We conducted this meta-analysis for a precise estimation of the association between BsmI (rs1544410), ApaI (rs7975232), TaqI (rs731236), and FokI (rs2228570) polymorphisms and acute pancreatitis.Several studies have been performed to investigate the association between Appropriate studies were retrieved by searching Web of Science, PubMed, Scopus, and Google scholar databases, until January 31, 2021. Two reviewers independently conducted data extraction and literature quality evaluation. Odds ratios and 95% confidence intervals were calculated to evaluate the strength of the association.All of the data were analyzed with Stata 16.0.The results of this meta-analysis will be submitted to a peer-reviewed journal for publication.This meta-analysis will summarize the association between BsmI, ApaI, TaqI, and FokI polymorphisms and the risk of acute pancreatitis.Ethical approval was not required for this study. The systematic review will be published in a peer-reviewed journal, presented at conferences, and shared on social media platforms.DOI 10.17605/OSF.IO/83W7R. The imbalance of active VD level is a major cause of hypercalcemia that is a risk element for AP. Due to polymorphisms in the VDR gene, the impairment of VDR can lead to a significant effect on the equilibrium of VD levels in the circulation and on its activity in the body. Moreover, Serum 25-hydroxyvitamin D3(25-(OH) D3) level was inversely related to the severity of AP and inflammatory markers such as C-reactive protein. VD also affects the innate and adaptive immune responses and reduces oxidative stress, which could initiate AP.Vitamin D (VD) dysregulation was proposed to be associated with the production of AP.VDR polymorphism and the risk of AP, but the results of these studies are not consistent.,11 In this study, we performed a systematic review and a meta-analysis to evaluate the association between different genetic variants of VDR gene and AP, and to evaluate the association of BsmI, ApaI, TaqI, and FokI polymorphisms within different ethnic subgroups. The results were pooled together to obtain reliable conclusions.In recent years, there are more and more studies on the relationship between 22.1The protocol of this review was registered in OSF (OSF registration number: DOI 10.17605/OSF.IO/83W7R). It was reported to follow the statement guidelines of preferred reporting items for systematic reviews and meta-analyses protocol.2.2Qualified studies have to meet the following criteria:(1)Predefined diagnosis criteria for AP;(2)Case-control studies evaluated the association between BsmI, ApaI, TaqI, and FokI polymorphism and AP risk;(3)Provided detailed genotype frequencies;(4)If multiple reports based on the same study population were available, the most recent or largest study will be selected.2.3The main exclusion criteria are as follows:(1)Insufficient data offered to calculate Odds ratio (OR) estimate;(2)Non-case-control studies;(3)Duplicate data from a same cohort.2.4vitamin D receptor, VDR, polymorphism, BsmI, rs1544410, ApaI, rs7975232, TaqI, rs731236, FokI, rs2228570, pancreatitis, acute pancreatitis.” Furthermore, studies were identified through manual searches on reviews and retrieved studies. The search strategy for PubMed is displayed in Table Two investigators independently performed a systematically computerized search for English studies through Web of Science, PubMed, Scopus, and Google Scholar databases up to January 31, 2021. The keywords for searching included the combination of “2.52.5.1Two reviewers complete the screening process independently, and any differences are decided by a third reviewer. The screening process of the article includes reading the title, the abstract, and the full text, so as to determine whether it meets the inclusion criteria. The researchers record the reasons to exclude each study in light of the preferred reporting items for systematic reviews and meta-analysis guidelines and report the screening results as well. The flowchart is exhibited in Figure 2.5.2P value for Hardy–Weinberg equilibrium (HWE) of controls, single nucleotide polymorphism detection method and so on. If the extracted data are inconsistent, discussion should be made to solve it.The collected information from each article are as follows: The first author and year, country, region, race, gene frequency of case group and control group, source of control group, 2.5.3 This scale uses a star rating system to judge the methodological quality and consists of three parts, namely selection, comparability, and ascertainment of exposure. The full score is 9 stars, and the score of 5 or more is regarded as “high quality.” Otherwise, the study is regarded as “low quality.” Any disagreements on the Newcastle-Ottawa scale score of the studies were resolved through a comprehensive reassessment by other authors.Two authors independently assessed the quality of the included studies based on Newcastle Ottawa Scale.2.5.4The reason for the loss of data in the period of data screening and extraction is identified here. If the data of potential studies are insufficient, missing, or vague, we would attempt to contact the authors. These studies would be excluded only if the data are not available through the method described above.2.5.5Z test and P < .05 was considered as statistically significant. In the meta-analysis, the overall pooled ORs were calculated for four models, including the allelic, dominant, recessive, and homozygous models. Deviation from HWE was examined by carrying out Chi-square test and P value < .05 indicated a departure from HWE. The between-study heterogeneity was assessed by the I2 statistic, which was calculated from the Q statistic. If the heterogeneity was statistically significant (P < .05 for Q-test or I2 > 50%), the random-effects model (based on DerSimonian-Laird method) was adopted to get the pooled estimates. Otherwise, the fixed-effects model (based on Mantel–Haenszel method) was adopted. All of the statistical analyses were conducted by STATA 16.0 , and the P values were 2-sided.Crude Odds ratio (ORs) with corresponding 95% confidence intervals were used to estimate the strength of the association between the BsmI, ApaI, TaqI, and FokI polymorphism and AP risk. The statistical significance of the pooled ORs was examined by performing 2.5.6We will conduct subgroup analyses based on age, ethnicity, and disease severity2.5.7The eligible study was sequentially removed to perform the sensitivity analysis.2.5.8P < .05.,16Publication bias was assessed by Begg rank correlation and Egger linear regression. The publication bias was regarded as statistically difference when 2.5.9The content of this article does not involve moral approval or ethical review and would be presented in print or at relevant conferences.3In recent years, studies on the relationship between inflammatory mediators and the risk of AP have proved that the severe systemic inflammatory response triggered by AP is not mediated by a single vector, but has complex pathophysiological pathways. Among these pathways, oxidative stress and cytokines are particularly important. Kim et al discovered that the concentration of 25-(OH) D3 in AP dogs was significantly lower than that in healthy dogs. Active VD metabolite, and 1,25-dihydroxyvitamin D selectively bind to specific VDR. Afterward, the VDR regulates the transcription of genes that are involved in calcium metabolism, cell proliferation and differentiation, senescence, and t-cell-mediated immune response.–28VDR is also expressed in pancreatic β cells and may play an important role in maintaining normal insulin levels consistent with glucose concentration and glucose tolerance.–31 The polymorphism of VDR may have key effects on the balance of circulating VD concentrations and the activity of final metabolites throughout the body, because VD works through VDR.The 25-(OH) D3 participates in human immune and inflammatory response.VDR polymorphism and AP susceptibility, the cumulative evidence of this association has not been systematically evaluated. In this study, we will conduct a systematic review and meta-analysis to combine the results of numerous studies and generate more reliable risk association estimation to guide the prevention and treatment of AP. The advantages of this study are as follows:So far, although many researchers have focused on the relationship between (1)Large data sets from all eligible latest studies will be merged;(2)In order to explore heterogeneity, we will try to avoid post-subgroup analysis;(3)Sensitivity analysis will be conducted on each genetic model for the improvement of the reliability of the results. Therefore, the release protocol will avoid potential biases related to data mining as much as possible and help to obtain convincing evidence.Conceptualization: Mengqin Li.Data curation: Xiaofeng Li, Xianghai Gan.Formal analysis: Mengqin Li, Xianghai Gan, Junzuo Gong.Methodology: Xianghai Gan, Tianyi Mou.Project administration: Mengqin Li.Resources: Junzuo Gong.Software: Junzuo Gong, Hua Zhou.Supervision: Mengqin Li.Validation: Tianyi Mou, Hua Zhou, Xiaofeng Li.Visualization: Xiaofeng Li, Hua Zhou.Writing – original draft: Mengqin Li, Xiaofeng Li.Writing – review & editing: Mengqin Li, Xiaofeng Li.


