Gastric cancer (GC) is a prominent global health issue, as it ranks as the fifth most prevalent type of cancer and the fourth most significant cause of cancer-related mortality worldwide. Although H. pylori is known to play a role in the development of GC, genetic factors also play a role in its onset and progression. Recent studies have shown that genetic polymorphisms are strongly associated with the development of GC and that certain single nucleotide polymorphisms (SNPs) can be used as biomarkers for early diagnosis and prevention. Epigenetic disturbances, such as DNA methylation, are involved in the development of GC, and mutations in the DNA methyltransferase (DNMT) gene have been found to increase the risk of GC. However, previous findings on the association between DNMTs SNPs and GC risk have been inconsistent. In this study, an updated meta-analysis of three well-studied and controversial DNMTs polymorphic loci, DNMT1 rs16999593, DNMT3A rs1550117 and DNMT3B rs1569686, was performed to provide more reliable results. It was found that DNMT1 rs16999593 was not associated with GC, DNMT3A rs1550117 may have a positive association with GC risk, and DNMT3B rs1569686 may be a protective factor for GC. These findings may provide valuable information for early diagnosis and prevention of GC, but further studies are needed to confirm these results. Gastric cancer (GC) is a major health problem worldwide, with an annual incidence of over one million and causing more than 700,000 deaths globally , 2. AlthThe development of GC is the result of the interaction between genetics and epigenetics . In epigRecent studies suggest that mutations in DNMTs genes are associated with an increased risk of GC. However, previous research results are inconsistent and even contradictory. Some studies, such as those conducted by Yang et al. , Gao et Based on the PRISMA guidelines , we condInclusion criteria were as follows: (1) case-control or cohort studies; (2) studies that described the correlation between rs16999593, rs1550117, and rs1569686 polymorphisms and GC risk; and (3) studies that provided sufficient genotype data for both case and control groups. Exclusion criteria were: (1) duplicate studies; (2) studies without available data; and (3) case reports, reviews, letters, and meta-analyses.The data extraction table for this study has already been prepared in advance. Based on the established inclusion and exclusion criteria, the data was independently extracted and cross-checked. If there were any disagreements, they were discussed and negotiated until a consensus was reached. We will invite a third author to extract the data again and conduct a final check and confirmation. If the data is not detailed or there are any questions, we will try to contact the original authors to supplement and confirm the accuracy and completeness of the data. Each study collected the following items: country, region, study type, race, matching criteria, age, polymorphisms, number of cases and controls. Data on all polymorphisms included in the study were also extracted, including data on genotype distribution and relative risk.The quality of all eligible studies was independently assessed by the two authors  and 95% confidence interval (CI). A significance level of P < 0.05 was considered to be statistically significant. To comprehensively evaluate different genetic models, five genetic models were compared, including: (1) allele model; (2) additive model; (3) dominant model; (4) recessive model; and (5) over-dominant model. The Hardy-Weinberg equilibrium (HWE) was examined using the chi-square goodness-of-fit test. If P > 0.05, the control group was considered to be in HWE. The heterogeneity test was conducted using the Chi-square-based Q-test and was used . Otherwiwas used . Sensitiwas used  and Eggewas used . When thwas used . In addiwas used  and Veniwas used  were useWe conducted a search based on our inclusion and exclusion criteria, resulting in 282 articles. After applying our criteria, we selected 14 studies that met our requirements, involving a total of 3539 cases of GC and 6106 controls. Within these studies, 5 investigated the association between rs16999593 and GC risk, involving 1846 cases of GC and 2554 controls; 4 studies reported on rs1550117, involving 1363 cases of GC and 2134 controls; and 7 studies investigated rs1569686, involving 1932 cases of GC and 4149 controls. Regarding quality, there were 2 high-quality studies and 3 moderate-quality studies regarding rs16999593, 2 high-quality studies and 2 moderate-quality studies regarding rs1550117, and 2 high-quality studies, 4 moderate-quality studies, and 1 moderate-quality study regarding rs1569686. We present the detailed characteristics and scores of each study in The association results between DNMT1 rs16999593 and cancer risk are shown in Overall analysis showed a significant reduction in GC risk in the rs1569686 genotypes  . Using eI2 68.5%, Phet 0.013), TC vs. TT  and C vs. T . Similarly, heterogeneity was evident in rs1550117, including AA vs. GG , AA+AG vs. GG , AA vs. AG+GG  and A vs. G . Furthermore, rs1569686 was also found to be heterogeneous, including GG+GT vs. TT , GT vs. TT  and G vs. T.The sensitivity analysis demonstrated that the study conducted by Liu et al [I2 20.6%, Phet 0.287). For rs1550117, the study conducted by Fan et al [I2 0.00%, Phet 0.780. Similarly, the study conducted by Wang et al [I2 0.00%, Phet 0.832.We compared the characteristics of the three studies and conducted subgroup analyses using population area and study quality as two factors explaining heterogeneity  we comprehensively searched all major repositories and manually screened articles to minimize the omission of any studies relevant to the topic, regardless of language or study year; (ii) for included studies, we performed quality assessment; (iii) for controls, we performed the HWE test; (iv) we used the FPRP and Venice criteria to assess significant associations in the current Meta-analysis; and (v) we used a larger sample size than in the previous Meta-analysis. However, this study still has some limitations. First, we did not control for confounding factors such as H. pylori infection, smoking, and alcohol consumption, which may have influenced the results. Second, we observed significant heterogeneity in some genetic models. Although we performed publication bias testing, sensitivity analyses, and subgroup analyses to clarify the sources, we were unable to find all potential factors. Third, the language of publications was limited to English and Chinese. Fourth, although this meta-analysis was based on the whole population, most of the studies were from the Chinese population, except for one Iranian case, pending results from other populations in future studies.Finally, the small number of studies per SNP made it difficult to draw strong conclusions, and further studies are needed to determine the generalizability of these findings.After conducting a thorough analysis of 15 articles examining SNPs in DNMTs, we have concluded that DNMT1 rs16999593 does not exhibit a significant association with GC. However, our findings suggest that DNMT3A rs1550117 may be positively associated with GC, while DNMT3B rs1569686 may serve as a protective factor against GC. These SNPs could serve as valuable biomarkers to predict the risk of GC development and aid in the development of timely prevention strategies. However, further studies in diverse populations are necessary to confirm the association between these SNPs and GC risk. Additionally, investigating the biological significance of functional SNPs in DNMT activity and expression is essential for a comprehensive understanding of the impact of these SNPs on GC.S1 Checklist(PDF)Click here for additional data file.S2 Checklist(DOC)Click here for additional data file.S1 Table(DOCX)Click here for additional data file.S2 Table(DOCX)Click here for additional data file.S3 Table(DOCX)Click here for additional data file.


Specifically, longer genetically predicted LTL is associated with a reduced risk of SSc. Sensitivity tests highlight the significant roles of the variants rs10936599 and rs2736100 annotated to the TERC and TERT genes, respectively. Our findings suggest an influence of telomere length in leukocytes on the development of SSc.Although previous studies have suggested a relationship between telomere shortening and systemic sclerosis (SSc), the association between these two traits remains poorly understood. The objective of this study was to assess the causal relationship between telomere length in leukocytes (LTL) and SSc using the two-sample Mendelian randomization approach, with the genome-wide association study data for both LTL and SSc. The results of inverse-variance weighted regression (OR = 0.716 [95% CI 0.528–0.970],  Systemic sclerosis (SSc) is an immune-mediated inflammatory disease (IMID) characterized by vascular damage, chronic inflammation and fibrotic involvement of connective tissues . SSc patSignificant progress has been made in the genetic understanding of SSc in recent years through extensive genome-wide association studies (GWAS) and Immunochip studies ,4,5. TheTelomeres are nucleoprotein structures located at chromosome ends that are implicated in the preservation of genome integrity and stability . These sMendelian randomization (MR) studies allow for exploring the potential causal relationship between a risk factor and a disease . These sIn the present study, we aimed to investigate the causal relationship between LTL and SSc through an MR study.Seven IVs were used in our study based on their association with LTL . This inSince none of the heterogeneity tests were significant, the inverse-variance weighted (IVW) fixed-effects (FE) method was used for the three datasets analyzed . In addip = 0.031). In addition, three other MR methods, maximum likelihood, weighted median and MR-PRESSO, showed the consistency of our results, with a significant association and the same direction of effect (p = 0.263). p = 0.241 and p = 0.102, respectively; Our results for IVW-FE show an association between genetically predicted longer LTL and a reduced risk of SSc  were the primary contributors to the obtained results. Interestingly, these SNPs and their corresponding annotated genes,  disease . Moreove disease ,24 and h disease ,18. TakiOur results underscore the potential involvement of LTL in SSc development. However, the mechanism by which LTL affects SSc remains unknown. In this context, we hypothesize some potential pathways. First, short TL could contribute to the dysregulation of the immune system by affecting T-cell numbers and receptor diversity . NotablyTo determine the possible causal relationship between LTL and SSc and its main clinical subtypes, lcSSc and dcSSc, we carried out a two-sample MR (2SMR) study . This app < 5 × 10−8) with LTL, were obtained from a GWAS meta-analysis comprising 37,684 European-descent individuals [Genetic association data for seven non-palindromic independent SNPs, associated at genome-wide significance level , accessed on 26 September 2023 [In order to evaluate the effect of each SNP, we carried out a LOO sensitivity analysis, which provides the IVW estimate sequentially excluding one of the IVs at a time , applyinIn conclusion, our study points towards a possible causal relationship between increased LTL and a reduced risk of SSc. It is important to interpret these results with caution until further investigation is conducted in this area, acknowledging the inherent limitations of MR studies in fully assessing the assumptions related to the exclusive impact of the IVs on the outcome through the risk factor. Future studies with the aim of clarifying specific associations and evaluating possible mechanisms are warranted.


