Vitamin D receptor (VDR) FokI polymorphism has been reported to influence the risk of spinal diseases. However, several studies suggest inconsistent results. Therefore, we performed this analysis to reveal the accurate relationship between VDR FokI polymorphism and spinal diseases.8 articles accord with the strict inclusion and exclusion criteria. 1116 cases and 1263 controls are entered into this analysis. The pooled odds ratios (ORs) and 95% confidence intervals (CI) are calculated to evaluate the association between VDR gene polymorphism and spinal diseases.p > 0.05). However, in recessive model (FF vs. Ff + ff), there is a significant association between VDR polymorphism and spinal diseases . In subgroup analysis, the results show that allele F is a risk factor for spinal diseases in each estimation. In hospital-based subgroup, the significant differences exist in FF vs. ff and FF vs. Ff + FF models. In degenerative spine diseases group, the results are consistent with that of overall studies.The results suggest that allele F is a risk factor for spinal diseases and the difference is significant . For the genotype analysis of VDR FokI, no statistical differences exist in the models of heterozygote comparison (Ff vs. ff), homozygote comparison (FF vs. ff) and dominant model (FF + Ff vs. ff) (According to results of this meta-analysis, allele F is associated with the increased risk of spinal diseases. FF genotype may contribute to the susceptibility of spinal diseases. Therefore, VDR FokI polymorphism is related with spinal diseases. Cervical spondylosis and lumbar spine pathologies are common diseases of musculoskeletal disorder. Neck and low back pain are the typical symptoms , 2. The Therefore, it is necessary to update the research results of the association between VDR FokI polymorphism and vertebral diseases, which could provide further evidence to discover the real relationship between them. In this study, a meta-analysis is performed to assess the available articles.The complete searching procedure is shown in Figure p = 1.000), homozygote comparison model  and recessive model   and dominant model   Figure . However) Figure . “Trim a) Figure . Therefo2 = 16.0%, p = 0.304). The pooled results indicate that allele F is a risk factor for vertebral diseases compared with allele f  and the difference is statistically significant .The results of VDR FokI allelic comparison (F vs. f) in case and control groups are listed in Figure 2 = 2.0%, p = 0.414). The pooled OR is 1.261  and there is no significant difference in Z test .In order to reveal the relationship between FokI polymorphism and spinal diseases, subgroup analysis is divided into 4 parts: homozygote comparison (FF vs. ff), heterozygote comparison (Ff vs. ff), dominant model (FF + Ff vs. ff) and recessive model (FF vs. Ff + ff). The results of these subgroups are described in Figures 2 = 5.8%, p = 0.385). The pooled results of fixed-effect model suggest that Ff genotype is not a risk factor for spinal diseases compared with ff carriers . The Z test of OR has no statistical differences .In Figure p = 0.158). In fixed-effect model, the result of heterogeneity chi-squared test has no significant statistical differences . The results of recessive model (FF vs. Ff + ff) are shown in Figure 2 = 18.6%, p = 0.282). The pooled OR is 1.209  and Z test shows significant difference .As shown in Figure In order to evaluate the influence of each study on the pooled OR, sensitivity analysis is performed and the STATA command “metaninf” is used. One study is expurgated from all eligible articles each time, then the new combined ORs are compared with the original pooled ORs, which are calculated using the data in all the eligible articles. As shown in Figure Source of control, quality of literature, population, diagnostic methods and classification of diseases are considered as influential factors for subgroup analysis. Noponen-Hietala N's study is population-based study, while, the quality of the evaluation results is low. Therefore, other 7 hospital-based studies have become a new research subgroup. Furthermore, subgroup analysis is also divided into caucasian group, MRI detection method group and degenerative spine diseases group according to ethnicity, different diagnostic method and classification of disease (Idiopathic scoliosis is excluded). The contrastive analysis results of these subgroups are shown in Table p < 0.05). In the recessive model (FF vs. Ff + ff), the results suggest that FF carriers have a higher risk of spinal diseases compared with the population who carry the recessive gene with statistically significant differences (p < 0.05). However, in the genotype analysis of VDR FokI including dominant and (FF + Ff vs. ff), heterozygote comparison (Ff vs. ff) and homozygote comparison (FF vs. ff), any kind of genotype do not increase the risk of spinal diseases and no statistical difference exists in any estimates (p > 0.05). In the eligible literatures, there is one literature with poor quality or different control source. The vast majority of eligible articles are hospital-based study and only one paper is population-based study. When the individual study is removed, the results of studies with hospital-based controls are slightly different from the results of all enrolled studies. We found that FF genotype carriers are vulnerably threatened by the spinal diseases compared with ff genotype and the population carried the recessive gene, meanwhile, the difference has the statistical significance (p < 0.05). Furthermore, subgroup analysis is also divided in to caucasian group, MRI detection method group and degenerative spine diseases group according to ethnicity, different diagnostic method and classification of disease. In the subgroup of caucasian and MRI detection method, only the difference in the allelic model (F vs. f) is statistically significant (p < 0.05). In degenerative spine diseases group, the results are consistent with that of all enrolled studies.The onset of spinal diseases is affected by many factors. Behavioral factors, environmental factors and genetic factors are the most thoroughly researched influential factors. Currently, genetic factors turn into research highlights in spinal disease instead of non-genetic factors. A large number of studies indicated that VDR gene plays crucial roles in the etiology of spinal diseases. FokI, BsmI, ApaI and TaqI are the most common types of VDR gene. Various studies have shown that FokI polymorphism is the only one that results in different structure of VDR protein and it is associated with increased risk of spinal diseases . HoweverOur meta-analysis is quite rigorous and has many prominent advantages. First of all, this paper is focused on VDR FokI polymorphism and the risk of spinal diseases. Literatures are chosen from PubMed and Web of science etc., which are the open classic biomedical databases. A reasonable search strategy is designed. Language type and the period covered by the publications are also limited strictly. Secondly, reasonable search strategy, objective quality evaluation, specific inclusion criteria and strict exclusion criteria are conducted to ensure the credibility of this meta-analysis. Finally, suitable statistical methods are used to calculate the results. Sensitivity analysis and stratification analysis are also performed to control the confounding factors.However, some limitations of this study should not be neglected. The results of the meta-analysis may be influenced by certain limitations. Firstly, pathogenesis of spinal diseases could be affected by non-genetic or genetic factors separately, that is certified by numerous investigations. However, it is worthy to notice that the interaction between genetic factors and non-genetic factors may impact the occurrence of spinal diseases. For example, genetic factors with the interaction of environmental factors increase the risk of spinal diseases. Secondly, confounding variables such as sex, age, smoking and alcohol habits, etc. should be considered accurately and controlled preferably. The data of this meta-analysis are collected from published literatures and it is impossible to eliminate publication bias completely. We can only minimize the effect of publication bias to obtain more reliable results.A detailed literature search strategy is performed using the keywords “Vitamin D receptor (VDR)”, “FokI polymorphism”, “cervical vertebra pathologies” and “lumbar spine pathologies” to search all the related articles in PubMed, which is a widely used online biomedical database . We also searched the papers in the frequently-used databases, such as “Web of Science”, “Medline”, etc. The search strategy consist of the terms “Vitamin D receptor (VDR)”, “FokI polymorphism” combined with “cervical vertebra pathologies” and/or “lumbar spine pathologies”. Two independent investigators screened the relevant articles using standardized screening guide.a. The included studies must be concentrated on the relationship between VDR FokI polymorphism and the diseases of spine.b. Case-control study is the unique methodology in selected articles.c. Studies with enough data to calculate odds ratios and corresponding 95% confidence intervals  are included.d. Cases are diagnosed by diagnostic radiology and clinical diagnosis.a. Abstracts, letters, comments, editorials, reviews, single-case reports and family-based studies are excluded.b. The patients who received chemotherapy, radiotherapy or surgical operation treatment are excluded.c. The articles with insufficient data or overlapped data are excluded.We evaluate the quality of eligible studies in accordance with an improved 10-point scale, which is the appropriate quality assessment for case-control study , 13. Qua2 index and p value of the chi-squared test are used to inspect the heterogeneity among enrolled literatures. If notable heterogeneity exist (p < 0.05 and/or I2 > 50%), the random-effect model is used to estimate ORs [p value of 0.05 are used to judge whether the differences of OR values have statistical significance. Stratification analysis is decided according to source of controls, ethnicity, different diagnostic method and classification of disease. The chi-square test for Hardy-Weinberg equilibrium (HWE) is performed and the p value greater than 0.05 is considered to be balanced in control population. Sensitivity analysis is conducted to assess the influence of individual studies. Begg's test is applied to evaluate the publication bias.This meta-analysis is performed using STATA software . Crude Odds ratios (ORs) and corresponding 95% confidence intervals (CIs) are calculated to assess the strength of association between VDR gene polymorphisms and the susceptibility of spinal diseases. Pooled ORs are calculated using the data of eligible articles in random-effect model (M-H heterogeneity method) or fixed-effect model (Mantel and Haenszel method). Imate ORs , 15; conmate ORs . ORs are