Background and objectiveFragility Index (FI) of meta-analyses determines their stability in terms of the level of confidence and strength behind the results depicted by them. The present study was conducted to estimate the FI of recently published meta-analyses in the Journal of Pediatric Urology (JPUrol).MethodTwenty recently published articles on meta-analyses were screened to identify the eligible ones. The baseline data of each meta-analysis including the details of the author, number of included studies, total sample size, the total number of events, the status of the overall outcome (significant or non-significant), type of effect measure, type of method used for pooling the estimates, and type of effects model were recorded. FI was calculated by doing each single status modification. The 95% CI of the treatment effect was re-calculated until the statistical significance of the meta-analysis was reversed.ResultsA total of seven articles incorporating 22 meta-analyses were included. Seven (32%) of them had a statistically significant outcome prior to FI estimation. The risk ratio  was the most commonly used effect measure. The random-effects model  and the Mantel-Haenszel method  of pooling the estimates were utilized in the majority of meta-analyses. The median  FI of statistically significant, non-significant, and total meta-analyses were 5 , 5 , and 5  respectively. FI of ≤5 was noticed in four out of seven (57%), 9/15 (60%), and 13/22 (59%) of these meta-analyses respectively.ConclusionBased on our findings, the majority of the recently published meta-analyses in the field of pediatric urology are fragile and depend upon the event status of ≤5 participants. The first meta-analysis in medical literature was published by Karl Pearson in 1904 . The FraFI was first devised by Walsh et al. for randomized controlled trials (RCTs) . Later, Study selectionwww.jpurol.com) were searched on June 9, 2021, and 20 recently published articles with the term ‘meta-analysis’ in their title were screened. For each eligible article, all the meta-analyses reported in that paper were included. The ‘meta-analyses reporting a significant outcome’ were not considered as an explicit inclusion criterion, and those reporting a non-significant result were also included. Consistent with the previous studies [The online archives of the JPUrol  FI of statistically significant, non-significant, and all (significant + non-significant) meta-analyses were calculated and recorded.The FI was calculated as done in previous studies ,5. AfterData extraction and analysisThe baseline data of each meta-analysis including the name of the author, year of publication, number of included studies, total sample size, the total number of events, the status of the overall outcome (significant or non-significant) prior to FI estimation, the type of effect measure used (risk ratio or odds ratio or risk difference), the type of method used for pooling the estimates (Mantel-Haenszel or inverse variance or Peto), and the type of effects model (random or fixed) were recorded in an extraction table using MS Excel (Version 15.24). The data analysis was performed using RevMan 5.4  and Stata 12.0 .The present study involves the collection of already available data in the archives of a journal. This primary data was used to generate secondary data. Since no direct patient contact was established during the course of this study, approval from the Institutional Review Board was not needed.The 20 recent articles -25 on meThe FI of all individual meta-analyses is depicted in Table In the evidence-based practice of medicine, meta-analyses and systematic reviews are considered to provide the highest level of evidence . HoweverRecently, the FI has been developed as an additional tool to confirm the stability of meta-analyses in terms of the confidence and strength behind the results depicted by them . A meta-Consistent results were observed by Schröder et al. , depictiOn the other hand, Atal et al.  have demThe values of FI can be influenced by a number of factors. Of substantial importance are the sample size and p-value . The greTo the best of our knowledge, ours is the first study to focus on the estimation of FI among the meta-analyses related to pediatric urology. Unlike other studies , we haveThe results of this study must be interpreted within the context of a few limitations. First, we have screened 20 recently published meta-analyses from one journal only. Although the reason for exploring the JPUrol was due to the fact that this journal focuses exclusively on pediatric urology, it might be possible that the FI of previously published meta-analyses from the same journal or those from other journals have a higher FI. Second, FI was initially designed for RCTs. It was later adopted for meta-analyses of RCTs as well. In the present study, we have estimated the FI of meta-analyses including studies other than RCTs as well. It is believed that the FI of meta-analyses of observational studies must also be calculated due to the obvious challenges of conducting high-quality RCTs in surgical specialties . Third, Based on our findings, almost 60% of the recently published meta-analyses in the field of pediatric urology have high fragility and their outcomes depend upon the event status of ≤5 participants. Similar to the PRISMA guidelines, a mandatory reporting of the FI can be done to provide additional information about the p-values depicted within the meta-analyses. In fact, at the time of consideration for publication, the FI can serve as an adjunctive tool for critical appraisal of meta-analyses related to common pediatric urologic ailments.


The aim of this study was to systematically update the evidence for associations between host genetic variants and subgingival microbial detection and counts.Following a previous systematic review : 889-900, IL1 composite genotype and subgingival detection of Aggregatibacter acinomycetemcomitans, Poprhyromonas gingivalis, Tannerella forsythia, Treponema denticola, and Prevotella intermedia. Promising evidence is emerging from other genetic variants and from sub-analyses of data from genome-association studies. Among other studies with candidate-gene, target SNPs were mainly within the IL10, IL6, IL4, IL8, IL17A, and VDR gene.A total of 19 studies were included in the review, from an initial search of 2797 titles. Studies consisted mainly of candidate gene studies and of one genome-wide analysis. A total of 62 studies were considered for summary findings, including 43 identified in the previous systematic review of studies published up to 2015. Meta-analyses were done when appropriate including both papers in the original review and in the update. Meta-analyses revealed lack of associations between IL1 composite genotype does not seem to be associated with subgingival microbial detection. Promising associations should be pursued by future studies, including studies employing -OMICS technologies.A better knowledge of which host genetic variant predispose to subgingival microbial colonization and to the development of progression of periodontal disease could potentially help to better understand periodontal disease pathogenesis and help with its management.The online version contains supplementary material available at 10.1007/s00784-021-04233-8. Humans are considered ‘holobionts’ who have evolved with their colonizing microbes. A large part of the human microbiota is vital for health and survival, although some microbes may have harmful effects and predispose to human disease . Periodo‘Infectogenomics’ has been introduced as a term to define the effect of host genetic variants  in influencing the response to infective agents and therefore the risk to develop disease . DysbiotA systematic review protocol was written in the planning stages and the PRISMA checklist  was follWhat is the association between host genetic variants and detection of specific microbes subgingivally?Broad question:Population: subjects with measures of periodontal disease or periodontal healthExposure: analysis of host genetic variantsComparisons: genotypes/allele frequency at different SNPsOutcomes: detection of specific microbes subgingivallyStudies: case–control, cross-sectional, cohort or randomized controlled trials (RCTs)PECOS outline:Following a previous systematic review , the seath September 2015 (after the close date of the previous review) and 30th May 2021 were searched.The search strategy used a combination of MeSH terms and key words described in supplemental material Study designs:Case–control studiesCross-sectional studiesLongitudinal/cohort studies or RCTs providing baseline genetic and microbial dataReporting measures of periodontal disease reported Reporting analysis of host genetic variants (SNPs)Reporting data on microbial detection subgingivally (by host genetic variant)Human studies reporting measures of associations between host genetic variants and detection of subgingival microbes were considered suitable for this review. Inclusion criteria were:ReviewsCase reportsStudies on animal modelsExclusion criteria were:Initial screening of potentially suitable titles and abstracts against the inclusion criteria to identify potentially relevant papersScreening of the full papers identified as possibly relevant in the initial screeningStudy selection was conducted by two independent reviewers  in the following stages:Studies were excluded if not meeting the inclusion criteria . Following the screening of titles and abstracts (steps 1 and 2), the studies included by both reviewers were compared and a complete database for step 3 was formed joining all studies selected by at least one reviewer. Following step 3, in case of a disagreement between reviewers, the decision about study eligibility was made trying to reach a consensus between the two reviewers. In case of continued disagreement, a third reviewer or arbitrator (author LN) judged study inclusion. The agreement value between reviewers will be calculated after step 2 and after step 3 using Kappa statistics.Data were extracted based on the general study characteristics  and population characteristics . Specific data on genetic and microbial analysis, genetic variants analyzed, microbes analyzed, method used for genetic analysis, and method used for microbial sampling and microbial detection/identification were extracted, as previously described .http://www.ohri.ca/programs/clinical_epidemiology/oxford.htm) was used to assess risk of bias for longitudinal studies.The risk of bias of the included case–control and cross-sectional studies was assessed through sensitivity analysis by using a recently proposed score of 0 to 20 adapted to genetic analyses of periodontal studies . The ‘NeThe study outcomes were the risk ratio of detection of specific subgingival microbes (primary outcome) or the overall microbial counts or proportions (secondary outcome) in patients with different genotypes. We aimed to stratify results separately according to periodontal diagnosis  if possible. The studies identified in the current updated review were pooled with the 43 studies identified in the original review  to assesp value = 0.05 was used as the cut-off level for significance. Heterogeneity was assessed with chi-square tests and I2 test, which ranges between 0 and 100% and where lower values represent less heterogeneity.The study outcomes were the risk ratio of detection of specific subgingival microbes (primary outcome) or the overall microbial counts or proportions (secondary outcome) in patients with different genotypes. Meta-analysis could be performed in the presence of at least 3 papers investigating the same combination of SNPs and subgingival bacteria. The risk ratios of primary and secondary outcomes were estimated using a computer program . The contribution of the included articles was weighted using inverse-variance method. Random effects meta-analyses of the selected studies were applied if the heterogeneity is considered moderate to high among the pooled studies; otherwise, fixed effects meta-analyses were applied if the heterogeneity is low. Forest plots were produced to graphically show the difference in outcomes of groups with different genotypes using number of SNP with each genotype as the analysis unit. A th September 2015 (after the close