four pooled ORs using a subgroup-type stratification analysis: (a) the minor allele carriers compared with the carriers of the homozygous major allele ID users . AlthougPTGS2 polymorphism and NSAID use with the risk of cancer following the steps below. First, since the effects of both the PTGS2 polymorphism and NSAID use were unclear before our analysis, a factorial stratification analysis was used to observe the effect sizes. As shown in Table PTGS2 rs5275 polymorphism did not significantly change the risk of developing cancer , although the unilateral effect of NSAID use decreased this risk significantly , supporting the conclusion in the study by Nago M et al. [P < 0.001). These ORs implied that only NSAID use may have a protective role in cancer development and the PTGS2 rs5275 polymorphism was not significantly associated with this risk.In our work, we performed a standard stratification analysis to investigate the relationship of the M et al. . When boPTGS2 rs5275 polymorphism was much weaker than that of NSAID use. We chose to study the effect of the PTGS2 rs5275 polymorphism. In the following analysis of the effect of the PTGS2 rs5275 polymorphism in the overall study population rather than that of NSAID use status, it was necessary to control for the potential confounding effect of NSAID use. As shown in Table 2-based Q test showed no significant difference across strata . The combined OR demonstrated that the PTGS2 rs5275 polymorphism was not associated with cancer risk in the overall study population . Similarly, to investigate the effect of NSAID use on the risk of cancer, we can calculate the crude pooled OR  because the PTGS2 rs5275 polymorphism did not have an influence on this risk. In summary, in the standard stratification analysis, (a) the real effects of both the PTGS2 rs5275 polymorphism and NSAID use on cancer risk were determined in the overall population after controlling for confounding variables, and (b) a standard computing process could mitigate increases in type I errors that may be caused by repeated pairwise comparisons.As shown by the earlier analysis, the effect of the et al. [et al. [CYP46A1 and MTHFR polymorphisms and AD, the effect of the ApoE ε4 allele was considered because different ApoE ε4 allele distributions between exposed and unexposed groups may represent a confounding variable.Many genetic association analyses demonstrated that ApoE ε4 represents the most important genetic risk factor for AD . Therefoet al.  and Zhan [et al.  investigCYP46A1 rs754203 among ApoE ε4 carriers and non-ApoE ε4 carriers [MTHFR rs1801133 polymorphism and the risk of AD according to ApoE ε4 status [CYP46A1 rs754203  and MTHFR rs1801133  polymorphisms showed significant associations with the risk of AD among non-ApoE ε4 carriers, but not among ApoE ε4 carriers (P = 0.284 and P = 0.086). However, the results of these two meta-analyses were still limited due to the following issues: (a) in the study of the CYP46A1 rs754203 polymorphism, the researchers assumed that the negative results in the ApoE ε4 carrier group were caused by stronger effects of the ApoE ε4 allele compared to those of the above polymorphisms [MTHFR rs1801133 polymorphism, the researchers performed a meta-analysis of non-ApoE ε4 carriers but not ApoE ε4 carriers [In the second example, researchers calculated the pooled ORs of homozygous mutant genotype CC compared with the TC or TT genotypes of carriers . In the 4 status . Howeverorphisms , althougcarriers ; thus, aCYP46A1 rs754203 polymorphism, the statistical results of the factorial stratification analysis are shown in Table CYP46A1 rs754203 polymorphism was significantly associated with AD among the non-ApoE ε4 carriers . Additionally, positive ApoE ε4 status increased the risk of developing AD , and the OR was 3.39-times greater than that of the CYP46A1 rs754203 polymorphism. With the combined effect of the CYP46A1 rs754203 polymorphism and positive ApoE ε4 status, the OR increased compared with that for ApoE ε4 only, indicating a significantly increased AD risk . For the MTHFR rs1801133 polymorphism, data from the factorial stratification analysis are also shown in Table MTHFR rs1801133 polymorphism significantly increased the AD risk  and also verified that ApoE ε4 was a significant risk factor for AD . The OR for the effect of ApoE ε4 on the increasing the AD risk was 2.36-times greater than that for the effect of the MTHFR rs1801133 polymorphism. With the combined effect of the MTHFR rs1801133 polymorphism and positive ApoE ε4 status, the OR increased from that for the effect of ApoE ε4 alone, indicating a significantly increased AD risk . Compared with the subgroup-typed stratification analysis conducted in the original meta-analyses, the factorial stratification analysis provided insight into the role of ApoE ε4 and its combined effect with the CYP46A1 rs754203 or MTHFR rs1801133 polymorphism on the risk of AD.In our study, a standard stratification analysis was conducted to solve the above issues in these two meta-analyses. For the CYP46A1 rs754203 or MTHFR rs1801133 polymorphism was much weaker than that of ApoE ε4. We chose to study the effects of the CYP46A1 rs754203 and MTHFR rs1801133 polymorphisms and control for ApoE ε4 as a potential confounding variable in the analyses. We examined the variation between the two ORs calculated in the following confounder-controlling stratification analysis in each polymorphism study based on the χ2-based Q test. Significant variations were not observed between the results ; therefore, the fixed-effect model was used to combine these ORs , which is slightly more pronounced than the crude OR  [CYP46A1 rs754203 polymorphism significantly increased the AD risk in the overall study population. In the study of the MTHFR rs1801133 polymorphism, the combined OR with a 95% CI was 1.572 (1.115–2.028), indicating that the MTHFR rs1801133 polymorphism also increased the AD risk in the overall study population significantly. However, the crude OR, which was not calculated in the original paper [P = 0.069, not shown in the tables). Therefore, the ApoE ε4 allele had a strong confounding effect on the association. Consequently, it would be difficult to show the real effect of the MTHFR rs1801133 polymorphism on the AD risk in the overall study population rather than among the non-ApoE ε4 carriers only without confounder-controlling stratification.As shown in the earlier analysis, the effect of the 0–1.380) . Both re1.572 1.1–2.028, iStep I) and confounder-controlling stratification analysis (Step II). Three examples of previous meta-analyses meeting the criterion of stratified data are used for illustration.In this study, we focus on providing a systematic study of stratification analyses. Based on the M-H and I-V approaches  as well PTGS2 rs5275 polymorphism and NSAID use on the risk of cancer, we calculated ORs using the factorial stratification analysis, which is an earlier stratified method of exploring the unilateral and additive effects of genetic and pharmaceutical factors. A confounder-controlling stratification analysis was further employed to control for the confounding effects of NSAID