Epigenetic variations in peripheral blood have potential as biomarkers for disease. This systematic review assesses the association of lung function and chronic obstructive pulmonary disease (COPD) with DNA methylation profiles in peripheral blood from population-based studies.Online databases Medline, Embase, and Web of Science were searched. Google Scholar was searched to identify grey literature. After removing duplicate articles, 1155 articles were independently screened by two investigators. Peer reviewed reports on population-based studies that examined peripheral blood DNA methylation in participants with measured lung function  or known COPD status were selected for full-text review. Six articles were suitable for inclusion. Information regarding study characteristics, designs, methodologies and conclusions was extracted. A narrative synthesis was performed based on published results.Three of the six articles assessed the association of COPD with DNA methylation, and two of these also included associations with lung function. Overall, five reports examined the association of lung function with DNA methylation profiles. Five of the six articles reported ‘significant’ results. However, no consistent CpG sites were identified across studies for COPD status or lung function values.DNA methylation patterns in peripheral blood from individuals with reduced lung function or COPD may be different to those in people with normal lung function. However, this systematic review did not find any consistent associations of lung function or COPD with differentially methylated CpG sites. Large studies with a longitudinal design to address reverse causality may prove a more fruitful area of research.CRD42016037352.PROSPERO 2016: The online version of this article (doi:10.1186/s12890-017-0397-3) contains supplementary material, which is available to authorized users. Epigenetics is the regulation of gene expression which is independent of the underlying DNA sequence, and is instead brought about by modifications to histones, changes in chromatin structure, microRNAs, non-coding RNAs, and DNA methylation. DNA methylation is fundamental for normal development and growth—responsible for imprinting of genes, inactivation of the X chromosome and cell differentiation , 2. In hThe use of DNA methylation profiles in peripheral blood as a biomarker for risk of disease, risk of disease progression, response to therapy and as a biomarker of exposure to environmental insults that may influence disease is an attractive concept as it could be translated into clinical practice with relative ease. Recent advances in microarray-techniques have meant that epigenome-wide association studies (EWAS) are possible, although the cost of these arrays remains high, limiting application to all relevant studies.th in the all-cause global disability-adjusted life-years lost. The main risk factors for COPD are age and cigarette smoking [Chronic obstructive pulmonary disease (COPD) is estimated to affect 334 million people worldwide , 8 with  smoking –13, but  smoking , and per smoking  is also  smoking , which m smoking , but metThis systematic review examines the association of COPD and lung function with global, epigenome-wide, and locus-specific DNA methylation in peripheral blood from population-based studies.The protocol was registered with PROSPERO (CRD42016037352), and methods and reporting followed the PRISMA guidelines .Searches comprising of 92 terms were applied to Medline, Embase and Web of Science (WoS)  and Embase (Embase Classic + Embase) were based on “Title” and “Abstract”. The WoS platform was used to search for “Title” of articles in its Core Collection. Simplified searches consisting of topic headings for COPD, lung function and DNA methylation were carried out in Google Scholar to identify grey literature .Duplicates were identified and removed.Titles and Abstracts of articles were screened by two authors (DLJ and MM) against the predefined inclusion criteria Table  to identOnly articles assessing the association of lung function and COPD with DNA methylation were included for full-text review. Independent full-text reviews were performed on potentially relevant studies. The reference list of all included studies was screened to identify further studies, and citations published before the date of the main search (identified through WoS Citation Indexing tool) were examined. Articles without an abstract in English were excluded.Articles selected for inclusion had participants with lung function measures or COPD status ascertained. COPD patients are at increased risk of lung cancer , but stuStudy identification , study design , methodologies , results  and conclusions were extracted into a standardised template in Microsoft Excel 2013.All studies were observational, had different designs, and different methodologies. The validity and bias of each study were assessed in regards to study size, study design, participant selection, adjustment for confounders, laboratory quality control, and validity of conclusions.Quantitative analysis was not performed. Studies were heterogeneous, and tested and reported effect estimates for different CpG sites. A narrative synthesis was performed to summarise the included articles.Searches of electronic databases identified 2242 articles . Searches using Google Scholar identified 111 articles resulting in a total of 2353 potential articles  left 1155 articles for title and abstract screening. This step excluded 1139 articles, leaving 16 articles for full-text review. Having read the full articles, six studies were considered as appropriate for systematic review.Removal of duplicates  with other reports consisting of lower population numbers. The Lothian Birth Cohort 1936 cohort and the Normative Ageing Study were older than participants in other studies. COPD participants in the EOCOPD cohort (47.5 ± 7.1) were younger due to selection criteria (cohort selected for age <53).Number of participants included in the reports ranged from 172  to 1458 . Marioni et al. also had a relatively large population  and a lower proportion in Wielscher et al. The Twins UK cohort used by Bell et al. was based entirely on women, whereas the Normative Aging Study was entirely male.Definitions of COPD differed across studies.Previous cigarette exposure, assessed as pack-years, varied widely across individuals and across studies. Reported means and standard deviations of pack-year histories from ICGN, EOCOPD and Normative Ageing Study imply highly skewed distributions. Information on previous smoke exposure was not reported for three of the studies , 22, 24.Forced expiratory volume in 1 s (FEV1) was reported in four studies , 25, 26.Tables The two studies with a candidate-loci approach performed either bisulfite conversion, followed by polymerase chain reaction (PCR) and pyrosequencing , or methAll three EWAS –23 used Two studies , 26 repoFive studies , 25, 26 All three studies assessing DNA methylation with COPD status reported associations.p-value < 0.05) when analyses were conducted without adjustment for confounders . Of note, CpG site cg02181506 in the SERPINA1 gene on chromosome 14 was top ranked in unadjusted analyses (hypomethylation associated with COPD) , second ranked in the adjusted analysis , and replicated in the EOCOPD cohort  in the unadjusted analysis. Between both cohorts there were 349 CpG sites associated with all three phenotypes: COPD, FEV1 and FEV1/FVC values , one of these being another SERPINA1 CpG site (cg24621042).Qiu et al. reported that COPD was associated with 3565 differentially methylated sites in the ICGN discovery cohort  CpG sites - cg05979020 (HOXD10), cg05964935 (N/A), cg05769349 (TBX5) and cg10384245 (ADCYAP1) - from their COPD case-control comparison. All four were hypermethylated in the presence of COPD and none were included in any published tabulations of results from the Qiu et al. report.Wielscher et al. reported four significant  with a lower odds ratio of COPD (OR 0.80: 0.64–0.99). No significant association with LINE-1 methylation was observed.Lange et al. reported that hypermethylation of Alu elements was significantly associated , but 2 sites reached p < 1 × 10−5 . None were included in any published tabulations of results from Qiu et al. or Bell et al.Marioni et al. in an EWAS of FEV1 adjusted for age, sex, height, and smoking, based on the Infinium HumanMethylation450 BeadChip, in 1092 older adults found no probes passed the Bonferroni significance threshold  which were associated with both measures . All, except one, of the top CpG sites (cg21969640) from Qiu et al. were also present in the more recent Infinium HumanMethylation450 BeadChip, which was used by Marioni et al. but were not found to be significant in this other study.Qiu et al. reported significant associations of lung function measures (FEV1 and FEV1/FVC), publishing effect estimates for the top 100  CpG site associated with FEV1-cg16463460 in the WT1 gene . This CpG site, which was present in both Infinium HumanMethylation BeadChips, was not reported as significant in the other included studies.Bell at al. identified one significant  may alter with age. Hypomethylation of IFNγ and IL6 was associated with better lung function. Although Lepeule et al. had lung function and methylation data at two time points  authors reported changes in lung function were too small to consider examining longitudinal change in lung function with longitudinal change in methylation. The sites tested by Lepeule et al. could not be compared to those tested in the other studies.Lepeule et al. examining associations of methylation in the promoter region of nine genes, involved in inflammation and oxidative stress, with FEV1, FVC, FEV1/FVC and maximum mid-expiratory flow (MMEF) observed decreased methylation in n = 362) of African-American smokers from the PA-SCOPE study, who were recruited during inpatient hospitalisation for acute exacerbation of COPD [FXYD1-cg27461196) with an FDR-corrected p-value of 0.08 and none of the others had been reported previously.To our knowledge, this is the only systematic review examining associations of lung function, or COPD, with DNA methylation profiles in peripheral blood. This review assessed 1115 unique articles, subsequently including six. DNA methylation profiles in those with reduced lung function or COPD may differ to that of a person with normal healthy lung function, but we found no consistent findings within the published data. While our manuscript was under review, Busch et al. published findings from a small sample . However, most were population-based studies with collection of samples either at baseline or at follow-up for all participants, making it unlikely that samples were collected during an exacerbation episode. Wielscher et al. who used COPD patient samples collected from a medical university may be an exception, but we could not check it further. The adjustment for confounders varied considerably across studies, and although most adjusted for age and smoking, the way the latter was treated varied too. Three studies reported and adjusted for the proportions of smokers and pack-years , 25, 26,There is currently no standardised, or optimal, procedure for the measurement of DNA methylation, and although apparently rigorous, laboratory quality control for both EWAS –23 and cNotably, there were also differences in the statistical approach to adjustment for potential confounders. This is important as epigenome association studies are susceptible to confounding (i.e. observed associations with one outcome may be due to true associations with another factor), and may be of particular relevance to COPD, which is strongly related to smoking and is associated with multiple morbidities. While the association of an outcome with DNA methylation can be confounded by several exposures, DNA methylation may be the result of exposures that are on the causal pathway to disease (i.e. exposures not acting as confounders) and a careful approach to adjustment for such exposures is needed - this should depend on the underlying scientific question that is being considered.No power calculations for identification of epigenome wide associations were reported, but attempts were made by many to adjust for multiple testing. Lepeule et al. (using a candidate-loci approach) failed to correct for multiple testing. Except for Qiu et al., who attempted to replicate their findings from ICGN within EOCOPD, all other reports were based on single studies.This systematic review was carried out with a predefined protocol registered with PROSPERO and in accordance with the PRISMA checklist. Articles were identified using three large online databases using a comprehensive selection of search terms (consisting of both MeSH and free text) with additional simplified searches to identify grey literature. To make the searches as sensitive as possible, no limits were imposed on the search regarding date of publication, language, or article type.Screening of articles, full-text review and data extraction was carried out by two investigators independently, with each providing reasons for exclusion for each article  and methylation patterns could be the cause of, or a result of, altered lung function. Some reported associations might have been related to smoking rather than to disease or lung function SERPINA1), and targeted analyses to investigate these further are warranted.Future longitudinal studies, with serial measurements of DNA methylation at yearly intervals, are required to assess the temporal sequence of disease onset and peripheral blood methylation. Harmonization of methods, and established guidelines for quality control and processing of DNA methylation data would facilitate meta-analysis of epigenome association studies. There is some evidence of associations with highly biologically plausible areas (e.g.


