SLC30A8) is located on chromosome 8q24.11. This study aimed to identify the association of SLC30A8 rs13266634 C/T gene polymorphism with T1D in a sample of T1D children in Tamil Nadu, India.Zinc transporter 8 (ZnT8) is a multi-transmembrane protein situated in the insulin secretory granule of the islets of β-cells and is identified as a novel auto-antigen in type 1 diabetes (T1D). The gene coding for ZnT8, solute carrier family 30 member 8  in T1D patients as compared to the controls.No significant differences were observed in either allele  and genotype  frequencies of rs13266634 C/T between T1D patients and controls. Transmission disequilibrium test has identified over-transmission of mutant T allele from parents to affected children (T: U=9:7) without statistical significance. Metaanalysis on the overall effects of rs13266634 C allele frequency was not different (p=0.10 and PThe present study along with the meta-analysis does not show any substantial association of the rs13266634 C/T polymorphism with T1D development in this population. What is already known on this topic?SLC30A8 rs13266634 C/T polymorphism in type 1 diabetes (T1D) patients from four different populations was previously reported. This gene polymorphism is associated with T1D in the German population, but not in Danish, Japanese and British populations.What this study adds?SLC30A8 gene polymorphism in South Indian patients. The present study and the meta-analysis show that the rs13266634 C/T polymorphism is not associated with type 1 diabetes in this population.To our knowledge, this is the first family-based report addressing SLC30A8) gene, located in chromosome 8q24.11, encodes for the ZnT8 auto-antigen and comprises 369 amino acids (SLC30A8 rs13266634 (C/T polymorphism) encodes either arginine (R) by the C allele or tryptophan (W) by the T allele at aa325 of ZnT8  is a complex, multifactorial disease caused by the selective destruction of insulin-producing pancreatic β-cells . The autno acids ,13. Nota of ZnT8  suggesti of ZnT8 . Thus, tThe study subjects comprised 121 T1D patients from the Department of Diabetology, Government Rajaji Hospital in Madurai, Tamil Nadu, India, along with 214 their first degree relatives (120 parents and 94 siblings) as controls. All patients were evaluated by clinical history and routine laboratory tests. The patients met the revised criteria of the American Diabetes Association (ADA) for the screening of T1D . GenomicEthic board consent for the study was approved by the Institutional Ethics Committees of Govt. Rajaji Hospital (Ref. No. 23339/E4/3/10) and Madurai Kamaraj University (MKU/IRB/11/11) and consented in writing by the participants.SLC30A8 gene by polymerase chain reaction (PCR)-restriction fragment length polymorphism  studies should be case-controlled; and 2) all patients should meet the diagnostic criteria for T1D according to the ADA. Studies were excluded if they did not report on genotype frequency or if they had insufficient data.An extensive literature search was done to examine the association between T1D and 2 test after segregating the data based on age, number and sex of the subjects. Odds ratio (OR) and their p-values were calculated by logistic regression, which was performed using STATA 14v software . In addition, the transmission/disequilibrium test (TDT) was employed to detect preferential transmission from heterozygous parents to affected offspring  without statistical significance.The demographic details of the T1D subjects and controls are given in SLC30A8 gene of T1D, which met the required criteria, were included in the present meta-analysis. Along with the present study, a total of five eligible studies with a total of 10,376 T1D patients and 10,027 control subjects were included in the meta-analysis. Meta-analysis of the data via literature survey was able to retrieve 18 studies. Of these, nine were excluded after screening the abstracts, review and irrelevant subject matter. Three studies did not provide comprehensive information. Two studies were not considered as they provided insufficient genotype frequencies. The remaining four studies ,26,27,282= 0.29; I2=0%; Pheterogeneity= 0.99) which indicated that no publication bias crept in the meta-analysis (Characteristics of the said studies and the distribution of rs13266634 C/T genotypes and alleles in T1D patients and controls are given in analysis . In the Forest plot the area of squares, horizontal lines and diamond shows the weight of specific study, confidence intervals and the summary of fixed-effects OR, respectively .In the Funnel plot the open circle represents various studies considered for this plot correlation . No evidSLC30A8 gene seems to predispose to the risk of T1D in individuals of non-European descent (ZnT8 is highly expressed in the pancreatic islet β-cells and recognized as one of the four major auto-antigens in T1D patients. It has been observed that autoantibodies are generated against ZnT8 prior to the onset of disease. It is known that rs13266634 C/T SNP is responsible for the autoimmune response to ZnT8 . The rs1 descent .SLC30A8 gene with susceptibility to T1D. However the result of the meta-analysis indicated that the C allele conferred no risk in the development of T1D. Nevertheless, we should point out that one of the previous meta-analyses on T2D revealed that the rs13266634 C/T polymorphism is significantly associated with impaired glucose tolerance (Until now, several publications have investigated the correlation of rs13266634 C/T polymorphisms with T1D ,26,27,28The study is limited by a relatively small number of subjects. Varied studies from different ethnicities with large sample size are required to conclusively confirm the role of rs13266634 C/T polymorphism in T1D.This result demonstrates that the allele, genotype, genetic models and allele transmission of rs13266634 C/T polymorphism are not strongly associated with T1D in the children of a Tamil Nadu population. The meta-analysis also indicates that the rs13266634 C/T polymorphism was not associated with T1D.


The cause of colorectal cancer (CRC) is multifactorial, involving both genetic variants and environmental risk factors. We systematically searched the MEDLINE, EMBASE, China National Knowledge Infrastructure (CNKI) and Wanfang databases from inception to December 2016, to identify systematic reviews and meta‐analyses of observational studies that investigated gene–environment (G×E) interactions in CRC risk. Then, we critically evaluated the cumulative evidence for the G×E interactions using an extension of the Human Genome Epidemiology Network's Venice criteria. Overall, 15 articles reporting systematic reviews of observational studies on 89 G×E interactions, 20 articles reporting meta‐analyses of candidate gene‐ or single‐nucleotide polymorphism‐based studies on 521 G×E interactions, and 8 articles reporting 33 genome‐wide G×E interaction analyses were identified. On the basis of prior and observed scores, only the interaction between rs6983267 (8q24) and aspirin use was found to have a moderate overall credibility score as well as main genetic and environmental effects. Though 5 other interactions were also found to have moderate evidence, these interaction effects were tenuous due to the lack of main genetic effects and/or environmental effects. We did not find highly convincing evidence for any interactions, but several associations were found to have moderate strength of evidence. Our conclusions are based on application of the Venice criteria which were designed to provide a conservative assessment of G×E interactions and thus do not include an evaluation of biological plausibility of an observed joint effect. AICRAmerican Institute for Cancer ResearchCIconfidence intervalCNKIChina National Knowledge InfrastructureCRCcolorectal cancerCTNNB1cadherin‐associated protein β1CUPcontinuous update projectCYP24A1cytochrome P450 family 24 subfamily A member 1G×Egene–environmentGATA3GATA binding protein 3GECCOGenetics and Epidemiology of Colorectal Cancer ConsortiumGWASgenome‐wide association studyHIATL1hippocampus abundant transcript‐like 1MINK1misshapen like kinase 1NAT2N‐acetyltransferase 2NSAIDnonsteroidal anti‐inflammatory drugORodds ratioPI3Kphosphatidylinositol‐4,5‐bisphosphonate 3‐kinasePIK3C2Gphosphatidylinositol‐4‐phosphate 3‐kinase catalytic subunit type 2 gammaPTCHD3patched domain containing 3RRrelative riskSHMT1serine hydroxymethyltransferase 1SNPsingle‐nucleotide polymorphismSOCCSScottish Colorectal Cancer StudyTCF7L2transcription factor 7 like 2WCRFWorld Cancer Research Fund InternationalColorectal cancer (CRC) is the third most common cancer worldwide, with 746,000 new cases in men and 614,000 new cases in women.CRC risk is determined by a complex interplay of both genetic variants and environmental exposures. It has been speculated that their interactions ‐ known as gene–environment (G×E) interactions should also be important determinants of CRC risk. To date, genome‐wide association study (GWAS) have shown that up to 50% of CRC heritability can be explained by common and rare variants included in popular genotyping arrays.A number of systematic reviews, meta‐analyses and genome‐wide G×E interaction analyses that explored interaction effects in CRC have been published. We recently collected and evaluated the evidence across existing meta‐analyses of observational studies in dietary factors and gene‐diet interactions for the 5 most common cancers.We systematically searched the MEDLINE, EMBASE, China National Knowledge Infrastructure (CNKI) and Wanfang databases from inception to December 2016. The search strategy and Medical Subject Headings terms are displayed in Supporting Information Table S1. All identified publications went through a 2‐step review before being included. Titles were reviewed by 1 investigator (TY). Abstracts and full texts were reviewed by 2 investigators (TY and MT). Any discrepancies were resolved by discussion.Three types of studies were eligible for this umbrella review: (i) systematic reviews of observational studies assessing interaction effects between genes and environmental exposures in CRC; (ii) meta‐analyses of candidate gene‐ or SNP‐based studies and analyses combining individual level data from multiple studies exploring G×E interactions and (iii) genome‐wide investigation of G×E interactions on CRC risk within GWAS consortia. We excluded reviews without explicit systematic literature searches; and systematic reviews or meta‐analyses of observational studies that explored associations between CRC risk and genes or environmental exposures only.p value for interaction and the p value (or I2) for heterogeneity.One investigator (TY) extracted data which were then checked by a second investigator (ZM). For each eligible article, we extracted the first author, year of publication, the dietary and genetic risk factors examined, study design and the number of studies included. For meta‐analyses, we extracted the summary study‐specific relative risk estimates  along with the corresponding 95% confidence intervals (CIs), the number of cases and total participants, the For systematic reviews, we performed descriptive analyses and presented the authors’ main conclusions. The evaluation process is described in detail in Supporting Information methods.p value for interaction <0.05, or for G×E