Gene expression is negatively regulated by microRNAs (miRNAs), which commonly act as tumor oncogenes or suppressors. Previous results were inconsistent concerning the relationship between polymorphisms in miRNAs and risk of urological neoplasms. Here, we conducted a comprehensive literature research on diverse databases aiming at enrolling all eligible studies up to August 31, 2016. A total of 13 publications comprising 29 case-control studies were enrolled for three polymorphisms in three miRNAs. Overall analyses suggested significant associations between miR-146a rs2910164 polymorphism and urological neoplasms risk in allelic, homozygote and recessive models. In the stratified analysis by ethnicity, we uncovered a significant association between rs2910164 polymorphism and risk of urological neoplasms in Asian populations in allelic, homozygote and recessive models. Highlighted, when stratified analysis was conducted by cancer type, rs2910164 polymorphism was also significantly associated with an increased risk of bladder cancer in allelic, homozygote and recessive models. Although for rs11614913 and rs3746444 polymorphisms, overall analyses suggested negative results, for rs11614913 polymorphism, when subgroup analysis was conducted by cancer type, a significantly decreased risk of renal cell cancer was identified in recessive model. In brief, current work indicated that miR-146a rs2910164 polymorphism is a risk factor for urological neoplasms, particularly for bladder cancer. The etiology and pathogenesis of urological neoplasms have a lot in common. In order to prevent urological neoplasms, it is necessary to investigate the relevant predisposing factors, such as environmental factors, diet, intake of non-steroidal and anti-inflammatory drugs, and endogenous factors, which may affect individual risk of urological neoplasms. In addition, a new mechanism which may regulate transcription by microRNA (miRNA) has been found to be related to various diseases, including cancer2. Among them, the secondary structure of miRNA corresponds to the shape(or topology) induced by all base pairings A-U, G-C, and G-U from the single-stranded molecule. It is thus composed by matched regions, called stems (or helices), and unpaired regions, called loops. A secondary structure is essentially defined by its shape, and more precisely by the configuration of stems and the nature of unpaired bases. Such a shape can be involved in: (1) formation of the tertiary structure; (2) biological functions of the miRNA; (3) interactions with other RNAs or with proteins. Additionally, miRNAs indeed represent a crucial mechanism transcriptionally regulating gene expression. To date, plenty of evidences have indicated that mature miRNAs participated in the degradation or translational suppression of mRNA by linking to the 3′ untranslated region of target genes, and eventually related to the regulation of various critical biological activities, including cell metabolism, proliferation, differentiation, proliferation, and apoptosis, even working as tumor suppressors or oncogenes for the sake of participating in tumorigenesis through posting expression regulation of homologous target genes5. Some key miRNAs, which have been regarded as biomarkers, could ameliorate diagnosis and prediction of prognosis and treatment response for cancer patients6.MiRNAs are a kind of non-coding single chain with 22 nucleotides in length, existing extensively in viruses, plants and animals, which are evolutionarily conserved. MiRNAs have three levels of structure: primary structure, secondary structure, tertiary structure9. It has been proposed that SNPs found in miRNA genes affect miRNA transcription, processing, and interactions with target mRNAs. SNPs in miRNA genes are widely-accepted to affect function in one of three ways: firstly, through the transcription of the primary transcript; secondly, through pri-miRNA and pre-miRNA processing; and also through effects on miRNA-mRNA interactions10. For example, miR-196a gene encodes many mature products including miR-196a and miR-196a*, and it also comprises rs11614913 polymorphism. In a study conducted by Hoffman et al.11, they demonstrated that relative to empty vector control, expression of miR-196a can be increased when transfected with pre-miR-196a-C or miR-196a-T in breast cancer cells. In addition, miR-196a was doubly more expressed in cells transfected with pre-miR-196a-C when compared with miR-196a-T. Namely, rs11614913 polymorphism can influence pre-miRNA processing into mature and functional form, and subsequently activating tumorigenesis. In recent years, plenty of investigators have studied the relationship between genetic polymorphisms in precursor or mature miRNA sequence and diverse cancer risks, including gastric cancer (GC)13, colorectal cancer (CRC)14, liver cancer15, gallbladder cancer (GBC)16, esophagus cancer (EC)17, bladder cancer (BCa)18 and prostate cancer (PCa)19. However, these results were controversial and inconsistent. In current work, we performed an update meta-analysis at the aim of precisely verifying the relationship between genetic polymorphisms in miRNAs and urological neoplasms risk.Single nucleotide polymorphisms (SNPs) are defined as a kind of genetic polymorphisms related to disease susceptibility, population diversity, drug metabolism and genome evolutionWe carried out a literature research for all eligible articles that investigated the relationship of three polymorphisms in miRNAs  with urological neoplasms on Embase, PubMed, Science Direct, and Web of Science  through using the following terms: (miRNA 146a/499/196a2) AND (polymorphism OR variation) AND (carcinoma OR cancer OR neoplasm OR adenocarcinoma OR tumor OR tumour). All the fields of the retrieved articles were screened through titles and abstracts. We also checked reference lists of the review articles and enrolled articles.Eligible studies were included while they met the following inclusion criteria: (1) assessment of microRNA 146a/499/196a2 polymorphisms and risk of urological neoplasms; (2) case-control design independently for human, and; (3) providing useful data of genotype frequencies. At the same time, the exclusion criteria were presented as follows: 1) duplicate data, 2) reports of clinical cases, comments, series, reviews and editorial and 3) insufficient data. Studies published in some other languages instead of English were also excluded. Articles involved with two or more case-control tests were regarded as two or more different studies.We reviewed carefully for each publication, and extracted the following data which conformed to the selection criteria: first author, year of publication, original country, ethnicity of the population studied, genotyping method, source of controls, cancer type, numbers for cases and controls of all genotypes, whether verified Hardy-Weinberg equilibrium (HWE). If original data of genotype frequency was not provided in relevant studies, we could send a request to the corresponding author for additional information.20 . In addition, subgroup analyses were conducted by source of controls, genotyping methods, ethnicity, cancer type and HWE status. Chi-square-based Q-tests were used to check heterogeneity across all the included studies, and P < 0.05 level was considered statistically significant21. If there existed heterogeneity in the included studies, the random-effect model (DerSimonian and Laird method) was performed to assess the pooled OR, otherwise the fixed-effect models (DerSimonian and Laird method) were adopted22. We determined publication bias using Begg’s funnel plot and Egger’s test. Sensitivity analyses were conducted in order to assess the data stability. We removed each study involved in this meta-analysis one at a time, and the residual studies were analyzed. STATA version 12.0 was applied in carrying out all data analyses. Statistical significance was set at the level of P < 0.05.We used crude ORs and 95% CIs to evaluate the relationship of the miRNA polymorphisms and urological neoplasms under five genetic models, including allelic (B vs. A), recessive (BB vs. BA + AA), dominant (BA + BB vs. AA), homozygous (BB vs. AA), and heterozygous (BA vs. AA) models33. The study selection processes were presented in Figs Table 32. Controls of six studies were hospital-based (H-B), while five studies were population-based (P-B). Additionally, genotype frequencies of control groups were consistent with HWE, except for two studies32 , homozygote  and recessive models . Furthermore, in the stratified analysis by ethnicity, we uncovered significant associations between rs2910164 polymorphism and risk of urological neoplasms in Asian populations rather Caucasian in allelic , homozygote  and recessive models . More importantly, when stratified analysis was conducted by cancer type, rs2910164 polymorphism was significantly associated with an increased risk of BCa in allelic , homozygote  and recessive models . As for subgroup analyses by source of control and HWE status, we also found significant associations between H-B and HWE (Y) subgroups and risk of urological neoplasms in allelic, homozygote and recessive models, respectively.Overall analysis suggested significant associations between miR-146a rs2910164 polymorphism and urological neoplasms risk in allelic . While stratification analyses were conducted by source of control, ethnicity and HWE status, null result was uncovered (Table 2).Overall, no significant association was uncovered for rs11614913 polymorphism and urological neoplasms risk. However, when subgroup analysis was conducted by cancer type, a significantly decreased risk of RCC was identified in recessive model , source of controls (Pheterogeneity = 0.007), HWE status (Pheterogeneity = 0.009) and cancer type (Pheterogeneity = 0.009) showed potential sources of between-study heterogeneity. For rs2910164 and rs3746444, no source was observed contributed to the substantial heterogeneity.Both overall comparisons and subgroup analyses identified that heterogeneity was across the studies. Thus, we estimated source of heterogeneity within each model by performing further analyses stratified by source of control, ethnicity, HWE status and cancer type. For rs11614913 polymorphism, ethnicity . For miR-196a2 rs11614913 polymorphism, publication bias was existed both in H-B and HWE (N) subgroups. Therefore, we further conducted sensitivity analyses using the trim and fill method36, and imputed studies provide a symmetrical funnel plot (data not shown), indicating that no publication bias was existed in both H-B and HWE (N) groups.Both Begg’s funnel plot and Egger’s regression tests were conducted to evaluate potential publication bias. In the funnel diagram, if publication bias was not existed, data obtained from each study will be presented with an inverted funnel-like symmetric distribution on the graph, whereas asymmetric inverted funnel graph suggested publication bias. As for miR-146a rs2910164 and miR-499 rs3746444 polymorphisms, neither Begg’s funnel plot nor Egger’s regression showed evidence of publication bias  and their risk of urological neoplasms. Current study was a kind of stratified investigations based on large populations and various cancer types. Significant relationship between miR-146a rs2910164 polymorphism and urological neoplasms risk was found, particularly in Asians instead of Caucasians. Especially, in the stratification analysis by cancer type, rs2910164 polymorphism was significantly associated with an increased risk of BCa. Although for rs11614913 and rs3746444 polymorphisms, overall analyses uncovered negative results, however, for rs11614913 polymorphism, when subgroup analysis was conducted by cancer type, a significantly decreased risk of RCC was identified in a recessive model. In addition, as for Begg’s funnel plot and Egger’s test, the adjusted results indicated that publication bias was not existed.In human, miRNA genes are approximately 50% situated in genomic areas that are frequently related to tumor. Polymorphisms are the most common type of genetic factors within the human genome, which may cause differences of phenotypeet al.35, they indicated that miR-146a rs2910164 B allele is associated with a decreased risk of BCa and PCa in Asians.Several investigators have paid attention to a single polymorphism or several polymorphisms in miRNA and cancer risk. Anyway, none of these meta-analyses have exhaustively enrolled all the available miRNA polymorphisms or commonly studied miRNA polymorphisms. In the current analysis, we covered the previously published eligible publications, and added some updated works. By comparing with previous meta-analysis works, several advantages in current study should be pointed. First of all, a more comprehensive analysis was conducted with large sample sizes, for the purpose of strengthening statistical power and reliability of conclusions. Secondly, we performed various subgroup analysis by ethnicity, source of controls and so on, in order to provide the sources of heterogeneity and the tumor and/or race markers. Thirdly, data in our work was adjusted referring to a commonly used formula in genome-wide association study (GWAS); our conclusions suggested that miR-146a rs2910164 polymorphism was associated with an increased risk of urological neoplasms, particularly for BCa in Asians, whereas in the study conducted by Xiao Nevertheless, there were still several limitations that should be noted in present work. Firstly, for miR-196a2 rs11614913 polymorphism, relatively heterogeneity existed between some studies, although we conducted this analysis with severe inclusion criteria and explicit extraction data. Therefore, after stratified analysis by ethnicity, source of control, HWE status and cancer type, we observed that the subgroup heterogeneity reduced significantly. It can be assumed that the heterogeneity possibly derived from difference of ethnicity, source of control, HWE status and cancer type. Secondly, the small sample sizes of studies may cause low power for data, particularly in subgroup analyses. Thirdly, we did not assess potential interactions due to lack of relevant data across the included studies; this is of extreme significance because interactions between gene-gene and gene-environment may modify kinds of disease risk. Last, our study was in lack of African ethnicities; therefore, more studies based on African populations were needed to be included in our analysis for drawing a more comprehensive conclusion.In summary, our conclusions demonstrate that rs2910164 polymorphism is a risk factor for urological neoplasms in Asians, particularly for BCa. Further studies with larger sample sizes are warranted to clarify the possible roles of these polymorphisms in urological neoplasms.Supplementary Information


