An association between weight status and migraine has been previously reported; however, this relationship has only been studied in adults, not in the paediatric population.To evaluate the association between weight status and migraine in the paediatric population.We searched PubMed/Medline, Scopus, Web of Science, Ovid Medline, and Embase using a cut-off date of May 2023. We included observational studies that evaluated the association between weight status  and migraine in the paediatric population (children and adolescents). Normal weight was the comparator. The outcome was migraine . We performed meta-analyses using a random-effects model to estimate the pooled effects for each outcome. Sensitivity analysis was performed based on study design and risk of bias . Certainty of evidence was assessed using the GRADE approach.I2 = 32.3%, p = 0.224) and that excess weight may increase the odds of migraine . Additionally, cohort and case-control studies found that obesity probably increases the odds of migraine. No studies analysed the association between underweight and migraine.Eight studies  covering 16,556 patients were included. The overall certainty of evidence was very low for the association between overweight, obesity, and excess weight with migraine. In the sensitivity analysis, meta-analyses of studies with a low risk of bias found that the overweight population probably had an increased odds of migraine (OR: 1.70; 95% CI: 1.14 to 2.53; The associations between overweight, obesity, excess weight and migraine were uncertain, but studies with better methodological quality reported increased odds. Future longitudinal studies with proper confounding control are needed to disentangle their causal relationship.PROSPERO, identifier CRD42021271533. GloballFactors such as comorbidities, diet, harmful habits, lifestyle, and weight status are associated with the development of migraine in the adult population , 7. AddiHence, the weight status in children could be an important modifiable risk factor for paediatric migraine and the study of this association could serve to understand the complex multifactorial etiology of migraine. However, some studies of children and adolescents report variable results regarding the relationship between weight status and migraine , 12–15; 2.This systematic review and meta-analysis were conducted in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines  Supplem. The pro2.1.Structured searches were performed in PubMed, Scopus, Web of Science, Ovid-Medline, and Embase until 10 May 2023. No restrictions on publication date or language were used. The complete search strategy is presented in 2.2.We included cross-sectional, case-control and cohort studies that reported relative risk (RR), hazard ratio (HR), odds ratio (OR), or prevalence ratio (PR) and studies that reported data that allowed estimating these measures between weight status  and migraine in paediatric patients (<18 years). Reviews, letters to the editor, meeting or conference abstracts, editorials, comments, case reports and case series, and articles not available in full text were excluded.2.3.Duplicate articles were removed automatically with Endnote and then manually with Rayyan. The authors  evaluated the inclusion of the studies on the basis of the title and abstract. The full text of potentially eligible studies was reviewed , and those that met the inclusion and exclusion criteria were included. This process was performed independently by two pairs of authors , and discrepancies were resolved by consensus or by a third author (DS and CA-D).2.4.Weight status in the paediatric population was measured using body mass index (BMI) percentiles: underweight (<5th percentile), normal weight (5–85th percentile), overweight (85–95th percentile), and obesity (>95th percentile) . Excess 2.5.The authors  extracted the relevant data from each study using an Excel sheet designed for this review. The following information was collected: study design, country, participant characteristics , sample size, study setting, migraine diagnostic criteria, and migraine type. This process was carried out independently and in pairs, and discrepancies were resolved by consensus or by a third author (DS and CA-D). Numerical variables presented as medians and interquartile ranges were converted to means and standard deviations. We converted relative risk (RR) to odds ratio (OR) as stipulated in the Cochrane manual, considering an assumed comparator risk of 0.09 , 18.2.6.The Newcastle Ottawa Scale (NOS) was used to assess the methodological quality of the studies. This scale measures three sections: study group selection, comparability among groups and association of the exposure with the outcome of interest. The scale is scored with stars; the higher the number of stars, the higher is the methodological quality. For cross-sectional studies, a maximum of 10 stars can be obtained, and for cohort and case-control studies, a maximum of 9 stars  can be o2.7.I2 statistic. Heterogeneity was defined as low if I2 < 40%, moderate if I2 = 40%–80%, and high if I2 > 80%. Additionally, associations were evaluated by type of migraine (episodic or chronic). Subgroup analyses were performed by study design and sensitivity, i.e., risk of bias classification. STATA version 16.0  was used.The association of interest  was evaluated, and the variable excess weight, which included overweight and obesity, was created, given the availability of data. The comparator was the normal weight group. We calculated crude odds ratios (ORs) with 95% confidence intervals (95% CIs) and meta-analysed them using DerSimonian and Laird  random e2.8.The Grading of Recommendations Assessment, Development and Evaluation  was used2.9.This systematic review included published and open information, and no human subjects participated in this review. Thus, no ethics committee approval was needed.3.3.1.A total of 1,511 studies were retrieved, of which 726 remained after removing duplicates. After reading the title and abstract, 678 were eliminated. The full text of the remaining 48 was read. Ultimately, eight studies were included , 26–29 . The exc3.2.Regarding study design, among the eight studies selected, six were cross-sectional studies , 26, 29,The studies evaluated overweight and obesity; only two evaluated obesity only , 28, andAll studies evaluated either episodic or chronic migraine or only migraine in general. To classify migraine type, the studies used the 2nd edition of the International Classification of Headaches (ICHD II) . The maj3.3.In the cross-sectional studies, the scores ranged from 4 to 9 out of 10. The domain with the highest score was “outcome,” and the domain with the lowest score was “selection”; the majority of studies , 26, 29 3.4.I2 = 67.5%), with very low certainty (⨁◯◯◯). However, by including only the two studies with a low risk of bias (I2 = 32.3%), with moderate certainty for this estimate (⨁⨁⨁◯)  , 26, 29  of bias , 14, thee (⨁⨁⨁◯) .I2 = 22.7%) and chronic migraine, finding a trend towards a greater association with the latter . However, both results had very low certainty (⨁◯◯◯)  .3.5.I2 = 62.8%), with very low certainty (⨁◯◯◯). Considering only the case-control study (560 patients) (I2 = 32.8%), with low certainty (⨁⨁◯◯)   patients, 29 evalatients) , there watients) , 14, 27,y (⨁⨁◯◯) .I2 = 60.0%), and for chronic migraine, for three cross-sectional studies (I2 = 61.8%); both results had a very low level of certainty (⨁◯◯◯). Considering only the cohort study , with very low certainty (⨁◯◯◯). When only the three studies with a low risk of bias were included (I2 = 83.7%), with low certainty (⨁⨁◯◯)  , 26, 29 included , 15, they (⨁⨁◯◯) .I2 = 55.5%), and for chronic migraine, the estimated OR was 1.00 ; both results had very low levels of certainty (⨁◯◯◯)  .3.7.I2 > 40) and imprecision in the confidence intervals of the meta-analysed estimates (The body of evidence of the association presented very low certainty (⨁◯◯◯) due to the cross-sectional design of most studies, the moderate risk of bias in more than 50% of the included studies, the high heterogeneity  and migraine. In our analyses, we observed findings similar to those previously reported, i.e., studies with a low risk of bias reported that excess weight could be associated with migraine. These findings could be explained by the increase in pro-inflammatory cytokines in adipose tissue, for example, TNFα, IL-1 and IL-6, which are associated with the development and chronification of migraine . However4.3.a priori. In addition, we found that the low risk of bias was mostly due to inadequate methodology in participant selection, especially in sample size, nonresponse rate, and lack of consecutive or randomized sampling. However, when we analysed only the studies with the best design and lowest risk of bias, the certainty improved. All this suggests that future studies should improve the precision of estimates and control for systematic error to confirm the relationship between weight status and migraine. For this purpose, longitudinal studies with a larger sample size and longer follow-up periods are needed to evaluate the association by adjusting for confounding variables such as age, sex, physical activity, and the consumption of prescription or recreational drugs, family history of cardiovascular conditions and migraine, mental health, and other comorbidities which are important factors that may affect this possible association , and the frequency and duration of episodes, similar to what has already been done in the adult population , 38. For4.5.Our study has some limitations. First, it was not possible to carry out subgroup analyses by age range, degree of obesity, geography, sex, or migraine characteristics  because not enough studies were found with such information. Second, the number of studies evaluated was small and most of them cross-sectional; however, we included all the observational studies found and did not limit the sample to cohort studies. Third, because we did not search the grey literature in local nonindexed journals, it is possible that we did not include some relevant studies; however, we searched the main databases and included all available articles without time or language restrictions. In addition, we used the GRADE approach to assess the certainty of the body of evidence and subgroup and sensitivity analyses to assess possible sources of heterogeneity in our findings.5.Studies with a low risk of bias report that being overweight, obese or overweight are possibly associated with the higher occurrence of migraine. However, the current evidence is still insufficient. Well-design and well-powered studies should be carried out. We recommend improving the control of overweight, obesity and excess weight in the paediatric population because doing so could prevent the occurrence or chronification of migraine, in addition to the well-known benefits on other health outcomes.The original contributions presented in the study are included in the article/CQ-V, DS, KP-B, and CA-D: article evaluation. CQ-V, DS, AD-L-R-P, LD-L, and DF-G: data extraction, results interpretation, and drafting the article. CQ-V, DS, and CA-D: data analysis. CQ-V, DS, AD-L-R-P, LD-L, DF-G, KP-B, and CA-D: critical revision and final approval of the manuscript: All authors contributed to the article and approved the submitted version.


