PA < 0.001, odds ratios >1), but not the homozygote or recessive models (PA > 0.05). Similar results were observed in a meta-analysis of central retinal vein occlusion (CRVO) and when comparing Caucasian subgroups to population-based controls. These data demonstrate that the G/A genotype of Factor V G1691A is associated with an increased risk of RVO/CRVO in a Caucasian population.We performed a meta-analysis to investigate the association between the Factor V G1691A polymorphism and the risk of retinal vein occlusion (RVO). This analysis included 37 studies involving 2,510 cases and 3,466 controls. Factor V G1691A was associated with an increased risk of RVO in the allele, heterozygote, dominant, and carrier models ( Factor V, Prothrombin (Factor II), and PAI-1, may contribute to the pathogenesis of RVO  , but not homozygote and recessive models . Forest plots are shown for the meta-analysis under A vs. G   Figure , AA vs. ) Figure , GA vs. ) Figure , AA vs. ) Figure , and A v) Figure  models. Factor V G1691A was associated with an increased risk of RVO compared to controls in a Caucasian population under A vs. G  , GA vs. GG , GA+AA vs. GG , and A vs. G (carrier)  models. Factor V G1691A was also associated with an increased risk of RVO among cases compared to PB controls. Eight BRVO and 13 CRVO studies were included in the disease subgroup meta-analysis. We observed an increased risk of CRVO, but not BRVO, under allele, heterozygote, dominant, and carrier models. Forest plots for the subgroup analysis under the A vs. G  and GA+AA vs. GG models are shown in Factor V G1691A is associated with an increased risk of RVO (particularly CRVO) in Caucasians.We next performed a subgroup analysis based on ethnicity, source of controls , and disease type (BRVO/CRVO) under all genetic models. PBegg > 0.05 and PEgger > 0.05). Begg’s funnel plots with pseudo 95% confidence limits under the A vs. G  and GA+AA vs. GG models are shown in Figure Our analysis indicated there was no publication bias (Table Factor V G1691A was previously associated with an increased risk of RVO in French ) OR Proaccelerin) OR AC Globulin) OR Coagulation Factor V) OR Factor V, Coagulation) OR Factor Pi) OR Blood Coagulation Factor V) OR FV Leiden) OR Factor V G1691A). We excluded duplicate articles, and then screened and removed ineligible articles using the following exclusion criteria: (1) review article or editorial, (2) case or trial report, (3) meeting abstract or poster, (4) meta-analysis, (5) cell- or animal-based study, (6) unrelated disease, gene, or SNP (7) departure from HWE, and (8) lack of available genotype data.Using the guidelines of the “Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA)” , we retrhttp://www.ohri.ca/programs/clinical_epidemiology/oxford.asp).Three authors independently extracted data from eligible articles including the name of the first author, publication year, country, ethnicity of the study population, genotype frequencies, disease type, genotyping assay, study number, sample size of case/control populations, and source of controls. We assessed the methodological quality of eligible studies using the NOS system (PH) > 0.1 or I2 < 50 %. ORs, 95% CIs, and PA values were calculated in allele (A vs. G), homozygote (AA vs. GG), heterozygote (GA vs. GG), dominant (GA+AA vs. GG), recessive (AA vs. GG+GA), and carrier (A vs. G) models. Subgroup analysis was performed according to ethnicity, source of controls (PB/HB), and disease type (BRVO/CRVO) under all genetic models. Publication bias was evaluated using Begg’s and Egger’s tests and sensitivity analysis was performed.Mantel-Haenszel statistical analysis under fixed- or random-effect models was performed with the Stata/SE 12.0 software . A fixed-effect model was utilized where Cochran’s Q statistic (


The association between CD14-159C/T polymorphism and sepsis has been assessed but results of current studies appeared conflicting and inconstant. This analysis was aimed to determine whether the CD14-159C/T polymorphism confers susceptibility to sepsis or is associated with increased risk of death from sepsis.The authors conducted a comprehensive search of PubMed, EMBASE, ISI Web of Science, Cochrane library, ScienceDirect, Wiley Online Library and CNKI databases according to a prespecified protocol. Language limits were restricted to English and Chinese. Two reviewers independently selected the articles and extracted relevant data onto standardized forms. Disagreements were settled by discussion and suggestions from senior consultants. The strength of association were evaluated by odds ratio (OR) and 95% confidence interval (CI). Studies failed to fit the Hardy-Weinberg-Equilibrium were excluded.p = 0.03) and recessive model . Of note, three out of the five papers included in the subgroup focused exclusively on burn ICU patients.The research identified a total of 2317 full-text articles of which 14 articles met the predefined inclusion criteria. Meta-analysis was performed for allele frequency of C versus T, as well as genotypes CC + CT versus TT (dominant model), CC versus TT + CT (recessive model), CT versus TT and CC versus TT (additive model). All control samples were in Hardy-Weinberg proportion. No significant association between CD14-159C/T polymorphism and sepsis susceptibility or mortality were detected in the overall population. Nonetheless, subgroup analysis of Asian ethnicity revealed significant association between the CD14-159C/T polymorphism and susceptibility to sepsis in additive model  contains supplementary material, which is available to authorized users. Sepsis, a complex clinical syndrome due to a systemic inflammatory response to bacteria and/or bacterial products, is imposing a huge burden on modern health care systems , 2. IndiWith the sequencing of human genome and the recognition of the degrees of genetic variation, it has become clear that an individual’s genetic makeup is likely to have impact on the incidence as well as outcome of sepsis , 5. As iCD14 gene, counting from the transcription start site, with a potential role of decreasing or increasing gene expression in process of sepsis [CD14 plays a crucial role in sepsis. CD14 is involved in LPS recognition and mediates the activation of monocytes/macrophages in gram-negative bacterial infection. In addition, it is also claimed that LPS is associated even with the deterioration of gram-positive bacterial infection because products from such bacteria induce hypersensitivity to LPS . Additiof sepsis . Thus, iSeveral studies tried to establish an association between CD14 polymorphism and sepsis , 11. HowThis meta-analysis was performed according to the recommendations of the Meta-analysis of Observational Studies in Epidemiology (MOOSE) guidelines . Two indp < 0.05 was considered representative of a departure from HWE.Studies were included in our meta-analysis if 1) they were focused on the relationship between CD14-159C/T polymorphism and the susceptibility to sepsis or sepsis-related mortality, 2) they studied humans, 3) enough data of selected SNP of both patients and control groups were clearly stated to calculate odds ratio (OR) and 95% confidence interval (CI), 4) the genotype frequency of control group was in accordance with Hardy-Weinberg equilibrium (HWE) . Sepsis-Exclusion criteria were 1) editorials, letters, comments, practice guidelines, consensus development conferences, and book chapters 2) causes of the systemic inflammatory response syndrome (SIRS) were not reliably defined;3) duplications. Disagreements on including/excluding studies were resolved by consensus. For studies without full text or sufficient data, we contacted the corresponding authors for additional information by e-mail. If no reply or the author refused to provide the data required in this meta-analysis, the study was excluded.Using a standardized form, two investigators independently extracted data from the included studies. For each eligible study, the following characteristics were collected: authors, publication date, ethnicity, numbers of cases, sample size, allele and genotype frequencies. The frequencies of alleles were calculated for cases and controls from the corresponding genotype distributions. Both of the investigators checked the extracting data results and disagreements were settled by discussion. Senior investigators were invited to discuss if disagreement still existed.p < 0.05 was considered as statistically significant. Due to the exploratory nature of the study, p-values were not adjusted for multiple testing.We recorded data from all publications with a computerized spreadsheet (Microsoft Excel). ORs and 95% CIs were calculated to estimate the association between CD14-159C/T polymorphism and susceptibility or mortality of sepsis. Pooled ORs were calculated for allele frequency comparison (C versus T), additive model , dominant model (CC + CT versus TT), and recessive model (CC versus TT + CT), respectively. Since the studies included are not as high-quality as RCTs, random effects models were used in each analysis whether or not heterogeneity exists. Therefore the result will be more conservative and can be interpreted in a homogenous way. The significance of pooled ORs was determined by Z-test and 2 value less than 50% indicated no significant heterogeneity. Meta-regression was adopted to determine the potential source of heterogeneity.Statistical heterogeneity among the studies was checked by chi-square-based Q-test. An IP value of Egger’s test less than 0.05 was considered statistically significant. All analyses were performed using the software program RevMan Analyses software (RevMan 5.0.17) from the Cochrane collaboration. All statistical analyses were performed with Stata 12.0 software . The statistical methodology was approved by principal investigator  and the statistical analysis were performed by trained investigators .Sensitivity analysis was carried out by omitting one single study each time to examine the influence of individual data sets on the pooled ORs. Publication bias of literature was assessed using funnel plots and Egger’s test. An asymmetric plot suggested a possible publication bias and The literature search identified a total of 2317 potentially relevant studies in PubMed, EMBASE, ISI Web of Science, Cochrane Central Register of Controlled Trials, ScienceDirect, Wiley Online Library and CNKI databases. As was shown in Fig. Characteristics of included studies were summarized in Table All studies were published in English except for three in Chinese. Nine studies were performed in Caucasian populations and five in Asian. In the studies included, sepsis was commonly defined according to the American College of Chest Physicians/Society of Critical Care Medicine guidelines .2 less than 50%) in the pooled analyses of susceptibility . No significant association was found in other genetic models in Asian population and in Caucasian population , and board-line significant association was also observed in additive model  , In Caucasian populations, association was identified only in TC versus TT  .Supplementary Material.


