TGF-β1), interleukin-4 (IL-4), and IL-10 genes have been implicated as risk factors for chronic kidney disease (CKD), but the results from published studies are inconsistent. Our meta-analysis reviews and summarizes the cumulative evidence for these associations.Anti-inflammatory cytokine polymorphisms in the transforming growth factor-β1  and 95% confidence intervals (CIs) were generated from random-effects or fixed-effects models using Stata 12.0.TGF-β1 rs1800469 was associated with decreased susceptibility to CKD , whereas TGF-β1 rs1800471 was associated with increased CKD susceptibility . In stratified analyses based on ethnicity, TGF-β1 rs1800469 was associated with CKD susceptibility in Asians and Caucasians, and there was an association of TGF-β1 rs1800470 and IL-4 rs8179190 with CKD in Asians. Stratified analyses also associated TGF-β1 rs1800471 with CKD susceptibility in Caucasians. Neither overall meta-analyses nor stratified analyses identified an association of the IL-10 rs1800869 and rs1800871 polymorphisms with susceptibility to CKD.Nineteen studies from 10 countries satisfied our inclusion criteria and were included in the meta-analysis. Overall, the pooled analysis showed that TGF-β1 and IL-4 genes including rs1800469, rs1800470, rs1800471, and rs8179190 may be important genetic contributors to CKD susceptibility.Available data suggest that common polymorphisms in the  Progres3 months .It is generally accepted that inflammation is an essential part of CKD. In the Chronic Renal Insufficiency Cohort (CRIC) study, biomarkers of inflammation were inversely associated with measures of kidney function and positively with albuminuria in CKD patients . An exceTGF-β1 is well known for its contribution to renal inflammation and fibrosis. The overexpression of TGF-β1 decreases the accumulation of macrophages and T cells and reduces inflammatory mediator release in CKD mice . Kidney Given the important role these cytokines play in the development of CKD, we sought to evaluate the relationship between common genetic polymorphisms within these cytokine genes and susceptibility to CKD using a meta-analysis study design.TGF-β1, IL-4, and IL-10 genes with CKD susceptibility. A literature search was performed within five scientific databases  from inception until October 2019. The following keywords were used in combination: transforming growth factor-β1, interleukin-4, interleukin-10, TGF-β1, IL-4, IL-10, chronic kidney disease, chronic renal failure, end-stage renal disease, polymorphism, and variant. After removing duplicates, two reviewers (MM and YJ) independently reviewed the titles and abstracts. Relevant studies were selected and underwent a full text review. Discrepancies between the two reviewers were resolved by discussion and consensus. When institutions published duplicate studies with accumulating numbers of CKD patients, only the most complete studies were included in the final analysis. The electronic search was supplemented by a hand-search of reference lists from the selected publications and relevant meta-analyses.This meta-analysis was conducted in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA) reporting guideline . The sea2, or markers of kidney damage, or both, of at least 3 months duration, regardless of the underlying cause  case-control studies with human subjects, 3) for case-control study, cases were CKD patients, whereas comparators (controls) were subjects without CKD; 4) standard methods used for genotyping, and 5) summary odds ratios (ORs) and 95% confidence intervals (CIs) were used to evaluate the susceptibility to CKD. We excluded case-only studies, conference papers, abstracts, meta-analyses, reviews, and studies that had no sufficient data.CKD was defined as decreased kidney function shown by glomerular filtration rate (GFR) of less than 60 ml/min per 1.73 mng cause . We inclTwo reviewers (MM and YJ) independently performed data extraction. Data were extracted from texts or tables as required. The following data were recorded for each report including publication details , characteristics of study subjects , genotypes, and methods used for genotyping. Quality assessment of the included studies was based on the Newcastle Ottawa Scale (NOS) for case-control studies.TGF-β1, IL-4, and IL-10 polymorphisms and CKD susceptibility was assessed using ORs and 95% CIs under dominant, recessive, and homozygote genetic models. The Z-test was applied to determine the significance of the pooled OR; P-value less than 0.05 was considered statistically significant. Between-study heterogeneity was evaluated by calculating the Q-value and I2 statistic that quantified the proportion variation that is owing to heterogeneity rather than chance . The strength of association between polymorphisms in the n chance . The fixTGF-β1 polymorphisms  (IL-4 rs8179190 polymorphism (IL-10 polymorphisms (rs1800896 and rs1800871) (A detailed flow diagram of study selection is shown in 1800470) , 3 studimorphism , and 8 s1800871) . The incTGF-β1 rs1800470 and its association with susceptibility to CKD  (2 > 50%). In the stratified analysis based on ethnicity, we found that rs1800470 was associated with increased susceptibility to CKD in Asians  .Seven studies including 614 cases and 747 controls evaluated y to CKD . The resTGF-β1 rs1800471 with susceptibility to CKD  and in Caucasians under dominant model  .Six studies with 714 CKD patients and 743 controls evaluated the relationship of y to CKD . The oveTGF-β1 rs1800469 polymorphism and susceptibility to CKD was evaluated in 3 studies (251 cases and 280 controls). There was an association between rs1800469 and susceptibility to CKD in dominant  and homozygote  models (2 = 49.4%). When stratifying the results of meta-analysis by ethnicity, we found that rs1800469 was associated with CKD susceptibility in Asians  and Caucasians  .The association between the = 0.028) Table 2,IL-4 rs8179190 polymorphism and its relationship with CKD susceptibility  but not recessive and homozygote models  (2 = 0). There was no association of rs8179190 with susceptibility to CKD in Caucasians. No publication bias was observed .Three studies assessed the tibility . The met= 0.800) Figure 3IL-10 rs1800896 polymorphism was assessed in 6 studies with 2,514 cases and 2,538 controls and was not found to be associated with CKD susceptibility . Stratified analyses did not find any significant association with CKD susceptibility in Caucasians and Asians. Significant heterogeneity was found under all genetic models (I2 > 50%). The results of Egger's test suggested that publication bias was unlikely to have been present .The IL-10 rs1800871 polymorphism and its association with susceptibility to CKD . Stratified analyses by ethnicity did not identify any association in Asians and a Caucasian population. We did not find any evidence of publication bias for this polymorphism .Five studies containing 1,623 cases and 3,733 controls evaluated the y to CKD . The resTGF-β1 rs1800470 and rs1800471 polymorphisms , without significantly influencing the pooled ORs. For IL-10 rs1800896, removing the studies by Wu et al., Babel et al., and Manchanda et al. abolished between-study heterogeneity  but did not alter the corresponding pooled ORs. For IL-10 rs1800871, the studies by Manchanda et al. and Okada et al. were the main contributors to heterogeneity.Funnel plots were symmetrical for the studies assessing the TGF-β1, IL-4, and IL-10 genes and CKD susceptibility. The results indicated a significant association of TGF-β1 rs1800469, TGF-β1 rs1800470, TGF-β1rs1800471, and IL-4 rs8179190 with CKD susceptibility, while no association was revealed for the IL-10 rs1800896 and rs1800871 polymorphisms.CKD is an irreversible and progressive disease with an inverse relationship between inflammation and kidney function. Even early stage CKD displays marked levels of inflammation . GeneticTGF-β1 gene, namely rs1800469 (C509T), rs1800470 (T869C), and rs1800471 (G915C) in the susceptibility to CKD. In our meta-analysis, there was an association between SNP rs1800469 and CKD susceptibility under dominant and homozygote models . Subgroup analysis by ethnicity showed significant results in Asians and Caucasians, respectively. SNP rs1800469 is located in the promoter region of TGF-β1 that can alter the rate of secretion of TGF-β1 and hence the circulating levels of mature protein. According to the Genotype-Tissue Expression (GTEx) Consortium  turnover in the kidney . In CKD nsortium , this pon humans . Accumuln humans . This coTGF-β1 rs1800470, an analysis of data from seven studies demonstrated a significant association with CKD susceptibility under all genetic models in Asians but not Caucasians. Frequencies of genetic markers often showed high variations among various ethnic and racial groups, but we found that the median prevalence of the rs1800470 C allele carriers was similar in Asians and Caucasians (66 and 67%). We noticed that this polymorphism was in strong linkage disequilibrium (LD) with TGF-β1 rs1800469 in Asians including Chinese  and contains a variable number of tandem repeat polymorphism  located in intron 3 . The funpression . In addipression . We founIL-10 polymorphisms (rs1800896 and rs1800871) and CKD susceptibility. rs1800896 is an SNP at the –1,082 position in the gene promoter in which guanine (G) replaces the ancestral adenine (A). Studies have shown that G carriers significantly overproduce IL-10 (IL-10 promoter region which can regulate the levels of circulating IL-10 (IL-10 polymorphisms. When data were limited to distinct ethnic groups, the results did not make a difference.We found eight studies assessing the association between ce IL-10 . rs18008ng IL-10 . Our metng IL-10 , whereasng IL-10 . PoolingIL-10 rs1800896 and rs1800871 polymorphisms with CKD susceptibility, additional functional variants may exist in the IL-10 gene. There were two studies that assessed other IL-10 polymorphisms, including rs1800872, rs3024509, rs1554286, rs3024505, rs3021094, and rs1800894 (IL-10 