Homocysteine, a key component in one-carbon metabolism, is of great importance in remethylation. Many epidemiologic studies have assessed the association between homocysteine and risk of digestive tract cancer, but the results are inconsistent. The objective of our meta-analysis is to assess the association between homocysteine and digestive tract cancer risk. Comprehensive searches were performed on the PubMed, Embase, Cochrane, and Web of Science databases up to September 25, 2018, to identify relevant studies. Thirteen studies were included in the meta-analysis. Odds ratios (ORs) and their corresponding 95% confidence intervals (95% CIs) were used to estimate the strength of the relationship between homocysteine and the risk of digestive tract cancer.P = 0.798, I2 = 0.0%). Moreover, the dose-response analysis revealed that each 5μmol/L increase in homocysteine increased the incidence of digestive tract cancer by 7%. The pooled OR of digestive tract cancer risk for patients with the highest categories of blood homocysteine levels versus the lowest categories was 1.27  with no significant heterogeneity observed ( Generally, our results indicated that elevated homocysteine was associated with higher risk of digestive tract cancer. That is, homocysteine concentration may be a potential biomarker for occurrence of digestive tract cancer. Gastric cancer, ranked as the third leading cause of cancer death, results in 723,000 deaths in both sexes annually all over the world. In total, digestive tract cancer constitutes 22.2 percent of the mortality due to cancer, leading to 1,817,000 cases of cancer death every year [Digestive tract cancers, which refer to malignant diseases of the gastrointestinal tract and mainly including esophageal, gastric, and colorectal cancers, are still major public health burdens. According to Globocan 2012 estimates, the standardized incidences of colorectal cancer, stomach cancer, and esophageal cancer were the 3rd, 5β-synthase [Previous studies have found that DNA methylation, which varies in the development of many cancer types, has a link to cancer . Hypermesynthase . The patsynthase . Deficiesynthase . The relsynthase –12. Addisynthase . Studiessynthase .Homocysteine has been widely studied in terms of cardiovascular disease and has been recognized as a potential risk factor for stroke, coronary vascular disease, ischemic heart disease, and other vascular occlusive diseases , 16. BesBased on the central role of homocysteine in carbon metabolism, its predictive role in cardiovascular disease, and the inconsistency of existing epidemiologic studies and inadequate statistical power in many primary studies, we chose to study the role of homocysteine in digestive tract cancers. The meta-analysis was conducted to assess the evidence from the literature on the relationship between blood levels of homocysteine and the risk of digestive tract cancers and to further to evaluate the dose-response relationship. We hope that we can provide a basis for the use of homocysteine as a tumor marker of digestive tract cancers in the future.We conducted a comprehensive, computerized literature search of the PubMed, Embase, Cochrane, and Web of Science databases for relevant epidemiologic studies with the aim of investigating the connection between homocysteine and digestive tract cancers published on September 25, 2018. The key words used were as follows: (1) gastrointestinal neoplasms, colorectal neoplasms, colorectal carcinoma, colorectal cancer, colonic neoplasms, colonic carcinoma, colonic cancer, colon neoplasms, colon carcinoma, colon cancer, rectal neoplasms, rectal carcinoma, rectal cancer, rectum neoplasms, rectum cancer, rectum carcinoma, esophageal neoplasms, esophageal cancer, esophageal tumor, esophageal carcinoma, gastric neoplasms, gastric cancer, gastric tumor, gastric carcinoma, stomach neoplasms, stomach cancer, stomach tumor, and stomach carcinoma; (2) homocysteine, 2-amino-4-mercaptobutyric acid, hcy, thcy, and hyperhomocysteinemia. In addition, references from the relevant original reports were also scrutinized and hand-searched. No restrictions were imposed. We also made efforts to contact the authors of the primary studies for additional information. Our meta-analysis did not include unpublished reports.Studies were recognized as eligible for our analysis if they met the following criteria: (1) the study design was a case-control or cohort study and investigated the association between homocysteine and digestive tract cancer incidence; (2) articles were written in English; (3) two comparison groups (cancer group vs. control group) were included in the study; (4) the outcome of this review, the incidence risk data of univariate and/or multivariate analyses presented as OR, had a 95% CI when comparing the highest levels of homocysteine with the lowest levels.The exclusion criteria were as follows: (1) the articles only composed of an overview and summary (without data); (2) animal study; (3) the same population in another study; and (4) studies with incomplete data.μmol/L.The two authors independently extracted all the data according to the selection criteria and reached a consensus on all projects. If there was a disagreement, another author would reevaluate these articles. All data was extracted using a standardized data-collection form. Study features were recorded as follows: (1) name of first author and publication year; (2) cancer type; (3) the location of the study conducted and study design; (4) the number of cases and controls; (5) method of measurement; (6) gender and age of cases; (7) source of controls; (8) ORs from the most fully adjusted model for the highest homocysteine compared with the lowest homocysteine and their corresponding 95% CIs; and (9) confounders adjusted for in multivariate analysis. Homocysteine was expressed uniformly as We used a scale called the Newcastle-Ottawa-Scale (NOS) to assess the quality of eligible studies. According to the NOS, studies were evaluated according to 3 aspects, including selection, comparability, and measurement of exposure for case-control studies or outcome for cohort studies. The NOS scale ranges from zero to nine stars. A study with ≥ 6 stars would be regarded as a high-quality study . I2 (inconsistency index) statistic were calculated to quantify the proportion of total variation attributable to heterogeneity between eligible studies [ P < 0.05 corresponded to heterogeneity across eligible studies, and a random-effects model (DerSimonian and Laird method) was appropriate to compute the summary risk estimates. Otherwise, the fixed-effects model (the Mantel-Haenszel method) was preferred. Moreover, I2 values >50% represented strong heterogeneity between studies and I2 values < 25% suggested no significant heterogeneity [ P < 0.05 represented significant publication bias [Odds ratios and their corresponding 95% confidence intervals were used to measure the strength of the correlation between homocysteine and the risk of digestive cancer. The Q-test and the studies . Generalogeneity . We alsoion bias , 35. Allμmol/L increment.To estimate the trend in correlated OR with the elevated concentration of homocysteine, we conducted a dose-response analysis using the method recommended by Greenland and Longnecker  and the By the search strategy, a total of 792 citations were obtained from the PubMed, Embase, Cochrane, and Web of Science databases, of which 37 eligible studies were considered of potential value; then, we retrieved the full text for further evaluation. According to inclusion criteria, 24 of the 37 articles were subsequently excluded from the meta-analysis . Finally, 13 articles , 23–30 . DetailsP = 0.798, I2 = 0.0%). Thus, we utilized fixed-effects models to analyze the association. Significantly increased incidence of digestive tract cancer was observed in patients with high homocysteine levels compared with those with low levels, from the data.The multivariable-adjusted ORs for each study and all studies combined for the highest compared with the lowest categories of homocysteine level are shown in Firstly, in the subgroup analysis of study design, the OR was 1.16  for nested case-control studies and 1.33  for case-control studies. We further analyzed the results by study location: studies conducted in Asia  and America  indicated that higher homocysteine was associated with the increased occurrence of digestive cancer in people living in Asia and America. However, we did not see this effect in Europeans . Additionally, the subgroup analysis of cancer type suggested that colorectal cancer risk was positively associated with homocysteine levels , while the relationship became weaker in esophageal cancer  and gastric cancer . Of the 13 articles, 3 articles measured homocysteine in serum , and 10 articles measured homocysteine in plasma . Additionally, in the subgroup analysis of method of measurement, the positive association was still significant in studies using HPLC , while the association was not significant in studies using GC-MS and FPIA. Details can be seen in μmol/L increase in homocysteine was 1.07    which reTo assess the stability of the results, we performed an influence analysis in which one study was omitted at a time each term and we calculated the pooled ORs for the rest of the studies. The analysis showed that the result was mostly affected by the study of F.-F. Chiang et al. . After e P value of Egger's test was 0.59 (P > 0.05), which indicated that there was no existence of publication bias.Begg's funnel plot and Egger's test were conducted to evaluate the publication bias of the studies included. The shape of the funnel plots seemed symmetrical  and the μmol/L increase in concentration of homocysteine enhanced the chance of developing digestive cancer by 7%.On the basis of the pooled OR of our meta-analysis, people with higher homocysteine were more likely to suffer from digestive tract cancer. According to the influence analysis, we can see that after removing the study of F.-F. Chiang et al., the pooled OR was changed most greatly, but the trend did not change, which indicated the stability of our result. The rest of the article included in our analysis classified the level of homocysteine by three or more categories, but the study of F.-F. Chiang et al. divided homocysteine levels into two categories (higher and lower), which may be the reason why it had significant influence on the pooled results. Moreover, the dose-response analysis further demonstrated the positive effect of homocysteine on digestive tract cancer: each 5When stratified by study design, the positive association between homocysteine and digestive cancer risk was only seen in case-control studies and was weaker in nested case-control studies. Such results can be explained because it is difficult to distinguish the chronological sequence of exposure and occurrence of diseases because of the retrospective nature of case-control studies. Additionally, according to the subgroup analysis of location, elevated homocysteine levels seemed to have more predictive value for digestive cancer in people living in Asia and America. Moreover, in the separate analysis of cancer type, the positive association between homocysteine and digestive cancer risk was weaker in esophageal cancer and gastric cancer. The discrepancy in results could be attributed to the fact that there are limited studies on the relationship between homocysteine, the esophagus and gastric cancer risk, which affected the stability summary effects. Thus, more studies are needed to confirm our conclusion.As we know, runaway gene expression and the unbalanced genome integrity whose major epigenetic mechanism is DNA methylation are considered causes of oncogenesis . DNA metHowever, the mechanisms by which elevated homocysteine enhances digestive tract carcinogenesis have not yet been clearly elucidated. Homocysteine is a non-protein–forming sulfur amino acid whose metabolism is of great importance in remethylation, in which SAM plays the part of a universal methyl donor to various acceptors . A byproAs far as we know, this meta-analysis is the first to explore the relationship between homocysteine and digestive cancer risk by the method of dose-response analysis. Additionally, there was no significant heterogeneity or publication bias observed in our analysis, which suggested that the articles included were compatible for our meta-analysis despite including a variety of studies concerning different populations, living environments, family history, habits, and customs. However, we must acknowledge the limitations of the meta-analysis. First, unpublished studies were not included in our meta-analysis, which can result in publication bias although the statistical data did not reflect this. Second, only studies published in English met our inclusion criterion, so meaningful data may have been ignored. Additionally, only 13 comparisons were contained in the meta-analysis, which might, to a certain extent, have affected the reliability of the results.In conclusion, our meta-analysis suggested that, with higher concentration of homocysteine, the incidence of digestive tract cancer increased despite the limitations of our meta-analysis. Further studies with larger sample size and more integral data are necessary to confirm our finding.


