Mounting evidence suggests that there may be a causal relationship or common pathogenic pathway between inflammatory bowel disease (IBD) and dementia. However, inconsistent results have emerged from epidemiological studies. We therefore conducted this review to clarify the relationship between IBD and dementia.We systematically searched PubMed, Web of Science, Embase, and Cochrane library to identify all studies exploring the relationship between IBD and dementia published as of September 2021. Risk estimates were pooled using both fixed and random-effects models.P = 0.001), which did not vary by age, gender, dementia subtype, or IBD subtype. Whereas, the dementia incidence before IBD diagnosis and the comorbidity rate of dementia in IBD patients were similar to those without IBD . However, current evidence was insufficient to establish a causal relationship.Six studies involving 2,334,472 subjects were included. Pooled results suggested that the risk of developing dementia significantly increased after IBD diagnosis (HR = 1.27, 95% CI: 1.10–1.47, This study shows an unidirectional association between IBD and dementia; patients with IBD have an increased risk of dementia, and it may be beneficial to develop individualized dementia screening strategies for this population. Future research needs to further investigate whether effective therapies of IBD can reduce this risk and pathophysiological mechanisms of the association. Inflammatory bowel disease (IBD), consisting mainly of Crohn's disease (CD) and ulcerative colitis (UC), is a chronic inflammatory disease of the intestine, the exact etiology of which remains unknown . CurrentDementia is a syndrome characterized by chronic or progressive cognitive decline, caused by the interaction of genes, lifestyle, and environmental factors, and is usually irreversible . Dementiin vivo may cause neuroinflammation and thus have a pathogenic effect on cognitive aging and neurodegeneration  guidelines . The proTwo reviewers extensively searched the PubMed, Embase, Web of Science, and Cochrane library for relevant papers published from inception date to September 2021. Search strategies were developed based on Medical Subject Headings terms  in combination with free terms , without language restrictions or use of filters. We also manually searched the reference lists of included studies and related reviews to identify potentially eligible studies. Detailed search strategies for each database are provided in the Reports that met all of the following criteria would be considered for inclusion: (a) IBD and dementia were identified according to standard diagnostic criteria or according to disease classification or prescription codes in the patient medical record database; (b) study results reported hazard ratio (HR), risk ratio, or odds ratio with their corresponding 95% confidence intervals (CIs) for the association between IBD (CD and/or UC) and any dementia subtype, or provided sufficient data to calculate these ratios; and (c) the study design was case-control, cohort, or cross-sectional;Reviews, meta-analyses, animal studies, commentaries, case reports or series with sample sizes of <10, conference abstracts published in full elsewhere, and single arm studies would be excluded. We would examine the period and location of population selection for each report, and for reports containing overlapping data, we included reports with larger sample sizes.Two authors independently reviewed the titles and abstracts of initially retrieved records according to inclusion and exclusion criteria. Potentially eligible reports were assessed by full-text reading. Reasons for exclusion after full-text reading would be recorded. Any disagreements were resolved by consensus through discussion.For the final included studies, data were extracted including: first author, year of publication, region, age, gender, study period, study design, source of subjects, sample size, identification methods for IBD and dementia, confounders being matched or adjusted for, and duration of follow-up. Data was recorded by one author and checked by another.For cohort and case-control designs, the quality of included studies was assessed using the Newcastle-Ottawa Scale (NOS), which includes eight specific quality items in three domains: selection, comparability, and exposure/outcome . All iteQ test and Higgin's I2 statistics were used to examine the heterogeneity of the included studies , dementia subtype , IBD subtype , sex, age . The Begg's and Egger's tests were used to assess publication bias. When the P-values for both ≥0.05, there was no publication bias, otherwise the impact of potentially unpublished studies was assessed by using the trim-and-fill method.In this study, all data analyses were performed using Stata MP/16.0. The inverse variance method of DerSimonian and Laird was used to pool the risk estimates and corresponding 95% CIs . For stu studies . HeterogA total of 323 records were identified through the initial search strategies. After excluding duplicates and reviewing titles, abstracts, and/or full texts, six population-based studies fulfilled eligibility criteria to be included in the analysis –26. The The six included studies were all population-based and comprised a total of 2,334,472 subjects, of which 132,445 were patients with IBD. All studies were cohort studies except one, which was cross-sectional design . Five stAll cohort studies were high quality population-based studies with appropriate subject selection, good comparability, and adequate outcome assessment . The croP = 0.018), with high heterogeneity  , and the statistical significance of the association between IBD and dementia remained .Pooled results from six studies involving 2,334,472 subjects suggested a statistical association between IBD and dementia  . HeterogSubgroup analysis was conducted based on the temporal relationship between dementia and IBD diagnosis, dementia subtype, IBD subtype, gender, and age. There was only one study assessing the incidence of dementia prior to the diagnosis of IBD and one assessing the dementia comorbidity in patients with IBD; given the power of pooled analysis, the subgroup analysis for dementia subtype, IBD subtype, age, and sex was limited to the group of dementia risk after IBD diagnosis.P = 0.001), whereas such risk prior to IBD diagnosis and the comorbidity rate of dementia in IBD patients were slightly lower compared to controls  , which decreased after excluding the study from Taiwan  and the significance of the result remained . There were no obvious differences in subgroups by age, sex, IBD subtype, dementia subtype; in some cases there was high heterogeneity, but all reduced to <50% after excluding the study conducted in Taiwan and remained statistically significant  . There wnificant .To assess the stability of the combined results, sensitivity analyses were conducted by excluding one study at a time and checking the influence of each study on pooled results. The results showed that the effect estimates of all groups were consistent and stable when any single study was omitted. In addition, the results from the random-effects and fixed-effects models were consistent in all pooled analyses. However, the statistical significance of the pooled results disappeared when certain studies were excluded in the AD, UC, age <65 years, and male subgroups.P-values for the Begg' and Egger' tests were 0.386 and 0.362, respectively, indicating no publication bias.The After pooling data from over 2.3 million participants, our results suggested that IBD is associated with an increased incidence of dementia. Patients with IBD had a 1.23-times higher risk of developing dementia, whereas dementia incidence prior to IBD diagnosis and the comorbidity rate of dementia in IBD patients were similar compared with non-IBD controls. No dementia subtype, IBD subtype, gender, or age- dependent association was observed. However, sensitivity analyses indicated that the statistical significance of association between IBD and dementia risk would disappear when certain studies were excluded in the AD, UC, age <65 years, and male subgroups, which may be attributed to the relatively small sample size and therefore lack of statistical power.Interestingly, the high heterogeneity shown in subgroup analysis was all significantly reduced after excluding the study from Taiwan . The HR The exact mechanism for the interaction between IBD and dementia remains unknown, and current evidence suggests that IBD may increase the risk of developing dementia through several pathways. IBD is a chronic inflammatory disease involving digestive tract and other organs; despite achieving the therapeutic goal of sustained remission, histological activity, microscopic inflammation, and extraintestinal symptoms can still be found, which leads to an increased systemic inflammatory burden –31. FurtThe current studies emphasized the unidirectional association between IBD and subsequent dementia onset; consistency in these studies were shown, and as discussed above, there appears to be biological plausibility that IBD causes dementia. However, current studies did not sufficiently consider temporality; dementia is a slow-progressing disease with an incubation period of several years, so reverse causality and the result of co-exposure are possible. Studies in Taiwan and Korea found that dementia risk seemed to accelerate over time with chronic course of IBD diagnosis , 26, butAlthough further clarification is needed on the causal relationship between IBD and dementia, current data suggest that IBD onset is associated with subsequent dementia diagnosis, whether as a risk factor or prodromal symptom. Moreover, the study in Taiwan indicated that the average age of dementia onset was 7 years younger in IBD patients than in controls . CurrentThere are some limitations in this study to note. First, there was only one study exploring the dementia incidence prior to the IBD diagnosis and one cross-sectional study exploring the dementia comorbidity among patients with IBD, so it is unclear whether there were consistent trends across regions and ethnicities. Moreover, there was only one study that assessed the relationship between IBD and dementia subtypes other than AD. Second, IBD is most commonly diagnosed in younger adults, but due to limited available data, age was divided into only two subgroups (<65 and >65 years). The characteristics of dementia risk in younger people with IBD are unclear. Third, the Begg' and Egger' tests indicate that available studies are not subject to publication bias, but the smaller number of eligible studies may limit the ability to test, so there is still a potential risk of publication bias. Several limitations of the included studies also need to be noted. Despite the large sample sizes in the included studies, the study data were all obtained from medical or insurance record databases; these data were not collected based on specific research questions, so the effects of some potential confounding factors such as physical activity, smoking, alcohol consumption, body mass index, or medications associated with dementia risk, were not adjusted/matched. In addition, some studies did not consider surveillance bias and competing risks. Moreover, there is time error in the diagnosis of IBD and dementia, such as delay in diagnosis, which is one of the reasons why causality cannot be concluded. Finally, the use of diagnostic codes to identify diseases suffers from inevitable underdiagnosis and misclassification.In conclusion, our study demonstrates that there is a correlation between IBD and dementia, and people with IBD have an increased risk of developing dementia, regardless of gender, age, IBD subtype, and dementia subtype. Education and dementia screening for individuals with IBD to achieve early diagnosis and improve quality of life is worth considering. However, current evidence does not support drawing a conclusion about causality. Future studies from different regions and ethnicities are needed to validate this association and further elucidate the precise pathological mechanisms underlying the association between IBD and dementia; interventions on pathogenic mechanism may help develop new approaches to dementia treatment.The original contributions presented in the study are included in the article/ZS: conception and design, development of methodology, critical review of the manuscript, and study supervision. ML and DL: database search, literature review, and data extraction. DL and XH: statistical analysis. ZS and ML: interpretation of data. ML, DL, and XH: drafting of manuscript. The final version of the manuscript was approved by all authors.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