interactions reaching genome‐wide significance threshold, we used an extension of the Human Genome Epidemiology Network's Venice criteriaFor meta‐analyses of G×E interactions with a 2‐sided amount of evidence, the extent of replication and the protection from bias . Each G×E association was graded based on the as Table . On the et al.,Second, we established a prior score category (expected) for the G×E interactions using a framework presented in Boffetta Third, we examined the overall plausibility of each interaction by combining the prior score and the strength of the observed evidence. Higher weight was given to the observed evidence in case of conflicting results between the observed evidence and the prior scores.p value for interaction <0.05) that were identified from the candidate meta‐analyses or for interactions that were concluded as suggestive by the authors of the systematic reviews, we tested the interactions in the Scottish Colorectal Cancer Study (SOCCS)Finally, for the statistically significant G×E interactions : N‐acetyltransferase 2 (NAT2) and processed meat intakeNAT2 and red meat intakeSHMT1) C1420T polymorphism and folate intakeSHMT1 C1420T polymorphism and folate intake and rs6983267 and aspirin use in the SOCCS,α = 0.05) in our data (data not presented) or in the GECCO (data not presented). Each identified G×E interaction is described in detail in Supporting Information results.Twenty articlesp value for interaction from each genome‐wide G×E interaction analysis are presented in Supporting Information Table S7). The following G×E interactions were identified that reached genome‐wide significance threshold specified by the authors of the original publication: rs4143094 (10p14) and processed meat intake(PTCHD3) at 10p12.1, misshapen like kinase 1 (MINK1) at 17p13.2 and NSAID useEight articlesp < 10−5; Fig. Here, we present the evidence for the identified G×E interactions in relation to CRC risk with main genetic effects ; p = 7.8×10−11; I2 = 91.1%],16) in a meta‐analysis including 13,348 cases and 26,438 controls of European ancestry Aspirin use was associated with a reduced risk of CRC on the basis of a meta‐analysis of 39 studies with 151,367 cases  and the association was classified as class IV (weak)  CRC reportsk) Table  main genp > 10−5; Fig. Here, we present the evidence for the identified G×E interactions in relation to CRC risk with no main genetic effects  due to the high heterogeneity between the studiesp > 10−5) in a meta‐analysis of 7 GWAS from Europe including 8,749 cases and 18,245 controls PTCHD3, 17p13.2/MINK1 and NSAID use could not be properly evaluated due to the missing elements of the extension of the Venice criteriaIn a meta‐analysis of 10 studies including 8,634 cases and 8,553 controls, regular use of aspirin and/or NSAIDs, compared to non‐regular use, was associated with lower risk of CRC The RR for use of estrogen plus progestogen therapy on CRC risk was 0.74  (Table p > 10−5) of 18,299 cases A meta‐analysis of 8 prospective studies in the latest CUP CRC reportsNAT2 based on the weak (Weak – 3) prior score and the weak observed score  prior score and a weak observed score (Tables No evidence was found for the interaction between k Weak –  prior scNo evidence was found for the interaction between rs1944511 11q23.3) and overweight based on a weak (Weak – 3) prior score and a weak observed score . In particular, the benefit of regular aspirin use on CRC risk was confined to individuals with T allele of rs6983267, which has been associated with impaired binding of cadherin‐associated protein β1 (CTNNB1)/ transcription factor 7 like 2 (TCF7L2) and lower expression of MYC.CTNNB1 expression in colon cancer cell lines.CTNNB1/TCF7L2 binding is not constitutively active is necessary for the susceptibility to the effects of aspirin on the Wnt/CTNNB1 pathway.Based on the prior and observed scores, only the interaction between rs6983267 (8q24) and aspirin use was found with a moderate overall plausibility score and a main genetic effect (p > 10−5), even though they may provide clues to discovering novel CRC susceptibility loci that have not been readily detected in GWAS by their marginal effects of genetic factors.Moderate overall plausibility scores were also found for the interactions between rs4143094 (10p14) and processed meat intake, rs9409565 (9q22.32) and light‐to‐moderate alcohol drinking (1–28 g/day), rs964293 (20q13.2) and use of estrogen plus progestogen therapy, as well as rs2965667 (12p12.3), rs16973225 (15q25.2) and aspirin and/or NSAID use. However, these interactions are regarded as tenuous due to the lack of main genetic effects (PIK3C2G) locus, rs16973225 (15q25.2/interleukin 16) and aspirin and/or NSAID use on CRC risk. PIK3C2G gene encodes a protein of the phosphatidylinositol‐4,5‐bisphosphonate 3‐kinase (PI3K) family,PI3K.Interleukin 16 may stimulate monocyte induction of proinflammatory cytokines associated with tumorigenesis,interleukin 16 gene may be associated with the production of inflammatory cytokines that modify the chemopreventive effect of aspirin and/or NSAIDs on CRC.Little is known about the underlying molecular mechanisms of the interactions between rs2965667 at the 12p12.3/ phosphatidylinositol‐4‐phosphate 3‐kinase catalytic subunit type 2 gamma (CYP24A1) is biologically plausible. CYP24A1 is greatly expressed in malignant colon tumours,CYP24A1 have been associated with CRC risk.p <0.001].p = 0.037).CYP24A1 may only be a metabolizing enzyme for progestrogens but not estrogen, since an interaction effect was only found with the use of combined estrogen‐progestogen therapy and not with estrogen‐only intake.The interaction between use of estrogen plus progestogen hormone preparations and the rs964293 variant in the 20q13.2, known as cytochrome P450 family 24 sub‐family A member 1 (GATA3) region, on the association between processed meat intake and CRC risk is even less clear. GATA3 has been described as a master regulator of T‐helper 2 cell differentiation in mature CD4 (+) T cells and has been associated with T cell development.GATA genes or silencing of expression can increase CRC risk.The mechanism of the modifying effect of the rs4143094 variant at the 10p14 locus, near GATA binding protein 3 ((HIATL1) locus and CRC risk has not been understood. HIATL1 is a member of the solute carrier group of membrane transport, which makes the move of substances  directly into or out of cells possible.HIATL1 that interact with alcohol on CRC risk through genome‐wide G×E interaction analyses can also impact HIATL1 expression,HIATL1 on CRC risk through its influence on HIATL1 expression levels.Furthermore, the mechanism of the modifying effect of alcohol consumption on the association between rs9409565 at the 9q22.32/ Hippocampus Abundant Transcript‐Like 1 Studies of G×E interactions require much larger sample sizes than main effect analyses due to small effect sizes, multiple testing, misclassification due to imperfect measures of environmental exposures and more model parameters.The strengths of umbrella reviews have been described elsewhere.I2 to assess the heterogeneity in the evaluation, however, it has been reported that I2 represents what proportion of the observed variance would remain if we could eliminate the sampling error rather than how much the effect size varies.Our study has limitations. First, interaction effects that have not yet been assessed through meta‐analyses or systematic reviews would not have been included, since umbrella reviews do not focus on individual studies. Second, we did not use an established tool to assess the risk of bias in the included observational studies, because available tools such as Q‐Geniep value thresholds are typically used to evaluate the creditability of findings. Candidate gene studies with liberal p value thresholds might be biased toward false positive findings, while the genome‐wide approach is prone to false negative observations, because true interactions may not reach a stringent genome‐wide significance threshold. This, as well as other issues such as genotyping and imputation problems, could be a reason why none of the interactions identified in the candidate gene studies were replicated in genome‐wide G×E studies.We combined systematic literature reviews, candidate and genome‐wide G×E studies together. The main reason for this was to provide a comprehensive overview of the existing literature on G×E interaction in relation to CRC risk. However, an issue is that in each of these types of studies, different criteria and The extension of Venice criteria used here has been applied in the past to assess joint effects of environments and genes on risk of multiple cancers.Our assessment maps the status of evidence on the associations between G×E interactions and CRC risk. Despite the identified studies exploring a wide variety of G×E interactions on CRC, we conclude that we did not find highly convincing evidence for any interactions, but several associations were found to have moderate strength of evidence using our set of guidelines.Though most of the evaluated G×E interactions in our study were with no main genetic effects, it has been suggested that such kind of risk loci that have not been readily detected in traditional GWAS may be identified by testing for interactions between SNPs and environmental risk factors, even though there is no strong evidence for a G×E interaction.None declared.Not required.No additional data available.Study design: MT and ET; Literature search: TY; Study selection: TY and MT; Data extraction: TY and ZM; Data analysis: TY and MT; Study draft and revision: TY, MT, ET, XL, ZM, JL, SMF, JPAI, MGD and HC; Article guarantor: Dr. Maria Timofeeva and Dr. Evropi Theodoratou.Appendix S1: Supplementary methodsClick here for additional data file.Figure S1 Categories for the credibility of cumulative epidemiological evidence. The 3 letters correspond (in order) to amount of evidence, replication and protection from bias. Evidence is categorized as strong, when there is A for all 3 items, and is categorized as weak when there is a C for any of the 3 items. All other combinations are categorized as moderate .Table S1. Keywords and Search Strategies for Meta‐analyses of G × E Interactions used in the Umbrella Review.Table S2. Score Categories for Credibility of an Interaction Between an Environmental Exposure and a Genetic Variant Based on the Strength of Evidence for a Main Effect of Each of Them  .Table S3. Search Strategy Used for the Identification of Main Environmental Effects for Colorectal Cancer Risk.Table S4. Search Strategy Used for the Identification of Main Genetic Effects for Colorectal Cancer Risk.Table S5. G × E Interactions in Relation to CRC Risk That Were Reported From Meta‐analyses of Candidate Gene‐ or SNP‐based Studies.Table S6. General Characteristics of 33 Genome‐wide G × E Interaction Analyses in GWAS Consortia.Table S7. SNPs With the Smallest P for G × E Interactions in Relation to CRC Risk That Were Reported From Each Genome‐wide G × E Interaction Analysisa.Table S8. General Characteristics and Main Findings of the Systematic Reviews of Observational Studies. Suggestive Associations for G × E Interactions in Relation to CRC Risk That Were Identified by the Authors of the Original Systematic Reviews are Shown in Bold.Click here for additional data file.


