The aim of this study was to analyze the risk factors for primary Sjögren’s Syndrome (pSS) by conducting a meta-analysis of observational studies.Four electronic databases were searched from inception to August 2022. The search strategy included medical subject headings (MeSH) and text words. Outcomes were calculated and reported as the odds ratio (OR) and 95% confidence interval (CI).Twelve studies consisting of nine case–control and three cohort studies were analyzed. Significant positive relationships between infection, a family history of autoimmune disease in first-degree relatives, negative stressful life events, CGGGG insertion/deletion polymorphisms in the IRF5 gene and the onset of pSS were found, with pooled ORs and 95% CIs of 2.73 , 5.93 , 1.69  and 2.69 , respectively. In contrast, the results showed that a history of smoking was not associated with the onset of pSS, with a pooled OR and 95% CI of 1.39 . However, a statistically significant negative association between current smoking and pSS was detected, with a pooled OR and 95% CI of 0.4 .Our research indicated that infection, a family history of autoimmune disease in first-degree relatives, negative stressful life events and CGGGG insertion/deletion polymorphisms in the IRF5 gene might be risk factors for pSS. In contrast, our study demonstrated that a history of smoking was not associated with the onset of pSS, whereas current smoking was negatively associated with pSS onset.https://inplasy.com/ ) under registration number INPLASY202230005.We registered this review on INPLASY (The online version contains supplementary material available at 10.1007/s10067-022-06474-8. Primary Sjögren’s syndrome (pSS) is a chronic and heterogeneous disorder characterized by a wide spectrum of glandular and extra-glandular features, leading to mononuclear cell infiltration of exocrine glands, notably the lacrimal and salivary glands –3. It isAlthough pSS is a complex disease, with genetic and epigenetic factors potentially contributing to its occurrence, there is still no consensus among experts regarding the risk factors and pathogenesis of pSS. An increasing number of studies have been performed in recent years to improve our understanding of pSS, and potential risk factors have been identified, which include various types of infection, genetic susceptibilities and some environmental factors , 7. One To date, a meta-analysis that comprehensively summarizes the risk factors of pSS based on existing evidence has been lacking. Here, we analyze the risk factors in patients with pSS by conducting a meta-analysis of observational studies.A systematic review of the electronic databases  was conducted independently by two authors from their inception to August 30, 2022, without any language restrictions. We also searched ClinicalTrials.govThe inclusion criteria for considering studies for this review were as follows: (a) the design is a case–control study, cohort study or cross-sectional study based on unrelated individuals; (b) study published in full-text form; (c) patients meet the diagnostic criteria according to the American–European classification criteria ; (d) stuThe risk of bias among the included studies was assessed according to the Newcastle–Ottawa quality assessment scale (NOS) . A studyA standardized form was designed for this meta-analysis. Two authors independently extracted data using the form to identify eligible studies. The following information was collected from each study: authors, year of publication, study design, statistical method, risk factors (including the hazard ratio or OR and 95% CI of each risk factor) and number of cases and controls. Discrepancies were resolved by discussion and by consultation with other authors. When the necessary information was unclear, we attempted to contact the authors of the original reports to provide further details.I2. The fixed-effects model was used for meta-。analysis when p ≥ 0.10 and I2 ≤ 50%, which indicated that the homogeneity was appropriate. Otherwise, the random-effects model was used. Funnel plots, Egger’s regression asymmetry tests and sensitivity analysis were used to analyze potential publication bias and to test the stability of the results of the meta-analysis. If required, for each risk factor, the final-effect ORs and 95% CIs were pooled by means of both random and fixed-effect models, and the results were compared. When the number of included studies was greater than five, studies with a significant deviation from the 95% CI in the funnel plot were excluded from the meta-analysis, and the results were compared with those contained in all included studies. p values < 0.05 were considered statistically significant.The synthesis, calculation and analysis of extracted data were all performed using Review Manager 5.3 and STATA14 software. Pooled statistics were calculated as ORs with 95% CIs. Assessment of statistical heterogeneity was conducted using Cochran’s Q statistic, and Higgins and Thompsons’ A total of 464 relevant citations were retrieved through electronic databases and other search sources , of which, 64 citations were duplicates. After excluding all ineligible articles and evaluating 41 report, 22 studies –36 were The 12 studies included nine case–control studies and three cohort studies. A total of 158,539 research objects were recruited, including 18,215 cases and 140,324 healthy controls. Control groups in 11 studies were sex-matched. All studies were scored using the NOS, and the results showed that four studies , 32, 35 The 12 included studies examined associations between several factors and pSS. The original data regarding the six selected factors  were pooled using the random-effects model or fixed-effects model based on the results of the heterogeneity test. The details of these analyses are provided in Table I2 = 81%, p = 0.0001). Using a random effects model, we found a statistically significant positive association between infection and pSS, with a pooled OR and 95% CI of 2.73   so a random-effects model was used, and the results revealed no statistical association between a history of smoking and pSS, with a pooled OR and 95% CI of 1.39   so a random effects model was used, and the results revealed a statistically significant negative association between current smoking and pSS, with a pooled OR and 95% CI of 0.49   so a fixed-effects model was used, and the results revealed a statistically significant positive association between a family history of AD in FDR and pSS, with a pooled OR and 95% CI of 5.93   so a fixed-effects model was used, and the results revealed a statistically significant positive association between a history of pregnancy and pSS, with a pooled OR and 95% CI of 2.09   so a random effects model was used, and the results revealed a marginally significant association between CGGGG Indel polymorphisms in the IRF5 gene and pSS, with a pooled OR and 95% CI of 1.69   so a fixed-effects model was used, and the results revealed a statistically significant positive association between the number of negative stressful life events being > 1 and pSS, with a pooled OR and 95% CI of 2.69   secretion after stimulation of innate immunity and in type I IFN signal transduction. The CGGGG Indel polymorphism in the IRF5 gene is in the promoter region of the IRF5 transcript containing exon 1A, where the risk allele carries four copies of a repeated CGGGG unit. This type of IRF5 polymorphism has been generally associated with ADs, such as systemic lupus erythematosus, rheumatoid arthritis, inflammatory bowel diseases and multiple sclerosis . It is wAccording to our results, experiencing a greater number of negative stressful life events significantly increases the risk of pSS onset. In fact, it is acknowledged that many patients with pSS suffer from anxiety or depression, which are considered complications of pSS . HoweverWe qualitatively analyzed and summarized 10 studies that could not be included in the quantitative synthesis because of the lack of other similar studies, and identified some other potential risk factors, including genetic risk factors, for pSS.McCoy et al. performed the largest study to date to evaluate sex hormone exposure with pSS, and their findings suggested that female sex hormones might be protective for pSS (16), or conversely, that reduced female sex hormones might be associated with pSS. This would be consistent with the epidemiology of pSS, in that onset typically occurs during perimenopause when estrogen and progesterone levels drop . Lu et aChao et al. investigated the correlation between the use of antibiotics for non-tuberculous mycobacterial infection and the risk of SS through a population-based dataset and found that the use of new macrolides, fluoroquinolones and tetracyclines was positively associated with the development of SS, whereas the use of amikacin appeared to play a protective role . This coMachowicz et al. examined the association between a Mediterranean diet and SS and found that a Mediterranean diet was linked with a lower likelihood of pSS . Given tIt is commonly agreed that pSS is an autoimmune disease with a complex genetic background. Our current study confirmed this view and, based on the five studies mentioned below, identified more than five genetic risk factors for pSS. Du et al. identified functional leucocyte immunoglobulin like receptor A3 as a susceptibility factor for pSS . This faThere were some limitations of our study. First, a considerable quantity of literature could not be included in this meta-analysis because of ineligible statistical methods, and this limited the supportive evidence for our findings. Second, some included studies were heterogeneous. For example, regarding infection, different studies included cases with different types of infection, and regarding smoking, the duration and frequency of exposure varied for each smoker. However, sensitivity analysis was conducted, and the results of the fixed-effects and random effects models were compared. In addition, as most of the included literature was a case–control study, the extent to which our findings could be demonstrated was limited.Our research indicated that infection, a family history of AD in FDR, a history of pregnancy, the CGGGG indel polymorphism in the IRF5 gene and negative stressful life events might be risk factors for pSS. In contrast, our study demonstrated that a history of smoking was not associated with pSS, while current smoking was negatively associated with pSS. These differences may potentially reflect early pathological changes, highlighting the chronic, insidious but progressive nature of pSS. The literature on pSS is currently limited, and more prospective, genetic and epidemiological studies on larger and more geographically diverse populations are needed to clarify the range of risk factors for pSS, which may allow for more timely and accurate patient stratification.Supplementary file1 (DOCX 32 KB)Supplementary file2 (DOCX 32 KB)Below is the link to the electronic supplementary material.