haplotypes and CKD susceptibility, but this could not be carried out because of limited data. Further studies are needed to address this interesting question.Some limitations of the included studies should be acknowledged. First, significant between-study heterogeneity was found in the meta-analyses evaluating rs1800470, rs1800896, and rs1800871. We used sensitivity analysis to identify the studies contributing to heterogeneity and confirmed the stability of the results. Future studies should clearly describe the information on study characteristics, including sex ratio and average age. This may be helpful in exploring precise sources of heterogeneity. Second, although Egger's test did not suggest publication bias for studies that analyzed rs1800470, the funnel plot showed some asymmetry. Statistical methods may not successfully detect publication bias when the number of eligible studies is relatively small. Third, in terms of control sources, not all studies recruited healthy individuals as controls. The studies by Cuenca et al. and Kamei et al. recruited patients without CKD after liver transplantation . The stus1800894 . We wereTGF-β1 and IL-4 genes including rs1800469, rs1800470, rs1800471, and rs8179190 may be important genetic contributors to susceptibility to CKD. Future prospective studies based on large sample sizes are needed to further substantiate and enrich the present findings.In summary, this meta-analysis of genetic association studies shows that common polymorphisms in the All datasets generated for this study are included in the article/WZ designed and supervised the research. MM, YJ, XW, GL, and YZ participated in the acquisition of data, performed the meta-analyses, and helped draft the manuscript. WZ revised the manuscript. All authors reviewed and approved the manuscript.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


Previous studies displayed that thrombomodulin gene polymorphisms are closely associated with venous thromboembolism (VTE), while the results are inconsistent. Therefore, we conducted a meta-analysis to accurately determine the association between thrombomodulin gene polymorphism and the risk of VTE.Wanfang, Chinese Biomedical Literature Database, Chinese National Knowledge Infrastructure, the Chongqing VIP Chinese Science and Technology Periodical Database, PubMed, EmBase, and Web of Science databases were searched, and the time to build the database was set until January 2021. The association between thrombomodulin gene polymorphism and the risk of VTE was evaluated. Meta-analysis was performed with STATA 16.0 software, and the odds ratio and its 95% confidence interval were applied to estimate the relationship between thrombomodulin gene polym‘orphism and the risk of VTE.The results of this meta-analysis will be submitted to a peer-reviewed journal for publication.This meta-analysis will summarize the relationship between thrombomodulin genepolymorphism and VTE risk.Ethical approval was not required for this study. The systematic review will be published in a peer-reviewed journal, presented at conferences, and shared on social media platforms. This review would be disseminated in a peer-reviewed journal or conference presentations.DOI 10.17605/OSF.IO/UEHJP. The incidence of VTE in China is increasing year by year. However, despite extensive studies, the exact pathogenesis of VTE is still unclear. Inactivity, active cancer, neurological disorders with motor disorders of the lower extremities, trauma/fracture, pregnancy, and oral intake of contraceptive are potential risk factors of VTE.,6 However, most individuals with these risk factors do not eventually develop into VTE, which indicates that genetic susceptibility plays an important role in its occurrence and development.Venous thromboembolism is caused by genetic, environmental, behavioral, and other factors. Some studies have revealed that about 50% to 60% of the disease is attributed to genetic factors,–10 Thrombomodulin is mainly synthesized by vascular endothelial cells and attached to the surface of endothelial cells, so it plays a very important role in inhibiting blood coagulation and activating fibrinolysis.,12 Animal studies have proved that thrombomodulin deficiency or dysfunction and the reduced production of activated Protein C in circulation result in hypercoagulability and prethrombotic state.,14 Therefore, perhaps, thrombomodulin plays an important role in the pathogenesis of venous thromboembolism.Thrombomodulin is a transmembrane glycoprotein and distributed on the surface of vein, artery, and capillary endothelial cells.–17 Abnormal expression of thrombomodulin is significant in the occurrence and development of VTE, which clearly indicates that thrombomodulin gene polymorphism can be used as a biomarker to evaluate the risk of VTE. Many studies have explored the relationship between thrombomodulin gene polymorphism and VTE risk.,17–22 However, the results of these studies are not consistent. Therefore, we performed a meta-analysis to examine the accurate correlation between thrombomodulin gene polymorphism and VTE risk susceptibility.Based on the key anticoagulant effects of thrombomodulin, it has been identified that genetic defects in many thrombomodulin genes are associated with the risk of primary and recurrent thrombosis.22.1The protocol of this review was registered in OSF (OSF registration number: DOI 10.17605/OSF.IO/UEHJP). It was reported to follow the statement guidelines of preferred reporting items for systematic reviews and meta-analyses protocol.2.2Studies would be included in this meta-analysis on the basis of the following criteria:1.Types of studies: All case control studies that are associated with the susceptibility of thrombomodulin gene polymorphisms to VTE would be incorporated in our review.2.Types of participants: Participants suffering from VTE will be included in the meta-analysis. Control subjects should be defined as without VTE or healthy individuals. No restrictions would focus on age, sex, or country.3.Data of the thrombomodulin gene polymorphism could be available on genotype distributions for the estimation of the odds ratio (OR) with its 95% confidence interval (CI), or adequate data are provided to estimate the corresponding estimate effects .4.Outcome: VTE risk comparison.2.3Studies would be excluded from the meta-analysis based on the following criteria: Animal experiments. Controls did not meet Hardy-Weinberg's law of genetic equilibrium. Repeated reporting of the same set of research data. The study was a review, systematic review, meta-analysis, or case series.2.4Electronic searching would focus on the databases of Wanfang, Chinese Biomedical Literature Database, Chinese National Knowledge Infrastructure, the Chongqing VIP Chinese Science and Technology Periodical Database, PubMed, Web of Science, and Embase, from the inception of database to January 2021. The search strategy in PubMed is illustrated in Table 2.52.5.1Data extraction and quality evaluation were performed independently by 2 researchers. In case of missing data in the included studies, the corresponding author was emailed to obtain additional information or raw data. Any disagreements between the 2 researchers were resolved through discussion. The researchers record the reasons to exclude each study in light of the preferred reporting items for systematic reviews and meta-analysis guidelines and report the screening results. The flowchart is demonstrated in Figure 2.5.2P value) of Hardy-Weinberg's equilibrium (HWE) was calculated based on the genotype frequency of thrombomodulin polymorphism in the control group. Group discussions are conducted to resolve any disagreements in the extraction process. If the data for a paper is incomplete or unconvincing, we would try to contact the author via email.Data extracted from all included studies are as follows: name of the first author; year of publication; country and ethnicity of the participants; number of cases and controls; and genotype distribution of thrombomodulin gene polymorphism in cases and controls. The probability value  that is adopted to evaluate the quality of observational studies.2.5.4The reason for the loss of data in the period of data screening and extraction is identified here. We would attempt to contact the authors if the data of potential studies are insufficient, missing, or vague. These studies would be excluded only if the data are not available through the method described above.2.5.5χ2 test, and P < .05 was regarded as significant disequilibrium. The strength of association between thrombomodulin gene polymorphisms and the susceptibility of cancer was assessed by computing the crude ORs with 95% CIs. The pooled ORs were conducted for 4 genetic models . The significance of the pooled ORs is determined by Z test, and P < .05 is considered statistically significant. χ2 test-based Q statistic and I2 would be applied to assess the overall heterogeneities. If I2 value < 50% and P > .1, heterogeneity is deemed to be low, and a fixed-effect model would be selected for data integration. Otherwise, a random-effect model could be adopted.Statistical analyses were conducted by using Stata 16.0 . The HWE for control subjects of each study was evaluated by carrying out 2.5.62 statistic. A fixed-effects or random-effects model is utilized to measure pooled OR in the absence or presence of heterogeneity, respectively. When a set of studies exhibit an obvious heterogeneity, factors, including the characteristics of patients and the variation degree in exposure leading to the heterogeneity, should be discussed. Subgroup analysis and sensitivity analysis would be conducted to explore potential sources of heterogeneity across studies when statistical heterogeneity is detected.Heterogeneity among the included studies will be evaluated by I2.5.7According to different ethnicity, genotyping method, and so on, we carried out subgroup analyses of the relationship between thrombomodulin gene polymorphisms and the risk of VTE.2.5.8Through