A correlation was established between the alleles associated with the cardiac form of CD in this review, members of the C haplotype of the gene (HHC–TGTG), and the cardiac form of CD.Our aim was to carry out a qualitative and quantitative synthesis of the influence of CCR5 genetic variants on Chagas disease (CD) through a systematic review. A total of 1197 articles were analyzed, and eleven were included in the review. A meta-analysis was conducted along with principal component analyses (PCAs). The polymorphisms found were analyzed using the SNP2TFBS tool to identify possible variants that influence the interaction with gene binding sites. Eleven studied variants were identified: rs2856758, rs2734648, rs1799987, rs1799988, rs41469351, rs1800023, rs1800024, Δ32/rs333, rs3176763, rs3087253 and rs11575815. The studies analyzed were published between 2001 and 2019, conducted in Argentina, Brazil, Spain, Colombia and Venezuela, and included Argentine, Brazilian, Colombian, Peruvian and Venezuelan patients. Eight polymorphisms were subjected to the meta-analysis, of which six were associated with the development of the cardiac form of CD: rs1799987—G/G and G/A in the dominance model and G/G in the recessiveness model; rs2856758—A/G in the codominance model; rs2734648—T/T and T/G in the dominance model; rs1799988—T/T in both the codominance and recessiveness models; rs1800023—G allele and the G/G genotype in the codominance and recessiveness models, and the G/G and G/A genotypes in the dominance model; and rs1800024—T allele. The PCA analyses were able to indicate the relationships between the alleles and the genotypes of the polymorphisms. The SNP2TFBS tool identified rs1800023 as an influencer of the Spi1 transcription factor ( Trypanosoma cruzi), the patient enters the acute phase, followed by the chronic phase. The chronic phase can be asymptomatic (patients with an undetermined outcome), it can present symptoms related to cardiac complications or those associated with the digestive system, or a mixed form of cardiac and digestive system complications simultaneously [Chagas disease (CD)—known as American trypanosomiasis—is a neglected disease that affects seven million patients worldwide. About 12–20 thousand patients die every year and approximately 75 million people are exposed to CD, which mainly affects low-income populations. . After ianeously .Some investigations have associated the genetics of the patient with the infection and evolution of CD ,5, indicGene expression and bioinformatics analyses continue to list CCR5 as a fundamental element in the development of CD ,15,16. Ihttps://www.crd.york.ac.uk/PROSPERO/, March 2023). The searches were based on the PICOS strategy [T. cruzi; intervention (I) was the identification of CCR5 gene polymorphisms in patients; comparisons (C) were made with any groups (asymptomatic or uninfected) or subgroups (chronic Chagas disease) of patients with well-defined profiles; the outcome (O) was the presence or absence of the polymorphism in patients; and the types of studies (S) were genetic association studies.This review has been registered on the PROSPERO platform  “chagas disease”, (2) “Trypanosomiasis” and (3) “Trypanosoma cruzi infection” were listed. Using the Health Sciences Descriptors tool—DeCS, from the BVS portal —the general synonyms of each term were sought, whose returns were: (1) “chagasic disease”, (2) “American trypanosomiasis”, “Trypanosome parasite”, “Trypanosome infection”, “Trypanosoma cruzi”, “Trypanosomosis”, and (3) “T. cruzi”, “Trypanosoma cruzi” and “cruzi’’ . The same method was used for the term “Genetic polymorphism”, whose results were “Polymorphic gene”, “Single nucleotide polymorphism”, “Genetic variation”, “Allelic variation” and “SNP’’ .To choose the groups of words used in the database searches, the guides and protocols established by WHO and PAHO for the management of CD patients were primarily read ,18, as wAfter finding all the synonyms of each descriptor, through the direct counting method, both commonly repeated words and those that appeared only once were highlighted. Applying Boolean operators, the highlighted terms were used with variations in the suffixes to form the search strings and their variations: “Chaga* disease” OR “Trypanosom*” OR “cruzi” AND “Polymorphi*” OR “Gene*” OR “Variation” OR “SNP”.The searches were carried out without time restrictions in the advanced mode of the databases, using the string mentioned above and its variations in the “title” field. The databases used were the CAPES Journal Portal, SciELO, Scopus, Web of Science, PubMed and ScienceDirect. The searches took place between January and February 2022. Only peer-reviewed articles written in English  were used as filters for returns. Subsequently, the articles included in the qualitative synthesis had their references checked to include articles that were not detected during the database searches. Information about the manuscripts  were organized using LibreOffice Calc.The following inclusion criteria were used: (1) original publications, (2) genetic association studies, and (3) electronically available articles. The following exclusion criteria were used: (1) duplicate studies, (2) studies using an animal model, (3) in vitro studies, (4) correlation studies between the genetics of the parasite and the patient, (5) genetic analysis studies of vectors, (6) studies that did not evaluate CCR5 genetic polymorphisms, (7) literature reviews, and (8) conference abstracts.The publications were initially analyzed by title and abstract using the inclusion and exclusion criteria in the returns. Two independent reviewers (JMF and BRCS) reviewed all the returns; inconsistencies between reviewers were resolved by discussing the publication with a third reviewer (ACMS).The analysis of the quality of the studies was performed after the selection of the studies. The quality analysis tool “Strengthening the Reporting of Genetic Association Studies (STREGA)” consists of a genetic study checklist, evaluating each part of the scientific article , where a total of 22 points can be reached in the study. Using STREGA, studies were classified as low quality (<9), medium quality (9–16) and high quality (17–22).Data on the study design, year of publication, country of origin of the study (based on the first author), the studied population, sample size, information on the profile of the patients in the analyzed group, analyzed polymorphisms, main results, laboratory confirmation method for infection and confirmation method for polymorphism were extracted.p ≤ 0.05. Study heterogeneity was assessed using the standard test (Q test and I-squared). When I2 ≥ 50% and p ≤ 0.05, indicating significant heterogeneity, probably caused by variations between studies, we chose to use the random analysis model (standardized by the software); for all other cases, the fixed analysis model was used [Data from the selected articles were organized using Google Sheets. Global allele and genotype frequencies, using data from the included articles, were calculated using the same tool. Meta-analyses were performed on all polymorphisms available in the RevMan 5.4 software, where the strength of association was calculated using a 95% confidence interval and odds ratio (OR), with the generated value OR < 1 considered as a protective factor (trend) or risk reduction, while OR > 1 values were considered as a risk factor (trend), the results were considered significant when was used . All stuwas used .The influences of the alleles (mutated vs. wild) and genotypes in different models of inheritance were evaluated: codominance , recessiveness (mutated homozygote vs. heterozygous + wild homozygote) and dominance (mutated homozygote + heterozygote vs. wild-type homozygote). Funnel-plot graphs were inspected for possible publication bias.https://ccg.epfl.ch/snp2tfbs/, May 2022).To identify single nucleotide polymorphisms (SNPs) that may affect transcription factor binding, we used the Mapping SNPs to Transcription Factor Binding Sites (SNP2TFBS) web interface (T. cruzi infection were termed as non-infected (nonCP), while patients infected with T. cruzi were generically termed as chronic patients with Chagas disease (CP). The “CP” group was subdivided according to the presence or absence of symptoms into two smaller groups: chronic asymptomatic patients in an undetermined form (UndCP) and chronic symptomatic patients (SympCP). Patients in the “SympCP” group were divided into three groups according to the clinical manifestation of the chronic form of CD: patients who presented any type of cardiac alteration/complications resulting from CD were grouped as cardiac patients (CardCP), while those with complications associated with the digestive system were grouped as digestive patients (DigCP). Patients who had both forms involving heart and digestive problems were grouped as mixed complications (CarDigCP). In our comparisons, the presence of someone involved in the CardCP or DigCp group did not exclude them from participating in the CarDigCP group, and vice versa.Using the data available in the publications, patients were grouped into two main groups: healthy individuals without https://www.nhm.uio.no/english/research/infrastructure/past/, May 2022). For significant comparisons found in the quantitative synthesis, sample power was calculated using G*Power 3.1.9.7  [In order to evaluate the interaction between the sample data, for the results of the global comparisons  where there was significance, principal component analysis (PCA) was performed using the Past 4.10 software ( = 0.05) , to deteExploratory statistical analyses were performed to establish comparisons between genetic factors and the development of the cardiac form of CD. For this purpose, the possible correlations between haplotypes and their respective constituent alleles were verified using Pearson’s correlation. The χ2 test was used to compare the frequency of the HHC haplotype between groups, adopting a significance level of 5%.Our work gathered the available information from the literature on the influence of CCR5 gene variants on CD. The flow of searches, analyses and the collection of articles is outlined in In our bibliographic survey, data on the genetic variants of CCR5 associated with CD were pooled, and eight genetic variants were subjected to a meta-analysis based on the number of studies: rs1799987, rs333, rs2856758, rs2734648, rs1799988, rs41469351, rs1800023 and rs1800024. The characteristics of the groups used in the studies that constituted the “CardCp” grouping are shown in Eight studies evaluated the influence of the rs1799987 SNP on CD ,26,27,28p = 0.008, OR = 1.92, 95% CI (1.18–3.11), I2 = 0%, fixed model). Similarly, in the dominance model (A/G + G/G vs. A/A), an association of G carriers with the risk of developing the cardiac form was also verified , I2 = 0%, fixed model). Both associations are summarized in The comparison between patients in the healthy control group vs. cardiac patients  showPCA was used to correlate the results obtained in the meta-analysis and establish inter-relationships between the variables, creating a smaller set of principal components (PCs). PC1 corresponded to 89.58% of the variance, while PC2 corresponded to 9.33%, totaling 98.91% of the variance. PC1 was dominated by the A and G alleles and the A/G genotype, while PC2 was predominantly dominated by the homozygous A/A and G/G genotypes. The analysis of the generated graphs suggested a correlation between the A allele and the A/A genotype, as well as suggesting a correlation between the G allele and the G/G genotype, while there was a distancing of the A/G genotype from these two groups a.p = 0.04, OR = 1.38, 95% CI (1.02–1.85), I2 = 53%, fixed model), as shown in Although the global comparison between cardiac patients vs. asymptomatic patients showed no difference between the groups, when the analysis was limited to patients from countries originating from Spanish colonization, ,26,27,28Four studies analyzed the Δ32 deletion ,10,13,27p = 0.05, OR = 0.17, 95% CI (0.03–1.03), I2 = 94%, random model), as shown in The rs2856758 polymorphism was analyzed in three studies ,26,28. OThe PCA showed in our analysis that PC1 corresponded to 70.91% of the variance, while PC2 corresponded to 21.16%, totaling 92.07% of the variance b. The vap = 0.05, OR = 1.24, 95% CI (1.00–1.55), I2 = 38%, fixed model). In the PCA, PC1 corresponded to 72.67% of the variance, while PC2 corresponded to 27%, totaling 99.67% of the variance , I2 = 62%, fixed model); similarly, the recessiveness model (T/T vs. T/C + C/C) also indicated an association of the homozygous T/T genotype with the same risk of developing the cardiac form of CD , I2 = 35%, fixed model), as shown in Flórez, Martín and González, Juiz et al., Machuca et al., and Nogueira et al. ,25,26 evOnly three studies involving patients from Spanish colonization countries analyzed the SNP rs41469351 ,24,28. Cp = 0.03, OR = 0.83, 95% CI (0.70–0.98), I2 = 29%, fixed model)  d. The PCd model) d.p = 0.003, OR = 1.30, 95% CI (1.09–1.55), I2 = 42%, fixed model). Significant differences were observed in almost all genetic models. The risk of developing the cardiac form of CD was associated with the G/G homozygote in the codominance model (G/G vs. A/A): p = 0.01; OR = 1.53; 95% CI (1.10–2.12); I2= 35%; fixed model. The mutated G/G genotype was also associated in the recessiveness model (G/G vs. G/A + A/A): p = 0.01; OR = 1.60; 95% CI (1.10–2.34); I2 = 42%; fixed model. In the dominance model (G/G + GA vs. A/A), genotypes associated with the G allele were also associated with the risk of developing the cardiac form of CD , I2 = 0%, fixed model). Representations of allelic and genotypic associations in the codominance, recessiveness and dominance models are shown in Only three studies analyzed the rs1800023 polymorphism ,26,28 anPCA indicated variances of 56.00% and 42.48% for PC1 and PC2, respectively, totaling 98.48%. The G allele and the G/A and G/G genotypes dominated PC1, while the A allele and the A/A genotype dominated PC2. The analysis of the graphs indicates a correlation between the A allele and the A/A genotype, as well as a correlation between the G allele and the genotypes carried by this allele (G/G and G/A) e.Following our analyses, no other association between the analyzed variants and CD was verified. Considering that all the analyses showed a sample power >80% (good sample representativeness), and with the exclusion of the SNPs rs333 and rs41469351 , it is possible to suggest that the other alleles and genotypes that did not show any association with CD are not factors capable of influencing this infection.As our analysis had less than ten studies in each comparison (K < 10), we considered inspecting the funnel-plot graphs to assess possible biases in the results of our comparisons. However, the scarcity of studies that analyzed the polymorphisms included in this review did not allow for a distribution that was considered sufficient to infer the existence of publication bias. In addition, studies were found on both sides of the funnel-plot graphs , which m2 = 94%, p < 0.00001), as shown in In our investigation, only a significant comparison of the rs2856758 SNP  indicated substantial heterogeneity (Ip < 0.05), as represented in The prediction by the analysis tool indicated only one polymorphism  of the CCR5 gene capable of affecting the Spi1 transcription factor binding , rs1799987 (G), rs1799988 (T) and rs1800023 (G) were the same ones that constituted a haplotype described in the literature as HHC. To verify the possible correlations between the alleles associated with the development of the cardiac form of CD and the haplotype, an exploratory analysis was performed between HHC and the associated alleles. For the analysis, information from the studies included in this review was used ,26,28. QTwo conditions were considered in order to assume the correlation between HHC and its alleles (HHC–TGTG) with the development of the cardiac form of CD as true. First, all correlations between the presented alleles and HHC should be strong (>0.800) and positive, and second, the results should be maintained when adding data from the alleles of undetermined patients to the correlation. The inclusion of allelic data from undetermined patients in this second step aims to avoid misinterpretation of the correlation, given that the allele and HHC data came from the same groups of patients with the cardiac form. If both HHC–TGTG and HHE (HHE–GACA) are strongly positively correlated with their respective alleles, then it is suggested that the correlation between the alleles and haplotypes occurs due to structural dependence. However, if the pre-established correlations are observed only in HHC and its respective alleles, then a correlation with the development of the cardiac form of CD and HHC–TGTG is suggested. The correlation procedures were the same for both haplotypes.p > 0.001), rs1799987-G , rs1799988-T  and rs1800023-G . For the tested conditions, the correlations between HHE and its respective alleles were classified as median or low (<0.700). Complementarily, using data from the articles, the total frequencies of HHC in cardiac and asymptomatic patients were compared using the χ2test. The results showed a significant difference between HHC frequencies between cardiac and asymptomatic patients ; SP = 100%; n = 1338, CardCp = 898/UndCP = 440), which suggests a correlation between HHC–TGTG and the cardiac form of CD.All correlations between HHC and cardiac patient alleles were strongly and positively correlated (>0.950). These correlations were not found between HHE and its respective alleles. Considering the indeterminate patients in the analysis, all correlations between HHC and the alleles remained strong and positive: rs2734648-T  capable of specifically recruiting subgroups of leukocytes. There are two major subfamilies of chemokines based on the position of their cysteine residues: CXC (neutrophil chemotactics) and CC (monocyte chemotactics and lymphocyte subtypes) –15 kDa c,30, all T. cruzi parasite [CCR5 is expressed in monocytes, macrophages and lymphocytes such as TH1 cells (known to aid in the destruction of pathogens) . In seveparasite ,13.T. cruzi parasite. Cardiomyocytes produce cytokines, chemokines and nitric oxide that attract leukocytes to the inflammatory site, and thus promote the control of parasitic intracellular replication [Studies indicate that the chronic inflammation of the cardiac form of CD results in tissue damage and is caused by the attempt to eliminate the lication . The explication ,36. Therlication ,13.The CCR5 gene is one of the most studied genes in CD ,37. In vIn our work, we compiled the information present in the literature about the influence of genetic variants of the CCR5 gene on CD. In the qualitative synthesis, we observed that all studies were carried out with samples from South America, which points to the concern about the disease in that region. The patients’ parasitemia was scarcely evaluated in the works found. The lack of verification of parasitemia leaves a gap in the influence of the studied genetic variants. There was also a shortage of studies that analyzed the influence of CCR5 and its genetic variants on the digestive form of the disease, since only one study analyzed this relationship .For the construction of the groups used in the comparisons in the quantitative synthesis, we observed that the authors of the studies used different criteria to group the participants as chronic cardiac patients . We obseThe most notorious polymorphism studied in the works was rs1799987 (59029A/G). In the quantitative synthesis, by comparing the CardCp and nonCP groups, our analyses showed that the A/G genotype was associated with the risk of progression to the cardiac form. However, it is important to highlight that the comparison between infected patients and healthy controls presents a non-optimized experimental design, since the comparison is based on a control group that has no chance of evolving to the cardiac form, which would be optimized by the comparison with asymptomatic infected patients (UndCP), allowing a comparison between groups that are in the same infectious condition but presenting forms that evolve in different ways .Our global comparison between CardCp and UndCP did not indicate an association with rs1799987; however, the subgroup of patients from countries originating from Spanish colonization indicated an association of the G/G genotype (recessive inheritance model) with the risk of developing the cardiac form, contradicting the study by Calzada et al. [The rs2856758 SNP A/G genotype in the codominance model was associated with a decreased risk of developing the cardiac form of CD. This result was not observed in other studies ,26,28;, Our global analysis of the rs1799988 polymorphism did not find differences between the analyzed groups, but in the analysis of the subgroup of countries formed after Spanish colonization ,26,28, tThe SNP rs1800023 indicated four different associations, all to some degree related to the G allele when comparing CardCP vs. UndCP . MachucaIn this work, we used principal component analysis (PCA), an exploratory statistical analysis that establishes relationships between data without the prior definition of a model, unlike the meta-analysis, which was based on standardized models of genetic inheritance. These relationships are important in order to evaluate how the interaction of alleles and genotypes occurs in the samples. According to PCA, it is possible to observe data dispersion, which may not be fully represented in analyses using pre-established models, as in the meta-analysis performed in our study. Unfortunately, few inferences could be obtained using PCA due to the small number of available studies and, consequently, the limited data. In the PCA, the graphs  and/or tThe PCA of rs1799987, based on the interaction between the genotypes, alleles and the condition of the participants (CardCp vs. nonCP), indicated that there was a correlation between the G carriers (G/A and G/G) and the G allele, as well as a correlation between the A allele and the AA genotype. The analysis performed using the SNP rs2856758 reported a cluster formed between the A allele and the A/A genotype, and a second cluster formed by the G allele and the G/G genotype, while the A/G genotype did not seem to be related to any of these groups. Additionally, it should be noted that in our study the A/G genotype obtained an association in the codominant inheritance model. Interestingly, this graphic representation was similar to the phenotypes that were presented in genetic inheritance in the codominance model, that is, the presentation of three different profiles.The T/T and T/G genotypes of the rs2734648 SNP were both associated with the development of the cardiac form in the dominant inheritance model. In the multivariate analysis, these two genotypes were correlated with the T allele, while the G allele and the G/G genotype formed a second cluster in the PCA. This graphical representation is also similar to the phenotypic profiles in dominant/recessive-type inheritance . Our observations from the multivariate analysis of the rs1800023 SNP indicated a correlation between the A allele and the A/A genotype. Despite being relatively distant, a correlation was also observed between the G allele and the G/G and G/A genotypes . FinallyThe SNP2TFBS tool was used to verify the influence of polymorphisms in this review on transcription factors. The rs1800023 SNP was shown to be able to influence Spi1 factor activity. Spi1 is an important transcription factor that binds to a sequence known as PU-box (a purine-rich DNA sequence), located near the promoter region of target genes. Thus, Spi1 co-regulates the expression of target genes together with other transcription factors and cofactors. The Spi1 protein can also bind to RNA to regulate the selective splicing of target genes .Mummidi et al.  have alrMany of the polymorphisms analyzed in this