However, due to other conflicting results, a clear consensus is lacking in the matter.Many studies suggest that the SUMO4 M55V gene polymorphism and T2DM. Depending on the heterogeneity of the data, either a fixed or random-effects model would be used to assess the combined odds ratio (ORs) and their corresponding 95% confidence interval (CI).A meta-analysis consisting of 6,823 subjects from 10 studies was conducted to elucidate relationship between the SUMO4 gene M55V polymorphism was significantly associated with T2DM in the whole population under allelic , recessive , dominant , homozygous , heterozygous , and additive genetic models . In our subgroup analysis, a significant association was found again in the Chinese population, but not in Japanese or Iranian population.SUMO4 gene M55V polymorphism may correlate with increased T2DM risk. Chinese carriers of the V allele of the SUMO4 gene M55V polymorphism may be predisposed to developing T2DM. Type 2 diabetes mellitus (T2DM) is a long-term metabolic disorder characterized by high blood sugar, insulin resistance, and impaired insulin secretion. Recently, research found the nuclear factor-κB (NF-κB) signaling pathway to be mechanistically implicated in the pathogenesis of T2DM by increasing pancreatic β-cell apoptosis –3. SmallSUMO4 gene is located at position 6q25 in the IDDM5 locus and spans 688 bp. The gene contains a single exon, encoding 95 amino acids  in the Caucasian population  T2DM diagnosis and classification meets guidelines proposed by the World Health Organization in 1999: fasting blood sugar no less than 7.0 mmol/l and the 2 h postprandial blood sugar no less than 11.1 mmol/l. Other metabolic conditions, such as acute and chronic complications of diabetes mellitus, T1DM, ketosis, hepatic and renal dysfunction, and other conditions were excluded. (c) Officially published case–control or cohort studies. (d) Genotype in control group follows Hardy–Weinberg equilibrium (HWE).The included studies should meet the following inclusion criteria: (a) assessment of the association of small ubiquitin-like modifier 4, Met55Val, diabetes.” Retrieved studies were published between 2003 and 2017.The following electronic databases were used to conduct a search: China National Knowledge Infrastructure, VIP database, Wanfang database, China Biological Medicine Database, and PubMed. Keywords used for the search were “SUMO4, rs237025, diabetes.” When searching Chinese databases, researchers employed corresponding Chinese terms. Nine publications were retrieved in our initial search of the Pubmed database and four papers met the inclusion criteria for this meta-analysis. Using keyword combination “SUMO4, Met55Val, diabetes” yielded three additional papers, all of which were eligible for our analysis. The eligible studies’ characteristics conform to the above inclusion criteria. Finally, “SUMO4, M55V, type 2 diabetes” retrieved another two additional papers. Both papers met inclusion criteria. Additional three Chinese papers were retrieved in the China National Knowledge Infrastructure database by using the keywords combination as “Data were extracted by three investigators using a standardized protocol Table . Two invSUMO4 gene M55V polymorphism and T2DM were compared by using the odds ratios (ORs) corresponding to its 95% confidence intervals (CIs).Statistical analyses were performed by using Revman 5.0 and STATA 12.0 software . Six genetic models, allelic , recessive (VV vs. MV + MM), dominant (MM vs. MV + VV), homozygous (VV vs. MM), heterozygous (MV vs. MM), and additive , were used in the current meta-analysis. The association of P < 0.05 level , recessive , dominant , homozygous , heterozygous , and additive genetic models  , recessive , dominant , homozygous , heterozygous , and additive genetic models .In the subgroup analysis, a significant association was also found in the Chinese population under allelic , recessive , dominant , homozygous , heterozygous , or additive genetic models .No significant association was detected in Japanese population under allelic . No significant association was detected in Iranian population under allelic , dominant , homozygous , heterozygous  or additive genetic models .In the Iranian population, a marginally significant association was detected only under recessive genetic model , a fixed-effect model was used to determine the pooled OR. While only under the recessive genetic model, significant heterogeneity was detected (Pheterogeneity < 0.05) and random-effect model was used  under the recessive genetic model by using Egger’s test , recessive (OR: 1.59), dominant (OR: 0.815), homozygous (OR: 1.415), heterozygous (OR: 1.191), and additive (OR: 1.184) genetic models. In the subsequent subgroup analysis, a significant association was only detected in the Chinese population, but not in the Japanese or Iranian population. Significant heterogeneity was only found under the recessive genetic model. Heterogeneity was reduced in the Chinese and Japanese populations, suggesting that ethnicity was the main source for the heterogeneity. As only one study of the Iranian population was present in our analysis, the heterogeneity detection was not applicable.In the current meta-analysis, a significant association was detected between SUMO4 gene M55V polymorphism is located in the evolutionarily crucial CUE structural domain and involves the substitution of the highly conserved Met (ATG) for Val (GTG). This substitution could modify site at which PKC phosphorylates the protein. The change in molecular conformation and functional activity reduces its ability to regulate NF-κB, resulting in increased NF-κB activation enhancement and overexpression of NF-κB-dependent gene products. In 2004, Guo et al. found that the M55V substitution resulted in 5.5 times greater NF-κB transcriptional activity and approximately two times greater expression of IL12B   . However < 0.05) . HoweverSUMO4 rs237024 and rs600739 polymorphisms, diet, smoking, and hypertension (SUMO4 gene M55V polymorphism affects patient T2DM susceptibility.This study is not without limitations. Many factors can influence plasma concentrations of SUMO4, such as rtension . In addiSUMO4 gene M55V polymorphism and T2DM risk in the current meta-analysis. Individuals with the Val allele of SUMO4 gene M55V polymorphism may be more susceptible to T2DM in the Chinese population. This association in Japanese or Iranian population needs to be further verified in the larger samples studies. This conclusion may help researchers formulate an individual treatment strategy to prevent T2DM in the future.In conclusion, we found a significant association between Conceived and designed the meta-analysis: Y-yL and HW. Performed the meta-analysis: Y-yL, X-xY, H-yG, and GG. Analyzed the data: Y-yL. Contributed material/analysis tools: Y-yL. Wrote the manuscript: Y-yL and HK. Reference collection and data management: Y-yL and J-jW. Statistical analyses and paper writing: Y-yL, Y-hZ, and HK. Study design: Y-yL and J-jW.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. The reviewer MH and handling editor declared their shared affiliation.


When we conducted the subgroup analysis by gender, the significant association was found in men’s populations , but not in the women’s populations. In the further subgroup analyses for study design and geographic locations, we did not find any association between dietary carbohydrate intake and CRC risk in the subgroup results respectively. No significant publication bias was found either by the Egger’s regression asymmetry test or by the funnel plot. This meta-analysis suggested that higher dietary carbohydrate intake may be an increased factor for CRC risk in men populations. Further studies are wanted to confirm this relationship.The association between dietary carbohydrate intake and colorectal cancer (CRC) risk remains controversial. We therefore conducted this meta-analysis to assess the relationship between them. A literature search from the databases of PubMed, Embase, Web of Science and Medline was performed for available articles published in English (up to September 2016). Pooled relative risk (RR) with 95% confidence interval (CI) was calculated to evaluate the association between dietary carbohydrate intake and CRC risk. The random-effect model (REM) was selected as the pooling method. Publication bias was estimated using Egger’s regression asymmetry test and funnel plot. A total of 17 articles involving 14402 CRC patients and 846004 participants were eligible with the inclusion criteria in this meta-analysis. The pooled RR with 95% CI of dietary carbohydrate intake for CRC, colon cancer and rectum cancer risk were 1.08 (95% CI =0.93–1.23,  Colorectal cancer (CRC) is the third most common cancer and the third most common cause of high mortality cancer rates in the United States . AlthougDietary carbohydrate intake is the main dietary component affecting an individual’s insulin secretion and glycaemic response, but the effect varies depending on the amount of carbohydrate consumed . AlthougA search was conducted for available articles that were published in English (up to September 2016) from the following databases: (i) PubMed, (ii) Web of Science, (iii) Embase and (iv) Medline. Search terms included ‘carbohydrate’ OR ‘nutrition’ OR ‘dietary’ AND ‘colorectal cancer’ OR ‘colon cancer’ OR ‘rectum cancer’ without any restriction. Moreover, we reviewed the reference lists of the studies included to identify additional studies not captured by our database searches.The inclusion criteria were as follows: (i) observational study published as the original study to assess the association between dietary carbohydrate intake and CRC risk; (ii) the exposure of interest was dietary carbohydrate intake; (iii) the outcome of interest was CRC; (iv) relative risk (RR) or odds ratio (OR) with corresponding 95% confidence interval (CI)  were available; (v) the complete and recent study was chosen if a study had been published more than once.All included studies were independently reviewed by two investigators carefully to determine and identify whether an individual study was eligible for inclusion in this meta-analysis. Articles for which it was unclear whether or not eligible for the inclusion criteria were discussed at consensus meetings including both investigators.Two investigators who reached a consensus on all of the items independently extracted the data. The following information was extracted from each study: the first author’s name, publication year, mean age or age range, country, gender, study design, disease type, number of cases and participants, variables adjusted for the analysis and RR  with their 95% CI.2I [2I values of 75, 50, 25 and 0% represent high, moderate, low and no heterogeneity [P values) were two sided at the level of 0.05.When available, we extracted the RR adjusted with the most potential confounders in the original articles. Pooled RR was calculated as the inverse variance-weighted mean of the logarithm of multivariate adjusted RR with their 95% CI to evaluate the association between dietary carbohydrate intake and CRC risk . For artI2  and I2 vogeneity  respectiogeneity . Publicaogeneity . STATA vIn our search strategy, 7574 articles from the databases of PubMed, Embase, Web of Science and Medline were selected. After reviewing the title/abstract, 38 articles were reviewed in full, 2 studies identified from reference list, 23 of which were subsequently excluded due to the following reasons: four articles were reviews, three articles reported duplicated data, six articles were animal studies, seven articles were lacking RR (or OR) or corresponding 95% CIs and three articles were letters to the editors. Ultimately, 17 articles –11,17–27We pooled the overall meta-analyses to assess the association between dietary carbohydrate intake and CRC risk. Subgroups analyses were also conducted by study type, geographic locations, sex and disease type. The detailed summary risk estimates are summarized in 2I =68.3%, Pheterogeneity<0.001) (2I =48.3%) (2I =17.8%)  . The ass =48.3%)  or rectu =17.8%)  for high2I =68.3%, Pheterogeneity<0.001) was demonstrated