Growing evidence suggests that inflammatory bowel disease (IBD) and dementia share pathological mechanisms and pathogenic risk factors. However, the previously diagnosed IBD and the subsequent risk of developing dementia are largely unknown.The purpose of this review is to assess the association between IBD and subsequent dementia diagnosis.We searched PubMed, Embase, and Cochrane library from database inception to February 1, 2022. Two reviewers independently extracted data and assessed methodological quality and risk of bias. Observational study that reported the possibility of dementia in IBD and non-IBD populations were included. Eligible studies were pooled effect estimates for relative risk (RR) through fixed-or random-effects models as appropriate.More than 3,181,549 participants from nine studies met the inclusion criteria. Previous IBD diagnosis did not increased the risk of subsequent all-cause dementia  and AD-dementia . Subgroup analyses based on study design indicated that cohort studies  reported an increased risk of all-cause dementia, but were not applicable to AD-dementia . Positive associations between IBD patients and all-cause dementia did not differ by age and gender in cohort studies. Both ulcerative colitis (UC)  and Crohn’s disease  are associated with increased risk of all-cause dementia.Evidence regarding dementia risk assessment in IBD patients is conflicting, which may be influenced by study design. More prospective cohort studies are needed to determine their relationship.https://www.prosper-isd.net], identifier [CRD42021284116].[ Inflammatory bowel disease (IBD), mainly including ulcerative colitis (UC) and Crohn’s disease (CD), is characterized by chronic inflammation in the gastrointestinal tract . The etiDementia is a progressive neurodegenerative disease that leads to a decline in cognitive ability, Alzheimer’s disease (AD) is the most common type . PatientThis study has been registered on PROSPERO (CRD42021284116). This study followed the Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA) reporting guideline and the Meta-analysis of Observational Studies in Epidemiology (MOOSE) reporting guideline.We searched PubMed, Embase, and Cochrane Library from database inception to October 9, 2021 for articles designed in observational study that reported available data on the risk of dementia in IBD and non-IBD populations. The search strategy included terms related to dementia and IBD, such as Alzheimer’s disease, vascular dementia, dementia with Lewy bodies, Crohn’s disease, and UC. These headings and keywords are combined with the Boolean operators “and” and/or “or.” There are no restrictions on the language of publication. We also performed a gray literature search of the database and checked the references of related studies to obtain other studies that were not identified by electronic search. The detailed search strategy is presented in Two authors independently reviewed the titles and abstracts, and then screened the full-text articles and cross-checked them. All the discrepancies were resolved through discussion. All eligible studies had to meet the following inclusion criteria: (1) Observational studies with a control group; (2) studies that recorded data on the risk of dementia in patients with IBD and non-IBD. Preclinical studies, case reports, letters to the editor, conference papers, and studies for which the full text was not available were excluded.Two authors independently extracted data using a pre-designed extraction form and resolved any discrepancies through discussion. The extracted information included study characteristics , characteristics of participants (age and gender), selection of cases and controls, diagnosis of IBD and dementia, evaluation of results, i.e., number of participants, odds ratio (OR), relative risk (RR), hazard ratio (HR) reported, and associated raw data for re-calculation or any multivariate analyses adjustment factors (if applicable).via the 11-item checklist recommended by the Agency for Healthcare Research and Quality (AHRQ),  was used to assess the risk of bias for cohort and case-control studies and arbitrarily judged studies with scores greater than 6 had a lower overall risk of bias, . Cross-s (AHRQ), .P < 0.10) and by estimating I2. Heterogeneity was classified as moderate (I2 = 25–50%), substantial (I2 = 50–75%) or considerable (I2 ≥ 75%). The preplanned subgroup analysis was performed based on gender, study design, and type of disease. Sensitivity analysis of the primary endpoint was conducted by sequential removal of each trial to assess the impact of individual studies on overall pooled estimates. The possibility of publication bias was assessed if sufficient primary studies were included (≥ 10). Variables were compared using χ2 tests. All tests were 2-tailed. We considered the P-value < 0.05 to be statistically significant.Meta-analysis was carried out using the STATA metan command. Fixed (Mantel-Haenszel) or random effects (DerSimonian-Laird) models were used to calculate pooled effect estimates. Effect estimates extracted or recalculated from existing data were HRs and ORs. Because of the low absolute risk of dementia, the two measures of association are expected to yield similar RR estimates. Therefore, we pooled all RR estimates appropriately to ensure comprehensiveness of the analysis and maximize statistical power . The adjA total of 672 records were identified by database search. After the reviewing of titles and abstracts, 17 articles were retained for full-text reading, and finally, nine studies met the review criteria, including seven cohort studies , one croThe nine studies totaled more than 3,181,549 participants, including 442, 320 IBD patients and more than 2,739, 229 non-IBD participants. The average age of the participants was over 55 years old. All cohort studies are retrospective, with follow-up ranging from 8 to 31 years. The diagnosis of dementia and IBD was based on the International Classification of Diseases (version 9 or 10) classification code. Eight studies adjusted for confounders, generally covering age, gender, and IBD type. The population characteristics of the included studies are summarized in Quality assessment scores using the NOS tool range from 7 to 9 stars . All stuSeven studies reported all-cause dementia risk in IBD and non-IBD populations, including five cohort studies, one case-control study and one cross-sectional study. Pooled effect estimates suggest that previously diagnosed IBD did not increase the risk of subsequent all-cause dementia  .In the subgroup analysis based on study design, cohort studies  and case-control studies  reported an increased risk of all-cause dementia in patients with IBD. However, the only cross sectional study did not support such an association  . AdditioWe also pooled the risk estimates of IBD types and found that both UC  and CD  were positively associated with the risk of all-cause dementia. These associations remained statistically significant when stratified by study type .Five studies reported the risk of AD-dementia in IBD and non-IBD populations, including four cohort studies and one case-control study. The results showed that previously diagnosed IBD did not increased the risk of subsequent AD-dementia  .In subgroup analyses based on study design, the case-control study  reported an increased risk of AD dementia, but this was not applicable to cohort studies  . InteresFor stratified analysis by IBD type, pooled effect estimates supported a positive association between UC and AD-dementia , but not CD . However, when stratified by study type, pooled results from cohort studies showed that neither UC  nor CD  increased the risk of AD-dementia .Significant heterogeneity was found for all comparison groups. Sensitivity analysis was performed by removing each study in turn and reanalyzing the dataset, which did not result in a substantial change in risk estimates. Since none of the comparison groups had more than 10 primary studies, publication bias was not assessed.We performed a systematic review and meta-analysis to comprehensively investigate the risk of dementia in IBD patients. The findings, including more than 3,181,549 participants, suggest that the previous IBD diagnosis did not increased the risk of subsequent all-cause dementia and AD-dementia. However, in the subgroup analysis based on the study design we found an increased risk of all-cause dementia but not AD dementia in IBD patients in the cohort study. Interestingly, UC increased the risk of subsequent all-cause dementia and AD dementia, whereas CD only increased the risk of all-cause dementia.Our findings are somewhat inconsistent compared with previously published meta-analyses. The study by Several additional studies were not included in the meta-analysis. A cohort study by Although there is no clear evidence to support IBD as a risk factor for dementia, we cannot rule out this possibility. The following explanations can illustrate their potential relationship. One explanation is linked to systemic inflammation. AD is the most common type of dementia, which is characterized by amyloid plaques, neurofibrillary tangles, and neuroinflammation . StudiesSeveral limitations need to be addressed. First, retrospective observational studies were included, and most participants were recruited from Medicare databases or hospitalization registries. Therefore, differences in disease diagnosis and data integrity may affect the results. Second, most studies lack complete demographic information, failing to consider other potential confounding factors that affect dementia, such as diet, exercise, mental health, and drug use. Therefore, further research is needed to include more comprehensive demographic data on other confounding factors. Third, limited primary study, especially in subgroup analysis, led to insufficient statistical strength. Fourth, results from epidemiological studies suggest potential associations but are still insufficient to confirm causality. Further studies are needed to verify the causality between them, such as large-scale prospective studies and randomized controlled trials. Fifth, significant heterogeneity may confuse current results. However, we used a random-effects model to pool effect estimates, which can truly reflect the degree of deviation of estimates between studies to obtain more conservative results. Sixth, peripherally acting anticholinergics used for IBS can worsen dementia as well, possibly due to increased use prior to IBD diagnosis, while detailed documentation of this information may be lacking in electronic medical records.This meta-analysis underlines that study design may have influenced the assessment of dementia risk in IBD patients and more prospective cohort studies are needed to determine their relationship. However, although controversial, these findings highlight the possibility that IBD is associated with subsequent dementia development and the importance of increased clinical vigilance and early active interventions for this population.The original contributions presented in this study are included in the article/NL, XW, LH, and HL designed the study. JS performed literature search. NL and YW analyzed the data and drafted the first version of the manuscript. XW revised the manuscript. All authors interpreted the data, contributed substantially to the manuscript, and approved the final manuscript for submission, were responsible for the integrity, accuracy, and presentation of the data.


However, we would like to state the following methodological issues.We read with interest the recent publication by Li et al.First, we thought that the data extracted by the authors might be incorrect: (1) the authors extracted a sample size of 50 individuals with sarcopenia and 32 patients with depression, respectively, from Olgun Yazar and YazarFigure 2 of the original article was generated by the authors based on the metan command of the Stata software, but in fact, the results of the meta‐analysis of single group rates using the metaprop command may be more realistic; the latter was published by Nyaga et al.Q statistic = 134.910, corresponding P < 0.001; I2 = 89.623%, suggesting heterogeneity among studies and the prevalence of depression based on a random‐effects model was 25.6% (95% CI: 19.3–31.8%) ], but the authors extracted data for men and women separately for inclusion in the analysis; the results of our re‐perform meta‐analysis showed that the overall adjusted OR between sarcopenia and depression was 1.565 (95% CI: 1.315–1.862) . Although this result is comparable with the authors' effect size, the results of the analysis incorporating data from the total population reported by the original authors may be more relevant and accurate.Third, we note that the study published by Kitamura In addition to the methodological issues mentioned above, the heterogeneity of the meta‐analyses reported in the original article was considerable. We thought that the authors should conduct further meta‐regression to determine the sources of heterogeneity among studies. In addition, assessment of publication bias by graphical method (funnel plot) or quantitative analysis (Begg's and Egger's test) is also an indispensable part of a meta‐analysis.Despite the methodological shortcomings, we acknowledge that the results of the study conducted by the authors should draw the attention of medical professionals to the fact that depression is present in approximately one‐third of individuals with sarcopenia. In other words, it is essential not to neglect psychological issues such as depression while addressing muscle dysfunction.Figure S1. Forest plot of the adjusted ORs between sarcopenia and depression.Click here for additional data file.


