In subgroup analysis, anemia was also associated with an increased risk of all-cause dementia (adjusted RR (aRR), 1.39 ), Alzheimer’s disease , and mild cognitive impairment ). Conclusion: This updated meta-analysis shows that patients with anemia appear to have a nearly 1.39-fold risk of developing OCI than those without anemia. The magnitude of this risk underscores the importance of improving anemia patients’ health outcomes, particularly in elderly patients.Background: Cognitive impairment is one of the most common, burdensome, and costly disorders in the elderly worldwide. The magnitude of the association between anemia and overall cognitive impairment (OCI) has not been established. Objective: We aimed to update and expand previous evidence of the association between anemia and the risk of OCI. Methods: We conducted an updated systematic review and meta-analysis. We searched electronic databases, including EMBASE, PubMed, and Web of Science for published observational studies and clinical trials between 1 January 1990 and 1 June 2020. We excluded articles that were in the form of a review, letter to editors, short reports, and studies with less than 50 participants. The Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA) guidelines were followed. We estimated summary risk ratios (RRs) with random effects. Results: A total of 20 studies, involving 6558 OCI patients were included. Anemia was significantly associated with an increased risk of OCI (adjusted RR (aRR) 1.39 (95% CI, 1.25–1.55;  The prevalence of cognitive impairment including dementia is increasing worldwide. It is estimated that more than 75 million people will have dementia by 2030 and 131.5 million by 2050 . PreviouNumerous epidemiological studies have highlighted an association between anemia and the increased risk of developing overall cognitive impairment (OCI) ,7. AlthoPeters et al.  reportedThe PRISMA  guidelines, which are based on the Cochrane Handbook for Systematic Reviews of Interventions, were used to conduct this study  14,15].,15.14,15We conducted an updated systematic review and meta-analysis. We searched electronic databases including EMBASE, PubMed, and Web of Science for published observational studies and randomized control trials between 1 January 1990 and 1 June 2020, with no language restrictions. We used the search terms “anemia” OR “iron deficiency”, and “dementia”, OR “Alzheimer”, OR “cognitive impairment”. We also searched the bibliography of retrieved articles to obtain relevant articles.Studies that assessed the association between anemia and OCI risk and matched all of the following criteria were included: (a) the primary outcome was OCI, (b) were observational or randomized control trials, (c) included at least 50 participants aged over 18 years old, (d) identified OCI patients using the standard diagnostic criteria (Feighner criteria or Research Diagnostic Criteria/DSM-III/DSM-III-R/DSM-IV/DSM-5/ICD-10), and (e) the diagnosis of anemia patients was based on WHO criteria, i.e., men: Hb < 13 g/dL, women: Hb < 12 g/dL, measured in blood samples.Studies were excluded if they were (a) published as a case report or case series with a low number of participants ; (b) published as editorial, letters, commentary, review articles, or conference abstracts; (c) provided insufficient information on OCI patient selection, confounding factors, adjusted variables, and effect size; or (d) used the same database with overlapping study durations.Required data were retrieved from selected articles by two independent authors (M.M.I. and S.A.). They developed a standard data collection form, checked all of the variables, and collected the needed information to calculate the risk ratio (RR). Afterwards, they retrieved and compiled the following information: (a) study characteristics: the author’s first name, publication year, country, study duration, study design; (b) demographic characteristics: the number of participants, number of male and female patients, and age; (c) the reference standard of dementia diagnosis, and number of dementia patients; and (d) the adjusted effect sizes such as hazard ratios (HRs) and odds ratios (ORs) with 95% confidence intervals (CIs). We considered the adjusted effect sizes because it helps to reduce potential bias.Study quality was assessed using the Newcastle Ottawa Scale for observational studies. The summary score was used to categorize the study quality into the following two groups: high (NOS score higher than 7) and low .2) were considered to examine whether there was significant heterogeneity in their effect sizes. Previous studies were followed to categorize the heterogeneity as low , medium , and high  [Comprehensive meta-analysis  software was used to analyze the data. An adjusted risk ratio ) with the corresponding 95% CI was calculated to show the magnitude of the association between anemia and OCI. However, the random effect model was used to calculate the heterogeneity between the studies. The Cochran Q statistic and inconsistency statistics (Ihan 50%) ,17. FinaThe articles search was conducted on electronic databases and garnered 987 articles. We reviewed the titles and abstracts of those articles and excluded 962 articles due to a lack of adherence to our pre-specified selection criteria. Afterwards, we screened the full text of the selected 25 articles and assessed the reference lists of relevant articles, retrieving no additional articles. We further excluded 6 articles that did not fulfil the inclusion criteria. Finally, 20 articles were considered in the meta-analysis ,34,35,36This updated meta-analysis comprised of 12 cohort studies and 8 case-control studies that are presented in The publication years ranged between 1997  and 2020We used the Newcastle Ottawa scale (NOS) to evaluate the methodological quality of the included studies, which is recommended by the Cochrane collaboration for nonrandomized studies. The NOS score included case-control and cohort studies ranging from 5 to 8, with a mean score of 7.3.n = 20, aRR, 1.39 . There was a moderate risk of heterogeneity among the studies   in patients with anemia. The risk of OCI was significantly higher in patients when compared to those who did not have any kind of anemia ( = 0.02) .p < 0.001)) .Among 15 studies, participants who had anemia were more likely to develop all-cause dementia compared to those who did not have any kind of anemia ) . There wp = 0.002)) .Moreover, six studies assessed the risk of Alzheimer’s disease (AD) in patients with or without anemia. The patients with anemia were significantly associated with an increased risk of AD ) . There wp = 0.02)). There was a moderate risk of heterogeneity among the studies   .We also evaluated the risk of overall cognitive impairment (OCI) in the patients with anemia by methodological quality, study design, and study locations. The subgroup analyses are presented in p < 0.001)). Nine studies with a low methodological quality assessed the risk of anemia and OCI risk. The overall risk of OCI was significantly higher in patients with anemia ).Eleven high methodological quality studies (based on NOS) examined the risk of OCI in patients with anemia in comparison to patients without anemia. The risk of OCI was higher in patients with anemia ). However, the risk of OCI in the patients with anemia was noticeably higher