use during the analysis of the effects of the PTGS2 rs5275 polymorphism. The combined OR across stratum showed that the PTGS2 rs5275 polymorphism was not associated with the risk of cancer in the overall study population. To analyse the effect of NSAID use, we directly calculated the crude OR by the fixed-effect model without stratification because the PTGS2 rs5275 polymorphism was found to have no influence on the risk of cancer.In the first example, because of the unknown effects of both the CYP46A1 rs754203 and MTHFR rs1801133 polymorphisms with the risk of AD, a factorial stratification analysis was first used to verify the role of ApoE ε4 in the disease, and then the second step was carried out to control for the confounding factors. In the study of the CYP46A1 rs754203 polymorphism, we found that the adjusted OR by stratification was slightly higher than the crude OR, although both were larger than 1 with P values smaller than 0.05, suggesting that the CYP46A1 rs754203 polymorphism was significantly associated with the increased AD risk in the overall population. In the study of the MTHFR rs1801133 polymorphism, the adjusted OR by stratification suggested that this polymorphism increased the risk of AD significantly, but it showed no effect on the AD risk by the crude OR. We consider our adjusted OR to be more reliable than the crude OR, which explains the real relationship between the MTHFR rs1801133 polymorphism and the AD risk. This finding is reasonable because combining the effect sizes based on the heterogeneity in each stratum first and then across strata provides better results than using the overall heterogeneity among studies in the meta-analysis. The difference between these two ORs implied that ApoE ε4 status was a strong confounder in this meta-analysis. Compared with the results of the unilateral analysis for the MTHFR rs1801133 polymorphism in individuals not carrying ApoE ε4 alleles in the original meta-analysis, the result from using both the ApoE ε4 and non-ApoE ε4 carriers in our standard stratification analysis had stronger statistical power because of the larger sample size. The cases mentioned above demonstrate the important role of standard stratification analysis in the exploration and control of confounding factors in meta-analyses.In the second and third examples, ApoE ε4 was defined as a risk factor for AD . During LIPC rs493258 polymorphism and the risk of macular degeneration [VEGF rs157036 polymorphism with AD risk, a subgroup analysis of ApoE ε4 status was mistaken for a stratified analysis [In the Introduction, the difference between the subgroup and stratification concepts was comprehensively discussed. One point that we should stress is that conceptual confusion between the subgroup and stratification concepts occurs in meta-analyses. For example, in a study of the association between the neration , the teranalysis . In the analysis . As mentFactorial stratification analyses represent the first step for calculating ORs for stratified data as mentioned in the Introduction, and they have been widely used for stratification analysis in some retrospective studies –11. In tCHAT rs3810950 polymorphism with AD risk according to ApoE ɛ4 carrier status [P = 0.014, shown in Table CHAT rs3810950 polymorphism and the AD risk among non-ApoE ε4 carriers , which is quite reasonable. However, an opposite trend  was observed by simple calculation, as in a single case-controlled study calculation. The forest plot from the random-effects model showed that significant between-study heterogeneity existed in the genetic comparison among non-ApoE ε4 carriers (I2 = 55.6%), and two studies with OR values less than 1 had relatively larger weights than the other two, which caused the combined CI to cross 1 , the homogeneity test is performed to estimate variants across strata. Significant variants indicate a potential interacting effect between the stratified moderator and the investigated factor. However, effect sizes of strata are not combined but reported respectively, or further statistical analyses of biological interactions are performed to verify the finding. Second, standard stratification analysis may not be suitable for data with a large number of concomitant variables. With an increasing number of concomitant variables, the number of strata will increase and the actual frequencies of cases or controls within each stratum will decrease, possibly resulting in failure to achieve the necessary conditions of the hypothetical test. Finally, standard stratification analysis cannot be performed when the stratified data are incomplete. In fact, all statistical techniques, including logistic regression methods, require complete data to estimate the difference between the theoretical frequency and the actual frequency . Data deficiency always causes difficulty in performing statistical analyses in meta-analyses and systematic reviews; however, researchers can contact the authors of original publications to ask for the missing data. Despite these restrictions, this method can be effectively used by researchers conducting meta-analyses to synthesize data from multiple case-control studies or cohort studies to determine more accurate estimations and more robust conclusions.Although standard stratification analysis has advantages for controlling confounders and revealing potential interactions between exposure and confounding factors, three potential limitations can be recognized in meta-analyses. First, the statistical results of standard stratification analysis may suggest the existence of a potential interaction but cannot quantify its strength. In confounder-controlling stratification analysis (With the development of sequencing technology and the increasing number of genome-wide association studies (GWASs) conducted, an increasing number of issues, such as gene-gene or gene-environment effects on disease development, will occur. By unifying the superior aspects of previous stratification methods, a standard stratification analysis will be applicable in controlling for confounding variables and exploring the effects of their interactions on the development of complicated diseases on a multivariate level.This paper systematically introduced the methodology, computing method and application of stratification analyses for meta-analyses. Our main contributions include the (a) establishment of a computing method and interpretation of the results of standard stratification analyses; (b) differentiation between stratification and subgroup analyses; and (c) discussion and resolution of incorrect computing methods for factorial stratification that are frequently used in previous studies. The examples here provide a good understanding of the methodology for standard stratification analyses used for meta-analyses, and they also show the important role of this method in controlling for confounding variables and identifying interacting effects in genetic association studies. More extensive multi-centre studies designed to determine gene-gene, gene-drug and gene-environment interactions are necessary to authenticate this standard stratification method in the future.