The summary adjusted RR of type 2 diabetes associated with residential proximity to major roadways was 1.12 . After excluding two cross-sectional studies, the summary results suggested that residential proximity to major roadways could increase type 2 diabetes risk . Bayesian meta-analysis showed that the unadjusted RR and adjusted RR of type 2 diabetes associated with residential proximity to major roadways were 1.22  and 1.13 , respectively. The meta-analysis suggested that residential proximity to major roadways could significantly increase risk of type 2 diabetes, and it is an independent risk factor of type 2 diabetes. More well-designed studies are needed to further strengthen the evidence.Research indicates that higher levels of traffic-related pollution exposure increase the risk of diabetes, but the association between road proximity and diabetes risk remains unclear. To assess and quantify the association between residential proximity to major roadways and type 2 diabetes, a systematic review and meta-analysis was performed. Embase, Medline, and Web of Science were searched for eligible studies. Using a random-effects meta-analysis, the summary relative risks (RRs) were calculated. Bayesian meta-analysis was also performed. Eight studies  with 158,576 participants were finally included. The summary unadjusted RR for type 2 diabetes associated with residential proximity to major roadways was 1.24 (95% confidence interval [CI]: 1.07–1.44,  The epidemic of type 2 diabetes has increased in both, developed and developing countries, and has become a serious health issue worldwide ,2. ThereRecent studies have focused on the causal role of air pollution in the development of type 2 diabetes, and have found that air pollution increases risk of type 2 diabetes ,13,14,15There is an important need to develop a better understanding of the association between road proximity and type 2 diabetes. In this meta-analysis, we presented a quantitative assessment of the evidence from studies reporting data on the association between residential proximity to major roadways and type 2 diabetes. The present meta-analysis was registered at PROSPERO (CRD42014009214).Embase, Medline, and Web of Science were searched to identify eligible studies. The following key words were used:  and (diabetes OR diabetic). Additional articles were identified by reviewing the reference lists of relevant studies. There was no language restriction. The literature search was performed on 20 September 2016.The following inclusion criteria were used in the meta-analysis: (1) Cohort or cross-sectional studies; (2) Estimating the association between residential proximity to major roadways and type 2 diabetes; (3) Type 2 diabetes was diagnosed by World Health Organization criteria or criteria recommended by countrywide guidelines; (4) Reported relative risks (RR) or other risk estimates for type 2 diabetes. Studies without usable data or those with overlapping data were excluded.For each included study, the following data were extracted: name of the first author, date of publication, study location, exposure type, time of follow-up, number of participants, events of type 2 diabetes, adjusted factors, and RRs with 95% Confidence Interval (CI). Both unadjusted and adjusted risk estimates were extracted, and we would perform meta-analyses on unadjusted and adjusted risk estimates separately. The quality assessment was performed by Newcastle Ottawa scale (NOS) . Methodo2 statistic was used to estimate the heterogeneity, and I2 > 50% indicated high degree of heterogeneity among included studies [7) was used for coefficient parameters, and an inverse gamma distribution  was for the variance. The summary statistics for parameters were calculated after 200,000 iterations . Publication bias was judged by funnel plot and Egger’s test [p-value less than 0.05 was statistically significant.The I studies . The sum studies . We perf studies ,24. Furtr’s test . BayesiaOf 354 identified articles, eight studies from seven articles with 158,576 participants were finally included into the meta-analysis  9,10,11,13,18,192 = 48.1%). The summary unadjusted RR for type 2 diabetes associated with residential proximity to major roadways was 1.24   . 2 = 17.9%). The summary adjusted RR of type 2 diabetes associated with residential proximity to major roadways was 1.12  . However, meta-analysis of those two cross-sectional studies didn’t find an association between residential proximity to major roadways and type 2 diabetes risk .Heterogeneity in the meta-analysis of adjusted risk estimates was low (I = 0.01) . Sensitip = 0.001, I2 = 1.3%). Meta-analysis of those two studies examining residential proximity to roads with high traffic intensity also found that residential proximity to roads with high traffic intensity significantly increased risk of type 2 diabetes . After excluding those studies in which roadway proximity was based on self-report data or how it was defined was not provided, meta-analysis of five studies using the residential address and the public traffic data to define major roadways still showed that residential proximity to major roadways could increase type 2 diabetes risk .There were six studies examining residential proximity to major roadways and two studies examining residential proximity to roads with high traffic intensity . After eBayesian meta-analysis showed the unadjusted RR and adjusted RR of type 2 diabetes associated with residential proximity to major roadways were 1.22 (95% CI: 1.06–1.55) and 1.13 (95% CI: 1.01–1.31), respectively. The outcomes from Bayesian meta-analysis provided further evidence for the association between residential proximity to major roadways and type 2 diabetes risk.p value from Egger’s test was 0.46.Publication bias was not evident in the funnel plot of this meta-analysis , and thep = 0.001). After adjusting for possible confounding factors, residential proximity to major roadways independently increased type 2 diabetes risk . Therefore, the meta-analysis suggested that residential proximity to major roadways could significantly increase risk of type 2 diabetes, and it’s an independent risk factor of type 2 diabetes.The meta-analysis provided a quantitative assessment of the evidence from studies assessing the association between residential proximity to major roadways and type 2 diabetes. It’s the first meta-analysis on this topic and has great reference value for future studies. Eight individual studies with 158,576 participants were finally included, which would provide a reliable quantitative assessment. The findings from the meta-analysis suggested residential proximity to major roadways significantly increased type 2 diabetes risk , which suggested the consistency of the influence of residential distance to main road on type 2 diabetes. In addition, sensitivity analysis found that no single study had obvious influence on the summary RRs, which proved the credibility of the summary risk estimates. Finally, the outcomes from Bayesian meta-analysis provided further evidence for the association between residential proximity to major roadways and type 2 diabetes risk. Thus, the meta-analysis provided strong evidence for the association between residential proximity to major roadways and type 2 diabetes.Heterogeneity in the meta-analysis of adjusted risk estimates was low (I2, PM2.5 and PM10 are main pollutants of traffic-related air pollution, and previous studies have suggested that road proximity is significantly correlated with higher concentrations of particulate matters and NO2 [2, PM2.5 and PM10, can lead to insulin resistance, which may further result in the development of diabetes [2, PM2.5 and PM10 can lead to increased risk of diabetes and diabetes-related mortality [There are several possible explanations for the association between residential proximity to major roadways and type 2 diabetes. Firstly, NO and NO2 ,27,28. Pdiabetes ,29,30. Sortality ,32,33. Iortality ,35,36. Tortality ,38. Becaortality ,39. Indiortality .The impact of residential proximity to major roadways on human heath has gained more and more attentions in recent years ,44,45,46There were several limitations in the meta-analysis. Firstly, a major limitation of the meta-analysis was the inherent biases from observational studies. Both cohort studies and cross-section studies are unable to eliminate the risk of inherent biases caused by residual confounding factors. Some factors were not considered in the exposure assessment of those included studies. There are several other factors needing to be considered to improve the validity of methods used for exposure assessment, such as the method for geocoding addresses, types of traffic, number of years at the residence, types of buildings included or predominating, types of windows and the number of hours at home or away. Future studies considering these factors above in the exposure assessment are needed. Secondly, there were obvious differences in the exposure characterization approach and adjustment factors used in those included studies. Those differences could cause the high-degree heterogeneity across included studies, and could not be ignored when interpreting the pooled results in the meta-analysis. Though both random-effect meta-analysis and Bayesian meta-analysis found a significant association between residential proximity to major roadways and type 2 diabetes risk, the finding of this meta-analysis still needs to be validated by more studies in the future. Thirdly, cohort studies are usually better than cross-sectional studies in exploring risk factors of diseases. However, there were limited numbers of studies assessing the association between residential proximity to major roadways and type 2 diabetes, and thus both cross-sectional studies and cohort studies were considered eligible in the meta-analysis. More cohort studies are needed to further strengthen the evidence. Fourthly, there was risk of information bias caused by the low reliability of self-report data in some included studies. Two included studies used self-report data to define the high traffic intensity, and the reliability of these two reports may be low. However, a sensitivity analysis of five studies using the residential address and the public traffic data to define major roadways still found a significant impact of residential proximity to major roadways on type 2 diabetes risk. Finally, there was a lack of relevant studies from developing countries or non-white population. All those included studies were performed in developed countries. Additional studies from non-white population or developing countries are needed to assess the impact of residential proximity to major roadways on type 2 diabetes risk. In summary, the meta-analysis suggested that residential proximity to major roadways significantly increased risk of type 2 diabetes, and it’s an independent risk factor of type 2 diabetes. More well-designed studies are needed to further strengthen the evidence. In addition, further studies from non-white population or developing countries are also needed.