for dietary carbohydrate intake and CRC risk. Therefore, univariate meta-regression with covariates was conducted. However, the results showed that no covariates including publication year, gender, study type, disease type and study region had a significant impact on heterogeneity. Considering three included studies [Pheterogeneity=0.231. The result for highest category of dietary carbohydrate intake for CRC risk was consistent with the whole result .As shown in  studies  showing Influence analysis showed that no individual study was found to have excessive influence on the pooled estimate for dietary carbohydrate intake and CRC risk.P=0.14) and the funnel plot . Therefore, these three studies were the potential causes of heterogeneity.Heterogeneity among studies is common in meta-analysis , and it As a meta-analysis, our study has several strengths. Firstly, large number of participants included reduced sampling error to a great extent to reach more reasonable conclusions. Secondly, most of all studies on dietary carbohydrate intake and CRC risk had adjusted for major risk factors, such as family history of colon cancer among first-degree relatives, smoking intensity, alcohol drinking and total energy intake, suggesting that the results were more reliable. Thirdly, most of the studies were prospective, allowing a much greater possibility of reaching reasonable conclusions. As prospective studies do not suffer from recall bias and are anticipated to be less likely to have selection bias relative to case-control studies. Finally, no significant publication bias was found, indicating that our results are stable.However, this meta-analysis also has some limitations. Firstly, as we all know, observational studies are susceptible to potential bias inherent in the original studies, especially for case-control studies in the meta-analysis. There is some case-control studies included in this meta-analysis. Overstated association could be expected from the case-control studies because of recall or selection bias, and early symptoms in patients may have resulted with a change in dietary habits, although we found no differences in risks between cohort studies and case-control studies. Secondly, although we extracted the multivariable-adjusted RRs, the extent to which they were adjusted in the original studies varied. Thirdly, most of the included studies came from America, however, no significant association was found. There are only two studies that came from Asia and two studies came from Europe, which are difficult to obtain reliable conclusions with such a small sample size for Asian and European populations. Therefore, more studies conducted from Asia and Europe are wanted to assess this relationship. Finally, despite the correction for potential confounders, the RR ratio remains an association. Considering the variability of results in the included studies, this would influence the final result of the study. Therefore, further studies with more cases and participants are wanted to confirm the association.In summary, results from this meta-analysis suggested that higher dietary carbohydrate intake may be an increased risk factor for CRC risk in men populations. Further studies are wanted to confirm this relationship.


G: OR = 1.35, 95% CI = 1.12-1.63; GA vs.GG: OR = 1.41, 95% CI = 1.12-1.76; AA+GA vs. GG: OR = 1.41, 95% CI = 1.13-1.7). In the stratified analyses based on ethnicity, we found a significant association in Caucasians and in Mexicans. In the subgroup analyses by gender, a significant association was found between PD1.3 polymorphism and SLE risk in males. The results also suggested an association between the PD1.6 polymorphism and decreased SLE risk . Our meta-analysis revealed that PD1.3 polymorphism may increase the susceptibility to SLE, particularly in Caucasians, while PD1.6 may be a protective factor to SLE.The association of polymorphisms in programmed cell death 1 (PDCD1) gene with systemic lupus erythematosus (SLE) risk is inconsistent across different studies. This meta-analysis is aimed to provide reliable evidence to the association of five common PDCD1 polymorphisms  with SLE risk. A total of 28 studies with 4,344 SLE cases and 5,474 healthy controls were included in this meta-analysis. PD1.3 polymorphism was significantly associated with SLE in the overall population (A  Systemic lupus erythematosus (SLE) is a multisystem autoimmune disease. And its characteristics are autoantibody production, hyperactive T and B cells, immune complex deposition and multi-organ damage  . SLE is PDCD1 encodes PD-1 molecule, which has a tyrosine-based inhibitory motif, is an immunoinhibitory receptor belonging to the CD28/B7 family. PDCD-1 is inductively expressed on CD4+/ CD8+ T cells, NKT cells, B cells and monocytes after being activated [PDCD1 can develop an SLE-like disease [Programmed cell death 1 (PDCD1), which is also called PD-1, is located on the chromosome 2q37 region. And it is recently suggested to be an important candidate for SLE susceptibility. ctivated . It playctivated –8. And m disease .PDCD-1 is widely expressed on T cells in comparison to the specific expression of other members in CD28 family. And its expression is much broader than PD-L2 in PD-Ls. Both PD-L1 and PD-1 are expressed on CD4+CD25+ T cells, but it is not clear whether they can influence the function of these regulatory T cells [The PD-1/PD-L pathway comprises PD-1 receptor and two ligands of it: PD-L1 and PD-L2. PD-1 receptor is inductively expressed on the activated T- and B cells, as well as myeloid cells. PDCD-1: PD-1.1 is located in the promoter (position -531 from the translation start), PD-1.2 is located in intron 2 , PD-1.3 is located in intron 4 , PD-1.4 is located in intron 4 , PD-1.5 is located in exon 5  and PD-1.6 is located in the 3′UTR  [PDCD1 polymorphisms with the susceptibility of SLE, but the results were inconsistent [et al. [et al. [et al. [et al. [et al. [PDCD1 polymorphisms have been widely discussed, but no conclusive relationships have been determined. In addition, the sample size in most of these studies were relatively small, thus were not powerful enough to assess whether there is an association between SLE and PDCD1 polymorphisms.There are several common polymorphisms in n 8,738) . Recentlnsistent –14. Prok [et al. first ide [et al. findings  [et al.  did not  [et al.  supportePDCD1 polymorphisms  with SLE risk by combining original data from relevant primary studies and achieve a more comprehensive evaluation.Herein, the aim of this meta-analysis is to provide a reliable estimation of the association of five common PDCD1 polymorphisms and SLE. The study selection process was shown in Figure PDCD1 polymorphisms on SLE risk.All studies in this meta-analysis were published prior to March 30, 2016 through a systematic search in the PubMed, Web of Science, WanFang and CNKI databases. 364 articles were identified that evaluated the association of Among the eligible 28 studies, 16 were carried out in Caucasians. Eight studies were based on subjects with an Asian background, 7 were performed in China and one was from Iran. Only one study came from African American and two from Mexico. The NOS score of all articles was more than 6, indicating each included study was of high-quality. The characteristics of the included studies were shown in Table P > 0.05). Moreover, the Hardy-Weinberg equilibrium (HWE) of one study about PD1.2 was 0.01 (PHWE < 0.05). We omitted this study and the results were in accordance with the overall population before omitting this study . However, in the stratified analyses based on ethnicity, results demonstrated a significant relationship in Caucasians  and in Mexicans  subgroup, but no association in African America and Asian group. In the subgroup analyses by gender, we found a significant relationship between PD1.3 polymorphism and increased SLE risk in the males . The distribution of genotypes in the controls conformed to HWE except for one study by Mahmoudi (PHWE = 0.02), the results were in accordance with the overall population when we omitted this study . However, in the subgroup analyses based on ethnicity, we failed to find any significant association in all subgroup.13 studies for PD1.5 contained 1,852 cases and 1,342 controls, and the average minor allele frequency was 0.37. 14 studies for PD1.6 contained 1,923 cases and 1,591 controls, and the average minor allele frequency was 0.41. No significant association was observed under any genotype model in PD1.5  was rejected. So the results of this meta-analysis are statistically reliable .We constructed a funnel plot and performed Egger's test to evaluate the extent of publication bias in our dataset. As shown in Figure PDCD-1 gene, as a new member of the CD28 family, has 23% homologues in amino acid sequence with CTLA-4 [PDCD-1 gene, there are two characteristic tyrosine domains: one immunotyrosine inhibitory motif and one immune receptor tyrosine-based switch motif [PDCD-1 gene was isolated from thymus T cell under the stimulation of apoptosis [PDCD1 to be a strong susceptibility locus for SLE because it belongs to the immunoglobulin family [PDCD-1 developed spontaneous autoimmune diseases, which suggested a negative costimulatory function [PDCD-1 gene in C57BL/6 mice developed lupus-like glomerulonephritis [PDCD1 act as a crucial regulator of acquired immunity, and its genetic variants may have an effect on other diseases [h CTLA-4 . In the ch motif . PDCD-1 poptosis . We consn family . Mice defunction . Disruptephritis . FurtherPDCD-1 gene is a SLE susceptible locus in whole genome-wide linkage studies [PDCD-1 with SLE has been demonstrated in some studies [PDCD-1 though affecting RUNX1 binding [PDCD-1 gene polymorphisms were associated with SLE risk [PDCD1 and SLE. As we know, this is the most comprehensive meta-analysis to date to evaluate the association between PDCD1 polymorphism and SLE risk.The  studies . Several studies . PD1.3 p binding . Many stSLE risk –17, 31. SLE risk , 31. ButSLE risk , 32or thSLE risk –17, 31 iPDCD1 polymorphisms and SLE. Liu et al. [population.In the meta-analysis by Lee et al. [Two previous meta-analyses , 34 wereu et al. paid attee et al. , they inPDCD1 polymorphisms  and SLE risk. Conversely, PD1.3 polymorphism may increase SLE susceptibility, and PD1.6 polymorphism may be a protective factor against SLE. However, Lee et al. found no relationship between PD1.6 polymorphism and SLE risk in Europeans [Our meta-analysis included 28 independent case-control studies involving 4,344 SLE patients and 5,474 healthy controls. We have used Newcastle-Ottawa Scale to assess the quality of our studies and they are all high quality.  