Background: Many publications have investigated the association between metal ions and the risk of Alzheimer's disease (AD), but the results were ambiguous.Aims: The objective of this study was to assess the association between the serum levels of metals (copper/zinc/iron) and the risk of AD via meta-analysis of case-control studies.Methods: We screened literatures published after 1978 in the Pubmed, Embase, Cochrane library, Web of Science and ClinicalTrials.gov. Electronic databases. By using Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) guidelines, we performed a systematic review of the 407 publications, there are 44 of these publications met all inclusion criteria. The Review Manager 5.3 software was used to calculate available data from each study.Results: Consistent with the conclusions of other meta-analysis, our results demonstrated serum copper levels were significantly higher , and the serum zinc levels were significantly lower in AD patients than in healthy controls . Serum iron levels were significantly lower in AD patients than in healthy controls after excluded two studies .Conclusion: The results of our meta-analysis provided rigorous statistical support for the association of the serum levels of metals and the risk of AD, suggesting a positive relationship between the serum copper levels and AD risk, and a negative relationship between the serum zinc/iron levels and AD risk. Alzheimer's disease (AD) is a progressive neurodegenerative disorder that leads to intellectual decline including memory loss, and language breakdown The deposition of β-amyloid peptide (Aβ) in the brain is one of the pathology hallmarks of AD Citron, . AlthougOn the basis of these evidences, the potential role of Cu/Zn/Fe dysfunction in the pathogenesis of AD has been the object of much investigation over the past decades. Therefore, the aim of our study was to provide more useful information about the relationship between the serum levels of Cu/Zn/Fe and AD susceptibility by carried out the present meta-analysis of case-control studies published in the past few years on the role of Cu, Zn, and Fe in AD.2+”), “zinc” (or “Zn” or “Zn2+”), “iron” (or “Fe” or “Fe2+”), “metals.” Additional studies were obtained from the reference lists of identified studies.We searched for case-control studies articles published from 1978 to May 2016 via systematically screening in the PUBMED, EMBASE, Cochrane library, Web of science and clinical trials.gov electronic databases according to PRISMA guidelines  non-case-control studies trials, such as case reports, reviews, and meta-analysis; (2) family-based studies; (3) without original data; (4) non-human; and (5) were published in a language other than English.Studies were identified by two independent authors using the aforementioned search strategy. When there was uncertainty regarding eligibility, the other two authors were consulted. The following information was collected from each study: name of the first author; year of publication; country; criteria for AD diagnosis, sample sizes of patients and controls, mean age, and percentage of females in groups. We assessed how metal levels were measured with the following data: type of sample; assay method; laboratory or kit used; collection, process, and storage of sample; blinding of laboratory personnel; and the use of quality control sample. Data in other forms  were converted to mean ± SD according to the Cochrane Handbook , we ran a sensitivity analysis and found that the I2 = 89% after excluding the study of Singh et al. , the mean age of the subjects (p = 0.66), and the percentage of women (p = 0.18) as a possible confounder, which revealed no variation of serum Cu level strictly associated with them.Of the 35 included studies, results are discordant, as shown in Table h et al.  and Ozcah et al. . After tCharacteristics of the 22 included studies are summarized in Table p = 0.0005]. As a high heterogeneity between the included studies was observed (I2 = 96%), we ran a sensitivity analysis and found that the I2 = 92% after excluding the study of Alimonti et al. , the mean age (p = 0.55), and country  as a possible confounder, which revealed no variation of the serum Zn strictly associated with them.Of the 22 included studies, results are discordant. As shown in Table i et al.  and Gonzi et al. . After tCharacteristics of the 25 included studies are summarized in Table p = 0.26]. As a high heterogeneity between the included studies was observed (I2 = 99%), we ran a sensitivity analysis and found that the I2 = 93% after excluding the study of Alimonti et al. , the mean age (p = 0.31), and country (p = 0.68) as a possible confounder, which revealed no variation of the serum Fe strictly associated with them. From the above, we concluded that the variation of the serum Fe concentration found in some of the studies might be associated with the percentage of women of the subjects.Of the 25 included studies, results are discordant, as shown in Table i et al.  and Ozcai et al. . After tt Figure . MoreoveAD is the most common form of irreversible dementia, and is clinically characterized by the progressive memory problems. Abnormal homeostasis of trace metals is also observed in AD patients, especially the metabolism of Cu, Zn, and Fe. The purpose of this meta-analysis was to evaluate the association between serum Cu/Zn/Fe and AD risk. To do so, we performed a meta-analysis of selected case-control studies papers from 1975 to 2016 that enriched the literatures on these topics. We ran a meta-analysis for the serum Cu/Zn/Fe in AD patients and healthy controls in case-control studies.Senile plaques are the typical pathology change of AD brains, which are composed of variety of components, including extracellular deposits of Aβ, lipids, heavy metal, and so on. Based on this evidence, Aβ plaques are sought to be a therapeutic targets of AD. In the past decades, several different classes of drugs were generated to prohibit Aβ plaques formation. But finally, many of them failed to improve the cognition of AD patients in the phase III clinical trials, suggesting that inhibition of senile plaques formation by using γ-secretase inhibitors or Aβ antibodies are not effective ways to dealing with AD. Another approach to reduce the toxicity of Aβ aggregation is to modulate the homeostasis of heavy metals, especially Cu, Zn, and Fe. Potential association between these metal ions and AD were showed. Dysfunction of metal ions could be an indicator for AD diagnosis and a good target for AD therapy.In vivo, Cu was absorbed in the small intestine by amino acid transporters, and then was transported into the blood in two forms, ceruloplasmin (Cp) and non-Cp Cu. Non-Cp Cu is also called “free copper” which is more freely available for cellular uses. The study form Dr. Rosanna Squitti et al. has proved that “free Cu” level is elevated in AD patients when compared with age-matched controls , to activate the expression of MTs. Then the elevated MTs bind with Cu+ and take Cu+ out into the stool as intestinal cells are shed.The conclusion of our meta-analysis revealed a significant increase of serum Cu and a decrease of serum Zn in AD patients than in healthy controls. In this study, we also ran a subgroup analysis for the possible confounders, including mean age of the subjects, percentage of women and the country. Differences of the serum Cu and Zn are unlikely to be affected by these factors, which suggesting that the overall results of our meta-analysis on Cu/Zn level were statistically robust. Focusing on the relationship between Cu and Zn, we found that these metal ions are competed with each other. They shared binding partners, such as melallothionein (MT), copper/zinc-superoxide dismutase 1 (SOD1), and Aβ. Zn-binding MT and the other Zn-binding proteins are function as a buffer system to buffer serum Cu, and then maintain the Cu homeostasis. Except for forming Zn binding proteins, Zn also mediated cell signaling pathways via modulating the activity of transcription factors, including Sp1, MTF-1, EGR1, GR, RAR, Ikaros, and Churchill. + effectively. But when Cu level is rising for a long term, cells will change their status to fit the Cu disorder. In this situation, metal binding protein, such as MTs  which is consistent with previous meta-analysis resulted from Tao et al. (Fe is another essential metal that plays a key role in AD pathophysiology. Similar as Cu, Fe has a high binding affinity to Aβ peptides, and it can provoke oxidative stress through the Fenton's reaction (Sayre et al., o et al. . We exclo et al.  because The present study still had some potential limitations that warrant mention. First, significant heterogeneity existed in the meta-analysis of the serum Cu/Zn/Fe in AD patients and healthy controls. The heterogeneity of the involved case-control studies are partially associated with the confounders, therefore, heterogeneity was still a problem that may affect the precision of the overall results in this meta-analysis. Second, the case-control studies means that the findings are might be affected by varying levels of bias owing to the quality evaluation of literature. Although the involved studies were reasonably homogeneous in most areas, there were some between-study variations that may have effects on the outcomes and thus the results of our meta-analysis. Third, we must acknowledge that the present case-control studies meta-analyses included limited number of studies; hence, the results must be clearly interpreted with some degree of caution. Fourth, only a few studies involving North America, Oceania were included in this meta-analysis. More studies are needed from other countries to evaluate the association between the serum metal levels and AD.In conclusion, from our current study, we provide statistical support that serum Cu was significantly increased and serum Zn/Fe was significantly decreased in AD patients. For serum Cu, the present case-control studies meta-analysis along with other four meta-analyses reached the same conclusion Citron, . For serPZ and ZW designed the study. DL and WZ performed the experiments and data analysis. PZ and WZ wrote the paper.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. The reviewer BMM and handling Editor declared their shared affiliation.