Both hepatitis B virus (HBV) and hepatitis C virus (HCV) infections are important risk factors for hepatocellular carcinoma. However, their effect on other hepatobiliary cancers, such as biliary tract cancers (BTCs), is not well established. We aimed to investigate associations between HBV or HCV infection and BTCs risk by conducting a systematic review and meta-analysis.We searched PubMed to identify all relevant articles published before June 9, 2021. Meta-analysis was performed to calculate pooled odds ratios (ORs) and corresponding 95% confidence intervals (CIs). The meta-analysis was evaluated by heterogeneity testing, sensitivity analyses, and publication bias assessment.. 1.51, P = 0.014). In particular, significantly increased risks of intrahepatic cholangiocarcinoma (ICC) were identified in individuals with HBV  or HCV infection .In total, 48 articles involving 69,723 cases and 4,047,574 controls were obtained to calculate the associations between HBV or HCV infection and the risk of BTCs. We found that both HBV and HCV infections were associated with the risk of BTCs, with pooled ORs of 2.16 (95% CI 1.73–2.69) and 2.12 (95% CI 1.62–2.77), respectively. Subgroup analyses by ethnicity suggested that HBV infection could increase the risk of BTCs in both Asian  and Caucasian  populations. In addition, HCV infection resulted in a higher increased risk of BTCs in Caucasian populations than in Asian populations (OR = 3.93 vsThis study suggests that both HBV and HCV infections are risk factors for BTCs, particularly ICC, highlighting the necessity of cancer screening for BTCs in patients with either HBV or HCV infection.The online version contains supplementary material available at 10.1186/s13027-022-00457-9. Viral hepatitis infections remain a major public health problem worldwide, affecting hundreds of millions of people; an estimated 257 million people are living with hepatitis B virus (HBV), and 71 million people are living with hepatitis C virus (HCV) [Both HBV and HCV are potential risk factors for liver cancer, non-Hodgkin lymphoma, multiple myeloma, and thyroid cancer –6. ImporHistorically, BTCs are classified into cancers of the gallbladder, intrahepatic bile duct, extrahepatic bile duct, and ampulla of Vate, which are relatively rare but highly fatal malignancies , 9. PrioThis meta-analysis followed the guidelines of the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) statement and Meta-analysis Of Observational Studies in Epidemiology (MOOSE) , 21. TheA comprehensive literature search of related studies was conducted using PubMed  with the keywords “hepatitis B virus or hepatitis C virus or HBV or HCV” and “cholangiocarcinoma or CCA or ECC or ICC or bile duct cancer or bile duct carcinoma or biliary tract cancer or gallbladder cancer or gallbladder neoplasm” as query terms. The titles and abstracts of the studies, or full texts if necessary, were reviewed to identify all relevant publications. Additionally, the reference lists of all included studies were manually reviewed, as were references from reviews and meta-analyses of relevant potential studies.Inclusion criteria for studies investigating the associations between HBV or HCV infections and the BTCs risk were as follows: (1) a case‒control or cohort study design; (2) exposure to HBV or HCV infection (with serum HBsAg and hepatitis C antibody used as the positive markers of hepatitis virus infection); (3) the outcome of interest was BTC, cholangiocarcinoma (CCA), intrahepatic cholangiocarcinoma (ICC), extrahepatic cholangiocarcinoma (ECC), and gallbladder cancer (GBC) ; (4) risk estimates with 95% confidence intervals (CIs) or data to calculate them were provided; and (5) publication in English. Abstracts, reviews, letters, case reports and studies that did not provide sufficient data to calculate risk estimates were excluded. Two investigators independently selected studies, and any discrepancies were resolved by consensus.The following information was extracted from each study: first author, year of publication, country, ethnicity, cancer type, study design, source population, numbers of cases and controls, odds ratios (ORs), relative risks (RRs), adjusted odds ratios (aORs) and corresponding 95% CIs, adjustments, sample sizes and follow-up years (only for cohort studies).P values less than 0.05  were considered significant unless otherwise stated. Associations between hepatitis virus infection and BTC risk were evaluated by ORs and corresponding 95% CIs using a random effects model (DerSimonian Laird). The significance of the overall OR was determined by the Z-test.Statistical analyses were performed with Stata version 15 , and two-sided P values less than 0.10 were considered indicative of statistically significant heterogeneity) and I2 statistic . Subgroup analyses and meta-regression analyses were conducted by ethnicity and study design to explore potential sources of heterogeneity. In addition, sensitivity analyses were performed to investigate the influences of single studies on the overall risk estimate by removing one study at a time. Potential publication bias was evaluated using both Begg’s funnel plot and Egger’s test, and P values less than 0.10 were considered to be statistically significant [The meta-analysis was evaluated by heterogeneity testing, sensitivity analyses, and publication bias assessment. Statistical heterogeneity among the studies was assessed by the Cochran Q statistic  and Caucasian  populations. Otherwise, HBV infection was associated with an increased risk of CCA in Asian populations, with a pooled OR of 2.48 (95% CI 1.81–3.38), and the pooled OR was 2.01 (95% CI 1.43–2.84) in Caucasian populations. In addition, we found that HBV infection could increase the risk of ICC  and ECC  in Asian populations. However, no association was observed between HBV infection and ECC in Caucasian populations.A significant association was found between HCV infection and BTCs risk  Fig. . The resSubgroup analyses by ethnicity showed that HCV infection could increase the risk of BTCs in both Asian  and Caucasian  populations. In addition, HCV infection was associated with an increased risk of CCA , especially ICC  in these two populations. However, an increased risk of ECC with HCV infection was observed only in Caucasian populations  and not in Asian populations .P = 0.014), CCA (P = 0.005), including ICC (P = 0.019) and ECC (P = 0.025). Ethnicity could explain 18% of the heterogeneity in the HCV infection–BTCs risk association, 26% of the heterogeneity in the HCV infection–CCA risk association, 46% of the heterogeneity in the HCV infection–ICC risk association, and 35% of the heterogeneity in the HCV infection–ECC risk association.Large heterogeneity was found in our study, and therefore, both subgroup analyses and meta-regression analyses were conducted by ethnicity and study design to explore the potential sources of heterogeneity Table . We founP > 0.1 in all the analyses, except for the associations between HBV and BTC risk and HBV and CCA risk).Sensitivity analysis was performed to evaluate the stability of the results, and we found that removing a single study could not significantly change the pooled ORs and 95% CIs (data not shown), indicating that any single study had little impact on the overall ORs. In addition, the funnel plot appeared generally symmetrical . In addition, patients with HCV infection had an approximately 2.12-fold increased risk of BTCs, with a 2.90-fold increased risk of ICC and a 1.60-fold increased risk of ECC. However, no evidence of an association between HCV infection and GBC was found. These results suggest the importance of considering ICC and ECC separately since both HBV and HCV infections have a higher risk of ICC than ECC. In addition, these findings highlight aetiologic heterogeneity across BTCs, and the disparity might be explained by the different anatomic locations. Both HBV and HCV prefer to replicate in hepatocytes, which could lead to liver cirrhosis and HCC . Since hP = 0.186), whereas HCV infection was associated with a higher increased risk of BTCs in Causation populations than in Asian populations . HBV infection, but not HCV infection, was reported to be associated with BTC in some Asian countries, such as China, where HBV infection is highly endemic [Subgroup analyses by ethnicity showed that both Causation and Asian populations had comparably higher increased risks of BTCs with HBV infection  in the literature since cancers arising from the ampulla of Vater are sometimes included under the term BTCs. Histologically, such cancers can be pancreatobiliary, intestinal, or mixed tumours and account for only 0.2% of gastrointestinal cancers . In addiIn conclusion, this meta-analysis shows that both HBV and HCV infections are risk factors for BTCs, especially ICC. Furthermore, we found that both Asian and Causation populations have comparably higher risks of HBV-related BTCs, whereas Causation populations have a higher risk of HCV-related BTCs. Findings from our study suggest an important role of hepatitis virus infection in the pathogenesis of BTCs, highlighting the necessity of cancer screening for BTCs in individuals with HBV or HCV infection.Additional file 1: Table S1. Characteristics of studies included in the meta-analysis. Figure S1. Funnel plot of studies evaluating the association between hepatitis B virus (HBV) and biliary tract cancer risk. (a) HBV and biliary tract cancer, (b) HBV and cholangiocarcinoma, (c) HBV and intrahepatic cholangiocarcinoma, (d) HBV and extrahepatic cholangiocarcinoma risk. Figure S2. Funnel plot of studies evaluating the association between hepatitis C virus (HCV) and biliary tract cancer risk. (a) HCV and biliary tract cancer, (b) HCV and cholangiocarcinoma, (c) HCV and intrahepatic cholangiocarcinoma, (d) HCV and extrahepatic cholangiocarcinoma risk.


