Journal Impact Factors (JIFs) are closer to truth than findings from studies in less-cited journals via a meta-epidemiological approach.Scientists, physicians, and the general public legitimately expect scholarly publications to give true answers to study questions raised. We investigated whether findings from studies published in journals with higher We screened intervention reviews from the Cochrane Database of Systematic Reviews (CDSR) and sought well-appraised meta-analyses. We used the individual RCT study estimates’ relative deviation from the pooled effect estimate as a proxy for the deviation of the study results from the truth. The effect of the JIF on the relative deviation was estimated with linear regression and with local polynomial regression, both with adjustment for the relative size of studies. Several sensitivity analyses for various sub-group analyses and for alternative impact metrics were conducted.In 2459 results from 446 meta-analyses, results with a higher JIF were on average closer to “truth” than the results with a lower JIF. The relative deviation decreased on average by −0.023 per JIF (95% CI −0.32 to −0.21). A decrease was consistently found in all sensitivity analyses.Our results indicate that study results published in higher-impact journals are on average closer to truth. However, the JIF is only one weak and impractical indicator among many that determine a studies’ accuracy. The deviation of study results to the truth was measured using pooled estimates of good-quality meta-analyses as proxies for truth.RCT study results published in higher-impact journals lie closer to “true” values than results published in lower-impact journals.As the association found is weak, the Journal Impact Factor is of limited practical value in predicting the trueness of a study result.“In 1955, it did not occur to me that “impact” would one day become so controversial. Like nuclear energy, the impact factor is a mixed blessing. I expected it to be used constructively while recognising that in the wrong hands it might be abused.” – Eugene Garfield, 2005Journal Impact Factor(JIF) [Academic publications are an important and much-noticed output of scientific work. The general public, policymakers, the scientific community, and physicians legitimately expect publications to give true answers to their study questions. However, there is increasing concern that the majority of published research findings are false . In a cotor(JIF) . Despitetor(JIF) –5, existtor(JIF) –8. Furthtor(JIF) , 10. Taktor(JIF) .To our knowledge, the direct association between the impact factor of a journal in which a study is published and the extent to which the study results tell us the truth has not been investigated to date. One possible reason for this is the obvious difficulty of determining the truth in empirical science. This is all the more the case when it is not a question of truth as a categorical statement, but of the magnitude of a true value. In medical science, this epistemological challenge can be met in a practically suitable manner. The idea is that the pooled effect estimates from distinctly reliable meta-analyses represent true values sufficiently well to serve as a proxy measure of truth. Cochrane reviews provide pertinent meta-analyses. Compared with other systematic reviews, Cochrane reviews use more rigorous methods –14, laidFollowing this assessment, we postulated that effect estimates from Cochrane meta-analyses based on evidence of “high” or “moderate” quality are sufficient proxies of true values. To distinguish between the actual, unknown truth and a proxy estimate of the truth, we refer to the latter as “truth” (in quotation marks). We examined whether the results of intervention studies published in higher-impact journals lie closer to the “truth” compared with those published in lower-impact journals. To better understand the hypothesised relationship, we fitted non-linear regression models, adjusted for confounders, and conducted sensitivity analyses for various subgroups and for alternative metrics of impact.We prospectively screened all intervention reviews published in the Cochrane Database of Systematic Reviews (CDSR) from July 2018 to January 2019 for meta-analyses. We restricted the selection to meta-analyses generating evidence of “moderate” or “high” quality. In this respect, we adopted the review author’s assessment based on the GRADE framework. Therein, “moderate” or “high” quality of evidence represents the upper two of four levels (the others being “low” and “very low”). These judgments imply that the review authors consider the pooled effect estimates close to the true values with “moderate” or “high” certainty, respectively . We deciReview Managersoftware, version 5.3 [In case a review contained at least one meta-analysis of moderate or high-quality evidence, we obtained the accompanying data file from the Cochrane website and extracted the corresponding data via Cochrane’s sion 5.3 . We enteFor each study result included in eligible meta-analyses, we identified the source publication. When more than one publication was referenced, we screened the articles’ abstracts and full texts to identify the correct publication reliably, as not in every case the designated primary publication was the source of all data used in meta-analyses.For each publication included in eligible meta-analyses, we obtained information on the publishing journal’s JIF of the publication year from the Journal Citations Report . We preferred this indicator to others , Eigenfactor (EF), and H factor) for primary analysis, as it represents the de-facto standard of citation metrics in science and is most likely known outside the scientific community. Information on the JIF was available from 1997 to 2018. We excluded results reported in older publications from the analysis.As the average reference list in the medical sciences and other fields became lengthier over time, JIFs are subject to inflation , 19. We relative deviation. The point estimates were centred around their respective “true” value and then standardized with the average distance of the point estimates to the pooled effect estimate of studies in the meta-analysis. Risk ratio (RR), odds ratio (OR), and hazard ratio (HR) were transformed from their multiplicative scale to an additive scale to enable a comparison of different effect measures . As it does not matter if a deviation from the truth is an over- or underestimation, negative signs were removed from the deviation measures.The purpose of most scientific medical studies is to estimate some unknown true parameter value based on data from a study sample. The study estimates can deviate from the true value because of systematic error (bias) and random error . Obviously, it was impossible for us to measure the true values, and a proxy variable for truth had to be employed. Meta-analyses combine the results of multiple studies to derive a pooled estimate that approximates the true value more accurately. In the absence of better alternatives and aware of the limitations of this choice, we defined the pooled effect estimate of the respective meta-analysis as “truth.” The outcome of our analysis, the closeness of a single study’s point estimate to the “truth,” was operationalised as the In summary, a relative deviation of 1 means that a point estimate’s deviation from its pooled effect estimate corresponds to the average of all deviations of the point estimates of a given meta-analysis. When a meta-analysis reports a treatment vs. control pooled effect estimate of RR = 0.70 while one of the meta-analysis’ studies reports an RR of 0.80, that study has a distance to its pooled effect of |log(0.70)–log(0.80)|= 0.13. When the studies in the meta-analysis scatter around the pooled effect estimate with an average distance of 0.10, our example displays a relative deviation of 0.13/10 = 1.30; a value of > 1 indicating an above-average deviation.Our approach becomes questionable when a single study dominates a meta-analysis. The pooled effect estimate is calculated as a weighted average of the intervention effects estimated in the individual studies . The morTo control for confounders, we considered variables that we expected to be  predecessors of both the JIF and the relative deviation.First, we suspected that larger studies with a correspondingly smaller variance of the effect measure are more likely to be published in higher impact journals than smaller studies with larger variance. As smaller variance translates to bigger weight in meta-analyses, the suspected association would result in the observation that studies from higher impact journals show systematically smaller relative deviations. As the risk of confounding has been mitigated but not eliminated by the exclusion of studies with a weight of 50% or more, we considered the study weight as a confounder.Second, we assumed that a higher methodological quality of studies  reduces the relative deviation and that studies with a lower risk of bias tend to be published in journals with a higher JIF than studies with a higher risk of bias. On the other hand, the range in risk of bias of studies that a journal usually accepts for publication can be seen as a characteristic of a journal. Then, the effect of the JIF on the relative deviation is partly due to the risk of bias acceptable to that journal. We adopted the latter view for analysis and quantified the proportion of explanatory power that is due to the risk of bias.For every study included in Cochrane reviews, the results of a detailed and mostly standardized risk of bias assessment conducted by the review authors are reported . Usuallyt test.The mean of the relative deviation, i.e., the weighted deviation of a study estimate published in that journal to the “truth” as defined above, was compared between study results from journals with an associated JIF and those without a JIF by using a Welsh Focusing on publications with a JIF, we assessed the effect of the JIF on the relative deviation by fitting a linear regression. The regression was adjusted for the assumed confounder, “study weight,” and weighted with the JIF to account for heteroscedasticity of the model residuals. Then, visual model diagnostics  indicated at most small deviations from the modelling assumptions. The assumption of normality of residuals was not met, but the central limit theorem came into force due to the large sample size. From a theoretical point of view, the observations were clustered within their respective meta-analyses and were consequently not independent. However, a mixed effects model with random intercept and slope for each meta-analysis found a variance of 0 for both random intercept and slope, indicating that a fixed effects regression is sufficient. Three extreme values  were excluded for more robust results and a better graphical representation.As the linearity of the relationship between JIF and relative deviation is a quite strong assumption, we additionally allowed for a relationship without a pre-specified functional form by fitting a local polynomial regression (R function stats::loess). Again, the model was adjusted for study weight in the meta-analysis and observations were weighted by their JIF.The regression curves of both models were plotted together with their 95% confidence bands while fixing the value for the adjustment variable “study weight” at its median value of 7.41%.Subgroup analyses were conducted for (1) “high” vs. “moderate” quality of evidence, (2) open access vs. traditional publishing, (3) large (ten or more included studies) vs. small meta-analyses, (4) meta-analyses using random effects vs. fixed effects for the included studies, (5) low vs. moderate study weight (median split at 7.41%), (6) low vs. high risk of bias (median split at 0.21), and (7) study results using RR vs. OR vs. mean differences as effect measures. In all cases, the linear regression and the local polynomial regression are shown, both adjusted and weighted as in the main analysis.In addition to the JIF, the scientific journal ranking , eigenfactor , and the H-factor were analyzed. Fourteen observations (EF ranging between 43 and 69) were excluded because they exhibited a strong leverage effect. Again, linear as well as local polynomial regressions were fitted for each outcome. The regressions were weighted with the respective measure of impact to control heteroscedasticity of residuals.R2 when adding JIF as predictor to the basic adjusted and weighted model, that could be attributed to the risk of bias predictor: ((R2JIF model—R2basic model) + (R2ROB model—R2basic model)—(R2JIF and ROB model—R2basic model))/(R2JIF model—R2basic model).The proportion of explanatory power of the JIF due to the risk of bias was calculated as the proportion of the increase in multiple We further assessed if study results from small studies published in high-impact journals are especially far from “truth” by adding an interaction term between JIF and study weight to the main linear regression.R software in version 4.1.3 [All statistical analysis was performed with the on 4.1.3 .We obtained 4226 study