UCP2 polymorphisms are candidate riskfactors for DKD; however, their associations with this complication are stillinconclusive. Here, we describe a case-control study and a meta-analysisconducted to investigate the association between UCP2 -866G/Aand Ins/Del polymorphisms and DKD. The case-control study comprised 385 patientswith type 1 diabetes mellitus (T1DM): 223 patients without DKD and 162 with DKD.UCP2 -866G/A (rs659366) and Ins/Del polymorphisms weregenotyped by real-time PCR and conventional PCR, respectively. For themeta-analysis, a literature search was conducted to identify all studies thatinvestigated associations between UCP2 polymorphisms and DKD inpatients with T1DM or type 2 diabetes mellitus. Pooled odds ratios werecalculated for different inheritance models. Allele and genotype frequencies of-866G/A and Ins/Del polymorphisms did not differ between T1DM case and controlgroups. Haplotype frequencies were also similar between groups. Four studiesplus the present one were eligible for inclusion in the meta-analysis. Inagreement with case-control data, the meta-analysis results showed that the-866G/A and Ins/Del polymorphisms were not associated with DKD. In conclusion,our case-control and meta-analysis studies did not indicate an associationbetween the analyzed UCP2 polymorphisms and DKD.Uncoupling protein 2 (UCP2) decreases reactive oxygen species (ROS). ROSoverproduction is a key contributor to the pathogenesis of diabetic kidneydisease (DKD). Thus,  Diabetic kidney disease (DKD) is a common microvascular complication that affects 40%of patients with diabetes mellitus (DM) . The maiAt the cellular level, chronic hyperglycemia causes renal damage through five mainmechanisms: increased formation of advanced glycation end-products; increasedexpression of the receptor for advanced glycation end-products; activation ofprotein kinase C isoforms; increased flux of glucose through the polyol pathway; andupregulation of the hexosamine pathway . SeveralUncoupling protein 2 (UCP2) is a mitochondrial anion carrier protein expressed in anumber of tissues, including adipose tissue, liver, kidney, and retina .UCP2 gene are associated withROS-related pathologies  in the promoterregion; the Ala55Val polymorphism (rs660339) in exon 4, and the 45 bpinsertion/deletion (Ins/Del) polymorphism in the 3’ untranslated region  was associated with risk for proliferative diabetic retinopathy (DR)in type 1 and type 2 diabetic patients . Additionally, weconducted a systematic review and meta-analysis of the literature on the subject aspart of the ongoing effort to evaluate if UCP2 polymorphisms areassociated with DKD in T1DM or T2DM patients.Therefore, here we performed a case-control study to investigate if theThis case-control study was designed in agreement with STROBE and STREGAguidelines for reporting of genetic association studies . A standSerum and plasma samples were taken after 12 h of fasting for laboratoryanalyses -1,209 x 0,993age x 1,018 [if female] x 1,159[if black]  in atleast two out of three consecutive 24 h timed urine samples in a 6-monthperiod. Patients were classified as having normal to mildly increased UAE, moderately increased UAE (UAE 30 –299 mg / 24 h) or severely increased UAE (UAE > 300 mg / 24 h) . TherefoIn addition, we also included a third group constituted of 489 healthy blooddonors recruited from the same hospital, and who did not have diabetes orfamily history of this disease. These subjects were used as non-diabeticcontrols; thus, only subjects with HbA1c < 5.7% were included in thisgroup . All subUCP2 -866G/A polymorphism(rs659366) was genotyped using primers and probes contained in the TaqManSNP Genotyping Assay 20 . Real-Time PCR reactions were performed in 384well plates, in a total 5 μL volume, using 2 ng of DNA, TaqMan GenotypingMaster Mix 1 (Thermo Fisher Scientific) and TaqMan Genotyping Assay 1. Theassays were done in a real-time PCR thermal cycler  with the following protocol: heating for10 min at 95 °C, followed by 50 cycles of 95 °C for 15 s and 62 °C for 90 s.Genotyping of the UCP2 45 bp Ins/Del polymorphism wasperformed by direct separation of the PCR products on 2.5% agarose gelstained with GelRed , as previously described  in ourpopulation, only the UCP2 -866G/A and Ins/Del polymorphismswere analyzed in the present case-control study . Multivariate logistic regression analyses were done toevaluate the independent association of each individualUCP2 polymorphism or haplotypes with DKD, adjusting forpossible confounding factors. Variables with significant associations withDKD in the univariate analysis, or with an important biological associationwith this complication were chosen for inclusion in the multivariate model.T1DM duration was not included as an independent variable in these analysessince T1DM control group was selected based on this characteristic.Statistical analyses were performed using the SPSS 18.0 software , and P-values < 0.05 were consideredsignificant.Clinical and laboratory characteristics were compared between group ofpatients categorized according to the different genotypes of the twoMeSH terms. The search was restrictedto human studies and English, Portuguese, or Spanish language articles, andwas completed on December, 2018. References from all articles identifiedwere searched manually to find other relevant studies.This study was designed and reported in accordance with the PreferredReporting Items for Systematic Reviews and Meta-Analyses (PRISMA) andMeta-analysis of Observational Studies in Epidemiology (MOOSE) statements, as previously described(Necessary information from each study was independently extracted by twoinvestigators (C.D. and N.E.L.) using a standardized extraction form (Two investigators (C.D. and N.E.L.) independently evaluated the quality ofeach selected study using the Newcastle-Ottawa Scale (NOS) . The NOS2 test.Associations between polymorphisms and DKD were analyzed using OR (95% CI)calculations based on allele contrast, dominant, recessive and additiveinheritance models  was used to calculate OR (95% CI) for each studyand for the pooled effect; where heterogeneity was not significant, thefixed effect model was used. Sensitivity analyses were performed torecognize important studies with a considerable impact on inter-studyheterogeneity. All statistical analyses were performed using Stata 11.0software .Genotype distributions in control groups were tested for conformity with theHardy-Weinberg Equilibrium using a goodness-of-fitness χvs. 5.4% of black subjects in the control group(P = 0.093). Frequencies of the minor alleles of the-866G/A and Ins/Del polymorphisms in white and black subjects were: 40.5%vs. 44.8% for the -866A allele (P =0.814), and 30.7% vs. 20.3% for the Ins allele(P = 0.386).Comparisons of clinical and laboratorial characteristics between T1DM case andcontrol groups, categorized according to UAE values, are show in P ≥ 0.05), and they were similar between DKD cases andT1DM controls (vs. patients with moderate UAE vs. severeUAE), -866G/A and Ins/Del frequencies also did not differ significantly amonggroups (Table S1).controls . Of notecontrols . Accordicontrols . It is w2= 0.311) in our population. Fourhaplotypes (Ht) produced by the combination of these two polymorphisms wereinferred in the total sample of T1DM patients: -866G/Del , -866A/Del, -866G/Ins  and -866A/Ins . Distributions ofthese haplotypes were similar between T1DM controls and cases with DKD(P = 0.892) .The -866G/A polymorphism is in moderate linkage disequilibrium with the Ins/Delpolymorphism  . MoreoveP = 0.117). In contrast, presence of DR was increased inpatients carrying the Ins/Ins genotype (81.8%) compared to patients with theDel/Del or Ins/Del genotypes . Frequency of DR was 70.4% in patients carrying 3 or 4 minor alleles ofthe -866G/A and Ins/Del polymorphisms, 49.7% in patients with 0/1 minor allele,and 54.1% in patients with 2 minor alleles (P = 0.029). Noother characteristic described in Table S2), suggesting that these twopolymorphisms are not associated with T1DM risk.In an exploratory analysis, all clinical and laboratory characteristics showed inUCP2 polymorphisms in two differentpopulations from South India and North India, and, because of that, theirresults are shown separately.UCP2 polymorphisms in case and controlsamples from the different studies, as well as their respective ORs (95% CI) forassociation with DKD, are shown in Table S3. Quality assessment using the NOSscale showed that most studies were considered as having good quality since 8stars were given for the studies by et al.(2015), and 7 stars for the study by With exception of the present case-control study, the other three articlesincluded only T2DM patients. Two studies comprised Caucasian populations  strongly targeted the sequence where the Ins/Delpolymorphism is located. The 45 bp Ins allele disrupts the ligation site for thismiRNA; thus, probably changing UCP2 expression  also reportedthat T2DM patients carrying the -866A/55Val/Ins haplotype (dominant model) showedlower estimated GFR compared to patients with the reference haplotype, which was notobserved in the present study. These discrepancies may be explained by differencesin DKD pathophysiology between T1DM and T2DM  to detect an OR = 2.0 for theassociation with the -866G/A and Ins/Del polymorphisms, we cannot rule out thepossibility that these polymorphisms would be individually associated with DKD withlower ORs. There is also a possibility that these two polymorphisms are onlyassociated with DR, an association observed in both T1DM and T2DM patients (Also, we cannot fully exclude the possibility of false-negative results whenanalyzing associations between the Meta-analysis has been regarded as a powerful method for pooling data from differentstudies because it could overcome the problem of small sample sizes, as well asinsufficient statistical power of genetic association studies for common diseases(UCP2 polymorphisms could have interfered with the presentfindings. First, meta-analysis is prone to publication bias, and although we haveattempted to trace unpublished observations, we cannot be sure that smaller negativestudies were overlooked. Second, although the meta-analysis increased thestatistical power, the total sample power might still not be sufficient to showassociations with lower ORs. Third, heterogeneity is potentially a significantproblem when interpreting the results of any meta-analysis, and our meta-analysisshowed significant inter-study heterogeneity when analyzing the -866G/A polymorphismin the dominant model of inheritance. The exclusion of the study by Therefore, to date, most studies indicated that the -866G/A and Ins/Del polymorphismsare not risk factors for DKD. We acknowledge that certain factors unrelated to theUCP2polymorphisms and DKD, functional studies have suggested that changes inUCP2 expression play an important role in the development ofrenal damage. UCP2 resisted obstruction-induced kidney fibrosis.Moreover, UCP2 knockdown in NRK-52E tubular cells abolished theeffect of TGF-β1 treatment, decreasing extracellular matrix production (UCP2 knockdown in renal mesangial cells of ratsalso increased oxidative stress, inflammation, and apoptosis invitro (Despite these negative results regarding associations between UCP2 -866G/A andIns/Del polymorphisms are not important risk factors for DKD, classified accordingto UAE values. Further additional studies with large sample sizes are necessary toelucidate the effects possibly played by UCP2 polymorphisms in thepathogenesis of DKD.In conclusion, data reported here suggest that the