To determine whether Tumor Necrosis Factor alpha (TNFα) –308 G/A polymorphism is associated with oral lichen planus (OLP).A systematic electronic search of the literature was conducted to identify all published studies on the association between TNFα –308 G/A polymorphism and OLP. All case-control studies evaluating the TNFα –308 G/A polymorphisms in OLP were selected. A meta-analysis of the studies that fulfilled the inclusion criteria was performed. Odds ratios (OR) with 95% confidence intervals (CI) were also calculated.Seven studies comprising 450 OLP cases and 867 controls were included in the meta-analysis. In the pooled analysis, TNFα –308 G/A polymorphism was associated with OLP with random effects and OR of 2.33 , assuming a dominant mode of inheritance (AA+GA vs. GG). In the subgroup analysis by ethnicity, TNFα –308 G/A was associated with a significantly increased odds ratio of OLP in mixed ethnicity , but not in Asians  or Caucasians . For subgroup analysis based on HCV (hepatitis C virus) infection status, significant increased risk of OLP was found among patients with mixed HCV infection status , but not in patients without HCV infection  and patients with HCV infection .Our results suggest that –308 G/A polymorphism in TNFα is a potential genetic marker for OLP. Oral lichen planus (OLP) is a chronic inflammatory oral condition characterized by oral lichenoid lesions. The etiopathogenesis of OLP involves an interaction between genetic, environmental, and lifestyle factors. OLP lesions can be induced by drugs or dental materials, and are often called idiopathic, involving abnormal immune responses,,,,,–,,Cytokines play an important role in the pathogenesis of OLP, and a bulk of evidence suggests that OLP is a T-cell-mediated diseaseA comprehensive literature search was conducted on Medline, PubMed, Embase, and Science Citation Index for all articles that were published by April 17, 2017 on the association between TNFα –308 G/A polymorphism and OLP risk. The search terms were used as follows:  and (“tumor necrosis factor” or “TNF” or “tumor necrosis factor-α” or “TNF-α” or “TNF-α” or “tumor necrosis factor-a”) and (“Polymorphism” or “mutation” or “variant”). Selection criteria of an eligible study were: (a) investigation of TNFα gene polymorphism and OLP risk; (b) case-control studies; (c) sufficient genotype distributions for cases and controls so that an OR with 95%CI could be assessed. Two investigators collected the information from all eligible publications based on the abovementioned criteria. Data including publication year, first author, country, ethnicity, HCV infection status, case number and control number, and genotyping method were extracted independently by these two investigators and reached conformity on all items by consultation.vs. GG). The statistical significance of the ORs was analyzed by Z-test, and p<0.05 was considered statistically significant. In addition to the comparison among all subjects, we also performed subgroup analyses by HCV infection and ethnicity.Odds ratios with 95% confidence intervals were used to estimate the association between the TNFα –308 G/A polymorphism and OLP risk. The pooled ORs were performed assuming a dominant genetic model . Random-effects models should be used in the prescience of heterogeneity. Thus, the pooled effect was calculated with a random-effects model. In the pooled analysis, the TNFα –308 G/A polymorphism appeared to be a genetic risk factor for susceptibility to OLP with a random effects and odds ratio (OR) of 2.33   and Caucasians  for AA+GA vs. GG comparison , but not in Asians , but not in patients without HCV infection  and patients with HCV infection  (vs. GG) in Additionally, we performed subgroup analysis based on HCV infection status. Significant increased risk of OLP was found among patients with mixed HCV infection status  . This suIn addition, sensitivity analysis was performed by removing one data set at a time. Statistically excluding any individual study did not resolve the genetic heterogeneity. The pooled OR was recalculated in the absence of each study, and OR was not significantly changed .p=0.54 for AA+AG vs. GG).The shape of the funnel plots did not reveal any evidence of obvious asymmetry , suggest,,p=0.03). Hepatitis C virus has been proposed to be involved in the etiopathogenesis of OLP. Significant increased risk of OLP was found among patients with mixed HCV infection status , but not in patients without HCV infection  and patients with HCV infection . HCV infection induces an abnormal immune response in the host, enhancing TNFα production, and potentially influences the pathogenesis of OLP lesions. Thus, distinct pathogenetic mechanisms are involved in OLP patients with HCV infection and without HCV infection. The HCV status may be a confounding factor in association studies between OLP risk and TNFα gene polymorphisms, underlying the need for future studies to investigate the HCV status of OLP patients. When we excluded one research that only studied HCV infected OLP patients, the overall meta-analysis yielded higher significance of association with random effects OR of 2.91 .OLP is a complex disorder influenced by multiple genes and environmental factors. Genetic factors that influence the host immune response have been suggested to contribute to OLP riskSubgroup analysis based on ethnicity revealed that A allele carriers (AA+AG) had significantly higher risk of OLP in mixed ethnic population, but not in Asians and Caucasians. The two studies with mixed populations are from Brazil and Saudi Arabia respectively. Considering their complex racial genetic makeup, it may be one of the ethnic groups that account for the increased risk. Interestingly, both countries may have mixture of African ethnicity. This also justifies future studies focusing on African populations.vs. GG). Other models [i.e. recessive model (AA vs. GG+GA) and homozygote model (AA vs. GG)] could have been conducted. Despite those limitations listed aforementioned, our meta-analysis also has its advantages. First, it contains the latest data on the association between TNFα –308 G/A polymorphism and OLP risk. Also, to our knowledge, it is the first meta-analysis that investigated the association between TNFα –308 G/A polymorphism and oral lichen planus risk.The current meta-analysis has several limitations. First, the sample size for some subgroup analysis was limited. Second, obvious heterogeneity was found in overall analysis. Moreover, one of the studies included in our meta-analysis did not follow HWE. Deviation from HWE reflects potential mistakes, such as laboratory or genotyping errors, population stratification or selection bias in controls. When we excluded one study that deviated from HWE, the pooled ORs was altered and the association became statistically insignificant (p>0.05). Lastly, in this study, we only evaluated a dominant model (AA+GA Our meta-analysis suggests that the substitution of G to A of TNFα –308 G/A polymorphism is a risk factor for OLP. Its effect exists among mixed populations with mixed HCV status. The TNFα –308 G/A polymorphism may be a useful genomic marker for oral lichen planus.