Background: Some pilot studies already tried to investigate potential associations of leptin (LEP) and LEP receptor (LEPR) variants with coronary artery disease (CAD). However, the results of these studies were not consistent. Thus, we performed the present meta-analysis to explore associations between LEP/LEPR variants and CAD in a larger pooled population.Methods: Systematic literature research of PubMed, Web of Science, Embase and CNKI was performed to identify eligible case–control studies on associations between LEP/LEPR variants and CAD. The initial search was conducted in September 2018 and the latest update was performed in December 2018. Q test and 2I statistic were employed to assess between-study heterogeneities. If probability value of Q test was less than 0.1 or 2I was greater than 50%, random-effect models (REMs) would be used to pool the data. Otherwise, fixed-effect models (FEMs) would be applied for synthetic analyses.Results: A total of ten studies published between 2006 and 2018 were eligible for analyses (1989 cases and 2601 controls). Pooled analyses suggested that LEP rs7799039 variant was significantly associated with CAD under over-dominant model  = 1.36, 95% confidence interval (CI): 1.14–1.63, 2I = 41%, FEM) in overall population, and this significant finding was further confirmed in East Asians in subsequent subgroup analyses. However, no positive findings were observed for LEPR rs1137100 and rs1137101 variants in overall and subgroup analyses.Conclusions: Our meta-analysis suggested that LEP rs7799039 variant might affect individual susceptibility to CAD. Coronary artery disease (CAD) is the primary cause of mortality and morbidity worldwide ,2. HowevLEP/LEPR variants, which may result in altered LEP levels, may also impact individual susceptibility to CAD.Leptin (LEP), a hormone that is primarily secreted by adipocytes, plays a pivotal role in regulating food intake, energy homeostasis and lipid metabolism by binding with LEP receptor (LEPR) ,8. PreviLEP/LEPR variants were correlated with higher plasma levels of LEP [LEP/LEPR variants and CAD. But the results of these studies were not consistent. Thus, we performed the present meta-analysis to explore associations between LEP/LEPR variants and CAD in a larger pooled population.Previous studies showed that functional s of LEP ,13. As aIn accordance with Reporting Items for Systematic Reviews and Meta-analyses (PRISMA) guideline, eligible studies were retrieved from PubMed, Web of Science, Embase and CNKI using the following searching strategy: (leptin receptor OR LEPR OR leptin OR LEP) AND  AND  . The iniLEP/LEPR variants and CAD; (ii) provide genotypic frequency of investigated variants in cases and controls; (iii) full text in English or Chinese available. Studies were excluded if one of the following criteria was fulfilled: (i) not relevant to LEP/LEPR variants and CAD; (ii) in vitro studies or animal studies; (iii) case reports or case series; (iv) abstracts, reviews, comments, letters and conference presentations.Included studies should meet all the following criteria: (i) case–control study about LEP/LEPR variants in cases and controls. The probability value  of Hardy–Weinberg equilibrium (HWE) was also calculated. When necessary, we wrote to the corresponding authors for extra information. We used the Newcastle–Ottawa scale (NOS) to assess the quality of eligible studies [We extracted following data from the included studies: (i) the name of the first author; (ii) publication time; (iii) country and ethnicity; (iv) sample size; and (v) genotypic distributions of  studies . This scP-values ≤0.05 were considered to be statistically significant. All investigated LEP/LEPR variants contain a major allele (M) and a minor allele (m), the dominant comparison is defined as MM versus Mm + mm, recessive comparison is defined as mm vs. MM + Mm, over-dominant comparison is defined as Mm versus MM + mm, and the allele comparison is defined as M versus m. Q test and 2I statistic were employed to assess between-study heterogeneities. If P-value of Q test was less than 0.1 or 2I was greater than 50%, random-effect models (REMs) would be used to pool the data. Otherwise, fixed-effect models (FEMs) would be used to pool the data. Subgroup analyses by ethnicity of participants and type of disease were performed. Sensitivity analyses were performed to evaluate the statistical robustness of the findings, and publication biases were evaluated with funnel plots.We used Review Manager Version 5.3.3  to conduct statistical analyses. We calculated odds ratios (ORs) and 95% confidence intervals (CIs) to estimate strength of associations in all possible genetic models, and We found 80 potential relevant articles. Among these articles, a total of ten studies published between 2006 and 2018 were eligible for pooled analyses  see Fig. The NOSLEPR rs1137100 A/G variant (798 cases and 1841 controls), six studies about LEPR rs1137101 G/A variant (1235 cases and 2156 controls) and six studies about LEP rs7799039 G/A variant (1136 cases and 989 controls) were eligible for synthetic analyses. Pooled analyses showed that LEP rs7799039 G/A variant was significantly associated with CAD under over-dominant model  in overall population, and this significant finding was further confirmed in East Asians in subsequent subgroup analyses . However, no any positive findings were observed for LEPR rs1137100 and rs1137101 variants in overall and subgroup analyses .LEP/LEPR variants with CAD, and our pooled analyses suggested that LEP rs7799039 G/A variant might affect individual susceptibility to CAD. There are several notable points about this meta-analysis. First, there are two possible explanations for our positive findings regarding LEP rs7799039 G/A variant and CAD. First, this variant may lead to alteration in gene expression or change in LEP protein structure, which may subsequently affect biological functions of LEP, result in elevated level of LEP and ultimately impact individual susceptibility to CAD. Second, it is also possible that LEP/LEPR variants may be linked to each other or even linked to other unidentified genes, which could also impact individual susceptibility to CAD [LEPR variants were also well established [LEP/LEPR variants and CAD in every genetic comparison. Therefore, further studies with larger sample sizes still need to test the associations between LEPR variants and CAD. Third, the pathogenesis of CAD is extremely complex, and therefore the probability that a specific genetic variant could significantly contribute to its development is low, and we strongly recommend further studies to perform haplotype analyses and explore potential gene–gene interactions. Fourth, to more precisely measure the effects of certain endogenous/exogenous factors on disease occurrence and development, molecular pathologic epidemiology (MPE) analyses should be adopted. However, since included studies only focused on the effects of LEP/LEPR variants on individual susceptibility to CAD, such analyses were infeasible in the current meta-analysis. But to better elucidate the underlying pathogenesis mechanisms of CAD, future studies should try to investigate the interaction of LEP/LEPR variants (as endogenous factors) with potential pathogenic environmental factors (as exogenous factors) as an MPE approach [LEP/LEPR variants and CAD. Nevertheless, only three variants were analyzed because no other variants were studied by at least two eligible studies.As far as we know, this is the first meta-analysis on associations of y to CAD . Second,ablished ,28, yet approach . Fifth, LEP/LEPR variants and CAD might also be modified by gene–environmental interactions. However, we could not perform relevant analyses accordingly since most of studies did not investigate these associations [Like all meta-analysis, the present study certainly has some limitations. First, due to lack of raw data, adjusted analyses were inapplicable, and we have to admit that failure to perform further adjusted analyses for potential confounding factors might impact the reliability of our findings . Second,ciations ,32. Consciations . Third, ciations . On accoLEP rs7799039 variant might affect individual susceptibility to CAD. However, further well-designed studies with larger sample sizes still need to confirm our findings. Moreover, future investigations are also warranted to explore potential roles of other LEP/LEPR variants in the development of CAD.In conclusion, our meta-analysis suggested that


Significant association was also found among Asian populations . The association was positive in subgroup analysis of population-based case–control studies (PBCC) , but not in hospital-based case–control studies (HBCC). Through a sensitivity analysis, we did not identify any single study to strongly influence the results of our serum copper levels and cervical cancer risk. No publication bias was found in our analysis. In conclusion, our study provided significant evidence of higher serum copper levels in patients with cervical cancer than in controls, suggesting that serum copper exposure was a risk factor on cervical cancer.Whether serum copper levels were higher in patients with cervical cancer than that in controls was controversial. Hence, we conducted the present study to explore the relationship between serum copper levels and cervical cancer. We searched PubMed, WanFang, and China National Knowledge Internet (CNKI) for relevant studies before November 30, 2017. Standardized mean difference (SMD) and 95% confidence interval (CI) were used to combine results across studies using the random-effect model. A total of 14 publications involving 747 patients with cervical cancer and 1014 controls were eligible through inclusion criteria. In comparison with controls, serum copper levels were significantly higher in patients with cervical cancer , with significant heterogeneity ( Cervical cancer is a malignant tumor derived from cervical cells. Cervical cancer is the second most common cancer among women worldwide , accountCopper is one of the trace elements in our bodies, and to date, a subset of investigators have researched the associations between serum copper levels and cervical cancer risks, and a number of epidemiologic studies have been published in this field. Eleven studies  suggesteStudies were identified through searching the databases of PubMed, WanFang, and China National Knowledge Internet (CNKI) up to November 30, 2017. The following search terms were used: ‘serum’ AND ‘copper’ OR ‘Cu’ AND ‘cervical cancer’ OR ‘cervical carcinoma’ without restrictions. Reference lists and the studies retrieved were also examined to find any additional study potentially unidentified. Two investigators independently performed the article search and reviewed the relevant references.Inclusion criteria were the following: having a prospective cohort design or a case–control design or a cross-sectional study; evaluating the association between serum copper levels and cervical cancer risk; reported mean and standard deviation (SD) of copper levels both in cervical cancer patients and controls; studies published in English language or Chinese language. The selection process was independently performed by two authors and retrieved articles were examined.Data were abstracted from each identified study by using a standardized extraction form. The following information was collected: first author name; publication years; study design; country; number of cervical cancer cases and controls; age range or mean age of the cases; mean and SD of copper levels both in cervical cancer patients and controls. This process was independently performed by two authors (M.Z. and M.S.) and discrepancies were discussed and resolved by consensus.The quality of studies was assessed by the Newcastle–Ottawa Scale (NOS) . The NOS2I of Higgins et al. .Forest plots of the association between serum copper levels and cervical cancer risk across all studies are shown in I2 = 98.9%; Pfor heterogeneity<0.001]. When we conducted the subgroup analysis by HBCC or PBCC, the association was only significant in the group of PBCC , but not in HBCC .Within subgroup analysis by geographic location, we only pooled the result for Asian populations because 13 of the 14 articles were from Asia. However, only one article comes from Europe, and the result was not pooled. The relationship was significant among Asian populations [summary SMD = 1.39; 95%CI: 0.06–2.71; 2I= 98.8%; P<0.001). Thus, univariate meta-regression was performed to explore whether the reason of heterogeneity was associated with covariates of publication year, case numbers, and geographic location. No significant contribution to between-study heterogeneity was found in this analysis .As seen in the pooled result, we found evidence of significant heterogeneity in our pooled result summary (P=0.722) and funnel plot . This enzyme plays an important role in protecting the body against free radicals . The car2I= 98.8%; forP heterogeneity< 0.001), which is common in meta-analysis [As seen in analysis . We thenOur meta-analysis has the following advantages. First, we performed the first meta-analysis to expound the risk factor of serum copper levels in cervical cancer patients. Second, according to our final pooled analysis for each individual study, larger numbers of cervical cancer patients and controls were included. This may strengthen the accurate comparisons between serum copper levels and cervical cancer risk. Third, subgroup analyses were performed to find the detailed results for location and sources of controls, and we observed that the association was significant in the subgroup analysis of Asian populations. The same conclusion was acquired in the subgroup analysis of PBCC. Fourth, no publication bias was found due to Egger’s test and funnel plot, which indicated that our results were stable across included studies.Although, we attempted to evaluate the association between serum copper levels and cervical cancer risk more comprehensively, some limitations still existed. First, all the included researches were case–control studies. Although some recollection bias and selection bias would be appeared in case–control studies, they were an important method in observational studies. Nevertheless, more studies with prospective design are needed in the future. Second, heterogeneity among studies was relatively large in our meta-analysis on account of the absence of valuable information, which might reduce the persuasive power of the pooled estimation. However, we used a random-effect model to combine the results. As we all know, random-effect model had wider range about 95%CI than fixed-effect model, and could obtain more accurate results. Furthermore, sensitivity analysis by exclusion of one study at the time was performed to assess the stability of results and potential sources of heterogeneity. No study had essential effect to the significant between-study heterogeneity and the whole result. Finally, 13 of the 14 included studies were from Asia; however, only one included study was from Europe, and thus, additional studies from other countries are required to further define associations between geographic location and cervical cancer risk.In summary, our study provided significant evidence of higher serum copper levels in patients with cervical cancer than in controls, suggesting that serum copper exposure was a risk factor on cervical cancer.