date of the previous review) and 30th May 2021 were searched at Ovid Medline, Embase, Cochrane Library, and LILACS combined. Following first-stage screening of titles and abstracts, 70 articles qualified for full-text screening . After full text reading, 19 articles met the defined inclusion criteria and 51 were excluded , USA (n = 5), Czech Republic (n = 3), China (n = 2), Italy (n = 2), Spain (n = 1), Thailand (n = 1), and Macedonia (n = 1). The number of study participants ranged from 39 to 4910. The majority of studies had a case–control design in a University setting, while only 1 study was a longitudinal treatment study.Table Aggregatibacter acinomycetemcomitans, Poprhyromonas gingivalis, Tannerella forsythia, Treponema denticola, Prevotella intermedia, and Fusobacterium nucleatum.Included cases ranged from chronic periodontitis (CP), aggressive periodontitis (AgP), gingivitis, and healthy periodontia. Some papers focused only on patients with specific medical history, such as HIV , diabeteA total of 62 studies were considered for summary findings, including 43 identified in the previous systematic review of studies published up to 2015 and 19 identified in the current update. Some studies reported positive associations between genotypes and detection or counts/proportions of specific bacteria, while other papers reported lack of associations (see Table IFI16) and absent in melanoma 2 (AIM2) genes and higher levels of periodontal micro-organisms [.Genome-wide significant association between host genetic variants and subgingival bacteria from participants in the Atherosclerosis Risk In Communities (ARIC) study was previously only detected at gene-centric analysis for 2 genes (KCNK1 and DAB2IP) , 23. Furrganisms .IL1 +) was defined as the presence of at least one copy of ‘allele 2’ for SNPs IL1B rs 1,143,634 (previously reported as IL1B + 3953 or + 3954) and IL1 A rs 1,800,587 (previously reported as IL1A -889). The present review identified two studies on IL1 composite genotype [Positivity for ‘composite genotype’      -SNP positive, while the group with the homozygous C allele (CC genotype) was named IL-1B(3954)-SNP negative. Concurrent presence of all red complex periodontal pathogens in IL-1B (3954)-SNP positive periodontitis patients was identified [F. nucleatum and T. forsythia was significantly higher in healthy sites in IL-1B(3954)-SNP positive compared to IL-1B(3954)-SNP negative participants. In addition, the frequency detection of F. nucleatum was found to be significantly higher in periodontitis sites in IL-1B(3954)-SNP positive compared to IL-1B(3954)-SNP negative subjects. However, due to the mixed ethnicity of the study’s participants, this study was not included in our meta-analysis.In a study conducted in Boston , the carentified . The freIL6 -174 G  genotypes and higher detection of A. actinomycetemcomitans, although no meta-analysis could be conducted [IL6 SNPs and the amount of red complex species P. gingivalis, T. forsythia, and T. denticola in 326 patients with periodontitis in Italy. No meta-analysis was possible.Some consistent associations were previously found between onducted . An addionducted  using PCIL10 SNPs were available. In Asian populations with periodontitis, ATA/ATA haplotype carriers exhibited increased bacterial counts of A. actinomycetemcomitans [A. actinomycetemcomitans counts in periodontitis [IL10 variant allele carrier’ showed higher P. gingivalis, T. forsythia, and T. denticola detection compared with ‘non carrier’ but no statistically significant associations [IL10 rs6667202 was associated with increased counts of P. gingivalis [P. gingivalis, T. forsythia, T. denticola, and A. actinomycetemcomitans [IL10 ATA/GCC haplotypes and the presence of T. forsythia, Rothia dentocariosa, Cardiobacterium hominis, P. gingivalis, T. denticola, Peptostreptococcus micros, Synergistetes, and Eikenella corrodens in subgingival samples . Similar association was detected for GCC/GCC haplotypes with A. actinomycetemcomitans and Porphyromonas endodontalis [. However, the results of this study need to be considered with caution due to the risk of bias identified. Meta-analysis was not possible, due to heterogeneity of SNPs analyzed and the diversity of ethnicity in the studied populations.Combining publications included in the previous review  and the comitans . Consistdontitis . No metaciations . In Brazngivalis , while rcomitans . In a cadontalis . HoweverIL4 haplotypes were significantly associated with levels of A. actinomycetemcomitans before and after periodontal treatment. On the other hand, there was no significant association between IL8 haplotypes and subgingival levels of A. actinomycetemcomitans before and after periodontal treatment [In 104 patients with periodontitis, reatment .IL17A polymorphism was significantly associated with the counts of T. forsythia and T. denticola in healthy Czech patients with periodontitis and in those with type 1 diabetes mellitus and periodontitis, respectively [ectively . HoweverVDR/FokI rs2228570 risk genotypes (CC + CT) were significantly associated with elevated P. gingivalis proportions and increased mean CAL. The effect of the FokI polymorphism on P. gingivalis proportions appeared greater in smokers. In another study on 326 patients with periodontitis in Italy, no significant association were reported between VDR gene and red complex bacteria [A cross-sectional study in 1460 Thai patients  revealedbacteria .NPY polymorphism rs2521634 mutant carries proved significantly associated with subgingival T. forsythia, Actinomyces gerencseriae, Fusobacterium periodonticum, and Prevotella nigrescens [. TBC1D1 SNP rs10010758 was associated with increased counts of P. gingivalis, while FBX038 SNP rs10043775 proved significantly associated with decreased counts of P. intermedia [CCR5Δ32 (rs333) and the presence or counts of the periodontal pathogens P. gingivalis, T. forsythia, and T. denticola in the subgingival biofilm of included patients [TLR genes (TLR1 (n = 2), TLR2 (n = 1), TLR4 (n = 1), TLR6 (n = 1), TLR8 (n = 2), and TLR9 (n = 1)) were positively associated with P. gingivalis (2 SNPs), T. denticola (6 SNPs), and T. forsythia (1 SNP) [P. gingivalis [In a Brazilian study, the patients . In a st (1 SNP) . A multiTable IL1 composite genotype and detection of periodontopathogenic bacteria A. actinomycetemcomitans, P. gingivalis, T. forsythia, P. intermedia, and T. denticolaNo association is seen between Several genetic variants have been proposed as potentially having an influence on the subgingival microbiotaWhen patients with periodontitis are clustered in different sub-phenotypes using microbial and inflammatory data, the association between genetic variants and disease appears to be strongerThere is still a paucity of well-conducted studies, and in particular of studies employing -OMICS approaches in periodontal infectogenomicsThis systematic review update investigated the associations between host genetic variants and detection and counts/proportions of periodontopathogenic bacteria subgingivally, based on the concept of periodontal infectogenomics. This was defined as the effect of host genetic variants in influencing the composition of the subgingival microbiota . SeveralIL1 host genetic variants and subgingival periodontopathogenic bacteria had been observed in a previous systematic review [IL1 composite genotype were identified, allowing meta-analysis of their association with five periodontopathogenic bacteria assessed by PCR from subgingival plaque samples. The absence of association with A. actinomycetemcomitans, P. gingivalis, T. forsythia, P. intermedia, and T. denticola suggests that the IL1 composite genotype may not have any effect on influencing the composition of the subgingival microbiota, or at least not with regards to the most studied periodontopathogenic bacteria.Nineteen studies were included in the present review. The genetic and microbial analyses typically involved the study of one or a selected panel of SNPs and one or a selected panel of bacteria supposed to have an effect on periodontal pathology. A lack of association between c review . Two addIL10, IL6, IL4, IL8, IL17A, and VDR genes. Meta-analysis was not possible for these SNPs due to heterogeneity of SNPs analyzed and the diversity of ethnicity in the studied populations. From our analysis of these findings, some consistent associations were found for IL10 genotypes, in Asian population and increased bacterial counts of A. actinomycetemcomitans [IL10 rs6667202 was associated with increased counts of P. gingivalis [VDR/FokI genotypes, which were associated with elevated P. gingivalis proportions in a Thai population [The FokI rs2228570 CC + CT genotypes were associated with elevated P. gingivalis proportions. The effect of the FokI polymorphism on P. gingivalis proportions was greater in smokers compared to non-smokers and in alcohol drinkers compared to non-drinkers [VDR gene and red complex bacteria [IL4 haplotypes were associated with levels of A. actinomycetemcomitans before and after periodontal treatment [IL17A polymorphism was associated with increased counts of T. forsythia and T. denticola in healthy Czech patients with periodontitis and in those with type 1 diabetes mellitus and periodontitis, respectively [Among other studies with candidate-gene and candidate-bacteria approach included in this review, target SNPs were mainly within the comitans  and in angivalis . New evipulation . The Fokdrinkers . In contbacteria . IL4 hapreatment  while ILectively .A. actinomycetemcomitans and P. gingivalis, respectively [IFI16) and absent in melanoma 2 (AIM2) genes SNPs were associated with higher levels of periodontal micro-organisms in the 1q12-locus. SNPs rs6940 and rs1057028 were significantly associated with increased P. gingivalis, T. forsythia, and C. rectus and haplotype block rs1057028 was also significantly associated with pathogens F. nucleatum and A. actinomycetemcomitans [Recent technology enabled researchers to expand this candidate-gene/candidate-bacteria approach and to perform large-scale high throughput genetic and microbiological analyses. The advantages and disadvantages of these approaches often both lie in their explorative nature which, while allowing concomitant analysis of a wide array of potentially relevant genes and bacteria, carries the risk of losing power and focus by multiple testing and by not taking into consideration a possible functional relevance to the periodontium. However, GWAS could also be interpreted with a more focused approach in the context of biological relevance. The cohort of the GWAS included in this review , 22 was ectively . When paectively . Interescomitans . In addicomitans , as wellcomitans , which scomitans .IL1 composite genotype, including a considerable number of subjects, thus increasing the sample size to assess potential genetic-microbial associations. A limitation of the included studies is their heterogeneity in data reporting and different ethnicities, as due to high variation in genotype distributions across ethnic groups, pooling data from different ethnic groups is not advisable. Moreover, three of the included studies were identified as having high risk of bias and therefore, their results should only be considered with caution. In fact, only 7 out of 19 included studies reported a priori sample size calculation for the main outcome.A strength of the studies in the current systematic review is the inclusion of studies carried out in several different populations and employing similar analytic strategies. It was also possible to carry out meta-analyses for IL1 composite genotypes are not associated with specific subgingival microbial colonization patterns. We suggest that other gene variants showing promising associations with detection and counts of periodontopathogenic bacteria subgingivally, such as for example IL10 gene variants, need replication in large independent samples. Furthermore, adherence to STREGA guidelines for the conduct and reporting of periodontal genetic-microbial association studies is of paramount importance in order to produce good-quality data [Based on this review, we conclude that the ity data . Genome-Supplementary file1 (DOCX 15 KB)Supplementary file2 (PDF 48 KB)Below is the link to the electronic supplementary material.