Genetic polymorphism of viral receptors is relevant to risks of HIV-1 infection, while it is still under debated whether the polymorphism of SDF1, a unique ligand for HIV-1 coreceptor CXCR4, is associated with HIV susceptibility and AIDS disease progression. Therefore, we provided an updated quantitative assessment by meta-analysis from 16 case-control and 7 cohort studies.Articles reporting the relationship between SDF1 polymorphism and HIV susceptibility or AIDS progression were retrieved from PubMed, Embase and Ovid electronic databases up to Apr 2017. Data were pooled by odds ratios (ORs) for HIV-1 infection with 95% confidence intervals (CIs) and summary relative hazards (RHs) for AIDS progression with 95% CIs using 1987 Center for Disease Control (CDC) case definition of AIDS (CDC87) and 1993 Center for Disease Control (CDC) case definition of AIDS (CDC93) and death as endpoints.As a result, 16 studies regarding susceptibility to HIV-1 infection with 2803 HIV-infected patients and 3697 healthy individuals and 7 studies regarding disease progression with 4239 subjects were included in the meta-analysis. For risks of infection, no evidences indicated SDF1 polymorphism was associated with the risk of HIV-1 infection in all genetic models , Furthermore, we failed to find an delayed AIDS progression except in some specific cohorts including MACS cohorts .Overall, no significant association was found between SDF1 polymorphism and HIV susceptibility. A protective effect of SDF1 on AIDS progression and death was seen especially in two studies based on the same cohorts. In conclusion, SDF1 polymorphism exerts a moderate protective effect against AIDS disease deterioration in some specific populations. Viral entry of human immunodeficiency viruses (HIV) required CD4 molecule and one member of CC or CXC chemokine-receptor families as co-receptors on cell membrane of lymphocytes. Genetic polymorphism of chemokines or chemokine-receptors was reported to be relevant to risks of human immunodeficiency viruses infection and disease progression. HIV viruses can be categorized to two major types according to the usage of co-receptors: R5 tropic viruses that use CC chemokine receptor (CCR) 5 and X4 tropic viruses that use CXC chemokine receptor (CXCR) 4. The stromal cell derived factor SDF1), also designated as CXCR12, is the unique ligand for one of HIV-1 coreceptor CXCR4. A guanine to adenine mutation at nucleotide position 801 in the 3’ untranslated region (UTR) of SDF1 (abbreviated as SDF1-3’A) was reported to confer resistance to HIV infection , also de. OtherwiThe meta-analysis followed the Preferred Reporting Items for Systematic Reviews and Meta-analysis criteria . A comprStudies were included if they met the inclusion criteria as follows: (1) case-control or cohort study; (2) provide contingency table in which the number of HIV+/- infected patients with each genotype was present in the content of HIV susceptibility. To analyze AIDS disease progression, relative hazards (RHs) with 95% CIs was provided by means of Cox proportional hazards models; (3) fulfil the Hardy-Weinberg Equilibrium (HWE). In addition, the major exclusion criteria were as follows: (1) lack of enough controls; (2) reviews, conference abstracts or case report; (3) poor study design or quality of paper; (4) small sample study; (5) not human studies; (6) duplicates of previous literature.http://getdata-graph-digitizer.com).Two investigators reviewed and extracted information from all the eligible publications independently according to the exclusion and inclusion criteria. Disagreement was solved by a discussion with a third investigator to reach a consensus on all the extracted information. For each article, the following characteristics were collected from each study: author name, year of publication, country, ethnicity, genotyping method, number of case and controls in HIV-infected patients and healthy controls, respectively and RHs of disease progression or death with 95% CIs. When RHs were provided in a graph, we obtained exact values using GetData Graph Digitizer 2.26  for nonrandomized studies. NOS allot eight points to each study . A study can be allotted for a maximum of one star in quality of selection and outcome categories and a maximum of two stars in comparability category. We evaluated studies with scores of more than 6 as high-quality studies.2 based Q test. If P value of heterogeneity >0.05, indicated an absence of heterogeneity between studies, then we adopted a fixed-effect model (Mantel-Haenszel) to assess the pooled ORs. If P value of heterogeneity <0.05, indicated a presence of heterogeneity between studies, then we adopted a random-effect model (DerSimonian and Laird) to assess the pooled ORs. Summary RR estimates were calculated using random-effect model. Heterogeneity between studies was evaluated by Q test and I2 statstic. We predefined heterogeneity . In order to explore the source of heterogeneity, a sensitivity analysis and subgroup analysis were conducted. These included subgroup analysis stratified based on sources of research subjects, ethnicity, seroconversion and whether or not the studies was adjusted for potential important confounders. Publication bias was tested by Egger’s test or Begg’s test and described by a funnel plot. Probable publication bias was corrected using “trim and fill” method. All statistical analysis except for the funnel plot was performed in Stata 14.0 software package. The funnel plot was conducted in Revman 5.3. A value less than 0.05 denoted a statistical significance.Crude ORs with 95% CIs were used to assess the strength of association of SDF1 polymorphism and susceptibility of HIV infection, under four different genetic model , based on genotype frequency distribution in cases and controls. A OR value >1 indicated a significant increase in susceptibility to HIV-1 infection and OR value <1 stood for a protective effect of HIV infection. The goodness-of-fit chi-square test was adopted to check the HWE. Heterogeneity was assessed by χAs shown in There were 9 high quality case-control studies, as determined by Newcastle-Ottawa Scale score of 6 or higher  and 4 hiAfter pooling the data from 16 studies regarding susceptibility of HIV-1 infection, the results were calculated using four genetic models , respectively. The studies were divided into two subgroups based on their NOS scores, thereby a more convincing conclusion could be made using both high quality evidence and all data collected. Overall, our results did not show a significant association between SDF1 polymorphism and susceptibility to HIV infection in all four models  . The con2 = 73.1%) with the data for AIDS according to CDC87 as the endpoint (2 = 38.9%) with the data for AIDS according to CDC93 as the endpoint (2 = 77.7%) with the data for death  with the data for AIDS (CDC87) as the endpoint, 0.63  with the data for AIDS (CDC93) as the endpoint and 0.27  with the data for death as the endpoint. In contrast, no significant association was found between SDF1-3’A and AIDS clinical progression or death in Group 2. CCR5 and CCR2 polymorphism have been demonstrated to be a risk factor in disease progression, which underscored the importance of controlling for confounding factors . Other genetic models was checked by Begg and Egger test’s in the same fashion, neither publication bias nor small sample effect was detected in other three models . In addition, visual inspection of funnel plot asymmetry has been performed for recessive model by Revman5.3 . HoweverA potential role of host genetic factors in predisposition to HIV infection has been described in several reports , 33. SDF2>50, p<0.01). Of note, two studies which demonstrated the SDF1-3’A polymorphism had a protective effect against AIDS progression based their researches on the same cohorts. So we divided the studies into two groups: in the first group, the subjects were selected from five epidemiologic cohorts (AMHHS cohorts), and in another group, the subjects were selected from other cohorts distinct from the first group. In AMHHS cohort, the RHs for disease progression (CDC87) and death is 0.38  and 0.27  respectively. In contrast, the RHs for both disease progression and death became not significant in another group. The positive effect of SDF1-3’A in AMHHS combined cohort is mainly attributable to that of MACS cohort. Of note, MACS cohort is a US-based ongoing prospective study of HIV-infected adult (ages 18–70) homosexual and bisexual men in Baltimore, Chicago, Pittsburgh and Los Angeles enrolled from 1984 to 1991. The racial distribution is 83.3% Europe American, 10% African American, 5% Hispanic and 2% others. A significant majority of these men in the cohort reported having 50 or more lifetime sexual partners, and over 80% had engaged in receptive anal intercourse with at least some of their partners in the previous two years [Whether SDF1 polymorphism accelerates or delays AIDS disease progression is a matter of ongoing debate. Data of RHs and its 95% CIs for disease progression (according to CDC87 and CDC93 criteria for AIDS separately) and death was obtained from original paper. Pooled RHs were calculated using random-effect model weighted by inverse variance method. We included 7 studies with 4239 HIV-infected patients. Considering the high statistical heterogeneity when all samples were counted into, we performed subgroup analysis for exploring the sources of heterogeneity. The heterogeneity was not reduced when the analysis were stratified by ethnicity, country, year of publication or study design  is a priority for infection risk and analysis of infection routes should be performed, however, the detailed data of HIV infected patients by different routes are lacking. Nevertheless, our study indicates SDF1 polymorphism exert little effect on HIV-1 infection whereas it exert a moderate influence AIDS progression in some specific populations.Our study conforms to the PRISMA statement  and the S1 Fig(A) Forest plot of RHs of SDF1-3’A homozygosity for AIDS progression (CDC87) which are adjusted for coreceptor tropism and stratified by cohorts. (B) Forest plot of the RHs of SDF1-3’A homozygosity for death which are adjusted for coreceptor tropism and stratified by cohorts.(TIF)Click here for additional data file.S1 Table(TIF)Click here for additional data file.S2 Table(TIF)Click here for additional data file.S3 Table(TIF)Click here for additional data file.S4 Table(TIF)Click here for additional data file.S5 Table(DOC)Click here for additional data file.S6 Table(DOCX)Click here for additional data file.


