TCF7L2 were most frequently investigated and showed interactions with fiber and whole grain on T2D incidence. Further gene‐diet interactions were reported for, eg, a western dietary pattern with a T2D‐GRS, fat and carbohydrate with IRS1 rs2943641, and heme iron with variants of HFE. Physical activity showed interaction with HNF1B, IRS1, PPARγ, ADRA2B, SLC2A2, and ABCC8 variants and weight loss interventions with ENPP1, PPARγ, ADIPOR2, ADRA2B, TNFα, and LIPC variants. However, most findings represent single study findings obtained in European ethnicities. Although some interactions have been reported, their conclusiveness is still low, as most findings were not yet replicated across multiple study populations.The pathophysiological influence of gene‐lifestyle interactions on the risk to develop type 2 diabetes (T2D) is currently under intensive research. This systematic review summarizes the evidence for gene‐lifestyle interactions regarding T2D incidence. MEDLINE, EMBASE, and Web of Science were systematically searched until 31 January 2019 to identify publication with (a) prospective study design; (b) T2D incidence; (c) gene‐diet, gene‐physical activity, and gene‐weight loss intervention interaction; and (d) population who are healthy or prediabetic. Of 66 eligible publications, 28 reported significant interactions. A variety of different genetic variants and dietary factors were studied. Variants at  DESIREpidemiological Study on the Insulin Resistance SyndromeDPPDiabetes Prevention ProjectDPSDiabetes Prevention StudyEPICEuropean Prospective Investigation into Cancer and NutritionG × Dgene‐dietG × Egene‐environmentG × Lgene‐weight loss due to lifestyle changesG × PAgene‐physical activityGRSgene risk scoreHPFSHealth Professionals Follow‐Up StudyKOGESKorean Genome and Epidemiology StudyLTPAleisure‐time physical activityKAREKOGES and the Korea Association ResourceMDCSMalmö Diet and Cancer studyNOSNewcastle‐Ottawa Quality Assessment ScaleNHSNurses' Health StudyPAphysical activityPREDIMEDPrevención con Dieta MediterráneaPRISMAPreferred Reporting Items for Systematic Reviews and Meta‐AnalysesRCTrandomized controlled trialSDPPStockholm Diabetes Prevention ProgramT2Dtype 2 diabetes mellitus1Type 2 diabetes mellitus (T2D) represents an important health problem, causing enormous costs and individual burden, in part due to related macrovascular and microvascular complications.The evidence of G × E interaction in relation to T2D has been highlighted by several recent reviews.Several new findings of G × E interactions on the risk to develop T2D were recently reported by prospective studies which as well have not yet been systematically summarized. Hence, we aimed to systematically review the current state of evidence relating to G × E interaction and T2D incidence. We focused on prospective studies investigating interactions of genetic variants with diet (G × D), PA (G × PA), or weight loss due to lifestyle changes (G × L).2www.crd.york.ac.uk/prospero/index.asp, identifier CRD42015023898) and followed the Preferred Reporting Items for Systematic Reviews and Meta‐Analyses (PRISMA) guidelines This review was registered in PROSPERO  using the electronic databases PubMed, EMBASE, and ISI Web of Science, with restriction to English written publications. MeSH terms and other terms were used (list S1). Moreover, the reference lists of reviewed articles were checked to identify further eligible publications. S.J., J.S.Z., and S.D. screened titles, abstracts, and full texts in parallel, with disagreement resolved by consensus.2.2Studies were included if they met the following criteria: (a) prospective study design , (b) outcome: T2D incidence, (c) populations who are healthy or prediabetic, and (d) a description of G × D, G × PA, or G × L interaction. Non‐English literature and studies on populations with specific diseases  and/or medications  were excluded. Moreover, we excluded studies without the application of a formal statistical test for interaction Table . Studies2.3P values and risk estimators of interactions.Data extraction was conducted by S.J. or S.D. for the following information: title, authors, publication year, study name, study design, number of participants, ethnicities, age, gender, country, follow‐up time, exposures, exposure assessment methods, interaction terms, and 2.4Significant and nonsignificant interaction findings were objectively equally treated in this review.2.5The risk of bias for cohort study publications was assessed by the Newcastle‐Ottawa Quality Assessment Scale (NOS).The Cochrane Collaboration's tool was applied to assess risk of bias for RCTs.The specific methodological quality of G × E interaction research was assessed by a score following quality criteria important for genetic association studies.3Out of 3002 screened publications Figure , 1075 an3.1Eight publications3.2Fifteen publications of RCTs were published from the Finish Diabetes Prevention Study (DPS), 14 from the American Diabetes Prevention Project (DPP), one from Prevención con Dieta Mediterránea (PREDIMED),In DPP Table , 3234 paIn PREDIMED, participants with high cardiovascular risk were randomized into three groups: two Mediterranean‐diet groups with extra‐virgin olive oil (1 L/week) or mixed nuts (30 g/day), and a control group with advice on a low‐fat diet.3.3The investigated dietary exposure varied considerably across the publications Figure  and incl3.4The NOS quality assessment resulted in high and average quality for 30 and five publications of cohort studies Table , respectThe Cochrane risk of bias assessment resulted in low risk of bias for all RCT Figures  and S2. The evaluation of methodological quality Table  resulted3.5TCF7L2 gene were most frequently investigated, while findings of other genetic variants were often limited to one publication only. Furthermore, publications showed a considerable heterogeneity in investigated dietary factors.Of the 66 eligible publications, 19 cohort studies3.63.6.1TCF7L2 variants have been the most widely studied so far.TCF7L2 variant rs7903146 is of interest as it is considered to be strongly associated with T2D risk and known to modify the effect of incretins on insulin secretion. Some publicationsTCF7L2 variants with fiber and whole‐grain intake on T2D incidence  and rs4457053 in the ZBED3  gene but without replication in another study. Several further variants were investigated in MDCSIn MDCS,3.6.2IRS1 rs2943641 (known to increase insulin sensitivity) IPR rs10423928 (known to decrease insulin secretion).IRS1 rs2943641 findingsGIPR rs10423928 interaction findings could not be replicated in EPIC‐InterAct (12). In NHS,TCF7L2 rs12255372 showed no interaction with carbohydrates, but with glycemic load. A replication of this finding in EPIC‐InterAct failed as well.Interaction of carbohydrates and total fat was reported Figure  by MDCS P = .05) was observed between fat intake and the PPARγ (receptor for fatty acid storage) variants rs1801282 and rs3856806. Furthermore, H‐allele carriers of the FFAR4 variant rs116454156  had a fourfold higher T2D incidence in DESIR CAV2 variant rs2270188 showed an interaction with total fat and saturated fatty acids but a replicate of the CAV2 findings in EPIC‐InterAct failed (12). In addition, no evidence was found in EPIC‐InterAct, NHS, and HPFS for interactions of T2D‐GRSs and IR‐GRS with fat and carbohydrates.Potential interaction of fat with genetic variants was also reported by DESIR3.6.3ADH1C *1/*2 variant ,SLC40A1 (iron‐regulated transporter),TMPRSS6 gene (transmembrane serine proteinase).HFE variants rs1799945 (H63D) or rs1800562 (C282Y). A similar trend was also observed in EPIC‐InterAct for women with the HFE rs1799945 variant, but the corresponding interaction was not significant.SLC40A1 rs744653 variant in men and with borderline significance (P = .046) for the TMPRSS6 variant rs855791 in the total sample.Findings Figure  indicateSLC30A8 (zinc transporter) variants rs16889462, 8_118252314, and 8_118252435, but without a clear trend for an association with T2D incidence in exposure strata.For zinc intake, DPP reported an interaction with the 3.6.5P = .05) with the GIPR variant rs10423928 in EPIC‐InterAct.24Several individual foods have been investigated for potential G × D interaction including red meat, processed meat, olive oil, dairy, and coffee Figure . InteracLCT gene.LCT rs4988235 T‐allele had higher T2D risk compared with persons with the lactose nonpersistence CC‐genotype when they did not consume milk.TCF7L2 variant rs12255372.IGF2BP2), rs7754840 G‐allele (CDKAL1), and rs5215 CC‐genotype (KCNJ11) had a reduced combined prediabetes and T2D risk compared with nonconsumer.The association of dairy products with T2D incidence may be modified by the genetic variant rs4988235 near the lactase persistence 3.6.6P = .052) between the CLOCK variant rs4580704 and the Mediterranean diet was observed indicating lower T2D incidence among persons with the rs4580704 G‐allele compared with persons with the CC‐genotype.Evidence suggests that dietary patterns that reflect Western diet and enhance T2D incidence and those that reflect Mediterranean diet lower T2D incidence.3.6.7HNF1B rs4430796 A‐allele weakened and possibly reversed the protective effect of higher PA on T2D incidence which was observed in persons with the GG‐genotype.IRS1 rs1522813 A‐allele and low levels of PA had a higher risk to develop T2D compared with women with the GG‐genotype, but not if they were physically active.Several findings indicate that the protective effect of PA on T2D risk is modified by genetic variants Tables  and S7. PPARγ rs1801282 Ala‐ and rs17036314 C‐alleles compared with persons with the ProPro‐ and GG‐genotypes.ADRA2B Glu12‐allele, with the ABCC8 rs3758947 GG‐genotype and with the nonrisk haplotype of the four SLC2A2 variants rs5393, rs5394, rs5400, and rs5404, whereas persons with the risk allele seem to be unresponsive to change in LTPA.In DPS, reduced leisure‐time physical activity (LTPA) Table , S8, con3.6.8TCF7L2 rs12255372 risk TT‐genotype showed lower T2D incidence in the intervention group than in the control group.Findings of DPP and DPS indicate that interaction between weight loss interventions to reduce diabetes risk and genetic variants may modify the risk to develop T2D Tables  and S9. ENPP1 rs1044498,MC4R rs17066829.PPARγ rs1152003 CC‐genotype,TNFα rs1800629 GG‐genotype,ADRA2B Glu9‐allele,LIPC rs2070895 A‐alleleOther genetic variants have been investigated only in one RCT study populations, respectively. In DPP, the weight loss intervention was associated with a reduced T2D incidence among persons with the diabetogenic variant of 4TCF7L2 gene were most frequently investigated and showed potential interactions with whole‐grain and fiber intake, although not consistent across all studies. Other G × D interactions were reported for, eg, a western dietary pattern with a T2D‐GRS, fat and carbohydrate with IRS1 rs2943641, and heme iron with variants of HFE. G × E Interactions were also reported for PA with HNF1B, IRS1, PPARγ, ADRA2B, SLC2A2, and ABCC8 variants and for weight loss interventions with ENPP1, PPARγ, ADIPOR2, ADRA2B, TNFα, and LIPC variants. However, the evidence of the reported interactions is hitherto limited as most findings were observed in only one study, mostly of European ethnicity, without validation and replication across multiple study populations.This is the first systematic review summarizing the comprehensive evidence of G × E interactions regarding T2D incidence. Of the 70 eligible publications, 28 publications reported statistically significant G × E interactions. However, the synthesis of findings is limited by the heterogeneity of the investigated genetic variants and dietary exposures. Variants of the TCF7L2, GIPR, CAV2, and HFE gene with various dietary factors could be confirmed in EPIC‐InterAct.TCF7L2 variant rs7903146 with fiber intakeCAV2 rs2270188 variant with fat intake.P values may also cause true interactions to be obscured, especially if the study population is small, and many exposures are tested hypothesis‐free. With regard to the potential interaction of TCF7L2 variants with whole‐grain or fiber intake, false‐positive findings are an unlikely explanation for the lack of reproducibility in EPIC‐InterAct given that such interaction has been observed in some populations without a multiple testing problem.Findings from EPIC‐InterAct demonstrated that replication of interaction findings poses a major challenge. Surprisingly, none of the previous interaction findings for variants of TCF7L2, it is noteworthy that measurement error is a potential explanation. That the commonly observed inverse association between cereal fiber and diabetes risk has also not been detected in EPIC‐InterAct Another issue in replication of G × E interaction findings concerns exposure measurement errors and reporting bias due to self‐reports of dietary intake and PA. This may introduce variations of measured exposures across studies, and in consequence interaction findings may be distorted and thus consistent replication of interaction findings is hampered.−4.P values of the few reported significant interactions in DPS LCT], rs17066829 [MC4R]), which display significant interactions with lifestyle, have previously not been identified to be associated with T2D incidence in GWAS studies. This may indicate that the effect on T2D risk emerges only as consequence of the interaction between the respective genetic variant and the respective lifestyle factor. Of note is also that DPP often applied interaction tests for three study groups: weight loss intervention, placebo, and also Metformin which could have masked potential interaction.An appropriate sample size to avoid statistical underpowered analysis is another important issue in G × E interaction research.TCF7L2, while those with risk alleles benefit less.TCF7L2 rs7903146 T‐risk allele, which has been linked to ß‐cell dysfunction, attenuated insulin secretion and incretin effects, and enhanced rate of hepatic glucose production.TCF7L2 risk alleles can reduce their enhanced T2D risk by weight loss due to a healthier lifestyle.Although some findings are subject to statistical uncertainty and others are unconfirmed, several of the findings suggest that interactions between genetic variants and the modifiable lifestyle factors diet, PA, and weight loss may modify the risk to develop T2D. Findings indicate that a Western diet high in meat intake enhances the risk to develop T2D especially for individuals with higher genetically susceptibility for T2D.TCF7L2 rs7903146 variant is quite common in European ethnicities but not in Asians.KCNQ1 have been found to be more common in Asians than in Danes.Of note, a large proportion of current evidence rely on study samples with European ethnicities resulting in an underrepresentation of other ethnicities. This is of importance as genetic variants occur with varying frequency in the genome depending on ethnicity. For example, the 4.1A strength of the present review is that a comprehensive overview of G × E interaction research regarding T2D incidence is given by including the most important modifiable T2D risk factors diet, PA, and weight loss. Moreover, the focus on prospective studies strengthens the evidence in terms of temporal sequence between G × E interaction and T2D risk development. Finally, this review assessed risk of bias and specific methodological quality of included studies, which mostly showed intermediate and high quality. However, a tool, which is specifically tailored to G × E interaction studies and evaluates both the risk of bias and methodological quality, is not available, to date. The development of such a specific tool should be prioritized in future. Substantial heterogeneity was found with respect to the reported genetic variants and dietary factors, which inhibited meta‐analysis of findings and an evaluation of a possible publication bias. Although our search strategy identified several publications with significant but also nonsignificant interaction findings, a publication bias due to the preferred reporting of significant findings cannot be excluded. In addition, the generalizability of the current evidence was hampered by mostly single study findings and restriction of weight loss interventions to individuals under high risk. Moreover, our systematic review is restricted to the lifestyle factors diet, PA, and weight loss intervention only. Indeed, interactions with genetic variants have also been reported for other lifestyle factors. However, the three reviewed lifestyle factors are considered as main risk factors for T2D.5TCF7L2 rs7903146 with whole grain have been consistently replicated in more than one independent study. A large number of other potential interactions have to be validated first in order to strengthen their evidence. Other potential interactions may be obscured due to limited statistical power. Further analysis in large‐scale studies and formation of collaborative project will possibly bring clarity. However, gene‐environment meta‐analyses across several collaborating studies may be powerful but are very likely to be confronted with issues of comparability and quality of exposure measures. Hence, a prerequisite for such projects is well‐validated, accurate, and precise measured exposures in collaborative studies. Moreover, usage of repeated exposure measurements would also be desirable to facilitate temporal inference. As T2D is a complex, multicausal disorder, limited heritability may be explained by individual variants. The ensemble of genetic variants to form genetic risk scores may therefore be more appropriate to investigate gene‐lifestyle interactions for T2D incidence and should be used more intensively. Investigation of mediator effects of epigenetic markers, which may influence gene‐lifestyle interactions, may also give new insights in the future.Although several studies reported gene‐lifestyle interactions, the strength of evidence for modifying effects of these interactions on T2D incidence is still weak. Most of the interaction findings have not yet been replicated across multiple study populations. So far, only interactions of No conflict of interest was declared.This work was funded by the European Union's Seventh Framework Programme for research, technological development, and demonstration under grant agreement no 602068. This work was partly supported by a grant from the German Ministry of Education and Research (BMBF) and the State of Brandenburg (DZD grant 82DZD00302). J. Zheng was funded by the Medical Research Council Epidemiology Unit MC_UU_12015/5 and the European Union's Horizon 2020 research and innovation programme under the Marie Sklodowska‐Curie grant agreement no 701708.Table S1: Judgment score for methodical quality of studiesTable S2: Quality assessment of cohort studies by using the Newcastle‐Ottawa ScaleTable S3: Methodical quality assessments of included cohort studies and randomized control trialsTable S4: Prospective studies which investigated gene‐lifestyle interaction regarding T2D incidence but did not apply a statistical test for interactionTable S5: Assessment of T2D incidence in cohort studiesTable S6: Findings for interaction between genetic variants and diet in relation to T2D incidenceTable S7: Findings for interaction between genetic variants and physical activity in relation to T2D incidenceTable S8: Findings for interaction between genetic variants and change in leisure‐time physical activity in relation to T2D incidenceTable S9: Findings for interaction between genetic variants and lifestyle intervention for weight reduction in relation to T2D incidenceFigure S1: Risk of bias summary: review authors' judgements about each risk of bias item for each included intervention studyFigure S2: Risk of bias graph: review authors' judgements about each risk of bias item presented as percentages across all included studiesClick here for additional data file.