results from 619 eligible Cochrane meta-analyses and presented in 148 reviews .Of the included study results, 368 13%) were published in journals without an assigned JIF in the respective publication year. Study results from journals with a JIF had more often a high quality of evidence, were less published in open access, and had on average a higher study weight in the meta-analyses and a slightly lower average risk of bias , but is a statistically significant predictor (F test: p < 0.001). Up to 4.8% of the explanatory power of the JIF could be attributable to the risk of bias.The JIF explains only a small proportion of the variability in relative deviations  study results, the EF for 1722 (60.9%) study results and the H-factor for 2743 (97.0%) study results. Also the SJR, the H-factor and the EF showed a decreasing relative deviation with increasing values of the impact measure  interaction was found.When an interaction term for study weight and JIF was added to the main linear model, a very small (0.0005) but significant . We used adjusted impact factors to account for inflation. In our sample of medical journals, the inflation-adjusted JIF of 1 roughly translates to an actual JIF of 2.32 in the year 2000 or 4.37 in the year 2018. An increase of the inflation-adjusted JIF by 1 yields an average decrease of the relative deviation by about 0.03. The JIF was found to be a significant predictor, but its predictive power for the accuracy of a study result is very low. It is, therefore, problematic and a practice that should be strongly discouraged to trust a particular study result on the basis of the journal impact; just as it is problematic to infer the impact of an individual study from the journal impact .Is the hypothesized and observed association between the journals’ impact and study findings’ closeness to “truth” a mere reflection of the strengths of well-designed studies? The results from regression analysis suggest that this is not the case. The predictive power of the JIF could be explained by methodological quality only to a small amount (less than 5%). This finding was somewhat surprising initially, as we deemed high methodological standards to be a major journal characteristic to convey the hypothesised advantage of high-rank journals. The low impact we found may, in part, be due to the pragmatic manner by which we assessed the methodological quality. A more elaborate assessment might result in a stronger influence of methodological strength. However, this consequence would affect to which extent the explanatory power of the JIF can be attributed to methodological quality, but it would not affect the predictive power of the JIF as such.By putting study results in relation to effect estimates from good-quality Cochrane meta-analyses, we applied a direct and feasible approach to measure the deviation of study results from “truth.” Within this concept, study results with large effect estimates are not suspicious per se, even if the results have not been replicated by more extensive studies. Large effects may occur not only by chance or because of bias due to methodological limitations but also because of (highly) selected study populations or optimized study conditions. These reasons would rather jeopardize the generalizability of the study results than their closeness to “truth.”The downside of our approach is that the chosen proxy measure for truth is only available for a small proportion of all studies. Studies not meeting the inclusion criteria of Cochrane reviews  and large RCTs, which are excluded due to their inherent correlation with the pooled effect estimate, were not considered. Observational studies, the results of which are less likely to be accurate, might be published rather in lower-impact journals, while large RCTs are published in higher-impact journals. We, therefore, assume that if our study is not representative of the medical literature as a whole, our approach underestimates the association between the JIF and the closeness of study results to the unknown truth.Our study relies largely on the judgments of review authors regarding the inclusion of studies into the review, the risk of bias assessment and the subsequent valuation of the quality of evidence. These judgments are ultimately at the review authors’ discretion but are not arbitrary, as they are guided by transparent rules. Furthermore, flawed assessments, which are likely to put the validity of specific review findings into question, are less likely to bias our results based on several thousand observations.As the hypothesized association was confirmed, we state that higher-rank journals’ manuscript selection process is effective in identifying research results that are, on average, closer to “truth.” This is the more meritorious as publishing true results may not always be the most promising way to achieve a maximum of citations, a goal appealing to both authors and journal editors. An alternative, potentially incompatible strategy might be to focus on studies in “hot” scientific fields, but the findings of which are supposed to be less likely true . Within In all this, we should keep in mind that giving true answers to specific study questions is an essential but not the only function of useful research. False findings caused by intelligent mistakes, unorthodox and immature methods, and diverse samples may broaden the spectrum of scientific thinking more effectively than adding certainty to what is already known to be true.


VEGFA) rs3025039 polymorphism and biliary atresia (BA). However, this conclusion is controversial and there is no published pooled evidence of this association.Previous studies have suggested an association between vascular endothelial growth factor A (VEGFA rs3025039 polymorphism. We selected relevant studies based on the following inclusion criteria: (1) the study design was case–control and cohort and (2) the patients carried standard clinical diagnoses of BA, etc. The exclusion criteria were as follows: (1) patients with other related diseases, (2) lack of requisite information and (3) duplicate data. The OR (odd ratio) and the corresponding 95% CI  were calculated to estimate the association.This study was conducted and reported according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses. The protocol was registered with PROSPERO . A thorough search was performed on databases including PubMed, Embase, and Chinese Biomedical Database up to August 2020. This study included 846 cases of BA and 2821 controls concerning VEGFA rs3025039 polymorphism in the Chinese population included 846 cases and 2821 controls. The results showed that there was no significant association between rs3025039 and susceptibility to BA under four genetic models. The results of the subgroup analysis were similar to the overall results.This study on This meta-analysis shows that rs3025039 was not associated with susceptibility to BA in the Chinese population. Further validation may entail additional research.CRD42020203812. The etiology of biliary atresia (BA) is still not well understood.VEGFA) plays an important role in the pathogenesis of BA.Previous studies supported that vascular endothelial growth factor A  (rs3025039) in the VEGFA rs3025039 polymorphism may not be associated with an elevated risk of BA in Chinese.The association between rs3025039 and risk of BA was not significant in southern Chinese.This meta-analysis did not demonstrate a definite association between rs3025039 and BA in northwestern Chinese because only one study was included in this subgroup.Additional research is needed to obtain a definite conclusion of this association.Future studies are recommended to identify other possible genetic markers.ADD3, XPNPEP1, VEGFA, and EFEMP1, are associated with risk of BA.Biliary atresia (BA) is a type of progressive obliterative disorder in neonates that interferes with the function and anatomy of the intrahepatic and extrahepatic bile ducts.VEGFA gene is one of the members of the VEGF  family located on chromosome 6p21.3. This gene comprises a type of heparin-binding protein that is in the form of a disulfide-linked homodimer. VEGFA participates in various developmental processes, including endothelial cell proliferation, cell migration, and apoptosis.VEGFA gene may provide markers for diagnosis and treatment to reverse progression of BA.The human VEGFA gene and susceptibility to BA; however, the results remain controversial due to the lack of consistency among the studies. In China, rs3025039 has been found to be associated with susceptibility to BA.Previous studies have explored the correlation between rs3025039 within the VEGFA rs3025039 polymorphism with susceptibility to BA.This discrepancy needs further verification by increasing the sample size. In the mean time, a meta-analysis is a suitable method to summarize previous genetic association studies (GAS) and to draw relatively reliable conclusions.This study protocol was registered with an international registration platform of systematic review, PROSPERO . We conducted and reported the study according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) statement10.1136/wjps-2021-000344.supp2Supplementary dataPatients and the public were not involved in this study.PubMed, Embase, and the Chinese Biomedical Database (CBD) were searched from inception until August 17, 2020 (see 10.1136/wjps-2021-000344.supp1Supplementary dataWe established the inclusion and exclusion criteria based on discussion studies. We selected relevant studies based on the following inclusion criteria: (1) the study design was case–control and cohort; (2) the patients carried a standard clinical diagnosis of BA; (3) the studies explored the target association; and (4) the authors presented enough data on genotype distribution. The criteria used to exclude studies were as follows: (1) patients with other related diseases; (2) lack of requisite information; and (3) duplicate data.At the first stage, duplicates from three electronic databases were screened and removed independently by three reviewers . At the second stage, the title and abstract of each of the remaining studies were reviewed independently by the same reviewers  to select eligible studies. At the final stage, the same reviewers  independently retrieved and assessed the potentially eligible full text of the remaining publications. Any discrepancy with regard to eligibility of articles was discussed among the three reviewers in consultation with a third reviewer (RD or SZ).All data were recorded independently by three reviewers  in accordance with a record form, with regard to (1) study characteristics ; and (2) genotype data  of the control group, and genotyping methods). All related data were found in the original studies so we did not need to contact the study authors to request for missing data.Three reviewers  independently appraised the quality of the GAS using a checklist revised from previous studies,2).2>50% or p value <0.1 indicated substantial heterogeneity. We conducted subgroup analyses according to the several study subject areas. We performed a sensitivity analysis by excluding every publication individually to evaluate the reliability and stability of the overall OR. We appraised publication bias using Egger’s test and Begg’s testIn a GAS, research candidates should be categorized into three groups  and usually B is used as the susceptibility allele.We conducted the search process and reported the findings according to the PRISMA statement2 range: 66.2%–87.9%, p=0.0003–0.0518; see There was a significant between-study heterogeneity in four genetic models of rs3025039 polymorphism . We performed a subgroup analysis by area. et al2 decreased from 87.9% to 66.9%)  .Both tests supported the absence of publication bias (p value for Begg’s test=0.602, p value for Egger’s test=0.422; VEGFA rs3025039 polymorphism and susceptibility to BA. Lee et alVEGFA as a susceptibility locus for BA in Chinese patients. This result has biological plausibility; VEGFA is a mediator of the pathogenesis of BA. As a proinflammatory cytokine, VEGFA participates in various processes including cell proliferation, cell migration, and apoptosis, all of which occur in cell-mediated immune-inflammatory diseases, such as BA.We conducted a systematic review of the association between We found a difference between cases and controls in comparisons of all genotypes of rs3025039, suggesting that rs3025039 polymorphism may not correlate with susceptibility to BA. A subgroup analysis by area further suggested no significant association between rs3025039 and susceptibility to BA. Given that only one study included the subgroup of northwestern Chinese, our study did not demonstrate a solid correlation between rs3025039 and BA in northwestern Chinese patients.The absence of significant findings may stem from the fact that we included only three original studies in this study. In addition, as the sample size increased, the association between rs3025039 and susceptibility to BA decreased in the three studies. We observed statistical significance in only two studies with small numbers of participants. Therefore, the