The pooled RRs showed no association with prostate cancer , pancreatic cancer , and colorectal cancer . There was no indication of nonlinearity for total cancer (Pnon-linearity = 0.99), however, there was evidence of a nonlinear association between BMI and breast cancer (Pnon-linearity = 0.004) with steeper increases in risk from a BMI around 35 and above respectively. Higher BMI was associated with a higher risk of total, and breast cancer but not with risk of other cancers, in patients with T2D, however, further studies are needed before firm conclusions can be drawn.Although obesity has been associated with an increased cancer risk in the general population, the association in patients with type 2 diabetes (T2D) remains controversial. We conducted a dose–response meta-analysis of cohort studies of body mass index (BMI) and the risk of total and site-specific cancers in patients with T2D. A systematic literature search was conducted in PubMed, Scopus, and Medline until September 2020 for cohort studies on the association between BMI and cancer risk in patients with T2D. Summary relative risks (RRs) and 95% confidence intervals (CIs) were calculated using random effects models. Ten prospective and three retrospective cohort studies  were included in the meta-analysis. Each 5-unit increase in BMI (kg/m Both adiposity and diabetes are major risk factors for cancer and a range of other complications and are associated with a substantial public health burden globally7. According to the World Cancer Research Fund (WCRF) and evidence from some meta-analyses adiposity is an established risk factor for 12 different types of cancer including cancers of the oesophagus (adenocarcinoma), stomach (cardia), colorectal, gallbladder, pancreas, kidney, liver, endometrial, breast , ovaries, and thyroid15. In addition, there is some evidence to suggest increased risk of leukaemia, Hodgkin's disease, non-Hodgkin's lymphoma and multiple myeloma16.The prevalence of high body mass index 20, and alterations in sex hormones and sex hormone-binding proteins21 are some of the proposed mechanisms through which obesity and type 2 diabetes increase the risk of cancers. Moreover, higher circulating levels of adipo-cytokines23 and inflammatory markers24 are the two common abnormalities in obesity and diabetes, both of which are associated with cancer development24. It has been estimated that 5.6% of all incident cancer cases were attributable to the synergistic effects of high BMI and type 2 diabetes in 201225.Type 2 diabetes is strongly related to excess body weight26, such associations in patients with existing type 2 diabetes have been less investigated and results are less clear than in general population-based studies32. Some studies have found a positive association with risk of total cancer33, however, not all studies have been consistent35, and results have been less convincing for hormone-related38 and colorectal cancer32.Although the associations between BMI and the risk of total and site-specific cancers in the general population have been widely investigatedTo our knowledge, no systematic review and meta-analysis has assessed the association of BMI with the risk of total and site-specific cancers in patients with T2D. Thus, we aimed to clarify the strength and shape of the associations between BMI and the risk of overall and site-specific cancers in patients with T2D by systematic review and meta-analysis of cohort studies.The initial database search yeilded 11,070 articles, of these, 55 were selected for full-text checking after removal of duplicate articles and the abstract/title screening. The Cohen’s κ coefficient was 0.75 at the abstract screening stage and 1.0 at the reviewing of the full-text.41, including 3,345,031 patients with T2D with age ranging from 18 to 90 year fulfilled our inclusion criteria. Detailed characteristics of the studies included in the present meta-analysis are shown in Supplementary Table 41 and three were retrospective cohort studies38 and in all of the them, measurement of BMI was performed at baseline of the study and prior to diagnosis of cancer. Six studies had an average follow-up duration of ≥ 10 years40. Four studies were conducted in North America40, four studies in Europe38, and five in Asia41. Two studies included only females37, three studies included only males39, and the remaining studies included both males and females41.The included studies were published between 2001 and 2019 and included over 37,412 cancer cases . In 10 studies, BMI was based on measured height and weight39, another three studies used self-reported anthropometry41. Cancer cases were recognized through national cancer registries in all studies40. Overall, eight primary studies were of high quality39, with a Newcastle Ottawa study quality score ranging from 7 to 9, and another four studies had moderate quality scores, with a score of five or six  in patients with T2D.Figure 35 with 89,261 participants and 7553 cases were included in the analysis of the association between BMI and the risk total cancer in patients with T2D. The analysis indicated that each 5-unit increase in BMI (kg/m2) was associated with a 6% higher risk of total cancer incidence , with high heterogeneity, I2 = 55.4%, Pheterogeneity < 0.001   Fig. .Figure 340 with 611,083 participants and 666 cases were included in the analysis of the association between BMI and the risk pancreatic cancer in patients with T2D. There was no association between a 5-unit increase in BMI (kg/m2) and the risk pancreatic cancer  with high heterogenity, I2 = 71%, Pheterogeneity = 0.06  and the risk colorectal cancer  with high heterogenity, I2 = 65.9%, Pheterogeneity = 0.09  and the risk prostate cancer  with high heterogenity, I2 = 64.4%, Pheterogeneity = 0.04   was associated with an 12% increased risk of breast cancer incidence , with no evidence of heterogeneity, I2 = 0%, Pheterogeneity = 0.78  . Findings from epidemiological studies also suggest that patients with T2D have increased risk of developing several cancers including colorectal (15), pancreatic (55), liver (56), gallbladder (57), breast (14), and endometrial (58) cancer. Metabolic, hormonal and physiological imbalances associated with diabetes and obesity could explain the positive association between higher BMI and cancer risk in patients with T2D. Adiposity is accompanied by insulin resistance, which is implicated in the development of cancer and tumor cells proliferation (20). Obesity-induced oxidative stress and low-grade systemic inflammation may also contribute to the increased risk of cancer in patients with T2D and excess weight (59). In addition, obesity-induced hypoxia and migrating adipose stromal cells are the emerging mechanisms linking obesity to carcinogenesis in patients with T2D (60). Moreover, estrogens and estradiol which are mainly produced by fat tissue, can activate cell proliferation and increase DNA damage in the breast and endometrial cells; resulting in tumor progression and cancer development (61).43. It is possible that use of certain anti-diabetic medications could have attenuated the association between BMI and some cancers in the current analysis as some studies have suggested metformin could reduce risk of some cancers44.Some limitations may have affected the results of the current analysis. As meta-analyses of observational studies are prone to the biases that are inherent in the included studies, it is possible that confounding or reverse causation could have impacted the results, and heterogeneity between studies was also observed in several analyses. Obesity and diabetes is usually associated with other unhealthy behaviors such as sedentary lifestyle, alcohol intake, smoking, dietary habits etc., which are also related to cancer incidence. Although we cannot completely rule out the possibility that confounding could have affected the results, the associations were similar in most subgroup analyses stratified by the most important confounders  and there was no heterogeneity between these subgroups, however, there were few studies that adjusted for dietary factors or took into account use of antidiabetic medications or diabetes duration. One exception was the positive association between BMI and total cancer, which was restricted to studies with adjustment for smoking, and not observed among studies without adjustment for smoking . Smoking is a strong risk factor for a large range of cancers (62–65) and lack of adjustment or stratification for smoking could potentially confound the association with cancer toward the null as smokers tend to have a lower BMI than non-smokersWe also conducted subgroup analyses stratified by study duration, study design, geographic location, but found little indication of differences across these subgroups. Reverse causation could have affected the results because several cancers are accompanied by weight loss, however, no studies excluded early follow-up to take this into account. However, since the results were similar when studies were stratified by duration of follow-up it is less likely that this may have had a substantial impact on the results as reverse causation would have affected studies with short follow-up to a larger degree than studies with long follow-up.45. The association between BMI and colorectal cancer risk was also not significant in the current analysis, however, this was based on only two studies and the the summary estimate is overlapping with the summary estimate from a meta-analysis of studies in the general population 46, and suggest further studies are needed in patients with T2D. Our null finding for BMI and overall prostate cancer risk is consistent with the results from the WCRF report, however, we were not able to look at fatal and advanced cancers, for which there is evidence of increased risk8. There was a limited number of studies across specific cancers and further studies are therefore needed across most cancer sites.For pancreatic cancer our null finding was inconsistent with a previous meta-analysis which found a positive association between higher BMI and increased risk of this cancer in general population studies38, we were unable to examine the association of central fatness with risk of cancer in diabetes. Another point also to be noted is that there are gender differences in the incidence of some cancers48, and we had limited possibility to conduct gender-specific analyses because most studies did not report stratified analyses. Although menopausal status is an effect modified of the association between BMI and breast cancer risk, only one study reported results stratified by menopausal status. Finally, because almost all studies included in the present study only performed a single baseline BMI assessment, we were not able to take into account potential changes in BMI that may have occurred after baseline.Although, indices of central obesity and body fat distribution may be more appropriate predictors for cancer risk than BMIThe present meta-analysis has several strengths. We included only prospective and retrospective cohort studies in the current meta-analysis and this reduced the possibility that recall bias and selection bias, which to a larger degree can affect case–control studies, could explain our findings. By combining data from several studies we had better statistical power than any of the individual studies to detect an association, but there may still have been insufficient statistical power for several of the cancers investigated because of the low number of studies. We investigated the associations for total and site-specific cancers to provide a comprehensive overview of the available data. Lastly, a relatively comprehensive search strategy was developed to identify all the relevant published