Barrett's esophagus (BE) is a well‐established risk factor for esophageal adenocarcinoma. Our objective was to investigate the effectiveness of lifestyle interventions on BE risk.We searched PubMed, Embase, and Web of Science up to 30 September 2020. The summary relative risks (RRs) and 95% confidence intervals (CIs) for the highest versus lowest categories of exposure were assessed. Analyses of subgroup, dose–response, sensitivity, and publication bias were conducted.p < 0.05). There was no publication bias for smoking and alcohol. Although the analysis suggested significant evidence of publication bias for BMI, sensitivity analysis showed that the changes in the recalculated RRs were not significant.Sixty‐two studies were included that involved more than 250,157 participants and 22,608 cases. Seven lifestyle factors were investigated: smoking, alcohol, body mass index (BMI), physical activity, sleep time, medication, and diet. We observed statistically significant increased BE risks for smoking , alcohol intake , body fatness , less sleep time , and proton pump inhibitors use . Reduced risks of BE were found for aspirin  and the intake of vitamin C , folate , and fiber . The quality of most included studies was high and the subgroup analysis according to the quality score showed significant results (The large meta‐analysis revealed that lifestyle modifications could reduce the risks of BE and, consequently, esophageal adenocarcinoma. We conducted a large meta‐analysis based on sufficient evidence and a quantitative synthesis of the eligible data published up to 30 September 2020, to investigate the effectiveness of lifestyle interventions on Barrett's esophagus risk. In this meta‐analysis of 62 studies that involved more than 250,000 participants and 22,000 cases, we found that smoking, alcohol intake, high levels of body fatness, and less sleep time are associated with Barrett's esophagus risk. There are statistically significant reduced risks of Barrett's esophagus with aspirin use and vitamin C intake, folate, and dietary fiber. In a word, these findings strengthen our understanding of the potential mechanisms of Barrett's esophagus development and highlight awareness that lifestyle intervention could reduce the risks of Barrett's esophagus and, consequently, esophageal adenocarcinoma. Details of the search terms  were: “lifestyle,” “risk(s),” “diet/dietary,” “food(s),” “smoking/smoker/tobacco/cigarette(s),” “drink/drinking/alcohol/ethanol/alcoholic/beverage(s)/wine/beer/spirits/liquor,” “fatness/obesity/obese/obeseness/adiposity/overweight/weight/body mass index/BMI/waist hip ratio/waist circumference/hip circumference,” “physical activity/exercise,” “sleep/nocturnal,” “medicine/medical/medication(s),” “nonsteroidal anti‐inflammatory drug(s)/NSAID(s)/ibuprofen/diclofenac/naproxen/indomethacin/mefenamic acid/piroxicam/ketoprofen/etodolac/meloxicam/rofecoxib/flurbiprofen/phenylbutazone/aspirin,” “proton pump inhibitor(s)/PPI(s)/omeprazole/pantoprazole/esomeprazole/lansoprazole/dexlansoprazole,” “statin(s)/hydroxymethylglutaryl‐CoA reductase inhibitor/simvastatin/lovastatin,” “nutrition/nutrient,” “vitamin(s),” “folate/folic acid,” “fiber(s)/fibre(s),” “meat(s)/fish/poultry/chicken/turkey/duck,” and “selenium” in combination with “Barrett's/Barrett,” “esophagus/oesophagus/esophageal/oesophageal/neoplasia/neoplasm/neoplasms,” The two sets of keywords were combined individually, and the eligibility criteria were independently judged by two reviewers (ZZ and ZY).2.3The study quality of cohort studies and case–control studies was assessed using the Newcastle–Ottawa Scale (NOS).A data extraction sheet was generated for each study. Detailed information included the first author, publication year, country, study type, study period, study population, assessment method, type of exposure measured, exposure categories, adjusted RR (95% CI), adjusted variables, and quality score.2.4SPSS 22.0  was used to collect and extract data. RevMan5.3  software was used for the synthesis and analysis of data based on relative risks (RRs) and 95% confidence intervals (95% CIs).We conducted this meta‐analysis for the risk of BE and smoking, BMI, physical activity, sleep duration, medications, and dietary factors. Medications included aspirin, NSAIDs, PPIs, and statins. Dietary factors included alcohol, vitamin C, folate, selenium, total meat, and white meat. Stratified analysis was not performed for alcohol. Beer, wine, and spirits were included. Fruits, vegetables, fat, red meat, and processed meat were excluded because we have previously analyzed these issues.2I statistics p < 0.1 was considered representative of significant heterogeneity). Gastroesophageal reflux disease (GERD) is a well‐established risk factor for the development of BE.Heterogeneity among studies was detected using p < 0.1 was considered significant publication bias).Publication bias was assessed using funnel plots and Egger's test   Figure . The cha3.2.3p = 0.72, 2I = 0%). The dose–response analysis of the number of cigarettes/day was not conducted due to the limited studies.Four studies were included in the analysis for the highest to lowest number of cigarettes/day Figure , and a f3.2.4Pnonlinearity < 0.01, Figure We conducted a dose–response analysis to further explore the association between pack‐years of smoking and BE risk. Six studies were included, and the results of 1.10 (1.05–1.14) indicated that BE risk increases by 10% for each 10‐year increment. We further checked for nonlinearity of the dose–response association, and the evidence suggested that the best‐fitting model was a nonlinear model , but subgroup analyses (Table There was significant heterogeneity (3.2.6p < 0.1), but the funnel plot provided a visible result of no publication bias observed in Figure A funnel plot, Begg's test, and Egger's test were used to explore the publication bias. Indeed, Egger's test indicated evidence of publication bias   and subgroup analyses also suggested that the positive association was stable by all of the confounders (Table There was no significant heterogeneity (rs Table .3.3.3p = 0.34) suggested no significant evidence of publication bias. Additionally, the sensitivity analysis also showed that the changes in the recalculated RRs were not significant, with a range from 1.20 (1.09–1.33) when excluding Avidan 2001 (27.2%) to 1.26 (1.15–1.37) when excluding Park 2009 (5.9%).The funnel plot Figure  and Egge3.43.4.1Twenty‐two studies that involved 211,607 participants were included, and a random‐effects model yielded a positive association  Figure .3.4.22 increase in BMI. We further checked for nonlinearity of the dose–response association, and the evidence suggested that the best‐fitting model was a nonlinear model  indicated that the BE risk increases by 8% for each 5 kg/m3.4.3p < 0.01, 2I = 63%), but subgroup analyses (Table There was significant heterogeneity (es Table  suggeste3.4.4p < 0.1) suggested significant evidence of publication bias. However, sensitivity analysis showed that the changes in the recalculated RRs were not significant, with a range from 1.06 (1.02–1.10) when excluding Edelstein 2007 (1.4%) to 1.18 (1.08–1.29) when excluding Thrift 2014 (29.8%).Indeed, the funnel plot Figure  and EggeFive studies investigated BE risk and aspects of waist‐to‐hip ratio (WHR), waist circumference, hip circumference, waist‐to‐thigh ratio, and visceral adiposity. These investigations suggested that high WHR,3.53.5.1p = 0.77, 2I = 0%)  without heterogeneity  Figure . Additio3.5.2p = 0.53, 2I = 0%)  without heterogeneity  Figure . The cha3.63.6.1p = 0.04, 2I = 52%)  with heterogeneity  Figure . Additio3.6.2Six studies were included that involved 3,742 participants. A fixed‐effects model yielded an inversed association  Figure . The cha3.6.3Seven studies were included that involved 14,908 participants. A significant increased BE risk was observed  Figure . The sen3.6.4Four studies were included that involved 4,845 participants. A significant reduced BE risk was observed  Figure . The sen3.7Significant inverse associations were observed between BE risks and the highest versus lowest intakes of vitamin C  may be an independent risk factor for BE development. Nevertheless, long‐term smoking cessation may diminish this risk,http://monographs.iarc.fr/ENG/Classification/ClassificationsGroupOrder.pdf) and the World Cancer Research Fund International  have classified alcohol as a Group 1 carcinogen for esophageal cancer. Thus, a decreased intake of alcohol is advisable to reduce the risk of BE and esophageal adenocarcinoma.A significant 23% increased risk was observed for highest versus lowest alcohol intake and BE risk, which was supported by the results of detailed subgroup analyses. Moreover, the International Agency for Research on Cancer . Thus, keeping the weight as low as possible within the healthy range is helpful to reduce the risks of BE and esophageal adenocarcinoma.Although Qumseya et alPhysical activity is often considered an inverse factor for esophageal cancer.H pylori infection in combination with antibiotics, and positive H pylori infection is associated with a reduced risk of BE.It is surprising that aspirin use was protective against BE with a 30% lower risk, but that nonaspirin NSAID use was not. The results were consistent with the results of the largest studyA 41% decreased risk was observed for the highest versus lowest intake of vitamin C and BE risk. Protective associations were also observed with the intake of folate and dietary fiber. Analyses for the highest versus lowest intakes of total meat, white meat, and selenium yielded nonsignificant risks of BE. Systematic analyses could not be conducted for other common dietary factors, such as other vitamins, calcium, tea, and coffee, because of the limited studies, and further studies are required to further validate our findings and to reveal these uncertain conclusions.Our study has several strengths. The first strength was that our systematic analysis was based on the main modifiable lifestyle factors, the substantial sample size and the quantitative synthesis of the eligible data, which provided sufficient robust and reliable evidence and increased the statistical power of our findings. Second, we performed detailed subgroup analyses and dose–response analyses to further detect the associations rather than simply conducting categorical comparisons. These independent analyses provided accurate evaluations and strengthened the conclusion. Third, we broadly and systematically searched three large databases to identify studies published from inception through 30 September 2020, and the reference lists of the included studies were also searched manually to identify additional literature. Two reviewers selected the studies and extracted the data independently and in duplicate, which increased the validity of our analyses. Last, 62 included studies were identified from 16 countries or regions in the Americas, Europe, Asia, and Australia, which increased the generalizability of our results.Hpylori infection, and hiatal hernia) could still not be obtained in several studies. Thus, our results should be considered carefully due to possible confounding. Third, the ranges of the highest to lowest category varied in the included studies, which influenced the accuracy of the results to some extent, and we cannot thoroughly exclude the potential bias. Nevertheless, to reduce the bias to a large extent, the pooled results for the highest compared with lowest category were adopted and pooled, and the results were further verified by dose–response analyses, which yielded results similar to the original analyses. Finally, the language of the included studies was limited to English, which may lead to potential selection bias.However, the limitations of the present meta‐analysis should be taken into consideration. First, the diagnostic criteria of BE may vary among the included studies.5This large systematic analysis demonstrated that smoking, alcohol intake, high BMI, and less sleep time are associated with BE risk. There are statistically significant reduced risks of BE with aspirin use and the intake of vitamin C, folate, and dietary fiber. Our findings strengthen our understanding of the potential mechanisms of BE development and highlight an awareness that lifestyle interventions may reduce the risks of BE and, consequently, esophageal adenocarcinoma.The authors declare no potential conflict of interest.Zhanwei Zhao and Zifang Yin wrote the main manuscript and participated in the study design and the data analysis. Chaojun Zhang completed the design of the work. All the authors have reviewed the manuscript text. ZZ and ZY contributed equally to this work.Fig S1‐S4Click here for additional data file.


