KCNJ11) gene is one of the essential genes for type 2 diabetes (T2D) predisposition. However, the relevance of this gene to T2D development is not consistent among diverse populations. In the current study, we aim to capture the possible association of common KCNJ11 variants across Iranian adults, followed by a meta-analysis. We found that the tested variants of KCNJ11 have not contributed to T2D incidence in Iranian adults, consistent with similar insulin secretion levels among individuals with different genotypes. The integration of our results with 72 eligible published case–control studies  as a meta-analysis demonstrated rs5219 and rs5215 are significantly associated with the increased T2D susceptibility under different genetic models. Nevertheless, the stratified analysis according to ethnicity showed rs5219 is involved in the T2D risk among disparate populations, including American, East Asian, European, and Greater Middle Eastern, but not South Asian. Additionally, the meta-regression analysis demonstrated that the sample size of both case and control groups was significantly associated with the magnitude of pooled genetic effect size. The present study can expand our knowledge about the KCNJ11 common variant's contributions to T2D incidence, which is valuable for designing SNP-based panels for potential clinical applications in precision medicine. It also highlights the importance of similar sample sizes for avoiding high heterogeneity and conducting a more precise meta-analysis.Due to the central role in insulin secretion, the potassium inwardly-rectifying channel subfamily J member 11 ( In recent years, urbanization and a sedentary lifestyle have been significantly increasing the T2D incidence. It is estimated the number of affected individuals with this metabolic disorder may enhance up to about 642 million by 20402.Type 2 diabetes (T2D), the most common form of diabetes, is one of the leading life-threatening diseases worldwide, with 4.2 million deaths globally, as reported in 2019 by International Diabetes Federation (IDF) consortium4. The genetic architecture of the disease is driven by multiple causal genetic variants with small to modest effect sizes, which influence either sufficient insulin secretion by pancreatic beta cells or proper insulin response5. Genome-wide association studies have identified many genetic variants associated with T2D and fasting glucose levels6. In recent years, several studies revealed the association of different genetic variants with the risk of T2D development among Iranians8. Sadeghi et al. (2021) deciphered the positive association of rs28514894 and rs2303044 belonging to the NR1H2 gene with the risk of T2D development. They also showed a significant difference in the blood urea nitrogen levels among diabetic carriers of these polymorphisms9. It has been reported that polymorphisms at the 3’UTR of SLC30A8 (rs2466293 and rs2466294) probably increased the Iranian susceptibility to T2D by affecting the binding site of some miRNAs and reducing the stability of SLC30A8 mRNA transcripts10. In addition to the coding genes, genetic variants on the non-coding genes contribute to type 2 diabetes development, pointing to the crucial role of gene expression regulation in the disease incidence. According to Jahantigh et al., some polymorphisms at the promoter region of the miR-143/145 cluster could significantly influence the T2D development in the Iranian population. The involvement of target genes of these miRNAs in the glucose and lipid metabolism pathways may explain their contribution to the T2D predisposition11. HOTAIR, one of the well-documented long non-coding RNAs, has a prominent role in glucose metabolism regulation. Sargazi et al. figured out that several SNPs belonging to this gene can affect the risk of type 2 development through modulating various biological pathways12. Among the numerous T2D-related genes, the adenosine triphosphate-sensitive potassium channel (KATP) plays a major role in regulating glucose-stimulated insulin secretion by beta cells through coupling cell membrane potential with cell metabolism. The KATP is a homo-tetramer of potassium inward channel (Kir6.2) and regulatory sulfonylurea receptor SUR1 subunits (ABCC8). The Kir6.2 subunit is coded by potassium inwardly-rectifying channel subfamily J member 11 (KCNJ11) gene with high expression in the pancreas. Mutation in both genes, KCNJ11 and ABCC8, can result in neonatal diabetes and congenital hyperinsulinemia in humans5. In 2002, Schwanstecher et al. reported that a non-synonymous polymorphism in KCNJ11 (rs5219) that substitutes glutamate for lysine at position 23 (E23K). It could change protein function via inducing pancreatic beta-cell over-activity, leading to defective insulin secretion15. A recent simulation study by computational approaches demonstrated the normal coupling between the open and close states of the KATP channel, resulting in normal insulin secretion, unlike the mutant Kir6.216. Hence, this polymorphism plays a key role in developing T2D as also represented by various association studies in different populations10. However, the effect of rs5219 on T2D susceptibility is not consistent among diverse ethnicities. For instance, the lack of association of this polymorphism with T2D was reported in Iranian19, Emirati20, Moroccan21, and Asian Indian13 populations. rs5210 and rs5215 are other KCNJ11 well-known polymorphisms. The rs5210 and rs5215 are located at the 3’ untranslated region (UTR) and the coding region of the KCNJ11 gene, respectively, which the later polymorphism alters the amino acid of valine to isoleucine at residue 250 of the corresponding protein13. Although researchers have less surveyed these polymorphisms of KCNJ11, their inconsistent contribution to T2D development was also reported across populations. In 2007, Koo et al. claimed that both rs5210 and rs5215 were significantly associated with type 2 diabetes incidence in the Korean population22, but they have not been confirmed in the Mexican population23. Due to the potentially broad clinical applications of single genetic polymorphisms in precision medicine for early diagnosis and treatment, it is critical to recognize their explicit relevance to disease incidence. However, different sample sizes and ethnic groups used by various researchers limited our ability to realize the clear effect of the genetic polymorphisms on complex disease development, such as type 2 diabetes. A meta-analysis of the genetic association studies is an efficient approach to gain a better understanding of genetic variants' impact on the disease incidence. Despite the importance of KCNJ11 polymorphisms, the lack of a comprehensive genetic association study among Iranians, a population with an unknown genetic makeup, motivated us to discover the possible involvement of the KCNJ11 polymorphisms in type 2 diabetes development across Iranians. The current study aims to (1) survey the association of common KCNJ11 polymorphisms  with T2D risk in a large sample size of the Iranian population; (2) examine pancreatic-cell function in individuals with different rs5219 genotypes; and (3) integrate our findings with all previous case–control studies and present the most comprehensive and up-to-date meta-analysis to precisely uncover the impact of common KCNJ11 polymorphisms on type 2 diabetes predisposition; (4) identify the probable sources of heterogeneity among studies used for the meta-analysis.Type 2 diabetes is raised by genetic, non-genetic  factors, and interaction between themThe nested case–control study was performed on 1326 diabetic and 1594 non-diabetic unrelated adults who participated in the ongoing TCGS cohort project; the average age of the case and control groups were 50.41 and 37.5 years, respectively. Demographic and biochemical characteristics of participants at the baseline are available in Supplementary Table KCNJ11 SNPs, rs5210, rs5215, and rs5219 complied with the Hardy–Weinberg equilibrium . According to our association results under six genetic models , none of the KCNJ11 SNPs could significantly influence T2D development in Iranian adults. The same finding was obtained in the model adjusted for age, sex, and BMI as covariates  = 0.37)  under all four genetic models. Therefore, the random-effects model was applied for the analysis, representing a significant positive association of rs5219 with type 2 diabetes development under all genetic models except for the over-dominant model , we applied the random-effects model for the meta-analysis. According to our results, rs5210 appears not to be associated with T2D risk under any genetic models , the meta-analysis was performed using the random-effects model. As Table For the rs5215 polymorphism, we recognized ten eligible studies, one of which deviated from the Hardy–Weinberg equilibrium. Hence, the meta-analysis was conducted on the nine included studies that covered 4487 cases and 4943 controls, as presented in Table The sensitivity analysis was conducted to assess each study's weight on the combined OR, via sequential elimination of each study under four genetic models . The meta-analysis results of all three polymorphisms remained constant  of rs5219 from 0.02 in African to 0.4 in South Asia27. Our findings can also be attributed to the distinct linkage disequilibrium (LD) structure among rs5219 and adjacent probable causal variants in different ethnic groups. Both RAF and LD patterns have become different across ethnicities during evolutionary processes28. Additionally, differences in non-genetic factors, including the disease age of onset and lifestyle, can lead to the discrepancy. Between-study heterogeneity is the common issue in the meta-analysis, which can refer to differences in age, BMI, and genetic background of participants in each study, over-estimation of genetic effect, differences in LD structure among the population, and sample size used for each study28. For instance, Peng et al. suggested that differences in mean BMI in the control group is one of the heterogeneity sources of association meta-analysis of rs9939609 FTO with obesity risk28. Despite detecting significant heterogeneity for all three polymorphisms in the present study, we could perform the meta-regression analysis for rs5219 but not for rs510 and rs5215 due to insufficient information. Among the tested variables, the sample size of both case and control groups had significantly influenced the combined effect size under all tested genetic models. We indicated that studies more than 1000 cases or 1000 controls have been generated a lower pooled odds ratio as compared to studies with less than 1000 subjects. It is highlighted the importance of adequate case and control sample size in genetic association studies to achieve the accurate results, especially when the expected effect size is low. Similarly, the previous studies recommended the higher samples size for conducting the genetic association analysis with rare variants or common variants with low effect size30. In a prior meta-analysis of rs521933, the authors hypothesized that heterogeneity might have resulted from the younger mean age of the control group as T2D is a disease with a relatively late-onset. But our meta-regression results demonstrated that the mean age and the mean BMI of case and control groups were not associated with the pooled effect size and could not be the source of heterogeneity. rs5210 can positively affect T2D susceptibility, which is in line with a previous meta-analysis conducted on only two studies34. Although we performed an extensive meta-analysis association of this polymorphism with nine eligible studies for the first time, all of them belonged to East and South Asia, which may introduce a potential bias to the result. Consequently, further studies from diverse ethnicities are required to generalize the findings.Based on our meta-analysis, the role of rs5219 is varied among different ethnicities. It can raise the risk of T2D development in all tested ethnicities, except for South Asia, where this polymorphism is not apparently involved in T2D