the study of large weight of elimination effect, the sensitivity analysis was performed to test the stability of the results of meta-analysis.2.5.9,27The funnel plots will be applied to examine the publication bias if there are over 10 eligible studies.2.5.10The content of this article does not involve moral approval or ethical review and would be presented in print or at relevant conferences.3 Stroncek et al used the antithrombotic effect of inner Thrombomodulin that is overexpressed by Thrombomodulin. Raife et al established a mouse model, knocked 2 independent human Thrombomodulin genes into apolipoprotein E-deficient mice and propagated them, and then tested the antithrombotic and anti-inflammatory effects of human thrombomodulin in vivo.Thrombomodulin gene is located on chromosome 20 P12 (20p12) and encodes 5 specific gene domains: plant hemagglutinin-like fragment, 6 epithelial growth factor-like repetitive domains, glycosyl binding region rich in serine and threonine, transmembrane region, and cytoplasmic region. The last 3 epithelial growth factor-like structures are the binding sites of thrombin and play a very important role in the anticoagulant function of Thrombomodulin. Several polymorphisms or mutations in the coding and promoter regions of the Thrombomodulin gene have been identified. The effects of these polymorphisms on the level or activity of Thrombomodulin are not clear. Some reports exhibited that this polymorphism was not associated with VTE.,18,21 However, some studies have suggested that this polymorphism is related to VTE.It has been previously reported that the frequency of Thrombomodulin mutation in patients with thromboembolic disease is about 5%.Up to now, although many studies have focused on the relationship between thrombomodulin gene polymorphism and VTE susceptibility, the accumulated evidence has not been systematically evaluated. In this study, we will conduct a systematic review and meta-analysis in terms of many research results to obtain more reliable risk association estimates and provide guidance for the prevention and treatment of VTE. The advantages of this study include the following aspects: We will include the latest literature; for the exploration of heterogeneity, we will try to avoid post-group subgroup analysis. To improve the credibility of the results, we will conduct a sensitivity analysis of each genetic model.In summary, this study will provide the latest evidence to support the susceptibility of thrombomodulin gene polymorphism and VTE, and provide strategies for the prevention and treatment of VTE.Data curation: Meihao Wei and Xiaohui Xue.Formal analysis: Meihao Wei and Xiaohui Xue.Funding acquisition: Yan Wu.Methodology: Meihao Wei.Project administration: Yan Wu, Yipeng Pan.Resources: Meihao Wei, Yipeng Pan.Software: Meihao Wei.Supervision: Xiaohui Xue, Yipeng Pan.Validation: Xiaohui Xue and Yan Wu.Visualization and software: Xiaohui Xue and Yan Wu.Writing – original draft: Meihao Wei, Xiaohui Xue and Yan Wu, Yipeng Pan.Writing – review & editing: Yan Wu, Meihao Wei, Xiaohui Xue, Yipeng Pan.


Subgroup analyses showed that follow‐up time is a source of heterogeneity; for low‐density lipoprotein cholesterol (LDL‐C), RR was 0.95 . Subgroup analyses indicated that adjusting for heart failure explains the source of heterogeneity; for high‐density lipoprotein cholesterol (HDL‐C), RR was 0.97 ; for triglycerides (TGs), RR was 1.00 . Subgroup analysis showed that gender, age, follow‐up time, and adjustment for heart failure are sources of heterogeneity. Higher levels of TC, LDL‐C, and HDL‐C were associated with lower risk of new‐onset AF. TG levels were not associated with new‐onset AF in all subjects.Lipid levels are closely associated with health, but whether lipid levels are associated with atrial fibrillation (AF) remains controversial. We thought that blood lipid levels may influence new‐onset AF. Here, we used a meta‐analysis to examine the overall association between lipid levels and new‐onset AF. PubMed and EMBASE databases were searched up to 20 December 2019. We conducted a systematic review and quantitative meta‐analysis of prospective studies to clarify the association between lipid levels and the risk of new‐onset AF. Sixteen articles with data on 4 032 638 participants and 42 825 cases of AF were included in this meta‐analysis. The summary relative risk (RR) for a 1 mmol/L increment in total cholesterol (TC) was 0.95 (95% CI 0.93‐0.96, I There is, however, a “cholesterol paradox” in AF2We followed the preferred reporting protocol for systematic reviews and meta‐analyses set out in the PRISMA Statement2.1We conducted a comprehensive literature search in the PubMed and EMBASE databases to evaluate the association between lipid levels and incidence of AF using the following search terms: “AF,” “TC,” “LDL‐C,” “HDL‐C,” “triglyceride (TG),” “AF”, “TC”, “LDL”, “HDL” and “TG”.2.2We used the following inclusion criteria: (a) the paper evaluated associations of lipid levels with the incidence of AF, (b) the paper provided adjusted relative risk estimates  with 95% confidence intervals (CIs), and for dose‐response analyses, provided a quantitative measure of exposure and the total number of cases and person‐years or continuous risk estimate, and (c) when multiple publications were available from the same study, we used the study with the largest number of cases. We excluded the literature using the following criteria: (a) when multiple publications were available from the same study, we used the study with the largest number of cases; (b) when the publication was a meeting abstract with insufficient data available online; and (c) when the publication described a meta‐analysis, review, case‐control study, or cross‐sectional study. We reviewed all relevant studies and identified 64 published articles that discussed the association between blood lipid levels and incidence of AF; 16 articles met our inclusion criteria.2.3Data abstraction was carried out independently by two authors (Yisong Yao and Yangyang Wang) and disagreements were resolved through discussion. The following information was abstracted: first author's last name, publication year, country where the study was conducted, study period, sample size, number of cases/controls, exposure variables, exposure levels, adjusted relative risk estimates , and 95% CIs for the highest vs the lowest level of the exposure variable in the publication.2.4(Supplementary Table ).Quality assessment of the publications was carried out using the Newcastle‐Ottawa Quality Assessment Scale (NOS), which has a nine‐point scale . The literature was divided into high quality (score ≥8) and low quality (score <8) 2.52 was lower than 50%, otherwise the random effect model was used. Publication bias was estimated using Egger's test and Begg's test. Subgroup analyses were completed using the characteristics of studies to find sources of heterogeneity. To verify the stability of our results, a sensitivity analysis was performed, in which one study at a time was removed and the remaining studies analyzed to evaluate whether the result could have been affected markedly by a single study.All of our results were analyzed using the Stata 14.0 software. We calculated summary relative risks (RRs) and 95% CIs for 1 mmol/L increments in TC, LDL‐C, HDL‐C, and TGs, using a random effects model or a fixed effects model, which takes into account heterogeneity between studies. RRs were pooled using a fixed effect model if IWe used the method described by Orsini et al to analyze dose‐responses of lipid levels and calculated study‐specific slopes (linear trends) and 95% CIs from the natural logarithms of the reported RRs and CIs across categories of each lipid level.3(Supplementary Table). Four articles were excluded because they were a duplicate report on the same cohort population and 28 articles were excluded because insufficient information was available online. Two reviews, one meta‐analysis, two case‐control studies, and 11 cross‐sectional studies were also excluded, leaving 16 articlesThe processes for retrieving and filtering articles, together with outcomes, are shown in Figure The characteristics of these 16 publications are shown in Table 3.12 = 74.6%, P < .001) . Six studies were included in the nonlinear dose‐response analysis.Supplementary FigureThirteen articles, with 40 979 cases of new‐onset AF among 3 987 985 participants3.2P < .001) . Four studies were included in the nonlinear dose‐response analysis.Ten articles with 34 665 cases of new‐onset AF among 3 898 670 participants3.3P < .401) . Five studies were included in the nonlinear dose‐response analysis.Eleven articles with 35 639 cases of new‐onset AF among 3 914 734 participants,3.42 = 81.1%, P < 0.001) .Eight articles with 33 996 cases of new‐onset AF among 3 869 363 participants3.5Significant heterogeneity was found in our meta‐analysis. TC, LDL‐C, and TGs were used for subgroup analyses. Subgroup analyses may be the source of heterogeneity.age < 50 years , age < 60 years , and age < 70 years ; the subgroup age > 70 years showed less heterogeneity . Second, follow‐up time may also influence heterogeneity. Greater heterogeneity  was found in the subgroup follow‐up time > 10 years, and less heterogeneity was found in the subgroups follow‐up time < 5 years  and follow‐up time 5‐10 years   was found in the subgroup women; higher heterogeneity was found in the subgroups men  and men and women . Second, heart failure should also be taken into account since this has a significant impact on AF. Less heterogeneity  was found in the subgroup that was adjusted for heart failure and higher heterogeneity  was found in the subgroup that was not adjusted for heart failure  and men ; higher heterogeneity was found in the subgroup men and women . The summary RR was 0.93  per 1 mmol/L increment in men. Second, age has a significant impact on AF. Less heterogeneity was found in the subgroup age 51 to 60 years  and the subgroup age ≥ 70 years . Third, follow‐up time has