Previous studies have demonstrated that patients with posttraumatic stress disorder (PTSD) caused by different types of trauma may show divergence in epidemiology, clinical manifestation and treatment outcome. However, it is still unclear whether this divergence has neuroanatomic correlates in PTSD brains. To elucidate the general and trauma-specific cortical morphometric alterations, we performed a meta-analysis of grey matter (GM) changes in PTSD (N = 246) with different traumas and trauma-exposed controls  using anisotropic effect-size signed differential mapping and its subgroup analysis. Our results revealed general GM reduction (GMR) foci in the prefrontal-limbic-striatal system of PTSD brains when compared with those of TECs. Notably, the GMR patterns were trauma-specific. For PTSD by single-incident traumas, GMR foci were found in bilateral medial prefrontal cortex (mPFC), anterior cingulate cortex (ACC), insula, striatum, left hippocampus and amygdala; and for PTSD by prolonged traumas in the left insula, striatum, amygdala and middle temporal gyrus. Moreover, Clinician-Administered PTSD Scale scores were found to be negatively associated with the GM changes in bilateral ACC and mPFC. Our study indicates that the GMR patterns of PTSD are associated with specific traumas, suggesting a stratified diagnosis and treatment for PTSD patients. For instance, high-resolution structural MRI combined with voxel-based morphometry (VBM) provides opportunities to investigate subtle structural abnormities121316et al. conducted a functional MRI meta-analysis to assess the role of the traumatic events in PTSDet al. also reported that different kinds of trauma may have acted as a potential moderator or a source of method variance that contributed to the heterogeneous findings of PTSD studies1220A couple of meta-analysis studies have been performed to address these questions. Boccia We therefore in this study performed a VBM meta-analysis on GM changes of different PTSD subtypes, i.e., single-incident vs. prolonged trauma types451112202213131418192526272829303132333435As shown in Subgroup analysis in PTSD patients by single-incident traumas  revealed GMRs in bilateral mPFC, ACC, insula and striatum, left hippocampus and amygdala, as depicted in Heterogeneity analysis showed that no regions in our pooled meta-analysis have significant heterogeneity between studies. Analysis of publication bias showed that the Egger test is insignificant for the right ACC (p = 0.264), the right mPFC (p = 0.409), the right striatum (p = 0.439), the right insula (p = 0.593), the right hippocampus (p = 0.957), the right amygdala (p = 0.524), the left mPFC (p = 0.083), the left striatum (p = 0.894), the left insula (p = 0.458), the left hippocampus (p = 0.457), the left amygdala (p = 0.626), except for the left ACC (p = 0.021).Whole-brain jack-knife sensitivity analysis showed that GMRs in bilateral ACC and mPFC were highly replicable and well preserved throughout all combinations. The findings in left striatum, insula, hippocampus, and amygdala remained significant in all but one combination, which is the same for the findings in right striatum, insula, and amygdala. The findings in the right hippocampus remained significant in all but two combinations.Symptom severity (CAPS scores) of PTSD patients was negatively associated with GM changes in bilateral ACC and mPFC, as shown in In the present study, we conducted a voxel-wise meta-analysis using AES-SDM for general and trauma-specific GM alteration patterns of PTSD brains compared with those of TECs. Our pooled meta-analysis obtained a general pattern of GMR in PTSD compared with TEC, mostly in bilateral mPFC, ACC, striatum, insula, hippocampus, and amygdala. In terms of trauma-specific GM alteration patterns, PTSD patients by single-incident traumas were characterized by GMRs in bilateral mPFC, ACC, striatum, and insula, the left hippocampus, and amygdala. While patients by prolonged traumas demonstrated quite a different pattern of GMR, with most affected regions found in the left striatum, insula, amygdala and MTG. In addition, GM alterations in the bilateral ACC and mPFC were negatively correlated with the severity of PTSD symptoms as measured by CAPS.Our pooled meta-analysis indicates that general brain regions affected by PTSD are mostly located in the prefrontal-limbic-striatal system. The prefrontal-limbic circuit is associated with fear conditioning and has been widely reported to exhibit both anatomical and functional deficits in PTSD patients123942Single-incident traumas are acute stressful events, limited in time12In contrast to the above subgroup, PTSD patients having prolonged traumas revealed a different pattern of GMR, which mainly involved left striatum, insula, amygdala and MTG, but not the mPFC structures. This difference may be due to that the long-term fear expression induced by prolonged and repetitive trauma exposure may not be mediated by the mPFC-limbic network50Our results demonstrated that for PTSD patients by acute single-incident traumas, GMRs generally appear in the mPFC-limbic-striatal system while for PTSD by prolonged traumas, GMRs are dominant in the limbic-striatal structures. This difference indicates that there may be different brain mechanisms underlying PTSD by different traumas. One hypothesis is that for PTSD by acute single-incident traumas, mPFC is easily impaired due to its susceptibility to uncontrollable stress1239Notably, there are some potential confounds that might contribute to the resultant differences in GMRs of the subtypes, such as gender and illness duration. Males with PTSD have been found to exhibit increased activation in left ACC during extinction recall compared with female PTSD patientsOur results revealed that the regions of GMR in PTSD are subjected to trauma. However, the findings should be interpreted in light of several possible limitations of the present study. First, only sixteen studies are included in this study. This limited number of studies prevents us from more detailed subgroup meta-analyses. It may also affect the generalisation of our results. Second, it should be considered that gender and illness duration differences might confound the between-trauma findings in the PTSD group. Third, whether the GMRs are predispositions or consequences of PTSD is difficult to elucidate. Further studies with longitudinal structural changes may be necessary to disentangle this. Fourth, some original studies included in our meta-analysis did not exclude PTSD with other psychiatric disorders . Finally, publication bias was shown in the left ACC, and relevant findings should be interpreted with caution.In this study, we performed a quantitative voxel-wise meta-analysis of GM changes in PTSD by different traumas using AES-SDM, and found that GMR regions were generally located in the prefrontal-limbic-striatal system. Notably, subgroup analyses revealed that the GMR patterns were associated with specific trauma categories. This study provides further evidences of different neural correlates underlying PTSD by different traumas, and suggests that stratified diagnosis and treatment of PTSD are necessary in clinics.A systematic search strategyStudies were included according to the following criteria: (1) used VBM to analyze brain GM changes in PTSD patients; (2) compared PTSD patients with TECs; (3) clearly reported traumatic types. For studies reported both corrected and uncorrected results, only the corrected ones were used in the subsequent analyses.A candidate study was excluded if (1) it belonged to reviews, case reports or meta-analysis studies; (2) full-text record is non-English or unavailable; (3) recruited patients were younger than 18 years old; (4) the included patients had co-morbidity such as headache, traumatic brain injury or other physical diseases; (5) results were based on small volume correction; (6) different thresholds were used for different brain regions, and (7) peak coordinates of reported brain regions or the trauma type could not be determined.The quality of an included study was independently assessed by two authors , using a checklist See  adapted 55http://www.sdmproject.com). This is a well-established meta- analysis toolkit with following features: (1) reconstruction of positive and negative differences in the same signed differential map to avoid any voxel appearing significant in opposite directions; (2) using effect sizes to combine reported peak coordinates with statistical parametric maps; (3) applying complementary analyses such as sensitivity, subgroup and meta-regression analysis to assess the robustness and heterogeneity of the resultsRegional differences in GM between PTSD patients and TECs were analyzed using AES-SDM  were conducted for the trauma-specific GM alteration. For all meta-analyses, the statistical significance of each voxel was determined using standard randomization testsIn the pooled meta-analysis, the statistical heterogeneity of individual clusters between studies was examined using a random effect model with Q statisticIn order to test the replicability of the results, a systematic whole-brain voxel-wise jack-knife sensitivity analysis was performedSeveral relevant socio-demographic and clinical characteristics were listed in How to cite this article: Meng, L. et al. Trauma-specific Grey Matter Alterations in PTSD. Sci. Rep.6, 33748; doi: 10.1038/srep33748 (2016).