Genetic variability in DNA double-strand break repair genes such as RAD51 gene and its paralogs XRCC2、XRCC3 may contribute to the occurrence and progression of breast cancer. To obtain a complete evaluation of the above association, we performed a meta-analysis of published studies.Electronic databases, including PubMed, EMBASE, Web of Science, and Cochrane Library, were comprehensively searched from inception to September 2022. The Newcastle-Ottawa Scale (NOS) checklist was used to assess all included non-randomized studies. Odds ratios (OR) with 95% confidence intervals (CI) were calculated by STATA 16.0 to assess the strength of the association between single nucleotide polymorphisms (SNPs) in these genes and breast cancer risk. Subsequently, the heterogeneity between studies, sensitivity, and publication bias were performed. We downloaded data from The Cancer Genome Atlas (TCGA) and used univariate and multivariate Cox proportional hazard regression (CPH) models to validate the prognostic value of these related genes in the R software.P=0.03). Furthermore, ethnic analysis showed that SNP was associated with the risk of breast cancer in Arab populations in homozygous models . For the XRCC2 R188H polymorphism, no significant association was observed. Regarding polymorphism in XRCC3 T241M, a significantly increased cancer risk was only observed in the allelic genetic model .The combined results showed that there was a significant correlation between the G172T polymorphism and the susceptibility to breast cancer in the homozygote model (OR= 1.841, 95% CI=1.06–3.21, In conclusion, this meta-analysis suggests that Rad51 G172T polymorphism is likely associated with an increased risk of breast cancer, significantly in the Arab population. The relationship between the XRCC2 R188H polymorphism and breast cancer was not obvious. And T241M in XRCC3 may be associated with breast cancer risk, especially in the Asian population. In all countries around the world, cancer is the leading cause of death and an important obstacle to improving life expectancy. Female breast cancer (BC) has overtaken lung cancer as the leading cause of global cancer incidence in 2020, with an estimated 2.3 million new cases, representing 11.7% of all cancer cases gene, located in 7q36.1, is an essential part of the homologous recombination repair pathway and a functional candidate for involvement in cancer progression. Its XRCC2 protein product, together with other proteins encoded by the XRCC2 gene such as RAD51L3, forms a complex that plays a critical role in chromosome segregation and the apoptotic response to DSBs , 17. As We examined whether polymorphisms in these three genes involving homologous recombination with DSB were associated with the risk of breast cancer.All studies investigating the association between polymorphisms in the RAD51 gene and paralog genes, such as the XRCC2 & XRCC3, and the risk of breast cancer, were identified by comprehensive computer-based searches of the PubMed, Embase, Web of Knowledge, and Cochrane Library databases(the last search update on September 2022). The search was carried out using various combinations of keywords such as (‘RAD51 gene’ OR ‘RAD51 recombinase gene’ OR ‘XRCC3 polymorphism’ OR ‘XRCC3 Thr241Met polymorphism’ OR ‘XRCC2’ OR ‘XRCC2 Arg188His polymorphism’) AND (‘polymorphism’ OR ‘variant’ OR ‘variants’).Studies included in our meta-analysis needed to have met the following criteria: 1)published in public, full text only; 2) case-control study; 3) sufficient data (genotype distributions for cases and controls) to calculate an odds ratio (OR) with its 95%CI; 4) studies published in English; 5) genotype distribution of the control population consistent with the Hardy-Weinberg Equilibrium (HWE).Two authors independently extracted information from all eligible publications according to the inclusion criteria listed above. Disagreement was resolved by evaluating a third reviewer and discussing until a consensus was reached. The following characteristics were collected from each study: first author, year of publication, country, ethnicity, methods in experiments, source of control groups and genotype frequencies in case and control groups, and the value of HWE. Duplicated primary studies were deleted and only one version of duplicated documents was kept.https://cancergenome.nih.gov/). In total, 903 patients with BC were selected from the TCGA cohort. For the transcriptome data from TCGA-BRCA, we download their series files. Some important clinical characteristics including age, pathologic stage , and pathology stage  are available. The datasets listed in The transcriptome data and clinical information of BC patients were obtained from The Cancer Genome Atlas (TCGA) database (P <0.05. Crude odds ratios (OR) with 95% confidence intervals (CI) were used to assess the strength of the association between the RAD51 gene and its paralog polymorphisms and breast cancer susceptibility. The pooled ORs for the RAD51 G172T polymorphism were performed under the dominant model (GG vs. TT+GT), recessive model (TT vs. GG+GT), homozygote model (TT vs. GG), and allelic genetic model (T vs. G). T and G represent the minor and the major alleles, respectively. The same methods were applied to the analysis of other polymorphisms. Stratified analyzes were performed on ethnicity and source of control. A Q-test was performed to assess statistical heterogeneity among studies. The pooled OR was calculated using a fixed effect model if the result of the p-value of the Q test<0.1 indicated significant heterogeneity according to the previous study  and the departure of HWE genotype frequency among control subjects was determined by er,1997) , 30. If ion bias . Cox regThe Newcastle-Ottawa Scale (NOS) was applied to assess the quality of all studies. The NOS checklist includes three parameters of quality: (i) selected population, (ii) comparability of groups, and (iii) assessment of either the exposure or outcome of interest for case-control studies. The studies scored greater than or equal to 7 were considered to be high quality articles.According to our first database search, 272 items were identified Figure 1P= 0.003) (P= 0.027), suggesting that it is marginally related to the population-based group.Among these 9 case-control studies from 9 publications with 4111 cases and 2669 controls for the RAD51 G172T polymorphism –40. The = 0.003) Figure 3For the R188H polymorphism XRCC2, 20 case-control studies from 11 articles with 20183 cases and 20321 controls for the XRCC2 R188H polymorphism –51. No sP = 0.003) and allelic genetic models   5.Given the significant heterogeneity between studies for the polymorphisms, the random-effect model was used to calculate the pooled results if the heterogeneity was significant. Meanwhile, we also performed a sensitivity analysis to assess the effects of each study on the pooled ORs by omission of individual studies. The sensitivity analysis showed that, for each polymorphism, no single study qualitatively changed the pooled ORs, suggesting that the results of this meta-analysis were statistically stable and reliable.P value were 0.67, and Egger’s test P value was 0.319. Begg’s test result of the allelic genetic model in XRCC3 T241M P = 0.65 and Egger’s test result showed P = 0.52, suggesting no publication bias. All P-values> 0.05, suggesting that there was no publication bias.We further identify potential publication biases of the literature using the Egger test and funnel plot. In all studies, no funnel plot asymmetry was found. The results of Egger’s test for the RAD51 G172T polymorphism did not show any evidence of publication bias. For the homozygote model, the funnel plot p-value was 0.47, and Egger’s test p-value was 0.185. In the dominant model, Begg’s test results of the R188H Screening for some frequent polymorphisms has improved our understanding of the critical roles that inheritance plays in BC susceptibility. To date, associations between genetic variants in HRR genes and BC development have been investigated, but the results remain unexplained to the best of our knowledge. However, new discoveries in drug research aimed at these gene mutations are always innovative. Some experiments suggest that the inhibition of HR will be selective against breast tumor cells. Inhibitors of HR proteins can be used in combination with radiotherapy or chemotherapy to sensitize the cells [5]. A more intriguing possibility would be to use anti-HR agents alone, avoiding the toxicity of DNA-damaging agents. Such a strategy has been applied to selectively kill BRCA2-deficient cells using poly-ADP-ribose-polymerase inhibitors (PARP). The first phase III clinical study of PARP inhibitor for adjuvant treatment of early breast cancer, OlympiA study, aims to evaluate the efficacy and safety of olaparib compared with placebo in the adjuvant treatment of early breast cancer with clinically and pathologically high-risk, HER2-negative, BRCA1/2 mutation. And randomized phase II GeparOLA study showed olaparib plus paclitaxel (PO) in early HER2-negative homologous recombination deficiency (HRD) breast cancer. In conclusion, germline BRCA 1/2 status and HRD predict a higher pathological complete response (pCR) rate in the neoadjuvant treatment . The molPh = 0.014)  . Further= 0.014) , 87, 88.= 0.014) . Similar= 0.014) , 65. Int= 0.014) . Chai pe= 0.014) . Our res= 0.014) , 54. TheCompared with studies before, our study has some improvements. First, Our study had the advantage of including higher numbers of cases and controls. Second, these polymorphisms in RAD51 and paralog genes were analyzed and associated with the risk of specific cancer, breast cancer. Third, we provided a more comprehensive analysis of the data by calculating four different genetic models and performing a subgroup analysis by ethnicity, and source of controls . Finally, we excluded studies in which the distribution of genotypes in the control group was inconsistent with HWE because they might influence the results. The results of this study further revealed the correlation between the polymorphism in these genes and the occurrence and development of breast cancer, providing a direction for the study of molecular mechanisms of cancer in the future.The main limitations of our meta-analysis are: 1) This meta-analysis only searched published studies in English, ignoring some unpublished studies or studies in other languages that may also meet the inclusion criteria. 2) Some studies did not provide enough clinical data such as patient family history, ER/PR, HER-2 hormone receptor status, tissue type, and tumor grade, leading to failure to conduct a comprehensive subgroup analysis to explore the source of heterogeneity. 3) Gene-gene and gene-environment interactions were not considered in current meta-analyses. Possible gene-gene and gene-environment interactions between Rad51 gene polymorphism and cancer susceptibility need to be further studied. 4) some patients were chosen from hospital-based groups, and these women may have benign breast disease, corresponding to an increased potential risk of breast cancer. 5) Most of the patients in our study were Caucasian, which may limit the general application of our results.The original contributions presented in the study are included in the article/All authors contributed to the study’s conception and design. Material preparation, data collection, and analysis were performed by JY and C-GW. Manuscript drafting and reviewing: All authors. All authors contributed to the article and approved the submitted version.