The etiology of amyotrophic lateral sclerosis (ALS) remains largely unknown. This study aimed to summarize the relationship between ALS and its genetic and non-genetic risk factors.A search of relevant literature from PubMed, Embase, and Cochrane Database from inception to December 2022 was performed. Random-effects or fixed-effects models were performed by Stata MP 15.0 to pool multivariate or adjusted ratios (OR). PROSPERO registration number: CRD42022301549.SOD1 (2.2%), C9orf72 (2.1%), ATXN2 (1.7%), FUS (1.7%), TARDBP (0.8%), VCP (0.6%), UBQLN2(0.6%) and SQSTM1 (0.6%) in all the ALS patients.230 eligible studies were included, of which 67 involved 22 non-genetic factors, and 163 involved genetic factors. Four aspects of non-genetic factors, including lifestyle, environmental and occupational exposures, pre-existing diseases/comorbidity and medical exposures, and others, were analyzed. Exposure to heavy metals (OR = 1.79), pesticides (OR = 1.46), solvents (OR = 1.37), previous head trauma (OR = 1.37), military service (OR = 1.29), stroke (OR = 1.26), magnetic field (OR = 1.22) and hypertension (OR = 1.04) are significant risk factors, but use of antidiabetics (OR = 0.52), high BMI , living in urban (OR = 0.70), diabetes mellitus (OR = 0.83), and kidney disease (OR = 0.84) decrease the risk for ALS. In addition, eight common ALS-related genes were evaluated, the mutation frequencies of these genes were ranked from highest to lowest as Our findings suggested that effective intervention for risk exposure and timely modification of lifestyle might prevent the occurrence of ALS. Genetic mutations are important risk factors for ALS and it is essential to detect genetic mutations correctly and scientifically.https://www.crd.york.ac.uk/PROSPERO/display_record.php?RecordID=301549, identifier: CRD42022301549. Amyotrophic lateral sclerosis (ALS) is a fatal nervous system disease characterized by degeneration of upper and lower motor neurons, leading to severe muscle weakness and respiratory failure. It has a prevalence of 5 in 100,000, with an incidence of 2.6–3.0 cases per 100,000 people, reflecting a short average survival period . C9ORF72, SOD1, FUS, and TARDBP are the most common causative genes, accounting for 54.5% of FALS and 7.5% of SALS in Caucasians. However, the remaining cases, especially for most of SALS were not accounted for by variants of wellknown causative genes. As for environmental factors, many advanced epidemiologic studies have examined the potential risk factors for ALS, suggesting heavy metal exposure, pesticides, electromagnetic fields and lifestyle are also considered major associated factors for ALS  disease” OR “MND.” We also performed a hand search to search the bibliographies of relevant original studies more comprehensively if necessary. Two investigators  performed literature selection, and if there were any discrepancies, the other two researchers  would determine the conclusion. A protocol was registered with PROSPERO https://www.crd.york.ac.uk/PROSPERO/display_record.php?RecordID=301549, registration number: CRD42022301549.We adapt the recommendations and methods made by the Meta-analysis Of Observational Studies in Epidemiology (MOOSE) Group, the PRISMA 2009 guidelines for systematic review and meta-analysis  diagnostic criteria of ALS were clearly stated, either by clinical criteria, El Escorial criteria/revised El Escorial criteria or Awaji criteria; (2) the non-genetic studies reported original data concerning the odds ratio (OR) of ALS using a longitudinal cohort study or case-control study design, while the genetic study presented mutation frequencies of the candidate ALS genes using case-control studies or cohort study; (3) familial history of the patients with ALS enrolled was clear; (4) published in English. When the same patient group appeared in more than one publication, the group with the largest sample size or the latest publication date was used for the study of data extraction. When the number of articles about a particular research factor is less than three, this factor was excluded. The detailed flow chart is shown in Studies eligible for further review were clinical literature related to mutation/variants frequencies in https://www.ohri.ca/programs/clinical_epidemiology/oxford.asp) . For non-genetic studies, data were extracted independently by three authors  as follows: Name of the first author, publication year, country of the study, population/cohort, study design, diagnosis criteria, the sample size of ALS (FALS and/or SALS) cases and controls, numbers of different predefined exposures FALS and/or SALS cases and controls. For each eligible genetic study, data were extracted independently by three authors  as follows: Name of the first author, publication year, country of the study, population, study design, diagnosis criteria, gene detection method, the sample size of FALS and/or SALS cases, numbers of mutation carriers in FALS and/or SALS. We use the Newcastle–Ottawa Scale (NOS) to assess the quality of articles  Stang,   Stang, . Here, sSOD1, C9orf72, TARDBP, FUS, UBQLN2, VCP, SQSTM1 and ATXN2 genes in all FALS or SALS cases with 95% confidence intervals (CI). Statistical heterogeneity between studies was assessed by Cochrane's Q test and I2 statistic, if p>0.05, it is considered that the test authenticity and reliability of this factor are high. If I2 <50%, we combined OR or mutation frequencies of the studies with a fixed-effects model. Otherwise, I2 ≥ 50% indicated substantial heterogeneity across studies, and a random-effects model was used  with a 95% confidence interval (CI). To assess which factor had a greater impact on increasing the risk of ALS, we graded all non-genetic factors with the following criterion: OR ≥ 1.3 in risk factors or OR ≤ 0.7 in protective factors were defined as Class I and the rest was Class II for poor pathogenic factors ; “grade II-A evidence” was defined as pooled population >5,000 but with higher heterogeneity (I2 ≥ 50%); “grade II-B evidence” was defined as lower heterogeneity (I2 <50%) but with pooled population <5,000; “grade III evidence” was defined as both pooled population <5,000 and higher heterogeneity.In addition, according to the previous study  existed in studies screening SOD1, TARDBP, FUS, C9orf72, SQSTM1, UBQLN2, VCP, and ATXN2 mutations.Between-study heterogeneity was calculated using the Ip-values for all study factors were >0.05. To assess the impact of each study on the overall results, we performed a sensitivity analysis for both genetic and non-genetic factors. For studies reporting pesticide exposure, sensitivity analysis showed that when the study of Peters et al. (2 decreased to 33.5%). For studies reporting living in urban, sensitivity analysis showed that when the study of Korner et al. (2 decreased to 47.0%). For lead exposure, sensitivity analysis showed that when the study of Andrew et al. , SOD1 (2.0%), ATXN2 repeat expansions (1.9%), FUS (1.6%),TARDBP (1.4%), VCP (0.8%), UBQLN2 (0.7%), SQSTM1 (0.6%), while in Asian populations was SOD1(3.2%), C9orf72 repeat expansions (1.9%), ATXN2 repeat expansions (1.5%), FUS (1.6%), TARDBP (0.6%), SQSTM1 (0.6%), UBQLN2 (0.3%) and VCP (0.3%). In addition, we summarized the mutation frequencies of FALS and SALS. In FALS, the most common gene was SOD1 , followed by C9orf72 repeat expansions , FUS , TARDBP , ATXN2 repeat expansions , VCP , SQSTM1  and UBQLN2 . In contrast, In SALS, the most common gene was ATXN2 repeat expansions , followed by SOD1 , FUS , C9orf72 repeat expansions , TARDBP , UBQLN2  VCP  and SQSTM1   –8.We discussed the mean age of onset of the study population for each factor, results showed that the average age of onset in patients with gene mutation and exposed to environmental risk factors were 41.90 to 57.46 and 62.53 to 73.60, respectively . As expeTill now, the etiology of ALS is largely unknown. As far as we know, this is the most comprehensive meta-analysis investigating non-genetic and genetic risk factors of ALS patients. 22 non-genetic factors, including lifestyle, pre-existing diseases/comorbidity and medical exposure, environmental and occupational exposure, and other non-genetic risk factors, were analyzed. We found 8 of which significantly increase the risk of future ALS development and 5 of which decrease the incidence of ALS. In addition, we also comprehensively summarized the mutation frequencies of ALS patients, including different ethnic and whether they have family history.Previous studies reported the link between lifestyle and ALS; however, conflicting evidence was found. Here, we analyzed three common lifestyles, including smoking, alcohol consumption and coffee drinking. We found alcohol consumption, coffee drinking and smoking might not be associated with a higher incidence of ALS, but the findings need to be further determined due to sample size and heterogeneity. Then, a subgroup analysis of smoking and drinking was performed to resolve this partially. The results of the subgroup analysis showed that current smoking significantly increased the risk of ALS compared with previous smoking, emphasizing the favorable role of quitting smoking in the prevention of ALS. But our subgroup analysis did not find a significant correlation between different drinking doses and ALS incidence, which was confirmed in the previous study , and we need to be alert to the occurrence of ALS in groups exposed to these factors over long periods of time, especially exposure to multiple risk factors. And we are in favor of better nutrition and moderate obesity in ALS patients, which may have a protective effect on the disease. Prospective studies with large samples will be necessary in the future for a more precise identification of the risk factors for ALS.Although genetic factors play a limited role in the development of ALS, they could essentially reveal the mechanism of the disease. Here, by the pooled analysis, the candidate seven ALS causative genes accounted for about 10% of ALS in all the patients, and the mutation frequencies were more in European patients or FALS, suggesting genetic factors should not be ignored.SOD1, 2.1% for C9orf72, 1.7% for ATXN2, 1.7% for FUS, 0.8% for TARDBP, 0.6% for VCP, 0.6% for SQSTM1 and 0.6% for UBQLN2. Subgroup analysis of whether there was a family history of ALS, and several conclusions could be drawn from the results. The mutation frequencies of these eight major ALS-related genes are 28.6% in FALS and 8.1% in SALS. We can conclude that family ALS has a higher gene mutation rate than sporadic ALS, suggesting that gene mutation might be one of the important risk factors and gene detection is of great significance in FALS. In FALS patients, the top three mutated genes are SOD1, C9orf72, and FUS, which are ATXN2, SOD1 and FUS in SALS. Our findings emphasize the distinct genetic structure between FALS and SALS, which need to be given appropriate consideration when performing genetic testing on patients with ALS.Our analysis showed that the overall pooled mutation frequencies of these major ALS-related genes are 2.2% for C9orf72 repeats, followed by SOD1, ATXN2 repeats, FUS, TARDBP, VCP, UBQLN2 and SQSTM1, In Asian populations, the most common mutations in ALS were the SOD1, followed by C9orf72 repeats, FUS, ATXN2 repeats, SQSTM1, TARDBP, UBQLN2, and VCP. Our results suggested that gene mutations are more common in European than Asians, and the mutant spectrum in different populations is significant differences.Subgroup analysis also showed the difference in gene mutation results between European and Asian populations. In European populations, the most common mutations in ALS were the Several limitations in this meta-analysis should be noted. First, most of the literature included in the present meta-analysis was case-control, without prior interventions in the experimental and control groups, making it difficult to quantitatively evaluate and scientifically categorize exposure levels for monitoring. Second, it was difficult to ensure that each study patient was exposed to a single risk factor, and not all studies made adequate adjustments for confounders. Third, the number of pooled populations included was small for some risk factors, resulting in low reliability and evidence. Fourth, we only included English-language literature, and all of them were from the above-mentioned databases. Finally, we only assessed risk factors for which the number of literature searches was ≥ 3, and there may be many risk factors that we did not discuss.In conclusion, our findings emphasized the effect of lifestyle, environmental and occupational exposure, pre-existing disease/comorbidity and medical exposure, and other non-genetic risk factors. At present, the replicable and definitive non-genetic risk factors of ALS have not been identified. In addition, we found diversity in mutation rates across family histories and populations from different ethnic. Our findings support the future assessment of these non-genetic and genetic factors as potential etiologic contributors to the risk of ALS, while the relationship between phenotype, genetics, and pathology remains to be further investigated.The original contributions presented in the study are included in the article/Q-QD, BC, and Y-PC conceived and designed the study. Q-QD, ZJ, W-MS, X-JG, and Y-PC selected the articles and extracted and cross-checked the data. Q-QD, HW, X-JG, and Y-FC contributed to the statistical analysis. Q-QD and Y-PC wrote the first draft of the manuscript. Q-QD, XG, YW, and Y-PC revised and discussed the final edition. All authors contributed to the article and approved the submitted version.