We analyuropeans . This diOverall, there was an increased association between PD1.3 and SLE. In the stratified analyses of race, the association only was found in Caucasians and Mexicans subgroup, and failed in African America and Asian group. Furthermore, of all the eligible studies, only two studies were based on African American population and two studies were based on Mexican. So following we still need more related studies to confirm and supplement this result.In the subgroup analysis of PD1.3 gender, we found an increased SLE risk in male based on the allele model and no relationship in women. However, as we know, SLE prevalence rate of women is much higher than that of male, it may be the reason that the male subjects was less than the female. Afterwards we may need added the larger sample to evaluated the result. Ethnicity and gender may be the essential biological factor that influences PD1.3 polymorphism through gene-gene interactions, which was particularly prominent in SLE.vs. G: OR = 0.84, 95% CI = 0.73-0.96, P = 0.01), but no in there Subgroup analyses by ethnicity. Furthermore, in other four genotype models of PD1.6, no significantly increased risk of SLE is associated. There was no marked association between the PD1.1, PD1.2, PD1.5 polymorphism and SLE susceptibility in five genotype models and the overall analysis.The A genotype of PD1.6 was associated with decreased SLE susceptibility  studies may affect the results. Therefore, further large scale studies from different races, with more available individual data, are needed to validated the association between PDCD1 polymorphisms and SLE risk.Despite our considerable efforts to search the possible relationship between the In conclusion, our meta-analysis provides clear evidence that PD1.3 polymorphism may increase the susceptibility to SLE, particularly in Caucasians, while PD1.6 may be a protective factor to SLE. There was no association between PD1.1, PD1.2, PD1.5 polymorphism with the risk of SLE. Large studies involving more detailed individual data are required to validate our results.PDCD1 gene polymorphisms and SLE risk up to March 30, 2016 was performed using the following search terms: “programmed cell death 1” or “PDCD1”, “PD1” or “CD279 antigen”, “polymorphism” or “SNPs”, “allele’ or ‘genotype”, “systemic lupus erythematosus” and “SLE”. Only articles published in English or Chinese were eligible for inclusion. Furthermore, the reference lists of all eligible articles, including review articles, were also checked to find additional relevant publications.A PubMed, Web of Science, Wanfang and Chinese National Knowledge Infrastructure (CNKI) search for studies that examined the association between PDCD1 polymorphisms and SLE susceptibility; d) participants in control groups were selected from normal individuals. Non case-control design studies and review papers were excluded. If two or more studies contained overlapping cases or controls, the study with the largest sample size was included in the meta-analysis.Studies were considered eligible for further meta-analysis if they met the following criteria: a) studies containing useful genotype frequencies or odds ratio (OR) data; b) case-control studies; c) articles that evaluated the association between Two independent investigators (Li Wang and Kang Liu) reviewed the articles, and any discrepant data were discussed by all authors to reach a consensus according to the meta-analysis of observational studies in epidemiology guidelines.  The follPDCD1 polymorphisms  and SLE risk. The Z test was used to assess the significance of all pooled ORs and P < 0.05 was considered significant.Hardy-Weinberg equilibrium (HWE) was firstly assessed in control groups of each study using chi-squared or Fisher's exact test. Based on the genotype and allele frequencies in cases and controls, crude odds ratios (OR) with 95% confidence intervals (95% CI) was used to measure the strength of the associations between vs. A) among all the studies, with A defined as the major allele and B defined as the minor allele. However, because of the missing information about genotypes, we evaluate the association under other different genetic models  using all the studies except the studies by Prokunina [We assess the association under the allele model . Potential publication bias was evaluated with Egger's test and the inverted funnel plot, and P < 0.05 was considered statistically significant. All statistical analyses were performed by STATA statistical software Version 12.0 .Statistical heterogeneity among studies was checked using the Q test and I


The associations between saturated fatty acid (SFA) consumption and risk of breast cancer (BC) remains inconclusive. Therefore, we conducted this meta-analysis to determine the quantitative relations between dietary SFA intake and incidence of BC.Literatures published up to April 2015 were systematically screened through Pubmed and Web of Science. Relevant publication quality was evaluated by conducting the Newcastle-Ottawa scale. We used fixed effects models or random effect models to calculate the summary relative risks (RRs) and odds ratios (ORs), and conducted sensitivity analyses and evaluated the publication bias.We identified a total of 52 studies (24 cohort studies and 28 case–control studies), with over 50,000 females diagnosed with BC. The associations between dietary SFA intake and risk of BC were 1.18 for case–control studies  and 1.04 for cohort studies (95% CI = 0.97–1.11). When restricted analyses to population-based studies, positive associations were observed for both cohort (RR [95% CI] = 1.11 [1.01–1.21]) and case–control studies (OR [95% CI] = 1.26 [1.03–1.53]). Additionally, for case–control studies, significant positive associations between higher SFA intake and BC risk were observed for Asian (OR [95% CI] = 1.17 [1.02–1.34]) and Caucasian (OR [95% CI] = 1.19 [1.00–1.41]), as well as for postmenopausal women . In contrast, higher dietary SFA intake was not associated with risk of BC among premenopausal women, in cohort studies or hospital-based studies.A positive association between higher dietary SFA intake and postmenopausal BC risk was observed in case–control but not in cohort studies. More studies are warranted to confirm these findings. In contrast, Smith-Warner et al8 reported null associations . Moreover, a cohort study in Japan was performed. And the total mortality of Japanese women was inverse to the intake of SFA (hazard ratio [HR] [95% CI] = 0.91 [0.83–1.00]).9 Due to different races background, people have varied eating habits. And menopausal processes and postmenopausal endocrine events can affect the development of BC.10 Nevertheless, none of the aforementioned publications included any subgroup analyses, such as ethnicity, population -based females, menopausal status, and so on. Hence, the objective of the present study was to further investigate the association between dietary SFA intake and the incidence of BC with the more detailed analyses among observational studies.However, the relationship between dietary SFA intake and the incidence of BC remains uncertain. For example, the meta-analysis conducted by Boyd et al11 to conduct this meta-analysis. Ethical approval was not necessary, because all publications included in this study were published officially.We followed the Meta-analysis of Observational Studies in Epidemiology (MOOSE) guidelinesWe systematically searched the literatures published in English in Pubmed, Web of Science up to April 2015, using search terms:  and (“breast” or “mammary”) and (“tumor” or “carcinoma” or “neoplasm”). The search was restricted to human studies. Reference lists from each study, systematic reviews and meta-analyses were reviewed to identify potential relevant literatures as well.Two investigators independently reviewed these studies. Studies were included when the following criteria were met: published openly; evaluated the association between SFA intake from food and the incidence of female BC only; specified diagnosis of BC; contained odds ratios (ORs), RRs, or HRs with corresponding 95%CIs or data could be estimated; and selected when data were most sufficient if they were from the same population. Studies were excluded when they were: animal or vitro experiments, review articles, repeated literatures, or mechanism studies; not related to human subjects; not of appropriate control groups; without analysis method provided; and were excluded when lack of access to full texts.12 was used to evaluate quality of literatures independently by 2 investigators. The literatures with scores ≥5 were included in the meta-analysis.We obtained from each study the information on author's name, publication year, country, community, or study design based on hospital, dietary assessment method, and outcomes (RRs/ORs/HRs [95% CIs]). Ethnicity was classified as Asian and Caucasian. ORs, RRs, or HRs were extracted only when articles employed adjusted models with most confounders in original publications. The estimates from 1 study were recorded as much as possible including premenopausal and postmenopausal population. Newcastle-Ottawa Scale13 the fixed-effects model was used when I2 was lower than 50% and P of the value of heterogeneity was ≥0.05. Otherwise we used the random-effects model. Second, we conducted subgroup analyses by ethnicity , menopause status , and study type . Finally, Begg funnel-plot and Egger test were conducted to examine publication bias with significance when the value of P is <0.05.15We used STATA  to perform the meta-analysis. We used RR as an approximate for HR in cohort studies. First, adjusted ORs or RRs comparing highest versus lowest category of dietary SFA intake were gathered with the corresponding 95% CIs as possible and meanwhile were calculated by the logarithmic transformation of RRs and ORs with the corresponding 95% CIs. As described in previous study,39 and 28 case–control studies67) were eligible for this meta-analysis after checking the full text while 14 articles81 were excluded for additional reasons.The flow chart for selected articles was shown in Figure Characteristics of 52 studies were shown in Table We analyzed cohort and case–control studies separately owing to the relatively higher incidence of BC.The forest plots of 52 studies together were shown in Figures In addition, following subgroup analyses, menopause status affected the risk of BC among case–control studies. SFA intake increased the risk among postmenopausal women and was not related to premenopausal women . However, as for hospital-based study, higher SFA intake was not associated with BC risk.Additionally, only case–control study was conducted by ethnicity. As for cohort studies, most of which were from the same race. Publications suggested that higher SFA intake could increase the risk of BC .Sensitivity analyses were conducted to evaluate the effect of excluding any individual study. By exclusion of 1 literature at a time in turns, summary results of remained literatures did not substantially change.Begg funnel-plot and Egger test were used to examine the potential publication bias. All funnel plots indicated no evidence of possible publication bias  increased the risk of disease and odd-chain decreased the risk. In the vitro study, Hardy et al84 observed SFA palmitate inhibited BC cells and resulted in the apoptosis, and unsaturated fatty acid promoted the process of proliferation simultaneously. Together, BC is not only related to dietary SFA intake but more associated with the free fatty acid in organism. Determination of internal metabolites of SFA may help us understand the occurrence and development of BC clearly.Micha and Mozaffarian85 Second, possible bias may contain in case–control studies, such as selection bias and recall bias with the contribution of different results of population or hospital-based design. Although, all of our studies considered the confounding factors, such as sex, age, education, BMI, energy intake, smoking, drinking, and nutrient intake, and they reduced the effect of confounding factors to some degree. We still cannot explain potential effects of other dietary habits or behavior and asserted etiology relationship between dietary SFA intake and BC events. The exact mechanisms are not well-established whereby higher SFA intake increasing risk of BC. Since reform and openness, the Chinese have obtained better life conditions and meanwhile their dietary patterns have been changing all the time with tendency to western countries. Hence, we increased our dietary fat intake rapidly, especially saturated fat. Additionally, only 6 articles of subgroup analyses conducted in Asia were referred in our meta-analysis. Small sample size may contribute to the heterogeneity. As for recruit source, due to selection bias, subjects from hospital may result in significant association and different results between case–control and cohort studies. Besides, subjects from hospital are not more representative when compared to the population-based ones.Our meta-analysis consists of some limitations. First, only the literatures published in English were included. Several unpublished null articles may be missing because of study with positive results which were searched easier.In conclusion, relationship was found between SFA intake and incidence of BC in case–control studies, and a positive association between higher dietary SFA intake and postmenopausal BC risk was observed in case–control but not in cohort studies. In future, dietary fatty acid intake and serum fatty acid level should be combined to analyze the more detailed relationship with BC.