incidence. Based on FPRP value, the detected genetic association can be categorized as strong (FPRP < 0.05), moderate (0.05 ≤ FPRP ≤ 0.2), or weak (FPRP > 0.2)KCNJ11 rs5219 and rs5215, but not rs5210, in designing an efficient SNP-based panel for potential clinical usage in the near future. Consequently, the individuals with the highest genetic risk can be detected early and subjected to lifestyle improvement and other interventions to prevent or delay T2D. Eventually, it will reduce the type 2 diabetes rate and its complications, gradually increase life quality and decrease the economic cost of the disease.From the potential clinical usage perspective, molecular biomarkers like SNPs have broad clinical applications in disease screening, early diagnosis, and treatment efficiency, which are the primary goals of precision medicine. Hence, genetic association studies are critical for recognizing risk variants associated with complex diseases, including type 2 diabetes. The identified risk variants can be utilized to detect genetically susceptible individuals at a very young age before appearing T2D symptoms. However, on the one hand, the associated genetic variants display diverse impacts among different ethnic groups. On the other hand, single genetic association studies are usually not robust enough to detect effective variants due to the small sample size. But association meta-analysis studies can overcome this issue by combining all eligible studies, generating more precise results. Alongside other risk SNPs, the present meta-analysis results could guide us to consider the 33, the present study comprised up-to-date and more literature reviews with the homogenous study design (case–control or nested case–control). Secondly, unlike the previous meta-analyses, we classified different ethnicities according to genetic information, not geographical area, for performing subgroup analysis based on ethnicity. Thirdly, we made an effort to find the potential source of between-study heterogeneity by doing an extensive meta-regression analysis with all genetic models and obtaining the consistent results. However, type 2 diabetes has a complicated genetic architecture that numerous genetic variants shape it, while we considered only essential variants of the KCNJ11 gene in the current study. Additionally, due to the lack of accessibility to the appropriate covariates, such as age, gender, and BMI, in the various studies, we could not adjust the pooled genetic effect size for those variables. Hence, the meta-analysis results should be interpreted with caution. Publication bias is a usual event in any meta-analysis study. Here, we found it only for rs5219 and adjusted it using the trim and fill method, however, the lack of publication bias cannot be a certainty sign of meta-analysis since the studies with significant association results usually have more chance of being published than studies with insignificant association results. Therefore, we should note that even if the publication bias is not detected in the meta-analysis, it cannot be completely ruled out.There are some strengths and weaknesses in our study that merit discussion. Firstly, compared with previously published meta-analysesKCNJ11 polymorphisms with type 2 diabetes incidence, which can expand our knowledge about the role of this gene in T2D incidence. However, more investigations are required to dissect the impact of KCNJ11 polymorphisms on insulin secretion levels. Furthermore, we recommend that researchers consider collecting studies with similar sample sizes to reduce the between-study heterogeneity in the meta-analysis studies.To conclude, the current study can be considered the most comprehensive association meta-analysis of 36. The ethics committee approved all procedures performed in this study on human subject research at Research Institute for Endocrine Sciences, Shahid Beheshti University of Medical Sciences (code of “IR.SBMU.ENDOCRINE.REC.1395.366″), which were following the 1964 Helsinki Declaration and its later amendments or comparable ethical standards. At each visit, written consent was obtained from each subject and referred to trained physicians and laboratories for clinical examinations and blood sampling. Considering the attendance of individuals from the main Iranian ethnic groups living in Iran, including Persians, Azeris, Kurds, Lors, Arabs, Baluchs, Turkmans, Mazanis, and Gilaks at the TCGS cohort, this cohort could represent the Iranian population. In summary, weight and height were recorded using the standard protocols. Body mass index (BMI) was calculated as weight in kilograms divided by height in square meters. Fasting plasma glucose (FPG), 2-h plasma glucose, and fasting insulin concentration was measured using the standard protocols37. Type 2 diabetes was diagnosed based on the fasting plasma glucose ≥ 126 mg/dL or 2-h plasma glucose ≥ 200 mg/dL during an oral glucose tolerance test or usage of anti-diabetic drugs. The first occurrence of type 2 diabetes in individuals during the follow-up period was considered as diabetic condition38.In the current study, Iranian subjects were opted from an ongoing TCGS project that is a branch of a Tehran Lipid and Glucose Study (TLGS) longitudinal project, in which participants have been genotyped and followed up for cardio-metabolic risk factors every three years since 1999 (1999–2017)39. Three common KCNJ11 polymorphisms  were chosen for this study. Before running the analysis and for making sure that our sample size is adequate, we estimated the statistical power using GAS power calculator40. For power calculation, we considered the given sample size, disease prevalence of 11.4% in Iran41, the minor allele frequency of 0.34, the average odds ratio of 1.2 for three SNPs of interest obtained from previous studies, and the significance level of 0.01.Genomic DNA of TCGS participants was extracted from the white blood cells via an alkaline boiling method, and its quantity and quality were evaluated by electrophoresis and spectrophotometry. DNA samples of 13,693 TCGS participants were genotyped using Illumina Human OmniExpress-24-v1-0 bead chip at the deCODE genetics company (Iceland) according to manufacturer's specifications After relevant quality control of genotyped SNPs and individuals using the PLINK program (V. 1.9), we investigated their association with type 2 diabetes incidence through the logistic regression analysis under six genetic models  adjusted for the BMI, sex, and age. The false discovery rate (FDR) at the 5% significance level was considered for correcting multiple testing. For this study, we considered 2991 (1326 diabetic and 1593 non-diabetic) unrelated adults (≥ 20 years old). Participants less than 20 years old, related adults, and pre-diabetic adults were excluded from the analysis.42. We used an analysis of variance (ANOVA) test to compare the β-cell function mean among non-diabetic participants with various rs5219 genotypes possessing zero, one, and two risk alleles. p-value < 0.05 was considered significant.The β-cell function was estimated using the Homeostatic Model Assessment of β-cell function (HOMA-β) formula: HOMA-β = 20 × fasting insulin (μU/mL)/fasting glucose (mmol/L) − 3.5)43. A PRISMA checklist is provided as supplementary file The current meta-analysis study was done based on the criteria of the Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA)KCNJ11 rs5210, res4215, and rs5219 with type 2 diabetes incidence were selected from PubMed, Scopus, and Google Scholar, and ProQuest based on publication date from 1990 to Jan. 2022. We used the following key words: “KCNJ11”, “Kir 2.6 channel”, “potassium inwardly-rectifying channel, subfamily J, member 11 protein, human”, “Potassium Voltage-Gated Channel Subfamily J Member 11”, and “Inwardly-Rectifying Potassium Channel Subfamily J Member 11”, T2D”, “type 2 diabetes”, “Non-Insulin-Dependent Diabetes Mellitus”, “Ketosis-Resistant Diabetes Mellitus”, “polymorphism”, “SNP”, “rs5210”, “rs5215”, and “rs5219”. The search was restricted to human studies published in the English language.Manuscripts that investigated the genetic association between In the current meta-analysis, we included the eligible studies with the following criteria: (1) published in peer-reviewed journals with available full text. (2) Designed as the case–control, nested case–control, and genome-wide association study. (3) Provided distributions of genotypes or alleles in case and control groups for calculating the odds ratio (OR) with the corresponding 95% confidence intervals (CI) and p-value. (4) Diagnosed T2D based on American Diabetes Association (ADA). In contrast, we excluded (1) case-only studies, (2) review, meta-analysis, editorial, and conference abstracts, (3) family-based association studies, and (4) studies without sufficient genotype frequency data that OR could not be calculated.44. The score on this scale ranged between zero and nine; studies with the at least score of seven were presumed to be a high-quality study.Two reviewers independently evaluated studies for relevance in a standardized manner. In case of disagreement, it was resolved by discussion and adjudication with the third reviewer. After removing duplicated records, the following information was extracted: first author’s name and publication year, participants’ ethnicity, the distribution of polymorphisms of interest in cases and controls, genotyping method, mean age, and BMI of cases and controls, and sample size of cases and controls. Newcastle–Ottawa scale (NOS) was applied to assess the methodological quality of eligible studies as previously described2 statistics. The random-effect model was applied if there was a significant heterogeneity . Otherwise, the fixed-effect model was used. Furthermore, we computed the false positive report probability (FPRP) to detect the potential false-positive results among the significant association meta-analysis findings. As proposed by Wacholder et al., the prior probability of 0.05 and the FPRP cutoff value of 0.2 were considered for this analysis45. To survey the potential source of heterogeneity, we conducted meta-regression analysis using the ethnicity, mean age of control group, mean age of case group, mean BMI of case group, mean BMI of the control group, and sample size of case and control groups (N ≥ 1000 or < 1000). BMI and age were considered quantitative traits while sample size was considered a binary trait. Studies with fewer than 1000 samples and those with more than or equal to 1000 samples were categorized as small and large sample sizes, respectively, studies with small sample size were considered as reference for doing the regression analysis. Ethnic groups were specified as American, African, Central Asian, European, Eastern Asian, Greater Middle Eastern, Southern Asian, and others based on Morales et al.'s genetic ancestry classifications46. Additionally, we appraised the robustness of our meta-analysis results by leave-one-out method in the sensitivity analysis via consecutive excluding only one study each time to assess the possible impact of a single study on the final result (pooled OR). Besides, we examined the potential publication bias using Egger’s test and visualized it by drawing a funnel plot. If the publication bias was detected, a Duval and Tweedie trim-and-fill method was applied to compute the adjusted effect size47. All analysis was performed using STATA (version 11). We used the MetaGenyo tool for presenting the high-resolution figures48.For the present meta-analysis, the Hardy–Weinberg Equilibrium (HWE) was assessed in the control group of each study. The studies that have not obeyed the HWE rule  were removed from further analysis. The effect of polymorphisms of interest  on type 2 diabetes development was evaluated under four genetic models, allele contrast, recessive, dominant, and over-dominant. Results were presented as the pooled odds ratio (OR) with 95% confidence interval (CI), and the related p-value. To detect the possible heterogeneity among the studies, we utilized the Cochrane Q-test and ISupplementary Information.