Increased cell-free DNA (cfDNA) levels in circulating blood have been associated with higher possibility of breast cancer, however, researchers have not reached an agreement on its analysis.We conducted a meta-analysis of 12 retrospective studies to clarify the value of cfDNA quantification in screening and diagnosis of breast cancer. PubMed, EMBASE, Web of Science and Cochrane library were searched from January, 2000 to October, 2016. Pooled analyses were estimated using a random effects model.2 = 86.2%, P < 0.001) in discriminating between breast cancer and healthy controls; the area under the summary receiver operating characteristic (SROC) curve measured 0.91 (95% CI: 0.17~1.00). Analysis of available data in distinguishing breast cancer and benign breast disease showed a pooled DOR of 35.30  with an area under SROC of 0.91 (95% CI: 0.89~0.93). Ethnic group distribution based geographical factors suggested by meta-regression and subgroup analyses explained most of the heterogeneity.In total, 1003 primary breast cancer patients, 283 cases with benign breast disease and 575 healthy individuals were included. Pooled diagnostic odds ratio (DOR) was 27.63 (95% confidence interval [CI]: 10.96~69.61, IQuantification of cfDNA is a promising test in screening and diagnostic of breast cancer, but population-based standardization of test methods require completion prior to clinical use. Breast cancer is the most frequently diagnosed cancer and the leading cause of cancer death among females worldwide . The incCirculating cell-free DNA (cfDNA) is the tumor-derived fragmented extracellular DNA which has been detected in human body fluids. As early as 1977, Leon et al. have reported that breast cancer patients contained increased cfDNA in their serum . With adFour main databases were searched for related studies: PubMed, EMBASE, Web of Science and Cochrane library (from 2000 to October 2016), without language limitations. The combinations of search terms included “breast neoplasms,” “cell-free,” “DNA,” and all of their possible variations. The search strategy was manually adapted according to the citation lists of retrieved articles for sensitivity.Inclusion criteria consisted of studies in breast cancer and availability of diagnostic data, such as area under the receiver operating characteristic (ROC) curve, specificity and sensitivity. No restrictions on methodology or types of the study were included. Case reports, reviews, conference presentations and duplications were excluded. Two independent reviewers, Z. Liu and H. Wang, evaluated the eligibility of studies. Disagreements were resolved by consensus.All the data analyzed were from published papers. Predesigned forms were applied in data collecting. Details listed as follows: first author, year of publication, country, type of study, numbers of cases categorized by age, estrogen receptor (ER), progesterone receptor (PR), human epidermal growth factor receptor 2 (HER-2), tumor stage, and lymph mode metastasis. Sample materials, testing methods, reference genes were collected as well. Diagnostic data was directly extracted from articles or estimated from ROC curves based on the Youden index (sensitivity+specificity-1), as others have published , 18. WheQuality assessment was conducted according to Quality Assessment of Diagnostic Accuracy Studies (QUADAS-2) , 20, whiP < 0.05.Fourfold tables (tables) for diagnostic test were rebuilt according to the primary publications. Pooled analysis for sensitivity, specificity positive likelihood ratio (PLR), negative likelihood ratio (NLR), and area under the ROC curve (AUC) with their corresponding 95% confidence intervals (95% CI) were calculated using the bivariate random effects model. The diagnostic accuracy for discriminating between breast cancer patients and healthy individuals or patients with benign breast disease were presented as diagnostic odds ratios (DORs) with 95% CI. The statistical heterogeneity was tested through the Q statistic and the variation in OR attributable to heterogeneity was evaluated by statistic. The source of heterogeneity was further investigated using meta-regression and subgroup analyses based on regions, time points of sample collection, sample materials, test methods, and reference genes. The test of publication bias was performed according to the methods described by Deeks et al. . SensitiA total of 1385 records were retrieved and 12 studies , 22–30 iEvaluation of the risk of bias and concerns regarding applicability are graphically displayed in P = 0.56) and no small-study effects (P = 0.67) were detected. The pooled sensitivity was 84%  and specificity was 85% [95% CI, 79~90%; 2I = 77.74%, Q = 49.43 (P < 0.01); Figure 2I = 86.2%, P < 0.001; Figure All studies reported data for discriminating between breast cancer and healthy controls, and showed that high levels of cfDNA significantly pointed to breast cancer. The scatter plot showed in 2I = 68.54%, P = 0.02) and 85% , respectively . The area under the SROC curve was 0.91 , in multivariate analysis, “test methods” and “regions” altogether explained 87.09% of the between-study variance. The other factors could not explain the heterogeneity, and details of the calculation were showed in Table To find the source of heterogeneity, firstly, random effects meta-regression analysis was used to assess covariates involved in these studies. Factors including “regions ”, “sample materials (plasma or serum)”, “test methods (polymerase chain reaction (PCR)-based or not)”, “time of sample collection (before or after treatment)”, and “Method of extraction ” were included in univariate analysis. The results suggested that “regions” accounted for most of heterogeneity and explained 82.54% between-study variance. Although “test methods” alone was not responsible for the heterogeneity  in Europe, 22.84  in Middle East, and 195.21  in East Asia. The diagnostic performance in East Asia increased dramatically while the heterogeneity went down to a low level. Pooled analysis of European studies showed moderate heterogeneity, however, the DOR did not indicate fairly good discriminatory test performance. Many other factors still needed to be considered and analyzed in studies from Middle East.The geographical grouping  contained the differences in ethnic and genetic characteristics in these regions, thus, subgroup analysis was used to further evaluate the accuracy of diagnosis test in each region. As shown in Figure Sensitivity analysis, using leave-one-out sensitivity analysis  with a rThe incidence and mortality of breast cancer in less developed countries are still increasing. Early screening and reliable diagnosis are essential for breast cancer treatment. Various investigations , 22–34 h2I < 30.0%), and studies from China [2I = 0%), both of which used real-time quantification PCR. Four studies from Middle East were done in Egypt and Israel, and there remained moderate heterogeneity within this subgroup. The major covariate affecting these trials was “testing method”: Hadshad et al. [Sources of heterogeneity was evaluated by meta-regression and subgroup analyses. Nearly 90 percent of the heterogeneity could be explained by the mixed effect of “regions” and “test method”, and the former is the main factor. Subgroup analysis provided more details of the ethnicity-based regional grouping. The heterogeneity between three studies from East Asia , 27, 29 om China , 27 had d et al.  and Mahmd et al.  quantifid et al.  to measun = 12 vs. n = 9). Our comprehensive literature search was supported by a lack of evidence for publication bias through Deeks and Harbord test. Although significant heterogeneity was observed in their study, Lin et al. demonstrated that none of the methodological covariates (“country” and “assay methods”) produced major heterogeneity (P > 0.05) in meta-regression analysis. This may be caused by the loose inclusion criteria, which introduced too many influencing factors. Moreover, among the data from Kohler et al.'s study [P < 0.001) for distinguishing BC from patients with benign breast diseases.Recently, Lin et al.  publishe's study , Lin et However, there remain limitations in our meta-analysis. Firstly, due to the restriction of systematic review and meta-analysis, only population-level data could be extracted; more correlation of subtypes defined by ER, PR or HER-2 as well as lymph node metastasis could not be further analyzed. Secondly, there remained about 1need a space: 10% heterogeneity which did not have clear source. At the pre-analytical phase of cfDNA quantification, the methods of cfDNA extraction and the following assessment lacked standard protocols, which may lead to heterogeneity .In conclusion, this meta-analysis suggests that the quantification of cfDNA can be a potential biomarker for accurately discriminating BC patients from healthy individuals. Sensitivity analyses using various criteria to improve the quality of included studies or reduce the systematic errors in the process of calculation did not alter the results substantially, suggesting that the results of our meta-analysis are robust. Combination of cfDNA quantification and other biomarkers maybe a future direction in BC early diagnosis.