Background. Age-related macular degeneration is the leading cause of blindness in elderly individuals where aetiology and pathophysiology of age-related macular degeneration are not absolutely clear. Purpose. To determine the frequency of the genotype of rs2108622 in patients with early and exudative age-related macular degeneration. Methods. The study enrolled 190 patients with early age-related macular degeneration, 181 patients with exudative age-related macular degeneration (eAMD), and a random sample of 210 subjects from the general population (control group). The genotyping of rs2108622 was carried out using the real-time polymerase chain reaction method. Results. The analysis of rs2108622 gene polymorphism did not reveal any differences in the distribution of C/C, C/T, and T/T genotypes between the early AMD group, the eAMD group, and the control group. The CYP4F2 (1347C>T) T/T genotype was more frequent in males with eAMD compared to females ; also T/T genotype was less frequently present in eAMD females compared to healthy control females . Conclusion. Rs2108622 gene polymorphism had no predominant effect on the development of early AMD and eAMD. The T/T genotype was more frequent in males with eAMD compared to females and less frequently present in eAMD females compared to healthy females. The pathological hallmark of the disease is amorphous deposits of protein and lipid, termed drusen [Age-related macular degeneration (AMD) is a progressive neurodegenerative disease and the leading cause of irreversible blindness among individuals aged 65 and older, particularly in western countries . With thd drusen . Drusen—d drusen . Drusen  CYP4F2 is involved in the production of 20-hydroxyeicosatetraenoic acid (20-HETE), a molecule that is proinflammatory and can induce hyperlipidemia [ CYP4F2 is also an important endobiotic metabolizing enzyme involved in the metabolism of fatty acids such as arachidonic acid, medium and very long polyunsaturated fatty acids, eicosanoids such as leukotriene B4 (LTB4), prostaglandins, and lipoxins implicating its importance in maintaining liver polyunsaturated fatty acids (PUFA) levels and inflammatory status [AMD is a complex disease to which many factors contribute: body ageing together with pathological changes, such as pathogenic oxidative stress, inflammatory processes, changes of the extracellular matrix, biological activity changes in the retinal pigment epithelium, and genetic factors—all of them are important in the pathogenesis of this disease . With adipidemia , 12. CYPy status . The poly status  found thKnowing that the main pathological changes of age-related macular degeneration are drusen, which include about 40% lipids, the attempts to find a relation between age-related macular degeneration and the gene rs2108622 controlling lipid metabolism have been made.Permission to undertake the study was obtained from the Ethics Committee for Biomedical Research. The study was conducted in the Lithuanian University of Health Sciences (LUHS), Neuroscience Institute, Ophthalmology laboratory (Number BE-2-/13).n = 190) with early age-related macular degeneration and patients (n = 181) with exudative age-related macular degeneration and a random sample of the population n = 210 (control group).The study included patients .All study subjects were evaluated by slit-lamp biomicroscopy to assess corneal and lenticular transparency. Classification and grading of lens opacities were performed according to the Lens Opacities Classification System III. Best corrected visual acuity (BCVA) was measured using the standard procedure adapted for the Age-Related Eye Disease Study (AREDS) at a 5-meter distance from the chart (letters on the ETDRS logMAR chart), for subjects with sufficiently reduced vision, at 1 meter. At each examination, intraocular pressure was measured. Pupils were dilated with tropicamide 1%, after which fundoscopy, using slit-lamp biomicroscopy with a double aspheric lens of +78 diopters, was performed. For detailed analysis of the macula, colour fundus photographs of the macula, centred at 30° to the fovea, were obtained with a Visucam NM Digital camera .μm in diameter) or retinal pigment epithelial abnormalities; intermediate AMD was characterized by the presence of extensive intermediate drusen and at least one large drusen (≥125 μm in diameter) or geographic atrophy (GA) not involving the centre of the fovea; and advanced AMD was characterized by GA involving the fovea and/or any of the features of neovascular AMD [The classification system of AMD formulated by the Age-Related Eye Disease Study  was usedular AMD . Early AThe following subject exclusion criteria were used: (i) unrelated eye disorders, for example, high refractive error, cloudy cornea, lens opacity  except minor opacities, keratitis, acute or chronic uveitis, glaucoma, or diseases of the optic nerve; (ii) systemic illnesses, for example, diabetes mellitus, malignant tumours, systemic connective tissue disorders, chronic infectious diseases, or conditions following organ or tissue transplantation; (iii) ungraded colour fundus photographs resulting from obscuration of the ocular optic system or because of fundus photograph quality. CYP4F2 (rs2108622) were carried out at the Laboratory of Ophthalmology at the Institute of Neuroscience of LUHS. DNA was extracted from 200 μL venous blood (white blood cells) using a DNA purification kit based on the magnetic beads method  or the silica-based membrane technology utilizing a genomic DNA extraction kit , according to the manufacturer's recommendations.The DNA extraction and analysis of the gene polymorphism of CYP4F2 (rs2108622) was carried out using the real-time polymerase chain reaction (PCR) method. Single-nucleotide polymorphism was determined using TaqMan® Drug Metabolism assay (Thermo Scientific).The genotyping of CYP4F2 (rs2108622) were prepared for determining a single-nucleotide polymorphism.The genotyping was performed using a Rotor-Gene Q real-time PCR quantification system . The real-time PCR reagents  were taken out from an environment of −20°C and were thawed at room temperature. The thawed reagents were centrifuged (10 000 rpm) and stored in an ice tub. Appropriate real-time PCR mixtures ofμL) was poured into the 72 wells of the Rotor-Disc, and then 1 μL of matrix DNA of the samples (~10 ng) and 1 μL of negative control (−K) were added.A PCR reaction mixture . After that, the Allelic Discrimination program was completed, and the genotyping results were received. The program determined the individual genotypes according to the fluorescence intensity rate of different detectors (VIC and FAM).The Allelic Discrimination program was used during the real-time PCR. Then, the assay was continued following the manual provided by the manufacturer . The data are presented as absolute numbers with percentages in brackets, average values, and standard deviations (SD). The frequencies of genotypes (in percentage) are presented in χ2 test in all groups. The distribution of the rs2108622 single-nucleotide polymorphism (SNP) in the AMD and control groups was compared using the χ2 test or the Fisher exact test. Binomial logistic regression analysis was performed to estimate the impact of genotypes on AMD development. Odds ratios and 95% confidence intervals are presented. The selection of the best genetic model was based on the Akaike Information Criterion (AIC); therefore, the best genetic models were those with the lowest AIC values.Hardy-Weinberg analysis was performed to compare the observed and expected frequencies of rs2108622 using the p < 0.05.Differences were considered statistically significant when n = 161) of women in the control group, 70% (n = 133) of women in the early AMD group, and 67.4% (n = 122) of women in the eAMD group.A total of 190 patients with early AMD and 181 patients with eAMD were enrolled into the analysis according to the subject inclusion and exclusion criteria. The control group comprised 210 persons. There were 76.7%  T/T genotype was more frequent in males with eAMD . When comparing the CYP4F2 genotype distribution in females with eAMD and healthy females significant differences were also revealed. The CYP4F2 (1347C>T) T/T genotype was less frequently present in eAMD females compared to healthy controls   .Binomial logistic regression analysis in the patients with NeAMD and in the control group was performed . Similarp value = 0.049) variables were statistically significant  variables were statistically significant in the group aged over 65 years.Binomial logistic regression analysis in the males with early AMD and in the control group by age was performed . This anp = 0.026) variables in the group under 65 years old were statistically significant (p = 0.037) and additive (p = 0.015) variables were statistically significant in the group of over 65 years  and 27-hydroxycholesterol (27-Hch), respectively [ CYP46A1 gene was evaluated by Fourgeux et al. and no significant difference between the CYP46A1 genotypes in the AMD and control groups was observed [ CYP4F2 (which is involved in lipid metabolism) in patients with early and exudative AMD. The data of our analysis of rs2108622 polymorphism did not reveal any differences in the distribution of C/C, C/T, and T/T genotypes between the early AMD group, exudative AMD group, and the control group . The CYP4F2 (1347C>T) T/T genotype was more frequent in males with exudative AMD , and the comparison of the CYP4F2 genotype distribution in females with exudative AMD and healthy females determined significant differences. The CYP4F2 (1347C>T) T/T genotype was less frequently present in exudative AMD females compared to healthy control females . To interpret our results, the research should be repeated with a larger sample size. However, exposure to environmental hazards has been associated with diseases in humans. The identification of single-nucleotide polymorphism (SNP) in human populations exposed to different environmental hazards is vital for detecting the genetic risks of some important human diseases [It is known that changes in the metabolism of cholesterol, particularly of HDL, may influence drusen accumulation and consequently promote AMD development . Cholestectively . High leectively , 21. Theectively , 21. Theobserved . At the diseases . Yang et CYP4F2  gene polymorphism in patients with age-related macular degeneration; thus we can only compare the genotyping results in our control group with results obtained in other studies.We found no studies analyzing theResults of the control groups with rs2108622 polymorphism distribution in the other studies are shown in p value < 0.05, 0.03, resp.), and that higher low density lipoprotein (LDL) is associated with an increased risk of advanced AMD and the NV subtype . When looking for an association of serum lipids with advanced AMD in multivariate modeling, Reynolds et al. also found significant trends with a higher quartile of LDL and increasing AMD risk . Higher total cholesterol was also associated with AMD risk when controlling for all covariates and genotypes [There are many studies analyzing genes that take part in lipid metabolism and about systemic lipoproteins, but results are conflicting. Moreover, conflicting results have been reported with regard to the associations of AMD with serum HDL concentration and the genes involved in lipid metabolism. Some authors agree that increased serum cholesterol levels increase AMD development , 26; howenotypes . CETP rs1864163 and LPL rs281 were protective against the development of large drusen area in the macula and CETP rs3764261 was protective against the development of pure GA. In the RS, CETP rs3764261 was linked to an increased risk of late AMD, pure GA, and exudative AMD. CETP rs1864163 was related to a decreased risk of the incidence of early AMD and large drusen area. ABCA1 rs1883025 was associated with a decreased risk of large SI drusen. LPL rs281 was associated with a decreased risk of a large drusen area in the macula. In the BMES, none of these SNPs were found to be significantly associated with any AMD outcomes, although marginally nonsignificant associations were found between ABCA1 rs1883025/T and pure GA and between LIPC rs7163555 and exudative AMD. Mean HDL-C increased in each of the three cohorts with each additional risk allele for CETP rs376426 and decreased with each additional risk allele for CETP rs1864163 and ABCA1 rs1883025 . There were no consistent statistically significant associations between LIPC or LPL and HDL-C or between any of the lipid genes and total cholesterol [A Klein et al. study  examinedlesterol . In a melesterol .In addition to being one of the major AMD-susceptibility genes, perhaps accounting for approximately 30%–50% of AMD patients, CFH might interact with lipid metabolism to affect the disease risk . Such in CYP4F2 (1347C>T) T/T genotype was more frequent in males with eAMD compared to females , and the comparison of the CYP4F2 genotype distribution in females with eAMD and healthy females did not reveal significant difference. The CYP4F2 (1347C>T) T/T genotype was less frequently present in eAMD females compared to healthy control females , but this must be replicated with a larger sample size to prove these results, as we know that T/T genotype is wild and very rare.In conclusion, our results suggest that the CYP4F2 (rs2108622) gene polymorphism had no predominant effect on the development of early and eAMD when compared to the control group. But the