literature. We also conducted dose–response analyses to clarify the shape of the associations in the analyses of total cancer and breast cancer.In conclusion, this dose–response meta-analysis of cohort studies found a significant positive association between higher BMI and increased risk of total cancer and breast cancer in patients with T2D. Although, these results are limited by the low number of studies published to date, they are largely consistent with results from studies in the general population. Given the increasing global prevalence of diabetes, further high-quality prospective cohort studies are needed to evaluate the association between BMI across a larger number of cancers in patients with T2D to obtain a more complete picture of these associations.49 and Meta-analyses Of Observational Studies in Epidemiology (MOOSE) guideline50 were followed as guidance for reporting this meta-analysis .The Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) statementWe searched PubMed, Scopus, and Medline databases to September 08, 2020 without any language restrictions for cohort studies in adult humans on the relationship between BMI and incidence of overall and site-specific cancers. The search query was as follows: (obesity OR body mass index) AND (cancer OR neoplasm OR carcinoma) AND (cohort OR follow-up OR prospective) AND (diabetes). The detailed search strategy is provided in Supplementary Table 34 and Iowa Women's Health Study (IWH)33 to obtain the risk estimates of cancer in patients with T2D and data about category-specific numbers of participants/cases across categories of BMI.We contacted the authors of the Malmo Diet and Cancer Study (MDCS)34. Two authors (SS and Sh A) independently screened the title and abstract of all articles retrieved. Any disagreements were resolved by consensus with the third author (AJ). The Cohen’s κ coefficient was calculated to indicate the interrater agreement at the abstract and full-text screening stages.The following inclusion criteria were used to find the potential eligible articles: the studies had to be (1) based on prospective/retrospective cohort, or nested case–control study design, (2) conducted in adults with existing type 2 diabetes, (3) use self-reported or measured BMI as exposure and incidence of overall or site-specific cancers as the outcome, and (4) reporting adjusted risk estimates  with their 95% confidence intervals (CIs) and the numbers of cases and person-years or participants/non-cases across three or more quantitative categories of BMI, or reporting sufficient data to calculate these values. Studies that presented results on a continuous scale per standard deviation (SD) or unit increment in BMI were also included. Studies were excluded if they had retrospective case–control or cross-sectional design. In case of studies that did not provide sufficient information to calculate effect sizes, corresponding authors were contacted, and of those, two studies provided additional data from their studies51. This scale is based on the following items: representativeness of the study populations, exposure assessment, adjustment for potential confounding factors, assessment of outcome, and length and adequacy of follow-up.One author (SS), by using a pre-defined data-collection form, extracted the following data from included studied, which was double checked by another author (AJ): the first author’s name, publication year, study location, study design (prospective/ retrospective cohort), follow-up duration, gender and age of participants, number of participants and cases, the criteria used for type 2 diabetes diagnosis, type of cancer , the method of exposure assessment (self-reported or measured), corresponding risk estimates with 95% CIs in fully adjusted models, and the confounding variables that were adjusted for. The crude crude/unadjusted risk estimates were calculated for each cancer in a sensitivity analysis to assess the potential impacts of confounding bias. The study quality was evaluated with the use of a 9-star Newcastle– Ottawa Scale52. The method proposed by Greenland and Longnecker53 was used for the linear dose–response analysis. The distribution of cases and participants/non-cases or person-years and adjusted risk estimates with their 95% CI for different categories of BMI were requisite input for using this method. The median point of BMI in each category was used when it was provided in the articles. For studies that reported the BMI categories in ranges we estimated the upper and lower cut-off value for open-ended categories (the first and the last categories) by using the width of the adjacent category. For studies in which the risk estimates were reported for a 1-SD or a 3- or 4-unit increment in BMI, the reported risk estimates were recalculated for a 5-unit increment in BMI. For those studies in which the lowest BMI category was not the reference category, the effect sizes were recalculated assuming the lowest category as reference, by the method described by Hamling54. If the study reported risk estimates in different age or gender subgroups, these risk estimates were pooled by a fixed-effects model to generate an overall estimate before inclusion in the main analysis. The Cochrane’s Q test and I2 statistics were used to evaluate the heterogeneity among the included studies. The following cut-off points were concidered to interperet the I2 statistic: < 25% (low heterogeneity), 25–50% (moderate heterogeneity), > 50–75% (high heterogeneity) and > 75% (severe heterogeneity)55. Subgroup analyses and meta-regression were performed by the study design (prospective vs/retrospective cohort), study location , study duration (cut-off point: 10 years), and confounders adjusted  to detect the potential sources of heterogeneity. When there was at least four studies included in the analysis we also conducted sensitivity analyses excluding one study at a time to examine the impact of each study on the summary estimate. Publication bias was not evaluated owing to the small number of studies (less than 10 studies)56. Nonlinear analyses were conducted using fractional polynomial models and we determined the best fitting second order polynomial regression model, defined as the one with the lowest deviance57. A likelihood ratio test was used to assess the difference between the nonlinear and linear models to test for nonlinearity57. All statistical analyses were conducted using STATA software, version 13.0 .Estimates of the relative risk (RR) and its 95% CIs such as hazard ratios and odds ratios were considered as equal to RRs given the rarity of cancer cases in patients with T2D. Summary RRs and 95% CIs of total and site-specific cancer were estimated for each 5-unit increase in BMI with the use of a random-effects modelSupplementary Information


To investigate that whether an association between marital status and the female breast cancer risk exists.The MEDLINE, EMBASE and PsycINFO databases were searched from their inception to July 2019. The Newcastle-Ottawa Scale was used to rate the methodological quality of included studies. Study data were pooled using random-effects meta-analyses to compare the breast cancer risk between unmarried, widowed, divorced or lifelong single women and married women. This study is registered with PROSPERO (number CRD42018112368).2: 46% to 82%), which may be partially explained by differences in geographic regions, publication years and control types. Possible publication bias was indicated by the funnel plot and Egger’s test (P = 0.03).Forty-nine publications were included in the meta-analysis. Compared with married women, unmarried and lifelong single women had an elevated risk of breast cancer, and the pooled ORs of case-control studies were 1.20 (95% CI: 1.07 to 1.35) and 1.24 (95% CI: 1.05 to 1.45), respectively. In the subgroup analyses under these two comparisons, hospital-based estimates and multivariate-adjusted estimates demonstrated a strong association, while population-based estimates and age-adjusted estimates produced nonsignificant results. The pooled OR of cohort studies examining the effect of being a lifelong single woman was 1.10 (95% CI: 1.04 to 1.16). Heterogeneity was moderate to substantial across case-control studies (IMarital status may correlate with the risk of developing female breast cancer. However, suboptimal selection of controls, insufficient exploration of confounding effects, inadequate ascertainment of marital status, and possible publication bias may have limited the quality of the available evidence. Overall, conclusions that marital status is an independent risk factor for breast cancer could not be drawn, and further prospective rigorous cohort studies are warranted. Breast cancer is the most frequently diagnosed cancer and the leading cause of cancer death among women worldwide . ApproxiOne factor that may be associated with breast cancer is marital status. Married individuals typically enjoy a higher socioeconomic status than unmarried individuals, which may translate into better access to healthcare . MarriagSome epidemiological studies have detected higher rates of breast cancer in unmarried people than married people ,12, whilWe conducted a systematic review and meta-analysis in accordance with the MOOSE  S1 Tabl and PRIShttp://www.sign.ac.uk/search-filters.html). We confined our search to papers published in English. The reference lists of all eligible articles were also checked to identify additional studies. The search strategy is shown in The MEDLINE, EMBASE and PsycINFO databases were searched for electronic journals. The search duration was from the inception of the databases to July 2019. The search strategy included terms related to marital status and breast cancer combined with SIGN filters for observational studies  independently assessed the titles, abstracts and keywords of each record retrieved. The full texts of all potentially relevant articles were investigated. Disagreements were resolved by discussion between the two reviewers and, if necessary, a third reviewer (SQZ).The data were independently extracted from the included trials by two reviewers (MLL and NZ) and entered into a structured characteristics table. The extracted data included the following: the name of the first author, publication year, study design, features of the study population, strategies used to confirm breast cancer, assessment of marital status, research findings and other required information. We resolved any differences in opinion through consultation with a third person (MH).We rated the methodological quality of the included studies using an adapted version of the Newcastle-Ottawa Scale (NOS) . The NOSWe provided a narrative synthesis of the findings from the included studies and pooled the results if the studies adopted the same methods to categorize marital status. Married women were used as the reference category. In all analyses, we generated inverse-variance weighted random-effects models with the DerSimonian-Laird estimator to account for the high expected heterogeneity across studies resulting from differences in the samples, measures, and designs. Subgroup analyses by control type  and by adjustment level (multivariate-adjusted estimates versus age-adjusted estimates) were performed to determine the association between marital status and the risk of breast cancer.The odds ratios (ORs) were used to measure the effect. If a study where the control subjects were matched to cases by age did not report the ORs, we calculated the ORs using a 2 × 2 cross tabulation. If a study did not use married people as the reference group, we inverted the ORs based on the method proposed by Greenland and Longnecker  with Mic2) statistic, which describes the variation in the effect size attributable