in a case-control study design ).In the cohort studies, patients with anemia were more likely to develop OCI when compared to those who did not have anemia ). In North America, four studies have evaluated the risk of OCI in patients with anemia. The pooled RR of OCI was 1.37  in patients with anemia. Furthermore, in Asia, nine studies have assessed the risk of OCI in patients with anemia. The pooled RR of OCI was 1.29  in the patients with anemia when compared to those who did not have any kind of anemia.Seven European studies demonstrated that patients with anemia were significantly associated with an increased risk of OCI . Our findings support previous evidence that anemia increased the risk of OCI -fold. Ou. The finSeveral biological mechanisms can be used to explain the association between anemia and OCI. First, the most convincing explanation would be related to the limited oxygenation of peripheral tissues due to a low hemoglobin concentration. Previous studies reported that anemia is linked to a decreased cerebral blood flow, leading to OCI ,38. A liConsidering the worldwide prevalence of anemia, and the significant association between anemia and OCI risk presented in our updated study, the follow-up of anemia patients with symptoms of MCI could have a considerable effect on public health. Although MCI is mainly asymptomatic, it provides a possibility to prevent the progression of AD or dementia and its complications. Previous studies reported that lifestyle changes could be more effective in preventing OCI, and early psychosocial intervention would be cost effective ,47. A raOur study has several strengths that need to be addressed. First, it is a comprehensive study that included 20 articles and showed an association between anemia and different types of cognitive impairment risk. Second, the heterogeneity of our study is moderate; therefore, the evidence provided here is less biased. Third, we have considered adjusted effect sizes while collecting our data from the retrieved articles; hence, there is less possibility of confounding factors because they adjusted the possible confounding factors in their studies.Our study also has several limitations. First, our study could not provide any findings based on the severity of anemia. Second, our study did not provide results based on the duration of anemia and the risk of OCI, although the included studies had at least a one-year follow-up period.The main purpose of this updated meta-analysis is to clarify the association between anemia and the risk of OCI. The findings of this study show that anemia is significantly associated with an increased risk of OCI. The early identification of a patient’s conditions and the proper management of anemia patients might contribute to the prevention of OCI risk. Future studies should focus on the biological mechanism of the association between anemia and OCI risk, as well as whether interventions designed to improve hemoglobin levels are effective in reducing OCI risk.


Meta-analyses typically consider multiple outcomes and report univariate effect sizes considered as independent. Multivariate meta-analysis (MVMA) incorporates outcome correlation and synthesises direct evidence and related outcome estimates within a single analysis. In a series of meta-analyses from the critically ill literature, the current study contrasts multiple univariate effect estimates and their precision with those derived from MVMA.A previous meta-epidemiological study was used to identify meta-analyses with either one or two secondary outcomes providing sufficient detail to structure bivariate or tri-variate MVMA, with mortality as primary outcome. Analysis was performed using a random effects model for both odds ratio (OR) and risk ratio (RR); borrowing of strength (BoS) between multivariate outcome estimates was reported. Estimate comparisons, β coefficients, standard errors (SE) and confidence interval (CI) width, univariate versus multivariate, were performed using Lin’s concordance correlation coefficient (CCC).n = 49) and RR (n = 48), there was substantial concordance (≥ 0.69) between estimates; but this was less so for tri-variate meta-analyses for both OR  and RR . A variable change in the multivariate precision of primary mortality outcome estimates compared with univariate was present for both bivariate and tri-variate meta-analyses and for metrics. For second outcomes, precision tended to decrease and CI width increase for bivariate meta-analyses, but was variable in the tri-variate. For third outcomes, precision increased and CI width decreased. In bivariate meta-analyses, OR coefficient significance reversal, univariate versus MVMA, occurred once for mortality and 6 cases for second outcomes. RR coefficient significance reversal occurred in 4 cases; 2 were discordant with OR. For tri-variate OR meta-analyses reversal of coefficient estimate significance occurred in two cases for mortality, nine cases for second and 7 cases for third outcomes. In RR meta-analyses significance reversals occurred for mortality in 2 cases, 6 cases for second and 3 cases for third; there were 7 discordances with OR. BoS was greater in trivariate MVMAs compared with bivariate and for OR versus RR.In bivariate meta-analyses, for OR (MVMA would appear to be the preferred solution to multiple univariate analyses; parameter significance changes may occur. Analytic metric appears to be a determinant. Meta-analyses typically consider more than one outcome, and the conventional approach is to report multiple univariate effect size estimates of these separate outcomes. Such an approach has two attendant consequences; it ignores the effect of outcome correlation upon individual estimates, assuming that they are independent , and engIn meta-analyses of interventions in the critically ill, where mortality is a common primary outcome, it would be expected that secondary outcomes such as intensive care unit (ICU) and hospital length of stay, infections and the requirement for mechanical ventilation would demonstrate substantial correlation , and witThe data for this study was abstracted from published studies and an Ethics clearance was not appropriate.A previous study  was usedTo facilitate rapid data processing over a large number of models, initial univariate meta-analytic point estimates and standard errors (SE) were computed within Stata™ V17 ; default(i)The two sets of univariate estimates were subsequently compared.(ii)Under persistent convergence difficulties of “mvmeta”, the model was refit assuming the overall correlation matrix was fixed and known, with values set equal to the estimates from either the BGFS or DFP algorithm using the “bscovariance” option of “mvmeta” z values for coefficient significance and very small p-values and CI width, such that the estimates were implausible.In the MVMA note was taken of very small β coefficient standard errors (SE) with consequent large (iv)To avoid the requirement for specific within study correlations , the “al(v)rth component of β under the uni- and multivariate models RV refers to relative variance [I2 for each outcome.The use of the “Riley” method  excludedDirect information and BoS between estimates were also reported , 10, 11,variance . An equianalyses , albeit analyses  using “Panalyses .The use Subsequently, both univariate and multivariate outcomes were estimated using the user written Stata command “mvmeta” Estimate differences average and standard deviation (SD); univariate versus MVMA.