CD147/EMMPRIN  plays an important role in tumor progression and a number of studies have suggested that it is an indicator of tumor prognosis. This current meta-analysis systematically reevaluated the predictive potential of CD147/EMMPRIN in various cancers. We searched PubMed and Embase databases to screen the literature. Fixed-effect and random-effect meta-analytical techniques were used to correlate CD147 expression with outcome measures. A total of 53 studies that included 68 datasets were eligible for inclusion in the final analysis. We found a significant association between CD147/EMMPRIN overexpression and adverse tumor outcomes, such as overall survival, disease-specific survival, progression-free survival, metastasis-free survival or recurrence-free survival, irrespective of the model analysis. In addition, CD147/EMMPRIN overexpression predicted a high risk for chemotherapy drugs resistance. CD147/EMMPRIN is a central player in tumor progression and predicts a poor prognosis, including in patients who have received chemo-radiotherapy. Our results provide the evidence that CD147/EMMPRIN could be a potential therapeutic target for cancers. The incidence of various cancers has been increasing and cancer is the most deadly disease threatening human life. One of the main causes of deaths is the inherent metastatic property of malignant tumors. This poses great difficulty in developing clinical therapeutics. The multi-linked pathological processes of tumor metastasis include: basement membrane degradation, matrix permeability, forward movement of tumor cells including secondary growth, and interaction between tumor cells and host stromal cells. CD147/EMMPRIN, also known as basigin or M6 antigen, has been shown to play an important role in tumor metastasis by stimulating tumor stromal cells to produce matrix metalloproteinases (MMPs) and degrading basement membrane and stromaCD147/EMMPRIN is a member of the immunoglobulin family and is widely expressed in a variety of human tissues and cells1567It is highly upregulated in several malignant tumors10111213141516101516Our systematic literature search of CD147 and its correlation with different outcomes identified 910 articles. Among these, 836 articles were excluded and only 82 studies satisfied the inclusion eligibility for the meta-analysis. Upon further review of the full text articles, eight additional articles from reference sources were included. An additional 29 studies were excluded due to the following reasons: two articles were reviews; three were duplicate reports; four had insufficient data; four included only a few cases; nine explored the prognostic value of CD147/EMMPRIN in combination with other biomarkers, such as VEGF, MMP-2, CD44s, MCT-1; and seven studies were determined to be too complicated for subgroup analysis. The remaining 53 studies5679101112131415161719202122232425262728293031323334353637383940414243444546474849505152535455565758The 53 eligible studies represented 26 different carcinomas or sarcomas and a median number of 204.5 patients . CD147/EMMPRIN expression in these studies was mainly detected by immunohistochemistry (IHC). One publication had three groups of subjects from different cancers and was therefore considered as three different studies12131416252938395156The 44 studies, with data from a total of 5,813 patients, showed that CD147/EMMPRIN expression was associated with worse OS, both in multivariate (meta-hazard ratio (HR) = 1.92; 95% confidence interval (CI): 1.58–2.32) and in univariate models  . In addiFurthermore, the subgroup analysis of tumors stratified by cancer type also demonstrated a significant association between higher CD147/EMMPRIN expression and adverse outcome of OS for breast carcinoma , bladder cancer , colorectal cancer , ovarian cancer  and osteosarcoma  in the multivariate model. However, an association was only observed in renal cell carcinoma  and bladder carcinoma , using the univariate model. In contrast, there was no association in gastric carcinoma  and lung cancer  . We alsoAnalysis of 19 studies, with a total of 2,472 patients, showed that CD147/EMMPRIN expression was associated with worse PFS/MFS/RFS, both in multivariate  and univariate  models . MoreoveSimilarly, the subgroup analysis of tumors stratified by cancer type again demonstrated a significant association between CD147/EMMPRIN overexpression and adverse outcome of PFS/MFS/RFS in breast carcinoma  and ovarian cancer  in the multivariate model and renal cell carcinoma  in the univariate model. Similar associations were observed in hepatocellular carcinoma, bladder cancer, prostate cancer, colorectal cancer, osteosarcoma, gastric cancer and hypopharyngeal squamous cell carcinoma. However, we did not observe an association in cases of endometrial carcinoma, and the results were not consistent for cervical carcinoma .Quantitative analysis of five different studies, representing 1,031 patients, linked CD147/EMMPRIN overexpression with DSS in four different solid tumors, namely breast cancerAmong the included articles, three studies562 > 50%) between OS and PFS studies, but not among DSS studies .To assess confidence in our study, we performed a publication bias analysis using the funnel plot and Egger’s and Begg’s rank correlation tests. There was no significant publication bias in both models for the DSS and PFS/MFS/RFS groups . In the CD147/EMMPRIN is a glycosylated, multifunctional molecule that participates in tumor progression60626465CD147/EMMPRIN has been shown to be involved in the regulation of tumor cell invasion, metastasis, angiogenesis, anti-apoptosis, adhesion and facilitation of drug resistance through its association with various proteins10Previous meta-analyses didn’t explore any significant association between CD147/EMMPRIN and susceptibility of radio-chemotherapy. In the view of more studies are warranted to validate CD147/EMMPRIN association with chemotherapy and radiation resistance prediction5677The results of the present study must be interpreted with caution due to the presence of substantial heterogeneity. In addition, there were several limitations in this study. First, the composition of the cancer type or stage varied between studies, and the detection and corresponding cut-offs varied. For instance, some studies only included pediatric patientsIn summary, this meta-analysis indicated that higher expression of CD147/EMMPRIN potentially may be a prognostic marker for most cancers, and thus can serve as a potential therapeutic target. We further verified that CD147/EMMPRIN had a complex role in tumor progression by crosstalk with numerous factors. However, additional multicenter prospective studies are warranted to confirm these findings, especially in various types of tumors.We searched PubMed and Embase databases through March 2015 to identify relevant studies for inclusion in our meta-analysis. The following keywords were used in the literature searches; “CD147”, “extracellular matrix metalloproteinase inducer”, “EMMPRIN”, “basigin”, “survival”, “prognosis”, “tumor”, “cancer”, “carcinoma”, “neoplasm”, or their combinations. Eligible articles were selected based on title, abstract and full text. If the same patient cohort was reported in multiple publications, only the most complete and most recent publication was selected. We also searched of the reference lists from electronically identified articles.Based on the Reporting Recommendations for Tumor Marker Prognostic Studies (REMARK) guidelines, we included studies that met all of the following criteria: (1) written in English; (2) reported quantitative outcomes from prognostic association studies of tumor and CD147/EMMPRIN; (3) described outcomes as overall survival (OS), disease-specific survival (DSS), progression-free survival (PFS), metastasis-free survival (MFS) or recurrence-free survival (RFS), depending upon the study; (4) had a minimum of 40 case numbers describing prognosis; (5) provided a detailed protocol, including the source of raw materials, methodology, quantification methods, and scoring criteria; (6) precisely defined the time-to-event outcome, time to follow-up, and the median follow-up time; and (8) data were presented as the estimated hazard ratios (HRs) with 95% confidence intervals (CIs) or in the case where HRs or 95% CIs were not reported directly, a calculation was used to determine if the conditions of the study were suitable for inclusion2 test2 < 50%) and a random-effect model when there was considerable heterogeneity. If heterogeneity was present, meta-regression was used to determine the source. Variables were synthesized using HR/OR. By convention, an overall HR (OR) > 1 with a 95% CI implied a poor outcome (high risk) for the group with either positive or negative biomarker expression. The high HR value corresponded with poor survival. To evaluate the effects of individual studies on the pooled HR estimates, we performed a sensitivity analysis omitting one study at a time. The statistical significance was set at 0.05. We used funnel plot asymmetry using Egger’s linear regression test and Begg’s rank correlation test to assess the publication bias. If both test results were inconsistent, Nonparametric Trim and Fill method was used to verify the resultsWe assessed heterogeneity using the ChiHow to cite this article: Xin, X. et al. CD147/EMMPRIN overexpression and prognosis in cancer: A systematic review and meta-analysis. Sci. Rep.6, 32804; doi: 10.1038/srep32804 (2016).