statistical power was relatively low, suggesting that the association might be spurious. For the first reason, more original studies regarding the association between rs3025039 and susceptibility to BA are necessary to generate accurate results. However, for the second reason, the effect may be relatively small or not at all, and explicit exploration of this association may result in an unnecessary study. Therefore, it may be more cost-effective to devote more resources to explore other potential biomarkers.Several limitations need to be acknowledged when considering the results of our study. First, the quantity and sample size of the included studies were insufficient to obtain high power for making a confirmatory conclusion, even though we undertook a comprehensive literature search. Second, all study subjects came from China only; therefore, we could not avoid potential selection bias. Third, residual confounders were possible because BA is a multifactorial and complicated disease, involving gene–environment and gene–gene interactions. We could not detect these effects due to limited information. Fourth, there was a lack of sufficient data; therefore, we did not conduct a subgroup analysis of familial and other BA types. Finally, there was significant heterogeneity that might distort the results. Several aspects may cause heterogeneity, including differences in experimental methods across studies. Therefore, readers should interpret our results with caution.VEGFA rs3025039 polymorphism in BA. The sample size of each study included in this study was insufficient to make a definite conclusion regarding the association between rs3025039 polymorphism and susceptibility to BA; however, the pooled OR calculated from the three studies significantly increased the statistical power. It is worth noting that the sample size increased from 205 subjects in the first study to 3667 subjects in our study. Reaching a relative sufficient statistical power is crucial in GAS. Furthermore, no significant publication bias was found in this study and the result was relatively stable in the subgroup analysis.Despite the limitations, we believe that the present study provides useful evidence regarding the role of VEGFA rs3025039 polymorphism might not be associated with an elevated risk of BA in Chinese population. Obtaining a definitive conclusion regarding this association may entail additional research. Future studies are recommended to identify other possible genetic markers.In conclusion,


Aim: This study aims to investigate the association between the use of dipeptidyl peptidase-4 inhibitors (DPP-4i), glucagon-like peptide-1 receptor agonists (GLP-1 RAs), or sodium-glucose cotransporter-2 inhibitors (SGLT-2i) and the risk of fracture among patients with type 2 diabetes mellitus.Methods: Medline, Embase, Cochrane Library, and Clinical-Trials.gov databases were searched for randomized controlled trials (RCTs). Network meta-analysis was performed for total fracture and a series of secondary outcomes.Results: A total of 177 RCTs  involving the risk of fracture were identified (a median follow-up of 26 weeks). DPP-4i, GLP-1 RAs, and SGLT-2i did not increase total fracture risk compared with insulin , metformin , sulfonylureas , thiazolidinediones , α-glucosidase inhibitor , and placebo .Conclusions: The use of DPP-4i, GLP-1 RAs, or SGLT-2i is unlikely to increase the risk of fracture among type 2 diabetes mellitus patients. There is an increased risk of fracture observed in both female and male patients with type 2 diabetes mellitus (T2DM) compared with non-diabetic individuals . FracturA lot of research in this field has been carried out, and there are studies which reported the effects of different hypoglycemic drugs on fracture risk in T2DM . DipeptiThis network meta-analysis was performed to investigate the association between the use of DPP-4i, GLP-1 RAs, or SGLT-2i and the risk of fracture among patients with T2DM by synthesizing the data from all available randomized controlled trials (RCTs).This study was conducted according to the Preferred Reporting Items for Systematic Reviews and Network Meta-Analyses (PRISMA-NMA) checklist.ClinicalTrials.gov, and the Cochrane Library were searched from inception to 7 September 2019. We used “Glucagon-Like Peptide-1 Receptor,” “Dipeptidyl-Peptidase IV Inhibitors,” and “Sodium-Glucose Cotransporter 2 Inhibitors” as keywords or MeSH terms, accompanied with relevant free words, to search these above databases. Details of search strategies are provided in Medline, Embase, Only RCTs involving DPP-4i, GLP-1 RAs, or SGLT-2i compared with placebo or other antidiabetic agents  in patients with T2DM and reporting on any fracture as an outcome were included in this analysis . No otheData extraction was conducted by using the Aggregate Data Drug Information System (ADDIS version 1.16.5). Data extracted from eligible studies included trial information , baseline characteristics of patients , and results on bone fracture . Two investigators (FL and SC) extracted data independently, in duplicate.The quality of studies was assessed according to the extracted information by Cochrane Collaboration’s tool for assessing risk of bias  . The Gra2 was used to assess the heterogeneity of direct treatment.Traditional pairwise meta-analysis was performed by using the DerSimonian–Laird random-effects model . The oddThe primary outcome of this study is total fracture. In all studies included, some studies have reported the events of fractures in different body parts, for instance, spinal fracture, hip fracture, upper limb fracture, lower limb fracture, and other fractures. So network meta-analysis was also performed for a series of secondary outcomes of the specific fractures of our concern, including spinal fracture, hip fracture, upper limb fracture, lower limb fracture, and other fractures. We performed a frequentist random-effects network meta-analysis. OR with 95% CI was summarized and integrated into a network evidence body for each fracture outcome. We obtained the result of any pairwise comparison in the network evidence body through direct or indirect comparison. A node-splitting model  and a loThe difference between the observed effect size and the comparison-specific summary effect for each study was calculated. Publication bias was evaluated according to whether the funnel diagram was symmetrical.All analyses were conducted by using STATA Version.14.0  and R Version.4.1.1.n = 165,081 patients) met the eligibility criteria and were included in this network meta-analysis. Trial duration ranged from 12 to 384 weeks, with a median follow-up of 26 weeks (IQR: 24–54 weeks). The average age of the included patients was 57.68 years (SD: 5.08). The mean diabetes duration at baseline was 8.20 years (SD: 4.45) and the mean baseline HbA1c level was 8.14% (SD: 0.53%).The flow chart of the literature search is shown in Eight classes of treatments were analyzed, including DPP-4i , GLP-1 RAs , SGLT-2i , and five other classes of active antidiabetic agents  and placebo. 166 trials (93.8%) were two-arm studies, 10 trials were three-arm studies, and one trial was a four-arm study . The boxFor the total 177 studies included in this analysis, majority of the studies were determined to be at “low risk” of bias in random sequence generation , blinding of participants and personnel , and blinding of outcome assessment . Some studies were judged as “low risk” of bias in allocation concealment , complete outcome data , and selective reporting . Only a few studies were rated as “high risk” of bias in the above items. 46.3% of the studies were funded by enterprises. Overall, the risk of bias across the evidence network was relatively low .We conducted a series of traditional paired meta-analysis on all interventions that have a direct comparison between these two for total fracture. The comparison includes placebo vs. DPP-4i, DPP-4i vs. AGI, SGLT-2i vs. GLP-1 RAs, SGLT-2i vs. metformin, SGLT-2i vs. placebo, TZD vs. DPP-4i, GLP-1 RAs vs. metformin, GLP-1 RAs vs. placebo, TZD vs. placebo, GLP-1 RAs vs. DPP-4i, GLP-1 RAs vs. insulin, GLP-1 RAs vs. sulfonylurea, GLP-1 RAs vs. TZD, sulfonylurea vs. placebo, DPP-4i vs. metformin, DPP-4i vs. SGLT-2i, DPP-4i vs. sulfonylurea, metformin vs. placebo, and SGLT-2i vs. sulfonylurea. The results of all the above comparisons show that there is no significant statistical difference between the two compared; that is, there is no significant difference in the risk of fracture in the above comparisons .GLP-1 RAs did not increase fracture risk compared with insulin , Met , SU , TZD , AGI , and placebo , respectively.SGLT-2i did not increase fracture risk compared with insulin , Met , SU , TZD , AGI , and placebo , respectively.Secondary outcomes based on fracture of different parts indicated that DPP-4i, GLP-1 RAs, and SGLT-2i did not increase the risk of fracture, respectively .p = 0.97). The predictive interval plot showed that there was little heterogeneity in this study. Summary estimations of network meta-analysis were relatively robust . The quality of most studies was moderate. With concern of within-study bias and imprecision, the quality of evidence in the total fracture was rated as moderate  there was no evidence to indicate an increased risk of fracture associated with DPP-4i–, GLP-1 RA–, and SGLT-2i–based therapies in patients with T2DM compared with other antidiabetic agents or placebo; 2) secondary outcomes based on fracture of different parts indicated that DPP-4i, GLP-1 RAs, and SGLT-2i did not increase the risk of fracture, respectively.In agreement with our findings, a meta-analysis of observational studies did not support an association between the use of DPP-4i, GLP-1 RAs, or SGLT-2i and the risk of fracture . A meta-However, contrasting evidence emerged from a meta-analysis suggesting exenatide treatment was associated with an elevated risk of incident fractures  compared to placebo or other active drugs . The reaAs a robust predictor of fracture risk, bone mineral density (BMD) is a surrogate reflecting bone strength. While  reportedPrevious studies have reported fracture-prone sites in diabetic patients. A systematic review  indicateRecently, studies on GLP-1 RAs, DPP-4i, and SGLT-2i in fracture risk of type 2 diabetes patients showed that gender differences may not be enough to play a decisive role in the increase of fracture risk in T2DM . TherefoOur study has several strengths. First, the predictive interval plot showed that there was little heterogeneity in this study. Second, in addition to assessing the overall risk of fractures, our study also assessed the risk of fractures in different parts of the body.This study also has some limitations. First, none of the RCTs included were tested for BMD and bone metabolism indexes, which are closely related to the risk of fracture. Second, some CIs in results are particularly wide, for example, the result range of AGI compared with placebo. The reason is that both interventions are used as the control group in this study, and the comparison of results between AGI and placebo are based on indirect comparison. Third, this study regards all the same types of hypoglycemic drug (regardless of the dose or specific drug) as the same intervention and has not considered the effects of specific drugs and their doses on the outcomes. Fourth, most of the included RCTs were conducted in Western countries, but whether they are suitable for other regions, such as Asian populations, remains to be discussed. Fifth, the literature retrieval time of this study is up to 2019, so the results of the latest research could not be included in time.In conclusion, our meta-analysis suggests that the use of DPP-4i, GLP-1 RAs, and SGLT-2i is not associated with an increased risk of fracture in patients with T2DM. In addition, more RCTs are required to investigate the number of fractures with the use of DPP-4i, GLP-1 RAs, and SGLT-2i, as a primary endpoint, rather than an adverse event.Dipeptidyl peptidase-4 inhibitors (DPP-4i) and the glucagon-like peptide-1 receptor agonists (GLP-1 RAs) have become widely used in T2DM patients as a novel class of blood glucose–lowering drugs with improved weight loss, low risk for hypoglycemia, and reduction in glycated hemoglobin. Sodium-glucose cotransporter-2 inhibitors (SGLT-2i) are another type of novel glucose-lowering agents and have also gained increasing use in recent years, which reduce plasma glucose concentrations by inhibiting proximal tubular reabsorption of glucose in the kidney.There are two new findings in the study. First, the use of DPP-4i, GLP-1 RAs, or SGLT-2i is unlikely to increase the overall risk of fracture among type 2 diabetes mellitus patients. Second, secondary outcomes based on fracture of different parts indicated that DPP-4i, GLP-1 RAs, and SGLT-2i did not increase the risk of fracture, respectively.Our results offer the best available evidence of the impact of DPP-4i, GLP-1 RAs, or SGLT-2i on fracture risk based on randomized controlled trials. Therefore, more evidence is provided for the rational use of these drugs in clinical trials.