Dietary patterns are commonly used in epidemiological research, yet there have been few studies assessing if and how research results may vary across dietary patterns. This study aimed to estimate the risk of mortality/recurrence/metastasis using different dietary patterns and comparison amongst the patterns.Dietary patterns were identified by Cluster Analysis (CA), Principal Component Analysis (PCA), Alternate Mediterranean Diet score , Recommended Food Score (RFS) and Dietary Inflammatory Index (DII) scores using a 169-item food frequency questionnaire.Five hundred thirty-two colorectal cancer patients diagnosed between 1999 and 2003 in Newfoundland were followed-up until 2010. Overall Mortality (OM) and combined Mortality, Recurrence or Metastasis (cMRM) were identified. Comparisons were made with adjusted Cox proportional Hazards Ratios (HRs), correlation coefficients and the distributions of individuals in defined clusters by quartiles of factor and index scores.One hundred and seventy cases died from all causes and 29 had a cancer recurrence/metastasis during follow-up. Processed meats as classified by PCA  1.07–3.09), clusters characterized by meat and dairy products  and total grains, sugar, soft drinks  were associated with a higher risk of cMRM. Poor adherence to AltMED increased the risk of all-cause OM . Prudent vegetable, high sugar pattern, RFS and DII had no significant association with both OM and cMRM.Estimation of OM and cMRM varied across dietary patterns which is attributed to the differences in the foundation of each pattern.The online version of this article (10.1186/s12937-018-0362-x) contains supplementary material, which is available to authorized users. Diet and behavioural factors have crucial roles in the risk and progression of several chronic diseases including colorectal cancer (CRC) . EpidemiData-driven and hypothesis-driven are two major approaches to identify dietary patterns . ClusterBriefly, cluster analysis (CA) divides individuals into mutually exclusive, non-overlapping groups based on mean dietary intakes (gm) . Food inRecommended food score (RFS)  and alteDietary patterns are commonly used in epidemiological research. Studies on how outcome estimation may vary across these different patterns are limited and comparing across the patterns are recommended to better understand disease diet association ; howeverThis study used data from the Newfoundland Familial Colorectal Cancer Registry (NFCCR). Five hundred and thirty-two pathologically confirmed  CRC patients diagnosed between 1999 and 2003 and residents of Newfoundland and Labrador, aged 20–75 years, were included in the study. A detailed description of the study population is published elsewhere . BrieflyIndividuals who were lost to follow up, still alive or who did not have a recurrence or metastasis by the end of the follow-up period were censored at the time of the last contact. We conducted follow-up questionnaires with participants and linked records to death certificates, pathology reports, autopsy records, physicians’ notes, and surgical reports. Additional data were obtained from the Dr. H. Bliss Murphy Cancer Care Foundation ; many ofParticipants completing the consent were asked to complete validated food frequency questionnaire (FFQ) , personaMSI (Microsatellite instability) and BRAF have been associated with cancer prognosis and survival , 20. P VFor CA, 169 food items were classified into 39 different food groups depending on the ways they are taken and nutrient profile. Food groupings are attached in the Additional file Three patterns were identified using the PCA correlation matrix as the variables were on different scales. Briefly, exploratory principal component factor analysis was conducted using the same 39 predefined food groups. A varimax rotation  was applied to identify uncorrelated food groups. Factor Eigen-value greater than 1.15, the scree plot and proportion of variance explained were used to identify the number of factors. Patterns were labelled based on factor loading ≥0.5. The factor score of each participant was obtained by summing the intake of each food group multiplied by optimal weights and divided into quartiles. A higher factor score represents greater adherence to that particular dietary pattern. Factor loading and explained variances for three major dietary patterns are shown in Additional file The RFS method developed by Kent, et al.  is basedThe altMED score is based on the Mediterranean diet scale ; scoringDetailed descriptions of the DII score are provided elsewhere , 26. BriAdjusted hazards ratios were estimated using Cox proportional hazard analysis using SAS version 9.4 . Comparisons across patterns were made with adjusted HRs, correlation coefficients and distributions of individuals in clusters by quartile of factor and index scores. Potential confounding factors include age; sex; body mass index (BMI) ; physical activity as measured by the Global Physical Activity Questionnaire (GPAQ) ; MetabolP < 0.10. Potential confounders were first selected based on the previous studies as well as a literature search. Initially, potential confounders were screened by the univariate test. Those variables that were statistically non-significant in the univariate test but have an important role in the etiology were included in the model selection step as default; those variables included marital status, the location of a tumor, smoking status, physical activity and reported chemotherapy. As there were too many variables, a stepwise procedure was employed in order to include potential confounding variables that have a detectable effect on the association of interest while retaining the above-mentioned variables in the model.The basis for assessing potential confounding factors included: existing evidence, biological plausibility, whether the regression coefficient of the primary variable of interest changed by 10% or more after addition of the potentially confounding variable for every covariate entered in the model at Mean age of participants and mean age at diagnosis was 62.53 ± 9.06 years and 60.42 ± 9.02 years, respectively. A total of 170 cases died from all causes and 29 had a cancer recurrence or metastasis at the end of the follow-up. Mean time between the date of diagnosis to the end of follow up or date of death from all causes (OM) was 6.27±1.98 years and mean time between the date of diagnosis to the end of follow up or the date of death, recurrence, or metastasis (CMRM) (whichever came first) was 5.70±2.38 years). Almost 68% of the participants were censored for OM and 62.6% for cMRM during analysis.Table Table p = 0.09), the highest quartile of processed meat pattern significantly increases the risk of cMRM , however, there was no significant association with OM. Neither the prudent vegetable pattern nor the high sugar pattern showed a significant association with both cMRM and OM.Three dietary patterns were identified using PCA: processed meat pattern, prudent vegetable pattern and high sugar pattern. Though the overall trend was not significant  but had no significant association with the cMRM.p = 0.001). Significant negative correlations were found between the DII score and the altMED  and RFS  scores.Spearman’s correlation coefficients amongst the index-based scores are described in Table Table This study is a prospective analysis of mortality among CRC patients diagnosed between 1999 and 2003 from a Canadian population. Both data-driven and hypothesis-driven dietary patterns were determined and relation with CRC patient’s mortality, recurrence and metastasises was estimated. The hypothesis-driven pattern showed how study population is adherent to dietary recommendation while data-driven pattern explains how whole population dietary practice can be classified into different categories. As each dietary pattern was designed to answer the different question, the discrepancy in the outcome estimation was expected despite some level of similarity in the foundation of dietary patterns.In the current study as identified by CA, the meat and dairy product cluster was associated with increased risk of cMRM while the refined grains, sugar, soft drinks cluster was associated with increased risk of both cMRM and OM. A processed meat pattern as identified by PCA was associated with an increased risk of cMRM. Low adherence to the Mediterranean diet was associated with increased OM. RFS and DII had no significant association with the survival outcomes. The magnitude of estimated HR also varied accordingly.Epidemiological studies reveal inconsistent results while assessing the relation between dietary patterns and disease outcome in the same population, which is in line with the current study. The study by Reedy J; et al.  showed tIn the Nurse’s Health study  index-baThe current study suggested good evidence of comparability between PCA and CA in identifying the dietary pattern as seen in other studies , 34 despThe hypothesis-driven dietary patterns give higher weight for fruits and vegetables, which is evident by having the majority of individuals in the lowest quartile of DII and the highest quartile of altMED and RFS in Cluster I, characterized by fruits and vegetables. Correlations between index scores were relatively strong and statistically significant as scores were based on similar food recommendations. An increasing score of altMED, RFS and a decrease in DII score are characterized by the higher amount of plant-based food .Indexing systems vary in the definition of optimal diet quality and in their scoring which leads difference in their sensitivity to estimate the disease outcome. Differential classification of food leads to differential exposure. RFS accounts for intake of vegetables, fruits, healthy protein sources, grains and dairy products but does not differentiate between different types of fatty acids or penalize for consumption of foods that are not recommended. Alcohol, energy dense food items and meat products are associated with survival outcomes as seen from empirical approaches but are not considered in scoring. Hence, RFS is likely to underestimate the true association. Further, in the RFS approach, energy cannot be adjusted so the effect of body size, physical activity and higher basal metabolic rate cannot be taken into account for the analysis . Energy Multiple reasons could be suggested for the inconsistent results. First, several studies have suggested dietary guidelines have been more strongly related to coronary heart disease than to cancer mortality, even though guidelines are directed toward lowering cancer risk . More exEach method has its own strengths and limitations . EmpiricThis is a prospective follow-up study. Detailed data for the variables  are available including the genetic data on MSI status. Multiple dietary patterns are used for comparison.This study has a number of limitations. First, the sample is relatively small, which may not offer desirable statistical power and precision in multivariate analysis. Further, cases were followed until 2010 only. Recall error and possible bias are likely to exist as the cases were asked to remember their dietary patterns a year prior to their diagnosis. However, we believe the recall bias, if any, could be non-differential, which is likely to attenuate the observed association. Although bias may exist and sample size is less, it may have little impact on cross-comparison, which is the primary focus of the study. Some cases might have changed their dietary patterns, lifestyle and behaviour after diagnosis or even in the disease induction/latent period; this may lead to possible reverse causation bias, which should be explored in future studies.The present study showed that the estimation of OM and cMRM amongst the CRC patients varied with the type of diet pattern used. Hazards ratios for cMRM varied from 1.82; 95% (CI- 1.07-3.09) for processed meat pattern identified by PCA to HR 2.19; 95% CI 1.03–4.67 for cluster characterized by meat and dairy products and HR 1.95; 95% CI 1.13–3.37 for cluster characterized by refined grains, sugar, soft drinks. Only cluster characterized by refined grains, sugar, soft drinks had higher risk of OM . All the diet indices showed similar null associations with both cMRM and OM except Poor adherence to altMED increased the risk of all-cause OM . On the average estimates were higher for data driven methods than hypothesis driven. The variations in the estimated hazards ratios is attributed to the foundation of each dietary pattern identified by various approaches.Additional file 1:Table S1. Food groupings. Table S2. Characteristics of Cluster. Table S3.. Factor loading and explained variances (VAR) for the three major dietary patterns identified from food frequency questionnaire at baseline using the principal component factor analysis, Newfoundland. Table S4. Recommended food Score. Table S5. Alternate Mediterranean Diet Score. (DOCX 23 kb)