COVID-19 outbreak has engulfed different parts of the world, affecting more than 163 million people and causing more than 3 million deaths worldwide due to human transmission. Thus, it has become critical to identify the risk factors and laboratory parameters to identify patients who have high chances of worsening clinical symptoms or poor clinical outcomes. Therefore, the study aims to identify inflammatory markers that can help identify patients at increased risk for progression to critical illness, thus decreasing the risk of any mortality. Our study focussed on the predictive utility of C-reactive protein, Interleukin-6, D-dimer and Procalcitonin in assisting the management of COVID-19 patients with adverse clinical effects. Through literature search in electronic databases, we included the retrospective studies that evaluated the biomarkers among confirmed COVID-19 patients before initiation of treatment and who had a definite outcome (dead or discharged). Biomarkers were expressed in standardized difference in mean value, calculated based on study sizes and mean values between survivors and non-survivors considered the effect size. We carried out a meta-regression analysis to identify the causes of the heterogeneity between the studies.P < 0.05) inferring that the mean scores of these marker were significantly higher among the non-survivors compared to the survivors. Two studies were eligible for Procalcitonin marker and there was no heterogeniety (I2-statistics = 0) between these studies. Therefore, fixed-effect model revealed that the overall effect size (95% CI) for Procalcitonin was 0.75 (0.30–1.21) Nanograms/millilitre was also high among non-survivors.Number of studies eligible for C-reactive protein, D-dimer and Interleukin-6 markers were eight, seven and four, respectively. Using random effect model revealed that the overall effect size with 95% confidence interval (CI) for C-reactive protein, D-dimer and Interleukin-6 were 1.45 (0.79–2.12) milligrams/litre, 1.12 (0.64–1.59) micrograms/millilitre Fibrinogen Equivalent Units and 1.34 (0.43–2.24) picograms/millilitre respectively was statistically significant (The study found that serum levels of C-reactive protein, Interleukin-6 and D-dimer showed significant elevation in non-survivors compared to survivors. Raised inflammatory markers aid in the risk stratification of COVID-19 patients and their proper management. Thus, the COVID-19 pandemic has become a principal concern to nations worldwide. Thus, it has become critical to identify the risk factors and laboratory parameters to identify patients who have high chances of worsening clinical symptoms or poor clinical outcomes. Studies have suggested that the Cytokine storm has emerged as an essential factor in the etiopathogenesis of fatal effects of COVID-19, predisposing the COVID-19 patients to heightened lung damage called acute respiratory distress leading to higher morbidity and mortality  retrospective study analysed the laboratory investigations of rRT-PCR confirmed COVID-19 patients who had a definite outcome (dead or discharged) (2) studies investigating serum C-reactive protein (CRP), D-dimer, Interleukin-6 (IL-6), and Procalcitonin (PCT); (3) blood samples were collected before initiation of treatment. We excluded the studies if (1) language of the abstract or full paper was in any language except English (2) Median, interquartile range of the laboratory investigations in survivor and non-survivors were not present (3) they were case series, case reports, meta-analysis, systematic reviews and editorials.Through searching the electronic databases such as Medicine: MEDLINE (through PUBMED interface), EMBASE, Google Scholar, Science Direct and Cochrane library, we identified articles. We included the articles published from December 2019 to May 2020, with search keys “C-reactive protein”, “Interleukin-6”, “D-dime”, “Procalcitonin”, “COVID-19”, and combinations of these keys. We reviewed the full text of the articles to decide their inclusion for meta-analysis.We extracted data from the selected studies such as author, publication year, country, study design, outcome, laboratory values. PRISMA flow diagram describes the number of studies screened and included for meta-analysis.The primary outcome was to assess the levels of various biomarkers such as CRP, D-dimer, IL-6 and PCT. We presented these biomarkers as median and interquartile range (IQR) values in the majority of studies. Therefore, we derived mean values and standard deviations (SD) for the present analysis, prerequisites to calculate the effect size of continuous variables in the meta-analysis. We derived mean and SD values using the formula as recommended in an earlier study  software version 3.0 . We calculated individual study-specific effect size with its 95% confidence interval (CI) in the first stage. We obtained an overall effect size as a weighted (inverse of the effect size variance) average of the individual summary statistics in the second stage. Since each study had different samples, the sampling error variability is likely high in a meta-analysis. The other source of heterogeneity might be due to characteristics of the patients, variations in the treatment, design quality and so on. Therefore, assessing the heterogeneity in meta-analysis is crucial because the presence versus the absence of true heterogeneity (between studies variability) can affect the statistical model. We tested the presence of true heterogeneity using the P < 0.05.We depicted the effect size with a 95% confidence interval (CI) for each study and the overall effect size in forest plots. We tested the effect size consistency using sensitivity analysis by leaving one study approach. Using the funnel plot and Egger regression test, we assessed publication bias between the studies. Further, to identify potential factors for heterogeneity, we carried out a meta-regression analysis of the effect size on various covariates such as age, fever rate and cough rate of the patients. For statistical significance, we considered PRISMA flow diagram Fig.  shows thP < 0.050), inferring that the mean score of CRP marker was significantly higher among the non-survivors compared to the survivors  was about 90%, and therefore the random effect model revealed that the overall effect size (95% CI) was 1.45 (95% CI: 0.79–2.12) milligrams/litre. To ensure the consistency of the effect size, we carried out a sensitivity analysis by leaving one study approach. The analysis  demonstrated that the effect size was statistically significant (P = 0.944), confirming the absence of publication bias. Since there was high heterogeneity between the studies, we carried out meta-regression to identify possible significant factors among the reported covariates, such as age, fever, and cough rate. Effect size  and fever rate . The variable cough rate could not establish a significant (P = 0.967) factor  to determine publication bias, showing Fig. A that thize Fig. B was tenP < 0.050), inferring that the mean score of the D-dimer marker was significantly higher among the non-survivors compared to the survivors  was 82%, and therefore the random effect model revealed that the overall effect size (95% CI) was 1.12 (95% CI: 0.64–1.59) micrograms/millilitre . Sensitivity analysis  demonstrated that the effect size was statistically significant (% demonstP = 0.851), confirming the absence of publication bias. Meta-regression of the effect size on covariates showed that effect size  and fever rate . The variable cough rate did not emerge as a significant (P = 0.957) factor .The funnel plot showed Fig. A that thize Fig. B was tenP < 0.050), inferring that the mean score of the IL-6 marker was significantly higher among the non-survivors compared to the survivors  was 87%, and therefore the random effect model revealed that the overall effect size (95% CI) was 1.34 (95% CI: 0.43–2.24) picograms/millilitre. Sensitivity analysis , confirming the absence of publication bias. In addition, meta-regression of the effect size on covariates showed that the effect size was not significantly influenced by age , indicating that the IL-6 marker was significantly higher among non-survivors with higher cough rates , inferring that the mean score of the PCT marker was significantly higher among the non-survivors compared to the survivors  was found to be 0%, and therefore the fixed-effect model revealed that the overall effect size (95% CI) was 0.75 (95% CI: 0.30–1.21) nanograms/millilitre. Sensitivity analysis  to assess the association of severity with lab parameters. They concluded markers such as D-dimer, CRP, ferritin, PCT were significantly elevated in patients with severe COVID-19 (Henry et al. Additionally, they reported that IL-6 levels were associated increased risk of mortality. However, in October 2020, Leisman et al. suggested that role of cytokine release syndrome is questionable in the etiopathogenesis of severe or critical cases of COVID-19 as mean IL-6 in these conditions were significantly lower as compared to that in other inflammatory syndromes such as Sepsis, ARDS and CAR Tcell induced cytokine release syndrome (Leisman et al. Our study suggests incorporating these CRP, IL-6 and D-dimer markers to design discriminatory tools and risk stratification tools to adequately identify COVID-19 patients with poor clinical outcomes.


TYK2 gene and autoimmune diseases; however, results arestill inconclusive. Thus, we conducted a systematic review followed bymeta-analysis. A literature search was performed to find studies thatinvestigated associations between TYK2 SNPs and autoimmunediseases . Pooled odds ratios (OR) with 95 % CI werecalculated using random (REM) or fixed (FEM) effects models in the Stata 11.0Software. Thirty-four articles were eligible for inclusion in the meta-analyses,comprising 9 different SNPs: rs280496, rs280500, rs280523, rs280519, rs2304256,rs12720270, rs12720356, rs34536443, and rs35018800. Meta-analysis results showedthe minor alleles of rs2304256, rs12720270, rs12720356, rs34536443, andrs35018800 SNPs were associated with protection against autoimmune diseases.Moreover, the A allele of the rs280519 SNP was associated with risk for systemiclupus erythematosus. Our meta-analyses demonstrated that the rs2304256,rs12720270, rs12720356, rs34536443, rs35018800, and rs280519 SNPs in theTYK2 gene are associated with different autoimmunediseases.Autoimmune diseases are characterized by the loss of self-tolerance, leading toimmune-mediated tissue destruction and chronic inflammation. Tyrosine kinase 2(TYK2) protein plays a key role in immunity and apoptosis pathways. Studies havereported associations between single nucleotide polymorphisms (SNPs) in the After IFN-α binding to IFNAR1, TYK2 and JAK1 proteinsare activated, leading to the recruitment and phosphorylation of the signal oftransducers and activators of transcription (STAT) 1 and 2. STAT1/2 heterodimersthen translocate to the nucleus, where they are major regulators of the expressionof a number of IFN-stimulated genes . TYK2 isTYK2 SNPsand autoimmune and inflammatory diseases. The authors showed an association betweenthe TYK2 rs2304256 and rs34536443 SNPs and MS, RA, SLE, CD, and UC.TYK2SNPs with SLE and RA, showing the rs2304256 and rs1270356 minor alleles wereassociated with protection against these rheumatic diseases. Five other SNPs were not associated withthese diseases; however, the number of studies included in their meta-analyses wassmall. A recent meta-analysis suggested the association of the TYK2rs2304256 C allele with risk for SLE in Europeans (3 studies) but not in Asians (3studies), while the rs12720270 and rs280519 SNPs were not associated with SLE (3studies each)  in this gene are associated withdifferent autoimmune diseases, including multiple sclerosis (MS) , SLE Ta, Crohn’sTYK2 SNPs and autoimmunediseases, we performed a literature search in Embase and PubMed resources. Forthis, the following MeSH terms were applied:  AND . The search was completedon June, 2020, and was restricted to papers written in English, Spanish orPortuguese. Studies were also searched in the GWAS Catalog(https://www.ebi.ac.uk/gwas/).This systematic review was performed and described following PRISMA and MOOSEguidelines , and itsEligibility assessment was done by reviewing titles and abstracts of all articlesselected, and when abstracts did not provide necessary information, the fulltext of the article was analyzed. This was performed independently, in astandardized manner, by two investigators (C.D. and F.M.P.), as previouslydescribed in other meta-analyses from our group . DiscordTYK2 SNPs in patients with some of the autoimmunediseases included in the