The present study was conducted to determine the association of transforming growth factor-beta (TGF-β) gene polymorphism and myopia.Four hundred twelve articles were identified, of which 11 articles with 5213 participants in 4 countries were included in the final analysis. Review Manager software  was used for data analysis.Odds ratio (OR) value of TGF-β1 rs1800469 is 1.33  in the allelic model; in the dominant model is 1.76 (95% CI = 1.16–2.67); in homozygous model is 5.98 (95% CI = 4.31–8.06). OR value of TGF-β1 rs4803455 is 0.62 (95% CI = 0.43–0.88) in recessive model. TGF-β2 is not associated with myopia. Relevant study on TGF-β3 is scarce.Our systematic review and meta-analysis found that TGF-β1 rs4803455 and rs1800469 were correlated with myopia. Myopia comes on mainly with children and teenagers. In East Asia and other regions, the proportion of myopia among teenagers is increasing year by year; the highest can reach 80% to 90%, among which high myopia has accounted for >20%. Myopia has become one of the most common eye diseases in the world. The pathogenesis of myopia is still unclear, but research shows that the formation of myopia is promoted by many factors, such as environmental factors, education level, and so on. However, whether genetic factors affect the occurrence and development of myopia have been controversial. Dirani et al. found that myopia in identical twins was also correlated. The discovery of >20 regions of the genome associated with myopia also indicates that the origin of myopia is polygenic and heterogeneous and offers the prospect of specific targeted therapy.A growing number of researchers believe that there is a direct link between myopia and heredity. Ip et al Found that compared with children with no myopic parents, children with 1 myopic parent and those with 1 myopic parent had a 2-fold and 8-fold higher risk of developing myopia, respectively. In addition, increased myopia in parents also increases the risk of myopia.–9 In recent years, experiments have shown that TGF-β also affects scleral remodeling and the formation of high myopia.,11 The subtypes of TGF-β that can act on the sclera are mainly divided into 1, 2, and 3. At present, most human studies focus on TGF-β1, while TGF-β2,3 type studies are relatively few. Different single nucleotide polymorphisms (SNPs) in TGF-β1 have different effects on myopia. Rs1800469, rs1982073, rs2241716, and rs4803455 are the 4 SNPs that attract more attention. The results were not the same.Transforming Growth Factor-Beta (TGF-β) is a family of cytokines that regulate biological development. TGFβ has evolved to regulate epithelial and neural tissue expansion systems, the immune system, and wound repair. In the field of the tumor, the TGF-β signal contributes to tumor progression, and in fibrotic diseases, TGF-β promotes the occurrence and development of pulmonary fibrosis.This article aims to present all existing researches on TGF-β polymorphisms and myopia through meta-analysis and systematic review to explore the relationship between TGF-β polymorphisms and myopia.The systematic review protocol was registered at the International Prospective Register of Systematic Reviews (PROSPERO) under identification number CRD42021284441. All stages of this study were performed under PRISMA guidelines.Inclusion criteria were as follows: there are no restrictions on the subjects’ age, race, and gender. However, studies with other complications were not included; the case-control study on the association between TGFB1 gene polymorphism and myopia; the average spherical refractive error (SRE) of the cases is less than or equal to −0.50d, and the average SRE of the controls is more excellent than −0.50d; only case-control studies were included; ff there are multiple research reports, include an extended version; and unregistered studies were not included.Exclusion criteria were as follows: reviews, editorials, opinion papers, and other studies presenting nonoriginal data; conference papers are not included; animal study; studies with other complicating diseases; articles providing incomplete data.We searched English databases such as PubMed, EMBASE, ProQuest, PsycINFO, CINAHL, and Cochrane Library and Chinese databases such as CNKI, Sinomed, and Wanfang databases, from their establishment to October 9, 2021. The language is not limited, and the similar literature and references attached to the search results are consulted simultaneously.We take “myopia” or “nearsightedness” or “shortsightedness” or “near‐sight” or “near‐sighted” or “near‐sightedness” or “short‐sight” or “short‐sighted” or “short‐sightedness” or “refractive error” and “Transforming Growth Factor beta” or “Milk Growth Factor” or “Factor, Milk Growth” or “Growth Factor, Milk” or “TGF-beta” or “TGFbeta” or “Platelet Transforming Growth Factor” or “Bone-Derived Transforming Growth Factor” or “Bone Derived Transforming Growth Factor” or “TGFB” and “Polymorphism, Single Nucleotide” or “Nucleotide Polymorphism, Single” or “Nucleotide Polymorphisms, Single” or “Polymorphisms, Single Nucleotide” or “Single Nucleotide Polymorphisms” or “SNP” or “SNPs” or “Single Nucleotide Polymorphism” as English keywords, and “Jinshi” or “Quguangbuzheng” or “Quguang” and “Zhuanhuashengzhangyinziβ” or “Zhuanhuashengzhangyinzibeta” or “TGFbeta” or “TGFβ” or “TGFB” and “Yiganjiyinduotaixing” or “Danhegansuanduotaixing” or “Jiyinduotaixing” or “SNP” as Chinese keywords, and use these to formulate retrieval strategies. The specific retrieval strategy is shown in the appendix.Two authors independently screened all search results. The 2 authors independently extracted the data in the study using a precustomized data table. The data sheet extracted the information of the first author, the year of publication, the polymorphism of the study, the total sample size, genotype frequency, allele frequency, age, race, the definition of the case group and the control group, whether each study met the Hardy-Weinberg balance, and whether the polymorphism in the experiment was associated with myopia. First, randomly select ten publications, and 2 reviewers perform calibration exercises to clarify whether the data extraction method and the variables extracted by the data extraction form need improvement. After 2 people extract data independently, they will consult where they disagree. If 2 authors cannot reach an agreement, ask another author to intervene. No automated tools were used except Noteexpress for duplicate checking. If there is no allele or genotype data in the report and whether the Hardy-Weinberg equilibrium is reached, we will calculate it ourselves. to evaluate the included studies. Evaluation criteria include:We used CASP Systematic Review ChecklistWas there a clear statement of the aims of the research?Is a qualitative methodology appropriate?Was the research design appropriate to address the aims of the research?Was the recruitment strategy appropriate to the aims of the research?Was the data collected in a way that addressed the research issue?Has the relationship between researcher and participants been adequately considered?Have ethical issues been taken into consideration?Was the data analysis sufficiently rigorous?Is there a clear statement of findings?How valuable is the research?2 tested the statistical heterogeneity of the included studies. The fixed effect model is used if P > .1, I2 ≤ 50%, indicating low heterogeneity, If P < .1, I2 > 50%, indicating high heterogeneity, the random effect model is used. One-way sensitivity analysis was performed by sequentially excluding each study in the meta-analysis. The possibility of publication bias was assessed by visual inspection of the funnel chart. The standard error of each study’s logarithm (OR) was plotted with its corresponding logarithm (OR). If there is an asymmetric chart, it indicates that there may be publication bias.The meta-analysis was performed if the SNP had at least 2 study evaluations. Dominant, recessive, homozygous, heterozygous, and allele models were used to analyze the correlation between SNP and myopia. We used Review Manager software  for data analysis. The fixed effect and random effect models were combined with odds ratio (OR) and 95% confidence interval (95% CI) to evaluate the association strength between SNP and myopia in the combined sample. The Cochrane IThe article screening process is shown in Figures Four SNPs of TGFbeta1 were finally investigated by more than 2 studies; they were rs1982073, rs2241716, rs1800469, rs4803455. Only rs7550232 of TGFbeta2 was investigated by 2 studies. The meta-analyses under the 5 genetic models are shown in Tables P value <.1, I2 > 50%, which showed that TGFbeta1 rs1982073 had no association with myopia. Allelic model , the OR value range of Rs1982073 is >1, which is consistent with Meng et al. However, it is different from Meng’s conclusion that Rs1982073 is closely related to myopia, the results of the meta-analysis show that the heterogeneity of the included studies is high , so the conclusion can not prove the correlation. Therefore, we are relatively conservative and believe that in the absence of clear evidence, Rs1982073 was not associated with high myopia. Sensitivity analysis was made for Rs1982073, and the exclusion of Wang et al’s study significantly reduced heterogeneity in the allelic model (C vs T), homozygous model (CC vs TT), and recessive model (CC vs CT+TT). We considered that the source of heterogeneity might be caused by different inclusion and exclusion criteria of selected subjects, which had nothing to do with race.Rs1982073 is located in introns, and we included 4 pieces of literature related to Rs1982073, 3 of which suggested that Rs1982073 was associated with myopia.In the study of Rs1800469, all models except the heterozygous model had good heterogeneity, and the 95% CI was more significant than 1. We regarded that Rs1800469 was directly related to myopia development, and allele A has a protective effect on myopia. Rs1800469 was the SNP with the highest positive result in this meta-analysis, and subsequent experiments focusing on Rs1800469 might obtain better results. Among the 4 groups of positive models, the allelic model included Hayashi et al’s study and other models. Only Hayashi et al’s experiment believed that Rs1800469 at the allelic model level had nothing to do with myopia.,26,29,30, but Biler et al found no association between Rs4803455 and myopia in the study of Turkish subjects. The sensitivity analysis, excluding Biler et al’s investigation, did not affect the results, indicating that race does not have a significant influence on the study. However, when the survey of Khor et al, was excluded, the heterogeneity of all models except the recessive model was significantly reduced. At that time, the allelic model and dominant model produced positive results, indicating that allele T was also a risk factor for myopia after excluding the study of Khor et al, which is consistent with the previous results of the recessive model. So, we estimated Rs4803455 was correlated with myopia.As can be seen from the results, Rs4803455 showed a correlation in the recessive model (TT vs TG+GG), and it can be believed that allele T may promote myopia. Among the 5 included studies, Shi et al found that Rs4803455 was correlated with myopia in 4 Chinese studies,29,30 However, after meta-analysis, we found that all model analyses of Rs2241716 had no positive results, and the sensitivity analysis had no change. Therefore, we believe that Rs2241716 has nothing to do with myopia.All 3 trials involving Rs2241716 involved Chinese subjects, and the authors all considered Rs2241716 was associated with myopia.,32 Meta-analysis showed that RS7550232 was not associated with myopia. Only Zha et al reported a study on TGF-β3 and found that TGF-β3 did not affect high myopia. Therefore, we believe that the current study cannot prove that TGF-β2 and TGF-β3 have an impact on the occurrence and development of myopia, and we expect that more relevant studies will be reported in the future, and new conclusions may be made.Up to now, there are few studies on TGF-β2 and TGF-β3. We retrieved 2 pieces of literature on the analysis of TGF-β2 SNP RS7550232. Shi et al reported that TGF-β2 was not associated with myopia, while Lin et al. estimated TGF-β2 is associated with high myopia.The retrieval scheme of this meta is the study on TGF-β and myopia. However, a meta-analysis could not be conducted due to the small literature related to TGF-β3 and the cumulative number of SNPs involved in the study being <2. Therefore, the retrieval of this meta-analysis centered on TGF-β, but the main conclusions were centered on TGF-β1 and TGF-β2.The retrieval object of this study is myopia. However, most studies of TGF-β still focus on high myopia, so the results of SNP Rs1982073 can only prove that there is no correlation with high myopia but not with myopia.The search scope is mainly English and Chinese databases, and the languages are English and Chinese. Other languages are not included, so there may be omissions.Through the above studies, it can be concluded that Rs1800469 and Rs4803455 in TGF-β1 are associated with myopia, suggesting that further research and treatment of myopia on this basis may be of great significance. Rs1982073, Rs2241716 of TGF-β1, and Rs7550232 of TGF-β2 were not significantly associated with intolerance. TGF-β3 needs to be further studied.Thanks to Professor Jing Zhang  and Ziyan Yu  for their helps with this article. Thanks to all reviewers for their valuable comments.Xiaoyu Zhu:Selected direction,Data statistics,Writing article;Bowei Xu;Selected direction,Literature retrievalLingxue Dai;Writing article;Zuoyuan Wang:Data statistics,Li Feng;Literature retrievalJiangyue Zhao*:Selected direction.