review are constituents of CCR5 haplotypes and capable of influencing its expression. Interestingly, all our findings involving an association with the risk of developing Chagas cardiomyopathy are within the C haplotype (HHC), according to Mummidi et al.: rs2734648 (T), rs1799987 (G), rs1799988 (T) and rs1800023 (G), this haplotype seems to induce a low transcriptional activity in Jukart cells (T lymphocyte lineage derived from leukemia), while in Nk cells (CD56+) and CD16+ it presents a high activity ,42. The p < 0.05). These results were not found in the correlation study using HHE as a control. Thus, we assume the relationship between HHC–TGTG and the development of the cardiac form of CD to be true. This result is supported by the fact that there was a significant difference in the frequency of HHC between the cardiac and indeterminate groups.To verify possible correlations between HHC and its alleles (HHC–TGTG) and the cardiac form of CD, a Pearson correlation was performed. Our findings demonstrated that there was a very strong correlation between HHC and alleles. All the correlations were positive and significant , localized damage , and finally, the patient’s genetics ,25,43.Among the hypotheses described, we believe that the patient’s genetics is the one that best explains the development of the cardiac form, since studies have indicated the lack of influence between the genetic variability of the parasite and the development of this form of CD ,44. FurtAccording to the authors of previous studies, it is possible that the course of CD in a patient is not the result of the mere presence or absence of a single genetic variant. It is believed that combinations between several different variants in several genes modestly contribute to the pathogenesis of the disease ,26,28. TSome limitations of this study can be highlighted. Our quantitative synthesis presents results based on the included studies. These studies used different methodologies, as well as different comparison groups, which may imply inconsistencies in our results. It was also not possible to analyze the influence of environmental factors, such as the exposure time of patients, or factors intrinsic to the participants, such as age and gender. The scarcity of data was a limitation for our analyses and, consequently, our inferences. However, despite these limitations, carrying out this study had advantages, such as a better understanding of CD.In short, we emphasize that meta-analysis is a tool capable of narrowing the relationship between the genetic profile of the hosts and the clinical outcome. In our analysis, the rarity of the Δ32 alleles of the SNP rs333 and the T allele of the SNP rs41469351 was evident in the studied samples from South American countries. In addition, the C allele (rs1800024) and the A/G heterozygote (rs2856758) were associated with a reduced risk of developing the cardiac form of CD. All alleles associated with an increased risk of developing the cardiac form of CD comprised human haplotype C (HHC). Our HHC–TGTG analysis was also shown to be correlated with the cardiac form of CD. In general terms, exploratory statistical analysis, such as PCA, made it possible to infer correlations between different factors without the need to establish genetic models. Based on our findings, PCA appears to be a strong tool that can help in the understanding of infectious diseases such as CD. Due to the heterogeneity of the data, we advise that the inferences indicated here be analyzed with caution.


Identifying risk factors for metachronous colorectal cancer (CRC) and metachronous advanced neoplasia could be useful for guiding surveillance. We conducted a systematic review and meta-analysis to investigate risk factors for metachronous CRC and advanced neoplasia.Searches were conducted in MEDLINE, Embase, Web of Science and Cochrane Central Registry of Controlled Trials for articles  that reported the results of an association between any factor and metachronous advanced neoplasia or metachronous CRC. There were no restrictions on the publication date or language. Random effects models were fitted to estimate the combined association between the risk factors and metachronous CRC or advanced neoplasia. The Risk of Bias In Non-Randomised Studies of Interventions tool (ROBINS-I) was used to assess the risk of bias of included studies.In total, 22 observational studies with 625,208 participants were included in the systematic review and meta-analysis. Of these, 13 studies investigated risk factors for metachronous CRC and 9 for advanced neoplasia. The risks of metachronous CRC or advanced neoplasia were higher if the first CRC was diagnosed in the presence of a synchronous advanced lesion (pooled risk ratio (RR) from 3 studies: 3.61, 95% confidence interval (CI): 1.44–9.05; and pooled RR from 8 studies: 2.77, 95% CI: 2.23–3.43, respectively). The risk of metachronous CRC was lower, but the risk of metachronous advanced neoplasia was higher if the first CRC was distal  . The risk of metachronous advanced neoplasia increased with age . There was no evidence that any lifestyle risk factors studied were associated with the risk of metachronous CRC or advanced neoplasia.The identified risk factors for metachronous CRC and advanced neoplasia might be useful to tailor the existing surveillance guidelines after the first CRC. There were potential limitations due to possible misclassification of the outcome, confounding and risk of bias, and the findings cannot be generalised to high-risk genetic syndrome cases.The online version contains supplementary material available at 10.1186/s12876-023-03053-2. Colorectal cancer (CRC) is the third most commonly diagnosed cancer in the world . The majFollowing treatment of the first CRC, surveillance colonoscopy is currently recommended for detecting metachronous CRC and metachronous advanced adenoma. However, current surveillance guidelines , 7 are tPrevious reviews –8 have fWe reported a systematic review using the Preferred Reporting Items for Systematic Review and Meta-analyses-2020 , Embase (Ovid), Cochrane Central Registry of Controlled Trials and Web of Science. The searching period was set from 1945 to February 2021. An updated search was conducted on 25th July 2022. Search strategies were developed with a biomedical librarian using the following terms:  AND “risk factors”).The full search strategies are provided in Supplementary Material For studies that met the eligibility criteria, the following data were independently extracted by two authors (YZ and YKA): study characteristics ; participant characteristics ; CRC related characteristics (definition of metachronous CRC or advanced neoplasia); risk factors assessed, and factors that were adjusted for in multivariable analyses. Disagreements were resolved by a third author (MAJ). Where data were not provided in the published manuscript, two authors (as a joint email from YZ and MAJ) contacted the corresponding authors by email (with one reminder) seeking unpublished data.Observational studies meeting the eligibility criteria were critically appraised for risk of bias independently by two authors (YZ and YKA) according to the Risk of Bias in Non-Randomised Studies of Interventions tool (ROBINS-I) . The folτ2) were fitted to estimate the pooled RRs and corresponding 95% confidence intervals (CIs). The 95% CIs were calculated using the Knapp-Hartung method when more than four studies were available [2 statistic was used to assess heterogeneity across selected studies. Results were reported separately for metachronous CRC and metachronous advanced neoplasia. We planned to use funnel plots to assess small study effects if more than 10 studies were included in the meta-analysis for each risk factor. We conducted sensitivity analyses where we excluded studies that did not adjust for age and/or sex because age and sex are both considered to be important confounding variables. We also conducted a post-hoc sensitivity analysis for the association between age and metachronous CRC to exclude one study [Associations that were reported in single studies could not be pooled and were presented as reported. Where multiple studies were available, hazard ratios and risk ratios were combined as a measure of pooled risk ratio (RR) given they estimate similar effects since the outcomes are rare . For eacvailable . Forest ne study  that repThe associations between age at diagnosis of the first CRC and metachronous CRC, and advanced neoplasia were reported for various categorisations of age: continuous e.g., per year of age; binary e.g., ≥60 years vs. < 60 years; or nominal e.g., decades of age. To combine these, we converted the effect estimates for the reported associations to correspond to ‘per year of age’ and pooled them using a random effects model . To convAll statistical analyses were performed using Stata version 17.0  .Our search identified 6327 studies. After removing duplicates, 4277 titles and abstracts were screened for eligibility. Of these, 133 appeared to meet the eligibility criteria, 4 studies were unable to be retrieved, and 129 studies were assessed for eligibility using the full text. Of these, 108 studies were excluded leaving 21 studies included in this review. An additional study was found through a manual search from the references of the eligible studies. In total, 22 articles of 625,208 participants were included in this systematic review and meta-analysis , and non2 = 66%, Fig. 2 = 94%, Fig. Having a synchronous lesion was associated with a 3.6-fold increased risk of metachronous CRC with relatively high heterogeneity  is likely to be affected by improvements in clinical practice (e.g. instrument improvement). For this risk factor, we included eight studies and when we ordered the studies by the midpoint of the study period, we found no indication that the effect changed with time; therefore, we did not conduct the meta-regression.None of the articles included all of the important confounders that we determined a priori. Accordingly, we assessed most articles as having a moderate to serious risk of bias in the confounder domain. All but one study  was clasIn this systematic review, we reviewed 22 articles to summarize the evidence of risk factors and the outcomes, metachronous CRC and advanced neoplasia. We confirmed the findings of previous systematic reviews that the risks of metachronous CRC and advanced neoplasia are 3–4 fold greater if the first primary CRC had a synchronous advanced lesion. We also found that those with a first CRC in the distal colon  were at increased risk of metachronous advanced neoplasia but lower risk of metachronous CRC. None of the other risk factors (demographic or lifestyle) we investigated appeared to be associated with metachronous CRC or advanced neoplasia.We employed a comprehensive search strategy of four databases to maximise the identification of eligible studies, resulting in a larger number of studies. We also conducted a dose-response analysis for age at diagnosis of first CRC to include as many reported associations; this has not been done previously. There are some potential limitations to our study. We limited our analysis to articles that defined metachronous as neoplasia diagnosed at least 6 months after the primary and metachronous CRC. While this definition reduces misclassification of synchronous tumours as metachronous, it potentially excludes studies of true metachronous CRC diagnosed within 6 months. We also excluded studies that focused on high-risk individuals, including those with a genetic syndrome, and therefore we cannot generalise the findings to such patients. As all studies were observational (not randomised as the risk factors we assessed cannot be randomised), they all had a potential to be biased, at least in one of the domains assessed by the risk of bias assessment. Although most studies attempted to adjust for potential important confounders, we cannot rule out residual confounding that might have biased the effect estimates. To minimise this issue, we