CYP2D6 and CYP2C19 genotype frequencies, across populations and ethnic groups worldwide. To that end, we conducted a comprehensive meta-analysis using Embase, PubMed, Web of Science, and PsycINFO . A non-normal metabolizer (non-NM) probability estimate was introduced as the equivalent of the sum-prevalence of predicted poor, intermediate, and ultrarapid metabolizer CYP2D6 and CYP2C19 phenotypes. The probability of having a CYP2D6 non-NM predicted phenotype was highest in Algeria (61%) and lowest in Gambia (2.7%) while the probability for CYP2C19 was highest in India (80%) and lowest in countries in the Americas, particularly Mexico (32%). The mean total probability estimates of having a non-NM predicted phenotype worldwide were 36.4% and 61.9% for CYP2D6 and CYP2C19, respectively. We provide detailed tables and world maps summarizing clinically relevant data regarding the prevalence of CYP2D6 and CYP2C19 predicted phenotypes and demonstrating large inter-ethnic differences. Based on the documented probability estimates, pre-emptive pharmacogenetic testing is encouraged for every patient who will undergo therapy with a drug(s) that is metabolized by CYP2D6 and/or CYP2C19 pathways and should be considered in case of treatment resistance or serious side effects.Extensive migration has led to the necessity of knowledge regarding the treatment of migrants with different ethnical backgrounds. This is especially relevant for pharmacological treatment, because of the significant variation between migrant groups in their capacity to metabolize drugs. For psychiatric medications, CYP2D6 and CYP2C19 enzymes are clinically relevant. The aim of this meta-analysis was to analyze studies reporting clinically useful information regarding  In the psychopharmacological treatment of migrants, variation in drug metabolism is an important aspect that must be taken into account2. In psychiatry, CYP2D6 and CYP2C19 are important drug-metabolizing enzymes8. For example, drugs that are metabolized by CYP2D6 include first- and second-generation antipsychotics, selective serotonin receptor inhibitors, and tricyclic antidepressants6. Among those metabolized by CYP2C19 are benzodiazepines, selective serotonin receptor inhibitors, and tricyclic antidepressants6. Individuals’ genetic variation is the most important factor influencing the kinetics of drug metabolism and thus may contribute to intolerability-related discontinuation or treatment failure9.Migration is a growing global phenomenon so that Western-trained psychiatrists are increasingly treating migrants with different cultural and ethnic backgrounds15.The outcome of a pharmacogenetic test  can be translated into a predicted phenotype. A combination of functional and non-functional alleles is responsible for the activity of the enzymes. There are four phenotype groups: poor (PM), intermediate (IM), normal (NM) (previously referred to as “extensive”), and ultrarapid metabolizers (UM), which are used to predict whether and how well a drug is metabolized. The same drug dosage may lead to a higher plasma level in PMs and IMs, compared to NMs, because of slower drug clearance, while UMs may have lower plasma levels than NMs because of a higher rate of drug clearance. Plasma levels are often related to the efficacy of a drug and the risk of dose-related side effects, with more severe side effects found in PMs and IMs than in NMsCYP polymorphisms also varies considerably across ethnic groups and plays a major role in inter-individual and inter-ethnic differences in drug metabolism and response16. For example, in European populations, just 2–3% of the population have a CYP2D6 UM profile, compared to 20–29% in East-African populations18. In contrast, CYP2C19 PMs are considerably more frequent in Asians (~12%) than in Europeans (~2%)19. Some allelic variants such as CYP2D6*40 and *45 are only seen in specific populations21.The prevalence of 25.The Clinical Pharmacogenetics Implementation Consortium (CPIC) has published guidelines with recommendations for drug choice and dosage based on phenotype predictionshttps://www.pharmgkb.org/guidelines). CYP2D6 and CYP2C19 allele frequency information has been compiled by CPIC and is available at https://www.pharmgkb.org/page/cyp2d6RefMaterials. Gaedigk et al., Fricke-Galindo et al., and Llerena et al. have described CYP2D6 and CYP2C19 phenotype prediction from genotype across world populations26.Other groups, including the Royal Dutch Association for the Advancement of Pharmacy—Pharmacogenetics Working Group, have also published guidelines  probability estimate, for which we calculated the sum-prevalence of a population for having a poor, intermediate, or ultrarapid CYP2D6 or CYP2C19 predicted phenotype. The sum-prevalence of these three predicted phenotypes presents a single measurement for non-normal metabolism in the populations of interest. It is defined as the equivalent of the prevalence of PM + IM + UM predicted phenotypes of the enzyme in percentages.The aim of our meta-analysis was to assess studies reporting clinically useful information about 41. The protocol for the current systematic review was not registered prior to the review.For this study, we followed the checklist in the Preferred Reporting Items for Systematic Reviews and Meta-Analyses statementWe conducted a literature review using the Embase, PubMed, Web of Science, and PsycINFO databases (1990–2019). The terms “CYP2D6” AND/OR “CYP2C19” AND “prevalence” OR “ethnicity” OR “race” AND “healthy subject” OR “normal control,” and variations on these terms and the names of different countries and continents were used in all fields. The last search was conducted on July 3, 2019.CYP2D6 or CYP2C19 allele frequencies from original data were reported; (2) the evaluated subjects did not have a specific disease (controls from case–control studies were included); (3) ethnicity was reported; (4) the article was published in English; (5) a minimum of 20 participants was investigated. If only an abstract was available, the article was included if all the above information was available; and (6) in order to be able to calculate a probability estimate, it was necessary to assess a minimum number of non-functional alleles and alleles with decreased function as well as a number of gene duplications. The genotyping assay included at least the following allelic variants: CYP2C19*2 and *17 in Africans, Americans, Europeans, Middle Easterners, Central/South East-Asians and African Americans; CYP2C19*2, *3, and *17 in Oceanians and CYP2C19*2 and *3 in East Asians; CYP2D6*2, *5, *17, *29, and *41 in Africans; CYP2D6*2 and *4 in Americans; CYP2D6*2 and *10 in East Asians; CYP2D6*2, *4, and *41 in Europeans; CYP2D6*2, *4, *10, and *41 in Middle Easterners and Central/South East-Asians; CYP2D6*5 in Oceanians; and CYP2D6*2, *4, *17, and *29 in African Americans.Our inclusion criteria were: (1) An eligibility assessment was performed independently in a standardized manner by A.B.K. and D.J.V. The first screening was based on the article abstracts; the next selection was based on the full text. Disagreements between the two reviewers were resolved by discussion to reach a consensus.The data were independently extracted from the studies by two investigators (A.B.K. and P.B.) and randomly checked by two investigators (D.J.V. and A.B.K.). For some studies, authors were contacted for clarification of the data.Information was extracted from each study as follows: (1) ethnicity of the participants; (2) definition of ethnicity (self-reported or genetic ancestry); (3) country of the studied population; (4) number of study participants; (5) study design ; (6) allele frequencies; (7) diplotype frequencies; (8) predicted phenotype frequencies.43.Star (*) alleles were assigned according to the Pharmacogenetic Variation (PharmVar) Consortium at PharmVar.org44. Seven major regions were considered: Africa, Americas (including Latino Americans and indigenous inhabitants of North America and Canada), East Asia, Europe (including North Americans and Canadians), the Middle East, Oceania, and Central/Southeast Asia; with one exception, namely, that African Americans were listed separately from Africans17. Here the frequencies of PM, IM, normal metabolizer (NM), and UM are reported by ethnicity, whereas the probability estimates of being a non-NM are reported by country. In many studies, these two factors—country and ethnicity—overlap, but for some studies we had to assign an ethnicity to a country to be able to show the information in world maps . The origin of the investigated ethnicity determined the country and region to which a population was assigned. An exception was made for Latin America, in which the population is an admixture of multiple origins  and no clear lineages can be determined; they were all considered as populations of the Americas and determined as belonging to the country they live in46. For some ethnicities, we could not determine a country of origin  so we have indicated them as “missing” in the figures.To be able to compare outcomes with previous meta-analyses, reported ethnicity was assigned to geographic regions, as done in previous meta-analysis about this subject, according to the Human Genome Diversity Project47. Briefly, a normal function allele was valued as 1, decreased function alleles as 0.25 or 0.5, a non-functional allele as 0, and increased function allele as 1.5. Gene duplications received double the value of their singleton counterparts. Homozygous carriers of non-functional alleles were classified as PMs (AS = 0). Carriers with one functional or decreased function allele and one non-functional allele and those carrying two decreased function alleles were classified as IMs (AS = 0.25–1)47. Homozygous carriers of normal function alleles and heterozygous carriers with one decreased function and one normal function allele were classified as NMs (AS = 1.25–2.25)47. Carriers of one or more increased function alleles and carriers of a duplication or multiplication of a functional allele were classified as UMs (AS > 2.25)47 (https://cpicpgx.org/resources/term-standardization/). CYP2C19 rapid and ultrarapid metabolizers were pooled and analyzed as UMs. The functionality of the CYP2D6 and CYP2C19 alleles was classified as listed by PharmVar in Table For each geographical region, the mean frequency of alleles was determined. In order to predict CYP2D6 and CYP2C19 phenotype frequencies from genotype data, we applied the activity score (AS) system to both genes (the AS system is widely used for CYP2D6 and was adapted to CYP2C19 to facilitate the translation process)In this meta-analysis, we applied strict criteria. To maximize the accuracy of the frequencies of the predicted phenotypes, we only predicted a phenotype if the original publication reported a minimum of non-/decreased function alleles and the assays included tests for gene duplications. Since the prevalence of alleles differed greatly per region, we used criteria specific for each geographical region. Alleles more prevalent than 0.05 (5%) in the major region Table  had to bp2 + 2pq + q2 = 1). For studies that did not report the prevalence of CYP2D6*1, the allele frequency was calculated as 100% minus the sum of variants17. The diplotypes were translated into predicted phenotypes according to the CPIC.All analyses were performed with IBM SPSS Statistics Version 25. If only diplotypes were reported, single allele frequencies were calculated. If only single allele frequencies were reported, diplotype frequencies were calculated using the Hardy–Weinberg equilibrium  of all the non-normal phenotypes in a population. We use the term probability estimate exclusively in this sense and it is in fact a proportion of the possible outcomes in a population. It is equal to 100% minus the percentage of NM in a given population.The studies were weighted by sample size (number of participants) when we calculated the mean predicted phenotypes per country and ethnicity.n = 200 papers) and CYP2C19 (n = 159 papers) included 261,296 and 257,745 healthy individuals. The alleles most frequently investigated were CYP2D6*1–*6, *10, *17, and CYP2C19*1–*3. Allele frequencies are shown per major geographical region in Table CYP2D6*2, *4, *10, and *41 and CYP2C19*2 and *17. As expected, allele frequencies varied substantially among ethnicities and countries. We found 89 studies that reported on more than one ethnic group. Overall, African and Middle Eastern countries were underrepresented, while European populations were the most frequently investigated.Of the 2873 publications retrieved from the database, 318 original