a significant impact on AF. Less heterogeneity was found in the subgroup follow‐up time < 5 years (P = .364 I2 = 7.5%) and the subgroup follow‐up time > 10 years . Fourth, heart failure has a significant impact on AF. Less heterogeneity was found in the subgroup that was adjusted for heart failure  and higher heterogeneity was found in the subgroup that was not adjusted for heart failure   per 1 mmol/L increment in men. The dose‐response curve between TC and AF was U‐shaped and that between LDL‐C and AF had a reversed spoon shape; a linear association was observed between HDL‐C and AF.To our knowledge, this is the first meta‐analysis of lipid levels and the risk of new‐onset AF. There was a 5% decrease in RR per 1 mmol/L increase in TC, a 5% decrease in RR per 1 mmol/L increase in LDL‐C, and a 3% decreases in RR per 1 mmol/L increase in HDL‐C. No association was observed between TGs and new‐onset AF in all subjects, but subgroup analysis found that the summary RR was 0.93  association between TC and new‐onset AF. We also found a nonlinear (reverse spoon‐shaped) association between concentrations of LDL‐C and the risk of new‐onset AF. Our study suggests that as a risk factor for new‐onset AF, neither “the higher, the better” nor “the lower, the better” is correct in terms of cholesterol concentration.4.3Although most of the studies included here found that high cholesterol levels were associated with lower risk of new‐onset AF, the dose‐response curve was not clear and the best concentration range was unknown. Our quantitative meta‐analysis found a non‐linear relationship between both TC and LDL‐C and new‐onset AF. A negative relationship between HDL‐C and new‐onset AF was also found.4.4In our meta‐analysis, although higher TC, LDL‐C, and HDL‐C were associated with lower risk of new‐onset AF, there are still some problems. First, our meta‐analysis showed an obvious heterogeneity between studies, which may affect the reliability of the results of the meta‐analysis and means that careful interpretation is needed. Second, there was publication bias with TC, which may affect the reliability of the results. Third, the heterogeneity was partially improved after subgroup analysis of follow‐up years, study quality, lipid‐lowering therapy, valvular atrial fibrillation, and diabetes. We believe that despite significant heterogeneity among studies, higher TC, LDL‐C, and HDL‐C were associated with lower risk of new‐onset AF. Our results provide an epidemiological basis for the underlying trials, but follow‐up studies on the relationship between new‐onset AF and cholesterol are needed.5Our meta‐analysis suggests that higher levels of TC, LDL‐C, and HDL‐C were associated with a lower risk of new‐onset AF and that TG levels were not associated with new‐onset AF across the entire study population. TC concentrations in the range 232 to 238 mg/dL (6.009‐6.164 mmol/L) were associated with lower risk of new‐onset AF, LDL‐C concentrations in the range 133 to 149 mg/dL (3.445‐3.859 mmol/L) were associated with lower risk of new‐onset AF, and HDL‐C concentrations in the range 31 to 80 mg/dL (0.803‐2.072 mmol/L) were linearly and negatively correlated with new‐onset AF.The authors declare no potential conflict of interest.Yisong Yao: Conceptualized the study; Yisong Yao, Yangyang Wang, and Feng Liu: Design of study; Yisong Yao, Feng Liu, and Yangyang Wang: Literature retrieval, study selection, data extraction, statistical analyses, interpretation of the data and drafting of the initial manuscript; Zengzhang Liu: Quality evaluation; Zengzhang Liu: Critical revision and comment for important intellectual content; all authors reviewed and approved the final manuscript.Supplementary Figure 1 Dose‐response curve for relationship between TC and risk of new‐onset AF.TC = total cholesterol, CI = confidence interval, AF = atrial fibrillation.Click here for additional data file.Supplementary Figure 2 Dose‐response curve for relationship between LDL‐C and risk of new‐onset AF.LDL‐C = low‐density lipoprotein cholesterol, CI = confidence interval, AF = atrial fibrillation.Click here for additional data file.Supplementary Figure 3 Dose‐response curve for relationship between HDL‐C and risk of new‐onset AF.HDL‐C = high‐density lipoprotein cholesterol, CI = confidence interval, AF = atrial fibrillation.Click here for additional data file.Supplementary Figure 4 Forest plot for TGs and risk of new‐onset AF, per 1 mmol/L TGs increase.TGs = triglycerides, CI = confidence interval, AF = atrial fibrillation.Click here for additional data file.Supplementary Figure 5 Dose‐response relationship and subgroup analysis between TC and risk of new‐onset AF.TC = total cholesterol, NOS = New Castle‐Ottawa Quality Assessment Scale,CI = confidence interval, AF = atrial fibrillation.Click here for additional data file.Supplementary Figure 6 Dose‐response relationship and subgroup analyses between LDL‐C and risk of new‐onset AF.LDL‐C = low‐density lipoprotein cholesterol, NOS = New Castle‐Ottawa Quality Assessment Scale, CI = confidence interval, AF = atrial fibrillation.Click here for additional data file.Supplementary Figure 7 Dose‐response relationship and subgroup analyses between TGs and risk of new‐onset AF.TGs = triglycerides, NOS = New Castle‐Ottawa Quality Assessment Scale, CI = confidence interval, AF = atrial fibrillation.Click here for additional data file.Supplementary Table 1 Results of quality assessment.Supplementary Table 2 Exclusion reasonsClick here for additional data file.


Recent studies have shown that prior antiplatelet drug use could ameliorate the risk and mortality of acute respiratory distress syndrome (ARDS). However, the connection between prior acetylsalicylic acid (aspirin) use and the risk of ARDS is unknown. Our primary objective was to perform a meta-analysis on the currently available studies to assess the association between aspirin use prior to ARDS onset and ARDS incidence in at-risk patients.Two investigators separately searched four research databases: MEDLINE, EMBASE, Cochrane Library, and Web of Science for relevant articles from the earliest available data through to July 14, 2019. In this paper, we performed a meta-analysis of the fixed effects model using the inverse variance-weighted average method to calculate the pooled odds ratios (ORs) and 95% confidence intervals (CIs). The primary outcome was risk of ARDS, and the secondary outcome was the hospital mortality of at-risk patients.P = 0.018) with low statistical heterogeneity (I2 = 1.7%). Additionally, difference between prior aspirin use and non-aspirin use was not remarkable for hospital mortality in at-risk patients , and this analysis did not involve statistical heterogeneity (I2 = 0%).This article included seven studies altogether, enrolling 6,764 at-risk patients. Our meta-analysis revealed that, compared to non-aspirin use, prior aspirin use was linked with a significantly lower incidence of ARDS in at-risk patients (OR, 0.78; 95% CI, 0.64–0.96; This article indicates an association between prior aspirin use and a lower incidence of ARDS in at-risk patients, suggesting that aspirin use could potentially lower the risk of ARDS, and the investigation of such an effect is an interesting area for future clinical studies. Acute respiratory distress syndrome (ARDS) is a clinical syndrome with high morbidity and mortality. ARDS is characterized by severely hypoxemic respiratory failure that requires mechanical ventilation and often induces organ failure. In addition, ARDS results in lower quality of life in patients after hospital discharge .Mechanistically, ARDS is described as an inflammatory syndrome characterized by inflammatory injuries to the lungs mediated by immune cells, such as neutrophils and macrophages . The infPrevious reports demonstrate that platelets play a substantial role in the onset of ALI or ARDS , as wellhttp://www.crd.york.ac.uk/PROSPERO), and the registration number is CRD42018103854.This meta-analysis was performed in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses . The proA flowchart of the screening procedure for relevant studies is presented in This article includes the relevant studies to analyze the effects of aspirin on ARDS incidence in at-risk patients if: (1) the studies were observational cohort studies or randomized clinical trials (RCTs); (2) the study's participants were at-risk of ARDS or ARDS patients in the original articles; (3) the experimental arms of the studies were aspirin use prior to ARDS onset; (4) the control arms of the studies were at-risk ARDS patients who did not use aspirin or used placebo; (5) the studies measured the risk of ARDS or hospital mortality in at-risk patients that used aspirin and at-risk patients that did not use aspirin; and (6) the studies were published in the English language. The patients included in this meta-analysis were all adults. At-risk patients are defined as patients with the presence of at least one major risk factor for acute lung injury (ALI), including aspiration, pneumonia, sepsis, shock, pancreatitis, high-risk trauma, or high-risk surgery.First, two investigators (HoL and YY) extracted the data from the above four databases. In cases where disagreements occurred, the determination of whether the study would be included was discussed with the presence of a third reviewer (HuL). Then, the relevant data, including the first name of the first author in the original studies, the publication date, the represented country of the articles, the design type of the included studies, the number of experimental arms and control arms, the study period, the dose of aspirin used, the ARDS or acute lung injury definition, and the ARDS risk and hospital mortality were extracted from the original reports. Moreover, we evaluated the risk of ARDS as the primary outcome and hospital mortality as the secondary outcome.According to the principle of the Cochrane Collaboration , all dom2I and P values. The degree of variability was due to the heterogeneity, not the sample error  