restricted our analysis to those that at least minimally adjusted for age and sex.The association of age with metachronous CRC or advanced neoplasia remains inconclusive. Our results differ from previous meta-analyses  as our aConsistent with a previous systematic review , we alsoWe found no evidence that lifestyle risk factors contributed to the risk of metachronous CRC or advanced neoplasia. However, it is difficult to rule out such associations given most of the studies in our review collected information about lifestyle risk factors before the primary CRC, when ideally the exposure would occur since the primary CRC diagnosis. A diagnosis of CRC could result in changes to lifestyle and behavior, for example quitting smoking and drinking less alcohol . We alsoExisting guidelines  have cleIn conclusion, we comprehensively reviewed the current evidence for a series of risk factors for metachronous CRC and metachronous advanced neoplasia and conducted meta-analyses to obtain pooled estimates. Similar to other studies, we found that synchronous advanced lesions and the location of initial CRC  were associated with the risk of developing metachronous CRC and metachronous advanced neoplasia, and we did not identify any additional risk factors. Existing studies were generally small with short follow-up periods. Future studies aiming to identify new risk factors will need to be larger, i.e., thousands of cases of colorectal cancer followed prospectively over 10 years and have detailed information on a wide range of risk factors.Additional file 1. Supplementary material 1: PRISMA checklist.Additional file 2. Supplementary material 2: Full search strategies for the systematic review of the literature on risk factors for metachronous colorectal cancer and advanced neoplasia.Additional file 3. Supplementary material 3: Summaries of amendments from registered protocol.Additional file 4: Figure S1. Forest plot of the association between age and metachronous advanced neoplasia and metachronous colorectal cancer. (a) including all selected studies (b) sensitivity analysis of excluding one study reported the opposite association. All relative risks (RRs) were calculated through dose-response analysis. CI, confidence interval. Figure S2. Forest plot of the association between sex and metachronous advanced neoplasia and metachronous colorectal cancer. (a) including all selected studies (b) sensitivity analysis of excluding unadjusted effect estimates. RR, relative risk; CI, confidence interval. Figure S3. Forest plot of the association between family history and metachronous advanced neoplasia and metachronous colorectal cancer. (a) including all selected studies (b) sensitivity analysis of excluding unadjusted effect estimates. RR, relative risk; CI, confidence interval. Figure S4. Forest plot presenting meta-analysis for the association between type 2 diabetes (vs no) and metachronous advanced neoplasia and metachronous colorectal cancer. RR, relative risk; CI, confidence interval. Figure S5. Forest plot presenting meta-analysis for the association between hypertension (vs no) and metachronous advanced neoplasia. RR, relative risk; CI, confidence interval. Figure S6. Forest plot presenting meta-analysis for the association between body mass index (> = 25 kg/m2 vs < 25 kg/m2) and metachronous advanced neoplasia. RR, relative risk; CI, confidence interval. Figure S7. Forest plot presenting meta-analysis for the association between aspirin use and metachronous advanced neoplasia and metachronous colorectal cancer. (a) including all selected studies (b) sensitivity analysis of excluding unadjusted effect estimates. RR, relative risk; CI, confidence interval. Figure S8. Forest plot presenting meta-analysis for the association between current smoking and metachronous advanced neoplasia. RR, relative risk; CI, confidence interval. Figure S9. Forest plot presenting meta-analysis for the association between presence of synchronous advanced lesions and metachronous advanced neoplasia and metachronous colorectal cancer. (a) including all selected studies (b) sensitivity analysis of excluding unadjusted effect estimates. RR, relative risk; CI, confidence interval. Figure S10. Forest plot presenting meta-analysis for the association between location of first colorectal cancer  and metachronous advanced neoplasia and metachronous colorectal cancer. (a) including all selected studies (b) sensitivity analysis of excluding unadjusted effect estimates. RR, relative risk; CI, confidence interval. Figure S11. Forest plot presenting meta-analysis for the association between advanced TNM stage and metachronous advanced neoplasia. RR, relative risk; CI, confidence interval.Additional file 5: Table S1. Risk of bias assessment summary by Risk of Bias in Non-Randomized Studies of “Interventions” (ROBINS-I) tool.


To quantify the dose–response relation between yogurt consumption and risk of mortality from all causes, CVD and cancer.Systematic review and meta-analysis.We conducted a comprehensive search of PubMed/Medline, ISI Web of Science and Scopus databases through August 2022 for cohort studies reporting the association of yogurt consumption with mortality from all causes, CVD and cancer. Summary relative risks (RR) and 95 % CI were calculated with a random-effects model.Seventeen cohort studies (eighteen publications) of 896 871 participants with 75 791 deaths (14 623 from CVD and 20 554 from cancer).2 = 47·3 %, n 12 studies) and CVD , but not with cancer . Each additional serving of yogurt consumption per d was significantly associated with a reduced risk of all-cause  and CVD mortality . There was evidence of non-linearity between yogurt consumption and risk of all-cause and CVD mortality, and there was no further reduction in risk above 0·5 serving/d.High intake of yogurt compared with low intake was significantly associated with a lower risk of deaths from all causes (pooled RR 0·93; 95 % CI: 0·89, 0·98, ISummarising earlier cohort studies, we found an inverse association between yogurt consumption and risk of all-cause and CVD mortality; however, there was no significant association between yogurt consumption and risk of cancer mortality. Healthy diet plays a prominent and profound role in these conditions. Yogurt, a dairy product, made from the fermentation of lactic acid in milk by Lactobacillus bulgaricus and Streptococcus thermophilusis, is among the most widely consumed fermented foods. Due to the close contribution of gut microbiota to human health and the effect of yogurt consumption on gut microbiota, its consumption might affect health outcomes. Moreover, yogurt provides energy and beneficial compounds such as proteins, minerals, multivitamins and conjugated linoleic acid that may jointly favour long-term health. However, it is also a source of saturated fats and added sugar, which is presumed to increase the risk of CVD and mortality,5.Annually, more than half of the deaths are due to CVD and cancer worldwide. Regular consumption of yogurt has been associated with a lower risk of hypertension, type 2 diabetes and certain cancers,10. However, findings on the contribution of yogurt consumption to longevity are limited and conflicting. While some prospective studies have indicated an inverse association between yogurt intake and overall mortality,13, some others have failed to reach such findings,15. Moreover, different associations were reported between yogurt consumption and mortality outcomes in men and women,16. In an earlier meta-analysis of prospective cohort studies, high yogurt intake was not related to a lower risk of all-cause, CVD and cancer mortality. However, this review had several limitations. For example, it missed several large and some smaller studies,16,18–22. In addition, two ineligible studies were included in the analysis,24. The exposure in one of these studies was a combination of dairy products not just yogurt, and in the other study, patients with cancer were included. Furthermore, several studies have since been published,25–28. Given the above-mentioned points, a comprehensive review and meta-analysis is required to quantitatively summarise earlier studies in this regard. The present study was therefore performed to comprehensively examine the association between the contribution of yogurt consumption to the risk of mortality from all causes, CVD and cancer based on earlier publications and to do a dose–response meta-analysis of cohort studies in this regard.Several epidemiological studies have previously examined the association between yogurt consumption and risk of chronic diseases.The present study was performed based on Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelinesAll relevant studies were identified by searching PubMed/Medline, ISI Web of Science and Scopus databases, without any language restriction, from inception to August 2022. Two independent authors (SN and FN) developed and performed the literature search. The following keywords were used in our search strategy: ((‘dairy products‘(Mesh) OR ‘dairy products’(tiab) OR ‘dairy’(tiab) OR ‘yogurt’(Mesh) OR ‘yogurt’(tiab) OR ‘yoghurt” (tiab)) AND  OR ‘death’(tiab) OR ‘fatal’(tiab) OR ‘survival’(tiab) OR ‘mortality’(Subheading) OR ‘neoplasm’(tiab) OR ‘cancer survivor’(tiab) OR ‘cardiovascular disease’(tiab) OR ‘coronary disease’(tiab) OR ‘myocardial ischemia’(tiab) OR ‘coronary artery disease’(tiab) OR ‘myocardial infarction’(tiab) OR ‘stroke’(tiab) OR ‘mortality’(Mesh) OR ‘death’(Mesh) OR ‘neoplasms’(Mesh) OR ‘cancer survivors’(Mesh) OR ‘cardiovascular diseases’(Mesh) OR ‘coronary disease’(Mesh) OR ‘myocardial ischemia’(Mesh) OR ‘coronary artery disease’(Mesh) OR ‘myocardial infarction’(Mesh) OR ‘stroke’(Mesh))). In addition, the bibliographies of the retrieved articles and previous systematic and narrative reviews were also scanned manually to identify potential publications.Two independent authors (SN and FN) screened the title and abstract of publications found in the systematic search to identify eligible studies. The publications were considered for inclusion in this meta-analysis if they (1) were cohort studies (2) reported hazard ratios (HR) or relative risks (RR) and their corresponding 95 % CI for the association between yogurt consumption and mortality from all causes, CVD and cancer. If multiple papers were published using the dataset of a single cohort, we included the one with the most comprehensive information.We excluded ecologic studies, non-original research papers  and meta-analyses. We also did not include unpublished studies as well as those studies where individuals were included based on the existence of a specific disease at baseline. In addition, if a study reported the risk estimates for yogurt consumption in combination with other dairy foods, it was not included in our analysis.Using a standardised form, pairs of authors (HT and SN) independently reviewed the title and abstract of all articles and extracted the required data. In case of any disagreements, consensus was reached. For each study, we recorded the following information: the first author’s name, year of publication, study location, mean age or age range, sex, sample size, length of follow-up, type of outcome, number of deaths from all causes, CVD and cancer, dietary assessment method, the amount of yogurt consumption in each category, adjustment for confounding variables in multivariable analysis as well as RR and 95 % CI of mortality for each category of yogurt consumption. We extracted RR with the most adjusted model. For one study that reported multivariable models with and without additional adjustment for dietary Ca intake, we selected the multivariable model without adjustment for dietary Ca intake.. The total score of Newcastle–Ottawa scale ranges between 0 and 9. Studies achieving nine points were considered to provide the highest quality.Quality assessments were performed in duplicate by two independent reviewers (SN and FN). The Newcastle–Ottawa scale was used to assess the quality of each studyv. the lowest categories