Acute myocardial infarction occurs when blood supply to a particular coronary artery is cut off, causing ischemia or hypoxia and subsequent heart muscle destruction in the vascularized area. With a mortality rate of 17% per year, myocardial infarction (MI) is still one of the top causes of death globally. Numerous studies have been done to identify the genetic risk factors for myocardial infarction, as a positive family history of heart disease is one of the most potent cardiovascular risk factors. The goal of this review is to compile all the information currently accessible in the literature on the genes associated with AMI. We performed a big data analysis of genes associated with acute myocardial infarction, using the following keywords: “myocardial infarction”, “genes”, “involvement”, “association”, and “risk”. The analysis was done using PubMed, Scopus, and Web of Science. Data from the title, abstract, and keywords were exported as text files and imported into an Excel spreadsheet. Its analysis was carried out using the VOSviewer v. 1.6.18 software. Our analysis found 28 genes which are mostly likely associated with an increased risk for AMI, including: PAI-1, CX37, IL18, and others. Also, a correlation was made between the results obtained in the big data analysis and the results of the review. The most important genes increasing the risk for AMI are lymphotoxin-a gene (LTA), LGALS2, LDLR, and APOA5. A deeper understanding of the underlying functional genomic circuits may present new opportunities for research in the future. Myocardial infarction (MI) is the interruption of the blood flow to a certain coronary artery that results in necrosis of the heart muscle in the vascularized area ,2. MI stMyocardial infarction is difficult to diagnose since it occurs suddenly and unexpectedly. Hypertension, circulating blood lipid levels , smokingAll available data suggests that a greater knowledge of the underlying functional genomic pathways may open new avenues for neutralizing a huge proportion of the population’s hereditary predisposition to MI. This review aims to identify, in an unbiased manner, all the associations between genetic abnormalities and myocardial infarction documented in the literature using big data analysis. As a secondary objective, we aimed to see if there was homogeneity in the findings on the three main medical databases of scientific articles .A total of 6436 articles  that met the search criteria were found on all three search engines, and none were excluded, as can be seen in the PRISMA flow diagram below .The results obtained after the analysis can be seen in the figures below .The program divided the topics of interest found in the database into clusters. Seventeen clusters were obtained, ordered according to the number of connections. “Myocardial infarction” was the largest cluster with 28 gene connections, as can be seen in the figure above  represenAccording to the big data analysis performed on the database, the direct links with high-risk genes/alleles in the production of a myocardial infarction are represented by: PAI-1, Based on the graphical representation, there were no obvious differences regarding the density of these genes in the studied databases.The most important genes/alleles identified as being associated with acute myocardial infarction are listed in Most studies actually analyzing genes associated with an increased risk for MI began to be published in 1995, when Eriksson et al.  proved tp 0.01). p = 0.002). The In 1996, Weiss et al.  investigge of 60 . In anotge of 60  in 1997,farction . Therefo(p = 0.001). The combined H7H5 and H7H6 polymorphisms for the polymorphism affecting hypervariable region 4, H6H5 genotypes were related to the highest risk, followed by H6H6, H6H7, and H7H7 genotypes in that order (p = 0.001) ), a tyrosine kinase (ROS1 [OR 1.75]), and two G protein–coupled receptors (TAS2R50 [OR 1.58] and OR13G1 [OR 1.40]) are all encoded by these gene variants; all Ors are for carriers of two versus zero risk alleles [Four gene variants were found to be associated with MI by Shiftman et al.  . The incidence of CAD or MI was not significantly correlated with genetic variations in ADH, though. In conclusion, the ALDH2 rs671 polymorphism may be linked to an increased risk of CAD and MI [A meta-analysis on the relationship between genetic variants in ADH and ALDH2 and the risk of coronary artery disease and myocardial infarction was carried out by Han et al.  in 2013.Do et al.  sequenceIn 2017, Yang et al. [Our analysis is based on a graphical representation of the associations between MI and various genetic profiles. A more in-depth, quantitative approach should be performed to evaluate the actual risk increase associated with distinct genetic profiles. Any analysis of this type (including this one) is affected by a non-recency bias: associations studied earlier usually generate more subsequent research to confirm the findings than associations studied more recently. This would lead to a potential underrepresentation of recently identified associations. This shortcoming may be solved by either performing studies using a shorter time-frame, or by performing in-depth, narrative, or systematic reviews of recent literature in the field.Considering that studies showed a multitude of genes that can be associated with myocardial infarction, genetic test kits available on the market were designed. The kits evaluate over 6000 genes, including genes that were presented in this literature review. Among the tested genes we list: APOE, LRP6, LDLR, APOB, PCSK9, ACTC1, CALR3, CAV3, CSRP3, GLA, JPH2, LAMP2, LIPA, MYBPC3, MYH6, MYH7, MYL2, MYL3, MYLK2, MYOZ2, NEXN, PLN, PRKAG2, SLC25A4, TNNC1, TNNI3, TNNT2, TPM1, TTN, TTR, and VCL.We searched PubMed’s, Web of Science, and Scopus databases for articles that met the study’s objectives. The following keywords were used for the generic search: “myocardial infarction”, “genes”, “involvement”, “association”, “risk”, “genes involved in myocardial infarction”, and “genes associated with increased risk of myocardial infraction”. After performing the search, the option chosen was to display only the articles that have the abstract available for viewing. Another inclusion criterion was the display of studies published starting with the year 1995. The next step was to save all the results offered by the search engine in a CSV document. This algorithm for searching and displaying the results was applied to all the databases mentioned above.The analysis was carried out using VOSviewer version 1.6.18 from Leiden University’s Centre for Science and Technology Studies (CWTS), which establishes maps and networks based on the association of terms in relation to the user’s subject of interest. In the network visualization, each item is represented by a label and a circle, whose diameter is proportionate to the number of mentions of that keyword in the mined texts.The big data analysis revealed the most relevant genes/alleles that present an increased risk of myocardial infarction, represented by: PAI-1, Stromelysin-1, 5A-1171/6A, LTA, LGALS2, AT2 (−1332 G/A), RS671, LDLR, AKAP12, OR8D2, GLRA2, the 5’-franklin region of the nitric-oxide synthase gene. Looking back, research carried out from 1995 until now has confirmed some of the genes discovered by us through the analysis. This suggests that conducting new research on population subgroups can enrich our knowledge about the mechanisms of myocardial infarction.


Both genetic background and diet are important determinants of cardiovascular diseases (CVD). Understanding gene-diet interactions could help improve CVD prevention and prognosis. We aimed to summarise the evidence on gene-diet interactions and CVD outcomes systematically.® via Ovid, Embase, PubMed®, and The Cochrane Library for relevant studies published until June 6th 2022. We considered for inclusion cross-sectional, case–control, prospective cohort, nested case–control, and case-cohort studies as well as randomised controlled trials that evaluated the interaction between genetic variants and/or genetic risk scores and food or diet intake on the risk of related outcomes, including myocardial infarction, coronary heart disease (CHD), stroke and CVD as a composite outcome. The PROSPERO protocol registration code is CRD42019147031.We searched MEDLINEWe included 59 articles based on data from 29 studies; six articles involved multiple studies, and seven did not report details of their source population. The median sample size of the articles was 2562 participants. Of the 59 articles, 21 (35.6%) were qualified as high quality, while the rest were intermediate or poor. Eleven (18.6%) articles adjusted for multiple comparisons, four (7.0%) attempted to replicate the findings, 18 (30.5%) were based on Han-Chinese ethnicity, and 29 (49.2%) did not present Minor Allele Frequency. Fifty different dietary exposures and 52 different genetic factors were investigated, with alcohol intake and ADH1C variants being the most examined. Of 266 investigated diet-gene interaction tests, 50 (18.8%) were statistically significant, including CETP-TaqIB and ADH1C variants, which interacted with alcohol intake on CHD risk. However, interactions effects were significant only in some articles and did not agree on the direction of effects. Moreover, most of the studies that reported significant interactions lacked replication. Overall, the evidence on gene-diet interactions on CVD is limited, and lack correction for multiple testing, replication and sample size consideration.The online version contains supplementary material available at 10.1186/s12872-022-02808-1. Cardiovascular diseases (CVDs), including ischemic heart disease and stroke, are the leading cause of mortality and morbidity and are responsible for more than 18 million deaths globally in 2019   and [OR 2.56 ], respectively, compared to subjects in the highest tertile of EPA intake and DHA consumption, who are also carriers of rs174547 C/C genotype. No association was observed in the middle tertile of EPA or DHA intake  compared with non-coffee drinkers  compared with those who consumed < 50 g/day. Additional analysis comparing different alcohol intake categories through B2B2 heterozygotes with B1B1 and B1B2 genotypes found that the protective effect of B2/B2 genotype was significant (p < 0.02) in the category of ≥ 6 drinks per week, Table p = 0.02) among drinkers of 5–14.9 g/day of alcohol who were B2 carriers, who had a lower odds of MI , compared with B1B1 genotype   . Other sd ADH1C γγ2, compCXCL12 rs1746048 and PCSK9 rs11206510 variants , compared to carriers of the TT genotype in the lowest quintile of ALA intake. At the same time, no association was observed in CC and CT genotypes in the other quintiles  and GG genotypes [OR 2.99 ] had an increased odds of having a stroke compared with non-drinkers and carriers of GG genotype. On the other hand, non-drinkers with the rs1800947 GC genotype had an OR of 2.95  [p = 0.003) interaction between drinking status and the Fgβ 148CT variant. Drinkers who are also carriers of CT/TT genotype had increased odds of having a stroke  compared to non-drinker carriers of the CC genotype [p = 0.048) interaction between drinking status and rs852426 β-actin (ACTB) variant on stroke risk [HR 0.54 ]  in comparison to the TT genotype. At the same time, no association was reported for the control group (fat intake reduction)  compared to the AA homozygote genotype. At the same time, no association was observed when total alcohol intake was evaluated [Concerning other dietary components, in the MDC cohort, a significant  articles were classified as high quality, 36 (61.0%) as intermediate quality, and two (3.3%) as poor quality. Small sample size, lack of correction for multiple testing (11 (18.6%) articles adjusted for multiple comparisons), lack of generalisation  often limited the methodological quality  variants were the most frequently assessed and were shown to interact with alcohol to modify the risk of MI and CHD. Other studies investigating plausible biological interactions such as FADS gene and fatty acids