to the heterogeneity across studies. The confidence intervals for I2 were also calculated using the formula proposed by Higgins [2 values of <25%, 25–75%, and >75%, respectively. The potential sources of heterogeneity were explored if high heterogeneity was detected using a random-effects-weighted meta-regression based on the following variables: (1) adjustment level: multivariate-adjusted estimates versus age-adjusted estimates; (2) control type: population-based studies versus hospital-based studies; (3) geographic region: Asian versus non-Asian populations; and (4) publication year (continuous). Funnel plots and Egger’s test results were generated to detect publication bias for the comparison with the largest number of studies. Review Manager V.5.3 (Cochrane Collaboration) and Stata SE.15 software were used for the statistical analyses.Heterogeneity was tested using the Cochran Q statistic and quantified with the I-squared  versus married people (n = 41);widowed versus married people (n = 14);divorced versus married people (n = 16); andlifelong single  versus married people (n = 27).2 = 82%; 95% CI: 77% to 87%). The meta-regression identified geographic region as a potential source of heterogeneity (P = 0.004) , but no significant association was observed among the population-based studies . However, the subgroup difference was not significant (P = 0.49) , whereas the studies that adjusted for age produced a nonsignificant OR of 1.16 . However, a subgroup difference was not detected (P = 0.61)  . When we = 0.49) . Accordi = 0.61) .2 = 46%, 95% CI: 0% to 72%). Subgroup analyses did not identify any statistically significant association between being widowed and the breast cancer risk. The population-based studies produced an OR of 0.99  while the hospital-based studies produced an OR of 0.94 . The studies with maximal adjustment produced an OR of 1.02  while the studies that adjusted for age produced an OR of 0.89 . A significant subgroup difference was not observed in both stratified analyses   .2 = 80%, 95% CI: 67% to 87%). The meta-regression showed that geographic region (P = 0.017) and publication year (P = 0.024) were significantly associated with the effect estimates and may have been the causes of heterogeneity , while the population-based analysis produced a nonsignificant OR of 1.03 . The multivariate-adjusted subgroup analysis resulted in an OR of 1.25 , whereas the age-adjusted analysis produced a nonsignificant OR of 1.02 . A significant subgroup difference was not observed in both stratified analyses  (One cohort study  investigogeneity . The hos = 0.23) .2 = 0%, 95% CI: 0% to 23%), while 25 case-control studies [2 = 69%, 95% CI: 54% to 80%). The subgroup analysis according to control type detected a significant subgroup difference (P = 0.02), but the subgroup analysis according to adjustment level failed to detect any difference (P = 0.56). The hospital-based studies produced a combined OR of 1.51 , while the population-based studies yielded a nonsignificant OR of 1.00 . The multivariate-adjusted estimates increased the overall OR to 1.28 , while the age-adjusted estimates produced a nonsignificant combined OR of 1.17   .There was evidence of significant funnel plot asymmetry (Egger’s test: P = 0.03), suggesting publication bias among the studies that investigated the breast cancer risk in unmarried women (n = 41) .Our study summarized all accessible published evidence and demonstrated that unmarried women, especially lifelong single women, had a higher risk of developing breast cancer than married women. Even though positive results were obtained, we could not draw the conclusion that marital status is an independent factor associated with breast cancer.As indicated by the subgroup analysis where only hospital-based studies showed positive associations between marital status and the breast cancer risk, control type may affect the effect estimates. According to another subgroup analysis, the multivariate-adjusted estimates demonstrated a significant association in several comparisons, but the age-adjusted estimates revealed such associations in no comparisons. We speculate that these results might be attributed to interactions among the independent variables analyzed in the multivariable-adjusted models. Unfortunately, we could not investigate whether there are interactions since none of the original studies provided enough details.Heterogeneity was moderate to substantial across the included case-control studies, which may be partly explained by geographic region, control type and publication year according to the subgroup analyses and meta-regression analyses. The results were consistent with those reported in previous studies. For instance, individuals of different races may experience different relationships between marriage and health , and theWe also identified possible publication bias, suggesting that some studies that failed to show a significant association between unmarried status and the breast cancer risk may have not been published.Thus far, no formal attempts have been made to systematically review data regarding the potential relationship between marital status and the risk of breast cancer. Therefore, the data included in our analysis represent the only available evidence for the enrichment of clinical screening and prevention decisions. However, considerable caution is warranted when interpreting these results due to the limitations of this review.First, most case-control studies used a hospital-based design, resulting in controls that often fail to reflect the distribution of key characteristics of the population from which the cases were drawn. Another concern is that retrospective studies are believed to be subject to recall bias caused by memory distortion. However, major life events, including divorce, bereavement and getting married , show miProspective cohort studies with sufficient details regarding the marital status and adequate adjustment for confounding factors should be carried out to explore the dose-response effect of different marital status categories and provide support for a causal relationship between marital status and breast cancer incidence. The moderating and mediating mechanisms of this relationship deserve substantial consideration to develop tailored interventions and strategies to detect breast cancer at an early stage.This review detects a higher rate of breast cancer in unmarried women, especially lifelong single women, than married women. However, the quality of the available data is limited by possible publication bias and several methodological drawbacks, including suboptimal selection of controls, insufficient exploration of confounding effects, and inadequate ascertainment of marital status. Overall, conclusions that marital status is an independent risk factor for breast cancer could not be drawn, and fully adjusted prospective cohort studies with sufficient details regarding marital status should be conducted in the future.S1 Table(DOC)Click here for additional data file.S2 Table(DOC)Click here for additional data file.S3 Table(DOCX)Click here for additional data file.S4 Table(DOCX)Click here for additional data file.S5 Table(DOC)Click here for additional data file.S1 File(PDF)Click here for additional data file.S2 File(DOCX)Click here for additional data file.S3 File(DOCX)Click here for additional data file.


Background: Some studies have reported biological linkages between periodontitis and esophageal cancer, prostate cancer, kidney cancer, hematological malignancy, and melanoma of the skin. This meta-analysis aimed to assess the relationship between periodontitis and the aforementioned five cancers.Methods: Eligible studies on the association between periodontitis and the aforementioned five kinds of cancers were retrieved. The statistical analysis was conducted using Stata 12.0.Results: Ten articles  were included. With statistical significance, participants with periodontitis might have enhanced risks of esophageal cancer , prostate cancer , hematological malignancy , and melanoma of skin , compared with those without periodontitis. However, the evidence regarding the correlation between periodontitis and the susceptibility to kidney cancer was lacking .Conclusions: The present meta-analysis revealed a potential link between periodontitis and esophageal cancer, prostate cancer, hematological malignancy, and melanoma of the skin. However, multi-center studies with large sample sizes and multivariable adjustments are still needed to support the conclusion. Periodontitis is an inflammatory disease that destroys periodontal tissues . About 10%-15% of the global population suffers from substantial tooth loss caused by severe periodontitis However, the correlation between periodontitis and other types of cancers, including esophageal cancer, prostate cancer, kidney cancer, hematological malignancy (HM), and melanoma of the skin, remains unclear. The aforementioned cancers are characterized by high morbidity and fatality rate. According to the GLOBOCAN 2018 database Porphyromonas gingivalis  9, Fusobacterium nucleatum (F. nucleatum) Treponema denticola (T. denticola) P.gingivalis was found to be correlated with the differentiation, metastasis and clinical stage of esophageal cancer. A high level of immune globulins against P.gingivalis was related to the poor prognosis of esophageal cancer P. gingivalis and T. denticolaRecently, mounting evidence suggested that periodontitis was potentially associated with esophageal cancer, prostate cancer, kidney cancer, HM, and melanoma of the skin, of which esophageal and prostate cancer were the most studied. Periodontal pathogens with their DNA or virulence factors were detected in esophageal cancer tissues, including Although there were biologically possible links between periodontitis and the aforementioned five types of malignant tumors, epidemiological researches revealed conflicting results. Due to the lack of statistical effectiveness of a single epidemiological study, this meta-analysis was conducted to provide the latest evidence on the relationship between periodontitis and the aforementioned five cancers.Articles published up to August 2020 on the association between periodontitis and the aforementioned five kinds of cancers, were retrieved from PubMed, Web of Science, Embase, and Cochrane Library. The key words were as follows: (1) “esophageal cancer” OR “esophagus tumor” OR “prostate cancer” OR “prostate tumor” OR “kidney cancer” OR “kidney tumor” OR “hematologic malignancy” OR “hematologic cancer” OR “hematologic neoplasm” OR “leukemia” OR “lymphoma” OR “multiple myeloma” OR “melanoma” AND (2) “periodontitis” OR “attachment loss” OR “probing depth” OR “alveolar crestal height”. Meanwhile, the references of related studies were also reviewed.A total of 375 records were obtained, and studies were selected using the following inclusion criteria: (1) studies on human participants; (2) exposure of interest being patients with periodontitis and the control being healthy participants without periodontitis; (3) result of concern being the incidence of esophageal cancer, prostate cancer, kidney cancer, HM, or melanoma of the skin; (4) studies designed as cohort studies, with the full text available; and (5) adjusted hazard ratio (HR) and 95% confidence intervals (95% CI) provided and adjusted confounders reported.Figure The retrieval and selection were done by two independent authors. Ten articles (referring to 8 studies) were finally included. The detailed process of searching and selection was illustrated in A diagnosis code for periodontitis (ICD-9-CM: 523.0-523.5) At least two teeth with interproximal (between teeth) attachment loss ≥4 mm or at least two teeth with interproximal pocket depth ≥5 mm  