of yogurt consumption were applied to estimate log RR and HR ± se. If a study reported risk estimates per standard deviation (sd) or per unit increment in yogurt consumption, the following method was used to translate per sd or per unit increment risk estimate to the top compared with the bottom categories of population baseline distribution of yogurt values. Briefly, we calculated the difference between the means/medians of the highest and lowest categories of yogurt consumption in other publications included in the analysis. Then, the mean difference between the mean/median of the highest and lowest categories was calculated. Finally, per-sd or per unit increase risk estimate was transformed to per ‘calculated mean/median difference’ and was included in the analysis. When the exact amount of sd was not presented in the published paper, we assumed a scaling factor of 2·18 times the log risk estimate for a 1-sd increase in yogurt consumption. The analyses were conducted using a random-effects meta-analysis. Cochran’s Q test and I2 were used to estimate between-study heterogeneity. A value of I2 more than 50 % was considered as substantial heterogeneity. When a study provided results by sex, we first pooled these estimates using a fixed-effects model and included the pooled value in the main analysis, but sex-specific results were presented separately in subgroup analyses. For studies that reported results separately for CHD and stroke mortality or cancer subtypes, we used the method developed by Hamling et al to combine the risk estimates, and the obtained pooled risk estimate was included in the main meta-analysis. For studies that did not provide the information required to apply the Hamling method, we used a fixed-effects model to pool the results. Yogurt intake was converted into serving/d based on standard portion sizes developed by the US Department of Agriculture (i.e. one serving = 244 g).Reported RR and HR and their 95 % CI for comparison of the highest . The sensitivity analysis was conducted to find the effect of any specific study on the pooled effect size.In case of between-study heterogeneity, subgroup analysis was done based on study location, sex, follow-up duration, methods used for assessing dietary intakes and adjustment for confounding factors (BMI and energy intake) to find potential sources of heterogeneity. The possibility of publication bias was examined using the Egger’s and Begg’s tests as well as visual inspection of funnel plots and Orsini et al was used to examine dose–response analysis. We computed study-specific slopes (linear trends) and 95 % CI from the natural logarithm of the RR, or HR, and their corresponding CI across categories of yogurt consumption. In this method, information on the number of deaths, number of participants and the effect sizes with the variance estimates for ≥ 3 quantitative categories of exposure was required. We used the median or mean amount of each category. For studies that reported the range of yogurt consumption rather than mean, the midpoint of the upper and lower limits was used to determine the amount of yogurt consumption. If the highest and lowest categories of yogurt consumption were open-ended, the width of the adjacent interval was used to calculate an upper or lower cut-off value.A method suggested by Greenland and Longnecker. To assess a non-linear dose–response association, restricted cubic splines with three knots at fixed percentiles of 10, 50 and 90 % of the distribution were applied. First, a restricted cubic spline model with a generalised least-squares trend estimation method was calculated after taking into account the correlation within each set of reported RR/HR. Second, all the study-specific estimates were combined with the use of the restricted maximum likelihood method in a multivariate random-effects meta-analysis,38. A probability value for non-linearity was evaluated by null hypothesis testing in which the coefficient of the second spline was considered equal to zero. All statistical tests were performed using the Stata software version 14 . For all tests, P-values less than 0·05 were considered statistically significant.A two-stage generalised least-squares trend estimation method was applied to assess a linear dose–response association of each additional serving of yogurt consumption with mortality risk. In this method, the study-specific slope lines are estimated and combined with a random-effects model to obtain an overall average slope, two was performed on colorectal cancer and myocardial infarction patients, three were conducted on the same population with the same outcome,15,41, five studies had reported the risk estimates for combination of dairy products, not yogurt separately,42–45, and one study had insufficient data. Finally, we considered data from seventeen cohort studies (eighteen publications)–28,47–50. One cohort study in Japan had three publications on colorectal, stomach and ovarian cancer mortality. The article of Schmid et al was published on two cohort studies including Nurses’ Health Study (NHS) and Health Professionals Follow-up Study (HPFS). Out of these, eleven publications had considered all-cause mortality, ten were about CVD mortality and thirteen about cancer mortality.As presented in Fig. ,18,27, one in the United Kingdom, three in the Netherlands,16,49, one in Australia, five (seven publications and three of them were reports from one study) in Japan,26,28,48, one in Italy, one in Iran and the study of Dehghan et al on population from twenty-one different countries. The sample size of the included studies varied from 1529 to 293 888 participants with an age range of 18–90 years. Overall, 896 871 individuals with 75 791 cases of all-cause mortality, 20 554 cases of cancer mortality and 14 623 cases of CVD mortality were included. One publication included only men, one included only women, and the others included both men and women. However, statistical analyses were separately done for males and females in six papers. In most studies, yogurt consumption was evaluated using FFQ, although one study had applied food recall. All studies had adjusted for age. Moreover, most publications had controlled for BMI (n 14), physical activity (n 11), smoking status (n 14), alcohol intake (n 13), energy intake (n 11) and other dietary variables (n 10). Based on the Newcastle–Ottawa scale score, thirteen papers which had a total score above the median (≥ 7) were defined to be high quality (online Supplementary Table 1).Table ,12,27,28 and six showed no significant association,47,49,50. However, in the publication of Schmid et al, an inverse association was seen in women (NHS) but not men (HPFS). Moreover, in the study of Goldbohm et al., yogurt consumption was inversely associated with all-cause mortality among men but not women. In terms of CVD mortality, yogurt consumption was not associated with a lower risk of CVD mortality in nine cohorts–28,47,49. However, two studies,16 revealed that yogurt consumption was inversely related to deaths from CVD. For the association of yogurt consumption and cancer mortality, one publication reported an inverse association and nine did not find any significant association,41,48,49. However, Schmid et al found an inverse association between yogurt intake and risk of cancer mortality among women (NHS) but not men (HPFS).Out of twelve cohorts (eleven publications) investigating the association between yogurt consumption and overall mortality, four studies reported an inverse association,47,49,50, which included a total of 476 160 participants with 75 791 cases. Comparing the highest v. lowest intakes of yogurt, the pooled RR for all-cause mortality was 0·93 , indicating a significant inverse association between yogurt consumption and overall mortality. There was evidence of moderate between-study heterogeneity  ,41,47,49 were included in the analysis of yogurt consumption and CVD mortality. These studies involved a total of 331 261 participants, among them 14 623 mortality cases were found. The pooled RR for CVD mortality was 0·89 , indicating a significant inverse association, with a low between-study heterogeneity  –28,48,49. These studies included a total of 741 973 participants with 20 926 deaths. Combining the RR from these publications, we failed to find any significant association between yogurt consumption and risk of cancer mortality . No significant between-study heterogeneity was also observed  ,47,49,50 were included in the dose–response analysis. Each additional serving/d yogurt consumption was inversely associated with risk of all-cause mortality  . There was evidence of non-linear association between yogurt consumption and all-cause mortality (P-non-linearity < 0·001), and there was no further reduction in risk above 0·5 serving/d  . There was evidence of departure from linearity (P-non-linearity = 0·009), and there was no further reduction in risk above 0·5 serving/d  . There was no evidence of non-linear association (P-non-linearity = 0·08) , which can in turn stimulate β-cell proliferation and improve insulin sensitivity. Yogurt intake was longitudinally associated with less weight gain and lower waist circumference, supporting the growing evidence that changes in gut bacteria may affect weight gain.Regular consumption of fermented yogurt products in experimental studies has been associated with an increased level of HDL-cholesterol,15. Findings from clinical trials have also indicated that consumption of yogurt may be effective in reducing chronic inflammation,59. Ca content of yogurt might also play a role in its inverse association with mortality. Ca appears to interact with SFA, forming fatty acid-insoluble soaps, consequently reducing SFA absorption, lowering TAG concentrations and improving the HDL-cholesterol:LDL-cholesterol ratio. Diets rich in Ca were associated with beneficial changes in blood pressure and lowering risk of stroke,61. Taken together, these findings support the notion that yogurt consumption may be effective in reducing risk of all-cause and CVD mortality.Health-promoting effects of fermented yogurt products may be attributed to the biosynthesis or release of bioactive peptides with antihypertensive, antimicrobial, antioxidative and immune-modulatory propertiesThe present meta-analysis has several strengths. A large sample size (896 871 participants and 75 791 cases) provides an adequate level of statistical power to detect the associations of interest. Moreover, findings were adjusted for numerous potential confounding variables in the included studies. Publication bias that can affect the results of studies is possible in any meta-analysis, but we found no evidence of such bias. Furthermore, a dose–response analysis in the current study adds to the present literature. However, a number of limitations should be considered. First, there was not sufficient information available to investigate the association of yogurt consumption with risk of mortality based on its fat content. Second, the present meta-analysis was based on observational studies; therefore, causality cannot be inferred. Third, given the use of FFQ as a method of dietary assessment in most included studies, measurement errors and misclassification of participants in terms of yogurt intake cannot be ignored. Fourth, the units of yogurt consumption were different across studies. Fifth, most included studies had measured yogurt consumption only at study baseline. Sixth, some studies did not provide sufficient information for the dose-dependent meta-analysis. Moreover, nutrient content of yogurt is dependent on various factors such as animals’ diet, food fortification, biosynthesis and physicochemical conditions, which might be different across studies.In conclusion, we found an inverse association between yogurt consumption and risk of all-cause and CVD mortality; however, we failed to find any significant association with cancer mortality. To shed light on this issue, it seems that further studies, particularly pooled analyses, are required.