Each study conducted multivariable linear regression with IOP as the outcome variable and results were pooled using random effects meta-analysis. The association of standardized study IOP with latitude was tested using meta-regression. Higher IOP was observed in men  and with higher body mass index , shorter height , higher systolic blood pressure  and more myopic refraction . An inverted U-shaped trend was observed between age and IOP, with IOP increasing up to the age of 60 and decreasing in participants older than 70 years. We found no significant association between standardized IOP and study location latitude (P = 0.76). Novel findings of our study include the association of lower IOP in taller people and an inverted-U shaped association of IOP with age. We found no evidence of significant variation in IOP across Europe. Despite the limited range of latitude amongst included studies, this finding is in favour of collaborative pooling of data from studies examining environmental and genetic determinants of IOP in Europeans.Raised intraocular pressure (IOP) is the most important risk factor for developing glaucoma, the second commonest cause of blindness globally. Understanding associations with IOP and variations in IOP between countries may teach us about mechanisms underlying glaucoma. We examined cross-sectional associations with IOP in 43,500 European adults from 12 cohort studies belonging to the European Eye Epidemiology (EThe online version of this article (doi:10.1007/s10654-016-0191-1) contains supplementary material, which is available to authorized users. Glaucoma is the second commonest cause of blindness globally following cataract, accounting for 8 % of world blindness . Raised Several European population studies have reported IOP data –9. Howev3) consortium is a collaborative network of 38 population-based studies across Europe with the overarching aim of developing and analysing large pooled datasets to increase understanding of eye disease and vision loss [3 studies from 6 countries (Table The European Eye Epidemiology (Eion loss . Data ones Table . All datIOP was measured using Goldmann applanation tonometry (GAT) in 6 studies and non-contact tonometry (NCT) in 6 studies Table . We defiFactors to be tested for association with IOP were decided a priori, based on common measures available in all studies with IOP data available; these were age, sex, height, body mass index (BMI), systolic blood pressure (SBP), refractive error , and history of cataract surgery.For initial analyses, we excluded participants with a history of a glaucoma therapy  or intraocular surgery (other than cataract surgery) in either eye. After examining the association of cataract surgery with IOP, we further excluded all participants with a history of cataract surgery, given the strong effect on IOP. Our main analyses were conducted on phakic patients only.To examine the associations between IOP and the variables of interest, we used linear regression. Primary multivariable models included all the main variables of interest . We also further adjusted for central corneal thickness (CCT) in the subset of participants with CCT data available (“Model 2”). There was no evidence for multicollinearity among variables included in the multivariable regression models. For all regression analyses, residuals were plotted and displayed normality. Regression analyses were conducted for each individual study, and then random-effects meta-analysis was used to combine the effect estimates. A random effects approach was decided a priori given the between study heterogeneity in IOP measurement methods. We conducted an influential analysis that examined the contribution of each study to the heterogeneity by sequentially omitting one study and reanalysing the pooled estimate for the remaining studies. We further examined the association between age and IOP, stratified into age groups based on initial results. Additionally, to address the potential bias of participants with the highest IOP being excluded due to using IOP-lowering therapy, we repeated analyses including participants on IOP-lowering medication; for these participants we imputed pre-treatment IOP by dividing measured IOP by 0.7 (“Model 3”). This approach assumes an average IOP reduction of 30 % on medical treatment and has been used successfully in the study of genetic associations with IOP . For the2, SE 0). To compare IOP in different regions in Europe, we divided the studies into “northern” and “southern” groups using an arbitrary latitude cut-point of 50° to derive two similarly sized groups (i.e. the definitions of “northern” and “southern” are based on dividing the included studies into two groups rather than being representative of geographic regions in Europe). We used random-effects meta-analysis to derive pooled standardized IOP estimates, and these were compared using the independent samples t test. We examined the association between standardized IOP and latitude as a continuous variable using meta-regression. We also compared standardized IOP in GAT studies with standardized IOP in NCT studies, and further examined the association between latitude and standardized IOP stratified by tonometry method.Comparing raw mean IOP values between studies is problematic given the different distribution of IOP-associated parameters across the studies. We therefore calculated a standardized IOP for each study using multivariable linear regression, based on fixed covariable parameters; these parameters were set to values likely to be included within the range values of values for each study  was used for all analyses.A total of 46,081 participants from 12 population-based studies were included. The mean age of participants ranged from 49 to 81 years, and 57 % were women  had undergone cataract surgery in at least one eye; on average, these participants had 0.61 mmHg lower IOP (Table P = 0.004). Both BMI and SBP were positively associated with IOP in crude and adjusted analyses . Height was negatively associated with IOP in crude and adjusted analyses . A more myopic refraction was associated with higher IOP . The R2 for IOP in the maximally adjusted multivariable models for each study ranged from 0.09 in the Rotterdam Study II to 0.27 in the Gutenberg Health Study. An influential analysis did not identify one study that consistently contributed to heterogeneity and omitting one study at a time did not meaningfully change any of the results (Supplementary Section B).Table P = 0.005). There was consistent evidence for decreasing IOP with older age in participants 70 years or older . There did not appear to be a significant relationship between IOP and age for participants aged 60–69 years in primary analyses. To further explore whether the reduction of IOP with increasing age in the oldest participants was due to exclusion of participants with higher IOP following commencement of IOP-lowering medication, we repeated the analysis including participants on IOP-lowering medication and imputing their pre-treatment IOP, and observed similar associations . We also carried out a meta-regression to examine whether standardized IOP was associated with latitude considered as a continuous variable . As shown in Supplementary Figure 1, the standardized IOP for all studies that used NCT  was higher than the standardized IOP for all studies that used GAT , though the difference was not statistically significant (P = 0.32). We therefore also compared northern versus southern standardized IOP stratified by tonometry method ; there were no significant differences for either the GAT studies (P = 0.56) or the NCT studies (P = 0.83). Further, we also carried out meta-regressions using latitude as a continuous variable, stratified by tonometry method ; there was no significant association for the GAT studies (P = 0.51) or the NCT studies (P = 0.85).Figure ble Fig. ; we founIn this large study examining IOP in over 40,000 participants from six European countries, we confirmed previously reported relationships of IOP with SBP, BMI, refractive error and previous cataract surgery. More novel findings include a negative association between IOP and height and an inverted-U-shaped association between IOP and age. The mean standardized IOP was 14.8 mmHg across all studies, and we did not find any significant geographical trends.While the IOP-lowering effect of cataract extraction in individuals has been consistently reported in longitudinal surgical case series , it is lThere is no consensus on the direction of association between IOP and age in the literature, with studies reporting increasing IOP –17, decrThe reported association between IOP and sex is also inconsistent between studies; most studies  have reported higher IOP in women , 21, 22,We found a significant decrease in IOP with greater height, even after adjustment for possible confounders. This is a relatively novel finding; while a negative crude association of height with IOP was reported in the Tanjong Pagar Study, this was not significant after adjustment for confounders . Our finThe significant associations we found between IOP and BMI, SBP and spherical equivalent are consistent with the literature. The majority of published studies have reported higher IOP with higher BMI , 29, 30,We did not find striking variability of IOP levels between the European countries participating in this study, and did not find any variation in IOP with latitude. This may be in part due to relative genetic and cultural homogeneity among the predominantly Caucasian populations in this study, and in contrast to the significant difference seen in IOP of Japanese people compared with Europeans . It is a2 statistics ranging from 0 to 98 %. While random effects meta-analysis assumes a distribution of the true effect due to between study heterogeneity, it may not always be appropriate to statistically combine results from studies that used vastly different methods. For this reason, we also ran analyses for the major associations Supplementary material 2 (DOCX 69 kb)Supplementary material 3 (DOCX 7616 kb)Supplementary material 4 (DOCX 136 kb)Below is the link to the electronic supplementary material.


Although investigators have implicated hypoxic-ischemia (HI) as a potential cause of periventricular leukomalacia (PVL), the role of clinical risk factors or markers for HI in the development of PVL remains controversial. The aim of this study was to identify perinatal HI-related factors associated with PVL.The PubMed, EMBASE, and Cochrane Library databases were searched. The last search was performed on January 2017. Summary effect estimates (pooled odds ratios [ORs]) were calculated for each risk factor using fixed or random effects models with tests for heterogeneity and publication bias.Fifteen studies with a total of 12,851 participants were included in this meta-analysis, and 14 potential risk factors were analyzed. The pooled results showed that mothers with oligohydramnios , preterm infants with acidemia , 1-minute Apgar score <7 , 5-minute Apgar score <7 , apnea , respiratory distress syndrome , and seizures  were associated with increased risk of PVL.This study identified perinatal HI-related risk factors for the development of PVL in preterm infants. Future large-scale prospective clinical studies are required to validate and extend these findings. Periventricular leukomalacia (PVL) is the most common form of cerebral white matter injury in preterm infants and results in cerebral palsy in 60–100% of survivors . The majThere are several perinatal HI-related risk factors implicated in numerous retrospective studies that may be related to PVL –9. TheseThe study was conducted according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses criteria (PRISMA) as shown in The literature on PVL was scrutinized in detail by two authors (Yafei Li and Fengyan Zhao) to identify all possible risk factors which might cause hypoxia ischemic injury. The two authors independently extracted the following data from each study included in the meta-analysis: first author, publication year, country of origin, study design, sample size, gestational age, birth weight, assessment methods for PVL and exposure, and risk of bias. The ORs and 95% CIs were extracted from the studies or computed by authors using Review Manager 5.3 software if they were not provided. After data extraction was completed, the results were compared by two authors. Any disagreement was resolved by a third author (Jichong Huang).The methodological quality of each included study was independently examined by two authors (Bingyao Kang and Tingting Zhu) using the Newcastle-Ottawa Scale (NOS) . The qua2 statistics and I2 tests. Data were considered statistically heterogeneous if I2 was >50% and the P-value was <0.05. Forest plots were used to show ORs and 95% CIs for each individual study, as well as pooled ORs and 95% CIs. Publication bias was evaluated using funnel plots and the Begg’s test [We reviewed the possible perinatal HI-related risk factors for PVL, which included prepartum complications, intrapartum complications, and infant characteristics. We only analyzed risk factors that were evaluated in two or more studies. A separate meta-analysis was conducted for each risk factor and the pooled OR was calculated using a fixed-effect model or a random-effect model. A fixed-effect model was used when heterogeneity across studies was not detected . Otherwise, a random-effect model was used , 18. Hetg’s test , whereinA total of 2,183 studies, excluding duplicate publications, were identified by our search strategy. Twenty-seven potential studies were eligible for further full-text review after the screening of titles and abstracts. After reading the full texts, 1 study was excluded for overlapped populations, 2 studies were excluded for not having a control group, and 9 studies were excluded for unavailable data. Fifteen studies were ultimately included in this meta-analysis. A flow diagram detailing the selection process is shown in The included studies were published between 1996 and 2016. The sample sizes varied from 30 to 3,187. All of the studies involved preterm neonates . All of the studies used cranial ultrasound for the assessment of PVL, but the timing of assessment varied from 72 hours to 40 weeks after birth. A summary of the characteristics of each included study is presented in The 15 studies included 8 case-control studies and 7 cohort studies. The methodological quality scores ranged from 7 to 8 points, and thus all studies were deemed to be of high quality. All studies used the medical records from one specific hospital or university rather than from a national health department, so the selection of cases in these studies may lack representativeness. Although all studies used cranial ultrasound as the method of diagnosis, the time of diagnosis varied between studies. Given that PVL can be diagnosed using a cranial ultrasound within 6 weeks of birth, the follow-up time was insufficient in three cohort studies , 22. NonAfter scrutinizing the literature, we found 16 possible perinatal HI-related risk factors. Preterm premature rupture of membranes was excluded for its association with not only hypoxia-ischemia but also inflammation. Chronic lung disease  was excluded for the inconsistency of the diagnostic criteria. Finally, 14 possible perinatal HI-related risk factors were identified , includi2 = 3.56, P = 0.31, I2 = 16%). Thus, a fixed-effect model was used. There was a significant association between oligohydramnios and PVL .2 = 4.65, P = 0.46, I2 = 0%). A significant association between acidemia and PVL was identified .Arterial umbilical cord pH <7.2 or base excess (BE) <-12.0 mmol/l was considered acidemia. There are 6 studies included in the meta-analysis . No sign2 = 16.95, P = 0.002, I2 = 76%). Thus, a random-effect model was used. No significant heterogeneity was found in studies for 5-minute Apgar score <7 . Increased risk for PVL was found in infants with 1-minute Apgar score at birth <7  or 5-minute .Five of the studies used 1-minute Apgar score <7 and 7 studies analyzed 5-minute Apgar score <7 . Signifi2 = 1.62, P = 0.20, I2 = 38%). Analysis of the findings showed that apnea is associated with PVL .2 = 8.12, P = 0.15, I2 = 38%). Analysis of the findings showed that RDS is associated with PVL .2 = 10.04, P = 0.07, I2 = 50%). Analysis of the findings showed that seizures are associated with PVL .2 = 16.73, P = 0.05, I2 = 46%). The pooled results indicated that maternal preeclampsia or eclampsia did not increase the risk of PVL in infants .Ten ORs of preeclampsia/eclampsia were pooled using the fixed-effects model . No signP > 0.05).Publication bias was assessed in pooled analyses of more than two studies. A funnel plot was used for visual assessment of publication bias  and an aOur meta-analysis included 15 studies and a total of 12,851 participants. The pooled results indicate that increased risk for PVL was associated with oligohydramnios in mothers , acidemia , 1-minute Apgar score <7 , 5-minute Apgar score <7 , apnea , RDS , and seizures  in preterm infants.Previous epidemiological studies have shown that perinatal HI and inflammation are two major risk factors in the development of PVL . ChorioaThe fetus is highly susceptible to changes in maternal conditions, especially those associated with HI-related complications. Oligohydramnios may contribute to umbilical cord compression. In this meta-analysis, we found a significant difference between the relationship of oligohydramnios and the risk of PVL. However, oligohydramnios can occur secondary to some inflammatory events, such as preterm prelabor rupture of membranes . Therefoet al. [Neonatal acidemia at birth is a crucial indicator for monitoring neurological outcomes after prenatal or perinatal hypoxic-ischemic events. It has been proposed that metabolic rather than respiratory acidosis is associated with neonatal neurologic deficits . In mostet al.  reportedet al. [et al. [et al. [An Apgar score less than 7 is often considered to indicate fetal distress, and is more commonly associated with adverse neurologic outcomes . Iliodroet al.  have rep [et al.  study wh [et al.  study fr [et al. , 41. ThePrevious studies have shown that apnea , RDS 4343, and sTo our knowledge, this is the first meta-analysis based on currently available studies to assess the association between perinatal hypoxic-ischemia and the development of PVL. However, this study has several limitations. First, all of the studies included in our analysis used medical records from one particular hospital or university rather than national health departments. As a result, our analysis lacks power. Second, all studies in our analysis were performed in the last 10 to 20 years. The diagnosis method used during this period was cranial ultrasound. However, in current clinical practice, the combination of magnetic resonance imaging and cranial ultrasound is used as the recognized diagnosis method to detect subtle lesions indicative of PVL. Thus, some cases of PVL may have been missed in our analysis. Third, none of the included studies adjusted for confounding variables such as necrotizing enterocolitis, which can also lead to systemic inflammation associated with subsequent development of PVL. Finally, the relatively small number of included studies resulted in the pooled results being underpowered.Although this study was subject to a number of limitations, our meta-analysis identified perinatal hypoxic-ischemic risk factors for the development of PVL in preterm infants. Future large-scale prospective clinical studies are required to validate and extend these findings.S1 File(DOC)Click here for additional data file.


Meta-analyses are increasingly used for synthesis of evidence from biomedical research, and often include an assessment of publication bias based on visual or analytical detection of asymmetry in funnel plots. We studied the influence of different normalisation approaches, sample size and intervention effects on funnel plot asymmetry, using empirical datasets and illustrative simulations. We found that funnel plots of the Standardized Mean Difference (SMD) plotted against the standard error (SE) are susceptible to distortion, leading to overestimation of the existence and extent of publication bias. Distortion was more severe when the primary studies had a small sample size and when an intervention effect was present. We show that using the Normalised Mean Difference measure as effect size (when possible), or plotting the SMD against a sample size-based precision estimate, are more reliable alternatives. We conclude that funnel plots using the SMD in combination with the SE are unsuitable for publication bias assessments and can lead to false-positive results. Systematic reviews are literature reviews intended to answer a particular research question by identifying, appraising and synthesizing all research evidence relevant to that question. They may include a meta-analysis, a statistical approach in which outcome data from individual studies are combined, which can be used to estimate the direction and magnitude of any underlying intervention effect, and to explore sources of between-study heterogeneity. Simultaneously, meta-analysis can be used to assess the risk of publication bias: the phenomenon that published research is more likely to have positive or statistically significant results than unpublished experiments . Meta-anThis calls for methodological research to ascertain whether approaches to data analysis routinely used in the clinical domain are appropriate in the pre-clinical domain and for resources that guide and inform researchers, reviewers and readers on best practice. In this light, we present findings which show that the use of the standardized mean difference (SMD) measure of effect size in funnel plots can introduce a risk of incorrect assessment of publication bias, particularly in meta-analyses of preclinical data characterised by a large number of individually small studies with large observed effects.e.g. the effects of an intervention which has been tested in several studies, outcome measures first need to be expressed on a common scale. Such scales include (for binary outcomes) the risk or odds ratios; and for continuous data a raw mean difference (RMD), SMD or normalized mean difference (NMD).To combine data statistically on i.e. the reported measurement unit of the change in outcome has the same meaning in all studies). The RMD is calculated by subtracting the mean outcome value in the control group (Mctrl) from the mean in the intervention group (Mint):The RMD can be used when all outcome data are in the same measurement unit, and the interpretation of the outcome is the same in all settings (The observed standard deviation (SD) is likely to differ between experimental groups, and therefore the standard error (SE) of the RMD is calculated as:where n is the sample size per group.e.g. a given change in infarct size measured in mm3 has a different consequence in the mouse brain than in the rat brain), the intervention effect may be expressed as an SMD. For each study the SMD is obtained by dividing the RMD by that study’s pooled standard deviation (SDpooled) to create an effect estimate that is comparable across studies:In cases where the measurement unit, or the interpretation of the outcome, or both differ between studies  of the SMD can be approximated using the sample sizes (n) and the effect estimate (SMD):d. However, Cohen’s d tends to overestimate the ‘true’ SMD and its variance when the sample sizes in the primary studies are small (e.g. <10). This bias can be corrected using the approach of Hedges , which relates the magnitude of effect in the intervention group to that seen in untreated animals, with reference to the outcome in a normal, healthy animal . A condisham is the mean score for normal, unlesioned and untreated subjects. The corresponding SE is calculated as:where Msee  for a coSMD is correlated to the SMD effect size, whereas the SEs of the RMD  imprecise  studies will deviate further from the summary effect compared to precise (larger) studies and 2) studies are equally likely to overestimate or underestimate the true effect (e.g. in this study).Funnel plots are scatter plots of the effect sizes of the included studies e effect . Assessme effect . Importae effect . In addiSMD on the observed SMD will impact the appearance of the funnel plot. When we review the equation for the SESMD,  will have a relatively small SE  will be zero, and the SE will therefore depend solely on the sample size . If an ismall SE . This wismall SE . The smasmall SE .In summary, a funnel plot using both the SMD and its SE may become asymmetrical in the absence of publication bias. When funnel plot distortion is assessed by visual inspection, this skewing might cause the plot to be interpreted as being asymmetrical and lead the observer to erroneously conclude that publication bias is present. Furthermore, funnel plot asymmetry is often tested statistically using Egger’s regression  or DuvalWe investigated the reliability of RMD, SMD and NMD-based funnel plots for the assessment of publication bias in meta-analyses, using both empirical datasets and data simulations. We investigate the effect on the severity of funnel plot distortion of the study sample size, the number of studies in the meta-analysis and the magnitude of the intervention effect. We assess whether distortion can be avoided by using a precision estimate based on the sample size