A total of twelve published articles, involving 3996 cases and 8876 controls were included in this meta-analysis. The pooled results from rs1501299 polymorphism showed decreased risk in homozygote model . Heterogeneity was detected in our study. Sensitivity analysis and subgroup analysis were conducted in the meta-analysis. For rs2241766 polymorphism, an increased risk was detected in Caucasian subgroup in heterozygote model . In genotyping method (PCR-RFLP) subgroup, an increased risk was found in recessive model . In the sensitivity analysis of rs1501299, decreased risk was detected in allelic model  and recessive model . Publication bias is not observed in our results. Our meta-analysis suggests that the rs1501299 polymorphism may play a protective role in CAD in diabetic patients. The rs2241766 polymorphism is found to be associated with a significant increase in CAD risk in Caucasian and genotyping method (PCR-RFLP) subgroups. Further studies are needed to confirm the prediagnostic effect of the two gene polymorphisms in CAD risk in diabetic patients.ADIPOQ gene polymorphisms were indicated to be associated with coronary artery disease (CAD) in diabetic patients, however, published studies reported inconsistent results. We performed this meta-analysis to reach a more accurate estimation of the relationship between two common ADIPOQ genetic polymorphisms (rs2241766 and rs1501299) and CAD risk in diabetic patients. Eligible studies were retrieved by searching PubMed, Embase, Wangfang, VIP database and China National Knowledge Infrastructure databases. Included and excluded criteria were formulated. The case group was diabetic patients with CAD, and the control group was diabetic subjects without CAD. Summary odds rations (ORs) and 95% confidence intervals (CIs) were used to evaluate ADIPOQ polymorphisms associations with CAD risk in diabetic group. Heterogeneity was evaluated by Q statistic and I Type 2 diabetic mellitus (T2DM) is the predominant type of diabetic mellitus which is a complex metabolic disorder with hereditary and environmental factors. CoronaryThe gene coding for adiponectin, ADIPOQ, is located on chromosome 3q27, which is the susceptible locus for CAD and T2DM., 11 A wiThe polymorphisms of rs2241766 and rs1501299 are two common single nucleotide polymorphisms of ADIPOQ. The rs2241766 polymorphism is a T/G substitution in exon2 and the rs1501299 is a G/T substitution in intron2. The influence of the two SNPs on CAD have been investigated in patients with T2DM,  and Fili214 articles were obtained by online and manual search. After removing duplicates and screening title and abstract, eighty articles were included. Seven articles were excluded for lack of detailed genotype distribution. Finally, a total of twelve published articles, , 29–34 iThe characteristics of all the included articles are summarized in Table h=0.001) in the subgroup of sample size(more than 500)Figure . In the 5)Figure .The results of associations between ADIPOQ rs150299 polymorphism and CAD risk in type 2 diabetic subjects were showed in Table rs1501299=0.088; Prs2241766= 0.799) and CAD risk in diabetic patients , which implies different genotyping method may have an influence on the accuracy of genetic analysis. In the subgroup of sample size (more than 500), a relative low risk of GG genotype was detected, which needed further research to confer whether the sample size may have an effect on associations between rs2241766 polymorphism and CAD with T2DM. In the study by Mofarrah and colleagues, rs2241766 of ADIPOQ had a significant association with CAD and G allele carriers had an increased adjusted risk ratio of developing CAD. Besides,For the rs1501299 polymorphism, pooled result was only significant in homozygote model, which implied a 33% decreased risk of TT genotype in the developing of CAD in T2DM subjects. When the study of Mohammadzadeh et al. was remoIn haplotype analysis, the haplotypes 45T-276T and 45G-276T in compared to the referent haplotype 45T-276G was indicated a protective effect reported by Esteghamati et al., howeverThere were several limitations in this meta-analysis. First, only English and Chinese articles were included in our study, which thus may bias the results. Second, patient heterogeneity and confounding factors might have distorted the analysis. Third, there was significant heterogeneity in some of the pooled analyses, which may have affected the meta-analysis results, even though we adopted the random effects model. Fourth, the number of included studies was relatively small in some subgroups, thus results should be interpreted with caution. In addition, the potential influence on genotype-CAD associations by environment factors is worthy of consideration.In conclusion, Our meta-analysis suggests that the rs1501299 polymorphism may play a protective role in CAD, and the possible protective role in T allele and TT genotype in CAD patients with T2DM needs more researches. The rs2241766 polymorphism is found to be associated with a significant increase in CAD risk in Caucasian subgroup based on our analysis. Further studies are needed to confirm the prediagnostic effect of ADIPOQ gene polymorphisms in CAD risk in diabetic patients.The systematic review was written in adherence to the PRISMA  checklist. Ethical Embase, PubMed, Wangfang, VIP databases and China National Knowledge Infrastructure databases were thoroughly searched in June 2016 by the first two investigators to identify potential studies addressing the associations between the ADIPOQ polymorphisms and coronary artery disease. The terms “coronary artery disease,” “coronary heart disease,” “ADIPOQ,” “APM,” “diabetic mellitus,” “T2DM,” “variant,” “polymorphism,” and “polymorphisms” were used. The missing data (the data that we failed to identify during the electronic search) were obtained by reviewing the citations of review articles and all eligible studies.Studies in the meta-analysis must meet the following inclusion criteria: (1) evaluation of the association between ADIPOQ polymorphisms and coronary artery disease in diabetic patients. Subjects with T2DM were based on American Diabetes Association (ADA). The case group was T2DM patients with CAD, the criteria for CAD, conformed by coronary angiography, was ≥50% stenosis of at least one segment of a major coronary artery or its main branches. The control group consisted of T2DM subjects with normal exercise tolerance test and negative history of CAD; (2) case-control study or cohort design; (3) detailed genotype data could be acquired to calculate odds ratios (ORs) and 95% confidence intervals (CIs); Exclusion criteria: (1) duplication of previous publications; (2) comment, review and editorial; (3) study with no detailed genotype data. The selection of the studies was achieved by two investigators independently, according to the inclusion and exclusion criteria by screening the title, abstract and full-text. Any dispute was solved by discussion.From each study, the following data were independently extracted by the first two investigators using a standardized form: first author's last name, year of publication, study country, ethnicity, region, genotyping methods, Hardy–Weinberg equilibrium, number of cases and controls, genotype distribution in cases and controls for ADIPOQ, quality score. Different ethnicity descents were classified as Caucasian and Mongoloid. Disagreements were resolved through discussion with a 3rd investigator.The first two authors independently assessed the quality of the included studies, according to a set of criteria  modified2 statistic (greater than 50% as evidence of significant inconsistency). To adjust for multiple comparisons, we applied the Bonferroni method to control the false discovery rate (FDR) [Hardy–Weinberg equilibrium (HWE) was evaluated for each study by Chi-square test in control groups, and P < 0.05 was considered as a significant departure from HWE. Odds ratio (OR) and 95% confidence intervals (CIs) were calculated to evaluate the strength of the association between ADIPOQ polymorphisms and CAD in T2DM subjects. Pooled ORs were performed for allelic model , heterozygote model , homozygote model , dominant model , recessive model , respectively. The statistically significant level was determined by Z-test with P value less than 0.05. Heterogeneity was evaluated by Q statistic (significance level of P < 0.1) and Ite (FDR) , 27.2 test, and a higher I2 values means higher levels of heterogeneity . In heterogeneity evaluation, when the I2 < 50%, the fixed-effects model would be used; if the I2 =50% to 90%, a random-effects model was used; if the I2 > 90%, the studies would not be pooled.[Heterogeneity between studies was evaluated with the Ie pooled. If heter