Although the association between MDM2 rs2279744 and TP53 rs1042522 polymorphisms and cervical cancer has been reported, the results of its correlation were contradictory. Thus, we conducted a meta-analysis to precisely verify the relationships between MDM2 rs2279744 and TP53 rs1042522 polymorphisms and cervical cancer.We thoroughly searched the PubMed, Web of Science, Embase, and Scopus databases for all potential articles from inception to June 2022 and used R Version 4.1.2 and STATA software 12.0 for the meta-analysis. The odds ratios (ORs), 95% confidence intervals (CIs) and 95% prediction intervals (PIs) were calculated to evaluate the associations. Subgroup analyses stratified by ethnicity, source of control, quality score and adjustment were further conducted to assess the relationship between MDM2 rs2279744 and TP53 rs1042522 polymorphisms and cervical cancer.A total of 30 case-control studies involving 5025 cases and 6680 controls were included. All the included studies were population-based or hospital-based studies. The overall analysis showed that MDM2 rs2279744 polymorphism was closely related to an increased risk of cervical cancer in the recessive model  and homozygote model . A significant correlation between TP53 rs1042522 polymorphism and cervical cancer was observed in two models .This meta-analysis revealed that MDM2 SNP309T>G and TP53 rs1042522 C>G polymorphisms were associated with the increased risk of cervical cancer. Cervical cancer is the second most common gynecological cancer worldwide, with an approximately 570,000 new cases and 311,000 deaths occurring for 2018 worldwide , 5. NeveThe TP53 gene, encoding tumor protein 53 (p53), has been well documented for its involvement in DNA damage repair and control of cell cycle . In addiTherefore, it is worth to explore the association between MDM2 rs2279744 and TP53 rs1042522 polymorphisms and susceptibility to cervical cancer. However, the reported results are contradictory and ambiguous –21. AddiOur study was analyzed and reported according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) . We thorStudies should meet the following inclusion criteria: (1) study investigating the association of MDM2 rs2279744 or TP53 rs1042522 polymorphism with cervical cancer; (2) meet case-control design; (3) containing available allele or genotype information to estimate an odds ratio (OR) and 95% confidence interval (CI); and (4) results include human subjects. Accordingly, the exclusion criteria were as follows: (1) studies with duplicate data; (2) studies in which subjects were not human; (3) Genome-wide association studies (GWASs) or targeted gene association studies; and (4) conference abstracts, reviews, case reports, and meta-analyses.Two independent researchers screened the literature and extracted all needed information from the included studies. All disagreements were resolved by discussion with a third investigator. The following information was extracted from each article using the predesigned data-collection form: name of first author, year of publication, country, ethnicity, sample size, genotyping method, source of control, genotype frequency of investigated SNPs, effect allele, P value for Hardy-Weinberg equilibrium in the control and adjustments. Case-control studies were assessed using the Newcastle-Ottawa scale (NOS)  consisti2) and the Hartung-Knapp adjustment to calculate ORs with 95% CIs and 95% prediction intervals (PIs). The degree of heterogeneity was assessed with Q, I2 and the PI  and STATA Version 12.0 . All the P-values are two-sided.The association between MDM2 rs2279744 or TP53 rs1042522 polymorphism and cervical cancer were estimated by the ORs and their 95% CIs in the five genetic models: dominant model , recessive model , heterozygote model , homozygote model  and allele model . Because the number of included studies was small and sampled population varied between the studies, random-effects meta-analyses were conducted with the DerSimonian-Laird estimate of heterogeneity  was performed by the TSA v0.9.5.10 Beta software to validate whether the results of the meta-analysis present a definite conclusion . Our stuDepending on the search strategy, 30 studies with 5025 cases and 6680 controls –61 were 2 = 0.0%, Tau2 = 0)  . In subgroup analysis conducted by ethnicity or adjustment, we obtained the same significant results in Asians or studies with adjusted results in recessive model . No significant results were observed in subgroup analysis of dominant, heterozygote, homozygote or allele model. 2 = 82.3%, Tau2 = 0.2481; GG vs CC: I2 = 79.2%, Tau2 = 0.3518)  and in population-based studies in one model .In a subgroup analysis conducted by quality score, we obtained significant results in high-quality studies that TP53 rs1042522 polymorphism increased the risk of cervical cancer in four models . No significant results in low-quality studies were observed in all models.In a subgroup analysis conducted by adjustment, we found that TP53 rs1042522 polymorphism was associated with an increased susceptibility to cervical cancer in covariates-adjusted studies in two models . We also obtained a significant result that TP53 rs1042522 polymorphism increased the risk of cervical cancer in studies without definite adjustments in one model .To assess whether the pooled results were affected by a single study, we conducted a sensitivity analysis by computing the pooled ORs and the corresponding 95% CIs after individual studies were omitted. The results were shown in After TSA estimation, the results on MDM2 rs2279744 polymorphism showed that the cumulative sample size has exceeded the target sample size Figure 5MDM2 rs2279744 polymorphism was in the MDM2 intronic promoter and shown to cause overexpression of MDM2 RNA and protein . A previIn the current meta-analysis, the MDM2 rs2279744 polymorphism showed a significant correlation with cervical cancer in the recessive model (GG vs GT + TT) and homozygote model (GG vs TT). Women with the GG genotype showed a tendency of higher rate of cervical cancer than those with the GT/TT or TT genotype. MDM2 is the critical negative regulator of TP53 and the dysfunction of these genes might be related to the increase of the cumulative rate of genetic errors, thus promoting the progress of the disease . It has Additionally, our finding suggested that cervical cancer was significantly associated with TP53 rs1042522 polymorphism in the dominant model (CC + CG vs GG) and homozygote model (GG vs CC). The pooled analysis of C allele as the effect allele indicated that the risk of cervical cancer in women with CC/CG genotype was 1.759 times higher than that with GG genotype . however, women carrying GG genotype was 2.452 times more likely to develop cervical cancer than those with the CC genotype in pooled analysis of G allele as the effect allele . Therefore, our study failed to determine that whether C or G allele was the risk allele. The increased risk of cervical cancer among women with GG genotype in this meta-analysis can be attributed to its susceptibility to degradation by ubiquitin-dependent proteolysis of HPV early oncoprotein . Based oWhen stratified by ethnicity, the subgroup analysis suggested a significant association between TP53 rs1042522 polymorphism and cervical cancer in Asians and Europeans, and no significant in other population, which means that TP53 rs1042522 polymorphism may increase the risk of cervical cancer for Asian and European people not others. This difference may be caused by cultural or lifestyle factors and dietary differences between different populations . In addiFurthermore, the prediction intervals were calculated to evaluate the heterogeneity for the random effects in present meta-analysis. The large 95% prediction intervals in all genetic models suggested a considerably high variation between studies, indicating that the associations of MDM2 rs2279744 and TP53 rs1042522 polymorphisms with cervical cancer risk could be found to be significantly changed in future studies. We speculated that different ethnicity of study population may lead to high between-study heterogeneity. The trim-and-fill analysis showed that there existed publication bias in seven genetic models. And the existence of publication bias changed the results in three models . Correction for potential publication bias did not alter the association in four models . Thus, more relevant studies are needed to verify the significant relationship of MDM2 rs2279744 polymorphism (GG vs TT) or TP53 rs1042522 polymorphism (GG vs CC) and cervical cancer risk.The present meta-analysis still has several limitations. First, only two studies were included in the recessive models (MDM2: GG vs GT + TT and TP53: GG vs GC + CC), although the pooled result was statistically significant in the recessive model of MDM2 rs2279744 polymorphism, the number of included studies was not convincing enough. Second, Given the small number of studies included, East, South and West Asian were unified into Asians for subgroup analysis. Thus, there are strong differences in terms of genetic background among these populations. Furthermore, the study population in certain countries were from different races, making it difficult to identify the ethnicity of the subjects. Therefore, more included studies are needed to further explored the subgroup analysis conducted by ethnicity. Third, Due to the small number of studies and different ethnicity of study population, there are significant chances of type I error. Although random-effects meta-analyses were conducted for all quantitative synthesis, Numerous studies with large sample need to be included to reduce the possibility of type 1 error. Finally, five studies did not report P values for the Hardy-Weinberg equilibrium in the control group, which may affect the pooled results.In summary, this meta-analysis suggested that MDM2 SNP309T>G and TP53 rs1042522 C>G polymorphisms were associated with the increased risk of cervical cancer. Further studies are needed to explore the effect of gene-gene interaction between MDM2 SNP309T>G and TP53 rs1042522 C>G polymorphisms on cervical cancer risk.The original contributions presented in the study are included in the article/MY and XZ conceived and designed the study. MY and QZ searched the literatures and extracted the data. MY and XZ carried out the statistical analyses and data interpretation. MY and QZ wrote the manuscript. All authors contributed to the article and approved the submitted version.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.