Findings on the association between intake of red and processed meat with renal cell carcinoma (RCC) risk are mixed. We conducted a meta-analysis to investigate this association.Eligible studies up to August 31, 2016, were identified and retrieved by searching the MEDLINE and Embase databases along with manual review of the reference lists from the retrieved studies. The quality of the included studies was evaluated using the Newcastle-Ottawa Quality Assessment Scale. The summary relative risk (SRR) and corresponding 95% confidence interval (CI) were calculated using a random-effects model.Twenty-three publications were included in this meta-analysis: four cohort studies, one pooled study, and 18 case-control studies. The SRR (95% CI) for the highest vs. lowest intake of red meat was 1.36 ; that for processed meat was 1.13 . Linear dose-response analysis yielded similar results, i.e., the SRR for per 100 g/day increment of red meat and per 50 g/day increment of processed meat was 1.21  and 1.16 , respectively. A non-linear association was observed only for red meat (Pnonlinearity = 0.002), and not for processed meat (Pnonlinearity = 0.231). Statistically significant positive associations were observed for intake of beef, salami/ham/bacon/sausage, and hamburger.This meta-analysis indicates a significant positive association between red and processed meat intake and RCC risk. In the United States, the incidence of kidney cancer is the seventh and tenth highest in men and women, respectively . Renal cN-nitroso compounds (NOCs) [The consumption of red and processed meat has long been recognized as a risk factor of human cancer, as such meats are rich in well-established carcinogens, such as heterocyclic amines (HCAs), polycyclic aromatic hydrocarbons (PAHs), and s (NOCs) , 14. Mans (NOCs) –28. Reces (NOCs) , 30 of os (NOCs) , which is (NOCs) –40 evalus (NOCs) .Two investigators (Z.S.J. and H.J.J.) conducted a computerized literature search independently in MEDLINE  and Embase  through to August 31, 2016. We searched the relevant studies using the following words and/or Medical Subject Heading (MeSH) terms: 1) intake OR consumption OR diet OR red meat OR processed meat OR preserved meat OR beef OR pork OR veal OR mutton OR lamb OR ham OR sausage OR bacon; 2) kidney OR renal; 3) carcinoma OR cancer OR neoplasm OR neoplasia; and 4) case-control OR cohort OR prospective OR retrospective. Furthermore, we reviewed the reference lists of the relevant articles to identify additional studies. Only studies published in English were included.In the present analysis, red meat was defined as beef, veal, pork, lamb, or a combination thereof ; processwere published as an original article;used a case-control or cohort design;reported relative risk (RR) estimates with corresponding 95% CIs for the association between red and/or processed meat intake and the risk of RCC.Non–peer-reviewed articles, abstracts, commentaries/letters, ecologic assessments, correlation studies, experimental animal studies, and mechanistic studies were excluded. When multiple reports on the same study were available, only the most informative one was considered.A standardized data collection sheet was designed before the extraction. Two investigators (Z.S.J. and H.J.J.) separately extracted the basic information , data of interest , and adjustments. From each study, we extracted the risk estimates that reflected the greatest degree of control for potential confounders.We used the NOS checklist to assess study quality , where tWe used a random-effects model to calculate the SRRs (95% CIs) for the high vs. low and dose-response analyses. This model accounts for heterogeneity among studies . As outc2 test to assess heterogeneity among studies, defining significant heterogeneity as P < 0.10. We also used the I2 statistic to explore the extent of inconsistency, with I2 > 50% indicating high heterogeneity and I2 < 25% indicating no significant heterogeneity [We used the χogeneity . We perfWhen possible, we performed linear dose-response meta-analysis per 100 g/day increment of red meat intake and per 50 g/day increment of processed meat intake using generalized least squares trend estimation (GLST) , 46. TheThe search strategy generated 2,211 citations, of which 59 were considered of potential value and for which the full text was retrieved for detailed evaluation. An additional seven articles were identified from a review of the references. Forty-three of these 66 articles were subsequently excluded from the meta-analysis. The studies by Di Maso et al.  and Bravn = 11), Europe (n = 7), Asia (n = 1), and South America (n = 3). The pooled study was conducted in the United States and in Europe. The methods used in all studies for assessing meat consumption were based on the food items semiquantitative Food Frequency Questionnaire (FFQ). The Newcastle-Ottawa Scale (NOS) scores ranged 5–9; 19 studies were deemed to be of high quality (≥ 7 stars) (The characteristics of these 23 publications are described in Tables 7 stars) .heterogeneity < 0.001, I2 = 71.3%; Figure Nineteen studies reported on the highest vs. lowest levels of red meat intake and RCC risk. The summary relative risk (SRR) was 1.36 ; there was evidence of high inter-study heterogeneity . Visual inspection of the curve suggested that the risk increased linearly up to approximately 240 g/day red meat intake. Above that, the risk increase became even steeper , with evidence of high heterogeneity , and there was evidence of moderate inter-study heterogeneity , and there was high inter-study heterogeneity (P2 = 68.6%) and South America , but not in those conducted in Europe  and Asia . There was significant between-subgroup heterogeneity in stratified analysis of location (P for difference = 0.038). Adjustments by body mass index (BMI), smoking, history of hypertension, total energy intake, intake of vegetables and fruits, and alcohol consumption did not significantly change the SRR for RCC risk.Table 2 = 55.4%) and cohort studies . The SRR was significant for studies conducted in North America , but not for studies conducted in South America, Europe, and Asia.For high vs. low consumption of processed meat, we observed a borderline significant risk of RCC in both case-control . In addition, repeat analysis of high vs. low intake using the studies included in the linear dose-response analysis yielded results similar to that of the original analysis .P = 0.087) and Begg's test (P = 0.005), indicated publication bias. The trim-and-fill method indicated that eight additional risk estimates were needed to balance the funnel plot  and Begg's test (P = 0.183), did not indicate publication bias. The trim-and-fill method indicated that two additional risk estimates were needed to balance the funnel plot , hamburger , and ham/salami/bacon/sausage . RCC risk was not positively associated with the intake of pork or barbecued/pan-fried/broiled meat .The results of this comprehensive meta-analysis show that the consumption of red and processed meat is associated with increased RCC risk, as per the high vs. low and linear dose-response meta-analyses. There was significant heterogeneity across studies for both red and processed meat intake. In non-linear models, RCC risk appeared to increase approximately linearly with increased intake of processed meat, whereas there was evidence of non-linear increased risk with increased intake of red meat. Among individual red and processed meat types, there were statistically significant positive associations for the intake of beef, salami/ham/bacon/sausage, and hamburger.Several mechanisms have been proposed to explain how the consumption of red and processed meat enhances cancer risk, and include the high intake of proteins and fats and intake of carcinogens  , 50. A lIn comparison with previous meta-analyses , 30, theWe found that red and processed meat consumption was significantly associated with increased risk of RCC in the case-control studies, which might drive the overall epidemiological findings of the present study, but not in the cohort studies. Case-control studies are more susceptible to recall and selection bias than are cohort studies, as lifestyles and diet habits in retrospective case-control studies are determined after the diagnosis of cancer. Although the meta-regression results suggested that study design did not significantly alter the aforementioned associations, we observed that the positive association was weaker in the cohort studies than in the case-control studies. Therefore, the finding that red and processed meat consumption is associated with increased RCC risk should be received with caution.The present meta-analysis has several limitations. First, inaccurate assessments of dietary intake could have led to overestimations of the range of intakes and consequent underestimation of the magnitude of the aforementioned relationship , 57. NotSecond, there was great inter-study heterogeneity. Stratified and meta-regression analyses revealed a significant positive association between studies from North America (but not from Europe), and study location was the only significant factor in the association between intake of red meat and RCC risk. This might be attributed to the fact that different populations consume different types, levels of meat, and their cooking practices differ, which may partly explain the high heterogeneity among the included studies. Additionally, there was considerable heterogeneity in the dose-response analysis models, which might be ascribed to a consequence of the conversions of the intake units.Third, the residual confounders inherent in primary observational studies are always of concern. Although most of the included studies reported adjusted risk estimates of RCC for confounders, some appeared to have failed to fully control for confounders. For example, only seven studies used adjustments for history of hypertension, which is one of the established risk factors of RCC . High inFourth, HCA and PAH formation increases with cooking temperature and duration; however, data on the degree of meat doneness in the included studies were not available. Additionally, the non-linear trend with intake of red meat should be interpreted with caution due to the low statistical power in the extremes of red meat intake distribution. This is an issue of the fractional polynomial method. Most of the included studies were based on data from Western populations; additional research in other populations is warranted to generalize these findings.Lastly, we acknowledge the presence of significant publication bias in the results for red meat intake. The overall risk estimates for the association for red meat consumption were probably an overestimation, as small studies with null results tend not to be published. Indeed, the trim-and-fill method indicated that eight additional risk estimates were needed to balance the funnel plot, and the summary risk estimates were attenuated and not statistically significant.In conclusion, our limited data suggest that high intake of red and processed meat may increase RCC risk. However, because the effect was only found in case-control studies and might be a consequence of bias, confounding factors, and importantly, publication bias, further prospective epidemiological studies that control for possible confounders and that examine the association between meat consumption and RCC risk are required.