Subgroup analysis revealed that PM2.5 has a significant correlation with ALT , AST , and GGT  in Asia. Our meta-analysis showed that increments in PM2.5 exposure were significantly associated with a higher level of ALT, AST, and GGT. In addition, investigations into liver enzyme subtypes and specific chemical components of PM2.5 are important directions for future research.Although previous studies have presented that fine particulate matter (PM2.5) regulates liver enzyme levels in the development of liver diseases, the evidence regarding the relationship between PM2.5 exposure and liver enzyme is not robust. We further aimed to conduct a systematic review and meta-analysis of observational studies to summarize the recent evidence on the effects of PM2.5 on liver enzyme in humans. In the meta-analysis, we retrieved online databases including PubMed and Web of Science database from 1982 up to 2022. A random-effects model was applied to evaluate the correlation between PM2.5 and liver enzyme level. A total of 10 studies fulfilled the inclusion criteria, including five prospective cohort studies, two cross-sectional studies, two longitudinal studies, and one time-series analysis. Each 10 μg/m Fine particulate matter PM2.5) represents fine inhalable particles, with diameters of generally 2.5 μm and smaller . Because.5 represAir pollutants have been found to be involved in various oxidative stress mechanisms, which leads to inflammation and affects the liver and other organs ,6. IncreLiver enzymes are important parameters in the liver function examination , mainly 3 was significantly correlated with liver cancer   × 100% when β was not log-transformed. In this equation, β represents the regression coefficient and M is the arithmetic mean of the liver enzyme level. Natural log-transformed data were anti-log transformed. We use Cochran’s Q and I2 to test the heterogeneity in the study. I2 from 0 to 100% means that the research has changed from no observation to maximum heterogeneity. I2 > 50% showed serious heterogeneity. We used the Begg funnel plot and Egger test to calculate the publication bias. We used Stata statistical software 11.0 for all statistical analyses.Here, 10 μg/mn = 108); no circulating biomarkers measure (n = 18); no PM2.5 data (n = 10); and no PM2.5-increment data (n = 15). The remaining ten studies, including five cohort studies [The article filtering process is shown in  studies ,10,11, t studies ,22, two  studies ,25, and  studies , were inThe characteristics of 10 studies included in the final meta-analysis are shown in 2 = 98.26%, p = 0.07), ALT , AST , and GGT .Heterogeneity was observed among studies investigating ALP  increase in the liver ALP level. There was significant correlation between PM2.5 concentration and liver ALT, AST, and GGT levels. PM2.5 rose every 10 μg/m3, ALT level increased by 4.45% , AST level increased by 3.99% , and GGT level increased by 2.91% . The result of the Egger’s test for asymmetry showed p > 0.05, indicating no publication bias in all types of liver enzymes.The subgroup analysis of PM2.5 concentration on changes in liver enzyme level is shown in At present, studies have pointed out that PM2.5 pollution is one of the main reasons for the increase in the global incidence rate ,28. An i3 in PM2.5 was associated with a 4.45% increase in ALT , 3.99% increase in AST , and 2.91% increase in GGT , respectively.PM2.5 exposure not only promoted the incidence rate and mortality of respiratory diseases, but also had a significant impact on digestive diseases ,31. LiveWe tried to propose some mechanisms to explain the relationship between PM2.5 and liver enzyme levels. The first mechanism involved oxidative stress causing liver function damage. The increase in liver enzymes could indicate that PM2.5 causes liver system diseases caused by oxidative stress ,34. ExpoAnother important mechanism is inflammation. The induction of PM2.5 will aggravate liver inflammation and lead to abnormal liver function . As an iThe advantage of our meta-analysis is that it covers more than 14 million participants and analyzes the impact of PM2.5 exposure on different types of liver enzymes. At present, meta-analyses on air pollution and liver focused on analyzing liver diseases caused by air pollution. In this study, we focused on the mediating role of liver enzymes in various liver system diseases. As far as we know, our meta-analysis is the first to explore the relationship between PM2.5 and various liver enzyme levels, which will also be our greatest advantage.Meanwhile, our study has some limitations. First, few articles focused on the relationship between PM2.5 exposure and liver enzyme level, and only 10 articles were included in the analysis. Therefore, there may be heterogeneity caused by differences between individuals and different observation characteristics, which may affect the results. Second, the number of studies in different groups included in the subgroup analysis was small, and the results may be biased. Finally, because the increase in liver enzymes may be caused by reasons other than liver cell damage, it is necessary to further explore biomarkers with high liver specificity.Our results indicated that there was a close relationship between air pollution and liver enzyme levels. When the exposure concentration of PM2.5 increases, the level of human liver enzymes will rise accordingly. Although specific liver enzyme data were limited, our data showed that PM2.5 exposure will increase the levels of ALT, AST, and GGT, causing liver damage. Although increasingly more studies have focused on the impact of air pollution on the liver system, there is a lack of research focusing on air pollution and liver enzymes. Up to now, no meta-analysis of PM2.5 and liver enzymes has been conducted, and our research attempts to propose two mechanisms to explain them. As one of the important mediators of air pollution affecting liver disease, the relationships between other air pollutants and liver enzymes need to be further studied.