of the primary studies, as previously suggested for mean difference outcome measurements . We then−5), but no additional studies were imputed in trim and fill analysis  and 196 missing studies were imputed by trim and fill analysis, leading to adjustment of the estimated SMD effect size from 2.8 to 1.9.Dataset 1 (ischaemic preconditioning) contains 785 individual effect sizes . In the analysis . When ex−10, Egger regression), but no missing studies were imputed (Dataset 2 (stem cell treatments) contained 95 individual effect sizes . Funnel  imputed .Results of our first simulation (in the absence of publication bias) are shown in When we simulated the presence of an intervention effect , again around 5% of the RMD funnel plot analyses were judged asymmetrical . Trim and SMD instead of Hedges’ g, or using Begg and Mazumdar’s test, we found highly similar results in all scenarios simulated  and the performance of SMD vs. 1/√n funnel plots and NMD funnel plots in the presence of an intervention effect as alternatives to the SMD vs. SE funnel plot. As in simulation 1, SMD vs. SE funnel plots of unbiased simulations were identified as asymmetrical by Egger’s test . HoweverIn our final simulation we tested the performance of these different approaches in the presence of simulated publication bias. In the majority of these simulations of meta-analyses of individually small studies, asymmetry was detected both visually , and usiSince a sample size-based precision estimate might be more suitable for asymmetry analysis, we used data from five previously published meta-analyses which used an SMD vs. SE funnel plot and claimed funnel plot asymmetry as a result of publication bias. In the original publications, all five of these funnel plots were asymmetrical according to Egger’s regression test. In three out of five cases, this asymmetry was not present in funnel plots using 1/√n as a precision estimate . Furthere.g. Begg and Mazumdar’s test eLife. Your article has been favorably evaluated by a Senior Editor and three reviewers, one of whom, M Dawn Teare (Reviewer #1), is a member of our Board of Reviewing Editors. The following individuals involved in review of your submission have agreed to reveal their identity: Marcus Munafo (Reviewer #2); Jack Vevea (Reviewer #3).Thank you for submitting your work entitled "Standardized mean differences cause funnel plot distortion in publication bias assessments" for consideration by The reviewers have recognised this is an important topic but you have not really presented what the impact of this problem is. There are many queries relating to selection of simulation parameters, choice of statistic and test, and what the impact is in realistic practice.eLife (two-three months), so we are rejecting the paper now but would welcome a resubmission in the future, with no guarantees of acceptance or re-review. This should be a de novo submission, and we would endeavour to recruit the same editors to assess the revisions.Addressing these issues will likely exceed the time span for major revisions in Reviewer #1:This manuscript identifies an important potential problem when using the SMD in meta-analyses. When sample sizes are small and effect sizes large, funnel plots using SMD vs. SE can show asymmetry and hence suggest evidence of publication bias when there is none.The authors demonstrate that this issue should not be so surprising as the SMD SE is a function of the effect size and hence studies with small sample sizes will tend to show a bias. Meta analyses that contain large numbers of studies are therefore more likely to show this effect. This could be an important issue as the meta-analysis may over correct the SMD and actually lead to an underestimate of an effect size.This manuscript has a clear simple message that using funnel plots of the SMD vs. 1/√n do not result in the same bias.I do have a number of concerns with the paper especially as they do not tackle the issue of what is the impact on the overall estimate of the effect size resulting from such meta-analyses. My understanding is that the funnel plot, trim and fill is used to estimate corrected effect sizes (so if publication bias is suspected the SMD is adjusted). This manuscript has not really focused on the impact on the overall estimates coming out of meta-analyses.g is often reported for studies of small sample size rather than Cohen's d, but the authors appear to have not performed their simulations using this version. (Apart from a single figure shown in the supplementary methods). It seems odd not to have fully evaluated the performance of both summary measures.1) Hedges' 2) The simulations have only been performed under quite a limited number of scenarios, the null, for one very large effect size and then for a specific form of publication bias. Though a range of sample sizes has been explored. While this is helpful to show the weakness in the method researchers will want to know when it is important. Is an effect size of 1SD a realistic effect size in practice? Surely if an effect size is so large as that not many studies will be required to confirm it. 3) Much more detail on the meta analyses and why they were selected. What was the impact on the estimates of SMD using the different funnel plots? What was the distributions of sample sizes and SMDs in the studies making up each meta-analysis?Reviewer #2:The authors highlight an interesting aspect of commonly used tests to assess for the presence of small study bias, which may be caused by publication bias. This is supported by analyses of simulated and real data.My main comment is that there are other tests (e.g. Begg and Mazumdar). Do the issues described here apply to all tests, and if so does the extent to which they do so differ ?It may also be worth briefly discussing other approaches, such as the Excess Significance Test developed by Ioannidis, that rely on different assumptions and therefore allow triangulation of methods.Reviewer #3:This manuscript presents a simulation study looking at the identification of publication bias for measures of effect size based on the difference between means: standardized mean difference (SMD), raw mean difference (RMD), and normalized mean difference (NMD). Using Egger's regression and Duval and Tweedie's trim and fill analysis to detect funnel plot asymmetry, the authors discover a problem of overidentification for SMD when analysis is based on funnel plots of SMD against standard error. Overidentification of publication bias was found to be much greater for SMD in comparison to RMD and NMD. This problem was found to be mitigated by plotting SMD against 1/√n.g in the fourth paragraph of Section 1.1. Many practitioners incorrectly use the term g and d interchangeably, and a description of how these two estimates are different will be beneficial. Related to this, in the following sentence the paper describes the shortcomings of SMD when the sample size is small. It would be useful to point out that Hedges' bias-corrected estimate is meant to address bias when sample sizes are small .The authors provide a good description of the different types of measures of effect size. However, they provide only a mention of Hedges' In Section 1.2, the authors describe the relationship between funnel plot asymmetry and publication bias. It would also be worth pointing out that funnel plot asymmetry does not necessarily indicate publication bias. There are other reasons why funnel plots may appear asymmetrical, such as systematic heterogeneity related to the inclusion of two different modes of inquiry that differ both in effect magnitude and in typical standard error.There are two sections of the paper with the heading "Data Simulations" (Section 2.2 and Section 4.1). Results of the simulations are in Section 2.2. Section 4.1 is at the end of the paper and describes the process for simulating the data. This organization is confusing. It would be better if the description of the simulation were provided before these results, as it provides context for understanding the outcome of the author's simulation. Also, headings that clearly define these different sections will make it easier for readers to navigate the article.d and Hedges' g effect-size estimates. Based on the information in the paper . This manuscript has not really focused on the impact on the overall estimates coming out of meta-analyses.Although not our primary outcome, we have added the results of trim and fill analyses for simulation 1 to Amendments to the manuscript:Section 2.2: “Trim and fill analysis resulted in on average 7% extra studies filled in preclinical simulation scenarios using the RMD. […] As a result, the adjusted overall effect estimate after trim and fill in SMD funnel plots tended to be an underestimation of the true effect size.”Added trim and fill results to Table 11) Hedges' g is often reported for studies of small sample size rather than Cohen's d, but the authors appear to have not performed their simulations using this version. (Apart from a single figure shown in the supplementary methods). It seems odd not to have fully evaluated the performance of both summary measures.g provides a more appropriate estimate of the SMD for studies with small sample sizes, which is why all simulations and re-analyses were performed in Hedges’ g. We infer from your question that this aspect of the methodology was not stated clearly enough, and have therefore improved the description in the first paragraph of the Materials and methods and Results sections to make it more clear that we used Hedges’ g in all main analyses.Indeed, Hedges’ g and Cohen’s d more extensively. We have therefore extended g and Cohen’s d, so that these can be easily compared. We have also extended g and Cohen’s d. The data presented provide further support for our conclusion that the differences between the results of simulations using Hedges’ g versus those using Cohen’s d are marginal .Related to this, we have also added a more detailed description of the difference between Hedges’ Amendment(s) to the manuscript:d and Hedges’ g, as well as Egger’s test and Begg and Mazumdar’s test, perform similar in multiple illustrative scenario’s (simulation 1)Added g, or using Begg and Mazumdar’s test, we found highly similar results in all scenarios simulated  […]. As a sensitivity analysis, all scenarios of simulation 1 were also performed using Cohen’s d.”Section 4.2: “[…] the unbiased SMD (Hedges’ 2) The simulations have only been performed under quite a limited number of scenarios, the null, for one very large effect size and then for a specific form of publication bias. Though a range of sample sizes has been explored. While this is helpful to show the weakness in the method researchers will want to know when it is important. Is an effect size of 1SD a realistic effect size in practice? Surely if an effect size is so large as that not many studies will be required to confirm it.The chosen intervention effect size RMD = 10 / SMD =1 was based on empirical data from preclinical meta-analyses, where such large effects are commonly observed. For instance, all meta-analyses re-analyzed in this manuscript have overall effect sizes (considerably) larger than RMD=10  or SMD=1We have also added a sentence explaining the rationale behind the chosen effect sizes to Section 4.1. Furthermore, we have added the fact that our simulations do not cover the entire spectrum of possible simulations scenarios as a limitation of this study under Section 3.1 Limitations.Amendment(s) to the manuscript:Section 1: “Importantly, preclinical studies are, generally, individually small, with large numbers of studies included in meta-analysis, and large observed effects of interventions. This contrasts with clinical research, where meta-analyses usually involve a smaller number of individually larger experiments with smaller intervention effects.”Section 2.2: updated text and Section 4.2: updated text and Section 4.2: “Of note, an intervention effect of SMD = 1 may appear large to those experienced in meta-analyses of clinical data, but is typical of those observed in animal studies, as are the group sizes reported see e.g. .”Section 3.1: “We acknowledge that our current range of simulation scenarios does not enable us to predict the impact of funnel plot distortion in every possible scenario, but we present those scenarios which most clearly illustrate the causes and consequences of funnel plot distortion.”We agree with the reviewer that the decision whether or not to pool heterogeneous results from animal studies is a recurrent matter of discussion, and should be carefully considered for each meta-analysis of animal studies. However, our goal here is not to assess whether the authors of the published meta-analysis made the correct