Increasing evidence from observational studies and clinical experimentation has indicated a link between the gut microbiotas (GMs) and polycystic ovary syndrome (PCOS), however, the causality and direction of causality between gut microbiome and PCOS remains to be established.We conducted a comprehensive search of four databases–PubMed, Cochrane Library, Web of Science, and Embase up until June 1, 2023, and subjected the results to a meta-analysis. In this study, a bidirectional two-sample Mendelian randomization (MR) analysis was employed to investigate the impact of gut microbiota on polycystic ovary syndrome (PCOS). The genome-wide association study (GWAS) data for PCOS comprised 113,238 samples, while the GWAS data for gut microbiota were derived from the MiBioGen consortium, encompassing a total sample size of 18,340 individuals. As the largest dataset of its kind, this study represents the most comprehensive genome-wide meta-analysis concerning gut microbiota composition to date. Single nucleotide polymorphisms (SNPs) were selected as instrumental variables at various taxonomic levels, including Phylum, Class, Order, Family, and Genus. The causal associations between exposures and outcomes were assessed using four established MR methods. To correct for multiple testing, the false discovery rate (FDR) method was applied. The reliability and potential biases of the results were evaluated through sensitivity analysis and F-statistics.Actinomyces , Streptococcus , and Ruminococcaceae UCG-005  were identified as risk factors for PCOS. Conversely, Candidatus Soleaferrea , Dorea , and Ruminococcaceae UCG-011  were found to be protective factors against PCOS. Furthermore, the MR-PRESSO global test and MR-Egger regression indicated that our study results were not affected by horizontal pleiotropy (p > 0.05). Finally, the leave-one-out analysis corroborated the robustness of the MR findings.The meta-analysis incorporated a total of 20 studies that met the criteria, revealing a close association between PCOS and specific gut microbiota species. As per the results from our MR analysis, we identified six causal associations between the gut microbiome and polycystic ovary syndrome (PCOS). At the genus level, Both our meta-analysis and MR study indicates that there is a causal relationship between the gut microbiome and PCOS, which may contribute to providing novel insights for the development of new preventive and therapeutic strategies for PCOS. The AndRandomized controlled trials (RCTs) are considered the gold standard for etiological research; however, their application in actual clinical research is often limited due to factors such as high costs, operational difficulties, and ethical considerations. Previous research on the relationship between gut microbiota and PCOS has primarily originated from observational studies. Researchers have examined patients’ feces or transplanted human gut microbiota into germ-free mice for experimental investigation . This prMendelian randomization (MR) has emerged as a widely employed epidemiological research method in recent years . As a reWith the increasing number of large-scale genome-wide association studies (GWAS) on the gut microbiome and its relationship to various diseases , Mendeli2.2.1.2.1.1.This systematic meta-analysis follows the PRISMA reporting guidelines . We anal2.1.2.in vitro studies.Inclusion criteria included:  studies 2.1.3.We used a standardized data extraction list for data collection, which was recorded in an Excel spreadsheet, including study type, publication year, first author, diagnostic criteria, age range of participants, sample size and type, testing methods, and statistical results. Given that the selected articles are observational studies, we employed the “Agency for Healthcare Research and Quality (AHRQ)” for cross-sectional data quality assessment and the “Newcastle–Ottawa Scale for assessing the quality of cohort studies in meta-analysis (NOS)” for cohort studies.2.1.4.Given the significant discrepancies in the results of studies on gut microbial communities at different microbial levels, and the limited data available on the association between a specific type of microorganism and PCOS, a quantitative consolidated analysis could not be conducted. Therefore, we only carried out a narrative synthesis of the research results.2.2.2.2.1.This study employs a bidirectional two-sample Mendelian randomization approach to investigate the causal relationship between the gut microbiota and PCOS, using summary data from published genome-wide association studies for analysis. In Mendelian randomization research, the instrumental variables must satisfy three core assumptions: relevance, exclusion, and independence: (1) Instrumental variables must be strongly associated with the exposure factors; (2) Instrumental variables can only affect the outcome through the exposure, and there must be no direct relationship between the instrumental variables and the outcome; (3) Instrumental variables must be independent of confounding factors that influence the relationship between exposure and outcome. The flowchart of the study is presented in 2.2.2.The gut microbiota GWAS data is derived from the MiBioGen consortium summary statistics , which cThe GWAS data for PCOS was sourced from a large-scale meta-analysis conducted by 2.2.3.p < 1 × 10−5 to filter significant SNPs  in the Mendelian randomization (MR) analysis, determining the presence of weak instrument bias  Setting a threshold of ant SNPs ; (2) To 0,000 kb ; 3. The ent bias . To furtociation .N represents the sample size of the exposure factor, R2 denotes the proportion of exposure variation explained by the instrumental variable . We used four MR analysis methods, including inverse-variance weighted (IVW), MR-Egger regression, weighted median, and weighted mode methods.The IVW method is the primary approach for MR analysis. It first calculates the Wald ratio for each valid SNP and then combines the Wald ratios using the inverse of variance as weights, further assessing the influence of exposure factors on outcomes , 2019.The weighted median estimator provides a valid causal effect estimate even when a certain proportion (less than 50%) of invalid instrumental variables are present .MR-Egger regression fits a linear function by calculating the association of each SNP with the outcome and its association with exposure. The slope represents the estimated causal effect, and we assess the average pleiotropy based on the regression intercept .Weighted mode clusters SNPs into groups based on the similarity of individual ratio estimates, calculates the cubic variance weighting for SNPs in each cluster, and generates causal estimates based on the cluster with the highest SNP weighting .2.2.4.1.For the primary MR results, we applied the false discovery rate (FDR) multiple testing correction at each taxonomic level .2.2.5.Q test was employed to assess the presence of heterogeneity between the two samples  and the Two sample MR (version 0.5.6) package, MR–PRESSO analysis was performed by the R package “MRPRESSO” (version 1.0).2.3.The GWAS data utilized in this study are publicly available de-identified datasets. The Institutional Review Board (IRB) has approved these data; therefore, no additional ethical approval is required.3.3.1.A total of 219 articles were retrieved from databases and other sources. After removing 82 duplicate articles and excluding 117 articles that did not meet the inclusion criteria, a total of 20 studies were included in the systematic review . These c3.2.Faecalibacterium prausnitzii) showed inconsistent results in three studies , comprising 124 phylum, 223 class, 279 order, 475 family, and 1717 genus. Following linkage disequilibrium (LD) clumping and harmonization, the number of candidate IVs associated with specific bacterial taxa for each outcome ranged from 4 to 22. It was found that all instrumental variables had an F-statistic >10. Using the MR-PRESSO global test (p > 0.05) and MR-Egger regression (p > 0.05), the results indicated no horizontal pleiotropy for the SNPs used as IVs, suggesting that there was no confounding in this study. Detailed IV information is listed in At the 3.4.IVW.” Actinomyces , Streptococcus , and RuminococcaceaeUCG005  were identified as risk factors for PCOS. Meanwhile, Candidatus Soleaferrea , Dorea , and RuminococcaceaeUCG011  were identified as protective factors for PCOS  method, with denoting this as the “ORfor PCOS . Furtherfor PCOS , 4.3.5.p > 0.05; p > 0.05). The MR-PRESSO global test confirmed the same results (p > 0.05), suggesting that there was no confounding in this study. Leave-one-out analysis indicated that the MR findings were robust , with statistically significant differences. It has been reported that the abundance of Dorea in the intestines of PCOS patients is significantly reduced compared to healthy individuals (p = 0.005), consistent with this finding. However, another study found that Dorea was significantly more abundant in the intestines of prediabetic PCOS patients than in healthy individuals (FDR = 0.03), suggesting that Dorea may increase the risk of developing metabolic subtypes of PCOS. Candidatus Soleaferrea is known to exert gut-protective effects through the secretion of metabolites (2) and 19 non-obese PCOS patients (BMI < 24 kg/m2) found that Candidatus Soleaferrea was significantly more abundant in non-obese PCOS patients, suggesting that it may reduce the risk of obesity in PCOS patients .The meta-analysis indicates that there are specific microbial communities at the phylum, order, family, genus, and species levels that are closely associated with PCOS, involving a total of 154 bacterial taxa. The results of our Mendelian Randomization (MR) analysis confirm the existence of a causal relationship between gut microbiota and PCOS disease. A previous study on PCOS employed 16S rRNA sequencing of the V3-V4 region and found a higher abundance of bundance . These f = 0.005). Yin et abolites . A studypatients . HoweverRuminococcaceae UCG011 reduced the risk of PCOS, while Actinomyces and Ruminococcaceae UCG005 increased the risk. However, there is currently no direct evidence linking Actinomyces, Ruminococcaceae UCG005, or Ruminococcaceae UCG011 specifically with PCOS.We also found that Ruminococcaceae UCG011 is positively correlated with acetate concentration . However, our study suggests that Ruminococcaceae UCG005 is a risk factor for PCOS . Since there is currently no research on the relationship between Actinomyces, Ruminococcaceae UCG005, and PCOS, our study provides a reference for further exploration.It has been reported that ntration . Acetaten health , while obundance . RuminocGut microbiota can promote the induction or development of PCOS through various mechanisms, but the exact mechanism by which gut microbial communities cause PCOS has not yet been determined. Many dietary components can influence disease onset and progression by targeting gut microbiota . VitaminThere are some limitations to this study. First, GWAS are unlikely to explain all genetic traits of complex phenotypes . Human b5.By providing a comprehensive assessment of the causal relationship between gut microbiota and PCOS, our study contributes valuable insights into the potential role of gut microbes in the development and progression of this condition. Future research should aim to further elucidate the underlying mechanisms by which these microbial communities influence PCOS and explore potential therapeutic strategies targeting the gut microbiota to mitigate the disease’s impact on affected individuals.https://mibiogen.gcc.rug.nl/, and the PCOS dataset is accessible at: https://www.repository.cam.ac.uk/handle/1810/289950.This study analyzed publicly available datasets. The primary summary statistics for gut microbiome can be downloaded from: The study utilized publicly available de-identified data from participant research approved by the ethics standard committees. Therefore, no additional separate ethical approval was required for this research.QM conceived the study, conducted data analysis, and authored the manuscript. HG contributed to the experimental design and participated in the revision of the manuscript. QG provided valuable assistance in data analysis. MX was involved in the design of experimental methods and provided critical review and editing of the manuscript. All authors contributed to the article and approved the submitted version.This research was supported by the National Natural Science Foundation of China (82104913) and the National Administration of Traditional Chinese Medicine National Renowned TCM Master Studio Construction Project (1199ws02).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.