MESH terms (cases) and individuals without anyautoimmune condition (controls). Exclusion criteria were as follows: 1) Studiesthat did not have sufficient data to estimate an OR with 95 % CI; and 2) Studieswhere genotype distributions in control group deviated from those predicted bythe Hardy-Weinberg equilibrium (HWE).Studies were considered eligible if they had case-control designs and evaluatedone or more Data were individually extracted by two researchers (C.D. and F.M.P.) using astandardized form , and agrThe Newcastle-Ottawa Scale (NOS) for case-control studies was used to analyze thequality of each eligible study . Two inv2 tests were used to evaluate whether the genotypefrequencies in the control groups were in agreement with those predicted by theHardy-Weinberg Equilibrium (HWE). Gene-disease associations were measured usingOR (95 % CI) calculations based on allele contrast, additive, recessive, anddominant inheritance models, which were categorized as suggested by 2-based Cochran’s Q statistic and inconsistencywas calculated using the I2 metric. When P < 0.10 for the Q testand/or I2 > 50 %, heterogeneity among studies was consideredsignificant. Then, the DerSimonian and Laird random effects model (REM) was usedto calculate OR (95 % CI) for each study and for the pooled effect. In the lackof significant heterogeneity, the fixed effect model (FEM) was used for thiscalculation ; When significant inter-study heterogeneities were observed, sensitivity analyseswere carried out in order to estimate the influence of each individual study onthe meta-analysis results obtained when assuming the allele contrast model. Thiswas performed by repeating meta-analyses excluding a different study each time.Our results showed two studies  explaineFunnel plots and Egger’s tests were performed to investigate the presence ofpossible publication bias in those meta-analyses containing at least 10 studies,which were those performed for the rs12720356, rs2304256, rs280519, andrs34536443 SNPs . No signTYK2 SNPs onsusceptibility for autoimmune diseases, we performed meta-analyses of 34 publishedarticles on the field. Our results suggest the minor alleles of rs2304256,rs12720270, rs12720356, rs34536443, and rs35018800 SNPs are associated withprotection against autoimmune diseases, while the rs280519A allele is associatedwith risk for SLE. The rs280496, rs280500, and rs280523 SNPs do not seem to beassociated with autoimmune diseases in the investigated populations.To further investigate the possible effects of the TYK2 gene  and showed the A allele of this SNP is associated with risk for SLE. ThisSNP does not seem to be associated with CD, UC, and Pso. In contrast, two previousmeta-analyses did not show any association of this SNP with autoimmune diseases. The disYK2 gene . No studYK2 gene ; thus, tOur meta-analysis for the rs2304256 SNP included 25 studies (23 827 cases and 35 760controls) and showed the A allele of this SNP is associated with protection againstautoimmune diseases  in all inheritance modelsanalyzed. This association was confirmed in SLE and MS diseases, although the lackof individual associations with CD, UC, T1DM, and RA might be due to the smallnumber of studies/sample sizes for each disease. In addition, after stratificationby ethnicity, this SNP remained associated with autoimmune diseases in Caucasians but not in Asians orpopulations of mixed ethnicity (from Southern Brazil), which can be attributed tothe fact that the studies in Asian and Brazilian populations presented a smallnumber of subjects. Our results regarding this SNP are in agreement with the resultsof two previous meta-analyses . ThemetIFNAR1 on cell membranes . Interestingly, TYK2 inhibitiondecreased cytokine-induced apoptosis and pro-inflammatory pathways in pancreaticbeta-cells via inhibition of the IFN-I signaling and consequent decrease in STAT1/2phosphorylation  region, which is a crucial domain forinteraction of TYK2 with IFNAR1 and its function, maintaining the expression ofembranes . Li et aembranes  showed trylation . Thus,tTYK2. This region is required for the binding of IFN-I toIFNAR1  ofthis SNP. PBMCs from subjects carrying the C allele showed reduced phosphorylated(p)-STAT4 levels after induction with IL-12 compared to the A/A genotype, suggestingthe rs12720356 C allele may have a functional impact on TYK2 function and,consequently, immunity  and showed the C allele of this SNP provides protection for autoimmunediseases  under allele contrast and dominantmodels. Among the 20 studies that evaluated this SNP, 18 were performed in Caucasiansubjects. Accordingly, the meta-analysis conducted by oIFNAR1 . Enerbäcimmunity .-8), which is in accordance to our results  and showed the C allele is associated with protection against autoimmunediseases  under both allele contrast and dominantmodels. This association was confirmed for SLE and MS; however, for IBD, Pso, andT1DM, we had a small number of studies to individually conclude about theassociations with these diseases. Of note, most of the studies were performed inCaucasian subjects. Moreover, two studies  were not results  alsoperThe rs34536443 SNP is located in exon 21 and causes a change of a proline to alanineat position 1104 within the kinase domain of TYK2 . The C aTYK2 gene, mostspecifically 36 nt upstream of the intron 7/exon 8 boundary  and showed the A allele of this SNP was associated with protection againstSLE. In contrast, 3 previous meta-analyses , includiboundary . The rs1Our meta-analysis for the rs35018800 SNP included 8 studies (61 241 cases and 163 386controls) and demonstrated the A allele of this SNP is associated with protectionagainst autoimmune diseases  in Caucasiansubjects. No study has evaluated this SNP in other ethnicities. We were not able todetermine if this SNP provides differential protection for a given autoimmunedisease since we had a small number of studies for each disease. The rs35018800 SNPcauses a substitution of an alanine to valine at position 928 within the kinasedomain of TYK2 . TodateAutoimmune diseases share common etiological pathways and, as a result, they may alsoshare some similar genetic factors . Indeed,Despite all the efforts, the results of the present meta-analysis should beinterpreted within the context of few limitations. First, we tried to retrieve allpublished articles, but we cannot exclude the possibility that small negativestudies could have been lost. Although we did not observe publication bias for thers2304256, rs280519, and rs34536443 SNPs, a significant publication bias was presentin the meta-analysis of the rs12720356 SNP. However, Trim and Fill analysisdemonstrated that the adjusted effect did not change significantly, indicating thatthe number of missing studies needed to reverse the bias is smaller than the numberof missing studies needed to nullify the effect . Second,TYK2 SNP on susceptibility for different autoimmune diseases.Functional studies are also needed to elucidate which are the TYK2SNPs with the highest impact on TYK2 function and, consequently, onautoimmune diseases.In conclusion, our results suggest that the minor alleles of the rs2304256,rs12720270, rs12720356, rs34536443, and rs35018800 SNPs are involved in theprotection against autoimmune diseases, and the A allele of the rs280519 SNP isassociated with risk for SLE. In addition, our results indicate that the rs280496,rs280500, and rs280523 SNPs are not associated with autoimmune diseases. Additionalstudies with larger sample sizes are necessary to clarify the impact of each


White matter hyperintensities are the commonest manifestation of cerebral small vesseldisease, associated with stroke, functional impairment, and cognitive decline. They arecommonly preceded by hypertension, but the magnitude and clinical importance of thisassociation is unclear.Quantify the relationship between blood pressure and white matter hyperintensitiesacross studies.PubMed and EMBASE were searched for studies reporting associations between concurrentor historic blood pressure and white matter hyperintensities. Beta coefficients fromlinear models were extracted, whether standardized, unstandardized, unadjusted oradjusted for age, sex, and cardiovascular risk factors. Beta-coefficients were combinedby fixed and random effects meta-analysis, combining standardized beta-coefficients orunstandardized coefficients measured by consistent methods.2 = 65%), including for concurrent readings  orreadings five years previously , and for younger or olderpopulations . Unstandardized, adjustedassociations were similar for raw WMHV, log-transformed WMHV, or WMHV as percentage ofintracranial volume. Unadjusted associations with systolic blood pressure (SBP) weregreater . However, whileassociations with DBP were weaker than SBP ,they were minimally affected by adjustment for age.Twenty-five of 3230 papers were eligible, including 53,392 participants. Systolic bloodpressure was significantly associated with white matter hyperintensity volume (WMHV)after maximal adjustment (standardized beta 0.096, 95%CI 0.06–0.133, p < 0.001,IA standard deviation increase in SBP is associated with 10% of a standard deviationincrease in WMHV, providing the current best estimate of the potential reduction inprogression of white matter hyperintensities expected with good control of bloodpressure. They are associated with up to 30% of strokes and 40% of dementia, as well as later life refractory depression and functional impairment. They are most strongly associated with hypertension, both systolic blood pressure, particularly in later life and diastolic bloodpressure, particularly in mid-life, reflecting increased arterial pulsatility and increased arterial stiffness. Intensive treatment of hypertension reduces progression of these markers of vascularaging, and in post hoc analyses of clinical trials, it reduces rates of progression of WMH. However, the magnitude of the potential benefit in preventing WMH by control ofhypertension is unclear, as well as the optimal time of intervention and blood pressure target.White matter hyperintensities (WMH) of presumed vascular origin are the commonestmanifestation of cerebral small vessel disease, This reflects transition from mid-life hypertensive phenotypes characterized bysympathetic overactivity, transitioning to late-life phenotypes characterized by increasedarterial stiffness, small vessel rarefraction, and greater arterial pulsatility in agingblood vessels. This interaction may therefore be reflected in the long-term relationship between SBPor DBP with WMH.The relationship between SBP or DBP and WMH appears to interact with age. Systolic bloodpressure (SBP) increases linearly with age, while DBP initially rises before declining afterapproximately 55 years of age for men and 58 years of age for women.Therefore, we performed a systematic review and meta-analysis to estimate the magnitude ofthe association between different blood pressure parameters and WMH, stratified by age andtime of exposure. Publication bias was assessed by funnel plots. The protocol for this search wasregistered and published, via PROSPERO. The data that support the findings of this studyare available from the corresponding author upon reasonable request, and are all availablein published journals.PubMed and EMBASE  were searched from databaseinception to 1 December 2020, restricted to human studies in English . Secondary searches were performed in the largeststudies in which the primary report did not provide adequate data to enable meta-analysis.Included studies reported a quantitative association between blood pressure level , or pulse pressure (PP) and WMH on magnetic resonanceimaging (MRI), assessed on either T2 or FLAIR imaging, using validated software forcalculation of WMH. Study titles, abstracts, and appropriate full texts were searchedsequentially (IW), with all potentially included full text articles independently reviewedby two reviewers  in accordance with pre-specified criteria defined in theprotocol. Reference lists of relevant studies were also searched. Assessment of studyquality for included papers was carried out using the NIH study quality assessment tool. Where the standard error of the beta-coefficient was not reported, it was estimatedfrom reported beta, p-value, and degrees of freedom of the model (n−1-number ofcovariates), or else it was imputed by the ratio of the study size of the study inquestion to the size of studies where an inverse variance could be determined.The primary extracted outcome was the beta coefficient between blood pressure index andvolume of WMH reported in linear models, where WMH volume was reported quantitatively,whether raw, log-transformed, logged, or normalized to the