Abnormal expression of lncRNAs is relevant to the occurrence and development of gastric cancer (GC), but the significance remains inconclusive. We performed a diagnostic meta-bioinformatics analysis to elucidate the association between lncRNA expression and GC risk.  Published datasets were selected from PubMed, Embase, CNKI, and Web of Science, up to 1st December 2021. The pooled sensitivity (SEN), specificity (SPE), positive likelihood ratio (PLR), negative likelihood ratio (NLR), diagnostic odds ratio (DOR), and area under the curve (AUC) were calculated to evaluate the diagnostic value. RNA sequencing data were downloaded for validation.  54 studies with 4671 patients and 4652 matched controls were included in the meta-analysis. The pooled SEN, SPE, PLR, NLR, DOR, and AUC were 0.71, 0.76, 2.9, 0.39, 8, and 0.79, respectively. Subgroup analyses showed that the DOR and AUC of intergenic lncRNAs, circulating lncRNAs, larger sample size (>200), and high-quality (NOS score ≥ 7) groups were superior to antisense lncRNAs, tissue lncRNAs, smaller sample size (≤200), and low-quality (NOS score < 7) groups, respectively. However, only circulating lncRNAs had significantly higher diagnostic utility than that tissue lncRNAs. Nine differentially expressed lncRNAs in the meta-analysis were verified in TCGA-STAD. PVT1 was the most effective single lncRNA, with AUC of 0.949, SEN of 0.808, and SPE of 0.969, while PVT1 and C5orf66-AS1 were the most effective combination, with AUC of 0.972, SEN of 0.941, and SPE of 0.937.  Abnormally expressed lncRNAs, especially circulating lncRNAs, might be potential diagnostic biomarkers for GC risk. A novel combined model of lncRNAs might achieve better GC diagnosis performance. Long noncoding RNAs (lncRNAs) regulate cell proliferation, apoptosis, differentiation, and metastasis, which all are associated with multiple diseases, including tumorigenesis . In tumoGastric cancer (GC) is the top 5 cancer type and is the fourth leading cause of cancer-related death worldwide according to Global Cancer Statistics 2020 . ApproxiTherefore, we conducted a diagnostic meta-analysis exploring the association between lncRNA expression and GC risk, taking genome location and sample source into account. Additionally, using TCGA database, we constructed individual and combined lncRNA models of GC risk assessment for bioinformatics analysis and validation.This systematic review meta-analysis was conducted in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines , 19.We systematically searched PubMed, Embase, CNKI, and Web of Science for studies focusing on the relationship between lncRNA expression and GC. Our medical subject heading terms (for PubMed), EMTREE terms (for Embase), and text (for others) were “ AND (Stomach Neoplasms OR stomach cancer OR gastric cancer).” We searched the databases of each primary study up to December 1, 2021.β-actin were used as qRT-PCR reference genes; and (5) the studies provided sufficient information to construct a 2 × 2 contingency table, with false/true positives/negatives provided. Studies were excluded if (1) the studies or data were duplicated, letters to the editor, commentaries, and review papers; (2) they were not relevant to GC diagnosis/risk or lncRNA expression; and (3) contained a lack of variable data or tables [Eligible studies met the following criteria: (1) studies that reported lncRNA expression data from patients were identified as GC by postoperative pathologic check according to the guideline of the National Comprehensive Cancer Network (NCCN), the European Society for Medical Oncology (ESMO), and the Chinese Society of Clinical Oncology (CSCO); (2) studies provided sufficient data to evaluate the relationship between lncRNA expression and the diagnosis of GC versus different control types ; (3) datasets generated using qRT-PCR; (4) GAPDH and r tables .Two investigators (Yingying Dong and Quan Zhou) extracted all the data independently and reached a consensus regarding all items. Controversial sections were verified and resolved by Dr. Liping Sun. The following is the information extracted from the included literature: the author's name, year of publication, country of origin, number and source of cases and controls, differential lncRNA expression, area under the curve (AUC) of the summary receiver-operating characteristic (SROC) curve, cut-off, sensitivity (SEN) and specificity (SPE), positive likelihood ratio, and negative likelihood ratio.Yingying Dong and Quan Zhou assessed the data quality using the Newcastle–Ottawa quality scale (NOS). A form which comprised three parts was used to assess the quality of nonrandomized studies in meta-analyses: (1) the selection of study groups, (2) the comparability of study groups, and (3) the assessment of exposure or outcomes. Each study was assigned a score of 0–9, with a score of at least 7 (of 9) indicating high quality.http://www.genenames.org/). Genomic positions of lncRNA in relation to protein-encoding genes were identified using the UCSC database (http://genome.ucsc.edu/) and LNCipedia version 5.2 (https://lncipedia.org/). A total of 407 GC samples from TCGA project (https://cancergenome.nih.gov/), including 375 cancer cases and 32 cancer cases with adjacent nontumor tissue (ANT), were downloaded. Morpheus database (https://software.broadinstitute.org/morpheus/) was used to identify genes that were differentially expressed in GC. RNA-seq raw read counts were converted to transcripts per million (TPM) values to normalize all samples.lncRNA classification was based on the latest nomenclature outlined on the HGNC website , Meta-Disc 1.4 , RevMan 5.3 , SPSS 21.0 , and GraphPad Prism 7.0 software  were used for statistical analysis.Q test, and inconsistency index (I2) test were used to confirm the heterogeneity of threshold or nonthreshold effects. If there was heterogeneity (I2 ≥ 50% or P ≤ 0.05), the random effects model was adopted. If there was no heterogeneity, the fixed effects model was used. The subgroup differences of AUC were conducted by using a two-sided Z-test at a significance level of 0.05. Sensitivity analysis was performed by removing each study from the analysis to determine its impact on the overall effect. Metaregression was performed to find the origin of heterogeneity. Pooled SEN, SPE, diagnostic odds ratio (DOR), positive likelihood ratio (PLR), and negative likelihood ratio (NLR) values were generated using bivariate analysis. Deeks' funnel plots and symmetry tests were used to investigate publication bias, with the significance threshold set at P < 0.01. lncRNA expression differences were analyzed by the Mann-Whitney U test. Binary logistic regression analysis (enter method) was used to construct the combined diagnostic model. AUC was used to evaluate diagnostic efficacy. Z-test was conducted to determine the difference of AUC between different GC stages. P < 0.05 was considered statistically significant. The Spearman correlation coefficient, Cochran's The study selection process is shown in We assessed the quality of included studies using NOS and found that the quality of the enrolled studies was acceptable. Thirty-eight studies , 65, 66 I2 values in SEN , SPE , PLR , NLR , and DOR  (Because significant heterogeneity was observed between studies for the high < 0.001) , we choo< 0.001) . The AUC< 0.001) , indicat< 0.001) .P = 0.069). There was no heterogeneity from the threshold effect. In addition, the Cochran Q of DOR is commonly used to detect nonthreshold effect heterogeneity; we analyzed heterogeneity with Cochran's Q test and I2 test and found that their DOR values were 251.75 (P < 0.001) and 78.9% , sample type (circulating vs. tissue), sample size (≤200 vs. > 200), and quality (NOS score < 7 vs. ≥7). The details are shown in I2 of variates such as lncRNAs extracted from tissue and sample size ≤ 200 decreased obviously from the different groups, which suggested that these variables may be the sources of potential heterogeneity  vs. 0.68 (95% CI: 0.63–0.72), SPE 0.77 (95% CI: 0.71–0.82) vs. 0.75 (95% CI: 0.69–0.79), PLR 3.2 (95% CI: 2.5–4.2) vs. 2.7 (95% CI: 2.2–3.2), NLR 0.33 (95% CI: 0.26–0.41) vs. 0.43 (95% CI: 0.39–0.48), DOR 10 (95% CI: 6–15) vs. 6 (95% CI: 5–8), and AUC 0.82 (95% CI: 0.79–0.86) vs. 0.76 (95% CI: 0.72–0.80), respectively. In terms of the study quality, the studies of NOS score ≥ 7 had a little higher diagnostic value than the studies of NOS score < 7, SEN 0.71 (95% CI: 0.66–0.75) vs. 0.71 (95% CI: 0.65–0.77), SPE 0.76 (95% CI: 0.72–0.80) vs. 0.75 (95% CI: 0.64–0.83), PLR 3.0 (95% CI: 2.5–3.5) vs. 2.8 (95% CI: 2.0–4.0), NLR 0.39 (95% CI: 0.34–0.44) vs. 0.38 (95% CI: 0.31–0.47), DOR 8 (95% CI: 6–10) vs. 7 (95% CI: 5–12), and AUC 0.80 (95% CI: 0.76–0.83) vs. 0.78 (95% CI: 0.74–0.82), respectively. Additionally, we also detected the heterogeneity from subgroups; the ogeneity .P value from large to small, “location,” “quality,” and “sample size” were eliminated one by one. The results showed that the significant heterogeneity of sample size groups and sample type groups was not affected by other covariables; this suggested that the sample type  and sample size  could be considered as the source of heterogeneity in the detection of gastric cancer.Then, we constructed a metaregression in terms of the specified covariates including location, sample type, sample size, and quality , A–E. DuBy excluding individual studies, sensitivity analysis was used to test the impact on overall effects and changes in heterogeneity. As displayed in supplement Figure P value was 0.74 , PVT1, urothelial cancer-associated 1 (UCA1), C5orf66-AS1, and LINC00086. However, the opposite was found in six lncRNAs, abhydrolase domain containing 11-antisense RNA1 (ABHD11-AS1), gastric cancer-associated transcript 2 (GACAT2), LINC00982, RP11-731F5.2, TINCR, and long intergenic nonprotein coding RNA, regulator of reprogramming (linc-ROR). In addition, for 8 upregulated lncRNAs in the published data, no significant difference was detected. We could not find any data for the remaining 14 lncRNAs in TCGA database .P) = −1.307 + 7.129 × PVT1 − 1.204 × C5orf66 − AS1 , SEN (0.76), PLR (2.9), NLR (0.39), and AUC (0.79) for the diagnosis of GC than did certain protein markers. Many proteins, such as CEA and CA19-9, are used as biomarkers for GC diagnosis and have been used clinically . However, SEN , and high quality (NOS score ≥ 7) groups were superior to antisense lncRNAs, tissue-based lncRNAs, smaller sample size (≤200), and low quality (NOS score < 7) groups, respectively; however, only circulating lncRNAs had significantly higher AUC than that of tissue lncRNAs.When interpreting meta-analysis results, heterogeneity should be considered. The result of Spearman correlation analysis suggested there was no threshold effect. In addition, the The genomic location of the lncRNA directly affects lncRNA function. However, in this study, no significant difference was found between the intergenic and antisense groups. The results of the subgroup analysis indicated that circulating group shares better performance than the tissue group; the SEN, SPE, PLR, NLR, and DOR in the blood samples were 0.76, 0.79, 3.7, 0.3, and 12, respectively. In the articles analyzed, the AUC of serum in the GC diagnosis using H19, HULC, and LINC01061 reached 0.943, 0.888, and 0.93, respectively. Future studies of lncRNA expression in the circulation of patients with early stage are necessary to identify a better diagnostic biomarker. The choice of the control group may explain some of the differences between the tissue and circulation groups. NAT was selected as a control for the tissue group, and healthy human serum was selected as a control for the serum group. However, NAT may be affected by the tumor microenvironment which may be why our results show that lncRNAs are not suitable for GC diagnosis from tissue samples. Besides, lncRNA encapsulated by exosomes is more stable in the serum and is not easily degraded by RNase. This suggests that serum-based detection of lncRNA expression is the preferred approach for future studies . Of noteThe reported lncRNA expression in GC most was corroborated by the results of TCGA database. We selected 9 lncRNAs with the same trend in meta- and TCGA analysis for subsequent diagnostic efficacy evaluation. Most previous studies have focused on single lncRNAs as potential biomarkers. However, several lncRNA combinations may have a better diagnostic performance . Here, wThere are several limitations to be noted in the current meta-analysis: firstly, remarkable heterogeneity was observed in this study, although the results of subgroup analysis could explain some sources of heterogeneity. Secondly, due to a lack of sufficient sample size, we only found that circulating lncRNA was of higher diagnostic efficacy than tissue-based lncRNA, so the predictive ability between serum-based lncRNA and plasma-based lncRNA needs to be further studied. Finally, although we validated most of the results using bioinformatics analysis, the majority of patients included in our study were Chinese except for six studies; thus, the applicability to other races might be limited.Together, these results provided evidence that abnormally expressed lncRNAs might be potential diagnostic biomarkers for GC diagnosis, especially circulating lncRNAs showed superior predictive ability, convenience, and feasibility. Furthermore, the novel combination model of PVT1 and C5orf66-AS1 might achieve better diagnostic efficacy and clinical potential in the prediction of GC. Due to the potential limitations, this study's clinical application warrants further investigation.