(ii)95% (Bland and Altman) limits of agreement (LOA)(iii)An F test (Bradley-Blackwood) of equality of means and variances; non-significance implies concordance.Agreement or otherwise between univariate and multivariate estimation results was undertaken using Lin’s concordance correlation coefficient (CCC) via the user written Stata command “concord” . The CCCBoxplots  were usep < 0.05.Statistical significance was ascribed at 2 index, of ≥ 50 and ≥ 75% was found in mortality, second and third outcomes in 12, 31 and 50%, and 0, 16 and 23%, respectively. Of the 49 mortality meta-analyses, five [p < 0.05); for the second outcome, five [p < 0.05) and the increased frequency of “query” for contour-enhanced funnel plot assessment in second and third outcomes versus mortality outcome  was noted and may be a function of the power of the test  number of RCT per meta-analysis for the primary mortality outcome was 13; for the second outcomes, 8; see Tables Z values. Concordance estimate analysis is given in Table In the MVMA, 49 meta-analyses were considered and there were no instances of “large” The bivariate distributions of BoS are displayed in Fig. The bivariate total marginal correlations, mortality vs second outcome, are shown in Fig. Z values [For the 30 meta-analyses, the median  number of studies per meta-analysis for the primary mortality outcome was 13; for the second outcome 8; and the third 7. In only 2 meta-analyses , 49 was Z values , 77, 78 Concordance estimate analysis is given in Table Z values [Of the 30 tri-variate meta-analyses, there was one instance of complete convergence failure  and seveZ values , 73, 78 Z values , 79, 80.Z values , 62, 67.Z values , 62 withZ values , 79, conZ values , 57, 77;Z values , 60, 67 The tri-variate distributions of BoS are displayed using boxplots in Fig. The tri-variate total marginal correlations for both OR (left) and RR (right)are shown via boxplots in Fig. It is easy to forget that the MVMA approach has a long history dating back to at least 1993  and has ith study is a function of the within-study variance matrix One of the principal attractions of MVMA is estimation of the BoS between parameters, well demonstrated in Fig. le sizes . The magle sizes , which ile sizes . Any nexle sizes . The notle sizes , albeit le sizes ; in partle sizes . Perhapsle sizes , MVMA hale sizes , 88.Computationally, MVMA requires both within- and between-study correlations and the former are typically not known and are likely not to be available, especially in higher order (trivariate) models . Riley p = 0.999 , and the -0.986  in Table f Riley  would be unwieldy and potentially uninterpretable. This being said, the recommendation of Riley et al. in the landmark 2008 paper , was tha studies , 77, 78  studies , 73, 78,The current study has adopted a workable and practical solution to the particular requirements of MVMA. Future studies should replicate or otherwise the findings in this paper using the “alternate” meta-analytic model of Riley and consider meta-analyses from specific disciplines, moving beyond the bivariate data structure to encompass “…three or more end points…” , albeit MVMA elucidates the structure and correlation between multiple reported outcomes in univariate meta-analyses and resolves outcome reporting bias. Change in estimate precision and CI width with MVMA appeared context dependent. The BoS entailed in this technique may be quantified and change of parameter significance may be a consequence. MVMA is a feasible solution to the meta-analytic estimation of multiple univariate effects.


Background: The association between Parkinson's disease (PD) risk and alcohol intake is a controversial topic.Objectives: To systematically assess the association between PD risk and alcohol intake.Methods: PubMed and Embase databases were searched for eligible studies with prospective design on PD risk and alcohol intake. A meta-analysis with a random-effects model and dose-response analysis was performed. Relative risk ratios (RRs) with 95% CIs were calculated.Results: Eleven prospective studies were included. Overall, a higher intake of alcohol was inversely associated with PD risk . Significant differences existed between the specific types of alcoholic beverages and geographic area. Specifically, a significant association existed for beer  and studies conducted in Asia . Dose-response analysis indicated a nonlinear relationship between PD risk and alcohol exposure. No evidence for publication bias was detected.Conclusions: In summary, our meta-analysis suggests that alcohol consumption was associated with a decreased risk of PD, with a nearly U-shaped association. Future studies are warranted to clarify the question of a specific type of alcoholic beverage-dependent association, geographic area effect, and possible threshold effects regarding both the adverse and beneficial effects of alcohol. Parkinson's disease (PD) characterized by three cardinal motor impairments: bradykinesia, rigidity, and tremor, is the second most common neurodegenerative disorder after Alzheimer's disease , 2. The Alcohol consumption is another important lifestyle exposure following cigarette smoking and coffee consumption and is associated with multiple health outcomes, namely, cancers, neuropsychiatric disorders, and cardiovascular diseases , 6. Somevia mitochondrial lipid peroxidation and DNA or protein damage , and reference lists of relevant original papers, meta-analyses, and review articles. The following search terms were used: “{Parkinson[Title/Abstract] AND }”. There were no publication years or language restrictions, but the search was limited to human studies.We included prospective cohort studies or case-control nested in prospective cohort studies that reported an association between alcohol consumption and PD risk and provided adjusted relative risks (RRs) and 95% CIs, or other data to estimate the association and risk. If multiple articles were based on the same case series, only the study with the longest follow-up period or largest sample size was included.The last name of the first author, publication year, country, sample size, identification of PD, exposure variables of interest , assessment of alcohol exposure, the adjusted RRs with corresponding 95% CIs in the multivariable analysis, and potential confounders considered were extracted from each eligible study.I2 test .I2 = 37.3%), 0.88 , and 0.98  for Asian, American, and European studies, respectively. No significant association between alcohol consumption and PD risk was demonstrated when a stratified analysis was based on sex. The pooled RRs were 0.81  in male studies and 0.79  in female studies. Considering subgroups of specific types of alcoholic beverages, the pooled results showed a RR of 0.78  for beer, 0.98  for liquor, and 0.95  for wine.Significant differences existed based on stratified analyses by geographic area. The pooled RRs were 0.66 .Sensitivity analyses suggested that our results were not stable. Exclusion of the national program of 6,795,816 Koreans aged ≥ 40 years  did not I2 = 8.1%).Another sensitivity analysis was restricted to those studies that provided risk estimates adjusted for smoking and coffee –27, 29. p = 0.697), no evidence of publication bias was found.As suggested by Begg's funnel plots  and Eggep = 0.0018) between alcohol consumption and the risk of PD was confirmed  , but theIn the dose-response analysis, a nearly U-shaped association between PD risk and alcohol consumption was identified, with the strongest inverse association observed for consumption of 26–35 g/day and no further reduction in risk of PD with increasing consumption of alcohol over this amount. Similarly, a 2019 systematic review of meta-analyses covering 34 risk factors concluded that light or moderate alcohol consumption was associated with a decreased risk of Alzheimer's disease . RegardiSeveral meta-analyses of the relationship between alcohol consumption and PD risk have been published , 43–45. The main strengths of this meta-analysis included a large number of prospective studies with large sample sizes, estimation of total and specific types of alcoholic beverages, and dose-response analysis with the one-stage method. Also, some potential limitations need to be addressed. First, significant heterogeneity was identified. As indicated by the results of sensitivity and subgroup analyses, confounding factors may have accounted for, in part, heterogeneity. Second, because of the nature of observational design, a residual confounding effect could not be neglected. Third, we noted that the potential measurement bias of alcohol exposure may twist the true relationship between alcohol consumption and PD risk. For example, alcohol exposure was only measured in the past 12 months before baseline, but not lifetime , and thuIn conclusion, our meta-analysis suggested that alcohol consumption was associated with a decreased risk of PD (a nearly U-shaped association). Due to the above-listed limitations, the need for caution regarding our findings is warranted. The specific type of alcoholic beverage-dependent association, geographic area effect, and possible alcohol threshold for adverse and beneficial effects remain to be clarified in future studies.The original contributions presented in the study are included in the article/supplementary material, further inquiries can be directed to the corresponding author/s.CS and HT designed the research and wrote the first draft. CS and XW performed the second literature search. CS, HT, XW, and PW extracted and confirmed the data. CS performed the statistical analysis. CS, HT, XW, PW, NW, and JH reviewed and rewrote the study. All authors contributed to the article and approved the submitted version.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


Recent genetic association studies showed that there are contradictory results on the relationship between vitamin D receptor (VDR) gene polymorphisms and type 1 diabetes mellitus (T1DM) risk in children. The purpose of this systematic review is to collect the currently available evidence to evaluate the relationship between VDR gene polymorphisms and the risk of T1DM in children.Such medical databases as Wanfang Data, Chinese Biomedical Literature Database, Chinese National Knowledge Infrastructure, Chongqing VIP Chinese Science and Technology Periodical Database, PubMed, Embase, and Web of Science were extensively searched for relevant literatures published before June 2021 with the focus on the relationship between VDR gene polymorphisms and the risk of T1DM in children. The risk of bias was evaluated as per the Newcastle-Ottawa Scale by 2 independent researchers. Meta-analysis was performed to quantify the relationship between VDR gene polymorphisms and T1DM risk in children.The results of this meta-analysis would be submitted to a peer-reviewed journal for publication.The relationship between VDR gene polymorphisms and T1DM risk in children is explored via this meta-analysis.Ethical approval was not required for this study. The systematic review will be published in a peer-reviewed journal, presented at conferences, and shared on social media platforms.DOI 10.17605/OSF.IO/Q8XA5. As is known to all, T1DM is a multi-factorial autoimmune disease induced by the interaction of genetic and environmental factors.Type 1 diabetes mellitus (T1DM) refers to an autoimmune disease that is mainly mediated by T cell immunity, and its pathogenesis is very complex.–8 Besides, it has been suggested in some studies that vitamin D deficiency is associated with the autoimmune destruction of β cells and the onset of T1DM induced by loss of immune regulation.,10 Vitamin D has a protective effect on T1DM. Dietary vitamin D intake in early childhood could reduce the risk of T1DM.,13 In addition, vitamin D supplementation during pregnancy may prevent the development of islet cell autoantibodies in newborns. Vitamin D could exert its actions via a nuclear vitamin D receptor (VDR). Therefore, the VDR gene can be considered a candidate/predisposing gene for T1DM.In recent years, many genes related to T1DM have received extensive attention. It has been demonstrated in abundant studies that vitamin D may exert significant impacts on the pathogenesis of T1DM through vitamin D receptor (VDR) gene.–22 Therefore, the purpose of this study is to further verify the relationship between VDR gene polymorphism and the risk of T1DM in children by collecting relevant literature.In recent years, it has been demonstrated in related studies that VDR gene polymorphism may be associated with the genetic predisposition to T1DM. However, the relationship between VDR gene polymorphism and the risk of T1DM in children has not been confirmed yet, and there are contradictory results on such relationship.22.1The protocol of this review was registered in OSF (OSF registration number: DOI 10.17605/OSF.IO/Q8XA5). It was reported in accordance with the statement guidelines of preferred reporting items for systematic reviews and meta-analyzes protocol.2.2All eligible studies included in this study shall fulfill these inclusion criteria:1.studies with the focus on the relationship between VDR gene polymorphisms and the risk of T1DM;2.participants with the age less than 18 years old;3.studies with sufficient data to calculate the odds ratio (OR) and its 95% confidence intervals (CIs);4.case-control or cohort studies.2.3The exclusion criteria were: letters, case reports, meta-analysis, review papers, papers without a control group, literature with abstract only, and literature without detailed genotype data.2.4Medical databases  were systematically searched for papers published before June 2021 with respect to the relationship between VDR gene polymorphisms and the risk of T1DM in children. The search strategy for PubMed is presented in Table 2.52.5.1Two researchers independently screened the literature for data extraction and cross-checking. If there are differences, the discussion or consultation with a third party would be required. During the screening process, the title was read at first in an attempt to exclude the obviously irrelevant literature, followed by the abstract and the full text to determine its eligibility. The flowchart is presented in Figure 2.5.2The following data were independently extracted by 2 researchers: first author, year of publication, country of origin, ethnicity, number of cases and controls, genotype frequency, source of controls, age, genotyping method, sample size, and Hardy-Weinberg equilibrium (HWE).2.5.3 