and 95% confidence intervals (CIs) for each patient outcome. To confirm the statistical significance, we performed a two-sided t-test, in which a P-value smaller than 0.05 was considered statistically significant. The software Stata 14.0  was used for all the statistical analyses in this meta-analysis.The outcomes in this meta-analysis were the risk of ARDS and the hospital mortality in at-risk patients. In the meta-analysis, the heterogeneity included methodological heterogeneity, clinical heterogeneity, and statistical heterogeneity. Most of the meta-analysis contained statistical heterogeneity, which was evaluated by le error , and wheBased on the combined keywords, a total of 1,470 articles were found in our search of the four databases, 272 of which were duplicate studies. Among the remaining reports, 37 studies were considered potential articles based on their titles and abstracts. In the end, based on the inclusion criteria, seven studies  were conA total of seven articles  with 6,7Six of the included articles  were obsP = 0.018; I2 = 1.7%; P = 0.204; I2 = 0%; In the seven eligible studies , the risSince all of the included studies were observational studies or an RCT with a low risk of bias Table 1,P = 0.600 and 0.544; A funnel plot revealed that no asymmetry was found upon visual inspection , Provincial Ministry Co-construction Project from Medical Scientific and Technological Research Program of Henan Province (Grant No. SBGJ2018020), and Natural Science Foundation of Henan Province (Grant No. 182300410369) supported this study.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


The aim of this study was to assess the impact on quality of life from informing patients with cancer of their diagnosis and disease status.neoplasm, cancer, tumor, tumor, carcinoma, disclosure, truth telling, breaking bad news, knowledge, knowing, awareness, quality of life, QOL. Pairs of reviewers independently screened documents and extracted the data, and the meta-analysis was performed using Revman 5.0 software.We searched the follow databases, PubMed, CENTRAL , PsycINFO, WEB OF SCIENCE, Embase, CBM , WANFANG database (Chinese Medicine Premier), and CNKI , using the following terms: P > 0.05). There were also no differences found between the patient groups in physical function, role function, cognitive activity, and emotional function (P > 0.05). In terms of vitality, patients who were completely informed about their diagnosis showed higher vitality than uniformed patients. Uninformed patients seemed to have lower social function scores. Between partly informed and uninformed cancer patients, no differences were found in their general quality of life, function domains, and disease-related symptoms (P > 0.05).Eleven thousand seven hundred forty records retrieved from the databases and 23 studies were included in the final analysis. A meta-analysis revealed that there were no differences in either the general quality of life and symptoms of fatigue, pain, dyspnea, insomnia, appetite loss, and diarrhea, between informed and uniformed cancer patients (Informing cancer patients of their diagnosis may not have a detrimental effect on their quality of life.CRD42017060073. In 2015, an estimated 17.5 million new cancer cases and 8.8 million cancer deaths occurred worldwide  and althMany researchers are also interested in this topic and one study showed that patients’ awareness of disease status significantly increased rates of psychiatric disorders, such as depression and anxiety . ConversIn this review, we have systematically collected and reviewed studies focusing on the association between diagnosis disclosure and QoL in cancer patients, and have conducted a meta-analysis to quantitatively present this association by pooling effect estimates.The following inclusion criteria were used to optimize selection of appropriate articles: articles needed to (1) be written in either English or Chinese; (2) explore the concept of awareness of disease status among cancer patients; (3) explore the impact of disease awareness on patients’ quality of life; (4) be randomized controlled studies, cohort studies, or case control studies. The following exclusion criteria were used: (1) the article was a conference abstract; (2) the full text was unavailable.No patients were directly involved in this study.We searched the following databases, PubMed, CENTRAL , PsycINFO, WEB OF SCIENCE, Embase, CBM , WANFANG database (Chinese Medicine Premier), and CNKI . The terms used were: neoplasm, cancer, tumor, carcinoma, disclosure, truth telling, breaking bad news, knowledge, knowing, awareness, quality of life, and QOL. Reference lists of obtained articles were hand searched and authors were contacted if articles couldn’t be easily obtained. Pairs of reviewers independently screened the literature and the third reviewer resolved any disagreements. The systematic review was registered in 2015 with PROSPERO registration number CRD42017060073. A complementary search using the above terms was performed in February 2018.Pairs of reviewers independently extracted the following data from included studies: first author, publication year, country, journal, the setting where the research was carried out, the time when the study began and ended, the definition of exposure in the research, study design, financial support, conflicts of interests, patients’ characteristics, and quality of life. The third reviewer resolved any disagreements.The included studies used self-reported participant measures of QoL as primary or secondary end points.General quality of life;i.physical capability ;ii.social capability ;iii.role function ;iv.emotional wellbeing ;v.cognitive capacity (e.g. ability to focus attention and form/retain memories);vi.vitality ;vii.economic ability QoL domains:Disease-related symptoms (or both), including fatigue, pain, dyspnea, insomnia, appetite loss, and/or diarrhea.Pairs of reviewers independently assessed risk of bias in the included studies by using the ROBINS-I assessment tool  for non-If we included at least 10 studies in a meta-analysis, we generated funnel plots of effect estimates against their standard errors  using Review Manager software (RevMan). We assessed the potential risk of publication bias through a visual analysis of the funnel plots. Roughly symmetrical funnel plots indicated a low risk of publication bias and asymmetrical funnel plots a high risk. One should be aware that this is a rather subjective judgement and that funnel plot asymmetry might also arise from other sources and that publication bias does not always lead to asymmetry. We further attempted to avoid publication bias by searching trials registries and conference proceedings for unpublished studies. We addressed duplicate publication bias by including only one study with more than one publication. If we had doubt about whether multiple publications referred to the same data, we attempted to contact trial authors by email to resolve this issue.Based on the results of the systematic review, the GRADE system was applied to evaluate the quality of the evidence, with results divided as follows: High quality (or A) - very confident that the real effect value is close to the estimated effect value, Moderate quality (or B) - having a moderate degree of confidence in the estimated value of the effect, and while the real value may be close to the estimated value there is still the possibility of large difference between the two groups, Low quality (or C) - limited confidence in the effect estimate and the true value may be quite different from the estimate, and Very low quality (or D) - little confidence in the effect estimate, with the true value likely to be very different from the estimate. Although evidence based on randomized controlled trails (RCT) is initially classified as high quality, confidence in such evidence may be diminished by five factors: (1) study limitations, (2) inconsistency in research results, (3) use of indirect evidence, (4) inaccurate results, and (5) publication bias. Evidence can be upgraded based on the following three factors; (1) large effect value, (2) existence of a dose-effect relationship, and (3) a possible confounding bias which may reduce efficacy.P ≤ 0. 05), we used the random effects model to combine the studies. To assess reporting biases, we investigated publication and other reporting biases using funnel plots.Measures of treatment effect: We analyzed continuous outcomes as standardized mean differences (SMD) between groups with 95% CIs. To assess heterogeneity, we determined statistical heterogeneity using theχ2 test. If heterogeneity was low , we used the fixed effects model to calculate the combined effect. If heterogeneity was high  participants were enrolled. Detailed information on overall study characteristics are shown in Table 0 to 352 Included studies were assessed for risk of bias using the ROBINS-I assessment tool. For each trial the risk of bias is detailed in Table There was no difference in the change in QoL from baseline between totally informed and uninformed of diagnosis in 1593 study patients , and no difference between partly informed and uninformed of diagnosis in 219 participants . Details shown in Figs. Meta-analyses comparing totally informed with control intervention showed no differences in role function among 1250 patients. The same result was seen with patients partly informed of diagnosis. See Table We found no significant effect on cognitive activity from totally informing cancer patients of diagnosis. See Table No difference in scores was observed between totally informed and uninformed of diagnosis groups in 1150 cancer patients. See Table P > 0.05), while in liver cancer, patients totally informed of diagnosis did better than uninformed patients . No difference was seen between the partly and totally uninformed of diagnosis groups  in 296 patients. See Figs. Compared to patients uninformed of diagnosis, totally informed patients did better, and their social function was significantly