The objective of this study is to introduce methods to use all of the information without omission when individual studies provide multiple effect sizes according to multiple cut-off values (thresholds) during diagnostic test accuracy (DTA) for data integration. For diagnostic test meta-analysis, a general performance method for synthesizing data according to one cut value in one study and a performance method for synthesizing data according to two or more cut values in one study were compared and analyzed.As sample data for meta-analysis of DTA studies, 13 DTA studies on prostate cancer  were collected. The summary statistics were calculated and the summary line was analyzed using the “meta”, “mada”, and “diagmeta” packagesof the R software.The summary statistics of the random effect model univariate analysis of the “meta” package with a single cut-off corresponding to the highest Youden index in a single study and those of the bivariate analysis of the “mada” package were highly similar. However, in the bivariate analysis of the “diagmeta” package including all cut-off values, the sensitivity decreased and the specificity increased as the amount of data increased.Considering the heterogeneity of the summary receiver op erating characteristic curve and the use of all given cut-offs, the use of the bivariate analysis model of the “diagmeta” package is recommended. This study focused on practical methods of DTA rather than theoretical concepts for use by researchers whose fields of study are non-statistics related. By performing this study, we hope that many researchers will use R software to determine the DTA more easily, and that there will be greater interest in related research. In this paper, we present a methodology for meta-analysis of diagnostic test accuracy (DTA) studies when the individual studies have multiple effect sizes according to multiple cut-off values (thresholds). The proposed method allows the use of all the information without omission in cases of multiple thresholds and multiple effect sizes. Therefore, this study requires basic understanding of meta-analysis of DTA studies as well as prior knowledge in relation to statistical models for calculation of summary statistics by referring to existing works -4.In the case of general pairwise meta-analysis, one effect size is calculated, whereas in meta-analysis of DTA studies, two paired effect sizes are combined simultaneously; for example, the estimates of sensitivity and specificity are calculated at the same time. In the meta-analysis of DTA studies, it is assumed that the cut-off values (thresholds) of the target studies are similar; however, in practice, the cut-off values are not identical, which may be an important risk factor of heterogeneity in the summary receiver operating characteristic (SROC) curve .A number of existing systematic reviews have investigated the methods for calculating effect sizes according to multiple thresholds in meta-analysis of DTA studies -9. In thTo address this problem, previous research works on meta-analysis of DTA studies have used all the effect sizes according to multiple cut-off values -17. We iDTA studies are typically represented by summary statistics and a summary line of a basic 2× 2 table consisting of true positive (TP), false positive (FP), false negative (FN), and true negative (TN) values. In general, representative summary statistics include sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV). An example of a summary line for summary statistics is the SROC curve.For calculation of the summary statistics of DTA studies, an appropriate model needs to be selected, as in the case of the general pairwise meta-analysis. Models with simultaneous consideration of sensitivity and specificity include the bivariate and hierarchical models.In the bivariate model, a binominal distribution that directly models the sensitivity and specificity is assumed for within-study variations and a bivariate normal distribution is assumed for between-study variations. The hierarchical model assumes a binominal distribution for within-study variations; for between-study variations, a hierarchical distribution is assumed for the parameters included in the logistic model by applying the logistic regression model to determine the probability of a binominal distribution ,4. HowevIn this study, estimation of the values will be performed using the “meta”, “mada”, and “diagmeta” packages of the R software, and these packages need to be installed before running the software.• install.packages(“meta”)• install.packages(“mada”)• install.packages(“diagmeta”)As sample data for meta-analysis of DTA studies , 13 DTA In univariate and bivariate analyses using meta and mada functions, one threshold with the highest Youden index was selected in a single study for analysis. In bivariate analysis using the diagmeta function, all thresholds were included in the analysis and the optimal threshold was estimated.This study used publicly available data and did not include human participant research. Therefore, this study was not submitted for institutional review board approval and did not require informed consent procedures.The “metaprop” function from the “meta” package calculates the total effect size when there are a number of events (event) and a number of samples (n) in the proportion-type data.In sensitivity analysis, the number of events is TP and the number of samples (n) is TP+FN. Among the methods for calculating the effect size in proportion-type data, the method based on logit transformation and then back transform was used (the logit transformation and Clopper–Pearson method). When the assumptions of the statistical model are properly applied for consistency and considering the symmetry and distribution of the data, it is preferable to transform (log transformation or logit transformation) the proportion-type data because this produces conservative results through the transformation. The inverse variance method is a basic method of meta-analysis, which utilizes the inverse variance of the applicable study when calculating the weights of individual studies, and the tau value, which is the between-study variance, was calculated using the DerSimonian–Lair estimator in a random effect model.2 of both models was 79.5% in sensitivity and 96.7% in specificity; thus, it is reasonable to converge to the random effect model  or only the cells of the corresponding study  (correction.control=“single”) were corrected to prevent error in the calculation. In the options, it is possible to adjust to an arbitrary value such as “correction= 0.5,” where 0.5 is the default value. For models using the reitsma function, “fit” was assigned.Examining the result values through summary(fit), the values of sensitivity 0.863 and specificity 0.483 (= 1−0.517 FP rate) were obtained. The area under the curve (AUC) value of 0.794 can be observed in the middle of the console window, and values corresponding to the HSROC model are also shown .The SROC curve uses the object calculated with the reitsma function. When a graph is drawn on the order of a command, the SROC curve first drawn with the “plot” function is temporarily saved in the computer memory, so individual studies are additionally indicated with the “points” function.The function “plot” draws graphs. In the function, the set model was entered into “fit,” and “sroclwd= 2” represented the thickness of the SROC curve. The units of the x and y axes were adjusted by adjusting “xlim” and “ylim”, respectively. The current graph shows the range from a minimum of 0 to a maximum of 1. The information of individual studies is entered in “points.” “fpr” and “sens” respectively represent the FP rate and sensitivity of individual studies, and “pch= 2” indicates the shape. Different numbers indicate different shapes as follows: rectangle (0), circle (1), triangle (2), cross (3), scissors (4), rhombus (5), inverted triangle (6), star (8), and black dot (20). The shape that allows the best discrimination was selected considering the visibility .The summary statistics and the SROC summary line described above are the deliverables that must be presented for DTA studies. In the subsequent analysis, the heterogeneity of individual studies is tested in the same way as the general pairwise meta-analysis, and if there is any significant factor, it should be tested and reported.The basic assumption of the SROC curve is that the shape of the ROC curve is identical in all studies. However, this basic assumption cannot hold if there is heterogeneity between studies.There are a variety of causes of heterogeneity such as chance, difference in cut-off value, difference in study design, prevalence, study environment, and the demographic factors of the sample population ,4. In meThe symmetry of the SROC curve indicates the agreement between the models of a divided SROC curve when this curve is divided by an arbitrary line from the top of the y-axis to the bottom right of the x-axis. That is, a diagnostic test with high accuracy will show that the SROC curve is symmetrical, the inflection point is drawn to the top left corner, and the curve will have a sharp turn to increase its AUC. Then, the value of Youden’s J index (J=sensitivity+specificity−1) will increase.According to the judgment based on visual discrimination, the SROC curve in this example does not appear to have a high asymmetry and the degree of scattering of individual studies also does not seem to be large. However, since the specificity is low compared to the high sensitivity, the inflection point of the entire curve is located in the center and top right instead of top left, which indicates that the diagnostic test does not have a sufficiently high accuracy. However, as the example diagnostic test is a test for diagnosing prostate cancer, a major chronic disease, through biomarkers, the abovementioned characteristic of the curve is acceptable from the point of view that the test is more specialized in the sensitivity for identifying the diseased. The specificity of the test can be improved through additional biopsy or radiological examination afterward, and thus the above example may be considered acceptable as the benefit of finding more immediate risk or threat to life (sensitivity) is greater than the benefit of accurately judging that the person is not a patient with a prostate cancer (specificity).The diagmeta function from the “diagmeta” package was used for the calculation. In the bivariate model of the “mada” package, which was considered above, 13 data extracted from a single cut-off value with the highest Youden index for each study were entered, but in the case of the diagmeta function, 34 data corresponding to all cut-off values were used. It is not necessary to match the number of cut-offs for each study.The variable names in the example data of this study  were chaThis model considered continuous biomarkers of the observed diseased group and the non-diseased group. In individual studies, when TP, FP, FN, and TN corresponding to each cut-off are given according to a random number of cut-offs, sensitivity and specificity can be calculated for the respective studies. In particular, this model was developed in accordance with the specificity and 1-sensitivity corresponding to the negative test result in the diagnostic test.where h denotes the normal model or logistic