interactions, vitamin B6, vitamin B12 and folic acid did not show consistent findings. While several studies investigated the interactions between genes and dietary factors on CVD risk, the current literature is limited and not consistent in showing gene-diet interactions with clinical and public health impacts, mainly because the reported positive findings were derived from case–control studies and lacked replication.Of the 59 included articles, 32 reported a statistically significant gene-diet interaction. Dietary and genetic exposure were very heterogeneous, which precluded us from conducting a meta-analysis of the results. Previous systematic reviews on gene-diet interactions and CVD have primarily focused on specific genes or diets. In contrast, our study provides a comprehensive assessment of all genes and dietary exposures interactions on CVD. Similar to previous findings, we identified a lack of consistency in the results of interaction studies , 82. In Another methodological concern of studies looking at gene-diet interaction and CVD is the sample size of the studies. Low statistical power leads to a reduced capacity to detect interactions. Genotyping errors, allele frequency and the precision of the dietary exposure and outcome measures are some of the criteria that researchers should consider when calculating adequate sample size to evaluate interactions . NeverthSimilarly, studies with 95% of power and a MAF of 20% looking for interactions of 1.5 of magnitude between genetic variants and continuous exposures require a sample size of up to 30,906 subjects . In thisComparing specific foods and gene variants generates multiple comparison scenarios that could increase the Family-wise error rate , where tAlcohol was the most evaluated exposure; its interaction with the CETP polymorphism (rs708272) was not consistent for CHD. The results did not agree with the direction of reported interactions, and most of the interactions lost statistical significance in the sex-stratified analysis. The low prevalence of alcohol intake could explain this difference and hypertriglyceridemia in the populations evaluated. , 45. In ADH1C variant and alcohol intake that decreases the incidence of MI in men who drank daily and were homozygous for the γ2 allele. Carriers of the γ2 allele are slow metabolisers of alcohol, which could enhance the beneficial effect of moderate alcohol consumption on lipid metabolism. In addition, the study stated that up to 50% of the observed decrease in MI risk could be attributed to increased HDL levels [ADH1C polymorphism and alcohol interactions were not homogeneous, and five studies did not report significant interactions, even though different alcohol intake categories were tested among these studies [ADH1C variant on CVD might be mediated through mechanisms independent of HDL cholesterol [Similarly, concerning lipid metabolism, a matched case–control study reported an interaction between the L levels . However studies , 47, 55.lesterol .MTHFD1 variant (rs1076991) minor T-allele has been associated with an approximately 62.5% drop-in transcription rate of the MTHFD1 enzyme, which could also be associated with intercellular SAM accumulation, conditions that lead to dyslipidaemia and the consequent increased CVD risk [The increased risk of MI in the WENBIT trial could be explained by the association of vitamin B6 and folate intake with elevated hepatic adenosylmethionine (SAM). SAM is an inhibitor of betaine-homocysteine methyltransferase, an enzyme that regulates hepatic lipids and induces ApoB expression and VLDL assembly. Furthermore, the CVD risk . HoweverCVD risk . It is iCVD risk .A significant strength of this paper is the comprehensive search strategy implemented to retrieve gene-diet interaction studies. We included all food and dietary exposures and epidemiological designs, providing a comprehensive overview of the literature. Also, we provided a critical evaluation of the quality of the current evidence on the topic. In addition, the included studies point to several biological mechanisms that could underlie the differences in the susceptibility to food/diet exposures and cardiometabolic diseases. However, it is a limitation for this study that, so far, no gene-diet interaction critical appraisal tool has been developed. This tool could standardise the evaluation of the studies' risk of bias and methodological quality, identifying the most significant weaknesses. Other issues were the lack of replication in the evaluation of interactions, few studies evaluated the same dietary and genetic exposures . Moreover, authors evaluating the same genetic variants used different genetics models . This heterogeneity limited the synthesis of the findings and are also a great weakness for the progress in the identification of population at higher risk of cardiometabolic diseases due to their genetic background and food/diet exposures.Identifying the mechanisms underlying gene-diet interactions is a priority; therefore, variants identified in GWAS are required to be investigated in functional studies, a challenge that could benefit from computational modelling. In addition, studies assessing interactions should provide more information on the origin of biases in the genetic exposures assessed . Future studies should analyse samples with a suitable size for evaluating interaction hypotheses, for which data sharing through consortia may play a crucial role. Replication in independent samples is also essential, for which the selection of a single reference group is a critical factor in facilitating the comparability among studies. Besides, studies should provide information on the size of interactions and the effects of gene and dietary exposures separately and in joint effect. Even though it was out of the focus of the current study, recent studies have shown that environmental factors including dietary compounds may modulate gene expression, influence DNA methylation processes, and regulate histone and microRNA assembling, which on the other hand may affect risk of diabetes and cardiovascular disease . TherefoCurrent evidence for gene-diet interaction in CVD is limited, as most interactions have been evaluated in single studies, without multiple correction testing, and mainly in European ethnicities; furthermore, studies have limited information to assess the robustness of sample size. Therefore, data-sharing platforms that combine large studies are needed to address current methodological problems and facilitate replication. In addition, priority should be given to the inclusion of diverse ethnicities and sample size-focused reporting to provide more conclusive evidence of gene-diet interaction with CVD that allows the development of nutritional personalized interventions.Additional file 1. Search strategy.Additional file 2. Supplemental material.


Chronic systemic inflammation may be associated with neurocognitive decline, but the relationships between inflammatory bowel disease and related medications and the risk of Alzheimer's disease remain unclear.We performed a meta-analysis to evaluate the associations of ulcerative colitis, Crohn's disease and related medications with risk of Alzheimer's disease. We identified cohort and case-control studies by searching PubMed, Embase and Web of Science up to August 2022.Seven eligible studies with 20,174 cases of Alzheimer's disease were included in the meta-analysis. Six studies reported the association between ulcerative colitis and risk of Alzheimer's disease; five studies reported the association between Crohn's disease and risk of Alzheimer's disease. Meta-analysis combining these studies did not reveal any significant association of ulcerative colitis or Crohn's disease with risk of Alzheimer's disease. The pooled relative risks were 1.16  and 1.17  for ulcerative colitis and Crohn's disease, respectively. High heterogeneity was detected across the studies. Of note, there was an inverse association between inflammatory bowel disease related medication exposure and risk of Alzheimer's disease. The pooled relative risk of three studies for Alzheimer's disease was 0.86 . No publication bias was detected.This study does not support the association of ulcerative colitis and Crohn's disease with the risk of Alzheimer's disease. However, medications for the treatment of inflammatory bowel disease might be associated with a lower risk of Alzheimer's disease. When more than one study was conducted in the same study population, we selected the most recent study in the review.The following information were extracted: first author, year of publication, study location, study design, age at baseline, sample size, number of AD cases, exposure ascertainment, AD ascertainment, duration of follow-up, potential risk factors or confounders considered in the data analysis, the risk estimates with their 95% CIs. When multiple RRs were reported, we extracted the risk estimate that was adjusted for the largest number of possible confounders.We used the Newcastle-Ottawa Scale to evaluate the study quality. In this 9-star evaluation system, each cohort or case-control study was judged on the basis of three broad perspectives/domains: “Selection,” “Comparability,” and “Exposure/Outcome.” A study can be awarded a maximum of one star for each numbered item within the Selection and Exposure/Outcome categories. A maximum of two stars can be given for “Comparability” category to assess the control for confounders. Studies with more stars indicated higher quality.Q test and the I2 statistic  were eligible in the meta-analysis  (P = 0.335)  to 6.67 ; the other two studies reported null association. Meta-analysis combining the data did not reveal an association between ulcerative colitis and risk of AD. The pooled RR was 1.16  with high heterogeneity across the studies ( <0.001) . In sens= 0.335) .I2 = 78.5%, P for heterogeneity = 0.001) (P = 0.430)  . In sens= 0.430) .P = 0.043). Moderate heterogeneity was detected   and risk of AD, and one study did not detect significant association between steroid usage and risk of AD. Meta-analysis of these three studies indicated a pooled RR of 0.86  .After comprehensively reviewing all the published data from cohort and case-control studies, this updated meta-analysis did not support the association of ulcerative colitis and Crohn's disease with risk of AD. Of note, this is the first meta-analysis to evaluate the association between IBD-related medication and risk of AD. The result from the meta-analysis might indicate a possible lower risk of AD in IBD patients with a history of medication treatment compared with those without medication exposure.Previous epidemiological studies observed a positive association between IBD and all-cause dementia  was small.The study has several limitations. First, there was significant heterogeneity in this study. The heterogeneity may be due to the differences in study characteristics, such as study population and diagnosis criteria of AD, etc. Due to the limited number of included studies, we failed to perform the meta-regression to explore the sources of heterogeneity. Of note, we have conducted sensitivity analysis by excluding Zhang et al.  study frIn conclusion, the current evidence does not support the association of ulcerative colitis and Crohn's disease with risk of AD. However, IBD-related medication exposure might be inversely associated with the risk of AD. Since high heterogeneity may exist across the studies, future well-designed, large-scale studies are still needed to explore the relationship between IBD and related anti-inflammatory medications and the risk of AD.jjjiang@cmu.edu.cn.The data analyzed in this study is subject to the following licenses/restrictions: The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request. Requests to access these datasets should be directed to JJ developed the research design and had primary responsibility for the final content. PL, KZ, ML, and YJ collected and analyzed the data. JJ and YX interpreted the results and drafted manuscript. All authors critically reviewed and approved the manuscript.