To evaluate the relationship between the aqueous humor levels of VEGF, TNF- PubMed, Web of Science, Embase, China National Knowledge Infrastructure (CNKI), and Wanfang databases were searched up to October 2018. Systematic review and meta-analysis were conducted. α had no correlation with DR and DME, whereas there was a significant relationship between IL-8 and DME ).  18 studies comprising 362 cases with DR (100 with DME) and 620 controls without DR were included in this meta-analysis. There was a significant association between VEGF levels in the aqueous humor and DR (standardized mean difference (SMD) 1.94 (95% CI 1.05-2.83)) and DME ). Furthermore, a significant correlation was observed between levels of IL-6 and DR ), and similarly correlation with DME ). The relationship between MCP-1 and DR and DME was significant, in which the SMD was ) and ), respectively. However, IL-12, IP-10, and TNF- Elevated levels of VEGF, IL-6, and MCP-1 in the aqueous humor were associated with the risk for the presence of DR, and levels of VEGF, IL-6, IL-8, and MCP-1 were associated with the risk of DME. Furthermore, these biomarkers may be used as potential predictors or therapeutic targets for DR/DME. Recentl with DR . Hence, α, IP-10, and MCP-1, are among the possible responsible mechanisms or are involved in the development of DME [Diabetic macular edema (DME) is also one of the most serious causes of visual disability and blindness and affects approximately 14% of patients with diabetes . The patt of DME –9. HowevTaken together, the association between levels of inflammatory cytokines in the aqueous humor and DR is still controversial , 11. Morα, interleukin 6, IL-6, interleukin 8, IL-8, interleukin 10, IL-10, monocyte chemoattractant protein-1, MCP-1), and  or , and , without year restriction. Moreover, an extensive hand search was performed and the additional relevant studies were further reviewed in reference lists. The selection process was according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) flow diagram [We identified the relevant evidences of mediators or cytokines in the aqueous humor among patients with type 2 diabetes mellitus (T2DM) by systematically searching PubMed, Embase, Web of Science, China National Knowledge Infrastructure (CNKI), and Wanfang databases, from inception to October 2018. The following key words were used to perform searches in the databases mentioned above:  or  and DR/DME; (2) it should be written in English or Chinese with the full text available; (3) patients with any age, gender, region or race were considered.The inclusion criteria were as follows: (1) the study should report the correlation between the aqueous humor levels of mediators or cytokines  literature reviews, case reports, and cell lines or animal studies; (2) duplication: same studies came from different databases; (3) no sufficient data to perform meta-analysis; (4) no DM group or no health control group.Two reviewers (Lei Liu and Jingyang Wu) independently reviewed each included study. The disagreements on eligibility during the reviewing were discussed and resolved by the third reviewer (Song Yue). The following data were extracted by three reviewers : the first author, location, year of publication, number of subjects in DR, DME, and control groups, definition of DR, cytokine measurements, and name of cytokines in detail. We defined that diabetic patients without retinopathy and/or matched healthy persons constituted the controls and patients with DR or DME were the cases.The quality of the included studies was assessed using the Newcastle-Ottawa scale (NOS) . A qualiI2 tests were used to evaluate the statistical heterogeneity among the included studies, and the heterogeneity was considered statistical when P < 0.05 and I2 ≥ 50%. Additionally, sensitive analysis was also performed to evaluate the influences of individual studies on the final effect. The funnel plots were performed to evaluate the potential publication bias. This meta-analysis was performed using the Stata 11.0 statistical software . A two-tailed P less than 0.05 was considered as significant difference.The weighted standardized mean difference (SMD) was measured with 95% confidence interval (CI) using random-effects method. The subgroup analyses were conducted based on location, DR definition, and cytokine measurements. The study selection flowchart was shown in 18 case-control studies involving 362 cases with DR including 100 cases with DME and 620 controls with type 2 diabetes but without DR were included in the current meta-analysis. The characteristics of the included studies were presented in I2 = 93.4%, 93.1%, and 97.8%, respectively) after the subgroup analysis .Bending with significant heterogeneity, it was necessary to perform subgroup and sensitivity analyses. Location, DR definition, and cytokine measurements may be the main causes of heterogeneity, but heterogeneity was still significant . Moreover, the funnel plot showed that publication bias was also not significant.There were significant differences in the aqueous humor level of VEGF between the DME and controls with SMD 1.07 , P = 0.028), but with significant heterogeneity , ogeneity . Howeverogeneity .P = 0.010, I2 = 86.4%, P = 0.001) between these two groups. Subgroup analysis showed that cytokine measurements were the reason for obvious heterogeneity . Performing subgroup analysis according to the cytokine measurements, the association was still significant , P = 0.001). The funnel plot showed that publication bias was not significant  in aqueous humor . The publication bias was not significant , us humor  with no nificant .P < 0.001, I2 = 63.9%, P = 0.040). Subgroup analysis was performed according to the cytokine measurements , P < 0.001), and the heterogeneity was not significant . The funnel plot showed that publication bias was not significant , nificant .P = 0.087) for the association between IL-8 levels in the aqueous humor and risk for DR (The pooled SMD was 0.38 (95% CI (-0.05-0.81), k for DR . MoreoveP < 0.001) in IL-8 levels . Dividing into subgroups according to countries, definition of DR, and cytokine measurements, respectively, heterogeneity was still evident . The funnel plot showed that publication bias was not significant , 8 levels . However evident . After rnificant .P = 0.488) in IL-12 levels , 2 levels . The pubP = 0.193, I2 = 20.5%, P = 0.262). The publication bias was not significant.Two studies were included in the meta-analysis for the association of IP-10 level with the risk of DR. The pooled SMD for DR was 0.31 , α and its risk for DR. α for DR was 0.51 (95% CI (-0.04-1.06), P = 0.067) and without significant publication bias.There were 4 studies which involved the aqueous humor levels of TNF-α among six studies, MCP-1 among nine studies, and VEGF among thirteen studies) on the risk of DR or DME. In the current meta-analysis, we found that elevated levels of mediators or cytokines  in the aqueous humor were strongly associated with DR, while the levels of VEGF, IL-6, IL-8, and MCP-1 were strongly associated with DME. Moreover, our meta-analysis presents strong points: the literature review was updated to comprise the latest evidence; a set of inclusion and exclusion criteria were applied for study selection; and outcomes were robust in the sensitivity or subgroup analysis.To the best of our knowledge, the present meta-analysis evaluates the influence of the aqueous humor concentration of inflammatory cytokines  and DME was not evaluated. Hence, further researches are needed for the evidence-based consequence.Although some studies revealed that there are potential associations between the aqueous levels of IL-12, IP-10, or TNF-α and DR , 24, othα and DR , 26, 27.α to provide enough evidences to demonstrate the association between the biomarkers and risk for both DR and DME. Hence, large-sample, longitudinal, population-based studies are needed to validate our findings in the future.The strengths of the current study include the comprehensive exploration of the evidence on the association between the cytokines in the aqueous humor and the risk of both DR and DME. However, there were some limitations in our study. First, there was a lack of studies available on the severity of DR for comparisons. Hence, our meta-analysis could only incorporate studies regarding any DR and controls. Second, we cannot obtain powerful outcomes adjusting potential confounders between the levels of cytokines and DR although we have done the subgroup analysis based on whether included studies had differences between case and control group or not. Last but not the least, there were insufficient studies for some cytokines such as IL-12, IP-10, and TNF-α, and inflammatory cytokines and risk for both DR and DME. Our findings indicated that lower mediator or cytokine levels in the aqueous humor may work best to attenuate DR and DME risk. This meta-analysis was the first comprehensive quantitative assessment of the aqueous humor levels of VEGF, IL-6, and MCP-1 on DR, which suggested that they may be used as biomarkers of the occurrence or development of DR, and reminded a new treatment besides intravitreal injection.In conclusion, we reviewed the literatures and conducted a current comprehensive meta-analysis to evaluate the association between the aqueous humor levels of VEGF, TNF-