model, Sp is the specificity, Se is the sensitivity, x is the threshold, and denote the mean and variance for non-diseased, and and represent the mean and variance for diseased.From individual data, a normal distribution or a logistic model is used to derive the corresponding model (h) (1), and this model is transformed to a linear mixed effects model for fitting (2) and (3). In general, a linear mixed effects model includes fixed effects  and random effects , and the structure of the model is changed according to how the weights of these parameters are set, which correspond to the intercepts and slopes for the respective cases.e is residual error for non-diseased, and f is residual error for diseased.where TN is true negative, FN is false negative, s denotes study, i is the threshold of the study, α0 and α1, β0 and β1 are fixed intercepts and fixed slopes for non-diseased and diseased, respectively; a0 and a1, b0 and b1 are random intercepts and random slopes for non-diseased and diseased, respectively; As for the model of the diagmeta function, the weights of the individual intercept and slopes are selected when calculating the linear mixed effects model. For the selection, eight options of distribution function are given as follows: “DIDS”, “CIDS”, “DICS”, “CICS”, “DS”, “CS”, “DI”, and “CI” . For exa“log.cutoff” sets whether to use the respective cutoffs after log transformation or use them as the raw data. In general, as the number of data is not large, it is necessary to normalize the values through log transformation.Examining the results through the summary(diag) function, we can observe and verify the frequency of the cut-offs in detail as well as the number of cut-offs for individual studies. At the bottom of the result window, the total number of studies (13) and the number of used cut-offs (34) are shown as a summary, and the DICS function was used as the model. The optimal cut-off value was 33.666, sensitivity was 0.718, specificity was 0.669, and AUC value was 0.749 .Along with summary statistics, figures for deriving the statistics are presented . In the Comparing the bivariate model of the “mada” package with the SROC curve, the bivariate model of the “diagmeta” package, which has larger number of data by using all cut-offs, shows a better distribution in terms of heterogeneity. That is, the distribution of all studies was symmetrical and concentrated, showing no significant heterogeneity. In addition, as described above, as the distribution of all effect sizes is located at the top right corner compared to the SROC inflection point , it can be observed that this example test is a diagnostic test specialized for sensitivity, showing relatively low specificity.In this work, for meta-analysis of DTA studies, summary statistics were calculated and the summary line was analyzed using the “meta”, “mada”, and “diagmeta” packages . The sumIndeed, the “diagmeta” model also shows some limitations as follows. First, the model does not take into account the uncertainty of variance in individual studies, so there may be a problem of continuity correction. In particular, if there is “0” in a data cell in the 2× 2 table, calculation is not possible. Therefore, in this case, data preprocessing needs to be considered, such as the method of augmentation in which an identical set value  is entered in each cell. Second, if there is excessive heterogeneity in the cutoff values, the fit may be poor. However, given that this problem is unavoidable owing to the nature of meta-analysis, it is necessary to perform cross-validation using various tools for meta-analysis of the DTA studies discussed above.In this study, I aimed to present the minimal theory of statistics and concentrate on the practical methods of meta-analysis of DTA studies for data integration so that researchers who are non-statistics majors can also perform the analyses with ease. Through this study, it is expected that researchers will be able to utilize the ready-made statistical tools that have been already developed and interpret them appropriately for each field of their research.In particular, for systematic review and meta-analysis of DTA studies, when there are multiple effect sizes due to two or more cut-off values (thresholds), I presented a method that allows the use of all information without omission and a comparative analysis with the existing method. Therefore, the results of this study will provide researchers with a useful guideline for selecting an appropriate model for their studies.In addition, it is expected that the findings of this study will facilitate the process of meta-analysis and promote related studies for many researchers in the relevant fields in Korea.


This systematic review and meta-analysis assesses all reported genetic modifiers of sickle cell disease and evaluates the design and data reporting of each study to provide guidelines for future analyses to accelerate discovery and validation of genetic modifiers in this disease. What genetic modifiers of sickle cell disease (SCD) are currently defined, and what are potential approaches to improve future studies?In this systematic review and meta-analysis of 571 studies examining 29 670 individuals with SCD, 17 757 associations involving 1552 genes and 25 SCD phenotype categories were discovered; of these, only 173 associations met the study design, reporting, and phenotype or genotype harmonization required for meta-analysis. Gene variants regulating fetal hemoglobin and α-thalassemia were frequently identified, but other associations remained unconfirmed.While major genetic modifiers of SCD severity were identified, including some that are clinically relevant, validated genetic associations were lacking, in part due to suboptimal study design and data reporting. Sickle cell disease (SCD) is a monogenic disorder, yet clinical outcomes are influenced by additional genetic factors. Despite decades of research, the genetics of SCD remain poorly understood.To assess all reported genetic modifiers of SCD, evaluate the design of associated studies, and provide guidelines for future analyses according to modern genetic study recommendations.PubMed, Web of Science, and Scopus were searched through May 16, 2023, identifying 5290 publications.At least 2 reviewers identified 571 original, peer-reviewed English-language publications reporting genetic modifiers of human SCD phenotypes, wherein the outcome was not treatment response, and the comparison was not between SCD subtypes or including healthy controls.z score meta-analyses and pathway analyses were conducted.Data relevant to all genetic modifiers of SCD were extracted, evaluated, and presented following STREGA and PRISMA guidelines. Weighted Outcomes were aggregated into 25 categories, grouped as acute complications, chronic conditions, hematologic parameters or biomarkers, and general or mixed measures of SCD severity.BCL11A, HBS1L-MYB, and HBG2 were significantly associated with fetal hemoglobin , and α-thalassemia deletions were significantly associated with increased hemoglobin level and reduced risk of albuminuria, abnormal transcranial Doppler velocity, and stroke . However, other associations remain unconfirmed. Pathway analyses of significant genes highlighted the importance of cellular adhesion, inflammation, oxidative and toxic stress, and blood vessel regulation in SCD (23 of the top 25 Gene Ontology pathways involve these processes) and suggested future research areas.The 571 included studies reported on 29 670 unique individuals (50% ≤ 18 years of age) from 43 countries. Of the 17 757 extracted results (4890 significant) in 1552 genes, 3675 results met the study criteria for meta-analysis: reported phenotype and genotype, association size and direction, variability measure, sample size, and statistical test. Only 173 results for 62 associations could be cross-study combined. The remaining associations could not be aggregated because they were only reported once or methods  and genotype or phenotype definitions were insufficiently harmonized. Gene variants regulating fetal hemoglobin and α-thalassemia (important markers for SCD severity) were frequently identified: 19 single-nucleotide variants in The findings of this comprehensive systematic review and meta-analysis of all published genetic modifiers of SCD indicated that implementation of standardized phenotypes, statistical methods, and reporting practices should accelerate discovery and validation of genetic modifiers and development of clinically actionable genetic profiles. Hemolysis-related cellular injury, partly mediated by circulating free heme released from red blood cells, is thought to drive progression of cerebrovascular disease, kidney disease, pulmonary hypertension, priapism, and leg ulcers,5 whereas vaso-occlusion is thought to precipitate acute pain episodes, acute chest syndrome, and avascular necrosis.2Most cases of SCD are caused by a homozygous variation in the 6 and high-income countries, where enhanced medical support extends patient lifespan, although most patients still suffer considerably and die prematurely.7Despite being a monogenic disorder, the symptoms of SCD vary between affected individuals. The influence of environment on SCD is illustrated by markedly different outcomes between sub-Saharan Africa, where approximately half of affected children die before 5 years of age,8 is largely determined genetically. Coinherited hereditary persistence of fetal hemoglobin, caused by variants in the extended β-like globin locus, results in extremely high levels of HbF, eliminating many symptoms of SCD.9 Genome-wide association studies have shown that 20%-50% of the variation in HbF can be explained by single-nucleotide variants (SNVs) in 3 loci: BCL11A, HBS1L-MYB, and the extended β-like globin locus.13 The erythroid-specific enhancer BCL11A encodes a potent transcriptional repressor for the γ-globin genes (HBG1 and HBG2).14 This discovery led to gene therapy strategies aimed at reducing erythroid BCL11A expression, some of which are showing early signs of efficacy in clinical trials,16 illustrating how understanding the genetic modifiers of SCD can have profound therapeutic implications.Manifestations of SCD are also influenced by genetic factors. For example, residual expression of fetal hemoglobin  in postnatal red blood cells, which reduces SCD severity by interfering with polymerization of sickle hemoglobin,17 A more complete understanding of how genetics influences pathophysiology could improve therapy by providing tools to predict outcomes and identifying new modes for therapeutic intervention. To assess current knowledge, we performed a systematic review of, to our knowledge, all publications reporting genetic modifiers of SCD, cataloged the findings by subdividing genotype-phenotype associations by quality of data analysis and reporting, and performed meta-analyses and pathway analyses. Based on our findings and current guidelines in the field of human genetics, we provide recommendations for analytical approaches and reporting to enhance scientific rigor, reduce spurious results, and facilitate cross-study data synthesis.The genetic contributions to SCD-related complications are poorly defined, despite a preponderance of publications on this topic. As a motivating example, a recent polygenic score incorporating 21 SNVs in 9 genetic loci, including