Health care-associated infections (HAI) have been shown to increase length of stay, the cost of care, and rates of hospital deaths :306–11, 2014; Roberts and Scott, Med Care 48(11):1026–35, 2010; Warren and Quadir, Crit Care Med 34(8):2084–9, 2006; Zimlichman and Henderson, JAMA Intern Med 173(22):2039–46, 2013). Importantly, infections acquired during a hospital stay have been shown to be preventable . In particular, due to more invasive procedures, mechanical ventilation, and critical illness, patients cared for in the intensive care unit (ICU) are at greater risk of HAI and associated poor outcomes. This meta-analysis aims to summarise the effectiveness of chlorhexidine (CHG) bathing, in adult intensive care patients, to reduce infection.Staphylococcus aureus (MRSA); vancomycin-resistant Enterococcus; and Clostridium difficile. Summary estimates were calculated as incidence rate ratios (IRRs) and 95% confidence/credible intervals. Variation in study designs was addressed using hierarchical Bayesian random-effects models.A systematic literature search was undertaken to identify trials assessing the effectiveness of CHG bathing to reduce risk of infection, among adult intensive care patients. Infections included were: bloodstream infections; central line-associated bloodstream infections (CLABSI); catheter-associated urinary tract infections; ventilator-associated pneumonia; methicillin-resistant Seventeen trials were included in our final analysis: seven of the studies were cluster-randomised crossover trials, and the remaining studies were before-and-after trials. CHG bathing was estimated to reduce the risk of CLABSI by 56% , 0.26, 0.75)), and MRSA colonisation and bacteraemia in the ICU by 41% and 36%, respectively ; and IRR = 0.64 ). The numbers needed to treat for these specific ICU infections ranged from 360 (CLABSI) to 2780 (MRSA bacteraemia).This meta-analysis of the effectiveness of CHG bathing to reduce infections among adults in the ICU has found evidence for the benefit of daily bathing with CHG to reduce CLABSI and MRSA infections. However, the effectiveness may be dependent on the underlying baseline risk of these events among the given ICU population. Therefore, CHG bathing appears to be of the most clinical benefit when infection rates are high for a given ICU population.The online version of this article (doi:10.1186/s13054-016-1553-5) contains supplementary material, which is available to authorized users. Health care-associated infections (HAI) have been shown to increase length of hospital stay, the cost of care, and rates of hospital deaths –4. ImporStaphylococcus aureus (MRSA); vancomycin-resistant Enterococcus (VRE); and Clostridium difficile (C-diff).The skin of patients is considered a major reservoir for pathogens associated with hospital-acquired infections , and hasThis meta-analysis was planned, undertaken, and reported using the Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA) guidelines .A systematic literature search was undertaken of medical literature databases including MEDLINE, EMBASE, and Cochrane Library published up until March 2016. Keywords and title searches included a combination of: “Chlorhexidine”, “bath$”, “intensive care”, “prevention”, “infection$”, and “effectiveness”. Hand searching of the references of research papers was also undertaken until no new studies were identified.The eligibility for inclusion of research papers into in this meta-analysis was considered independently by two authors (SAF and M-CA). Only trials of the effectiveness to reduce infections in adult ICU patients were included. Review papers, non-adult populations, non-ICUs, and papers that did not report the rates per ICU-days at risk were excluded.Data extracted from each paper included: first authors’ names and publication year, country of study, duration of study, study site, study design  type of ICU setting , infection of interest, and number of events and ICU-days at risk.I2 statistic and p < 0.1 was chosen as evidence of statistical heterogeneity. Initial analysis identified the presence of statistical heterogeneity, and therefore summary estimates for fixed effects and random effects (RE) models are presented [Individual study and combined estimates of the effectiveness of CHG bathing to prevent infections (incidence rate ratios (IRRs)) are presented as forest plots . Heterogresented .p values to assess statistical significance; in a similar manner to the use of confidence intervals (CIs), credible intervals (CrIs) are used to identify the significance of results. For instance, an estimate of effect as a ratio (i.e. a relative risk) with a 95% CrI that does not include the null (1.0) is considered statistically significant. To assess the potential role of baseline risk for the observed heterogeneity between individual studies, a Bayesian meta-regression approach was also used [Because of the inclusion of both before-and-after and randomised-cluster crossover trials, summary estimates are also presented using the method suggested by Sutton and Abrams . This aplso used .In this approach, any regression estimates exhibiting a negative relationship between baseline risk and treatment effect are showing that treatment benefit increased when baseline risk of infection was higher among the given ICU population. Traditional meta-analysis models were developed using the R metafor and meta packages . BayesiaThe electronic search resulted in 114 potential papers to be included. Following review of the abstract, or the complete paper when required, 17 before-and-after or randomised-cluster crossover trials were included in our analysis. Reasons why papers were excluded from our final analysis were due to being them being review papers, not among adult ICU patients, and trials that included multiple interventions. Characteristics of the 17 studies are presented in Additional file Summary estimates of the effectiveness CHG bathing to reduce infections among adult ICU patients are presented in Table Daily bathing with CHG was estimated to reduce BSI in the ICU by approximately 20% . Summary estimates for specific study designs were IRR = 0.86  and IRR = 0.79  for before-and-after and RCT studies respectively. Meta-regression of the relationship between risk among the control group and effectiveness of CHG bathing . Summary estimates for specific study designs were IRR = 0.47  and IRR = 0.50  for before-and-after and RCT studies respectively. From meta-regression of the relationship between risk among control group and effectiveness of CHG bathing . Summary estimates for specific study designs were IRR = 0.77  and IRR = 1.19  for before-and-after and RCT studies respectively. From meta-regression of the relationship between risk among control group and effectiveness of CHG bathing . Summary estimates for specific study designs were IRR = 0.98  and IRR = 0.89  for before-and-after and RCT studies respectively. From meta-regression of the relationship between risk among control group and effectiveness of CHG bathing Table , slope =Daily bathing with CHG was estimated to reduce MRSA colonisation and bacteraemia in the ICU by approximately 41% and 36%, respectively . For MRSA colonisation, summary estimates for specific study designs were IRR = 0.52  and IRR = 0.68  for before-and-after and RCT studies respectively. From meta-regression of the relationship between risk among control group and effectiveness of CHG bathing to prevent MRSA colonisation . From meta-regression of the relationship between risk among control group and effectiveness of CHG bathing to prevent VRE colonisation . Summary estimates for specific study designs were IRR = 0.0.98  and IRR = 0.89  for before-and-after and RCT studies respectively. From meta-regression of the relationship between risk among control group and effectiveness of CHG bathing .Relative risk, median baseline risk during control periods, risk with treatment, absolute risk reduction, and number needed to treat (NNT) to prevent a single event are presented in Table This meta-analysis presents a summary of the estimated benefit of CHG bathing to prevent infection in the ICU. CHG bathing was most effective for the prevention of CLABSI among ICU patients, demonstrating a 56% reduction. However, the magnitude of benefit is affected by the underlying risk of CLABSI among ICU populations. Even among an average risk group of five CLABSI per 1000 central-line-days, 360 patients will need to be bathed with CHG to prevent a single event. If the underlying risk of CLABSI is only 1 per 1000 central-line-days than the NNT increases to 1780. Effectiveness was also shown for reducing MRSA colonisation and MRSA bacteraemia. However, even among average baseline-risk populations, the NNT is approximately 600 and 2800, respectively. Because of varying study designs , there remains uncertainty in the effectiveness of CHG-B to prevent other infections among adults in the ICU.Previous reviews of daily CHG bathing to reduce infections in the ICU have been undertaken –24, and A potential strength of our meta-analysis is the inclusion of a wide range of infections associated with an ICU stay. In particular, we have attempted to account for the uncertainty of average estimates by using a Bayesian approach to account for variation due to study design. We have also presented the potential clinical effectiveness of CHG bathing for various ICU infections by estimating the absolute rates of infection with treatment and therefore the absolute risk reduction and NNT.Any systematic review and meta-analysis has a potential weakness of missing unpublished trials, and potential individual trial heterogeneity that is difficult to account for in analysis. It is obvious from the published trials that before-and-after trials tend to overestimate effectiveness, and even variation in the length of a randomised trial may affect the ability to detect underlying benefit. The study by Huang et al.  reports The length of the study intervention and the control period have been proposed to suggest the difference between the results obtained by Climo et al.  and NotoAnother potential limitation of our meta-analysis is inclusion of the study by Huang et al. , in whicC-diff, UTIs and VAP, many studies included these as secondary outcomes, and may have lacked adequate sample sizes to assess a lesser effectiveness of CHG bathing.The results of our meta-analysis have some important clinical implications. Each ICU must assess the potential benefit of instituting daily CHG bathing to reduce infections in the ICU such as CLABSI and MRSA. For instance, in an ICU with specific bundles to prevent CLABSI, such as those developed by Pronovost , 27, basThere are concerns regarding the use of daily bathing of ICU patients with CHG. Although CHG bathing aims to reduce HAI, it may promote the emergence of CHG resistance, and increase Gram-negative organism bacteraemia . HoweverOur meta-analysis of the effectiveness of CHG bathing to reduce infections among adults in the ICU has found evidence for the benefit of daily bathing with CHG to reduce CLABSI and MRSA infections in the ICU. However, the effectiveness was dependent on the underlying risk of these events in the given ICU.