affected among 2130 cancer patients . Subgroup analysis based on cancer types showed that there was no difference in lung and gastrointestinal cancer patients . No information on partly informed versus totally uninformed patients was found for use in this study. More information is shown in Fig. No difference was seen between the totally and partly informed diagnosis groups compared to totally uninformed groups. See Table We observed that in terms of economic function, totally informed performed significantly worse than uninformed of diagnosis groups in 1123 participants when looking at the change in scores across instruments from baseline to follow-up . Totally informed of diagnosis patients more often felt economic difficulty than those uninformed of diagnosis. See Fig. We observed no significant effect between totally informed and uninformed of diagnosis groups in assessments of fatigue, pain, dyspnea, diarrhea, constipation, appetite loss, insomnia, nausea, and vomiting. Details shown in Tables Results based on systematic reviews were graded low and very low. Details in Table Because we included 10 studies in the meta-analysis of overall quality of life between totally informed and totally uninformed of diagnosis cancer patients, we generated a funnel plot of effect estimates against their standard errors  using Review Manager software (RevMan). The funnel plot was nearly symmetrical and every meta-analysis exited negative and positive results, which meant that there is little possibility of publication bias in this study. See Fig. 2 was larger than 50%, sample sizes had fewer than 300 participants included in the total, and the 95% confidence interval was too wide.We included 23 trials with 3322 participants distributed over totally informed, partly informed, and uninformed of diagnosis groups. Conference abstracts and studies whose full text was unavailable were excluded. Almost all the included studies were of low quality, among which 20 studies had an existing bias due to various confounding factors such as age and degree of education, and only 5 had an adjusting analysis. The 3 other studies were bias-free due to the consistency of their confoundings and baselines. Results based on systematic reviews were graded low and very low. The main reasons for their downgrading were that the confidence interval overlaps were low and IP > 0.05). There was also no difference in the physical function, role function, cognitive activity, and emotional function, of the groups (P > 0.05). However, in terms of vitality and social function, totally informed patients did better than uninformed patients. Subgroup analysis based on cancer types showed that liver cancer patients who were totally informed of their diagnosis did better than those uninformed in social function, but informed patients seemed to get higher scores in financial difficulty. Between the partly informed and uninformed groups, no differences were found in general quality of life, function domains, and disease-related symptoms (P > 0.05).Through meta-analysis, cancer patients who were totally informed or uninformed of the diagnosis had no differences in either their general quality of life and symptoms of fatigue, pain, dyspnea, insomnia, appetite loss, and diarrhea , we used the random effects model to combine the studies. The sub-subgroups were then divided into lung, liver, and gastrointestinal cancer to decrease heterogeneity.The results of this study will give clinicians and patients’ family some enlightenment on communication with cancer patients. Our conclusion relies on both the quality and quantity of the original studies available for review, and the low-quality evidence in our studies may affect any extrapolation of our conclusion. Because our research went on for a long period of time, we conducted a complementary search to avoid missing the latest original studies. The biggest limitation in our study was the different health-related quality of life scales which increased heterogeneity and made comparing findings between trials extremely difficult. However, we were still able to analyze these continuous outcomes as standardized mean differences (SMD) between groups with 95% CIs. To assess heterogeneity, we determined statistical heterogeneity using the χ2 test. If heterogeneity was low , we used the fixed effects model to calculate the combined effect and if heterogeneity was high (I2 ≥ 50%, Informing cancer patients about their diagnosis may not have a detrimental effect on their quality of life, but more studies based on high quality evidence are still required.


A systematic review reported that coffee consumption would decrease risk of colon cancer in Asian women. But the systematic review arises the issue of duplication, so that a meta-epidemiological study was conducted. The selection criteria were defined that a prospective cohort follow-up study conducted to evaluate coffee consumption and risk of colon cancer in Asian and showed adjusted relative risk and its 95% confidence interval. In order to conduct meta-analysis, the highest versus lowest method was applied to extract relative risk and its 95% confidence intervals of the highest category. Random effect model was applied if I-squared value was over 50%. After avoiding duplication, 9 cohort data were selected for meta-analysis. The summary relative risk  [I-square value] were 0.90 (95% CI: 0.79-1.03) [0.0%] in men, and 0.64 (95% CI: 0.36-1.15) [65.9%] in women, respectively. The results suggest that coffee consumption is not associated with the risk of colon cancer in Asian men and women. The findings of this study are consistent with the results of two systematic reviews conducted under the same hypothesis and selection criteria. Additional epidemiological studies are needed for the inflection of colon cancer risk as the dose of coffee increases and the difference in the protective effect by sex.  Colorectal cancer ranks third in incidence and second in mortality worldwide . While dietary habits are known as major risk factors , it has been suggested in the hypothesis that the consumption of coffee might decrease colon cancer risk based on the anti-oxidants and anti-mutagens effects of coffee ingredients .The results of 4 systematic reviews on prospective cohort studies conducted to elucidate the hypothesis are as shown in Therefore, the purpose of this study is to conduct a meta-epidemiological study  to re-evaluate the risk of colon cancer associated with coffee consumption in Asians.In that the previous systematic review was reevaluated, the selection criteria is the same as Sartini et al., (2019), but adds that it is limited to Asians. In other words, the selection criteria were defined that a prospective cohort follow-up study conducted to evaluate coffee consumption and risk of colon cancer in Asian and showed adjusted relative risk (RR) and its 95% confidence interval (95%CI). In response, five cohorts selected by Sartini et al., (2019) were subject to priority consideration . Meanwhile, the RR and its 95%CI was extracted using the ‘highest versus lowest method’ (HLM) in the selected papers as performed by Sartini et al. (2019). For each paper, logarithm RR (logRR) and standard error of logRR (SElogRR) were calculated from the information extracted by HLM method.The heterogeneity level was evaluated by the I-squared value (%), and meta-analyses applying random effect model was performed in the case of more than 50% . Subgroup analysis was performed by sex. Statistical significance level was set as 0.05Instead of selecting Kashino et al., (2018) in order to include eight Japanese cohort data, three papers  were excluded to avoid duplication. As a result, 9 cohort data from two papers  were selected for meta-analysis, finally. All were cohorts of Japanese population.In the two final papers selected for meta-analysis , the RR values extracted by applying the HLM method are presented in The results suggest that coffee consumption is not associated with the risk of colon cancer in Japanese men and women . The conclusion differs from what Sartini et al. (2019) claimed to have statistically significant preventive effects in Asian women .This different result could be considered by two things. First, it is due to the duplication of cohort databases used in meta-analysis . As pointed out in the introduction, the results of this study was obtained after excluding three papers with duplication of cohort data. Second is the choice of effect model according to the level of heterogeneity. Since the I-squared value of this study was 65.9%, a random effect model was applied and the result was statistically insignificant. However, I-squared value of Sartini et al., (2019) was 43.1%, and authors stated clearly that a fixed effect model was applied. When this study ignored the heterogeneity level and applied the fixed effect model, a statistically significant result was obtained . In other words, the elimination of duplication led to differences in the statistical significance of sRR as the level of heterogeneity increased.The findings of this study are consistent with the results of two systematic reviews conducted under the same hypothesis and selection criteria . Although this study selected Kashino et al., (2018) instead of the three papers  they had selected, but the same results were obtained. Of particular note, the pooled hazard ratios for the eight cohorts  showed a significantly dose-response relationship with a P for trend of 0.05 when categorized into 1-2 cups or 3 cups or more, based on the intake group below 1 cup per day. In response, the dose-response meta-analysis  showed that the RR were lower than 1 as the daily intake increases from 1 cup to 3 cups, and then higher as more than 4 cups. These results indicated that there was a change of RR direction between 3 cups and 4 cups daily. Therefore, additional epidemiological studies are needed for the inflection of colon cancer risk as the dose of coffee increases and the difference in the protective effect by sex.