Cryptosporidium infection causes gastrointestinal disease and has a worldwide distribution. The highest burden is in developing countries.Cryptosporidium risk factors in Low and Middle Income countries (LMICs).We sought to conduct a systematic review and meta-analysis to identify Medline Ovid and Scopus databases were searched with no restriction on year or language of publication. All references were screened independently in duplicate and were included if they presented data on at least 3 risk factors. Meta-analyses using random effects models were used to calculate overall estimates for each exposure.Cryptosporidium risk, odds ratio 1.06 (95%CI: 0.77–1.47). Breastfeeding was protective with pooled odds ratio 0.4 (95%CI: 0.13–1.22), which was not statistically significant.The most frequently reported risk factors in the 15 included studies were overcrowding, household diarrhoea, poor quality drinking water, animal contact, open defecation/ lack of toilet and breastfeeding. The combined odds ratio for animal contact was 1.98 (95%CI: 1.11–3.54) based on 11 studies and for diarrhoea in the household 1.98 (95%CI: 1.13–3.49) based on 4 studies. Open defecation was associated with a pooled odds ratio of 1.82 (95%CI: 1.19–2.8) based on 5 studies. Poor drinking water quality was not associated with a significant Cryptosporidium cases in LMICs. Future studies investigating Cryptosporidium risk factors should have a good study design and duration, include appropriate number of cases, select suitable controls, investigate multiple relevant risk factors, fully report data and perform multivariate analysis.Based on the included studies, crowded living conditions, animal contact and open defecation are responsible for the majority of  Cryptosporidium is a parasite that causes diarrhoea and is transmitted through faecal contamination of water and food. Though it occurs in developed nations, it is much more prevalent in developing countries and is associated with high mortality in children under 2 years old. In this review, we looked at published studies on factors that increase the risk of contracting cryptosporidiosis in Low and Middle Income Countries. These factors could be targeted to limit the transmission of the disease. Based on the selected studies, the most important risk factors identified were contacts with animals and presence of infected people in the household. Open defecation was also contributing to the risk of infection by this parasite transmitted through the faecal oral route. Breastfeeding was protective from the infection. Poor drinking water was not responsible for causing the disease. Cryptosporidium is a protozoan parasite with a worldwide distribution infecting humans and animals. In high income countries, Cryptosporidium occasionally causes sizable outbreaks due to contaminated water supplies or food sources   software [Population attributable fraction (PAF) was calculated using the formula PAF = Pepooled)] . Pepoolesoftware , enterinCryptosporidium specific risk factors, cryptosporidiosis outbreak/ case report, review, cryptosporidiosis in animals, treatment studies, seroprevalence surveys, detection in water/rivers, and cryptosporidiosis in immunocompromised patients.The combined search retrieved 3830 studies, which was reduced to 3356 after duplicate removal . Based oA total of 15 papers were included for meta-analysis . Table 1Cryptosporidium transmission pathways are: i) person to person , ii) water related , iii) environmental transmission , iiii) inadequate sanitation (lack of toilet and/or open defecation) and iiiii) hygiene . Details of transmission pathways are provided in For each study, odds ratios  for each relevant risk factor were extracted and are detailed in 2 score of 87%.Animal contact was investigated in all 15 included studies. The type of animal species (when available) is provided in 2 = 33%).The impact of non-piped drinking water on cryptosporidiosis was investigated in all 15 studies. Meta-analysis was possible for 10 studies. The combined odds ratio was 1.06 (95%CI: 0.77–1.47), which was not significant . This wa2 = 81%). Despite the relatively moderate combined risk associated with open defecation, all studies consistently showed increased cryptosporidiosis risk.This risk factor was investigated in 7 studies. The combined odds ratio from 5 studies was 1.82 (95%CI: 1.19–2.8) p = 0.006 . HeterogCryptosporidium infection in 7 studies. The combined odds ratio based on 5 studies was 1.37 (95%CI: 1.07–1.75) p = 0.01 (2 = 72%). Only one study reported that overcrowding is protective [Overcrowding was reported as a risk factor for p = 0.01 . Heterogotective .2 = 38%). One study reported that diarrhoea in the household was protective [Household diarrhoea was a cryptosporidiosis risk factor in 4 studies, all of which were included in the meta-analysis. The combined odds ratio was 1.98 (95%CI: 1.13–3.49), which was statistically significant . Heterogotective .2 = 83%). Only one study reported that breastfeeding was conducive to acquiring cryptosporidiosis in infants [Breastfeeding was investigated in 10 studies. Meta-analysis was restricted to 5 studies that provided the required information. The combined odds ratio was 0.4 (95%CI: 0.13–1.22) suggesting a protective overall effect . However infants .For each exposure, publication bias was assessed using funnel plots. All funnel plots had a symmetrical shape suggesting minimal publication bias . CalculaCryptosporidium infection from LMICs based on good quality studies. Although the search strategy retrieved > 3000 papers, only 15 studies were of acceptable quality warranting inclusion. The pitfall of this strategy is the exclusion of relevant risk factors and decreasing the power of meta-analysis by including fewer studies. Nevertheless, this review identified six risk factors that are likely to be the main drivers of Cryptosporidium infection in LMICs. Animal contact had the highest combined odds ratio 1.98 (95%CI: 1.11–3.54), which was statistically significant. This is in accordance with reviews from developed countries where animal contact/ farm visits/ petting zoo visits were significantly associated with acquiring cryptosporidiosis [Cryptosporidium infection risk, pooled odds ratio 1.98 (95%CI: 1.13–3.49), which was also statistically significant. Case contact is understandably a risk factor for transmitting any infectious disease and this is relevant in both developed and developing nations. However, as the majority of Cryptosporidium infections are associated with mild self-limiting symptoms in healthy adults and could be asymptomatic in children, the number of diagnosed cases are substantially underestimated, contributing to further Cryptosporidium transmission unless proper hand hygiene and prevention measures are implemented. Similarly, overcrowded living conditions are associated with an increased risk of Cryptosporidium infection, pooled odds ratio 1.37 (95%CI: 1.07–1.75). Another person to person transmission pathway is children attending nursery. This risk factor was not assessed in many studies and therefore could not be included in the meta-analysis. This was also the case in developed countries, where only a few studies reported that nursery attendance and changing nappies are risk factors for Cryptosporidium infection [This systematic review aimed to identify the most frequently reported risk factors for ridiosis , 11. Dianfection .Cryptosporidium and other gastrointestinal infections. The search strategy was not restricted to focus on WASH. Nevertheless, lack of appropriate sanitation/ open defecation was associated with a significant risk of acquiring cryptosporidiosis, pooled odds ratio 1.82 (95%CI: 1.19–2.8) based on 5 studies. This result is comparable to the systematic review by Speich and colleagues, who reported that lack of sanitation was associated with Cryptosporidium infection risk, pooled odds ratio 1.47 (95%CI: 0.37–5.88) based on 5 studies [Poor WASH  conditions are paramount to the spread of  studies . Interes studies . Open deCryptosporidium infection, however, this was not statistically significant. This was due to the contradicting findings of the included studies and wide confidence intervals. We considered the absence of piped water an indicator of poor drinking water quality. However, this is not necessarily true. The microbiological quality of spring and well water could be satisfactory for the majority of time unless contamination events occur. Indeed, many of the papers retrieved by our search strategy highlighted the increased risk of Cryptosporidium infection in the wet season and/or following extreme rain events. Regular consumption of contaminated drinking water, though not recommended from a public health view, could be associated with building protective immunity [In this systematic review, poor drinking water was not associated with immunity , 30. Furimmunity .Cryptosporidium specific antibodies in breast milk [Breastfeeding (or lack of) was investigated in numerous studies that focused on childhood cryptosporidiosis. Breastfeeding was associated with a protective effect, however, this was not statistically significant. The protection potentially conferred by breastfeeding could be due to the passive immunity acquired through ingestion of ast milk . Additioast milk , most liast milk , which dCryptosporidium cases, explored less than 3 risk factors  or reported their results incompletely or inappropriately for inclusion in meta-analysis. This resulted in a small number of studies for each risk factor, which in turn reduced the power of meta-analyses performed. Additionally, this could have inevitably resulted in the exclusion of some other relevant risk factors for Cryptosporidium infection, that are investigated less frequently and/or not in conjunction with well-known transmission pathways. The main limitations of some of the included studies are the small number of cryptosporidiosis cases and poor quality in terms of study design and duration, number of exposures investigated and data reporting. Another shortcoming was that several papers presented risk factors for diarrhoeal diseases in general without categorisation/ sub group analysis for each etiologic agent. Some did not even seek to diagnose diarrhoeal pathogens. This limits the usefulness of such epidemiological studies and hinders the identification of relevant risk factors and the formulation of specific prevention measures. A heterogeneity between the included studies was noted. While, the majority of studies used diarrhoea free, Cryptosporidium negative control groups, some used diarrhoeal subjects that were Cryptosporidium negative. Both sets were undistinguishably included in the meta-analysis, however, combining them would introduce bias in the overall risk associated with each exposure.The main limitation of this systematic review is the small number of studies included. As strict inclusion criteria were applied, a large number of papers that could have added to the body of evidence were excluded. This was because they had a small number of Cryptosporidium infection in LMICs. Improving sanitation coverage is one of the Sustainable Development Goals and progress is likely to happen despite the high number of people still practising open defecation globally. Animal contact and case contact/ household diarrhoea are relevant for both developed and developing countries and prevention measures should include awareness campaigns and better hand hygiene. Other relevant risk factors could have been omitted from the systematic review because of the paucity of data and poor quality of several studies. Considering the significant morbidity and mortality of cryptosporidiosis in sub-Saharan Africa and South Asia, especially for under 5 years (and HIV+), strategies to reduce the prevalence and burden of cryptosporidiosis and other gastrointestinal opportunistic diseases should be prioritised and offered adequate funding.In summary, this systematic review identified animal contact, diarrhoea in the household and open defecation as the most relevant risk factors associated with S1 File(DOC)Click here for additional data file.S2 File(DOCX)Click here for additional data file.S1 Table(DOCX)Click here for additional data file.S2 Table(DOCX)Click here for additional data file.S1 Fig(TIFF)Click here for additional data file.