decision in pooling these 1400 studies for their publications bias assessment, but rather to show that their use of an SMD versus SE funnel plot led to an overestimation of publications bias in their analysis. We chose this example especially because the pattern of skewing in the published SMD versus SE plot led to a high number of imputed studies. This is why we present the authors’ published analysis as published, and compare the results with the SMD versus 1/√n analysis.3) Much more detail on the meta analyses and why they were selected.The selected datasets were from our own groups, or from our recent collaborations. This allowed for easy identification of meta-analyses using SMD vs. SE funnel plots, and easy access to the data required for re-analysis. We did not apply any selection criteria to the datasets, other than consent of the corresponding author. We limited the number of empirical examples to 5, because we felt that any more examples would be redundant. In addition to these practical advantages, we feel that we present the need for implementation of our approach best by reflecting critically on our own past work and point out limitations, rather than by directly criticizing the work of others. We have provided additional information in the text to better describe how the datasets were selected.Amendment(s) to the manuscript:Section 4.3: “The selected datasets were from our own groups, or from recent collaborations, which allowed for easy identification of meta-analyses using SMD vs. SE funnel plots, and easy access to the data. There were no selection criteria in terms of e.g. the number of studies in the analysis, or the outcome of the publication bias assessment.”What was the impact on the estimates of SMD using the different funnel plots? What was the distributions of sample sizes and SMDs in the studies making up each meta-analysis?We have now reported the original overall effect estimate, as well as the adjusted overall effect estimates after trim and fill in The range of SMDs in the studies making up each meta-analysis can be most easily examined using the funnel plots in Amendment(s) to the manuscript:Section 4.3: “The distribution of the total number of subjects per datapoint in the selected studies is (in median(min-max)): 11.7(6-38) for Wever et al.2012, 20(12-46) for Groenink et al.2014, 11(424) for Yan et al. 2015, 14.5(6-35) for Kleikers et al. 2015 and 12(4-66) for Egan et al. 2016”Reviewer #2:[…] My main comment is that there are other tests (e.g. Begg and Mazumdar). Do the issues described here apply to all tests, and if so does the extent to which they do so differ ?Thank you for this interesting suggestion. We focus on the performance of Egger’s test since this is the test most commonly used in practice. However, based on our theoretical explanation of how the association between the SMD and its SE leads to funnel plot distortion, it is almost inevitable that the issues described will occur with any test that relies on an assessment of funnel plot asymmetry to detect publication bias . As an example, we have investigated the performance of Begg and Mazumdar’s test, the results of which are shown in For the reviewers interest, we present below the results for Amendment(s) to the manuscript:Section 4.2: “As a second sensitivity analysis, we assessed the robustness of our findings using Egger’s test by re-testing all scenarios of simulation 1 using Begg and Mazumdar’s test , and comparing the results.”d SMD instead of Hedges’ g, or using Begg and Mazumdar’s test, we found highly similar results in all scenarios simulated ).”It may also be worth briefly discussing other approaches, such as the Excess Significance Test developed by Ioannidis, that rely on different assumptions and therefore allow triangulation of methods.The aim of our study is to promote the correct use of funnel plots for publication bias assessments. We agree that methods not relying on funnel plots may provide suitable alternatives, and have discussed such methods in Section 3.2 Recommendations. We agree that the Excess Significance Test should be mentioned here as well and have added it to the list of proposed alternatives. Of note, the performance of this test with continuous outcome measures has not been thoroughly evaluated, which is why we recommend it to be used cautiously.Amendment(s) to the manuscript:Section 3.2: “[…] Excess Significance Test […]”Reviewer #3:[…] The authors provide a good description of the different types of measures of effect size. However, they provide only a mention of Hedges' g in the fourth paragraph of Section 1.1. Many practitioners incorrectly use the term g and d interchangeably, and a description of how these two estimates are different will be beneficial.g and Cohen’s d to the Introduction.Thank you for your compliment and suggestion, we have added a more detailed description of the difference between Hedges’ Amendment(s) to the manuscript:d. […] In many clinical meta-analyses, Hedges’ g will be almost identical to Cohen’s d, but the difference between the estimates can be larger in preclinical meta-analyses, where small sample sizes are more common.”Section 1.1:“Of note, equations 3 and 5 estimate the SMD using the approach of Cohen ; this estimate is therefore termed Cohen’s Related to this, in the following sentence the paper describes the shortcomings of SMD when the sample size is small. It would be useful to point out that Hedges' bias-corrected estimate is meant to address bias when sample sizes are small .d into the description of the difference between Cohen’s d and Hedges’ g .We fully agree and have now incorporated the shortcoming in the estimation of the variance in Cohen’s In Section 1.2, the authors describe the relationship between funnel plot asymmetry and publication bias. It would also be worth pointing out that funnel plot asymmetry does not necessarily indicate publication bias. There are other reasons why funnel plots may appear asymmetrical, such as systematic heterogeneity related to the inclusion of two different modes of inquiry that differ both in effect magnitude and in typical standard error.We agree and have therefore added an explanation of the fact that asymmetry may result from different sources to the Introduction.Amendment(s) to the manuscript:Section 1.2: “Importantly, there are other causes of asymmetry in funnel plots. For instance, the true effect size in smaller (and therefore less precise) studies may be genuinely different from that in large studies . […] In addition, artefacts and chance may cause asymmetry (as shown e.g. in this study).”There are two sections of the paper with the heading "Data Simulations" (Section 2.2 and Section 4.1). Results of the simulations are in Section 2.2. Section 4.1 is at the end of the paper and describes the process for simulating the data. This organization is confusing. It would be better if the description of the simulation were provided before these results, as it provides context for understanding the outcome of the author's simulation. Also, headings that clearly define these different sections will make it easier for readers to navigate the article.eLife’s guidelines for authors when organizing the sections of the manuscript, which dictate that the Materials and methods section should come last, after the Results and Discussion. We have, however, changed the titles of the subheadings of Section 2.2 and 4.1 to avoid confusion of the sections.Thank you for pointing this out. We followed Amendment(s) to the manuscript:Section 2.2: changed section heading to “Data simulation results”Section 4.2: changed section heading to “Data simulation methods”In the third paragraph of Section 2.2 the authors state that results were similar for Cohen's d and Hedges' g effect-size estimates. Based on the information in the paper  to the manuscript:In the fourth paragraph of Section 2.2 the authors use SMD-1/√n to refer to their analysis of funnel plots looking at SMD with 1/√n on the y axis. The hyphen could be interpreted as arithmetic minus sign, so additional clarity of notation is needed.Thank you for this helpful comment, which was also mentioned by reviewer #1. As mentioned above, we have replaced “SDM-”, “NMD-” and “RMD-” with “SMD vs.”, “NMD vs.” or “RMD vs.” throughout the manuscript, to avoid confusion of the hyphen with the minus sign.In the fourth paragraph of Section 2.2 the authors state that the distortion is not seen in plot D of We agree that it is important to emphasize that visual inspection of funnel plots is subjective, and that the results of Egger’s regression test (reported in Amendment(s) to the manuscript:Section 2.2: “As in simulation 1, SMD vs. SE funnel plots of unbiased simulations were identified as asymmetrical by Egger’s test . HoweverIn We agree that our method to introduce publication bias can be refined and have therefore improved this analysis by introducing bias using a step wise function.Amendment(s) to the manuscript:Added description of step-wise method to section 4.2: “We then introduced publication bias in these meta-analyses using a stepwise method, Publication bias was introduced stepwise, by removing 10% of primary studies in which the difference between the intervention and control group means was significant at p<0.05 (Student-t test), 50% of studies where the significance level was p≥0.05 to p<0.10, and 90% of studies where the significance level was p≥0.10.”Added R script to Supplemental figure now shows example plots of the new step function.In Section 2.3, the authors compare the results of the analyses using examples from real-world studies. Providing this shows how their research may be applicable in real world settings. It would also be useful to report effect-size estimate from the original meta-analysis.We agree that this would indeed be helpful to the reader, and have therefore added the original and adjusted effect sizes of the meta-analyses in The Discussion section is missing a discussion of the limitations of the current study. The simulation had a limited number of conditions. Providing limitations identifies the scope of the presented findings.We have added the limitations of our data simulations to section 3.1 Limitations.Amendment(s) to the manuscript:Section 3.1: “We designed our data simulations to closely resemble empirical data in terms of the range of sample sizes, effect sizes and numbers of studies in a meta-analyses. […] However, research on how to optimally simulate these parameters is first needed, and was beyond the scope of this study. Instead, we used re-analyses of empirical data to test our proposed solutions on a number of real-life meta-analyses which include all of these aspects.”Also, the authors should include a rationale explaining why they got these results Without that, the findings atheoretical; entirely empirical methodological findings are harder to accept with confidence.We have added Section 1.3 and a new now  in whichAmendment(s) to the manuscript:Added section 1.3 Theoretical explanation of SMD funnel plot distortion:“1.3 Theoretical explanation of SMD funnel plot distortion[…] The effect of the second component on the SE, and the resulting distortion, is largest if the sample size is small and the SMD is large  under conditions where the population magnitude is small or moderate.We interpreted ‘population magnitude’ as the effect size introduced. Although this method might indeed reach significance later, the methodological problem still exists. Slightly more power may be needed to detect it, but chances of false positive claims are still higher than they should be.Also, the simulation did not include heterogeneity of studies in the model: all studies were sampled from exactly the same fixed-effect distribution. Studies conducted in the real world tend to have heterogeneity. Conditions of heterogeneity often lead to failure of trim and fill and Eggers regression, and would influence the outcome of an analysis of publication bias.We fully agree that heterogeneity in “real” meta-analyses may greatly influence the outcomes of Egger’s regression and trim and fill analyses. However, the goal of our current simulations is to illustrate the cause of the problem (why are SMD vs. SE funnel plots distorted), and how is this influenced by three straightforward variables: sample size, effect size and number of studies in the analysis. We feel that introducing heterogeneity into these simulations will not make the message more clear. Because heterogeneity is such a complex phenomenon, we felt it was more informative to perform re-analyses of empirical datasets, in which heterogeneity and the aforementioned variables are combined and which therefore optimally serve the purpose of showing how relevant our findings are in practice.