Cumulative studies have utilized high-throughput sequencing of the 16SrRNA gene to characterize the composition and structure of the microbiota in autism spectrum disorder (ASD). However, they do not always obtain consistent results; thus, conducting cross-study comparisons is necessary. This study sought to analyze the alteration of fecal microbiota and the diagnostic capabilities of gut microbiota biomarkers in individuals with ASD using the existing 16SrRNA microbial data and explore heterogeneity among studies. The raw sequence and metadata from 10 studies, including 1,019 samples, were reanalyzed. Results showed no significant difference in alpha diversity of fecal microbiota between ASD and the control group. However, a significant difference in the composition structure of fecal microbiota was observed. Given the large differences in sample selection and technical differences, the separation of fecal microbiota between ASD and controls was not observed. Subgroup analysis was performed on the basis of different country of origin, hypervariable regions, and sequencing platforms, and the dominant genera in ASD and healthy control groups were determined by linear discriminant analysis (LDA) of the effect size (LEfSe) algorithm and Wilcoxon rank-sum test. Machine learning analyses were carried out to determine the diagnostic capabilities of potential microbial biomarkers. A total of 12 genera were identified to distinguish ASD from control, and the AUC of the training set and verification set was 0.757 and 0.761, respectively. Despite cohort heterogeneity, gut microbial dysbiosis of ASD has been proven to be a widespread phenomenon. Therefore, fecal microbial markers are of great significance in diagnosing ASD diseases and possible candidates for further mechanistic study of the role of intestinal microbiota in ASD.IMPORTANCE This study provides an updated analysis to characterize the gut microbiota in ASD using 16SrRNA gene high-throughput sequencing data from 10 publicly available studies. Our analysis suggests an association between the fecal microbiota and ASD. Sample selection and technical differences between studies may interfere with the species composition analysis of the ASD group and control group. By summarizing the results of 16SrRNA gene sequencing from multiple fecal samples, we can provide evidence to support the use of microbial biomarkers to diagnose the occurrence of ASD. Our study provides a new perspective for further revealing the correlation between gut microbiota and ASD from the perspective of 16SrRNA sequencing in larger samples. Autism spectrum disorder (ASD) is a group of neurodevelopmental disorders characterized by deficits in social interactions, communication, and restricted and repetitive behavior , 2. PatiBacteroides, Akkermansia, Coprococcus, and different species of Ruminococcus increased in children with ASD relative to the controls , observed operational taxonomic units (OTUs) (P = 0.356), and Pielou’s evenness index (P = 0.822), indicating that no significant difference in richness and uniformity of the gut microbiota was observed between ASD and control groups. Next, subgroup analysis was performed in accordance with different studies. In two of the 10 studies, the alpha diversity of the ASD group was lower than that of the healthy control group. The random model was selected for meta-analysis, and the results showed that no significant difference in Shannon diversity index , observed OTUs , and Pielou’s evenness index  was observed between ASD and control groups. The results are in First, we assessed whether variation in alpha diversity was associated with the occurrence of disease. Analysis of fecal samples showed that no statistical differences were observed using the Shannon diversity index (P = 0.001) and unweighted uniFrac distances  revealed substantial variation among ASD and control groups regarding microbial community composition. The difference among groups was more significant that within groups . However, the PCoA revealed apparent overlap distinguishing distributions between the two groups, in which the center points were close . However, the difference between groups based on different studies , different variable regions , and different sequencing platforms  was more significant than that within groups.Ordination analysis based on Bray–Curtis dissimilarity . The gut microbiota composition between patients with ASD and healthy control sequenced using primers that targeted the V3 to 4 and V4 to 5 regions differed significantly . In addition, the gut microbiota composition between patients with ASD and healthy control sequenced using sequencing platform Illumina MiSeq, Illumina HiSeq 2500, and Illumina HiSeq 4000 differ significantly . The PCoA plots based on Bray–Curtis dissimilarities between the ASD group and control group classified by projects, variable regions, and sequencing platform are shown in The microbiota composition across ASD and healthy controls in subgroups was compared using PERMANOVA based on Bray–Curtis dissimilarities and unweighted uniFrac distance. Significant intergroup differences were observed in five out of 10 studies . The overlapping feature of the dominant genera of ASD and control groups in different studies is shown in Lachnospiracea_incertae_sedis, Clostridium_XVIII, Eubacterium, Anaerostipes, Clostridium_sensu_stricto, Coprococcus, Dorea, and Faecalibacterium. The dominant genera in the control group included Gemmiger, Bacteroides, Roseburia, Dialister, Akkermansia, Haemophilus, Megamonas, Parabacteroides, and Streptococcus. The results of dominant genera in ASD and control group subgrouped by three variable regions using LEfSe and Wilcoxon rank-sum test are summarized in Tables S12 to S15. The dominant genera of the ASD group included Lachnospiracea_incertae_sedis, and Eubacterium. The dominant bacteria in the control group included Dialister, Prevotella, Megamonas, Parabacteroides,Clostridium_XVIII, and Roseburia. Then, subgroup analysis was performed on the basis of different sequencing platforms, and the dominant genera in ASD and control groups were screened (Tables S15 to S19). The dominant genera of the ASD group included Eubacterium, Bifidobacterium, Blautia, Dialister, Coprococcus, and Lachnospiracea_incertae_sedis. The dominant bacteria in the control group included Parabacteroides, Prevotella, Ruminococcus2, Romboutsia, Megamonas, and Clostridium_XlVa.We identified the dominant genera in the ASD and control groups using LEfSe and Wilcoxon rank-sum test an included the intersection of the results in the two groups. The results showed that no significant dominant genera were found in the ASD group, whereas the dominant genera in the control group included Next, we used the dominant genera identified in different subgroups to build prediction models based on the random forest model. The top 12 important genera in the ASD and healthy control groups determined by subgroup analysis of different studies can distinguish the two groups, and the area under the receiver operating characteristic curve (AUC) of the training set and verification set was 0.688 and 0.706, respectively . The eigIn this study, we found significant differences in fecal microbiota composition between ASD and healthy controls. However, the batch effect between the two groups was too large. Study projects, sequencing platform, and hypervariable region sequenced were important interference factors for intergroup differences among fecal sample groups, and the influence of the country of origin was relatively small. Then, we selected 12 dominant genera in ASD and healthy control groups by LEfSe and Wilcoxon rank-sum test, and these genera were analyzed to identify and validate microbiota-based biomarkers that could be used to classify individuals as ASD or healthy controls. Random forest classification models constructed to differentiate individuals with ASD from healthy controls using fecal samples showed good performance.Compared with a single study, the data set of multiple studies can comprehensively detect the changes in ASD-related microbiota composition by increasing the sample size and evaluating interference factors. In our results, PCoA showed that the ASD group was closer to the control group in the same research project, indicating that the factors such as sample selection and technical differences greatly affected microbiota analysis. Previous studies have shown that geographical and ethnic groups are crucial in forming specific microbial communities . Chen etEubacterium coprostanogenes . For example, children with ASD often exhibit an immune response disorder. Using the mouse model of maternal immune activation, Kim et al.  found thThis study can identify some studies and pool their results, with a relatively large number of participants. It is also an update and extension of previous similar studies. Nevertheless, this study has several limitations. First, the metadata information of existing research is incomplete. The composition of individual feces varies greatly, which is affected by many confounding factors, such as region, diet, exercise, gender, age, body mass index, and specimen collection method. Therefore, we hope to integrate other possible confounding factors in future research. Second, the projects included in this study involve different sequencing platforms and variable regions targeted. We tried to eliminate the batch effect by using the combat function  and remoOur analysis suggests an association between the fecal microbiota and ASD. Sample selection and technical differences among studies may interfere with species composition analysis of the ASD group and control group. By summarizing the results of 16SrRNA gene sequencing from multiple fecal samples, we can provide evidence to support the use of microbial biomarkers to diagnose the occurrence of ASD. Our study provides a new perspective to reveal the correlation between gut microbiota and ASD based on 16SrRNA sequencing in large samples. Nevertheless, future research is necessary to understand the exact contribution of changing intestinal microbiota to the clinical manifestations of ASD, which lead to the development of prevention and treatment methods.We searched within the PubMed database. The search strategy and search terms were constructed as follows: (((((Autism Spectrum Disorder [MeSH Terms]) OR (Autism Spectrum Disorder [Title/Abstract])) OR (Autistic Spectrum Disorder [Title/Abstract])) OR (Autism [Title/Abstract])) OR (Autistic Disorder [Title/Abstract])) AND (((((((Microbiota [MeSH Terms]) OR (Microbiota [Title/Abstract])) OR (Microbiotas [Title/Abstract])) OR ) OR ) OR (Microbiome [Title/Abstract])) OR (Microbiomes [Title/Abstract])). Studies were limited to those published from inception to April 1, 2022. In addition, we restricted the search to papers written without language restrictions.https://gmrepo.humangut.info), and the search condition was set as follows: (i) the phenotype was “autism spectrum disorder”; (ii) healthy controls were mandatory; (iii) studies with fecal microbiota analyzed using 16SrRNA sequencing; and (iv) the percentage of failed runs was set to less than 90% to eliminate the projects whose percentage of failed runs exceeds 90%.The GMrepo (data repository for Gut Microbiota) database is a curated database of human gut metagenomes, which can provide data resources with high reusability and accessibility . It inclRaw sequence data and metadata were retrieved from the NCBI Short Read Archive (SRA), European Nucleotide Archive (ENA), GMrepo database, or article’s contents. Next, we excluded samples if the metadata were not available. Study information from the selected research included demographic information and methodologies, such as the recruiting area, sample size, mean age, diagnostic methods of ASD, PCR primer, sequencing platform, hypervariable region sequence, and whether or not the raw data available were recorded. The samples were grouped before downstream analyses.All downloaded raw sequencing reads of 16SrRNA gene sequences were processed using USEARCH . When thP-value < 0.05 was characterized as significant difference. Cochrane’s Q test and I2 statistics were used to assess the extent of heterogeneity among studies. A high degree of heterogeneity was expected; thus, pooled estimates was obtained through random-effect models. Beta diversity (between-sample) was assessed on the basis of Bray–Curtis dissimilarity and unweighted uniFrac distances and visualized by principal coordinate analysis (PCoA). Moreover, analysis of similarities (ANOSIM) was conducted on the basis of Bray–Curtis dissimilarity to test whether the difference between the two groups was significantly more significant than that within groups. We used the permutational multivariate analysis of variance (PERMANOVA) to assess microbiota differences between patients with ASD and healthy controls.The Shannon diversity index, observed OTUs, and Pielou’s evenness index were used to compare the differences in alpha diversity between ASD patients and healthy controls in different projects. Stata version 16.0  was used for meta-analysis. For continuous variables, comparisons between the two groups assessed the weighted standardized mean difference (SMD) and 95% confidence intervals (CIs), and Hedges’ g was selected as the final effect size. 10) of 2.0 was used as the cutoff . The relative importance of each genus in the predictive model was evaluated using the mean decreasing accuracy and Gini coefficient. If more genera are screened, the top 12 genera of importance were retained for subsequent analysis. Finally, subject operating characteristic (ROC) curve analysis was used to evaluate the clinical diagnostic ability of microbial biomarkers. Furthermore, 70% data and 30% data were randomly selected as the training set and verification set, respectively.Next, the LEfSe method using the Kruskal–Wallis test was performed to identify the microbiota biomarkers. A linear discriminant analysis (LDA) score (loge cutoff . The gen