HbF modifiers, explained only 3.5% of the variation in acute pain episodes.CRD42021274466) and was reported following the Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA) reporting guideline and the Strengthening the Reporting of Genetic Association Studies (STREGA) reporting guideline. We searched PubMed, Web of Science, and Scopus for all studies reporting genetic modifiers of SCD, irrespective of SCD subtype, published before May 16, 2023 , with a third reviewer (J.H.E.) blinded to other screening opinions resolving disagreements. To comprehensively aggregate all published mutations associated with SCD-related outcomes and to avoid excluding important genetic modifiers due to incorrect phenotypic or genotypic attribution, we included all reported phenotypes and genetic polymorphisms. Studies were excluded if the manuscript was unavailable in English, the research was not conducted in humans, individuals without SCD were included, the only analysis was nongenetic, the only comparison was between SCD subtypes, the outcome was treatment response only, the manuscript was not peer reviewed, or no original research was included. A total of 571 publications passed this screening . We did not exclude results based on these categories; however, some sections only used meta-suitable and contemporary results  was considered statistically significant. For pathway analyses, an adjusted P < .05 was considered significant.Unless stated otherwise, analyses were conducted with R, version 4.2.1 (CRAN). For the meta-analyses, a Bonferroni-corrected 2-sided 583 Approximately 52% of individuals resided in USA, Canada, France, or Brazil, while just 6628 individuals studied (22%) were from African cohorts  from 43 countries  were meta-suitable, containing all elements required to perform a meta-analysis, and only 44 results from 2 studies met all contemporary criteria .Across the 571 publications analyzed, 17 757 association results in 1552 unique genes were reported, along with 249 078 genome-wide association summary statistics . The numWhile 3675 of the 17 757 total results (21%) were categorized as meta-suitable, due to differences in specific phenotypes, genotypes, or statistical methods, studies analyzing similar outcomes were often insufficiently harmonized for cross-comparison or were not replicated . Only 17Z = 5.12; P = 3.11 × 10−7) and reduced risk of albuminuria , abnormal transcranial Doppler velocity , and stroke   were significantly associated with HbF . An increased number UGT1A1 promoter repeats was associated with increased risk for cholelithiasis 7/(TA)7; Z = 4.27, P = 1.93 × 10−5 for (TA)7/(TA)8; compared with (TA)6/(TA)6). Single-nucleotide variants in RPS24  and TBC1D1  were significantly associated with increased rate of vaso-occlusive crisis.Meta-analysis results indicated that α-thalassemia deletions were significantly associated with increased hemoglobin level (o event) . Ten SNVAPOL1 variants were frequently associated with numerous markers for kidney dysfunction  (COMT (rs4680) was nominally associated with vaso-occlusive crisis in our meta-analyses  (While high-risk G1/G2  = .005) . Similar × 10−4) , but num × 10−4) .BCL11A, or HBS1L-MYB, with few repeated or meta-suitable results outside of these regions  reporting a total of 3952 associations involving 452 genes . SignifiConcurrent α-thalassemia was consistently associated with increased hemoglobin levels  and reduced risk for elevated markers of hemolysis , hepatomegaly , biliary dysfunction , stroke , and kidney dysfunction  . While lConsistent with the known pathophysiology of SCD, genes with at least 1 significant result for any outcome were enriched for cellular adhesion, oxidative and toxic stress, inflammation, and blood vessel regulation, with 23 of the 25 most enriched GO pathways representing these processes . PhenotyThis comprehensive systematic review and meta-analysis consolidates current knowledge of SCD genetic modifiers, incorporating data from 571 publications from 1981 to 2023 and describing at least 29 670 unique individuals residing in 43 countries. These 571 studies were assessed for quality of study design and reporting, according to STREGA guidelines. Remarkably, only 1% of results reported across the last 43 years of work met minimum standards for cross-study meta-analysis due to variability in study designs, reporting practices, and phenotype or genotype definitions.The differing methodologies and reporting practices between screened studies limited our ability to include, aggregate, or analyze published data. Many manuscripts used individuals without SCD as controls and were excluded, as this does not assess the genetic modifiers of SCD severity. Similarly, studies using biochemical measures as surrogates for genotypes  were excluded because biochemical measurements do not always align with genotype. Other limitations included use of rudimentary statistical tests, rather than regression-based techniques adjusting for confounders, and inconsistent phenotype or genotype definitions. Despite these methodological challenges, we resolved some previous discrepant results and provide guidelines for future studies.585 identified several genes associated with SCD complications in at least 2 studies. However, among those validated in meta-analysis, most were related to HbF levels. Specifically, meta-analyses confirmed the polygenic regulation of HbF: 10 SNVs in BCL11A, 8 in HBS1L-MYB, and 1 HBG2 were significantly associated with HbF. Remarkably, 137 additional genes were reportedly associated with HbF but have not been confirmed due to lack of validation in a separate cohort or insufficient harmonization of phenotype  or genotype. Only 20%-50% of the genetic variability in HbF can be explained by currently validated variants ,13 which have relatively large association sizes. Most likely, many other loci with small association sizes or low frequency account for the remaining heritability. These additional modifier variants may be represented among the genes that lack validation.Across 571 manuscripts, we, as in a recent review,232 our analysis consolidated the data to identify consistent trends among nonharmonized phenotypes with conflicting results.This review also clarifies the role of α-thalassemia as a modifier of SCD severity by demonstrating that α-thalassemia was associated with reduced risk of clinically relevant SCD symptoms thought to be driven by hemolysis, including severe anemia, hyperbilirubinemia or gallstones, kidney dysfunction, and stroke. While this was a commonly accepted belief,586 Discovering such modifiers of SCD will require well-designed studies in larger cohorts using modern approaches to genetic analyses, including genome-wide association studies, adjustment for covariates, and, as discussed by Pincez, et al,585 multiomics. Future studies following best practices may also confirm candidate associations that have been reported in only 1 study. Moreover, studies in African cohorts could identify heretofore undiscovered variants with different frequencies in European and admixed cohorts. Most high-income countries, such as the US, have relatively few patients with SCD available for genetic studies. By contrast, while only 22% of individuals studied to date were from African cohorts, millions of individuals with SCD reside in sub-Saharan Africa, reflecting a fertile region for future research.To date, most validated studies of SCD modifiers have identified common variants with large association sizes  in relatively small cohorts. However, most genetic variation in health-related traits is driven by the interplay of many variants with small association sizes or low allele frequencies.544 pain,104 kidney,170 and cerebrovascular outcomes132 in SCD. However, those scores generally account for a small fraction of heritability and thus lack clinical utility. Improving polygenic scores for SCD phenotypes will require identifying more variants, validation, and rigorous testing, all of which would benefit from larger, more diverse cohorts and could be informed by analogous studies in non-SCD cohorts. Similarly, mendelian randomization, a method to explore causal relationships, has been used infrequently in SCD cohorts.576 In addition to increasing studies in Africa of both individuals with or without SCD, local ancestry inference in admixed individuals may help deconstruct associations driven by African ancestry.Advanced statistical and machine learning approaches, will also prove beneficial. For example, polygenic scores combining variants of small association sizes have been generated, including for HbF,588 and in SCD590), which were enriched for genes associated with hemolysis, anemia, and hyperbilirubinemia or biliary complications.Pathway analysis is another avenue for identifying potential candidate genes and generating clinically relevant hypotheses, even when traditional meta-analysis is not possible, as it can identify biologically meaningful pathways based on genes with significant associations and indicate other genes in these pathways that may contribute to disease risk. Among genes significantly associated with any SCD outcome in at least 1 study, we found enrichment in pathways controlling cellular adhesion, inflammation, response to toxic and oxidative stress, and blood vessel regulation, aligning with known disease pathophysiology. Other genes in those pathways are potential candidates for future investigation. We also identified potential therapeutic targets, such as flavonoid metabolic processes (of interest generallyThis study has limitations that may confound or reduce the generalizability of our results. Subtype of SCD, ancestry, and hydroxyurea treatment status were not often reported in detail or, in the case of SCD subtype and ancestry, determined genetically; thus, we made no attempt to assess the difference between SCD subtypes or ancestries or to examine treatment response. Because most studies used a candidate gene approach, our results may be biased toward genes or pathways that were historically of high interest. Similarly, our analysis could be affected by unreported negative or contradictory results arising from positive publication bias. There are some methods, such as bayesian approaches, that cannot be integrated into a meta-analysis, resulting in some high-quality results being classified as exploratory. Finally, while our analysis categories allowed for a measure of study design and reporting rigor, they did not constitute a formal risk of bias assessment.592 Combined, these steps will generate the high quality results necessary to develop clinically actionable genetic tools.Although this systematic review and meta-analysis assessed 571 manuscripts that collectively reported 17 757 genetic associations with outcomes related to SCD severity, those associations validated in cross-study meta-analysis were largely related to HbF. To accelerate the understanding of the genetic etiology of SCD, future genetic association studies should report sufficient information for results to be included in meta-analyses. Elements of contemporary study design and international collaborations will improve scientific rigor, reduce the risk of false positives, and expand generalizability of study results. To facilitate cross-study analysis, the use of consensus measures is recommended for phenotypes and exposures.