Whole mouth mean alveolar crestal height (ACH) ≥2 mm, or at least one site ACH ≥4mm  >2 interproximal sites with attachment loss (AL) >3 mm, and >2 interproximal sites with probing depth (PD)>4 mm (not on same tooth) or one site with PD>5 mm >10% of examined sites having AL>3 mm Self-reported teeth mobility  Diagnosis codes for the 5 cancers, such as ICD-9-CM 150 , ICD-10-CM C61 (prostate cancer) Records on cancer registries or death certificates where cancer was the underlying cause of death Table Detailed criteria of classification of periodontitis severity were shown in Tables 3 illustrated the extracted data and NOS scores. The risk of bias was evaluated by two authors based on a scale in Review Manager 5.2, which included evaluation for cohort design, blinding, missing results, incomplete reporting and so forth. The result was shown in Figure S1 and Figure S2 in the Supplementary File.The following information was obtained from 8 involved studies (referring to 10 articles): author, published date, origin of participants, study design, mean follow-up period, mean age, sex, sample size, ascertainment of periodontitis and cancers, severity of periodontitis, cancer types, adjusted covariates and information for quality assessment. Besides, two authors independently evaluated the quality of eligible literatures using the Newcastle-Ottawa scale (NOS). The maximum score could reach 9 points for each study. The specific NOS score was proportional to study quality. NOS scores were divided into three levels: a score of 0-3, relatively low quality; a score of 4-6, moderate quality; and a score of 7-9, relatively high quality. I2 metric values and their 95% CIs 2I were calculated based on the classical formulas proposed by Higgins We employed the inverse-variance model in Stata 12.0 to obtain overall estimates. HR worked as an indicator to evaluate the correlation between periodontitis and cancers. The heterogeneity was evaluated using Table  Overall estimates were calculated employing random‐effects models because of the insufficient number of studies (<10 studies) on each type of cancer. With statistical significance, participants with periodontitis might have enhanced risks of esophageal cancer, prostate cancer, hematological malignancy, and melanoma of the skin, compared with those without periodontitis. However, the evidence regarding the correlation between periodontitis and the susceptibility to kidney cancer was lacking.The included studies were pooled and analyzed by different cancers, as illustrated in P = 0.009, 2I = 60%, P for heterogeneity = 0.056, Figure ). As shown in Figure , the pooled results remained robust when individually leaving out one study at a time. Funnel plot was not applied because of the small number of included studies (<10 studies).Among all the included studies, four focused on periodontitis and the risk of esophageal cancer  Table 5). When stratified by research quality, the value of 2I decreased significantly, indicating that the main source of heterogeneity was study quality. As for sex, the heterogeneity of the male group was not significant while that of the female group was obvious, suggesting that sex contributed partly to heterogeneity. When pooling the studies with the mean age of participants ≥50, we found periodontitis was potentially linked to esophageal carcinoma . When pooling studies with adjusted covariates of smoking or socio-economic status, we discovered a significant correlation between periodontitis and esophageal carcinoma. None of the four studies had a classification of periodontitis severity; therefore, no subgroup analysis was performed on periodontitis severity.Since significant heterogeneity was discovered, a subgroup analysis stratified by sex, age, research quality and adjusted covariates was performed to find out the causes for heterogeneity . The sensitivity analysis illustrated the robustness of the results . The funnel plot was not used due to the insufficient number of included studies (< 10 studies).Four eligible studies  focused on periodontitis and prostate cancer Table  Among the four studies included, two studies investigated the association between periodontitis of different severity and prostate cancer The subgroup analysis stratified by severity of periodontitis, age, sample size and adjusted covariates was illustrated in Figure 4).Two eligible studies  focused on correlation between periodontitis and kidney cancer risk Figure ). Sensitivity analysis illustrated the homogeneousness and robustness of the results . Because of insufficient studies involved (<10 studies), the funnel plot was not applied to evaluate publication bias.Epidemiological studies concerning periodontitis and cancers always combined the cases of leukemia, lymphoma, and multiple myeloma (MM), collectively known as HM. Thus, this study evaluated the relationship between periodontitis and HM, while the respective correlation between periodontitis and leukemia, lymphoma, or MM was assessed in the subgroup analysis. Five eligible studies Table 7). A correlation between periodontitis and HM was observed among participants with mean age <60 years and male participants. When pooling studies with adjustments on smoking status or socioeconomic factors, a link between periodontitis and HM was discovered. Besides, an association between periodontitis and HM was observed in non-smokers. For the follow-up years, the longer the duration, the higher the HM risk for patients with periodontitis . For HM sub-types, periodontitis enhanced the susceptibility to non-Hodgkin's lymphoma (NHL) , but the evidence that periodontitis could increase the risk of leukemia and MM was lacking.Subgroup analysis was conducted stratified by periodontitis severity, adjusted covariates, age, sex, follow-up duration, sample size, and HM sub-types . A sensitivity analysis was subsequently conducted. After excluding Nwizu's study, the overall estimate was no longer statistically significant . The possible reason was that the sample size of this study accounted for a large part of the total sample size of the included studies. A subgroup analysis was not conducted because of the inclusion of insufficient studies.Three studies, including 87,139 participants, focused on the effect of periodontitis on melanoma of the skin A consensus about the correlation between periodontitis and five cancers  was still lacking, highlighting the necessity for a meta-analysis. This meta-analysis, was performed on 8 studies (10 articles), with a remarkable sample size of more than 100,000 samples for most cancers. By pooling the included studies, a preliminary conclusion was drawn that participants with periodontitis might have enhanced risks of esophageal cancer, prostate cancer, HM, and melanoma of the skin with statistical significance, compared with those without periodontitis. However, no evident correlation between periodontitis and kidney cancer was found. The result of subgroup analysis showed that the periodontitis severity, duration of follow-up, age, sex and the number of participants might influence the result of overall analysis. In addition, observational studies with fewer participants might overestimate the actual correlation, compared with those with more participants. The correlations between periodontitis and two cancers  were more significant among male participants than among female participants. Despite the confirmation of the correlation between periodontitis and HM, only the risk of NHL was found to be enhanced when studying the relationship between periodontitis and HM subtypes. The evidence of periodontitis enhancing the susceptibility to leukemia or MM was lacking, which might be attributed to the insufficiency of relevant studies. Therefore, more researches are needed to assess the correlations between periodontitis and HM subtypes.P.gingivalisT. denticolaF. nucleatumT. denticola could trigger matrix metalloproteinases (MMP-8 and MMP-9) expressions P.gingivalis could interfere with tumor suppressor p53 and promote carcinogenesis P.gingivalis and Actinobacillus gingivalis) might trigger autoimmune responses At present, the exact mechanism of periodontitis promoting esophageal cancer, prostate cancer, HM and melanoma remains unclear. Among these cancers, the mechanisms underlying the relevance between periodontitis and malignant tumors in the esophagus and prostate were studied the most. In esophageal cancer tissues, periodontal microorganisms were found, including However, the aforementioned findings were not sufficient to support a causal relationship between periodontitis and esophageal cancer, prostate cancer, HM and melanoma of the skin. More researches are needed to reveal the association between periodontitis and the aforementioned cancers.Age, smoking and socioeconomic status are closely related to the development of cancers. Meanwhile, the aforementioned factors are also significantly related to periodontal conditions. Therefore, the possible influence of the aforementioned confounding factors should be addressed. All of the involved studies adjusted for age. Most studies adjusted for smoking and socioeconomic status. To address possible residual effects caused by smoking, three studies reported analyses restricted to never-smokers et al. performed no such investigations. Similarly, Wu's meta-analysis recently reported a link between periodontitis and HM. et al. did not address the possible influence of some significant confounders, such as age and socioeconomic status, which were the risk factors for both periodontitis and cancers. Moreover, they did not calculate the respective risk of leukemia, NHL and MM for patients with periodontitis. On the contrary, the present meta-analysis carefully discussed covariates including age, socioeconomic status, follow-up years, research quality and sample size, in the subgroup analysis. More importantly, it further evaluated the correlation between periodontitis and HM sub-types, including leukemia, lymphoma and MM.No meta-analysis investigated whether periodontitis increased the risk of esophageal cancer, kidney cancer and melanoma of the skin. Corbella's meta-analysis reported a correlation between periodontitis and prostate tumor  et al.2I were not reported. This was because 95%CIs of 2I cannot be calculated when the number of studies included was insufficient or the value of 2Chi for heterogeneity was too small et al. caused a great change in the result Nevertheless, this study had certain limitations. First, due to the inadequate number of involved literatures, the results obtained were not robust enough. Therefore, it was tentatively suggested that the relevance between periodontitis and the aforementioned cancers was a potential link, not a clear correlation. Second, the confirmation on periodontitis varied from study to study. A majority of included studies were based on standardized dental examinations, while two studies confirming periodontitis based on self-reported tooth mobility The present meta-analysis of recent epidemiological studies supported that periodontitis was potentially associated with the development of esophageal cancer, prostate cancer, HM, and melanoma of the skin, highlighting the significance of early prevention and treatment of periodontitis. It must be noted that this meta-analysis was not sufficient to support a causal relationship between periodontitis and the aforementioned cancers. It only revealed a potential link. Moreover, the exact mechanism underlying this potential link remained unclear. Multi-center observational studies with an adequate number of participants and multivariable adjustments are required to further assess the correlation between periodontitis and cancers.Supplementary figures.Click here for additional data file.