intracranial volume.Standardized and unstandardized beta-coefficients were included, adjusted and unadjustedfor covariates. Other variables extracted included demographics of the included population, study characteristics, inclusion/exclusioncriteria, interval between BP measurement and MRI imaging, method of quantification ofWMH, and details of analytical models . Measures of uncertainty of variables were extracted where available,including standard deviation (SD), standard error or interquartile range. Where possible,unstandardized beta coefficients were converted to standardized via multiplication of thecoefficient by the ratio of SD of BP to SD of white matter hyperintensity volume (WMHV)and vice versa. Where necessary, SD of WMHV was estimated from the interquartile range,including for logged WMHV, by interquartile range/1.35, as recommended by the Cochrane Handbook. Heterogeneity was assessed via I2 statistics and Χ2 test. Theprimary analysis was a meta-analysis of standardized beta-coefficients adjusted for age,gender, and cardiovascular risk factors, including results with at least adjustment forage and gender. Further meta-analyses were performed including unadjusted, standardizedbeta-coefficients and non-standardized beta-coefficients, unadjusted and adjusted,stratified by whether WMH were logged or calculated as proportion of intracranialvolume.Beta coefficients were combined by fixed and random effects meta-analysis, weighted bythe inverse variance.3 to 13.9 cm3.A total of 3230 papers were identified in the primary search, and 578 in secondarysearches. Following screening of titles, 1198 papers were reviewed as abstracts and 786papers were reviewed in full, with 25 papers eligible for inclusion. Standardized betacoefficients were reported or could be calculated in 12 papers (13 populations) for SBP,nine papers for DBP, three papers for MBP, and two papers for PP. In total, there were53,392 participants, with participant numbers ranging from 56 individuals to 37,026individuals (Supplementary Table 1). The mean age ranged from 31.7 to 85.8, while the meanWMHV ranged from 0.012 cmOf the 25 included papers, quality varied from poor to high, with the majority of papers ofmoderate quality (Supplementary Table 2). There was limited evidence of publication bias, with evidence for reduced reporting of small studies with null ornegative associations, but not sufficient to have a significant impact on the summaryestimates.1320 In univariate comparisons, withoutadjustment for age, the standardized beta-coefficient was 0.273 reducing to a mean 0.096 standardized beta-coefficient after adjustment for cardiovascularrisk factors, partially reflecting the strong association between age and SBP. Thisrepresents an approximately 10% SD increase in WMH per SD increase in SBP. This was similareven when excluding the largest study .In 12 populations including 44,570 people, across all studies, increased systolic bloodpressure was significantly associated with increased severity of WMH , although this difference was not significant . However3greater WMH per 1 mmHg increase in SBP in non-transformed associations, or a 0.011% increasein the proportion of intracranial volume occupied by WMH. However, there was significantheterogeneity in the five studies reporting results by log of WMH, limiting conclusionsabout the magnitude of the effect. In papers reporting unstandardized beta-coefficients, or where this could be estimated fromstandardized beta-coefficients, SBP was consistently associated with WMHV, whether WMH werelog-transformed, divided by the intracranial volume or both . This coAssociations between SBP and WMH were not significantly different in populations with amean age below 65 comparedIn a moderate number of studies, including more than 50,000 participants, there was astrong and largely consistent association between systolic blood pressure or diastolic bloodpressure with severity of WMH. Overall, an SD increase in SBP was associated withapproximately a 10% of an SD increase in WMH, after adjustment for age, sex, and other majorcardiovascular risk factors. This effect was consistent with associations reportingunstandardized associations between SBP or DBP and WMHV. There was no significant differencein associations with concurrent or historic blood pressure, and only non-significant trendsto a stronger association in younger populations or a stronger association with SBP thanDBP. a 14.2 mmHg difference in blood pressure for intensive vs. standard treatment wasassociated with a reduction in progression of WMH by 0.54cm over four years, equating to approximately a 10–15% reduction in the population SD ofWMH volume at follow-up, although with no change in cognitive decline. This is a marginally greater magnitude of effect to the association demonstrated inthis meta-analysis, whether standardized or unstandardized, but the populations aredifferent and this analysis is cross-sectional rather than assessing the association withprogression of WMH. Nonetheless, this confirms that reducing blood pressure results in theexpected reduction in WMH, supporting a likely causative relationship between BP and WMHseverity.In the SPRINT-MIND study,16 This likely reflects theheterogeneity between studies in this meta-analysis. In particular, all studies included arange of patient ages, and the mean age in any specific population is a poor surrogate forestimating the effect in younger vs. older participants. The meta-analysis did demonstrate anon-significantly stronger association between concurrent SBP and WMHV than concurrent DBPand WMHV, which is consistent with the difference in effect size in older participants inother studies.In this meta-analysis, there was no significant difference between associations of SBP andDBP with WMHV, or between concurrent and historic measures. This is in contrast to limitedreports from individual studies suggesting a stronger concurrent association between SBP andWMHV, particularly in the elderly, but a stronger association between historic, mid-life DBPwith late life WMHV. which is significantly reduced after adjustment for age. This may result inunderestimation of the potential effect of controlling SBP in reducing WMHV, as theage-related increase in SBP may still be preventable. In contrast, there was littledifference in the association between DBP and WMHV before and after adjustment for age. Thisis also consistent with the non-linear association between age and DBP, with a rise in DBPuntil the age of 55, followed by a fall in DBP resulting in no significant impact ofadjusting for age. However, given that the association with DBP is largely independent of age, this mayalso demonstrate that a greater proportion of preventable WMH may reflect elevated DBP, andmay still be of greater significance in younger patients. Unfortunately, the limited numberof studies focused only on younger or older patients and reporting DBP, means that thismeta-analysis is not powered to test this hypothesis as suggested in large individualstudies.16In unadjusted analyses, there was a much stronger association between SBP and WMHV, aswould be expected from the strong linear association between SBP and age, vs. non open source, in-house methods), but there were too few studies with eachmethod to assess this. Finally, despite broad initial search terms and a secondary searchfor specific large studies, it is likely that not all reports of the relationship between BPand WMH were identified, as this value may often be reported in full texts of papers wherethe principal focus of the paper is elsewhere. As such, key terms are not always present inthe abstract or title, and may not be identified by a targeted search strategy.This meta-analysis has several limitations. Firstly, there were a relatively limited numberof papers that reported sufficient data to be included in the meta-analysis, with manypapers reporting qualitative or semi-quantitative outcomes, including from large, seminalcohorts . As a result, althoughsome stratification of standardized beta-coefficients for SBP was possible, this was notfeasible for other analyses. Secondly, there was a significant variation in the analysisused in different studies, with differences in studies reporting standardized orunstandardized beta coefficients, and whether WMHV was log-transformed or divided by theintracranial volume. Although in some studies it was possible to convert betweenstandardized and unstandardized values, there was frequently insufficient data for this.Furthermore, in many studies it was not clear whether standardized or unstandardizedcoefficients were reported. However, enough studies were reported for each method ofanalysis for SBP and DBP to enable a meta-analysis to be performed, although this was notthe case for MBP or PP. Ideally, reporting of this type of study should be standardizedacross studies, with standard outcome measures, reporting of both standardized andunstandardized coefficients, and should follow CONSORT guidelines. Thirdly, the timeintervals between measurement of BP and WMH varied significantly in the limited studies thatreported historic BP and current WMH, preventing a detailed assessment of any temporalgradient of the effect. Fourthly, there remained significant unexplained heterogeneitybetween studies, which may well relate to differences in methods of acquiring scans (T2 vs.FLAIR), differences in scanner field (1.5 T vs. 3 T), and differences in methods ofquantification of WMH (BIANCAOverall, this study identifies a 10% SD of WMH increase per SD increase in SBP. Thisstrongly covaries with age, while the relationship with DBP was largely unchanged byadjustment for age. The relative effect size was largely consistent across different studydesigns, populations, and methods of data acquisition, as well as between methods ofmeasurement, and was consistent with effect sizes from control of blood pressure inrandomized controlled trials.Click here for additional data file.Supplemental material, sj-pdf-1-wso-10.1177_17474930211043364 for Consistency ofassociations of systolic and diastolic blood pressure with white matter hyperintensities:A meta-analysis by Imogen Wilkinson and Alastair John Stewart Webb in InternationalJournal of Stroke


Yet there is still no explicit evidence strongly supporting that PM2.5 with per unit increment can increase the risk of hemorrhagic stroke (HS). Literatures were searched from PubMed, Cochrane, and Embase. After the systemic review of relevant studies, random effects model was used to perform meta-analysis and to evaluate the association between PM2.5 and risk of HS. Seven cohort studies were finally included, involving more than 6 million people and 37,667 endpoint events . Total scores of quality assessment were 50. Pooled hazard ratio (HR) for crude HRs was 1.13 (95%CI: 1.09–1.17) . Pooled HR of subgroup analysis for current smoking with exposure to growing PM2.5 was 1.14 (95%CI: 0.92–2.15) and for never and former smoking was 1.04 (95%CI: 0.74–1.46). Ambient PM2.5 level is significantly associated with the risk of HS, which might be a potential risk factor of HS. Smoking does not further increase the risk of HS under exposure of PM2.5.Ambient fine particulate matter of 2.5 μm or less in diameter (PM According to the data from the World Health Organization, 58% of premature deaths were related to outdoor air pollution. Especially, the fatal effects of air pollution were presented as ischemic heart diseases and strokes resulted from exposure to fine particulate matter of 2.5 μm or less in diameter (PM2.5). Moreover, hemorrhagic stroke (HS) accounted for one-third of strokes. Therefore, PM2.5 might be closely related to risk of HS. Recently, Sheng Yuan .This systemic review was performed according to the protocol published on the database of International Platform of Registered Systematic Review and Meta-analysis Protocols  for article management. All literatures were independently reviewed and analyzed by two authors (Kai Zhao and Yu Guo). If there is inconsistency between them, another author (Mingfei Yang) would be consulted or the literature search strategy would be modified to reach a consensus.The full strategy of searching literature in PubMed and Cochrane was as follows: (((((((((Stroke[MeSH Terms]) OR Strokes[Title/Abstract]) OR “Cerebrovascular Accident*”[Title/Abstract]) OR CVA*[Title/Abstract]) OR Apoplexy[Title/Abstract]) OR “Vascular Accident*, Brain”[Title/Abstract]) OR “Brain Vascular Accident*”[Title/Abstract]) OR stroke[Title/Abstract])) AND (((((((((((“Air Pollution”[MeSH Terms]) OR “Pollution, Air”[Title/Abstract]) OR “Air Quality”[Title/Abstract]) OR “Particulate Matter”[MeSH Terms]) OR “Airborne Particulate