Single nucleotide polymorphisms (SNPs) in genes encoding mitotic kinases could influence development and progression of gastric cancer (GC).Case-control study of nine SNPs in mitotic genes was conducted using qPCR. The study included 116 GC patients and 203 controls. In silico analysis was performed to evaluate the effects of polymorphisms on transcription factors binding sites.AURKA rs1047972 genotypes  and rs911160 , were associated with increased GC risk, whereas certain rs8173 genotypes  were protective. Association with increased GC risk was demonstrated for AURKB rs2241909  and rs2289590 . Furthermore, AURKC rs11084490  was associated with increased GC risk. A combined analysis of five SNPs, associated with an increased GC risk, detected polymorphism profiles where all the combinations contribute to the higher GC risk, with an OR increased 1.51-fold for the rs1047972(CT)/rs11084490(CG + GG) to 2.29-fold for the rs1047972(CT)/rs911160(CC) combinations. In silico analysis for rs911160 and rs2289590 demonstrated that different transcription factors preferentially bind to polymorphic sites, indicating that AURKA and AURKB could be regulated differently depending on the presence of particular allele.The AURKA (rs1047972 and rs911160), AURKB (rs2241909 and rs2289590) and AURKC (rs11084490) are associated with a higher risk of GC susceptibility. Our findings also showed that the combined effect of these SNPs may influence GC risk, thus indicating the significance of assessing multiple polymorphisms, jointly. The study was conducted on a less numerous but ethnically homogeneous Bosnian population, therefore further investigations in larger and multiethnic groups and the assessment of functional impact of the results are needed to strengthen the findings.Our results revealed that  AURKA) is involved in regulation of a several oncogenic signaling processes, including mitotic entry, cytokinesis, functions of centrosome, chromosome segregation, and chromosome alignment [AURKB) assists in chromatin modification, spindle checkpoint regulation, cytokinesis and plays a significant role in establishment of the correct kinetochore/microtubule binding [AURKC) acts as a chromosomal passenger protein, participating in the proper centrosome functioning [PLK1) is essential for cell division and regulates various cellular events including centrosome maturation, mitotic checkpoint activation, spindle assembly, kinetochore/microtubule attachment, exit from the mitosis, and cytokinesis [Gastric cancer (GC) represents one of the major causes of tumor-linked death, with geographical and ethnical variations in incidence . Accuratlignment , 7. Auro binding . Aurora ctioning . Polo-liokinesis .AURKA, rs2241909 and rs2289590 in AURKB, rs758099 and rs11084490 in AURKC and rs42873 in PLK1 mitotic checkpoint genes on GC susceptibility in Bosnia and Herzegovina population. In addition, the associations between single nucleotide polymorphisms and the histological types of gastric cancer  have been investigated. By conducting in silico analysis of SNPs, we evaluated the impact of the studied polymorphisms in introns and untranslated regions (UTRs) within candidate genes  on transcription factors binding sites.In this study, using a case-control approach, we estimated the impact of rs2273535, rs1047972, rs911160 and rs8173 in Our examined population consisted of 116 GC patients with diagnosed gastric adenocarcinoma from the Clinical Pathology and Cytology at the University Clinical Center Sarajevo, Bosnia and Herzegovina. General status of gastric cancer patients is given in Table Genomic DNA from FFPE GC tissues was isolated using the Chemagic FFPE DNA Kit special , according to manufacturer’s recommendations. Automated DNA washing and elution was conducted on Chemagic Magnetic Separation Module I robot , following manufacturer’s standard programme. All sample transfers were performed with 4-eye principle to avoid sample mix-ups. DNA from lymphocytes (control DNA) was extracted using the Promega™ Wizard™ Genomic DNA Purification Kit Protocol , in concordance with the manufacturer’s recommendations. The qualitative and quantitative analysis of extracted DNA was conducted by use of the DropSense96 photometer  and Synergy™ 2 Multi Mode Reader .AURKA), rs2241909 and rs2289590 (AURKB), rs758099 and rs11084490 (AURKC) and rs42873 (PLK1). The positions of selected genetic variants in mitotic genes are presented in Fig. https://snpinfo.niehs.nih.gov) [We selected nine polymorphisms in mitotic genes, namely rs2273535, rs1047972, rs911160 and rs8173 . The assay ID numbers are presented in Table http://www.tufts.edu) [P ≤ 0.05 was chosen as threshold value in significance testing. MAF plot was created by use of the PAST software package, version 3.18 (http://folk.uio.no/ohammer/past/) [The genotype frequencies of the investigated variants were tested for Hardy-Weinberg equilibrium (HWE) in the case/control groups separately, using Michael H. Court’s online HWE calculator (fts.edu) . The difr/past/) .Determination of the haplotype block structure and haplotype analysis, which encompassed subsequent corrections for multiple comparisons by 10,000 permutations, were evaluated using the Haploview software, version 4.2 ., and SNwww.ensembl.org/index.html) [Impact of the polymorphic DNA sequences (SNPs in introns and untranslated regions (UTRs)) on transcription factors binding sites (TFBSs) was estimated in silico. Bioinformatic functional evaluation was carried out using PROMO software (ALGGEN web-server), which is utilizing data from TRANSFAC database V8.3 , 16. FASex.html) . IdentifAURKA), rs1047972 (AURKA), rs911160 (AURKA), rs8173 (AURKA), rs2241909 (AURKB), rs2289590 (AURKB), rs758099 (AURKC), rs11084490 (AURKC), rs42873 (PLK1) was determined to be in HWE in both, case and control populations (P > 0.05). When chi-square test and Fisher exact test were conducted for the frequency distributions at the genotypic level, a significant differences for rs911160 in AURKA (P = 0.044), rs8173 in AURKA (P = 0.018), rs2289590 in AURKB (P < 0.001) and rs11084490 in AURKC (P = 0.009) between the cases and controls for all types of GC were observed  CT genotype had a higher risk of GC development in comparison with the reference CC genotype  (Table P = 0.030) and CT vs. reference (CC + TT) genotypes in overdominant model  were associated with higher disease risk (Table AURKA) CC genotype was positively associated with an increased gastric cancer risk in comparison with the reference GG genotype . Also, CC genotype was associated with disease risk in the recessive genetic model (GG + CG) vs. CC genotypes, . However, the confidence intervals in those two cases were wide; therefore, other factors might play a significant role in GC risk in interaction with this polymorphism. Comparison of genotype distributions for rs8173 (AURKA) showed that patients with GG genotype , and CG genotype  had decreased risk of gastric cancer. Analysis of genetic models showed that GG + CG genotypes in comparison with the reference CC genotype in dominant model  and GG vs. reference (CC + CG)  genotypes (recessive genetic model) were associated with decreased GC risk. According to the calculated AIC values, (CC + CG):GG recessive model had more statistical power than dominant model CC:(GG + CG). Analysis of rs2241909 (AURKB) demonstrated that G allele (dominant model: (GG + AG) vs. common AA genotype) was associated with higher risk of GC development . Comparison of the reference AA genotype with AC  and CC  genotypes of rs2289590 (AURKB) also revealed a significant effect of these two genotypes on increased GC risk. CC and AC genotypes in dominant model  as well as CC genotype in recessive model  and AC genotype in overdominant genetic model  were associated with an elevated disease risk. Since recessive genetic model had the lowest AIC value, when compared to the dominant and overdominant models, it was considered to be preferred model. However, in this model the confidence interval was wide, therefore, other factors could influence its effect. For rs11084490 (AURKC) polymorphism, (GG + CG) vs. CC genotypes in dominant model demonstrated statistically significant effect on higher GC risk . Additionally, the five polymorphisms rs1047972, rs911160, rs2241909, rs2289590 and rs11084490, associated with an increased GC risk individually in this study, were subjected to the combined analysis in order to determine polymorphism profiles related to the higher risk of this disease. The results of the synergistic effects of these SNPs are summarized in Table P = 0.034) for the combined rs1047972(CT)/rs11084490(CG + GG) risk genotypes to  for the rs1047972(CT)/rs911160(CC) combination. Another interesting combined effect was demonstrated for five-polymorphisms combination rs1047972(CT)/rs911160 (CC)/rs2241909 (AG + GG)/rs2289590(AC + CC)/rs11084490 (CG + GG). In this case, this combination was significantly associated with an increased GC risk . No significant effects on gastric cancer susceptibility were revealed for rs2273535 (AURKA), rs758099 (AURKC) and rs42873 (PLK1) polymorphisms (P > 0.05), when patients with both types of GC, intestinal and diffuse, were taken into account.Patients with rs1047972 (AURKA) was more frequent in patients with intestinal type  in comparison with the reference CC genotype. Likewise, (TT + CT) genotypes vs. reference CC  and CT vs. common (CC + TT) genotypes  were associated with higher risk for the development of intestinal subtype. According to the AIC values, (CC + TT):CT overdominant genetic model displayed stronger statistical confidence than dominant model CC:(TT + CT). The rs8173 (AURKA), GG genotype, in comparison with the reference CC genotype, was underrepresented in patients with diffuse GC type . Furthermore, both (GG + CG) genotypes as compared to its common CC genotype in dominant model  and GG vs. reference (CC + CG) genotypes in recessive model  were associated with the decreased diffuse type GC risk. In order to discriminate between these two competing models, in accordance with AIC, recessive model represents the preferred model in comparison with the dominant model. In stratified analysis for rs2241909 (AURKB), we found that carriers of AG genotype had elevated risk of developing intestinal type GC as compared to its reference AA genotype . Carriers of (GG + AG) genotypes had more frequently intestinal type of GC when compared to the carriers of the more common AA genotype in dominant model . In overdominant model  individuals with AG genotype had more frequently intestinal type GC in comparison with reference genotypes (AA+GG). According to the calculated AIC values, overdominant model had more statistical power than dominant, therefore it represents the model that better fitted the data. The higher risk for intestinal type GC development was also detected for the patients with CC genotype of rs2289590 (AURKB) . Dominant genetic model revealed that patients with (CC + AC) genotypes when compared to the AA genotype  had significantly more frequently intestinal GC subtype. AC genotype  was more frequently observed in patients with diffuse subtype. Regarding genetic models, (CC + AC) genotypes in dominant model , CC genotype in recessive model  and AC genotype in overdominant model  were associated with the increased risk of diffuse subtype, with a recessive model being the one that best suited the data , however, the confidence interval in this model was also the largest. For rs11084490 (AURKC), dominant model (GG + CG) vs. CC (ref.) genotypes reveled a significant effect of GG and CG genotypes on the higher risk of intestinal subtype .Next, we estimated the effects of genotypes on GC subtypes (presented in Table AURKA), rs911160 (AURKA), rs758099 (AURKC) and rs42873 (PLK1) no significant effect on any of the GC histological subtypes was noted (P > 0.05).For genotypes of rs2273535 . In this regard, the region comprising G allele of rs911160 in AURKA was linked with C/EBPalpha, C/EBPbeta and GR-beta proteins, whereas for C allele, additional binding sites for NF-Y, NFI-CTF and NF-1 were identified , if different alleles, either C or G, were present. The G allele of rs42873 in PLK1 was linked with an additional recognition motif for c-Jun transcription