Those with a score of 6 would be considered to be of high quality.The quality assessment of the included literature was investigated based on Newcastle-Ottawa Scale.2.5.4In case of any missing data in a literature, please contact the newsletter author or the first author by email for accurate data. If there is a failure in the data request, descriptive analysis, instead of meta-analysis, shall be conducted.2.5.5I2 test and the Q test. If the I2 value is less than 50%, the fixed-effect model would be adopted. If the I2 value is more than 50%, a random-effects model would be adopted. All of the statistical analyzes were conducted by the STATA 16.0 , and the P values were two-sided.In each included literature, HWE was examined to assess bias in genotype distribution. Besides, odds ratios (ORs) and 95% confidence intervals (95% CIs) were calculated for analyzes of the VDR gene polymorphisms and T1DM risk in children. In addition, the pooled ORs and 95% CIs were calculated in 5 genetic models, namely allele model (T vs C), heterozygote model (TC vs CC), homozygote model (TT vs CC), dominant model (TT + TC vs CC), and recessive model (TT vs TC + CC). Moreover, the heterogeneity was calculated with the Chi-Squared-based 2.5.6According to ethnicity, source of controls and genotyping method, the subgroup analysis was performed on the relationship between VDR gene polymorphisms and the risk of T1DM in children.2.5.7The eligible papers were sequentially removed in order to perform the sensitivity analysis.2.5.8,27Potential publication bias was estimated by Egger linear regression test, and Begg test was employed to estimate the funnel plot asymmetry.2.5.9The content of this article does not involve moral approval or ethical review and would be presented in print or at relevant conferences.3 VDR gene polymorphisms have been associated with susceptibility to a variety of autoimmune diseases over the past few decades.–31 In recent years, the relationship between VDR gene polymorphisms and T1DM has been investigated in several studies around the world. However, there is no meta-analysis of VDR polymorphism and the risk of T1DM in children. Meanwhile, the relationship between VDR gene polymorphisms and the risk of T1DM in children reported in the existing literature is inconsistent. In addition, the risk of T1DM is increasing due to vitamin D deficiency from year to year.,33 Therefore, a comprehensive meta-analysis may be the optimal way to address these problems.The VDR gene located at 12q13 consists of 9 exons and 8 introns, with multiple restriction endonuclease restriction sites. The common loci include BSM I, Apai, Taqi, and Foki.Conceptualization: Renjun Li, Yalin Ran.Data curation: Suyuan Hu.Funding acquisition: Renjun Li.Formal analysis: Xiaohua Yu.Investigation: Suyuan Hu.Methodology: Yalin Ran.Project administration: Renjun Li.Resources: Xiaohua Yu.Software: Xiaohua Yu.Supervision: Renjun Li.Validation: Yalin Ran.Visualization and software: Yalin Ran.Writing – original draft: Yalin Ran and Renjun Li.Writing – review & editing: Yalin Ran and Renjun Li.


Sarcopenia is a progressive generalized skeletal muscle disorder, which may increase the risk of osteopenia. The aim of this study was to systematically review studies on the association between sarcopenia and osteopenia by pooled analysis. The PubMed and Embase databases were searched from inception to October 2020 for studies focusing on the association between sarcopenia and osteopenia. Two reviewers independently extracted data and assessed study quality. A pooled analysis was performed to calculate odds ratios (ORs) and 95% confidence intervals (CIs) using random-effects models. Subgroup analysis was conducted to explore the source of heterogeneity and the stability of outcome. A total of 25 independent studies involving 47,744 participants fulfilled the inclusion criteria. Sarcopenia significantly increased the risk of osteopenia ; Sensitivity analyses indicated the outcome was stable. Subgroup analyses showed that sarcopenia significantly increased osteopenia risk in each subgroup. No evidence of publication bias among the studies existed. In this study, our findings showed that sarcopenia significantly increased the risk of osteopenia. Thus, we suggest that sarcopenia can be a predictor of osteopenia risk. Sarcopenia is a muscle disorder involving depletion of skeletal muscle mass with a risk of adverse outcomes, such as physical disability and poor quality of life , is assoSome studies have indicated that osteopenia is associated with an increased risk of sarcopenia , 7–16. HThis analysis was conducted in accordance with the Meta-analysis of Observational Studies in Epidemiology guidelines and the Preferred Reporting Items for Systematic Reviews and Meta-analyses standards , 21.We systematically searched PubMed and Embase  for studies conducted on the association between sarcopenia and osteopenia. Our core search keywords are as follows: “sarcopenia”, “osteopenia”, and “low bone mineral density”. Two researchers (TZW and ZY) independently reviewed the titles and abstracts of the studies retrieved from the databases. We included studies that reported sufficient data on sarcopenia increasing osteopenia risk, such as risk estimates  with 95% confidence intervals (CIs). The studies were assessed based on the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) statement [2 statistics were used to evaluate the statistical heterogeneity [2 value was > 50%, the data were considered to be heterogeneous, and a random-effects model [The data extraction and analysis were similar as our previous studies . The fologeneity . When thts model  was applts model , 26. STAA total of 1727 studies were retrieved from PubMed and Embase, after removing duplicates, 1475 were identified. After screening the title and abstract, 288 necessitated reading of the full article. Ultimately, 20 studies , 27–32 iheterogeneity = 0.000, I2 = 86.1%) (2 >50%)  . Substan(2 >50%) ; however(2 >50%) . Subgrou(2 >50%) . The Beg(2 >50%) .Osteopenia is characterized by loss of bone mass, reduced bone mineral density, which will develop into osteoporosis, may further lead to heavy economic and social burdens. Sarcopenia is one of the most important contributing factors related to osteopenia. Muscle and bone are interconnected biochemically and biomechanically, and they can mutually influence each other , 34. SarThis study has several limitations. First, the study design included cross-sectional studies, case-control studies, and others, which might have led to substantial heterogeneity. Second, some trials did not provide the data as estimates with 95% CIs, so we had to calculate these values according to specific numbers of participants, which might have influenced the accuracy of the results. Third, different studies used different diagnostic criteria for sarcopenia, which might have slightly affected the results. Therefore, the results should be interpreted with caution.In this study, our findings showed that sarcopenia significantly increases osteopenia risk. However, care should be taken when interpreting the findings, and large randomized controlled trials are still needed to further specify the association between osteopenia and sarcopenia.S1 Table(PDF)Click here for additional data file.S1 Checklist(PDF)Click here for additional data file.