Triglyceride-glucose (TyG) index has been reported to be associated with various cardiovascular diseases in recent years. However, the conclusive association between the TyG index and hypertension was not established in the last meta-analysis. Furthermore, it remains unclear whether a linear relationship exists between these two variables.We conducted a comprehensive search of the CNKI, VIP, WanFang Data, CBM, PubMed, EMbase, Web of Science, and The Cochrane Library databases up until May 10, 2023, to identify relevant studies conducted in China. We used Stata version 17.0 and Rstudio version 4.2.1 to analyze the data and assess the association between the TyG index and the risk of hypertension, as well as the dose-response relationship between these two variables. We will select either a random-effects model or a fixed-effects model based on the results of the heterogeneity tests and report 95% confidence intervals accordingly.I2 = 69.0% P < 0.001]. Through meta-regression analysis of multiple covariates, we found that study type, study region, sample size, database source, and study quality score, the above five variables were able to explain 63.0% of the total heterogeneity. The results of the dose-response Meta-analysis showed: The TyG index has a linear relationship with the risk of developing hypertension, as indicated by non-significant coefficients of higher-order terms in the nonlinear model (P > 0.05). The linear trend analysis showed that for every one-unit increase in the TyG index, the risk of developing hypertension increased by 1.5 times [1.532 95%CI  P < 0.001]. However, this result is influenced by the number of studies included in the dose-response analysis and requires further corroboration.In the end, our analysis encompassed 22 studies involving a total of 668,486 participants, comprising 12 cross-sectional studies and 10 cohort studies. Meta-analysis results showed: Analysis of data from China revealed that an elevated TyG index was associated with a higher risk of developing hypertension, as indicated by an OR/HR of 1.36 [95%CI (1.28–1.45) In the Chinese region, there was an independent association between TyG index and the risk of developing hypertension, with a linear trend. However, the results of the linear trend need to be corrected by the more number of related studies.https://www.crd.york.ac.uk/prospero/display_record.php?ID=CRD42023425836. The incidence of hypertension varies significantly across the globe, showing substantial differences. In the past 30 years, the prevalence and absolute burden of hypertension have continuously increased in low- and middle-income countries, including China and India—the two countries with the largest populations in the world. Blood pressure and hypertension incidence have been rising or have plateaued at most –4. In ChOur meta-analysis for this observational study was done under the PRISMA  statement  and MOOSWe searched Chinese and English databases for studies involving the Chinese region separately, and we also referred to the studies covered in the previous meta-analysis , which wArticles meeting the following criteria will be included in the meta-analysis: Inclusion criteria (1) Study design: Observational studies . (2) Methods and outcomes: Any study reporting associations between the TyG index and hypertension. Exclusion criteria: (1) Studies conducted outside of China; (2) Studies lacking relevant OR, RR, or HR values for necessary outcome reporting; (3) Studies without direct comparison of hypertension and the TyG index; (4) Studies without reporting of any excluded confounding factors; (5) Non-core journal papers published in Chinese databases; (6) The journal where the study is published has been removed from the database; (7) Repeatedly published articles, conference proceedings, and reviews; (8) Special groups, such as pregnant women, children, etc. Two researchers independently screened the studies and discussed and resolved the controversial ones.Two researchers independently extracted the study data using the same Excel spreadsheet. Any elements that generated controversy were referred to the third researcher(Q Fu) for discussion and resolution. The content of our subgroup analysis had been predetermined in advance, and thus, our main extracts included: (1) the First author's name and publication year of the study; (2) the specific region in China where the study was conducted; (3) the study design; (4) participant characteristics; (5) mean age ; (6) male to female ratio, along with corresponding effect sizes and intervals (only studies with relevant reports were extracted); (7) sample size. (8) Report format, cutoff value, and effect size with 95% confidence interval for TyG index {for studies that did not provide continuous variables, the effect sizes were obtained by reasonable selection or combination}. (9) Mean BMI {no studies providing mean BMI, obtained by calculation}. (10) Content of adjusted confounders. (11) Relevant data for dose-response analysis.Two investigators  independently evaluated the results of the included studies and the final meta-analysis, in which studies that were too controversial were referred to a third investigator (Q Fu) for discussion and resolution. Only two study types, cross-sectional studies and cohort studies, were ultimately included in this paper. Due to different types of studies requiring different assessment scales, therefore, the cross-sectional study was evaluated with The Agency for Healthcare Research and Quality (AHRQ) scale , which cI2 statistic, where P < 0.0001 was considered heterogeneous , P < 0.0001, I2 = 69.0%, indicating moderate heterogeneity in the meta-analysis (P < 0.0001). This indicates the need for subgroup analysis. Based on the results of individual covariate regression analysis, we found that study type, study region, sample size, database source, and study quality score may be sources of heterogeneity. Therefore, after including these variables in the multiple covariate regression analysis, we found that the results were statistically significant (P = 0.007). The heterogeneity among studies decreased from tau2 = 0.0122 to tau2 = 0.002693, indicating that the introduction of these five variables explains 63.0% of the total heterogeneity. Additionally, I2 also decreased to 10.63%, indicating that the remaining heterogeneity among different studies, relative to the unexplained variation, is 10.63% after the inclusion of these variables. This suggests that the heterogeneity is associated with these five variables. Finally, no type I error was found in the test using the Monte Carlo permutation method. Please refer to In the heterogeneity test results, we found that H = 1.8 > 1.5, analysis . Additioanalysis . In ordeStudy type may be a source of heterogeneity, with significantly lower heterogeneity results in cohort studies and higher heterogeneity results in cross-sectional studies . Sample P = 0.002. We speculate that the publication bias may stem from the regional nature of the study, as this study was based on the Chinese region. To assess the potential impact of publication bias on the results, we applied the trim-and-fill method to deal with publication bias. Finally, under the fixed-effect model, we found that the estimate before filling was 0.239 , and after filling, the estimate was 1.245  . Using the same method, we found that publication bias still existed , we later found through regression analysis that the partial heterogeneity was due to the study type, study region, sample size, database source, and study quality score, and these five variables can explain 63.0% of the total heterogeneity.Based on the results of our observational study's meta-analysis and dose-response analysis, we found a significant and linear association between TyG index and the risk of hypertension or the risk of elevated blood pressure in China [1.532 95%CI  P < 0.001]. In the subgroup analysis of gender, we found that the association between TyG index and hypertension is more pronounced in the female group. This may be because the average age of the participants in this study was around 50 years old, and there is study has shown that the prevalence of hypertension in women before menopause is significantly lower than in men, but after menopause, the prevalence doubles, and is significantly higher than in premenopausal women and age-matched men (According to the results of the regression analysis, the study type, study region, mean age, gender, sample size, mean BMI, database source, and study quality score have an impact on the final results, and subgroup analysis is required. In subgroup analysis, the north-south difference in China has a relatively small impact on the results, but its impact on the northern region is slightly higher than that on the southern region, which may be attributed to the fact that blood pressure is generally higher in the population of northern China than in southern China . In the ched men  while ma① (② (+/H+ transporter expressed in renal tubular cells while also promoting the deposition of intrarenal hyaluronic acid and lipids, which further increases intrarenal pressure. This leads to impaired urine outflow and reduced tubular flow, which in turn increases body fluid volume and drives hypertension. ③ (④ (In the present study, the ability of the TyG index to predict or monitor hypertension is mainly attributed to its effectiveness in reflecting the degree of insulin resistance. The relationship between insulin resistance and elevated blood pressure can be discussed from the following perspectives: ① , 52, 53 ① (② , 13, 53 nsion. ③ , 12 In ton. ③ (④ .The TyG index is often associated with insulin resistance or diabetes in predicting hypertension. However, more research is needed to explore other mechanisms that may explain the relationship between the TyG index and hypertension. One advantage of the TyG index over the HOMA-IR index is that it can reflect other factors and their relationship with cardiovascular disease to some extent . In addiHowever, there are still some limitations in this study. Firstly, the selection or processing of continuous variables included in the meta-analysis may have introduced some bias. Secondly, the results of the regional meta-analysis may not be representative of the association in all regions. Meanwhile, this study still has residual heterogeneity that has not been explained, which means that further in-depth exploration is needed. Finally, the small number of included studies in the dose-response analysis may have an impact on the final linear results.Due to the varying and significant differences in the incidence of hypertension worldwide, the hypertension status of populations in developed regions has greatly improved, while the blood pressure situation of populations in middle and low-income areas is not optimistic. Therefore, we conducted this regional discussion. In summary, there is substantial evidence to support the independent and linear association between the TyG index and the risk of hypertension within the middle and low-income populations in China. However, further large-scale clinical studies are needed to precisely determine the numerical value of this linear relationship. Additionally, it is crucial to expand research on the mechanisms underlying the association between the TyG index and hypertension beyond insulin resistance alone.