We aimed to perform a meta-analysis comparing the safety and efficacy of TAVI versus surgical aortic valve replacement (SAVR) during the early and mid-term follow-up period. (2) Methods: We conducted a meta-analysis of randomized controlled trials (RCTs) comparing 1- to 2-year outcomes between TAVI and SAVR. The study protocol was preregistered in PROSPERO and the results were reported according to PRISMA guidelines. (3) Results: The pooled analysis included data from eight RCTs totaling 8780 patients. TAVI was associated with a lower risk of all-cause mortality or disabling stroke , significant bleeding , acute kidney injury  and atrial fibrillation . SAVR was associated with a lower risk of major vascular complication  as well as permanent pacemaker implantation . (3) Conclusions: TAVI compared with SAVR during early and mid-term follow-up was associated with a lower risk of all-cause mortality or disabling stroke, significant bleeding, AKI and atrial fibrillation; however, it was associated with a higher risk of MVC and PPI. Since its introduction more than 20 years ago by Dr. Alain Cribier, transcatheter aortic valve implantation (TAVI) has become the intervention of choice for severe symptomatic aortic valve stenosis in high-risk and elderly patients ,2. ContiA recently published comprehensive meta-analysis compared the safety and efficacy of TAVI versus SAVR during 1 year of follow-up as well as for the longest data available . In thisWe therefore aimed to conduct an updated systematic review and meta-analysis comparing early and mid-term (1–2 years) safety and efficacy outcomes from all RCTs comparing TAVI and SAVR, using the subgroup analysis according to the valve system types and a meta-regression analysis examining the effect of surgical risk on the primary outcome.The study protocol was written by T.L. and A.L. We conducted a comprehensive search to identify studies in Pubmed, Embase, and Cochrane Central Register of Controlled Trials (CENTRAL), up to December 2022, using a combination of keywords and MeSH terms for: transcatheter aortic valve replacement, transcatheter aortic valve implantation and surgical aortic valve implantation. References of all the included trials and reviews identified were scanned for additional studies. All the titles and abstracts were screened, and those thought to possibly meet the inclusion criteria were screened for eligibility using the full text. The primary outcome was all-cause mortality. The secondary outcomes were cardiovascular mortality, stroke, disabling stroke, composite outcome of all-cause mortality or disabling stroke, acute kidney injury (AKI), major/life-threating or disabling bleeding (significant bleeding), myocardial infarction (MI), permanent pacemaker implantation (PPI), major vascular complications (MVC) and atrial fibrillation. We included studies with a follow-up period between 1 and 2 years. The longest follow-up available data were used in the pooled analysis. Additional data regarding the longest follow-up available regarding the primary outcome were extracted as well.Two reviewers  independently extracted the data and resolved conflicts by discussion.Two authors  assessed the risk of bias. Cochrane’s handbook tool was used to assess the studies . A funne2 measure of inconsistency. We used a fixed effect model with the Mantel–Haenszel method for pooling trial results throughout the review unless statistically significant heterogeneity was found (p < 0.10 or I2 > 50%), in which case we chose a random-effects model and used the inverse variance method. Dichotomous data were analysed by calculating the odds ratio (OR) for each trial with 95% confidence interval (CI). Reported values are 2 tailed, and hypothesis-testing results were considered significant at p < 0.05.Heterogeneity between the included trials was assessed using the chi-squared test for heterogeneity and the IA sensitivity analysis was carried out examining the effect of the exclusion of each study on the pooled results . A subgroup analysis according to the TAVI valve system used was done as well. A meta-regression examining the association between patient surgical risk according to STS score on the effect size of the primary outcome was carried out as well.The study was pre-registered in PROSPERO; ID CRD42023391959.A flow chart representing the study selection process is shown in All eight RCTs provided data regarding all-cause mortality. In all the included trials, there were data regarding the outcome at a two-year follow-up period, except for the UK TAVI which only reported one year of follow-up.p = 0.19, I2 = 0%). The forest plot of all-cause mortality is shown in p < 0.01, I2 = 40%), however, there was a significant heterogeneity between the follow up groups (p = 0.02).There were 542/4455 (12.2%) deaths in the TAVI group and 571/4325 (13.2%) in the SAVR group. Overall, there was no significant difference between the two groups . A meta-regression analysis is shown is In a meta-regression analysis, there was no correlation between the all-cause mortality effect size . Additionally, TAVI was associated with a lower risk of significant bleeding. There were 454/3434 (13.2%) cases in the TAVI group and 974/3324 (29.3%) in the SAVR group . The risk of AKI was lower as well: 90/2942 (3.1%) in the TAVI group versus 161/2920 (5.5%) in the SAVR group . The risk of new onset atrial fibrillation was lower as well among the TAVI group: 399/3255 (12.3%) in the TAVI group versus 1032/3116 (33.1%) in the SAVR group .TAVI was associated with a lower risk of all-cause mortality or disabling stroke. There were 569/4313 (13.2%) cases in the TAVI group versus 624/4191 (14.9%) in the SAVR group .There was a lower risk for cardiovascular mortality in the TAVI group that did not reach statistical significance . In addition, the risk of PPI was lower in the SAVR group: 551/3229 (17.1%) in the TAVI group versus 279/3110 (9%) in the SAVR group .SAVR was associated with a lower risk of MVC: 185/2976 (6.2%) in the TAVI group versus 106/2869 (3.7%) in the SAVR group , disabling stroke , MI , or endocarditis . Secondary outcomes forest plots are shown in There was no statistically significant difference regarding the risk of stroke  as well as AKI (p = 0.01). Subgroup analysis is presented in All the RCTs except the UK TAVI included the TAVI arm with the homogenous valve system used with regards to the balloon-expandable or self-expanding valve. Unfortunately, the UK TAVI study used heterogenous valve systems and did not provide a subgroup or sensitivity analysis comparing the valve systems and, therefore, sub-group analysis could not be conducted using this study. Consequently, it was excluded from the subgroup meta-analysis. As mentioned above, its exclusion did not result in a shift in the effect size direction of the statistical significance of any outcome of interest. There were significant interactions between the valve type and PPI  and atrial fibrillation compared to SAVR. On the other hand, SAVR was associated with a lower risk of MVC and PPI compared to TAVI. There was no significant difference in the risk of stroke, disabling stroke, MI, or endocarditis between the two groups.In the latest meta-analysis published by Ahmad et al., all-cause mortality was reported for 1 year of follow-up and the longest follow-up available separately . Both coOur analysis found that the risk of all-cause mortality was higher in the TAVI group when looking at the longest available follow-up period. However, the data showed a significant discrepancy between the follow-up periods, making it unclear whether this difference is significant. In studies with a 5-year follow-up, a survival advantage was found in the SAVR group; this contrasts with short-term studies in which there was a trend towards an advantage for the TAVI group. This difference is probably explained by the fact that early studies which reported long-term follow-up data were characterized by less experience of the operators, older patients with a higher rate of comorbidities and a higher surgical risk. In addition, only one study examined 8-year outcomes. Therefore, further research with a longer follow-up period is needed to fully understand and clarify this finding.Another key difference between the current study and the recent meta-analysis by Ahmad et al. compared to the previous meta-analyses by Sinotis et al. is that we did not observe a statistically significant difference in stroke rates ,12. The Our study revealed a notable correlation between the type of TAVI valve used and the risk of AKI. However, a previous meta-analysis conducted by our group did not identify any significant difference in the AKI risk between the latest generation two-valve systems . FurtherThe current study highlights two significant safety disadvantages of TAVI, which are well established ,12. The Our meta-analysis has several strengths that make it a valuable contribution to the field. Primarily, it is the most comprehensive and up-to-date meta-analysis published that compares early and mid-term “hard outcomes” between TAVI and SAVR, therefore, providing important insights for heart teams to consider when making treatment decisions. In addition, the pooled analysis only included data from RCTs, which provides a high level of quality and reliability for the analysis. The exclusion of observational study data further enhances the strength of our analysis by minimizing the potential for bias and increasing the generalizability of our findings. Moreover, we used advanced statistical methods, such as meta-regression analysis, to examine the relationship between surgical risk and the main outcome. Finally, we conducted a subgroup analysis by valve type to control for possible confounding factors and increase the validity of the study findings. There are several limitations to this study that should be noted. One limitation is that some of the studies included in our analysis used older generation valves, which may not accurately reflect the outcomes of modern TAVI procedures. Another limitation is that we did not have access to individual patient data and our analysis was based on aggregate data. This means that the results should be interpreted with caution and may not be generalizable to all patients. Additionally, some of the analyses revealed significant heterogeneity between the studies, which could indicate that the results may not be directly comparable. Lastly, our analysis examined only early and mid-term outcomes up to 2 years of follow-up. However, we believe that at present, there is insufficient long-term data to conduct a comprehensive “hard-outcome” meta-analysis and further studies with longer-term data are necessary to establish a definitive conclusion within this time frame.In conclusion, our analysis found no significant difference in the all-cause mortality between TAVI and SAVR. TAVI was associated with a lower risk of all-cause mortality or disabling stroke, as well as a lower risk of significant bleeding, AKI and atrial fibrillation. However, it was also associated with a higher risk of major vascular complications and the need for permanent pacemaker implantation. Our analysis provides insights that should be considered in the decision-making process for both the heart team and in the shared decision making between cardiologists and patients.