The random-effects model was used to estimate the relative risk (RR) for specific PM2.5 values. The meta-estimate for lung cancer risk associated with PM2.5 was 1.11 for mortality  and 1.08  for incidence. Analyses by continent showed that the meta-estimate for lung cancer mortality associated with PM2.5 was greatest in North America [1.15 ], followed by Asia [1.12 ], and then Europe [1.05 ]. Lung cancer incidence associated with PM2.5 was greatest in Asia [1.09 ], followed by North America [1.06 ], and then Europe [1.03 ]. In subgroup analyses of country, the mortality meta-estimate for developed countries was 1.14 , and for developing countries was 1.03 . The incidence meta-estimate for developed countries was 1.07 , and was similar to that of developing countries, 1.07 . In subgroup analyses of males and females, the meta-estimate for lung cancer mortality associated with PM2.5 was greater for males [1.26 ] than for females [1.17 ]. The meta-estimate for lung cancer incidence associated with PM2.5 was greater for males [1.23 ] than for females [1.15 ]. In subgroup analyses of smoking status, the meta-estimate for lung cancer mortality associated with PM2.5 for former smokers was 1.46 , for current smokers was 1.33 , and for never smokers was 1.16 , respectively. The meta-estimate for lung cancer incidence associated with PM2.5 for former smokers was 1.19 , for never smokers was 1.10 , and for current smokers was 1.03 . The relative risks of a relationship between PM2.5 and lung cancer incidence and mortality were 1.08  and 1.11 , respectively. These findings will provide some evidence for policy makers and public health practitioners worldwide.We conducted a meta-analysis to examinine the relationship between exposure to PM2.5 and lung cancer incidence and mortality. In total, 17 studies met our inclusion criteria and provided information necessary to estimate the change in lung cancer risk per 10 μg/m The between-study variance for mortality and incidence was 63.2% and 39.4% of the total variance, respectively.Figure Tables Subgroup analyses by continent showed that the mortality meta-estimates for North America, Europe and Asia were 1.15 , 1.05 , and 1.12 , respectively. The incidence meta-estimates for North America, Europe and Asia were 1.06 , 1.03 , and 1.09 , respectively. The full meta-estimates for North America, Europe and Asia were 1.11 , 1.03 , and 1.09 , respectively.Meta-estimates for the country subgroups showed that the mortality meta-estimate for developed countries was 1.14 , and for developing countries was 1.03 . Estimates for developing countries were only available from one study . The incidence meta-estimate for developed countries was 1.07 , similar to developing countries which was also 1.07 . Estimates for developing countries were only available from one study . The full meta-estimate for developed countries was 1.11 , and for developing countries was 1.05 .We also conducted subgroup analyses of males and females. The meta-estimate for LC mortality associated with PM2.5 was 1.26 for males and  and 1.17 for females . Estimates for females were only available from one study . The meta-estimate for LC incidence associated with PM2.5 was 1.23 for males and  and 1.15 for females . The full meta-estimate for LC risk associated with PM2.5 was 1.21 for males  and was 1.15 for females .Subgroup analyses of smoking status showed that the meta-estimate for LC mortality associated with PM2.5 in former smokers was 1.46 , in current smokers was 1.33 , and in never smokers was 1.16 , respectively. The meta-estimate for LC incidence associated with PM2.5 in former smokers was 1.19 , in never smokers was 1.10 , and in current smokers was 1.03 . The full meta-estimate for LC risk associated with PM2.5 in former smokers was 1.32 , in current smokers was 1.20 , and in never smokers was 1.16 .P = 0.005) to 41.8% (P = 0.100), and the pooled RR of the remaining studies on LC mortality associated with PM2.5 was more robust and stable. The article by Katanoda et al. 2011 was a cohort study, where the heterogeneity was explained by HR. However, we used RR instead of HR in our meta-analysis. Our study showed no significant heterogeneity between LC incidence and PM2.5, thus the sensitivity analysis was omitted. Begg's funnel plot and Egger's publication bias plot were used to evaluate publication bias in relation to LC mortality/incidence and PM2.5  or incidence .The sensitivity analysis showed that when the study by Katanoda et al. 2011 was omitted, the I-squared changed from 63.2%  and two studies on females, which contributed to the overall meta-estimates. These results are limited by a lack of sufficient studies, and we were unable to unravel the effects of PM2.5 on LC risk by gender.In addition, meta-estimates suggested that males may have an elevated risk of LC associated with PM2.5 compared to females, according to the mortality, incidence and full meta-estimates. This is presumably because men may have more exposure to tobacco smoke. In our meta-analysis, one study investigated the risk of LC according to gender, and showed that males had a higher LC risk . AnotherIn order to adjust for smoking status, some studies provided analyses of PM2.5 by smoking subgroups, allowing the assessment–differential effects of PM2.5 by smoking status . A comprehensive analysis of 26,957 patients with non-small cell lung cancer (NSCLC) showed that smoking status was an independent prognostic factor in NSCLC . Meta-esCertain limitations in our study may have influenced the results. Firstly, some sample sizes in the subgroups were too small. Secondly, some of the documents quoted were old. Thirdly, the mortality analysis showed significant heterogeneity, and some subgroups also reported significant heterogeneity mainly due to small sample size. Fourthly, the results of the pooled data in several eligible studies were too limited to evaluate the association between PM2.5 and LC.We identified potentially relevant studies by searching the PubMed, Ovid, Embase and Cochrane databases from their inception up to August 2016. Search terms included “particulate air” OR “particulate matter” OR “air pollution” And “neoplasms” or “cancer” with the results restricted to human studies. Titles and abstracts identified by electronic searches were examined by two independent reviewers, to select potentially relevant studies. In addition, the references from relevant original papers and review articles were scrutinized to obtain other pertinent publications. We considered all available cohort and case–control studies that provided information on the incidence of LC, and where the resources needed for participation were available.Eligible articles included studies which evaluated the association between PM2.5 and LC incidence and mortality. Articles were considered for inclusion in the current meta-analysis if they provided quantitative estimates of the change in LC incidence or mortality associated with exposure to PM2.5, and reported the odds ratio [OR] or hazard ratio [HR] or relative risk [RR], and its 95% confidence interval (CI) of LC associated with long-term exposure to PM2.5. Where multiple publications included overlapping study populations, the publication with the longest follow-up period was selected. Studies with poor quality and/or insufficient data, and duplicate publications were excluded from the analysis.A double abstraction process was performed for data extraction (by Feifei Huang and Liying Chen). Disagreements were resolved by consulting a third reviewer. The following data were collected from eligible studies: the first author, publication year, study region, number of LC patients exposed to PM2.5, incidence , study period, study source, gender distribution, country, smoking status, experimental method and primary outcomes . If any of the above information was not available in the study, the authors were contacted by email for additional information. Where two or more publications included overlapping cases, we included the publication which had the largest number and/or longest follow-up.The quality of each study was evaluated using a 9-point scoring system according to the Newcastle–Ottawa Scale (NOS) checklist for cohort and case-control studies  Supplem. The tot3 unit increase in exposure to PM2.5. In this meta-analysis, the relative risks (RRs) and 95%CIs were considered the effect size for all studies. Because the incidence of LC was low, the HR from cohort studies was approximated to RR. We reported all risk estimates produced by the current meta-analysis as RR for simplicity. As significant heterogeneity would contribute to the variance, the pooled RRs for specific PM2.5 values were estimated using a random-effects model. Subgroup analyses were performed according to continent, country, gender, and smoking status, respectively. Furthermore, sensitivity analysis was performed to assess the source of heterogeneity. Potential publication bias was detected using Begg's test and Egger's test. Two-tailed P values < 0.05 were considered statistically significant. All statistical analyses were performed using Stata 12.0 software.A dose-response meta-analysis was performed to examine the relationship between PM2.5 and LC. All study estimates were converted to represent the change in LC incidence or mortality per 10-μg/mIn conclusion, the relative risks of LC incidence and mortality following exposure to PM2.5 were 1.08  and 1.11 , respectively. The findings from the present meta-analysis may provide some evidence for policy makers and public health practitioners worldwide.


LEP rs7799039 , ADIPOQ rs2241766  and ADIPOR1 rs10920531  variants were identified to be correlated with increased risk of prostate cancer. On the contrary, LEPR rs1137101  and ADIPOR1 rs2232853  variants were associated with decreased risk of prostate cancer. From the pooled-review, we additionally recognized eight variants associated with cancer risk and another eight variants associated with cancer aggressiveness, respectively. These observations indicated important roles of leptin, adiponectin and their receptors in the development and progression of prostate cancer. The identified polymorphisms might assist in developing better risk-assessment tools, as well as generating novel targeted therapies, especially for obese cancer patients with impaired leptin and adiponectin signaling.Leptin and adiponectin signaling was associated with development and progression of various cancers. The present study aimed to clarify the role of genetic variants in leptin, adiponectin and their receptors in prostate cancer. After comprehensive search and manuscript scanning, a total of 49 genetic variants were enrolled and examined for their relations to cancer risk and aggressiveness. In the meta-analysis,  Prostate cancer (PCa) recently became the second most frequently diagnosed malignant tumor, and the fifth leading cause of cancer death in men throughout the world . ExtensiLEP) were associated with onset and progression of various cancers [In vitro studies revealed that leptin and its downstream signaling might induce cell proliferation [ADIPOQ) inhibited proliferation and invasion of PCa cells [LEPR) or adiponectin receptor 1/2 (ADIPOR1/ADIPOR2), to activate downstream signaling and functioning [Epidemiological evidence showed that high levels of serum leptin  were demonstrated to affect the risk and aggressiveness of PCa [By modulating the levels and functioning of leptin and adiponectin, the genetic factors , LEP rs12535708 , LEP rs12535747  and LEP D7S1875  were associated with decreased risk of total PCa , ADIPOQ rs3774262  and ADIPOQ rs822391  were negatively associated with cancer risk  increased both the prevalence and aggressiveness of PCa. The increasing trend in cancer risk was in accordance with previous studies [LEP. Several studies demonstrated that the variants might elevate the serum leptin level through transcriptional level, and increase human body weight as well [The ogenesis . In pres studies , 38. The as well . Higher  as well , 41.LEPR). Like LEP, LEPR was also highly polymorphic, with a number of single nucleotide polymorphisms identified. The present study revealed that LEPR rs1137101 (Q223R) variants were correlated with decreased PCa risk, whereas worse pathological grade and prognosis. The previous meta-analysis discovered that rs1137101 might increase the susceptibility to cancers in Asian and African patients, while decreasing the susceptibility in Caucasians, suggesting that race might explain for the heterogeneity and inconsistency among different studies [Leptin exerted its downstream functioning through binding to leptin receptor  case-control or cohort studies; (3) sufficient data, including the accurate population number of specific alleles, genotypes and microsatellites; (4) genotype frequencies in control groups should be examined by the Hardy-Weinberg equilibrium (HWE). If serial studies of the same study population were reported, the most recent study or the study with the largest population should be adopted. The exclusion criteria included: (1) animal or molecular studies; (2) reviews, meta-analyses or case reports; (3) insufficient information published in the study; (4) duplicated studies.The eligible studies were included according to the following criteria: (1) studies investigating the associations between Two investigators (Hu and Xu) independently evaluated the eligibility of all retrieved studies, and extracted key information. The collected data included: author's name, year of publication, study population, study design, genotyping methods, case and control population, and polymorphisms investigated. NOS was adopted to evaluate the quality of all eligible studies . The nin2 statistics. Fixed-effect model was adopted to estimate the OR and 95%CI when heterogeneity was low, otherwise random-effect model was adopted. Besides, we conducted a pooled-review for studies discussing risk or aggressiveness of PCa that could not be included in the meta-analysis. All analyses were performed using the STATA/SE 12.0 software . Statistical significance was defined as a two-tailed p-value <0.05.In the meta-analysis, the pooled odds ratio (OR) with 95% confidence interval (95%CI) was calculated to evaluate the correlation between genetic polymorphisms and total PCa risk according to allele contrast, homozygote, heterozygote, dominant and recessive models. The inter-study heterogeneity was verified by Q and I