TMEM18 gene, which plays a significant role in feeding behaviour; however, subsequent studies among different ethnic populations and age groups have shown inconsistent results. Therefore, this present meta-analysis examines the relationship between TMEM18 polymorphisms with the risk of obesity with regard to age group and ethnic population.Many studies have proposed that the pathogenesis of obesity has a genetic basis, with an important risk factor being the presence of polymorphisms in the region of the TMEM18. Pooled odds ratio (OR) and 95% confidence intervals (95% CI) were calculated by either a fixed-effects model or random effect model based on a heterogeneity test. The meta-analysis of rs6548238 and its surrogates examined the relationships between 53 395 obesity cases and 123 972 healthy controls from 27 studies and published data from the POPULOUS collection (Poland).A literature database search was conducted for available relevant studies investigating the association between obesity risk and the presence of rs6548238, rs4854344, rs11127485, rs2867125 and rs7561317 polymorphisms in A significant association is observed between rs6548238 (and surrogate) and obesity risk, with OR = 1.25 (95% CI: 1.08–1.45). Regarding population type, a significant association was revealed among groups of Europeans with OR = 1.32 (1.10–1.59) and Mexicans with OR = 1.39 (1.13–1.73). However, a lack of statistical significance was noticed in groups in Asia with OR = 1.11 (95% CI: 0.86–1.42). Regarding age, a significant association was observed among children with OR = 1.28 (95% CI: 1.18–1.39) but not in adults OR = 1.21 (95% CI: 0.92–1.58).TMEM18 appear to play a role in the development of obesity. Our findings indicate that differences exist between ethnic populations and age groups, supporting those of a previous study showing the various effects of genetic factors on age and ethnic groups.The polymorphisms near The online version contains supplementary material available at 10.1186/s12920-021-01025-7. Obesity is a nutritional disorder associated with weight gain  caused b2). In adults, the threshold for obesity is considered to be a BMI of 30 [Obesity is classified by Body Mass Index (BMI), an index of weight-for-height defined as weight in kilograms divided by the square of height in meters , which is located in the nuclear membrane. This protein is reported to be involved in weight regulation through transcriptional repression [TMEM18 and changes in BMI has been confirmed by a third wave of GWAS (Genome-wide association study) [Obesity is considered by many researchers to be a heritable disease , and manpression . TMEM18 n study) , and subn study) , 14. Apan study) .TMEM18 region and obesity, there is a clear need to determine more precisely the effect size of polymorphisms rs6548238, rs4854344, rs11127485, rs2867125, and rs7561317 localised near the gene itself with regard to age and population type. Therefore, the present meta-analysis reviews a range of data meeting the required criteria, with the age categorization.With the discovery of an apparent relationship between the Identification of relevant studies concerning the association of rs6548238 rs4854344, rs11127485, rs2867125, and rs7561317 polymorphisms with obesity was conducted by systematic literature search from PubMed, Science Direct, Web of Science databases and GWAS catalogue. The following search terms were used: obesity AND (TMEM18 OR GWAS). Obtained results were divided into two categories: age and population. No restriction was placed on the time period, sample size or language. If more than one article was published using the same case series, only the original study was included.The present meta-analysis included genetic association studies based on cohort or case–control models, in which the case subjects were obese and the control subjects were individuals within the normal BMI range. The following inclusion criteria were applied: (a) case–control or cohort studies presenting original data on the associations between the risk of obesity and TMEM18 polymorphisms , (b) sufficient data for the calculation of odds ratio (OR) with the 95% confidence interval (95% CI) or calculated OR with 95% CI. The following exclusion criteria were applied: (a) review articles and meta-analysis, (b) studies on the association between other gene polymorphisms and obesity risks, (c) no information about number of alleles and number of cases and controls (d) studies based on non-human models (e) quantitative traits such as Body Mass Index or Body Fat Percentage since the data were not uniform .Two researchers collected data independently. Disagreements were resolved by discussion with a third researcher. The following information was extracted from each study: (a) name of the first author, (b) year of publication, (c) country of origin, (d) ethnicity, (e) sample size, (f) association of the SNPs with obesity (OR and 95% CI), (g) analysed polymorphism, (h) number of case and controls, (i) mean age of case and controls.POPULOUS collection [POPUlation—LOdz UniverSity Biobank) at the Biobank Lab of the Department of Molecular Biophysics, University of Lodz. The POPULOUS population collection is currently registered in Directory (v. 4.0) of BBMRI-ERIC consortium under the registration number bbmri-eric:ID:PL_BLUL:collection:POPULOUS_BLUL. POPULOUS contains saliva samples from over 10,000 volunteers from all over the Poland [Additional data from adults of Polish origin were collected between 2010–2012 within the TESTOPLEK research project and registered as the llection , 17 , and each participant from the POPULOUS study gave their written consent to take part. All procedures were performed in accordance with the Declaration of Helsinki  .2, based on self-assessment of height and weight controlled by trained pollsters. People were considered as obese when BMI values were ≥ 30, and were normal when BMI was in the range of 18.5–24.9 [The BMI of each participant was calculated from the standard formula: BMI = weight/height8.5–24.9 . Allele The results indicate that 6047 unrelated Polish adult subjects were enrolled in the study group. Of these, 3 495 subjects remained after exclusion of underweight (BMI < 18.5) and overweight (BMI in the range of 25.0–29.9) individuals. Following this, 3 334 individuals, including 827 obese cases (BMI > 30) and 2667 control cases with normal weight (BMI in the range of 18.5–25.0), were successfully genotyped for SNPs rs6548238, rs2867125 and rs7561317. For rs4854344 there were 804 cases and 2 592 controls. 24 × 1 Infinium HTS Human Core Exome  microarrays were used for genotyping according to the protocol provided by the manufacturer.The Newcastle–Ottawa Scale  was usedp ≤ 0.05 was considered as statistically significant. The statistical analysis of the extracted data was conducted using Review Manager 5.3 [The association between obesity risk and the presence of rs6548238, rs4854344, rs11127485, rs2867125 or rs7561317 polymorphisms was estimated by calculation of pooled ORs and 95% CI in the allele model . The combined outcomes were displayed as forest plots. Statistical differences of the pooled ORs were assessed using Z-test, ager 5.3 . This prp-value threshold of 0.1 was considered as statistically significant. The heterogeneity between the studies was also tested by I2 statistics; I2 values of at least 50% were taken as indicators of heterogeneity. When heterogeneity existed, a random effect model was used to evaluate the pooled ORs and 95% CIs, otherwise a fixed effect model was used.Cochran’s Chi-squared test (based on Q test) was used to evaluate heterogeneity between studies, a Potential publication bias was assessed via visual inspection of the funnel plot, with an asymmetric funnel plot indicating publication bias. To identify the source of heterogeneity, a sensitivity analysis was applied by removing individual studies from the data for children or adults. Subgroup analysis was performed based on population type  and age: adults (> 18 years) vs. children (≤ 18 years). A total of five polymorphisms were evaluated independently , as was rs6548238 and its surrogates.The following studies were included in the meta-analysis: Albuquerque et al. , Almen eThe PRISMA  flow chart explaining study selection and literature search is presented in Fig. The sample size showed great variation, from n = 77 to n = 40 872. Throughout the meta-analysis, if the study contained results for more than one polymorphism, only rs6548238 was selected. Detailed information about the studies selected for the meta-analysis is gathered in Additional file 2 statistics. The results of the I2 test found low heterogeneity concerning rs6548238 and its surrogate in the group of children . Therefore the fixed effect model was used for further analysis. As the group of adults demonstrated high heterogeneity , the random effect model was used. Regarding population type, low heterogeneity was only observed in the Mexican group ; higher heterogeneity was observed among the European and Asian groups .The heterogeneity among studies was assessed by the Chi-squared test based on Q-test and the IThe overall results showed that rs6548238 and its surrogate was significantly associated with obesity risk  Fig. .Fig. 2FoBased on 1000 Genomes Project data, polymorphism rs6548238 is used in the main analysis since it is in a high linkage disequilibrium with rs4854344  with Northern and Western European ancestry); D’ = 1; r = 1 in CHB ; D’ = 1; r = 1 in MXL (Mexican Ancestry from Los Angeles USA), rs11127485 , rs2867125  and rs7561317 .In the analysis of each polymorphism without division into subgroups, independently pooled ORs indicate that allele C of rs6548238  . Despite all of these strengths, some limitations should be addressed. OR with 95% CI from genetic data was used to evaluate the sole genetic influence; however, when it was not given, calculated OR with 95% CI was used after covariate adjustment from the individual study. The potential interactions between the genetic factors and environmental factors were not addressed due to insufficient data, which might also affect the accuracy of the results.The second limitation concerns the variation in BMI cut-offs for obesity between studies. Most studies used the basic classification of obesity (BMI ≥ 30) and