research papers met our inclusion criteria 48, in non-Austronesian Melanesians (21.5%)48, and in the ethno-religious Druze from the Middle East (21.4%)48. High percentages of CYP2D6 PM were found in Europeans, for example, in the British (12.1%)49, the Danish (10.6%)50, and Basque (French) people (9.7%)48 due to the high frequency of CYP2D6*4. Frequencies of CYP2D6 predicted phenotypes by ethnicity are summarized in Table Predicted phenotype was reported or could be inferred from 51 studies for 116 ethnicities, covering h Africa 9.5%48, i*4, *17, *41, and duplications)48, Argentina 51, and France 48. The CYP2D6 non-NM probability estimate was lowest in several populations from Africa  and South-East Asia 49. See Fig. The probability of having a CYP2D6 non-NM predicted phenotype is the highest in Algeria 53, and ethnic groups living in the North Caucasus in Russia and in Burushu, Pakistan (39.0%)55 owing to a high prevalence of >20% of CYP2C19*17 in all these populations. High percentages of CYP2C19 PMs were found in Indian and Pakistani populations (Naik 31.0%56 and Saraiki 20.0%55), Tohoku Japanese (18.9%)57, and in Chinese Hui (28.0%)59 due to the presence of the CYP2C19*2 and *3 non-functional alleles. Table Data were retrieved from 73 studies describing 225,173 subjects of 85 ethnic groups/countries. These studies were selected for fulfilling the minimum number of alleles tested as prescribed by our region-specific criteria. The UM predicted phenotype is rather common in Ecuadorian Mestizos 41.4%)1.4%52, DCYP2C19*2 allele and/or the increased function CYP2C19*17 allele is highest in India (non-NM probability estimate 80.1%)61, Pakistan (non-NM probability estimate 74.8%)63, and Iran (non-NM probability estimate 69.2%)65. The probability is lowest in countries in the Americas, particularly Mexico (non-NM probability estimate 31.7%)68 and Costa Rica (non-NM probability estimate 33.9%)69. CYP2C19 non-NM probability estimates are shown in Fig. The probability of having a CYP2C19 non-NM predicted phenotype due to high frequencies of the non-functional 70, the results of our meta-analysis emphasize the importance of integrating pharmacogenetic information into clinical practice, especially when treating patients who have had adverse drug events or show treatment resistance.In this meta-analysis, we introduce a clinical useful concept of a non-NM probability estimate as the equivalent of the sum-prevalence (in percentages) of PM + IM + UM predicted CYP2D6 and CYP2C19 phenotypes. The mean totals of non-NM probability estimates worldwide were 36.4% (CYP2D6) and 61.9% (CYP2C19). This means that more than half of all psychiatric patients have a non-normal predicted CYP2D6 and/or CYP2C19 metabolizer phenotype. Since >75% of the psychopharmacological drugs are metabolized by one or both of these enzymesCYP2D6 and CYP2C19 genotypes in healthy populations of which genotype requirements were fulfilled by 118 studies (37%). This is an important requirement, because having too few CYP2D6 or CYP2C19 alleles in a study may not accurately determine predicted phenotype from genotype data.We included a total of 318 studies in our meta-analysis of 72. The prevalences of our CYP2D6 predicted phenotypes were in general agreement with those reported by Gaedigk et al.17. However, our data predicted a higher percentage of UMs for Africans . This difference may be explained by the fact that we weighted the number of genotyped subjects when calculating the mean allele frequencies for our meta-analysis. In Gaedigk et al.’s report, studies were not weighted by sample size, so small studies might have had a bigger influence on the mean. We also included two studies in Africans that were not included by Gaedigk et al. due to their small sample size and thus not meeting their requirements of at least 50 study subjects; they both reported higher frequencies of UMs73. In contrast, in the South Central Asian population the percentage of UMs found by Gaedigk et al. was higher than those reported here, 2.8% (n = 370) vs. 2.1% (n = 434), due to the inclusion of two studies in South East Asians in this meta-analysis, which found no UMs74.Although grouping populations can simplify reporting of pharmacogenetic alleles, we grouped ethnicities within geographical regions to enable comparisons with other published meta-analyses19 and the CPIC’s CYP2C19 allele frequency table , we found a much higher prevalence of 25.0% for CYP2C19 UMs in Oceania (vs. 0% and 1.5%). This is because we only included studies investigating the CYP2C19*17 increased function allele, which leads to rapid and ultrarapid metabolizer predicted phenotypes. Studies that do not investigate this allele yield underestimates of the prevalence of UMs.In contrast with Fricke-Galindo et al.CYP genotyping is beneficial for clinical outcomes in psychiatric practice. There are a few prospective studies analyzing the clinical utility of CYP genotyping and they report contradictory outcomes in diverse populations29. So far, it is still unclear which patient groups might benefit from genotyping and see better treatment outcomes. One reason for the uncertainty is the possibility of the transformation of genotypic EMs into phenotypic PMs by multiple causes, for example, due to smoking, CYP2D6 and CYP2C19 inhibiting medication , CYP2D6 and CYP2C19 inducing medication , and inflammation or co-morbidities76. This phenomenon is called phenoconversion: it has been described in diverse populations79. If this is happening on a large scale, it means genotypic outcomes could be unreliable for use in clinical practice. It may already influence outcomes of studies on the effectivity of CYP genotyping in clinical practice28. Another issue may arise with genotyping patients who have been on treatment for several years, because their brain has adapted to the changed levels of neurotransmitters and the side effects are no longer reversible81.Some issues need to be addressed in translating genotype data to information useful for clinical practice. Although prevalences of non-NM are high, there is no conclusive evidence whether 82. In this meta-analysis, we categorized the IM as defined by the CPIC47. Because IMs only show minor differences in metabolism from NMs83, one could categorize them in the NM group. However, other studies have indicated that IMs show lower oral drug clearance, higher blood serum levels, and have higher chances of side effects than NMs86. For this reason, we consider that the IM status is clinically relevant for psychiatric patients and guidelines for some medications are now advising dose adjustments for this predicted phenotype87. Estimations of the worldwide prevalence of only PM + UM were 7.75% (CYP2D6) and 32.94% (CYP2C19) , as well as controls from case–control studies, which increased the number of studies we could include. Bias was minimized by excluding studies of populations diagnosed with a disease to prevent confounding our data . On the other hand, some large studies had to be excluded due to their inclusion of non-healthy individuals, which might have biased the outcomes of some countries95.We included studies with a small number of participants 98, which did not identify any PMs.Studies reporting allele frequencies of merely CYP2D6*10 and *36 and in the CYP2D6*17 and *40 allele, a slight overestimation or underestimation of some predicted phenotypes might have been reported in some studies99.A limitation is that we were depended on the sensitivity of the tests of the individual studies. For example, because of overlap in single-nucleotide polymorphisms in the The inclusion of studies with a small number of participants (20–50) could have led to an overestimation or underestimation of predicted phenotypes in some populations, but the influence on the mean prevalence was minimized by weighting the number of genotyped subjects. We may still have made overestimates or underestimates where there are few studies for a certain region/country along with a relatively small number of studied subjects. Because few studies reporting specific minority ethnicities met our inclusion criteria, we did not want to exclude potentially valuable information from our meta-analysis by setting too-stringent participant number requirements.CYP genotypes has been based on self-reported ethnicity, while for a few minority populations, some genetic data were systematically analyzed. In a study of 103,006 participants with 23 ethnicities, a very high correspondence was found between self-reported ethnicity and genetic ancestry100. Only African Americans and Latino Americans demonstrated a higher degree of ancestral admixture than self-reported.Although we only included studies on homogenous ethnic groups in this meta-analysis, we are aware of the limitations of grouping ethnicities based on self-reported ethnicity. Although ancestry based on genetic information is more objective than self-reported ethnicity, much of the research into 44. Ethnic groups migrate, and although some ethnicities show almost no admixture with the local ethnicity even many years after migration, other ethnicities do show a mixture of multiple ancestors.Second, although studies of genetic ancestry show there is a strong linkage between belonging to an ethnic group and coming from a certain geographical region, ethnicity is not always the same as geographical region76. The probability estimates per country  and their predicted phenotype distributions are not adequately represented in the total estimated means.In this comprehensive meta-analysis of worldwide CYP2D6 and CYP2C19 genotype variation, , we found that the mean total probability estimates for a non-NM predicted phenotype are 36.4% for CYP2D6 and 61.9% for CYP2C19. The estimates reveal a large geographical variation . Our results suggest that more than half of the world population has a non-normal CYP2D6 and/or CYP2C19 metabolizer predicted phenotype. Based on the documented probability estimates, pre-emptive pharmacogenetic testing is encouraged for every patient who will undergo therapy with a drug(s) that is metabolized by CYP2D6 and/or CYP2C19 pathways and should be considered in case of treatment resistance or serious side effects.*17 to come to a reliable phenotype prediction. We therefore recommend that, when allele frequencies are being studied, a minimum number of alleles—depending on the geographical region—must be assessed to be able to predict phenotypes as accurately as possible101.Second, many of the studies were not relevant for clinical practice, because they only investigated a minimum number of allelic variants and thus any phenotype prediction is unlikely to be accurate. Especially when estimating the prevalence of the CYP2C19 UM predicted phenotype, studies in all regions except for East Asia should genotype on SUPPLEMENTAL FIGURE AND TABLE LEGENDSSupplemental Table 1: References for Table 2Supplemental Table 2: References for Table 3Supplemental Table 3: References for Table 4Supplemental Figure 1. Prevalence of CYP2D6 PM+UM per countrySupplemental Figure 2. Prevalence of CYP2C19 PM+UM per country