factor.Our in silico analysis suggested that polymorphic sequences in transcription factors binding sites (TFBSs), within AURKA), rs2241909 and rs2289590 (AURKB), rs758099 and rs11084490 (AURKC), and rs42873 (PLK1) mitotic kinases were screened for associations with the genetic susceptibility to gastric cancer (GC) in Bosnian population. We also examined genotype effects of the investigated polymorphisms for each GC subtype.In this study, SNPs rs2273535, rs1047972, rs911160 and rs8173  gene have a protective effect on overall survival in GC patients through decreased HMGB1 mRNA expression levels [AURKA 3’UTR, evaluated in our study, could be associated with an aberrant AURKA expression.Bioinformatic analysis of transcription binding sites reveled that in the case of rs911160 C allele, an extra NF-Y, NFI-CTF and NF-1 transcription factors were detected in comparison with G allele. NF-Y regulates some of the genes enrolled in regulation of cell cycle, which are also deregulated in certain human diseases . NF-1 fa GC risk , and our GC risk . Recent n levels . Thus, iAURKA confers major contribution to the processes, such as centrosome duplication, entry into mitosis and in spindle assembly checkpoint [AURKA overexpression leads to malignant transformation [AURKA have also been reported to exhibit an effect on the risk of cancer onset. Genetic variant rs2273535 was associated with colorectal and lung cancer [AURKA) and GC risk. Polymorphism rs1047972, one of the most investigated variants in AURKA gene, showed significant association with the increased esophagus cancer risk as well as with gastric cancer risk and progression [AURKA gene, rs1047972 and rs2273535 variants are located in exon 3 with high LD amongst them, suggesting that phenotypic effects of both polymorphisms could be consequence of a synergistic act. In addition, it was suggested that rs1047972 could possess a noticeable role in carcinogenesis by alteration of rs2273535 secondary structure and/or function [AURKA gene, suggest that rs1047972 and rs911160 polymorphisms could act as factors which contribute to GC susceptibility, whereas rs8173 variant might be protective factor for GC development.eckpoint . Severalormation . A numbeg cancer , 30. In gression –33. Our gression . AURKA igression . The absgression . Moreovefunction . Our finAURKB) is a subunit of chromosomal passenger complex (CPC), involved in the segregation of chromatids, cytokinesis and modification of histones [AURKB overexpression causes defects in chromosome segregation, aneuploidy and tumor development [AURKB and found a significant association between (AG/GG) genotypes and increased susceptibility to GC. In addition to this, in analyses of genetic models, AG genotype demonstrated an effect on a higher risk of intestinal type GC growth. In an earlier study, rs2241909 showed association with familial breast cancer risk [AURKB mRNA secondary structure [AURKB, rs2289590, demonstrated that CC genotype was associated with higher risk of GC onset. In stratified analysis of GC types, both CC and AC genotypes had an effect on diffuse type GC risk, whereas CC genotype was related to the increased risk of developing intestinal GC subtype. In silico analysis of rs2289590 region revealed binding of additional YY1 transcription factor, if C allele was present.Aurora kinase B  association with an increased GC risk, are in accordance with the findings from our previous study conducted in Slovenian population [The YY1 TF is associated with a cell cycle progression and it has been demonstrated that YY1 expression is with uncontrolled cell proliferation, apoptosis resistance and metastasis, thus acting as an initiator of carcinogenesis . Transcrigenesis . More impulation .AURKC) represents a catalytic chromosomal passenger protein, similarly as Aurora kinase B, which plays essential role mitotic events, segregation and centrosome function throughout meiosis [AURKC overexpression has been described in malignant thyroid cell lines and tissues [AURKC and its potential relationship with gastric cancer risk. A link between CG and GG genotypes and increased gastric cancer risk was observed. Stratified analyses revealed that these genotypes were more common in patients with intestinal type of GC. Polymorphism rs11084490 is situated within the 5’UTR region of AURKC. Eukaryotic 5’UTR various elements and structures e.g. hairpins, RNA G-quadruplexes (RG4s), Kozak sequences around the initiation codons, upstream open reading frames (uORFs) and start codons AUGs, internal ribosome entry sites (IRESs) and iron responsive elements (IREs) greatly influence mRNA translation [AURKC) polymorphism with the increased GC risk in our study could be due to altered AURKC translation mediated by risk genotypes affecting the above mentioned functional motifs in AURKC 5’UTR. Our results demonstrated that rs758099 (AURKC) polymorphism exhibited no effect on GC susceptibility.Aurora kinase C , rs911160 (AURKA), rs2241909 (AURKB), rs2289590 (AURKB) and rs11084490 (AURKC) polymorphisms in gastric tumorigenesis. However, considering different genes included in chromosome segregation process, it is difficult to explain the association of gastric cancer development with an individual polymorphism. Therefore, a combined analysis spanning various gene polymorphisms enables the assessment of gene-gene interactions, and consequently determination of genetic profiles associated with a risk of GC.As reported above, the results of our study demonstrated involvement of the rs1047972 (P < 0.001).In this study, combined analysis of the five polymorphisms and their risk genotypes associated with an increased susceptibility to gastric cancer, rs1047972(CT)/rs911160(CC)/rs2241909(AG + GG)/rs2289590(AC + CC)/rs11084490 influence the higher risk of GC, with an OR increased 1.51-fold for the rs1047972(CT)/rs11084490(CG + GG) to 2.29-fold for the rs1047972(CT)/rs911160(CC) combinations. It is also important to highlight that five-polymorphisms combination rs1047972(CT)/rs911160 (CC)/rs2241909(AG + GG)/rs2289590(AC + CC)/rs11084490 (CG + GG) showed significant effect on an increased GC risk  is essential for cell division and it has been demonstrated that PLK1 with other signal proteins is responsible for mitotic progression and has also been linked to cellular proliferation [PLK1 influence its expression, therefore they could potentially affect cancer risk and progression [PLK1) polymorphism for the assessment of its possible effect on an increased gastric cancer risk, however, our results showed no significant association between rs42873 genetic variant and GC risk.Polo-like kinase 1 (feration . Moreovegression . We seleAURKA (rs1047972 and rs911160), AURKB (rs2241909 and rs2289590) and AURKC (rs11084490) polymorphisms could affect the risk of gastric cancer, both individually and synergistically. Contrary, we found that AURKA (rs8173) polymorphism appeared to be associated with decreased GC risk. Collectively, these findings indicated the existence of the plausible roles of genetic variations in AURKA, AURKB and AURKC in stomach carcinogenesis. Our results could be beneficial in the further investigations of the functional impact of these polymorphisms. The present study is based on a reduced number of cases which represents its limitation, therefore it is important that larger prospective studies confirm our findings.The results of this study revealed that


Finally, a total of eighteen eligible case-control studies were included. BH (Benjamini & Hochberg) adjusted Passociation > 0.05). We also performed subgroup meta-analyses by the source of controls, ethnicity, country, Hardy-Weinberg equilibrium, and genotyping method. Similar results were observed in most subgroup meta-analyses (BH adjusted Passociation > 0.05). Besides, data of Begg’s and Egger’s tests excluded the significant publication bias; while the sensitivity analysis data further indicated the statistical reliability of our pooling results.Compared with the negative controls, no statistically significant difference in the odds of psoriasis was detected for the cases under any genetic models (BH adjusted VDR rs7975232, rs1544410, rs2228570 and rs731236 polymorphisms and psoriasis susceptibility, which still required the support of more case-control studies.The currently available data fails to support a robust association between  VDR gene is mapped on chromosome 12 and contains four common polymorphisms, namely rs7975232 A/C in intron eight (ApaI) rs1544410 G/A in intron eight (BsmI), rs2228570 T/C in exon two (FokI), and rs731236 T/C in exon nine (TaqI) . We then screened a total of 705 records after removing duplicate records from different databases. Next, we excluded an additional 620 records per the exclusion criteria. The detailed information was shown in Fig. Figure BASE n = 42, WOS  led to the utilization of a random-effects pooling model, and a fixed-effects pooling model was utilized for the other genetic models. Pooling results of Table Passociation  =0.640, BH-adjusted Passociation = 0.960], homozygote CC vs. AA , heterozygote AC vs. AA , dominant AC + CC vs. AA , recessive CC vs. AA+AC , and carrier C vs. A . Figure There are a total of thirteen case-control studies with 1654 cases and 1991 controls for the meta-analysis of the Passociation > 0.05, BH-adjusted Passociation > 0.05) except the subgroup of “China” under the carrier model . Additional file VDR rs7975232 polymorphism has no significant influence on the susceptibility to psoriasis.We also performed subgroup meta-analyses based on the factors of control source, ethnicity, country, HWE, and genotyping method. We observed no significant differences between cases and controls in any subgroup , whereas a fixed-effects pooling model was utilized for the others . We did not observe the statistical differences between cases and controls under any genetic model during the overall meta-analysis and subsequent subgroup analysis  with the exception of the “PHWE > 0.05” subgroup under the AA vs. GG + GA model  and “PCR-RFLP” subgroup under the GG + GA vs. GG model . Figure VDR rs1544410 polymorphism seems not to be linked to the psoriasis susceptibility.For VDR rs2228570. A fixed-effect pooling model was utilized for the TC vs. TT , whereas a random-effects pooling model was used for the others . As shown in Table Passociation > 0.05, BH-adjusted Passociation > 0.05). Figure VDR rs2228570 might not be associated with the risk of psoriasis.A total of eight studies involving 1308 cases/1253 controls were enrolled for meta-analysis of VDR rs731236 containing 1690 cases/1857 controls, a random-effect model was used for the allele C vs. T (Pheterogeneity = 0.034), TC vs. TT (Pheterogeneity = 0.043) and TC + CC vs. TT , and a fix-effect model was applied for others . As shown in Table Passociation > 0.05, BH-adjusted Passociation > 0.05). Figure VDR rs731236 polymorphism is not significantly associated with the odds of psoriasis disease.During the meta-analysis of P-value of Begg’s and Egger’s test was greater than 0.05 under all the above genetic models. Additional file We did not observe largely altered meta-analysis estimates in the results of our sensitivity analysis , rather than the name of restriction enzymes in polymerase chain reaction-restriction fragment length polymorphism (PCR-RFLP) assay, namely ApaI, BsmI, FokI, and TaqI. Moreover, six genetic models, including allele, homozygote, heterozygote, dominant, recessive, and carrier models, were employed. BH correction method was also utilized to adjust the P-values obtained from the multiple comparisons.In the current study, we searched eight online electronic databases, including PubMed, EMBASE, WOS, CNKI, WANFANG, OVID, Scopus and Cochrane , to enroll a total of 18 case-control studies. Based on the currently available data, we conducted a series of overall meta-analysis and subgroup analysis to evaluate the genetic relationship regarding VDR rs7975232, we enrolled thirteen case-control studies for pooling and did not detect any significant statistical association between the VDR rs7975232 polymorphism and the odds of psoriasis. In 2012, Lee, YH et al. included six case-control studies [VDR rs7975232 polymorphism and psoriasis susceptibility [VDR rs7975232 polymorphism