Adipocytes and their products, adipocytokines, play important roles in the generation and development of multiple myeloma (MM). Studies have demonstrated some adipocytokines to be associated with MM, although those results are controversial. Therefore, we conducted a meta-analysis to verify the association of adipocytokines with MM.We performed a systematic retrieval of literature published prior to 26 October 2021. Standardized mean difference (SMD) with a 95% confidence interval (CI) was calculated to evaluate pooled effects. Subgroup analysis and meta-regression analysis were conducted to detect sources of heterogeneity. Sensitivity analysis was performed to evaluate the stability of the study. Publication bias was assessed by funnel plots and Egger’s linear regression test.Ten eligible studies with 1269 MM patients and 2158 controls were included. The pooled analyses indicated that circulating leptin levels of MM patients were significantly higher than control levels , while the circulating adiponectin levels in MM patients were significantly lower than controls with a pooled SMD of -0.49 (95%CI: -0.78 to -0.20). The difference of circulating resistin levels were not significant between MM patients and controls . Subgroup analysis and meta-regression analysis found that sample size, age, and sex were possible sources of heterogeneity. Sensitivity analysis demonstrated our pooled results to be stable.Decreased circulating adiponectin and increased leptin levels were associated with the occurrence and development of MM. Adiponectin and leptin may be potential biomarkers and therapeutic targets for MM.The online version contains supplementary material available at 10.1186/s12902-022-00939-2. Multiple myeloma is a neoplastic plasma cell disease characterized by clonal proliferation of malignant mature B cells in bone marrow. Rapid proliferation of myeloma cells results in the accumulation of monoclonal proteins in the blood and urine. MM is the second most frequent hematological malignancy, accounting for 1.79% of all new cancer cases and 2.11% of all cancer deaths worldwide .The bone marrow microenvironment plays an important role in the generation and progression of myeloma. The major component of BMME is the bone marrow adipose tissue (BMAT), which comprises 50-70% of BM volume and accounts for 5-10% of total fat . It is wLeptin is a 16-kDa peptide hormone, found at increased levels in overweight individuals, regulates energy balance and suppresses appetite via the hypothalamus. Leptin is a pro-inflammatory and pro-angiogenic factor that regulates cell proliferation and immune responsiveness via paracrine signaling . ConversYoon et al. reported that circulating adiponectin and leptin were significantly associated with risks of obesity-related cancers, such as endometrial cancer and breast cancer . FurtherWe followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2009 checklist . This stTwo investigators independently conducted a systematic literature retrieval in PubMed, Web of Science, and EMBASE to identify relevant studies prior to 26 October 2021. The search terms were as follows; “multiple myeloma” or “plasma cell neoplasm” combined with “adipokines” OR “adiponectin” OR “leptin” OR “resistin” OR “visfatin”. In addition, we scrutinized the references of relevant articles and reviews manually to add additional studies to the meta-analysis. The detailed search strategy is shown in Publications were eligible if they met all of the following inclusion criteria: (1) case-control or cohort studies published as original articles, as well as other original articles containing relevant raw data; (2) studies evaluating associations among circulating adipocytokine levels and myeloma; (3) all patients were definitely diagnosed multiple myeloma; (4) sufficient data were available for the estimation of SMD with 95% CI.The exclusion criteria were the following: (1) review papers, opinions, editorials, nonoriginal studies, interim analysis, pooled analysis, and studies published in multiple journals that were based on the same data; (2) conference abstracts that lacked baseline information and could not conduct methodological evaluations; (3) lack of the control group.Two authors screened the relevant publications based on the above criteria. Discrepancies were resolved by discussion or consultation with another investigator.We performed data extraction based on Meta-analysis of Observational Studies in Epidemiology (MOOSE) checklist . VariablThe quality of the selected studies was evaluated by two independent reviewers based on the Newcastle-Ottawa Scale (NOS) . DisagreSMD with 95% CI was calculated based on the sample size, mean, and standard deviation of all eligible studies to assess the association between circulating concentration of adipocytokines and multiple myeloma. However, a few studies only reported median, quartiles, or maximum, and minimum. For those studies, we contacted the corresponding author for detailed information or estimated the sample mean and standard deviation from the available