Risk stratification in Brugada Syndrome (BrS) patients is still challenging due to the heterogeneity of clinical presentation; thus, some additional risk markers are needed. Several studies investigating the association between RVOT conduction delay sign on electrocardiography (ECG) and major arrhythmic events (MAE) in BrS patients showed inconclusive results. This meta-analysis aims to evaluate the association between RVOT conduction delay signs presented by aVR sign and large S wave in lead I, and MAE in BrS patients.th, 2022. We included studies consisting of two main components, including ECG markers of RVOT conduction delay (aVR sign and large S wave in lead I) and MAE related to BrS (syncope/VT/VF/SCD/aborted SCD/appropriate ICD shocks)The literature search was performed using several online databases from the inception to March 16p < 0.001; I2= 52%, Pheterogeneity = 0.02]. Subgroup analysis showed that aVR sign  and large S wave in lead I  were significantly associated with MAE. Summary receiver operating characteristics analysis revealed the aVR sign [AUC: 0.77 (0.73–0.80)] and large S wave in lead I [AUC: 0.69 (0.65–0.73)] were a good predictor of MAE in BrS patients.Meta-analysis of eleven cohort studies with a total of 2,575 participants showed RVOT conduction delay sign was significantly associated with MAE in BrS patients [RR = 1.87 ; RVOT conduction delay sign, presented by aVR sign and large S wave in the lead I, is significantly associated with an increased risk of MAE in BrS patients. Hence, we propose that these parameters may be useful as an additional risk stratification tool to predict MAE in BrS patients.https://www.crd.york.ac.uk/prospero/#recordDetails, identifier: CRD42022321090. Brugada syndrome (BrS) is an inherited channelopathy that predisposes to sudden cardiac death (SCD) due to ventricular tachyarrhythmias (VTA) . The preFurthermore, in some cases, the decision of ICD implantation may be challenging, especially in BrS type I pattern on ECG with unexplained syncope  and without prior cardiac arrest or documented VTA. Hence, several studies investigated the association between several ECG markers and major arrhythmic events (MAE) in BrS patients to predict the likelihood of SCD and VTA events. Several meta-analyses revealed that first-degree atrioventricular block, fragmented QRS, wide QRS complex, early repolarization, especially in the inferolateral region, atrial fibrillation, Tpeak-Tend dispersion, Tpeak-Tend interval, and (Tpeak-Tend)/QTc ratio were significantly associated with a higher risk of MAE in BrS patients –14.Studies by Coronel et al.  and PanoThis meta-analysis adhered to the Preferred Reporting Items for Systematic Reviews and Meta-analysis (PRISMA) guideline.th, 2022. Two independent authors performed title or abstract screening and eligibility assessment of the articles. Discrepancies were resolved by discussion.We conducted a systematic literature search from several databases, including Pubmed, EBSCO host, Europe PMC, and Proquest. The keyword of “(aVR sign or S wave in the lead I) AND (major arrhythmic events or syncope or ventricular tachyarrhythmia or ventricular tachycardia or ventricular fibrillation or sudden cardiac death or aborted sudden cardiac death or appropriate implantable cardioverter-defibrillator shocks) AND (Brugada syndrome)” was used. The timeframe was from the inception until March 16Studies that met the following criteria were included: prospective or retrospective cohort studies reporting ECG findings of RVOT conduction delay and MAE in BrS patients. BrS is diagnosed in patients with typical type 1 Brugada ECG patterns that occur spontaneously or drug-induced . There wTwo independent authors performed data extraction of the eligible studies using standardized extraction form for the first author, study design, location of the study, inclusion criteria, sample size, age, sex, male gender, RVOT conduction delay sign on ECG and its criteria, mean/median duration of follow up and the outcomes. Any discrepancies were resolved by discussion.Two independent authors performed the risk of bias assessment using the Newcastle-Ottawa Scale (NOS) . Scoringp-value was ≤ 0.05. Inconsistency index (I2) test ranging from 0 to 100% was used to assess heterogeneity among the studies, in which I2 values >50% or Pheterogeneity < 0.05 indicate moderate to high heterogeneity  and STATA software version 16. We calculated the pooled risk ratio (RR) and its 95% confidence interval using the Mantel-Haenzel formula to characterize the association between ECG findings of RVOT conduction delay and MAE in patients with BrS. The significance was obtained if the two-tailed ogeneity . If highn = 8), and (2) outcomes of interests were not supplied in categorical data (n = 1). Eventually, at the end of the preliminary search, 11 cohort studies  independent variables were not supplied in categorical data . The meta-analysis of ECG patterns of RVOT conduction delay and MAE in patients with BrS is described in The meta-analysis in 10 cohorts studies with pooled subjects of 2,575 participants showed that RVOT conduction delay sign presented by positive aVR sign and prominent S wave in lead I combined was significantly associated with MAE in patients with BrS ; p < 0.001; I2= 0%, Pheterogeneity = 0.40).Sub-group analysis of 4 cohorts studies –22, 26 wp = 0.01; I2= 60%, Pheterogeneity = 0.01]. Due to moderate heterogeneity, sensitivity analysis was performed by excluding the Calo et al. study resulting in reduced heterogeneity to 8%, and the association remained significant .Sub-group analysis of 9 cohort studies –24 with SROC analysis showed that positive aVR sign on ECG had a great diagnostic performance in predicting MAE in BrS patients . On the other hand, large S wave in lead I on ECG was also a good predictor of MAE in BrS patients . SROC analysis is described in p = 0.097).Begg's funnel plot analysis revealed qualitatively symmetrical funnel plots, suggesting no evidence of publication bias in association between RVOT conduction delay sign and MAE . MoreoveTo the best of our knowledge, this is the first meta-analysis to demonstrate the association between RVOT conduction delay on ECG and MAE in patients with BrS. There were several significant findings in our study. Firstly, the RVOT conduction delay sign on ECG was significantly associated with MAE in BrS patients. Secondly, a positive aVR sign and large S wave in the lead I were significantly associated with higher MAE risk in BrS patients.Up until now, there are two widely accepted hypotheses related to the pathomechanism of BrS, including repolarization and depolarization abnormalities, which all originated from RVOT , 34. PanThese RVOT conduction delay pathomechanism can also be elaborated in the case of widely known positive SCN5A mutation, which can increase the risk of SCD . AnotherFurthermore, Pannone et al.  also evaA vectorcardiography (VCG) study in BrS patients showed that right terminal conduction delay (RECD) was detected in the upper right quadrant and posterior quadrant of the VCG . These VModerate heterogeneity was detected in the analysis of large S wave in the lead I with MAE; thereby, sensitivity analysis by excluding Calo et al. study was performed, resulting in significantly reduced heterogeneity to 8% and without altering the significance. It is due to Calo et al. study, which has a remarkably high-risk ratio (RR) compared to the other studies. The possible explanation was because the 5 per cent of BrS patients with large S wave in the lead I in Calo et al. study appeared to have an MAE on the same day or within days of their diagnosis . It is questionable that MAE develops in such a short time after the initial diagnosis of BrS in the Calo et al. study. Nevertheless, it might be explained that all participants in this study had spontaneous type I Brugada ECG pattern and had most of the inducible VTA by electrophysiological study (EPS) compared to other included studies, which carried a high risk of MAE.A retrospective study conducted by Pannone et al.  showed tThe positive aVR sign and large S wave in the lead I on ECG are described in This meta-analysis has several limitations. Firstly, most of the included studies were retrospective designs, increasing the risk of recall and selection bias. Secondly, several confounding factors, including ECG pattern of spontaneous type I Brugada, prior syncope, family history of SCD, presence of SCN5A gene mutation, and inducible VTA in EPS, were not adjusted in most included studies, possibly leading to an increased risk of bias. Thirdly, high heterogeneity was noted in a sub-group analysis of large S wave in the lead I, which was likely caused by differences in included participants' characteristics in the Calo et al. study. Lastly, due to the lack of studies investigating the impact of ECG parameters indicating RVOT conduction delay sign on MAE in BrS patients according to patients' age and clinical presentation, further prospective cohort studies are still needed to understand this ECG pattern in BrS patients better.In conclusion, this meta-analysis shows that the RVOT conduction delay sign, including the positive aVR sign and large S wave in the lead I, is significantly associated with MAE in patients with BrS. Furthermore, the aVR sign and large S wave in lead I can be used as a potential ECG marker to predict MAE. Further prospective cohort studies are still needed to establish the association between these ECG parameters and MAE in BrS patients in the near future.The original contributions presented in the study are included in the article/MI conceived and designed the study. IP and MB performed study selection, data extraction, and interpreted the data. MI, IP, RP, and MB performed extensive search of relevant topics. IP and RP performed statistical analysis. MP, HG, MA, and AK performed review and extensive editing of the manuscript. All authors contributed significantly to the writing of the manuscript and approved the final manuscript.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.