Introduction. Chronic Periodontitis (CP) is suggested to be related to gene variations. Present study aims to quantitatively estimate the association between interleukin-6- (IL-6-) 174G/C polymorphism and CP susceptibility. Materials and Methods. Pubmed, Embase, and Web of Science were searched up to May 2016. The meta-analyses were performed using STATA 12.0. Results. 21 studies were yielded. Significant associations were found under heterozygote comparison and dominant model in studies fulfilling HWE ; significant associations were found under heterozygote comparison and dominant model in Caucasian studies fulfilling HWE ; significant associations were found under allele comparison, heterozygote comparison, and dominant model in Brazilian population . Conclusion. IL-6 174 polymorphism is associated with CP susceptibility. In Brazilian and Caucasian population, IL-6 174 GG genotype plays as a risk factor to CP. Chronic Periodontitis (CP) is a chronic pathological condition characterized by the inflammatory gingiva and the destruction of tooth-supporting structures . The devIL-6 is a pleiotropic cytokine not only exerting immunological effect but also functioning in haematopoiesis, bone metabolism, and tissue regeneration , 9. The Although observational studies have been performed to investigate the association between IL-6 -174 G/C polymorphism and CP, the findings were inconsistent , 14–19. Present meta-analysis was performed in accordance with the PRISMA-P  statement which was recommended for the establishment of a systematic review and meta-analysis .The eligibility criteria were as follows: (1) case-control studies; (2) the case groups consisted of patients diagnosed with CP, and the control groups consisted of periodontally healthy individuals; (3) IL-6 -174G/C polymorphism were detected and sufficient data of genotype distribution were provided for the calculation of odds ratio (ORs) and corresponding 95% confidence intervals (CIs); (4) studies with no repeated data. Studies which could not reach the eligibility criteria were excluded.A systemic manuscripts search was performed from the electronic databases including Pubmed, Embase, and Web of Science up to May 2016. In addition, the reference lists of the selected manuscripts and related reviews were also manually searched and screened for a comprehensive search result. The search strategies were presented as follows.Search StrategiesPubpmed  OR ＇＇Periodontics＇＇[Mesh]) OR  OR periodontal pocket[Title/Abstract]) OR periodontal tissue[Title/Abstract])) AND (((((＇＇Interleukin-6＇＇[Mesh] OR Interleukin-6[Title/Abstract]) OR Title/Abstract[All Fields]) OR IL-6[Title/Abstract]) OR IL 6[Title/Abstract]) OR Interleukin 6[Title/Abstract])) AND  OR Polymorphism[Title/Abstract]) OR Polymorphisms[Title/Abstract]) OR Genetic Variation[Title/Abstract]) OR rs1800795[Title/Abstract])Embase ＇periodontal disease＇/exp OR ＇periodontitis＇/exp OR ＇periodontics＇/exp OR ＇periodontal disease＇:ab,ti OR ＇periodontitis＇:ab,ti OR ＇periodontal pocket＇:ab,ti OR ＇periodontal tissue＇:ab,ti AND  AND Web of Science# 1TS =  ORTS = (periodontitis) OR TS =  OR TS = # 2TS = (interleukin-6) OR TS = (IL-6) OR TS = (interleukin 6) OR TS = (IL 6)# 3TS = (polymorphism) ORTS = (polymorphisms) OR TS = (genetic variation)#1 AND #2 AND #3For the study selection, titles and abstracts of the search results were screened firstly and the full-text paper screen was performed next according to eligibility criteria. The results were screened by two authors (JF Zhu and M Fu) independently and a third author (WS Guo) was consulted if any discrepancies existed. The following characteristics were extracted from the eligible studies by two authors (JF Zhu and M Fu) independently, and discrepancies were resolved through discussion as follows: (1) name of the first author and year of publication; (2) country and ethnicity; (3) group size; (4) smoking status; (5) gender ratio comparability; (6) methods of genotyping; (7) type of the controls; (8) genotype distribution; (9) Hardy-Weinberg equilibrium (HWE) for the controls.The methodological quality of the included studies was evaluated by two authors (JF Zhu and M Fu) independently. Newcastle-Ottawa scale (NOS) was applied for the quality assessment of the included case-control studies. The composition of NOS includes 3 chapters which are “Selection” (0–4 points), “Comparability” (0–2 points), and “Exposure” (0–3 points). For the chapter “Comparability,” ethnicity and gender were selected as the factors to be matched in present study. The final scores were calculated ranging from 0 to 9 . Studiesχ2 and I2, I2 > 50% and P < 0.05 was considered as significant heterogeneity then the random-effects model was used; otherwise, a fixed-effects model was used to assure the statistical efficiency [χ2 method was used to assess the HWE for the control groups. Subgroup analyses were conducted according to HWE fulfillment, ethnicity, smoking status, gender ratio, and source of the control. Moreover, the following characteristics were included as covariates in the meta-regression to explore the potential sources of heterogeneity: HWE fulfillment, smoking status, gender ratio, source of the controls, and genotyping method, a characteristic was considered as the source of heterogeneity if P < 0.05. Publication bias was measured by Begg's and Egger' linear regression test. A significant publication bias was considered if P < 0.05. All of the statistical analyses were processed using the software STATA 12.0.ORs and CIs were calculated to evaluate the association between IL-6 polymorphism and CP. Heterogeneity was estimated by ficiency . The folA total of 373 studies were yielded using the mentioned search strategies. After deleting the duplicates, record screen was performed. And 21 studies –36, 38 iOf the 21 included studies, 10 studies were conducted in Caucasian population , 34–36, P < 0.001; I2 = 27.3%  P = 0.184; CC + GC versus GG: OR = 0.690, 95% CI = 0.568–0.838, P < 0.001; I2 = 44.3%  P = 0.049) ; significant associations were found under allele comparison, heterozygote comparison, and dominant model in Brazilian population  ; significant associations were found under heterozygote comparison and dominant model in Caucasian studies fulfilling HWE   . In the = 0.236) ; signifi= 0.054) . The resP = 0.004; CC + GC versus GG: P < 0.0001). The results of metaregression were summarized in Metaregression showed that the HWE fulfillment was considered as the source of heterogeneity under heterozygote comparison and dominant model .No significant publication bias was detected under allele comparison and recessive model. However, the Begg test and Egger linear regression test demonstrated the presence of publication bias with statistical significance under the heterozygote comparison and dominant model  and demonstrated significant associations under model “C versus G,” “GC versus GG,” and “CC + GC versus GG,” which indicated that allele C play as a protection factor to CP, and genotype GG contributes to the susceptibility of CP. Those 2 findings were inequable but not paradoxical. With the accumulation of further evidences, we believe the association between IL-6 174 G/C polymorphism and CP susceptibility could be clearly elucidated.Shao et al. firstly performed the systematic review and meta-analysis based on the association between IL-6 174 G/C polymorphism and periodontitis in 2009 and demonstrated that GG genotype increased the risk of periodontitis, which was consistent with present findings . In 2013The present study however has some limitations. First, although 21 studies were included, the quantity of the included studies was considered insufficient especially for the Asian and Indian subgroups. Additionally, several relevant studies could not be included in present meta-analysis owing to lacking of raw data , 66–69 oIn conclusion, within the limitations of this study, the present systematic review and meta-analysis supports that IL-6 174 polymorphism is associated with CP susceptibility. In Brazilian and Caucasian population, IL-6 174 GG genotype plays as a risk factor to CP. Further large-scale studies were required to validate our findings.