Multiple epidemiologic studies have evaluated the relationship between dietary cholesterol and lung cancer risk, but the association is controversial and inconclusive. A meta-analysis of case-control studies and cohort studies was conducted to evaluate the relationship between dietary cholesterol intake and lung cancer risk in this study. A relevant literature search up to October 2017 was performed in Web of Science, PubMed, China National Knowledge Infrastructure, Sinomed, and VIP Journal Integration Platform. Ten case-control studies and six cohort studies were included in the meta-analysis, and the risk estimates were pooled using either fixed or random effects models. The case-control studies with a total of 6894 lung cancer cases and 29,736 controls showed that dietary cholesterol intake was positively associated with lung cancer risk . However, there was no evidence of an association between dietary cholesterol intake and risk of lung cancer among the 241,920 participants and 1769 lung cancer cases in the cohort studies . Due to inconsistent results from case-control and cohort studies, it is difficult to draw any conclusion regarding the effects of dietary cholesterol intake on lung cancer risk. Carefully designed and well-conducted cohort studies are needed to identify the association between dietary cholesterol and lung cancer risk. Lung cancer is one of the most common cancers. Cancer epidemiological data showed that, in 2012, there were about 1.8 million new lung cancer cases and 1.6 million cases of death, respectively, accounting for about 13% of the total number of cancer diagnosis and 20% of the total number of cancer deaths [The dietary factors and lung cancer risk studies that have found arsenic in drinking water and (in smokers only) pharmacological doses of beta-carotene to be causes of lung cancer are convincing. Fruits, as well as foods containing carotenoids, probably protect against lung cancer ,5. CholeA systematic literature search up to October of 2017 was performed in Pubmed, Web of Science, China National Knowledge Infrastructure (CNKI), Sinomed, and VIP Journal Integration (VIP) to identify relevant studies. Search terms included: “cholesterol” and “lung cancer” or “lung carcinoma” or “cancer of lung” or “lung neoplasm”. Moreover, we reviewed reference lists of retrieved articles to identify any potentially relevant studies.Eligible trials were selected by two reviewers independently. Disagreement between the two reviewers was resolved by discussing with a third reviewer. Inclusion criteria were: (1) exposure factor was dietary cholesterol, and language was limited to Chinese and English; (2) studies designed as a case-control study or cohort study; (3) the outcome of interest was risk of lung cancer; and (4) relative risk (RR), odds risk (OR), or Hazard Ratio (HR) estimates with 95% confidence intervals.A purpose-designed form was used by two independent reviewers to collect the following data: first author, publication date, country, sample size, sex of subjects, dietary cholesterol intake level, other lung cancer risk factors adjusted, and effect estimates with corresponding 95% CIs for the highest versus the lowest categories of dietary cholesterol intake levels.We chose to use the nine-star Newcastle-Ottawa Scale (NOS) to assess the methodological quality of case–control and cohort studies . The NOSI2 statistics. For the Q statistic, statistically significant for heterogeneity was set as p value < 0.1. The I2 test was used to provide further evidence of heterogeneity. A random-effects model was adopted for meta-analysis if heterogeneity was detected; otherwise, a fixed-effects model was employed. Then the Egger’s test and Begg’s test were used to analyze the publication bias. Finally, a sensitivity analysis was performed.On the basis of study design, two separate meta-analyses were conducted: one for case-control studies and the other for cohort studies. For the quantification of the ORs (or HRs) in our meta-analyses, we extracted ORs (or HRs or RRs) comparing the extreme categories of dietary cholesterol intake (highest compared with lowest) as defined within each study. If multiple ORs (or HRs) were analyzed in one study, we extracted risk estimates from the greatest degree of control for potential confounding factors. Stata version 12.0  was used for statistical analysis. Heterogeneity was assessed using the method of Cochran Q and I2 = 42.3%, P for heterogeneity = 0.067) and lung cancer risk. Using a random effects model, the pooled OR for the highest and lowest levels of dietary cholesterol was 1.70 (95% CI: 1.43–2.03). Six cohort studies provided the dietary cholesterol intake levels and lung cancer risk, with a total of 241,920 participants and 1769 lung cancer cases. There was no heterogeneity in the results of the association between dietary cholesterol  and lung cancer risk. Using a fixed effects model, the pooled RR for the highest and lowest levels of dietary cholesterol was 1.08 (95% CI: 0.94–1.25).The pooled results combined for the highest versus lowest dietary cholesterol intake levels are shown in I2 = 68.7%, P for heterogeneity = 0.004) and lung cancer risk. Using a random effects model, the pooled OR for the highest and lowest levels of dietary total fat was 1.64 (95% CI: 1.16–2.33).Among the 10 case-control studies, there were six case-control studies that also analyzed dietary total fat and lung cancer risk. The pooled results combined for the highest versus the lowest dietary total fat intake levels, and are shown in p = 0.737, Begg’s test, p = 0.213; cohort studies: Egger’s test, p = 0.459, Begg’s test, p = 1.000.The Egger’s test and Begg’s test were used to analyze the publication bias. There was no evidence of publication bias observed. Case-control studies: Egger’s test, To test the stability and credibility of the results from the dietary cholesterol and lung cancer risk meta-analysis, we recalculated the pooled effect measure after systematically removing each study and found no significant change in the results, indicating that our findings are stable and credible.The objective of this study was to use meta-analysis to evaluate the correlation between dietary cholesterol and lung cancer risk. An analysis of 6894 lung cancer cases and 29,736 controls in 10 case-control studies showed that the dietary cholesterol intake levels  were positively associated with lung cancer risk. An analysis of 1769 lung cancer cases among 241,920 participants in six cohort studies showed that there was no association between dietary cholesterol intake levels and lung cancer risk ; this is the same as the Smith-Warner’s pooled analysis of cohort studies .However, we found that the results of case-control studies and cohort studies were inconsistent in our meta-analysis. Although a positive correlation between dietary cholesterol intake and lung cancer risk was found in 10 case-control studies, the result needs to be carefully interpreted. Dietary cholesterol intake is often accompanied by dietary fat intake. Yang et al.  conducteCholesterol is an essential compound for many physiological processes in the body, and an important lipid for maintaining cellular homeostasis. Even though conflicting epidemiologic evidence leads to uncertainty regarding a role for cholesterol in cancer development, it is consistent that cholesterol levels tend to be high in cancer cells and intracellular cholesterol is needed for cancer progression . RecentlSerum total cholesterol (TC) was commonly used as a biomarker of cholesterol metabolic status in the body, especially for the risk of cardiovascular diseases. For the relationship between the serum TC level and lung cancer risk, an inverse relationship was observed in multiple case-control and cohort studies. If we excluded the impact of pre-diagnostic cancer progress on serum TC levels, a significantly inverse association between total cholesterol and lung cancer risk was observed from our recently-published meta-analysis  . TherefoWe acknowledge that the major limitations of this study may include not accessing individual participant data and inter-study variability in cut-off values of cholesterol. And cohort studies used just one baseline measurement of cholesterol intake across years of follow up (regression dilution bias) could possibly account for some disparity between case-control and cohort studies.Due to inconsistent results from cohort and case-control studies, it is difficult to draw any conclusions regarding the effects of dietary cholesterol intake on lung cancer risk. With concerns about the interaction between cholesterol metabolism-related genetic variants and dietary cholesterol intakes, carefully designed and well-conducted cohort studies are needed to elucidate the relationship between dietary cholesterol and lung cancer risk.


Previous studies have indicated that chitinase 3-like 1 (CHI3L1) gene rs4950928 polymorphism and acidic mammalian chitinase (AMCase or CHIA) gene rs10494132 polymorphism are associated with the risk of asthma. However, the results are inconsistent because of small sample size and varied ethnicity and age in studies. Therefore, a systematic meta-analysis was important to clarify the effect of CHI3L1 rs4950928 polymorphism and CHIA rs10494132 variant on asthma risk.An electronic literature search was conducted to identify all the eligible studies. Odds ratios (ORs) with 95% confidence intervals (CIs) were calculated and sensitivity analysis as well as publication bias were assessed to investigate the associations. All statistical analyses were performed using STATA 12.0.P = 0.000; GC vs. CC: OR = 0.612, 95% CI = 0.470–0.796, P = 0.000; G vs. C: OR = 0.696, 95% CI = 0.567–0.856, P = 0.001). When stratified population by age, there was no association in children under all genetic models. As for CHIA rs10494132 polymorphism, no evidence of association between CHIA rs10494132 polymorphism and asthma risk was identified. Furthermore, subgroup analysis by ethnicity revealed a positive correlation between CHIA rs10494132 polymorphism and asthma risk among Asians . Additionally, in the subgroup analysis conducted according to age, CHIA rs10494132 variant was also found to be associated with the increased risk of asthma in children .Eight published articles with 10 case-control studies were included, 5 studies were of CHI3L1 rs4950928 polymorphism and another 5 studies involved CHIA rs10494132 polymorphism. Overall, no significant association was found between CHI3L1 polymorphism and asthma susceptibility. After stratified according to ethnicity, CHI3L1 rs4950928 variant was associated with decreased asthma risk in Caucasians (GG + GC vs. CC: OR = 0.621, 95% CI = 0.484–0.797, The G allele of CHI3L1 rs4950928 might be a protective factor against the development of asthma. However, the rs10494132 polymorphism of CHIA might be a risk factor for asthma. Asthma is a chronic inflammatory respiratory disease that is characterized by airway hyper-reactivity to some environmental stimuli, reversible airway obstruction, and gradually appeared airway