GSTM1 present/null, GSTT1 present/null, and GSTP1 IIe105Val on breast cancer (BC) risk. However, their meta-analyses did not explore the combined effects of the three genetic polymorphisms on BC risk. In addition, they did not evaluate the credibility of statistically significant associations. Furthermore, a multitude of new articles have been published on these themes, and therefore a meta-analysis and re-analysis of systematic previous meta-analyses were performed to further explore these issues.Fourteen previous meta-analyses have been published to analyze the polymorphisms of individual GSTM1, GSTT1, and GSTP1 polymorphisms on breast cancer risk.To determine the association between the individual and combined effects of GSTM1, GSTT1, and GSTP1 polymorphisms on BC risk. To evaluate the credibility of statistically significant associations in the current and previous meta-analyses, we applied the the false-positive report probabilities (FPRP) test and the Venice criteria.Crude odds ratios (ORs) and their 95% confidence intervals (CIs) were applied to estimate the association between individual and combined effects of GSTM1, GSTT1 and GSTP1 polymorphisms on BC risk. Overall, statistically significant elevated BC risk was found in any individual and combined effects of GSTM1 present/null, GSTT1 present/null, and GSTP1 IIe105Val polymorphisms. However, when we restricted studies only involving with high-quality, matching, HWE, and genotyping examination performed blindly or with quality control, significantly increased BC risk was only found in overall population for GSTM1 null genotype, among all populations, Caucasians, and postmenopausal women for the combined effects of GSTM1 and GSTT1 polymorphisms, and in overall analysis for the combined effects of GSTM1, GSTT1, and GSTP1 IIe105Val polymorphisms. Further, less-credible positive results were identified when we evaluated the credibility of positive results of the current and previous meta-analyses.101 publications were selected to evaluate the individual and combined effects of GSTM1, GSTT1 and GSTP1 polymorphisms may be not associated with increased BC risk.This meta-analysis indicates that the individual and combined effects of  Breast cancer (BC) is one of the most common diseases and an important public health challenge among women worldwide, although the incidences of BC are not the same in different countries and ethnic groups , 2. RiskGSTs) are overwhelmingly important genes, which play key role in the detoxification of toxic, potentially carcinogenic compounds, and a host of basic physiological processes of the human body [GST enzymes have been found  [GSTA, GSTM, GSTP, GSTO, and GSTT genes). Among these genes, both GSTM1 and GSTT1 genes show deletion polymorphisms (null genotype) [GSTP1 polymorphism results in a change isoleucine (IIe) to valine , which also decreases enzymatic activity [In recent years, some genes have been confirmed as potential cancer susceptible genes. Glutathione S-transferases   and eachenotype) , 14, whienotype) . They arenotype) . An codoactivity , 18. TheGSTM1 null genotype and BC cancer susceptibility [Reference 1 in GSTT1 null genotype and the combined effects of GSTM1 present/null, GSTT1 present/null, and GSTP1 IIe105Val polymorphisms on BC cancer risk was published in 1998 [Reference 5 in GSTP1 IIe105Val polymorphism and BC cancer risk [Reference 110 in GSTM1 present/null, GSTT1 present/null, and GSTP1 IIe105Val polymorphisms on BC risk. However, their meta-analyses did not conduct the combined effects of the three genes on BC risk, in addition, they did not evaluate the credibility of statistically significant associations, furthermore, a lot of new studies have been published, and therefore a meta-analysis and re-analysis of previous meta-analyses were carried out to further explore the individual and combined effects of these genes on BC risk.In 1993, the first publication was reported on the association between GSTT1 OR glutathione S-transferase P1 OR GSTP1 OR glutathione S-transferase M1 OR GSTM1) AND breast AND . Language was not restricted in the present meta-analysis. It was implemented to identify additional studies manually . Finally, the corresponding authors were contacted via e-mail if necessary.Literature search was performed using PubMed and CNKI databases in this meta-analysis . The following search strategy was applied:  case–control study; (2) detailed genotype frequencies were afforded between case and control groups; (3) studies must assess the association between the individual and combined effects of GSTM1, GSTT1 and GSTP1 polymorphisms on BC risk.Information was carefully collected independently by two investigators from all selected studies. Potential disagreements were judged through the corresponding authors if necessary. The following information was collected: first author’s surname, year of publication, country, ethnicity, source of cases, source of controls, type of controls, matching, single nucleotide polymorphism (SNP), sample size, and genotype frequencies of the individual and combined effects of GSTM1 present/null, GSTT1 present/null, and GSTP1 IIe105Val polymorphisms with BC risk. They were considered as low quality studies if quality scores were ≤10, while scores of > 10 were regarded as high quality in this meta-analysis.The quality of the studies were appraised independently by two of all authors. We designed quality assessment criteria on the basis of two previous meta-analyses , 34. S1 GSTM1 present/null, GSTT1 present/null, and GSTP1 IIe105Val polymorphisms on BC risk. We used null vs. present model to calculate the pooled ORs with their 95% CIs for the GSTM1 present/null and GSTT1 present/null polymorphisms. Analysis was conducted employing the following genetic models for GSTP1 IIe105Val polymorphism: Val/Val vs. IIe/IIe, IIe/Val vs. IIe/IIe, Val/Val vs. IIe/IIe + IIe/Val, Val/Val + IIe/Val vs. IIe/IIe, and Val vs. IIe. For the combined effects of GSTM1 present/null and GSTT1 present/null polymorphisms, we applied the following genetic models: + − vs. + +, − + vs. + +, − − vs. + +, (+ −) + (− +) vs. + +, (− −) + (+ −) + (− +) vs. + +, and − − vs. (+ +) + (+ −) + (− +). − − was GSTM1 null/GSTT1 null, + + was GSTM1 present/GSTT1 present, + − was GSTM1 present/GSTT1 null, and − + was GSTM1 null/GSTT1 present. The following genetic models were used for the combined effects of GSTM1 present/null and GSTP1 IIe105Val polymorphisms: GSTM1 null/GSTP1 IIe/IIe vs. GSTM1 present/GSTP1 IIe/IIe, GSTM1 present/GSTP1 Val* vs. GSTM1 present/GSTP1 IIe/IIe, all one high risk genotypes vs. GSTM1 present/GSTP1 IIe/IIe, GSTM1 null/GSTP1 Val* vs. GSTM1 present/GSTP1 IIe/IIe, all high risk genotypes vs. GSTM1 present/GSTP1 IIe/IIe, and GSTM1 null/GSTP1 Val* vs. . For the combined effects of GSTT1 present/null and GSTP1 IIe105Val polymorphisms, the following genetic models were employed: GSTT1 null/GSTP1 IIe/IIe vs. GSTT1 present/GSTP1 IIe/IIe, GSTT1 present/GSTP1 Val* vs. GSTT1 present/GSTP1 IIe/IIe, all one high risk genotypes vs. GSTT1 present/GSTP1 IIe/IIe, GSTT1 null/GSTP1 Val* vs. GSTT1 present/GSTP1 IIe/IIe, all high risk genotypes vs. GSTT1 present/GSTP1 IIe/IIe, and GSTT1 null/GSTP1 Val* vs. . Finally, for the combined effects of GSTM1 present/null, GSTT1 present/null, and GSTP1 IIe105Val polymorphisms, we applied the following ten genetic models: GSTM1 null/GSTT1 present/GSTP1 IIe/IIe vs. GSTM1 present/GSTT1 present/GSTP1 IIe/IIe, GSTM1 present/GSTT1 null/GSTP1 IIe/IIe vs. GSTM1 present/GSTT1 present/GSTP1 IIe/IIe, GSTM1 present/GSTT1 present/GSTP1 Val* vs. GSTM1 present/GSTT1 present/GSTP1 IIe/IIe, all one high-risk genotypes vs. GSTM1 present/GSTT1 present/GSTP1 IIe/IIe, GSTM1 null/GSTT1 null/GSTP1 IIe/IIe vs. GSTM1 present/GSTT1 present/GSTP1 IIe/IIe, GSTM1 null/GSTT1 present/GSTP1 Val* vs. GSTM1 present/GSTT1 present/GSTP1 IIe/IIe, GSTM1 present/GSTT1 null/GSTP1 Val* vs. GSTM1 present/GSTT1 present/GSTP1 IIe/IIe, all two high-risk genotypes vs. GSTM1 present/GSTT1 present/GSTP1 IIe/IIe, GSTM1 null/GSTT1 null//GSTP1 Val* vs. GSTM1 present/GSTT1 present/GSTP1 IIe/IIe, and GSTM1 null/GSTT1 null//GSTP1 Val* vs. . We employed Q test to evaluate heterogeneity among selected studies. A statistically significant heterogeneity was regarded if P < 0.10 and I2 > 50% [P < 0.05. Heterogeneity sources were estimated according to a meta-regression analysis method. A sensitivity analysis was performed by using two methods: first, a single study was removed each time, second, a dataset was used that the comprised only high-quality studies, matching studies, HWE, and genotyping performed blindly or with quality control [P < 0.05) [We applied crude odds ratios (ORs) and their 95% confidence intervals (CIs) to estimate the association between individual and combined effects of I2 > 50% . A fixedI2 > 50%  was consI2 > 50% . Subgrou control . Publica control  Egger’s  < 0.05) . A nonpa < 0.05)  if publi < 0.05)  and the  < 0.05) . The FPRGSTM1 present/null, GSTT1 present/null, and GSTP1 IIe105Val polymorphisms on BC risk. GSTM1 present/null polymorphism , GSTT1 null genotype , GSTP1 IIe105Val , and the combined effects of GSTM1 and GSTT1 polymorphisms  among postmenopausal women, and seventeen, twelve, fifteen, and six case–control studies were conducted to analyze GSTM1 null genotype , GSTT1 null genotype , GSTP1 IIe105Val , and the combined effects of GSTM1 and GSTT1 polymorphisms  among premenopausal women, respectively, as shown in GSTM1, GSTT1, and GSTP1 polymorphisms, respectively, as shown in GSTM1 present/null, 23 high-quality studies and 44 low-quality studies on GSTT1 present/null, 30 high-quality studies and 26 low-quality studies on GSTP1 IIe105Val, 13 high-quality studies and 18 low-quality studies on the combined effects of GSTM1 and GSTT1, nine high-quality studies and six low-quality studies on the combined effects of GSTM1 and GSTP1, eight high-quality studies and five low-quality studies on the combined effects of GSTT1 and GSTP1, and eight high-quality studies and five low-quality studies on the combined effects of GSTM1, GSTT1, and GSTP1 polymorphisms as determined by quality assessment of molecular association studies . In addition, significantly elevated BC risk was also observed in a slice of subgroups, such as Asians, population-based studies, healthy women, cancer-free women, cancer-free patients, matching studies, no matching studies, large-sized studies, small-sized studies, high-quality studies, low-quality studies, postmenopausal and premenopausal women, as shown in At the overall analysis, the GSTT1 null genotype had a significantly elevated BC risk  in overall analysis. Significant association was also shown in quite a few subgroups, for instance, Caucasians, hospital-based studies, healthy women, cancer-free patients, matching studies, no matching studies, large-sized studies, small-sized studies, low-quality studies, and premenopausal women, as shown in The characters who carried GSTP1 IIe105Val polymorphism in pooling all studies. However, significantly increased BC risk was yielded in some subgroup analyses, such as Asians, Indians, hospital-based studies, no matching, and low-quality studies, as shown in No significantly raised BC risk was observed for GSTM1 and GSTT1 null genotypes on BC risk  + (+ −) vs. + +: OR = 1.17, 95% CI = 1.05–1.30, (− +) + (+ −) + (− −) vs. + +: OR = 1.27, 95% CI = 1.12–1.43, − − vs. (− +) + (+ −) + (+ +): OR = 1.41, 95% CI = 1.19–1.68) across overall analysis. In addition, a significantly increased BC risk was observed in all subgroup analyses, as shown in The pooled estimates showed an significant association between the combined effects of both GSTM1 present/null and GSTP1 IIe105Val polymorphisms on BC risk. Overall, a significant association was found between the combined effects of GSTM1 present/null and GSTP1 IIe105Val polymorphisms on BC risk : OR = 1.40, 95% CI = 1.12–1.75). Furthermore, a statistically significant association was also observed in a slice of subgroups, for example, Asians, Caucasians, Indians, no population-based studies, population-based studies, healthy women, cancer-free women, large-sized studies, small-sized studies, high-quality studies, low-quality studies, no matching studies, and controls in HWE studies.GSTT1 present/null and GSTP1 IIe105Val polymorphisms on BC risk. The results showed an raised BC risk : OR = 1.26, 95% CI = 1.03–1.54) in all eligible studies. Analyses of subgroups also showed an increased BC risk in Caucasians, Indians, no population-based studies, population-based studies, healthy women, large-sized studies, small-sized studies, high-quality studies, low-quality studies, and no matching studies.GSTM1 present/null, GSTT1 present/null, and GSTP1 IIe105Val polymorphisms on BC risk. The results indicated that a significantly increased BC risk was found in overall populations  for GSTM1 null/GSTT1 present/GSTP1 Val* vs. GSTM1 present/GSTT1 present/GSTP1 IIe/IIe, OR was 1.41 (95% CI = 1.08–1.83) for all two high-risk genotypes vs. GSTM1 present/GSTT1 present/GSTP1 IIe/IIe, OR was 1.79 (95% CI = 1.19–2.67) for GSTM1 null/GSTT1 null/GSTP1 