Hidradenitis suppurativa were associated with comorbidities in various organ systems. Inflammatory dermatological diseases such as pyoderma gangrenosum were reported to be associated with hidradenitis suppurativa. Nevertheless, as for the association between hidradenitis suppurativa and psoriasis, evidences were insufficient. In many studies, the association between psoriasis and hidradenitis suppurativa has been reported. However, some evidence seems to be controversial. The purpose of the systematic review and meta-analysis was to assess whether there was significant association between HS and psoriasis.On June 01, 2022, we appraised 2,795 articles from databases including PubMed, Web of Science and Embase. Search syntaxes were based on ‘hidradenitis suppurativa’ or ‘acne inversa’ with “psoriasis”, “comorbidities” or ‘epidemiology’. Synonyms were determined based on MeSH terms and Emtree. Observational results that evaluated the odds ratio for people with hidradenitis suppurativa who had psoriasis were extracted for qualitative synthesis.After the selection process of the initial 2,795 studies, ten observational studies, including 3 cohort studies, 1 case-control study, and 6 cross-sectional studies, were extracted for critical appraisal. Based on the integration of 7 studies , people with hidradenitis suppurativa had a higher risk of having psoriasis, with a 2.67-fold risk . The association remained in the sensitivity analyses utilizing strict adjustment models. In the analysis that only included studies with a similar study design and adjustments in obesity-related factors, the risk of people with hidradenitis suppurativa having psoriasis was 3.24 . In male patients with HS, the risk of having psoriasis was 4.30-fold higher than male patients without HS . Likewise, in an analysis including 3 cross-sectional studies, the risk of female HS patients having psoriasis was 3.94-fold higher than female HS-free patients .The co-occurrence of hidradenitis suppurativa and psoriasis can greatly increase the burden of the disease. Psoriasis could be one of the critical comorbidities of hidradenitis suppurativa and should be recommended for future screening and follow up. The association between the two diseases should be kept in mind in managing hidradenitis suppurativa patients. More prospective studies are needed to establish the true magnitude of the association between psoriasis and hidradenitis suppurativa. Identifying comorbidities in hidradenitis suppurativa (HS) is critical to give patients recommendations for the screening for comorbidities. Recently, HS has been reported to have a strong association with psoriasis. However, some evidence seemed to be controversial and the current guidelines for comorbidity screening recommendation have insufficient information regarding the HS-psoriasis association. In the current systematic review and meta-analysis, we report that, based on the integration of 7 studies with more than 560,000 participants included, people with HS had a more than 2.6-fold risk of having psoriasis. The significance of the association remained in the sensitivity analyses using a strict adjustment model and different study design. In the analysis that only included studies with similar study design and adjusted for obesity-related factors, the risk of people with HS having psoriasis was 3.24 . The coexistence of HS and psoriasis can greatly increase the burden of the disease that may require aggressive treatment. Further prospective studies are needed to establish the magnitude of the association between psoriasis and HS.Hidradenitis suppurativa (HS) is a chronic, inflammatory skin disorder that affects apocrine units. It is characterized by nodules and cysts that form sinus tracts, which is seen prominently in intertriginous areas . PsoriasSeveral reports documented associations with different autoimmune diseases and lifestyle diseases in both HS and psoriasis. Diseases including metabolic syndrome, diabetes mellitus, inflammatory bowel disease, and spondyloarthropathies, were reported to be associated with both HS and psoriasis , 9. EvenIn the current meta-analysis, the Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA) was utilized to ensure the screening process being precise and objective . Real-woOn June 01, 2022, we appraised 2,795 articles from databases including PubMed, Web of Science and Embase. Search syntaxes were based on ‘hidradenitis suppurativa’ or ‘acne inversa’ with “psoriasis”, “comorbidities” or ‘epidemiology’. Synonyms were determined based on MeSH terms and Emtree. The detailed study protocol on the search strategy and inclusion criteria were provided in the In this study, our aim was to provide integrated real-world evidence based on observational results. The following are the exclusion criteria: (1) studies that had no relationship with HS, (2) studies related to HS but not focusing on HS related comorbidities related to HS, (3) studies focused on genetic roles or pathophysiologic mechanisms in HS, and (4) studies reporting coexistence but not containing proper comparative groups. In the screening process, the study language and ethnicity of the participants were not established as specific exclusion criteria. Conference abstracts were also included in the selection process to address potential publication bias.Study characteristics were reported in In the current study, Review Manager 5.4  was used to perform statistical analyses. The results of the observational studies evaluating HS-psoriasis relationship were extracted and analyzed to present the pooled odds ratio of the association. Given that the clinical heterogeneity could possibly exist, we utilized the random-effects model in all of the qualitative synthesis. The odds ratio (OR) was evaluated based on 95% confidence interval (95% CI). For the pooled studies in each analysis, heterogeneity was determined by applying the value of I . If the The PRISMA flow diagram was reported in In Additionally, based on the current evidence, we have evaluated the HS-psoriasis association based on different genders. For HS patients, both genders presented approximately 4-fold risk of having psoriasis. In male patients with HS, the risk of having psoriasis was 4.30-fold higher 95% CI, 2.37, 7.78)  than those without HS  . In WestIn many diseases, the inflammatory state could play potential roles in the association with autoimmune or endocrinological comorbidities –32. The The present study has limitations that need to be addressed. First, the meta-analysis included a small number of studies. However, the studies included have generally observed the positive association between HS and psoriasis. Second, the included literature was retrospective observational studies that may not determine the true association between HS and psoriasis. Future prospective studies are recommended to establish the association between the two. Furthermore, there were some studies at high risk of bias in outcome assessment due to the application of administrative codes for disease definition. Third, confounders could cause potential bias. In observational studies, some residual confounders might not be adjusted as covariates –41. TherIn conclusion, the meta-analysis confirmed the strong association between HS and psoriasis. This supports the advantageous use of biologic therapies with or without HS patients due to its common inflammatory pathway. The co-occurrence of HS and psoriasis can greatly increase the burden of the disease that may require aggressive treatment. Further prospective studies are needed to establish the true magnitude of the association between psoriasis and HS.The original contributions presented in the study are included in the article and All the authors involved in drafting or revising the article and approved of the submitted version. Study conception and design: S-YG, Y-PH, KM, C-YL, Y-HK, S-EJ, and JW. Data analysis and demonstration: S-YG. Original draft preparation: S-YG and IP. All authors contributed to the article and approved the submitted version.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.


Serum pepsinogens are serological biomarkers of gastric atrophy, and the latter is a risk factor for esophageal squamous cell carcinoma (ESCC). However, the association of serum pepsinogens with ESCC risk remains unclear. This systematic review and meta-analysis aimed to assess the relationship between serum pepsinogen I (PGI) and pepsinogen I: pepsinogen II ratio (PGR) and ESCC risk.PubMed, Embase, and Web of Science were searched for articles on the effect of serum PGI and PGR on ESCC risk, published up to the end of February 2022. Meta-analysis with a random-effect model was used to calculate pooled odds ratios (ORs) and 95% confidence intervals (CIs).I2 = 60.2%, P = 0.028) rather than in serum PGI . In stratified analysis by study quality, the significant risk effect on ESCC was remained for PGI  and PGR  when only the studies with high quality were pooled.Five case–control studies and three prospective studies were included. In comparison with the high categories, the low categories of serum PGI  and PGR  were associated with an increased risk of ESCC, although a substantial heterogeneity was observed in serum PGR (Based on the available studies, although limited in number, this systematic review along with meta-analysis suggests that low serum PGI and low PGR may be related to an increased risk of ESCC. This present study provides evidence for using serum pepsinogen biomarkers in predicting ESCC. More delicate well-designed cohort studies with high study quality are needed, and dose–response analysis should be performed. Esophageal cancer is one of the most common malignant tumors worldwide. It ranks seventh in cancer incidence and sixth in cancer mortality worldwide , 2. EsopEarly detection, early diagnosis, and early treatment can improve the patient’s condition and thereafter increase the survival of patients. Invasive and costly endoscopy and biopsy for histopathology are the gold standard for diagnosis of esophageal cancer . HoweverTwo previous meta-analyses , 26 examTherefore, this systematic review and meta-analysis aimed to assess the association of ESCC risk with serum PGI and PGR by pooling the existing evidence.This systematic review and meta-analysis was conducted by following the PRISMA statement . The twoA systematic literature search for studies published in PubMed, Embase, and Web of Science was conducted up to the end of February 2022 for original studies in humans on the association between serum pepsinogens and ESCC, with no language restriction. The following search strategy was adopted:  AND . Reference and citation tracking was also conducted to retrieve potential studies. A more detailed search strategy is shown in Eligible studies met the following criteria: study design of the case–control study or cohort study, ESCC cases were diagnosed by histological examination, the association of serum pepsinogens with ESCC incidence was examined, and the relative risk (RR) or odds ratios (ORs) and their 95% confidence interval (CIs) were reported or could be calculated. Cross-sectional studies, comments, editorials, animal studies, and abstracts were excluded. For multiple reports of the same biomarker from the same research population, only the most recent or informative report was included.The quality of included studies was evaluated using the Newcastle–Ottawa Scale (NOS) , 31. ThiThe following information was collected from each included study, including first author, year of publication, country where the study was performed, study