To our knowledge, no study has assessed the association between heatwaves and risk of hospitalization and how it may change over time in Brazil. We quantified the heatwave–hospitalization association in Brazil during 2000–2015.Daily data on hospitalization and temperature were collected from 1,814 cities  in the hottest five consecutive months during 2000–2015. Twelve types of heatwaves were defined with daily mean temperatures of ≥90th, 92.5th, 95th, or 97.5th percentiles of year-round temperature and durations of ≥2, 3, or 4 consecutive days. The city-specific association was estimated using a quasi-Poisson regression with constrained distributed lag model and then pooled at the national level using random-effect meta-analysis. Stratified analyses were performed by five regions, sex, 10 age groups, and nine cause categories. The temporal change in the heatwave–hospitalization association was assessed using a time-varying constrained distributed lag model. Of the 58,400,682 hospitalizations (59% women), 24%, 34%, 21%, and 19% of cases were aged <20, 20–39, 40–59, and ≥60 years, respectively. The city-specific year-round daily mean temperatures were 23.5 ± 2.8 °C on average, varying from 26.8 ± 1.8 °C for the 90th percentile to 28.0 ± 1.6 °C for the 97.5th percentile. We observed that the risk of hospitalization was most pronounced for heatwaves characterized by high daily temperatures and long durations across Brazil, except for the minimal association in the north (the hottest region). After controlling for temperature, the association remained for severe heatwaves in the south and southeast (cold regions). Children 0–9 years, the elderly ≥70 years, and admissions for perinatal conditions were most strongly associated with heatwaves. Over the study period, the strength of the heatwave–hospitalization association declined substantially in the south, while an apparent increase was observed in the southeast. The main limitations of this study included the lack of data on individual temperature exposure and measured air pollution.There are geographic, demographic, cause-specific, and temporal variations in the heatwave–hospitalization associations across the Brazilian population. Considering the projected increase in frequency, duration, and intensity of heatwaves, future strategies should be developed, such as building early warning systems, to reduce the health risk associated with heatwaves in Brazil. Qi Zhao and colleagues reveal the association between heatwaves and hospital admissions between 2000 and 2015 in Brazil, with young children and the elderly most affected. Brazil has a rapidly growing morbidity burden and is experiencing some of the most severe effects of global warming. However, there is no national evidence regarding the heatwave–hospitalization associations.Using a data set covering >78% of the national population (>160 million residents), we performed a nationwide time series study to examine the relationship between heatwaves and risk of all-cause and cause-specific hospitalizations and how it varied over time among the Brazilian population.Exposure to heatwaves characterized by high temperatures and long durations was associated with higher risk of hospitalization compared with moderate heatwaves. The duration of severe heatwaves was additively associated with risk of hospitalization in the cooler south and southeast regions.Children and the elderly were most vulnerable to heatwaves. Hospital admissions for perinatal conditions were most strongly associated with heatwaves. Over the study period, the risk of hospitalization associated with moderate heatwaves declined substantially in Brazil , with the notable exception of an apparent increase in the southeast region.To the best of our knowledge, this is the first national study to explore the geographic, demographic, cause-specific, and temporal variations in the association between heatwaves and risk of hospitalization in Brazil.Considering the projected increase in the frequency, duration, and intensity of heatwaves, future strategies for the forecasting of healthcare utilization in the Brazilian population should take into account the association between heatwaves and hospitalizations, especially among the most vulnerable population subgroups.Given the unique location of Brazil and its climatic and geographic diversity, our findings may be of relevance for other South American countries. Globally, the average temperature has risen by 1.7 °C since 1910, accompanied by increased frequency, duration, and intensity of extreme heat events, otherwise known as heatwaves . In 2003There is no unified criterion regarding the critical temperature threshold and duration of heatwaves worldwide. For example, in Sweden, a heatwave is defined as at least five adjacent days when the daily maximum temperatures are above 25 °C . In MainSouth America is experiencing some of the most significant effects of global warming compared with many other areas of the world . Brazil—Given the populous nature of Brazil and the vulnerability of the South American continent to climate change, we have started to document the growing impact of climate change on healthcare utilization in Brazil –17. The This time series study is reported as per the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) guidelines . The dat2) and highest in the southeast (87 persons/km2) [There are five regions in Brazil—the north, northeast, central west, southeast, and south . This geons/km2) ,20. EconFor each city, time series data on hospitalization between 1 January 2000 and 31 December 2015 were collected through Brazil’s National Unified Health System. There were 5,570 cities in Brazil at the end of 2015, but the electronic medical records of some cities were not completed during the early years. To reduce the impact of missing data, we only applied for hospitalization records in 1,814 cities that had complete data for the 16-year study period. The population coverage of our data set ranged from 26% in the north to 87% in the south . Medical variables included information on patient’s sex, age , admission date, city of residence, and primary diagnosis coded according to the International Classification of Diseases, 10th revision (ICD-10). The hospitalization data were then divided into nine main cause categories according to primary diagnosis .For each city, daily minimum and maximum temperatures were extracted from a 0.25° × 0.25° meteorological data set developed by Xavier and colleagues  using anAlthough without a unified definition, a heatwave for a specific area is commonly considered as an extreme heat event that persists for ≥2 days, compared with its long-term weather status ,23. Howej is the daily hospitalization counts on day j. HWj is a binary variable representing heatwave days (yes or no). cb is the cross-basis function to fit the cumulative association on lag 0–7 days, with a natural cubic spline with 3 degrees of freedom (df) used for lag days. Stratumj is a categorical variable of the year and calendar month  to control for the within-season variation and long-term trend. In total, there were 192 strata (16 × 12). This design provides reliable control for temporal variations [j is a categorical variable representing day of the week. Holidayj is a binary variable representing public holidays in Brazil. α is the intercept. β, γ, and δ are coefficients.Data analyses were conducted for the hottest five consecutive months in each city, which were identified using the city-specific monthly mean temperatures during 2000–2015. A two-stage approach was applied to quantify the heatwave–hospitalization association ,26. In triations ,28. DowjIn the second stage, city-specific estimates fitted by Findings from previous studies have suggested that the association between heatwaves and mortality may be deconstructed into two parts : (1) thej, lag = 7) with a time variable centered at the specific time point in 2002 or 2013. In the second stage, the city-specific estimates were pooled at the national level using a random-effect meta-analysis with maximum likelihood estimation.A similar two-stage approach was applied to estimate the heatwave–hospitalization associations at two specific early and late time points of the study period—the middle days of the hottest five consecutive months in 2002 and 2013. This strategy has been described in detail by Gasparrini and colleagues  to exploSensitivity analyses were conducted to test whether our results were reliable by extending the lag from 0–7 days to 0–9 days and df of lag days from three to five. We evaluated the confounding effect of RH using observed data from 193 cities during 2000–2012.R software (version 3.4.1) was used to perform all data analyses. The “dlnm” package was used to examine the distributed lag relationship between heatwaves and hospitalizations . The “mvDuring the hottest five consecutive months between 2000 and 2015, there were 58,400,682 (59% women) hospitalization cases in the 1,814 Brazilian cities . SpecifiAt the national level, heatwaves characterized by high temperatures and long durations  were associated with higher risk of hospitalization than heatwaves with low temperatures and short durations  over lag 0–7 days . The risThere was no substantial sex difference in the cumulative association between heatwaves and hospitalizations (lag 0–7 days), but it varied by age group . The heaThe heatwave–hospitalization associations varied across the nine cause categories . The strAt the national level, the heatwave–hospitalization association declined temporally, especially for moderate heatwaves . RegionaResults of sensitivity analyses indicated that our results were robust to changed maximum lag days (7–9 days), df (3–5), and the inclusion of RH –S8 Figs.To the best of our knowledge, this is the first nationwide study to estimate the geographic, demographic, cause-specific, and temporal variations in the association between heatwaves and risk of hospitalization in Brazil. Using data covering more than 160 million residents, we observed that the risk of hospitalization—in particular for perinatal conditions—was higher during heatwaves with high temperatures and long durations than for moderate heatwaves. The nature of the association was complex and varied by health conditions, population demographics, and by region of the country. While there was no sex difference for heatwave susceptibility, children and the elderly were more vulnerable than adults. The association between heatwaves and risk of hospitalization was minimal in the north. In contrast, heatwaves with a threshold ≥97.5th percentile of year-round temperature showed excess health risk in the south and southeast after controlling for temperature. Over the study period, the risk of hospitalization associated with moderate heatwaves declined substantially at the national level, but regional differences existed.The health risks associated with heatwaves have been increasingly reported over the past half century, with nearly all findings derived from Asia, Europe, North America, and Oceania . In VietPrevious studies have speculated that the relationship between heatwaves and health outcomes may be due to the inadequate response of the body’s thermoregulatory system ,24,35. HThe adverse role of high temperature on the cardiovascular system has been well documented, including increases in heart rate and blood viscosity and declines in plasma volume and arterial blood pressure –46. ParaPopulation-level characteristics are important modifiers for the association between heatwaves and health outcomes within a country, with some studies suggesting that women may be more susceptible than men ,48. In cConsistent with findings from countries with wide geographic areas , we alsoIn this study, the heatwave–hospitalization association diminished across most Brazilian regions after controlling for daily mean temperature. This is in line with some previous studies in other countries ,35, indiNumerous studies have reported a long-term decline in the strength of the association between extreme heat events and health outcomes in some of the most developed countries, such as the US and Japan, indicating adaptive capacity at the population level ,52. In BIBGE estimates that the population of Brazil will remain relatively unchanged by 2060 . HoweverOur study has several strengths. First, this is the first nationwide study to explore the relationship between different definitions of heatwaves and risk of hospitalization across the Brazilian population and to quantify the temporal change. Our findings are relevant to policy makers and healthcare service providers because the results can inform future policy aimed at mitigating future impacts on healthcare utilization that are associated with sustained periods of high temperatures. Secondly, this study covers over three-quarters of the Brazilian population, suggesting that our results are broadly representative of the entire population of Brazil. At the global level, our study adds reliable and much-needed information to the sparse amount of knowledge that exists around the health risk associated with heatwaves.This study also has several limitations. We used gridded temperature data rather than personal measurement. The measurement bias, if existent, should be randomly distributed across the Brazilian population, which may result in an underestimation of the heatwave–hospitalization association . We wereIn Brazil, heatwaves are associated with increased risk of hospitalization, with heatwaves characterized by high daily temperatures and long durations more vulnerable and less adaptable than moderate heatwaves. The heatwave–hospitalization association was unequally distributed but exhibited geographic, demographic, cause-specific, and temporal variations. Considering the predicted increase in the frequency, duration, and intensity of heatwaves under the current climate change scenarios, the health risk associated with heatwaves warrants further investigation. Adaptation strategies  and health promotion campaigns may help to reduce the burden of heatwave-related risk of hospitalization in Brazil and elsewhere in the world where heatwaves are becoming increasingly common.S1 TextSTROBE, Strengthening the Reporting of Observational Studies in Epidemiology.(DOCX)Click here for additional data file.S2 Text(DOCX)Click here for additional data file.S1 TableICD-10, International Classification of Diseases, 10th revision.(DOCX)Click here for additional data file.S2 Table(DOCX)Click here for additional data file.S3 Table(DOCX)Click here for additional data file.S1 Fighttp://www.censo2010.ibge.gov.br/).City-specific population sizes are extracted from Brazilian Census 2010 ((TIF)Click here for additional data file.S2 FigA natural cubic spline with 2 df was applied for daily temperature. df, degrees of freedom.(TIF)Click here for additional data file.S3 Fig(TIF)Click here for additional data file.S4 Fig(TIF)Click here for additional data file.S5 Fig(TIF)Click here for additional data file.S6 Fig(TIF)Click here for additional data file.S7 Figdf, degrees of freedom.(TIF)Click here for additional data file.S8 FigRH, relative humidity.(TIF)Click here for additional data file.