Recently, serum exosomal circular RNAs (circRNAs) were applied to discriminate cancer patients from healthy individuals, indicating that exosomal circRNAs have the potential to be novel biomarkers for cancer diagnosis. This study aims to summarize the role of exosomal circRNAs in cancer diagnosis by a meta‐analysis.A comprehensive literature search was conducted up to July 2021 in PubMed, Web of Science, EMBASE, and Cochrane Database. To evaluate the diagnostic value, the sensitivity, specificity, positive likelihood ratio (PLR), negative likelihood ratio (NLR), diagnostic odds ratio (DOR), and area under the curve (AUC) were pooled. Threshold effect followed by subgroup analysis and meta‐regression were performed to explore the source of heterogeneity. Sensitivity analysis was performed to assess the stability of this meta‐analysis model. Fagan plots and likelihood ratio scattergrams were used to explore the potential clinical significance.Ten eligible studies with 514 controls and 557 patients were included in this diagnostic meta‐analysis. The pooled sensitivity, specificity, PLR, NLR, and DOR were 0.75 (95% CI: 0.65–0.83), 0.84 , 5.87 , 0.28 , and 21.15 , respectively. The AUC was 0.89 . Sensitivity analysis showed that four studies had an impact on the pooled results and mainly contributed to the heterogeneity. Fagan's nomogram revealed that the prior probability of 20%, the post probability positive, the post probability negative were 59% and 6%, respectively.Our results suggested that exosomal circRNAs might serve as powerful biomarkers in detecting cancers with high sensitivity and specificity. However, more well‐designed and multicenter diagnostic tests are needed to validate our results. Ten eligible studies with 514 controls and 557 patients were included. The pooled sensitivity, specificity, PLR, NLR, and DOR were 0.75 (95% CI: 0.65–0.83), 0.84 , 5.87 , 0.28 , and 21.15 , respectively. The AUC was 0.89 . If detected in the early stage, the prognosis of cancer patients will be greatly improved.Exosomes, 40–100 nm in diameter, are nanoscale extracellular vesicles, which can be secreted into the extracellular environment by most cells.To our knowledge, although some systematic reviews have reported the diagnostic value of cancer‐derived exosomal miRNAs and LncRNAs, there is no meta‐analysis about exosomal circRNAs in cancer diagnosis. Actually, the diagnostic value of exosomal circRNAs is still controversial. Therefore, this study aims to conduct the first meta‐analysis to summarize the role of exosomal circRNAs in cancer diagnosis.22.1A systematic search was conducted from the earliest up to July 2021 in EMBASE, the Cochrane Database, PubMed, and Web of Science to identify all potential literature. In addition, references were also retrieved and manually researched to find potential studies. The search strategy was performed by two investigators in our team and the relevant literature was independently screened by two investigators within 2 weeks. The detailed searching strategy was shown in Table 2.2The inclusion criteria were: (1) studies were based on diagnostic test accuracy; (2) it was not clear whether the participants had cancer before diagnosis; (3) The diagnosis of cancer was confirmed by pathology; (4) original studies could supply sufficient information; (5) circRNAs for diagnosis was encapsulated in exosomes.The exclusion criteria were: (1) repetitive literature; (2) studies not related to the research topic; (3) meeting abstracts, letters, case reports, reviews, and editorials; (4) animal experiments; (5) studies without complete data or cannot get the full article. Following a literature search, the title and abstract were independently checked by two of our researchers. Full articles were downloaded and reviewed if abstracts met the inclusion criteria. Discordant opinions were resolved through the consultation of a third investigator or group discussion.2.3Two researchers extracted the information and assessed the quality of included studies. Different opinions will be settled through group consultation. The following data were collected: author, year of publication, region, circRNAs profile, expression level, cancer type, exosomes source, sample size (case/control), control, true‐positive (TP), false‐positive (FP), false‐negative (FN), true‐negative (TN), sensitivity, specificity, and AUC. The quality of the diagnostic studies was assessed using the Quality Assessment of Diagnostic Accuracy Studies 2 (QUADAS‐2).2.4I2 and Cochran's Q tests. I2 > 50% were considered a significant heterogeneity.All analyses were performed with STATA 16.0, RevMan 5.2, and Meta‐Disc 1.4 software.33.1n = 29), circRNAs not in exosomes (n = 5), insufficient data (n = 5), patent paper (n = 3), prognosis study (n = 1), and full text unavailable (n = 1). Ultimately, 10 eligible studies were enrolled in this diagnostic meta‐analysis.An initial literature search yielded 336 potential articles from 4 databases. After excluding duplicate publications, 173 studies remained. After browsing the titles and abstracts, 79 articles were excluded for reviews and animal experiments. 40 articles were excluded for unrelated topics. Then, the 54 remaining studies were further evaluated through full‐text reading. Forty‐four papers were excluded for lacking diagnostic data  and specificity . Thus, we chose a random effects model to measure the accuracy of the combined diagnosis. The pooled sensitivity, specificity, PLR, NLR, and DOR were 0.75 (95% CI: 0.65–0.83), 0.84 (95% CI: 0.78–0.89), 5.87 (95% CI: 3.67–9.38), 0.28 (95% CI: 0.19–0.40), and 21.15 (95% CI: 10.25–43.68), respectively. The AUC was 0.89 (95% CI: 0.86–0.91), which demonstrated that exosomal circRNAs had diagnostic value. The above results were shown in Figure Cochran 3.4p = 0.567), which indicated that heterogeneity was not caused by the threshold effect. Therefore, further subgroup analyses and meta‐regression were conducted to explore potential causes of heterogeneity. As depicted in Table The Spearman correlation coefficient was −0.118  and specificity . The pooled sensitivity, specificity, PLR, NLR, DOR, and AUC were 0.83 (95% CI: 0.78–0.87), 0.85 (95% CI: 0.80–0.89), 5.44 (95% CI: 4.03–7.34), 0.20 (95% CI: 0.15–0.26), 27.32 (95% CI: 17.01–43.88), and 0.88 (95% CI: 0.84–0.90), respectively.A sensitivity analysis showed that two included studies had a great impact on the pooled results Figure . As show3.6p = 0.75).As shown in Figure 3.7Fagan's nomogram showed a prior probability of 20%. The post probability positive and post probability negative were 59% and 6%, respectively. Furthermore, the likelihood ratio scattergram showed the different clinical significances of exosomal circRNAs in tumors. On the left upper quadrant (LUQ), PLR was >10 and NLR was <0.1, which indicated these markers could be used to make an exclusion or confirmation diagnosis. On the right upper quadrant (RUQ), PLR was >10 and NLR was >0.1, which indicated these markers could be used to make a confirmation diagnosis only. On the right lower quadrant (RLQ), PLR was <10, and NLR was >0.1, which indicated these markers were not able to be used to make an exclusion or confirmation diagnosis. Fagan's nomogram together with the likelihood ratio scattergram was shown in Figure 4CircRNAs, initially found as viroids in 1976, were thought to be generated by splicing errors.A total of 10 studies with 557 cases and 514 controls were included in this study. The pooled sensitivity and specificity were 0.75 and 0.84, respectively. Moreover, the AUC was 0.89, which indicated a relatively preferable diagnostic value. Compared to traditional serum tumor biomarkers, exosomal circRNAs seem to have a better performance. A previous meta‐analysis reported that the pooled sensitivity and specificity of CEA in detecting pancreatic cancer were only 0.395 and 0.813, respectively.p = 0.567), which indicated that heterogeneity was not caused by the threshold effect. Subgroup analysis and meta‐regression were further performed. However, heterogeneity was not caused by circRNAs number, tumor species, exosomes source, and sample size. No significant difference between subgroups might be generated by too few studies included in this study. One meta‐analysis that evaluated the overall diagnostic value of circulating exosomes in cancers demonstrated that tumor types and exosomes sources could lead to heterogeneity.It is worth noting that significant heterogeneity existed in this study. The Spearman correlation coefficient was −0.118  studies were performed in China, which may restrict the generalization of these findings. Further studies in other countries are needed to prove whether these findings could be also in accordance with that of other populations. Fourth, different isolation methods of exosomes and the instability of instruments and experimental operation could also induce the deviation of results. Fifth, heterogeneity among included studies is still an essential issue of this meta‐analysis. Thus, the diagnostic performance of exosomal circRNAs and their clinical values should be interpreted carefully. More well‐designed and multicenter diagnostic tests are needed to validate our results.5In summary, this meta‐analysis suggested that exosomal circRNAs might serve as novel powerful biomarkers in detecting cancers with high sensitivity and specificity. However, more well‐designed and multicenter diagnostic tests are needed to validate our results.Yang Zhang and Xuyang Yang contributed the most to this article. Yang Zhang and Xuyang Yang designed the project. Yang Zhang wrote the manuscript. Xuyang Yang reviewed the manuscript. Zixuan Zhuang and Mingtian Wei checked the search, performed literature screening, conducted the quality assessment of the included studies and data extraction. Wenjian Meng guided data analysis. Yang Zhang and Xuyang Yang carried the data analysis. Ziqiang Wang and Xiangbing Deng reviewed the manuscript and approved for it to be published.This study was supported by the Department of Science and Technology of Sichuan Province (award number 2019YFS0375), Post‐Doctor Research Project, Sichuan University (20826041E4084), Post‐Doctor Research Project, West China Hospital, Sichuan University (2021HXBH033), the Ethicon Excellent in Surgery grant (EESG) (no. HZB‐20190528‐4).The authors declare no conflicts of interest.In this meta‐analysis, we analyzed the existing literature to assess the diagnostic value of exosomal circular RNAs in cancer patients. Ten eligible studies with 557 patients and 514 controls were included in this diagnostic meta‐analysis. The pooled sensitivity, specificity were 0.75 (95% CI: 0.65–0.83), 0.84 (95% CI: 0.78–0.89), The area under the curve (AUC) was 0.89 (95% CI: 0.86–0.91). These results suggested that exosomal circRNAs might serve as powerful biomarkers in detecting cancers with high sensitivity and specificity. One of the reasons for the high mortality of cancer is that many cancer patients are detected in the advanced stage, having lost the chance for radical surgery. Due to the invasiveness of biopsy, low sensitivity, and specificity of some serum tumor markers, such as CEA, it is necessary to explore new markers with high sensitivity and specificity for the early diagnosis of cancer. To our knowledge, this study is the first meta‐analysis to summarize the role of exosomal circRNAs in cancer diagnosis.Appendix S1 Supporting informationClick here for additional data file.