The aim of this meta-analysis is to systematically evaluate and summarize the risk factors of intensive care unit acquired weakness (ICU-AW), to provide evidence-based evidence for the formulation of prevention strategies for ICU-AW.PubMed, EMBASE, Web of Science, CBM , Chinese National Knowledge Infrastructure, Chinese WANFANG, and VIP will be searched to define relevant risk factors for ICU-AW. The databases search period is from January 1, 2005 to August 13, 2021. The Newcastle Ottawa Scale (NOS) is used to evaluate the quality of the included studies. RevMan 5.3 analysis software will be used for meta-analysis.P = .02), mechanical ventilation days , age , length of intensive care unit (ICU) stay , infectious disease , renal replacement therapy , use of aminoglucoside drugs , sepsis related organ failure assessment (SOFA) score , hyperglycemia .This systematic review and meta-analysis included a total of 12 cohort studies, including 9 international journals and 3 Chinese journals, with a total of 1950 patients, of which 856 had ICU-AW. The results showed that the significant risk factors for ICU-AW included female (odds ratio [OR] = 1.34, 95% confidence interval [CI]: 1.06–1.71; This meta-analysis provides comprehensive evidence-based on the assessment of the risk factors for ICU-AW, their multifactorial etiology was confirmed. This study indicated that female, mechanical ventilation days, age, length of ICU stay, infectious disease, renal replacement therapy, use of aminoglucoside drugs, SOFA score, and hyperglycemia are independent risk factors for ICU-AW. We have not found consistent evidence that corticosteroids, neuromuscular blockers, sepsis have any effect on ICU-AW risk. ICU-AW due to neuromuscular dysfunction is one of the serious complications in critically ill patients. In 1993, Ramsay proposed the concept of ICU-AW. Clinical manifestations include difficulty in weaning, paralysis or quadriplegia, decreased reflexes, and muscle atrophy. Some studies in the United States reported that the incidence of ICU-AW was 67%, and it was still as high as 36% after discharge. About the pathogenesis of ICU-AW is complicated and involves the functional and structural changes of muscles and nerves. ICU-AW can not only cause serious sequelae, including quadriplegia or paraplegia, but also cause permanent disability, which seriously affects the quality of life of patients after discharge from hospital. In view of the serious impact of ICU-AW on intensive care unit (ICU) patients, ICU-AW has become a hot research topic at home and abroad, attracting intensive attention from scholars at home and abroad. Relevant studies,8 have shown that a more analytical classification is proposed to divide risk factors for ICU-AW into the several categories, namely general information and risk factors related to underlying diseases and risk factors related to treatment. But the current evidence is still inconclusive and there is no particularly effective treatment. Controlling risk factors may be the only preventive measure to reduce its incidence. Furthermore, the pathogenesis and mechanism of ICU-AW have not yet been fully clarified, and there is no effective treatment, Therefore, early identification of risk factors for ICU-AW is important. Early assessment of ICU-AW risk factors, and provide targeted interventions to achieve the optimal recovery goals of patients. Our objective is to provide an updated comprehensive systematic review of prospective studies on risk factors for ICU-AW, to identify the risk factors of ICU-AW, hoping to provide scientific reference for clinical development of ICU-AW prevention strategies.For the past few years, with the continuous development of critical care medicine and medical technology, the attention of medical staff has gradually shifted to the long-term impact of critically ill patients. In 2014, the American Thoracic Society drafted the intensive care unit acquired weakness (ICU-AW) guidelines, This study did not require the approval of the ethics committee. Because all the data used for analyses were extracted from the published studies, the ethical approval and informed consent were not necessary.Systematic literature search and quantitative analysis were conducted according to the preferred reporting items in systematic review and Meta-analysis guidelines.As a basis for our analysis, PubMed, EMBASE, Web of Science, SinoMed, Chinese National Knowledge Infrastructure, Chinese WANFANG, VIP, and other databases will be searched systematically. The databases are searched from January 1, 2005 to August 13, 2021.In this systematic review and meta-analysis, 2 researchers independently conducted a preliminary search according to the inclusion criteria and selected eligible studies. The inclusion are as follows: Literature type: domestic and foreign prospective cohort research literature published in Chinese and English, the topic is related the risk factors of ICU-AW; Research object: ICU adult patients (age ≥18 years old). The case group is ICU-AW patients, the rest are the control group, regardless of race, gender, or disease type; The diagnosis of ICU-AW: such as the Medical Research Council Scale or Nerve Conduction Studies (NCSs) and so on; Outcome indicators: the incidence of ICU-AW. The inclusion are as follows: Exclude studies that are inconsistent with the above conditions, repeated publications, incomplete information, gray literature dissertations, and data that cannot be extracted and used.All studies were imported into EndNote X9. The 2 researchers read the title and abstract respectively according to the established inclusion and exclusion criteria for preliminary screening, further read the full text of the literature that meets the inclusion criteria for further screening to determine whether to include. Disagreements can be resolved by discussion or negotiation with third-party review (XBT). For each study, the 2 authors independently extracted data based on pre-designed table, including first author, publication year, country, sample size, incidence of ICU-AW, major risk factors, and diagnostic methods. was used independently by 2 reviewers to evaluate the quality of the included literature. With a full score of 9 points, 0 to 4 is considered low-quality research and 5 to 9 is considered high-quality research. After the evaluation, the 2 researchers cross-check the results, discuss the inconsistencies or consult a third party to help judge.The Newcastle-Ottawa Quality Scale (NOS)P > .1 and I2 < 50%, the included data were considered to be homogeneous and the fixed-effects model was used for analysis. When P ≤ .1 and I2 ≥ 50%, sensitivity analysis was used to analyze the source of heterogeneity and calculate the comprehensive effect after excluding the studies that caused heterogeneity. If heterogeneity remains large, use a random-effects model or discard the combined results and use descriptive analysis. If P < .1, I2 ≥ 75%, and the heterogeneity is too large to determine the source of heterogeneity, the data will be described without merging. When the number of articles included in the analysis of a single risk factor was more than 10, the funnel plot was used to analyze the publication bias of each risk factor.Meta-analysis of the extracted data will be performed using Revman 5.3. For dichotomous variables, odds ratio (OR) and its 95% confidence interval (CI) were used as effect statistics, while for continuous variables, mean difference (MD) and its 95% CI were used as effect statistics. After data input, heterogeneity test is performed first. If –21 met the inclusion criteria. The flowchat is shown in Figure A total of 11,648 related studies were obtained through systematic search, including 1478 in Chinese and 10,170 in English. Six thousand two hundred forty eight duplicate studies were removed and 5400 were retained. After reading the titles and abstracts, 5370 studies that did not meet the inclusion and exclusion criteria were excluded and 30 studies were retained. After further reading the whole study, 18 studies were removed, 10 articles were non-cohort studies, 4 articles did not mention relevant outcome indicators, and 4 articles had incomplete data. Eventually, 12 prospective cohort studiesThe cohort studies included in the meta-analysis are shown in Table The quality assessment of these 12 studies is shown in Table The related risk factors are shown in Figure ,19,21–24 explored this content and tested for heterogeneity, P = .03, I2 = 53%, indicating heterogeneity. After sensitivity analysis, Anastasopoulos was the main source of heterogeneity. After excluding this study, there was a significant change in overall estimate, OR = 1.34, 95% CI:1.06 to 1.71; P = .02, There was no heterogeneity among studies, I2 = 16%, P = .30.The result is shown in Figure ,20,21 discussed this content and tested for heterogeneity, P = .41, I2 = 0%, there was a good literature consistency, the fixed effects model was used, the combined effect was statistically significant, OR = 2.51, 95% CI: 1.54 to 4.08; P = .0002.The result is shown in Figure ,20,22–24 explored this content and tested heterogeneity, P = .001, I2 = 78%, indicating heterogeneity. Therefore, sensitivity studies were conducted, excluding trials with relatively small sample size (n < 50), but there was no significant change in the overall estimate, OR = 2.73, 95% CI:1.65 to 3.80; P < .00001, however, significant heterogeneity was still observed, I2 = 76%, P = .005. Except for the experiment with the largest sample size, there was little change in overall estimates, OR = 3.62, 95% CI:1.79 to 5.44; P = .0001, significant heterogeneity still exists, I2 = 83%, P = .0004. The exclusion of either study did not change the combined estimate and heterogeneity (data not shown).The result is shown in Figure ,20,22–24 explored this content and tested for heterogeneity, P = .001, I2 = 78%, indicating heterogeneity. Therefore, we conducted a sensitivity study, excluding trials with relatively small sample sizes (n < 50), with a substantial change in overall estimates, OR = 6.33, 95% CI:5.05 to 7.61; P < .00001. There was no heterogeneity among studies, I2 = 50%, P = .06.The result is shown in Figure ,20,22–24 explored this content and tested for heterogeneity, P < .0001, I2 = 88%, there was heterogeneity in the literature. Therefore, we conducted a sensitivity study, excluding trials with relatively small sample sizes (n < 50), there was no substantial change in overall estimates, OR = 3.39, 95% CI:1.76 to 5.03; P < .0001. However, significant heterogeneity was still observed, I2 = 89%, P < .00001. Excluding the experiment with the largest sample size, there was still no substantial change in overall estimates, OR = 3.02, 95% CI:1.51 to 4.54; P < .0001, significant heterogeneity is still observed, I2 = 75%, P = .008. Excluding any one study will not change the combined estimates and heterogeneity (data not shown).The result is shown in Figure –24 discussed this content and tested for heterogeneity, P = .60, I2 = 0%, the literature consistency was good, so we used the fixed effects model, and the combined effect was statistically significant, OR = 1.59, 95% CI: 1.11 to 2.28; P = .01.The result is shown in Figure ,20,22,24 discussed this content and tested for heterogeneity, P = .76, I2 = 0%, the literature consistency was good, the fixed effects model was used, and the combined effect was statistically significant, OR = 1.67, 95% CI: 1.20 to 2.33; P = .002.The result is shown in Figure ,21 discussed this content and tested for heterogeneity, P = .44, I2 = 0%, the literature consistency was good, the fixed effects model was used, and the combined effect was statistically significant, OR = 1.07, 95% CI: 0.24 to 1.90; P = .01.The result is shown in Figure ,19–21,23 explored this content and tested for heterogeneity, P < .0001, I2 = 77%, there was a heterogeneity in the literature. Therefore, we conducted a sensitivity study, excluding trials with relatively small sample sizes (n < 50), there was no substantial change in overall estimates, OR = 1.86, 95% CI:0.96 to 3.62; P = .07, However, significant heterogeneity is still observed, I2 = 75%, P = .0005. Excluding the experiment with the largest sample size, there was no substantial change in overall estimates, OR = 1.60, 95% CI:0.71 to 3.59; P = .26, significant heterogeneity was still observed, I2 = 79%, P < .0001. Excluding any one study will not change the combined estimates and heterogeneity (data not shown).The result is shown in Figure ,17,20,21 discussed this content and tested for heterogeneity, P = .52, I2 = 0%, the literature consistency was good, the fixed effects model was used, and the combined effect was not statistically significant, OR = 1.43, 95% CI: 0.92 to 2.22; P = .11.