The associations between periodontal disease, tooth loss, and lung cancer risk remain debatable. Therefore, the purpose of the present study is to evaluate whether periodontal disease and tooth loss are associated with lung cancer risk.  A literature search was performed for relevant studies using PubMed and Embase databases. Risk ratio (RR) with 95% confidence interval (CI) was applied as effect size to summarize the associations between periodontal disease, tooth loss, and lung cancer risk. A further dose-response analysis was also performed.  A total of twelve studies comprising 263,238 participants were included. The results indicated that periodontal disease was positively associated with lung cancer risk . There was a positive association between tooth loss and lung cancer risk . Moreover, there was a significantly linear dose-response relationship between tooth loss and lung cancer risk, and every 5 increment in tooth loss was associated with 10% increased lung cancer risk. Similar results were obtained in subgroup analysis.  Periodontal disease and tooth loss are increased risk factors for lung cancer. Prevention and treatment of periodontal disease may be effective potential prevention strategies for lung cancer. Worldwide, lung cancer is the most frequently diagnosed cancer and the leading cause of cancer-related death, with approximately 2.1 million new lung cancer cases and 1.8 million deaths [Periodontal disease is a chronic inflammatory disease of the periodontium caused by periodontal pathogen infection, resulting in the damage of tooth-supporting tissues and finally leading to tooth loss . Severe Therefore, the purpose of our meta-analysis is to evaluate whether periodontal disease and tooth loss are associated with lung cancer risk and whether prevention of periodontal disease is an effective potential prevention strategy for lung cancer.A systematic literature search was performed for the relevant studies on associations between periodontal disease, tooth loss, and lung cancer risk using PubMed and Embase databases (up to September 2019). The search strategy was as follows:  AND (lung cancer OR lung carcinoma OR lung tumor OR lung neoplasm). Furthermore, we manually searched the references of reviews and relevant studies to identify other eligible studies.Included studies must meet the following eligibility criteria: (1) the exposure factor was periodontal disease and/or tooth loss; (2) the endpoint outcome was lung cancer risk; (3) the effect size of outcome was hazard ratio (HR), odds ratio (OR), or risk ratio (RR) with 95% confidence interval (CI); (4) the study type was cohort study or case-control study and the full text was available. In addition, only the most comprehensive study was included for meta-analysis if there were several duplicated studies. Some data reported only in the excluded duplicated studies were extracted into the included duplicated study for further analysis.Two reviewers independently screened literatures and extracted data as follows: first author, study type, study name, publication country and year, follow-up time, age, type of exposure ascertainment, type of lung cancer ascertainment, sample size, number of cases, exposure type, effect size with 95% CI, and controlled confounding factors. Moreover, if the same study provided several risk estimates and these risk estimates had major gaps for confounder control, only the risk estimates with the greatest control for confounding factors were extracted for meta-analysis. The study quality of the included studies was assessed by the Newcastle-Ottawa Scale (NOS) . Any difRR with 95% CI was used as effect size to summarize the associations between periodontal disease, tooth loss, and lung cancer risk. OR value provided by case-control study could be almost equal to the RR because the lung cancer incidence was extremely low, and thus, OR was used as RR in the data synthesis . A pooleTo assess the impact of the number of tooth loss on lung cancer risk, we also performed a dose-response analysis which needed the assigned values of tooth loss, distributions of cases and noncases, and risk estimates in each category . A midpoQ test and the I2 statistic were performed to evaluate heterogeneity among studies, and the definition of statistically significant heterogeneity was p < 0.10 and/or I2 > 50% [The Cochran I2 > 50% . A randoI2 > 50% . PublicaI2 > 50% , 21. In I2 > 50% .p < 0.05 was considered statistically significant.All statistical analyses were conducted in Stata software version 12.0 . A two-sided There were 1228 studies initially obtained from the literature search, among which 662 studies were from PubMed database and 566 studies were from Embase database. 1185 studies were excluded according to the title and abstract, and the remaining 43 studies were needed to review the full texts. After reviewing these full texts, 31 studies were excluded because these studies did not meet the eligibility criteria, and 12 studies were included for our meta-analysis –34. The The twelve studies were published from 2003 to 2019. Among these studies, five studies were from the USA, two studies were from Japan, and one study was from Turkey, Finland, Greece, Australia, and UK, respectively. In the type of study design, nine studies were cohort studies , 33, 34 I2 = 62.7%), and the result indicated a positive association between periodontal disease and lung cancer risk . In terms of study design, subgroup analysis stratified by cohort study  and case-control study  also showed a positive association. Subgroup analysis based on the control for smoking status, amount, and duration obtained similar results, and the pooled RR was 1.80 after controlling for all potential important confounding factors   . Moreoveiabetes) .p for nonlinearity = 0.96). Indeed, a significant linear dose-response relationship was confirmed by the generalized least-squares regressions  and tooth loss  were positively associated with lung cancer risk. Moreover, similar results were obtained in subgroup analysis by study design, publication country, study quality, gender, sample size, cancer ascertainment, exposure ascertainment, and controlled confounding factors. The results of a dose-response analysis showed that there was a significantly linear relationship between tooth loss and lung cancer risk, and the lung cancer risk increased by 10% for 5 tooth increment in tooth loss, with a monotonically increasing trend.In exploring the relationship between periodontal disease and lung cancer risk, four studies used self-reported measure to identify periodontal disease. Researchers may be concerned about whether self-reported periodontal disease was validated. Previous several systematic reviews have demonstrated that self-reported periodontal disease had acceptable validity and self-reported measure was feasible for monitoring periodontal disease in epidemiological studies , 36. MorPeriodontal disease and lung cancer were affected by many common risk factors , 42. Theγ, and IL-1β [γ, and IL-1β were positively associated with lung cancer risk [The mechanism of the positive relationship between periodontal disease and lung cancer was unclear. The associations between infection, inflammation, and lung cancer may be the most probable explanation . Emerginnd IL-1β –49. Perind IL-1β , 51. Indcer risk –54. Dentcer risk . Pneumoncer risk –58. Sevecer risk , 60. UndBased on the present status that severe periodontitis and lung cancer were public health problems worldwide and there was a relationship between periodontal disease and lung cancer risk, it was a meaningful problem whether the risk of lung cancer could be reduced by effective prevention and treatment of periodontal disease , 4. SomeThere was considerable heterogeneity in the meta-analysis of the association between periodontal disease and lung cancer risk. The result indicated that the study by Guven et al. contributed relatively substantial heterogeneity . The reaThere were several limitations in our meta-analysis. First, the number of included studies was limited, which could affect the implementation of in-depth subgroup analyses. Second, the controlled confounding factors were varied, and the differences may be the potential source of heterogeneity. However, we could not completely control for all important confounding factors and eliminate the heterogeneity because the personal information could not be obtained from these published studies. Third, the exposure ascertainment of periodontal disease was discrepant. Some studies used self-reported measure to ascertain periodontal disease while the other studies used clinical periodontal examination to ascertain periodontal disease. Moreover, there was no uniform amount in the definition of tooth loss.Our results indicate that periodontal disease and tooth loss are positively associated with lung cancer risk. Moreover, there is a significantly linear relationship between tooth loss and lung cancer risk, with a monotonically increasing trend. Moreover, subgroup analyses based on different controlled confounding factors including smoking status, amount, and duration also confirm the validity of our results. Further large-scale, well-designed studies are urgently required to adequately control for multiple confounding factors, especially smoking, to explore the association between periodontal disease, tooth loss, and lung cancer risk.