Val* vs. GSTM1 present/GSTT1 present/GSTP1 IIe/IIe, and OR was 1.51 (95% CI = 1.10–2.06) for GSTM1 null/GSTT1 null/GSTP1 Val* vs. . The results of subgroups indicated that significant association was also observed in Caucasians, Indians, no population-based studies, population-based studies, healthy women, large-sized studies, small-sized studies, high-quality studies, low-quality studies, no matching studies, and controls in HWE studies, as shown in Finally, we also performed a pooled analysis to investigate the combined effects of ulations ; respectP = 0.027 for + − vs. + +), type of controls (P = 0.005 for − − vs. + +), and quality score of articles (− + vs. + +: P = 0.045 for − − vs. + +) were source of heterogeneity between the combined effects of GSTM1 present/null and GSTT1 present/null polymorphisms with BC risk. For the combined effects of GSTM1 present/null and GSTP1 IIe105Val polymorphisms, matching  was source of heterogeneity.Heterogeneity was observed in the current meta-analysis, as shown in Tables GSTM1, GSTT1, and GSTP1 IIe105Val polymorphisms , GSTM1 null genotype , all races , Caucasians  + (− +) + (+ −): OR = 1.58, 95% CI = 1.11–2.24), and postmenopausal women  + (− +) + (+ −): OR = 1.29, 95% CI = 1.03–1.61), and the combined effects of GSTM1 and GSTT1 polymorphisms, and, as shown in Tables GSTT1 and GSTP1 IIe105Val polymorphisms, GSTT1,GSTP1, and the combined effects of GSTM1 and GSTP1 IIe105Val polymorphisms, and, as shown in Tables Sensitivity analysis was estimated by applying two methods in this meta-analysis. First, results did not change when removing a single study each time to appraise the robustness in the current meta-analysis. However, when we restrained only high-quality studies, HWE, matching, and genotyping examination performed blindly or with quality control, significantly increased BC risk was found in the overall analysis for the combined effects of GSTM1  + (− +) vs. (+ +): P = 0.005, (− +) + (+ −) + (− −) vs. + +: P = 0.002, − − vs. (− +) + (+ −) + (+ +): P = 0.001), the combined effects of GSTM1 and GSTP1 IIe105Val polymorphisms , the combined effects of GSTT1 and GSTP1 IIe105Val polymorphisms , and the combined effects of GSTM1, GSTT1, and GSTP1 IIe105Val polymorphisms . GSTM1 and GSTT1 polymorphisms  + (− +) vs. (+ +): OR = 1.09, 95% CI = 0.96–1.23, (− +) + (+ −) + (− −) vs. + +: OR = 1.12, 95% CI = 0.97–1.29, − − vs. (− +) + (+ −) + (+ +): OR = 1.14, 95% CI = 0.95–1.37), the combined effects of GSTM1 and GSTP1 IIe105Val , the combined effects of GSTT1 and GSTP1 IIe105Val , and the combined effects of GSTM1, GSTT1, and GSTP1 IIe105Val polymorphisms  on BC risk in all populations.There was no evidence of publication bias for P value < 0.05 was observed in at least one of the two genetic model ; (2) FPRP < 0.2; (3) statistical power > 0.8; and (4) I2 < 50%. Associations were considered to be “less-credible positive results” if they did not meet the above criteria. Tables I2 value, statistical power and FPRP value for the current and previous meta-analyses, respectively. We identified “less-credible positive results” for the current and previous meta-analyses on the basis of above criteria.Statistically significant associations were considered as “positive results” when they met the following criteria : (1) P vGSTM1, GSTT1, and GSTP1 polymorphisms on BC risk. We also used FPRP test and Venice criteria to re-analyze the previously published systematic meta-analyses. As far as we know, this is the first meta-analysis to investigate whether there was an increased BC risk for the combined effects of GSTM1 present/null, GSTT1 present/null, and GSTP1 IIe105Val polymorphisms.A meta-analysis involving 101 publications was done to evaluate the relationship between individual and combined effects of GSTM1 and GSTT1 genes show deletion polymorphisms (null genotype), which cause the absence of expression and enzyme activity loss. GSTP1 IIe105Val polymorphism also decreases enzymatic activity. Given the involvement of GSTs in deactivating and detoxifying carcinogens, deletions in GSTM1 and GSTT1 and IIe105Val polymorphism in GSTP1 resulting in no enzyme activity may compromise an individual’s ability to deactivate carcinogens, thus increasing risk of cancer. The exact mechanism involved are still mysterious, these combined effects might be due to the involvement of GSTM1, GSTT1, and GSTP1 in metabolism. Moreover, each gene expresses an increased risk genotype , which may be involved in breast cancer susceptibility when more than one are expressed in each individual. Overall, statistically significant increased BC risk was found in any individual and combined effects of the GSTM1, GSTT1 and GSTP1 polymorphisms. In addition, significant association was also observed in some subgroups for these genes on BC risk. However, when we restrained only high-quality studies, HWE, matching, and genotyping examination performed blindly or with quality control, significantly increased BC risk was found in the overall analysis for GSTM1 null genotype, all populations, Caucasians, and postmenopausal women for the combined effects of GSTM1 and GSTT1 polymorphisms, and overall analysis for the combined effects of GSTM1, GSTT1, and GSTP1 IIe105Val polymorphisms. This was an attempt to avoid random errors and confounding bias that sometimes distorted the results of molecular epidemiological studies [P-value must be adjusted [I2 were important indicator by Ioannidis et al. [GSTM1 and GSTT1 polymorphisms and BC risk. For the combined effects of GSTM1 and GSTP1 IIe105Val polymorphisms, matching was source of heterogeneity in this meta-analysis. Therefore, we should perform subgroup analyses to reduce heterogeneity, because HB, patients, low quality studies and no-matching studies were important confounding bias. In addition, random error and bias were common in the studies with small sample sizes, and the results were unreliable, especially in molecular epidemiological studies [Among these genes, both  studies –47. Furtadjusted . With res et al. . Hence,  studies . FurtherGSTM1 present/null, GSTT1 present/null, and/or GSTP1 IIe105Val polymorphisms on breast cancer (BC) risk. I2 value, statistical power and FPRP value for the previous meta-analyses. Xue et al. [GSTM1 null genotype contributed to an increased CRC risk in Chinese population. Kuang et al. [GSTP1 IIe105Val polymorphism was associated with an increased BC risk in Asians. The examination of 17 studies of GSTM1 , 14 studies of GSTT1 , and 10 studies of GSTP1  by Song et al. [GSTM1 and GSTT1 null genotypes were associated with an increased BC risk in Asians and the GSTP1 IIe105Val polymorphism was associated with an increased BC risk in Caucasians. The examination of 27 studies of GSTM1, 23 studies of GSTT1, and 20 studies of GSTP1 by Tang et al. [GSTM1 and GSTP1 polymorphisms were associated with an increased BC risk in Asian population, especially in East Asian, while the GSTT1 polymorphism may be not associated with BC risk. Xiao et al. [GSTT1 null genotype contributed to an increased BC risk in Chinese population. Wan et al. [GSTM1 null genotype was associated with an increased BC risk in the Chinese population. Liu et al. [GSTP1 IIe105Val polymorphism was associated with increased BC risk in Asians. Chen et al. [GSTT1 null genotype may contribute to an increased BC risk in Asians and Caucasians. Economopoulos and Sergentanis [GSTP1 IIe105Val polymorphism was associated with an increased BC risk in Asians. Lu et al. [GSTP1 IIe105Val polymorphism with BC risk in all races in 30 published studies  indicated that the GSTM1 null genotype may be associated with an increased risk of BC in Asians. Qiu et al. [GSTM1 null genotype was associated with an increased BC risk in Caucasians and postmenopausal women. The examination of 41 studies of GSTT1  and 30 studies of GSTP1  by Sergentanis and Economopoulos [GSTT1 null genotype and GSTP1 IIe105Val polymorphisms seemed to be associated with an increased BC risk in a race-specific manner. The finding on GSTP1 IIe105Val polymorphisms was further investigated because of the small number of Chinese studies. Sull et al. [GSTM1 null genotype association with BC risk they found that the GSTM1 null genotype was associated with an increased BC risk in postmenopausal women. The examination of 19 studies of GSTM1 , 15 studies of GSTT1 , and 10 studies of GSTP1  by Egan et al. [GSTM1 and GSTT1 null genotypes were associated with an increased BC risk in postmenopausal and all women, respectively. However, quality assessment of the eligible studies was not assessed in 12 previous meta-analyses [I2 value was not showed in 11 previous meta-analyses [A total of fourteen previous meta-analyses –32 betwee et al.  performeg et al.  examinedg et al.  indicateg et al.  indicateu et al.  conducten et al.  selectedgentanis , the resu et al.  evaluateu et al.  identifil et al.  examinedanalyses –32, souranalyses –32. TherGSTM1 null genotype, 67 studies involving 23,092 BC cases and 26,381 controls for the GSTT1 null genotype, and 56 studies involving 25,331 BC cases and 27,424 controls in all populations. Second, this is the first meta-analysis to investigate the combined effects of these genes in overall population. Third, we evaluated quality assessment of the eligible studies. Forth, we used meta-regression analysis method to explore the source of heterogeneity. Fifth, we collected more detailed data. Sixth, an important sensitivity analysis was conducted on studies that were high-quality, matching, HWE, and or in which genotyping was performed blindly or with quality control. Seventh, we applied FPRP and Venice criteria to investigate the significant association with BC risk. The current meta-analysis also has several limitations. First, only published articles were included in the current meta-analysis, therefore, publication bias may be exist as shown in GSTM1 null effect may be observed. Second, we did not consider whether the genotype distribution in the controls was in HWE for GSTM1 and GSTT1 polymorphism because we cannot calculate the HWE on the both genes. Third, no data were extracted on other risk factors, such as hormonal readiness, obesity, smoking, and so on.This meta-analysis has several advantages over previous meta-analyses –32. FirsGSTM1, GSTT1 and GSTP1 polymorphisms may be not associated with increased BC risk.In summary, this meta-analysis indicates that individual and combined effects of S1 Table(PDF)Click here for additional data file.S2 Table(PDF)Click here for additional data file.S3 Table(PDF)Click here for additional data file.S4 Table(PDF)Click here for additional data file.S5 Table(PDF)Click here for additional data file.S6 Table(PDF)Click here for additional data file.S7 Table(PDF)Click here for additional data file.S8 Table(PDF)Click here for additional data file.S9 Table(PDF)Click here for additional data file.S10 Table(PDF)Click here for additional data file.S11 Table(PDF)Click here for additional data file.S12 Table(PDF)Click here for additional data file.S1 Fig(PDF)Click here for additional data file.S2 Fig(PDF)Click here for additional data file.S3 Fig(PDF)Click here for additional data file.S4 Fig(PDF)Click here for additional data file.S5 Fig(PDF)Click here for additional data file.S6 Fig(PDF)Click here for additional data file.S7 Fig(PDF)Click here for additional data file.S8 Fig(PDF)Click here for additional data file.S9 Fig(PDF)Click here for additional data file.S10 Fig(PDF)Click here for additional data file.S11 Fig(PDF)Click here for additional data file.S12 Fig(PDF)Click here for additional data file.S13 Fig(PDF)Click here for additional data file.S14 Fig(PDF)Click here for additional data file.S15 Fig(PDF)Click here for additional data file.S16 Fig(PDF)Click here for additional data file.S17 Fig(PDF)Click here for additional data file.S18 Fig(PDF)Click here for additional data file.S19 Fig(PDF)Click here for additional data file.S1 Appendix(PDF)Click here for additional data file.S2 Appendix(XLS)Click here for additional data file.S1 File(DOC)Click here for additional data file.S2 File(DOCX)Click here for additional data file.