·The result is shown in Figure ,20,23 explored this content and tested for heterogeneity, P = .005, I2 = 81%, there was a heterogeneity in the literature. Therefore, we conducted a sensitivity study, excluding trials with relatively small sample sizes (n < 50), there was no substantial change in overall estimates, OR = 1.27, 95% CI:0.41 to 3.96; P = .67, However, significant heterogeneity is still observed, I2 = 77%, P = .04. Excluding the experiment with the largest sample size, there was no substantial change in overall estimates, OR = 3.95, 95% CI:0.09 to 172.68; P = .48, significant heterogeneity was still observed, I2 = 88%, P = .003. Excluding any one study will not change the combined estimates and heterogeneity (data not shown).The result is shown in Figure ,17,21 explored this content and tested for heterogeneity, P = .04, I2 = 70%, there was a heterogeneity in the literature. Therefore, we conducted a sensitivity study, excluding trials with relatively small sample sizes (n < 50), there was no substantial change in overall estimates, OR = 1.55, 95% CI:0.47 to 5.12; P = .47, However, significant heterogeneity is still observed, I2 = 80%, P = .02. Excluding the experiment with the largest sample size, there was substantial change in overall estimates, OR = 2.95, 95% CI:1.70 to 5.11; P = .0001, There is no heterogeneity between studies, I2 = 0%, P = .82.The result is shown in Figure ,17,19–24 explored this content and tested for heterogeneity, P < .00001, I2 = 93%, The heterogeneity of the literature was obvious, so only describe. According to various literature studies and the occurrence of ICU-AW, APACHE II score is considered a risk factor that may cause ICU-AW.The result is shown in Figure In this study, the “female” index was included in the literature as a funnel plot, and the results showed that there was some asymmetry, possibly publication bias. Acquired muscle weakness – a serious and costly medical complication – has become increasingly common in ICUS due to improvements in survival rates for critically ill patients over the past few decades. In recent years, ICU-AW has begun to attract people’s attention. But so far, the pathological mechanism of ICU-AW has not been fully understood, and there is still no particularly effective treatment. It has been suggested that ICU-AW is caused by a variety of factors work together. The relationship between some factors and the pathogenesis of ICU-AW and the pathogenesis are still unclear.,9 The quality of the 12 articles included in this meta-analysis can be seen from the NOS scores. Most of the index data are were consistent during the analysis, laying a foundation for drawing reliable conclusions. We identified 32 risk factors for ICU-AW, few studies have shown the same risk factors. Thus, only 12 risk factors were used to calculate the size of the effect. To reach a consensus on the risk factors for ICU-AW, a large number of studies on individual risk factors are needed. In this study, the risk factors significantly associated with ICU-AW were female, days of mechanical ventilation, age, length of ICU stay, infectious diseases, renal replacement therapy, aminoglycoside use, SOFA score, and hyperglycemia. In conclusion, the risk factors identified in this study contribute to a better understanding of the pathogenesis and predictors of ICU-AW.Meta-analysis is the quantitative and scientific synthesis of research results. Since the term research synthesis and modern methods were first introduced in the 1970s, meta-analysis has a revolutionary impact in many areas of science, helping to establish evidence-based practice and resolve seemingly contradictory research findings. also included gender as a risk factor. A large number of studies have found that female is a risk factor for ICU-AW,–30 but the mechanism of female susceptibility to ICU-AW remains unclear. Therefore, health care providers should pay more attention to female patients who are susceptible to ICU-AW.This systematic review and meta-analysis revealed that female is one of the important risk factors for ICU-AW and plays a vital role in the prediction of ICU-AW. Female patients are prone to ICU-AW. The ICU-AW risk prediction model constructed by WitteveenThis study found that age predisposed to ICU-AW. As we gradually enter an aging society, the average age of admission to the ICU is gradually increasing. The elderly (>60 years old) develop normal age-related muscle mass loss before admission to the ICU, this decrease in muscle mass is slow. However, in the severe cases, where systemic inflammatory responses, protein loss and anabolic stimulation are reduced, muscle loss of the elderly may be particularly rapid, which is the so-called muscle attenuation syndrome. The longer the mechanical ventilation days, the longer the patient needs to be immobilized. Studies have shown that when mechanical ventilation lasts for more than 5 days, the incidence of ICU-AW is 25% to 60%, further confirming that mechanical ventilation is an independent risk factor for ICU-AW. Aminoglycoside administration was also associated with ICU-AW.–37 This finding is consistent with our research.This study found that mechanical ventilation was an independent risk factor for ICU-AW. It has been reported that normal people stay in bed strictly for 1 day, and their muscles decrease by 1%. Less than 10% of mechanically ventilated patients in the ICU reportedly do rehabilitation exercises in bed. Therefore, the longer the ICU stay in the hospital, the more likely ICU-AW will occur. In this study, length of ICU stay was an independent risk factor for ICU-AW. This is consistent with the findings of Hermans.Due to the special nature of the ICU, patient has been in a state of immobility for a long time. Even healthy people, after fixed 4 hours every day, their muscles will decrease by 1% to 1.3% every day, and catabolism of patients with serious diseases may be aggravated, leading to the loss of muscle mass. Hyperglycemia can lead to decreased diaphragm function, and strict control of blood glucose can reduce the risk of critically ill polyneuropathy. This study suggests that the use of glucocorticoids and neuromuscular blockers are not independent risk factors for ICU-AW. The relationship between glucocorticoids and ICU-AW reported in a few prospective studies is not completely clear.,38 Some studies have shown that glucocorticoid is a risk factor for ICU-AW and can lead to ICU-AW,,40 while some studies have shown that low to moderate doses of glucocorticoids  will not cause ICU-AW.,41,42 The relationship between neuromuscular blockers and ICU-AW is not fully understood, and has varied from one study to another.,43–46 Studies have shown that the occurrence of ICU-AW is related to the duration of neuromuscular blockers, and use of neuromuscular blockers for less than 48 hours does not increase the risk of ICU-AW.,46,47 In a multicenter double-blind ACURASYS study conducted by Papazian et al, including 340 ARDS patients, the use of cis-atracurium for 48 hours did not increase the risk of ICU-AW. In addition, the occurrence of ICU-AW is related to the specific type, dose, and concomitant drugs (such as glucocorticoids) of neuromuscular blockers, which need to be further studied. Infection disease is also a risk factor for ICU-AW. Ischemia and inflammation may contribute to systemic infection in ICU patients. This weakness is associated with the release of inflammatory mediators. Inflammation can increase the permeability of capillaries, and its toxicity is more likely to release neuromuscular complications.An important finding of our study was the significant association between hyperglycemia during ICU stay and ICU-AW, which is consistent with the results of Hermans.The relevant studies we reviewed in this study were found to have publication bias. This may be because we only selected studies published in academic journals, and studies with negligible results may not have been published. Second, the publication bias found in this study may be due to the limited overlap of risk factors between studies. Therefore, further research is needed to accumulate evidence on each risk factor.This study has a little limitations. First of all, we only reviewed prospective cohort studies, so other types of related studies may be excluded. The studies included in this meta-analysis were all from published literature. Gray literature is not included, which may have potential publication bias. Secondly, different types of studies have differences in sample size and case selection, which may lead to heterogeneity among studies and have a certain impact on research results. Finally, due to the limited number of studies, it is impossible to calculate the impact of all risk factors. Whereas, this article makes an important contribution to understanding the current situation by integrating research on ICU-AW risk factors and identifying areas that need further research. It is recommended that more high-quality, multi-center, and large-sample original studies for verification in the future, and conduct a more comprehensive and scientific evaluation of the influencing factors of ICU-AW, so as to provide early warning for the clinial practice.Through this systematic review and meta-analysis, the risk factors for ICU-AW were identified as follows: female, days of mechanical ventilation days, age, length of ICU stay, infectious diseases, renal replacement therapy, aminoglycoside drug use, SOFA score, and hyperglycemia. However, the evidence for the predictive value of glucocorticoids, neuromuscular blockers, and sepsis is insufficient and needs to be validated by more high-quality studies in the future. Based on the existing evidence, it is suggested that clinical medical staff should pay attention to the impact of ICU-AW on patients, improve the awareness of early warning, actively screen and identify high-risk groups, and classify risks. The occurrence and development of ICU-AW is a dynamic process, medical staff should take comprehensive intervention measures as soon as possible to effectively prevent ICU-AW.We would like to thank the support of the medical staff in the intensive care units of the Second Affiliated Hospital of Harbin Medical University.Conceptualization: Zi Yang, Faying Wang, Zeyu Peng.Data curation: Zi Yang.Formal analysis: Zi Yang.Funding acquisition: Yuying Fan.Investigation: Zi Yang.Methodology: Zi Yang.Resources: Zeyu Peng.Software: Xiaohui Wang.Supervision: Xiaohui Wang.Validation: Xiaohui Wang.Visualization: Xiaohui Wang.Writing – original draft: Faying Wang.Writing – review & editing: Faying Wang, Zeyu Peng.