We adopted this meta-analysis for further insight into the case–control studies.The association between To investigate the genetic association, we searched multiple databases, including Web of Science, EMbase, CBM disc, PubMed and CNKI. Also, we manually identified the searched references. All the statistical analyses were conducted using Stata 11.0.IL-6 gene −572 G^C polymorphism and MI, implying that the IL-6 gene −572 C allele may be a protective factor for MI . However, in the subgroup analysis with regard to ethnicity, no significant correlation was identified between IL-6 gene −572 G^C polymorphism and MI among Europeans.A total of five studies were identified, involving 2,526 MI cases and 3,027 controls. The results revealed a significant association between IL-6 gene −572 C allele may be a protective factor for MI. Future studies involving larger sample bases are still recommended.The  MI is caused mainly by the interruption of the blood supply in the heart, which may cause myocardial necrosis. Although a combined impact from environmental and genetic factors may play a critical role ,2, the eIL-6 gene is located on the chromosome 7p21, spanning about 5 kb, consisting of five exons and four introns [IL-6 gene and its secretion [IL-6 gene −572 G^C polymorphism may play a significant role in the development of MI.Interleukin-6 (IL-6), a proinflammatory cytokine, has a molecular weight of around 26 kD [ecretion ,9. Moreoecretion ,11, and ecretion . Ridker ecretion . Thus, IIL-6 gene −572 G^C polymorphism in human atherosclerotic disease leading to MI progression has been widely studied; however, the result is uncertain. To the best of our knowledge, few studies have managed to reveal a significant association between IL-6 gene −572 G^C polymorphism and MI risk [IL-6 gene −572 G allele and the elevated risk of MI [IL-6 gene −572 G^C polymorphism and the risk of MI.Within the past few years, the role of  MI risk ,15,16,18sk of MI . Therefo22.1This meta-analysis is consistent with the systematic review and meta-analysis (PRISMA) standards for priority reporting projects . A compr2.2IL-6 gene −572 G^C polymorphism and MI, (3) independent case–control studies, (4) adequate published data on the genotypes or allele frequencies for estimating odds ratio (OR) with 95% confidence interval (CI) and (5) unpublished data.The study of the combined meta-analysis must conform to the following criteria: (1) human studies, (2) studies on the relationship between 2.3p < 0.05 of HWE was considered as statistically significant).Data were independently analyzed by two authors (Ding HG and Yin YW), and the results were reviewed by the third author (Liu SL). Multiple items of information were derived and analyzed from each publication: first, the author’s surname, publication year, country and race, locus of control, case number and control number, genotype and allele information. In addition, Hardy Weinberg was also collected , which is applicable for both case–control and cohort studies . We asse2.5IL-6 gene −572 G > C polymorphism and MI risk was measured by ORs with 95% CIs. The pooled ORs were estimated for four genetic models . Heterogeneity between studies was formally tested by using Cochran’s Q statistic and considered statistically significant when p < 0.10. Heterogeneity was also measured with I2 statistic (I2 > 50% indicated evidence of heterogeneity) [p < 0.05 was considered representative of statistically significant publication bias) [The strength of the association between geneity) ,22. The geneity) ,24. The on bias) . Data fr33.1p < 0.05). The average score of the NOS results was 8 and was relatively acceptable.The study logistics are shown in 3.2I2 = 23.6%; for C/C vs G/G: PQ = 0.530, I2 = 0%; for C/C vs C/G + G/G: PQ = 0.900, I2 = 0%; for C/C + C/G vs G/G: PQ = 0.350, I2 = 9.9%), we used the fixed-effects model to calculate the pooled ORs. The overall results showed that there was a significant association between IL-6 gene −572 G > C polymorphism and MI, suggesting that IL-6 gene −572 C allele may be a protective factor for MI . The main results of the meta-analysis are presented in Given that no heterogeneity existed in overall comparisons . We did not perform the subgroup analysis for Asians as only one study involved Asians. The main results of the meta-analysis are summarized in However, in the subgroup analysis by ethnicity, no significant association was found between 3.3The sensitive analysis was used to evaluate the stability of the results. In the study by Coker et al., the sensitivity analysis was excluded due to the deviation of the genotype distribution in the control group from HWE. We found that the corresponding pooled ORs were not significantly altered in the recessive model and the dominant model. In contrast, the corresponding pooled ORs were significantly altered in the allelic model and the additive model, indicating that the study without HWE should be viewed as an influencing factor on the overall results. 3.4p = 0.118 for allelic model, p = 0.221 for additive model, p = 0.860 for recessive model and p = 0.083 for dominant model).Begg’s funnel plot and Egger’s regression test were performed to assess the publication bias of the studies. No obvious asymmetry was observed in any genetic model according to the visual assessment of the funnel plot . Further4IL-6 gene −572 G > C polymorphism and MI has been extensively studied. However, previous studies exploring the existing evidence were most frequently performed as case–control studies [IL-6 gene −572 G^C polymorphism and MI. Recently, the meta-analysis has been widely utilized in genetic studies because it is capable of detecting small effects between gene polymorphism and human disease [IL-6 gene −174 G^C polymorphism is not associated with the elevated risk of MI [IL-6 gene −572 G^C polymorphism and MI, and this meta-analysis is used to provide a more precise validation.MI is a complex disease associated with multiple factors including environmental and genetic impacts ,2. The a studies ,16,17,18 disease ,27,28,29sk of MI . Here, oIL-6 gene −572 G^C polymorphism and MI. In this meta-analysis, the integrated evidence indicates that the IL-6 gene −572 C allele may play a role as a protective factor for MI. Given that potential ethnic differences might have an indirect impact, we also performed the subgroup analysis with regard to ethnicity. The results show that among Europeans there is no significant association between IL-6 gene −572 G^C polymorphism and MI. The inconsistent results for Europeans in the subgroup analysis may partially result from the genetic diversity among ethnicities. MI is a complex disease; in addition to genetic factors, environmental factors also have certain impacts in MI etiology. Thus, this discrepancy may also be caused by varied geographic distribution, linked to climate, diet, financial status and living conditions. For Asians, we did not perform the subgroup analysis because of insufficient studies. Furthermore, considering that the genetic correlation results from the case–control study may be biased, the genotype distribution in the control group deviated from HWE [IL-6 gene −572 C allele may be a protective factor for MI.As far as we know, this is the first comprehensive meta-analysis to date whose main purpose is to explore the relationship between from HWE . We alsoIL-6 gene −572 G^C polymorphism and MI among Asians. Therefore, the results should be interpreted with caution. Third, meta-analysis has its own inherent limitations; its retrospective nature is associated with the deficiencies of the methodology  in this meta-analysis is relatively small. Further studies with enlarged enrollment of study subjects, e.g., with multicenter case–control studies, will be required to verify our findings within the near future.In conclusion, our meta-analysis including 5,553 subjects suggests that the


Q4 vs. Q1 = 1.16; 95% CI = 1.02–1.31). In subsites, we found a positive association between GI and laryngeal cancer  and an inverse association between GL and oropharyngeal cancer . This pooled analysis indicates a modest positive association between GI and HNC, mainly driven by laryngeal cancer.High dietary glycaemic index (GI) and glycaemic load (GL) may increase cancer risk. However, limited information was available on GI and/or GL and head and neck cancer (HNC) risk. We conducted a pooled analysis on 8 case-control studies  from the International Head and Neck Cancer Epidemiology (INHANCE) consortium. We estimated the odds ratios (ORs) and 95% confidence intervals (CIs) of HNC, and its subsites, from fixed- or mixed-effects logistic models including centre-specific quartiles of GI or GL. GI, but not GL, had a weak positive association with HNC (OR In addition, we calculated GI and/or GL intakes from study-specific food items and food composition databases for another five studies, giving a total of eight studies included in the analysis. Details on individual studies and data pooling methods have been previously describedCases were included if their cancer had been originally classified as invasive cancer of the oral cavity, pharynx, larynx or unspecified oral cavity/pharynx. Corresponding controls from the original studies were included in the analysis. We excluded subjects with missing information on the site of origin of cancer, or GI or GL value, and those with missing or implausible  non-alcohol energy intake. Thus, our analysis included 11,488 subjects, with 4081 HNC cases and 7407 controls . There were 810 oral cavity, 1172 oropharynx, 343 hypopharynx, 1338 larynx and 418 unspecified oral cavity/pharynx cancer cases.9 the GI of a food was expressed as a percentage of the glycaemic response elicited by white bread as a standard food with a GI of 100. The average daily GI for each subject was computed by summing the products of the GI value of each food times the amount of available carbohydrates in that food consumed per day, divided by the total amount of available carbohydrates (g) consumed per day. The average daily GL (g) was calculated by summing the products of the GI value of each food times the amount of available carbohydrates in that food consumed per day, divided by 100. Each GL unit represents the equivalent of 1 g of carbohydrate from white bread. Therefore, we initially converted frequencies of consumption into servings/day and servings/day into grams/day; then, we assigned the corresponding GI to each food item and applied the previous formulas to derive individual GI and GL values. For the North Carolina (2002–2006) study, information on individual values of GL was originally provided to the INHANCE Consortium Coordinating Center. We estimated GI as 100 multiplied with GL and divided by total available grams of carbohydrate intake  and food composition tables allowed us to calculate individual values of GI and GL for the four studies lacking information on both the exposures . In detail, as described previously,10 The models included the following potential confounders: age, sex, race/ethnicity, study centre, education, cigarette smoking intensity, cigarette smoking duration, cigar smoking status, pipe smoking status, alcohol drinking intensity and the product term of cigarette smoking and alcohol drinking intensities. For GI, models were further adjusted for energy intake without alcohol; for GL, models were further adjusted for energy intake without alcohol and carbohydrates. For both GI and GL models, we used centre-specific control-based quartiles of energy intake. Separate analyses were carried out by HNC subsites and in strata of selected covariates. In sensitivity analyses, we further adjusted for history of diabetes or excluded