To investigate whether maternal cigarette smoking during pregnancy is a risk factor for developing GDM.2) test and the I2 index was used to quantify it. The studies were evaluated for publication bias.MEDLINE, Scopus, CENTRAL and Google Scholar databases were searched from inception to December 2022 to identify eligible original articles. A systematic review and meta-analysis  were performed. The primary outcome was the development of GDM in pregnant women. The results were expressed as odds ratios (OR) with 95% confidence interval (CI) (inverse variance method). Subgroup analysis was planned according to the maternal smoking status and GDM diagnostic criteria. Statistical heterogeneity was checked with the Chi-squared , p = 0.12; I2 = 63%; Chi2 = 27; df=10; p < 0.002. Further subgroup analysis according to maternal smoking status was not performed due to missing data.Thirty-five studies, including 23,849,696 pregnant women, met the inclusion criteria. The pooled OR of smoking during pregnancy compared with non-smoking (never smokers and former smokers) was 1.06 (95% CI 0.95–1.19), p = 0.30; IThere is no evidence to support an association between maternal cigarette smoking during pregnancy and the risk for GDM. Universally accepted diagnostic criteria for GDM must be adopted to reduce heterogeneity and clarify the association between smoking and GDM. Studies involving pregnant women with pre-existing type 1 or T2DM, non-pregnant populations, pediatric, adolescent, or male populations, as well letters to the editor, editorials, case reports and case series were excluded.The primary outcome of interest was the development of GDM in pregnant women smoking during pregnancy. The first arm included the active smokers and the second the non-smokers (never-smokers and former smokers). Former smokers ceased smoking at the beginning of gestation.The study selection was performed by two researchers (KIA and EP) independently; disagreements were resolved with the contribution of a third researcher (SAP), leading to consensus. The study selection was guided by the eligibility criteria and outcome of interest. The identified studies were imported to a systematic review software platform (COVIDENCE). Their title and abstract were screened for relevance, and followingly the full texts of selected studies were screened further for the final selection. Finally, the references of selected articles were also screened for the detection of more eligible studies.® sheet by two researchers (KIA and EP) that collected data independently on the name of the first author, the paper’s title, the year of the paper’s publication, the number of participants, the study’s type, the journal of publication, the country of origin, the study’s duration, the primary outcome, the study’s conclusion, the number of women in each subgroup , the number of GDM cases in each subgroup, the diagnostic method of GDM, the confounders adjusted in each study, and the study’s conclusion. Any disagreements were resolved by a third researcher (SAP).The data extraction was performed in a pre-designed Microsoft Excel2) test  and was quantified with the I2 Higgins test  [Statistical analysis was performed using Review Manager (RevMan) software (version 5.4.1 for Mac OS), as recommended by the Cochrane Collaboration. Dichotomous data were analyzed using an odds ratio (OR) and a 95% confidence interval (CI), according to the Inverse Variance method. The level of significance was set at 5%. The data were combined using the random-effects model because of the high heterogeneity among the included studies. The presence of heterogeneity was checked with the Chi-squared (Chigeneity) . A sensiThe risk of bias was assessed by two independent researchers (KIA and EP) with the 9-star Newcastle-Ottawa Scale (NOS) for cohort and case control studies , and with the revised Joanna Briggs Institute (JBI) scale for cross-sectional studies  , 23. PubWith the initial search 2,574 articles were retrieved. After title and abstract screening, 243 studies remained for full text assessment. Finally, 35 articles were included in the present systematic review and meta-analysis. The PRISMA  flowchart of selected studies is presented in Fig. The characteristics of the included studies, published between 1992 and 2022, are summarized in Table The results of the meta-analysis performed are summarized in Fig. 2 = 90%; Chi2 = 344; df = 34; p < 0.001).OR 1.06 , indicating no significant association between cigarette smoking during pregnancy and GDM development. There was high heterogeneity  [Due to the high heterogeneity in the definition of GDM diagnostic methods, we proceeded to subgroup analysis of the studies using the two-step Carpenter-Coustan criteria. The results of subgroup analysis Fig.  did not < 0.002) . The hetAdditionally, we performed further subgroup analysis between the studies that provided: adjusted OR Fig. , unadjus2 index were observed.High heterogeneity was observed. Removing each study, no significant changes in the effect estimate and IThe quality scores using NOS and the revised JBI scales are summarized in Table The present systematic review and meta-analysis aimed to investigate the association between maternal cigarette smoking during pregnancy and the risk for GDM development. There is limited epidemiologic evidence supporting cigarette smoking as a risk factor for GDM. The present data synthesis of 35 studies concluded that maternal smoking during pregnancy is not associated with an increased risk for GDM , a result consistent with the findings of the previous meta-analyses. To our knowledge, this is the third systematic review and meta-analysis (SRMA) on the topic, as there are two SRMAs published in 2008 and 2018 [The present study is a meta-analysis of observational studies, and the potential confounders were acknowledged in Table Among the including studies, there were controversial results about the smoking effect on GDM development. Fourteen studies showed an association between smoking during pregnancy and GDM, while 21 studies did not reveal any association  altered the diagnostic approach of hyperglycemia in pregnancy . FollowiAs tobacco smoking during pregnancy may have detrimental implications for mother and fetus, women are encouraged to avoid it. Nevertheless, the actual mechanisms involved in the association of smoking and GDM manifestation need clarification. Nicotine increases cortisol, catecholamines and growth hormone secretion, hormones that counteract the actions of insulin, contributing to insulin resistance . PregnanAlthough women are strongly encouraged to cease smoking during gestation, it was unexpected that some studies showed an association between smoking cessation and increased risk for GDM. A possible cause is that smoking cessation leads to increased caloric consumption and weight gain, a risk factor for GDM. Moreover, changes in adipocytes metabolism and cumulative exposure to smoking before cessation are associated with a greater risk for GDM in women who cease smoking upon gestation . In the Smoking results in a toxic environment of inflammatory and oxidative stress, undermining the health of mothers and fetuses. Although policies have been adopted for the prevention of active maternal smoking during pregnancy, exposure to tobacco through passive smoking from partners or/and family remains a threat to pregnant women . The expThe current study presents several strengths and certain limitations. Regarding strengths, the meta-analysis included 23,849,696 subjects and 35 studies published between 1992–2022 and conducted in 16 countries from almost every continent , covering a representative population. Moreover, it is the third systematic review and meta-analysis evaluating the association between maternal cigarette smoking and the risk of GDM. Nine original studies were published since 2018 and incorporated in the present meta-analysis, along with several older studies.In terms of limitations, the present systematic review and meta-analysis consists of 35 observational studies, as they were only available in the literature. The only study type that can prove a causation between a risk factor and a certain condition is the randomized controlled trial; nevertheless, it is unethical to conduct a trial with smoking intervention in pregnant women, as it would pose mothers and fetuses at a definitive risk. In all meta-analyses of observational studies, the confounding factors have to be identified and considered for a safe conclusion to be reached. Unfortunately, many of the included studies did not assess the potential confounders and presented unadjusted data, while the corresponding authors did not respond to our request for providing further data. However, subgroup analyses to evaluate the effect of unadjusted results on the final conclusion were conducted; the pooled outcome remained unchanged. Another limitation is that subgroup analysis according to tobacco exposure levels and smoking status was not performed, due to a lack of relevant data. Finally, there was high heterogeneity among the studies, which could be attributed to major differences in participants’ demographic characteristics, smoking assessment, and GDM diagnostic methods.In conclusion, there is no evidence to suggest an association between maternal cigarette smoking during pregnancy and the development of GDM. However, further studies evaluating the impact of passive smoking and other potential confounding factors on GDM development should be performed. It is imperative, though, that the exposure of pregnant women to cigarette smoke should be limited even from the preconception period through investment in awareness policies, as it creates a toxic environment for the fetomaternal unit. GDM diagnostic criteria deserve a global consensus to establish a common ground on early identification and treatment.Supplementary material


Previous studies have been indicated that susceptible loci of several multifactorial diseases were non-randomly distributed on human genome. There is no published data on chromosomal distribution of genes associated with risk of ankylosing spondylitis. Therefore, the present study was carried out.Published meta-analyses indexed in the PubMed database were used in the present study. Non-randomness chromosomal distribution of these loci was evaluated by the statistical method of Tai et al.A total of 88 articles were obtained. There was 32 ankylosing spondylitis associated genes. The present study revealed that the human chromosome segments 6p11.2-p21.33, 19q13.2-q13.42, and 2q11.2-q14.1 were ankylosing spondylitis associated-rich regions by bearing 7, 6 and 4 susceptible loci, respectively.Ankylosing spondylitis associated-genes non-randomly have been distributed non-randomly on human chromosomes. A total of 88 articles were obtained. There were 11 studies with other purposes, such as those examine association of ankylosing spondylitis and cancers which were excluded. After removing these articles, the author screened the retrieved studies. For some genetic polymorphisms which more than one meta-analyses were published about their association with the risk of ankylosing spondylitis, the latest articles were included in the study and other articles were excluded. Loci reported from genome-wide association studies were not included in the analysis.Published meta-analyses indexed in the PubMed database were used in the present study. Keywords for searching were ankylosing spondylitis, meta-analysis and polymorphism. No time limitation was considered for the results and for selection of the studies. Last update was 27 August 2022. Meta-analysis study design was eligible for including in the study. List of the references of the relevant articles, as well as data presented in two articles 28 Statistical relationship between polymorphisms and the risk of the disease may had been stated for at least one human ethnicity.Genes with at least one genetic variation associated with the susceptibility to ankylosing spondylitis were included in the present study.https://www.omim.org).A non-electronic hand-written form was used for