A total of 31  studies were qualified for meta-analysis. In a meta-analysis of cohort studies, the risk of CTS did not differ between current and never smokers (pooled hazard ratio (HR) 1.09, 95% CI 0.84–1.43), current and past/never smokers , and past and never smokers . Furthermore, a meta-analysis of case control studies found no difference in the risk of CTS between current and never smokers (pooled odds ratio (OR) 0.92, 95% CI 0.56–1.53), current and past/never smokers , and past and never smokers . However, a meta-analysis of cross-sectional studies showed the associations of ever  and current smoking  with CTS. However, the association between ever smoking and CTS disappeared after limiting the meta-analysis to higher quality studies or after adjusting for publication bias. The association between current smoking and CTS also attenuated after limiting the meta-analysis to studies that confirmed CTS by a nerve conduction study or studies with low attrition bias. This meta-analysis does not support an association between smoking and CTS. The association between smoking and CTS observed in cross-sectional studies could be due to biases and/or confounding factors.To date, the role of smoking in carpal tunnel syndrome (CTS) is unclear. The aim of this systematic review and meta-analysis was to assess the association between smoking and CTS. The literature searches were conducted in PubMed, Embase, and Scopus, from inception until October 2021. Three reviewers screened the titles, abstracts, and full-text articles and evaluated the methodological quality of the included studies. A random-effects meta-analysis was used, and heterogeneity across studies was examined using I Compression of the median nerve at the carpal tunnel, known as carpal tunnel syndrome (CTS), is the most common entrapment neuropathy of the upper extremity ,2,3. TheSmoking is a major health concern . To dateAn earlier meta-analysis regarding the association between smoking and CTS, published in 2014 by Pourmemari and his colleagues, reported inconclusive results . That meThe aim of this systematic review and meta-analysis was to determine whether smoking is associated with CTS.We developed the protocol of this systematic review and meta-analysis according to the PRISMA guidelines . We retaLiterature searches were conducted in PubMed, Embase, and Scopus, from their inception until October 2021. We used a combination of MeSH terms (in PubMed), Emtree terms (in Embase), and text words . The seaThree reviewers  independently screened the titles and abstracts of the references retrieved. Both population- and hospital-based case-control, cross-sectional, and cohort studies that reported quantitative results for the association between smoking and CTS symptoms confirmed by nerve conduction studies or clinical signs were included in the meta-analysis. Studies conducted among volunteers and CTS patients without a control group were excluded. Moreover, studies defined CTS based on self-reports, studies defined CTS by symptoms only, or nerve conduction studies only were excluded. Lastly, studies conducted among pregnant women, patients undergoing dialysis, or among patients with toxic oil syndrome were excluded from the review. Disagreements between the reviewers were resolved through discussion.Characteristics of the included studies and quantitative data were extracted by two reviewers (S.H. and K.L.) and checked by a third reviewer (R.S.). The following characteristics of the included studies were extracted: study population, age and gender distribution, sample size, smoking, outcome assessment, summary results, and adjustment for confounding factors.Three reviewers  independently appraised the risk of bias of included studies. For methodological quality assessment, we used a checklist adapted from the Effective Public Health Practice Project tool . We rate2 statistic was used to assess the presence of heterogeneity across the studies  1.09, 95% CI 0.84–1.43, two studies,  for CTS . Among mIn this meta-analysis, we found no association between smoking and CTS in case control or cohort studies. Only a meta-analysis of cross-sectional studies showed an association between smoking and CTS. The results of the current meta-analysis are consistent with those of a previous systematic review and meta-analysis of studies published up to 2014 . LimitinThe lack of uniformity in using a comparison group for current smoking across the included studies reduced the statistical power of this meta-analysis. A meta-analysis of cross-sectional studies did not show a significant difference in the prevalence of CTS between current and never smokers, but showed a significant difference between current and past/never smokers. Furthermore, most of the studies included in the current meta-analysis did not assess the association between the number of cigarettes smoked per day and CTS.Recent studies have identified the relationship between workload factors and CTS ,59,60. OWith respect to the meta-analysis of case control studies, we found no association of ever, past, or current smoking with CTS. It is possible that hospital-based controls have influenced the outcomes, as most of the included studies in this meta-analysis used hospital-based controls ,49,51,52The studies included in the current meta-analysis had some limitations. Smoking was assessed subjectively, rather than objectively, which makes it prone to recall bias. Study participants may underreport their tobacco consumption . AnotherIn this meta-analysis, we found no association between smoking and CTS in the meta-analyses of case control and cohort studies. Smoking was associated with CTS only in a meta-analysis of cross-sectional studies. However, limiting the meta-analysis to higher quality cross-sectional studies did not support an association between smoking and CTS. It is likely that the association between smoking and CTS observed in cross-sectional studies is not a true association.


Background: Several dietary patterns are reported as risk factors for several chronic diseases including oral diseases. However, thus far, there has been no comprehensive quantitative analysis of nutrition and periodontal diseases. Methods: This systematic review was conducted according to the PRISMA guidelines. Cohort, case–control, and cross-sectional studies were eligible for inclusion in this study. The Newcastle–Ottawa scale was used to qualitatively assess the risk of bias in the included studies. The number of samples was used for odds ratio calculation, followed by the unadjusted value and 95% confidence interval. Results: Nine papers were included for the systematic review and meta-analysis. The results of screening for database search records showed that four diet patterns  have enough data for meta-analysis. The risk of periodontal disease in the western-diet group and the lowest dairy product intake group was 1.05 (0.51–2.13) and 1.28 (0.89–1.84), respectively. The risk of periodontal disease in the highest sugar intake group and the lowest vitamin C intake group was 1.52 (0.79–2.91) and 1.15 (1.08–1.23), respectively. Conclusions: With aging of the population globally, the prevalence of periodontal disease increases, and the associated cost also increases. Though this study, we found foods related to the risk of periodontal disease, and we are confident that it will contribute to lowering the incidence of the disease. Periodontal disease is an oral, chronic infectious, and inflammatory disease that can damage the supportive tissues surrounding the teeth and consequently cause tooth loss ,2. The pNutritional intake and dietary patterns have been studied as risk factors for several chronic diseases including oral diseases ,8,9. AccAs mentioned above, several review papers and systematic literature reviews have been reported on the association between various dietary habits and periodontitis, but a meta-analysis performed by quantifying the related papers has not been published. Therefore, the aim of this paper is to conduct a systematic literature review and meta-analysis on the association between specific dietary habits and periodontitis.PRISMA guidelines were followed in conducting this systematic review . A protoThe search was performed on 29 January 2021, using Embase and Medline. The search words were discussed by all authors. The detailed search terms are as follows:  AND  AND . We did not have restrictions by language or year of publication.We defined the PICO question as follows: Does the group with a particular dietary pattern increase the risk of periodontitis compared to the group without it? In this study, a term that includes various diets and nutritional intake such as western diet, sugar and vitamin C intake, and dairy product was defined as the term ‘dietary pattern’. The results of screening for database search records showed that these four diet patterns have enough data for meta-analysis. We included studies that presented the number of samples or effect measures. The western diet is defined as a diet that contains a large amount of saturated fat, refined carbohydrates, and salt and has a low proportion of fruits and vegetables . Dairy pThe titles and abstracts of each study were reviewed independently by two authors (JJ and KK). For inclusion, the same authors reviewed full-text articles. Disagreements were resolved through discussion.The following variables were extracted during the screening phase: title, abstract, journal, author name, publication year, and publication type. We extracted additional information from the full-text assessment, such as the study design, WHO region, number of samples, type of diet, diagnostic criteria, race, and age.Qualitative risk of bias assessments were conducted for the cohorts and case-control studies based on the Newcastle-Ottawa scale . We used2 statistics [Odds ratio and 95% confidence interval was extracted by a 2 × 2 contingency table of exposures and outcomes. We performed subgroup analysis by classifying the diet patterns into 4 categories; western diet, dairy product, sugar, and Vitamin C. Forest plots were drawn to clearly visualise the synthesised risk. Evaluation of the effect measures’ heterogeneity was conducted using the classification of Iatistics . In caseWe identified and screened 682 potentially relevant articles (including overlapping publications across the two databases); 495 articles were excluded on the basis of the title and abstract and 187 articles were further searched for a more detailed assessment. After further evaluation of the full articles, 168 were excluded because they had study designs other than the pertinent one, or no results on the association between diet pattern and periodontal disease were presented. Finally, 9 studies were included in the systematic review and meta-analysis . The chaTwo studies provided two data sets were included to derive the result. The risk of periodontal disease in the western-diet group was 1.05 (0.51–2.13) compared to that in the non-western diet group . The hetFour studies provided four data sets were included to derive the result. The risk of periodontal disease in the lowest dairy product intake group was 1.28 (0.89–1.84) compared with that in the highest dairy product intake group . The hetTwo studies provided two data sets were included to derive the result. The risk of periodontal disease in the highest sugar intake group was 1.52 (0.79–2.91) compared with that in the lowest sugar intake group . The hetTwo studies provided two data sets were included to derive the result. The risk of periodontal disease in the lowest vitamin C intake group was 1.15 (1.08–1.23) compared with that in the highest vitamin C intake group . The hetOne cohort study was rated good. One case-control study was rated Fair. Out of 13 cross-sectional studies, 8 were evaluated as very good, 3 as good, and 2 as satisfactory. Detailed evaluation results are presented in In this systematic review and meta-analysis of observational studies, we found that certain nutritional and dietary patterns significantly affected the prevalence of periodontal disease. Western diet, dairy products, and high sugar intake were not statistically significant with periodontitis, and vitamin C low intake was positively associated with risk of periodontitis. Because poor periodontal health may cause systemic diseases such as cardiovascular diseases, cancers, and diabetes, oral hygiene-related factors should be studied in detail.Western diet, which includes processed foods, red meat, snacks, and soft drinks, is a well-studied risk factor for inflammatory diseases and many types of malignancies ,34. LongDairy products  are one of the most studied foods for their association with several diseases. Although there is no association with the risk of all-cause mortality, many epidemiological studies have reported that dairy products have protective effects against periodontal diseases, cancers, and chronic inflammatory diseases, especially cardiometabolic diseases ,40,41,42A high-sugar diet contributes to the formation of dental biofilms and, consequently, caries and reduction of pH ,45. HypeWe found that vitamin C intake slightly reduced the prevalence of periodontal disease. According to two recently published systematic reviews of the literature, it was reported that low vitamin C intake and low vitamin blood levels increase the risk and progression of periodontitis ,12. Our Our meta-analysis had several limitations that could have affected the results. First, it was difficult to clearly identify the causal relationship because the majority of the included studies in the meta-analysis had a cross-sectional design. Second, errors in reporting, such as food frequency questionnaires, or uncertainties about the source and composition of food, during the course of the investigation may have affected drawing conclusions. Third, it was impossible to calculate the total number of patients with periodontal disease included in the study because the articles did not present information on the sample size. Finally, three (rather than 2) data sets are often considered the minimum for meta-analysis, but in this study, the number of included studies for each diet pattern was very small.In this study, we analysed the relationship between various food patterns and the prevalence of periodontal diseases through a comprehensive analysis of dietary habits. Overall, there were no deviations from the conventional common sense. The diet suggested in our study was considered to be similar to various diets used for the prevention of metabolic disease. With aging of the population globally, the prevalence of periodontal disease increases, and the associated cost also increases. Therefore, by studying this issue further and controlling the intake of foods that appear to be a risk factor, the risk of periodontitis could be reduced.