The Global Burden of Disease Study in 2016 estimated that the global incident cases of meningitis have increased by 320,000 between 1990 and 2016. Current evidence suggests that diabetes may be a prime risk factor for meningitis among individuals, including older adults. However, findings of prior studies on this topic remain inconsistent, making a general conclusion relatively difficult. This study aimed to quantitatively synthesize the literature on the risk of meningitis associated with diabetes and compare the risk across different global regions.  Literature search and study design protocol followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. The search was conducted in PubMed, Web of Science, African Journal Online, and Google Scholar using relevant MESH terms. A random effect model was used to pull effect sizes.  Initial search yielded 772 papers but 756 studies were excluded due to duplicity and not meeting inclusion criteria. In all, 16 papers involving 16847 cases were used. The pulled effect size (ES) of the association between diabetes and meningitis was 2.240 . Regional-base analysis showed that diabetes increased the risk of developing meningitis in Europe , Asia , and North America . These associations remained significant in the study design and etiological classe-based subgroup analyses. However, we surprisingly found no studies in Africa or South America.  Diabetes is a risk factor for developing meningitis. Given that no research on this topic came from Africa and South America, our findings should be contextually interpreted. We, however, encourage studies on diabetes-meningitis linkages from all parts of the world, particularly in Africa and South America, to confirm the findings of the present study. Hib and S. pneumoniae vaccines [According to the Global Burden of Disease Study, the global incident cases of meningitis have increased from 2.50 million : 2.19–2.91) in 1990 to 2.82 million 2.46–3.31) in 2016 –3.31 in . Even thvaccines . For exavaccines . TypicalS. pneumoniae and N. meningitidis, respectively, in more developed countries [p = 0.036) and the presence of seizures (p = 0.041) [Approximately 290,000 people lost their lives in 2015 due to meningitis infection . Howeverountries , 18. Theountries  found a ountries . For exa= 0.041) .Mycobacterium tuberculosis will not develop the disease until they are influenced by a noncommunicable disease such as diabetes [C. neoformans meningitis [S. pneumoniae risk, followed by L. monocytogenes [L. monocytogenes instead of S. pneumoniae [Type 1 and 2 diabetes have been associated with multiple infectious diseases such as tuberculosis and pneumonia . For exadiabetes . Particudiabetes . Some rediabetes –24. As adiabetes –27. Evenningitis . A similningitis . On the ningitis  found diytogenes . Converseumoniae . Since teumoniae . ConsideThis quantitative review was conducted using the PRISMA  guidelines .A comprehensive literature search was conducted by MA and AM in recognized databases of PubMed and Web of Science, African journal online (Ajol), and Google Scholar. MESH terms see  togetherWe also further screened downloaded papers for additional papers. All extracted papers were managed using Mendeley reference manager.Studies were included only if they reported the association between meningitis and diabetes (see Papers were retrieved from databases independently by the two researchers (MA and AM), and when there was any disagreement concerning eligibility criteria, it was resolved by the third author, RMG.a is the minimum value, m is the median, and b is the maximum value. If the first quartile and the third quartiles were reported in addition to maximum and the minimum value, we estimated the mean with the expression 2 as follows:q1 is the first quartile and q3 is the third quartile. If the median and the interquartile range was provided, we estimated the mean with the expression 3 as follows:A data extraction sheet was developed based on extensive discussion between MA and AM to make sure all information obtained was significant enough for the review. Our primary outcome variable was meningitis, and the independent variable was diabetes as reported in previously published studies . However, pooled odd ratios (OR) were calculated for different regions of the world  where data were enough using the OpenMEE software . This ise-N test  providedThere were no year restrictions on papers eligibility, even though almost all papers were between 2014 and 2021 , indicatThe individual study characteristics are presented in The total number of meningeal disease cases was 16847 across all studies. Only one study was conducted before the year 2000 . Four stn = 9) of the papers used retrospective study designs and approximately 35% (n = 6) used prospective study designs. Two studies used population-based active surveillance cohort [n = 10 studies; 59%) and fungal meningitis  (The longest follow-up period leading to the reportage on the association between diabetes and meningitis was conducted in Denmark (1977–2018) and Spain (1977–2017), and the shortest was in Greece (2006–2008) . The avee cohort  and obsee cohort  study dees; 41%) .p < 0.001) indicating that diabetes increases the risk of developing meningitis by two-fold. However, there was significant heterogeneity of ES among individual studies . The possible sources of heterogeneity were, therefore, investigated.Diabetes has been identified as a potential risk factor for meningitis. However, studies reporting on such evidence are significantly limited. The overall pooled OR and point estimate for the effective sizes for various studie are shown in p < 0.001), Asia OR = 2.490 , and North America OR = 2.819 , diabetes increased the risk of developing meningitis by nearly two-fold. However, there was significant heterogeneity among studies from all these continents: Europe , Asia (I2 = 78.06% p = 0.002), and North America . Both study designs; retrospective OR of 2.044  and prospective study designs OR of 2.428  found statistically significant association between diabetes and meningitis. This indicates that even if we divide studies between these separate study designs, diabetes will remain a significant risk factor.We did leave-one-out (LOO) analysis to assess the overall influence of individual studies on the general ES, and the significance of the risk remained apparent , suggestp=0.041) and bacterial meningitis OR = 2.190  even though the strength of association seems to be marked among cases caused by bacteria. Our publication bias diagnostics were conducted by using the funnel plot  suggested that additional 1023 studies may be needed to bring down the pooled ES of 2.240 to OR ≤1. Our funnel plot confirmed this diagnostic, as it showed moderate symmetry among studies. A univariate meta-regression (using maximum likelihood for the coefficient estimation) based on covariates indicated a significant variation in the covariate effects on the ES. Particularly, age explained 10.96% of the overall heterogeneity found, even though the covariate effect  was not statistically significant. Nevertheless, the positive regression coefficient  may suggest that increasing age will determine the risk of meningitis. The study sample size explained 7.83% of the heterogeneity, although the covariate effect was also not statistically significant . However, the subgroup analysis among studies with >500 or <500 sample size, the ES was significant in larger sample sizes. For example, association was significant for studies with sample size >500: OR = 2.283  but insignificant for sample size <500: OR = 1.692 . Study quality score was marginally significant . Study design explained approximately 0% of the heterogeneity . The year of publication was the most significant covariate that explained a significant portion of the heterogeneity in the effect sizes (r2 = 40.42%), and its covariate effect was statistically significant  We also found that diabetes increased the risk of meningitis regardless of the etiological class: fungal meningitis OR = 2.290   .Infectious diseases remain a significant threat to the wellbeing of people and the economies of countries worldwide , 50–55. Accumulating research provides evidence on the pathophysiology of the disease, but little knowledge exists on how its prevalence and mortality are further amplified by other coinfections such as diabetes. However, diabetes has been noted as an independent risk factor for multiple infections, including tuberculosis and pneumonia , 21 and However, the complex relationship between diabetes and meningitis was not broadly explored in variousstudies. Such an intricate mechanism remains largely unclear. Nevertheless, few studies have investigated this connection. Hyperglycemia, which is strongly associated with diabetes , is relaIt should, however, be noted that most of the studies that found no association between diabetes and meningitis were conducted in hospital settings, where other factors such as intraoperative cerebrospinal fluid leakage may expose patients to meningitis. While our meta-regression suggested that age, year of publication, study quality, and study design explained 79% of the finding's heterogeneity across studies, other factors such as smoking, alcoholism, malnutrition, and comorbidities still remain significant . In effeS. pneumoniae, followed by L. monocytogenes. Conversely, age, pregnancy, an underlying cancer condition, and immunosuppression were significant predictors of L. monocytogenes instead of S. pneumoniae [Although our current meta-analysis found no differences in the effect sizes between fungal and bacterial meningitis, the broader grouping into bacteria and fungus may sometimes mask the finer differences in how specific etiologic agents of these broader groups moderate the risk. For example, cryptococcal meningitis which is fungal has been discovered to be most prevalent among people living with HIV (PLHIV) whereas, among the general population, bacterial meningitis presents the greatest meningeal disease burden, currently infecting 1.2 million annually around the world. This may explain why about 60% of studies were based on bacterial meningitis. In one study, it was noted that diabetes mellitus increased the risk of eumoniae . The etip < 0.001) while research with a sample size less than 500 found no association (p=0.280). This suggests that our conclusion is based on larger sample size studies with higher statistical power, which implies how significant our meta-analysis is in increasing the statistical strength of the association. However, there are some limitations. Firstly, some studies evaluated the association based on retrospective case-control study designs which may be subjected to selection and recall biases [Our study has a major strength by increasing the statistical power due to the larger sample size as compared to individual studies. For example, subgroup analysis between studies based on sample size indicated that research with a sample size greater than 500 found a statistically significant association . While this finding corroborates with some other studies, no research was found from South America and Africa, which obviously are the most plagued in terms of meningitis and diabetes disease burden. The findings suggest that attention should be drawn to this research theme in Africa and other LMICs. Crucially, studies focusing on detailed subtypes of meningitis and their association with different classes of diabetes will be helpful to confirm our findings and provide comprehensive evidence for the pathology and etiology of meningitis.