Due to significant heterogeneity , the random-effects model was followed. The stratified meta-analysis showed that the selenium levels were lower in the cases compared with the normal controls in the third trimester ; p ≤ 0.01). The same trend was observed in the studies published before the year 2014 ; p ≤0.01) and those published in or after 2014 ; p = 0.05). None of the investigated covariates in the meta-regression analysis  were significantly associated with the selenium SMD. The current evidence indicates that selenium levels are lower among women with GDM in comparison to those without GDM; however, after the correction of the reporting bias, the result was no longer significant. Further studies with more prospective designs are needed to confirm this evidence and explain the function of selenium in GDM throughout pregnancy.Several studies have investigated the association between selenium levels and gestational diabetes mellitus (GDM); however, their results are not conclusive. This systematic review and meta-analysis aimed to update and draw conclusions regarding the evidence from published studies that investigated selenium levels in relation to GDM. PubMed, Google Scholar, Cochrane Library and ScienceDirect were searched for studies related to selenium and GDM, published from the inception of each database through to July 2022. The meta-analysis was conducted by measuring the standardized mean difference (SMD) between the selenium levels of women with GDM and those pregnant without GDM (control group). Stratified meta-analysis, meta-regression analysis and reporting bias were applied. The “meta” package in the open-access software R was used to analyze all of the data. A total of 12 studies, including 940 pregnant women with GDM and 1749 controls met this study’s inclusion criteria. The selenium levels were significantly lower in women with GDM compared with the control group : ;  Gestational diabetes mellitus (GDM) is a common endocrine problem during pregnancy, with a prevalence of 15% of all pregnancies worldwide . GDM is The definite cause of GDM has not yet been fully explored. However, many risk factors that may contribute to the susceptibility to GDM, such as maternal obesity, past history of GDM and family history of DM ,6,7,8, hSelenium is an important trace element necessary for optimal several physiological processes . SeleniuSeveral studies reported an association between selenium deficiency and T2DM ,18,19. ATwo previous systematic reviews and meta-analyses published in 2015 and 2016 reported significantly lower levels of selenium among cases with GDM compared with normal controls ,28. HoweThe protocol for this systematic review and meta-analysis was listed in an international database of prospectively registered systematic reviews in health and social care (PROSPERO), with the registration number CRD42021239431 .The Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines were strictly followed . An elecP (population): pregnancy OR pregnant women OR gestation;I (intervention): selenium OR selenate OR Se;C (comparison): euglycemic OR normal pregnancy OR healthy pregnancy;O (outcome): gestational diabetes OR gestational diabetes mellitus OR gestational hyperglycemia OR GDM;S (study type): case–control OR cross-sectional OR cohort.Two investigators (SZH and IA) independently screened and carefully chose eligible studies for this meta-analysis after perusal, reading and assessment. Any disagreement was resolved by a discussion with the referee investigator (HZH). The search was aided by LitSuggest , an autoThe articles were included if the studies (1) investigated the association between selenium levels and GDM; (2) employed case–control, cohort or cross-sectional designs; (3) reported mean (standard deviation (SD)), median (interquartile) or median (range) values of the selenium levels in the case and the control groups and provided a measurement unit; (4) described the methods used to measure the selenium levels and measured the selenium levels in blood, plasma or serum and (5) were written in the English language.Systematic review articles, case reports, commentaries, editorials, clinical trials, abstracts and duplicate publications were excluded. The studies that reported selenium levels from hair samples and those written in languages other than English were also excluded.The primary outcome of this study is the investigation of the association between selenium levels and the development of GDM in comparison to healthy normoglycemic pregnant women. According to Carpenter and Coustan , GDM is Two investigators (IA and SZH) assessed the quality of the included studies by using the Newcastle–Ottawa Scale (NOS) for the case–control and cohort studies; the modified version of the NOS was used to assess the cross-sectional studies . Three mThe Joanna Briggs Institute Meta-Analysis of Statistics Assessment and Review Instrument was used to extract the data from each eligible study . Brieflymeta package [2I. Cochrane Q with p < 0.010 and 2I > 50% indicated evidence of inter-study heterogeneity [The open-source statistical software R 4.0.3  was used to measure the standardized mean differences (SMDs) in selenium levels between the case and control groups by applying the function “metacont” in the  package . The eff package . The hetogeneity .p-value < 0.05 was considered statistically significant in this study.Due to the high inter-study heterogeneity, the random-effects model was followed in this meta-analysis. Sensitivity analysis was applied to recognize any study that significantly changed the selenium SMD upon exclusion. A graphical funnel plot and a quantitative Egger’s test were used to investigate reporting bias. Any evidence of reporting bias was further examined by means of the trim-and-fill method in order to correct the asymmetry in the funnel plot. Stratification meta-analysis was conducted by grouping the studies according to the trimester in which the blood samples were collected for selenium measurement  and the study’s year of publication (before 2014 and in or after 2014), as we noticed that studies published after 2014 had smaller selenium SMDs than those published before 2014. Meta-regression analysis was performed to assess the relations between the selenium SMD and the following factors: study quality score, publication year, study continent, selenium measurement trimester, selenium quantification methods, regional economic ranking and study sample size. A The preliminary search in the databases retrieved a total of 224 articles. Out of this total, 56 were excluded as duplicates and 85 were irrelevant. The two investigators screened the titles and the abstracts of the remaining 83 articles. After screening, 21 were removed as they were animal studies, and 2 more were removed because they not written in English. Out of the 60 articles that were retrieved, 48 were found to be irrelevant, and 7 were primarily about children. Finally, 12 studies were included for meta-analysis, as shown in In this systematic review and meta-analysis, the included 12 studies comprised a total of 940 GDM cases and 1749 controls ,38,43,44Of the 12 studies, 8 were case–control ,38,43,44Six studies were published before 2014 ,23,24,26p = 0.0007 . Accordingly, the random-effects model was followed.The overall pooled estimate of the standardized mean difference of selenium levels was SMD = −0.66; 95% CI, ; = 0.0007 . The meaSensitivity analysis revealed that none of the included studies significantly changed the SMD of selenium upon deletion, as shown in p ≤ 0.01); see p ≤ 0.01) was higher than in the studies published in or after 2014 ; p = 0.05); see Stratified meta-analysis based on the trimester of selenium quantification, revealed that the heterogeneity level increased with the trimester. Likewise, the SMD of selenium levels increased along with the trimester, and was the highest during the third trimester ; second trimester: SMD = −0.54 ; third trimester: SMD = −1.85 ; In the meta-regression analysis, none of the investigated factors—geographic location, trimester of selenium quantification, World Bank economic ranking, methods of selenium quantification, study design, study quality score, publication year and study sample size—showed any evidence of a relation with the overall estimate .t = −2.34; p = 0.032). Therefore, the trim-and-fill method was applied, which corrected the plot asymmetry by adding four studies. The newly corrected pooled measures for the 16 studies were SMD = −0.15; 95% CI: ; p = 0.473  meta-analysis . This reIn a previous meta-analysis , it was Despite the subgroup analysis, the level of heterogeneity did not decrease significantly. Therefore, sensitivity analysis was performed in an attempt to identify the study or studies that could significantly change the overall effect and had heterogeneity. However, none of the investigated studies was found to significantly change the overall effect. This study found evidence of reporting bias, and four studies were estimated to be possibly missed in this meta-analysis. The supposed four missed studies influenced the overall effect and shifted it from a significant to an insignificant level. Although in their meta-analysis Kong et al. observed an asymmetrical distribution of studies in the funnel plot, no evidence of reporting bias was found, which was also the case in the meta-analysis by Askari et al. ,28. The Although this study updates the current literature, it has some limitations that must be acknowledged when interpreting the findings. First, reporting bias was corrected by adding four studies. However, other types of bias, such as selection bias, could not be avoided. Second, although cohort studies were included, which could explain the causality, the conclusions of these studies were neither uniform nor clear enough to formulate a conclusion about causality. Third, BMI and participant age were not reported in all the included studies. Consequently, a more in-depth analysis regarding these two important covariates could not be conducted. Fourth, the included studies reported varying levels of selenium while providing minimal or no details about validation or quality control for the method of determination. Fifth, the diagnostic criteria for GDM were neither uniform nor reported in all of the studies. This could alter the diagnostic threshold for glucose levels, and hence the diagnoses.This updated systematic review and meta-analysis indicates that selenium is lower among women with GDM in comparison to pregnant women with no GDM, who comprised the control group. However, the difference was not statistically significant after the correction of the reporting bias by means of the trim-and-fill method. There is a need for further research that includes more well-designed cohort studies, as well as the measurement of selenium levels throughout the trimesters, to more clearly explain the kinetics of selenium in GDM.