normal weight (BMI 18.5–24.9); however, in some studies, mostly among Asian populations, other BMI thresholds were used for obesity (e.g. BMI ≥ 27.5) and normal weight (e.g. BMI 18.5–23.0) (Additional file TMEM18 polymorphisms and obesity across different population types based on ethnicity and age. Our findings indicate that rs6548238 was significantly associated with an increased risk of obesity and similar trends were found among subgroups of European and Mexican origin but not Asian. While the presence of this polymorphism appears to have a significant influence on obesity among children, no significant association was found with obesity risk among adults.The present meta-analysis is the first to systematically explore the association between TMEM18 and the risk of obesity. Differences appear to exist between ethnic populations and age groups. Our findings support those of a previous study showing the various effect of genetics in age and ethnic groups.The results of this meta-analysis suggest an association between rs6548238 polymorphisms and its surrogate near Additional file 1: Fig. S1. Meta-analysis of the association between rs6548238 and obesity risk using random effect model. Forest plot created within RevMan 5.3 software.Additional file 2: Fig. S2. Meta-analysis of the association between rs4854344 and obesity risk using random effect model. Forest plot created within RevMan 5.3 software.Additional file 3: Fig. S3. Meta-analysis of the association between rs11127485 and obesity risk using fixed effect model. The size of the red box corresponding to each study is proportional to the sample size. Forest plot created within RevMan 5.3 software.Additional file 4: Fig. S4. Meta-analysis of the association between rs2867125 and obesity risk using random effect model. Forest plot created within RevMan 5.3 software.Additional file 5: Fig. S5. Meta-analysis of the association between rs7561317 and obesity risk using random effect model. Forest plot created within RevMan 5.3 software.Additional file 6: Table S1. Characteristics of studies included in the meta-analysis.Additional file 7: Table S2. Sensitivity analysis of general results of rs6548238 and its surrogates.Additional file 8: Table S3. The quality of studies included in the analysis.


Photosensitizing medications are commonly used in the United States, and studies have begun exploring whether exposure to these medications may be a risk factor for skin cancer. As the population in the United States ages, there are more elderly patients being prescribed photosensitizing medications to treat chronic conditions. At the same time, the incidence of skin cancer is increasing. We summarize current evidence regarding the risk of skin cancer associated with use of each type of photosensitizing medication and highlight gaps in the literature to guide further study.(1) The incidence of skin cancer is increasing in the United States (US) despite scientific advances in our understanding of skin cancer risk factors and treatments. In vitro and in vivo studies have provided evidence that suggests that certain photosensitizing medications (PSMs) increase skin cancer risk. This review summarizes current epidemiological evidence on the association between common PSMs and skin cancer. (2) A comprehensive literature search was conducted to identify meta-analyses, observational studies and clinical trials that report on skin cancer events in PSM users. The associated risks of keratinocyte carcinoma  and melanoma are summarized, for each PSM. (3) There are extensive reports on antihypertensives and statins relative to other PSMs, with positive and null findings, respectively. Fewer studies have explored amiodarone, metformin, antimicrobials and vemurafenib. No studies report on the individual skin cancer risks in glyburide, naproxen, piroxicam, chlorpromazine, thioridazine and nalidixic acid users. (4) The research gaps in understanding the relationship between PSMs and skin cancer outlined in this review should be prioritized because the US population is aging. Thus the number of patients prescribed PSMs is likely to continue to rise. Skin cancers, including basal cell carcinoma (BCC), squamous cell carcinoma (SCC) and melanoma, are the most common cancers in the United States (US), and incidence rates are increasing . InvasivProlonged and excessive exposure to ultraviolet (UV) radiation from natural and artificial sources are major risk factors for skin cancer ,8. OtherHerein, we provide a comprehensive review of the current epidemiological evidence on the association between commonly used PSMs and skin cancer. When appropriate, in vitro and animal studies are discussed to highlight biological mechanisms that may explain the epidemiological findings. Gaps in the literature are highlighted to guide future research directions.We conducted a narrative review of the association between various phototoxic medications and skin cancer. Medications were selected based on a review article that highlights common PSMs, according to literature review and the authors’ experience . We searThis review explored antihypertensives, such as diuretics, calcium-channel blockers (CCBs), beta-blockers, angiotensin-converting enzyme inhibitors (ACEi) and angiotensin receptor blockers (ARB), antidiabetic drugs, amiodarone, nonsteroidal anti-inflammatory drugs (NSAIDs), statins, voriconazole, antibacterial agents and vemurafenib . These PWe included case-control studies, cohort studies and randomized-controlled trials (RCT). If multiple meta-analyses on an individual medication and skin cancer were available, we chose the most recent or most comprehensive analysis and described these findings. We also described individual studies, which were not included in the meta-analyses. If a meta-analysis of the medication was not available, we summarized individual studies of the medication and skin cancer. For most medications, we describe the findings of melanoma and KC separately.Since their introduction more than 60 years ago, diuretics have become a backbone for antihypertensive treatment regimens. Certain subtypes of diuretics have been linked to phototoxic reactions, including thiazides , thiazide-like diuretics , loop diuretics  and potassium-sparing diuretics  . The sulMultiple meta-analyses ,23,24,25The meta-analysis also found that the RR of melanoma associated with thiazide diuretics use was 1.17 (95% CI 1.12–1.24), with a dose-response relationship based on three case-control studies and one matched cohort study . The majCertain CCBs may have photosensitizing properties similar to that of thiazide diuretics . HoweverKC: RR 1.16, 95% CI 1.06–1.27). The study did not report separately on the risks of BCC versus SCC. Tang et al. included three studies  and found that CCB use was associated with small increased risk of BCC , but not with SCC  [Literature on the risk of skin cancer among CCB users includes two meta-analyses ,25. GandA meta-analysis including the results from five studies  found a modestly increased risk of melanoma  among CCB users .Tilisolol and nebivolol are beta-blockers that have been reported to cause photosensitive reactions ,32. On tUnfortunately, epidemiological studies have not evaluated the association between individual beta-blockers and skin cancer. Rather, the evidence has been presented with all beta-blockers grouped into a single category of exposure.A meta-analysis based on one case-control and two cohort studies found that beta-blocker use was associated with modestly increased risk of BCC  compared to non-use, but was not associated with the incidence of SCC  . The casIn a meta-analysis based on two case-control and two cohort studies, beta-blocker use was significantly associated with an increased risk of melanoma with RR of 1.21 (95% CI 1.05–1.40) . Other eACEi prevent angiotensin-converting enzyme (ACE) from converting angiotensin I to angiotensin II, whereas ARB prevent binding of angiotensin II to its receptor, angiotensin II type 1 receptor (AT1R) . AngioteA meta-analysis on ACEi use and the risk of KC included eight studies  with 37,618 cases of skin cancer . The RR Another meta-analysis explored the association between ACEi and ARB with type of KC . For BCCIn a meta-analysis, the OR for risk of melanoma was 1.08 (95% CI 0.95–1.23) for the use of ACEi or ARB including three case-control and one matched cohort study. For ARB, the OR was 1.12 (95% CI 0.95–1.31) including two case-control and one matched cohort study . In anotPhototoxic eruptions have been noted in patients taking certain antidiabetic medications, such as metformin, glibenclamide  and sitagliptin . MetformEven though metformin has been studied as skin cancer treatment in clinical trials, epidemiological studies have not demonstrated decreased risk of skin cancer among metformin users ,56. A reThere were no reported results on skin cancer risk for sitagliptin, which is the only known DDP4i with phototoxic potential. A large meta-analysis exploring the cancer risk for multiple DPP4i found no significant risk of BCC  based on 11 RCTs . Null fiIn a meta-analysis based on six RCTs, metformin use was not significantly associated with decreased risk of melanoma  . SimilarThe meta-analysis of multiple DPP4i found no significant risk of melanoma based on 12 RCTs . Again, Amiodarone has been used for decades in management and prevention of severe cardiac arrhythmias, including ventricular tachycardia and atrial fibrillation. Multiple case reports have documented phototoxicity and skin cancer development in association with amiodarone use ,60,61. IAmiodarone use has been linked to multiple cancers, including skin cancer ,66. Two NSAIDs are extremely common medications used for their analgesic properties. Within this class of medications, naproxen and piroxicam are non-selective cyclooxygenase (COX) inhibitors with photosensitizing potential, in addition to anti-inflammatory, anti-proliferative and pro-apoptotic effects. Naproxen has a propionic aromatic acid structure and can induce skin photosensitivity, DNA photocleavage and photo-hemolytic activity in red blood cells ,69,70. IIn a study of five anti-inflammatory medications, incubation of piroxicam under UVA induced the formation of damaged single-stranded DNA . On the A meta-analysis including 17 studies found a RR of 0.93 (95% CI 0.87–0.99) for non-selective COX inhibitors and skin cancer overall . ResultsNeither non-selective COX inhibitors nor selective COX-2 inhibitors was significantly associated with melanoma . The RR A case-control study in the Netherlands evaluated multiple commonly used PSMs and found that use of propionic acid derivative NSAIDs was associated with an increased risk of melanoma  . The mosStatins, also known as HMG-CoA reductase inhibitors, have been instrumental in reducing morbidity and mortality for patients at high risk of cardiovascular disease . SeveralMultiple meta-analyses have investigated the relationship between statins and KC ,96,97,98A large meta-analysis by Li et al. explored the risk of melanoma among statin users and non-users, based on 24 studies  with 8433 melanoma cases . There wTetracyclines are a class of broad-spectrum antibiotics used to treat various infectious diseases . CertainNalidixic acid and the derivative fluoroquinolone antibiotics have also been implicated in photosensitive and phototoxic reactions ,106. BioA recent US-based prospective cohort study, including 36,377 cases of BCC, found a small increased risk of BCC among tetracycline users , with higher BCC risk with increased duration of use (P-trend = 0.05) . While tA Danish national register-based cohort study with 35,328 BCC cases and 6550 SCC cases found significant associations between the risks of BCC and SCC and short-term use of any photosensitizing antimicrobial  . Some ofThe US cohort study by Li et al. found no association between tetracycline use and melanoma  . HoweverVoriconazole is a broad-spectrum triazole antifungal medication commonly used to treat candidiasis and invasive aspergillosis. It is also prophylactically administered to immunocompromised patients to prevent invasive fungal infections . VoriconA meta-analysis by Tang et al. explored voriconazole use and the risk of SCC and BCC among lung transplant (LTx) and hematopoietic cell transplant (HCTx) patients . Two cohSubgroup analyses by transplant type demonstrated increased risk of SCC associated with voriconazole use in both LTx  and HCTx patients . Even after adjusting for sunlight and UV exposure, there was significantly increased SCC risk with voriconazole exposure. Two included studies showed a dose-response for voriconazole and SCC risk. Additionally, longer duration of voriconazole use was significantly associated with increased risk of SCC .Few epidemiological studies examining the association between voriconazole and KC risk have been published since the meta-analysis. A multi-center case-control study including 40 cases of BCC and 90 cases of SCC found that voriconazole exposure was associated with worse 3-year survival for KC patients and with SCC occurrence (cases: 15% versus controls: 0%) . A recenSome case reports have documented melanoma development in patients with chronic photosensitivity associated with long-term voriconazole therapy . HoweverVemurafenib is a BRAF kinase inhibitor often used as a targeted therapy for late-stage and metastatic melanoma . This drA recent meta-analysis of 11 clinical trials  evaluated dermatological toxicities, such as rashes, photosensitivity reactions, SCC and keratoacanthoma, associated with vemurafenib use among melanoma patients . VemurafOnly a few studies evaluated the use of any PSM in relation to skin cancer risk. A population-based case-control study in New Hampshire with 1567 BCC and 1,599 SCC cases revealed a risk of SCC  and BCC  associated with use of any PSM, although not statistically significant . HoweverIn the previously mentioned Netherlands case-control study that demonstrated over 40% of the population used PSMs, Siiskonen et al. also found that propionic acid derivative NSAIDs and quinolones were associated with increased risk of melanoma . In a prThis review describes findings from epidemiological studies that explore use of PSMs and the risk of skin cancer. Evidence is summarized for common PSMs, including antihypertensives, antidiabetics, amiodarone, NSAIDs, statins, antibacterial agents, voriconazole and vemurafenib, that have at least one epidemiological study investigating their association with skin cancer.Of the common PSMs reviewed, the antihypertensive medications were most extensively investigated. Each antihypertensive medication class was explored in at least one meta-analysis. These meta-analyses revealed that diuretics were associated with increased risks of all skin cancer types, while CCBs and beta-blockers were only associated with certain types of skin cancer . For ACEThe relationship between statins and skin cancer was also extensively evaluated in multiple meta-analyses, relative to other PSMs. There was a modest positive association between statin use and KC in observational studies, but null findings in RCTs .There were a few studies on metformin and amiodarone that demonstrated no association with skin cancer. Even though only a few studies are available, the literature suggests a positive association between antibacterial agents and skin cancer. In the antifungal category, voriconazole was associated with increased risk of SCC, but not BCC. There were no epidemiological studies of voriconazole use and melanoma risk. Vemurafenib, a melanoma treatment option, was associated with increased risk of SCC.For some medications, such as naproxen, piroxicam and sitagliptin, there were no epidemiological studies that focused on skin cancer risk associated with exposure to the individual PSM. Instead, meta-analyses reported on drug classes, combining medications with and without photosensitizing properties. There were no epidemiological studies of other common PSMs, including glyburide, chlorpromazine, thioridazine and nalidixic acid.In addition to beta-blockers, there are other PSMs with properties that may afford skin cancer protection. These include metformin, naproxen and piroxicam. In fact, metformin has been evaluated in several clinical trials as a chemopreventive agent for patients with melanoma. Piroxicam may even mitigate the phototoxic effects associated with thiazide use. The potential carcinogenic effects of these PSMs may be balanced by their anti-cancer properties, attenuating the risk of skin cancer associated with use.We identified several research gaps in the literature. First, a limited number of studies evaluated the dose and duration of PSMs and the risk of skin cancer . UnfortuSecond, nearly all studies evaluated the association between use of a single PSM and skin cancer ,112. HowThird, for certain conditions, such as hypertension, many of the therapeutic drug options are potentially photosensitizing . In thisFourth, many previous studies were based on cancer registries with limited demographic information and almost no data on skin cancer risk factors such as constitutional factors and sun exposure ,23,26. TFifth, skin cancers related to PSMs could occur on sun-exposed body sites more frequently than on sun-shielded body sites. However, only a few studies on PSMs evaluated the risk of skin cancer based on tumor body site.Sixth, studies relying on prescription databases may not know whether filled prescriptions were actually used by the patients and may lead to misclassification of some non-users as users, potentially biasing results towards the null ,18.Seventh, there is also an issue of confounding by indication: PSM users may be at increased risk of skin cancer, due to their underlying disease, rather than their medication use. For example, since antifungal medications are more commonly used in immunocompromised patients such as transplant recipients, it is possible that the immunosuppressed state is driving the association with skin cancer, rather than the antifungal medication. However, many of the diseases included in this review such as hypertension and cardiac arrhythmias are not known risk factors of skin cancer.Finally, individuals taking PSMs may see doctors more frequently, resulting in a higher chance of detection and diagnosis of skin cancer during office visits . Many ofIn summary, there are several commonly used PSMs, which are associated with elevated risk of skin cancer based on review of epidemiological literature. Still, these studies often lacked adjustment for skin cancer risk factors and evaluation of dose-response relationships. There was limited evaluation of the interaction with skin cancer risk factors. Additionally, there is a lack of epidemiological studies on skin cancer risk associated with many PSMs. Delineating the effects of concurrent use of multiple PSMs is necessary since they are often taken in tandem with one another.It is important to note that while the reported RR of skin cancer associated with PSMs, which represent the strength of the associations, may be moderate or small, use of PSMs is very common, especially among older populations who are more susceptible to skin cancer development ,16. In a