data by statistical methods , 22.Q test and I2 statistics. Cut-offs of P < 0.10 and I2 > 50% were considered to be statistically significant heterogeneity. The pooled SMD was calculated by the fixed-effect model (FEM) if the heterogeneity was not significant. Otherwise, a random-effect model (REM) was calculated. Subgroup analysis and meta-regression analysis were conducted to detect underlying sources of heterogeneity. Sensitivity analysis was performed to confirm the effects reported by individual studies and to evaluate the stability of meta-analysis results.Heterogeneity was assessed by the chi-squared-based P < 0.05 indicated statistically significant publication bias. With publication bias, Trim and Fill analysis was used to assess the number of missing studies. The pooled results were re-evaluated with the addition of missing studies [Publication bias was assessed qualitatively by use of funnel plots and quantitatively by Egger’s linear regression test. Visual detection of asymmetry in funnel plots and a  studies .All data analysis was conducted with StataMP 14 software and Review Manager 5.4 software.As shown in Fig. 2). All studies were case-control studies, with two nested case-control studies. A total of four different adipocytokines were evaluated. Seven studies included leptin [The primary characteristics of all included studies are summarized in Table d leptin , 30, 32,d leptin , 30, 32,d leptin , 33, andDetailed statistics for each adipocytokine are presented in Table z = 3.14, P = 0.002). However, statistically significant heterogeneity was observed in these studies .Five out of seven studies found MM patients to have significantly increased circulating leptin concentrations compared to controls. As shown in Fig. z = 3.27, P = 0.001; Fig. P < 0.0001, I2 = 84%, 95%CI: 0.6507 to 0.9304).For adiponectin levels, significant reductions in four of five studies were found in patients with MM. 938 MM patients and 1715 controls were analyzed for adiponectin. Circulating adiponectin levels in MM patients were significantly lower than in controls with a pooled SMD of -0.49 . Significant heterogeneity was found among these five studies  was found to be a significant contributing factor for between-study variance, while sample size (P = 0.262), age (P = 0.072), region (P = 0.070), BMI (P = 0.246), study design (P = 0.380), and NOS quality (P = 0.642) did not significantly affect study variance.Subgroup analysis based on sample size, age, and BMI was performed to identify potential sources of leptin heterogeneity Fig. . First, P = 0.016) to be a significant contributing factor to heterogeneity, but sample size (P = 0.215), age (P = 0.440), region (P = 0.094), BMI (P = 0.197), sex (P = 0.919) and NOS quality (P = 0.665) were not.Subgroup analysis of adiponectin is summarized in Fig. Studies were individually excluded to evaluate the robustness of the results and the influence of each study on pooled SMD Fig.  and S4. P = 0.061). For leptin, evident asymmetry was observed in the funnel plot , indicating the significant publication bias. Therefore, Trim and Fill analysis was used to evaluate the effect of publication bias on pooled results. Analysis showed the imputed pooled results were identical to original results . No missing studies were added to the filled funnel plot . Thus, we suggest that future studies focused on adipocytokine should adjust for BMI, or make strict inclusion and exclusion criteria to avoid not cancer-related impact on adipocytokine levels.There are several limitations in this meta-analysis. First, all included studies were case-control, with inherent selection, information, and confounding bias. Second, data regarding myeloma types, cancer stage, risk stratification, past medical history, and lifestyle was not available. Third, the sample sizes of few eligible studies are small.In conclusion, results herein suggest that low circulating levels of adiponectin and high circulating levels of leptin are associated with an increased risk for MM, indicating that adiponectin and leptin may be potential biomarkers.Additional file 1: Table S1. Newcastle-Ottawa scale for the assessment of included studies. Table S2. Using GARDE to assess the credibility of the current evidence. Table S3. The pooled SMD and 95%CI of the eligible studies of leptin through sensitivity analysis. Table S4. The pooled SMD and 95%CI of the eligible studies of adiponectin through sensitivity analysis. Figure S1. Funnel plot for publication bias of the association between serum leptin levels and myeloma. Evident asymmetry was observed in the funnel plot, and the Egger’s test result was P=0.013, which indicates the existence of significant publication bias. Figure S2. Filled Funnel plot of the association between serum leptin levels and myeloma by the use of Trim and Fill method. Trim and Fill analysis was used to evaluate the effect of publication bias on the pooled result. The analysis showed the imputed pooled result was identical to our original result . No missing studies were added to the filled funnel plot.