subjects with diabetes (information available for 6 studies). Analyses were performed using the SAS software .Multiple logistic regression models were used to estimate the odds ratios (ORs) of HNC and the corresponding 95% confidence intervals (CIs) according to centre-specific quartiles of GI or GL among controls , but excluding laryngeal cancer cases, the ORQ4 vs. Q1 was 1.01  (data not shown). Little associations between GL and cancers of the oral cavity, hypopharynx and larynx were observed. An inverse association was found between oropharyngeal cancer risk and GL . Results did not materially change when excluding subjects with diabetes or when additionally adjusting models by diabetes history. No heterogeneity was observed in strata of covariates  and low-GL (e.g. meat) foods.10Inconsistent associations of GI and GL with HNC risk may be partly due to differences in the underlying dietary patterns. Indeed, higher dietary GL is strongly associated with higher carbohydrate intakes, while a higher GI is also associated with lower intakes of dairy products, legumes, fruit and vegetables.7 have examined the association between GI or GL and HNC risk, with one of them partially overlapping with the current dataset.6 An analysis6 of three Italian case-control studies on upper aero-digestive tract cancers reported a positive association with higher GI  and GL  in quintiles. Although in the same direction, the association was weaker with oral and pharyngeal cancers combined or laryngeal cancer.6 Findings from the National Institutes of Health–AARP Diet and Health Study  reported a null association with GI and a possible inverse association with GL in women , in the absence of a clear dose-response relationship.7Only two previous studies8Limitations of the current analyses included possible recall bias and non-differential misclassification of GI/GL quartiles. In addition, food items contributing to GI differed in part across regions (Supplementary Table In conclusion, findings from this large-scale pooled analysis support a positive effect of average daily GI on the risk of HNC, and in particular of laryngeal cancer.Supplementary Material


Recent publications have investigated the potential role of the protein level of matrix metalloproteinase-1 (MMP-1) in the susceptibility to rheumatoid arthritis (RA) and osteoarthritis (OA). However, no unanimous conclusion was obtained. Therefore, we carried out a meta-analysis to explore the association between MMP-1 expression and these two clinical disorders. After database searching and screening, we enrolled a total of eighteen articles for the pooled analysis. We observed a significant association between RA cases and controls in the whole population . There were similar positive results in the subgroup analysis of “population-based control”  and “synovial fluid” . In addition, we observed an increased risk in OA cases, compared with controls, in the overall analysis  and subsequent subgroup analysis of “knee OA” , “Asian/China” , “cartilage-Asian/China” , and “synovial fluid-Asian/China” . In summary, a high protein level of MMP-1 in synovial fluid may be associated with the susceptibility to RA, and the high MMP-1 level in the cartilage tissue or synovial fluid may be related to the pathogenesis of knee OA in the Chinese population. This should be confirmed by larger sample sizes. Rheumatoid arthritis (RA), a kind of systemic and chronic autoimmune inflammatory disorder, can affect multiple joints and lead to bone erosion and cartilage degradation . A serieOsteoarthritis (OA), a degenerative multifactorial joint disorder, mainly affects knee joints and can result in deeply damaged and irreversible morphological changes of articular cartilage, body pain, and loss of function –9. FacTo date, there is still no meta-analysis regarding the correlation between MMP-1 expression and the risk of RA. Furthermore, there is only one meta-analysis published in 2015 that investigated the potential role of MMP-1 expression in the OA susceptibility . In the We designed our analysis flowchart according to the guidelines of PRISMA  . ReferriAfter screening, we tried to extract the basic information  from the selected full-text articles. Articles with unavailable data were removed. In addition, after the evaluation of the Newcastle-Ottawa Scale (NOS) system, we excluded the studies with poor quality (NOS score ≤5).A , SMD (standard mean difference), and 95%CI  were calculated, under the random-effect model . Apart from the overall meta-analysis, we also performed the subgroup analyses by factors of ethnicity (Caucasian/Asian), control source [PB (population-based control)/HB ], and sample source .We used the STATA software  to perform the statistical analysis. In the Cohen statistics of our association test, the PAdditionally, a sensitivity analysis was performed to assess the stability of pooling results. Egger's test (Egger's publication bias plot) was also carried out to evaluate whether a remarkable publication bias existed.Based on the PRISMA-based flowchart , we inclA<0.05). We did not detect a significant association for the other subgroups (A>0.05). A total of ten studies (417 cases/332 controls) were enrolled for the meta-analysis of RA. There was an increased risk in the RA cases, compared with controls, in the overall pooling analysis and the subgroup analysis of “PB” and “synovial fluid” (A<0.05). Twenty-two studies (863 cases/433 controls) were enrolled for the meta-analysis of OA. As presented in 2>50.0%, PH<0.001), which led to the use of a random effect model. During the pooled analyses of RA, we observed a reduced heterogeneity in the subgroups of “Caucasian” and “HB” (There was a high heterogeneity in the meta-analyses of RA and OA (and “HB” , compareand “HB” . Thus, cThe results of sensitivity analysis  for OA sThere are several studies regarding the role of MMP-1 expression level from the synovial fluid, serum, or cartilage tissue in the risk of OA. For instance, the MMP-1 level of the synovial fluid, but not serum, in OA cases in China is higher than that in controls . HoweverIn 2015, Zeng et al.  enrolledLikewise, there are inconsistent conclusions regarding the effect of MMP-1 level of synovial fluid or serum on the risk of RA. For instance, the high MMP-1 level in the synovial fluid is linked to the risk of RA in China . HoweverSome potential limitations should be noted in our study. A small sample size was included in some analyses. For instance, there were no more than ten case-control studies for the evaluation of the correlation between the MMP-1 level in the specific serum or synovial fluid and susceptibility to RA. A similar question exists in the stratification analyses of OA risk by the different sample sources, such as “cartilage tissue”, or “synovial fluid”. Thus, although we did not detect a correlation between the serum MMP-1 level and the risk of RA or OA, we still cannot rule out the possibility of association. In addition, the high heterogeneity in the Cohen statistics of RA or OA in the overall population, and a slight publication bias for the meta-analysis of RA could be a source of bias. Due to the sample collection feature of OA or RA, hospital-based controls were mainly included. More population-based controls are needed for the meta-analysis of MMP-1 serum level. Lastly, we only investigated the role of MMP-1 level in the risk of RA or OA. The combined effect of the expression levels of MMP-1 and other MMPs, such as MMP-2 and MMP-3, or the MMP expression and genetic variants, on the risk of RA or OA, should be investigated based on the available case-control studies in the future.In summary, an elevated MMP-1 level in synovial fluid seemed to be related to a high risk of RA. Also, a high MMP-1 level in the cartilage tissue or synovial fluid may be associated with knee OA susceptibility in the Chinese population. A larger sample size is still required for the verification of our findings.


There is a growing body of evidence on low serum vitamin-D levels and the risk of uterine leiomyomas (UL). Therefore, this systematic review and meta-analysis was conducted to investigate the association between serum vitamin D levels and UL occurrence.2). Data was pooled using random effects modeling and expressed as standardized mean differences (SMDs).Searches were systematically conducted of the electronic databases PubMed, Scopus, EMBASE, Web of Science (ISI), Cochrane library, Ovid, and Google Scholar to identify relevant studies from inception until February 6, 2020. Heterogeneity across the included studies was examined using Cochran’s Q and I-square . Based on the findings of subgroup analyses, it was found that the SMD values across the included studies from Asia  were lower than those from Europe  and Africa , although the difference was not significant in Africa. Publication year was also found to be a potential contributor’s variable in the pooled SMD using the meta-regression method .Nine eligible studies with a total of 1730 participants (835 patients with UL and 895 controls) were included in the current meta-analysis. Pooled results with random effects modeling indicated that serum vitamin D levels were significantly lower in patients with UL than in the control group (To the best of our knowledge, the current meta-analysis showed for the first time that serum vitamin D levels were significantly lower in women with UL in selected populations. The term uterine leiomyoma (UL), also called uterine fibroids, represents the most common benign gynecological tumor affecting the female reproductive system and primarily originates from uterine smooth muscles . The preDespite identifying risk factors such as age, black race, premenopausal state, family history, hormonal factors, obesity, and nulliparity that play a role in the development of UL, the exact pathogenesis of this condition remains poorly understood , 2, 6. Ip < 0.001). In the latter study, the UL size increased proportionately with the decrease in 25-(OH) D3 levels (p = 0.014)  AND . To increase the sensitivity of our search results, we checked manually the reference lists of relevant pervious reviews and eligible studies.Studies were included that met the following inclusion criteria: study was an observational human article  in English language; investigated the association between serum vitamin D levels and UL; and reported sufficient data to calculate mean difference with 95% confidence intervals (95% CI) for investigating serum vitamin D levels in patients with UL group compared with control group without UL. Studies such as case report, case series, animal study, letter to editor, review study, abstracts without full text, and studies that did not control group were excluded.Data extraction was performed by two independent authors (M-H.B and P-N.S) from the included articles using standard sheet form of Microsoft Excel. Any discrepancies were resolved through censuses or discussion with a three author (RT or NA).The following data were extracted: the first author’s name, year of publication, study design, study setting, participant characteristics (case and control groups), method of vitamin D assessment, number of patients in UL group and control group, and mean (SD) vitamin D levels in case and control group.The Newcastle-Ottawa Scale were used to assess the quality of included studies. This scale assesses using three aspects: “participant selection, comparability of study groups and assessment of outcome or exposure”. Study with a NOS scored ≥7 was considered as a high quality .p < 0.1 and I2 ≥ 50% was considered to be significant heterogeneity across the included studies [The overall pooled effect size was indicated by standardized mean