The present study aims to investigate whether patients with nephrolithiasis have a prominently higher prevalence of osteoporosis than the controls and Four databases were used to detect the eligible studies. We calculated the relative risk (RR) with a 95% confidence interval (CI) to assess the combined effect. The methodologies for conducting this study followed the PRISMA guidelines and were registered in the PROSPERO (ID: CRD42023395875),P< 0.001; heterogeneity: 2 = I34.8%, P= 0.162). Conversely, osteoporosis was significantly correlated to an increased risk of nephrolithiasis as compared to the controls without osteoporosis . Sensitivity analysis on the two categories validated the above findings. No significant publication bias was identified in this study.Nine case-control or cohort studies with a total of 454,464 participants were finally included. Combined results indicated that there was a significantly higher prevalence of osteoporosis in patients with nephrolithiasis as compared to the general population without nephrolithiasis (overall RR from six studies= 1.204, 95%CI: 1.133 to 1.28, vice versa. This reciprocal association reminded the clinicians to conduct a regular follow-up assessment when managing patients with nephrolithiasis or osteoporosis, especially for the elderly.The present study highlighted a significantly high prevalence of osteoporosis in patients with nephrolithiasis and https://www.crd.york.ac.uk/PROSPERO/#searchadvanced, identifier CRD42023395875. Nephrolithiasis, a common urological disorder, constitutes an important health concern and a common cause of pain and hospitalization. The incidence of nephrolithiasis ranges from 7–13%, 5–9%, and 1–5%, in North America, Europe, and Asia, respectively –4. WithoP < 0.0001) (P < 0.05) after adjusting for multiple confounding factors.Osteoporosis is a chronic, metabolic bone disorder in the elderly, manifested by the deterioration of bone mineral density (BMD) and impaired bone micro-architecture . It affe 0.0001) . Interes 0.0001)  showed tRecently, there have been mounting trials demonstrating the reciprocal association between nephrolithiasis and osteoporosis. In the present study, we aimed to quantitatively summarize the current evidence from all the relevant published studies to evaluate the categorical association between nephrolithiasis and osteoporosis.This cumulative analysis was registered on the PROSPERO (ID: CRD42023395875), an international database of prospectively registered systematic reviews. Moreover, we followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines when conducting this cumulative analysis , the Cochrane Library databases, EMBASE (OVID), and PsychINFO, from their inception until December 1, 2022. Among the studies we included, only those involving human participants reported using English. PubMed search keywords with various combinations were as follows: ((((((((((((((((((((((“Osteoporosis”[Mesh]) OR (Osteoporoses)) OR ) OR ) OR (Post-Traumatic Osteoporoses)) OR (Post-Traumatic Osteoporosis)) OR ) OR ) OR (Senile Osteoporoses)) OR ) OR (Senile Osteoporosis)) OR ) OR ) OR ) OR (Age-Related Bone Loss)) OR (Age-Related Bone Losses)) OR ) OR ) OR (Age-Related Osteoporosis)) OR (Age Related Osteoporosis)) OR (Age-Related Osteoporoses)) OR ) AND (((((((((((((((((“Urolithiasis”[Mesh]) OR (Urinary Lithiasis)) OR ) OR ) OR ) OR ) OR ) OR (Nephrolith)) OR ) OR (Kidney Stones)) OR (Kidney Stone)) OR ) OR ) OR ) OR ) OR ) OR (Nephrolithiasis)).The BMD T-score criteria are commonly used to diagnose osteoporosis. A T-score ≤ −2.5 in any of the bones was applied for defined osteoporosis. Nephrolithiasis could be confirmed by ultrasound and radiograph examination .vice versa? The Patient, Intervention, Comparison, Outcome, and Study Design (PICOS) framework was applied. The components for this PICOS evidence contained the following factors: patients diagnosed with osteoporosis or nephrolithiasis (P); a history of nephrolithiasis or osteoporosis (I); compared with the healthy controls (C); the prevalence of osteoporosis or nephrolithiasis (O); any study designs (S). Furthermore, those articles that provided relative risk (RR) or odds ratios (OR) with the corresponding 95% confidence intervals (CI) were also considered to be eligible.This cumulative analysis included all epidemiologic studies that met the prior inclusion criteria. The question guiding the present study was: Does nephrolithiasis increase the risk of osteoporosis and in vivo or in vitro studies); (f) meta-analysis researches. Under the inclusion and exclusion criteria, two authors independently screened the potential included studies. The corresponding author or the third author was expected to resolve the ambiguities during the selection of the eligible studies.The exclusion criteria of this cumulative analysis were: (a) non-English studies, (b) lack of a control group; (c) review articles, comments, and case reports; (d) duplicated data; (e) experimental studies . Within this scale, nine domains were assessed and the score of conformity gained one score. Studies were assigned a score between 0 and 3 to indicate low quality, 4 to 6 to indicate moderate quality, and 7 to 9 to indicate high quality.I2 statistics and the Cochrane Q statistic. Significant heterogeneity was defined as I2 > 50%. In the Q test, a P-value < 0.10 is considered statistically significant. Considering the high likelihood of between-study variance for differences in the study design and demographics, a random-effect model is used in this cumulative analysis rather than a fixed-effect model. Additionally, we performed a sensitivity analysis to investigate the sources of heterogeneity. In sensitivity analyses, one study was eliminated at a time and the effect of this elimination was subsequently evaluated. An assessment of publication bias was conducted using the funnel plot and Begg’s rank correlation test.We conducted the current cumulative analysis by using the STATA . The combined RR with the corresponding 95% CI was applied to quantitatively evaluate the strength of the association between nephrolithiasis and osteoporosis. Two-tail P values of 0.05 were assumed to be statistically significant. The heterogeneity test was conducted using The selection process for screening the potential inclusions is shown in Following NOS’ methodological quality checker, six included studies gained a score of 7-9, so they were considered high quality. The remainder three included studies were judged to a score of 6, indicating these studies were of moderate quality. As a result, the proportion of high methodological quality in this cumulative analysis was 67% (6/9). The detail-specific scoring was listed in P< 0.001; heterogeneity test: 2 = I34.8%, P= 0.162).As shown in P< 0.001). However, substantial heterogeneity was observed in this synthesized analysis   Figure 3The above results suggested that there was a cross-correlation between nephrolithiasis and osteoporosis. Nephrolithiasis might elevate the risk of osteoporosis. Conversely, patients with osteoporosis might also be susceptible to suffering from nephrolithiasis.P= 0.028) to 1.206 . On the other hand, no substantial heterogeneity was observed after eliminating any one of the 7 included studies. The 2I ranged from 8.8% to 44.8% and all P>0.1.To evaluate the impact of individual studies on the overall RR, sensitivity analysis was conducted on the two topics. First, as shown in P< 0.001) to 1.614  after eliminating any of the four included studies. However, the substantial heterogeneity remarkably disappeared after excluding Kim et al.’s study  (2 = I89.8%) in this cumulative analysis.As regards to those studies reporting osteoporosis and the risk of nephrolithiasis, there was no substantial change in the new synthesized overall RR, which ranged from 1.422  (P > |z| = 0.734) . During the last 40 years, a considerable proportion of investigators found a reduction in both BMC and BMD in patients with calcium nephrolithiasis (P< 0.001). On the other hand, combined RR from four studies reporting the relationship between osteoporosis and risk of nephrolithiasis also yielded a 1.51-fold increased risk of nephrolithiasis in patients with osteoporosis as compared to the healthy population without osteoporosis . In this pooled analysis, the methodological quality of the nine included studies was either moderate or high quality. And the proportion of high methodological quality among the eligible studies was up to 67%. Besides, sensitivity analysis suggested that no single study dominated the overall RR in the two independent cumulative analyses. Moreover, no remarkable publication bias was detected when combining the RR from the included studies. Based on the above facts, our study is highly reliable and the evidence is robust.Approximately 40 years ago, an earlier report conducted by Fuss et al.  demonstrithiasis –29. In tithiasis . In a moithiasis . Howeverithiasis , 19. TheThough there has been a positive association between nephrolithiasis and osteoporosis found in the current cumulative analysis and other uncontrolled studies, a clear etiology for such a correlation is still elusive. Theoretically, both nephrolithiasis and osteoporosis are calcium metabolic disorders, indicating there are some commonalities between the two diseases. According to the findings from some relevant studies, the predominant etiological agents underlying the relationship between nephrolithiasis and osteoporosis might be hypercalciuria and dietary calcium intake .It is known that patients with hypercalciuria may be predisposed to bone demineralization . It has In addition to vitamin D, intact parathyroid hormone (PTH) is also one of the pivotal hormones responsible for the metabolism of calcium and phosphorous. A parathyroid-independent pathologic process may cause bone Ca efflux. Excess PTH levels induce additional loss of calcium in the sufferers, which leads to the formation of calcium stones , 51. In Alternatively, the diet has also been postulated to be an important factor underlying the correlation between osteoporosis and nephrolithiasis. Theoretically, nephrolithiasis patients may restrict calcium and vitamin D intake to prevent the recurrence of urinary tract stones, and this may contribute to the development of osteoporosis . Limitedvice versa through a cumulative analysis. Some strengths existed in this study, including large sample size , high methodological qualities of the relevant studies, fewer heterogeneities among the included studies, and an evidence-based etiological theory for the association between nephrolithiasis and risk of osteoporosis and vice versa. However, some inherent limitations should be noted when interpreting the present results. First, only nine relevant studies were included in the analysis, which may yield biased data. Second, all the included studies were retrospectively designed, which may downgrade the evidence of this cumulative analysis. Third, different geographic regions, sample sizes, mean age, study designs, variable factors, and characteristics of participants among the eligible studies might cause potential heterogeneities. Fourth, higher age, gender, hyperlipidemia, hypertension, diabetes mellitus, hyperuricemia, smoking, and being overweight might be the independent risk factors for nephrolithiasis so as well for osteoporosis. Given the inability to adjust for these potential confounders, it’s not possible to conclude that the causal relationship exists between nephrolithiasis and osteoporosis. Based on these facts, additional high-quality rigorous and prospective cohorts are still warranted to better illustrate the reciprocal correlation between nephrolithiasis and the risk of osteoporosis and vice versa.Nephrolithiasis is thought to be a risk factor for osteoporosis, but data assessing if osteoporosis predisposes to the risk of nephrolithiasis are lacking. In this study, we clarified the two phenomena underlying nephrolithiasis and osteoporosis by summarizing all the data from the relevant studies. To the best of our knowledge, the present study is the first study to quantify the association between nephrolithiasis and the risk of osteoporosis and vice versa. However, the evidence of the causal relationship between nephrolithiasis and osteoporosis is not so strong due to the study design and the confounding factors. Further study of these relationships is still required, given the possibilities to screen for these two conditions if necessary. Our study highlights that regular follow-up assessments are needed when managing patients with nephrolithiasis or osteoporosis, especially for the elderly.The present cumulative analysis provides evidence supporting the reciprocal relationship between nephrolithiasis and the high risk of osteoporosis and The original contributions presented in the study are included in the article/Conceptualization and methodology: SJ and WX. Software and formal analysis: YW and WZ. Writing original draft and editing: SJ, JJ and WX. All authors contributed to the article and approved the submitted version.