The major objective of this study was to investigate the effect of MTHFR polymorphisms, air pollution, and their interaction on the risk of HDP by using meta-predictive analytics. We searched various databases comprehensively to access all available studies conducted for various ethnic populations from countries worldwide, from 1997 to 2017. Seventy-one studies with 8064 cases and 13,232 controls for MTHFR C677T and 11 studies with 1425 cases and 1859 controls for MTHFR A1298C were included. MTHFR C677T homozygous TT (risk ratio (RR) = 1.28, p < 0.0001) and CT plus TT  were the risk genotypes, while wild-type CC played a protective role  for HDP. The meta-predictive analysis found that the percentage of MTHFR C677T TT plus CT (p = 0.044) and CT (p = 0.043) genotypes in the HDP case group were significantly increased with elevated levels of air pollution worldwide. Additionally, in countries with higher air pollution levels, the pregnant women with wild-type CC MTHFR 677 had a protection effect against HDP (p = 0.014), whereas, the homozygous TT of MTHFR C677T polymorphism was a risk genotype for developing HDP. Air pollution level is an environmental factor interacting with increased MTHFR C677T polymorphisms, impacting the susceptibility of HDP for women.Hypertensive disorders in pregnancy (HDP) are devastating health hazards for both women and children. Both  Hypertensive disorders in pregnancy (HDP) are major health hazards in perinatal care ,2. Womenmethylenetetrahydrofolate reductase (MTHFR) gene has been reported as a candidate gene associated with HDP [MTHFR polymorphism-mutation loci include C677T (rs1801133) and A1298C (rs1801131), both are associated with MTHFR enzymatic deficiency resulting in increased homocysteine concentrations [MTHFR C677T [MTHFR A1298C [The with HDP ,12,13. Mwith HDP . Two comtrations . Hyperhotrations ,16,17 antrations , atheros C677T rs01133 andtrations . Reducedtrations . To datetrations ,21,22 coFR C677T ,17,23,24R A1298C  polymorpR A1298C ,27,28.2.5) [3), carbon monoxide (CO), nitrogen oxides (NOx), and nitrogen dioxide (NO2) [MTHFR C677T polymorphism, CT or TT genotypes, and exposure to air pollution may significantly increase the risk of CVD across the lifespan [Further evidence has indicated that HDP can result from epigenetic factors, as well as modifications and interactions involving genetic factors and environmental toxicants ,30. Expo2.5) ,27, ozonde (NO2) ,34,35,36de (NO2) , though de (NO2) ,35. Furtlifespan .MTHFR C677T and A1298C polymorphisms with HDP [MTHFR C677T polymorphism had significantly increased the susceptibility of HDP for various ethnic groups. However, none of these meta-analyses addressed the effects of epigenetic factors including air pollution on the development of HDP. Therefore, the primary objective of this study is to examine the MTHFR gene polymorphism on HDP risk across the globe. In addition, the secondary objective is to investigate the impact of air pollution on MTHFR polymorphisms and the risks of HDP, using meta-predictive techniques.In summary, previous meta-analyses examined the association between with HDP ,21,22 anWe conducted a comprehensive literature search, following the guidelines of meta-analysis of observational studies in epidemiology (MOOSE)  and prefMTHFR gene” or “MTHFR polymorphism” or “methylenetetrahydrofolate reductase gene” or “MTHFR in pregnant women”, and mash terms for “new-onset hypertensive disorders during pregnancy” [We searched various online databases of PubMed, PubMed Central, Embase, and Airiti Library to access all available studies from the first relevant study in 1997 to 2017. We used combinations of the following keywords: “MTHFR C677T and MTHFR A1298C polymorphisms and HDP and that clearly reported appropriate genotype allele counts per case and control groups. We included studies if the articles included an abstract in English and tables that clearly listed the genotype allele counts. We excluded articles that (1) did not include genotypes per case and control groups and (2) did not include genotyping in pregnant women. MTHFR C677T or MTHFR A1298C polymorphisms and HDP. We excluded a total of 109 articles, including 7 irrelevant studies, 68 none-case-control studies, 33 studies with missing MTHFR genotype allele counts, and 1 study involving subsidiary data repeated in another study , and 4 South Asian), then Hispanic , Middle Eastern (7 studies), African (5 studies), and South American (4 studies) , followed by Asian (22 studies including 18 East Asian (1 study included only studies) . For HDPstudies) .We entered the air-quality data for various countries. Specifically, we verified from various sources for the most current and complete air-pollution data including the death rates from air pollution  ,43. The We evaluated the quality of each study using a set of indicators appropriate for the current state of science for the field ,49,50 T. The quaMTHFR polymorphism genotypes with HDP. We defined significant findings as those with p-values < 0.05. The results of RRs and ORs were similar  to pool data analyses. We calculated pooled relative risk ratios (RR), odds ratios (OR), and 95% confidence intervals (CI) between cases and controls for the associations of  similar ; howeverMTHFR polymorphism tests . Random effects instead of fixed effects models were used for the risk estimates when the heterogeneity tests were significant with p < 0.05.The Heterogeneity tests, Egger’s test, and funnel plots were used to detect publication bias. No significant publication bias was found on the meta-analyses of MTHFR poly-morphisms and risks, we used geographic-information-system (GIS) maps to visualize the pattern distributions of polymorphisms and risks on the global maps [R2 tends to yield a complex and overfitted model because R2 always goes up with additional predictors. On the other hand, Akaike’s information criterion (AIC) or AICc does not necessarily change with the addition of variables. Rather, it varies based upon the composition of the predictors; thus, it is more likely to yield an optimal model [Because the data showed heterogeneity with regional differences on bal maps . In addibal maps . To judgbal maps , it is ial model . Furtheral model . These mMTHFR C677T polymorphisms, we analyzed 8064 HDP cases and 13,232 controls in 71 study groups for populations from all continents worldwide. For clarity, we summarized significant findings in a schema Table (MTHFR C677T polymorphism homozygous TT and TT plus CT (where both TT and CT types were added together to see the dominant effects of polymorphisms) were risk genotypes for overall populations and for the PE-eclampsia subgroup , and its frequency in ethnic groups in rank order was Hispanic (28.10%), East Asian (23.59%), South American (16.40%), Caucasian (11.65%), Middle Eastern (7.26%), African (2.81%), and South Asian (2.67%) ; and the rank order of highest risk to lowest was African (RR = 5.82), East Asian (RR = 1.75), South American (RR = 1.40), and Caucasian (RR = 1.14)  for all populations combined, as well as for HDP subtypes of PE-eclampsia and GH ; its effects were mixed without significance for all other populations  . Homozyg = 1.14) . The proa and GH . For eth African . While Hulations .MTHFR A1298C polymorphisms, we included 1425 cases and 1859 controls from 11 study groups , Caucasian (8.96%), Middle Eastern (7.02%), Asian (6.99%), and finally African (2.04%) .For y groups . The fre (2.04%) . Pooled MTHFR C677T polymorphisms (MTHFR A1298C polymorphism (MTHFR C677T polymorphisms TT plus CT genotypes (TT as a risk genotype (RR > 1 in 21 countries) versus those that had it as a protective genotype (RR < 1 in 5 countries) and other countries that had varied effect (RR varied around 1 in 4 countries) (TT as a risk genotype (RRs > 1) were found in 49 studies from the regions of Europe, Americas, Asia, the Middle East, and Africa  (TT as a protective genotype (RR < 1) included Australia, Germany, Austria, Croatia, and India  . The cou 0.0001) . In contnd India . There w and HDP .MTHFR C677T polymorphisms demonstrated that the homozygous TT  and TT plus CT  were risk genotypes for the PE-eclampsia subgroup. Homozygous TT was also a risk genotype  for the GH subtype. The heterozygous CT  and TT plus CT  were risk genotypes for the mixed HDP subgroup. The wild-type CC was a protective genotype for the PE-eclampsia, GH, and mixed HDP groups.Subgroup-analysis by HDP disease types with CC plus CT polymorphisms were also protective for PE-eclampsia and GH HDP groups . There was no significant association between MTHFR A1298C genotypes and HDP risk, except CC plus AC genotypes were a risk and AA wild-type was a protective genotype for PE-eclampsia subgroup in the Middle East  and heterozygous CT (p = 0.043) genotypes were significantly higher in countries with higher (Level 4) air pollution than in countries with lower (Level 2) air pollution, while the percentage of wild-type CC genotype showed reversed findings to that of other two polymorphic genotypes combined in association with the levels of air pollution exposure (p = 0.044) (p > 0.05) while there was an upward trend of polymorphic genotypes. To further illustrate these significant findings, we plotted those results on nonlinear curves (TT plus CT genotypes showed a steeper increase in the HDP group than in the control group (CC wild-genotype showed higher protective effects in countries with level 4 than those in level 2 air pollution (Because findings were mixed on the effects of k of HDP . We used= 0.044) . Interesr curves . With inol group . Contrarollution . The heaollution .MTHFR C677T polymorphism and HDP risk for populations worldwide [homozygous TT as well as TT plus CT genotypes and HDP risk in East Asian [homozygous TT genotype was found to be a risk factor for HDP in the PE-eclampsia subgroup in African [Based on the results of this comprehensive analysis, we show a significant association between orldwide ,20,21,22st Asian ,20,21, Cst Asian ,20,21, a African  and Sout African . We addeOur pooled analyses provided summative evidence with the findings from individual studies on the direct effects of exposure to air pollution being associated with increased risk of HDP ,33,35,36MTHFR 677 polymorphism in HDP cases and thus HDP risks and that air pollution had an additive effect to MTHFR C677T polymorphism in increasing the risk of HDP. Noticeably, both the nonlinear plots and heat-maps clearly show that the MTHFR 677 polymorphisms significantly increased in the HDP case group from the countries with lower to higher levels of air pollution, but not in the control group. Previous studies presented that fine particles from air pollution can affect endothelial dysfunction causing abnormal placentation [With this meta-prediction study, we found that air pollution was associated with increased entation ,35,36 anentation , resultientation ,30, as HMTHFR polymorphisms on the risk of HDP with the effects of air pollution in pregnant populations worldwide. Our study demonstrated the adverse effect of air pollution with increased MTHFR C677T polymorphisms and women’s susceptibility to HDP. Further studies are needed to examine whether and how interventions such as diets rich in folate, vitamin B6, or vitamin B12 can counteract the effects of air pollution [Overall, this meta-prediction study provided a comprehensive analysis of ollution  on HDP a