We aim to evaluate the relationship between the use of metformin and the risk of pancreatic cancer in type 2 diabetes patients.We systematically searched the observational studies on PubMed, Embase, Web of Science, Cochrane Library, clinicalrials.gov, and CNKI databases, extracted relevant data, combined the OR value and 95% CI using the random effect model, and conducted a sensitivity analysis, subgroup analysis, and meta-regression to evaluate the size and stability of this relationship.Twenty-nine studies from twenty-four articles met our inclusion criteria, including more than 2 million subjects. Overall analysis showed that compared with no use of metformin, the use of metformin could reduce the risk of pancreatic cancer in patients with type 2 diabetes ). Subgroup analysis showed that compared with the use of hypoglycemic drugs, the use of metformin could reduce the risk of pancreatic cancer in patients with type 2 diabetes ). However, compared with no drugs or only diet therapy, metformin users might increase the risk of pancreatic cancer ). Sensitivity analysis confirmed the stability of the study, and there was no significant publication bias.Compared with the no-use of metformin, metformin users with diabetes can reduce the risk of pancreatic cancer. More research is needed to prove it works. According to GLOBOCAN 2020 statistics, pancreatic cancer ranks 14th in the global cancer incidence rate and 7th in the global cancer mortality . Early iThe relationship between diabetes and pancreatic cancer is particularly complex. Although there is disagreement on the relationship between the duration of diabetes and the risk of pancreatic cancer, almost all studies show that the risk of pancreatic cancer in diabetes patients is significantly higher –7. ClariMetformin is the first-line drug of type 2 diabetes mellitus (DM), and its role in reducing the mortality of patients with pancreatic cancer is widely recognized , 9. SpecThis paper is based on the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA). The agreement of this overview has been published in PROSPERO (Registration No: CRD42022359987).From the beginning of the database construction to August 31, 2022, We performed an electronic search on PubMed, Embase, Web of Science, Cochrane Library, clinicalrials.gov, and China National Knowledge Infrastructure (CNKI) databases, using the keywords "metformin" OR "biguanide" OR "dimethyl biguanide" AND "pancreatic cancer" OR "pancreatic tumor" in "Title/Abstract", with no language restriction. All the studies retrieved were independently screened by two authors (Jian Hu and Hong-Dan Fan). We will consult with a third person(Qing-Song Mao) if there are different opinions in the literature screening process. To include sufficiently accurate literature, we also searched and screened the references included in the literature.The inclusive criteria were as follows: (1) case–control or cohort study; (3) reporting or including studies on the association between metformin use and pancreatic cancer risk; (4) reporting the Relative Risk (RR), Hazard Ratio (HR) or Odds Ratio (OR) and 95% confidence interval (CI) of pancreatic cancer, or providing data that we can calculate them.The exclusion criteria were as follows: (1) cross-sectional studies; (2) duplicated studies; (3) preclinical studies ; (4) abstracts, case reports, reviews, conferences, letters, and books; (5) only showing the relationship between metformin and pancreatic cancer mortality; (6) no full-text studies; (7) contrast agent containing metformin; (8) lacking necessary data.Two investigators (Jian Hu and Hong-Dan Fan) independently extracted and then checked the extracted data by a third party (Qing-Song Mao). For each study, we recorded the following information: the first author, publication year, publication region/country, study design, basic characteristics , the time of diagnosis of diabetes in the study population, sample size, study period, outcome indicators , adjusted confounding factors and contrast agent. If there is no adjusted OR value and 95% CI, the crude OR value and 95% CI will be extracted. Suppose there are multiple groups (multiple control groups or test groups) in the literature that all meet the inclusion criteria. In that case, we extract or calculate the sample size data of each group and use the method of merging multiple groups of sample size into a new group to calculate the OR value and 95% CI . Since tThis analysis uses the Newcastle Ottawa Scale (NOS)  to evalu2 statistics. If the I2 values exceeded 25%, 50%, and 75% respectively, it represented low, medium, and high heterogeneity [2 value is greater than 50%, the random effect model is used; otherwise, the fixed effect model is used. We conducted sensitivity analysis by excluding each study or some studies that may affect the stability of the study results and conducted subgroup analysis and single factor meta-regression analysis on some characteristics of the included studies. We assessed publication bias by visual funnel plots and the Egger regression asymmetry test. Unless otherwise stated, the statistical significance level was set at P < 0.05 under a double-sided test.STATA MP 17.0 is adopted for all statistical analyses in this paper. The heterogeneity between studies was investigated by the Q test and measured by Iogeneity . When thThrough the search of the above databases, we have preliminarily obtained 1477 articles that may be relevant. After importing the received articles into Note-Express, we found 199 duplicate articles. After reading the title and abstract, we excluded 1218 articles irrelevant to the study. Then, the remaining 60 articles were reviewed in full text, and 36 studies were excluded again. Among them, 21 studies had no available data, 9 were conferences or abstracts, three were unable to obtain the full text, 2 were meta-analyses or reviews, and one was treated with metformin combined with dipeptidyl peptidase-4 inhibitors (DPP-4i) as the contrast agent. Finally, the remaining 24 studies that met the inclusion criteria were analyzed. The retrieval and filtering process is shown in Fig. We included a total of 24 articles –38  ), with significant heterogeneity  experiments. Metformin added in drinking water can prevent the pancreatic carcinogenesis induced by N-nitrosobis—(2-oxopropyl) amine in hamsters fed a high-fat diet . In obesHowever, as far as the published meta-analysis is concerned, its role is still uncertain. Wang Z , Yu X 5, and ZhaSince there is no consensus on the role of metformin in the existing meta-analysis results, we conducted this meta-analysis involving 24 articles. In this analysis, more than 2.28 million people participated. The overall analysis of the study showed that the use of metformin was negatively correlated with the occurrence of pancreatic cancer ), which was consistent with most previous studies. When subgroup analysis is conducted according to study quality, only the subgroup of the high-quality study shows that metformin is negatively related to the risk of pancreatic cancer, which may be due to the deviation of the research methodology of the low-quality study. When the subgroup analysis was carried out according to the status of diabetes of the study subject, only the subgroup of non-newly diagnosed diabetes suggested that metformin was negatively related to the risk of pancreatic cancer, which may be because the protective effect of metformin on pancreatic cancer needs a certain delay. When subgroup analysis is conducted according to the study design, metformin can reduce the risk of pancreatic cancer only in the cohort study subgroup, which may be caused by the relatively small sample size of the case–control study and the low statistical efficiency in the study. It is worth noting that when the contrast agent was sub-analyzed, the opposite results were obtained. Single-factor meta-regression showed that the contrast agent was one of the heterogeneities of the study. The overall sensitivity analysis indicated that the study was stable, and no significant publication bias was found through the funnel plot and Egger test.Compared with the previous meta-analysis, our research has some advantages. Firstly, this paper has included 24 articles from many countries, including more than 2 million participants, with high study quality, enhancing the statistical power of the data analysis and providing more reliable estimates. Secondly, we explored the research heterogeneity through subgroup analysis and single-factor meta-regression. Fortunately, we found the source of some research heterogeneity. Finally, since the existing evidence shows a relationship between the duration of diabetes and the occurrence of pancreatic cancer, we conducted a subgroup analysis on whether the study subjects were newly diagnosed with diabetes and obtained inconsistent results. As far as we know, this is the first meta-analysis of this subgroup analysis. When researchers later conduct relevant research, it can remind them to consider the diabetes status of the subjects.However, we must admit that this study has some limitations. First of all, the heterogeneity of the study is remarkable. Although we have carried out some subgroup analysis, sensitivity analysis, and meta-regression, we only found partial sources of heterogeneity. The rest of the heterogeneity may be attributed to retrospective studies, inconsistent adjustment of confounding factors, or inconsistent follow-up time. Second, although we think that the flushing period and lag period will significantly impact the research results due to the inability to extract relevant data in some studies, no further analysis can be conducted. Third, the contrast agents of all the studies included in the analysis differ. Most appear as "no metformin users", but the specific drugs they contain are unclear. Although we have conducted subgroup analysis, whether "no metformin users" includes "no drug users" is ambiguous, which may lead to errors and bias in the results. Fourth, part of the literature contains several studies. We calculated and combined the sample size to obtain data for analysis, which may be biased from the actual situation. Fifth, we have extracted risk estimates that reflect the maximum control of potential confounding factors. However, the results of adjustments based on specific confounding factors may be different from those based on standards.Metformin can reduce the risk of pancreatic cancer in patients with diabetes. Prospective research is needed to confirm our view in the future further.