A visual presentation of the funnel plot and Egger’s regression test showed no evidence of publication bias. Our meta-analysis indicated that persons using PPIs exhibited an increased risk of AKI. North American individuals had a higher risk of AKI compared to Asian and European individuals. However, the pooled effect from observational studies cannot clarify whether the observed association is a causal effect or the result of some unmeasured confounding factors. Hence, the biological mechanisms underlying this association are still unclear and require further research.Proton pump inhibitors (PPIs) are widely prescribed in medical practice for the treatment of several gastrointestinal disorders. Previous epidemiology studies have reported the association between PPI use and the risk of AKI, although the magnitude of the association between PPIs and the risk of acute kidney injury (AKI) remains uncertain. Therefore, we conducted a meta-analysis to determine the relationship between PPI therapy and the risk of AKI. We systematically searched for relevant articles published before January 2023 on PubMed, Scopus, and Web of Science. In addition, we conducted a manual search of the bibliographies of potential articles. Two independent reviewers examined the appropriateness of all studies for inclusion. We pooled studies that compared the risk of AKI with PPI against their control using a random effect model. The search criteria based on PRISMA guidelines yielded 568 articles. Twelve observational studies included 2,492,125 individuals. The pooled adjusted RR demonstrated a significant positive association between PPI therapy and the risk of AKI (adjusted RR 1.75, 95% CI: 1.40–2.19,  Kidney disease is one of the most common global health challenges, affecting more than 37 million Americans . Acute kProton pump inhibitor (PPI) is a widely prescribed medication in patients with gastric disorders ,12. WhilTo our knowledge, no comprehensive systematic review and meta-analysis of the association between the use of PPI and the risk of AKI has been published so far. Therefore, we performed an updated systematic review and meta-analysis of observational studies to determine whether the use of PPI is associated with an increased risk of AKI when compared to non-PPI users. In our study, we examined the risk of AKI among PPI users based on study design, region, quality of the study, and PPI types. This study might help clinicians to weigh the risk against overall benefits and provide clinical guidance.This systematic review and meta-analysis were conducted based on the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines ,26. A reWe searched electronic databases, such as PubMed, Scopus, and Web of Science, before January 2023 for relevant articles written in English. The following search terms were used to find potential articles: “proton pump inhibitor”, “PPI”, “anti-ulcer agent”, “esomeprazole”, “lansoprazole”, “rabeprazole”, “pantoprazole”, “acute kidney injury”, “acute renal failure”, and “AKI”. All references from previous review articles were manually examined. Two independent authors scrutinized retrieved studies using pre-established selection criteria. Any disagreement during the study selection process was arbitrated by a third author and resolved by discussion.We aimed to include both randomized controlled trials (RCTs) and observational studies, but there were no RCTs reporting PPIs and AKI risk. Therefore, in this study, we only included observational studies (such as case–control studies and cohort studies) published in English and which met the PICO (Patient Intervention Control Outcome) format. Studies were considered eligible if they met the following criteria: (a) PPI therapy was the exposure of interest; (b) AKI was the outcome; and (c) effect sizes were reported as an odds ratio (OR), hazards ratio (HR), or risk ratio (RR) with the corresponding 95% confidence interval (CI). We excluded studies published as reviews, letters, case reports, editorials, and animal studies.Using pre-established criteria, the same two independent reviewers extracted relevant information from the selected articles. For each study, the reviewers collected the author’s name, publication year, country, number of participants, number of PPI users, number of AKI patients, mean age, percentage of male and female patients, inclusion criteria, and effect size. Data from multiple observational studies were compiled into a single file and used to calculate the pooled effect size.The risk of bias was assessed using the Cochrane risk-of-bias tool and the Newcastle–Ottawa Quality Assessment Scale (NOS) . The risQ test. The I2 statistic was used to show the percentage of variability due to sampling error. The I2 values of 0~25%, 25~50%, 50~75%, and >75% were used to represent very low, low, moderate, and high levels of heterogeneity, respectively  compared to Asian [1.80 ] and European [1.64 ] individuals. There was a significant heterogeneity among studies from both North America and Europe .Seven studies from North America, four studies from Europe, and one study from Asia evaluated the association between PPI and AKI risk. North American individuals had a higher risk of AKI [1.80  in high-quality studies. However, there was a significant heterogeneity among studies . The pooled risk of AKI was 1.83  in moderate-quality studies. However, there was no significant heterogeneity among studies .Furthermore, nine high-quality and three moderate-quality study assessed the relationship between PPI and AKI risk. The pooled risk of AKI was 1.68 , we therefore conducted a sensitivity analysis. To examine the overall impact of each study on AKI risk, a sensitivity analysis was performed by excluding studies one by one .Egger’s regression was used to detect overall publication bias and also generated Begg’s funnel plots for the association between PPI use and the risk of AKI . It showA meta-analysis based on twelve observational studies showed that there was a significant association between PPI use and the risk of AKI. However, when stratified according to region, we found that the risk of AKI was high in North American individuals compared to Asian and European individuals. The risk of AKI was slightly lower among PPI users in high-quality studies than in moderate-quality studies. This may be due to the small number of studies focused on the association between PPI use and AKI, and the fact that some risk factors for AKI have not been fully adjusted. The findings of our study are consistent with previously published meta-analysis , which fAlthough the association between PPI use and the risk of AKI is uncertain, several possible pathophysiological mechanisms have been reported. Previous studies have shown that the development of AIN and a hypersensitivity reaction might responsible for the reducing glomerular filtration rate and adverse renal outcomes ,41. MoreKidney disease is the most common public health problem and the leading cause of death worldwide . The ecoWhile the major causes of AKI are always unclear, a number of possible risk factors for developing AKI have been identified. Previous studies reported that use of NSAIDs , remdesiThe findings of our study showed a higher risk of AKI among PPI users in three different geographical regions. Disease risk varies regionally and is always complex. Several studies have reported that physiological variation may be a factor ,63, inclThe risk of AKI was consistent in other subgroup analyses, which makes the evidence more robust. Although PPI has been shown to have a favorable safety profile ,69, it iOur study has several strengths. First, it included a larger number of individuals and studies compared to previously published meta-analysis, providing greater statistical power. Second, our study also analyzed the relationship between PPI use and the risk of AKI based on region and study quality, which was not included in previous studies. Third, to minimize the impact of potential confounding factors, our study calculated the pooled effect size using only adjusted OR/HR values, which makes our findings more robust. However, our study also has some limitations. First, it included only observational studies. Although most of the studies included were of high quality and adjusted for potential confounding factors, selection bias cannot be ignored. Therefore, the findings of our study cannot confirm the existence of causality. Second, we were unable to examine the risk of AKI among PPI users based on the duration of PPI use and various dosages due to data insufficiency. Third, only one study provided individual PPI use and the risk of AKI. More studies are warranted to draw a firm conclusion about different PPIs . Fourth, our study provided a subgroup analysis based on the region but only studies from Asia were included. Therefore, more studies are needed to evaluate the regional effect. Finally, most of the included studies defined AKI based on the ICD-9 or ICD-10; however, the absolute risk of AKI in the target population can be underestimated. Two studies used laboratory data to detect AKI among PPI users; however, the effect of PPIs might be significantly affected by existed confounding factors and those studies did not categorize the stage of AKI. In this meta-analysis, we found that PPI use was associated with an increased risk of AKI. The risk of AKI was high in North American individuals compared to Asian and European individuals. Given the high prevalence of PPI use among patients with gastric disorders, safe prescription practices are necessary to ensure patients are not put at undue risk. Well-designed, with an appropriate strategy for patient selection , and large prospective studies are needed in the future to gain further insight into the importance of PPI dose and duration, which can guide critical clinical decision-making. Moreover, animal studies can provide additional insight into the mechanisms by which PPI use may increase the risk of AKI.