Recent studies explored the association between insulin receptor substrate-2 (IRS-2) gene rs1805097 polymorphism and colorectal cancer (CRC) with contradictory findings. Therefore, we conducted a comprehensive meta-analysis by searching the databases of PubMed and Embase. Pooled odds ratios (ORs) and 95% confidence intervals (CIs) were calculated by using fixed-effect or random-effect models. A total of 5 citations containing 6 case-control studies involving 4,333 cases and 5,333 controls were included. Our data indicated that IRS-2 rs1805097 polymorphism was associated with decreased risk of CRC. Stratification analysis of ethnicity found that rs1805097 polymorphism decreased the risk of CRC among Americans. Stratification analysis of cancer type suggested that this polymorphism decreased the risk of colon cancer. In summary, this meta-analysis indicates that IRS-2 gene rs1805097 polymorphism plays an important role in the pathogenesis of CRC. Colorectal cancer (CRC) is the second most commonly diagnosed cancer worldwide . CRC is Insulin receptor substrates (IRSs) are involved in insulin signaling pathway . IRS-2 pWe yielded a total of 51 citations after initial search. 33 citations were removed after removing duplicates and screening the titles and abstracts. 18 citations were selected for further full text review. 13 citations were excluded: 1 investigated other polymorphisms; 5 were about other diseases; 2 were meta-analyses. Finally, 5 citations  containiP = 0.022, Figure P = 0.007, Figure P = 0.002, Figure As shown in Table We assessed sensitivity by omitting each study once at a time in every genetic model for rs1805097 polymorphism. Our data indicated that the findings of this meta-analysis were stable and trustworthy  studies that evaluated the association between IRS-2 gene rs1805097 polymorphism and CRC risk, (2) study provided sufficient data to calculate the odds ratios (ORs) and 95% confidence intervals (CIs), (3) case-control study.Relevant information was carefully extracted from all eligible studies. The extracted information from all eligible studies including: name of first author, publication year, country of origin, ethnicity, genotype methods and genotype numbers of cases and controls. Two authors independently performed the extraction of data and assessed the study quality based on the NOS . All disP < 0.1 or I2 > 50% indicated heterogeneity across studies, a random-effect model was used. Otherwise, the fixed-effects model was applied [P < 0.05 was considered to indicate statistically significant. The power of this meta-analysis was calculated with a significant value of 0.05 [All statistical analyses were performed using the Stata 11.0 software . ORs and 95%CIs were used to assess the strength of association between IRS-2 gene rs1805097 polymorphism and CRC risk. Stratification analyses were carried out by ethnicity, SOC and cancer type. When a Q test indicated  applied . Allele  applied ; P < 0.0 of 0.05 .


Background: The relationship of conventional cardiovascular risk factors  to coronary artery calcification (CAC) presence and extent has never before been assessed in a systematic review and meta-analysis. Methods: We included only English language studies that assessed at least three conventional risk factors apart from age, gender, and ethnicity, but excluded studies in which all patients had another confirmed condition such as renal disease. Results: In total, 10 studies, comprising 15,769 patients, were investigated in the systematic review and seven studies, comprising 12,682 patients, were included in the meta-analysis, which demonstrated the importance of diabetes and hypertension as predictors of CAC presence and extent, with age also predicting CAC presence. Male gender, dyslipidaemia, family history of coronary artery disease, obesity, and smoking were overall not predictive of either CAC presence or extent, despite dyslipidaemia being a key risk factor for coronary artery disease (CAD). Conclusion: Diabetes and hypertension consistently predict the presence and extent of CAC in symptomatic patients. The presence of conventional cardiovascular (CV) risk factors  have been shown to predict the 10-year coronary event risk ,3. In paThe methodology for this systematic review and meta-analysis conforms to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses statement .We systematically searched electronic databases  for observational human studies, assessing CAC and conventional CV risk factors. Articles were selected if the title or abstract indicated that the paper analysed original associations between CAC and CV risk factors using different combinations of the Medical Subject Headings (MeSH): “coronary calcification” or “coronary calcium” and “risk factors”, “hypertension”, “dyslipidaemia”, “hyperlipidaemia obesity”, “diabetes”, “smoking”, “family history”, “exercise” or “physical activity”. No date limit was applied to article selection. Since computed tomographic scanning for CAC was first introduced in the early 1990s, the study dates range from then to the present date. Two researchers performed the literature search, study selection and data extraction independently of each other, with the results placed in a spreadsheet; disagreements were resolved by discussion between the two researchers and a third adjudicated in case of disagreement. The selected reports were manually searched and other relevant articles, obtained from the reference lists, were retrieved. We also performed a quality assessment of each study included in the meta-analysis.Any clinical studies that reported the presence, extent, new development or progression of CAC, assessed by electron beam computed tomography (EBCT), multi-detector computed tomography (MDCT), or coronary angiography, were eligible, regardless of whether or not the study objective was an assessment of the association of risk factors with CAC.