design, follow-up period for cohort studies, period when subjects were recruited for case–control studies, number of cases and total individuals for cohort studies, number of cases and controls for case–control studies, median or mean of age, male proportion, serum pepsinogen biomarkers and boundary point, risk estimates and their 95% CIs, and the covariates. The cutoff points for serum PGI and PGR varied in different studies. When the results for several cutoff points were provided in a study, we selected those which were more consistent with the cutoff points in other studies, that is, PGI <25 to ≤ 70 μg/l and PGR≤ 2 to ≤3.I2 ≥ 50% and P ≤ 0.1); otherwise, the fixed-effect model was adopted , and two studies were found from reference tracking. A total of 1,549 studies were left after duplicate removal. Of 42 studies for further full-text reviewing after the abstract screening, 34 studies were excluded for the reason of unavailable outcomes of interest, inappropriate exposure, conference abstract, review, and studies in the same population. Finally, eight studies –24 were I2 = 46.4%, P-heterogeneity = 0.070). The Egger’s test (P = 0.156) and the funnel plot  and Europe   rather than with low quality .As shown in I2 statistic was 60.2% and the P value for Q-test was 0.028, suggesting a marginal heterogeneity. The Egger’s test (P = 0.812) and the funnel plot  or three studies with case–control design were pooled .For six studies that assessed the association between serum PGR and risk of ESCC, the pooled OR (95% CI) was 1.70  when comparing the low with high level Table 2.In the present study, we found that both low level of serum PGI and low level of serum PGR were associated with an increased risk of ESCC.Smoking and drinking were two risk factors for ESCC; hence, we conducted a subgroup analysis based on whether these two risk factors were adjusted. Two studies [one cohort study  and one For PGI, the subgroup analysis by region found that the association was stronger in European than in Asian, suggesting the geographical or ethnic variation. This was consistent with the finding by Islami et al. . When stHelicobacter pylori infection was associated with a higher risk of ESCC, and gastric atrophy may be an intermediate step in the pathway from Helicobacter pylori infection to ESCC. However, since most studies did not provide information on the combined stratification of Helicobacter pylori and serum PGI or PGR, we cannot examine it directly in the present meta-analysis.Gastric atrophy has been associated with ESCC, but its causal relevance has been questioned. It was suggested that gastric atrophy might be related to ESCC because of bacterial overgrowth and N-nitrosation reactions caused by the reduced gastric acid production during gastric atrophy or duodenal reflux –39. SomeThere are some advantages in this study. Firstly, as many eligible studies as possible were included in this study. Secondly, several subgroup analyses were conducted to identify potential sources of heterogeneity. Several limitations also existed. Firstly, substantial heterogeneity was observed among included studies. This heterogeneity may be due to various factors, such as diversity in the population characteristics, serum pepsinogen detection methods, and study design. Secondly, there is no consensus on the best cutoff point for serum pepsinogen biomarkers. Dichotomous comparisons may not be the most efficient use of data in estimating the association between gastric atrophy and ESCC risk. These comparisons may be misleading, because any cutoff point will put people with considerably different risks into the same category. Therefore, it is urgent to conduct the delicate well-designed longitude studies and dose–response analyses.In summary, this systematic review with meta-analysis, although based on a limited number of studies, suggested that both low level of serum PGI and low level of serum PGR were related to an increased risk of ESCC, respectively. This present study provides evidence for using serum pepsinogen biomarkers in predicting ESCC. More delicate well-designed cohort studies with high study quality are needed and dose–response analyses should be performed.Publicly available datasets were analyzed in this study. The data are available by contacting the corresponding author or extracting from originally published research.XL and WZ conceived and designed this study. Z-XY and L-BY researched the literature. Z-XY and PH performed the statistical analysis. Z-XY and PX drafted the manuscript. XL, XX, YL, WZ, and L-BY edited and reviewed the manuscript. All authors contributed to the article and approved the submitted version.This study was supported by the National Key R&D Program of China (No. 2018YFE0208000), the Guangdong Basic and Applied Basic Research Foundation (No. 2019A1515011599), and the Science and Technology Program of Guangzhou City (No. 202102080404). The funders had no role in the design, analysis, or writing of this manuscript.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.


Infertility affects one in every six couples in developed countries, and approximately 50% is of male origin. In 2021, sperm DNA fragmentation (SDF) testing became an evidence-based test for fertility evaluations depicting fertility more clearly than standard semen parameters. Therefore, we aimed to summarize the potential prognostic factors of a higher SDF.We conducted a systematic search in three medical databases and included studies investigating any risk factors for SDF values. We calculated mean differences (MD) in SDF with 95% confidence interval (CI) for exposed and non-exposed individuals.Chlamydia and HPV were negligible. Of lifestyle factors, smoking had the most disruptive effect on SDF – an increase of 9.19% (CI: 4.33–14.06). Different periods of sexual abstinence did not show significant variations in SDF values. Age seemed to have a more drastic effect on SDF from age 50 onwards, with a mean difference of 12.58% (CI: 7.31–17.86). Pollution also had a detrimental effect – 9.68% (CI: 6.85–12.52).We included 190 studies in our analysis. In the group of associated health conditions, varicocele  and impaired glucose tolerance  had the most significant increase in SDF. Among malignancies, testicular tumors had the highest impact, with a maximum of MD = 11.3% (CI: 7.84–14.76). Among infections, the overall effects of both Of the above risk factors, varicocele, impaired glucose tolerance, testicular tumors, smoking, pollution, and paternal age of over 50 were associated with the highest SDF.CRD42021282533.The online version contains supplementary material available at 10.1186/s12958-023-01054-0. Fertility rates are declining in western developed countries; currently, approximately 15% of couples are infertile . The reaIn recent years, there has been a growing demand for functional, objective parameters reflecting fertility status more clearly than classical parameters. Of these, sperm DNA fragmentation (SDF) and the DNA fragmentation index (DFI) – denoting the percentage of sperm with damaged DNA – seem to be of utmost importance . In 2021DNA breaks occur both physiologically and pathologically during the development and maturation of sperm cells . SeveralTherefore, we aimed to conduct a systematic review and meta-analysis on all risk factors that have been investigated as potentially increasing SDF.Our systematic review and meta-analysis are reported based on the recommendation of the PRISMA 2020 guideline , and compared the SDF values (O) between groups with and without a particular risk factor (I and C). Risk factors included all lifestyle-, environmental-, and additional health factors. For the outcome measures, all sperm DNA fragmentation assays were included , terminal deoxynucleotidyl transferase (dUTP), nick end labeling (TUNEL), sperm chromatin dispersion test (SCD), Comet assay, both neutral and alkaline). Eligible studies reported either the SDF difference between the groups with and without the risk factors in terms of mean difference (MD) or the rate of high SDF based on a specific cut-off value for each group. A change in SDF was considered clinically significant if it was around 10%, but SDF values were interpreted on a consensus basis, as there are no guideline recommendations that we could have followed.Cohort studies of both prospective and retrospective designs were eligible. No studies were excluded on the basis of language criteria.We excluded studies (1) with inaccurate data or if data were presented in a way that could not be further processed, (2) conference abstracts, (3) reviews, case series, and case reports.Our systematic search was conducted in Embase, MEDLINE (via PubMed), and Cochrane Central Register of Controlled Trials (CENTRAL) on October 17, 2021. During the systematic search, we used the following search key: (“sperm DNA fragmentation” OR “SDF” OR “DNA fragmentation index” OR “DFI”). We did not use filters or other restrictions.Endnote v9.0  reference manager software was used for the selection. After automatic and manual duplicate removal, the selection was preformed in pairs by four independent review authors for the two halves of the articles (JÁ-AF and TF-AS) at the level of title, abstract, and full text level of the references. Disagreements were resolved at each level by a third review author for each group (NÁ and TS). The Cohen’s kappa coefficient (κ) was calculated after each step to measure interrater reliability .Data from the eligible articles were collected by two authors (AS and JÁ) into a predefined data collection table. We extracted the following data: first author, year of publication, study design and period, number of participants and demographical data, fertility status, risk factors and groupings, SDF assay type, cut-off values for dichotomous outcomes, MD values with distribution of risk factor groups or high SDF in the risk factor groups, pregnancy or birth as additional outcomes in terms of either the risk factor or SDF, and information for assessing the risk of bias in the studies.The original study investigator was contacted when data were missing or insufficient.Wherever possible, we grouped participants in the selected studies by general population, fertility clinic, fertile, or a combination of these based on the fertility status of patients. Studies reporting similar SDF cut-off values were handled in the same group.The preferred data format for SDF was the mean with standard deviation (SD). Therefore, data reported as median were approximated by the interquartile range to mean with SD based on the work by Wan et al. . When weFor sexual abstinence, we used as a reference the number of abstinence days recommended when examining standard semen parameters . As these repeated measurement studies lacked SD of changes from baseline, we used a conservative approach and assumed a correlation of minus one to calculate the SD of changes, which is equivalent to the sum of the SDs.Two review authors (SV and HP) performed the risk of bias assessment independently using the Quality in Prognostic Studies (QUIPS) tool . Risk asAll statistical analyses were performed with R  using the meta (v5.5.0) and dmetar (v0.0.9) packages .The random-effects model was used with the inverse variance method for weighting and the Mantel–Haenszel method to pool odds ratio (OR) with a 95% confidence interval (CI) from 2 by 2 tables  and models with restricted maximum likelihood method to calculate MD with 95%CI from mean values (mean SDF in groups with and without risk factor) .Forest plots were used to graphically summarize