data extraction. Data regarding article identification (authors name and publication date) as well as main study attributes (gene name and locus symbol) were extracted from each selected study, by the author. Cytogenetic location and MIM (Mendelian inheritance in Man ID) of the susceptible loci were also extracted from Home - OMIM database  was used.28 A total 32 of susceptible loci were identified. Extracted data  were summarized in Table 1.After removing 11 unrelated studies and older articles concerning association a specific polymorphism with the risk of ankylosing spondylitis, as described in Methods section, 25 articles were included in the study.IL17F, IL17A, PSMB9, TAP1, TAP2, TNF, and HLA-B), 6  and 4  genes were located on the human 6p11.2-p21.33, 19q13.2-q13.42, and 2q11.2-q14.1 chromosome segments, respectively. This finding supports the non-randomness distribution of diseases associated genes on human chromosomes.42 There was no statistical evidence that the other ankylosing spondylitis associated-genes distributed non-randomly on the chromosomes .From 32 ankylosing spondylitis associated genes, 7 (46One of the important featured of multifactorial complex traits such as ankylosing spondylitis is that different polymorphic genes/alleles are involved in the pathogenesis. Each of above-mentioned chromosomal segments bearing several functional loci. We know that these loci are in linkage disequilibrium and their allelic frequencies differ between ethnic groups. Additionally, additive, dominance and epistatic effects might be involved between the susceptible genes. There is evidence that some of known polymorphic alleles involved in the pathogenesis of ankylosing spondylitis have synergistic and epistatic effects with each other.Application of knowledge gained from nonrandom accumulation of ankylosing spondylitis associated loci on the human chromosome segments 6p11.2-p21.33, 19q13.2-q13.42, and 2q11.2-q14.1 could be of crucial importance in development of a laboratory diagnostic test for mass screening programs to find high risk persons.


This study aims to conduct a meta-analysis to assess the effect of virtual reality-based therapy (VRBT) on balance dimensions and fear of falling in patients with multiple sclerosis (PwMS). Secondarily, to determine the most recommendable dose of VRBT to improve balance.PubMed Medline, Web of Science, Scopus, CINAHL and PEDro were screened, without publication date restrictions, until September 30th, 2021. Randomized controlled trials (RCTs) comparing the effectiveness of VRBT against other interventions in PwMS were included. Functional and dynamic balance, confidence of balance, postural control in posturography, fear of falling and gait speed were the variables assessed. A meta-analysis was performed by pooling the Cohen's standardized mean difference (SMD) with 95% confidence interval (95% CI) using Comprehensive Meta-Analysis 3.0.p < 0.001); dynamic balance ; postural control with posturography ; confidence of balance ; and in reducing fear of falling ; but not on gait speed . Besides, the most adequate dose of VRBT to achieve the greatest improvement in functional balance was at least 40 sessions, five sessions per week and 40–45 min per sessions; and for dynamic balance, it would be between 8 and 19 weeks, twice a week and 20–30 min per session.Nineteen RCTs, reporting 858 PwMS, were included. Our findings reported that VRBT is effective in improving functional balance (SMD = 0.8; 95%CI 0.47 to 1.14; VRBT may have a short-term beneficial role in improving balance and reducing fear of falling in PwMS.The online version contains supplementary material available at 10.1186/s12984-023-01174-z. Multiple sclerosis (MS) is an immune-mediated, inflammatory and neurodegenerative chronic disease that causes demyelination and axonal degeneration in the Central Nervous System (CNS) . MS has In the field of neurorehabilitation, along with pharmacological and conventional therapy (CT), virtual reality-based therapy (VRBT) is being employed in the last decade to reduce the impact of disabling sequelae and to improve the quality of life of these patients  and otheIn recent years, several reviews have analyzed the effect of VRBT on balance and/or risk of falls –34. Howe. It was registered in the PROSPERO database (CRD42021256768).A systematic review with meta-analysis was performed in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020 statement . It was A search was performed, independently, by two authors (ICP and FANE), in PubMed Medline, Web of Science (WOS), Scopus, CINAHL Complete and PEDro (Physiotherapy Evidence Database) and through the references of the retrieved records , independently, screened the titles and abstracts of all the retrieved studies for further examination. Disagreements were resolved by a third author (FANE).The following inclusion criteria were used: (1) RCTs or pilot RCTs with at least two groups; (2) assessing the effect of VRBT on the outcomes of interest (see outcomes section) in comparison to others controls; (3) in PwMS; (4) and studies that provided quantitative data about the outcomes to perform the meta-analysis. The exclusion criteria were: (1) RCTs including patients with different neurological diseases apart from PwMS in the same group; (2) studies reporting statistical data which cannot be meta-analyzed with our software.For each study the following data were extracted: (1) overall study characteristics ; (2) number of groups; (3) sample characteristics for each group ; (4) characteristics of the VRBT intervention ; (5) type of therapy used as control; (6) quantitative results for each variable at the end of the intervention  ; and evaThe variables assessed in this systematic review were three: postural balance, fear of falling and gait speed. Considering that postural balance is a complex function integrated by some dimensions, we independently assessed the functional balance, dynamic balance, confidence of balance and postural control measured with posturography.Risk of bias and methodological quality of the included studies was assessed using the PEDro Scale. This scale is composed by 11 binary items (“yes” if the criterion is met or “no” when the criterion is not met) . The totThe GRADE  approach was employed to assess the level of quality evidence of findings in each meta-analysis, through the assessment of risk of bias, inconsistency, inaccuracy, indirectness and risk of publication bias . With thComprehensive Meta-Analysis version 3.0  [P < 0.1 there exists a risk of publication bias) [P for Q-test and the degree of inconsistency (I2) from Higgins [P < 0.1 and it can be categorized as low (I2 < 25%), moderate (I2 25–50%) or large (I2 > 50%) [Statistical analysis was performed by two authors by using NJ, USA) . A meta-NJ, USA)  with 95%NJ, USA) . SMD proNJ, USA) . In addiNJ, USA) . The risNJ, USA)  using thon bias) . In addion bias) . Accordion bias) . Finally Higgins . Heterog2 > 50%) , 51.In order to assess the contribution of each study to the global effect in each meta-analysis, a sensitivity analysis was performed using the leave-one-out method . In addin = 303), 260 studies were screened by title/abstract. One hundred and ninety-seven studies were excluded by title/abstract whereas 44 did not meet the inclusion criteria. Finally, 19 RCTs [Five hundred and sixty-three references were identified . After removing duplicate records   and dynaAll studies were included in the quantitative synthesis. Table p < 0.001) in favor of VRBT (Table p < 0.001) on BBS is observed in favor of VRBT compared to other controls. A possible risk of publication bias has been identified (Egger p = 0.9 and Trim-and-fill variation of 16%) . Sensitivity analysis did not show variations.Thirteen RCTs , 69, 71 BT Table ; Fig. 2Ap = 0.005) in favor of VRBT when compared with UC [p = 0.001) in favor of VRBT when compared with CT [p = 0.011) in favor of VRBT + RAGT vs RAGT [p < 0.001) was observed in PwMS with moderate disability  [p < 0.001) [p = 0.045) [For specific subgroups comparisons, the analyses of the BBS show: an increase of 3.92 points  , 71; bei< 0.001) , 59; and= 0.045) , 69 (Addp = 0.002) on dynamic balance in favor of VRBT (Table p = 0.24 and Trim-and-fill variation of 40%) . Sensitivity analysis showed a variation of 19% in the effect size with respect to the original SMD when the study of Kalron [p = 0.008).Data from thirteen RCTs , 70, 75 BT Table ; Fig. 2Bf Kalron  was remop = 0.001) in favor of VRBT [p = 0.049) [p = 0.012) [p = 0.007) [p = 0.01) [Compared to CT, the analysis showed a medium effect of VRBT   (Additio= 0.012) , 68, 70;= 0.007) , 65, 70; = 0.01) , 68, 70 p = 0.017) was observed on Sway Area for the EC condition   [Three studies , 62, 69 on Table ; Fig. 3A= 0.004) , 62.Fig.p = 0.048) on CoP excursion with OE has been observed   in favor of VRBT (Table p = 0.001) on the ABC scale in favor VRBT, with no risk of publication bias . Sensitivity analysis did not show variations.Three RCTs , 68, 70 BT Table ; Fig. 4Ap = 0.001) on the ABC scale in favor of VRBT compared to UC [p < 0.001) compared to CT [Subgroup analysis revealed an increase of 10.03 points  (Table p = 0.002) in FES-1 in favor of VRBT. No risk of publication bias was found . Sensitivity analysis showed a variation of 51% in the effect size after removing the study of Kalron [p = 0.03).Three RCTs , 65, 68 5) Table ; Fig. 4Bf Kalron , althougp = 0.4) (Table p = 0.12 and Trim-and-fill change of 46%)  was observed. Sensitivity analysis did not report substantial variations. Subgroup analyses, according specific comparisons, did not show differences between VRBT vs UC , VRBT vs CT , and VRBT + CT vs CT .Seven RCTs , 68, 69 The aim of this systematic review with meta-analysis was to collect all previous RCTs assessing the effectiveness of VRBT to improve balance in its different dimensions and to reduce fear of falling in PwMS. A second aim was to define the optimal dose of the VRBT protocol to improve functional and dynamic balance in PwMS. The present findings suggest that VRBT: (1) improves functional and dynamic balance; (2) increases confidence of balance and postural control assessed with posturography; (3) reduces fear of falling; (4) but, does not improve gait speed in PwMS. Compared to previous reviews –34, the The present results have shown that VRBT is effective improving functional balance (with regard to ADLS) in PwMS. A large effect is also observed for VRBT, with an increase of 3.36 points on the BBS score when compared to other interventions or UC. Regarding functional balance results (assessed with BBS) from previous reviews, we must mention that not the all reviews found significant improvements in VRBT compared to other therapies. While Casuso-Holgado and Santos-Nascimento did not show statistically significant differences between VRBT and CT , 32, ourWe must note that in contrast to the reviews by Casuso-Holgado and Santos-Nascimento , 32, ourRegarding the effect on postural control assessed with posturography, the meta-analysis shows a medium effect on Sway Area in EC condition, and a small effect on CoP excursion in EO in favor of VRBT compared to CT. This points out that VRBT is helpful to reorganize the sensory inputs related with balance . Thus, VRBT leads to an increase of postural control when different sensory inputs are lost. In addition, our findings showed that VRBT does not improve gait speed, in agree to Casuso-Holgado .As shown in the review of Akkan,  the imprMultisensory information is crucial to produce an effective antigravity muscular response in order to avoid destabilization and to maintain balance . TherefoFindings reported in this study are clinically relevant and provide the most appropriate VRBT dose for treating functional and dynamic balance in PwMS. One strength of our findings is that we provide the most optimal dose to obtain the largest improvement on functional and dynamic balance. One strength of our findings, with large interest for clinical practice, is that we report the most appropriate dose of VRBT to obtain the largest improvement for functional , and for dynamic balance . Our findings provide support to the use of VRBT to recover balance in neurological diseases such as MS. The majority of the VR devices employed in the included studies are non-immersive, so the present results may be more valid for interventions based on non-immersive VR devices. As VRBT may be used both, in clinical settings and home, it may be also considered an excellent tool for tele-rehabilitation.Assuming that the results reported in this meta-analysis are clinically relevant, some limitations must be considered however. Thus, the low number of participants per meta-analysis may reduce the accuracy of our findings, although studies involving neurological patients usually have small sample sizes. Furthermore, the small number of studies that assess some outcomes, such as balance confidence, fear of falling or postural control may also reduce the generalization of the findings. In addition, the medium risk of bias in the included studies, resulting from the impossibility of blinding participants and therapists, and assessors in sometimes, increases the selection risk, performance and detection biases. Other limitation is related to the risk of publication bias observed in some meta-analysis, and the impossibility of assessing this variable in some studies, which also reduces the generalization of the findings. Sensitivity analysis surpasses 20%, being another limitation that reduces the precision of our findings. Another limitation is related to the low quality evidence found in some meta-analysis, which can affect to the robustness of our findings. Finally, all the included studies conducted the assessment in the short-time, so it has not been possible to assess the effect of VRBT in the medium- and long-term.This review provides evidence supporting the effectiveness of VRBT to improve postural balance in PwMS. VRBT is better than UC, CT or RAGT to increase the functional balance, being able to exceed the MCID for BBS reported by scientific literature after VRBT. Therefore, VRBT can be considered an excellent strategy for functional balance rehabilitation in PwMS showing moderate disability. To increase functional balance VRBT would be applied during 40 sessions or more, five sessions per week and between 40 and 45 min. Regarding dynamic balance, VRBT shows a small effect, especially in PwMS with only minimal signs of disability. Our findings recommend that the more appropriated dose of VRBT protocols to improve dynamic balance would be between 8 and 19 sessions, with a duration of 20–30 min per session and twice per week. VRBT also improves different parameters related to postural control in EO and EC conditions. In addition, VRBT reduces the fear of falling compared with CT, increases balance confidence associated to ADLS. However, further RCTs studies using a larger sample size and a control of risk of bias are required in order to increase the generalizability of the present findings.Additionalfile1. Supplementary Tables:TableS1. Description of each Test Reported by Included Studies. TableS2. Subgroup Analysis According Disability Status (EDSS) of PwMS in Studies Included. TableS3. Subgroup Analysis According Total Number of Sessions. TableS4. Subgroup Analysis Number of Sessions per Week. TableS5. Subgroup Analysis According Duration of each Session in Minutes.Additionalfile2. Supplementary Figures: FigureS1. Funnel Plot of the Effect of VRBT on Functional Balance. FigureS2. Funnel Plot of the Effect of VRBT on Dynamic Balance. FigureS3. Funnel Plot of the Effect of VRBT on Sway Area with Eyes Closed. FigureS4. Funnel Plot of the Effect of VRBT on Centre of Pressure Excursion with Eyes Open. FigureS5. Funnel Plot of the Effect of VRBT on Confidence of Balance. FigureS6. Funnel Plot of the Effect of VRBT on Fear of Falling. FigureS7. Funnel Plot of the Effect of VRBT on Gait Speed.


XRCC3 rs861539 on the risk of ovarian cancer (OC) are still unclear. Therefore, a meta-analysis of 10 studies containing 6,375 OC cases and 10,204 controls was performed for this topic. Compared with GG genotype, GA + AA genotypes could significantly decrease the OC risk, odds ratios (ORs) and their corresponding 95% confidence intervals (CIs) were 0.89 (0.83-0.95) and P=0.001, and 0.88 (0.82–0.95) and P=0.001 under the dominant and heterozygous genetic models. Compared with G allele, rs861539 A could significantly reduce the OC risk, OR and its corresponding 95% CI was 0.94 (0.89–0.98) and P=0.007. By subgroup analysis in ethnicity, protective effects on OC risk in Caucasians were observed . The authenticity of positive association findings was further confirmed by trial sequential analysis (TSA) and false-positive report probability (FPRP) analysis. The subsequent functional analysis revealed that rs861539 could regulate the post-transcriptional expression of XRCC3 by changing the activity of putative splice sites and types of splicing factors. rs861539 also may act as an expression Quantitative Trait Loci (eQTL) affecting the expression of genes such as XRCC3, MARK3, APOPT1, etc., and has an impact on the structure of XRCC3.The effects and underlying mechanism of  Worldwide, ovarian cancer (OC) is one of the most common gynecologic cancers, and has the highest mortality rate between them and occupy the third place in mortality, only after cervical and uterine cancer . At presXRCC3 gene may affect its DNA damage repair efficiency and be associated with the pathogenesis of tumors , the recessive model , the heterozygous model , the homozygous model  and the allelic model ). Thus, the subsequent meta-analysis was performed using a fixed-effects model.The Chi-square Q test showed that there was no significant heterogeneity in the included data under the five genetic models , P=0.001 in the heterozygous genetic model. Under the allelic model, compared with the G allele, the A allele could significantly reduce the OC risk, the OR and 95% CI was 0.94 (0.89–0.98), P=0.007. However, no significant association between rs861539 and OC risk was found under the recessive genetic model and the homozygous genetic model. As shown in The results of meta-analysis showed that rs861539 was significantly associated with the genetic susceptibility of OC. Comparing with the GG genotype, GA + AA genotypes could significantly reduce the risk of OC, the ORs and their 95% CIs were 0.89 (0.83–0.95), P<0.001), the homozygous model , the heterozygous model  and the allelic model  except for the recessive model. However, there was no significant association observed in the Asian populations. As demonstrated in The subgroup analysis in ethnicity suggested a protective effect of rs861539 on OC risk in Caucasian populations under the dominant genetic model .To verify the statistically significant associations, we performed the FPRP analysis with the presetting threshold of 0.2 and prior probability of 0.1. All significant associations found in the meta-analysis were considered to be true, and FPRP values for effects of rs861539 on OC risk in the dominant model, heterozygous model and allelic model were 0.009, 0.010 and 0.059, respectively. Moreover, it was found that the FPRP values were 0.001, 0.189, 0.001 and 0.026 for the effects of rs861539 on Caucasians OC risk in the subgroup analysis stratified by ethnicity under the dominant, homozygous, heterozygous and allelic models, respectively. The surprising findings indicated that the rs861539 leading a credibly decreased OC risk among Caucasian populations. As shown in The results of Trial Sequential Analysis (TSA) support the conclusions drawn from the meta-analysis. As depicted in XRCC3 gene splicing site is significantly associated with OC susceptibility, we evaluated the effect of this variant on the XRCC3 gene post-transcriptional splicing and structure or function of the coding protein of XRCC3. The analysis of Exonic Splicing Enhancer (ESE) method showed that the genetic variation might affect the efficacy of splicing factors (such as SRp 55 and SF2ASF1/2). As shown in XRCC3 rs861539 G>A may give rise to changes in the activity of putative splice sites. We found that score activation was 3.715 in the putative splice sites 102bp position of examined DNA sequence with rs861539 G allele, while the score activation changed to 3.272 in the same position with rs861539 A allele. The resulting change was that the putative splice site at 102 bp position changed from defined as unclasssified 3′ splice site to Alt.isoform/crytic 3′ splice site, which meaned rs861539 might have different post-transcriptional splicing regulation performance under different alleles. See Since the missense mutation rs861539 of XRCC3, MARK3, APOPT1, KLC1, etc and was associated with the risk of OC in individuals , affecting the expression of functional genes such as ividuals . In addiXRCC3 plays a key role in maintaining the functional integrity of the human genome by participating in recombination repair after genomic DNA double-strand breaks damage [XRCC3 gene sequence or the function of the coding product is disturbed, its DNA damage repair efficiency will be weakened. Experimental study had also confirmed that XRCC3-deficient cells are more sensitive to cisplatin and chemoradiotherapy [XRCC3 gene was significant associated with the onset of multiple malignancies, such as gynecological malignancies, oesophageal cancer, prostate cancer, breast cancer, etc [As a member of DNA damage repair genes family, the s damage . It is botherapy . Studiescer, etc ,7,31–33.In present meta-analysis, we confirmed that rs861539 was significantly associated with the risk of human OC under the most genetic models, particularly in Caucasians. The conclusion of the present study is similar to that of a previous meta-analysis  but is iHowever, the biological mechanisms behind the significant associations between rs861539 and the susceptibility of women to OC, as well as the role of rs861539 in the pathogenesis of OC, have not yet been elucidated. In this study, we further utilized bioinformatics analysis to explore the possible biological functions of rs861539 and preliminarily revealed the potential molecular mechanisms by which rs861539 affects individuals' susceptibility to OC. According to the functional type and the gene region localization of rs861539, we explored its possible biological functions.XRCC3 gene [XRCC3 post-transcriptional related splicing sites and may produce a different splicing of the post-transcriptional mRNA sequence. This indicates that rs861539 has a significant regulatory effect on the post-transcriptional splicing of XRCC3 gene, which may be one of the mechanisms to change the genetic susceptibility of individuals to OC. Meanwhile, eQTL analyses base on the GTEx database, which incorporates 127 tissue/cell type-specific epigenome data sets [XRCC3 gene or changing the structure of the XRCC3 product. To sum up, rs861539 affects the post-transcriptional splicing of XRCC3 gene and the expression of functional genes by changing the activity of splice sites and types of splicing factors. This evidence may provide new clues for revealing the mechanism of OC susceptibility.With the help of ‘SNP function prediction’ tool, it can be seen that rs861539 is located at the splicing site of This study still has some limitations. First of all, all studies included in this study have been published, and there may be some publication bias. Secondly, because the occurrence and development of OC is the result of multiple factors, such as genetic, environmental and endocrine factors, it is difficult to eliminate the effect of confounding factors in meta-analysis, which may affect the final results and conclusions. Also, evaluation of the potential gene–gene and gene–environment interaction effects on the risk of OC was limited because of lacking the original data of the reviewed studies. Finally, the present study only used bioinformatics methods to explore the mechanism of rs861539 affecting OC susceptibility, but did not carry out cell and molecular biological experiments. This weakened the strength of the evidence to some extent. Therefore, it must be cautious in accepting the conclusion of the present study.XRCC3 rs861539 G>A variant is significantly associated with the risk of human OC, especially in Caucasians. Involvement in the post-transcriptional regulation of XRCC3 gene and the change of the structure of the encoded XRCC3 protein may be the biological mechanisms of rs861539 leading to individual susceptibility to OC. Our finds above provide new clues for researchers to further explore the biological mechanism of OC.This study supports that