To conduct a comprehensive systematic review and meta-analysis to estimate the relationship between endocrine-disrupting chemicals (EDCs), including polychlorinated biphenyls (PCBs), poly-brominated diphenyl ethers (PBDEs), phthalates (PAEs), and per- and polyfluoroalkyl substances (PFAS) exposure and risk of gestational diabetes mellitus (GDM).I2 statistic. Publication bias was evaluated through the Begg and Egger tests.Relevant studies from their inception to November 2021 were identified by searching EMBASE, PubMed, and Web of Science. The cohort and case–control studies that reported effect size with 95% confidence intervals (CIs) of EDC exposure and GDM were selected. The heterogeneity among the included studies was quantified by n = 8). The risk of GDM was found to be associated with PBDE exposure . PAEs and PFASs exposure were also positively associated with the risk of GDM, with summary ORs of 1.10  and 1.09 , respectively. When only cohort studies were considered, the summary OR between PCBs exposure and the risk of GDM was 0.99 . Meanwhile, the summary ORs from cohort studies for PBDEs, PAEs, and PFASs exposure were 1.12 , 1.08 , and 1.06 , respectively. The Beggs and Egger tests did not show publication bias, and the sensitivity analyses did not change the results in this meta-analysis.Twenty-five articles with a total of 23,796 participants were found. Results indicated that exposure to PCBs has a significant influence on the incidence of GDM (OR = 1.14; 95% CI = 1.00-–1.31; These results support that exposure to certain EDCs, including PCBs, PBDEs, PAEs, and PFAS, increase the risk of GDM. Further large-sample epidemiologic researches and mechanistic studies are needed to verify the potential relationship and biological mechanisms. These results are of public health significance because the daily EDC exposure is expected to increase the risk of GDM development.The online version contains supplementary material available at 10.1186/s12940-022-00858-8. Meanwith GDM . Howeverwith GDM .Endocrine-disrupting chemicals (EDCs) are a special type of exogenous chemicals that can interfere with considerable normal endocrine signals . These cSeveral EDCs, including polychlorinated biphenyls (PCBs), poly-brominated diphenyl ethers (PBDEs), per- and polyfluoroalkyl substances (PFAS), and phthalates (PAEs), have been reported to be associated with impaired glucose metabolism and T2DM , 16. GroHumans are widely exposed to various EDCs, which may influence the regulation of glucose homeostasis. However, the current studies linking EDCs exposure to glucose homeostasis during pregnancy were inconsistent and have not been well systematically reviewed. This study aims aimed to conduct a comprehensive systematic review and meta-analysis to evaluate the relationship between EDCs exposure and risk of GDM. The objectives of this study are as follows: (i) to confirm the association between EDCs and the risk of GDM and provide up-to-date epidemiological clues on this association, and (ii) to determine the pooled data on different types of EDCs  and their influence on the risk of GDM.This meta-analysis was performed according to the guidelines of the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA)  and the A comprehensive electronic search was carried out in the EMBASE, PubMed, and Web of Science databases for relevant studies from their inception to November 2021. The search terms included exposure  and outcomes . The detailed search strategy is presented in the Additional file The titles and abstracts of the search results were independently screened by two investigators (DDY and YJ) according to the following inclusion criteria: (1) observational epidemiological studies  on the relationship between EDC exposure and risk of GDM; (2) the level of EDC exposure in humans is determined in biological samples ; (3) the outcome data reported the effect size with 95% confidence interval (CI) or sufficient data to calculate the effect size and 95% CI; and (4) provided data on sub-group EDC exposure, which have been studied in at least three studies; thus, the extracted data can be integrated. Studies that detected the level of EDCs through questionnaires or environmental measurements were excluded. Reviews, editorials, letters, and nonhuman studies were excluded because they could not provide the effect size and 95% CI on the correlations between EDCs and risk of GDM. The studies whose results could not be extracted or results could not be translated into odds ratio (OR) or 95% CI were also excluded.Data extraction was independently performed by two researchers (DDY and YJ) using the standardized data extraction sheet. The detailed data extraction sheet included the following items: first author, year of publication, location, study time period, design, sample size, covariate adjustment, diagnostic criteria for GDM, effect sizes, and 95% CIs. The comparison value between the highest versus the lowest EDC concentration category was selected to calculate the summary OR. The quality of the included studies was independently assessed by two investigators (DDY and YJ) using the Newcastle–Ottawa Scale . This scQ and I2 statistics were used to evaluate the possible heterogeneity among the included studies, and P < 0.10 and I2 > 50% represent a significant level of heterogeneity [P < 0.05 (two-sided).In the meta-analyses, the effect estimates were pooled if at least two original studies reported the same types of EDCs as follows: a) risk per unit increase in continuous exposure and b) risk of high versus low exposure level in the individual study. The OR and 95% CI were used as the primary measures to assess the relationship between EDCs and risk of GDM. The summary OR was calculated by using categorical exposure defined as high versus low because this method has been previously used in researches of environmental exposure studies . Cochranogeneity , 27. A fogeneity , 29. SenApproximately 392 articles were identified from three electronic databases using the search strategy, and five additional records were identified through citation review. Among those 397 literatures, 152 of which were left after removing duplicates. After screening the titles and abstracts, 45 articles were excluded for the following reasons: did not meet the selection criteria, irrelevant exposure or outcome, or studies not related to systematic review. According to the full-text article review, 25 studies were enrolled with a total of sample size of 23,796. The screening flow chart is displayed in Fig. Among those included articles, 16 were cohort studies, seven were case–control studies, and two were cross-sectional studies. In terms of study location, 11 studies were conducted in China, 7 in the USA, 2 in Canada, 2 in Spain, 1 in Iran, 1 in Norway, and 1 in the UK. More than half of those studies assessed the level of EDCs in serum (15 articles), six studies evaluated EDCs in urine, three studies detected EDCs in plasma, and one study assessed EDCs in meconium. In terms of laboratory technique, liquid chromatography was used in 17 studies and gas chromatography was used in eight studies. The general characteristics of the selected articles are displayed in Table According to the items in the inclusion criteria  risk per unit increase in continuous exposure and b) risk of high versus low exposure level in the individual study) and the searching results, the EDCs evaluated in this work are: PBDEs, PCBs, PAEs, PFAS, perfluorononanoic acid (PFNA), perfluorooctanoic acid (PFOA), perfluorodecanoic acid (PFDA), perfluorohexanesulfonic acid (PFHxS), perfluorooctanesulfonic acid (PFOS), perfluoroundecanoic acid (PFUnDA), perfluorododecanoic acid (PFDoA), perfluorobutane sulfonate (PFBS), 2-(N- ethyl-perfluorooctane sulfonamide) acetate (EtFOSAA), 2-(N-methyl-perfluorooctane sulfonamide) acetate (MeFOSAA), diethylhexyl phthalate (DEHP), mono-n-butyl phthalate(MBP), mono-benzyl phthalate(MBzP), mono-3-carboxypropyl phthalate (MCPP), mono-ethyl phthalate (MEP), mono-isobutyl phthalate (MIBP), and mono-(2-ethylhexyl) phthalate (MEHP).P = 0.000, I2 = 64.0%). Accordingly, a random-effect model was used, and the overall estimated OR exhibited a significant association between PCBs and the risk of GDM , indicating that PCBs is a risk factor for GDM. The forest plot is displayed in Fig. n = 5) and 1.60 , respectively , with a significant heterogeneity among the included studies . The forest plot is displayed in Fig. n = 2) and 2.21 , respectively . Accordingly, a fixed-effect model was used to combine data. The forest plot is shown in Fig. n = 7). In the subgroup analysis, MBP and MEP exposure were markedly associated with the risk of GDM . Meanwhile, the OR values of the other five PAE metabolites indicated no statistical significance. When the subgroup analysis was classified according to the design of studies, the summary OR from the cohort and non-cohort studies were 1.08  and 2.17 , respectively , suggesting that the results of the included studies were statistically homogeneous. Thus, a fixed-effect model was used to evaluate the overall estimated OR and 95% CIs. The summary OR of PFAS and the risk of GDM was 1.09 , indicating an additive effect on the risk of GDM. The forest plot is displayed in Fig. n = 8) and 1.22 , respectively , as shown in Table P = 0.130 for Egger’s test; P = 0.195 for Begg’s test). The P values of Egger and Begg tests for PAEs were 0.069 and 0.198, respectively. These results were not statistically significant. Furthermore, the Egger and Begg tests for PFAS also provided no publication bias . Thus, no publication bias was observed in the included studies. Sensitivity analyses were evaluated by leave-one-out-method. We omitted each study from the analysis one by one. As shown in Additional Fig. The Egger and Begg tests for PCBs provided no evidence of substantial publication bias  and risk of GDM. The data indicated that certain types of EDC exposure, including PCBs, PBDEs, PAEs, and PFASs, are risk factors for GDM. Our results suggest that exposure to certain types of EDCs are one of the modifying risk factors for GDM.PCBs belong to the persistent organic pollutants, which can bioaccumulate and biomagnify within the food chain. PCB congeners are frequently detected in human milk and serum due to their persistent characteristics. Previous epidemiology studies have indicated that PCBs differ in their effects on obesity, insulin resistance, or diabetes depending on the number of chlorine atoms; heavily chlorinated PCBs were more likely to be associated with these effects; however, such patterns were not consistent among studies , 64, 65.PBDEs are a class of persistent organic pollutants that are commonly used as flame retardants in household consumer products such as electronics and furniture. PBDEs can leach into the environment and enter the human body, and it is associated with the endocrine and numerous health problems . The resPAEs are plasticizers used in many