Introduction: Studies on the association between gene polymorphisms of various inflammatory factors and liver cirrhosis have been inconsistent. The purpose of this study was to comprehensively summarize the available evidence on the association between gene polymorphisms of inflammatory factors and liver cirrhosis through a systematic review.Methods: We searched databases of PubMed, EMBASE, Web of Science, and the Cochrane Library for relevant articles published from building databases to 25 September 2022. A systematic review and meta‐analysis were performed to investigate the association between gene polymorphisms of various inflammatory factors and liver cirrhosis. Odds ratios (OR) and 95% confidence intervals (CI) were used to assess the strength of association.Results: A total of 43 articles were included in the systematic review and of them, 22 articles were included in the meta‐analysis. The gene polymorphisms of IL-10–1082 GA + AA vs. GG , IL-10–1082 AA vs. GG , IL-18 -137 GG vs. CC , TGF-β1 -509 T vs. C , and IFN-γ +874 T vs. A  were associated with liver cirrhosis significantly and no association was observed in other gene polymorphisms included in the meta‐analysis. The review of inflammatory factors gene polymorphisms that were only reported by a single study indicated 19 gene polymorphisms were risk factors and 4 gene polymorphisms were protective factors for liver cirrhosis, while the association between other 27 gene polymorphisms and liver cirrhosis were not statistically significant.Discussion: This study suggests that IL-10 -1082G/A, IL-18 -137G/C, TGF-β1 -509T/C, and IFN-γ +874T/A were potentially associated with the risk of liver cirrhosis susceptibility. These findings may provide comprehensive evidence for genetic susceptibility and immunogenetic pathology of liver cirrhosis. Liver cirrhosis (LC) is a common chronic progressive liver disease, characterized by diffuse fibrosis, pseudo lobule formation, and eventually to portal hypertension, liver failure, or hepatocellular carcinoma (HCC) . It is rThe main causes of liver cirrhosis were chronic infection of hepatitis B virus (HBV) or hepatitis C virus (HCV), alcoholic liver disease (ALD), and non-alcoholic fatty liver disease (NAFLD). Other causes include primary biliary cirrhosis (PBC), primary sclerosing cholangitis (PSC), autoimmune hepatitis (AIH), and genetic and metabolic diseases, such as Wilson’s disease and hemochromatosis . These pFurthermore, inflammatory factors themselves are manipulated by polymorphisms in their genes. Single nucleotide polymorphisms (SNPs) are the commonest form of genetic variants in human being and they may affect the level of inflammatory factors production and the intensity of their response. There is increasing evidence on the association between individual genetic polymorphisms of inflammatory factors and liver diseases, including chronic hepatitis, liver cirrhosis, and HCC . The genSo far, several common inflammatory factors gene polymorphisms in liver cirrhosis have been investigated by meta-analysis . HoweverThis systematic review and meta‐analysis were performed according to the Preferred Re‐porting Items for Systematic Reviews and Meta‐Analyses (PRISMA) guidelines . We systin vitro cell experiments; 4) no full text; 5) no complete odds ratio (OR) and 95% confidence interval (95% CI).Studies that met all of the following criteria were included: 1) the study design was case-control, cohort or cross-sectional; 2) the study investigated the association between inflammatory factors gene polymorphisms and liver cirrhosis; 3) the study established liver cirrhosis group and control group without liver cirrhosis; 4) diagnoses for liver cirrhosis were reported; 5) genotype frequencies were in line with Hardy–Weinberg equilibrium (HWE). Studies that met any of the following criteria were excluded from the study: 1) duplicated studies retrieved from various databases; 2) reviews, meta-analyses, conference papers, comments or case reports; 3) animal or Endnote 20 was used to manage articles and three investigators assessed articles and extracted relevant data from eligible articles independently, then a customized data extraction Excel spreadsheet was used to extract the data for this study. Differences among the three independent reviewers were resolved by another reviewer. The following data were extracted from the eligible studies: 1) the last name of the first author and year of publication; 2) the SNP of various inflammatory factors; 3) study design and sample size; 4) OR and 95% CI; 5) country where the research was conducted and race of participants; 6) type of case and control and diagnostic criteria; 7) material of sample, storage temperature, and genotyping method; 8) patients characteristics such as Mean age, Male gender, Body Mass Index (BMI), Aspartate aminotransferase (AST), Alanine transaminase (ALT) and γ-Glutamyl transpeptidase (GGT). Finally, the Newcastle-Ottawa Quality Assessment Scale (NOS) was used to assess the quality of eligible studies.2 statistic to test the degree of heterogeneity. If p > 0.05 and I2 <50%, the results are homogeneous, and the fixed effect model is used for combined analysis; otherwise, the random effect model is used for combined analysis. Because the number of included studies of single SNP was less than 10, no formal assessment of publication bias was performed. Sensitivity analysis were conducted when I2 ≥ 50%. The sensitivity analysis adopted the method of eliminating individual studies item by item to explore the source of heterogeneity.RevMan5.3 software was used for meta-analysis. OR and 95% CI were used to assess the strength of associations. Using Q test and IThe search strategy retrieved a total of 2,622 studies, of which 1,305 were from PubMed, 522 from Embase, 340 from Web of Science, and 445 from Cochrane Library. The assessment of full texts of the 395 articles found that 318 articles examined the association between inflammatory factors concentration and liver cirrhosis and 34 provided incomplete OR and 95% CI, and were hence excluded, leaving 43 articles for the systematic review. Among them, 22 were included in the meta-analysis .Characteristics of the 22 included articles in the meta-analysis are presented in Gene polymorphisms of IL-10–1082 GA + AA vs. GG , IL-10–1082 AA vs. GG , IL-18 -137 GG vs. CC , and TGF-β1 -509 T vs. C  were significantly associated with liver cirrhosis. The sensitivity analysis showed that in the study of IFN-γ +874 T vs. A, the heterogeneity of other studies was significantly reduced and the result was reversed  after excluding the article by 2 = 0%, p = 0.660), while there was a significant difference of IFN-γ + 874 T vs. A between HCV-LC and HBV-LC groups  and heterogeneity was reduced in the subgroup of the HCV-LC group , IL-18 (-607A/C), IL-1β −511C/T, IL-28 rs12979860C/T, TGF-β1(Arg25ProC/G), TNF-α  with liver cirrhosis. One of the reasons for this result may be the limited number of eligible studies. Gene polymorphisms of IL-10, IL-18, TGF-β1 have been confirmed to be related to liver cirrhosis by our study, however, it is accepted that IL-1β, IL-28, especially TNF-α plays an important role in the occurrence and development of liver cirrhosis. More research is needed to explore the relationship between these inflammatory factor gene polymorphisms and liver cirrhosis.IL-6, a primary immunomodulatory cytokine, has been shown related to liver cirrhosis in a few studies. This study has some limitations that should pay attention to. Firstly, only four databases were searched and some relevant original studies may be missed. Secondly, there were many predisposing factors such as age and race for liver cirrhosis, but we did not carry out further subgroup analysis on factors due to the limited number of relevant studies. Thirdly, we did not conduct a further analysis due to the lack of data on the severity of liver fibrosis, these may influence the precision of the association. Fourthly, only SNP was considered in this review due to the dearth of studies on the combined impact of gene-gene polymorphisms and haplotype gene polymorphisms. As liver cirrhosis is a multi-factors disease, the combined effects of factors like gene-gene and gene-environment should be considered. Moreover, liver cirrhosis and inflammatory diseases of the intestinal tract like inflammatory bowel diseases may share susceptibility gene loci, and expanded search of more databases may unveal different biomarkers.Our systematic review and meta-analysis suggest that IL-10 -1082G/A, IL-18 -137G/C, TGF-β1 -509T/C and IFN-γ +874T/A are potentially associated with the risk of liver cirrhosis susceptibility. These findings may provide comprehensive evidence for genetic susceptibility and immunogenetic pathology of liver cirrhosis.