Macrophage migration inhibitory factor (MIF) gene rs755622 G/C polymorphism was suggested to be associated with CAD risk. However, due to the different results among the individual studies, no agreement has been reached till now. Therefore, the meta-analysis on the association of MIF gene rs755622 G/C polymorphism with CAD was performed.MIF gene rs755622 G/C polymorphism was significantly associated with CAD under the allelic , recessive , dominant , homozygous , and additive  genetic models.The association between them was evaluated by calculating the pooled odds ratios (ORs) and the corresponding 95% confidence intervals (CIs). The random-effects models were used because of the significant heterogeneity among them. In this meta-analysis, 8,488 subjects from 9 studies were included. The MIF gene rs755622 G/C polymorphism was significantly related to CAD, especially in the Chinese population. Persons with the C allele of the MIF gene rs755622 G/C polymorphism might be susceptible to CAD. Coronary artery disease (CAD) is a serious disease that endangers human health and life. In developed Western countries, such as Europe and the United States, CAD has decreased incidence compared with 30 years ago but remains the main cause of death in the population . In ChinThe pathogenesis of CAD is the atherosclerosis of the coronary artery. Risk factors for CAD include smoking, hyperlipidemia, hypertension, and diabetes. Studies found that CAD is also a chronic inflammatory disease, and a variety of inflammatory factors play important roles in the occurrence and development of CAD. The macrophage migration inhibitor (MIF) is a homologous trimer protein that can locally inhibit macrophage migration and promote the inflammatory response. MIF can be expressed in many cells and tissues, including mononuclear phagocytes, vascular smooth muscle cells, and cardiomyocytes . MIF is MIF gene, located in 21q11.2, spans about 2,119 bp, contains 3 exons and 2 introns, and encodes 115 amino acids. The MIF gene –173G/C polymorphism (rs755622) is located in the promoter, which is located at the binding site of the response element of transcription factor activator protein 4 (AP4). This rs755622 polymorphism may affect the affinity of the response element of AP4, thus affecting the transcription level of the MIF gene  the diagnosis of CAD was defined as the stenosis degree of the left main trunk, left anterior descending branch, left circumflex branch, right coronary artery, and any one of the main branches ≥ 50% by coronary angiography examination. (c) The research should be case-control or cohort studies. If multiple studies were retrieved by one author group, the data would be analyzed. If there was overlapped data, the data could be merely adopted for one time. The data with more sample size would be adopted. The studies that did not provide enough data should not be analyzed. The selected studies’ quality would be evaluated by using the Newcastle-Ottawa Scale (NOS) score.The included studies should conform to the subsequent criteria. The included studies should (a) evaluate the association of P-value of Hardy-Weinberg equilibrium (HWE) in the control group. The population in the case group might be affected by disease pressure (a type of selection pressure), which would make its genotype distribution deviate from HWE. However, the genotype distribution of the control group without disease should be consistent with HWE. Therefore, in the meta-analysis of genetic association studies, it is necessary to test whether the genotype distribution of the control group in the case-control study is consistent with HWE.The data extraction process was carried out by three researchers following the standard procedures. Two investigators completed literature retrieval, quality evaluation, and data extraction independently. If the two investigators have differences, they will discuss them with the third investigator. However, this did not often occur and had less effect on the overall NOS score. The obtained literature was then scored according to the NOS score evaluation system. According to the scoring rules, the score of the literature was between 0 and 9 points. Two independent researchers graded strictly according to the grading guidelines. A literature score greater than or equal to 6 is considered high quality. Data that met the inclusion criteria were extracted from the literature, and data extraction tables were developed. The table includes the first author, publication year, study region, race, number of genotypes in the CAD group and control group, genotyping method, the sample size in each group, and MIF gene rs755622 G/C polymorphism and CAD.In this study, there were six genetic models, namely, allelic , recessive (CC vs. GG + GC), dominant (GG vs. GC + CC), heterozygous (GC vs.GG), homozygous (CC vs. GG), and additive . The pooled odds ratios (ORs) and corresponding 95% confidence intervals (CIs) were adopted to assess the association between Pheterogeneity and I2 were calculated to assess the heterogeneity among the included studies , recessive , dominant , homozygous , and additive  genetic models. No significant relation between them was found under the heterozygous  genetic model , recessive , homozygous , and additive  genetic models. There was no significant association between them under the dominant  or heterozygous  genetic models.In the Chinese subgroup, a significant association between P = 0.279), recessive , dominant , heterozygous , homozygous , or additive  genetic models.In the Caucasian subgroup, no significant association between them was observed under the allelic , the random-effects models were utilized. After the population was stratified according to ethnicity, the heterogeneity was reduced in the subgroups under the heterozygous and the homozygous genetic models. Under the heterozygous genetic model, the I2 was reduced from 60.8% in the total population to 0 in the Caucasian subgroup. Under the homozygous genetic model, the I2 was reduced from 60.3% in the total population to 51.3% in the Chinese subgroup. It was indicated that the different ethnicity was associated with the heterogeneity source. In general, if Pheterogeneity is large, I2 must be small. The smaller Pheterogeneity is, the greater the heterogeneity is, and I2 represents how big it is. In Pheterogeneity value has the same change trend with the I2 in the Caucasian subgroup. In the heterogeneity test, the results of the Q-test are inconsistent with those of the H and I2-test, which is due to the small number of studies in the Caucasian subgroup, and the Q-test and interval range estimation are often inaccurate , dominant , heterozygous , homozygous , or additive genetic models  .No significant publication bias was observed through the Begg’s test under the allelic (= 0.095) . It was The sensitivity analysis has been performed under the six genetic models to analyze the results’ stability. It was observed that even if any of the individual studies were eliminated from this meta-analysis, the primary outcome was not influenced. It was suggested that the present meta-analysis’s results were considerably stable .MIF gene rs755622 G/C polymorphism is found to be remarkably associated with the development of CAD under allelic (OR: 1.213), recessive (OR: 1.945), dominant (OR: 0.781), homozygous (OR: 2.057), and additive (OR: 1.327) genetic models. The C allele of the MIF gene –173G/C polymorphism may increase the CAD risk. The significant association between MIF gene rs755622 G/C polymorphism and CAD is found in the Chinese subgroup (P < 0.05) but not in the Caucasian subgroup (P > 0.05). Given that only two individual studies are included in the Caucasian subgroup, the result in this subgroup needs to be confirmed in more studies in the future. Only the random-effects models are used because of the significant heterogeneity among individual studies. Heterogeneity is distinctly reduced in the subgroup analysis under heterozygous and homozygous genetic models. The heterogeneity source is suggested to be related to different ethnicities.In the current meta-analysis, the CAD is a complex multifactorial disease resulting from the interaction of genetic and environmental factors. CAD is common in the elderly, and the formation of atherosclerotic plaque in the coronary artery is the main cause of CAD. Atherosclerosis is a disease of fat deposition, and inflammation plays a key role in the formation and development of atherosclerotic thrombosis. Many inflammatory factors, chemokines, and enzymes are involved in the pathogenesis of atherosclerosis .MIF was discovered by Bloom and Bennett in a hypersensitivity experiment on guinea pigs in 1966 and was named for its ability to inhibit macrophage mobility . Later sWhen atherosclerotic plaques occur, serum MIF levels increase rapidly, suggesting that MIF may be involved in the occurrence of atherosclerosis. MIF can be rapidly released from macrophages, cardiomyocytes, and vascular endothelial cells under the stimulation of harmful factors . The relMIF gene has multiple polymorphic loci, including –173G/C, 254T/C, and 656C/G. Among these polymorphic loci, –173G/C polymorphism is closely related to CAD. Given that the MIF gene –173 C allele has an AP4 response element-binding site, the difference in the mononucleotide may result in different affinities with the AP4 response element, thus affecting the MIF expression level. Donn et al. found that healthy volunteers with the –173C genotype (GC + CC) have significantly higher serum MIF levels than those with the –173GG genotype (MIF gene –173G/C mutation can lead to a high MIF level and promote CAD development, which is in line with the outcome of the present meta-analysis.genotype . Hence, In the meta-analysis by Li et al. , OR valuPCSK9 gene E670G polymorphism (ALDH2 gene G487A polymorphism (CDKN2B-AS1 gene rs4977574 A/G polymorphism (FVII gene R353Q polymorphism (Limitations are present in this study. The sample size is relatively small. The data on the Caucasian population are especially limited. More studies from a large sample size of Caucasian populations are needed to cover the shortage. In addition, the environmental factors of hyperlipidemia, hypertension, and diabetes also have significant effects on CAD susceptibility. Other gene polymorphisms, e.g., morphism , ALDH2 gmorphism , CDKN2B-morphism , and FVImorphism , may alsMIF gene –173C/G polymorphism is associated with CAD risk. The C allele of the MIF gene –173C/G polymorphism may be the risk factor for CAD, especially in the Chinese population. More studies on the relationship between MIF gene –173C/G polymorphism and CAD need to be conducted to further verify this conclusion. MIF gene expression studies are necessary for confirmation.The The original contributions presented in this study are included in the article/Y-YL and HW researched the data. Y-YL wrote the manuscript, researched the data, contributed to the discussion, and reviewed/edited the manuscript. Y-YL and Y-YZ reviewed/edited the manuscript. All authors contributed to the article and approved the submitted version.