Breakfast, which is considered as an important meal of the day, is being ignored by an increasing number of people as the pace of modern life accelerates. Although a large number of previous studies have reported the relationship between skipping breakfast and type 2 diabetes mellitus, most of them were cross-sectional studies. It remains unclear how skipping breakfast affects such specific cardio-metabolic diseases as hypertension, strokes and hypercholesterolemia.The protocols and reports of this meta-analysis are based on a meta-analysis of observational studies in epidemiological guidelines (MOOSE). Relevant studies were systematically retrieved from PubMed, Embase, Web of Science and the Cochrane Library, and were restricted to English from the inception to May 10, 2019. All the results were obtained by RRs, and outcomes of interests should include the occurrence of cardiovascular and metabolic diseases.≦3times/week, those with a frequency>3 times/week have reduced the risk of type 2 diabetes mellitus, obesity, Metabolic Syndrome, Low high-density lipoprotein cholesterolemia, Cardiovascular Diseases, cardiovascular Mortality, hypertension and strokes, with , , , , , , , and , respectively.Fourteen cohort studies in total were eventually included. Compared with people having breakfast frequencyA regular daily breakfast habit benefits the cardio-metabolism to a great extent, reducing the risk of Cardiovascular Diseases, type 2 diabetes mellitus, obesity, hypertension, strokes, Metabolic Syndrome, cardiovascular Mortality, Low high-density lipoprotein cholesterolemia, and Abdominal obesity, while it is not significantly related to hypercholesterolemia and coronary heart disease regardless of gender. Nevertheless, skipping breakfast once a week may greatly reduce the benefits of cardio-metabolism. Therefore, public institutions should promote and encourage citizens to cultivate regular daily breakfast habits. For office workers, they may not have enough time to eat breakfast. For students, they may lack a good breakfast habit. As for obese patients, they may have a poor appetite or must limit their energy intake. As a matter of fact, regular breakfast habits can keep us energetic in work and study. In addition, eating breakfast can increase satiety, thereby reducing overeating later in the day to restrict weight gain. Recently, several studies have shown that skipping breakfast increases the risk of obesity, hypertension, hypercholesterolemia (HC), type 2 diabetes mellitus (T2DM), metabolic syndrome (MetS), coronary heart disease (CHD), and cardiovascular mortality (CVM). Conversely, can regular breakfast habits reduce cardiovascular and metabolic diseases?With the acceleration of the pace of modern life, breakfast, which is regarded as an important meal of the day, is being ignored by more and more people, which seemingly as a universal behavior, may have negative effects on your health.–14 such as dietary fat, cereals, and the Mediterranean diet patterns, while few pay attention to the effects of daily eating behavior on cardiovascular diseases (CVD) and metabolic diseases (MetD). Besides, although a large number of previous studies have revealed the relationship between skipping breakfast and diabetes, most of them were cross-sectional ones.–17 In this case, it is still unclear how skipping breakfast affects some other specific cardio-metabolic diseases. Therefore, the purpose of this meta-analysis is to systematically investigate the association between regular breakfast habits and cardio-metabolic diseases, and to update the epidemiological evidence so as to better serve public health and health promotion activities.Most of the previous dietary studies have focused on dietary components and combinations,22.1 Relevant studies were systematically retrieved (Zhi-hui and Xu) from PubMed, the Embase database and the Cochrane Library, and were restricted to English from the inception to May 10, 2019. Furthermore, the manual retrieval of the library was carried out. To ensure a comprehensive search, three sets of medical subject headings (MeSH) including “breakfast,” “cardiovascular diseases,” and “metabolic disease” were used. Generally, the Boolean operator “And” is employed between the two sets of keywords, and the “Or” is adopted within each group. Specifically, the first step was to use Boolean operator “Or” to combine the two sets of Mesh (cardiovascular diseases and metabolic diseases) and their corresponding synonyms, and then the Boolean operator “And” was applied to combine the Mesh of breakfast and its related synonyms. Besides, previous meta-analyses and systematic reviews were reviewed for comprehensive inclusion in the study. See Appendix 1  for a detailed search strategy.The protocols and reports of this meta-analysis rely on a meta-analysis of observational studies in epidemiological guidelines (MOOSE).According to PICOS criteria, the inclusion criteria for the study were as follows:1.The study population was the general population, aged > 18 years, with no previous history of metabolic or cardiovascular disease.2.Regular breakfast eaters as the intervention group.3.Irregular or non-breakfast eaters as the control or reference group.4.Outcomes of interests should contain the occurrence of cardiovascular and metabolic diseases.5.The study type was limited to cohort studies or randomized-controlled trials.6.Studies were provided with available maximum adjustment odds ratios (ORs), risk ratios (RRs), hazard ratios (HRs), and the corresponding 95% confidence intervals (CIs).7.Language was limited to English.Meanwhile, the exclusion criteria were shown below:1.The study subjects were people who previously had CVD or MetD.2.The exposure of the studies was non-breakfast frequency, such as specific-ingredients in the food spectrum.3.Cross-sectional studies, reviews, case reports, conference abstracts, and letters were excluded.4.The outcomes of studies were non-cardiovascular and non-metabolic diseases.5.The related ORs, RRs or HRs and the corresponding 95% CIs of studies could not be obtained.6.The language of studies was non-English.7.For duplicated publications, the longest follow-up data or the largest number of population would be included for analysis.2.2 was adopted to assess the quality of the study, with a total score of 9. To be specific, it is believed that studies with a NOS score over 6 stars are of high quality, while those with a NOS score below 6 stars are considered as low-quality ones.The following data, like the first author, the year of publication, the country, the duration of follow-up, the mean age, females, the sample size, exposure assessment, CVD/MetD assessment, intervention (breakfast frequency), control (breakfast frequency), outcomes, and main findings, were extracted with a unified data list made by two independent reviewers (Li and Liu). Any disagreements and disputes in the process of data extraction shall be resolved through negotiation. Besides, the Newcastle-Ottawa scale (NOS)2.3 If necessary, use the following formula to convert ORs to RRs and calculate the corresponding 95% CIs. Relative risk=odds ratio/[(1 − P0) + (P0 × odds ratio)], where P0 indicated the incidence of outcomes in the unexposed group. Then, convert the standard error (SE) of the RR with the following formula: SElog(relative risk) = SElog(odds ratio) × log(relative risk)/log(odds ratio). In addition to that, if P0 was rare (P < .05), ignore the differences among various measures of relative risk . All the results were shown by RRs. As the reference groups of each study were not identical, the frequency of breakfast was divided into the following five groups: ≤3 times/week, >3 times/week, 4 to 6 times/week, 0 to 6 times/week, and 7 times/week, to ensure homogeneity and effective consolidation of data. This group of one cohort 25 study was 0 to 4, and it was roughly classified as 0 to 3 for the effective combination of data. Besides that, the group with the lowest breakfast frequency was unified into the reference group through the Excel macro file made by Hamling et al based on Greenland and Longnecker's theory. In general, most studies reported specific breakfast frequency, but a few articles did not mention breakfast frequency. We contacted the original authors, and if relevant information was still not available, we would classify “Eats breakfast (yes) or eating breakfast” as 7 times per week and “Eats breakfast (No) or skipping breakfast” as 0 time per week. Besides, if the subject was specifically classified according to gender and age, we would tend to consider it as two studies. Statistical heterogeneity was assessed by using I2 statistics, where 25%, 50%, and 75% of I2 values represented low, medium and high heterogeneity, accordingly. If I2 was ≤50%, the fixed-effect model would be adopted. Otherwise, the random effect-model would be adopted. If the confidence intervals of 95 are on one side of 1, we think there is a significant difference. Meanwhile, Begg's test was performed to evaluate the potential publication bias, and subgroup and sensitivity analysis were used to explore sources of potential heterogeneity. All data analyses were conducted by Stata SE12.0 software.Our primary outcomes would focus on the risk of cardiovascular and metabolic diseases, as well as specific diseases such as hypertension, type 2 diabetes mellitus, obesity, and strokes. Broadly speaking, the HR was equivalent to the RR, and was thereby directly considered to be the RR.2.4The data analyzed in this study were extracted from previously published studies, and therefore ethical approval was not necessary.3A total of 18,334 studies were identified from the four electronic databases, namely PubMed, Embase, the Cochrane Library, and Web of Science, as shown in Figure 1.The exposure of interest was not breakfast frequency .2.Participants had a history of CVD or MetD (n = 8).3.The relevant RRs could not be obtained (n = 10).4.Cross-sectional and Case–control studies were excluded (n = 27).5.Reviews, Letters, and Conference Abstracts were excluded (n = 22).,24,29–39 were involved, and among them, 6 studies showed the relationship between breakfast frequency and T2DM; 5 studies revealed the risk of obesity; 4 studies reported the risk of hypertension; 3 studies indicated the risk of MetS and abdominal obesity, while 2 pointed out the risk of CVD, strokes, hypercholesterolemia, CVM, Low levels of high-density lipoprotein cholesterol (LHDL-c) and CHD. Table Finally, 14 cohort studies3.13.1.1≤3times/week, those with the frequency>3 times/week and frequency7times/week had a lower risk of T2DM  and , respectively. Besides, compared with people who had a breakfast frequency0∼6times/week, those with a frequency7times/week would significantly decrease the risk of inducing T2DM . However, risk for T2DM of those with a breakfast frequency4 to 6times/week would not be lower than that of those with a frequency ≤ 3 times/week .Figure 7times/week would have an obvious lower risk for T2DM than those with a frequency≤0∼6times/week . Similarly, males with a breakfast frequency 7 times/week could have an obvious lower risk than those with a frequency0∼6times/week . However, no significant association was found in females .From the perspective of gender, as shown in Figure 3.1.2≤3times/week, those with a frequency>3 times/week would have a decreasing risk for obesity . Besides, different from people who had a breakfast frequency of 0∼6times/week, those with a frequency7times/week could obviously be featured with a low risk of suffering from obesity . Nevertheless, people who had a breakfast frequency4∼6times/week would not have a lower risk of getting obesity than those with a frequency≤3times/week .According to Figure 7times/week would have a lower risk of inducing abdominal obesity than those with a frequency≤3times/week .It should be noted that people who had the breakfast frequency3.1.3≤3times/week, those with a frequency>3 times/week would be featured with the decreasing risk of MetS  and LHDL-c  accordingly.As shown in Figure 7times/week would not have a lower risk than those with a frequency≤3times/week . Similarly, people with a breakfast frequency>3 times/week could not reduce the risk of getting HC than those with a frequency≤3times/week .However, for HC, people having a breakfast frequency3.1.4≤3times/week, those with a frequency>3 times/week would significantly reduce the risk of CVD  and CVM , respectively. Similarly, people with a breakfast frequency7times/week would have an obvious lower risk than those with a frequency≤3times/week for CVD  and CVM .Figure 3times/week≤, those with a frequency>3 times/week would not have a significant reducing risk for CHD .However, compared with people who had a breakfast frequency3.1.5≤3times/week, those with a frequency>3 times/week would have a lower risk of developing hypertension  and strokes , respectively. Similarly, people with a breakfast frequency of 7times/week would see an obvious lower risk than those with a frequency of ≤3times/week for hypertension  and strokes .As shown in Figure ≤3times/week, those with a frequency4∼6times/week could not have a significantly reducing risk of inducing hypertension  and strokes .However, compared with people who had a breakfast frequencyP = .373). According to Figure S2 , the sensitivity analysis showed that the pooled results changed slightly after each study was removed one by one.For T2DM, Figure S1  revealed the funnel plot of the comparison between higher breakfast frequency and lower breakfast frequency. Besides, Begg's test suggested that no significant publication bias was observed (P = .929). As shown in Figure S4 , from sensitivity analysis, it could be found that after each study was eliminated in sequence, the summary results changed slightly.For strokes, the funnel plot of the comparison between higher breakfast frequency and lower breakfast frequency was displayed in Figure S3 . Besides, Begg's test revealed no obvious evidence of a publication bias  could significantly reduce the occurrence of cardiovascular and metabolic diseases, as well as such specific-diseases as T2DM, obesity, hypertension, strokes, hypercholesterolemia, MetS and abdominal obesity. Moreover, regular breakfast habits (7 times/week) yielded the maximum potential cardio-metabolic benefits, and even skipping breakfast once per week might reduce the benefits for T2DM, obesity, hypertension and strokes to a great extent. However, there was no significant correlation between regular breakfast habits and the occurrence of hypercholesterolemia. Besides, when stratified by gender, no significant correlation can be seen between regular breakfast habits and the risk for T2DM in females.–42 have revealed breakfast skipping was closely related to the occurrence of T2DM or obesity. However, most of these studies were cross-sectional, which might weaken the evidence value when being compared with cohort studies. Moreover, few studies have focused on the relationship between breakfast and other specific cardio-metabolic diseases. Diet patterns and physical activity go hand in hand with human health. Recently, Kwok et al have comprehensively investigated the relationship between dietary components and cardiovascular mortality, and then they found many dietary components appear to be beneficial to cardiovascular disease and mortality, including fish, grains, vegetables and nuts, but tinned fruit and processed meat seem to be harmful. Musse et al pointed out that within 30 days after hospital discharge, the association between skipping breakfast concomitant and late-night dinner increased the likelihood of death, reinfarction, and postinfarction angina four to five times. Simultaneously, Cheng et al systematically analyzed the relationship between physical activity and cardiovascular mortality, and it was shown that leisure-time physical activity shows a linearly negative correlation with the risk of cardiovascular mortality. In a sense, the current meta-analyses have enriched previous studies.Breakfast is taken as the most important meal of a day, and irregular breakfast habits were associated with the occurrence of various cardiovascular and metabolic diseases. Several meta-analyses,47 Besides, the effects of breakfast frequency on cardio-metabolic diseases might be explained by the following potential mechanisms. First, irregular breakfast is deemed to be a sign of unhealthy eating patterns and lifestyles, which might be a long-term behavior from childhood to adulthood.,49 Recently, several studies–52 have indicated that irregular breakfast can significantly decrease satiety, thus leading people to eat more at lunch, which will further increase the production of hunger-related hormones that are associated with higher glucose responses and obesity. Secondly, breakfast eating behavior may have a mediating effect on subsequent metabolic outcomes. Studies conducted by Wennberg et al and Nas et al,54 have shown that irregular breakfast could result in inflexibility in the metabolic system, thus causing an increase in postprandial hyperglycemia and fat oxidation, while the release rhythm of insulin in the body would not be altered, thus resulting in a low inflammatory state and the impaired blood glucose regulation system. Besides, the study by Myers C et al. showed that the consumption of nutritionally matched fruit smoothie at breakfast did not affect acute dietary intake, yet the effects of consuming fruit smoothie instead of cereal for breakfast on body weight and health biomarkers to be further clarified. Similarly, the study by Rosi A et al showed that food patterns at lunch did not alter after consuming different categories of breakfast, and further work is needed on the effect on the amount of food consumed at lunch.In the long process of human evolution, regular eating not only keeps the body at a stable rhythm, but also gradually forms a specific circadian rhythm system and the internal clock, whereas it has been reported that irregular breakfast is a key factor affecting the biological clock. suggested that irregular breakfast habits might have an impact on lipid levels, increase atherosclerotic LDL levels, and further cause atherosclerosis that leaded to cardiovascular disease, which was similar to our conclusions. However, no relationship was found between breakfast and hypercholesterolemia in our meta-analysis, which might be related to the current high-energy dietary structure similar to the results of a recent national survey by Jung et al suggesting that lower breakfast frequency was related with metabolic syndrome in men, yet there is no significant association in women. Recently, Yao et al conducted a meta-analysis of the effect of fatty acid composition in breakfast on postprandial lipids, and the results showed that triglyceride after breakfast was not significantly altered regardless of saturated or unsaturated fatty acid composition. However, when a subgroup analysis was performed with 8 h as the cut-off, it was shown that triglyceride levels were decreased until 8 h after the ones having breakfast with saturated acid composition, while triglyceride levels were increased after 8 h after after those having breakfast with unsaturated acid composition. In addition, there may be a cumulative temporal relationship between irregular breakfast behavior and subsequent disease onset, and more research is needed to further explain these complex mechanisms. Fourthly, skipping breakfast can also affect the regulation of hypothalamic-pituitary-adrenal axis on blood pressure, further causing hypertension in the morning.,61Thirdly, the study conducted by Uzhova et alBased on the current meta-analysis, this paper has the following advantages. First, this is the first systematic analysis on specific metabolic diseases, and all related specific diseases were reported in the original study. Secondly, four electronic databases were retrieved, and previous meta-analyses were reviewed to ensure the most comprehensive inclusion of the studies. In addition, all studies mentioned here were cohort ones, which guaranteed the evidence value. Thirdly, the included studies were featured with large sample size and high quality.Inevitably, the present meta-analysis possesses the following limitations. First, although most studies have adjusted the maximum mixed variable, the influence of residual confounding factors could not be excluded. Secondly, due to the limited number of current studies, sensitivity and subgroup analysis could not be conducted for explaining the high heterogeneity among several studies. Thirdly, in most of the studies, questionnaires were adopted to record the frequency of breakfast. In this case, the influence of subjective factors on the results of the studies could not be ruled out. Finally, as the included studies were mainly limited to Asia, Europe and the Americas, the impact on other regions remains unknown.5It can be concluded that daily eating habits would bring the greatest cardio-metabolic benefits, reducing the risk of T2DM, obesity, hypertension, etc, yet not be significantly related to hypercholesterolemia.ZH Li, LX, and RD have contributed equally to this work.Zhi-Hui Li had full access to all of the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis.Acquisition, analysis, or interpretation of data: zhi-hui, li-jie and Wang.Administrative, technical, or material support: zhi-hui.Concept and design: zhi-hui, xu, and Wang.Conceptualization: Zhi-hui Li, lei Xu.Critical revision of the manuscript for important intellectual content: All authors.Data curation: Zhi-hui Li, li-jie Li.Drafting of the manuscript: All authors.Formal analysis: Zhi-hui Li, lei Xu, rao dai, li-jie Li, haojie wang.Funding acquisition: rao dai.Investigation: lei Xu, li-jie Li.Methodology: Zhi-hui Li, lei Xu, haojie wang.Obtained funding: None.Project administration: lei Xu.Resources: rao dai.Statistical analysis: Li, liu.Supervision: rao dai, zhi-hui.Writing – original draft: Zhi-hui Li.Writing – review & editing: Zhi-hui Li.