(a)English language articles published in peer-reviewed journals;(b)In addition to age, gender, and ethnicity (where applicable), the study must include assessment of at least three risk factors out of dyslipidaemia, hypertension, obesity, diabetes, smoking, family history of premature CVD, or exercise. In some cases surrogate markers were used to indicate the presence of a risk factor, such as elevated low density lipoprotein (LDL) cholesterol to indicate hyperlipidaemia or elevated systolic blood pressure (SBP) consistent with hypertension. Any differences in risk factor criteria between studies are discussed in the narrative;(c)p-value of 0.05, with some taking it as borderline and others as significant but the exact p-value may not be shown in the narrative; for our purposes only p < 0.05 is taken as significant,The ability of risk factors to predict CAC presence, extent, new development, or progression must be displayed in a table rather than as a narrative. This criterion was included because narrative results in some studies did not adequately reflect the tabular results, for example, where a risk factor shown as significant in a table was not mentioned in the narrative. Studies also varied in their treatment of a (d)The patients must be symptomatic ; and(e)For the systematic review only, the study results must show risk factors as multivariate predictors of CAC presence, extent, or progression.Study inclusion criteria were:Those involving patients with a specific diagnosis, such as Type 1 diabetes or renal disease, which had no healthy control group.There were no specified requirements for the control groups, where applicable.Study exclusion criteria were:2 statistical test. When the I2 was greater than 50%, the analysis was considered significantly heterogeneous and the random effect model was applied. When the I2 was less than 50%, the analysis was considered not heterogeneous and the fixed effect meta-analysis model was applied. A p-value of <0.05 was regarded as significant.The data was extracted from each study and analysed using the Revman software 5.3 . The publication bias was tested using Egger’s regression interception test and funnel plot by comprehensive meta-analysis software. The unadjusted odds ratios (ORs) of each risk factors were estimated from the exposure distributions for CAC presence or absence. The ORs and 95% confidence intervals (CIs) were converted into Log OR and standard error (SE) using the calculator and the Revman software in order to obtain the forest plots for each risk factor. The statistical heterogeneity was evaluated using the IA total of 884 studies were identified. After exclusion of duplicates and review of the retrieved papers for the above criteria  was not predictive of CAC presence .When the studies are analysed by markers for diabetes:Dyslipidaemia: Thee studies showed that dyslipidaemia was predictive of CAC presence ,18, whilElevated LDL cholesterol was not predictive in all four studies of CAC presence ,15,16 anElevated total cholesterol was not predictive in two studies of CAC presence ,18, two Decreased HDL was not predictive in three studies of CAC presence ,15 and oLipid-lowering medication was predictive in three studies of CAC presence ,18, but When the studies are analysed by markers and biomarkers for dyslipidaemia:Hypertension: Although two studies, including the study of 9993 patients, ,17 foundSystolic blood pressure (SBP) was not predictive in three studies of CAC presence ,15 and tDiastolic blood pressure (DBP) was not predictive in three studies of CAC presence ,15 and tAntihypertensive medications were not predictive in four studies of CAC presence ,15,18 anPulse pressure was not predictive in one study of CAC extent  and the When the studies are analysed by markers for hypertension:Family history of premature CHD: The only study to assess predictive ability for CAC presence was the study of 9993 patients , which fObesity: Five studies found no ability for obesity to predict CAC presence ,15,17,18Body mass index (BMI) was inversely predictive in the angiographic study of CAC presence involving 9993 patients , but notWeight was not predictive in one study of CAC extent .When the studies are analysed by markers for obesity:Smoking: With respect to CAC presence, only one small study found it to be predictive , while tWhen the studies are analysed by markers for smoking, current smoking was not predictive in two studies of CAC presence .Out of the ten papers that were eligible for the systematic review ,17,18,19The meta-analysis investigated the predictive ability of age, male gender, diabetes, dyslipidaemia, hypertension, and smoking for CAC presence and extent . It was p < 0.00001), male gender , diabetes , and age . Smoking and dyslipidaemia were not predictive of CAC presence. The Egger’s regression interception test was not significant suggesting no significant publication bias , male gender to 1.74 (p < 0.00001), diabetes to 1.45 (p < 0.00001). Smoking and dyslipidaemia were still not significant.Due to the disproportionately large number of patients in the study by Kovavic et al. , we repeOnly three studies , with diabetes, dyslipidaemia, and smoking proving not to be predictive of mild-moderate CAC. The presence of severe CAC, compared with zero CAC, was predicted by hypertension  and diabetes ; dyslipidaemia and smoking were not independently predictive of severe CAC. It was not possible to analyse age or male gender as predictors of CAC extent.The presence of mild-moderate CAC, compared with zero CAC, was independently predicted only by hypertension , definition and duration of risk factor, and year of publication. As expected, we observed considerable heterogeneity between studies, so it is arguable whether a summary estimate should be presented. However, our objective was not to provide this but rather to present a general approximation of the prevalence of these risk factors to facilitate the message. In particular, two studies assessed CAC angiographically, which is not sensitive to CAC detection, while the remainder used 16- or 64-slice CT scanning. Fourthly, analysis of studies of CAC presence was overwhelmed by the study of 9333 patients, while the next largest study had only 1560. Fifthly, the lack of standardisation of definitions of risk factors limits our ability to provide summary estimates and we had no information on the duration of risk factors, which might have impacted the analysis. Sixthly, we were confined to those risk factors commonly measured in a clinical setting and, inevitably, there are others which might have been relevant.Our meta-analysis showed that hypertension followed by diabetes were the most important risk factors for prediction of CAC presence and extent, with age and male gender also showing predictive ability for CAC presence. The results from the systematic review were more equivocal, but the two forms of analysis were in general agreement that dyslipidaemia, obesity, and smoking were not predictive of CAC presence or extent. Irrespective of the mechanism for arterial endothelial damage, hypertension and diabetes seem to result in a common phenotypic arterial wall damage in the form of calcification. Finally, despite CAC and the conventional CV risk factors both being predictive of 10-year coronary event risk, only a few of the CV risk factors appear predictive of CAC.