A stronger association was observed for the alexithymia dimension “Difficulty in Identifying Feelings”  and “Difficulty in Describing Feelings”  than for “Externally Oriented Thinking” . The association was stronger in studies with clinical patient populations  than in those investigating general or student populations, and in studies with a majority of male rather than female participants. These findings suggest a strong overall association between alexithymia and substance use and a very strong association among clinical patient populations. The association may be stronger with the emotion‐related dimensions than with the cognition‐related dimension of alexithymia. As nearly all the studies were cross‐sectional, more longitudinal studies are needed.Alexithymia has been associated with substance use, but the magnitude of the association has not been evaluated and sub‐group differences, if any, are unknown. The aim of this meta‐analysis is to systematically review the association between alexithymia and substance use . We identified studies through a systematic review of PubMed and Web of Science and obtained a total of 52 publications using the Toronto Alexithymia Scale‐20 scale. Random effects meta‐analysis was used to evaluate the overall and sub‐group associations. Of the studies, 50 were cross‐sectional and two longitudinal. Alexithymia was associated with any substance use (Cohen’s  A poveret al., Substance use disorder (SUD) as a diagnosis in the DSM‐IV is a condition in which there is uncontrolled use of a substance  despite harmful consequence , age and sex distribution of the participants, and study region. We hypothesize that alexithymia is associated positively with alcohol and drug abuse and more strongly with DIF and DDF than EOT. We hypothesize that alexithymia is associated with both alcohol and drug abuse Furthermore, based on earlies findings .We followed the Prisma guidelines for systematic reviews and meta‐analyses (We searched the PubMed and Web of Science databases for available publications from January 2000 to September (PubMed)/October (Web of Science) 2019. The search terms we used to identify suitable publications are shown in Supplementary Materials Table The study was included in the meta‐analysis if it: (1) was a cross‐sectional, case–control or cohort study including adult individuals (≥ 18 years) with alexithymia and alcohol/drug use assessments; (2) used TAS‐20 to assesses alexithymia; and (3) reported estimates for the association between alexithymia and substance use , relative risk (RR)). In addition, patient populations were accepted if the study included a “healthy” comparison group. The selected articles had to be written in English and be published in a peer‐reviewed journal. The resulting abstracts were read through and were included in the meta‐analysis if they fulfilled the inclusion criteria. We did not include other types of addiction than substance use, thus excluding, for example, internet or game addiction. When multiple reports on the same data had been published, we chose the study which included the most informative estimates or was published first. In addition to the database searches, we searched the reference lists of the eligible studies for additional records.The description of the study population , the size of the study groups, the age of subjects, proportion of males, length of follow‐up and the method used for measuring alexithymia were extracted from each report. Two reviewers  independently checked the estimates derived in the studies on the association between alexithymia and substance use.https://www.nhlbi.nih.gov/health‐topics/study‐quality‐assessment‐tools)  Table . Each ofd) and their 95% confidence intervals. We expected to find heterogeneity between studies and therefore used random‐effects meta‐analysis to obtain pooled estimates for the retrieved studies. The magnitude of Cohen's d was considered low if d ≤ 0.3, medium if d = 0.31 to 0.5, high if d = 0.51 to 0.8, and very high if d ≥ 0.81  outcome ; (2) sample type ; (3) mean age of the participants (<30 vs. ≥30 years); (4) proportion male (≤50% vs. >50%); (5) region ; (6) substance type ; and (7) analysis type . We examined possible publication bias using the Egger's test for small‐study effects and a funnel plot of the estimates against their standard errors . Stata 13.1  was used for all meta‐analyses.As there were several types of analyses in the original publications , we transformed all original estimates to standardized mean differences   were from Europe, 10 studies were from Australia, five from USA, five from Asia, and two from Africa , with high heterogeneity between studies (I2 = 92.1%). Despite the high heterogeneity, all but one of the studies showed a positive association between alexithymia and substance use .There was a total of 46 estimates for the TAS‐20 total score. Meta‐analysis , as well as the five studies carried out in the USA  and the two studies with logistic regression as analysis type .Results from the sub‐group analyses are presented in Fig. d = 0.64, 95% CI 0.47–0.81) as the TAS‐20 total score, whereas the estimates for Difficulty in Describing Feelings (DDF) and Externally Oriented Thinking (EOT) were lower in magnitude , with a statistically significant interaction found in meta‐regression (P = 0.025).With regard to the outcome type, the estimate for Difficulty in Identifying Feelings (DIF) was of the same magnitude (P = 0.049). Clinical samples yielded higher estimates than student samples , whereas studies from general populations gave estimates between these two .There was also a significant interaction among sample types (P = 0.051 for interaction in meta‐regression), but those with a higher proportion of male participants suggested a stronger association between alexithymia and substance use . Other subgroup differences  were small .There was no significant difference between studies regarding age of the participants (P = 0.42), not even after exclusion of the two outlier estimates from the analysis .Study‐specific forest plots for the association of alexithymia with alexithymia dimensions DIF, DDF and EOT are presented in Figs. d = 0.62), and in the dimension the association appeared to be strongest for Difficulty in Identifying Feelings , followed by Difficulty in Describing Feelings , and the weakest association for Externally Oriented Thinking . The associations for alcohol use and illicit drug use were similar, whereas the strongest association was found in studies with clinical patient populations. Alexithymia was also more strongly associated with substance use in studies with most male participants.This meta‐analysis confirmed a strong association between alexithymia and substance use and identified some subgroup differences in the association. We identified 52 studies that provided estimates for the association between alexithymia and/or its dimension, and substance use measured by alcohol or illicit drug use. We found a strong positive association between alexithymia and any substance use   than with the cognition‐related dimension (EOT) of alexithymia. Several studies among patients with substance use disorder . However, these sub‐group differences  were imprecisely estimated and the meta‐regression analyses did not suggest significant interaction. Based on the present evidence, there is no clear evidence of differences in the association between alexithymia and substance use between different countries, when the outcome is alcohol or illicit drug use, or depending on the chosen analysis type in the study.Among different regions, very strong association between alexithymia and substance use was found for studies from Asia , a strong association for studies from Europe , and only a medium association for studies from Australia  and USA  , to those carried out in the USA (n = 5) and to investigations based on logistic regression analysis as a method (n = 2) did not suggest high heterogeneity. Many small meta‐analyses are not able to estimate heterogeneity with much precision; in fact, we may have little confidence in any estimate beyond the average effect size, thus any statistical analysis cannot change the limitations of small meta‐analyses , the US National Institute on Aging (NIA) (grant R01AG056477), the Academy of Finland (311492), NordForsk , and the Finnish Work Environment Fund (190424), outside the area of this study.Figure S1. Study‐specific associations between Difficulty in Identifying Feelings sub‐score of alexithymia and substance use.Click here for additional data file.Figure S2. Study‐specific associations between Difficulty in Describing Feelings sub‐score of alexithymia and substance use.Click here for additional data file.Figure S3. Study‐specific associations between Externally Oriented Thinking sub‐score of alexithymia and substance use.Click here for additional data file.Figure S4. The funnel plot for the TAS‐20 total score.Click here for additional data file.Table S1. Search terms for the search in PubMed.Click here for additional data file.Table S2. Quality assessment of the studies in meta‐analysis.Click here for additional data file.Supplementary materialClick here for additional data file.


Family socioeconomic position (SEP) in childhood is an important factor to predict some chronic diseases. However, the association between family SEP in childhood and the risk of lung cancer is not clear.A systematic search was performed to explore their relationship. We selected education level, socioeconomic positions of parents and childhood housing conditions to represent an individual family SEP. Hazard ratios (HRs) of lung cancer specific-mortality were synthesized using a random effects model. Two-sample Mendelian randomization (MR) was carried out with summary data from published genome-wide association studies of SEP to assess the possible causal relationship of SEP and risk of lung cancer.P < 0.001].Through meta-analysis of 13 studies, we observed that to compared with the better SEP, the poorer SEP in the childhood was associated with the increased lung cancer risk in the adulthood . In addition, the dose-response analysis revealed a positive correlation between the poorer SEP and increased lung cancer risk. Same conclusion was reached in MR [(education level) OR 0.50, 95% CI: 0.39 to 0.63; This study indicates that poor family socioeconomic position in childhood is causally correlated with lung cancer risk in adulthood.identifier: 159082. The relationship between everyday-life situations in childhood and chronic diseases has received increased attention across a number of disciplines in recent years –4. In adSEP is one of the interfering factors of health of family members , 9. FamiIn this study, we sought to investigate the association between SEP in childhood and lung cancer risk in adulthood. A meta-analysis with dose response and a two-sample Mendelian Randomization (MR) were performed using single or multiple single nucleotide polymorphisms (SNPs) as an instrument in instrumental variable analyses by studying known genetic determinants of the exposure variable of interest.MR uses genetic variants as instrumental variables for assessing causal relationships from observational data  and is aTwo investigators  independently searched, PubMed, Web of Science from their inceptions  to June 1, 2020, for cohort studies related to family SEP in childhood and lung cancer in adulthood, without language restrictions. Keywords used were “socioeconomic position (SEP)”, “cohort”, “lung cancer”, “risk”, as well as their Medical Subject Headings (MeSH) terms. Only papers published in English were included. Details of the search terms and inclusion/exclusion criteria are shown in The inclusion criteria were: (i) cohort study design; (ii) all cases were lung cancer patients, and data were classified according to different SEP, regardless of subtypes of lung cancer; (iii) In addition, the exclusion criteria were: (i) small sample sizes; (ii) non-English publications; (iii) lack of data and necessary information for meta analysis or dose response analysis.All the included studies were divided into two groups: (I) studies reported adjusted estimates