The relationship between Q576R polymorphism of IL-4 receptor (IL-4R) gene and pediatric asthma risk is still undefined. To this end, this meta-analysis was performed to explore the above controversy.In this study, we systemically retrieved CNKI, EMBASE, Web of Science, Scopus, Science direct and Pub Med to collect relevant researches, followed by calculation of odds ratio (OR) along with 95% confidence intervals (CIs). STATA 12.0 software was employed in this meta-analysis.We found an association between IL-4R Q576R polymorphism and pediatric asthma risk . Moreover, there was no obvious publication bias.Our findings suggested that G allele of IL-4R Q576R polymorphism is associated with increased risk of pediatric asthma. Anyhow, delicately-designed, large-scale studies should be conducted to further confirm the current outcomes. Asthma is a common chronic allergic disorder that involves the respiratory tract and affects millions of children worldwide. With the detection rate up to 10% of children, atopic asthma is a global public health problemInterleukin 4 (IL-4) not only is vitally involved in type 2 T-helper (Th2) reactions and isotype class switching of B cells to IgE synthesis, but participates in the recruitment of mast cellsIL-4R gene has been previously demonstrated as a potential asthma-related gene, located on chromosome 16p12.1. IL-4R Q576R polymorphism (rs1801275) was first revealed by Hershey et al. in 1997, which was potently correlated with atopyFurther investigations have indicated the possible relationship of IL-4R Q576R polymorphism with pediatric asthma, however, the conclusion is drawn from studies with inadequate statistical power, sample size and clinical heterogeneity. To this end, the present meta-analysis was performed to accurately examine the correlation of IL-4R Q576R polymorphism with pediatric asthma risk.Two investigators (Xiwu Chen and Jinhui Hu) screened each of the titles, abstracts and full texts to determine inclusion independently. The results were compared and disagreements were resolved by consensus. We systematically searched in Embase, Pub Med, Web of Science, Scopus, Science direct and CNKI databases with several key words: ‘interleukin 4/IL-4’, ‘576R’, ‘pediatric asthma’, and 'gene polymorphism’ (last search was updated on October 2021). Additional manual screening of references from reviewed studies was supplemented to comprehensively extract all relevant researches. Moreover, an information flowchart was constructed, covering screening, identification, eligibility and final selections based on Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA) guidelinesEligible researches were collected accordingly: i) All studies concerning the association between IL-4R Q576R polymorphism and pediatric asthma risk; ii) researches with adequate and effective genotype information; iii)We defined childhood asthma using strict age of onset criteria and considered asthma cases with onset < 12 years of age as childhood onset casesNecessary data were retrieved from all eligible researches by two independent investigators with agreement on all items (Xiwu Chen and Jinhui Hu), including: first author name, year of publication, region, number and genotypes of cases and controls, ethnicity and evidence of Hardy-Weinberg equilibrium (HWE) in controls.Newcastle-Ottawa Scale (NOS) was employed to evaluate the methodological quality of every enrolled researchhttp://biostat.mc.vanderbilt.edu/wiki/Main/PowerSampleSize).The deviation from HWE for the distribution of every genotype in control group was determined by Fisher exact test. In addition, odds ratio (OR) along with 95% confidence interval (95%CI) was adopted to assess the degree of correlation between IL-4R Q576R polymorphism and vulnerability to pediatric asthma under Dominant (GG+-GA vs. AA), Additive  and Recessive (GG vs. AA+GA) models between groups. I2 test was subsequently employed to evaluate the heterogeneity among researches, where I2 > 50% implicated heterogeneity as well as application of random effects model, otherwise fixed effects model should be performed. Sensitivity analysis was mainly conducted by sequentially omitting each study or non-HWE ones. Finally, the possible publication bias was visually assessed by Begg's funnel plot. STATA 12.0  was employed for statistical analysis and a P value <0.05 indicated statistical significance. The power of each study was computed as the probability of detecting an association between IL-4R Q576R polymorphism and pediatric asthma using a significance level of 0.05, assuming an OR of 1.5 . Power analysis was per¬formed using the statistical program PS: Power and Sample Size Calculation  and the threshold for statistical significance. During the analysis, the significance levels for type I and type II errors were set to 5% and 20%, respectively, and relative risk reduction (RRR) was set at 20%. When the cumulative Z-curve crosses the TSA boundary or enters the insignificance area, it demonstrates a sufficient level of evidence, and no further study is necessary. The TSA software (version 0.9.5.10 beta) was used for data processingThe flow diagram concerning study selection was displayed in The major outcomes of our meta-analysis were shown in The Begg's test was performed for evaluating publication bias. Consequently, there no obvious evidence of publication bias by visually assessing funnel plot , implicaFor reducing the random errors and increasing the credibility of the conclusions, TSA was performed. It showed that the cumulative Z-curves did not cross monitoring boundaries and futility boundaries and also did not reach the required information size . Thus, tAsthma is considered as the most prevalent chronic pediatric disorder. The complicated pathogenesis of asthma remains largely unclear, despite multiple genetic loci as well as diverse environmental factors which are indicated to play decisive rolesPrevious meta-analysis showed that IL-4R Q576R polymorphism is associated with asthma, included adults and children, but there was no subgroup analysis aiming at age in this articleNevertheless, the mechanism underlying the relationship between IL-4R Q576R polymorphism and pediatric asthma risk remains unknown. To be specific, IL-4R-encoded protein is a vital functional component of Th2 cells. Previous study showed that The IL-4R Q576R polymorphism may involve in the development of allergy through modulating specific serum IgE levelsThere were several limitations in our study. To begin with, based on unad¬justed information, we were unable to obtain genotype data after stratification for major confounding factors; additionally, there was great variation on confounding factors among different researches. Secondly, only seven articles from China were selected in the present meta-analysis. This may be one of the main limitations of the present work. Thirdly, with the merely published studies included in our meta-analysis, publication bias is very likely to occur, though no statistically significant publication bias is found in our meta-analysis. Fourth, age could be an important one, as the results for outcome onset before school age, before adolescence, and puberty can be different. Due to the small number of articles, we could not conduct a subgroup analysis for age. Finally, the predictive value of a single gene test in a complex disease is very limited for diagnostic or preventive purposes and thus cannot be recommended. Based on data of simulation studies and other complex diseases, the use of genetic profiling that incorporates multiple genetic risk factors holds promise for clinical application.The results of genome-wide association studies will be crucial in establishing this genetic risk profile for asthma. In the future, asthma prediction may be possible, based on a prediction model that incorporates genes, personal factors and environmental risk factorsIn conclusion, we demonstrate the significant relationship of IL-4R Q576R polymorphism with pediatric asthma. Population-based, large-scale, case-control studies should be performed to confirm the identified risk factors in our study, and to further explore other potential gene-environment and gene-gene interactions on pediatric asthma risk.


Besides, the rs2735971 mutation was connected with the digestive system tumor. In summary, the rs217727, rs2839698, rs2107425 and rs2735971 polymorphisms in H19 have associations with cancer susceptibility.The association between polymorphisms in lncRNA H19 and cancer susceptibility remains to be inconsistent. This study aimed to provide a more precise estimation of the relationship between lncRNA H19 polymorphisms and the risk of cancer based on all available published studies. 53 studies encompassing 32,376 cases and 43,659 controls were included in our meta-analysis by searching the Pubmed, Embase, Web of Science, WanFang, and China National Knowledge Infrastructure databases. Pooled ORs and their 95% CIs were used to estimate the strength between the SNPs in H19  and cancer susceptibility. The results showed that H19 rs2839698 polymorphism was associated with increased cancer risk in all participants under three genetic models. However, no significant association was identified between the other six SNPs as well as an overall cancer risk. Stratification by ethnicity showed that rs2839698 mutation indicated to be an important hazardous factor for the Asian population. While rs2107425 mutation had a protective effect on the Caucasian population. Stratification by cancer type identified that rs217727 mutation was linked to increased susceptibility to oral squamous cell carcinoma, lung cancer, and hepatocellular carcinoma; whereas rs2839698 mutation was associated with an elevated risk of hematological tumor and digestive system tumor ( Cancer is a serious disease affecting human health, which ranks as the leading cause of mortality worldwide . It was lncRNA, larger than 200 nts, is one of the most important members of the non-coding RNA family, which regulates gene expression through various mechanisms, such as inducing chromosome refactoring, synthesizing endogenous small interfering RNA, and changing protein localization . AlthougLncRNA H19, located on chromosome 11p15.5, belongs to the human imprinted gene and contains five exons as well as four introns . It has We searched the Pubmed, Embase, Web of Science, WanFang, and China National Knowledge Infrastructure (CNKI) databases up to May 2022 for relevant articles using the following terms: “lncRNA H19 OR long non-coding RNA H19 OR H19”, “cancer OR carcinoma OR tumor OR malignancy”, and “SNP OR polymorphism OR variant OR mutation”. Besides, we retrieved articles by reading the abstracts, and full articles published in English or Chinese without language restrictions. The references for each retrieved article were also investigated carefully for getting related studies.The studies included in our meta-analysis should meet the following criteria: 1) to evaluate the association between the H19 polymorphism and cancer risk; 2) case-control study or cohort study; and 3) genotype frequencies of case and control groups could be obtained to calculate the odds ratio (OR) and 95% confidence interval (CI). We excluded reviews, letters, meta-analyses, conference papers, and other studies that were short of sufficient genotyping data.The information collection was conducted by two investigators independently, and a final agreement was reached by discussing with the third person when there were divergences. The following information was collected from both cases and controls groups: the first author’s name, publication year, country, ethnicity, cancer type, source of the control group, genotyping method, and evidence of Hardy-Weinberg equilibrium (HWE) in the control group.2 statistic were used to calculate heterogeneity , the fixed-effect model was performed. Otherwise, a random-effect model was applied. Furthermore, sensitivity analyses were performed to examine the stability of the results. Egger’s test and Begg’s funnel plots were used to assess the publication bias  and cancer susceptibility in the following genetic models: allele model, recessive model, dominant model, homozygous model, and heterozygous model. Subgroup analysis was calculated concerning ethnicity, cancer types, sources of control, HWE status, and genotyping methods. Cochran’s Q test and Iogeneity . When noion bias . p > 0.0A total of 1,067 publications were preliminarily identified based on our search strategy using different search term combinations. All the abstracts and articles were scrutinized following the selection process . In the p = 0.034); dominant model ; and homozygous model  in all participates , dominant model  and homozygous model  (p = 0.040); recessive model ; and homozygous model . Disease-specific meta-analysis was performed on hepatocellular cancer (HCC), gastric cancer (GC), colorectal cancer (CRC), and other digestive system neoplasms. The analysis indicated that rs2839698 mutation increased the risk of HCC and GC rather than CRC (p < 0.05)  . Stratifthan CRC . Further < 0.05) . In anal < 0.05) .Thirty-six studies were selected to estimate the association of the rs217727 SNP with the risk of cancer. No significant association was indicated for rs217727 through the pooled risk estimation in all genetic models .p < 0.001; recessive model: OR = 1.67, 95% CI = 1.04–2.67, p = 0.034; dominant model: OR = 1.37, 95% CI = 1.16–1.60, p < 0.001; homozygous model: OR = 1.89, 95% CI = 1.19–2.99, p = 0.007; heterozygote model: OR = 1.27, 95% CI = 1.07–1.51, p = 0.005). The same association with increased lung cancer (LC) risk was also observed in four models . While the variant A allele of the rs217727 polymorphism resulted in significantly decreased risk for HCC in recessive model  and homozygous model   risk under all models  . In the The association between the H19 rs2107425, rs2735971, rs3024270, rs3741219, and rs3741216 polymorphisms and cancer risk were separately examined in 13 studies, 8 studies, 15 studies, 13 studies, and 4 studies. We did not find any significant association between these five SNPs and cancer risk in all participants. However, in subgroup analysis stratified by ethnicity, cancer type, genotyping methods, and source of control, these SNPs were related to cancer susceptibility except for rs3741216.p = 0.006), dominant model  and heterozygote model   .p = 0.021; dominant model: OR = 0.85, 95% CI = 0.74–0.98, p = 0.024; and heterozygote model: OR = 0.86, 95% CI = 0.74–0.99, p = 0.034)  . Additio= 0.034) . No signThere was heterogeneity in the analysis between these seven SNPs and cancer risk, except for the recessive and homozygous models of rs2107425 and rs3024270, the recessive model of rs3741219, the heterozygous model of rs2735971 as well as the dominant, homozygous and heterozygous models of rs3741216. Therefore, a random-effects model was adopted to analyze the cancer risk. Otherwise, a fixed-effects model was applied for others mentioned above. Sensitivity analyses observed no change of polled OR before and after the removal of each study in all results of all genetic models, suggesting the good stability and reliability of our study . Begg’s LncRNA plays a crucial role in regulating gene expression and maintaining cell function . AccumulOur combined studies demonstrated a clear increase in cancer risk associated with H19 rs2839698 polymorphism in the overall and Asian population group, which contradicts the results of The previous meta-analysis obtained inconsistent findings on the correlation between H19 rs217727 polymorphism and cancer susceptibility. In the previous meta-analysis, no association was found between the rs2735971 polymorphism and cancer risk in any genetic models . HoweverIn addition, the influence of genotypic methods and population information on cancer susceptibility was also explored in our study. When stratified by genotypic methods, rs217727 polymorphism was significantly correlated with PCR-RFLP, rs2839698, and rs3024270 polymorphisms were significantly correlated with TaqMan. Both PCR-RFLP and TaqMan have high confidence in the analysis of gene polymorphism due to their respective merits . In addiThere are some limitations to this research. Firstly, there is little published research about rs2735971 and rs3741216 polymorphisms, which may limit the statistical power and decrease the reliability of the results. Secondly, in addition to genetic factors, some characteristics of participants, such as age, alcohol intake, and tobacco smoking also influence cancer susceptibility. However, the interaction between these factors and cancer risk could not be assessed due to insufficient data. Thirdly, there exists intensity, moderate or low heterogeneity in this meta-analysis, which may reduce the reliability. Lastly, as mentioned above, because almost all the studies included focused on the Asian population, it is unreasonable to suggest that there is a significant ethnic difference between rs217727 and rs2839698 polymorphisms and cancer risk. This conclusion may not apply to all populations.In conclusion, our results demonstrated that the H19 rs2839698 polymorphism increased cancer susceptibility, whereas the rs2107425 mutation decreased cancer susceptibility in the Caucasian population. Rs2839698 polymorphism was associated with an increased risk of hematologic tumor and digestive system cancer. Rs217727 polymorphism was associated with OSCC, LC, and HCC risk. Rs2735971 mutation significantly increased the risk of digestive system cancer. Considering the limitations mentioned above, further well-designed case-control studies with larger sample sizes and more different ethnic groups are still warranted to explore the impact of H19 genetic variants on cancer risk.