SDF-1) gene rs1801157 polymorphism in systemic lupus erythematosus (SLE).Present meta-analysis was performed to investigate the role of stromal cell-derived factor . A similar result was also observed in Asian subgroup under the same comparison after stratification analysis by ethnicity .Five publications with 1,087 cases and 1,181 controls were incorporated in this meta-analysis. Overall, a marginal association between SDF-1 rs1801157 polymorphism and SLE risk. The stratification analysis was also performed according to ethnicity. In addition, heterogeneity was examined with Q test, and sensitivity analysis was used to test the stability of final results. Begg’s funnel plot and Egger’s test were adopted to evaluate publication bias.All included studies were retrieved from PubMed, EMBASE, Google Scholar Web and Chinese National Knowledge Infrastructure (CNKI). Odds ratios (ORs) with 95% confidence intervals (95% CIs) were calculated to appraise the strength of association between SDF-1 rs1801157 polymorphism may not influence the risk of SLE. However, more studies with larger sample sizes involving different populations are needed to further explore this issue. Systemic lupus erythematosus (SLE) is a diffuse connective tissue disease mediated by immune inflammatory reaction. As a recognized autoimmune disease (AID), SLE usually implicates multiple systems and organs, leading to complicated clinical manifestations, prolonged course and higher fatality rate. Along with the deterioration of environments and the improvements in diagnostic techniques, the incidence rate of SLE exhibits an obvious rising tendency worldwide. Until now, the pathogenesis of SLE has not been fully explained. It is generally accepted that the etiology of SLE is implicated intrinsic factors, such as heredity and sex hormones, and, environmental factors, including drugs, and infection. In addition, it is a key point in the pathogenesis of SLE that the excessive activation of autoreactive T cells can stimulate B cells to produce a large number of autoantibodies, thus aggravating disease progression .Chemokines, a class of micromolecular cytokines, contribute to the directional movement of immune cells. It plays important roles in various physiological and pathological processes, such as inflammation and immune responses, immune cell and organ developments, via combining with corresponding specific receptors on cell surface, thus participating in the regulation of T and B cell functions simultaneously . ChemokiSDF-1 gene, the coding gene of SDF-1, is reported to be able to influence individual susceptibility to several disease. A great number of investigations have been devoted to detect the roles of SDF-1 rs1801157 polymorphism in cancers [SDF-1 rs1801157 polymorphism and SLE risk have yielded inconsistent conclusions. We therefore conducted present meta-analysis to ascertain this topic.Numerous researches have demonstrated that SDF-1 may be involved in the pathogenic processes of diverse AIDs, such as rheumatoid arthritis (RA)  and SLE  cancers –8 and sy cancers . HoweverP < 0.05).As shown in Figure SDF-1 rs1801157 polymorphism and reduced SLE risk under GA vs. GG contrast [OR = 0.84, 95% CI = 0.70–1.00 , suggesting the stability and credibility of our results.P = 0.324 for AA vs. GG). Consequently, the publication bias in this study was negligible.Funnel plots visually seemed symmetrical under all comparison models Figure , and EggSLE is one of the most representative autoimmune inflammatory diseases which is characterized by excessive production of antibodies, abnormal activation of complement, pathological deposition of immune complex, and persistent disturbance of immune function. SLE accompanied by primary kidney damage is categorized into systemic lupus erythematosus nephritis (SLEN) and lupus nephritis (LN), with the latter greatly increasing the disease mortality. Although the etiology of SLE remains unclear, growing evidences have reported that both genetic and environmental factors play significant roles in its occurrence and development. It’s generally believed that the the over-activation of B lymphocytes induced by dysfunction of T lymphocytes may excessively produce autoantibodies, formate immune complexes and activate complement, thereby resulting in the onset of SLE, even multiple organ damages. Researches find that abnormal regulations of various cytokines, chemokines, growth factors, integrins, as well as external physical and chemical stimulation (such as ultraviolet and some drugs) may conduce to aberrant activation of T and B lymphocytes, thereby affecting the pathogenesis of SLE.Accumulating researches have demonstrated that chemokines may present a vital physiological and pathological effect on the organism. Some studies indicate that chemokines are significantly correlated with inflammation, allergic reaction, AIDs, tumors, as well as acquired immune deficiency syndrome (AIDS) –17. ChemSDF-1 rs1801157 polymorphism and SLE risk. Nevertheless, the results remain controversial. For instance, Teresa Warchol et al. [SDF-1 rs1801157 polymorphism might be linked to some clinical manifestations in patients with SLE. But Lian et al. [SDF-1 rs1801157 polymorphism on risk of SLE.SDF-1, a widely-studied chemokine, play an important role in propelling lymphocyte homing and maturation, regulating immune inflammatory reaction and promoting angiogenesis. Given its functional roles in immune inflammatory reaction, SDF-1 was implicated in SLE. In the last few years, several studies have been undertaken to investigate the correlation between n et al.  concludeSDF-1 rs1801157 polymorphism and SLE risk were incorporated in the meta-analysis, which were published between Feb, 2005 and May, 2012. A total of 2,268 subjects were enrolled, including 1,087 cases and 1,181 controls. The results of our meta-analysis demonstrated a marginal association between SDF-1 rs1801157 polymorphism and reduced SLE risk under GA vs. GG model in overall analysis. Furthermore, subgroup analysis by ethnicity revealed a similar relationship between the polymorphism and the disease risk in Asians. In addition to SLE, the meta-analysis on SDF-1 rs1801157 polymorphism was also constructed for other diseases. For examples, the significant association between SDF-1 rs1801157 polymorphism and hematological malignancy was observed by Zhang et al. based on 9 related studies [SDF-1 rs1801157 polymorphism showed close association with cancer risk among Asians, especially influencing the susceptibility of urologic and lung cancers [SDF-1 rs1801157 polymorphism and coronary hear disease based on 7 articles [Ultimately, five case-control studies on the relationship between  studies . The rel cancers . Howeverarticles . The conSDF-1 rs1801157 polymorphism with SLE risk through pooling the results of all available studies. However, there were still several limitations in our meta-analysis. Firstly, only five records were embraced in the meta-analysis, and the sample size was relatively small, particularly in stratified analysis. Secondly, the influences of gene-gene and gene-environment interactions were not taken into account in this meta-analysis, because most studies did not provide relevant information, thus impeding us for further analysis. Further well-designed investigations with larger sample size were urgently needed to verify the conclusion in our article.Current meta-analysis systematically assessed the relationship of SDF-1 rs1801157 polymorphism may tend to decrease the SLE risk, especially in Asian population. In view of the above shortcomings in the current work, further researches with larger sample size should be carried out to further identify the correlation of SDF-1 rs1801157 polymorphism with SLE risk.Taken together, SDF-1 or CXCL12”, and “polymorphism or variant or mutation”. There was no language restriction imposed on literature search. To avoid missing any related papers, we also scanned the references of all relevant publications.The relevant studies were identified from the electronic databases of PubMed, EMBASE, Google Scholar Web and Chinese National Knowledge Infrastructure (CNKI) with the following keywords and terms: “systemic lupus erythematosus or SLE”, “stromal cell-derived factor 1 or SDF-1 rs1801157 polymorphism on the risk of SLE; (3) studies presented adequate genotype data to calculate odds ratios (ORs) along with 95% confidence intervals (95% CIs). For multiple studies reported the same trial, the most recent or the largest one with whole data would be included in the meta-analysis. Besides of English and Chinese, the articles were published in other languages would be excluded. In addition, the meta-analysis, reviews, letters etc would not included in this meta-analysis.Selection of eligible studies for this meta-analysis was conducted based on the following criteria: (1) studies had a case-control design; (2) studies involved the influence of P values for Hardy-Weinberg equilibrium (HWE) in controls. If necessary, an experienced investigator was consulted to resolve all discrepancies over recorded data, thus reaching a consensus finally.In accordance with the selection criteria, two investigators identified all qualified publications, and independently abstracted the following information from them: the first author’s name, year of publication, original country, ethnicity , control source, genotyping method, numbers of cases and controls, genotype frequency in both case and control groups, and SDF-1 rs1801157 polymorphism on SLE risk under AA vs. GG, AA + GA vs. GG, AA vs. GG + GA, allele A vs. allele G, and GA vs. GG genetic models. Stratified analysis was performed on the basis of ethnicity to further explore the specific correlation between them. The statistical significance of pooled OR was determined by Z test. Deviation of genotype distribution from HWE in control populations was measured using the chi-square goodness-of-fit test. Between-study heterogeneity was checked by chi-square based Q-test. If statistically significant heterogeneity (P < 0.05) existed among included studies, random-effects model was adopted to estimate pooled ORs [P < 0.05 was considered to be statistically significant.Summary ORs and 95% CIs were calculated to assess the effect of oled ORs ; or elseoled ORs . Sensitioled ORs . All ana