CFI) gene polymorphisms have been reported to age-related macular degenerative (AMD) risk, nevertheless, above association is not consistent. We investigated a meta-analysis to evaluate the conclusions between CFI polymorphisms (rs10033900 and rs2285714) and AMD risk. An identification was covered with the PubMed and other databases through February 8, 2020. Odds ratios (OR) and 95% confidence intervals (CI) were used to assess the strength of associations. After a comprehensive search, 11 different articles  were retrieved. Individuals carrying C-allele or CC genotype of rs10033900 polymorphism may have a decreased risk to be AMD disease. For example, there has a significantly decreased relationship between rs10033900 polymorphism and AMD both in the whole group, Caucasian population and population-based source of control. Moreover, a similar trend in subgroup of genotype method group by MALDI-TOF MS was detected. To classify the type of AMD in further, decreased association was also observed in both neovascular disease and geographic atrophy AMD. No association was found about rs2285714 polymorphism. Our present groundbreaking study suggests that the CFI rs10033900 polymorphism is potentially associated with the risk of AMD development.The complement factor I ( Age-related macular degeneration (AMD) is a retinal degenerative disease that is an important cause of blindness and central vision loss in the elderly who are over 55 years ,2. The icomponent 2 (rs547154 and rs9332739) [component 5 [factor B (L9H) [factor H (Y402H) [component 3 gene polymorphisms and AMD risk and suggested rs2230199, rs11569536, rs1047286 and 2250656 SNPs may be related to AMD development [The complement system is an important mediator of natural and acquired immunity in humans . A dysfuponent 5 , factor  B (L9H)  and fact (Y402H)  polymorphttps://www.ncbi.nlm.nih.gov/gene/3426).CFI gene encodes a serine proteinase that is essential for regulating the complement cascade and is expressed by hepatocytes, macrophages, lymphocytes, endothelial cells and fibroblasts . The encCFI gene is rs10033900 , rs2285714  and rs141853578 . Fagerness et al. first found rs1003390 SNP remained the most highly associated SNP with a P-value of 6.46 × 10−8  for AMD [Three common polymorphisms in CFI gene two common polymorphisms (rs10033900 and rs2285714) and preformed a comprehensive meta-analysis to make convincing conclusions [In view of the foregoing, we realized the vital role of clusions .We searched relative studies from PubMed and Other databases  before February 8, 2020. The keywords were “age-related macular degeneration or AMD,” “polymorphism or variant,” and “CFI or complement factor I.” With these terms, a total of 11 different articles were included from above databases based on our inclusion criteria. Stages of AMD were assigned based on the classification of the Age-Related Eye Disease Study (AREDS) .CFI gene rs10033900 and/or rs2285714 polymorphisms; (b) case–control studies, and (c) adequate numbers of each genotypes  in case and control groups. Studies were excluded if they (a) included no control information; (b) didn't contain genotype frequency data, and (c) were duplicated studies with some other papers.Included studies were according with (a) the correlation between AMD risk and Two authors (Qianqian Yu and Chao Sun) independently screened all papers that according with the selection criteria. These data included the first author’s last name, publication year, country of origin, ethnicity, Hardy–Weinberg equilibrium (HWE) of control group, genotyping method and AMD disease types (neovascular disease and geographic atrophy in AMD). Ethnicity was categorized as Caucasian or Asian. The control subgroups were classified to population-based (PB) and hospital-based (HB).Z test [χ2-square-based Q test. If P-value > 0.10 for the Q test was indicated, a lack of heterogeneity among studies, other words, Mantel–Haenszel (fixed-effects model) was chosen, otherwise, the DerSimonian-Laird (random-effects model) was applied [χ2 test, P < 0.05 was considered significant [P-value < 0.05 was considered significant [http://biostat.mc.vanderbilt.edu/wiki/Main/PowerSampleSize#Windows).Based on the genotype frequencies for cases and controls, odds ratios (OR) with 95% confidence intervals (CI) were used to measure the strengths of associations. The statistical significance of the OR was determined with the Z test . The het applied ,37. We inificant . The funnificant . All stahttp://string-db.org/) [To more complete understanding of the role of CFI in AMD, the network of gene-gene interactions for CFI gene was utilized through String online server (db.org/) .CFI gene rs10033900 polymorphism and total AMD risk and 3 case–control studies about rs2285714 polymorphism and AMD risk. The available clinical information in all publications were shown in Supplementary Table S1. Eleven case–control studies were involved to neovascular disease and geographic atrophy. All case–control studies about rs10033900 polymorphism were consistent with HWE in control groups (https://www.ncbi.nlm.nih.gov/snp/rs10033900): Global (0.495), Europe (0.537), East Asian (0.388), South Asian (0.31), African (0.660), American (0.53) (https://www.ncbi.nlm.nih.gov/snp/rs2285714); Global (0.252), Europe (0.393), East Asian (0.202), South Asian (0.39), African (0.023), American (0.37)  and other databases (247 titles). As shown in l groups . In addin (0.37) A,B. Finan (0.37) . The genP = 0.001 for heterogeneity, P = 0.029; CC vs. TT: OR: 0.75, 95% CI: 0.58–0.97, P = 0.003 for heterogeneity, P = 0.025; CC vs. CT+TT: OR: 0.82, 95% CI: 0.68–0.98, P = 0.039 for heterogeneity, P = 0.028). In subgroup analysis by ethnicity, based on different frequency of races, there had decreased associations between this polymorphism and AMD in Caucasians not Asians in all models . In addition, regular analysis by source of control, also significantly trend were found for this SNP in PB rather than HB studies      . AMD hav= 0.004)   .t = −0.77, P = 0.46 for Egger’s test; z = 0.62, P = 0.537 for Begg’s test, t = 0.68, P = 0.62 for Egger’s test; z = 0.52, P = 0.602 for Begg’s test, The publication bias was evaluated by both Begg’s funnel plot and Egger’s test. At beginning, the shape of the funnel plots seemed asymmetrical in allele comparison for rs10033900 and rs2285714 by Begg’s test, suggesting no publication bias was existed. Then, Egger’s test was applied to provide statistical evidence of funnel plot symmetry. As a result, no obvious evidence of publication bias was observed , it is necessary to study its etiology and mechanism, then to development early diagnostic methods and effective treatments. Nowadays, vascular endothelial growth factor (VEGF) inhibitors are widely recognized as effective drugs in clinical application for CNV (wet AMD) . It is wCFI gene polymorphisms and AMD susceptibility.Next, to identify some novel detection markers and target drugs for different types of AMD is the current and future research focus on the direction. In the introduction section, we have enunciated the genetic factors may help us search potential high-risk group about AMD, which can be prevented and treated in advance. CFB, C2, C3, CFH in complement system has been widely reported. Another molecular CFI remains equivocal. Yang et al. made a meta-analysis that rs10033900 and rs2285714 SNPs had significant associations with AMD risk , whose rThe best part of our analysis is that decreased associations were found about rs10033900 SNP and AMD risk in Caucasians, positive correlations were also observed both in geographic atrophy and neovascular disease subtype. In other words, if individuals carry on CC genotype or C-allele from peripheral blood test, which may indicate that it is possible to have a lower incidence of AMD, on the contrary, individuals carrying T-allele or TT genotype may have a high susceptibility for AMD. Therefore, it should offer us some preventions to intervene, or carry out treatments as soon as possible. To sum up, we wish to use this method to reduce the incidence of AMD and improve the cure rate of early treatment. In addition, the power of present study was 0.76, which suggested our conclusions were relative stable and convincing, which should be included more clinical information to confirm.In addition, in order to identify the network correlation of CFI, the online analysis system-String was applied to predict potential and functional partners related to CFI, which can help us to better understand the value for detection and concern. Finally, ten genes were predicted. Among them, the scores are general high, and eight genes are members in complement system. In addition, researchers have focused on the complement pathways involved in AMD and their preventive/personalized medicine correspondingly ,53.H402, which may contribute to the pathogenesis of AMD [The associations among AMD development and these genes majority involves gene polymorphisms. The highest score of association was CFH (0.999), Harrison et al. suggested the decreased heparin-binding affinity caused by the Y402H polymorphism (a common SNP in CFH gene) may recognize of SCR7s of AMD . C3 genes of AMD ,55. Wangs of AMD . Rs54715CFI polymorphisms and AMD, including age, diabetes, smoking, familial history and hypertension. Third, whether the AMD patients have other complications, such as kidney disease, heart disease, all the included paper have not been reported. Further comprehensive studies should include above information, which may influence the function of CFI gene polymorphisms. Fourth, vision is the most concerned-clinical indicator of AMD, future studies should include the value of the vision and analyze the relationships between CFI polymorphisms and the degree of visual impairment, which may help us better detect disease progression.There are some inherent limitations of our study should be declared. First, further studies should focus on Mixed and African populations, which was vacant in present analysis and need many more studies to consider rs2285714 SNP. Second, gene–gene and gene–environment interactions were not well analyzed. It is possible that specific environmental and lifestyle factors alter the associations between CFI rs10033900 polymorphism may be powerful associated with AMD risk, which may be as a clinical biomarker for detection in the future.In conclusion, our present meta-analysis suggests that Click here for additional data file.