Matter”[Title/Abstract]) OR “Particulate Matter, Airborne”[Title/Abstract]) OR “Air Pollutant*, Particulate”[Title/Abstract]) OR “Particulate Air Pollutant*”[Title/Abstract]) OR “Pollutant*, Particulate Air”[Title/Abstract]) OR “Ambient Particulate Matter”[Title/Abstract]) OR “Particulate Matter, Ambient”[Title/Abstract]).The full strategy of searching literature in Embase was as follows:  and .2.5” and “hemorrhagic stroke.” Final exclusion criterion: absence of hazards ratio (HR) or “increase of PM2.5.” Screening of articles was conducted by one author (Kai Zhao) and examined and verified by another author (Jing Li). The third author (Mingfei Yang) would settle disagreement.Articles from three electronic databases were pooled to complete the preparation of screening. After deleting the duplications, we established two initial criteria for inclusion and exclusion, respectively. Inclusion criteria included cohort studies, “stroke” and “particulate matter” found in title or abstract. Exclusion criteria: studies without cohort design, animals, or special people enrolled as subjective of studies, and no MeSH term in abstract. After completion of initial screening, final criterion was used to select studies for meta-analysis. Final inclusion criterion for full texts of article: “PM2.5, and HR with 95% confidence interval (CI) were extracted. When there were different results of HR with 95%CI for different models adjusted for various covariates, data and relative information of all models were extracted.The data of first author’s name, publication year, study region, exposure, period, total numbers of participants, gender ratio , definition of endpoints, increase extent of PM2.5, and other factors that could influence the outcome, objectivity, and accuracy of determination of endpoint.All enrolled studies were independently assessed by two authors (Kai Zhao and Yu Guo) according to Newcastle-Ottawa Quality Assessment Scale Cohort Studies (NOS). All eligible studies were assessed from three main aspects: representativeness of participants included, levels of ambient PMThere were various factors that were adjusted to different proportional hazard models. If the coefficient of each factor was supplied in full text, crude data would be restored by conversion formula. The data adjusted by the least factors would not be selected to perform data processing, unless there was no way to acquire crude data. Likewise, data adjusted by different factors would be processed in the same way.2) was used to test the heterogeneity. Funnel plot asymmetry and Egger’s regression were used for detecting publication bias. Sensitivity analysis was used to attenuate heterogeneity, in which each study was omitted one by one and the pooled HR of the rest studies was retrieved or the random effects model was switched to fixed effects model. Meta-analysis was completed through corresponding modules in Software for Statistics and Data Science . All p values were two-sided with a significant level at 0.05.The pooled HR and 95%CI was calculated by random effects model. I-square . After removing duplicates, 1975 articles were initially included. After excluding reviews, researches of animals, and titles and abstracts that were not conformed to the theme of “PMAll the studies were assessed by NOS. Six studies were scored 7 points and one study was scored 8 points. All studies were scored in representativeness of the exposed cohort, ascertainment of exposure, comparability of cohorts on the basis of the design or analysis, assessment of outcome, and a long follow-up duration. Only one study , gender (5 studies), education (4 studies), body mass index (4 studies), and smoking status (5 studies). Besides, 2 studies exhibited crude HR . Nevertheless, different editions and ranges of items in detail might result in bias of the meta-analysis. Three studies , which indicated a positive association between exposure to growing PM2.5 and incidence or mortality of HS. Yet the heterogeneity of these studies was significant . There was no publication bias according to Funnel plot and Egger’s regression  until the study . Publication bias was not evident according to Funnel plot and Egger’s regression . Subgroup analysis and its sensitivity analysis carried out through switching the random effects model to fixed effects model is shown in Fig. According to the definite fact that smoking could damage the vascular endothelium, smoking might aggravate the effect of PM2.5 is a kind of the inhaled particulates that could enter the circulatory system via permeating alveolar epithelium and vascular endothelium. Therefore, PM2.5 might be transmitted to the arteries in the brain and deposited on the surface of vascular endothelium, further leading to HS. Thus, effect of deposition or particulates trundle might result in inflammation and injury of vascular endothelium. Finally, the suffered arteries might have corrosion damaging the vessel wall.PM2.5. Many articles reported relationship between long-term exposure to PM2.5 and increased risks of incident stroke  and the subgroup analysis outcomes of smoking status were 1.08  for never smoking, 1.11  for former smoking, and 1.08  for current smoking. This meta-analysis included less literatures than previous studies and had un-unified PM2.5 increment levels. Besides, due to limited data of included studies, the subgroups in this meta-analysis were divided into the current smoking subgroup and the subgroup of never smoking and former smoking.Chronic diseases  at the beginning period of a study. Exposure measurement is related to the sensitivity and accuracy of the outcome; therefore, the third scientific institutions and proper mathematical models would be used. As to adjustment for covariates, according to this meta-analysis, educated levels, classification of body mass index, and different age grade might be new researchable variables, which can be arranged to appropriate scales for subgroup analysis in the future. Definition of endpoints would be standardized and detailed according to professional items in the world; especially, adequacy of follow-up of cohorts will be manifested in articles.In perspective of recommendations for further research about relationship between PM2.5 was not unified, which ranged from 1.4 μg/m3 to 10 μg/m3. Numerous covariates were concealed in data synthesis, which might result in bias or heterogeneity of the meta-analysis and subgroup analysis.As previously mentioned, there were several limitations of this systemic review. Only 7 articles from 3 open electronic databases during 2017 to 2019 were included in meta-analysis. Therefore, those not published in open platform were lost. Moreover, increment of PM2.5 level was significantly associated with the risk of HS, which might be a potential risk factor of HS. Additionally, under exposure of PM2.5, smoking does not further increase the risk of HS.Ambient PM


Insulin resistance has been demonstrated to be involved in the pathogenesis of atherosclerotic cardiovascular diseases (ASCVDs). This study evaluated the association between the triglyceride–glucose (TyG) index, a novel surrogate indicator of insulin resistance, and the incidence of ASCVDs in people without ASCVDs at baseline by performing a meta-analysis.Cohort studies reporting the multivariate-adjusted association between the TyG index and the incidence of ASCVDs were obtained by searching the PubMed and Embase databases. A random-effects model incorporating intra-study heterogeneity was applied to combine the results.2 = 80%, P < 0.001]. This finding was consistent with the meta-analysis results with the TyG index analyzed as a continuous variable . Subgroup analyses suggested that the age, sex, and diabetic status did not significantly affect the association . Moreover, participants with the highest TyG index category were independently associated with a higher risk of coronary artery disease  and stroke .Eight cohort studies comprising 5,731,294 participants were included in this meta-analysis. The results showed that compared to those with the lowest TyG index category, participants with the highest TyG index category were independently associated with a higher risk of ASCVDs [hazard ratio (HR): 1.61, 95% confidence interval (CI) 1.29–2.01, IA higher TyG index may be independently associated with a higher incidence of ASCVDs, CAD, and stroke in people without ASCVDs at baseline. Despite significant advances in the prevention and treatment of cardiovascular diseases, atherosclerotic cardiovascular diseases (ASCVDs), which mainly include coronary artery disease (CAD) and stroke, remain one of the leading causes of death worldwide , 2. EstaThe Meta-analysis of Observational Studies in Epidemiology  StatemenElectronic databases including PubMed and Embase were searched with the combination of the following terms: (1) “TyG index” OR “triglyceride-glucose index” OR “triglyceride and glucose index” OR “triglyceride glucose index” OR “triacylglycerol glucose index;” and (2) “atherosclerotic cardiovascular disease” OR “ASCVD” OR “cardiovascular events” OR “MACE” OR “cardiovascular” OR “coronary artery disease” OR “CAD” OR “CHD” OR “stroke.” Filters were applied so that only studies conducted in humans and published in English or Chinese were considered. The reference lists of related original and review articles were manually searched for potential eligible studies. The final literature search was performed on January 5, 2021.Studies fulfilling all of the following criteria were included: (1) cohort studies published as full-length articles; (2) included an adult population without ASCVDs at baseline; (3) TyG index was measured at baseline; (4) the outcome of interest was CAD, stroke, or a composite outcome of ASCVD; and (5) reported the relative risk for the association after adjustment of potential confounding factors. The TyG index was calculated as ln[TG (mg/dL) × FPG (mg/dL)/2] . A compoThe literature search, data extraction, and quality assessment of the included studies were performed by two authors independently, according to the predefined criteria. Discrepancies were resolved by consensus. The extracted data were as follows: (1) name of the first author, publication year, and country; (2) study design characteristics; (3) participant characteristics, including health status, sample size, age, sex, and proportion of patients with diabetes; (4) patterns for TyG index analysis; (5) follow-up duration; (6) outcomes reported and methods for outcome validation; and (7) confounding factors adjusted in the multivariate analyses. The quality of each study was evaluated using the Newcastle–Ottawa Scale . This sc2 statistic [2 > 50%, a significant heterogeneity was considered. A random-effects model was used to synthesize the HR data because this model is considered as a more generalized method to incorporate the potential heterogeneity among included studies [Hazard ratios (HRs) and their corresponding 95% confidence intervals (CIs) were applied as the general measure for the association between the TyG index and ASCVDs, CAD, and stroke in an adult population without ASCVDs at baseline. For studies with the TyG index analyzed as a categorical variable, the HRs of the ASCVD incidence in participants with the highest TyG index level compared to those with the lowest TyG index level were extracted. For studies with the TyG index analyzed as a continuous variable, the HRs of the ASCVD incidence per 1-unit increment of the TyG index was extracted. Data of the HRs and their standard errors were calculated from the 95% CIs or P values; then they were logarithmically transformed for variance stabilization and distribution normalization . Cochrantatistic . If I2 > studies . Sensiti studies . Predefi studies . RevMan Figure The characteristics of the included studies are summarized in Table 2 = 80%, P < 0.001; Fig. 2 = 61%, P < 0.001; Fig. The pooled results of five studies , 22, 23 2 = 92%, P < 0.001; Fig. 2 = 89%, P < 0.001; Fig. 2 = 0%, P < 0.001; Fig. The pooled results of six studies , 22–25 sThe funnel plots depicting the association between the serum TyG index analyzed as a categorical variable and ASCVD and CAD are shown in Fig. In this meta-analysis of cohort studies, compared to patients with the lowest TyG index category, those with the highest category were independently associated with an increased incidence of ASCVDs, CAD, and stroke. For ASCVDs, subgroup analyses showed that the association between the TyG index and the subsequent incidence of an ASCVD was not significantly affected by the age, sex, or diabetic status of the participants. Moreover, meta-analysis with the TyG index analyzed as a continuous variable