structural remodeling . AlthougChitin is an abundant, naturally occurring polysaccharide functioning as a major structural polymer in many lower life forms as well as an important nutritional source for many organisms, which triggers allergies or asthma attacks in people –11. The Although several genetic studies have reported the correlation between CHI3L1 rs4950928 polymorphism or CHIA rs10494132 variant and the risk of asthma , 20, theThe systematic electronic databases of PubMed, Embase, Web of Science, China National Knowledge Infrastructure (CNKI) and Wanfang Database were comprehensively searched for all the eligible studies published up to May 2017 using the search terms: (“chitinase 3–like 1” or “CHI3L1”), (“single nucleotide polymorphism” or “SNP” or “polymorphism” or “variation” or “mutation”) and (“asthma” or “asthmatic”) or , (“single nucleotide polymorphism” or “SNP” or “polymorphism” or “variation” or “mutation”) and (“asthma” or “asthmatic”). Besides, the reference lists of all selected articles were manually searched for further potentially eligible articles. No restrictions with regard to language, population, publication date, or type of report were imposed; and unpublished data were excluded.All the potential studies were independently selected by two reviewers (Yanting Zhu and Xin Yan) according to the following inclusion and exclusion criteria. Inclusion criteria in this meta-analysis were: (1) studies that were using a case-control design; (2) studies that evaluated the correlation between CHI3L1 rs4950928 polymorphism or CHIA rs10494132 polymorphism and risk of asthma; (3) studies that provided sufficient data on the genotypic and allelic distributions of CHI3L1 rs4950928 polymorphism or CHIA rs10494132 polymorphism for estimating the odds ratios (ORs) and its corresponding 95% confidence intervals (CIs). Exclusion criteria were as follow: (1) studies without the control groups; (2) studies with no available data reported; (3) studies with inappropriate article types ; (4) studies with duplicated data. For overlapping studies, the largest or most recent publication was selected.The information of each eligible study was independently extracted by two investigators (Cui Zhai and Lan Yang). The following data were extracted: first author, year of publication, country, ethnicity, age, number of cases and controls, genotyping method and genotype distribution in cases and controls. Discrepancies were resolved by discussion or additional assessment by a third author (Manxiang Li), and consensus was achieved on each item.Subgroup analyses were performed according to the ethnicity and age. Ethnicity is determined by the descent of all the subjects in each eligible study. For CHI3L1 rs4950928 polymorphism, three studies of European descent from European countries Sweden, Germany and USA, were enrolled and belonged to Caucasian ethnicity. One study of Asian descent came from China, which belonged to Asian ethnicity. Another study of African descent from Mauritius belonged to African ethnicity. As for CHIA rs10494132 polymorphism, three studies of Asian descent, belonged to Asian ethnicity, came from China and were enrolled in this study. Another two studies of European descent came from North India and belonged to Caucasian ethnicity. As depicted in Table P < 0.05 was considered statistically significant in HWE testing. The between-study heterogeneity was assessed by Chi-square-based Q test and I2 statistics. The random-effect model was adopted for analysis, if there was statistical heterogeneity (P < 0.10). Otherwise, the fixed-effect model was applied for analysis as the pooling method. The pooled ORs and its corresponding 95%CIs were calculated to evaluate the associations of CHI3L1 rs4950928 polymorphism and CHIA rs10494132 polymorphism with asthma risk under five genetic models: recessive model ; dominant model ; codominant model ; homozygote model ; allele model . Z-test was performed to assess the significance of the pooled ORs, and P < 0.05 considered statistically significant. Publication bias was assessed by Begg’s test and Egger’s test and P < 0.05 was considered significant publication bias.This meta-analysis was performed using STATA version 12.0 software . The Hardy-Weinberg equilibrium (HWE) for the genotype frequencies was evaluated only in controls using Chi-squared test to check whether samples in eligible studies were from a large population in HWE and whether the sample bias existed. Samples in cases may show departure from HWE since the biases existed in case groups, such as age, the ratio of gender and so on. P > 0.05). As for CHIA rs10494132 polymorphism, HWE deviation existed in control of 1 eligible study [The literature retrieval process was presented in Fig. le study .Fig. 1FlThe genetic effects of CHI3L1 rs4950928 polymorphism and CHIA rs10494132 variant on asthma risk were presented in Table P = 0.791); dominant model ; codominant model ; homozygote model  and allele model . After categorizing included studies into different subgroups according to the ethnicity of study population, results indicated that CHI3L1 rs4950928 variant was associated with decreased asthma risk among Caucasians under the dominant model , the codominant model  and the allele model  . No subgroup analysis was performed in Asians and Africans due to only 1 study included in Asians and Africans. When stratified population by age, no statistically significant association was found in children under all genetic models. We could not perform analysis in adults since only one study was included.For CHI3L1 rs4950928 polymorphism, there was no statistically significant association observed under the following genetic models: recessive model . In the subgroup analysis done on the basis of ethnicity, a positive correlation was found between CHIA rs10494132 polymorphism and asthma risk among Asians under the recessive model  and the allele model  . No subgroup analysis was performed in Caucasians due to limited studies included. Additionally, in the subgroup analysis conducted according to age, CHIA rs10494132 variant was also found to be associated with the increased risk of asthma in children under the recessive model  and the allele model  . We could not perform subgroup analysis in adult group duo to insufficiency of relevant studies included.As for CHIA rs10494132 polymorphism, results based on all included studies provided no evidence of association between CHIA rs10494132 polymorphism and asthma risk in any genetic models in the overall population   Table .Fig. 5BeIn the present study, our results indicated that there was no significant association between CHI3L1 rs4950928 polymorphism and asthma susceptibility in overall analyses. However, following subgroup analysis by ethnicity, there was a protective effect of CHI3L1 rs4950928 polymorphism on asthma risk in Caucasians. The G allele of CHI3L1 rs4950928 might be a protective factor against the development of asthma. As for association between CHIA rs10494132 polymorphism and asthma risk, no evidence was found in the overall population, while a significantly increased risk of asthma was found in Asians as well as in children of all ethnicities.The gene CHI3L1, 10 exons long and located on chromosome 1q31-q32, codes for the chitinase-like protein YKL-40, which is a 40 kDa protein produced by cells in the airway mucosa, such as monocytes, macrophages and neutrophils , 25. YKLOur meta-analysis synthesized a total of 1062 cases and 1034 controls from 5 eligible studies to analyze the association between CHI3L1 rs4950928 polymorphism and asthma risk. The pooled results indicated no significant association between CHI3L1 rs4950928 polymorphism and asthma susceptibility. After stratified population by ethnicity, analysis indicated that the G allele of CHI3L1 rs4950928 had a reduced risk for asthma in Caucasians. No subgroup analysis was performed in Asians and Africans due to only 1 study included in Asians and Africans. When stratified population by age, the results remained non-significant in children. We could not perform analysis in adults since only one study was included.The gene for CHIA is present on chromosome 1q13.1–21.3 and produces acid mammalian chitinase, which is a 50 kDa acid-stable protein . In addiIn this meta-analysis, 796 cases and 744 controls in 4 published articles were included. Results suggested that there was no significant relationship between CHIA rs10494132 polymorphism and asthma susceptibility. When population was stratified based on ethnicity, a positive correlation between CHIA rs10494132 polymorphism and asthma risk was found in Asians. No subgroup analysis was performed in Caucasians due to limited studies included. Moreover, in the subgroup analysis conducted according to age, CHIA rs10494132 variant was also found to be associated with the increased risk of asthma in children. We could not perform subgroup analysis in adult group duo to insufficiency of relevant studies included.There are several limitations in this meta-analysis. Firstly, sample size in this meta-analysis was small, which might result in bias of the results when evaluating the association of CHI3L1 and CHIA gene polymorphisms with susceptibility to asthma. Secondly, the original data in some studies was lacking, which might limit sufficient statistical power to evaluate the potential effects of gene-gene and gene-environment interactions on the development of asthma. Thirdly, significant between-study heterogeneity was found in some pooled analyses. The exact sources of heterogeneity by meta-regression analysis were unable to further identify because of limited relevant data provided. Subgroup analysis was further performed based on ethnicity and age. However, heterogeneity was not resolved, indicating that other potentially relevant factors such as gender, genotyping method, phenotype of the disease may account for the heterogeneity. Fourthly, asthma is a heterogeneous disease with various genotypes and phenotypes. Although a genetic basis for asthma is undeniable, only a small proportion of heritability can be explained by the previously identified genetic polymorphisms associated with asthma because of the variability of the clinical phenotype. At last, only published studies were retrieved in the meta-analysis and possible publication bias might exist, despite no statistically significant publication bias was detected by Begg’s test or Egger’s test.In conclusion, the present meta-analysis demonstrates that there was no significant association between CHI3L1 rs4950928 polymorphism and asthma susceptibility in overall analyses. However, following subgroup analysis by ethnicity, there was a protective effect of CHI3L1 rs4950928 polymorphism on asthma risk in Caucasians. The G allele of CHI3L1 rs4950928 might be a protective factor against the development of asthma. As for association between CHIA rs10494132 polymorphism and asthma risk, no evidence was found in the overall population, while a significantly increased risk of asthma was found in Asians as well as in children of all ethnicities.