Sex hormones are associated with many cardiovascular risk factors, but their effects on atrial fibrillation (AF) incidence remain unclear. This systematic review and meta-analysis aimed to evaluate the association of circulating sex hormones with AF risk by pooling available data from observational studies.via five databases up to 7 July 2021. A meta-analysis with six cohort studies was conducted separately on men and women. Adjusted relative risk (RR) with a 95% confidence interval (CI) was derived by comparing the highest with the lowest levels of a specific sex hormone and by using a random-effect or fixed-effect model. Heterogeneity was tested using the I2 statistic and the Q-test.A systematic literature search for pertinent articles with case-control and cohort designs was conducted I2 = 50.0%, P–heterogeneity = 0.157) after combining results from two cohort studies; total testosterone was not associated with any risk of AF in men and postmenopausal women, and AF risk was not associated with estradiol in men after synthesizing available studies.A total of six cohort studies and four case-control studies were included. In a meta-analysis of cohort studies, dehydroepiandrosterone sulfate (DHEAS) was associated with a decreased risk of AF in men (RR: 0.729, 95% CI: 0.559–0.952, This study indicates that a higher endogenous DHEAS level was associated with a lower AF risk in men, whereas total testosterone and estradiol were not associated with AF risk. Longitudinal studies with multiple monitoring are needed to further promulgate the relationship between various circulating sex hormones and AF risk. Atrial fibrillation (AF) is the most common cardiac arrhythmia. Its incidence and prevalence increased during the past two decades and will continue to increase in the next 10 years . AF is tGender-related endogenous sex hormones have gotten much more attention recently and were reported to be associated with cardiovascular risk factors, such as hypertension , obesityUntil now, the association of circulating sex hormones with AF risk remains unclear. Thereafter, we conducted this systematic review with meta-analysis in light of MOOSE guidelines  to examiUp to 7 July 2021, five electronic databases, including PubMed, Embase, Web of Science, Chinese National Knowledge Infrastructure (CNKI), and WANFANG DATA, were searched. The following key words were used: “sex hormone,” “gonadal steroid hormones,” “estrogen,” “oestrogen,” “estradiol,” “oestradiol,” “progestins,” “progestagens,” “progestational hormones,” “progestogens,” “progesterone,” “androgen,” “testosterone,” “dehydroepiandrosterone sulfate,” “follicle stimulating hormone,” “luteinizing hormone,” “prolactin,” “androstenedione,” “sex hormone binding globulin,” “atrial fibrillation,” and “auricular fibrillation.” Details of the search strategy are shown in in vitro and in vivo experiments, reviews, editorials, comments, abstracts, effect estimates were not reported or could not be calculated, and studies in neither English nor Chinese.Two researchers (PH and WZ) conducted the literature search independently, reviewed, and identified relevant literature. Disagreements were resolved by group discussions with the third researcher (XL). Studies that met the following criteria were included: (1) case-control studies or cohort studies; (2) the serum or plasm levels of TT, bioavailable testosterone (BT), estradiol, dehydroepiandrosterone sulfate (DHEAS), free dihydrotestosterone (DHT), and sex hormone binding globulin (SHBG) were detected; (3) published in English or Chinese; (4) effect estimates , and hazard ratio) and 95% confidence intervals (95% CI) were reported or they can be calculated according to the reported data. Studies were excluded if they were duplicated studies, cross-sectional studies, case reports, animal studies, Detailed information from each study was extracted, and the information included the first author, publication year, study design, sample size, country in which the study was conducted, age, gender, sex hormone detection method, whether to take samples after fasting, whether to exclude subjects with hormone therapy, confounding factor for multivariable analysis, body mass index, median or mean of follow-up years for the cohort study, effect calculating approach, and specific sex hormone. The effect estimates and 95% CIs for each specific sex hormone obtained from the multivariable model were also extracted.The quality of each study was assessed using the Newcastle-Ottawa Scale (NOS) criteria for the included cohort studies or case-control studies . The maxA systematic review was conducted among cohort and case-control studies, and the meta-analysis was only conducted among cohort studies as the cause-and-effect timing sequence cannot be determined among case-control studies. The RR was used to uniformly display the pooled effect of cohort studies in this study. A meta-analysis was conducted separately in men and in women to generate the pooled RR for each specific sex hormone. The pooled RR with 95% CI was derived by comparing the highest sex hormone levels with the lowest and by using a random-effect or fixed-effect model. The effect estimates in Magnani’s report  were calI2 statistic and the Q-test were used to examine the heterogeneity. The I2 value of more than 50% or P-value from the Q-test of less than 0.1 suggested significant heterogeneity , and TT assessment method . A random-effects meta-regression analysis was also conducted to estimate these characteristics for the potential sources of heterogeneity in this study. Repeated analysis was conducted to examine the contribution of each study to the pooled estimates by omitting one study at a time. The publication bias was illustrated by using the funnel plot and Begg’s test. An asymmetric funnel plot or a P-value of Begg’s test of lower than 0.05 was considered to have significant publication bias. All statistical analyses were conducted using the Stata 12.0 software .The ogeneity . A randoThe flowchart of study selection is shown in The general characteristics of the 10 selected studies are shown in 2, and the average BMI in post-menopausal women varied from 24.08 to 29.00 kg/m2. Among them, four studies . Similarly, no significant association was found in any other subgroups stratified by regions, age, body mass index, and TT detection method . In sensitivity analysis, the pooled analysis was repeated three times by excluding one study each time, and no significant change was observed .As shown in I2 = 50.0%, P–heterogeneity = 0.157).When two cohort , 29 studP = 0.734) and in post-menopausal women (P = 1.000), the associations between estradiol levels and AF risk in men (P = 1.000), and the association between DHEAS levels and AF risk in men (P = 1.000).The funnel plots did not show any significant asymmetry . The BegTo the best of our knowledge, this systematic review and meta-analysis comprehensively examined the association of AF risk with circulating sex hormones. Based on the synthetization of limited evidence, the pooled results suggested that a higher level of DHEAS was associated with a lower risk of AF in men, whereas the association of AF risk with TT and estradiol was observed in neither men nor post-menopausal women. Similarly, the risk of AF was not associated with serum estradiol in men.The pooled results in this study indicated that a high DHEAS level was associated with a 27.1% reduced AF risk in men. A negative association between DHEAS level and the risk of heart failure and mortality in both genders was found in a cohort study with 8,143 participants . Lower DThe evidence investigating the association between TT level and the risk of AF was contradictory, no matter in the included cohort studies or case-control studies , 30, 32.There were insufficient studies to examine the association between the level of DHT, BT, and SHBG and AF risk. In the Cardiovascular Health Study in the United States with a median follow-up of 9.5 years, Rosenberg et al. found that men with serum-free DHT of less than 0.16 ng/dl were associated with a 1.48-fold risk of AF in men when compared with those with higher levels of DHT . By usinvia experimental modification of systemic hormones. Compared with controls, orchiectomized rats demonstrated greater repetitive atrial responses with left atrial pacing (The potential pathophysiology underlying the association between sex hormones and AF is complex. In rats, androgens depress spontaneous depolarization during atrial pacemaking, resulting in acute vasodilation, increased contractility, and an increased sino-atrial recovery time . Androgel pacing .There were some limitations in the included studies. Almost all the included studies assessed the levels of circulating sex hormones at a single point in time; however, the longitudinal changes in the circulating level of sex hormones and whether these changes influenced the incidence of AF were unknown. The study by Berger et al. cannot avoid the effects of asymptomatic AF cases that may be missed in an outpatient setting, and false-negative misclassification may have occurred . MagnaniThe advantage of this study is that different sex hormones were fully considered, and for the first time, a qualitative systematic review with a quantitative meta-analysis was conducted to reveal the relationship between sex hormones and AF. The meta-analysis and sensitivity analysis were performed successively, and the results indicated the robustness and stability of the main results. Nevertheless, some limitations should be acknowledged in our study. First, specific sex hormones were measured by different methods, which may lead to information bias and thus may affect the consistency of the results; however, sensitivity analysis was repeated by omitting one study each time, and no significant change in pooled results was observed. Second, this study did not differentiate the AF types when doing the meta-analysis, so the effect of sex hormones on different types of AF needs further evaluation. Third, there were only six studies included; only two studies could be pooled to reveal the association of DHEAS and estradiol with AF risk in men, respectively; pooling results from limited evidence may influence the stability of our results to draw strong conclusions on the association between sex hormones and AF risk. Fourth, the potential heterogeneity among the included studies may exist in terms of population source, sample size, and subject characteristics; however, due to the limited data, the subgroup analysis and meta-regression based on these factors could not be performed for TT in women, estradiol in women, and DHEAS in men.The findings of this systematic review and meta-analysis indicate that a higher endogenous DHEAS level was associated with a lower AF risk in men, whereas no association was observed between TT and estradiol concentration and AF risk. Longitudinal studies with multiple monitoring are needed to further promulgate the relationship between various circulating sex hormones and AF risk.Publicly available datasets were analyzed in this study. The data is available by contacting the corresponding authors or extracting from the original published research.XL and HD conceived, designed, and supervised the study. PH and WZ searched the data. PH analyzed the data. PH and JH drafted the manuscript. PH, MZ, HD, WZ, HL, YL, and XL reviewed and interpreted the findings. All co-authors provided comments and approved the final version.