Based on conventional measures of class performance, POGIL had a small effect on achievement outcomes  but substantially improved the odds of passing a class . That is, participants in the POGIL pedagogy had higher odds of passing a course and roughly performed 0.3 standard deviations higher on achievement measures than participants in standard lectures. In relative risk terms, POGIL reduced the risk of failing a course by 38%. These findings suggest providing opportunities to improve process skills during class instruction does not inhibit content learning but enhances conventional success measures. We compare these findings with those of recent large meta-analysis that examined the effects of global active learning methods on achievement outcomes and course failure rates in science, technology, engineering, and mathematics (STEM) fields.While the inquiry approach to science teaching has been widely recommended as an epistemic mechanism to promote deep content understanding, there is also increased expectation that process and other transferable skills should be integral part of science pedagogy. To test the hypothesis that coupling process skills to content teaching impacts academic success measures, we meta-analyzed twenty-one studies ( There is increased expectation that science education should encompass practices that embody the ways of doing science and the epistemic practices of the scientific enterprise –5. By scIn the work reported here, we focus on POGIL to answer the question, “how does coupling teaching scientific practices to content learning impact student success measures?” POGIL provides an appropriate venue to address this question as POGIL materials use a learning cycle based on three phases of inquiry: exploration of a model, concept invention, and application . This prWe had a second motive for conducting this study. In their recent large meta-analysis, Freeman and colleagues  examinedWhat is the impact of POGIL pedagogy on student achievement outcomes compared to standard lecture?What are the odds of passing a course using POGIL instruction as opposed to standard lectures?Do the findings of this study with respect to achievement and course pass/failure rates falsify or verify earlier meta-analysis that examined the global effects of active learning on these variables?H01: POGIL pedagogy is no more effective than standard lectures in improving student achievement outcomes, and 2) H02: the odds of passing a POGIL classroom are no better than passing in a standard lecture classroom. Here, we focus on pass rates as measure of student success and retention as opposed to the commonly reported failure rates in the literature .De-duplication of the initial hits resulted 408 studies for further consideration . This waTo facilitate data selection and analysis, we organized the data into a spreadsheet on Excel . We noten = 15) were from chemistry and there was a scattering of POGIL use in other STEM disciplines in the articles identified for inclusion. We thus used “chemistry” and “other” as possible “discipline” moderator variables on effect sizes. We did not use study setting (high school or college) as a variable because of the limited number of studies done at the high school level (n = 2) making it unsuitable for meta-analysis purposes. In addition to meta-analyses by the individual variables, we similarly used meta-regression to assess their effects on summary outcome measures.We used a previously established protocol  to desiget al. . Rather, POGIL appears to improve student achievement outcomes.Based on weighted standardized mean difference of independent studies  and other disciplines (n = 11). Both variables, class size and disciplinary domain, were not significant moderators . However, the test for residual heterogeneity was significant , possibly indicating other moderators not considered in our model are influencing the summary effect size. For example, it is possible that variations in how the different disciplines assess student learning influence observed effect sizes. It is also likely student perceptions of content difficulty and whether innovations in instructional strategies help them is a factor. Methodologically, most of these studies lack fidelity of implementation measures, which could explain most of the heterogeneity in the data. These hypotheses are speculations on our part–all we can say is that there are unaccounted moderators in our models that may explain the observed variations.To test these hypotheses, we meta-regressed the achievement omnibus effect by class size and disciplinary domains , the onlgsmall = 0.25, 95% CI = 0.069–0.430, p = 0.007, n = 11; gmedium = 0.29, 95% CI = 0.006–0.574, p = 0.045, n = 9; n > 100 students) and thus not enough experiments for meta-analysis purposes. Heterogeneity was nonsignificant in the small classroom data  but significant in the medium classroom data , suggesting underlying variations in study structures in medium classes contributed to substantial heterogeneity in this dataset. Synthesis across these studies therefore suggests a “general” small positive POGIL effect on achievement outcomes in small and medium classroom settings compared to standard lectures.To further assess the influences of class size and disciplinary domains on achievement measures, we meta-analyzed the studies by these factors. Classes with 50 students or less  and those with 51–100 students (medium) showed significant positive small effects favoring POGIL over standard lectures . This analysis indicates that POGIL pedagogy was more effective than the control group to varying degrees in both chemistry and the other domains analyzed.The summary effect size for achievement from chemistry studies  suggested considerable heterogeneity among the true effects that warranted further analysis.To test the null hypothesis that the odds of passing a classroom using POGIL instruction are no better than those of a class using standard lectures, we computed the odds ratio for passing a POGIL course versus control , the test for residual heterogeneity was significant , possibly indicating that other moderators not considered in the model are influencing the pass rates.To examine the influence of moderators on the odds ratio, we meta-regressed course pass rates by class size and instructor training, the only variables with sufficient information reported in the meta-analyzed studies . While tmedium = 1.86, 95% CI = 1.23–2.80, p = 0.003, n = 6; small 3.26 = 95% CI = 0.82–13.07, p = 0.095] but this interpretation is suspect due to small number of studies in this category [n = 2]. Heterogeneity was statistically significant in the medium classroom data , suggesting underlying variations in study structures in medium classes contributed to substantial heterogeneity in this dataset. Synthesis across these studies suggests “general” odds ratio pattern favoring POGIL over standard lectures.We further meta-analyzed pass rate patterns by class size and instructor training. Classes with 51–100 students (medium) showed significant odds ratio favoring POGIL over standard lectures  versus those that did not report such training . The general trend of these studies therefore suggests the odds of passing a class favor POGIL pedagogy over standard lectures.The odds for passing a course were roughly two times higher in a POGIL classroom versus standard lectures regardless of whether the studies reported instructor training . The POGIL approach reduced the relative risk of failure by 38%.(TIF)Click here for additional data file.S2 FigThe lack of symmetry suggests the absence of publication bias even when there is potential for such bias.(TIF)Click here for additional data file.


Association between let-7-KRAS rs712 polymorphism and cancer risk was inconsistent. We therefore conducted this meta-analysis to clarify the association between let-7-KRAS rs712 polymorphism and cancer risk with STATA 14.0 software. A systemic literature search in online databases  was preformed to obtain relevant articles. A total of 13 case-control studies involving 3,453 patients and 4,470 controls were identified up to May 16, 2015. The pooled results indicated that significantly increased risk were observed in Chinese population in T vs. G  and TT vs. GG + GT genetic models . Sensitivity analysis was conducted and the result without heterogeneity showed significant associations in all five genetic models. Subgroup analyses of cancer type indicated a similar result in digestive cancer . In summary, these evidences demonstrate that let-7-KRAS rs712 G > T polymorphism might be associated with digestive system cancer risk in the Chinese population. Cancer, one of the major common malignant diseases contributes to death worldwide, which has become an important healthy problem . In ChinTo date, a large numbers of studies have indicated that genetic abnormity maybe result in tumorigenesis , 7. MicrGene mutation, including single-nucleotide polymorphisms (SNPs), such as interleukin gene family polymorphisms and microRNA polymorphisms has been proved to be associated with cancer risk. Regarding KARS gene, several common SNPs located at the 3′-UTR region have been identified, such as rs712 G > T polymorphism. A recent study with luciferase vector reporter system demonstrated that the let-7 would decrease the activity of KRAS, but the rs712 minor allele would compromise the interaction between let-7g and KRAS 3′-UTR .From 2014, two meta-analyses were conducted, only 6 case-control studies were included in both meta-analyses , 13. TodA total of 89 studies were identified initially. Figure P = 0.03, I2 = 75.8%) and recessive model . Obviously heterogeneities were found in all analyzed genetic models. Sensitive analysis were conducted though deleting each study one by one, and the results indicated that the report by Hu et al.  