Common small-effect genetic variants that contribute to human complex traits and disease are typically identified using traditional fixed-effect (FE) meta-analysis methods. However, the power to detect genetic associations under FE models deteriorates with increasing heterogeneity, so that some small-effect heterogeneous loci might go undetected. A modified random-effects meta-analysis approach (RE2) was previously developed that is more powerful than traditional fixed and random-effects methods at detecting small-effect heterogeneous genetic associations, the method was updated (RE2C) to identify small-effect heterogeneous variants overlooked by traditional fixed-effect meta-analysis. Here, we re-appraise a large-scale meta-analysis of coronary disease with RE2C to search for small-effect genetic signals potentially masked by heterogeneity in a FE meta-analysis.Our application of RE2C suggests a high sensitivity but low specificity of this approach for discovering small-effect heterogeneous genetic associations. We recommend that reports of small-effect heterogeneous loci discovered with RE2C are accompanied by forest plots and standardized predicted random-effects statistics to reveal the distribution of genetic effect estimates across component studies of meta-analyses, highlighting overly influential outlier studies with the potential to inflate genetic signals.getspres R package entitled from https://magosil86.github.io/getspres/.Scripts to calculate standardized predicted random-effects statistics and generate forest plots are available in the Bioinformatics online. P-values of variants identified in FE meta-analysis to the discovery of additional and potentially novel loci. The RE2 and by extension RE2C random-effects method’s power advantage over traditional fixed and random-effects meta-analysis models is partly attributable to a relaxed null hypothesis, which assumes homogeneity of genetic effects under the null and thereby provides a greater contrast between the null and alternative hypothesesThe conservative nature of the traditional random-effects model (RE), which assumes the presence of heterogeneity under the null, has contributed to the dominance of fixed-effect (FE) meta-analysis methods in the discovery of small-effect variants  . Most usHeterogeneity of genetic effects might arise from biologically relevant differences among contributing studies in a meta-analysis, such as diverse: ancestries, linkage disequilibrium patterns, sub-phenotypes, ages-of disease onset, family-history of disease or gender. Alternatively, differences in the direction and/or size of genetic effect-estimates among participating studies in a meta-analysis could reflect genotyping error or population structure , where, for example, the average genetic effect estimate at a variant of interest is inflated by a few outlier studies showing outsized effects while the majority of study effects are marginal. Heterogeneity at individual variants can be explored through forest plots and the calculation of standardized predicted random-effects (SPREs), while heterogeneity patterns across multiple variants can be conveniently inspected through the calculation of M statistics  of coronary artery disease (CAD) with the RE2C random-effects method, to search for additional CAD loci potentially masked by heterogeneity in the primary FE meta-analysis.n = 1) and Hispanic American (n = 1), East  and South  Asian, Middle Eastern  and European (n = 38); meta-analysis was conducted for a set of ∼ 9 million variants with minor allele frequencies >0.005 (e(odds ratios) from an additive-effects-only association model) in each study were aligned such that the same risk allele was compared across the studies assembled in the meta-analysis. The studies contributing to the CARDIoGRAMplusC4D study obtained ethical approval from the ethics committees of the respective medical faculties, and informed consent was obtained from all participants, summary genetic association data were anonymously meta-analysed and reported here. Membership of the CARDIoGRAMplusC4D Consortium is provided in the www.cardiogramplusc4d.org).Summary data (i.e. logistic regression coefficients and their corresponding standard errors) were collated from 48 genome-wide association studies of coronary disease risk that comprised individuals from 6 different ancestry groups including: African (s >0.005 . Design s >0.005  was apple(odds ratios) and associated standard errors from an additive-effects-only logistic regression association model) from this analysis were downloaded from the www.cardiomics.net server. Design details of the UK Biobank participants to compare with the CARDIoGRAMplusC4D cohorts are included in The UK Biobank study (UKBB) is a large-scale prospective study of over half a million participants commissioned to assemble comprehensive data on genotypic, socio-demographic, lifestyle and environmental factors with the aim of developing better strategies for the prevention, diagnosis and treatment of common diseases . CoronarS studies sth study, and sth study, which can be calculated as Genetic association meta-analyses are typically performed under a RE when the objective is both to estimate a summary effect (i.e. average genetic effect) across studies in a meta-analysis and measure the amount of heterogeneity. Consider a meta-analysis comprising 2.2.1The traditional RE tests the null hypothesis that the average genetic effect, P-values corrected for small sample-size based on the assumption that the studies are equally weighted (i.e. same sample-size). The asymptotic RE2 summary association P-value is denoted by:P-value is given by:In contrast to the traditional RE which assumes the presence of heterogeneity under the null the RE2 model tests the null hypothesis that the average genetic effect is zero and there is no heterogeneity; that is, P-values produced by the RE2 model into two groups assigning variants with RE2 P-value ≤ FE P-value the RE2 summary association statistic, The RE2C approach is an adaptation of the RE2 model designed to: (i) facilitate discovery of small-effect heterogeneous variants and (ii) minimize redundancy between genetic variants identified by the FE and RE2 models; as it is commonplace to perform an FE analysis prior to a random-effects analysis when conducting genetic association meta-analyses. To reduce redundancies between RE2 and FE analyses the RE2C approach partitions summary association P-values the RE2 summary association statistic is decomposed into two component statistics, the first, Q-test of heterogeneity and asymptotically approximates a 50:50 mixture of 0 and K small bins P-value is approximated by:In contrast to the RE2 summary association statistic the RE2C statistic, P), comprising S GWAS V independently associated lead variants SPRE is then computed for each lead variant such that the SPRE for the vth lead variant in the sth study is:Standardized predicted random-effect statistics are precision-weighted residuals that capture the direction and extent with which individual genetic effects of studies in a meta-analysis deviate from the average genetic effect at a variant of interest. Consider a genetic association meta-analysis  measure , yielding 382 loci where lead variants were centered on a genetic distance window of Of 9 455 778 variants in a RE2C meta-analysis of 48 CARDIoGRAMplusC4D studies, 4645 showed genome-wide significant associations with coronary disease (± 0.5 cM .PFE<5×10−8) variants in 46 loci, and an RE2 analysis that afforded 5942 GWAS (PRE2<5×10−8) variants in 406 loci , with at least half of the lead variants showing relatively high levels of heterogeneity (I2>72.1%) (SPREs and generated forest plots to inspect heterogeneity patterns at lead variants of the 382 RE2C loci. Most n = 323) of the lead variants fell in the first category where at least one study showed outsized effects while the majority of the studies showed minimal effects (n = 28) in the second category generally showed heterogeneity patterns with outlier studies showing contrasting effects, in particular the forest plots showed both positive outlier studies P-values became more extreme  with increasing levels of heterogeneity (Most (85.6%) of the lead variants showed marked heterogeneity (Q-statistic 2>72.1%) . Next, wPRE=2.87 . An inspPRE=2.87 . Most   that systematically showed stronger than average effects (Bonferroni-corrected s <0.05) . A meta--history , design -history . We note=88.28%) .PUKBB < 5 × 10−5, P-values that were significant at genome-wide levels (−8) and just 2 of the 24 showed marked heterogeneity (I2>0.5)  from England, Scotland and Wales aged 45–69 years . Only 24(I2>0.5) . Further(I2>0.5) . These fM statistics for 323 RE2C lead variants in a combined CARDIoGRAMplusC4D and UK Biobank meta-analysis confirmed genomic control inflation as a potential source of systematic heterogeneity in genetic meta-analyses  were suggestively associated under the traditional FE method (5×10−8 < −5); and 2  of these were part of the group of RE2C lead variants that replicated in the UKBB analysis while the remaining 19 fell below the replication threshold (PUKBB<5×10−5)   and lower HDL cholesterol (HDL-C) in individuals of East Asian and European ancestry  . Of the 28×10−4) . Notablyn = 331) of lead variants discovered in the CARDIoGRAMplusC4D meta-analysis by the RE2C random-effects method fell outside the scope of tentatively associated CAD risk variants (−5)  and where −5, likely signify substantial heterogeneity of genetic effects at the individual variants rather than novel CAD signals.Altogether, the majority (>5×10−5) . SignifiP  4.2×10−7; P < 2×10−98), a meta-analysis dataset heavily weighted by European (69%), South Asian (20%) and East Asian (7%) data  that shows substantial heterogeneity (Q-statistic 7%) data . Other tSPRE statistics to explore the distribution of genetic effect estimates across participating studies. This can highlight overly influential outlier studies with the potential to inflate genetic signals prompting researchers to reflect upon the underlying data that gave rise to novel heterogeneous associations.Based on our experience of applying RE2C to the CARDIoGRAMplusC4D dataset, we recommend as best practice that reports of small-effect heterogeneous loci discovered with this method be accompanied by forest plots and btz590_Supplementary_DataClick here for additional data file.