I2 statistics were employed to assess between-study heterogeneities. If I2 was >50%, random-effects models were used to combine the data. Otherwise, fixed-effects models were applied for synthetic analyses. A total of 39 genetic association studies were included in the analyses. The combined analyses showed that TLR1 rs4833095, TLR1 rs5743557, TLR1 rs5743596, TLR2 rs3804099, TLR2 rs5743704, TLR2 rs5743708, TLR6 rs5743810 and TLR8 rs3764879 polymorphisms were significantly associated with susceptibility to TB in the overall population. Further subgroup analyses revealed similar significant findings for TLR1 rs4833095, TLR1 rs5743557, TLR1 rs5743596, TLR1 rs5743618, TLR2 rs3804099, TLR2 rs5743704, TLR2 rs5743708, TLR4 rs4986790 and TLR4 rs4986791 polymorphisms in certain ethnicities. In conclusion, our findings support that these TLR polymorphisms may be used to identify individuals at high risk of developing tuberculosis.Some genetic association studies have tried to investigate potential associations between TLR polymorphisms and tuberculosis. However, the results of these studies have not been consistent. Thus, we performed the present meta-analysis to explore associations between TLR polymorphisms and tuberculosis in a larger combined population. A systematic literature research of PubMed, Web of Science and Embase was performed to identify eligible studies for combined analyses.  Mycobacterium tuberculosis (MTB), and it could manifest as pulmonary tuberculosis and/or extrapulmonary tuberculosis.1 Despite rapid advancements achieved in early diagnosis and pharmacological therapy over the past few decades, TB remains a serious public-health threat. According to a recent epidemiological study, about 30% of the general population is currently infected with MTB, and around 5–10% of these infected individuals will eventually develop active TB.2 The course of MTB infection depends on a complex interaction of pathogen, host and environmental factors, and the fact that only a small portion of infected individuals eventually develop active TB suggests that host genetic background is crucial for its development.4Tuberculosis (TB) is a common chronic infectious disorder caused by 6 The binding of TLRs with their corresponding ligands leads to recruitment of adaptor proteins, activation of downstream signal transduction pathways, up-regulation of cytokine and chemokine production, and ultimately the development of immune responses against exogenous pathogens.8 Consequently, it is possible that TLR gene polymorphisms, which may impact biological activities of TLRs, might also be involved in the development of multiple infectious diseases, including TB.9TLRs are a group of type 1 transmembrane proteins expressed on a variety of immune cells that recognise stimuli from exogenous pathogens.TLR gene polymorphisms and TB. However, the results of these studies were not consistent, especially when they were conducted in different populations. Previous studies failed to reach a consensus regarding associations between TLR gene polymorphisms and TB, in part because of their relatively small sample sizes. Thus, we performed the present meta-analysis to explore the relationship between TLR gene polymorphisms and TB in a larger combined population. In addition, we also aimed to elucidate the potential effects of ethnic background on associations between TLR gene polymorphisms and TB.To date, numerous studies have already investigated potential associations between 10The current meta-analysis was reported according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses statement.Potentially relevant articles were searched in PubMed, Medline and Web of Science using the following keywords: ‘Toll like receptor’, ‘TLR’, ‘polymorphism’, ‘variant’, ‘mutation’, ‘SNP’, ‘variation’, ‘genotype’, ‘allele’, ‘tuberculosis’ and ‘TB’. The initial literature search was performed in January 2019, and the latest update was finished in May 2019. Moreover, we also screened the references of all retrieved articles to identify other potential relevant studies.TLR gene polymorphisms and TB in human beings, (b) genotypic frequency of investigated TLR gene polymorphisms provided in cases and controls and (c) full text available in English. For duplicate reports, only the most complete one was included. Excluded criteria were (a) not about TLR gene polymorphisms and TB, (b) not performed on human beings, (c) case reports or case series and (d) reviews, comments and conference presentations.Inclusion criteria were (a) genetic association studies on associations between TLR gene polymorphisms in cases and controls. The P-value for 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.11 This scale has a score range of zero to nine, and studies with a score of more than seven were thought to be of high quality. Data extraction and quality assessment were performed by two independent reviewers. Any disagreement between two reviewers was solved by discussion until a consensus was reached.The following data were extracted from the included studies: (a) last name of first author, (b) year of publication, (c) country where the study was conducted and ethnicity of study participants, (d) type of disease, (e) the number of cases and controls and (f) genotypic distributions of TLR gene polymorphisms and TB in dominant, recessive, over-dominant and allele models. Statistical significances of combined analyses were determined by the Z-test, with a P-value of ≤ 0.05 defined as statistically significant. I2 statistics were employed to assess between-study heterogeneities. If I2 was >50%, random-effects models  was used to combine the data because of significant heterogeneities. Otherwise, fixed-effects models (Mantel–Haenszel method) were used for synthetic analyses. Subgroup analyses by ethnicity of participants were subsequently performed to evaluate effects of ethnic background on investigated genetic associations. Sensitivity analyses were carried out to test the stability of combined results by omitting one study at a time and performing the analyses again based on the results of the remaining studies. Publication biases were evaluated with funnel plots.We used Review Manager v5.3.3  to conduct statistical analyses. We calculated odds ratios (ORs) and 95% confidence intervals (CIs) to estimate the strength of associations between https://osf.io). Data sets are also available to readers upon request.The initial literature search identified 573 potential relevant articles. After exclusion of irrelevant and duplicate articles by reading titles and abstracts, 78 potentially relevant articles were retrieved for eligibility assessment. Another 39 articles were subsequently excluded after reading the full text. Finally, 39 studies that met the inclusion criteria were included . Baseline characteristics of included studies are shown in TLR1 rs4833095 , TLR1 rs5743557 , TLR1 rs5743596 , TLR2 rs3804099 , TLR2 rs5743704 , TLR2 rs5743708 , TLR6 rs5743810  and TLR8 rs3764879  polymorphisms were significantly associated with susceptibility to TB in overall population. Further subgroup analyses revealed similar significant findings for TLR1 rs4833095, TLR1 rs5743557, TLR1 rs5743596, TLR1 rs5743618, TLR2 rs3804099, TLR2 rs5743704, TLR2 rs5743708, TLR4 rs4986790 and TLR4 rs4986791 polymorphisms in certain ethnicities (see The results of overall and subgroup analyses are summarised in ties see .We performed sensitivity analyses by deleting one study at a time to test the effects of individual studies on combined results. No altered results were observed in overall and subgroup comparisons, which indicated that our findings were statistically robust.We used funnel plots to assess publication biases. We did not find obvious asymmetry of funnel plots in any comparisons, which suggested that our findings were unlikely to be impacted by severe publication biases.6 The interaction of TLRs with their corresponding ligands activates the TLR signalling pathway, which leads to pro-inflammatory cytokine production and leucocyte infiltration.8 Given the crucial roles of TLRs in regulating immune responses against exogenous pathogens, the potential associations of certain TLR gene polymorphisms with susceptibility to infectious diseases such as TB were extensively studied, but the results of these studies were contradictory. Therefore, we performed the present meta-analysis of all published genetic association studies on the relationship between TLR gene polymorphisms and TB in order to obtain a more conclusive result.TLRs, a group of PRRs for structural conserved exogenous protospacer adjacent motifs, play vital roles in evoking immune reactions in response to infectious stimuli.TLR gene polymorphisms and TB, and our combined analyses suggested that TLR1 rs4833095, TLR1 rs5743557, TLR1 rs5743596, TLR1 rs5743618, TLR2 rs3804099, TLR2 rs5743704, TLR2 rs5743708, TLR4 rs4986790, TLR4 rs4986791, TLR6 rs5743810 and TLR8 rs3764879 polymorphisms were all significantly associated with TB in certain ethnicities. The stabilities of synthetic results were evaluated by sensitivity analyses, and no alterations of results were observed in any comparisons, which suggested that our findings were statistically robust.To our knowledge, this is the most comprehensive meta-analysis to date on associations between TLR1 rs4506565, TLR4 rs4986790, TLR4 rs4986791 and TLR9 rs5743836 polymorphisms, significant heterogeneities existed among the included studies. Thus, most of the combined analyses for these polymorphisms were performed with REMs. However, in further subgroup analyses, an obvious reduction tendency of heterogeneity was found in both Asians and Caucasians, which suggested that differences in ethnic background could largely explain observed heterogeneities between studies. The obvious heterogeneities that existed between included studies for TLR1 rs4506565, TLR4 rs4986790, TLR4 rs4986791 and TLR9 rs5743836 polymorphisms in the overall analyses also indicated that the distributions of these TLR polymorphisms vary greatly from population to population. Therefore, the genetic associations between these TLR polymorphisms and TB may be ethnicity specific, and we should not generalise these results to a broader population.As for evaluation of heterogeneities, we found that for TLR gene may lead to alterations in gene expression or changes in protein structure, which may subsequently affect the biological functions of the TLR signalling pathway and, ultimately, individual susceptibility to TB. Second, the pathogenic mechanism of TB is extremely complex, and hence despite our positive findings, it is unlikely that a single gene polymorphism could significantly contribute to its development. Thus, we strongly recommend further studies to perform haplotype analyses and explore potential gene–gene interactions.13 Third, to measure the effects of certain genetic factors on disease occurrence and development more precisely, gene–environment interactions should also be considered. However, since the included studies only focused on the effects of TLR gene polymorphisms on individual susceptibility to TB, such analyses were not applicable in the current meta-analysis.14 Fourth, the present meta-analysis aimed to explore associations between all TLR gene polymorphisms and TB. However, only 17 polymorphisms could be analysed in the current study because no other TLR polymorphisms were investigated by at least two different genetic association studies. Fifth, it should be noted that a recent meta-analyses conducted by Schurz et al.15 also tried to explore potential associations between TLR1, TLR 2, TLR4, TLR6 and TLR9 variants and TB. However, many related studies have been published in the last three yr. Therefore, an updated meta-analysis is warranted. The sample sizes of our analyses were also significantly larger than that of the previous meta-analysis, which could significantly reduce the risk of obtaining