ApaI, VDR FokI and PMOP susceptibility. Subgroup analysis showed that VDR ApaI polymorphism significantly decreased the osteoporosis risk in Caucasian postmenopausal women. In Asian populations, VDR BsmI and VDR FokI were associated with an increased risk of PMOP. As to the associations between VDR polymorphisms and BMD, Caucasian PMOP women carrying the ApaI aa genotype were at risk of high BMD in femoral neck, and low femoral neck BMD was observed in Caucasian PMOP women with FokI Ff genotype. PMOP women with the Cdx2 GA genotype had a lower lumbar spine BMD in overall and Caucasian populations compared with PMOP women with GG genotype. Different VDR gene polymorphisms have different impacts on PMOP risk and BMD.Results on the relationships between vitamin D receptor (VDR) gene polymorphisms and postmenopausal osteoporosis (PMOP) susceptibility and bone mineral density (BMD) are conflicting. The aim of the study is to identify more eligible studies that calculated pooled OR and WMD with 95% CI to assess their associations. Overall, there were significant correlations between VDR  The pathogenesis of PMOP remains unclear3. In recent years, the association between genetic factors and PMOP susceptibility has been highlighted7.Postmenopausal osteoporosis (PMOP) is a common metabolic bone disorder characterized by low bone mineral density (BMD) and increased fracture risks in postmenopausal women8. Vitamin D receptor (VDR) is the target receptor to regulate the transcription of Vitamin D, and is also thought to play a key role in cellular differentiation and proliferation9. Recently, VDR gene polymorphisms like VDR ApaI, VDR BsmI, VDR Cdx2, VDR FokI and VDR TaqI are getting an increasing recognition of importance as more studies have verified their significant associations with several diseases10.Vitamin D has a wide range of biological functions, including calcium and phosphate homeostasis, skeletal metabolism and vascular function14. Although previous meta-analyses reported associations between VDR polymorphisms and osteoporosis risk, the results are conflicting16. To the best of our knowledge, there lacks evidence to confirm the relationship between VDR ApaI, VDR BsmI, VDR Cdx2, VDR FokI and VDR TaqI polymorphisms and osteoporosis risk in postmenopausal women. In addition, the relationship between VDR gene polymorphisms and BMD in postmenopausal women has also been widely studied, but the results are also controversial26. The aim of the present meta-analysis is to determine whether there is any significant association between VDR gene polymorphisms  and susceptibility to osteoporosis and BMD in postmenopausal women.More attention has been paid to the relationship between VDR gene polymorphisms and PMOP risk and BMD in postmenopausal women. Nevertheless, there are sdiscrepancies over this issue71 meeting the inclusion and exclusion criteria were recruited in our meta-analysis, among which 47 studies62 explored the relationships between VDR gene polymorphisms and PMOP susceptibility in postmenopausal women, and 26 studies71 eported the BMD value in PMOP women with various VDR genotypes. The study selection and inclusion processes are shown in Fig. ApaI, VDR BsmI, VDR TaqI, VDR Cdx2 and VDR FokI polymorphisms are shown in Table A total of 58 studiesBefore this meta-analysis, a power analysis was conducted by using the Power and Precision V4 software to verify whether the included studies could offer adequate power (>80%). The statistical power in our study was sufficient to detect the associations between VDR gene polymorphisms and PMOP risk.ApaI polymorphism and PMOP risk. When stratified by ethnicity, subgroup analysis indicated that there was also a significant association between VDR ApaI polymorphism and PMOP risk in Caucasian populations, while there lacked a significant association in Asian populations. All the data are shown in Table Overall, our study showed a significant association between VDR BsmI polymorphism was found to be significantly associated with risk of developing PMOP in the overall populations and Asian populations (Table P > 0.05).VDR ns Table . In contCdx2 polymorphism and PMOP risk in Caucasian populations (P > 0.05), nor could we confirm the association in overall and Asian populations as there lacked relevant studies. The data are shown in Table We failed to find any significant association between VDR FokI polymorphism   Table .Figure 4TaqI polymorphism, no significant relationship was observed between VDR TaqI polymorphism and PMOP susceptibility in the overall populations and Caucasian populations (both P > 0.05) (Table TaqI and PMOP in Asian populations as only one study was been searched out and no sufficient dat could be used to draw any firm conclusions in Asians.Regarding VDR 5) Table . HoweverApaI was significantly associated with increased BMD in the femoral neck; while no significant difference of BMD was observed at lumbar spine between PMOP women carrying aa genotype and AA genotype (Table aa genotype of VDR pe Table . Howeverpe Table .Table 4MP > 0.05) (Table P > 0.05). As shown in Table P > 0.05).No significant difference of Ward’s triangle BMD was observed between the Bb genotype and bb genotype in Asian and overall populations (both 5) Table . In addiCdx2 polymorphism, the GA genotype was significantly associated with reduced lumbar spine BMD in overall and Caucasian populations, but no significant difference was observed in the femoral neck . In addition, VDR Cdx2 was also not significantly associated with BMD in lumbar spine and BMD in femoral neck in etither overall populations. All the data are shown in Table Among PMOP women with VDR FokI Ff genotype was significantly lower than that in women with VDR FokI FF genotype, while no significant difference was observed in lumbar spine BMD in either overall and Caucasian populations .The femoral neck BMD in Caucasian PMOP women with VDR ns Table . The VDRTaqI Tt, VDR TaqI tt and VDR TaqI TT genotypes   Table .We performed a leave-one-out analysis, and any single study could be omitted, without any effect on the overall statistical significance, indicating that the results were stable. The Begg’s and Egger’s tests were performed and the results indicated that there was minimal evidence of publication bias. The shape of funnel plot was symmetrical, which also indicated that there was no publication bias in our study Fig. .Figure 5ApaI polymorphism is located in the 3′-regulatory region of VDR gene (in intron 8), resulting in changes of biological functions of Vitamin D31. Overall, VDR ApaI polymorphism has a protective effect against the development of PMOP in the overall populations and Caucasian populations, suggesting that postmenopausal women with VDR ApaI mutant might have less opportunity to suffer from PMOP compared with wide genotypes, which is consistent with many other studies41. However, controversial results were reported in Douroudis’s study40. In addition, the meta-analysis by Zintzaras et al.15 reported that the allele contrast for Caucasian populations showed no association for ApaI, which is inconsistent with our finding. When we compared our study with this study15, we could find that several studies39 performed after the publication year of it15 were searched out and included in our pooled analysis, suggesting that our meta-analysis could provide a more precise evaluation of the relationship between VDR ApaI polymorphism and PMOP risk.VDR ApaI was significantly associated with increased BMD in the femoral neck, which is consistent with some studies27. However, no significant difference in BMD was observed at the lumbar spine, which is consistent with three case-control studies34. Marozik et al.27 reported a significant association between VDR ApaI polymorphism and lumbar spine BMD in PMOP women, and in their opinion, VDR ApaI polymorphism might be a useful marker for osteoporosis screening at least in Belarusian women. VDR ApaI polymorphism is found in the non-coding region of the VDR gene and may have no significant effect on the final protein product; therefore, why there are controversial results in lumbar spine and femoral neck BMD needs to be further studied. In addition, no significant difference was observed in either lumbar spine or femoral neck BMD between Caucasian PMOP women carrying Aa genotype and those carrying AA genotype, suggesting that different genotypes might have different effects on BMD.In our study, we found that the aa genotype of VDR BsmI is located in the 3′ untranslated region, and involved in regulating the stability of VDR mRNA. Our study showed that VDR BsmI was significantly associated with the increased risk of developing PMOP in the overall populations as well as Asian populations, which is consistent with three previous studies56. In contrast, no association was observed in some other studies57. The combination of different original data in each study might have great impact on the pooled distribution of each genotype, which might be an important contributor to the different results of our results and other studies. Our results are consistent with Jia et al.16 and Zintzaras et al.’s study15. However, no significant association was observed in Asian populations in other studies16. As Qin et al.9 included all the osteoporotic patients, and Zhao et al.8 only analyzed three studies, our study may provide a more precise evaluation than theirs. As no significant association was observed between VDR BsmI and PMOP risk in Caucasian populations, ethnicity might be a factor contributing to this difference with Asian populations.VDR BsmI may not affect the amino acid sequence of VDR, it is easily understood that BsmI Bb and BB genotype might not play a key role in BMD at lumbar spine, femoral neck, and Ward’s triangle. Two studies73 found no relationship between VDR BsmI polymorphism and fracture risk in PMOP women, which verifies our results on the other hand.We compared BMD at the lumbar spine, femoral neck or Ward’s triangle in PMOP women with BB, Bb and bb genotypes, and found that PMOP women carrying Bb genotype or BB genotype were not at a significantly higher risk of low BMD at lumbar spine, femoral neck, and Ward’s triangle than those carrying bb genotype. As VDR ApaI was associated with a decreased risk of PMOP, and high levels of BMD, whereas BsmI was associated with an increased risk of PMOP and did not play a key role in BMD. Theoretically, the consistent results should be observed in the subgroup analysis, for both VDR ApaI and VDR BsmI have influences on the stability of VDR mRNA. However, different gene locations of VDR ApaI and VDR BsmI may lead to different biological functions. Thus, the different role of VDR ApaI and VDR BsmI in the etiology and pathogenesis of PMOP and BMD may be an important contributor to the controversial findings in our study. However, the exact mechanism of the VDR ApaI and VDR BsmI polymorphism requires further investigation.Interestingly, our results showed consistency: VDR Cdx2 polymorphism is located in the promoter region of VDR gene, which is considered to be associated with the level of calcium absorption and the receptor’s activation to Vitamin D. It was found that VDR Cdx2 was not significantly associated with PMOP risk in Caucasian populations, which is consistent with the finding of Marozik et al.27. One previous study28 showed that VDR Cdx2 played a protective role against the risk of PMOP, which is inconsistent with the result reported by Mencej-Bedrac et al.46, while 74 postmenopausal women were examined in the study of Ziablitsev et al.28, which might contribute to this difference.VDR Cdx2 had an increased risk of developing low BMD at the lumbar spine in overall and Caucasian populations compared with GG genotype. In addition, no significant association was observed at femoral neck BMD, which is consistent with Marozik et al.’s study27 and inconsistent with other two studies46. As to the AA genotype of VDR Cdx2, no significant difference in lumar BMD or femoral neck BMD was observed between PMOP women with AA genotype and those with GG genotype in either overall or Caucasian populations. In Mencej-Bedrac et al.’s study46, they observed an association between the Cdx2 polymorphism and vertebral fracture risk; therefore, large sample-size studies are required before a more convincing conclusion can be made.We found that GA genotype of VDR FokI is a polymorphism of VDR near the 50-UTR region of the gene within the DNA-binding domain, and plays an essential role in message stability and post transcriptional processes74. In our meta-analysis, VDR FokI was significantly associated with higher risk of developing PMOP in