High-throughput data generation platforms, like mass-spectrometry, microarrays, and second-generation sequencing are susceptible to batch effects due to run-to-run variation in reagents, equipment, protocols, or personnel. Currently, batch correction methods are not commonly applied to microbiome sequencing datasets. In this paper, we compare different batch-correction methods applied to microbiome case-control studies. We introduce a model-free normalization procedure where features  in case samples are converted to percentiles of the equivalent features in control samples within a study prior to pooling data across studies. We look at how this percentile-normalization method compares to traditional meta-analysis methods for combining independent p-values and to limma and ComBat, widely used batch-correction models developed for RNA microarray data. Overall, we show that percentile-normalization is a simple, non-parametric approach for correcting batch effects and improving sensitivity in case-control meta-analyses. Batch effects are obstacles to comparing results across studies. Traditional meta-analysis techniques for combining p-values from independent studies, like Fisher’s method, are effective but statistically conservative. If batch-effects can be corrected, then statistical tests can be performed on data pooled across studies, increasing sensitivity to detect differences between treatment groups. Here, we show how a simple, model-free approach corrects for batch effects in case-control microbiome datasets. PLOS Computational Biology Methods paper.This is a Data generated by high throughput methods like mass-spectrometry, second-generation sequencing, or microarrays are sensitive to experimental and computational processing , 2. ThisSeveral tools for reducing batch effects in RNA microarray data have been developed. For example, surrogate variable analysis (SVA) estimates a set of inferred variables (eigenvectors) that explain variance associated with putative batch effects . These iUnfortunately, models that often work well for many types of ‘omics data may not be appropriate for microbiome datasets. In microbiome studies, batch effects are often diffuse and conflated with biological signals –12. The Here, we describe a model-free data-normalization procedure for controlling batch effects in case-control microbiome studies that enables pooling data across studies. Case-control studies include a built-in population of control samples  that can be used to normalize the case samples (e.g. diseased subjects). For every feature , the case abundance distributions can be converted to percentiles of the equivalent control abundance distributions . Study-sClostridium difficile induced diarrhea (CDI) [We used a collection of case-control datasets obtained from the MicrobiomeHD database  to validea (CDI) , 36. Forea (CDI) –20, we wTo perform OTU-level analyses across the CRC studies, we downloaded the raw data from all of the MicrobiomeHD datasets that sequenced the V4 region of the 16S gene. We quality filtered and length trimmed each V4 dataset as described in Duvallet et al. (2017) and concatenated these raw, trimmed FASTQ files into one file. We removed any unique sequences that did not appear more than 20 times and clustered the remaining reads with USEARCH  at 97% shttps://doi.org/10.5281/zenodo.569601). Raw data were downloaded from the original studies and processed through our in-house 16S-processing pipeline (https://github.com/thomasgurry/amplicon_sequencing_pipeline) as described previously [For genus-level analyses, OTU tables and metadata were acquired from the MicrobiomeHD database  . Non-met−9 . Due to the zero-replacement step, p-values can shift slightly upon re-analysis with a different random draw, which can lead to the loss or gain of significance for features very near the significance threshold. Within each study, control distributions for each individual OTU or genus were converted into percentiles of themselves and case distributions were converted into percentiles of their corresponding control distribution. We have written a python script that performs percentile-normalization given an OTU table, a list of case sample IDs, and a list of control sample IDs as inputs . A QIIME 2 (https://qiime2.org) plugin for running percentile-normalization is also available .In this procedure, control feature distributions are percentile-normalized against themselves (resulting in a uniform distribution between 0 and 100) and case feature distributions are converted into percentiles of their equivalent control features. Treating our controls as null-hypotheses is motivated by the idea that healthy patients should be treated as similar across datasets, even though we understand that they will differ due to biological as well as technical batch effects. Relative abundance distributions were converted to percentiles using the SciPy v 0.19.0  stats.peFor each disease, we applied ComBat  to the cIn addition to ComBat, we applied a linear batch correction method from the limma package in R . Relativscipy.stats.combine_pvalues; method = ‘fisher’ or method = ‘stouffer’). For Stouffer’s method, weights for each study were calculated as the square root of the number of cases plus the number of controls. OTUs/genera with significant responses in opposing directions across studies were excluded from Fisher and Stouffer analyses.To calculate statistical significance, we restricted our statistical tests to OTUs/genera that occurred in at least one third of control or one third of case samples in order to reduce our multi-test correction penalty. We used the Wilcoxon rank-sum test, as implemented in SciPy v0.19.0 (sicipy.stats.ranksums) , to detein silico titration experiment using the OTU-level data to simulate pooling of control samples from different datasets before calculating significant differences. Healthy samples from one study were mixed with healthy samples from another study at different proportions prior to calculating significant differences in OTU frequencies between cases and controls. Case and control groups were subsampled to 40 samples each. Control samples were substituted by randomly selected samples from another study along a fractional gradient (0–100% control samples from another study). We calculated significant differences between case and control groups using the Wilcoxon rank-sum test and applied an FDR correction. OTUs with q-values ≤ 0.05 were considered significant. The titration experiment was rerun 20 times, and the results were averaged.We ran an Similar to the titration experiment, we ran an OTU-level analysis of how batch-correction methods might impact false-positive rates by randomly selecting 40 control samples from the Baxter et al. (2016) study as artificial ‘controls’ and 40 control samples from the Zeller et al. (2014) study as artificial ‘cases’ (across 20 iterations) for each data type . We then calculated significant differences between these artificial ‘case’ and ‘control’ groups as outlined above to generate p-values for each OTU.To minimize possible biases across data sets, we identified three colorectal cancer (CRC) studies that sequenced the same region of the 16S gene (V4). We reprocessed the raw sequence data from each study in the same quality filtering and OTU picking pipeline to obtain bioinformatically-standardized results. OTUs that occurred in at least one third of case or one third of control samples  were retained for all downstream statistical analyses. Despite standardizing the bioinformatic processing of these data, we saw significant batch effects in healthy patients across studies  and calculated significant OTU-level differences between these groups. We repeated this process twenty times to generate a set of p-value distributions. We found that the fraction of p-values ≤ 0.05 can be as high as ~70% for the non-normalized data  showing significant differences in abundance between cases and controls (FDR q ≤ 0.05). For Zeller, there were 71 control and 40 case samples, with 18 OTUs  that differed significantly across cases and controls (FDR q ≤ 0.05).We next assessed the performance each cross-study analysis method by comparing OTU-level results across two independent CRC datasets. In the Baxter study, there were 172 healthy (control) samples and 120 CRC (case) samples, with 14 OTUs , but not in the within-study results or in the Fisher and Stouffer results. Pooled analysis of percentile-normalized data also identified on Enterobacter OTU enriched in cancer patients, two OTUs from the Lachnospiraceae family that were enriched in controls and one Lachnospiraceae that was enriched in cases, which were missed by the within-study analyses. In all, 18 OTUs were identified in the pooled, percentile-normalized results that were missed by the within-study analyses  are consistent with prior meta-analyses of CRC microbiome studies [In the absence of batch effects, pooling data across datasets of the same disease should increase sensitivity to detect significant cross-study associations. We pooled percentile-normalized, limma-corrected, and ComBat-corrected data, respectively, across Baxter and Zeller studies to look for OTUs that differed significantly across cases and controls. These pooled results were then compared to classic methods for combining p-values from each dataset’s individual results (above). For the percentile-normalized data, we found 39 OTUs that differed significantly between cases and controls (FDR q ≤ 0.05), 21 of which overlapped with the within-study results. The pooled limma-corrected and ComBat-corrected data resulted in 37 and 36 significant OTUs, respectively. 35 of the OTUs identified as significant by ComBat were also significant in the limma results. 