false-positive or false-negative results. So, our work should be considered as a valuable supplementary work to the existing literature.Several factors need to be pointed out about the current study. First, the exact underlying molecular mechanisms of our positive findings remains to be explored by experimental studies, but we speculate that investigated polymorphisms of the 17 Second, significant heterogeneities were detected in certain subgroup comparisons, which indicated that the inconsistent results of the included studies could not be fully explained by differences in ethnic background, and other unmeasured characteristics of participants may also partially attribute to between-study heterogeneities.18 Third, since only published articles were eligible for analyses, although funnel plots revealed no obvious publication biases, we still could not rule out the possibility of potential publication biases.19 Taken these limitations into consideration, the results of the current study should be interpreted with caution.This meta-analysis also has some limitations. First, although the methodology qualities of the included studies were generally good, it should be noted that we did not have direct access to genotypic distributions of investigated polymorphisms according to the base characteristics of the study subjects. Therefore, our results were derived from unadjusted estimations, and failure to conduct further adjusted analyses for baseline characteristics of participants such as age, sex and co-morbidity conditions may influence the reliability of our findings.TLR1 rs4833095, TLR1 rs5743557, TLR1 rs5743596, TLR1 rs5743618, TLR2 rs3804099, TLR2 rs5743704, TLR2 rs5743708, TLR4 rs4986790, TLR4 rs4986791, TLR6 rs5743810 and TLR8 rs3764879 polymorphisms were all significantly associated with TB in certain ethnicities. These results suggest that these polymorphisms may be used to identify individuals at high risk of developing TB. The exact underlying molecular mechanisms of our positive findings remain to be explored by future experimental studies, but we speculate that these TLR polymorphisms may lead to alterations in gene expression or changes in TLR protein structure, which may subsequently affect biological activities of TLR, impact immune responses against exogenous pathogens and ultimately alter individual susceptibility to TB. Moreover, it is worth noting that many genetic comparisons in the current study were only based on a limited number of studies. So, further well-designed studies are still warranted to confirm our findings.In conclusion, the present meta-analysis indicated that Click here for additional data file.Supplemental Material for Associations between genetic polymorphisms of TLRs and susceptibility to tuberculosis: A meta-analysis by Yong Zhou and Mengtao Zhang in Innate Immunity


The effects of dietary factors on prognosis of esophageal cancer remain unclear. This systematic review and meta-analysis aimed to assess the association between dietary intake and the risk of mortality among patients with esophageal cancer.Six electronic databases  were searched for studies published up to Oct. 2019 that examined the association between dietary intake and all-cause mortality, esophageal cancer-specific mortality and esophageal cancer recurrence. The pooled hazard ratio (HR) with 95% confidence interval (CI) were derived by comparing the highest with the lowest categories of each dietary item and by using random effect models.I2 = 0%, P = 0.788). When comparing the highest with the lowest categories of alcohol consumption, alcohol consumption was associated with an increased risk of all-cause mortality in patients with esophageal squamous cell carcinoma , but this increased risk was not significant in patients with esophageal adenocarcinoma .A total of 15 cohort studies were included in this study and all reported pre-diagnosis dietary exposure; two focused on dietary folate, 12 on alcohol consumption and three on other dietary components . When comparing the highest with the lowest categories, dietary folate intake was associated with a reduced risk of esophageal cancer-specific mortality in patients with esophageal squamous cell carcinoma , with low heterogeneity (This review with pre-diagnostic dietary exposure showed that dietary folate intake was associated with a reduced risk of mortality of esophageal squamous cell carcinoma, whereas alcohol consumption was associated with an increased risk. More studies are needed to investigate effect of dietary factors, especially post-diagnosis dietary consumption, on esophageal cancer prognosis. Esophageal cancer (EC) is one of the most malignant tumors worldwide, ranking seventh in cancer incidence and sixth in cancer mortality in 2018 –5.The prognosis of esophageal cancer is influenced by many different factors . NoteworTherefore, by summarizing the results of observational studies, this systematic review and meta-analysis was conducted to evaluate the association between food and its components intake and risk of mortality among patients with esophageal cancer, with addressing the difference in histology and the difference between all-cause mortality and cancer-specific mortality.Literatures published up to October. 2019 were systematically searched through four English databases  and two Chinese databases (CNKI and Wanfang). PRISMA statement for conducting and reporting meta-analysis of observational studies was followed . The seaTwo researchers (LP Sun & LB Yan) independently conducted the literature retrieval, identified potential studies, extracted information from the included papers, and assessed the quality of included studies. Discrepancies were settled down by group discussion with other two professionals (X LIU & CX Zhang). The titles and abstracts of initially identified papers were firstly reviewed, and then the full texts of the selected papers were reviewed to determine eligibility. To avoid omission of literatures, backward and forward citation tracking in both Web of Science and Scopus were also used to identify articles.Studies meeting the following conditions would be included: (i) study design was cohort study with esophageal cancer patients; (ii) the consumption of food and/or its components but not supplementations was accessible; (iii) prognostic outcomes included all-cause mortality, esophageal cancer-specific mortality or esophageal cancer recurrence; (iv) the hazard ratio (HR) or relative risk with 95% confidence interval (95% CI) were reported or could be calculated. When there were papers from the same study or covering the same population, only the most comprehensive or latest data was selected. Case reports, cross-sectional studies, editorials, abstracts, reviews, articles without full text, duplicated studies, animal studies and vitro studies were excluded.Data and information were extracted from the included studies, including name of the first author, publication year, country, sources of patients, sample size, histological type, follow-up duration, stage/grade grouping, dietary exposure, dietary assessments, outcomes, comparison method, effect size, confounders and covariates.Quality of the included papers was evaluated according to the Newcastle-Ottawa Scale (NOS) Criteria for non-randomized studies . A maximQ and I2 statistics. Sensitivity analysis was conducted by excluding the study one by one from the pooled results, by excluding the studies not reporting adjusted effects, and by excluding the studies not collecting information of stage and severity of esophageal cancer. The meta-analysis with crude HRs obtained from univariate analysis was also conducted. Publication bias was tested by funnel plot with Begg’s tests [Meta-analysis was conducted to estimate the pooled HR with 95% CI by comparing the highest with the lowest categories of dietary intake in each selected item. A random effect model was selected in the meta-analysis . Heterog’s tests . A power’s tests . StatistFigure Characteristics and study quality of the included studies are shown in Table All of 15 included studies provided pre-diagnosis dietary exposure information. In terms of types of dietary exposure, 12 studies were on alcohol consumption , 31–33, For study quality of 15 included studies, average NOS score was 6.65, ranging from 4 to 8; 12 studies were high quality (NOS score ≥ 7) and three studies were median quality.Pfor heterogeneity = 0.79, I2 = 0%). The funnel plot did not reveal asymmetry . Power calculation for random effect model was 96.5%.Only two studies , 25 repotry Fig.  and the Only one study on sugary beverages , one on A total of ten studies , 31–33 iPfor heterogeneity = 0.35, I2 = 4.2%) in EC and 1.29 (95% CI: 1.07–1.55) with moderate statistical heterogeneity  in ESCC, whereas no association was found in EAC . The funnel plots did not show significant asymmetry for any types of esophageal cancer . Power calculation for these three random effect models was all equal to 100.0%.The pooled results of the association between alcohol consumption and risk of death among different subtypes of esophageal cancer are shown in Fig. The sensitivity analysis was only performed on alcohol consumption. The analysis was repeated consecutively by removing one study from the pooled results each time, and significant change was observed only after the exclusion of Huang’s study  in EC anAs far as we know, there lacks of systematic review with quantitative analysis to evaluate ordinary dietary behavior and prognosis of esophageal cancer. The results from meta-analysis displayed that pre-diagnostic dietary folate intake was significantly related to a decreased risk of esophageal cancer-specific mortality in ESCC and pre-diagnostic alcohol consumption was associated with an increased risk of all-cause mortality in EC and in ESCC.Alcohol is one of the major determinant factors for developing esophageal cancer , but effOur results of meta-analysis also indicated that intake of dietary folate was associated with 59% reduced risk of esophageal cancer-specific mortality among ESCC patients. This finding is consistent with findings on other cancers , 37. FolThe evidence for other dietary components is limited. Lignans was found to reduce risk of all-cause mortality of ESCC by 42% , while pThis study has some strengths. First, the literature retrieval was from multiple databases and the selection was determined by independent reviewers, which was helpful to avoid literature omission. Second, we included studies according to the strict inclusion and exclusion criteria, which may augment the validity of our findings. Third, sensitivity analyses yielded similar results, indicating the stability of our findings.There were also some limitations in this study. There were only 15 studies included; most of them focused on alcohol consumption and only five focused on other dietary components; pooling results from limited evidence may influence the stability of our results, though the power of random effect models in our study is fairly strong and sensitivity analyses yield similar results. Consuming large amounts of fruits and vegetables after diagnosis could reduce the mortality of cancer and diets before diagnosis may reflect the changes in taste or appetite attributable to cancer . BesidesIn summary, this review with limited evidence suggested that folate intake was associated with a reduced risk of esophageal cancer-specific mortality for ESCC, whereas alcohol consumption was associated with increased risk of mortality for ESCC. More studies are needed to investigate effect of dietary factors, especially post-diagnosis dietary consumption, on esophageal cancer prognosis.Additional file 1: Figure S1. Summary of sensitivity analyses of alcohol consumption and mortality among (a) EC (b) ESCC (c) EAC. Abbreviation: HR, hazard ratio; CI, confidence interval; EC, esophageal cancer; EAC, esophageal adenocarcinoma; ESCC, esophageal squamous cell carcinoma.