Clinical and preclinical studies suggested that certain mutagens occurring as a reaction of creatine, amino acids, and sugar during the high temperature of cooking meat are involved in the pathogenesis of human cancer. Here we conducted a systematic review and meta-analysis to examine whether meat mutagens  present a risk factor for human cancer.We searched the following databases for relevant articles published from inception to 10 Oct 2021 with no language restrictions: Pubmed, Embase, Cochrane Central Register of Controlled Trials (CENTRAL), Baidu Academic, Zhejiang Digital Library. Two independent researchers screened all titles and obtained eligible texts for further screening. Independent data extraction was conducted, and meta-analysis was carried out using random-effects models to calculate the risk ratio of the meat mutagens exposure.p < 0.001), MeIQx , DiMeIQx , total HCA , and cancer risk was not significantly increased by intake of B(a)P .A total of 1,786,410 participants and 70,653 cancer cases were identified. Among these, there were 12 different types of cancer at various sites, i.e., breast, bladder, colorectal, colon, rectum, prostate, lung, Non-Hodgkin lymphoma, kidney, gastric, esophagus, pancreatic, hepatocellular carcinoma. Cancer risk was significantly increased by intake of PhIP (OR = 1.13;95% CI 1.07–1.21; Meat mutagens of PhIP, MeIQx, DiMeIQx, and total HCA have a positive association with the risk of cancer.www.crd.york.ac.uk/prospero], identifier [CRD42022148856].[ N-acetyltransferase 2 (NAT2) with N-acetylation and O-acetylation  on a topic, which is desirable to identify a clear relationship between the variables of concern. In this study, we study the risk estimate about meat mutagens and human cancer. We conducted a systematic review and meta-analysis to shed light on the relation between meat mutagens  and different types of human cancers. The study have registered on PROSPERO. ID: CRD42022148856.Our objectives were as follows:(i) Conduct a meta-analysis to examine whether meat mutagens  present a risk factor for human cancer.(ii) To identify which types of human cancers are especially vulnerable to meat related mutagens.(iii) To identify which categories of meat mutagens that warrant further in-depth evaluation according to harmfulness.In this study, meta-analysis was according to PRISMA .Exposure referred to heterocyclic amines  and benzo (a)pyrene; the outcomes of interest included all kinds of cancer. Following databases were searched from inception to 10 Oct 2021 with no language restrictions: PubMed, Cochrane Central Register of Controlled Trials (CENTRAL), Baidu Schoolar, Google scholar, and Zhejiang Digital Library. We used the following search keywords [meat mutagens OR heterocyclic amin OR PhIP OR MeIQx OR DiMeIQx OR benzo(a)pyrene] and (neoplasm OR cancer OR tumor). To maximize the search for relevant articles, the reference lists of identified articles and relevant systematic reviews were examined to identify additional relevant publications.Articles were considered eligible if they met the following inclusion criteria: (i) study design: epidemiological study and clinical study with case-control or cohort study; (ii) object: the association between heterocyclic amine  intake and cancer risk; (iii) outcomes: publications presented results of relative risk [RR], odds ratio [OR], or hazard ratio (HR) for the highest vs. lowest exposure.If more articles were produced based on the same cohort study, such as Nurses Health Study (NHS), and analyses presented utilized unique sites or exposures, the article with the largest number of cases was selected for statistical analysis. No studies were excluded due to issues related to data quality or design.(i) Study design with cross-sectional surveys, ecologic analyses, case reports, editorials.(ii) Exposure comes from the working environment or smoking.(iii) Experimental study on the animal model.After the removal of duplicates, two independent researchers screened all titles and obtained eligible texts for further screening. Disagreements between researchers were solved by the third author.The following data were extracted from the included studies; (i) basic information: first author, year of publication, study design, study region; (ii) individual data: sample size, tumor site, dietary assessment method, exposure quantification method, risk estimates , (iii) matched or adjusted variables. We abstracted those that adjusted for multivariate or most confounding factors.P < 0.05 was considered statistically significant; p-values and I2 values were used for the heterogeneity test.The random-effect model of the inverse variance weighting test was used to calculate OR and the corresponding 95% CI for the highest vs. the lowest level of exposures. We performed subgroup analyses according to cancer site, study design, and country. Forest plots were generated for PhIP, MeIQx, DiMeIQx, total HCA, and B(a)P. Subgroup analysis can be used to evaluate the potential association between exposure and influencing factors and describe sources of heterogeneity by stratifying factors.The existence of publication bias was evaluated by Begg’s test and Egger’s test, the Begg methods test was also used for funnel plot asymmetry. If the value of Begg’s Test (Pr > | z|) and Egger’s test Pr > | z| were < 0.05, the funnel plot was considered to be asymmetrical, thus suggesting the potential existence of publication bias. When potential bias was detected, a trim-and-fill analysis was further performed to assess the influence of the bias and to have the bias-corrected. We also performed a sensitivity analysis to investigate the influence of a single study on the pooled OR estimate by omitting one study in each turn. Stata12.0 software was used for all analyses.Among 1,141 publications that were initially identified from the data sources, 58 were included in the final meta-analysis. Details of the selection of publications included in the meta-analysis are shown in p = < 0.001. The result of the meta-analysis was a significant summary risk estimate.p = 0.000). The meta-analysis revealed a significant association between MeIQx intake and carcinoma risk.p = 0.013). The results indicated a weakly significant association between DiMeIQx intake and carcinoma risk.p = 0.016). The results revealed a statistically significant association between total HCA intake and carcinoma risk.Sixteen publications evaluated the association between total HCA consumption and cancer . The OR p = 0.206. The results revealed no statistically significant association between B(a)P intake and carcinoma risk.Thirty studies were included in the meta-analysis of the association between B(a)P intake and cancer risk . The OR p = 0.001), this result was the same as the original OR result, which was 1.13   .Sensitivity analyses were used to investigate the influence of a single study on the pooled OR estimates by omitting one study in each turn. The obtained results suggested that the estimates of PhIP, MeIQx, DiMeIQx, total HCA, B(a)P were not substantially modified by any single study .In this meta-analysis, we investigated the relationship between the meat mutagens  and all typical types of cancers.First, our results indicated a significant association between  and total cancer risk, and no association between benzo(a)pyrene and cancer risk. This research included 1,987,798 participants, 69,874 cancer cases, and 12 types of cancer at the following sites: breast, bladder, colorectal, colon, rectum, prostate, lung, Non-Hodgkin lymphoma, kidney, gastric, esophagus, and pancreas. The result clearly showed which types of cancers are particularly vulnerable to related mutagens. Future studies are needed to identify categories of meat mutagens that warrant further in-depth evaluation according to harmfulness.Second, we conducted meta-analyses for cancer risk and meat mutagens intake stratified by geographic location. We observe an increased risk in North America with exposure to PhIP, MeIQx, DiMeIQx; in South America, with PhIP and total HCA exposure, while no increased risk was observed in Europe, Asia, and Oceania. This difference may be related to dietary structure. As is well-known, the Mediterranean diet is preferred in Europe, and a previous meta-analysis suggested that the Mediterranean diet provides significant protection from the incidence of cancer of all types , 24. ThiThis systematic review and meta-analysis have several strengths. First, the present study had a large sample size and a large number of epidemiologic studies, with 63 trials being included in this meta-analysis. Second, to the best of our knowledge, this systematic review is the first that provided a comprehensive guide to the risk estimates for 12 types of cancers and meat mutagens exposure.I2 = 25.8%), while the results obtained without the CHARRED database revealed large heterogeneity (I2 = 80%). Accordingly, we found that the use of the CHARRED database could effectively improve the heterogeneity from the nutritional epidemiology study.Furthermore, the food frequency questionnaire was used to ascertain dietary information in this meta-analysis. It is challenging to ascertain respondents’ usual exposure from an FFQ. Some early epidemiological studies included a few items of cooking methods  as surrogate measures in the food frequency questionnaire. Meat intake mutagens were calculated as follows: frequency of consumption of pan-frying meat × [(portion size) × (PhIP content for each pan-frying meat according to literature data)] , which wThe present study also has some limitations: heterogeneity was statistically significant in case-control studies, while it was small in a cohort study , which sThe results indicated that PhIP, MeIQx, DiMeIQx, and total HCA have a positive effect on total cancer risk, while benzo(a)pyrene was not associated with an increased risk of cancer. Results support this basic tenet of prevention in public health, restricting processed meat intake is a healthy lifestyle. This meta-analysis paves the way for a prospective epidemiological study in meat intake and cancer risk.The original contributions presented in this study are included in the article/FJ conducted the analysis, generated the figures, and wrote the manuscript. All authors designed and conducted the systematic review, contributed to edits and revisions of the manuscript, and approved the submitted version.