N = 906, application of eligibility criteria yielded N = 46. Upon analysis of risk of bias N = 41 demonstrated high risk. Studies found a positive association between elevated PM2.5, PM10 and SARS-related outcomes. A geographic and temporal variation in both PM and C19’s role was observed. Conclusion: C19 is a high mortality and resource intensive disease which devastated the globe. PM exposure is also a global health crisis. Our systematic review focuses on the intersection of this impactful disease-exposure dyad and understanding the role of PM is important in the development of interventions to prevent future spread of viral infections.Background: Particulate matter (PM) exposure is responsible for seven million deaths annually and has been implicated in the pathogenesis of respiratory infections such as severe acute respiratory syndrome (SARS). Understanding modifiable risk factors of high mortality, resource burdensome C19 and exposure risks such as PM is key to mitigating their devastating effects. This systematic review focuses on the literature available, identifying the spatial and temporal variation in the role of quantified PM exposure in SARS disease outcome and planning our future experimental studies. Methods: The systematic review utilized keywords adhered to the PRISMA guidelines. We included original human research studies in English. Results: Initial search yielded  Coronaviruses (CoV) are a common cause of respiratory disease. However, at least two novel CoVs have plagued humanity ,2. In 202.5 is known to activate tumor-associated signaling pathways by microRNA dysregulation, DNA methylation and by increasing the levels of inflammatory cells, cytokines. Altered macrophage-mediated inflammatory response to viral infections due to PM exposure has been hypothesized to play a role in these adverse outcomes ..28]. We Studies that performed Nucleic Acid Amplification Test (NAAT) using reverse-transcription polymerase chain reaction (RT-PCR) to detect SARS-CoV-2 RNA from the upper respiratory tract, physician diagnosis or other clinical tests, were categorized as low risk for detection bias. For studies with unknown methods of diagnosis, we categorized them as unclear risk of detection bias.Studies that adjusted for age, gender, individual levels of exposure or any other relevant covariates were categorized as low risk for this domain. Studies that did not adjust results for at least one covariate were categorized as high risk.Studies that included a control group were categorized as low risk for this domain, whereas those that did not were categorized as high risk. The three key domains were assessed for overall risk of bias judgment. Studies were categorized as low overall risk of bias if it was at low risk for all key domains, and high if any of the domains were high. For the time series studies only two domains, i.e., assessment of outcomes and adjustment for confounding were considered to analyze the risk of bias.Based on the inclusion and exclusion criteria, we screened and selected manuscripts (EndNote™ 20.1). Each article was screened for study design, patient characteristics, sample size, tools used, incidence, severity and mortality of SARS in association with quantifiable PM exposure. Results from each database search were filtered for human subjects, English language, publication date (after November 2002) and imported into EndNote. The references were then screened for duplicates. Only original research papers were then reviewed for title, abstract and full text to ascertain eligibility. The references cited in the relevant articles were also examined. All results were screened by SP and MSF and further independently evaluated by AN. Disagreements were resolved by consensus see .Data was generated from sources using our review PEO question and summarized into tables and plots . QualitaN = 732 were assessed for inclusion (abstract and title review). Finally, 46 original research articles were considered eligible [A total of 906 studies (334 PubMed and 572 Embase) were identified after filtering for relevant studies . After religible ,72,73,74N = 2 studies were high risk for outcome assessment, N = 24 were high risk due to lack of adjustment for confounders and N = 39 were high risk due to lack of a control group in their studies. Overall, N = 3 studies had low risk of bias, whereas N = 40 had high risk of bias Risk of bias assessment was performed for outcome, confounders and control group assessment. Of the three domains assessed for cohort studies, N = 3 were low risk for outcome assessment and N= 1 was considered high risk for due to lack of adjustment for confounders. Overall, N = 2 were low risk for bias and N = 1 had high risk of bias.For the time series studies of the two domains assessed (outcome and confounders), N = 43), while N = 3 were time-series studies [2.5 and PM10 have played a role in SARS severity and spread.As the C19 pandemic swept the globe from 31 December 2019, understanding the phenotype of both the disease and associated risk factors of disease severity has been challenging. Cohort studies were the predominant type  of daily SARS mortality was 1.06 (1.00–1.12) [SARS-1 originated in Guangdong, China in 2003, and in six months had spread to more than two dozen countries resulting in at least 774 deaths . Due to 00–1.12) . There w00–1.12) . In cont00–1.12) .2.5 through a risks study using days with the highest and lowest incidence numbers in February [3 increase in PM2.5 and PM10 resulted in 2.21% (95% CI:1.13–3.29), 2.67% (95% CI: 0.33–5.01), increase in daily counts of C19 infected cases, respectively [N = 6); prevalence (N = 2); morbidity (N = 1) and mortality (N = 6). Negative association was observed with mortality (N = 1) and incidence (N = 1), and equivocal results were reported by N = 1 [N = 17); prevalence (N = 4); morbidity (N = 6) and mortality (N = 18). Negative association with incidence was observed in N = 2. Equivocal results reported by N = 4 [A decline in the incidence, mortality, and hospitalization was observed during the later pandemic period, from approximately late April–June 2020. A temporal analysis in Beijing from 25 April–31 May 2020 showed a declining trend in daily mortality count . While tby N = 1 ,52,63. Oby N = 4 .Few, if any, areas of the globe have been left unaffected by the C19 pandemic. Meo et al. studied 17 countries across the globe and noted a significant positive association between PM and C19 incidence . CertainN = 16) studies [2.5 and C19 cases was observed in Lombardy, Italy by Rovetta et al. [2.5 levels, the number of SARS-CoV-2 infections significantly increased by 0.1% in London [2.5 levels recorded in 2010 were significantly associated with an increased SARS-CoV-2 positive testing likelihood  [N = 9); incidence (N = 6); prevalence (N = 4); morbidity (N = 2). Equivocal results were reported by (N = 2) [Europe was the setting of  . Overall2.5 and C19 cases in Wuhan  and Xiagan  [3 rise in levels of PM2.5 (lag 0–14 was positively associated with RR of 1.050 (95% CI: 1.028–1.073) daily newly confirmed cases [N = 4); mortality (N = 3); prevalence (N = 1).Similarly, of the nine studies dedicated to Asia ,36,37,38 < 0.01) . In Hubeed cases . Overall10 positively correlated to daily cases of C19  [2.5 for several days showed significant association to confirmed cases [2.5 due to a sandstorm in Saudi Arabia was associated with a significant increase in the number of SARS-CoV-2 cases (Spearman’s correlation coefficient ρ = 0.944 (<0.0001)) [Six studies with a focus on the Middle East were identified ,69,70,71ctively) . Similared cases . Increas2.5 was associated with an increased risk of mortality  and ICU admission  per 1-µg/m3 increase in PM2.5 [2.5 . PM was positively associated with C19: incidence rate (N = 7); mortality (N = 4) and prevalence (N = 1). A negative association with mortality was observed by N = 2 [In the U.S, data from seven New York City (NYC) hospitals concluded that higher and long-term exposure to PMin PM2.5 . Similarby N = 2 ,64.3 in PM2.5 increased the mortality risk by approximately 7.4% in Mexico City metropolitan area in October 2020 [Similarly, as in 2/3 Latin American countries, PM and C19 incidence and mortality had a positive association. In contrast, one study reported equivocal results . Specifiber 2020 .2.5 (<2.5 mm) and coarse PM10 (<10 mm). PM2.5 is more likely to travel and deposit deeper in the lungs like the alveolus, whereas PM10 can deposit on the surfaces of larger airways inducing inflammation. Ambient air pollutants are risk factors for cardiopulmonary diseases and responsible for over 6 million annual deaths.PM is a heterogeneous mixture of solid particles and liquid droplets found in the air. It is commonly grouped by diameter into fine PM2.5 was assessed by 19/46 studies. PM2.5 was positively associated with C19: incidence (N = 11); mortality (N = 6); morbidity (N = 5); and prevalence (N = 1). Negative association with prevalence was seen in only one study [The role of PMne study .10 was evaluated in eight studies, and it was positively associated with C19 incidence (N = 5); prevalence (N = 1); and mortality (N = 1). Finally, PM10 and PM2.5 were evaluated by N = 19 studies. A positive association with C19 incidence (N = 9); mortality (N = 5) was seen. Negative association was seen in N = 2 and equivocal results were identified in N = 1.PMOur systematic review identified the role of PM to be important in the incidence, mortality and morbidity due to SARS infection. These studies had significant differences in the populations, methods, and outcomes that were studied . We idenLonger durations and higher levels of PM increased the risk of ICU admissions and deaths due to C19. Several mechanisms have been hypothesized. Oxidative stress and disruptive immune and/or neuroendocrine function can result in increased severity of viral pulmonary infections ,82.3 increase in PM2.5 was associated with increased measles incidence. A similar observation was also noted with respiratory syncytial virus, which causes bronchiolitis and pneumonia [2.5 in four study sites and found the highest levels of SARS-CoV-2 RNA on PM2.5 [PM has been associated with enhanced infection with RNA viruses such as SARS . PM concneumonia . A studyon PM2.5 . PM expoon PM2.5 ,86. Expoon PM2.5 ,88. Theron PM2.5 ,72.2.5. Spatial variation in the concentration of PM2.5 in some areas such as California’s central valley and Italy’s Po Valley can be contributed to geographical features with climate inversion events that trap these pollutants. The air trapping in these regions can also lead to chronic exposure to these particulates increasing the risk for respiratory and cardiovascular diseases which further enhances the risk [Variation in C19 outcomes in different regions could be attributed to social determinants of health such as poverty, access to health care facilities and health literacy. Populations with limited resources also have a high prevalence of chronic health conditions ,90. Urbathe risk .10 [2.5 is known to increase the risk of COPD, a known risk factor for severe C19 infection [Worsening asthma and COPD leading to hospitalization has been noted with short-term (up to 24 h) exposures to PM10 . Long-tenfection .An NYC hospital-based study noted that mortality rates dropped from 25.6% in March to 7.6% in August 2020 . This reStrengths of Systematic Review. This review focuses on the environmental inducers of infectious diseases, a global health issue. It incorporates the variation in PM and the risk of Coronaviruses geographically. Manuscripts from across the globe were reviewed, which made this study more generalizable. Each article was screened for study design and was further subcategorized to understand temporal, spatial variability.N = 6 studies analyzed the spatial variation [Our systematic review has several limitations. Since assessing the risk of bias inherently has some level of subjectivity, we categorized high- vs. low-risk studies using a determined set of criteria. While many of the studies that we assessed had a high risk of bias, future studies would benefit from assessing their data in the context of potential confounders including age, gender, and comorbidities. Several variants have been identified since the 2019; however, our manuscript does not discuss the disparity in disease outcomes for different variants . The manariation ,55,62,65In conclusion, these studies have expanded our knowledge of PM exposure and its association with SARS infection. The review highlights the clinical impact of PM and the need for implementing measures to combat climate change and dangerous levels of environmental toxin. There was a spatial and temporal variation in the characteristics of the disease. Overall, it was seen that exposure to quantified PM was associated with increased incidence, mortality and morbidity to C19. Measures can be taken on both global and a personal level, such as improving air ventilation design and systems in enclosed spaces and buildings, restricting wildlife trading and deforestation, and training our healthcare professionals to educate masses on taking personal steps to ensure less production and exposure to pollution, such as using facemasks, and walking/cycling instead of motorized transport.Future experimental studies will include developing our understanding of the role of PM in accentuating the response to pathogens such as C19, understanding the effects estimate for chronic vs. short-term exposure to PM, and in furthering our management of PM exposure to limit severity of viral infections. These projects will focus on quantifying the association of PM concentrations (by zip code and/or geocoding) and the incidence of C19 related morbidity and mortality.