Tofacitinib is a relatively novel therapy for immune-mediated inflammatory diseases, including rheumatoid arthritis, psoriatic arthritis, and ulcerative colitis. It is a small-molecule drug that exerts its effects by inhibiting Janus kinases. Recently, concerns have been raised about the drug’s safety in terms of cardiovascular side effects and cancer risk. This meta-analysis determined the risk of cancer in patients treated with tofacitinib for different clinical indications, compared to both a placebo and other therapies. We did not find any difference in the cancer risk between tofacitinib and either the placebo or biological drugs overall. In contrast, we found only a slightly higher risk of cancer in patients treated with tofacitinib compared with the patients treated with drugs that inhibit the tumor necrosis factor. Therefore, further studies are needed to better define the cancer risk of tofacitinib therapy.p = 0.95). In separate comparisons between tofacitinib and either a placebo or biological therapy, no difference was found in the overall cancer risk . When tofacitinib was compared to tumor necrosis factor (TNF) inhibitors, the overall cancer RR was 1.40 . Similarly, significant results were obtained for all cancers, except for non-melanoma skin cancer , and for this skin cancer alone . In conclusion, no difference in the overall cancer risk was found between tofacitinib and either a placebo or biological drugs, while a slightly higher risk was found in patients treated with tofacitinib than anti-TNF agents. Further studies are needed to better define the cancer risk of tofacitinib therapy.Tofacitinib is approved for several immune-mediated inflammatory diseases, but safety concerns have recently been raised. We searched PubMed (accessed on 27 February 2023) for original articles regarding tofacitinib’s cancer risk when used for rheumatoid arthritis, ulcerative colitis, Crohn’s disease, psoriatic arthritis, and ankylosing spondylitis. Of the 2047 initial records, 22 articles describing 26 controlled studies  were selected. In the comparison between tofacitinib and any control treatment, the relative risk (RR) for any cancer was 1.06 (95% CI, 0.86–1.31;  Tofacitinib is one of several Janus kinase (JAK) inhibitors that form a novel class of drugs with immunomodulatory and anti-inflammatory effects and are widely prescribed for several immune-mediated inflammatory diseases (IMIDs) ,2. JAKs Pyoderma gangrenosum [Tofacitinib was first approved for the treatment of rheumatoid arthritis by the US Food and Drug Administration (FDA) in 2012 . Soon afgrenosum ,10,11,12Despite tofacitinib’s efficacy in several IMIDs, major concerns about its safety have recently been raised. Some authors suggested that tofacitinib could be associated with increased risks of major adverse cardiovascular events, infections, blood clots, cancer (namely lung and skin cancers), and death ,14,15. AThe risk of cancer after treatment with tofacitinib has been assessed in many studies, with discordant results. For example, there was a higher risk of cancer in patients who took tofacitinib than an anti-tumor necrosis factor (TNF) agent after 18 months of therapy for rheumatoid arthritis, according to a post-hoc analysis  of a ranIn contrast, a study of a large cohort of RA patients in routine care did not find an increased risk of any malignancy associated with tofacitinib therapy compared with anti-TNF treatment . Given tThe present systematic review with meta-analysis was previously registered in the PRISMA database and was conducted following PRISMA reporting guidelines.PubMed.gov (“All Databases”) with the following search strings: “tofacitinib AND cancer”; “tofacitinib AND ulcerative colitis”; “tofacitinib AND Crohn”; “tofacitinib AND rheumatoid arthritis”; “tofacitinib AND psoriatic arthritis”; “tofacitinib AND ankylosing spondylitis”; and “tofacitinib AND juvenile idiopathic arthritis”. The bibliographic search was completed on 10 October 2022.We analyzed original research articles published in English about tofacitinib and cancer or about tofacitinib and its use for approved or experimental indications. Relevant articles were first identified by searching Three authors  independently reviewed the titles and abstracts of the articles identified by the bibliographic research and selected potentially relevant studies. The same authors read the full texts of the initially selected papers to check the inclusion and exclusion criteria. Inclusion criteria were as follows: (1) research comparing (as a primary or secondary outcome) the incidence of malignancies between patients treated with tofacitinib and controls ; (2) original papers reporting a clinical trial, cohort study or observational study; and (3) studies in the English language. Exclusion criteria were as follows: (1) the lack of a control group; (2) lack of comparability in baseline characteristics between case and control groups or between different arms of a clinical trial; (3) papers reporting individual cases, meta-analyses, pooled analyses, or analyses of data from other original papers; (4) reviews; and (5) studies conducted on children. Divergences in opinion among the three reviewers were solved by discussion until an agreement was obtained or, when a consensus was not reached, a fourth author (S.S.) was consulted. Finally, reference lists of articles selected for the analysis were screened for other titles that met the study’s inclusion and exclusion criteria.For each paper included in the analysis, the following information was collected: number of patients in the different study groups, mean follow-up time (in months), number of cancers in the different groups, and types of cancers (when available). For articles with more than one control group or more than one timepoint of follow-up, we divided the data into two or more parts and treated the parts as separate studies in the analysis.As different indications and different phases trials required different doses of tofacitinib in the treatment group (and the dosage frequently varied during the study), the dosage of the drug was not taken into account for the statistical analysis.The included articles were evaluated using the Newcastle–Ottawa Scale (NOS), which judges study quality according to the following three perspectives: selection of the study groups, comparability of the groups, and ascertainment of the exposure of interest.For the meta-analyses, we expressed the follow-up duration of each study in units of person-months and used this value as the denominator for calculating risks, thereby reducing the heterogeneity in follow-up time among the studies. When the number of malignancies was zero in all study groups, the study was removed from the statistical analysis because the relative risk was not calculable. The overall risk of malignancy was calculated, as were the risks for all cancers, except non-melanoma skin cancer (NMSC) and for NMSC alone. When the type of tumor was gynecological, only the female study population was taken into account for the risk calculation.To identify publication bias, funnel plots were created and visually evaluated. A symmetrical inverted funnel was taken to indicate the absence of publication bias, while asymmetry was interpreted as publication bias .2 statistics were used to estimate heterogeneity across the studies. When heterogeneity was observed , a random-effects model was used; otherwise, a fixed-effect model was employed. A p value < 0.05 was treated as statistically significant.Statistical analysis was conducted using Med Calc software . Cochran’s Q and IOur bibliographic searches gave a total of 2047 results . After dThe 22 selected articles had been published between 2011 and 2022 and reported on clinical trials (18 articles) and observational studies . The patThe quality of the included studies was assessed at the article level, resulting in a mean NOS score of 8.5 . Thirteep = 0.57; I2 = 0%). The corresponding funnel plot , indicating that the neoplastic risk is not increased with tofacitinib.We repeated the meta-analysis for all cancers excluding NMSC using data from 11 studies A. From tp = 0.23; I2 = 0%)  C. The coI2 = 0%) B,D were p = 0.97; I2 = 0%)   . Data were available and sufficient for statistical analysis regarding lung cancer, breast cancer, and cervical cancer . For lunI2 = 0%) A. For brI2 = 0%) C and forI2 = 0%) E. These p = 0.95; I2 = 0%)    A,B. SimiI2 = 0%) C,D and fI2 = 0%) E,F.p = 0.58; I2 = 31.18%)    A,B. For I2 = 0%) C,D. For I2 = 0%) E,F.p = 0.02; I2 = 0%). This finding indicates that the relative risk of malignancy is increased in patients treated with tofacitinib, in comparison with patients treated with anti-TNF agents    C,D. In cI2 = 0%) E,F.2 = 0.00%; p = 0.03)  . No sign = 0.03) .Our meta-analysis assessed the risk of cancer in patients treated with tofacitinib compared to control patients who received a placebo or an active IMID treatment. Our study identified 26 studies on the use of tofacitinib for an IMID in twenty-two randomized controlled trials and four observational studies. The pathology most often investigated was rheumatoid arthritis, but there also were studies on ulcerative colitis, ankylosing spondylitis, Crohn’s disease, and psoriatic arthritis. At least one cancer was reported during follow-up in 13 studies, which were included in our meta-analysis.Despite the plenitude of published papers on the efficacy and safety of tofacitinib in rheumatological, dermatological, and gastroenterological diseases, few of these studies had a control group. Indeed, only 22 of the initial 2047 articles retrieved from PubMed were included in our study. Moreover, even though malignancy was a safety issue for all of the 26 included studies, in 13 of these studies, no case of cancer was detected, so they were excluded from the meta-analysis because the relative risk was not calculable. The overall quality of the included articles was high (mean NOS score: 8.5) and the heterogeneity of data was low, allowing us to use a fixed-effect model in all of the analyses. The high quality was probably due to the fact that many of the included studies were randomized controlled trials, while just four of them were observational studies. Similarly, publication bias was low, as shown by the symmetric funnel plots.p = 0.57). Separate analyses limited to only studies with a placebo control and to those with biological therapy as an active control also showed no significant difference in the cancer risk. Only the analysis between tofacitinib and anti-TNF agents revealed a slightly increased overall risk of cancer in the tofacitinib groups (RR = 1.40 p = 0.02); this result was maintained when NMSC was excluded from the analysis  and was lost in the analysis of NMSC alone .Overall, the risk of developing cancer during therapy with tofacitinib was similar to that in patients treated with a placebo or other drugs  in patients who took tofacitinib than in those treated with anti-TNF agents should be interpreted with caution. This result cannot be generalized to every age group and every IMID, because aging is an independent risk factor for cancer and IMIDs increase the risk of specific neoplasms . Indeed,Overall, our results about the lack of an increased neoplastic risk associated with tofacitinib are reassuring. Further prospective studies, designed ad hoc in real clinical practice, are needed to better define the potential cancer risk of tofacitinib therapy and also of the whole anti-JAK class, controlling for confounders due to the different IMIDs, different baseline characteristics of the patients, and the impact of ongoing and previous immunomodulant and biologic therapies.