the results irrespective of the number of studies included in the pooled analysis. Forest plots with fewer than three studies were interpreted with limitations.Where applicable, we reported the prediction intervals  of results following the recommendations of IntHout et al. .p-value < 0.1 as a threshold for a statistically significant difference, and the I2 index was used to quantify between-study heterogeneity. In addition, Egger’s test and funnel plots were applied to report and visualize publication bias when at least ten studies were involved in the analysis.The Cochrane’s Q test was used to assess statistical heterogeneity with a p-value < 0.05 was considered statistically significant.Besides heterogeneity, a A subgroup analysis was carried out based on the fertility status of the population and the SDF assay used.Altogether, we found 26,901 articles using our search key, of which 190 were used for the meta-analysis or the systematic review .For malignancies, testicular tumors had both statistically and clinically significant effects on SDF, with a maximum of MD = 11.3% (CI: 7.84–14.76) when measured by SCD. The presence of Hodgkin’s lymphoma was statistically significant but not clinically . Lymphomas generally resulted in a higher SDF than those without . The presence of non-Hodgkin’s lymphoma and leukemias did not reach statistical significance.Chlamydia was not significant, either statistically or clinically. The presence of human papillomavirus (HPV) did not result in a higher SDF either and in general, viral infections had a negligible effect . However, the presence of bacterial infections  or sexually transmitted infections (STIs)  yielded ambiguous results.As for infections, the mean difference for Results are summarized in Fig. Overweight or obese patients had similar SDF values as those with normal BMI . Underweight men showed a slightly lower SDF than patients with normal BMI . However, the difference was non-significant.Abstinence for none of the generally recommended days seemed to result in clinically lower SDF compared to longer or shorter periods when results were compared for the same population or different patients.A summary of other risk factors is showing in Fig. Exposure to different pollutants significantly increased SDF . However, the type of pollutants was heterogeneous. On the other hand, exposure to pesticides or insecticides significantly increased SDF .The pooled results of studies measuring reactive oxygen species (ROS) showed a higher SDF for higher ROS values .Risk factors with a more significant impact on SDF in the systematic review were spinal cord injuries in two studies  and heroin use . The effects of chronic prostatitis and previous orchidopexy also seem to be significant risk factors for urology, with a MD value of around 10% increase in SDF in the presence of the risk present.The results of the risk of bias assessment are presented in Supplementary Table Egger’s test could only be performed for varicocele and age based on SDF cut-off values. Their p-values were 0.548 and 0.405, respectively.Heterogeneity was high for almost every risk factor examined due to the heterogeneity in risk factor definitions.This comprehensive meta-analysis provides a thorough summary of all potential risk factors of SDF that have been assessed to date. In addition, wherever possible, we performed subgroup analyses based on the severity or the quantity of exposure to the risk factor, the assay method used for measurement, and fertility status.Chlamydia, which had a higher effect on SDF [Although many studies have focused on the risk factors for standard semen parameters, much fewer data are available on the risk factors for SDF and even less, in turn, for pregnancy and birth outcomes in terms of SDF. Our study showed that several modifiable risk factors affect SDF notably. Amongst health conditions, varicocele and impaired glucose tolerance were found to have the strongest negative effect on SDF. With regard to infections, only a few studies are available that investigate the relationship between a specific pathogen and SDF. One of them was HPV, for which the literature is ambiguous, and the other one was t on SDF . Of malit on SDF . For thet on SDF .Among lifestyle factors, smoking had the greatest impact on SDF. It is known to affect health, including sexual health. Ramlau-Hansen et al. even showed a dose-dependency when investigating the effect of smoking on semen volume, concentration, and motility . SeveralCurrent guidelines suggest that 30–40% of infertility cases could be idiopathic in males, with a sole discrepancy in seminal parameters . The undAnother issue concerning fertility is that there is no clinically defined “normal range” for SDF to distinguish between infertile and fertile men. Moreover, the only certain impact of a higher SDF is on miscarriage rates, no definite results are available on other aspects of pregnancies . CurrentDespite these shortcomings, other meta-analyses of SDF have been conducted, most commonly on varicocele. Varicocele is a common condition in approximately 15% of males and is thought to be a reversible cause of infertility when the correct indications for surgery are applied , 38. It The best studied intervention is varicocelectomy, which, according to the meta-analysis by Qiu et al., reduces SDF by approximately 6% . AnotherThe above examples show that we do not clearly understand several risk factors affecting sperm DNA fragmentation, let alone the possible means of reducing it. However, once we clarify the above, we can move on to the more important questions, i.e., their impact on pregnancy rates and live births.Regarding the strengths of our analysis, we followed our protocol, which was registered in advance. A rigorous methodology was applied, and we included a large number of studies and a high number of patients, which resulted in the generalizability of our results. In addition, subgroup analyses led to more precise conclusions.There are several limitations to our study. First, the included studies have different study designs, data collection methods, inclusion and exclusion criteria, definitions of fertility, and risk factors outcome measures. Many studies did not account for confounding factors. Lastly, the outcome measurements were also different between the studies. All this produced substantial heterogeneity.Our results suggest that several lifestyle-, environmental-, and additional health factors are associated with increased sperm DNA fragmentation.On the basis of previous evidence, there are clear benefits of rapidly integrating results into clinical practice . For patAdditional file 1: Supplementary Appendix 1. Risk of bias assessment methodology. Supplementary Table 1. PRISMA 2020 checklist. Supplementary Table 2. Basic characteristics of the included article. Supplementary Table 3. Eligibility criteria in each included article. Supplementary Table 4. Risk factor and population definitions in each included article. Supplementary Table 5. Risk of bias assessment using the QUIPS tools. Supplementary Table 6. Articles also looking at pregnancy or birth as an outcome. Supplementary Figure 1. Comparison of patients’ sperm DNA fragmentation values with and without varicocele subdivided based on sperm DNA fragmentation assays used (continuous data). Supplementary Figure 2. Comparison of patients’ sperm DNA fragmentation values with and without varicocele subdivided based on different cut-off values. Supplementary Figure 3.Supplementary Figure 4. Comparison of patients’ sperm DNA fragmentation values with and without varicocele subdivided based on the fertility status of patients (continuous data). Supplementary Figure 5. Comparison of patients’ sperm DNA fragmentation values with impaired and normal glucose tolerance (continuous data). Supplementary Figure 6. Comparison of patients’ sperm DNA fragmentation values with and without testicular tumors subdivided based on sperm DNA fragmentation assays used (continuous data). Supplementary Figure 7. Comparison of patients’ sperm DNA fragmentation values with and without Hodgkin-lymphoma (HL) (continuous data). Supplementary Figure 8. Comparison of patients’ sperm DNA fragmentation values with and without non-Hodgkin lymphoma (NHL) (continuous data). Supplementary Figure 9. Comparison of patients’ sperm DNA fragmentation values with and without lymphomas (continuous data). Supplementary Figure 10. Comparison of patients’ sperm DNA fragmentation values with and without leukemia (continuous data). Supplementary Figure 11. Comparison of patients’ sperm DNA fragmentation values with and without human papilloma virus (HPV) infections (continuous data). Supplementary Figure 12.Supplementary Figure 13. Comparison of patients’ sperm DNA fragmentation values with and without viral infections (continuous data). Supplementary Figure 14.Supplementary Figure 15.Supplementary Figure 16. Comparison of smokers and non-smokers’ sperm DNA fragmentation values subdivided based on sperm DNA fragmentation assays used (continuous data). Supplementary Figure 17. Comparison of heavy smokers with non-smokers, and moderate smokers with non-smokers’ sperm DNA fragmentation values (continuous data). Supplementary Figure 18. Comparison of smokers and non-smokers’ sperm DNA fragmentation values subdivided based on the fertility status of patients (continuous data). Supplementary Figure 19. Comparison of drinkers’ and abstainers’ sperm DNA fragmentation values (continuous data). Supplementary Figure 20. Comparison of heavy drinkers with abstainers, and moderate drinkers with abstainers’ sperm DNA fragmentation values (continuous data). Supplementary Figure 21. Comparison of sperm DNA fragmentation values of patients with different BMI categories (continuous data). Supplementary Figure 22.Supplementary Figure 23.Supplementary Figure 24.Supplementary Figure 25.Supplementary Figure 26.Supplementary Figure 27.Supplementary Figure 28.Supplementary Figure 29.Supplementary Figure 30.Supplementary Figure 31.Supplementary Figure 32.Supplementary Figure 33.Supplementary Figure 34.Supplementary Figure 35.Supplementary Figure 36. Comparison of patients’ sperm DNA fragmentation values of different age groups (continuous data). Supplementary Figure 37. Comparison of patients’ sperm DNA fragmentation values of different age groups (continuous data). Supplementary Figure 38. Comparison of patients’ sperm DNA fragmentation values of different age groups with cut-off values being at 30% DNA fragmentation. Supplementary Figure 39. Comparison of patients’ sperm DNA fragmentation values of age groups over and below 40 years of age with different cut-off values for DNA fragmentation. Supplementary Figure 40.Supplementary Figure 41. Comparison of patients’ sperm DNA fragmentation values with exposures to different types of pollutants (continuous data). Supplementary Figure 42.Supplementary Figure 43.Supplementary Figure 44. Comparison of patients’ sperm DNA fragmentation values with more or less reactive oxygen species (ROS) (continuous data). Supplementary Figure 45. Comparison of patients’ sperm DNA fragmentation values with more vs. less reactive oxygen species (ROS) – reduced model (continuous data). Supplementary Figure 46. Comparison of patients’ sperm DNA fragmentation values within the risk group and control group (continuous data). Supplementary Figure 47. Comparison of patients’ sperm DNA fragmentation values within the risk group and control group . Supplementary Figure 48. Comparison of patients’ sperm DNA fragmentation values within the risk group and control group .