The association between hypertension and obesity has been confirmed, while no agreement has been reached about which anthropometric adiposity index is the best. This meta-analysis aimed to perform a systematic review and meta-analysis on the associations of hypertension risk with body mass index (BMI), waist circumstance (WC), waist-to-hip ratio (WHR), and waist-to-height ratio (WHtR), and a prospective urban and rural epidemiology study from China (PURE-China) was added into this meta-analysis as an individual study.Systematic literature searching was conducted to identify relevant articles published up to September 2018 in CNKI, WANFANG Data, Web of Science, SinoMed, PubMed, MEDLINE, EMBASE, Cochrane Library and cross-referencing. Literature reporting the association of hypertension risk with BMI, WC, WHR, and WHtR were defined as eligible. PURE-China data were analyzed and included as 1 eligible study into meta-analyses. Summary odds ratio (OR) and area under receiver operating characteristic curve (AUC) were pooled using meta-analysis methods. Heterogeneity and publication bias were evaluated. Subgroups based on gender, country and study design were conducted as well.Thirty-eight original articles including PURE-China were included into meta-analyses, involving 309,585 subjects. WHtR had the strongest association with hypertension risk  and prediction ability , which were also confirmed in subgroup analyses based on gender and country. However, BMI was found to have the highest prediction ability in adjusted models of PURE-China and followed WC, both of which were superior to WHtR (73.7% and 73.4% vs 73.2%).Our overall meta-analysis further confirmed WHtR as a good indicator at discriminating those individuals at increased risk of hypertension, and in some cases, it is better than BMI, WC, and WHR. Moderate numbers of studies provided strong evidence that hypertension contributes markedly to the global burden of diseases.–11 Although hypertension diagnosis seemed easier and cheaper than other cardiovascular diseases, no syndromes are reported by a number of people with high blood pressure. Additionally, some population is not engaged in annual physical examinations due to busy working, unlike to hospital, and self-feeling healthy and others. Therefore, the awareness, treatment, and control of hypertension are very low in some countries.–20Hypertension is not only a common disease itself, but also one of the main causes for risk of cerebrovascular and cardiovascular diseases, such as stroke, metabolic syndromes, and coronary artery diseases.–24 4 AAI are common to be used as risk evaluation indexes in many epidemiological studies,–34 including body mass index (BMI), waist circumstance (WC), waist-to-hip ratio (WHR) and waist-to-height ratio (WHtR), all of which can be self-measured. Two meta-analytic reviews were published in 2008 and provided more supports for centralized obesity, especially WHtR, while BMI was the poorest discriminator for detecting cardiovascular risk factors in both male and female.,36 Additionally, a robust association was observed among Asians compared to non-Asian populations.Thus, applying some simple anthropometric adiposity indexes (AAI) in evaluating and predicting the risk groups of hypertension is valuable. Since obesity has a strong association with hypertension, only searched MEDLINE database up to 2006, and another study used the original data of 19 cross-sectional studies from 10 countries in the Asia-Pacific regions. A number of individual studies were reported in the last decade.–44 Thus, we conducted an updated systematic review and meta-analysis and summarize literature evidence of association of hypertension risk with BMI, WC, WHR, and WHtR, as well as further evaluate sex-based and country-based difference for these associations. Our data in a prospective urban and rural epidemiology study in China (PURE-China) was added into meta-analyses as an individual study.However, Lee et al22.1 A systematic searching was conducted to identify the related articles in the following literature databases up to September 2018, including Cochrane Library (CENTRAL), PubMed, MEDLINE, EMBASE, Web of Science, WANFANG Data, China National Knowledge Infrastructure (CNKI), and SinoMed, and using the combinations of the following terms: (“body mass index” or “BMI”) and (“waist” or “waist circumference” or “WC”) and  and (“waist to height ratio” or “waist-height ratio” or “waist: height ratio” or “waist to stature ratio” or “waist-stature ratio” or “WHtR” or “WHTR” or “WSR” or “WHeiR”) and (“blood pressure” or “hypertension”). Corresponding Chinese terms with above-mentioned terms were used for searching in Chinese literature databases, such as CNKI, WANFANG Data, and SinoMed. All the bibliographical references found in target literature databases were imported into Endnote X8 for verifying eligibility checking. Each title and/or abstract was screened to evaluate its possible relevance after excluding duplicates. Full-text articles were downloaded for further review and eligibility determination if both titles and abstracts were not enough to make decision. All article-selecting were completed by 2 researchers (Deng GJ and Liu WD) independently, the senior researcher (Yin L) made final decision when any discrepancies were shown. Personal email contacts with authors were used to obtain data when needed data were not explicitly reported or not derived from data in the articles. Cross-referencing was also conducted to improve the study identification process.All procedures of this study followed the guidelines of the preferred reporting items for systematic reviews and meta-analyses (PRISMA) statement.2.2The inclusive criteria of article selection were described as follows:(1)only original articles were considered, and editorials, comments or reviews were excluded;(2)hypertension risk was evaluated in epidemiological studies;(3)only adults were included (age≥18-year-old), but studies with older adults (age≥60-year-old) were excluded;(4)odds ratio (OR) for the associations of hypertension risk with BMI, WC, WHR, and WHtR, and/or area under receiver operating characteristic curve (AUC) for prediction abilities of hypertension risk had to be reported in 1 study. Studies with lack of any one of the indexes above-mentioned were excluded.2.3If articles were regarded as eligible, at least 2 co-authors extracted the following data independently in a standardized manner and any disagreement was discussed and resolved in our research group, including author's name, publication year, country of study, study duration, study design, recruited participants , OR, and AUC with their 95% respective confidence interval (CI) for hypertension risk related to BMI, WC, WHR, and WHtR.2.4 which consists of 11 items scored 0 or 1. One score was counted if any item was answered “Yes”, while the score was 0 when any item was answered “No” or “Unclear”. The total score was calculated by adding all the scores of 11 items, and the quality level was determined as low if the total score≤3, medium if the score ranged from 4 to 7, and high if total score≥8.The assessment for the quality and potential bias of the included articles were executed by 2 researchers independently using forms from Agency for Healthcare Research and Quality (AHRQ),2.5,48 Based on 46,285 recruited participants, 1871 were excluded due to missing values of blood pressure, weight, height, WC, and hip circumference (HC) and 156 excluded due to implausible values for systolic blood pressure (SBP) (<70 or >260 mmHg), diastolic blood pressure (DBP) (<40 or >140 mmHg), weight (<30 or >130 kg), WC (>130 cm), and HC (<50 cm). Finally, 44,258 eligible subjects  were included for the analyses.Details of PURE-China have been reported elsewhere. hypertension is defined if 1 of the following 3 criteria is fulfilled:Guided by 2010 Chinese guidelines of hypertension management,(1)taking antihypertensive drugs regularly;(2)history of hypertension diagnosis;(3)2), WHR computed using WC (cm) divided by HC (cm), and WHtR using WC (cm) divided by height (cm).SBP≥140 mmHg and/or DBP≥90 mmHg. BMI was calculated as weight (kg) divided by height square  on study effect sizes using regression-based techniques.Stata 12.0 was used for the meta-analyses. OR and AUC with their respective 95% CI for hypertension risk with 4 AAI  was defined as effect sizes. Heterogeneity was present if The Statistical Analysis System  software was used for the statistical analyses of PURE-China. Only baseline data were used for analyses. Continuous variables were shown as the mean ± standard deviation (SD), and categorical variables as numbers (n) and percentages (%). The OR with 95% CI and AUC with 95% CI for hypertension risk in relation to BMI, WC, WHR, WHtR were computed using multivariate logistic regressions adjusted for age, sex , education levels, alcohol use, smoking status, living location, levels of physical activities, as well as taking anti-diabetics drugs and lipid-lowering drugs. Subgroup analyses stratified by gender country and study design also were conducted.33.1The details of search strategy and included procedure were shown in Figure  to 55,563. Only 6 studies had subjects less than 1000,,54,56–58 and there were 6 studies with more than 10 thousand subjects,,55,59–61 including PURE-China. According to AHRQ, the overall quality of the included studies was good with the average score 9.1, ranged from 7 to 10. 