Objectives: The association between non-genetic risk factors and cervical cancer (CC) remains controversial and unclear. This umbrella review was conducted to evaluate and synthesize previously published systematic reviews and meta-analyses related to non-genetic factors and CC risk.Methods: We searched PubMed, Web of Science, and EMBASE to identify studies investigating the association between extragenetic factors and CC risk. For each article, we calculated the summary effect size and the 95% confidence interval. Specific criteria were used to classify the association into four levels: strong, highly suggestive, suggestive, or weak.Results: A total of 18 meta-analyses of different risk factors for CC were examined; these studies covered risk factors related to diet, lifestyle, reproduction, disease, viral infection, microorganisms, and parasites. Oral contraceptive use and Chlamydia trachomatis infection were shown to increase CC risk, and this was supported by strong evidence. Additionally, there were four risk factors supported by highly suggestive evidence and six risk factors supported by suggestive evidence.Conclusion: In conclusion, there is a strong association between oral contraceptive use, Chlamydia trachomatis infection, and increased CC risk. Cervical cancer (CC) is one of the most severe malignancies affecting women. In 2020, CC was the fourth most frequently diagnosed cancer and the fourth leading cause of cancer death in women, with an estimated 604,000 new cases and 342,000 deaths worldwide . ResultiRecent evidence has identified some non-genetic risk factors of CC, such as oral contraceptive use, smoking, household air pollution, and trichomonas vaginalis infection –9. Oral In this context, we conducted an umbrella review of current systematic reviews as well as meta-analyses of observational studies to systematically assess the strength and validity of the association between extragenetic factors and CC risk . We perfWe completed an umbrella review, which is a systematic collection and assessment of multiple systematic reviews and meta-analyses of a specific research topic . We searEach systematic review and meta-analysis were reviewed independently by two authors (X-YL and CG) to identify studies that met the inclusion criteria. Differences were resolved by a third author (Q-JW). Inclusion criteria were as follows: 1) systematic reviews and meta-analyses of observational studies that assessed the association between extragenetic factors and CC risk in humans; 2) studies that provided effect sizes  of CC for various extragenetic factors; and 3) articles that were published in English. If multiple systematic reviews and meta-analyses discussing the same exposures and outcomes were found, we included the one with the largest number of original studies . We inclArticles were excluded if they met the following criteria: 1) study exposure and outcome were not of interest; 2) studies using animals; or 3) articles that did not report necessary study-specific data . Systematic reviews and meta-analyses that only reported pre-cancerous lesions  or that assessed pre-cancerous lesions in combination with CC were also excluded.Two authors (X-YL and CG) extracted data independently. When discrepancies arose, the decision was made by a third author (Q-JW). We employed a data-collection form to acquire data from eligible studies. The data-collection form included both the information of the original study and the information of the meta-analysis. For meta-analyses: first author, publication year, number of included studies, exposure, outcome, case number, total population, and comparison were recorded. For original studies: study design, case number, total population, most adjusted risk estimates , and corresponding 95% CIs were recorded.2 statistic, with I2 ≥ 50% or I2 ≥ 75% representing large heterogeneity and very large heterogeneity, respectively. Due to the uncertainty of heterogeneity between studies, we further calculated the 95% CI of I2  effect size in the largest study was smaller than the summary effect size , and no excess significance bias (p > 0.1). The criteria for highly suggestive evidence included random effects p < 10−6, number of cases >1,000, and p < 0.05 of the largest study in the meta-analysis. The criteria for suggestive evidence included random effects p < 10−3 and number of cases >1,000. The sole criterion for weak evidence was random effects p < 0.05. In cases with p > 0.05, there was no association.We used specific criteria to grade the association between extragenetic factors and CC risk. Our criteria for evaluation of evidence in concordance with the strategies used in previously published umbrella reviews –31. TherTwo authors independently evaluated the quality of each included systematic review and meta-analysis. The evaluation was based on the Assessment of Multiple Systematic Reviews (AMSTAR) tool, which provides 11 items to measure the methodological quality of meta-analyses ; the higThe literature search strategy retrieved 6,181 publications from three electronic databases . After rin vitro fertilization, intrauterine devices, and oral contraceptives); disease factors ; dietary intake factors ; and virus, microorganism, and parasite factors . All articles were published between 2001 and 2020. Of the 18 studies, the median number of original studies in each systematic review or meta-analysis was four (range: 3–16). The study design of the synthesized studies included case-control, cohort, nested case-control, cross-sectional, and friend/family studies. The number of cases and participants ranged from 23 to 4,945 and from 111 to 5,371,295, respectively. Outcome indicators included various types of CC, such as adenocarcinomas of the cervix, squamous cell carcinoma, invasive CC, cervical carcinoma in situ, and early stage CC.A total of 39 different associations between non-genetic risk factors and CC risk were examined in the 18 meta-analyses  10, 33––33–49. Tp < 0.05 was used as the threshold for statistical significance, the summary fixed-effects and random-effects estimates were significant in 31 (79%) and 29 (74%) of the meta-analyses, respectively. Of these associations, 18 reported increased risks of CC and 11 showed decreased risks of CC under the random effects model. A total of 18 (46%) associations generated significant summary results (p < 0.001) using the random-effects model, including smoking, HPV and HPV16, herpes simplex type 2, C. trachomatis, Epstein-Barr virus, intrauterine devices, oral contraceptives, endometriosis, vitamin A, vitamin E, and selenium intake. A total of 25 (64%) associations showed statistically significant effects (p < 0.001) using the fixed-effects model. At a more stringent threshold of significance (p < 1 × 10−6), the summary random-effects estimates were significant for nine (23%) associations and the summary fixed-effects estimates were significant for 18 (46%) meta-analyses. Of the nine associations, eight meta-analyses  showed increased CC risk, and only vitamin A showed decreased CC risk. The summary random effect estimates are presented in p < 0.05.In this study, we used the random-effects model and the fixed-effects model to re-analyze 39 associations in the 18 meta-analyses. When 2 statistic. Twenty-six (67%) showed low heterogeneity (I2 < 50%), eight (20%) meta-analyses had large heterogeneity (I2 = 50–75%), and five (13%) had very large heterogeneity (I2 > 75%). We calculated the 95% prediction intervals, and the null value was excluded in eight (20%) meta-analyses; this included herpes simplex type 2, C. trachomatis infection, oral contraceptive use, and selenium intake. Heterogeneity and 95% prediction intervals are shown in The heterogeneity of the 39 associations was evaluated using the Ip < 0.1, and effect size of the largest study smaller than the summary effect size); these included HPV and HPV16, Epstein-Barr virus, intrauterine device, gestational diabetes mellitus, and retinol intake. The 12 (30%) risk factors that had evidence of excess significance bias (based on O > E and p < 0.1) were: smoking, HPV and HPV16, C. trachomatis, cervicovaginal lactobacilli, oral contraceptives, endometriosis, carotenoid intake, and selenium intake  associations met the criteria for small-study effects  presented strong evidence. Four associations presented highly suggestive evidence and assessed the association between extragenetic factors and CC risk, such as C. trachomatis (exposed vs. unexposed) and smoking (smokers vs. non-smokers). Six extragenetic factors  presented suggestive evidence. In addition, 17 associations were supported by weak evidence and 10 associations presented no association. The detailed results of the analyses on which the evidence ratings were based are shown in We used specific criteria to grade the 39 associations in the 18 meta-analyses. After applying our credibility criteria, only two associations . With the evaluation of substantial heterogeneity between studies, small study effects, and excess significance bias, we found that C. trachomatis infection and oral contraceptive use can increase CC risk. Furthermore, four associations (C. trachomatis and smoke) were supported by highly suggestive evidence, six associations  were supported by suggestive evidence, and 17 associations were supported by weak evidence.This is the first umbrella review to provide a comprehensive overview and a critical evaluation of the association between extragenetic factors and CC risk. In this umbrella review, by summarizing the evidence from related systematic reviews and meta‐analyses, we examined 39 different meta-analyses of different non-genetic risk factors and CC risk. Of these, two risk factors were supported by strong evidence  in the highest intake (or serum level) group of vitamin E  was assessed by standard approaches. Of note, most of the meta-analyses we included were recognized as moderate-to-high quality.Several possible limitations of this umbrella review should be considered. First, all of the studies we included were systematic reviews and meta-analyses. The reliability of the included meta-analyses is indirectly dependent upon the original studies; there was no feasible way for us to control bias from the original research. However, we use AMSTAR tool to ensure the quality of included studies. Second, some of the included meta-analyses had scores indicating low quality methodology. Few of the included meta-analyses considered grey literature and also did not present a list of excluded studies. Even so most articles are rated as moderate to high quality, which aggrandize the credibility to our results. Third, we calculated the summary effect size from a combination of studies with different measures, such as OR, RR, and HR. When the outcome is uncommon, OR is statistically similar to RR . Lastly,Chlamydia trachomatis infection) were supported by strong evidence. Exposure to oral contraceptives or infection with C. trachomatis can increase CC risk. From studies with highly suggestive evidence and suggestive evidence, smoking is shown to be a risk factor for CC, whereas intrauterine device use, endometriosis, vitamin A, carotene, and vitamin E are protective factors.In conclusion, two risk factors (oral contraceptive use and