30 of the limma results and 29 of the ComBat results were also significant in the percentile-normalization results, respectively. Fisher’s method identified seven significant OTUs from analyses . These a studies , 43. It Clostridium difficile induced diarrhea (CDI), Crohn’s disease (CD), ulcerative colitis (UC), obesity (OB), and CRC. There were a total of 306 unique genera detected across studies. There were two CDI case-control studies: Schubert et al. (2014) had 154 control and 93 case samples [In order to assess the performance of different meta-analysis techniques across a larger set of studies and diseases, we summarized OTU abundances at the genus level for five diseases across 18 studies— samples ; Vincent samples . There w samples ; Morgan  samples ; Willing samples ; Gevers  samples . There w samples ; Goodric samples ; Zhu et  samples ; Jumpert samples ; Ross et samples ; Zupanci samples ; Baxter  samples ; Wu et a samples ; and Zee samples . There w samples , and Che samples .Most genera that differed significantly within a given study were not significant in other studies of the same disease. For example, of the 36 unique genera that showed significant differences within any given OB study, none were found to be significant in all studies I = 0; . Indeed,The number of genera that differed significantly across pooled cases and controls changed depending on how the data were batch-corrected . For eveGemmiger, Bacteroides, and Roseburia showed variable responses across studies, sometimes enriched in controls and other times enriched in cases. The remaining genera showed weak associations within one or two studies, but did not differ significantly across studies (i.e. q ≤ 0.05). These genera that show weak or inconsistent responses across batches may not be reliable disease biomarkers. However, by including larger numbers of CRC studies in future meta-analyses it is likely that some of these genera could pass the significance threshold. Two genera—Lactobacillus and Desulfovibrio—were not significantly different between cases and controls within an individual study, but became significant after pooling  were also found to be significant in the pooled, percentile-normalized results (Mogibacterium and Anaerovorax) showed highly irregular responses across the 11 obesity studies analyzed in this study . Methods for combining p-values from independent studies are quite robust and should probably be considered as a safe alternative to pooling . However, in the case of our obesity analysis, Fisher’s and Stouffer’s methods identified  studies .In conclusion, we present a robust, model-free procedure for transforming each feature in a microbiome case-control dataset into percentiles of its control distribution . The maiS1 Table(XLSX)Click here for additional data file.S1 FigRandom sets of 40 Baxter controls and random sets of 40 Zeller controls were selected for null case-control comparisons (20 iterations). Smaller points show the fraction of p-values ≤ 0.05 within a given iteration, while larger dots show the average value across all 20 iterations. Within each category, smaller points are randomly jittered along the x-axis for better visualization. The fraction of p-values ≤ 0.05 is highly inflated for all methods except percentile-normalization . All OTUs were included in this analysis (i.e. no abundance filter prior to running tests). ComBat and limma show highly skewed p-value distributions when including low-abundance OTUs.(TIFF)Click here for additional data file.S2 FigGray points show data pooled from all 11 obesity studies. Other colors show individual obesity studies. Studies listed in order from left to right: Zhu et al. (2014), Zeevi et al. (2015), Wu et al. (2011), Baxter et al. (2016), Schubert et al. (2014), Zupancic et al. (2012), Ross et al. (2015), Jumpertz et al. (2011), Turnbaugh et al. (2009), Goodrich et al. (2014), Escobar et al. (2014). X symbols indicate the average control percentiles, while + symbols indicate the average case percentiles.(TIFF)Click here for additional data file.S3 FigGray points show data pooled from all 11 obesity studies. Other colors show individual obesity studies. Studies listed in order from left to right: Zhu et al. (2014), Zeevi et al. (2015), Wu et al. (2011), Baxter et al. (2016), Schubert et al. (2014), Zupancic et al. (2012), Ross et al. (2015), Jumpertz et al. (2011), Turnbaugh et al. (2009), Goodrich et al. (2014), Escobar et al. (2014). X symbols indicate the average control percentiles, while + symbols indicate the average case percentiles.(TIFF)Click here for additional data file.


Mycobacterium tuberculosis (M.tb) and regulates immune responses against M.tb pathogen. MIF -173 G > C gene polymorphism may affect immunity in an individual and leads to susceptibility to tuberculosis (TB). A large number of studies have investigated the relevance of this polymorphism with TB risk, but their results were inconclusive. To obtain a precise conclusion, a meta-analysis was performed by retrieving six eligible studies from Google Scholar, PubMed (Medline), and EMBASE online databases. Overall combined analysis suggested increased TB risk between MIF -173 G > C polymorphism and overall risk in four genetic models, i.e., allelic , homozygous , heterozygous  and dominant model . Similarly, increased TB risk was observed in subgroup analysis of Asian ethnicity. No publication bias was observed. These results suggested that MIF -173 G > C variant is a significant risk factor for TB in overall and in Asian populations, and can be used as prognostic marker for TB susceptibility.The macrophage migration inhibitory factor (MIF) is a cytokine that plays an important role in inhibiting the growth of pathogenic  Mycobacterium tuberculosis (M.tb) is the responsible microorganism of tuberculosis (TB) that still remains a second leading cause of death from any infectious disease worldwide. It is estimated that ~2 billion people are affected with M.tb, however only 5–10% of the infected population develop the active form of the disease1. The development of TB is affected by many factors including M.tb virulence, environmental factors, and various other risk factors comprising malnutrition, HIV infection, receipt of immunosuppressive therapy, and diabetes mellitus2. As mentioned earlier, only minority of the infected individuals actually develops clinical disease, this highlights an increased interest in understanding the role of genetic background of M. tuberculosis in infection outcome. Host genetic factors identified, why some people are more or less susceptible to TB infection. Several lines of evidences, including twin studies3, genome-wide linkage studies4, and lately reported genome-wide association studies (GWAS)6 have proven that host genetics strongly influences the susceptibility of TB. Hence, understanding the contribution of host genetic factor(s) in the development of TB infection could be proven as an extremely valuable tool in clinical and medicine research, especially because the susceptibility to an infectious agent lies at least partly hidden or masked in inborn errors or immune response7.MIF) gene is located on chromosome 22q11.2 and encodes a multifunctional cytokine (MIF) that contributes to innate and adaptive immune responses against the pathogenic infection8. MIF is also known as a soluble factor produced by T-lymphocytes that can inhibit the migration and promote the aggregation of macrophages at the sites of local inflammation or infection9. In addition, MIF plays an important role in the regulation of the Th1/Th2 balance in the host’s inflammatory reaction and immune responses10. Two major polymorphisms are found in MIF gene, a functional variant  present within the 5′ promoter region, located at the position -173 involves a G > C substitution, appears to affect promoter activity in a cell-type dependent manner11. Previous clinical studies reported higher level of MIF in the serum of TB patients in comparison with the healthy controls12. Since, MIF protein appears to play a key role in mediating a wide variety of immune responses against the invading pathogens, for e.g., M.tb in humans. A large number of researchers performed genetic association studies to correlate MIF -173 G > C polymorphism with TB infection development18. However, the results from the published studies of -173 G > C gene polymorphism and TB susceptibility are inconsistent and inconclusive. The inconsistency in the results across many of the case-control studies could possibly because of lower sample size and individual studies. Furthermore, as the immune interactions determine TB susceptibility and severity, combined studies may give more meaningful information than a single, independent/individual study in determining the outcome of TB infection. Nowadays, meta-analysis, a statistical tool is used to pool the results from the individual studies to increase the sample size and provides more robust as well as reliable conclusion19. Therefore, a meta-analysis was carried out to evaluate the precise relationship between MIF -173 G > C genetic variant and the risk of developing TB, and its ethnicity based effect on TB infection. In this study, the quality of the included studies was checked by performing Newcastle Ottawa Scale (NOS) analysis. Additionally, publication bias and random errors  occurred due to sparse data were minimized by performing Trial Sequential Analysis (TSA) for quantifying the statistical reliability of the data included in the cumulative meta-analysis with the threshold of statistical significance. This is the primary meta-analysis dealing with the precise appraisal of the association between the MIF -173 G > C gene polymorphism and the risk of TB.Human macrophage migration inhibitory factor  and abstracts/summaries. Complete texts of the potentially germane research articles were further tested for their aptness for inclusion in the present meta-analysis. Studies either showing MIF genetic variant to predict survival in TB patients and/or considering MIF gene polymorphisms as indicators for response to therapy were disqualified straightaway. Likewise, research articles reporting MIF mRNA levels or protein expression or relevant reviews were omitted too. In this study, we included only case-control or cohort design based reports mentioning the frequency of all the three genotypes. Besides the database search, the references available in the retrieved studies were also examined for other probable articles. The major characteristics of all the six studies incorporated in this pooled study, i.e., distributions of genotypes, minor allele frequency (MAF) in the controls and cases, have been shown in Tables According to the literature search strategy applied in this study, as stated in the methodology section, six articlesIn order to evaluate the publication bias among the incorporated studies, Begg’s funnel plot and Egger’s test were carried out in this meta-analysis. The appearance of the shape of funnel plots and the outcomes of Egger’s test have not shown the indication of publication bias in all the five genetic models Table .Table 4S2 statistics were used. Heterogeneity was not observed in all the four studied genetic models. However, heterogeneity was observed in one genetic model CC vs GC + GG. Thus, random effects model was used for synthesizing the data , homozygous , heterozygous , and dominant model . However, recessive  model did not display any risk between MIF -173 G > C polymorphism and overall TB risk , the cumulative Z curve crossed with TSA monitoring boundary confirmed that this polymorphism is associated with an increased risk of TB and further relevant trials are unnecessary Fig. . Similarary