15 studies were scored at 10,–43,62–71 including PURE-China, 13 studies at 9,–61,72–77 9 studies at 8,,57,78–82 and 1 study at 7.The details of included studies were shown in Table 3.22), WC (84.4 vs 78.6 cm), WHR (0.88 vs 0.85), and WHtR (0.53 vs 0.49).Baseline characteristics of eligible participants in PURE-China were shown in Table rd was BMI . WC was found to be the poorest one .OR and AUC and their respective 95% CI for hypertension risk according to various AAI in unadjusted and adjusted models were shown in Table Regarding prediction abilities of hypertension risk, WHtR was the strongest in unadjusted model . However, BMI showed strongest prediction abilities in adjusted models  among both sexes, in males  and females .3.33.3.1,76,80,81 reported ORs for the associations with hypertension risk, 8 articles,74,75,81 reported ORs in men, 6 articles,66,74,75 reported ORs in women. ORs from all countries were combined using meta-analysis methods and found WHtR was the highest OR , followed WHR , the 3rd for BMI , and the lowest for WC , but large heterogeneity was observed across individual studies . Publication bias was found for BMI (Egger test P = .003), WC (Egger test P = .001) and WHtR (Egger test P = .044), but not for WHR (Egger test P = .093). Further trim and fill analyses were conducted to obtain filled ORs for BMI , WC  and WHtR , which still reached statistical significance. Sensitivity analyses were conducted to evaluate the stability of overall effect size, and no outliers were detected for overall effect sizes for WHR and WHtR. However, the study by Kaur 2008, Yu 2016 and PURE-China were identified as outliers for BMI and WC. After deleting these 2 studies, larger OR were observed for both BMI  and WC .The summary ORs of 4 AAI for hypertension risk in China, non-China countries and global were shown in Figure P for meta-regression = .59 for WHtR; .52 for WHR; .75 for WC; .95 for BMI). Additionally, no significant difference was observed for meta-regression based on gender among both China and non-China countries (P for meta-regression ≥.4 for the 4 indexes).Subgroup analyses were performed to evaluate the associations between 4 AAI and hypertension risk in China and non-China countries, as well as females and males, all of which were illustrated in Figure P for meta-regression<0.01 for the 4 indexes).Further subgroup analyses were conducted to evaluate the associations between cross-sectional, retrospective cohort study and prospective cohort study. And found that BMI was the highest OR among prospective cohort study  and retrospective cohort study  respectively. However, it was WHtR with the highest OR among cross-sectional study . Significant difference was observed for meta-regression based on study design , whatever males  and females . Prediction abilities were higher among China studies than other countries (P for meta-regression <.01 for the 4 indexes). Large heterogeneity was observed for all meta-analyses for AUCs . No outliers were identified in sensitivity analyses for WHtR, WHR, WC, and BMI, and no publication bias was found . Trim and fill analyses were conducted to evaluate prediction abilities after filling “missing studies”, filled AUC continued to show original prediction abilities for all 4 AAI.Summary AUCs of 4 AAI for hypertension risk was illustrated in Figure P <.01 for the 4 indexes). Significant difference was observed for meta-regression between males and females in China for BMI (P = .03), WC (P = .04) and WHtR (P = .02). However, no significant difference was observed for meta-regression between males and females among non-China countries (P >.2 for the 4 indexes).Subgroup analyses based on gender and China and non-China countries were also conducted, which were illustrated in Figure P for meta-regression <.01 for the 4 indexes).Further subgroup analyses were conducted to evaluate the associations between cross-sectional, retrospective cohort study, and prospective cohort study. WHtR had the strongest prediction abilities of hypertension risk among prospective cohort study , cross-sectional study  and retrospective cohort study  respectively. Significant difference was observed for meta-regression based on study design  were superior to WC  and WHR . We also combined effect sizes from 4 studies,61,69,76 with adjusted AUCs and found that the prediction ability of BMI, WC, and WHtR were almost the same , which little superior to WHR . Hence, more studies are needed to confirm this variation, and hitherto, BMI and WC are not excluded while predicting the risk of hypertension.Our meta-analyses updated the results of 2 previous meta-analytic reviews–37 significant heterogeneity among females and males was observed when discriminating hypertension risk, and higher combined AUCs were found among females than males, which indicated that the hypertension risk was estimated rather precisely in women. Furthermore, except for WC, the association of hypertension risk was stronger in men than women, although this correlation variation was not confirmed in meta-regression with respect to sex. Additionally, the difference in discrimination abilities for hypertension risk in China and other countries are notable. According to OR, WHtR is the best predictor for both Chinese population and other ethnic groups. When considering about AUC, while the best predictors are BMI and WHtR for China and non-China countries respectively. And current evidence indicated that the strength of the association between the anthropometric measures with hypertension risk is higher in other countries than China, irrespective of indexes. Central adiposity has been emphasized by a number of studies, particularly for Asian populations who may have a ‘normal’ BMI along with disproportionately large WC.,37 However, BMI showed the strongest prediction abilities in adjusted models in our PURE-China study, in either females or males, or both sexes.Similar to previous studies,–91 Second, most of studies included in our meta-analyses were observational studies, which have potential methodological limitations to detect causality between exposure and outcome. Third, 3 studies including our PURE-China were defined as outliers when assessing the stability of effect sizes of BMI and WC. Additionally, potential publication bias was detected using Egger tests, though Begg and Mazumdar rank correlation test not. Finally, although 8 databases were searched for the reviews and extensive checks for completeness by cross-referencing were employed, we cannot promise that a relevant study might be missed.Our study has specific strengths and limitations. A major strength is the application of systematic review strategies and comprehensive evaluation of the associations between adiposity measures and hypertension risk from available data, despite large heterogeneity and publication bias were observed. First, major limitations are related to limitations of the data provided by the individual studies. As a result, the risk estimation may be less accurate if individual-level data were not been available. Some studies were excluded due to no complete data used for meta-analyses, even if we contacted with authors via emails.5Despite these limitations, our systematic review and meta-analyses summarize the available studies so far and provide a comprehensive picture for the associations between hypertension risk and 4 anthropometric measures. The magnitude of these association was partly similar among Chinese and non-Chinese populations. WHtR was confirmed as a good indicator at discriminating those individuals at increased risk of hypertension.Besides co-authors listed in this study, we would like to thank Ononamadu, CJ from Department of Biochemistry and Forensic science, Nigerian police academy, who share the data we need with us. In addition, we would like to thank those who supported our study and what they did with our sincere gratitude, especially for site coordinators, physicians, nurses, questionnaire interviewers, and laboratory personnel in all participating centers and communities, included China Coordination Center Beijing Office: Lisheng Liu, Hongye Zhang, Jian Bo, Jian Li, Kean Wang, Xiaoru Cheng, Xixin Hou, Xingyu Wang, Xuan Jia, Yi Sun, Yang Wang, Xiaoyun Liu; Jishuitan Hospital, Beijing: Di Chen, Dong Li, Hui Jin, Jiwen Tian; Center for Disease Control & Prevention, Shunyi District, Beijing: Yindong Li, Kai You, Changqing Li, Songjian Zhang,Wenlong Cheng,Hongye Zhang; Hospital of Traditional Chinese Medicine, Shijingshan District, Beijing: Honghong Li, Qiang Zhou, Xu Xu, Yanhong Sun, Jinling Di, Jianquan Wu, Mei Wang; Bayannaoer Center for Disease Control & Prevention, Inner Mongolia: Minzhi Cao, Shiying Zhang, Aiying Han; Center for Disease Control & Prevention, Wujin District, Changzhou City, Jiangsu Province: Jianxin Zhou, Yihong Zhou, Deren Qiang, Jianfang Wu, Zhaowei Li, Jing Qin, Suyi Shi, Zhihua Fan, Alin Qian, Lingyun Pan, Minrui Xu, Yibing Cui; Jiangsu provincial hospital:Jun Li,Yongzhen Mo, Center for Disease Control & Prevention, Jiangsu Province: Quanyong Xiang; Ye Cao, Jiangxinzhou Community Health Service Center, Nanjing City, Jiangsu Province: Zhenzhen Qian, Zhengrong Liu; Health Service Center, Nanhu District, Nanjing City, Jiangsu Province: Xiangrong He; Changlin Dong, Ming Wan; Xiaohang Hospital, Nanjing City, Jiangsu Province: Jinhua Tang; Center for Disease Control & Prevention, Nanchang County, Jiangxi Province: Rensheng Lei, Lihua Hu, Shuwei Xiong; Qingshanhu Community Health Service Center, Nanchang City, Jiangxi Province: Ning Li, Xincheng Tang, Dan Zou, Qilu Gan, Shuli Ye; Shenyang 242 Hospital, Liaoning Province: Yu Liu; Health Center, Daxing District, Shenyang City, Liaoning Province: Minfan Fu, Qiuyuan Wang; RedCross Hospital, Shenyang City, Liaoning Province: Baoxia Guo, Huilian Feng, Xiaojie Xing; Center for Disease Control & Prevention, Xining City, Qinghai Province: Yuqing Yang, Wenqiang Xu, Haibin Ma, Yali Wang; Huizu Hospital, Xining City, Qinghai Province: Youzhu Yang, Xiaolan Ma, Yan Hai, Zhe Xie, Yuanting Ma; Huaxi Hospital, Chengdu City, Sichuan Province: Xiaoyang Liao, Qian Zhao,Chuan Zou; Jianshe Road Community Health Service Center, Chengdu City, Sichuan Province: Guofan Xu, Jiankang Liu; Health Center, Dayicaichang Town, Sichuan Province: Xiaolin Zhang, Wenqing Deng; Cardiovascular Disease Research Institute, Shandong Province: Fanghong Lu, Hua Zhang, Shangwen Sun, Yingxin Zhao, Zhendong Liu, Falian Sun; Jingle County Hospital, Shanxi Province: Yinsheng Wu, Guoqin Liu; Balingqiao CommunityHealthServiceStation, Xinghualing District, Taiyuan City, Shanxi Province: Yan Hou, Junying Wang, Hua Wei; Electronic Science and Technology University Hospital, Xi’an City, Shaanxi Province: Xiaoxia Li, Yahong Zhi, Tianlu Liu; Guanshan Town Hospital, Yanliang District, Xi’an City, Shaanxi Province: Peng Zhang; Center for Disease Control & Prevention, Hetian City, Xinjiang Province: Ayoufu Aideer Aili, Mitiwula, Reshalaiti, Hui Wang; Health Center, Damenglong Town, Xishuangbanna Prefecture, Yunnan Province: Qiyun Wang, Jinkui Yang, Kehua Li; Center for Disease Control & Prevention, Mengla County, Xishuangbanna Prefecture, Yunnan Province: Huaxing Liu, Chunmei Liu; Center for Disease Control & Prevention, Yunnan Province: Yize Xiao.Conceptualization: Guijuan Deng, Lu Yin.Data curation: Guijuan Deng, Xiaoyun Liu, Juntao Ma, Bo Hu.Formal analysis: Guijuan Deng.Funding acquisition: Wei Li.Investigation: Guijuan Deng, Lu Yin, Quanyong Xiang, Zhenzhen Qian, Hui Chen, Bo Hu.Methodology: Guijuan Deng, Lu Yin.Project administration: Lu Yin.Resources: Quanyong Xiang, Zhenzhen Qian, Juntao Ma.Software: Guijuan Deng, Weida Liu, Yang Wang.Supervision: Lu Yin, Quanyong Xiang, Wei Li, Yu Jiang.Validation: Weida Liu, Zhenzhen Qian.Visualization: Guijuan Deng.Writing – original draft: Guijuan Deng.Writing – review & editing: Lu Yin, Yu Jiang.