of the hazard ratios (HR) (including relative risk and odds ratios) and 95% confidence interval (95% CI); (II) studies missed data mentioned above. We also included studies that graded data based on parents' financial status and family life in childhood, and overall impact. The quintile or quartile method was incorporated and the first level was used as the reference.Data were extracted by two investigators  independently, and any disagreements came to consensus after discussion. Basic data were recorded from all eligible studies, including the first author's name, publication year, country, study period, period of follow-up, number of lung cancer patients and HR with their 95% Cl. The results were reviewed by two senior investigators .The quality of the studies was evaluated using a score system that was designed with reference to the Newcastle-Ottawa Scale (NOS) tool –40. The Two researchers (W.R. and G.F.) independently evaluated the methodological quality of each included published cohort study. The results of the quality assessment were used for descriptive purposes, to provide an overall assessment of the quality of the included studies , 41–44.2 statistic, we examined the heterogeneity across studies and statistical heterogeneity was considered if an I2 statistic ≥ 50% , otherwise a fixed effect model was conducted.A meta-analysis was performed following the Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA) guidelines and the study protocol of this study is 1,59,082 , 46. We ic ≥ 50% . A randoP-values were 2-tailed; statistical significance was set at P-value < 0.05  . All steP < 5 × 10−8). Besides, we applied some methods including weighted median and MR–Egger to test the second assumption. Then we evaluated the directional pleiotropy based on the intercept obtained from the MR Egger analysis to satisfy the third assumption (The MR method was based on the following three assumptions: (i) the instrumental variables are strongly associated with the family SEP in childhood; (ii) the instrumental variables affect cancer only through their effect on family SEP in childhood and not through any alternative causal pathway; and (iii) the instrumental variables are independent of any confounders , 59. To sumption . We alsoX) on the risk of lung cancer (Y) using genetic variants (g), and the causal estimate is equal to Yg/Xg (Xg), summary data were utilized from published Genome-Wide Association Studies (GWASs), , –77 inclu(GWASs),  78–80).–80.X) on(GWASs),  69)..X) on thYg and Xg, an Inverse Variance-Weighted (IVW) meta-analysis was employed to estimate the effect of genetically determined family SEP in childhood on the risk of lung cancer using the method of Burgess et al. .where In addition, MR-Egger and Weighted Median were also conducted to identify the causality. Ward ratio would only be observed when the first three models cannot be used due to lack of SNPs . Leave-oA total of eight cohort studies consisting of 2,779,242 cases were included in the final meta-analysis, , 23–28 aThe results of quality assessment did not suggest any evidence of publication bias , 2 [UnadP < 0.001) (P = 0.04) (p = 0.78) for male, a pooled HR of 1.10   for female, and a pooled HR of 1.39   for both sexes. Subgroup analysis based on sex that provided unadjusted original data resulted in a pooled HR of 1.52   for male and a pooled HR of 1.28   for female and a pooled HR of 1.63   for both sexes. The above results was consistent in dose response used the model summary with BMR of 1 Std. Dev. including the family economic conditions of parents and childhood housing conditions   . The ris = 0.04) . Subgrounditions .P: 0.001). Another gene pool with the same exposure factor was also included for verification in our study, showing one SD longer education (genetic predisposition by 4 SNPs) was associated with a 60% lower risk of lung cancer . With a sample size of 27 209, our sample provided sufficient statistical power (> 80%) to detect a causal effect of educational attainment on lung cancer. Power calculations on MR analysis were performed according to Brion et al.   and MR-Egger method  , but provided less precise estimates than with conventional MR (IVW method). Nonetheless, their causal estimates were similar in terms of direction and magnitude, and they were unlikely to occur by chance alone. These results were consistent with the hypothesis that genetic pleiotropy does not drive the result.Meanwhile, associations were consistent in sensitivity analyses using weighted median : ; mother's age at death : ; and average total household income before tax (MRC-IEU): . Nine out of the 15 independent exposure factor gene pools were unable to be analyzed due to the lack of SNPs corresponding to lung cancer.We also analyzed and observed the relationship between other exposure factors related to family SEP in childhood and lung cancer, and we found that the Based on data from 2,779,242 respondents, we observed that poorer family SEP in childhood means higher lung cancer risk in adulthood compared to controls. The overall dose-response of multiple models also demonstrated this trend. This phenomenon was partially verified by Mendelian randomization of two samples, but the causal relationship between some genes was still unclear. Through meta-analysis, we analyzed both the adjusted  and unadjusted data, and strengthened the credibility by mutual authentication. The results showed a link between family SEP in childhood and the risk of lung cancer in adulthood in the unadjusted group.P-value < 0.001 instead of 0.01 in the R package used for two-sample MR  and Key Project of Guangzhou Scientific Research Project (Grant No. 201804020030).The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


We recently studied the association between various human leukocyte antigen (HLA) alleles and end-stage renal disease (ESRD). According to our analysis, HLA-B*50 and HLA-DQA1*3 alleles were positively associated with ESRD, while B*40, DRB1*12, DRB1*13, and DQA1*6 alleles were negatively associated with ESRD. However, a single case-control study does not have enough statistical power to evaluate the possible impact of genetic polymorphism on any disease. Hence, the main objective of this meta-analysis is to determine the association between these abovementioned HLA alleles and ESRD.MEDLINE/PubMed, EMBASE, Web of Science, and Cochrane databases were searched through December 2020 for case-control studies on the associations between HLA polymorphisms and ESRD. Independent reviewers screened the texts of potentially eligible studies and assessed the risk of bias. The meta-analysis was conducted based on the checklists and guidelines based on PRISMA.We identified 26 case-control studies comprising 1,312 ESRD and 3,842 healthy subjects. A non-significant positive association was observed between HLA-B*50 , HLA-B*40 , HLA-DQA1*3, , DRB1*12  alleles and ESRD. In addition, a non-significant negative association was observed between HLA-DRB1*13 , HLA-DQB1*6  alleles and ESRD.Our meta-analysis indicates no significant association between HLA-B*50, HLA-DQA1*3, B*40, DRB1*12, DRB1*13, and DQA1*6 alleles and ESRD. Further studies with larger sample sizes and adjustments for confounders are required to confirm these conclusions. End-stage renal disease (ESRD) is the last stage of chronic kidney disease (CKD) . The pacHuman leukocyte antigen (HLA) alleles are known to confer susceptibility to various kidney diseases . MultiplThe review protocol of this study was pre-registered to Prospero (CRD42021243631).Several databases  were searched through December 2020 for all publications on the association between HLA polymorphism and ESRD. The studies were identified using a combination of the following keywords: (“HLA” OR “human leukocyte antigen”) AND  AND .The studies were assessed based on original data analyses to determine their eligibility for inclusion and exclusion in this qualitative analysis. The inclusion criteria for the study were: articles published in English; case-control studies focused on the associations between HLA polymorphisms and ESRD; providing sufficient data for estimating an odds ratio (OR) with a 95% confidence interval (CI). The exclusion criteria for the study were: the reviews, comments, editorials, basic science or animal studies; the studies that were not distributed into patient and control groups; focused only on novel alleles; were duplicate studies and lack of detailed information.We reviewed each included study independently to extract the data. The extracted data from the eligible studies include the following information: the first author, year of publication, country, study population, population size, number of cases, and controls for the particular HLA alleles.http://www.ohri.ca/programs/clinical_epidemiology/oxford.asp). The score of each article was calculated based on three items: selection, comparability, and exposure (maximum score = 9 points). The score of eligible studies must be higher than 5  scale . Three of the selected studies (P = 0.52) (Fifteen  case-con studies  showed t = 0.52) .2 = 223.17, P = 0.00001, I2 = 93%). Only five of the selected studies showed that B*40 was negatively associated with ESRD (P = 0.06)  .2 = 21.12, P = 0.22, I2 = 20%). Most of the studies included in this analysis displayed a positive association between DRB*12 and ESRD. Our previously published study (P = 0.43).For the meta-analysis aimed to determine the association of HLA DRB*12 with ESRD, eighteen studies were selected . These aed study  and Dai 2 = 28.01, P = 0.04, I2 = 39%). Seven studies showed a positive association between HLA DRB*13 and ESRD (P = 0.07)  .2 = 18.60, P = 0.005, I2 = 68%) was statistically significant (P = 0.13).Seven  case-connificant . OverallPerez-Luque et al. (P = 0.50).We found only three studies that reported an association between the HLA DQA1*3 allele and ESRD . We perfe et al.  showed aThe study conducted by Our meta-analysis indicates no significant association between HLA-B*50, DQA1*3, B*40, DRB1*12, DRB1*13, and DQA1*6 alleles and ESRD. There is a possibility that the association between the selected HLA alleles and ESRD may vary across diverse populations. For instance, HLA-B*50 is shown to be positively associated with ESRD in Pakistani , ChineseThese differences could be driven by interactions between genetic background and environmental factors, including differences in geographic location and lifestyle factors . The vari.e., only the studies written in English were included in the analysis. Further studies with larger sample sizes and adjustments for confounders are required to confirm our conclusions.Moreover, it is also crucial to consider that these discrepancies could be due to the diversity in the designs of studies included in this meta-analysis. It is important to note that the size of each study is a useful to measure reliability. Most studies included in the meta-analysis have a relatively small sample size, which is the main cause of variations among different studies. Smaller studies helpful in summarizing the presented information and producing hypotheses for future investigations. While larger studies are more authentic and reliable and can be clinically useful. We should also take the following limitations of this meta-analysis into account: (1) only unadjusted values were used to evaluate the strength of the association, and (2) selection bias, 10.7717/peerj.14792/supp-1Supplemental Information 1Click here for additional data file.10.7717/peerj.14792/supp-2Supplemental Information 2Click here for additional data file.10.7717/peerj.14792/supp-3Supplemental Information 3Click here for additional data file.10.7717/peerj.14792/supp-4Supplemental Information 4Click here for additional data file.10.7717/peerj.14792/supp-5Supplemental Information 5Click here for additional data file.10.7717/peerj.14792/supp-6Supplemental Information 6Click here for additional data file.10.7717/peerj.14792/supp-7Supplemental Information 7Click here for additional data file.10.7717/peerj.14792/supp-8Supplemental Information 8Click here for additional data file.10.7717/peerj.14792/supp-9Table S1Click here for additional data file.