Studies exploring the association between chronic obstructive pulmonary disease (COPD) and lung cancer have yielded mixed results. We conducted a meta-analysis of the published prospective cohort studies to have a clear understanding about this association.We searched the MEDLINE and EMBASE databases from inception to December 31, 2016. Bibliographies were also reviewed for additional information. Random-effects model was used to calculate summary relative risk (SRR) and corresponding 95% confidence interval (CI).Eighteen prospective cohort studies were part of this meta-analysis, involving 12,442 lung cancer cases with a median duration of follow- up of 5 years (range: 1.5–20 years). A history of COPD, emphysema or chronic bronchitis conferred SRRs of 2.06 , 2.33  and 1.17 , respectively. Stratification by COPD severity yielded SRR of 1.46 (95% CIs: 1.20–1.76) for mild, 2.05 (95% CIs: 1.67-2.52) for moderate and 2.44(95% CIs: 1.73-3.45) for severe COPD, respectively. There were similar risk estimations for never and ever smokers. The SRR was statistically higher for squamous cell cancer than that for adenocarcinoma and for small cell cancer of the lung (P<0.05).This meta-analysis indicated a significantly increased risk of lung cancer for COPD, emphysema, but not for chronic bronchitis. For the prevention of lung cancer, it is of importance for early detection of COPD in lung cancer surveillance. Lung cancer and chronic obstructive pulmonary disease (COPD) are two major public health problems. According to the Global Burden of Disease (GBD) 2013, these two diseases are among the six non-communicable leading causes of years of life lost globally .COPD is characterized by progressive and incompletely reversible airflow obstruction in the lungs. Epidemiological studies have observed an association between these two diseases and mechanisms are most likely related to systemic inflammation, oxidative stress, lung repair mechanisms . Though Based on the study selection criteria, we identified a total of 2,439 potentially relevant articles . Among these 2439 articles, 72 were considered potentially relevant and their full texts were retrieved for further evaluation. Fifty-nine were excluded for various reasons: 17 did not evaluate this association, 4 reported the same population , 34–36, Table heterogeneity <0.001, I2 = 96.7%; Figure heterogeneity =0.027, I2 = 72.3%; Figure heterogeneity =0.091, I2 = 53.5%; Figure Fourteen studies representing the association between COPD and lung cancer risk were used for this analysis. The SRR was 2.06 (95% CI: 1.50-2.85), and had high heterogeneity (Pheterogeneity<0.001), Europe  and the USA .When stratified by study locations, the strong association was observed in China . There was no gender difference in the risk estimation (P =0. 289).Most of the studies reported risk estimations for men and women combined, and three studies –22 reporThree studies , 13, 20 heterogeneity = 0.931, I2=0) were similar with those in self-report  or disease registry subgroups .We also performed subgroup analysis based on the methods of COPD diagnosis. The SRR in spirometry and radiographic evidence subgroup  for mild, 2.05 for moderate and 2.44 for severe COPD, respectively.Four studies , 16, 45 heterogeneity <0.001, I2= 89.3%) for studies with a median duration of follow-up less than 5 years, which was similar with those for studies with a median duration of follow-up large than 5 years 2.23.The SRRs were 1.87 .Among these 14 cohort studies, 10 used incidence rate ratios or hazard ratio as the measure of relative risk, while four used mortality rate ratios or hazard ratio , 22, 47.heterogeneity =0.147), but null for adenocarcinoma  and small cell cancer .Two cohort studies , 13 repoExclusion of studies in which all subjects were reported with a previous diagnosis of pneumonia , peripheWe conducted meta-regression analyses to investigate the impact of above study characteristics on the risk estimates of lung cancer. Only study locations (P=0.029) was found to be the significant factor, and the heterogeneity explained by the population source was 36.3%.P =0.511) and Egger's (P =0.356) tests did not reveal evidence of publication bias, but visual inspection of the funnel plots revealed significant asymmetry. The trim-and-fill method suggested that 7 additional risk estimates were needed to balance the funnel plot, and the summary risk estimates were still statistically significant, albeit weaker . In contrast to the well established finding that smoking is a stronger risk factor for squamous cell carcinoma and small cell carcinoma than for adenocarcinoma of the lung , our finOur study has several strengths as follows: (1) we were able to update a number of recently published studies on the COPD-lung cancer association; (2) we could derive risk estimates with high levels of precision and the less possibility of recall or selection bias, because our analysis was according to eighteen prospective cohort studies, involving more than 10,000 lung cancer cases and 9 million participants; (3) We performed several sensitivity analysis based on sex, geographic locations, smoking status, exposure assessment, COPD severity, duration of follow-up, lung cancer histology in the COPD-lung cancer association.There are several design and methodological limitations of this study that must be considered in the interpretation of the results. First, this meta-analysis detected great heterogeneity across studies. We performed meta-regression analyses showed that study locations have modified effects on this association, which might partially (36.3%) account for the high heterogeneity among studies.Second, misclassification bias may exist. A history of COPD was ascertained by self-report in four studies, which lead to the attenuation of risk estimates and the true COPD-lung cancer association may be stronger than we observed. Although the precise definitions and diagnoses of COPD vary, some studies , 58 haveThird, because of the observational nature of our analysis, unmeasured and residual confounding is likely to be present. It has been debated that the COPD-lung cancer relationship may be a product of residual confounding by tobacco smoking, the shared risk factor for both COPD and lung cancer . In our Forth, the effect of medications for COPD on this association should also be taken into account. To date, results from observational studies have suggested the potential that inhaled corticosteroids (ICS), which are commonly prescribed to COPD patients, may exert a protective role against the development and progression of lung cancer, particularly at high doses . HoweverFinally, the possibility of publication bias is of a concern, because small studies with null results tend not to be published. Although Begg's and Egger's tests did not reveal evidence of publication bias, visual inspection of the funnel plots did reveal significant asymmetry. The trim-and-fill method suggested 7 additional risk estimates were needed to balance the funnel plot, and the summary risk estimates were still statistically significant, albeit weaker (SRR =1.37).In conclusion, the present study suggests a significantly increased risk of lung cancer associated with history of COPD. However, the possibility that the association may be due to bias or confounding cannot be completely excluded.Two investigators (ZXY and JN) independently screened the original articles published in English language in MEDLINE and EMBASE databases from inception to December 31, 2016. We used the list of keywords in the form of following MeSH terms and Text Words: 1) COPD OR chronic obstructive pulmonary disease OR emphysema OR chronic bronchitis; 2) lung cancer OR lung neoplasm; 3) risk OR incidence OR prevalence OR mortality. References of selected retrieved articles were also manually searched. We did not consider abstracts or unpublished reports.COPD is a term generally used to describe two end phenotypes: emphysema  and chronic bronchitis (chronic inflammation and scarring of bronchi), and the majority of patients with COPD are found somewhere in the middle. The most common definitions of COPD involve either airflow limitation (American Thoracic Society) or reduced maximum expiratory flow (European Respiratory Society). The present meta-analysis was based on estimates reported for these three conditions combined as well as reported separately .Two authors (ZXY and JN) independently reviewed all the retrieved studies to determine if they meet the inclusion criteria and disagreements were settled through consensus with a third investigator (WLJ). The inclusion criteria were as follows:(1) published as original prospective cohort studies reporting incidence or mortality of lung cancer after the diagnosis of COPD (including emphysema and/or chronic bronchitis);(2) reported findings in terms of relative risk (RR), hazard ratio(HR), with corresponding 95% CIs or that reporting sufficient data to calculate them;(3) adjusted or matched the risk estimations with age and smoking at least.Non peer-reviewed articles, animal and mechanistic studies, ecologic assessments and correlation studies were not included for analysis. In case of several publications describing the same study, only the most recent or informative publication was included.The following information was extracted from each study by two researchers (ZXY and JN) independently: last name of the first author, study design, publication year, geographic locations, study size, study population, methods of COPD and lung cancer ascertainment, follow-up duration, the RR estimates with their 95% CI and adjustment variables. From each study, the risk estimates were extracted that have been adjusted for the greatest number of potential confounders. To ensure the accuracy of data extraction, this process was independently performed by three authors . Any data discrepancy was also resolved by consensus.Data analysis was performed using STATA, version 11.0  statistical software. A two-tailed P value of <0.05 represents significance. In light of the high likelihood of between study variance, we used a random-effect model rather than a fixed-effect model. This method of a random-effects model was developed by DerSimonian and Laird . BecauseQ and I2 statistics. P value of < 0.10 represents statistically significant heterogeneity. I2 values quantify the proportion of total variation across studies that is due to heterogeneity rather than chance, with the low, moderate, and high of 25%, 50%, and 75% respectively. Where the I2 value was 25% or lower, indicating no evidence of heterogeneity [P values of <0.10 indicates potential publication bias. In addition, we evaluated the number of unpublished studies that would have to exist to negate the results and the pooled RR adjusted for publication bias using the trim and fill method [Heterogeneity was assessed by Cochran ogeneity . Sourcesogeneity , 65. Thel method .