Puerperal infection (PI) is a severe threat to maternal health. The incidence and risk of PI should be accurately quantified and conveyed for prior decision-making. This study aims to assess the quality of the published literature on the epidemiology of PI, and synthesize them to identify the temporal trends and risk factors of PI occurring in Mainland China.This review was registered in PROSPERO (CRD42021267399). Putting a time frame on 2010 to March 2022, we searched Cochrane library, Embase, Google Scholar, MEDLINE, Web of Science, China biology medicine, China national knowledge infrastructure and Chinese medical current contents, and performed a meta-analysis and meta-regression to pool the incidence of PI and the effects of risk factors on PI.A total of 49 eligible studies with 133,938 participants from 17 provinces were included. The pooled incidence of PI was 4.95% , and there was a statistical association between the incidence of PI following caesarean section and the median year of data collection. Gestational hypertension (OR = 2.14), Gestational diabetes mellitus (OR = 1.82), primipara (OR = 0.81), genital tract inflammation (OR = 2.51), anemia during pregnancy (OR = 2.28), caesarean section (OR = 2.03), episiotomy (OR = 2.64), premature rupture of membrane (OR = 2.54), prolonged labor (OR = 1.32), placenta remnant (OR = 2.59) and postpartum hemorrhage (OR = 2.43) have significant association with PI.Maternal infection remains a crucial complication during puerperium in Mainland China, which showed a nationwide temporal rising following caesarean section in the past decade. The opportunity to prevent unnecessary PI exists in several simple but necessary measures and it’s urgent for clinicians and policymakers to focus joint efforts on promoting the bundle of evidence-based practices. Puerperal infection (PI) is a local or systemic inflammatory reaction caused by the invasion of pathogenic microorganism following childbirth . In sevePutting a time frame on 2010 to the present, we searched the databases  of Cochrane library, Embase, Google Scholar, MEDLINE and Web of Science for relevant English language literatures, and the databases of China biology medicine (CBM), China national knowledge infrastructure (CNKI) and Chinese medical current contents (CMCC) for relevant Chinese language literatures. Search strategies were customized to each electronic database and based on the following combinations of key search terms: maternal, obstetric, puerperal, postpartum, puerperium, after birth, after delivery, infection, sepsis, endometritis, risk factors etc. In case of duplicate publications, we included only the study with most detailed information. Moreover, the references cited in the identified literatures were further searched by manual retrieval.All observational, longitudinal studies  were included in our review if they recruited participants who had undergone childbirth in Chinese hospitals, reported on outcomes of PI following delivery and/or reported on the associations of PI with any maternal-related factors , delivery-related factors , or hospital-related factors . Besides, for the studies on risk factors associated with PI, the following criteria should be met: (1) The definitions of exposure were clear and basically consistent; (2) The OR value with 95% confidence intervals (CIs) could be calculated or converted from the raw data provided. And studies were excluded if they: (1) Fewer than 20 participants; (2) Only a subgroup of puerperae with higher risk  of PI other than the general population of puerperae; (3) Duplicate publications with identical participants; (4) Case report, review, poster or conference abstracts; (5) Incomplete or inconsistent raw data.PI was defined as maternal in-hospital infection after childbirth and post-discharge infection occurring at any time during the 6-week postpartum period, which consisted of genital tract infection, urinary tract infection, wound infection following cesarean section, infectious peritonitis, sepsis or other specified infectious complications of surrounding tissues . And we Two authors (L.P. and L.Y.) independently extracted data, which included first author, research method, study location, year of data collection, hospital level, sample size, social characteristics of participants, exposure factors, number of exposed and no exposed participants, number and subtype of PIs. And we contacted the authors to provide the necessary data. This review was registered on the PROSPERO database with a registration number of CRD42021267399 and conducted according to PRISMA guidelines . The quaGiven the incidence of PI is close to zero in most studies, the individual study-specific incidences were presented via the score method to avoid yielding inadmissible values (i.e. less than zero) . The subx2 test were used to calculate the Cochran’s Q statistic, along with I2 to quantify the between-study heterogeneity, according to which the random- or fixed-effects model was chosen to pool the subgroup and overall OR values with log transformation and back transformation. For studies of specific risk factors with heterogeneity, the random effects meta-regression was performed to identify the sources of heterogeneity, and then a subgroup analysis was conducted to assess the effects of these sources on heterogeneity. Next, to detect publication bias quantitatively, the fail-safe N coefficient was adopted to calculate the potential number of studies that would have been needed to reverse the current effect [The measures of association between exposures and PI were presented as OR with 95% Wald CIs. The t effect , and thet effect , as the t effect . Consider = 0.208, P = 0.165). And 27 studies published from 2006 to 2021 reported the risk factors for PI, 25 (92.59%) of which had a JBI score greater than 6, with a upward temporal trend in JBI scores . The detailed information and quality of included studies are available in Table A total of 49 eligible studies with 133,938 participants from 17 provinces in Mainland China were included after retrieval and screening  (Table P = 0.491). After stratified by delivery mode, the incidence of PI was 7.11%  following caesarean section and 4.75%  following vaginal delivery. And there was a significant association between incidence of PI following caesarean section and the median year of data collection (P < 0.01), but not between incidence of PI following vaginal delivery and the median year of data collection (P = 0.920) . The forest plot for studies reporting the incidence of PI are available in Fig. P < 0.01) (Table The factors of 1) gestational hypertension, GDM, multipara, genital tract inflammation and AP before childbirth, 2) caesarean section, episiotomy, PROM and prolonged labor during childbirth, 3) placenta remnant and hemorrhage after childbirth were significantly associated with PI (P < 0.10 and I2 > 50%) (Table P = 0.07 and 0.08), hospital levels for caesarean section and PROM (P = 0.01 and < 0.01), study design for caesarean section and PROM (P = 0.03 and 0.01), JBI scores for PROM (P = 0.06)  Table . Meta-re6) Table . After sP < 0.01) . The result of Harbord weighted linear regression test showed that asymmetry with larger intercept value existed in the funnel plots of the factors of gestational hypertension and GDM, and there were significant correlations between the sample size and OR values of these two factors’ included studies (P < 0.01).The publication bias was detected for the factors statistically associated with PI Table . If therTo our knowledge, this is the first systematic review on synthesizing and evaluating the incidence, temporal trends and risk factors of PI in Mainland China. The incidence of PI ranged widely from 0.30% to 32.67% across individual studies and averaged approximately 4.95% in pooled analysis. The incidence of PI following caesarean section was higher than that of vaginal delivery and appeared to be a temporal rising over the past decade.Various maternal- and hospital-related factors before, during and after childbirth showed their associations with PI. And our analysis found no statistically significant association between age, multiple pregnancy, premature delivery or operative vaginal delivery and PI, whereas primipara was associated with decreased risk of PI.Understanding the epidemiology of PI is the first step towards evidence-based clinical practice. And in this analysis, we found a much higher incidence of PI than in a recent study covered 46 countries between 2005 and 2016, with 90% of the data coming from HICs . Even soAlthough the general incidence of PI has not changed across the country, it should not be ignored that our results also showed the incidence of PI following caesarean section was higher than that of vaginal delivery and has increased strongly in the past decade. As we know, with the continuous development of perinatal medicine and the improvement of surgical safety, the rate of cesarean section is increasing globally . Due to In addition to caesarean section, episiotomy is another invasive operation characterized by high rate and high risk of PI as our findings indicated. As an internationally controversial operation in the past 10 years, episiotomy could alleviate the pain of prolonged labor, accelerate the delivery of fetus, prevent the aggravation of fetal distress, and even reduce risks of several maternal complications, which were thought to include subsequent urinary and fecal incontinence, pelvic floor dysfunction and prolapse, and sexual dysfunction . HoweverMoreover, in common with other studies, we found the underlying diseases such as gestational hypertension and GDM, prenatal symptoms of genital tract inflammation and AP, as well as placenta remnant and hemorrhage after childbirth, would increase the probability of PI to varying degrees. The mainstream explanation is that the existence of above risk factors leads to the hemodynamic disorder of endometrium easily, and hyperosmosis and hyperglycemia increase the colonization rate of pathogens, thus promoting the occurrence of infection. In addition, PROM changes the closed environment of the uterine cavity and weakens both the bactericidal effect of lysozyme in amniotic fluid and the self-purification effect of vagina, which leads to the imbalance of bacterial flora in maternal genital tract and increases the chance of intrauterine infection . TherefoWhat is also worth noticing is that, although there has been no clear consensus on the effect of parity on PI until now, our results showed that primiparae were less likely to develop PI than multiparae. One potential explanation is that the vaginal microbial environment is affected and modified strongly by pregnancy history . The absThe findings of our incidence rate-related analysis require interpretation with caution, given the following limitations: (i) We adopted the WHO definition of PI, but acknowledged that very few surveys were able to investigate infection up to 6 weeks postpartum, and some studies only interviewed or assessed puerperae during hospitalization; (ii) The heterogeneity was observed among included studies when pooling the incidence. In addition to the differences in study quality identified by meta regression, sources of heterogeneity may also be attributed to a variety of other parameters with limited access, such as regional differences in economic levels and population characteristics, hospital differences in diagnostic procedures, perioperative managements and infection control practices; (iii) The analysis of temporal trends in PI rates was based on the median year of study data collection, which may have resulted in an inability to accurately capture the specific perinatal and follow-up periods. Meanwhile, there are two major limitations to our risk analysis: (i) The studies included were observational in design, which may be subject to selection, recalling or attrition bias, hence the confidence of effect estimates may be biased and the validity of the findings could have been limited; (ii) Due to lack of data, our analysis could not identify the contribution of other potential risk factors, such as maternal financial situation and psychological characteristic, nor could it explore and reduce inter-study heterogeneity by stratification through a number of other important confounding factors, such as hospital location and PI type.Our study details the changing epidemiology and various risk of PI. Maternal infection is revealed to be a still common and crucial complication during puerperium in Mainland China, and it has shown a strong nationwide temporal rising following caesarean section in the past decade. Risk factors for PI include 1) gestational hypertension, GDM, multipara, genital tract inflammation and AP before childbirth, 2) caesarean section, episiotomy, PROM and prolonged labor during childbirth, 3) placenta remnant and hemorrhage after childbirth. The noticeable effects of various maternal- and hospital-related factors on the risk of PI indicates that the opportunities for preventing PI exist in several simple but necessary measures, such as strengthening prenatal screening and treatment of underlying diseases, regulating surgical indications, promoting restrictive over routine episiotomy, standardizing aseptic operation during labor, enhancing postpartum care and monitoring of uterine contraction. Moreover, great attempts are warranted to reduce unnecessary PI through evidence-based education, training and management. In an era of declining global fertility but rising antimicrobial resistance, our findings highlight the importance and urgency for clinicians and policymakers to focus joint efforts on promoting the bundle of evidence-based practices to prevent PI.