It has been debated for several years as to whether the antidiabetic drug pioglitazone increases the risk for bladder cancer. A series of recent large population studies yielded conflicting results. To investigate why the observational studies yielded conflicting results, we conducted stratified analyses to analyze the potential confounders behind these discordant outcomes. A total of 2,764,731 participants from observational (OB) studies and 9,999 from randomized control trials (RCTs) were identified for these analyses. The stratified analysis revealed that the study type, adjustment for age/sex, treatment duration, cumulative dose, agents used in a control group, mean period of follow-up and study population region might contribute to the discordant outcomes. In terms of population regions, pioglitazone increased the risk for bladder cancer could be found in European population, and patients who undergo treatment with pioglitazone for longer durations (>12 months) or are administrated a larger cumulative dose  might require more attention, and the long-term effects (≥3.6 years) of pioglitazone needs be monitored more carefully. These agents have been widely used in patients with diabetes mellitus (DM). However, troglitazone was discontinued because it caused hepatotoxicity3, and the safety of rosiglitazone was disputed due its effect on the cardiovascular system5. In contrast, pioglitazone has been shown to prevent the progression to diabetes and major cardiovascular events7 as well as nonalcoholic steatohepatitis6, thereby indicating a broader prospect for its clinical applications. However, globally, safety concerns pertaining to the potential effect of pioglitazone in increasing bladder cancer risk have been raised and debated for many years23. A series of large clinical studies26, such as the Kaiser Permanente Northern California (KPNC) study, United Kingdom Clinical Practice Research Datalink (UKCPRD) research, and Four European Countries Datasets (FECD) research, obtained discordant results, which led to more debate regarding this issue. Although this observation is still debated, the U.S. Food and Drug Administration (FDA) did warn about this risk on December 12, 2016 (http://www.fda.gov/Drugs/DrugSafety/ucm519616.htm).Thiazolidinediones (TZDs), agonists of the peroxisome-proliferator-activated receptor (PPAR) γ, reduce blood glucose levels primarily by increasing insulin sensitivity in peripheral tissues, without causing hypoglycemia27, risk for other cancers types were also evaluated in the study.We conducted stratified analyses to investigate why the observational studies yield conflicting results and to analyze potential confounders that caused inconsistent results in the previous studies, as well as to determine optimal future study designs. In addition, as pioglitazone use may be related to other malignancies as wellEmbase, PubMed, Web of Science, Cochrane Central Register of Controlled Trials (CENTRAL), and ClinicalTrials.gov were searched from inception through Jan 5, 2017 without language restriction. Two independent reviewers (Y.W. and R.Z.) searched and selected studies separately. Disagreements were resolved by discussion between the reviewers, and if necessary, consultation with other authors included in this study. Our search strategy included the following terms pertinent to pioglitazone: peroxisome proliferator activated receptor agonist/activator, PPAR, thiazolidinediones, TZDs, pioglitazone, Actos; and terms pertinent to cancer: cancer, tumor, carcinoma, neoplasm, malignancy (eAppendix 1 in the supplement). References of relevant studies were manually screened for eligible sources of data.All human studies that evaluated patients with DM, reported pioglitazone therapy, and provided cancer outcomes were included. Observational (OB) studies and randomized controlled trials (RCTs) that provided either relative risk estimates such as risk ratios (RR), hazard ratios (HR) or odds ratios (OR) and 95% confidence intervals (CI) for cancers or raw data were eligible. Studies with the greatest number of patients and the latest publications were selected when overlapping subjects were included in more than one study. Trials reporting serious adverse events or adverse events related to cancer following pioglitazone exposure were also included.We excluded duplicate reports and abstracts from meeting proceedings. Studies were also excluded if they were animal research, reviews, comments or replies.Three reviewers  independently extracted data from the primary texts and supplementary appendixes of all trials. Disagreements among the three reviewers were resolved by discussion, and if necessary, consultation with the other two reviewers (Y.Z. and H.Z.). The following data were collected for OB studies: authors, year of publication, age and sex, sample size, number of cancer events in both groups, RR, HR, OR with 95% CI, study type, adjustment factors, dose/duration response gradient, types of medications used in exposed and control groups, mean period of follow-up, target disease, and population region. For RCTs, data included trial registry number, number of study sites, study phase, number of cancer events in both groups, types of medications used in interventional and control groups, duration of follow-up, target disease, and population region.For relative risk estimates of each study, we selected the most adjusted value  and corresponding 95% CI, in addition to raw events data. Unadjusted estimates were selected if the outcome was not adjusted for any variable.28 was used to assess the risk of bias of cohort studies and case-control studies. The highest-quality score was 9 (maximum), and studies with scores ≥7 were considered as having a low risk of bias, scores of 4–6 as having a moderate risk of bias, and scores <4 as having a high risk of bias. The item “was follow-up long enough for outcomes to occur” for cohort studies was removed owing to the adequate duration of follow-up is uncertain and was analyzed as a result of this study. The Cochrane Collaboration’s tool29 was used to assess the risk of bias of RCTs. The judgmental items were “random sequence generation”, “allocation concealment”, “blinding of participants and personnel”, “blinded assessment of bladder cancer events”, “incomplete outcome data”, and “selective reporting”.Three reviewers  independently assessed the quality of all studies. The Newcastle-Ottawa quality assessment scale30. The secondary outcome was defined as the relationship between pioglitazone use and other cancer risks.The primary outcomes were included to examine the association between pioglitazone use and bladder cancer risk and whether this association varied based on the study design 31. We pooled relative risk estimates and reported pooled OR with corresponding 95% CI using random effects models according to the methodology proposed by DerSimonian and Laird32, with weights calculated by the inverse variance method in cases of heterogeneity. Otherwise, fixed-effects models were used. Subgroup analyses were conducted to explore whether the association between pioglitazone use and bladder cancer risk were varied by study design.As the incidence rates of cancers involved in our study are relatively rare (<5%) in overall and subgroup analyses, the distinctions among the RR, HR, and OR can be ignored33 for meta-analysis were followed, and PRISMA criteria34 were performed for reporting our meta-analysis. Publication bias was evaluated visually by funnel plots and quantified by the Egger’s test and the Begg’s test36. Heterogeneity across trials was assessed by theI2 statistic, with values greater than 50% indicating significant statistical heterogeneity37. If significant statistical heterogeneity was detected, a sensitivity analysis was conducted using the “leave one out” approach38 to identify the source of heterogeneity. This “leave one out” approach was also used for sensitivity analysis to detect the influence of a single study on the overall bladder cancer risk. In addition, sensitivity analyses were also conducted by study quality analysis, which was restricted to the highest-quality studies with Newcastle-Ottawa scale scores of 8–9, and to test the robustness of overall bladder cancer risk and subgroup analysis.The MOOSE guidelinesP-values less than 0.05 were considered statistically significant. All statistical analyses were performed using Stata Statistical Software: version 12.0 .Results with 2-sided 41 and 4 RCTs 42 published between 1986 and 2016 were identified. From the OB studies, 2,764,731 participants were included. Of these participants, 343,176 (12.4%) were exposed to pioglitazone, and 13,264 (0.5%) developed bladder cancer. The mean period of follow-up ranged from 2.1 to 7.9 years. In the RCTs, 9,999 participants were included. Of these participants, 4,515 (45.2%) were exposed to pioglitazone, and 24 (0.2%) developed bladder cancer. The mean period of follow-up ranged from 72 weeks to 48 months 42 were identified, and 17 other site-specific cancers were assessed.Figure P < 0.001) , indicating that pioglitazone use was associated with overall bladder cancer risk based on the existing evidence.First, we conducted a pooled analysis to determine whether pioglitazone use was associated with the bladder cancer risk at the global level. To date, a majority of the studies related to this topic are OB studies. Only 4 RCTs were identified; thus, at first the OB studies were analyzed. The results showed that the risk for bladder cancer risk increased by 15% , whereas case control studies showed negative results  , suggesting that study type might be a confounder for the global outcomes.Next, to explore the reason behind the inconsistent outcomes observed in previous individual studies, the potential confounders for study outcome, which are generally derived from study design 49. Although the risk factor “unhealthy diet and lack of physical activity” could not be analyzed (refer to the limitations list in the Discussion section for the reasons), the remaining 3 factors were evaluated. The adjustment for both smoking and use of other medications did not influence the outcomes , whereas adjustment for age/sex significantly affected the results  . These results indicated that adjustment for age/sex might also be a confounder that affected previous outcomes, and the results of the studies adjusted for age/sex seem more credible.With regard to adjustment factors, the main cancer risk factors that are commonly adjusted in medication related cancer risk studies include age/sex, smoking, unhealthy diet and lack of physical activity, and the use of other medications52. However, pioglitazone is only orally administrated, only the duration and cumulative dose were assessed in this study. Of the included OB trials, in 13, the duration or cumulative dose-response relation was