To systematically and quantitatively review the relation of abdominal obesity, as measured by waist circumference (WC) and waist to hip ratio (WHR), to total gastroesophageal cancer, gastric cancer (GC), and esophageal cancer. PubMed and Web of Science databases were searched for studies assessing the association between abdominal obesity and gastroesophageal cancer  up to August 2016. A random-effect model was used to calculate the summary relative risks (RRs) and 95% confidence intervals (CIs). Seven prospective cohort studies – one publication included two separate cohorts – from six publications were included in the final analysis. A total of 2130 gastroesophageal cancer cases diagnosed amongst 913182 participants. Higher WC and WHR were significantly associated with increased risk of total gastroesophageal cancer , GC , and esophageal cancer .Findings from our subgroup analyses showed non-significant positive associations between gastric non-cardia adenocarcinoma (GNCA) and both measures of abdominal adiposity, while gastric cardia adenocarcinoma (GCA) was positively associated with WC but not with WHR. On analysis restricted to studies that adjusted for body mass index (BMI), WC was positively associated with GC and esophageal cancer, whereas WHR was positively associated with risk of GC only. Although limited, the findings from our meta-analysis suggest the potential role of abdominal obesity in the etiology of gastric and esophageal cancers. Globally, esophageal cancer ranks eighth for cancer incidence and sixth for cancer death, while gastric cancer (GC) ranks fourth and second, respectively . There iDuring the past few years, evidence from several observational studies has shown that body fat distribution, particularly abdominal obesity, may better predict risk of several chronic diseases and mortality than general obesity (BMI) –18. MoreThis meta-analysis was planned, conducted, and reported according to ‘Meta-analysis of Observational Studies in Epidemiology (MOOSE) group’ guidelines . PubMed To be included in this meta-analysis, the studies had to meet the following inclusion criteria: (i) the study design was a prospective study ; (ii) investigated the association between WC and/or WHR and gastroesophageal cancer ; (iii) relative risks (RRs), hazard ratios (HRs), or odds ratio (ORs) with 95% confidence intervals (CIs) were available. Accordingly, retrospective studies or studies on gastroesophageal cancer mortality, or recurrence were excluded. If multiple publications from the same study were identified, the publication containing the largest number of cases and most applicable information was selected.Using a standardised data-collection form, the following data were abstracted from each study: the first author’s last name, publication year, country, study population, duration of follow-up, number of participants, number of cases, ascertainment of adiposity, measures of abdominal adiposity, most fully adjusted risk estimates with their corresponding 95% CIs for each category of abdominal adiposity measures, statistical adjustment for potential confounding factors, and outcome assessed. If multiple RRs of the association were available, we extracted RRs with their corresponding 95% CIs from the models that reflected the maximum extent of adjustment for potentially confounding variables. When studies provided specific risk estimates (i.e. anatomic subtypes of gastric adenocarcinoma), we extracted all of them and used the data in subgroup analyses. The study quality was assessed using the 9-star Newcastle-Ottawa Scale (NOS) , in whicQ and I2 statistics. For the Q statistic, P<0.1 was considered statistically significant; and for the I2 statistic, the following conventional cut-off points were used: <25% (low heterogeneity), 25–50% (moderate heterogeneity) and >75% (severe heterogeneity). Both Begg’s rank correlation test and Egger’s linear regression test were performed to investigate potential publication bias [P-values were two-sided and the level of significance was at <0.05, unless explicitly stated.RR was chosen as the common measure of association across the present study. Due to the rarity of cancer in the general population, HR and OR were directly considered as RR. DerSimonian and Laird  random-eion bias . If evidion bias . To explI2 =27.6%, P=0.182), as well as for esophageal cancer . On the other hand, no evidence of heterogeneity for GC . Higher WC was associated with an increased risk of GCA  but not with GNCA   and 2.13  for GC and esophageal cancer, respectively. The sensitivity analyses that omitted one study at a time and calculated the combined RR for the remaining studies yielded consistent results. No evidence of publication bias was observed across studies .Five prospective cohort studies ,25,27,28P=0.840) . AnalysiI2 =44.2%, P=0.064), as well as for GC , while high heterogeneity was observed for esophageal cancer . Regarding the anatomic subtypes of GC, no significant association was found between WHR and adenocarcinomas of gastric cardia  and gastric non-cardia  , whereas no association was found with esophageal cancer . The sensitivity analyses omitting one study at a time and calculating the combined RRs for the remaining studies showed that the combined RRs was not substantially affected by any single study. No evidence of publication bias was observed across studies .Five prospective cohort studies –27,29 weP=0.142) . In analThe present comprehensive systematic review and meta-analysis examined the associations between abdominal obesity and risk of total gastroesophageal cancer, GC and esophageal cancer. We found evidence of an increased risk of total gastroesophageal cancer, GC, and esophageal cancer with higher WC and WHR.To the best of our knowledge, this is the first systematic review and meta-analysis to summarise the available evidence from prospective studies for determining the associations between abdominal obesity and risk of total gastroesophageal cancer and GC. We are fully aware of previous meta-analysis of central adiposity and esophageal cancer . HoweverFindings on the associations between measures of abdominal obesity and anatomic subtypes of GC in prospective studies have been inconsistent. MacInnis et al.  found thAs mentioned earlier, obesity has been shown to be an important risk factor for certain types of cancer –7. HowevH. pylori infection and evidence of traditional reflux disease were enrolled in the present study to determine the associations between central obesity and the length of cardiac mucosa, inflammation of the distal segment of the lower esophageal sphincter, pH, and other measurements of gastroesophageal function. In the present study, inflammatory cells were detected at the gastric cardia in all participants, but those with large WC and higher total abdominal fat were found to exhibit greater cardiac mucosal lengthening and had more proximal acid reflux. Gastric mucosal lengthening may trigger the expansion and outward migration of cardia progenitor cells into the gastroesophageal junction, leading to gastroesophageal junction neoplasia [The link between esophageal adenocarcinoma is notably stronger for abdominal obesity than general obesity . A poteneoplasia .Abdominal obesity may affect the gastroesophageal junction not only mechanically but also systemically via metabolic/inflammatory pathways. One plausible molecular mechanism for obesity-associated carcinogenesis is that visceral adipose tissue, which is metabolically active, promotes the release of inflammatory cytokines and mediators, including free fatty acids, tumor necrosis factor α (TNFα), leptin, and resistin, inhibits the secretion of adiponectin and ultimately leads to development of insulin resistance ,9,57. InThis meta-analysis has several strengths, including incorporated evidence and relevant studies to the date. Because results from individual studies had insufficient statistical power, the enlarged sample size from our meta-analysis may enhance the power to detect a significant association and provide more precise estimates of the effects. All original studies included are of long follow-up durations, and used a prospective design which thereby reduced the likelihood of potential biases . Finally, the prospective design permits the evaluation abdominal adiposity measures before the weight loss that often accompanies gastroesophageal malignancies.There are several limitations in the present meta-analysis that should be acknowledged. First, heterogeneity was observed across studies, this may attribute to differences in the strength of the association, rather than due to differences in the directionality of effect as all studies showed a clear trend toward increased risk. The observed heterogeneity could also be explained by differences in duration of follow-up, sample sizes, population characteristics, and statistical adjustments for potential confounders. Second, data from original studies were insufficient to evaluate a dose–response relationship since most of included studies did not reported cut-off points for every quantiles of abdominal adiposity measures. Although contacting authors was an option to overcome this problem, however, our approach to overcome this issue was not successful. Third, only seven studies were eligible for this meta-analysis. One of them was conducted in participants with Barrett’s esophagus , which mAlthough limited, the findings from our meta-analysis suggest the potential role of abdominal obesity in the etiology of gastric and esophageal cancers. The findings from our present meta-analysis highlight the importance of maintaining a healthy weight and staying as lean as possible for reducing risk of gastric and esophageal cancers. Most current evidence suggest mechanical forces caused by increased abdominal pressure and/or endocrinological changes due to excess abdominal adipose tissue as potential risk factors for these cancers. Nevertheless, further research on the mechanisms linking abdominal obesity to adenocarcinoma of esophagus and gastric cardia is warranted to elucidate the exact pathogenic pathways underlying these malignancies and thereby develop better treatment strategies. Furthermore, it will be important for the future prospective studies to examine the potential influences of general obesity (BMI) on these associations, and to provide results according to histologic subtypes. Finally, there are several risk factors  common to both abdominal obesity and cancer; given this consideration, the observed association between abdominal obesity and gastroesophageal cancer may be partly due to similar factors shared by both conditions. Thus, further clarification for the issue of whether abdominal obesity itself, rather than a proxy for another cancer risk factor, associated with increased risk of gastroesophageal cancer is warranted.