overall and Asian populations, but not in Caucasian populations, which is inconsistent with Zintzaras et al.’s meta-analysis15.VDR FokI was significantly associated with decreased BMD in the femoral neck in Caucasian populations, but not in the lumbar spine. Besides, we did not observe overall associations between VDR FokI and BMD in either lumbar spine or femoral neck in either overall populations or Caucasian populations with ff genotype in our meta-analysis. A study performed by Wang et al.75 showed that VDR FokI was associated with BMD in postmenopausal Asian women, and could probably be used with other genetic markers together to identify individuals at high risk of osteoporosis. However, we could not make a certain conclusion whether VDR FokI plays a key role in BMD value in Asians since no available data could be used in meta-analysis. Four studies61 found by our searching terms were not included in Wang’s study. In addition, we excluded three studies76 that were recruited in Wang’s study, because no sufficient data could be collected in their original articles.Our analysis indicated that the Ff genotype of VDR BsmI, VDR TaqI has been proved to affect mRNA stability, leading to altered protein levels and biological functions of Vitamin D. In our study, there was no significant association in overall and Caucasian populations, which was consistent with Zintzaras et al.’s study15. More studies were included in our study compared with their study15, suggesting that our study might provide a more precise evaluation of the relationship between VDR TaqI and PMOP risk. In addition, we also did not find any significant difference in lumbar spine BMD or femoral neck BMD in comparison with PNOP women with TT, Tt and tt genotypes, which is inconsistent with two studies27. As our meta-analysis had larger sample sizes and higher statistical power, it provided a more precise evaluation of this association.Unlike VDR ApaI, VDR BsmI, VDR FokI and VDR TaqI and PMOP risk in different ethnicities, a conclusion might be drawn that these polymorphisms may be useful markers for osteoporosis screening in certain ethnicities. Second, screening of these genetic markers may enable an early identification of risk groups to perform preventive measures in a timely manner and also to improve treatment effectiveness, avoid complications, reduce disability and mortality rates in these patients, as well as cut down the treatment costs. Third, some more reports have confirmed the genetic background of BMD18. Therefore, our results could provide theories that these VDR gene polymorphisms may be potential targets for genetic therapy of PMOP.Futhermore, we should pay more attention to the implications of our results on public health and clinical practice. First, taking into consideration a significant association between VDR ApaI, TaqI polymorphism and BMD in Asian populations, between VDR Cdx2 and PMOP risk in Asian populations have been found. Mendelian randomization (MR) study is a method of using measured variation in genes of known function to examine the causal effect of a modifiable exposure on disease in non-experimental studies. We had planned to perform MR study to reinforce the findings of our meta-analysis. However, convicing evidence in the literature cannot be provided to support the MR criteria.Our meta-analysis has some limitations that should be addressed. First, it should be remembered that in many cases it is the environmental factor that determines the development of PMOP. We should also remember that the absence of control for confounders such as smoking is one of the main limitations of our work because phenotypes of many diseases may be the results of interactions between genotyps and environmental factors. Second, no studies that explored the association between VDR In conclusion, VDR gene polymorphisms play keys roles in osteoporosis susceptibility and BMD in postmenopausal women, although different VDR gene polymorphisms might have significantly different influences on the risk of osteoporosis and BMD in PMOP women with various ethnicities.Databases including PubMed, EMBASE, Web of Science, the Cochrane Library and China WeiPu Library were searched to identify case-control studies investigating the relationship between VDR gene polymorphisms and susceptibility to PMOP and BMD. The following search terms were used to find out eligible studies exploring the PMOP risk in postmenopausal women:  AND (‘VDR’ OR ‘vitamin D receptor’) AND (‘polymorphism’ OR ‘single nucleotide polymorphism’ OR ‘SNP’ OR ‘variation’). To analyze to pooled effects of VDR gene polymorphisms on BMD in postmenopausal women, we used the following search terms to find out eligible studies: ‘PMOP’ OR ‘Postmenopausal osteoporosis’ OR ‘Postmenopausal’) AND (‘VDR’ OR ‘vitamin D receptor’) AND (‘polymorphism’ OR ‘single nucleotide polymorphism’ OR ‘SNP’ OR ‘variation’) AND . Then, one-by-one screening was performed by two authors according the inclusion and exclusion criteria. No language restrictions were applied. Secondary searches of eligible studies were conducted by searching the reference lists of the selected studies, reviews or comments.The inclusion criteria of our meta-analysis were as follows: (1) case-control studies; (2) postmenopausal women with PMOP as case populations, and postmenopausal women without PMOP or healthy women as controls; (3) studies evaluating PMOP risk, alleles and genotypes of at least one of the VDR gene polymorphisms; (3) studies providing the sample size, mean and standard deviation of BMD at lumbar spine, femoral neck or Ward’s triangle in PMOP women with at least one of the VDR genotypes; (4) studies providing sufficient data .The exclusion criteria were: (1) reviews or case reports that were not case-control studies; (2) studies without reporting currently available data; (3) duplicated reports.Data from the eligible studies were extracted according to the inclusion and exclusion criteria by two authors, and a consensus was reached by discussion if the researchers disagreed. In the study of associations between VDR gene polymorphisms and PMOP risk, the following data were collected: author list, year of publication, ethnicity, sample size, and allele and genotype of each gene polymorphism. In the analysis of difference in BMD in PMOP women with various VDR genotypes, we collected the following data: author list, year of publication, ethnicity, the number of cases, and BMD values of the femoral neck, lumbar spine or Ward’s triangle in each VDR genotype in PMOP women.2 metric value. If I2 value was >50% or P < 0.10, ORs were pooled by the random-effects model; otherwise, the fixed-effects model was used. Sensitivity analysis was performed to assess the impact of each study on the combined effect of the present meta-analysis, and subgroup analysis was also performed according to the ethnicity of the study populations. RevMan 5.3 software was used and a P < 0.05 was considered as statistically significant.Odds ratios (OR) and 95% confidence interval (CI) were calculated to evaluate the association between VDR gene polymorphisms and PMOP. The strength of association between VDR gene polymorphisms and PMOP susceptibility was evaluated by OR and 95% CI under the allele contrast model, heterozygote model, homozygote model and dominant model. Regarding the associations between BMD and VDR gene polymorphisms, we compared BMD in PMOP women under heterozygote and homozygote models by using the weight mean difference (WMD) and 95% CI. Power analysis was performed using the Power and Precision V4 software . The heterogeneity of included studies was examined by a chi-squared-based Q statistical test and quantified by IAll data analyzed during this study are included in this published article (and its Supplementary Information files).


Glucose transporter-1 (GLUT-1) exhibits altered expression in colorectal cancer (CRC). The aim of this study was to explore the association between GLUT-1 and survival conditions, as well as clinical features in CRC by meta-analysis.Relevant studies were searched through predefined strategies, hazard ratios (HRs), odds ratios (ORs), and their 95% confidence intervals (CIs) were used as effective measures.A total of 14 studies with 2,077 patients were included in this meta-analysis. The results showed that GLUT-1 was not significantly associated with overall survival (OS)  or disease-free survival (DFS) . However, subgroup analysis indicated that GLUT-1 was a significant biomarker for poor DFS in rectal cancer . GLUT-1 expression was also found to be significantly correlated with the presence of lymph node metastasis , T stage , higher Dukes stage , female sex , and presence of liver metastasis .In conclusion, this meta-analysis showed that GLUT-1 was associated with poor DFS in rectal cancer (RC). Furthermore, GLUT-1 was also an indicator of aggressive clinical features in CRC. Colorectal cancer (CRC) is the third most prevalent cancer and the second leading cause of cancer-related death worldwide . It is eMalignant cells often have elevated metabolic rates than normal cells , and gluFour hundred and forty-four records were identified through initial searching as described in the methods. After duplicate records were discarded, 345 records were left for screening, of which 300 records were then removed by title and/or abstract inspection. Forty-five records were further evaluated by full-text reading, and 31 studies were excluded either owing to being a meeting abstract (n=1), lacking necessary information (n=27), being duplicate studies (n=1), or not using immunohistochemistry (IHC) methods (n=2). Finally, 14 studies –25 publiI2=75.7%, Ph<0.001; Table Eight studies , 22, 25 I2=61.8%, Ph=0.049; Table There were 4 studies , 22–24 wNine studies –22, 24 rBegg's test  and EggeTo our knowledge, the current meta-analysis was the first to investigate the prognostic significance of GLUT-1 expression in CRC. Our study demonstrated that GLUT-1 was associated with poor DFS in rectal cancer, but had a non-significant correlation with OS. Furthermore, GLUT-1 overexpression was positively connected with lymph node metastasis, T stage, higher Dukes stage, female sex, and presence of liver metastasis. Taken together, these results indicated that GLUT-1 was a promising prognostic biomarker for shorter DFS and aggressive clinical parameters.GLUT-1 mediates basal glucose transport in cancer cells and provides glucose for energy metabolism . InducinSeveral limitations still need to be acknowledged. First, significant heterogeneity among studies was detected on OS and DFS analyses. Although we selected eligible studies using uniform criteria, inherent differences among studies still existed. Second, the sample size was relatively small, where only four studies were included when the correlation between GLUT-1 and DFS was analyzed. Although Begg's test and Egger's test suggested non-significant publication bias, selection bias could be possibly exist owing to limited sample size.In conclusion, this meta-analysis showed that GLUT-1 was associated with poor DFS in rectal cancer. In addition, GLUT-1 was also an indicator of aggressive clinical features in CRC. Because several limitations exist in this study, further investigations are required to warrant our findings.This meta-analysis was carried out in line with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) statement . ElectroTwo independent investigators extracted the following information from eligible studies: first author's name, year of publication, study country, sample size, sex, cancer type, detection method, treatment methods, and research period. Discrepancies between the two investigators were resolved by discussion. The qualities of included studies were evaluated by using NOS . The NOSI2 statistics. If p-value for heterogeneity (Ph) <0.10 and I2>50% indicate significant heterogeneity, then a random-effects model was used; otherwise, a fixed-effects model was utilized. For further analysis, subgroup analyses according to location, cancer type, and treatment were conducted. Odds ratios (ORs) and 95% CIs were used to calculated the relevance of GLUT-1 and clinical features. Publication bias was tested by using Begg's funnel plot test [All statistical analyses were performed using Stata 12.0 . HRs and 95% CIs were used to evaluate the effect of GLUT-1 expression on overall survival (OS) and disease-free survival (DFS) in CRC patients. HR >1 without 95% CI overlapping 1 indicates a significant association between high GLUT-1 expression and poor outcomes. Heterogeneity among studies was tested using Cochran Q and lot test  and Eggelot test . P<0.05