High body mass index (BMI) has been inconsistently associated with overall survival (OS) of digestive system cancers (DSCs). This meta-analysis was conducted to investigate whether high BMI was associated with DSCs prognosis. 34 studies were accepted, with a total of 23,946 DSC cases. Hazard ratios (HRs) with 95% confidence intervals (95% CIs) for OS in BMI categories from individual studies were extracted and pooled by random-effect model. The overall HR of DSCs except pancreatic cancer for OS of adult overweight cases was 0.76 (95% CI = 0.67–0.85). DSC individuals except pancreatic cancer with adult obesity were at decreased risk for OS . Among DSC patients except pancreatic cancer, the overall HR for the highest versus the lowest BMI category was 0.82 (95% CI = 0.71–0.92). Additionally, comparing the highest and lowest BMI categories, the combined HR of pancreatic cancer was 1.22 (95% CI = 1.01–1.43). Our meta-analysis suggested an increased OS among adult overweight and obese DSC survivors except pancreatic cancer. Overweight and obesity in adulthood may be important prognostic factors that indicate an increased survival from DSC patients except pancreatic cancer. Approxi cancers . However cancers , 6. On t cancers –9. Where cancers –13. Neve cancers . This reWe carried out a meta-analysis of published articles to clarify the association between BMI and survival among DSC patients. Moreover, we summarized the evidence on adult and diagnosis BMI and analyzed the highest versus the lowest category of BMI and OS of DSC patients.Two authors performed the search independently in PubMed, Embase, and the Cochrane Library from its earliest available date to January 20, 2017. The following keywords were used: digestive system, esophagus, esophageal, stomach, gastric, colon, rectum, colorectum, liver, hepatic, gallbladder, pancreas, pancreatic, tongue, oropharynx, cancer, tumor, neoplasm, mortality, survival, BMI, and body mass index. Boolean logic words were jointly used to combine the key words. Potentially relevant articles were investigated seriously by two authors. We also checked the references of retrieved articles for further relevant studies. Disagreements were resolved by group discussion.Studies were considered eligible if they satisfied all the following items: (1) comparing OS of DSC patients with different BMI ranges, containing comparison and referent BMI group; (2) presenting an association estimate with 95% CI or survival curve; (3) only full texts written in English were included. If different articles reported the same study, we only included the publication with the largest size.Two authors independently drew up the evaluation program and assessed full texts included. The Newcastle Ottawa Scale (NOS) was used for methodological quality, which was recommended by the Cochrane Nonrandomized Studies Methods Working Group . This quThree authors extracted information independently, and disagreements were resolved by consensus. The following data were extracted from each eligible study: first author, year of publication, country where the study conducted, study type, study period, cancer type, cancer site, number of cases, BMI category, both univariate HR (95% CI) and multivariate HR (95% CI) from each BMI category, and covariates list. If data above had not been referred in original articles, items were deemed as “NA.” Engauge Digitizer software was used to extract relevant data and calculate the HR (95% CI) from Kaplan-Meier survival curves .Q and I2 statistics. A pooled HR > 1 revealed that comparison BMI group had worse prognosis than referent group for DSC patients. On the other hand, a pooled HR < 1 suggested comparison BMI group predicted a more favorable survival. When the 95% CI of HR did not overlap 1, the result was regarded as statistically significant. Begg funnel plots and Egger regression asymmetry test were used to evaluate publication bias. All P values were 2 sides. P < 0.05 was considered as statistically significant. All analyses were performed using STATA version 12.0 software . Our research did not need ethical approval or consent as this meta-analysis was a review of published studies.Our analysis evaluated the reported OS of DSC cases with different BMI categories. The highest and lowest BMI group were compared to reveal the mortality difference of DSC. Multivariate HRs were commonly adopted to estimate included studies. Univariate HRs were used instead if multivariate HRs were not available. The pooled HR with 95% CI was estimated by random-effect model. Study-specific study size and 95% CI was showed by forming forest plots. For dose-response evaluation, midpoint of comparison and referent BMI group was used to quantitatively calculate the OS change. If the BMI category was open-ended, midpoints were estimated using the width of the adjacent close-ended category . SubgrouThe search strategy identified 1633 articles. Excluding irrelevant articles and duplicates, remaining 35 full texts were assessed for eligibility. Additional one record was identified from reference lists. 36 articles met the inclusion criteria and were assessed for eligibility. Further examination led to exclude two studies . AlthougIn total, we included 34 studies in meta-analysis. We combined and evaluated 6 kinds of cancers: tongue , oropharI2 = 0% and Pheterogeneity = 0.876, 0.623, and 0.715, respectively. The study of Iyengar et al. just contributed to 1.62% of overall HR, while total weight of three prospective studies was 85.24%. Compared with normal BMI, increased adult BMI was related to lower risk of death with 9% for every 5-unit increment.Association between overweight and OS of DSC, excluding pancreatic cancer, was presented in six studies (five prospective and one retrospective study) . The pooI2 = 40.2%, Pheterogeneity = 0.153). Meta-analysis of four prospective studies on the association of adult obesity and OS of DSC participants revealed that pooled HR was 0.85 (95% CI = 0.72–0.98), without obvious heterogeneity . Combined HR of all nine studies was 0.86 . With every 5-unit of BMI increased in adulthood, risk of death was reduced by 3%.Nine studies (eight prospective and one retrospective study) were included in the analysis of obesity and survival of DSC patients except pancreatic cancer . Pooled I2 = 69.9%, Pheterogeneity < 0.001).Twenty-four studies on highest versus lowest BMI and mortality of DSC patients except pancreatic cancer were combined and analyzed . HighestI2 = 75.2%, Pheterogeneity < 0.001) , but the heterogeneity was high (< 0.001) . Regardi< 0.001) . However< 0.001) , and 9.In subgroup analysis, comparing with the lowest BMI category, the highest category had a statistically significant effect on OS of DSC patients except pancreatic cancer in both North America and non-North America group, with the HR of 0.77 (95% CI = 0.65–0.89) and 0.84 (95% CI = 0.70–0.99) . MoreoveP = 0.467) or obesity (Begg test P = 0.329) showed no asymmetry (P = 0.867) or other DSCs (P = 0.086) failed to reveal any significant publication bias  –46. AparThe relationship between BMI and DSCs has been discussed for decades. Three published meta-analyses had evaluated the association between BMI and survival of particular DSC patients, including esophageal cancer, colorectal cancer, and pancreatic cancer , 47–49. The mechanism behind the results that adult obesity enhanced the OS of pancreatic cancer had not been revealed thoroughly. Increased insulin resistance, DNA damage, adipokines, and proinflammation may contribute to the survival outcomes . AdditioPrognostic effect of overweight and obesity on DSCs has been searched. However, the role of BMI at diagnosis and in adulthood on the mortality of DSC patients is still unclear, excluding pancreatic cancer with a latest meta-analysis. Thus, we conducted the meta-analysis to identify the prognostic role of BMI on overall survival from DSCs except pancreatic cancer. Additionally, we analyzed the association between the highest BMI versus lowest BMI category and OS of pancreatic cancer to cover the shortage of Shi et al.'s research.P = 0.007 and P = 0.011, resp.) [Eighteen included studies were multivariately analyzed and nine studies provided both multivariate and univariate results. In this study, we conducted each analysis using multivariate results as many as possible. When estimating the highest versus lowest BMI category and mortality, we combined all studies to analyze the association. Some studies, which only provided univariate outcomes, were also adapted to achieve more credible pooled results. Subgroup analyses for covariates adjusting, especially for weight loss and tumor grade, were performed as a supplement. Overweight and obesity in adulthood significantly enhanced the OS of DSC patients except pancreatic cancer. But we failed to find significant association between overweight or obesity at diagnosis and OS of DSCs except pancreatic cancer. Pooled analysis of both overweight and obesity revealed positive effect on survival of DSC patients except pancreatic cancer: HR = 0.78 (95% CI = 0.69–0.87) and HR = 0.86 (95% CI = 0.76–0.95), respectively. Excluding pancreatic cancer, higher adult BMI was related to better survival. Considering the limitation of study numbers, we included all 24 studies and estimated the highest versus lowest BMI category and mortality of DSC patients except pancreatic cancer. It was easier to find the relationship of BMI and OS by analyzing the maximum and minimum BMI. The outcome provided the evidence that patients with higher BMI had lower mortality. Moreover, we included 10 studies on pancreatic cancer and OS to conduct further analysis. The results of supplementary study were coincident with the former study of pancreatic cancer patients . The cur, resp.) . Loss of, resp.) . Compare, resp.) . Campbel, resp.) . Additio, resp.) .Risk estimate of site-specific tumors from digestive system is necessary and concerned by many scholars. In esophageal cancer survivors, we obtained meaningful result , suggesting that highest BMI group had better survival than lowest group. Although higher BMI represented the trend of better overall survival, the significant effect was not found in groups of oropharyngeal, gastric, and colorectal cancers. By a clinical-based cohort and meta-analysis in 2013, Zhang et al. revealed that high BMI could significantly enhance overall survival of esophageal cancer , which was consistent with this study . AnotherThe underlying mechanisms of the effect of higher BMI on DSC patients were unclarified and rarely elucidated. Comparing with normal BMI cases, overweight and obese patients had a better nutrition status and potential survival advantages . DSC patTo our knowledge, this study is the first meta-analysis estimating the association between BMI and OS of DSC patients. Analysis of a functional system, adjusted risk factors, the relatively large sample size, and the summarized evidence of single study are strengths in our study. However, limitations of our study should be addressed. Most included studies were conducted in developed countries; research in developing countries may be restricted by techniques, devices, therapies, and other factors. Comprehensive and through analysis needs more research information from developing countries. Except pancreatic cancer, we analyzed BMI at two time points: adulthood and diagnosis, and weight loss as a significant prognostic factor was only considered in five included articles , 28, 31.This study revealed that overweight and obesity in adulthood increased the OS of DSC patients except pancreatic cancer. However, higher BMI at diagnosis did not show any association with the survival of DSC patients. In total, DSC patients, excluding pancreatic cancer, with higher BMI had better survival than lower BMI. To draw definite recommendations for DSC survivors, further studies are needed to find whether BMI and related clinical factors are potential predictors of mortality of DSC patients.