and psoriasis susceptibility [Passociation = 0.043) but not other genetic models [VDR rs7975232 polymorphism and psoriasis risk [VDR rs7975232 polymorphism in the odds of psoriasis, which is in line with the data of Lee, YH [In our updated meta-analysis of  studies , 21, 24 tibility . Data frtibility , 17 inditibility . In 2013tibility  for an uc models  but addesis risk , 29, 30 rs1544410, rs2228570, and rs731236 polymorphisms, compared with three previous meta-analyses [VDR rs1544410, rs2228570 and VDR rs731236 polymorphisms and psoriasis susceptibility were observed. The conclusions regarding the genetic effect of VDR rs1544410, rs2228570, but not VDR rs731236 polymorphisms on the odds of psoriasis disease were consistent with the pooling results of Lee, YH [VDR rs731236 polymorphism is linked to the risk of psoriasis in the Caucasian population under the recessive model, but not the allele, homozygote and dominant models [VDR rs731236. The potential slight genetic effect of VDR rs731236 polymorphism in the high susceptibility to psoriasis in the Caucasian population was masked by the adding of more sample size, and the utilization of BH correction of P-value. Despite of this, we cannot exclude the VDR rs731236 polymorphism in the odds of psoriasis in the Caucasian population, the support of more case-control studies is required.For analyses , 29, 30  Lee, YH –30. SubgVDR rs2228570 in the overall population. In addition, only one case-control study of the African population [VDR rs7975232 and rs731236 by the factor of ethnicity. Given the lack of sufficient genotype data, we did not detect the potential genetic influence of the other VDR variants (such as rs4516035) or the combined variants of VDR and other relevant genes. Second, high heterogeneity between studies was detected in some analyses of VDR polymorphisms and psoriasis susceptibility. We observed a decreased level of between-study heterogeneity in some subgroups of “Asian” or “Caucasian”, indicating that the factor of ethnicity may be implicated in the source of heterogeneity. Third, conflicting conclusions regarding the potential role of VDR polymorphisms in the partial resistance of psoriasis patients to calcipotriol therapy were reported [VDR polymorphisms and the response of psoriasis patients to drug treatments.In this study, three investigators tried the best to reduce the potential bias during database retrieval, study selection, data extraction, and statistical analysis. However, some limitations should be addressed. First, less than ten case-control studies were included in the meta-analysis of the pulation  is inclureported , 27. We VDR rs7975232, rs1544410, and rs2228570 polymorphisms with the odds of psoriasis. More case-control studies will be of assistance to us to further confirm the effect of the VDR polymorphisms on the psoriasis susceptibility in the Caucasian population.Above all, based on the presently available case-control studies, our pooling analysis data and previous reports do not provide the robust statistical evidence linking Additional file 1: Table S1. Searching terms for our meta-analysis .Additional file 2: Table S2. The clinical characteristics of included case-control studies.Additional file 3: Table S3. Quality assessment of included case-control studies.Additional file 4: Figure S1. The forest plot for VDR rs7975232 polymorphism in the subgroup analysis by ethnicity under the allele model.Additional file 5: Figure S2. The forest plot for VDR rs7975232 polymorphism in the subgroup analysis by the source of controls under the allele model.Additional file 6: Figure S3. The forest plot for VDR rs1544410 polymorphism in the subgroup analysis by ethnicity under the allele model.Additional file 7: Figure S4. The forest plot for VDR rs1544410 polymorphism in the subgroup analysis by the source of controls under the allele model.Additional file 8: Figure S5. The forest plot for VDR rs2228570 polymorphism in the subgroup analysis by ethnicity under the allele model.Additional file 9: Figure S6. The forest plot for VDR rs2228570 polymorphism in the subgroup analysis by the source of controls under the allele model.Additional file 10: Figure S7. The forest plot for VDR rs731236 polymorphism in the subgroup analysis by ethnicity under the allele model.Additional file 11: Figure S8. The forest plot for VDR rs731236 polymorphism in the subgroup analysis by the source of controls under the allele model.Additional file 12: Figure S9. Publication bias of VDR rs7975232 and rs1544410 polymorphism under the allele model. a-b rs7975232 polymorphism; c-d rs1544410 polymorphism.Additional file 13: Figure S10. Publication bias of VDR rs2228570 and rs731236 polymorphism under the allele model. a-b rs2228570 polymorphism; c-d rs731236 polymorphism.


Background and objective: The hepatitis C virus (HCV) is able to cause a life-threatening disease relating to lethal hepatocellular carcinoma. Previous, Toll-like receptor polymorphisms were proposed as promising biomarker for HCV-related hepatocellular carcinoma and disease progression. This study aimed to summarize the association of TLR4 polymorphisms and HCV infection through meta-analysis.Methods: We applied a systematic review and meta-analysis performed by using PubMed, EMBASE and Web of Science searches. The Modified Newcastle-Ottawa scale was used for quality assessment. The odd-ratio (OR) and 95% confidence interval (CI) were calculated to assess the association. In silico analysis was applied for proposing the function as microRNA (miRNA) of non-coding polymorphism. Finally, the miRNA target was predicted and annotated to suggest the possible relationship between polymorphism and HCV infection.Results: Our meta-analysis incorporated seven studies involving rs4986791, rs4986790 and rs2149356. No association exists between rs4986791 and HCV infection. However, the heterozygous model (AG vs GG) of rs4986790 significantly associates with HCV infection . Moreover, the rs2149356 TG genotype also associates with HCV infection in the over-dominant model . In silico analysis of rs2149356G allele showed that this mutation is siRNA, which targets the set of genes, especially in the autophagy pathway.Conclusion: We demonstrated that rs4986790 and rs2149356 are associated with HCV infection. Hepatitis C virus (HCV), which is composed of 6 genotypes and 90 subtypes, causes a disease that is life-threatening to mankind since it is associated with highly lethal hepatocellular carcinoma ,2. The vInnate immunity is a crucial response to pathogens, including HCV infection. Among such innate molecules, Toll-like receptors (TLRs) interact with pathogen-associated molecular patterns such as lipopolysaccharides (LPS), peptidoglycan and viral RNA and DNA. Nowadays, approximately 10 TLRs are identifiable in humans and located in different compartments of the cells. These receptors respond to various pathogens, including both extracellular and intracellular microorganisms. In viral infections, TLR activation promotes antiviral response in an interferon-dependent manner . ParticuIn recent decades, personal medicine has emerged as a novel therapy based on the best response and highest safety of patients. To achieve this regard, gene association studies are required for personalized information. Interestingly, TLR4 single nucleotide polymorphisms (SNPs) have been reported as a risk factor in several infectious diseases such as Malaria, Hepatitis A virus and Hepatitis E virus . At presin silico analysis, that TLR4 rs2149356G is a part of miRNA sequences, which control groups of genes. Our work will provide one piece of the puzzle to filling the gap in knowledge concerning HCV infection.To increase the validity of TLR4 rs4986790 and rs4986791 association with HCV infection, we conducted a systematic review and meta-analysis. Moreover, our study assesses whether there is a relation between TLR4 rs2149356 and HCV. We demonstrated, for the first time by using We performed a systematic review based on electronic literature databases in PubMed, Excerpta Medical Database (EMBASE) and Web of Science. The search was carried out and updated until 26 December 2018. The following keywords were used: ((HCV OR Hepatitis C) AND (variants OR Genetic Polymorphisms OR Genetic Polymorphism OR genotyping OR SNP OR SNPs OR Single Nucleotide Polymorphism OR Single Nucleotide Polymorphisms OR Polymorphisms OR Polymorphism OR Nucleotide Polymorphism) AND ). Two principal investigators independently searched the aforementioned databases and subsequently screened the title and abstract. To decide on the inclusion and exclusion criteria for disagreement, the authors held discussions to resolve issues for final consensus. Related review articles were screened to identify additional data.Selected publications included in our study were: (1) case-control or cohort study on the association of TLR4 gene polymorphisms and HCV infection; (2) Odd-ratio (OR) or relative risk with 95% confidence interval (CI); (3) human study. The exclusion criteria were: (1) Abstract, case-report review, systematic review and other non-original studies; (2) liver transplantation; (3) non-relevant study; (4) co-infection with other microorganisms; (5) lack of data to calculate genetic models; (6) lack of author information to access the full paper in other languages.Two independent authors evaluated the research quality according to the modified Newcastle-Ottawa scale (NOS) for genetic association study . The NOSThe data were extracted by using a data extraction form. The necessary information from the study was independently extracted by two reviews. Briefly, we extracted the name of the first author, the publication year, the number of cases and control, genotype of cases and control, country of study and genotyping methods.P-value for multiple testing via Bonferroni method. The heterogeneous studies were assessed by I2 value and Cochran Q test. I2 with <50% was considered a homogeneous population. Consequently, the pool OR was combined using the fixed-effect model or random effect model depending on the homogeneous population. Moreover, publication bias was tested by funnel plot and Egger’s regression test. Due to the fact that rs2149356 has a limited amount of evidence, we cannot calculate Egger’s regression for this allele. Thus, meta-analysis was performed using MetaGenyo [Hardy–Weinberg equilibrium (HWE) was applied for sample selection bias by using Chi-square test to identify low quality in individual study before pooling. The strength of association between TLR4 polymorphisms and hepatitis C infection were represented as OR with 95% confident intervals. All allelic models  were examined for association by using an adjusted etaGenyo .The chromosome 9 GRGh38p12 sequence was downloaded from NCBI accession NC_000009.12. The TLR4 gene sequence was identified and used to search for the rs2149356 position. The gene was analyzed using miRNAFold to identify potential microRNA (miRNA) precursor in the comparison between T and G nucleotides ,27. VeriThe databases  showed a total of 141 publications relating to TLR4 polymorphisms and HCV after duplicated articles were removed. Thereafter, 134 articles were removed based on those containing unrelated titles, abstracts and full text, resulting in 7 eligible articles. Accordingly, only seven eligible articles were enrolled in this study for quality assessment and meta-analysis  17–20,22,23,34. I2 = 79%, for example). Publication bias was assessed by using both the Egger’s test and funnel plot . Egger’s test showed no obvious publication bias (P=0.6104). Thereby, subgroups analysis was performed via excluded non-HWE study. However, the results were similar to pooled analysis, which showed no statistically significant association (Data not shown).Meta-analysis of rs4986791 involved in four studies showed there were no significant associations in any genetic models between TLR4 polymorphisms and HCV infection . In the P<0.0001 (P=0.0768). The funnel plot appeared to be symmetrical . As aforementioned result, there was a study that it was not agreed with HWE in a control group. Therefore, we applied sensitivity analysis by using sequentially omitting individual study to confirm our meta-analysis . The results did not