To summarize current knowledge on interactions between genetic variants and lifestyle factors (G×L) associated with the development of coronary artery disease (CAD) and prioritize future research.Genetic risk and combined lifestyle factors and behaviors have a log-additive effect on the risk of developing CAD.First, we describe genetic and lifestyle factors associated with CAD and then focus on G×L interactions. The majority of G×L interaction studies are small-scale candidate gene studies that lack replication and therefore provide spurious results. Only a few studies, of which most use genetic risk scores or genome-wide approaches to test interactions, are robust in number and analysis strategy. These studies provide evidence for the existence of G×L interactions in the development of CAD. Further G×L interactions studies are important as they contribute to our understanding of disease pathophysiology and possibly provide insights for improving interventions or personalized recommendations. Coronary artery disease (CAD) is a complex multifactorial disease leading to ischemic heart disease and myocardial infarction. Globally, CAD is an important cause of death and morbidity, with approximately 9 million deaths between 2007 and 2017 , which resulted in the development of drugs targeting these genes [A heritable component to CAD has been well established, and recent studies estimate the heritability of CAD to range between 40 and 50 % . GeneticAPOB , PCSK9 [B [PCSK9 ••, and LSK9 [LPA••, whichse genes .P < 5 × 10−8) loci associated with CAD. To understand the nature of these associations, possible shared genetic pathways with other traits or diseases were investigated and showed various associations with anthropometric measurements, lipids, inflammation markers, kidney function, diabetes mellitus, and blood pressure possibly providing an intermediate trait in the development of CAD [The most recent GWAS on CAD by van der Harst et al. included 122,733 cases and 424,528 controls and reported over 160 genome-wide significant  as an estimation of an individual’s genomic risk of CAD. A weighted GRS counts the number of risk-increasing alleles  per SNP for each individual and takes into account the effect size of each risk-increasing allele of each SNP as calculated in the GWAS. Since GRS’s are based on germline SNPs with alleles that are randomly allocated at conception, GRS’s are quantifiable from birth and potentially allow earlier risk stratification and primary prevention of events. GRS’s usually only include loci that reached genome-wide significance. For example, van der Harst et al. constructed a weighted GRS for CAD to investigate the risk of downstream cardiovascular diseases and observed associations with the development of atrial fibrillation and heart failure ••.P = 1 × 10−132) higher odds of CAD [Another strategy to estimate an individual’s genomic risk includes the creation of more extensive GRS’s using thousands or even millions of SNPs weakly or uncertainly associated with CAD. Another study constructed a CAD GRS with over 6 million SNPs and found individuals in the top 1% of the distribution were at almost 5-fold  diets, which are both rich in fruits, vegetables, and nuts, to be associated with lower risk of MI and improved cardiometabolic factors with effects on blood pressure, blood lipids, inflammation, endothelial function, and thrombosis . Robust Lifestyle behavior, especially smoking and alcohol use, can also be viewed as the willingness of an individual to take certain health risks. This risk tolerance or behavior may have a predisposing genetic architecture, as shown by Karlsson Linnér et al. who found hundreds of loci associated with risk tolerance and risky behaviors, including 124 SNPs associated with general risk tolerance . The genGenetic and lifestyle factors both contribute to each person’s risk of CAD and have a complex interplay. Lifestyle factors themselves are partly determined by genetic factors. In addition, increasingly more evidence indicates lifestyle can also modify the effects of genetic variants on CAD. This may be due to shared etiological pathways between the genetic variant and lifestyle risk factor, in turn leading to the disease. Another potential mechanism includes epigenetics, in which gene expression is altered by lifestyle or environmental factors through local chromatin environment changes affecting DNA accessibility despite the absence of DNA alterations , 43.Some of the complex gene and lifestyle interplay can be better understood through G×L interaction studies. These interactions can be investigated using several approaches, as discussed below.It is possible to construct a GRS based on the most highly associated variants of previous GWAS’s and analyze the interaction between this overall genetic risk and a lifestyle factor on CAD. However, the number of studies investigating the combined risks of both genetic and lifestyle factors is little. To date, two large studies investigated the combined risks of CAD associated with genetics and overall lifestyle ••, 8. ThAlthough neither study observed statistically significant interactions between lifestyle categories and genetic risk, Said et al. estimated that with 80% power and an alpha of 0.005, interaction effects between genetic risk and intermediate or poor lifestyle would range from 1.21 to 1.50 [The importance of physical activity in genetic risk groups of CAD has been reported as well . One stuInstead of selecting lead variants of previous GWAS’s and testing these for interactions with lifestyle factors, SNPs across the whole genome can be scanned in a genome-wide interaction study. Because unveiling G×L interactions requires well-powered studies, the CHARGE Gene–Lifestyle Interactions Working Group was formed . To dateOne twin study assessed G×L interactions amongst 51,065 Swedish same-sex twins. This study found higher BMI was associated with lower genetic variance of CAD, suggesting a more important role for genetics in the development of CAD in individuals with low BMI [ADAMTS7, and smoking [ApoE and smoking as well [The largest candidate gene study on G×L interactions is a meta-analysis which investigated the interaction between 45 CAD loci and smoking in 60,919 cases and 80,243 controls. In this study, a significant interaction was observed between rs7178051, located within  smoking . Several as well –55. Howe as well , 56. Thi as well . Many ot as well –65, alco as well , 65, 66, as well –69, and Only recently, studies with sufficient sample sizes have emerged and reported robust G×L interactions. Although the effect sizes are small and might add little to the explained variance of CAD heritability, they increase our knowledge on complex G×L interplays , 16. ThiIn line with most GWAS’s, the GWAS’s on CAD included only or mostly white Europeans in order to reduce heterogeneity . LogistiIn this review, we summarized current knowledge on the genetics of CAD, lifestyle factors, and the genetics of lifestyle factors associated with CAD. We focused on the interplay of genetics and lifestyle, especially the effect modifications as determined by G×L interaction studies. The majority of G×L interaction studies are small-scale candidate gene studies, lacking replication and therefore providing spurious results. Only few studies are robust in number and analysis strategy. These studies provide evidence of the existence of G×L interactions. Current data suggest that genetics and G×L interactions contribute little to the overall risk prediction for CAD next to lifestyle and other phenotypic risk factors. However, well-powered G×L interactions studies are important as they contribute to our understanding of disease pathophysiology and may provide insights into improving interventions or personalized recommendations.


GSTP1 (glutathione S-transferase pi 1) gene and the risk of different SCC (squamous cell carcinoma) diseases, such as ESCC , HNSCC (head and neck SCC), LSCC (lung SCC), and SSCC (skin SCC). Nevertheless, no unified conclusions have been drawn.There are several meta-analyses on the genetic relationship between the rs1695 polymorphism within the GSTP1 rs1695 on the susceptibility to different SCC diseases under six genetic models . Three online databases, namely, PubMed, WOS (Web of Science), and Embase (Excerpta Medica Database), were searched.Herein, an updated meta-analysis was performed to evaluate the probable impact of Pa  = 0.601; carrier, Pa = 0.587; homozygote, Pa = 0.689; heterozygote, Pa = 0.167; dominant, Pa = 0.289; dominant, Pa = 0.548]. Similar results were obtained after stratification by race (Asian/Caucasian), genotyping, control source, and disease type (ESCC/HNSCC/LSCC/SSCC) .Initially, we obtained a total of 497 articles. Based on our selection criteria, we eventually included 52 case-control studies  from 47 eligible articles. As shown in the pooling analysis, there was no difference in the risk of overall SCC disease between cases and controls [allele, GSTP1 gene is not associated with the risk of overall SCC or a specific SCC type, including ESCC, HNSCC, LSCC, and SSCC.The rs1695 polymorphism within the  CADM1 (cell adhesion molecule 1), AHR (aryl hydrocarbon receptor), and SEC16A , may be related with the risk of SCC . As shown in Table Pa = 0.601; carrier, Pa = 0.587; homozygote, Pa = 0.689; heterozygote, Pa = 0.167; dominant, Pa = 0.289; dominant, Pa = 0.548]. These data suggest that the rs1695 polymorphism within the GSTP1 gene does not contribute to the risk of overall SCC.First, we performed the overall meta-analysis, which included 52 case-control studies with 9763 cases and 15,028 controls (Table Pa > 0.05). The forest plot of the subgroup analysis by disease type under the allele model is shown in Fig. Next, we performed additional subgroup meta-analyses according to the factors of race (Asian/Caucasian), genotyping assay (PCR-RFLP), control source (PB/HB), and disease type (ESCC/HNSCC/LSCC/SSCC). As shown in Tables GSTP1 gene rs1695 polymorphism is not likely related to the genetic susceptibility of a specific SCC type, including ESCC, HNSCC, LSCC, and SSCC.Furthermore, we included all case-controls studies regarding the specific SCC type and conducted a series of subgroup analyses by race and control source. However, similar results were obtained (data not shown). As a result, the PE > 0.05, PB > 0.05), except for the heterozygote  and dominant (PE = 0.036) models. The funnel plot  is displayed in Fig. The publication bias analysis data obtained from Egger’s and Begg’s tests are shown in Table  PE = 0.06 models.GSTP1 rs1695 A/G polymorphism and the risk of overall SCC and then conducted subgroup analyses by the specific histological status. After rigorous screening, four main types of SCC, namely, ESCC, HNSCC, ESCC, and SSCC, were targeted.In the current meta-analysis, we first focused on the genetic relationship between the GSTP1 rs1695 A/G polymorphism is significantly related to the risk of ESCC in the Kashmiri population [GSTP1 rs1695 may be an independent risk factor for ESCC in Western populations [GSTP1 rs1695 A/G polymorphism in ESCC risk. Herein, we recruited 15 case-control studies involving 1934 cases and 3951 controls and performed a new meta-analysis to examine the association between the GSTP1 rs1695 A/G polymorphism and ESCC susceptibility. The carrier (carrier G vs. A) model, as well as the allele, homozygote, heterozygote, dominant and recessive genetic models, was used. Our results in the stratified analysis of specific ESCCs are consistent with the data of Tan et al. [ESCC, a type of squamous epithelium differentiation of a malignant tumour within the