A previous meta-analysis, entitled “The association between metabolic syndrome and bladder cancer susceptibility and prognosis: an updated comprehensive evidence synthesis of 95 observational studies involving 97,795,299 subjects,” focused on all observational studies, whereas in the present meta-analysis, we focused on cohort studies to obtain more accurate and stronger evidence to evaluate the association between metabolic syndrome and its components with bladder cancer. PubMed, Embase, Scopus, and Web of Science were searched to identify studies on the association between metabolic syndrome and its components with bladder cancer from January 1, 2000 through May 23, 2021. The pooled relative risk (RR) and 95% confidence intervals (CI) were used to measure this relationship using a random-effects meta-analytic model. Quality appraisal was undertaken using the Newcastle-Ottawa Scale. In total, 56 studies were included. A statistically significant relationship was found between metabolic syndrome and bladder cancer 1.09 , and there was evidence of moderate heterogeneity among these studies. Our findings also indicated statistically significant relationships between diabetes  and hypertension  with bladder cancer, but obesity and overweight did not present a statistically significant relationship with bladder cancer. We found no evidence of publication bias. Our analysis demonstrated statistically significant relationships between metabolic syndrome and the risk of bladder cancer. Furthermore, diabetes and hypertension were associated with the risk of bladder cancer. Metabolic syndrome is defined as a set of risk factors that jointly cause adverse outcomes, including type 2 diabetes mellitus and cardiovascular disease . These rBladder cancer has recently been linked to metabolic syndrome . BladderResearch has shown that low HDL levels are an important risk factor for urothelial carcinoma of the bladder. Furthermore, through excess secretion of insulin, metabolic syndrome could be associated with the malignant potential of urothelial carcinoma of the bladder . In addiConsidering the available data, the association of metabolic syndrome and its components, including obesity, hypertension, high blood triglycerides, low HDL cholesterol levels, and insulin resistance, with bladder cancer has yet to be well enough defined to reach reliable results. Furthermore, the previous meta-analysis published in 2018  focused Initially, to identify whether studies satisfied the inclusion criteria, we screened the titles and abstracts of all studies using EndNote 20, an update from version X9 . A wide search was performed of several electronic databases including PubMed, Embase, Scopus, and Web of Science from January 1, 2000, through May 23, 2021. The search terms comprised the following keywords: “bladder cancer,” “bladder neoplasms,” “bladder carcinoma,” “bladder tumor-associated antigen,” “urothelial carcinoma,” “noninvasive bladder cancer,” “non-muscle-invasive bladder cancer,” “muscle-invasive bladder cancer,” “metabolic syndrome,” “metabolic syndrome components,” “metabolic abnormalities,” “metabolic syndrome x,” “insulin resistance,” “obesity,” “overweight,” “hypertension,” “dyslipidemia,” “diabetes mellitus,” “hypertriglyceridemia,” and “low HDL.” We also investigated the references of all articles to identify studies that were not included during the initial search. The following inclusion criteria were selected for meta-analysis: the study had a cohort study design, the primary outcome was the risk of bladder cancer  associated with metabolic syndrome, and the study was published in English. The exclusion criteria were articles of the following types: letters to the editor, case reports, intervention studies, case-control studies, cross-sectional studies, reviews, and meta-analyses.Initially, to identify whether studies satisfied the inclusion criteria, we screened their titles and abstracts. For studies where this decision was difficult to make based only on their titles and abstracts, a full-text assessment was conducted. Two authors (MA and FK) screened the final full-texts, and after reading the fulltexts of all eligible articles, made a final decision for each study. In cases of disagreement, the third review author’s opinion was solicited, or the issue was resolved by discussion.A structured data extraction form was used. The extracted data included: the last name of the first author, publication year, country, study purpose, sample characteristics, sample size, exposure, mean age, gender, confounders, and definitions of the components of metabolic syndrome. Data were extracted independently by the same 2 review authors (MA and FK) who conducted the study selection.The quality of papers was assessed using the Newcastle-Ottawa Scale (NOS) designed for observational studies . The NOS2 statistic to evaluate heterogeneity. A subgroup analysis was carried out according to gender (men and women) and weight (obesity and overweight). To identify influential studies in the meta-analysis, leave-one-out sensitivity analysis was performed. Publication bias was determined by a funnel plot and the Begg and Egger tests. A p-value < 0.05 was considered to indicate statistical significance. All analyses were performed using Stata version 14 .Pooled RRs and 95% CIs were calculated to measure the association of metabolic syndrome and its components with the risk of bladder cancer using a random-effects meta-analytic model. We used adjusted estimates. We used the Cochran Q-test and the IEthical approval was not sought because this study was based on published articles and no human or animal intervention was performed.2=40.4%; p=0.152). A sensitivity analysis showed that no single study significantly changed the pooled RR. Based on the Begg (p=0.851) and Egger (p=0.801) tests, we found no evidence of publication bias.2=83.3%; p<0.001). 2=56.5%; p=0.014) and men , but heterogeneity was lower among women. A sensitivity analysis showed that no single study was a potential source of heterogeneity. We determined the possibility of publication bias using a funnel plot  and overweight  groups. Sensitivity analysis showed that the studies by Choi et al. [The results of the relationship between excessive body weight and bladder cancer stratified by obesity and overweight are shown in i et al.  and Ko ei et al.  were con2=30.3%; p=0.158). A sensitivity analysis found that no single study significantly changed the pooled RR, and the Begg (p=0.312) and Egger (p=0.139) tests showed no evidence of publication bias.The results for the relationship between hypertension and bladder cancer are shown in In the present study, PubMed, Web of Science, Embase, and Scopus were searched and 55 studies were identified. Our analysis demonstrated a statistically significant relationship between metabolic syndrome and bladder cancer, and there was moderate heterogeneity among these studies. Our findings also indicated statistically significant relationships between diabetes and hypertension and bladder cancer, but no association was found between obesity or overweight and bladder cancer.Previous studies have reported metabolic syndrome as an essential factor for the progression of different types of cancers ,79. In dIn our study, there was a statistically significant association between diabetes and the risk of bladder cancer in both genders. The previous meta-analysis also found that diabetes increased the risk of bladder cancer . It has In the present meta-analysis, we observed no association between obesity or overweight and bladder cancer. Some studies have similarly found no association between overweight or obesity and bladder cancer ,51,74, bWe found a statistically significant relationship between hypertension and bladder cancer. The results of previous studies similarly showed an association between hypertension and urinary bladder cancer ,89,90. A2 statistic to detect heterogeneity. Moderate heterogeneity was found for the relationship between metabolic syndrome and the risk of bladder cancer among studies. Regarding the components of metabolic syndrome, there was also significant heterogeneity in the relationship between diabetes and bladder cancer in both genders, and between obesity or overweight and bladder cancer. There can be various reasons for heterogeneity between studies. Most likely, this heterogenicity was due to the variety of definitions of metabolic syndrome, as well as because metabolic factors may not have been directly measured by the same method. For example, studies diagnosed high BP through direct BP measurements, self-reported diagnoses of hypertension, or specific drug use. This might have resulted in high heterogeneity between studies. Other relevant sources of heterogeneity may have been differences among studies in the sample size, publication date (the eligible studies were published from 2000 to 2021), geographical area, and the study population.We used the Q-test and the IA strength of this study is that we included the most recent studies in the last 5 years. The second strength of this study is that we only assessed cohort studies in this systematic review, because cohort studies provide reliable and unbiased evidence. The third strength is that we evaluated all articles accurately to ensure that all articles were of sufficient quality for inclusion . Finally, we performed subgroup analyses of diabetes, BMI, and BP, which are the most important components of metabolic syndrome, and we separately measured the effect of each of these factors on bladder cancer risk.One of the limitations of the study is that we used studies written in the English language because non-English language studies were unavailable. The second limitation is that the studies we reviewed were from 2000 to 2021, and we did not include articles published before 2000. The reason for this is that the number of these articles was very limited and these articles did not meet our inclusion criteria.Our analysis demonstrated a statistically significant relationship between metabolic syndrome and the risk of bladder cancer. Of the components of metabolic syndrome, diabetes and hypertension showed statistically significant relationships with the risk of bladder cancer, but there was no association between obesity or overweight and the risk of bladder cancer.