differences (SMDs) using the Hedges method. A random effects model was used to combine SMDs with the DerSimonian and Laird method. The Cochran (Q) statistic and I2 test were applied to assess heterogeneity among the included studies. A Q test of  studies . Sensiti studies , 20. StaIn the initial electronic database searches, 864 reports were identified as being related to serum vitamin D levels and UL. After removing 430 duplicates, 434 reports were screened based on titles/abstracts. Of these, 68 full-text articles were retrieved to be assessed for eligibility based on the study’s inclusion criteria. Finally, 9 articles (4 case-control –24 and 4The nine included articles comprised 1730 participants (ranging from 64 to 432), numbering 835 and 895 in case and control groups, respectively. The included studies were published between 2013 , 25 and p < 0.001; I2 = 89.3%, p < 0.001) compared with the control group.The forest plots for serum vitamin D levels in UL patients and controls are presented in Fig. 2 = 89.3%, p < 0.001), subgroup analyses were conducted based on potential suspected variables, including study area (Europe vs. Africa vs. Asia) and quality score (high vs. low). It was found that heterogeneity was decreased based on study area. Moreover, the SMD values across the included studies from Asia  were higher than those from Europe  and Africa . Although the difference was not significant in Africa, classification of pooled SMD according to quality score did not affect heterogeneity across the included studies.Because of the existence of significant heterogeneity (Ip = 0.02) was found to be significantly related to pooled SMD, but sample size was not . Moreover, it was found that the pooled SMD increased over time . The lower and higher pooled SMD for each study have been summarized in Fig. p = 0.03) and Egger’s (p = 0.01) statistics indicated that there was significant evidence for publication bias in the meta-analyses assessing the serum vitamin D levels in women with UL. However, we used the non-parametric to correct the findings . The results showed no significant differences between before and after inclusion of the censored studies. The findings of quality assessment using the Newcastle-Ottawa scale are presented in Table The Begg’s  in the latter meta-analysis [This systematic review indicated that lower vitamin D levels are correlated with occurrence of UL; however, this finding needed to be confirmed in larger observational studies. Similarly, several studies have evaluated the relationship between vitamin D serum concentration and the odds of other types of gynecological benign and malignant tumors; however, their findings differed. A meta-analysis conducted by Yin et al. found a weak inverse association between serum vitamin D levels and the risk of ovarian cancer in women . AnotherSubgroup analysis in the current meta-analysis showed that heterogeneity decreased based on two factors: 1) different study area, and 2) publication year. The studies conducted in Africa represented a non-significant correlation between serum concentrations of vitamin D and the risk of UL, while this association remained statistically significant among Asian and European studies. This finding is in line with previous reports suggesting that a low vitamin D level was correlated with UL in white but not black populations, which may be attributable to differences in sun exposure, racial and individual factors , 31. It The other factor leading to heterogeneity in this meta-analysis was publication year. Subgroup analysis showed that the inverse correlation between serum vitamin D level and odds of UL became more prominent among studies that had been published in recent years. This could be attributed to improved vitamin D deficiency detection methods. Subsequently, the detection rates of vitamin D deficiency among women with or without UL have risen . The incThe current systematic review and meta-analysis and its publication bias assessment were limited by the relatively small number of eligible included studies. Therefore, the results should be interpreted with caution. However, results of the current meta-analysis could be used, for the first time, to shed light on the low vitamin D levels and increased risk of UL in women, while addressing limitations from previous observational studies. The authors strongly recommend that further studies with larger populations be conducted to further elucidate the current evidence.In summary, vitamin D deficiency is a possible risk factor for the occurrence of UL. Further studies with larger populations are definitely needed to evaluate vitamin D level in women with UL.


CC+TC . Though no statistical significance was achieved between HMGB1 rs1045411 polymorphism and cancer risk in other four genetic models , a trend of increased risk could be drawn. In the subgroup analysis by type of malignancy and ethnicity, no obvious difference was found in the tumour risk regarding the HMGB1 rs1045411 polymorphism amongst the cancer types except for breast cancer  and hepatocellular carcinoma , while rs1045411 polymorphism was positively associated with risks of cancer amongst Hans  rather than Caucasians . These results suggest that the HMGB1 rs1045411 polymorphism might be associated with increased cancer risk.The high-mobility group box protein 1 (HMGB1) rs1045411 polymorphism has been demonstrated to be associated with cancer risk in some studies. However, the results regarding this topic are inconsistent. A meta-analysis was applied to elucidate the association between the HMGB1 rs1045411 polymorphism and cancer risk. Ten relevant studies were subjected to our analysis, and pooled odds ratios (ORs) and 95% confidence intervals (CIs) were calculated. In total, of 3,918 cases and 5,296 controls were included in this study. The pooled ORs were calculated using a random-effects or fixed-effects model according to the heterogeneity. The pooled results revealed that TT genotype was significantly related to increased cancer risk in the comparisons of TT  Cancer is the most frequently diagnosed disease in the world, and the exact mechanisms of which remain unclear As a highly conserved nuclear protein, HMGB1 functions as a chromatin structural protein in the nucleus or pro-inflammatory cytokine extracellularly As HMGB1 rs1045411 polymorphism is closely correlated with altered binding of miR-505-5P in the 3'-UTR of mRNA transcripts, HMGB1 gene polymorphisms could emerge as a crucial player in cancer development through a post-transcriptional mechanism Articles published up to April 2020 from PubMed, Embase, Wanfang Data Knowledge Service Platform and China National Knowledge Infrastructure were searched using the terms HMGB1 polymorphisms, with no language restrictions. The studies included in this meta-analysis were original studies that reported odds ratios (ORs) with 95% confidence intervals (CIs) or provided useful data to calculate ORs and 95% CIs. In this meta-analysis, all studies were independently verified against the inclusion and exclusion criteria by two investigators. Useful information was extracted from each included study. Allele frequencies were calculated from the corresponding genotype distributions when they were not given . These processes were also carried out independently by two investigators (Xia and Tao).vs. C), heterozygote model (TC vs. CC), homozygote model (TT vs. CC), dominant model (TT vs. CC+TC) and recessive model (CC vs. TC + TT) by using STATA  and Review Manager Software , respectively. Additionally, 2χ-based Q statistics and 2I metrics were used to assess the heterogeneity between studies. When 2I<50%, a fixed-effects model was used to calculate the pooled OR; otherwise, a random-effects model was used.Pooled ORs and 95% CIs were calculated for allele contrast model (T p HWE < 0.05) in the control was also included in this study but was excluded from the sensitivity analysis.A database that included each paper's first author, country, sample size, genotyping method and other useable information was set up based on the information extracted from 10 relevant studies that met the inclusion criteria . The same result was also detected by excluding one study vs. CC+TC genotype   except for breast cancer  and hepatocellular carcinoma  in the dominant model  rather than Caucasians  to low  in the dominant model (TT vs. CC+TC). However, after eliminating the Wang et al. study p=0.578 for T vs. C; p=0.268 for TT vs. CC+TC; p=0.982 for CC vs. TC+TT; p=0.253 for TT vs. CC; p=0.583 for TC vs. CC).Sensitivity analysis was performed by removing one study at a time to assess the stability of these results. After the removal of Wang During the past few years, some studies have reported the association between HMGB1 polymorphisms  and different cancer types HMGB1 is a tumour-related gene vs. CC+TC. Though no evidence of association was found between rs1045411 polymorphism and cancer risk in some other genetic models , HMGB1 rs1045411 polymorphism still emerged as a risk factor for cancer. And there were trends towards an association with higher cancer susceptibility, which might become more distinct with a larger sample size. Since the statistically significant differences in cancer risk amongst carriers of this SNP variant compared with non-carriers could not be detected. Whether it was covered up by the counterbalance of its pleiotropic roles in cancer progression or reflected in diversified statistical strategies requires further investigation.In this study, the significant connection of increased cancer risk and the TT genotype was indicated in the comparisons of TT et al.vs. CC+TC while statistical significance was not achieved in any genetic model for all polymorphisms studied by Li XY et al.et al. study, in which the subjects were non-Asian, subgroup analysis based on ethnicity was also conducted. But surprisingly, our results demonstrated that rs1045411 polymorphism was positively associated with risks of cancer amongst Hans rather than Caucasians. Therefore, more studies of HMGB1 polymorphism in different ethnic backgrounds, such as, Caucasian, African and others, should be conducted in the future. During the sensitivity analysis, Wang et al. study et al. study, the pooled ORs were not distinctly changed, with stable results, which is consistent with previous study Compared with the former meta-analysis conducted by Kumari T In spite of the considerable efforts to explore the possible relationship between the HMGB1 rs1045411 polymorphism and cancer risk, some limitations of the current meta-analysis should be noted. First, although we tried to gather as much evidence as possible from the present literature, due to the lack of usable data, we could not perform a methodological assessment of certain studies. More studies must be pursued in the future. Second, potential publication bias might arise because several unpublished articles and abstracts were not considered because they were not available. Additionally, due to our language criteria, only studies published in English or Chinese were included; this language restriction might also lead to bias risk and affect the results. Finally, this meta-analysis may have been too underpowered to obtain original data from the included studies.Despite all the above limitations, by means of investigating associated cases of large samples and analyzing all five genetic models, our study provides new evidence that the HMGB1 rs1045411 polymorphism may be associated with increased cancer risk. However, due to the limitation of heterogeneity and sample size, the results of this study should be interpreted with caution and more work need to be done in the future to validate our findings.