Osteoarthritis (OA) is a common disease characterized by articular cartilage degeneration and secondary hyperosteogenesis. Genetic factors are associated with the occurrence of OA. While several studies have shown that the matrix metalloproteinase-1- (MMP-1-) 1607 1G/2G (rs1799750) polymorphism may be related to the occurrence and development of OA, there is inconsistency in the literature. To better estimate the relationship between the MMP-1 gene polymorphism and OA, a comprehensive meta-analysis of relevant literature was carried out. P < 0.01; recessive: 2G2G vs. 1G1G+1G2G: OR = 2.411, 95%CI = 1.658–3.504, P < 0.01; and homozygote: 2G2G vs. 1G1G: OR = 2.313, 95%CI = 1.341, 3.991, P = 0.003), the younger subgroup , the larger subgroup (N > 300), and the hospital-based case-control study (HCC) subgroup. The sensitivity analysis suggested that the results of the meta-analysis were stable and reliable. Begg's funnel plot and Egger's test indicated that there was no publication bias in this study.  In total, seven studies comprising 1245 OA patients and 1230 controls were included in this meta-analysis. The combined results revealed no significant association between the MMP-1-1607 1G/2G polymorphism and risk of OA in the five genetic models. However, after Bonferroni correction, the results of subgroup analysis revealed a significant correlation between the MMP-1-1607 1G/2G polymorphism and OA susceptibility in the temporomandibular joint (TMJ) OA subgroup (allelic: 2G vs. 1G: OR = 1.575, 95%CI = 1.259–1.972,  Our meta-analysis indicated that although the MMP-1-1607 1G/2G polymorphism was not significantly associated with OA susceptibility among the whole sample, it played a key role in the etiology and development of TMJ OA and OA in people aged less than 60 years. Osteoarthritis (OA) is a cartilage degenerative disease that is more common among middle- and older-aged people. The main pathological manifestations include degeneration of articular cartilage, formation of osteophytes, and subchondral bone sclerosis . ClinicaThe role of matrix metalloproteinase-1- (MMP-1-) mediated destruction of articular cartilage in the pathogenesis of OA has attracted widespread attention. MMP-1, also known as collagenase 1, is the most widely expressed proteolytic enzyme in the MMP family. MMP-1 is primarily produced by interstitial cells, epithelial cells, and endothelial cells. Articular cartilage is composed of chondrocytes and extracellular matrix (ECM), and MMP-1 can degrade collagens I, II, and III in ECM. Thus, when MMP-1 is overexpressed in chondrocytes, there is an increase in the degradation of cartilage collagen and proteoglycan, resulting in pathological cartilage damage; this underlies the development of OA , 5. ExprSingle nucleotide polymorphisms (SNPs) are polymorphisms of DNA sequences caused by single nucleotide variation at the gene level . SNPs loBarlas et al. first studied the 1G/2G site of the MMP-1-1607 gene in Turkish patients with OA. It was reported that the MMP-1 gene polymorphism may be associated with susceptibility to OA; in particular, the 1G allele and 1G gene model were associated with susceptibility to OA. Subsequently, many researchers attempted to replicate this finding; however, the results were inconsistent –15. GiveManual searches of the PubMed, Embase, China Knowledge Network, and Wanfang databases were performed. The following keywords were used to identify relevant literature: “MMP-1 or matrix metalloproteinase-1,” “polymorphism or variation,” “rs1799750 or MMP-1 1607,” “susceptibility or risk,” and “osteoarthritis or joint degeneration.” In addition, we also carried out literature tracing; that is, we used reference lists and citations in the relevant retrieved literature to trace and identify further relevant literature that was not identified in the database searches. There was no language restriction on the literature search. The last retrieval date for relevant studies was July 2019. Two researchers used the same retrieval method to search the databases independently. When there was a disagreement, they negotiated a solution.The inclusion criteria included (1) original case-control studies examining the relationship between the MMP-1-1607 1G/2G (rs1799750) polymorphism and susceptibility to OA, (2) all cases with a diagnosis of OA and a control group composed of healthy individuals or individuals without a family history of OA, (3) genotype distribution in the control group consistent with the Hardy-Weinberg equilibrium (HWE), and (4) enough genotypic data for the case group and control group to calculate odds ratios (ORs) and 95% confidence intervals (CIs). The exclusion criteria included (1) duplicate studies, (2) non-case-control studies, and (3) failure to provide detailed genotypic frequency data. In addition, we excluded unpublished reports, abstracts, reviews, and editorial comments. Two researchers independently judged the identified research according to the inclusion and exclusion criteria. Disputes were settled via discussion among the researchers.P < 0.05 was a significant deviation from the HWE). The data extraction was carried out independently by two researchers using a standardized form; disputes were resolved via discussion.The information extracted from each included study is described in Tables Quality evaluation of the included literature was carried out independently by the two researchers. Since the literature included in the present study comprised observational case-control studies, the Newcastle-Ottawa Scale (NOS) was used to evaluate the quality of the included literature. The maximum score of the NOS is nine. Studies scoring greater than five points can be included in a meta-analysis. We considered studies with scores greater than or equal to seven to be of high quality .Pooled ORs and 95% CIs were used to evaluate the relationship between MMP-1-1607 1G/2G (rs1799750) and OA susceptibility. We evaluated the following five genetic models: allele genetic model (2G vs. 1G), homozygote genetic model (2G2G vs. 1G1G), heterozygote genetic model (2G1G vs. 1G1G), dominant genetic model (2G1G+2G2G vs. 1G1G), and recessive genetic model (2G2G vs. 1G1G+1G2G).Q test based on the chi-square test was used to evaluate heterogeneity among the included studies [P < 0.1, statistically significant heterogeneity was assumed, and a random effects model (DerSimonian-Laird) [P < 0.05, the pooled OR value was considered meaningful. To maximize the avoidance of possible false positive results in the subgroup analysis, we also performed strict Bonferroni correction for P values less than 0.05 [The  studies . When P n-Laird)  was usedn-Laird) . If P < han 0.05 .P < 0.05 indicated that the corresponding independent variable was the source of heterogeneity [When significant heterogeneity was present, the possible sources of heterogeneity were identified by metaregression analysis. For these analyses, logistic regression was performed with the OR as the dependent variable and the factors that might influence heterogeneity, such as ethnicity, location of OA, study design, patient surgery, sample size, average age, and study quality score, as the independent variables. In these analyses, ogeneity .P < 0.05 indicated that there was significant publication bias. All statistical analyses were carried out using STATA 14.1 software .We also conducted subgroup analysis based on ethnicity, location of OA, study design, patient surgery, sample size, average age, and study quality to evaluate the possible effects of different subgroups on the results of the study. In addition, we performed sensitivity analysis to investigate the effect of each single study on the pooled effect to explore whether a single study was the source of heterogeneity. We used Begg's funnel plot and Egger's test to identify possible publication bias in our meta-analysis. A nonsymmetrical funnel plot or Fifty-five potentially relevant articles were retrieved. After initial exclusion of eight duplicate documents, 37 articles with unmatched titles and abstracts were excluded. Finally, the full texts of the remaining 10 articles were carefully read. One study did not provide enough genotypic data to calculate the OR and 95% CI , one wasP = 0.21; recessive: 1.478 , P = 0.102; dominant: 1.203 , P = 0.373; heterozygote: 1.512 , P = 0.179; and homozygote: 1.088 , P = 0.627).In the total sample, the MMP-1 1G/2G polymorphism was not found to be significantly associated with risk of OA in the five genetic models (OR(95% CI): allelic: 1.232 , P < 0.025, the pooled OR value was considered meaningful)   , and the hospital-based case-control study (HCC) subgroup . The detailed results are shown in The results of the subgroup analyses showed that there was a significant correlation between the MMP-1-1607 1G/2G polymorphism and OA susceptibility in the temporomandibular joint (TMJ) OA subgroup after Bonferroni correction , study design (HCC or population-based case-control study (PCC)), and age  as we considered these analyses to be valuable. Subgroup analyses showed that the heterogeneity in all contrast models was significantly decreased in the TMJ subgroup and the older age subgroup. Therefore, the location of OA and the age of the patients are possible sources of heterogeneity. Detailed results are shown in Nonetheless, we still carried out subgroup analyses according to ethnicity , location of OA , total sample size .Galbraith plot analysis indicated that the study by Barlas et al. was an outlier and the main contributor to the heterogeneity in all contrast models . HoweverNext, we conducted sensitivity analysis to explore the effects of each study on heterogeneity and pooled effects. The results showed that when any one study was eliminated and the remaining studies were included in a meta-analysis, there was no significant change in the pooled effect. This suggests that the results of the meta-analysis are stable and reliable Figures ; howeverP values for Egger's test and Begg's test for the five genetic models were greater than 0.05, indicating that there was no significant publication bias in the present study. Taking the allele contrast model (2G vs. 1G) as an example, we analyzed the funnel plots and found no apparent asymmetry , and age as these analyses are valuable. We found significant differences in the TMJ OA subgroup analysis in the allelic gene model, recessive gene model, and homozygous gene model; this may be due to differences in the pathogenesis of OA in different regions of the body, suggesting that MMP-1 plays a more important role in the pathogenesis of TMJ OA. Similarly, the younger age subgroup (less than 60 years old) showed that the MMP-1-1607 1G/2G gene polymorphism was associated with OA susceptibility in the younger age group in the allelic gene model, dominant gene model, recessive gene model, and homozygous gene model. Although the reasons for this finding are unclear, we suspect that OA patients of different ages may have different underlying pathogenesis. For young patients, the role of genetic factors in the pathogenesis of OA may be more important. As patients get older, they may be affected by an accumulation of environmental and lifestyle factors, and the effects of genes on OA susceptibility may become weaker.Our meta-analysis has several advantages. First, to the best of our knowledge, this is the most comprehensive meta-analysis of the relationship between the MMP-1-1607 1G/2G gene polymorphism and OA risk. All relevant data were included in the meta-analysis and subgroup analysis, sensitivity analysis, and metaregression analysis which were carried out to provide more comprehensive and reliable results. Secondly, based on the quality evaluation, all included studies are of high quality. Finally, Begg's funnel plot and Egger's test confirmed that there was no publication bias evident.There are several limitations in our meta-analysis that should be noted. First, the number of studies included in this meta-analysis is small, especially with respect to the reliability of subgroup analysis; thus, it may not be possible to fully evaluate the true association between the MMP-1-1607 1G/2G gene polymorphism and risk of OA. Second, due to the inability to obtain the original data included in the study, it is impossible to adjust the analyses for other variables such as sex and body mass index (BMI), which may affect the accuracy of the results. Third, in some genetic models, we found significant intergroup heterogeneity. Although we used metaregression analysis, subgroup analysis, and sensitivity analysis to identify the source of heterogeneity and minimize its impact, heterogeneity is still an inevitable problem affecting the accuracy of the results.In summary, this meta-analysis assessed all relevant published data on the association between the MMP-1-1607 1G/2G polymorphism and OA risk. Our meta-analysis showed that although the MMP-1-1607 1G/2G polymorphism was not significantly associated with OA susceptibility in the whole sample, it played a key role in the etiology and development of TMJ OA and OA in people aged under 60 years.