I2 > 90%, p < 0.001). The results demonstrated that PFASs might be potential risk factors for breast cancer, and the compounds in low exposure levels could have a more harmful impact on human health.Polyfluoroalkyl substances (PFASs) are persistent pollutants that may cause breast cancer. However, associations between exposure to PFASs and the risk of breast cancer are controversial. We retrieved studies on the association between PFASs—perfluorooctanoic acid (PFOA), perfluorononanoic acid (PFNA), perfluorohexane sulfonic acid (PFHxS), and perfluorooctane sulfonic acid (PFOS)—and breast cancer risk in women from PubMed, Embase, and the Web of Science. The pooled odds ratios (ORs) or relative risks (RRs) and their 95% confidence intervals (CIs) were extracted or calculated from provided data. Moreover, subgroup and metaregression analyses were performed to distinguish the potential sources of heterogeneity between studies. Lastly, eight original studies were included in the meta-analysis. PFOA and PFHxS were positively correlated with breast cancer risk, and the pooled ORs (and 95% CIs) were 1.32 (1.19 and 1.46) and 1.79 (1.51 and 2.11), respectively. PFNA was negatively correlated with breast cancer risk and the pooled OR (and 95% CIs) was 0.76 (0.6 and 0.96), and PFOS was shown to have no correlation with breast cancer risk and the pooled OR (and 95% CIs) was 1.01 (0.87 and 1.17). All results were merged in a random-effects model with significant heterogeneities ( Per- and polyfluorinated alkylated substances (PFASs) are a wide group of synthetic compounds that are water and oil repellent, and have been used in many industrial and consumer applications . Direct PFASs are important environmental contaminants in drinking water, and have been linked to various adverse reproductive health outcomes in women . A growiA systematic literature search was performed using PubMed, Embase, and Web of Science up to February 2022. The following relevant keywords were randomly combined to acquire the most comprehensive data: “Fluorocarbons” or “Perfluorocarbons” or “Fluorocarbon” or “Telomer Fluorocarbons” or “Fluorinated Telomer Alcohols” or “Fluorinated Telomer, Alcohols” or “Polyfluorinated Telomer Alcohols” or “Emulsion, Fluorocarbons” or “PFOA” or “PFNA” or “PFHxS” or “PFOS” and “cancers” or “tumors” or “neoplasms” or “malignancies”. A detailed search strategy is presented in To identify eligible studies, specific criteria were used as follows: (1) restricted to English language articles and pathological breast cancer patients; (2) outcomes of interest were the association between PFASs and breast cancer; (3) OR, RR, or hazard ratio (HR) with 95% CIs of breast cancer were provided or could be calculated from the available data; (4) studies that measured exposures to PFOA, PFNA, PFUDA, PFHxS, and PFOS levels in blood. Additionally, the criteria for article exclusion were as follows: (1) studies focused on animal or cell experiments; (2) letters, reviews, editorials, case reports, or abstracts; (3) no data available to evaluate the correlation between PFAS exposure and breast cancer patients; (4) duplicates or samples used in previous publications.Data from all included studies were extracted by two reviewers (H.-H.J. and H.L.), and uncertain results were assessed by another investigator (H.W.). The extracted data included the following information: the first author, publication year, country, sample size, type of PFAS, study type, OR, RR, or HR, and 95% CIs. For significant heterogeneities among included studies, the random-effects model was used to merge results for meta-analysis.Whether each included literature met the quality standards separately was assessed by two authors (H.-H.J. and H.L.). Any discrepancies were resolved through discussion and consensus. The quality of involved studies was evaluated with the Newcastle–Ottawa scale (NOS), which contains eight items and three dimensions . The totIn this meta-analysis, a combination of the pooled OR or RR and 95% CI was calculated to evaluate the relationship between PFASs and breast cancer. The study data were analyzed by Stata version 14-MP for Windows.I2 statistics were applied to evaluate the heterogeneity of the pooled results [p < 0.05 or I2 > 50% was recognized as statistically significant. If studies were proven to be homogenous, a fixed-effects model was utilized for further analysis. If not, the random-effects model was used. Finally, Begg’s and Egger’s tests were used to assess publication bias [Subgroup analyses based on PFAS concentration levels, regions (Asian and Occident), and study type were performed to identify the potential sources of heterogeneity. Cochrane’s Q test and  results . p < 0.0ion bias , and triion bias . SensitiThe entire process of the literature collection and screening is illustrated in The main characteristics of the eligible studies are summarized in Moreover, the detailed quality assessment of each study scored following the guidelines of NOS is shown in p < 0.05). In addition, regions of these cases were the heterogeneity source of PFOA (p < 0.05). The concentration group might not have affected the reliability of the pooled results (p > 0.05).The subgroup analysis and metaregression were carried out to identify the potential sources of heterogeneity . ResultsTo evaluate the concentration-dependent difference in the association between PFASs and risk of breast cancer, studies were divided into three exposure groups on the basis of their quartile of the serum level of PFASs: Q2 (low), Q3 (medium), and Q4 (high). The pooled results are shown in I2 = 98.5%, p < 0.001). The subgroup analysis showed that different regions and study types of the included studies (p < 0.05) might have been important heterogeneity sources. The sensitivity analysis results revealed that the stability of the pooled OR might have been influenced by article [p value of Egger’s test for PFOA was 0.659. This finding indicated that no publication bias existed in the included studies for PFOA. Through a trim-and-fill analysis, the pooled results were found to not be significantly affected for PFOA  compared with no trimming performed, indicating that the pooled OR was stable.Eight studies provided data suitable for the meta-analysis of correlations between PFOA exposure and breast cancer risk a. The ov article , which wI2 = 99.9%, p < 0.001). The subgroup analysis failed to find the heterogeneity sources (p > 0.05). The sensitivity analysis results showed that the stability of the pooled OR might have also been influenced by article [p value of Egger’s test for PFOA was 0.481. This finding indicated that no publication bias existed in the included studies for PFNA. Through a trim-and-fill analysis, the pooled results were different between merging models  in fixed-effects model, but 0.731  in random-effects model) and with no trimming performed, indicating that the pooled OR was unstable.Six studies provided data suitable for the meta-analysis of correlations between PFNA exposure and breast cancer risk b. The ov article , which wI2 = 99.3%, p < 0.001). The subgroup analysis showed that the different study types of included studies (p < 0.05) might have been primary heterogeneity sources. Additionally, the sensitivity analysis results showed that the stability of the pooled OR might have also been influenced by article [p value of Egger’s test for PFHxS was 0.904. This finding indicated that no publication bias existed in the included studies for PFHxS. Through a trim-and-fill analysis, the pooled results were found to not be significantly affected for PFHxS  with no trimming performed, indicating that the pooled OR was stable.Six studies provided data suitable for the meta-analysis of correlations between PFHxS exposure and breast cancer risk c. The ov article , which wI2 = 99.8%, p < 0.001). The subgroup analysis failed to find the heterogeneity sources (p > 0.05). The sensitivity analysis results showed that no outlier study was found  in the fixed-effects model, but 1.01  in the random-effects model) with no trimming performed, indicating that the pooled OR was unstable.Eight studies provided data suitable for the meta-analysis of correlations between PFOS exposure and breast cancer risk d. PFOS was found d. Begg’sas found d. HoweveIn the meta-analysis, the results demonstrated that PFOA and PFHxS were positively correlated with breast cancer risk , whereas the PFNA was negatively correlated with breast cancer risk  and PFOS was not associated with breast cancer risk . In the included studies, researchers found some common risk features of patient cases, such as a family history of breast cancer, smoking, BMI, full-term pregnancy, etc. Although there was significant heterogeneity, the pooled results of PFOA and PFHxS were stable and reliable. The pooled results of PFNA and PFOS were different between merging models, indicating that the pooled ORs were unstable, which might have been influenced by the quantities and qualities of the included studies. The causes of breast cancer are constantly being updated by researchers. Some established breast cancer risk factors include early age at giving first birth, genetic inheritance ,22, an uSome in vitro studies demonstrated that the exposure of human breast epithelial cells to PFOA and PFOS induced an increase in cell proliferation, cell migration, and invasion potential by differentially affecting proteins such as cell-cycle regulators, β-integrin, E-cadherin, and occlusion, as well as global DNA methylation and histone modifications . PFOA alThe epidemiological studies on the association between PFAS exposure and breast cancer risk are usually diverse. A study from China found that PFHxS and PFOS positively correlated with ER+ breast cancer risk in younger women . LikewisPFASs might increase the risk of disease by disrupting hormone-mediated processes. However, the impact of the mixtures of environmental chemicals along the carcinogenic process can be triggered or boosted by individual chemicals through different mechanisms . The studies included in this meta-analysis exhibited differences, which might have caused heterogeneity. In addition, most of the included studies were case–control studies, which had some limitations. Case–control studies are always retrospective, which could introduce bias during the recollection of samples for exposure estimations , and theA cross-sectional study was also incorporated in this meta-analysis, and the subgroup-analysis results showed that the study might have been an important heterogeneity source. The outcome of breast cancer (yes/no) was determined without clinical confirmation evidence measuring recruited patients, which was significantly different from other included studies . NeverthOur meta-analysis provided evidence suggesting that PFASs, especially PFOA and PFHxS, might be potential risk factors of breast cancer. Although the results show that PFOS is not associated with breast cancer risk and PFNA is negatively associated with the risk, in the context of some in vitro studies, PFOS and PFNA are potential risk factors for breast cancer. In addition, PFOS and PFOA are mostly detected in aquatic environments, and our results showed that compounds in low-exposure levels would have a more harmful impact on human health. Therefore, more well-designed large studies, especially some prospective studies, are warranted to further explore the association between PFAS exposure (single and combined) and breast cancer risk to uncover the underlying biological mechanism for preventing breast cancer and other PFAS-related diseases.