Higher educational attainment (EA) has proven to be beneficial for preventing and treating various types of cancers. Currently, there is little evidence on the association between EA and prevention of oral cavity and pharyngeal cancer (OCPC).Several databases were searched until October 1, 2022, and a meta-analysis was performed. A Mendelian randomization (MR) study was conducted with EA  data derived from the Social Science Genetic Association Consortium and 6,034 cases of OCPC  selected from the Integrative Epidemiology Unit genome-wide association study. Five methods were used to evaluate the causality between EA and OCPC. The leave-one-out sensitivity test, MR-Egger regression, and multivariable MR (MVMR) analysis were applied to evaluate the MR results.P < 0.001). MR analysis revealed that the risk of OCPC, oropharyngeal cancer, and oral cavity cancer decreased with an increase in education . Even after correcting for mediators, high EA still significantly reduced the risk of OCPC .The meta-analysis included 36 eligible studies. EA was significantly and negatively associated with OCPC risk (odds ratio [OR]: 0.439, 95% confidence interval [CI]: 0.383–0.503, Both the meta-analysis and MR results demonstrated that high levels of EA can reduce the risk of OCPC in the general population. The prevalence of oral and oropharyngeal cancer (OCPC) has increased in recent years, making it the sixth most frequent neoplasm , 2. Due This meta-analysis was registered in PROSPERO (ID: CRD4202365834). Databases including PubMed, Web of Science, Embase, and Cochrane Library were searched until October 1, 2022. The searches were conducted using the following medical subject heading (MESH) terms: “education,” “oral cavity,” and “pharyngeal cancer;” these terms and their variants were linked using Boolean operators.Studies meeting the following criteria were included: (1) focus on education and OCPC (including broader definitions such as head and neck cancer) that reported odds ratios (ORs), with 95% confidence intervals (95% CIs), or adequate data to calculate these measures, (2) inclusion of participants aged 18 years or older, (3) availability of the full text of the study and published in English, and (4) sample size of ≥200. Review articles, meta-analyses, case reports, and editorials were excluded. Two researchers (GC and JX) independently assessed the full texts after screening all the titles and abstracts.Data on the full title of a study, first author, year of publication, study design, follow-up period, sources of cases and participants, number of cases and controls, and ORs were independently extracted by two investigators (GC and JX). Since the studies included education levels assessed using different methods and scales, we redefined education level to facilitate data collection and comparison. Therefore, for each study, we defined the lowest category reported in the study as the lower education level and the highest category as the higher education level. The Newcastle Ottawa Scale (NOS) tool was used to evaluate the quality of each study . Any disagreements between the researchers were resolved through consensus.I2 analysis was used to assess heterogeneity, and an I2-value >50% indicated heterogeneity between the studies. Data synthesis based on different research populations and methods was conducted using a random-effects model. Subgroup and meta-regression analyses were performed. The robustness of the literature results was evaluated by sensitivity analysis. Funnel plots derived from the Egger and Berger bias tests were used to check for publication bias.Statistical analyses were conducted using Stata 14.0. A genome-wide association study (GWAS) dataset, which included 766,345 individuals of European descent who had received an education, was selected as the exposure set  16). Th. Th16). The Integrative Epidemiology Unit GWAS database, which included 6,034 cases of OCPC and 6,585 controls, was selected as the outcome set  20). Th. Th20). To explore the potential mediators of the EA–OCPC pathway, we used the inverse-variance weighted (IVW) method to assess the potential causal relationship between EA and common risk factors for OCPC. Based on the current database, we analyzed the following common risk factors of OCPC, including HPV, number of sexual partners in lifetime, hypertension, type 2 diabetes, smoking, and alcohol consumption. According to the guidelines for MR, several MR approaches, including the IVW method, weighted median method, MR-Egger method, simple mode, and weighted mode, were used to estimate the relationship between EA and OCPC. The IVW method is primarily used to evaluate causal relationships since it possesses the highest statistical power. ORs were transformed with effect estimates , and the results were represented with 95% CIs. Since many instrumental variables (IVs) were associated with multiple traits (pleiotropy), it was necessary to apply sensitivity analysis to the results. Therefore, we applied MR-Egger regression and the leave-one-out sensitivity test. If the regression intercept was close to 0, it indicated the absence of horizontal pleiotropy . The leaA total of 36 studies on OCPC (including 13 studies that delineated their focus on OCC) were finally included in the meta-analysis . All incI2 = 92.7%, P < 0.001). Random-effects model analysis showed a significant negative association between EA and OCPC, and the pooled OR was 0.439 . The forest plots showed 36 studies (P = 0.775), and Berger's test results (P = 0.505) confirmed that there was no publication bias (P < 0.001) (P = 0.913), and Berger's test results (P = 0.054) showed no evidence of publication bias.The meta-analysis of the studies on the relationship between EA and OCPC revealed significant heterogeneity ( studies . In the  studies . The met studies . The comion bias . We furt< 0.001) . Subgrou< 0.001) . Further< 0.001) . After tP < 0.001). Additionally, a positive result was obtained by applying the weighted median method . Although no statistical significance was found, similar results were found with the MR-Egger, simple mode, and weighted mode methods (P = 0.800), it was unlikely to have horizontal pleiotropy   . To cont< 0.001) .In our study, we found that EA was negatively associated with the risks of OCPC, OPC, and OCC. Specifically, according to MR, 4.2 years of additional education lowered the risk of OCPC, OPC, and OCC by 65.1%, 65.7%, and 65.8%, respectively. These results revealed a cause-and-effect relationship between high EA and low risk of OCPC.Cancer prevention has long been considered one of the most effective strategies to overcome this public health problem, and some experts consider certain cancers to be major but preventable chronic life-threatening diseases . TherefoA previous meta-analysis showed that higher education level was protective against OCPC , 26. HowAdditionally, we also explored the mediators in the EA–OCPC pathway. The preliminary results showed that BMI, hypertension, type 2 diabetes, and smoking may play important roles in this pathway. A previous MR study evaluated the impact of smoking on OCPC risk and indicated that smoking significantly increased OCPC risk . To corrHowever, the mechanism by which education affects OCPC remains unclear. Several studies have posited the following potential explanations. People with higher education may have a healthier lifestyle and access to better healthcare , 31 and The current meta-analysis had several limitations. First, the analysis incorporated case-control studies, and questionnaires or interviews were used to collect information in most studies. Second, this meta-analysis had some heterogeneity, which may be related to the following factors: different methods for measuring education level, potential confounding factors, different data sources, and inclusion of participants from all over the world. Finally, our analyses were not adjusted for age, sex, smoking, or alcohol abuse.There were also several limitations to the current MR analysis. First, the data used for analysis could not be stratified by covariates such as age, sex, smoking, and alcohol abuse. Second, the study population used to determine the exposure and outcome was of European descent, which may have reduced the bias caused by population stratification. However, it is unclear whether these findings can be extrapolated to populations of other nationalities. Third, the selected EA-related dataset of 766,345 people included 442,183 participants from the UK Biobank. Therefore, we must recognize that the overlapping of participants between exposure and outcome may lead to substantial bias . FinallyThis meta-analysis and MR study provided robust evidence on the effect of EA on the risk of OCPC. Therefore, reducing the prevalence of OCPC in individuals with low educational levels to ensure early detection and treatment should be the focus of public health policies. Moreover, we urgently need to expand the level of education and implement science/education programs for promoting healthy behaviors, especially in educationally disadvantaged areas.The original contributions presented in the study are included in the article/GC, JX, XZ, and AT: conceptualization and design. XZ and AT: project administration. GC, DL, and JX: data analysis and interpretation. All authors: manuscript writing and final approval of manuscript.