evaluated, and in 8, the unified categories were used. These 8 trials were analyzed. Increased risks of bladder cancer were identified in longer duration and larger cumulative dose subgroups , while the shorter duration and smaller cumulative dose subgroups did not increased risks of bladder cancer  . These results indicated that the treatment duration and cumulative dose might also be confounders, and that long durations and large cumulative doses of pioglitazone use should be carefully monitored.With regard to intervention measures, administration routes as well as the treatment duration and cumulative dose were commonly considered53. Of the included OB trials, non-specific agents were used in the control groups of 17 studies, and specific agents (insulin and rosiglitazone) were reported in 2. A positive relationship was identified in the non-specific comparator subgroup , whereas the result was negative in the specific comparator subgroup  , indicating that comparator agents might influence the outcomes. Moreover, insulin and rosiglitazone have been reported to potentially influence bladder cancer risks55, this likely leads to the incorrect estimation of the association between pioglitazone use and bladder cancer risk. Thus, the results of studies that used non-specific agents as the control group might be more reasonable.The medication used in the control group can also be a potential confounder56. This factor was analyzed using the tertiles method  in our study. The middle tertile exhibited a positive association between pioglitazone and bladder cancer risk , whereas the lower  and upper tertiles  did not show any association , indicating that the follow-up duration might influence the outcomes. However, the pooled result of the upper tertile was inexplicable at the global level.The follow-up duration mainly refers to the mean period of follow-up, as the maximum period cannot describe the maturity of the study data and the quality of follow-up57. In terms of target disease, the relationship between pioglitazone use and bladder cancer risk was positive both in the T2DM (with T1DM excluded) and DM (with T1DM included) subgroups  . However, for the population region, the European region exhibited an increased risk of bladder cancer after pioglitazone exposure , whereas the results in both American  and Asian  regions were negative , indicating that the population region might be a confounder at the global level.The study population commonly includes the target disease and the population region02) Fig. . Howeve60. Therefore, we re-analyzed the potential confounders in different population regions. For American and Asian regions, the results were negative (P > 0.05), further confirming the negative pooled results . In the European region, the results were positive (P < 0.05) except for the subgroups unadjusted for age/sex , moking , the shortest treatment duration  and follow-up period , and specific agent (rosiglitazone) used in the control group . As mentioned above, the results of studies adjusted for age/sex and smoking, with longer treatment durations, and non-specific agents used in the control group were considered more credible; therefore, the positive results achieved in these subgroups in both Europe and globally further confirmed the positive pooled European and global results  but negative in the American and Asian populations . This finding might explain the inexplicable negative result of the longest follow-up subgroup at global level  was identified as a common contributor for these heterogeneities  . The re-summary results showed that pioglitazone use was associated with increased risks of prostate and pancreatic cancer , and decreased risks of liver and brain cancer  . These results indicated that pioglitazone use might also be related to other cancer risks.In addition to bladder cancer, pioglitazone exposure has also been reported to be associated with prostate and pancreatic cancer risks61. Therefore, we combined all the OB studies with the 4 RCTs and reanalyzed the results. Given that the 4 RCTs were not involved in the adjustment factors and intervention measures analyses, we included comparator agents, follow-up duration, and study population in the subgroup analyses. As for other cancers, 17 site-specific cancers were analyzed. All the re-analytical results with OB studies plus RCTs were similar to those obtained from the OB studies  and using the “leave one out” approach, and none of the results showed significant changes. For the subgroup analyses of bladder cancer risk, “study quality” sensitivity analysis was conducted, and the results remained similar. With regard to risks for other cancer types, a sensitivity analysis was conducted using the “leave one out” approach, the results of which are presented in the “Pioglitazone and other cancer risks” section of the Results.P = 0.22) or Begg’s test (P = 0.57 [continuity corrected])  study was conducted, more than a decade ago64. In addition, the negative results were achieved from the subgroups unadjusted for age/sex, short treatment durations and small cumulative doses, and the specific agents (insulin and rosiglitazone) used as comparators during the stratified analysis with potential confounders. These also strengthen the positive relationship, as adjusted results are more credible, and diabetic patients require long-term treatment65, and hence insulin and rosiglitazone may influence bladder cancer risk55.Our analysis suggested that pioglitazone use was associated with bladder cancer risk at the global level (15% increase), which was consistent with the results of most of the previous meta-analyses (17% to 23% increase)025. Similarly, the risk factors for stroke, disabling sequelae from bacterial meningitis, and obesity were also reported to have regional differences60. Interestingly, when the relationship was considered in terms of different population regions, the study type no longer influenced the outcomes, and the inexplicable results of follow-up duration analysis at the global level could be interpreted reasonably. Based on these findings, we suggest that the bladder cancer risk related to pioglitazone should be considered according to the different population regions, and the European population require more attention. Notably, although we identified the regional differences across continents, the possibility of variation among different countries and regions may exist. Whether the regional differences that we observed in this study are associated with race and ethnicity remain unclear, as only the KPNC study24 described the racial composition of its study population. Further studies are required to clarify these issues.Most importantly, our results indicated for the first time that the association of pioglitazone use and bladder cancer risk exhibits a significant regional variation. While the pooled and stratified results were negative in the American and Asian regions, results from studies conducted in the European region showed a striking positivity from the pooled and most of the stratified analyses. Although the subgroups unadjusted for age/sex and smoking, short treatment duration and follow-up period, and rosiglitazone used in the control group showed negative results, based on the reasons mentioned above, the results further confirmed a positive relationship in European populations. This regional difference might explain the discordant results of several previous studies, such as the KPNC and UKCPRDMoreover, when performing future studies, the authors need to be aware of the long-term effects of prolonged pioglitazone use and should adjust for age/sex and smoking and adopt non-specific agents in the control group. Although our results indicated that the study design did not influence the outcomes of the American and Asian populations, it still had an effect on the European population. In the European population, positive results were achieved with long treatment durations (>12 months) and follow-up periods (≥3.6 years); therefore, we should pay more attention to this population. In addition, adjustment for age/sex and smoking, and the specific comparator agent (rosiglitazone) influenced the outcomes; therefore, these factors should also be noticed in future related studies.24, and decreased risks for liver and brain cancer. Additionally, one study recently suggested that pioglitazone might influence cancer progression66. However, studies on these other cancers are very limited and more studies are needed to answer several key questions in this field: Does pioglitazone influence the risk and/or progression of other cancers? Does the association between pioglitazone use and risk for other cancer types show regional differences? If so, what is the mechanism behind the role of pioglitazone in tumorigenesis?Our results indicated that pioglitazone use might also be related to increased risk for other cancer types, such as prostate and pancreatic cancer, which is consistent with the observations of the KPNC studyOur study has important implications in future clinical practice and trials in that (1) more attention regarding bladder cancer risk related to pioglitazone should be given to European, especially those with long treatment durations and a large cumulative dose; (2) studies should adjust for age/sex and smoking, and adopt non-specific agents in the control group; and (3) the risks of other site-specific cancers, such as prostate, pancreatic, liver and brain cancer, need to be addressed.12 in the form of alcohol intake. Second, some factors that were adjusted for in previous individual trials were not analyzed for the following reasons. Only one18 and two trials24 have adjusted for body mass index (BMI) and income, respectively; therefore, the data are inadequate for meaningful analyses. The comorbidities selected for adjustment different from trial to trial, and the diabetes duration and date of cohort entry were defined inconsistently among the trials41; thus, these 3 factors were also unsuitable for analysis. Only 3 trials adjusted for hemoglobin A1c concentration (HbA1c)25. Although the analyzed results were accordant with our conclusions, more data might be required for further analysis. The results obtained from analysis at the global level showed that adjustment for HbA1c affected the outcome  but negative in the American and Asian regions   or a larger cumulative dose  might should be followed up more carefully. The long-term effects (≥3.6 years) of pioglitazone might also need to be noted. In addition, future related studies should adjust for age/sex and smoking and adopt non-specific agents in the control group. Moreover, pioglitazone use may relate to risks for other cancer types.Information about how the data can be accessed is available from the corresponding author.Supplementary material


TP53 gene product is an important regulator of cell growth and a tumor suppressor. The association between TP53 Arg72Pro polymorphism and ovarian cancer risk has been widely investigated, but the results are contradictory. We therefore searched the PubMed, EMBASE and Chinese Biomedical databases for studies on the relation between TP53 Arg72Pro polymorphism and ovarian cancer risk. Our final meta-analysis included 24 published studies with 3271 cases and 6842 controls. Pooled results indicated that there was no significant association between TP53 Arg72Pro polymorphism and ovarian cancer risk . Likewise, stratified analyses failed to reveal a genetic association. Despite some limitations, the present meta-analysis provides statistical evidence indicating a lack of association between TP53 Arg72Pro polymorphism and ovarian cancer risk.The  With 238,700 new cases worldwide, ovarian cancer was the seventh most frequently occurring cancer among women in 2012, and was responsible for 151,900 deaths. In developing countries, newly diagnosed cases of ovarian cancer have exceeded those of cervical cancer , 5. ConsTP53 is a tumor suppressor gene is located on the chromosome 17p13 short arm and encodes a protein with 393 amino acids , and recessive [Pro/Pro vs. (Arg/Pro + Arg/Arg)] models, as well as allele comparison (Pro vs. Arg). The Chi square-based Q test was used to calculate the heterogeneity between studies. If the P value was less than 0.1, the pooled ORs were calculated using a random-effects model (the DerSimonian and Laird method) [P < 0.05 were regarded as deviations from the HWE. All statistical tests were conducted using STATA software . Values of P < 0.05 was considered significant, and all tests were two-sided.The strength of the association between  method) . Otherwi method) . Subgrou method) –51. The  method) . HWE was