change after excluded them. It suggested that this meta-analysis was stable.For rs4986790, the pooled analysis showed heterozygous model , signiP=0.00; TG vs TT + GG: OR = 0.53, 95% CI = 0.40–0.72, P<0.0001 . The fThe rs2149356 is located in the un-translational region at 7749 of the TLR4 gene. Therefore, a possible function of this position may relate to miRNA due to the fact that most miRNA sequences are located in the intergenic region or part of introns [The TLR4 polymorphisms are proposed to associate with various diseases such as gastric cancer, prostate cancer, primary open-angle glaucoma and bacterial infection . TLR4 isIn our meta-analysis, we could not detect any association between rs4986791 and HCV infection, suggesting that this position might not be a risk or protective factor. It is possible that the genotyping showed only one case of TT genotype which is a limitation in the genetic model calculation, such as the dominant model. To confirm these proposed, larger studies are required to increase genotype distribution. However, several studies have suggested that this polymorphism is involved in other biological processes of HCV evasion. The heterozygous and homozygous Asp299Gly/Thr399Ile (rs4986790 and rs4986791) polymorphisms are correlated with high viral loading and delayed response to viral therapy ,16. On tin silico analysis suggested that G allele was able to produce miRNA, in which the main functional annotation of targets showed the highest enrichment in the autophagy pathway. This is interesting since HCV induces autophagy to inhibit host innate immunity and cell death [Previous reports have suggested that rs2149356, located in the non-coding region, has functions under-investigated that are associated with the development of HHC. The TLR4 rs2149356 T allele reduces the risk of HHC compared with HCV patients and healthy controls . Moreovell death ,44. We sOur study possesses two hallmarks that are different from previous studies. First, we combined the HHC and chronic HCV as a patient group, including responder and non-responder patients, to compare with healthy control. Therefore, our results were able to represent protective genotypes to HCV infection. Second, our meta-analysis incorporated two studies of rs214956 to increase population size. Altogether with previous studies, we postulated that rs2149356 plays an essential role during HCV-host interaction. TG genotype protected the HCV infection and TT genotype increased the success rate of HCV therapy via peg-IFN-α2b-ribavirin treatment. Finally, T allele of this SNP decreased the risk of HHC.The inadequacies and restrictions existent in this study are significant and should be taken into account for subsequent research concerning similar topics. First, this study only merged seven previous reports concerning HCV infection, including two studies for rs2149356: five analyses of rs4986790 and four investigations of rs4986791. The individual studies carried on small samples which may decrease the power of meta-analysis, especially rs2149356 composed of 391 cases and 430 controls. There are some studies in our meta-analysis that are not based on the HWE. Therefore, in order to enhance the validity of the link with HCV infection and genotype distribution, future studies should consider conducting research employing a larger sampling comprised of a variety of ethnic groups. Additionally, research concerning rs2149356 and rs4986790 should exhibit a comparatively enhanced sample size. Thus, additional research employing a more expansive sample size should be involved as a way to improve validity. Besides, the genotyping methods in our meta-analysis are heterogeneity between realtime-PCR and PCR-RFLP which may affect our results. Several original articles performed the genotyping via only one method, which should be confirmed by others, and did not calculate call rate. Furthermore, additional elements may influence the results of research, such as supplementary gene polymorphisms. Our study could not assess the haplotype between HCV patients and healthy controls. Additionally, we did not analyze the association of TLR4 polymorphisms with other outcomes of HCV infection such as fibrosis, drug treatment and HCC due to lack of sufficient data for eligible analysis.In summary, the studies of TLR4 polymorphisms and HCV are still required in several aspects to verify these phenomena in an epidemiological effort. Especially, the TLR4 gene polymorphisms should be compared between persistence infection and spontaneous viral clearance due to the fact that uncommon cases are self-limitation which occurs approximately 20% . The undin silico prediction. Our study and results may be applied and considered in planning for personal and precise medicine to eradicate HCV.We found the association between TLR polymorphisms and HCV infection from collected publications in related topics, although our study did not show strong evidence due to insufficient material. Our result implied that the rs4986790 decreased a risk of infection with HCV. The rs2149356 TG genotype was a protective factor via the possible mechanism relating to miRNA, which regulated the autophagy partway. This knowledge was rediscovered from a systematic review, meta-analysis and


Bioscience Reports (2019) 39, BSR20180923) recently published a meta-analysis about the association between the K469E (rs5498) polymorphism and risk of coronary heart disease (CHD). Authors included 14 studies based on their inclusion criteria. They indicated that only studies which their genotyping data were in Hardy–Weinberg equilibrium (HWE) were included in their meta-analysis. They also tested HWE for these studies and found all the control groups in HWE. As their main finding, they concluded that ‘K469E polymorphism is associated with CHD risk and the K allele is a more significant risk factor for developing CHD amongst Chinese and Caucasians populations’. However, there seems to be presenting some mistakes in HWE test which strongly affects included studies and the final conclusion. Here we aim to comment on the issue.Yin et al. ( Dear Editor,P-values for HWE test and ineligible studies, based on ‘HardyWeinberg’ package in R programming language (https://cran.rproject.org/web/packages/HardyWeinberg/HardyWeinberg.pdf). Our results were double checked with STATA , and also manually. In manual method, P-value of HWE test was calculated based on four following steps. (i) We calculated allele frequencies in control group: K = [(2 × KK) + KE]/, so E should be E = 1 − K. (ii) We calculated expected genotypes based on allele frequencies: KK = K2 × total, KE = (2 × K × E) × total, and EE = EE2 × total. (iii) We carried out chi-square test between observed and expected genotypes (χ2 = Σ(Ob − Ex)2/Ex). (iv) Finally, results were interpreted based on chi-square routine distribution table (steps (i–iii) are shown in Unfortunately, based on our analysis, contrary to meta-analysis by Yin et al. , studiesup: K = [ × KK + Kup: K = [ × KK + KAfter deleting studies with deviation from HWE and meta-analysis of included articles, we found completely different results. Genotyping data related to seven finally included articles , involvi


In a recent issue of the BMC Public Health journal, Bian et al. described the results of an interesting meta-analysis on the association between dairy products consumption and risk of hip fracture. Although the results are important and valuable, some critical points should be noticed in the explanation of these findings. We prepared these critical points in a letter to the editor and hope to be an interest of you. In a recent issue of the journal, Bian et al. described the results of an interesting meta-analysis on the association between dairy products consumption and risk of hip fracture based on 18 observational studies [First, as we all know, literature search is the most important step in any meta-analysis. Lack of inclusion of relevant studies might influence the whole findings of the meta-analysis. In the study of Bian et al., several publications have been missed and were not included –7. For iIn conclusion, there is a great value in summarizing the relation between consumption of dairy products and risk of hip fracture in a meta-analysis; however, in order to avoid any incorrect conclusions, careful search of literature and data extraction must be done.


Meta-analyses frequently include studies with small sample sizes. Researchers usually fail to account for sampling error in the reported within-study variances; they model the observed study-specific effect sizes with the within-study variances and treat these sample variances as if they were the true variances. However, this sampling error may be influential when sample sizes are small. This article illustrates that the sampling error may lead to substantial bias in meta-analysis results.We conducted extensive simulation studies to assess the bias caused by sampling error. Meta-analyses with continuous and binary outcomes were simulated with various ranges of sample size and extents of heterogeneity. We evaluated the bias and the confidence interval coverage for five commonly-used effect sizes .g, which is a bias-corrected estimate of the standardized mean difference within studies, might lead to larger bias than Cohen’s d in meta-analysis results.Sampling error did not cause noticeable bias when the effect size was the mean difference, but the standardized mean difference, odds ratio, risk ratio, and risk difference suffered from this bias to different extents. The bias in the estimated overall odds ratio and risk ratio was noticeable even when each individual study had more than 50 samples under some settings. Also, Hedges’ Cautions are needed to perform meta-analyses with small sample sizes. The reported within-study variances may not be simply treated as the true variances, and their sampling error should be fully considered in such meta-analyses. I2 statistic is supposed to quantify the proportion of variation due to heterogeneity rather than sampling error  range. We could successfully generate meta-analyses by truncating such improper pi1’s and constraining them to be between 0 and 1; however, this constraint would produce bias, which cannot be distinguished from the bias caused by sampling error that is of primary interest in this article.When the outcome was binary, we first simulated meta-analyses based on the OR. The event numbers on event , and it τ = 0. Thus, the produced CIs might be conservative. Also, the between-study variance was estimated using the popular method of moments by DerSimonian and Laird [For each simulation setting above, 10,000 meta-analyses were generated. The random-effects model was applied to each simulated meta-analysis , even ifnd Laird . The resnd Laird , 52, 53,nd Laird , 55, whid and Hedges’ g), log OR, log RR, and RD, respectively. In addition, N = 5) and the sample sizes were also very small (between 5 and 10) within studies.d and Hedges’ g were almost unbiased. The box of Cohen’s d was slightly larger vertically than that of Hedges’ g when the sample sizes within studies were small, so the point estimates of Hedges’ g were more concentrated around the true SMD. The CI coverage was also close to the nominal 95% level. However, as the true SMD increased from 0 to 0.5 and to 1, both Cohen’s d and Hedges’g began to have bias, and the bias increased as the sample sizes decreased within studies. Cohen’s d generally produced less bias in the estimated overall SMD than Hedges’ g, as shown in d was still close to 95% when the true SMD increased, but that of Hedges’ g dropped below 80% when the sample size was fairly small (between 5 and 10), the true SMD was fairly large (θ = 1), and the number of studies was large (N = 50).When the true SMD was zero in the left panels of τ = 0, N = 5, θ = 1.5, and the sample size of each study was between 5 and 10, the bias of the estimated overall log OR was −0.30 and the CI coverage was 97.8%. The log OR underestimated the true value θ. Among the simulated meta-analyses whose CIs did not cover θ, 2.2% had CIs entirely below θ, while only one meta-analysis (0.01%) had a CI entirely above θ. As the number of studies increased to N = 50 and other parameters unchanged, the