Fig. .Figure 4MIF -173 G > C (rs755622) polymorphism with TB susceptibility. The distribution of odds ratios, 95% CI, odds ratio and p-values for all the five genetic models  of overall analysis (2025000 subjects) have been shown in Table MIF -173 G > C (rs755622) polymorphism with TB risk in Asian ethnicity (1283000 subjects). Similar to the re-sampling results of overall analysis, it was found that MIF -173 G > C (rs755622) polymorphism is also linked with TB susceptibility  files provided with this manuscript. A total of 1000 re-sampling groups were created for both, overall and Asian ethnic groups that yielded 2025000 and 1283000 subjects, respectively. Re-sampling statistics of overall analysis revealed significant association of 20. Thus, prevention and control of TB is attracting more and more attention of the medical world. Identification of the host genes responsible for the susceptibility and resistance to TB may lead towards a better understanding of the pathogenesis of TB and management or even help in the treatment strategies. Evidences indicated that host genetic factors may play an important role in TB susceptibility that contains single nucleotide polymorphisms (SNPs) as a major factor21. Immune responses play a major role in the host defense against M. tuberculosis infection and can cause a broad spectrum of clinical manifestations23. Macrophages are the first line of the defense against M.tb infection. Invasive M.tb is eliminated or suppressed by phagosomal and lysosomal fusion, apoptosis induction, and free radicals23. The cytokines produced at the site of the disease after the interaction between T lymphocytes and infected macrophages are necessary for the pathogenesis of TB24.As it is established that TB is a very complex, multi-factorial disease and environmental and host genetic factors are strongly involved in the development of TB infectionMIF is an immunoregulatory cytokine that is expressed by T cells, macrophages and pulmonary epithelial cells, and has been considered as potential defense against the invading pathogen25. In addition, this gene has a key role in innate immune response against the bacteria and intracellular pathogens26. In the recent year, many researchers paid attention on the possible role of MIF gene in the innate immune system, pathogenesis of infection and inflammation28.MIF gene in view, previously many studies examined the possible link between MIF -173 G > C gene variant and the development of TB infection but failed to give concrete conclusion, and still the said relationship is controversial. Until now, there is no well-organized meta-analysis available that evaluated the precise association between MIF -173 G > C gene polymorphism and risk of TB infection development. Therefore, we conducted the present meta-analysis to precisely investigate this possible relationship. On the basis of six independent publications, our meta-analysis clearly produced statistical evidence that MIF -173 G > C variant is significantly associated with an increased risk of TB infection. As we know that host genetic factors are potentially responsible for the development of TB infection20. Hence, a single genetic polymorphism is normally inadequate to prophesized the susceptibility towards this dreadful disease. The important feature of this gene variant is that its occurrence may vary sufficiently among different ethnic populations or races. Therefore, ethnicity-wise sub-group analysis using stratification analysis was performed, which revealed that MIF -173 G > C is more prominent in the Asian subjects. Also, the precise association suggested by the current meta-analysis was confirmed by the TSA, which further escalated the conclusion that MIF -173 G > C polymorphism is correlated with an increased risk of TB. The polymorphism of the promoter region may alter the expression of mRNA and the level of protein. Earlier studies reported that MIF -173 C allele significantly increase the promoter activity of MIF gene26. Earlier, studies stated that circulating levels of MIF concentration were significantly higher in those affected with TB infection than in the healthy controls13. Promoter sequence analysis has deciphered that MIF -173 C allele creates a potential activator protein 4 transcription factor binding site, and the circulating levels of MIF are significantly vary among MIF -173 G > C genotypes29. MIF is also considered an essential component of the host antimicrobial alarm system and plays an important role in the stress response that promotes the proinflammatory functions of immune cells30. Although, it is worth mentioning that the immune interactions between the host and the molecular structure of M.tb are not solely depend upon one single factor or one single gene variant, but rather on many gene variants or gene-environment interactions.Considering the importance of et al.16 did not fit with the HWE test in the control group. The most probable reason for this variation is either the controls were not recruited from exactly the same genetic population as the TB patients or may controls suffered from other infectious diseases. But, after the omission of the study of Li et al.16 during the sensitivity analysis, it did not alter the conclusions observed in the meta-analysis. Fourth, the results presented here in this study were based on unadjusted estimates, as ORs in all the included studies were not adjusted by the same potential confounders, such as age, sex, and exposure. Fifth, some studies included in the present meta-analysis used PCR-RFLP whereas other studies adopted sequencing for the genotyping purpose. The basic difference between these two techniques is that, RFLP-PCR exploits variations in homologous DNA sequences and allows rapid detection of the mutation after genomic sequences PCR amplification followed by specific restriction enzyme digestion and visualization in stained agarose gel. Whereas, DNA sequencing is any chemical, enzymatic or technological procedure for determining the linear order of nucleotide bases in DNA, it is generally used to detect any mutation in the DNA sequence being analyzed and this method is often considered as the ‘gold standard’ for DNA sequence analysis. Both of these methods have their own limitations such as sensitivity, performance ease, and instrumentation constraints etc., that might affect the overall results in one or another way.Regardless of achieving some crucial findings, the present meta-analysis suffered with several limitations. First, we considered the research articles that were published only in the English language, which resulted into limited number of studies. The research studies published in other languages and probable unpublished articles were not included in this meta-analysis, which may cause publication bias. Second, in the stratification analysis by sub-group ethnicity-wise analysis, the numbers of individual studies are less that might have a small statistical power to detect the actual association. Third, the study Li Notwithstanding above limitations, there are some advantages associated with this meta-analysis. First, the quality assessment of the selected studies was stringently performed by applying NOS scale, where most of the studies scored five or more stars in quality assessment score and indicated moderate to good quality of each study by clearly mentioning about the sample size, genotype, and inclusion criteria of confirmed TB patients and healthy controls. Second, in order to achieve more robust conclusion, publication bias analysis was done and the results did not show any publication bias. Third, we minimized the likelihood of bias by following a detailed protocol of study identification, statistical analysis and data selection. Fourth, sensitivity analysis, Trial sequential analysis, and re-sampling statistics further strengthened the conclusion of the present study.MIF -173 G > C gene polymorphism is associated with the development of TB infection in overall and Asian population. MIF genotyping may offer the possibility of identifying individuals those are at a greater risk of TB. Overall, these results would help in understanding the role of this promoter variant in TB infection development and can aid in identifying new molecular targets by focusing on TB patients most likely to get benefit from the pharmacological targeting of MIF. Further well-designed and large sample studies are warranted to confirm the findings reported in this study. Additionally, studies investigating the effect of gene-environment interactions in relation with TB risk are also required.In conclusion, this meta-analysis provides a strong evidence that MIF -173 G > C (rs755622) gene polymorphism and the development of TB conducted on internet using online databases, i.e., PubMed (Medline), Google Scholar, and EMBASE to retrieve the compatible and peer reviewed research articles. The last search updated on February 2017. The pertinent study search was performed using the combination of the following keywords: ‘macrophage migration inhibitory factor OR MIF AND Tuberculosis OR TB susceptibility OR TB risk. Combination of single nucleotide polymorphism (SNP) OR genetic variant OR mutation OR genetic polymorphism of MIF -173 G > C AND/OR MIF rs755622 was also tried for the retrieval of the relevant studies. The search was focused on the studies that had been conducted in humans only. All the retrieve studies were evaluated by reading their titles (headlines) and abstracts/summaries, and all the published articles matching with the pre-set eligibility norms were retrieved for the present meta-analysis. We also did some manual search of the references listed in the retrieved articles for other eligible articles.A comprehensive and systematic search of the relevant studies based on the association between MIF -173 G > C gene polymorphism and susceptibility to TB, (b) adopted a case-control study design, (c) must recruited confirmed TB patients and healthy control subjects, (d) availability of genotype frequency in cases (confirmed TB patients) and controls to count the odds ratio (OR) and 95% confidence intervals, (e) must published in the English language. Likewise, some preset criteria for the study exclusion were also applied. The major reasons for the study exclusion were (i) not designed as a case-control study, (ii) reviews, and abstracts or overlapping studies, (iii) not reported the genotype frequencies or numbers in the studies. The schematic flow diagram of the selection of the pertinent studies for this meta-analysis following the pre-set including/excluding criteria is shown in Fig. In order to include the research articles in the current meta-analysis, retrieved studies had to fulfill all the pre-set criteria, (a) it must have evaluated the relationship between For every retrieved published study, the procedural data extraction was independently performed in replica copies by two independent (self-regulating) investigators (SAD & RKM) by following a standard validated protocol. In order to ratify the accuracy of the abstracted data, strict pre-set selection criteria were followed during the collection of the data by filling a standard data-collection form. The major characteristics summarized from the retrieved articles include the name of the first author, year of publication, the number of cases and controls, the country of origin, type of study, TB type, methods of genotyping and genotype frequencies for the cases and the controls. Cases pertaining to any type of disagreement or discrepancy in any of the items of the abstracted data from the selected articles were fully resolved by conducting many rounds of group discussions involving adjudicator (SH) to accomplish a final agreement.31. The NOS criteria predominantly comprised of three aspects: (i) subject selection: 0–4 points; (ii) comparability of subjects: 0–2 points; (ii) clinical outcomes: 0–3 points. The selected studies, which were awarded 5 stars or more, can be considered as moderate to high quality32. The quality assessment of the selected individual study was performed independently in duplicate by two investigators (as stated above SAD & RKM) and in case of any discord, it was resolved by a systematic discussion or expert consultation, if necessary with the help of adjudicator SH).The methodological quality evaluation of the selected studies was also performed separately by two independent investigators (SAD & RKM) by following the preset NOS criteriaMIF -173 G > C gene variant with the chances of developing TB was assessed by using OR and 95% CI. The pooled ORs were computed for allele contrast, log-additive, recessive, and dominant genetic models33. Heterogeneity assumption between the studies across the eligible comparison was done by the chi-square based Q-test34. Heterogeneity was deliberated significant in case p-value < 0.05, to avoid underestimation of the presence of heterogeneity. A fixed effect model (if p > 0.05) or a random effect model (if p < 0.05)36 was applied for the pooling of the results. In addition, I2 statistics was used to efficiently check the heterogeneity among the chosen studies37. Hardy-Weinberg equilibrium (HWE) in the controls was computed by employing chi-square test. Additionally, funnel plot asymmetry was measured by Egger’s linear regression test (a type of linear-regression approach) that calculates the funnel plot asymmetry based on the natural logarithm scale of the ORs. The significance of the intercept was determined by the t-test, where p-value < 0.05 was deliberated as the symbol of significant statistical publication bias38. The entire statistical analysis involved in this meta-analysis was performed by using Comprehensive Meta-Analysis (CMA) version 2 software program . All the p-values were two sided and the statistical significance level was considered as p-value < 0.05 for this pooled analysis.The precise strength of the association of MIF polymorphism (rs755622) with TB susceptibility40. The details of re-sampling program along with its results are shown in the Supplementary Information (Microsoft Excel File SI5 and Microsoft Excel File SI6). In the re-sampling program, Microsoft excel file data of the recruited studies  comprising 1069 confirmed TB patients and 956 healthy controls were coded as follows: number 1 for TB patients with GG genotype, 2 for TB patients with GC genotype, 3 for TB patients with CC genotype, 4 for healthy controls with GG type, 5 for controls with GC genotype, and 6 for healthy controls with CC genotype. For each re-sampling group, genotypes of 2025 samples were generated by bootstrap method and ORs were calculated for various genetic models. A total sum of 1000 re-sampling groups were created for robust and replicable results. Overall, ORs, 95% CI, range of ORs and p-values were calculated for all the five genetic models  was utilized for performing the present investigation.According to Cochrane handbook, meta-analyses are considered to be the best if all the eligible trials are included in the analysis, however, it may not be able to provide adequate and satisfactory evidence. As meta-analysis may leads to systematic errors (bias) or random errors (play of chance). In order to minimize these errors, a novel statistical analysis program, Trial Sequential Analysis (TSA) tool from Copenhagen Trial Unit, Center for Clinical Intervention Research, Denmark) has been introduced that estimates required information size and adjusts the threshold for the statistical significance and estimates the power of current conclusionSupplementary Information SI1-SI4Supplementary Information: Microsoft Excel File SI5Supplementary Information: Microsoft Excel File SI6


Recently, some studies have focused on the relationship between dietary protein intake and the risk of type 2 diabetes mellitus (T2DM), but the conclusions have been inconsistent. Therefore, in this paper, a systematic review and meta-analysis of cohort studies regarding protein consumption and T2DM risk are conducted in order to present the association between them. We searched the PubMed and Embase databases for cohort studies on dietary protein, high-protein food consumption and risk of T2DM, up to July 2017. A summary of relative risks was compiled by the fixed-effect model or random-effect model. Eleven cohort studies regarded protein intake and T2DM . The summary RR and 95% CI  of T2DM was 1.12 (1.08–1.17) in all subjects, 1.13 (1.04–1.24) in men, and 1.09 (1.04–1.15) in women for total protein; 1.14 (1.09–1.19) in all subjects, 1.23 (1.09–1.38) in men, and 1.11 (1.03–1.19) in women for animal protein; 0.96 (0.88–1.06) in all subjects, 0.98 (0.72–1.34) in men, and 0.92 (0.86–0.98) in women for plant protein. We also compared the association between different food sources of protein and the risk of T2DM. The summary RR (Relative Risk) and 95% CI of T2DM was 1.22 (1.09–1.36) for red meat, 1.39 (1.29–1.49) for processed meat, 1.03 (0.89–1.17) for fish, 1.03 (0.64–1.67) for egg, 0.89 (0.84–0.94) for total dairy products, 0.87 (0.78–0.96) for whole milk, 0.83 (0.70–0.98) for yogurt, 0.74 (0.59–0.93) in women for soy. This meta-analysis shows that total protein and animal protein could increase the risk of T2DM in both males and females, and plant protein decreases the risk of T2DM in females. The association between high-protein food types and T2DM are also different. Red meat and processed meat are risk factors of T2DM, and soy, dairy and dairy products are the protective factors of T2DM. Egg and fish intake are not associated with a decreased risk of T2DM. This research indicates the type of dietary protein and food sources of protein that should be considered for the prevention of diabetes. Type 2 diabetes is rapidly increasing in the world. Ninety percent of patients with diabetes have type 2 diabetes . Type 2 Evidence suggests that dietary factors may influence the risk of T2DM . Many prIn the present study, we conducted a systematic review and meta-analysis of cohort studies to clarify the association of protein consumption with risk of T2DM. In order to provide a better dietary instruction for the lay public, we also conducted a systematic review and meta-analysis of cohort studies to study the association between different kinds of high-protein food and the risk of T2DM.We searched relevant studies from the Embase and PubMed electronic databases from their starting dates to July 2017. Protein: search items included: ‘dietary protein’ or ‘protein intake’ or ‘plant protein’ or ‘animal protein’ or ‘food’ and ‘diabetes’ or ‘diabetes mellitus’ or ‘T2DM’ and ‘population’ or ‘human’.Meat: search items included: ‘red meat’ or ‘processed meat’ or ‘food’ and ‘diabetes’ or ‘diabetes mellitus’ or ‘T2DM’ and ‘population’ or ‘human’.Fish: search items included: ‘fish’ or ‘seafood’ or ‘food’ and ‘diabetes’ or ‘diabetes mellitus’ or ‘T2DM’ and ‘population’ or ‘human’.Egg: search items included: ‘egg’ or ‘food’ and ‘diabetes’ or ‘diabetes mellitus’ or ‘T2DM’ and ‘population’ or ‘human’.Dairy: search items included: ‘dairy’ or ‘milk’ or ‘dairy product’ or ‘yogurt’ or ‘food’ and ‘diabetes’ or ‘diabetes mellitus’ or ‘T2DM’ and ‘population’ or ‘human’.Soy: search items included: ‘soy’ or ‘legume’ or ‘soy product’ or ‘food’ and ‘diabetes’ or ‘diabetes mellitus’ or ‘T2DM’ and ‘population’ or ‘human’.Eligible studies are selected by further manual scanning of all included studies and relevant reference lists.All included studies should match the following criteria:(1) the study must have a cohort design; (2) the endpoint of the study was the incidence or mortality of the T2DM; (3) the study had to report risk ratios and the results had to be within a 95% CI in the paper ; and (4) the study had to show the participants’ intake of dietary protein or other high-protein foods such as meat, fish, egg, dairy, soy.We extracted the following information from each publication we selected: first the author of the publication, publication year, the country where the study was conducted, the number of the samples and cases, T2DM diagnosis and criteria, the year the study began and finished, the years of follow-up, the methods of diet exposure assessment, the RRs and 95% CI, and the adjustment factors .p < 0.05 was considered statistically significant. The black square in the forest plots represents the weight contribution of every study. In order to evaluate the extent of variability, the I2-test statistic was adapted to estimate the heterogeneity [I2 < 50% and p > 0.05, there was no heterogeneity; we used the fixed-effect model. The random-effect model was selected when I2 > 50% or p < 0.05. We used the Egger linear regression test and Begg rank correlation test to search for publication bias. When p > 0.05, there was no publication bias. Comprehensive Meta-Analysis V2 was employed for data analysis.We used effect models to calculate the summary RRs and 95% confidence to compare the highest dietary protein consumption with the lowest dietary protein consumption . Two-sidogeneity .When I2 We included 11 cohort studies in the analysis . Six of Total Protein Eleven cohort studies [I2 = 18.72, p = 0.25) in all subjects, 1.13 (1.04–1.24)  in men, and 1.09(1.04–1.15)  in women. studies ,16,17,1804–1.24) b (I2 = 104–1.15) c  in all subjects, 1.23 (1.09–1.38)  in men, and 1.11 (1.03–1.19)  in women. studies ,16,17,1809–1.38) b, (I2 = 03–1.19) c,  in all subjects. We used a sensitivity analysis to exclude the most influential studies: the summary RR and 95% CI ranged from 0.92 (0.87–0.98) when the European men’s study [I2 = 14.85, p = 0.31). The summary RR and 95% CI was 0.98 (0.72–1.34)  in men. A sensitivity analysis was used to exclude the most influential studies: the summary RR and 95% CI ranged from 0.88 (0.76–1.02) when the European study [I2 = 45.72, p = 0.12) in women. studies ,16,17,18’s study  was excl’s study  was excl’s study  and when72–1.34) b . We used a sensitivity analysis to exclude the most influential studies: the summary RR and 95% CI ranged from1.20 (1.07–1.35) when the U.S. Study [I2 = 27.68, p = 0.17). studies ,29,30,31S. Study  was exclS. Study  was exclS. Study , and wheProcessed Meat Eleven cohort studies [I2 = 49.32, p = 0.03). studies ,29,30,31Fish Nine cohort studies [I2 = 79.71, p < 0.001). We used a sensitivity analysis to exclude the most influential studies: the summary RR ranged from0.99 (0.89–1.10) when the American study [I2 = 79.41, p< 0.001). studies ,36,37,38an study  was exclan study  was exclan study , and wheEgg Five cohort studies [I2 = 91.12, p< 0.001). We used a sensitivity analysis to exclude the most influential studies: the summary RR ranged from 0.97 (0.49–1.88) when the Lithuanian study [I2 = 69.43, p = 0.06). studies ,40,41,42an study  was exclTotalDairyProduct Consumption Eleven cohort studies [I2 = 48.81, p = 0.03).  