also showed that a higher TyG index at baseline was independently associated with an increased risk of the subsequent incidence of ASCVD or CAD. Taken together, these results suggested that a higher TyG index may be an independent predictor of an increased risk of ASCVD incidence in a population without ASCVDs at baseline.To the best of our knowledge, this study is the first meta-analysis to summarize the association between the TyG index and the ASCVD incidence in a general population without ASCVDs at baseline. Only cohort studies were included; therefore, the potential recall bias associated with studies having a cross-sectional design was avoided. In addition, only studies with multivariate analyses were included. The results indicated a potential independent association between a higher TyG index and an increased risk of ASCVDs. Moreover, meta-analyses were performed separately with the TyG index analyzed as a categorical variable as well as a continuous variable, and similar results were obtained, thus confirming the robustness of the findings. Furthermore, multiple sensitivity and subgroup analyses were performed to confirm the stability of the findings, which were not driven by a single study or affected by participant characteristics such as age, sex, or diabetic status. Pathophysiologically, the current results reflect the previously suggested role of insulin resistance in the pathogenesis of atherosclerosis , 10. It The current study supports the potential of the TyG index to be used as an indicator of ASCVD risk in a general population without ASCVDs. Methodologically, the TyG index can be easily calculated in real-world clinical practice based on routine blood biochemical tests in a cost-effective manner. A previous study has shown that the TyG index is highly sensitive (96.5%) and specific (85.0%) for the detection of insulin resistance, compared to the hyperinsulinemic-euglycemic clamp test . FurtherDespite the above strengths and potential clinical implications, this meta-analysis has some limitations that should be considered when interpreting the results. First, a limited number of studies was available for this meta-analysis, and significant heterogeneity was detected among them. Additional studies are needed to determine whether other study characteristics can affect the results, such as ethnicity and comorbidities of the participants, follow-up duration, and concurrent medications. In addition, the sample sizes of the included studies varied significantly. For example, for the outcome of stroke, the weight of the study by Hong 2000 is significantly larger than the other two, and the result of the meta-analysis was mainly driven by the result of this study. Second, it remains unknown whether the association between the TyG index and an increased risk of ASCVDs is linear and what the optimal cut-off value of the TyG index is for the prediction of future risk of ASCVDs. Third, residual confounding factors possibly affecting the association between the TyG index and the ASCVD risk, such as the dietary and nutritional factors that may affect the TyG index, could not be excluded . FinallyIn conclusion, the existing evidence from cohort studies suggests that a higher TyG index may be an independent predictor of ASCVD risk in people without ASCVDs at baseline. Due to the convenience of measuring the TyG index in clinical settings, future studies are needed to determine whether incorporation of the TyG index on top of the ASCVD risk prediction tools currently used can improve their predictive efficacies.


Several large-scale studies suggest that Bacille Calmette–Guerin (BCG) vaccination in early childhood may reduce the risk of atopic diseases, but the findings remain controversial. Here, we aimed to investigate the potential correlation between early childhood BCG vaccination and the risk of developing atopic diseases.  Eligible studies published on PubMed, EMBASE, and Cochrane CENTRAL were systematically sourced from 1950 to July 2021. Studies with over 100 participants and focusing on the association between BCG vaccine and atopic diseases including eczema, asthma, and rhinitis were included. Preliminary assessment of methods, interventions, outcomes, and study quality was performed by two independent investigators. Odds ratio (OR) with 95% confidence interval (CI) was calculated. Random effects of the meta-analysis were performed to define pooled estimates of the effects.  Twenty studies with a total of 222,928 participants were selected. The quantitative analysis revealed that administering BCG vaccine in early childhood reduced the risk of developing asthma significantly , indicating a protective efficacy of 23% against asthma development among vaccinated children. However, early administration of BCG vaccine did not significantly reduce the risk of developing eczema  and rhinitis . Further analysis revealed that the effect of BCG vaccination on asthma prevalence was significant especially in developed countries .  BCG vaccination in early childhood is associated with reduced risk of atopic disease, especially in developed countries. Allergy is malfunctioned immune response caused by various foreign antigens, which can increase the risk of asthma, atopic dermatitis (eczema), and rhinitis (hay fever) . Atopic tuberculosis [Vaccination is used worldwide for preventing infectious diseases . Childhorculosis . Th2 immrculosis . Recentlrculosis . Howeverrculosis . To clarOur systematic review was performed and reported in accordance with Meta-analysis Of Observational Studies in Epidemiology (MOOSE)  and the We performed a systematic literature search in PubMed/Medline (1950 to Jul 2021), EMBASE (1980 to Jul 2021), and Cochrane CENTRAL (1950 to Jul 2021) for association between BCG vaccination and atopic disease by using relevant keywords including asthma, eczema, rhinitis, BCG vaccines, and other synonyms. The search strategy suitable for PubMed is provided in Appendix 1. The analysis was restricted to studies published in English. We screened bibliographies of relevant review articles to ensure that all relevant studies were included.Each study was first selected based on their titles and abstracts (when available) by two independent investigators at the same time. Then, they retrieved full texts and performed further screening when studies were deemed eligible. Studies had to be cohort studies with information of authors, year, geographical area, study design, sample size, exposure , and outcome (confirmed diagnosis of specific atopic disease). Case reports, preclinical studies, and some studies without confirmed diagnosis of a specific atopic disease will not be used for this study. Disagreements were resolved by discussion and, if necessary, in consultation with a third senior investigator.Quality of all included trials was assessed by two authors independently by using the STROBE checklist for cohort study and JADAD scale for RCT study. The risk of bias in each domain was judged as low, high, or unclear. The overall risk of bias in a study was classified as low if all domains had low risk; as high if one or more domains had high risk; or as unclear otherwise. Based on these standards, we defined the studies into the following three grades: A, high quality and low risk of bias (scored ≥66.6%); B, moderate quality and moderate risk of bias (scored 33.3–66.6%); and C, low quality and high risk of bias (<33.3%). Discrepancy in quality assessment between the reviewers was resolved by discussion with involvement of a third senior investigator if necessary.Two authors extracted data independently using a standard data extraction form. The following baseline characteristics were extracted from the included studies: first author, year of publication, study design, and location in which the study was performed, number of included participants, and diagnosis of atopic disease. Studies were excluded if any of the above information is not available.p < 0.05 [I2 statistic were used for investigating heterogeneity, and statistical heterogeneity was defined at I2 > 50%. We used random-effects modeling to perform the meta-analysis for all pooling. In case heterogeneity exists, analysis to investigate whether the heterogeneity is related to the participant's race will be performed. Different ethnic background in different continents may be considered potentially important to heterogeneity because of living habit diversity. Funnel plots were used to display the publication bias graphically, both specifically and officially with Egger's test.We used R (version 3.6.3) to perform the data analysis; odds ratios (ORs) and their associated 95% confidence intervals (CI) were used to assess the strength of association between BCG vaccination in early life and the risk of getting atopic disease. Statistical significance was defined at p < 0.05 . I2 statOur search strategy generated 4127 citations from 3 databases. Among them, 3946 articles were removed after exclusion of duplicates and screening of titles and abstracts. Of the remaining 181 studies, 161 studies were excluded after reviewing the full text. In total, 20 articles including 222928 participants met the inclusion criteria and were included in the meta-analysis , 23–41.TFour studies were conducted in developing countries, and sixteen studies were in developed countries. None of the included studies were at low risk of bias (rated A) as all trials had an element of pragmatism in using different methods. Fifteen studies including 87% participants were deemed to be at moderate risk of bias (rated B). We judged five studies including only 13% participants to have high risk of bias (rated C) in the field of study participation or statistical reporting.In the pooled analysis, we found that participants receiving BCG in childhood associated with a lower risk of atopic disease than that of the non-BCG group .The total publication bias was outlined in the funnel plot . Based oIn this systematic review and meta-analysis, we revealed that BCG vaccine might be effective in preventing atopic disease, especially for asthma among developed countries. However, compared with asthma, the risk of developing atopic disease was not reduced for both eczema and rhinitis. In developing countries, there was no association between BCG vaccine and atopic diseases. As far as we know, this systematic review and meta-analysis is the latest study investigating the association between BCG vaccination and atopic diseases.γ influences Th2 responses, but it may involve macrophage activation and inhibition of Th2 lymphocytes development or change antigen presentation directly [In this study, we found that receiving a BCG vaccine in early childhood especially reduced the risk of asthma. According to other studies on BCG vaccination and asthma, BCG vaccine, which was proved to prevent the inflammation caused by mycobacteria, has been tested to inhibit allergen-induced airway inflammation in a murine model of atopic asthma . The immdirectly . The mecdirectly , and we tuberculosis are endemic; therefore, the effects of BCG vaccine on reducing prevalence of allergic diseases were compromised [In developed countries, the association between BCG vaccination and reduction of the risk of asthma is more significant than the other developing countries of the world. This could be attributable to the fact that, in the regions other than developed countries, helminth infections and promised . The propromised . It is apromised . HoweverAs for rhinitis and eczema, there is no significant association, even though they may share similar genetic characteristics with asthma . This isOur meta-analysis has several strengths. Compared with a similar meta-analysis , six newAs with all systematic reviews, we may have failed to identify some studies, especially those with negative results, several studies are missing in this meta-analysis, including RCTs concerning neonatal BCG vaccination and wheeze and other possibly atopic diseases. Therefore, this may have influenced our findings. Finally, the length of time that the early effective protection from BCG vaccination lasts remains unanswered. The age of participants in this review may partially explain the result of the protective effect of BCG vaccination. In Linehan's study, it was shown that any benefits of BCG vaccine are likely to be transient . TherefoOur results provide evidence that BCG protects against the risk of atopic diseases with the most protective effects on asthma occurrence from multiple epidemiologically different settings. Our results also suggest that that BCG vaccination in early childhood is associated with reduced risk of atopic disease, especially in developed countries.