consumer applications, such as food packaging, personal care products, and medical devices. They can migrate into food and enter the human body. Several metabolites of PAEs can be detected in the urine of > 75% of the general population . The resPFASs have been extensively used in a series of industrial applications, such as product surfactants, paper and textile coatings, nonstick frying pan coatings, and repellents, resulting in ubiquitous contamination and worldwide exposure . It can At present, the biological mechanisms of the potential relationship between EDC exposure and risk of GDM are not fully elucidated. As previously mentioned, GDM may share certain similar pathways for development in common with T2DM in terms of EDC exposure because both of them are characterized by continuum hyperglycemia and insulin resistance. Women with GDM are more likely to acquire T2DM later in life. Therefore, certain hypothesized mechanisms, such as mitochondrial dysfunction, oxidative stress, inflammation response, and insulin resistance, which have been considered to be associated with the development of diabetes may also be applicable to GDM . The higHowever, several limitations should be recognized in our present study. First, the types of EDCs and complications of pregnancy in this study are limited due to the insufficient number of relevant literatures and the failure of data extraction,. Second, the dose–response relationship between the individual pollutants and the risk of GDM was not established in the present study because humans are generally exposed to a complex mixtures of pollutants. The effects of individual compounds are difficult to distinguish. Third, the publication bias may be inevitable, even though no publication bias was observed in the Egger and Begg tests, because the unpublished data were not retrieved. Moreover, we did not perform a subgroup analysis to assess the relationship between the EDCs and the risk of GDM in different gestational stages (various trimesters) due to the limited number of included studies. In addition, the summary overall effect sizes reported in our meta-analysis are relatively small, particularly for cohort studies, even though all of our four results showed significant impact on the incidence of GDM due to the limited number of existing epidemiological studies and sample size in a single study. Finally, we are unable to perform more additional subgroup analyses to explore the possible sources of heterogeneity among the included studies due to the limited number of included studies especially for the PBDEs and PCBs exposure. Thus the study heterogeneity may be inevitable.In conclusion, the results of our present meta-analysis indicated that PCB, PBDE, PAE, and PFAS exposure have significant effects on the risk of GDM. These results provide strong evidence to a certain extent, supporting the hypothesis that certain EDCs  exposure is related to an increased risk of GDM. Further large-sample and high-quality epidemiologic studies with improved methods for documenting cases of GDM and in vivo and in vitro studies are needed to verify the potential relationship and biological mechanisms.Additional file 1: PRISMA Report (modified) for Systematic Reviews.Additional file 2: Search strategy for electronic databases.Additional file 3: Table 2. Quality assessment of the included studies.Additional file 4: Datasets.Additional file 5: Figure 1. funnel plot of PCBs (A), PBDEs(B), PAEs(C), and PFAS (D) exposure and risk of GDM.


Dear EditorWe recently read an article written by Leo Nherera et al. . The autFirst, the research conducted by Yu et al.  was actuSecond, in the “Study selection and eligibility criteria” section, the inclusion criteria were described as “Adults with intertrochanteric hip fractures with subtrochanteric extension or subtrochanteric fractures,” which did not correspond to the purpose of this article, because subtrochanteric fractures are much different from intertrochanteric fractures, not to mention that this leads to a greater heterogeneity.Third, only 188 patients were recruited in 2 RCTs and these cases were far from enough, given this meta-analysis involved 987 cases and 6 researches. Furthermore, although the authors claimed RCTs were assessed with the Cochrane Collaboration's risk of bias tool, the risk of bias graph was not found in this study. Meanwhile, the Newcastle–Ottawa Scale  was usuaFourth, the included researches were either conducted in Turkey or China; however, the authors did not mention ethnic bias which could be critical to the conclusion. Also, only 2 or 3 included researches were applied in some subgroup analysis.The controversies above may weaken the reliability of this meta-analysis. As a result, more studies need to be carried out to clarify these issues.


The use of the CD38 monoclonal antibody daratumumab in combination with standard myeloma chemotherapy regimens has been studied extensively in recent years. We undertook an updated meta‐analysis of phase III randomized controlled trials (RCT) to determine the efficacy of daratumumab combination regimens. The relative risk for progression was significantly lower in daratumumab‐treated cohorts  and this was consistent across newly diagnosed and relapsed cases. No statistically significant improvement was identified in newly diagnosed patients with high‐risk cytogenetics and this group remains a therapeutic challenge. MM rema2th April 2020 using the keywords “multiple myeloma AND daratumumab,” OR “plasma cell disorder AND daratumumab.” The references of all potential studies were also reviewed for any additional relevant studies. We limited the search to “humans” and “randomised controlled trials.” All studies written in English or non‐English languages were obtained. The studies that were eligible to be included in the meta‐analysis had to conform with the following characteristics: phase III RCTs utilizing daratumumab in patients with newly diagnosed/untreated multiple myeloma or relapsed/refractory multiple myeloma.The systematic review was performed as per the Cochrane Handbook for Systematic Reviews and reported in accordance with the Preferred Reporting Items for Systematic Reviews and Meta‐Analyses (PRISMA) guidelines . We perfTable I2 and Cochran's Q statistic [P‐value” of <.05 was considered significant and I2 > 50% is considered substantially heterogeneous. An HR < 1.0 or RR < 1.0 was in favor of daratumumab. The risk of bias for each study was evaluated by Cochrane RevMan 5.3 software. Five main salient biases  were categorized and were rated as low, high, or unclear risk [The primary outcome of our meta‐analysis was progression‐free survival (PFS). The secondary outcome was the overall response rate (ORR), including stringent complete response (sCR), complete response (CR), and MRD negativity (molecular response). We summarized the characteristic features of incorporated studies in Table tatistic . A “P‐vaear risk . Publica3I2 statistic showed some heterogeneity among RCTs and the random‐effects model was applied to provide a more conservative result. The pooled HR for overall PFS was statistically significant at .46  Figure P < .00001) and RRMM in Figure P < .0001). Although the pooled HR for PFS was significant in standard‐risk cytogenetic NDMM cohort in Figure P < .00001), PFS was not statistically significant in high risk cytogenetic NDMM cohort in Figure P = .15). A PFS benefit was observed in both standard‐risk cytogenetic and high‐risk cytogenetic cohorts in RRMM with the pooled HR of 0.38  and the HR of 0.46 , respectively in Figure P = .35) in patients with NDMM who harbored high‐risk cytogenetics.The P = .03). In RRMM, ORR was 87% versus 71.3% in the control arm . In NDMM, the rate of CR and sCR was 17.9% higher in daratumumab combination regimens compared to the control group , whereas the rate of CR and sCR was 22.5% higher in daratumumab arm in the RRMM subgroup . Higher MRD 10−5 negativity was also observed in both NDMM and RRMM. In NDMM, molecular remission was reported in 38.8% in the daratumumab arm versus 22% in the control arm . In RRMM, molecular remission was reported in 18.4% of patients in the daratumumab arm versus 3.4% in the control arm and the pooled RR was significant at 5.73 .The benefit in ORR was observed in both NDMM and RRMM who have received a daratumumab‐containing regimen. In NDMM, ORR was reported in 92.2% in daratumumab arm versus 82.8% in the control arm  was reported in the daratumumab group in NDMM in the CASSIOPEIA study compared to 16% in patients with NDMM who received daratumumab combination regimens in the ALCYONE study and 24.1% in the MAIA study. The ALCYONE and MAIA trials enrolled transplant‐ineligible patients and are relatively older populations with the mean age of ∼70 years whereas the CASSIOPEIA study included relatively younger patients with a mean age of 59 years and they were eligible to undergo autologous stem cell transplantation. The differences in patient selection criteria may explain the differences in the rate of response and impact on survival.A higher molecular remission rate of 64% (MRD10Despite the consistency of our findings, some caution should be used in their interpretation. First, the studies used different standard combination regimens, trial design, and methodology, which might confound the analysis. Second, the overall survival data are still immature for a few trials and we used the abstract data for the CANDOR trial. Individual patient data pooled meta‐analysis would provide more detailed and accurate analyses and confirm our findings. Lastly, translating clinical trial data into the real‐world setting is of paramount importance.5Our meta‐analysis showed that daratumumab combination regimens significantly improved PFS, ORR, CR, and sCR, and MRD negativity compared to control arms in patients with NDMM and RRMM. The improvement in PFS was noted across all subgroups except in NDMM with high‐risk cytogenetics. More randomized studies are necessary in the future to explore further novel therapies and the optimal combination of anti‐myeloma therapies to improve survival in patients with NDMM within the high‐risk cytogenetic subset.Thura W. Htut and Kyaw Z. Thein contributed to the study conception and design. Material preparation, data collection, and analysis were performed by Thura W. Htut and Kyaw Z. Thein. The first draft of the manuscript was written by Thura W. Htut and Kyaw Z. Thein, and Alastair Lawrie, Jane Tighe, and Gavin Preston commented on the manuscript. All authors read and approved the final manuscript.Gavin Preston has received honoraria or meeting sponsorship from Abbvie, Janssen‐Cilag, and Takeda, and has attended meetings sponsored by Celgene, Roche, Bristol‐Myers‐Squibb, Novartis, Gilead, Pfizer, and Napp pharmaceuticals. Jane Tighe has received educational support and travel for meetings from Amgen, Celgene, Sanofi Aventis, Takeda, and Janssen over the years. We had no financial support for this project.