bias was still −0.30, but the CI coverage decreased to 82.5%. The CIs of the meta-analyses not covering θ were all below θ. Therefore, the low CI coverage was likely because the CI became shorter as the number of studies N increased while the bias remained.The patterns in τ = 0, N = 5, θ = 0.3, and the sample size of each study was between 5 and 10, the bias of the estimated overall log RR was 0.26 and the CI coverage was 86.2%. The log RR overestimated the true value θ. The CIs of the simulated meta-analyses not covering θ were all above θ. When N increased to 50 and other parameters unchanged, the bias was 0.35 and the CI coverage dropped dramatically to 9.7%. The CIs of the simulated meta-analyses not covering θ were also all above θ.Compared with the log OR, the log RR in d and Hedges’ g), log OR, log RR, and RD. They are plotted using the same idea of the funnel plot for assessing publication bias [yi and their within-study variances yi and its variance In addition, Figures A–F in ion bias , and theThis article has shown that the bias in the overall estimates of the SMD, log OR, log RR, and RD may be substantial in meta-analyses with small sample sizes. The estimated overall MD was almost unbiased in nearly all simulation settings, mainly because its point estimate and within-study variance were independent. However, for the other four effect sizes except the MD, the intrinsic association between their point estimates and estimated variances within studies may be strong, so the meta-analysis results were biased in many simulation settings. Therefore, when the collected studies have small sample sizes, researchers need to choose a proper effect size and perform the meta-analysis with great cautions.d led to noticeably less bias than using Hedges’ g in our simulation studies, although Hedges’ g was designed as a bias-corrected estimate of the SMD within individual studies. For example, in one of our simulated fixed-effect meta-analyses with 50 studies and 5 to 10 samples in each study (the true SMD was 1), the average of Cohen’s d in the 50 studies was around 1.29, while the average of Hedges’ g 1.07 was closer to the true value 1. This was consistent with the fact that Hedges’ g was generally less biased within individual studies. However, the meta-analytic overall Cohen’s d was 0.98, which was much closer to 1 compared with the meta-analytic overall Hedges’ g 0.89, because of the sampling error in these effect sizes’ variances that caused the association between the effect sizes and the variances. Note that, instead of advocating that Cohen’s d is always preferred than Hedges’ g in meta-analyses, this article only reminds researchers that Cohen’s d may be less biased in at least some meta-analytic results, and the argument for the use of Hedges’ g in the presence of small sample sizes needs to be carefully examined.Surprisingly, to estimate the overall SMD, using Cohen’s g besides the one used in our article. Specifically, our simulation studies used gi is the point estimate of Hedges’ g in study i; this calculation was introduced on page 86 in Hedges and Olkin [g is calculated by multiplying Cohen’s d by a bias-correction coefficient; that is, gi = Jidi, where di is the point estimate of Cohen’s in study i. Therefore, the variance of Hedges’ g can be alternatively estimated as d; see, e.g., page 226 in Cooper et al. [g, the combined SMD may remain biased. For example, consider a special case that all N studies in a meta-analysis have the same sample size n, so the bias-correction coefficients in all studies are equal: Ji = J. Using the fixed-effect model, the expectation of the combined Cohen’s d isg isJ is a coefficient always less than 1, we have μg < μd if assuming μd is positive. If the true overall SMD θ is also positive and the combined Cohen’s d underestimates it (as in our simulation studies), then μg < μd < θ, indicating that the combined Hedges’ g is more biased. However, if the combined Cohen’s d overestimates the overall SMD , then the combined Hedges’ g might be less biased.In addition, there are alternative methods to estimate the within-study variance of Hedges’ nd Olkin . Recall This article helps explain the phenomenon of the inflated type I error rates for testing for publication bias. To detect potential publication bias in meta-analyses, it has been popular to check for the association between the study-specific effect sizes and their standard errors using the funnel plot or Egger’s regression test . HoweverMoreover, our findings imply that the magnitude of sample size may not be viewed as an absolute concept in meta-analyses; we may not determine whether a sample size is small or large without taking other parameters into account. For example, using the log OR as the effect size, The bias of the estimated overall log RR was particularly substantial in This article performed the simulated meta-analyses using the popular inverse-of-variance method in a frequentist way. Alternatively, several exact models have been proposed for binary outcomes; they do not require the normal approximation to estimate the study-specific effect sizes and their within-study variances –62. The S1 File(PDF)Click here for additional data file.S2 File(ZIP)Click here for additional data file.S3 File(ZIP)Click here for additional data file.S4 File(ZIP)Click here for additional data file.S5 File(ZIP)Click here for additional data file.S6 File(ZIP)Click here for additional data file.S7 File(ZIP)Click here for additional data file.


PON1) rs854560 polymorphism with plasma lipid levels, but the results are inconclusive. This meta-analysis aimed to clarify the associations of the rs854560 polymorphism with plasma lipid levels.Previous studies have investigated the associations of paraoxonase 1 , Wanfang and VIP database up till August 2018. The pooled standardized mean difference (SMD) with 95% confidence interval (CI) was used to assess the differences in lipid levels between the genotypes. Begg’s funnel plots and Egger’s test were used to examine the publication bias.P < 0.01) and apolipoprotein A-I (APOA1)  than the non-carriers. Subgroup analysis by ethnicity revealed that the effect on HDL level was significant in Caucasians and the subjects of other ethnic origins. No publication bias was detected in this meta-analysis.A total of 41 studies  were identified for the associations of rs854560 polymorphism with plasma lipid levels. The M carriers had lower levels of high-density lipoprotein Cholesterol (HDL-C)  contains supplementary material, which is available to authorized users. Coronary heart disease (CHD) is the leading cause of death in China and most of the developed countries . While tHigh-density lipoprotein (HDL) has been shown to play a critical role in reverse cholesterol transport (RCT) by eliciting cholesterol efflux from macrophage foam cells which prevents the progression of atherosclerotic lesions and induces the regression of existing plaques . HDL-C hPON1 gene is located on the long arm of human chromosome 7 (7q21–22), containing 9 exons and 8 introns. Although some studies have demonstrated that PON1 gene rs854560 polymorphism was significantly associated with CHD [PON1 rs854560 polymorphism and the different plasma lipid levels by using a larger sample size and to put into consideration ethnicity and disease condition particularly atherosclerosis.The with CHD –12, TC [with CHD , 14, LDLwith CHD –15, and with CHD  and apoAwith CHD  while rewith CHD –21. HencA comprehensive search of the literature was carried out by using databases which include Medline, Google Scholar, Web of Science, Embase, Cochrane Library, China National Knowledge Infrastructure (CNKI), Wanfang and VIP database (up till August 2018). The terms “paraoxonase 1” or “PON1”; “polymorphism” or “mutation” or “variant” or “SNP” or “rs854560” or “L55 M”; “blood lipid” or “serum lipid” or “lipids” were used for the search. The variables were limited to TG, TC, LDL-C, HDL-C and APOA1. The languages of the articles were limited to English and Chinese. All references cited in the included articles were reviewed to check for published works that were not indexed by Medline, Google Scholar, Web of Science, Embase, Cochrane Library, CNKI, Wanfang and VIP database.Studies that fulfill the following criteria were included: (1) studies in which the mean serum lipid values and standard deviations (SD) or standard errors (SE) by the rs854560 genotypes were available; (2) data reported on at least one of the five variables ; (3) data reported on fasting lipid variables; (4) pre-intervention baseline data that were used for interventional studies. Reports with incomplete data, studies based on pedigree data, case reports, review articles, abstracts and animal studies were excluded from the meta-analysis.The studies that do not meet the inclusion criteria were excluded after being reviewed independently by two reviewers. All data were double-checked, compared after extraction and disagreements between reviewers were discussed and resolved. From each paper, the following information was collected: first author’s name, year of publication, average age, country, ethnicity, gender, health condition and the mean of serum lipid levels and SD by genotypes. If a paper’s data were unconvincing, we tried to contact the correspondent author by e-mail. All the information was extracted using a standardized data collection form.P-value smaller than 0.05 for any test or model was considered to be statistically significant. Standardized mean difference (SMD) with 95% confidence interval (CI) was used for the meta-analysis. A fixed effect model (a Mantel-Haenszel method) was used to evaluate the results when heterogeneity among studies investigated by Cochrane Q statistic was not significant . Otherwise, the random effect model (DerSimonian and Laird) was used [P-value < 0.05 was considered statistically significant. Possible publication bias was tested by Begg’s funnel plots and Egger’s test using P < 0.05 to indicate the presence of potential publication bias.All statistical tests were two-sided and conducted by the STATA software package . was used . Where tInitial search of the databases yielded 903 articles. Eight hundred thirty-four studies were excluded according to the titles and abstracts. Then full-text articles were retrieved and assessed on the basis of the inclusion criteria. Twenty-eight studies were ineligible for the following reasons: 21 studies presented data for other polymorphisms, 5 studies were based on pedigree analysis, and 2 studies had subjects overlapping with other publications. In the end, 41 studies were selected for this meta-analysis  and APOA1  than the non-carriers (Table P = 0.05) and APOA1  were also detected (Table P = 0.87), TC , and LDL-C   and higher levels of LDL-C  were detected in Caucasians. The rs854560 polymorphism was significantly associated with lower levels of HDL-C  and APOA1  in the subjects of other ethnic origins. The rs854560 polymorphism was also significantly associated with higher levels of TG  in Asians. When health status was taken into account, the significant association of the rs854560 polymorphism with lower levels of HDL-C was detected in the T2DM patients, the healthy/control subjects and the case-control subjects . Six comparisons , 5 comparisons , 5 comparisons , 4 comparisons  and 4 comparisons  were identified as the main contributors to the heterogeneity for TG, TC, LDL-C, HDL-C and APOA1, respectively, by using Galbraith plots. The SMD values and 95% CIs of TG , TC , LDL-C  and HDL-C  did not change substantially after excluding these outlier comparisons. However, SMD value and 95% CI of APOA1  changed significantly after excluding the outlier comparisons.The heterogeneity among studies was significant for the five lipid variables .The Begg’s funnel plot and Egger’s test were used to evaluate the publication bias in the literature. In the present study, Begg’s funnel plot showed no publication bias, and this was confirmed by Egger’s test Additional file 2:Table S1. Characteristics of the individual studies included in the meta-analysis between the PON1 rs854560 polymorphism and plasma lipid levels; Table S2. Plasma lipid levels by the genotypes of the PON1 rs854560 polymorphism. (DOC 387 kb)