AND (“proto-oncogene proteins p21(ras)”[MeSH Terms] OR (“proto-oncogene”[All Fields] AND “proteins” [All Fields] AND “p21(ras)”[All Fields]) OR “proto-oncogene proteins p21(ras)”[All Fields] OR “kras”[All Fields]) AND  OR “genetic polymorphism”[All Fields] OR “polymorphism”[All Fields]) AND (“neoplasms”[MeSH Terms] OR “neoplasms”[All Fields] OR “cancer”[All Fields])”, up to 10 June, 2016. Only studies written with English and Chinese were selected.All included studies according to these inclusion criteria: (1) case-control studies; (2) research focus on rs712 G > T polymorphism and cancer risk; and (3) the publication with adequate information to calculate the odds ratio (OR) and 95% confidence interval (CI). The exclusion criteria: (1) review studies; (2) molecular fundamental studies; (3) data not about the related locus or with in sufficient outcome; and (4) duplicated or overlapping data of the same author or issue.Two independent reviewers (Du and Xie) review and extracted the relevant the data from all included studies: the first author's name, study published date, sources of controls, genotyping method, cancer location, adjusted OR and its 95% CI, MAF  in cases and controls, and the distributed number of genotypes in cases and controls. Quality assessment of included studies was evaluated with modified Newcastle-Ottawa scale (NOS) by two authors, and the scores ranged from 0 points (worst) to 9 points (best) Table  35]..35].I2 value and Cochran's Q test. The fixed-effect model (the Mantel-Haenszel method) was applied when the I2 value less than 50% and P > 0.10 for the Q test; otherwise, a random effects model (the DerSimonian and Laird method) was adopted. Third, cumulative meta-analyses were conducted to identify a possible trend of the pooled results with new studies added. Sensitivity analyses were also conducted to examine the stability of the results through deleting each study one by one. Finally, publication bias was assessed with Begg's funnel plot and Egger's test. All statistical analyses were performed using STATA version 14.0 . A P value < 0.05 was considered statistically significant.First, Hardy-Weinberg equilibrium (HWE) in controls of every included study was calculated by Chi-square test. Second, Crude ORs with 95% CIs were used to assess the association between rs712 G > T polymorphism and cancer risk. Five genetic models were analyses, involving allele contrast (T vs. G), co-dominant (GT vs. GG and TT vs. GG), dominant (GT + TT vs. GG) and recessive (TT vs. GG + GT) models. Stratified assessments were calculated based on cancer location, control resource, and genotype methods. Heterogeneity between studies was calculated with the


P = 5.80E-10 in European population. In Chinese population, several genetic association studies have investigated the association between rs3802842 variant and CRC risk. However these studies reported both positive and negative association results. It is still necessary to evaluate a specific variant in a specific population, which would be informative to reveal the disease mechanism. Until recently, there is no a systemic study to evaluate the potential association between rs3802842 and CRC risk in Chinese population by a meta-analysis method. Here, we aim to evaluate this association in Chinese population by a meta-analysis method using 12077 samples including 5816 CRC cases and 6261 controls. We identified the T allele of rs3802842 to be significantly related with an increase CRC risk  in Chinese population.A genome-wide association study identified a common genetic variant rs3802842 at 11q23 to be associated with CRC risk with OR=1.1 and  P = 5.80E-10 in European population . In CC vs. CA+AA model, we applied the fixed-effect model to perform the meta-analysis, which indicated significant association between rs3802842 CC genotype and CRC risk with P=2.22E-07, OR=1.39, and 95% CI . In CC+CA vs. AA model, we applied the random-effect model to perform the meta-analysis, which indicated significant association between rs3802842 CC+CA genotype and CRC risk with P=9.00E-03, OR=1.37, and 95% CI . The detailed information is described in Figure In C vs. A model, we applied the random-effect model to perform the meta-analysis, which indicated significant association between rs3802842 C allele and CRC risk with P=0.81 for C vs. A model.The possible publication bias of meta-analysis is evaluated by both funnel plot and a regression based statistical approach. Based on the shapes of funnel plots, we did not observe any asymmetric signal in all these three models as described in Figure A leave-one-out sensitivity analysis showed that the pooled ORs were not significantly changed when all these studies were excluded one by one, which indicated that the meta-analysis results were robust and reliable (data not shown).2 = 4.21, df = 3 (P = 0.24); I2 = 29%. We applied the fixed-effect model to perform the meta-analysis, which indicated significant association between rs3802842 C allele and CRC risk with P=9.19E-15, OR=1.31, and 95% CI . In the combined Hong Kong Chinese and Taiwan Chinese subgroup, we did not identified significant heterogeneity in these four studies with Heterogeneity: Chi2 = 0.00, df = 1 (P = 0.98); I2 = 0%. We applied the fixed-effect model to perform the meta-analysis, which indicated no significant association between rs3802842 C allele and CRC risk with P=0.08, OR=1.08, and 95% CI .In Han Chinese subgroup, we did not identified significant heterogeneity in these four studies with ChiTenesa et al. identified rs3802842 to be significantly associated with CRC risk . InteresUntil recently, six independent case-control association studies have been conducted to investigate the association between rs3802842 and CRC risk in Chinese population. Three studies reported positive association results –20, and In our study, we identified significant heterogeneity in these six genetic association studies. We think this may be caused by the substantial genetic variation in Han Chinese population . Chen et2 = 29%) is higher compared with that in combined Hong Kong and Singapore Chinese subgroup (I2 = 0%). Meta-analysis further showed the rs3802842 variant to be significantly associated with CRC risk in Han Chinese subgroup, but not in the combined Hong Kong and Taiwan Chinese subgroup.Considering the significant heterogeneity, we further performed a subgroup analysis in the Han Chinese subgroup, and the combined Hong Kong and Taiwan Chinese subgroup. The results are consistent with previous findings. The heterogeneity in Han Chinese subgroup  using the key words ‘rs3802842’ + ‘colorectal cancer’ . We also manually examined additional studies from the references cited in the original literature using Google Scholar database (https://scholar.google.com/), especially all associated publications citing the original CRC GWAS [Two reviewers independently selected the potential studies by systematically searching the PubMed database  be a case-control design in Chinese population; (2) evaluate the association between rs3802842 and CRC risk; (3) provide the original genotype number, or allele number, or odds ratio (OR) with 95% confidence interval (CI) for one of the three genetic models; or (4) provide sufficient data to calculate the genotype number, or allele number, or OR and 95% CI for one of these three genetic models. We excluded those studies that did not meet the inclusion criteria in following meta-analysis.We extracted (1) the name of the first author; (2) the year of publication; (3) the population; (4) the numbers of CRC cases and controls. Two reviewers independently extracted the data carefully. Meanwhile, a third reviewer resolved any disagreement. More detailed information has been widely described in previous studies using the meta-analysis methods –44.In brief, we used Review Manager 5.1 to investigate the potential heterogeneity in all the selected studies by a Cochran's Q test, calculate the pooled OR by a fixed effect model or a random-effect model based on the potential heterogeneity, determine the significance of pooled OR by a Z test.We calculated the Hardy-Weinberg equilibrium by a chi-square test in R program, if one study provides the control genotype number , 46. If We investigate potential publication bias by a funnel plot based approach, and a regression based statistical approach proposed by Egger. We performed a sensitivity analysis by a leave-one-out method . We evalWe performed a subgroup analysis in the Han Chinese subgroup including four studies, and in the combined Hong Kong Chinese and Taiwan Chinese subgroup including two studies using C vs. A model.