As an immune-modulator, vitamin D is known to regulate immune response and is implicated in disease pathogenesis. Celiac disease (CD) is a systemic autoimmune disease and susceptibility conferred by vitamin D metabolism is under investigation. Studies on the association of vitamin D metabolism and genetic polymorphisms are expected to explain CD pathogenesis. We performed a systematic review–based meta-analysis to investigate the 25(OH)D serum levels and susceptibility conferred by the genetic variants of VDR in CD.p-value was assessed for 25(OH)D serum levels. Meta-odds ratio and Z-test p-value were evaluated to estimate the allelic susceptibility of VDR variants.Systematic review was conducted through a web-based literature search following stringent study inclusion–exclusion criteria. The Newcastle–Ottawa Scale and GRADE tools were used to assess the quality of evidence in studies and the study outcome. Cohen's κ value was estimated to access the reviewer's agreement. RevMan 5.4.1 was used to perform the meta-analyses. Weighted mean difference and Meta Bsm1, Apa1, Fok1, and Taq1) of VDR. Significantly higher levels  of 25(OH)D were observed in healthy controls than in patients with CD. rs2228570-T (Fok1)  was confirmed to be predisposing allele for CD.A total of 8 out of 12 studies were evaluated for “25(OH)D” serum level, while four studies were found eligible for SNPs D and association of  Componeve genes . Some ofIn vitro study performed on Caco-2 cell layers reported the protective role of 1,25(OH)2D3 on the damage of tight junction, which was induced by the pepsin–trypsin digested gliadin (PT–G). Vitamin D was observed to increase the expression of the tight junction–associated proteins and was also able to minimize epithelial permeability  were genotyped. Fok1 was reported to be a risk genotype in 25.64% of CD cases as compared to 9.89% of controls  (VDR marker (Bsm1) or serum vitamin D level  . Another D level . Two sigobserved . A recenobserved  did not VDR gene polymorphism with CD. Four SNPs  of VDR were evaluated.In this systematic review and meta-analysis, we intended to assess an association of serum level of 25(OH)D and VDR gene polymorphism with celiac disease was searched. For this literature search, keywords were used as follows: “Celiac disease” AND “serum vitamin D concentration” OR “serum 25(OH)D concentration”. For the genotype association study, the key terms used were as follows: “Celiac disease” AND “VDR polymorphism” OR “VDR genotype” OR “VDR variants” OR “Bsm1 polymorphism” OR “Apa1 polymorphism” OR “Fok1 polymorphism” OR “Taq1 polymorphism”.For the search and retrieval of relevant published literature, various web search engines for scientific databases such as Google scholar, NCBI (PubMed/MEDLINE), SCOPUS, EMBASE, and Web of Science were used. All the published literature until May 2022 on the association of vitamin D serum concentration and VDR gene polymorphism association study with celiac disease and healthy controls, and (v) Only articles written in the English language. Our study was not restricted to any specific population or ethnicity, and all the relevant articles available till May 2022 were considered.To limit the screening to relevant articles, the inclusion and exclusion criteria were defined. Articles were considered eligible for the study if they met the following inclusion criteria: (i) published full-text original research article, (ii) case–control study design, (iii) mean serum 25(OH)D concentration can be obtained, (iv) The exclusion criteria for the study were as follows: (i) studies other than case–control, (ii) if the CD patients were on a gluten-free diet or vitamin D supplementation, and (iii) review articles. Apart from this, duplicate publications were also excluded.VDR genotype in patients with CD and healthy controls were retrieved along with the name of the author and the year of publication. For the concentration of vitamin D, the serum levels of 25(OH)D [mean ± standard deviation (SD)] in patients with CD and healthy controls were extracted from every eligible article, and for the vitamin D concentration, the units considered were in ng/ml, data obtained in other units (such as nmol/L) were converted to ng/ml (1 ng/ml = 2.5 nmol/L). Few other information about the study population such as the mean age of the patients with CD and healthy controls and the male:female ratio in the study were also obtained.Two researchers independently performed a literature search checked the eligibility criteria, and extracted data from the shortlisted literature. From all the studies which we considered eligible according to our inclusion and exclusion criteria, relevant data for serum vitamin D concentration and VDR genotype association studies, VDR SNPs data of CD cases and controls were obtained. Four SNPs of the VDR gene: Bsm1-rs1544410, Apa1-rs7975232, Fok1-rs2228570, and Taq1-rs731236 were analyzed, and the genotype frequencies in CD cases and controls were obtained. After data extraction, to carry out a systematic review, standard checklists were used to analyze methodological quality and strength of association, which also included the risk of bias evaluation in observational studies as recommended by the Cochrane handbook (https://training.cochrane.org/handbook). The Preferred Reporting Items for Systematic Reviews and Meta-analysis (PRISMA) statement was followed for reporting the findings (http://www.prisma-statement.org/) (For the nt.org/) . The PRICohen's kappa (κ) value was calculated in order to estimate the extent of concordance between two reviewers, who performed a literature search, and checked eligibility and data extraction . Based oGRADEpro.v.3.6 was used to assess the quality of evidence (QoE) for each of the outcomes using The Grades of Research, Assessment, Development, and Evaluation (GRADE) tool . Based oVDR gene were utilized to calculate Meta-OR using Mantel–Haenszel (M–H) method with 95% CI). The statistical analysis for this study was carried out using the RevMan  software. For this meta-analysis, the assessment of study heterogeneity was done by chi-square p-value and I2 value. The meta-analysis was carried out using the DerSimonian and Laird random effect model for I2 > 50% and p < 0.05, and for I2 < 50% and p > 0.05, the fixed effect model was used.For the meta-analysis of the serum 25(OH)D level, the mean difference was used to analyze the pooled continuous data of serum 25(OH)D concentration which was obtained as mean ± standard deviation (SD). To evaluate the combined MD, mean differences (MD) with 95% confidence intervals (CIs) were presented for all relevant studies on a forest plot. A collective study was carried out without dividing the study based on age group due to very few studies were obtained with relevant data in the adult age group. The dichotomous data of the alleles of the four SNPs of the in silico, in vitro, in vivo studies, case studies), and articles without full text. Initially, a total of 16 studies were considered for this study but due to the lack of relevant data, four articles were excluded. Finally, 12 studies that satisfied our inclusion and exclusion criteria and had the data of serum 25(OH)D level and VDR genotype in CD cases and control were included in this meta-analysis as shown in VDR SNPs Bsm1 rs1544410, Apa1 rs7975232, Fok1 rs2228570, Taq1 rs731236, and their summary statistics are provided in After employing all the above-described literature retrieval strategies, we got a total of 17,656 studies. Overall, 17,196 studies were excluded based on the inclusion and exclusion criteria because of irrelevant titles or because they were review articles or were not in the English language. After screening the rest of the articles, 444 articles were excluded from our study because of duplicate publications, irrelevant abstracts . All the 12 eligible studies included in this meta-analysis are given in Based on the evaluation of the quality of evidence, all 12 studies were identified to have a low risk of bias (NOS = 7–8) . Becausep-values  of <0.05. The mean difference was evaluated for all the studies included, and a forest plot was plotted using the mean differences with 95% CI, of each of these studies to estimate the combined mean difference  D concentration of patients with CD and controls, the list of same is given in fference . The out0.00001) .VDR SNPs  were given for patients with CD and healthy controls, basic information of which is provided in Bsm1 rs1544410 and Fok1 rs2228570 was performed using the fixed effect model for insignificant heterogeneity (I2 < 50% and p > 0.05), whereas for Apa1 rs7975232 and Taq1 rs731236, the random effect model was used because of a high degree of data heterogeneity. Significant associations were defined as Z-test p-values  <0.05. The Meta-OR was used to predict risk.For this meta-analysis of allelic association, four studies were included in which the allelic frequencies for the four p = 0.20]. The meta p-value was declared insignificant   with CD. All the studies were performed in the last decade and on a modest sample size D [MD = 5.49; ple size . Cross-sThe extra-calcium role of vitamin D and its involvement in immune modulation suggested that in genetically predisposed individuals, vitamin D deficiency can be an underlying cause for the onset of CD in children. Moreover, vitamin D deficiency can lead to dysregulated immune responses that result in abnormal intestinal mucosa and a greater risk of developing acute gastrointestinal infection . SeveralBsm1 , Apa1 , Fok1  and Taq1  were considered where at two studies were available for the meta-analysis. The association of the Fok1-T allele with CD  suggested the putative role of this gene in the disease pathogenesis  , in exon 2 of the VDR has been shown to alter the structure of the VDR. The change in C > T also represented as F > f leads to a threonine to methionine substitution and provides two possible sites for the initiation of translation 2D3 thus restricts VDR activation, and therefore, may limit regulating the expression of specific genes that are implicated in immune regulation.The s (T2DM) . The Foknslation . The shocapacity , while rVDR gene  did not show any significant association with CD, but Fok1 (rs2228570-T) was identified to be providing significant risk for CD. However, due to limitations in the number of studies performed on the association of VDR gene polymorphism and CD, strong evidence to support this association is still lacking.In this meta-analysis, lower levels of serum 25(OH)D were observed in patients with CD, which indicates that deficiency of vitamin D may play a significant role in the pathogenesis of CD. The SNPs of the The original contributions presented in the study are included in the article/SS conceptualized the study. SS and TS designed the study and performed the initial literature search. PB and TS performed the systematic review. SS and PB performed the analysis and wrote the manuscript. All the authors read the final manuscript and approved it for publication.