studies ,50,51,52Whole Milk Seven cohort studies [I2 = 52.20, p = 0.01). We used a sensitivity analysis to exclude the most influential studies: the summary RR ranged from 0.85 (0.76–0.94) when the American study [I2 = 46.87, p = 0.04). studies ,46,47,48an study  was exclYogurt Seven cohort studies [I2 = 62.06, p = 0.01). We used a sensitivity analysis to exclude the most influential studies: the summary RR ranged from 0.81 (0.67–0.97) when the Japanese study [I2 = 40.61, p = 0.11). studies ,47,48,49Soy Eight cohort studies [I2 = 86.57, p < 0.01). We used a sensitivity analysis to exclude the most influential studies: the summary RR ranged from0.82 (0.68–0.98) when the American study [I2 = 69.34, p < 0.01). However, the summary RR for high vs. low soy was 0.74 (0.59–0.93)  in women. We used a sensitivity analysis to exclude the most influential studies: the summary RR ranged from0.66 (0.49–0.90) when the American study [I2 = 0.00, p = 0.67). studies ,55,56,57an study  was excl59–0.93) b, (I2 = an study  was exclNo significant publication bias was detected in the Begg-Mazumdar’s test and Egger’s test of our meta-analyses, provided in According to the results of this meta-analysis, the intake of total protein and animal protein was associated with a high risk of T2DM both in males and females. The intake of plant protein was associated with low risk of T2DM in females, but not in males. In high animal protein food, red meat, and processed meat were associated with a high risk of T2DM in all subjects, while total dairy products, low-fat dairy, and yogurt were associated with a low risk of T2DM in all subjects, and egg and fish were not associated with a decreased risk of T2DM. In high plant protein food, soy was associated with a low risk of T2DM in females.Higher intake of dietary protein is often associated with lifestyles, including physical activity, body weight, smoking, drinking. For example, we already know that overweight and obesity are risk factors for T2DM, and a meta-analysis showed that each unit increase of BMI would increase the risk for T2DM by approximately 20% . In our The statistical power of the results could be significantly increased as the number of studies and the sample size increase, but it could also lead to heterogeneity. Some heterogeneity was due to different participants’ characteristics and regions, and different dietary assessment methods. Thus, heterogeneity is usually used to explain the study characteristics and is difficult to interpret. In our study, the heterogeneities of total protein and animal protein were in the acceptable range, but the heterogeneity of plant protein was outside of the range. We found that the European study  contribuAs meta-analysis is based on published studies, the publication bias affect is inevitable. It is particularly important to evaluate publication bias. In our meta-analysis, we used the Egger linear regression test and Begg rank correlation test to determine publication bias, and we found that there was no publication bias in our study.Dietary protein and amino acids are involved in the modulations of insulin sensitivity and glucose metabolism. However, the results from human studies were still inconsistent. Some studies showed that high intake of dietary protein had negative effects on glucose homeostasis by facilitating insulin resistance and increasing gluconeogenesis ,59,60. AFrom the current literature available, the inconsistency in association between plant protein and T2DM was probably due to gender difference. The negative association between plant protein or soy and T2DM was observed mainly in women, while most of the studies in men found null results. Therefore, results from all subjects without considering gender difference were different and the proportion of women in the study may influence the results. However, the exact mechanism is unclear.In order to further refine and compare the association between different food sources of protein and T2DM, and facilitate dietary guidance, we have analyzed the relationship between different high-protein food and the risk of type 2 diabetes. We found that different high-protein foods play different roles in T2DM, even if they are all animal-based foods. Our results indicated that the intake of red meat and processed meat are risk factors for T2DM. They are also positively associated with weight gain , stroke Our meta-analysis also had limitations. First, publications into our research were adjusted for BMI, but some studies had measurement errors because of self-reporting of height and weight, resulting in the BMI relying on self-reporting, which could lead to confounding results. Second, some important factors that influence T2DM such as fiber, lipids, and carbohydrates, were only adjusted in some of these studies, which may also lead to confounding results. Additionally, limitations might be due to temporal bias. Studies with longer follow-up might beless influenced by temporal bias. In our studies, the follow-up period of each research study was different, so the temporal bias might impact the association between dietary protein intake and the risk of T2DM.In summary, we found that total protein and animal protein consumption were the risk factors for T2DM, and plant protein was the protective factor for T2DM in women, but not in men. We also found that different high-protein foods have a different effect on T2DM risk, even if they all belong to animal proteins. These results underline the significance of taking into account what kind of dietary protein and food sources of protein are recommended for the prevention of diabetes.


TNF-α rs361525 polymorphism has been demonstrated, but remains conflicting and controversial. Therefore, a meta-analysis was conducted to more precisely estimate this relationship. PubMed, Web of Science, EMBASE and CNKI were comprehensively searched to find out relevant articles through October 5, 2017. The strength of the relationship was assessed using pooled odds ratios and 95% confidence intervals. Totally 20 articles were included involving 4,084 cases and 7,010 controls. No significant relationship between TNF-α rs361525 polymorphism and increased GC risk was found in the whole populations. Subgroup analyses uncovered TNF-α rs361525 polymorphism intensified the risk of GC among Asians under five models, but decreased the risk of GC among Caucasiansin the allelic and dominant models. Subgroup analysis by genotyping methods revealed increased risk for other methods. In conclusion, this meta-analysis suggests TNF-α rs361525 polymorphism is related to the risk of GC, especially for Asians.Tumor necrosis factor (TNF)-α, a major part in inflammatory, infectious and tumor processes, and is pivotal at the early stages of gastric cancer. Relationship between its risk and  Helicobacter pylori infection contributes to causing the progression of chronic inflammation to GC  is the fourth major malignancy and the second dominant cause of cancer-induced death in the world -α belonging to the TNF/TNF receptor cytokine superfamily can be found in plasma or serum of healthy people, as well as some cancer patients  and−308 (rs1800629). Some studies demonstrated the relationship between TNF-α rs361525 polymorphism and GC risk  evaluation of relationship between GC risk and TNF-α rs361525 polymorphism; (2) study on humans; (3) provision of enough data for computation of odds ratios (ORs) and 95% confidence intervals (CIs); (4) case- control study. Exclusion criteria were: (1) duplication; (2) case report or review; (3) lack of genotype data; (4) irrelevant topic.From each included study, data including name of first author, country of origin, publication year, ethnicity, age, and genotype numbers in cases and controls was extracted. When more than one ethnicity were involved, genotype data was processed separately. Data extraction and study quality assessment based on the Newcastle-Ottawa Scale (NOS) Stang,  were conhttps://www.gtexportal.org/home/), respectively , the recessive model (AA vs. GA+GG), the heterozygote model (GA vs. GG), the homozygote model (AA vs. GG), and the allele model (A vs. G). Stratification analyses were carried out by ethnicity, source of control (SOC), Hardy-Weinberg equilibrium (HWE), genotyping method and NOS score , a random-effect model was used; otherwise, a fixed-effect model was applied .The strength of relationship between GC risk and The process of study selection is shown in Figure TNF-α rs361525 polymorphism and GC risk were summarized in Tables P = 0.646, Figure TNF-α rs361525 polymorphism intensified the risk of GC among Asians in most of the comparisons  and transformed fibroblasts (P = 6.65 × 10−10) . We believe this meta-analysis has several strengths over previous meta-analyses. Firstly, our sample size was larger. Secondly, sensitivity analysis proved the reliability and stability of our data. Thirdly, we conducted subgroup analyses of ethnicity, HWE, SOC, NOS score, and genotyping methods, and explored the potential sources of heterogeneity. Finally, we calculated false-positive report probability and statistical power.Many studies have reported the relationship between H. pylori infection were conducted. Secondly, potential gene-gene or gene-environment interactions were not assessed. Thirdly, only one SNP of TNF-α gene was investigated. Finally, the relationship between this SNP and clinical manifestations of GC was not examined.However, this study also has some limitations. Firstly, no subgroup analyses of confounding factors such as age, sex, smoking or TNF-α rs361525 polymorphism increases the risk of GC among Asians, but decreases the risk of GC among Caucasians. This finding should be validated by larger case-control studies in other ethnicities.In conclusion, this meta-analysis indicates the ZK conceived the entire study. TX and HZ analyzed the data. TX and ZK performed statistical analysis. ZK and HZ wrote the paper. All authors read and agreed with the final version of this paper.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.