oesophagus, accounts for the vast majority of oesophageal cancers , 65. ESCpulation . Similarulations . Neverthulations  or a Chiulations . Therefon et al. .GSTP1 rs1695 A/G polymorphism and LSCC risk have been reported in different races and geographical locations [GSTP1 rs1695 and LSCC susceptibility, consistent with the prior meta-analysis of Feng in 2013 [Similarly, inconsistent results regarding an association between the ocations , 60, 63. in 2013  and Xu i in 2013 .GSTP1 rs1695 A/G polymorphism on overall head and neck cancer [GSTP1 rs1695 A/G polymorphism and the risk of overall head and neck cancer. Nevertheless, the potential role of GSTP1 rs1695 in the susceptibility to HNSCC was not assessed. Therefore, we performed a subgroup meta-analysis of HNSCC involving 18 case-control studies, but did not identify an association between GSTP1 rs1695 and HNSCC risk.Head and neck cancer comprises cancers of the mouth, nose, sinuses, salivary glands, throat, and lymph nodes in the neck, and HNSCC is the major pathologic type . In 2012GSTP1 rs1695 A/G polymorphism and SSCC risk, consistent with the prior meta-analyses regarding the correlation between GSTP1 rs1695 and the susceptibility to cutaneous cancer in 2015 [SSCC, SBCC  and  are the three main types of cutaneous cancer . Herein, in 2015 , 71.GSTA , GSTM1 (glutathione S-transferase mu 1), GSTT1 (glutathione S-transferase theta 1) and GSTP1, encode phase II enzymes and are thus important for the body defence, metabolic detoxification of mutagens or chemical drugs, or cellular elimination of carcinogens [GSTP1 gene can result in the substitution of Ile (isoleucine) for Val  at amino acid position 105, which may lower the cytosolic enzyme activity of GSTP1 protein [GSTP1 rs1695 A/G polymorphism.Human GST family genes, mainly including cinogens , 10. The protein , 73. AltGSTP1 gene. Other possible functional polymorphisms of the GSTP1 gene, such as rs1138272, or relative haplotypes will be important to examine in the future. We should also pay attention to the genetic relationship between GSTP1/GSTM1/GSTT1 polymorphisms and the risk of SCC.There are still some limitations to our meta-analysis that should be clarified. Even though our findings were considered reliable by our sensitivity analysis and publication bias assessment, more eligible investigations are still warranted to further enhance the statistical power. We note that population-based controls were not utilized in each case-control study. The currently available data of genotypic and allelic frequency from the on-line databases led us to only target the rs1695 polymorphism of the GSTP1 gene rs1695 polymorphism is not associated with the susceptibility to overall SCC diseases, including ESCC, HNSCC, LSCC, and skin SCC. The confirmation or refutation of this conclusion merits further evidence.In general, based on the currently published data, the


Studies had investigated the associations between proprotein convertase subtilisin/kexin type 9 SNP rs562556 and serum lipids levels and response to statin treatment, however, the results remained inconclusive. We conducted this meta-analysis to elucidate the relationship of rs562556 and serum lipids levels.All eligible studies met the inclusion criteria were retrieved from multiple databases. Relative data were extracted from each study. Review Manager (version 5.3.5) and STATA 12.0 software was used to perform this meta-analysis. Pooled standardized mean difference (SMD) with 95% CI was employed to evaluate the association of rs562556 with serum lipids levels.P = 0.001) and LDL-C than the non-carriers. The statistical results also illustrated that the G carriers had lower relative risk  than the non-carriers.A total of 7 eligible articles involving 4742 subjects were included in the final meta-analysis. The results revealed that the G carriers had lower levels of total cholesterol  contains supplementary material, which is available to authorized users. Proprotein convertase subtilisin/kexin type 9 (PCSK9), the ninth member of the subtilisin family of kexin-like proconvertases, was formerly known as neural apoptosis regulated convertase 1 (NARC-1) which was responsible for the cleavage of a variety of precursor proteins including neuropeptides, prohormones, cytokines, growth factors and other cell surface proteins . PCSK9 wThe pre-processed PCSK9 comprised four domains: the signal peptide (residues 1–30), the N-terminal pro-domain (residues 31–152), the catalytic domain (residues 153–425) and the C-terminal cysteine histidine rich domain (residues 526–692) . ResiduePCSK9 is a highly polymorphic gene. Variants in the PCSK9 gene have been associated with variability of serum lipids levels especially the level of LDL-C . Gain-ofThe PCSK9 rs562556  variant in exon9, located within the linker domain between catalytic domain and C-terminal domain, was reported as a gain-of-function mutation . NumerouAll studies focused on the associations of PCSK9 rs562556 with plasma lipids level and the effect of statin treatment were identified. A systematic search was performed in Pubmed, Elsevier and Web of Science up to December, 2018 without restrictions. Different combinations of the following terms were applied: “PCSK9”, “proprotein convertase subtilisin/kexin type 9”, “neural apoptosis regulated convertase”, “NARC-1”, “rs562556”, “polymorphism”, “variant” and “mutation”. Two authors  conducted the research independently. We also performed a full manual search from the references of relevant articles for studies potentially missed in the primary searches.This meta-analysis aimed to investigate the relationship between rs562556 and serum lipids levelS including total triglycerides (TG), total cholesterol (TC), LDL-C, and high-density lipoprotein cholesterol (HDL-C) before and/or after statin treatment. Studies were included in the meta-analysis when mean plasma lipid levels and standard deviations or standard errors by genotype were available. Reviews, case reports, pedigree-based studies, animal studies and reports with incomplete date were excluded.The unqualified and repeated studies were excluded after reviewed independently by two authors . Disagreements were consulted by the third person (MP). The following information was extracted from each included study: first author, year of publication, origin country, ethnicity, sample size, genotyping method, mean lipids level and standard deviations before and/or after statin treatment.P value < 0.05 was regarded as a statistically significant difference. Heterogeneity between studies was assessed using the chi-square-based “Q” test and inconsistency value (I2); P ≤ 0.05 and/or I2 > 50% indicated a significant heterogeneity. When P ≥ 0.05 and/or I2 < 50%, the Inverse-Variance fixed-effect model was used for the combined data, otherwise Inverse-Variance random-effects model was applied. Sensitivity analysis was performed by omitting each individual study in turn and performing all calculations using the remaining studies to assess the stability of the pooled results. Publication bias was assessed using Begg’s rank correlation test which was shown as a funnel plot . Funnel plot asymmetry was also examined by using Egger’s linear regression test. All statistical analyses were conducted with STATA 12.0 software  and Review Manager (version 5.3.5) .Due to the low frequencies of the GG genotype, more than half of the included studies reported the serum lipids level by pooling the GG and AG genotype. So a dominant model [(AG + GG) versus AA] was employed for calculation. Distributions of continuous variables were presented as mean ± standard deviation. We recalculated the mean ± standard deviation under dominant model when the candidate study did not report in this way. The strength of association was assessed and interpreted as pooled standardized mean difference (SMD) and its corresponding 95% confidence interval (CI). A PRISMA Flow Diagram documenting the details of study selection process is illustrated in Fig. 2 = 100%. The sensitivity analysis was conducted via sequential analysis after omitting one study at a time to assess the effects of individual studies on the overall meta-analysis estimate. Sensitivity analysis suggested that the study Shioji 2004 obviously affected the overall Std Mean Difference values of correlations. Heterogeneity was specifically decreased (I2 ≤ 41.3%), when the study by Shioji 2004 was removed. So the reference study Shioji 2004 was excluded when assessing the association of PCSK9 rs562556 with serum lipid levels.If all of the 7 studies were included for statistical analysis, significance of heterogeneity was observed and the value of IP = 0.001) and LDL-C than the non-carriers  and TG was detected between the G carriers and the non-carriers  than the non-carriers . Another study investigated the influence of PCSK9 variants on plasma lipid profile on hypercholesterolemic and normolipidemic individuals (Anderson 2014). While the third study included myocardial infarction group and controls to assess the association between this polymorphism and the incidence of myocardial infarction (Shioji 2004). A total of 2274 with subjects were involved in the three studies. The statistical results also illustrated that the G carriers had lower relative risk  and rs11591147 . SNP rs505151 , a common gain-of-function mutation, was accompanied with higher TC and LDL-C and interacted with statin treatment. Variant allele carriers of E670G had no significant benefit from statin treatment compared to homozygous wild type carriers who did benefit , 34. SNPSince no meta-analysis has yet been conducted on rs562556 , our primary purpose was to assess the role of rs562556 on the serum lipids levels. To our knowledge, this was the first meta-analysis to comprehensively investigate the association between rs562556 and serum lipids levels. We did this meta-analysis and found that this variant were positively associated with lower levels of TC and LDL-C under dominant model. Subgroup analysis based on ethnicity suggested that rs562556 had correlation with lower levels of TC and LDL-C in Caucasian, African Canadian, Brazilian but not in Thai population. A study based on Japanese revealed that rs562556 had strong correlation not only with lower levels of TC, LDL-C and TG but also with higher levels of HLD-C (data not shown). This difference may be attributed to different regional influences, ethnicities, and lifestyles. No significant differences in the response to statin treatment were observed between G-carriers and non G-carriers. Only two studies were included to assess the response to statin treatment, this result should be treated prudently. More related studies were needed to verify this conclusion and to give more robust evidence.Several unavoidable limitations of the present study should be noted. Firstly, the number of published studies and sample size were relatively small. A total of 2949 objects were included in the final analysis. Secondly, the objects including healthy population, hypercholesterolemics patients, polycystic ovary syndrome patients, varied widely considering their disease characteristics, which may weaken the statistic power of the findings of this meta-analysis. Thirdly, some of the included studies were case-control designed while the others were not. We recalculated and combined the data together, which may introduce inevitable interference for the final results.The current meta-analysis provided evidence of a positive association between rs562556 and lower TC and LDL-C levels.Additional file 1:The funnel plot of publication bias detection between PCSK9 rs562556 polymorphism and the serum lipid levels . (PNG 9 kb)