Background: A meta-analysis was conducted to summarize the evidence from prospective cohort and case-control studies regarding the association between coffee intake and biliary tract cancer (BTC) and liver cancer risk. Methods: Eligible studies were identified by searches of PubMed and EMBASE databases from the earliest available online indexing year to March 2017. The dose–response relationship was assessed by a restricted cubic spline model and multivariate random-effect meta-regression. A stratified and subgroup analysis by smoking status and hepatitis was performed to identify potential confounding factors. Results: We identified five studies on BTC risk and 13 on liver cancer risk eligible for meta-analysis. A linear dose–response meta-analysis did not show a significant association between coffee consumption and BTC risk. However, there was evidence of inverse correlation between coffee consumption and liver cancer risk. The association was consistent throughout the various potential confounding factors explored including smoking status, hepatitis, etc. Increasing coffee consumption by one cup per day was associated with a 15% reduction in liver cancer risk . Conclusions: The findings suggest that increased coffee consumption is associated with decreased risk of liver cancer, but not BTC. Coffee is one of the most consumed beverages worldwide and it has been associated with a number of benefits on human health including a decreased risk of all-cause, cardiovascular, and cancer mortality ,2,3. CofGallbladder (GB) cancer is a highly fatal malignancy with notable geographical variations and a higher incidence in women. The aetiology of biliary tract cancer (BTC), including GB cancer and bile duct cancer (BDC) is poorly understood. One of the main risk factors has been hypothesized to be prolonged exposure to gallstones associated with chronic inflammation , which mAmong the main targets of the healthful effects of coffee, the liver in particular, appears to benefit from coffee consumption due to the improvement of lipid metabolism regulation and decreased rates of liver steatosis and non-alcoholic steatohepatitis, which in turn may decrease the risk of consequent inflammation and fibrosis . A recenMeta-Analysis of Observational Studies in Epidemiology (MOOSE) protocols were followed throughout design, execution, analysis and reporting of this meta-analysis  16]..16].http://www.ncbi.nlm.nih.gov/pubmed/) and EMBASE (http://www.embase.com/) databases from the earliest available online indexing year to March 2017, with English-language restriction. Search terms included the following: (coffee OR caffeine OR beverages) and  and (cancer OR carcinoma OR neoplasm) (We conducted a comprehensive literature search using PubMed (eoplasm) . Two auteoplasm) . ReferenData were abstracted from each identified study using a standardized extraction form. The following information was collected: (1) first author name; (2) year of publication; (3) study cohort name; (4) country; (5) number of participants; (6) sex of participants; (7) age range of the study population at baseline; (8) categories of consumption; (9) follow-up period; (10) endpoints and cases; (11) distributions of cases and person-years, HRs, and 95% CIs for all categories of exposure; and (12) covariates used in adjustments. This process was performed independently by two authors and discrepancies were discussed and resolved by consensus. The quality of included studies was assessed according to the Newcastle–Ottawa Quality Assessment Scale , which cQ test and I2 statistic. The level of significance equal to 0.10 was used for the Q test. The I2 statistic represented the amount of total variation that could be attributed to heterogeneity. I2 values ≤25%, 25–50%, 50–75%, and >75% indicated no, small, moderate, and significant heterogeneity, respectively. A sensitivity analysis by exclusion of one study at a time was performed to evaluate the stability of results and potential sources of heterogeneity. Subgroup analysis was only performed for liver cancer risk, in order to check for potential source of heterogeneity according to study design, gender and geographical area. To test for potential confounders/effect modifiers, subgroup analyses were performed according to smoking status, coffee type and hepatitis. Publication bias was evaluated by a visual investigation of funnel plots for potential asymmetry.In this meta-analysis, ORs and HRs were deemed equivalent to relative risks (RRs) . ORs, RRP-value by testing that the 2 regression coefficients were simultaneously equal to zero. We then calculated a P-value for non-linearity by testing that the coefficient of the second spline was equal to zero. All analyses were performed with R software Version 3.0.3, using dosresmeta and mvmeta packages .A dose–response analysis was performed using the method of Greenland and Longnecker to calculate study-specific liner and non-linear trend  based on results across categories of coffee intake ,20. DataThe study selection process of eligible studies is presented in Thirteen studies ,36,37,38I2 = 0%, p = 0.58 , but not for decaffeinated  was found  (heterogeneityP = 0.692) was observed.For the dose–response analysis on the association between coffee consumption and liver cancer risk, seven studies were eligible ,32,34,38= 0.954) , Table 4In the present meta-analysis, the inverse association between coffee consumption and risk of liver cancer was consistent when taking into account key potential confounding factors. In contrast, no significant association between coffee consumption and risk of BTC was evident. Notably, a non-significant decreased risk was found especially for lower intake of coffee ; however, higher intake was associated with no further benefit or rather an increased risk in two out of the three cohorts examined. Furthermore, the limited number of the studies eligible for meta-analysis is not sufficient to draw conclusions on the association between coffee consumption and BTC risk. From a mechanistic point of view, intake of both caffeinated and decaffeinated coffee stimulates gallbladder contraction caused by increased concentration in plasma cholecystokinin induced by coffee and decreases gallbladder volume by approximately 30% . FurtherS-transferase class Alpha 1, UDP-glucuronosyl transferase 1A6, and the glutamate cysteine ligase catalytic subunit, all involved in the antioxidant response of the organism [Findings on coffee consumption and liver cancer risk were more consistent: all sensitivity and subgroup analyses performed showed significant decreasing risk of cancer with a linear dose–response relation. Molecular targets involved in the chemopreventive effects of coffee include the nuclear factor E2-related factor 2 (Nrf2), responsible for transcription of enzymes involved in detoxification processes and in cellular antioxidant defences : a diet organism . With sporganism , reducedorganism , and redorganism ,51. A number of experimental studies provided the biological rationale for the components responsible for beneficial effects of coffee on liver cells. In this meta-analysis, a significant decrease in risk of liver cancer for caffeinated coffee, but not for decaffeinated, was found. Caffeine has been reported to reduce fibrosis in in vitro and animal studies, inhibiting TGF-beta-induced CTGF (Connective Tissue Growth Factor) expression in hepatocytes by stimulation of degradation of the TGF-beta effector SMAD 2, inhibition of SMAD3 phosphorylation and up-regulation of the PPARgamma-receptor ,53, as wIt has been shown that the caffeine metabolite paraxanthine may be responsible for the down-regulation of the expression of the fibrogenic protein CTGF in hepatic stellate cells and reduction of liver fibrosis and lipid peroxidation . More reThe results of the present study should be considered in the light of a number of limitations. First, some analyses reported moderate heterogeneity. As previously mentioned, several factors may explain differences across studies, including type of coffee bean (Arabica or Robusta), roasting, and beverage preparation. Secondly, genetic variants associated with caffeine metabolism are not considered in prospective cohort studies but were included in the meta-analysis and may contribute to the observed heterogeneity. Coffee consumption was assessed before outcome, thus recall bias is unlikely. However, misclassification of the actual amounts consumed may have affected the dose–response relation. Reverse causation may have affected the results if individuals changed coffee intake due to a diagnosed medical condition or disease; however, any such effects would be muted in studies with a long duration.In conclusion, coffee may represent a valid functional food for liver protection. Current evidence is sufficient to guide future clinical randomized trials to test the hepatoprotective effects of coffee, which in turn may lead to more definitive recommendations. However, further observational studies with better in depth analyses of potential confounding factors are needed to test the association between coffee consumption and BTC.