This study compared the results of meta-analysis with and without adjustment for the healthy worker effect on the association between working in the semiconductor industry and cancer mortality.x/SMRnot x) were calculated using observed and expected counts for the specific cause of interest  and the observed and expected counts for all other causes of mortality. Then, the rSMR results were combined to estimate the summary rSMRs (95% CIs).Six studies that reported standardized mortality ratios (SMRs) for cancers were selected for meta-analysis. Using a random-effects model, the SMR results from each study were combined for all cancers and leukemias to estimate the summary SMRs . To adjust for the healthy worker effect, the relative standardized mortality ratio  whereas the summary rSMR was 1.38 . The summary SMR for leukemia was 0.88 , and the summary rSMR was 1.88 .Our results suggest that adjustment for the healthy worker effect  may be useful in meta-analyses of cohort studies reporting SMRs. Semiconductor manufacturing has become the core of the entire microelectronics industry. The concern of the semiconductor industry for the health of its workers includes the cancer risk from recirculated air for particle control and suspected carcinogens including ionizing radiation, asbestos, arsenic and arsenical compounds, chromium compounds, sulfuric acid mist, ultraviolet light, trichloroethylene, carbon tetrachloride, nickel, and antimony trioxide ,2. The sThe relevance of epidemiological research reporting the standardized mortality ratios (SMRs) for workers in the semiconductor industry is often limited by the healthy worker effect. The healthy worker effect refers to the consistent tendency for actively employed people to have more favorable mortality outcomes than the population at large, and includes the effects of initial hiring into the workforce  and those of continuing employment  . BecauseIn this meta-analysis of retrospective cohort studies reporting SMRs for workers in the semiconductor industry, the rSMR method was explored to adjust for the healthy worker effect and the summarized results with and without this method were compared. The purpose of this study was to compare summary SMRs and summary rSMRs in a meta-analysis of SMR studies among workers in the semiconductor industry.A literature search was conducted for studies on mortality for workers in the semiconductor industry using the PubMed, EMBASE, and JSTOR databases up to November 2020. Search terms were semiconductor AND  AND cancer. The number of potentially relevant studies  identified for further review was 4 from JSTOR, 9 from EMBASE, and 10 from PubMed. After excluding duplicated studies, potentially relevant studies were identified from each database (n= 11). A total of 6 studies were selected after excluding again the studies without SMR data (n= 5) and adding one study through reference search (n= 1) . When th2 and statistical significance evaluated by the Cochrane Q-test. Publication bias was assessed using the Egger test. All meta-analyses were conducted using the meta package in R version 4.0.0.The data were combined using a random-effects model to estimate the summary SMR  considering significant inter-study heterogeneity. Heterogeneity among the studies was evaluated as Ix/SMRnot x) was calculated for all cancers and leukemias by comparing the ratio between observed and expected counts for the specific cause of mortality  and the ratio between observed and expected counts for the other causes of mortality . The reference values were calculated as observed (or expected) counts for all causes minus observed (or expected) values for the specific cause. This measure is in essence the ratio of 2 Poisson variables for mutually exclusive events. When Ototal and Etotal is defined as Ox+Oy, and Ex+Ey, respectively, the rSMR and its CI  are expressed as follows:By applying methods described in the report from the Agricultural Health Study , the rSMwhereandIn the equation, F denotes an F-inverse function to obtain a point  from an F-distribution given 2 degrees of freedom and a p-value (α/2). When both the unadjusted SMR and adjusted SMR were reported, it was decided to adopt the adjusted SMR rather than the unadjusted SMR. Observed counts  and SMR values were retrieved from each study selected, and expected counts were calculated using SMRs and observed counts. For several studies, it was necessary to restrict the results to men or women.heterogeneity and peggers.test were also included in The same meta-analytic procedures were adopted to estimate the summary rSMR and 95% CI for all cancers and leukemias to compare with the corresponding summary SMR and 95% CI. Forest plots were then used to collectively show the SMRs and the rSMRs for comparison . AlthougThe meta-analysis of those 6 studies included 364,263 men and women workers in total with a follow-up of more than 4.6 million person-years. The SMR values for all causes ranged from 0.27 to 0.80, suggesting strong healthy worker effects for the selected studies . Howeverheterogeneity= 0.001) and no significant publication bias was found (peggers.test= 0.18).The summary SMR and 95% CI for the association between working in the semiconductor industry and all cancers was 0.70 . Inter-study heterogeneity was 69% (pheterogeneity= 0.001) and no significant publication bias was found (peggers.test= 0.75).When the rSMRs were summarized, a significant positive association was found  . Thus, a2: 7% and pheterogeneity= 0.38; peggers.test= 0.44).For leukemia, the summary SMR (95% CI) for the association between working in the semiconductor industry and leukemia risk was 0.88 . No significant interstudy heterogeneity or publication bias was found (Iheterogeneity= 0.02) and no significant publication bias was found (peggers.test= 0.25).When the rSMRs were summarized, a significant positive association was found  . Thus, aWe adopted the rSMR method to adjust for the healthy worker effect in a meta-analysis of 6 retrospective cohort studies which evaluated whether semiconductor industry workers are at increased risk of cancer mortality. After adjusting for the healthy worker effect, we found that there were significant associations between working in the semiconductor industry and increased cancer mortality.It should be noted that previous studies on mortality or incidence of cancer among semiconductor workers have mentioned the healthy worker effect as one of their limitations ,9,11,14.Many different types of statistical adjustment for the healthy worker effect including the rSMR analysis had already been developed: for example, setting a minimum follow-up duration, using a lag time (at least 2-3 years) to adjust for the healthy worker effect, G-estimation methods, and others -26. AmonGiven that the results of the SMR and rSMR analyses were very much in contrast, the results from the SMR analysis should not be regarded as conclusive. According to Lee et al. , the oveIn this study, limitations of the meta-analysis include the small number of studies analyzed and the inherent weakness of an SMR study. The inter-study heterogeneity of all cancers can be attributed in part to the types of cancer included in each study and whether the reported data were classified by gender. Therefore, a random-effect model was used to estimate summary measures. The SMR studies did not control for potential confounding factors and the follow-up period tended to be short. Moreover, the rSMR method cannot use the standardized incidence ratio (SIR), which may be a better measure of risk than SMR; therefore, studies reporting only the SIR were excluded from our study.Despite these limitations, our results show that the rSMR method may be useful in determining summary risk adjusted for the healthy worker effect through meta-analysis.In conclusion, our results suggest that the healthy worker effect may be partly adjusted by summarizing the rSMRs in a meta-analysis of cohort studies reporting SMRs. Other types of statistical adjustment could be evaluated for application in future epidemiological study designs.


The association of obesity with colorectal cancer (CRC) may vary depending on metabolic status.This meta-analysis aimed to investigate the combined impacts of obesity and metabolic status on CRC risk.The Scopus, PubMed, and web of sciences databases were systematically searched up to Jun 2021 to find all eligible publications examining CRC risk in individuals with metabolically unhealthy normal-weight (MUHNW), metabolically healthy obesity (MHO), and metabolically unhealthy obesity (MUHO) phenotypes.A total of 7 cohort studies with a total of 759,066 participants were included in this meta-analysis. Compared with healthy normal-weight people, MUHNW, MHO, and MUHO individuals indicated an increased risk for CRC with a pooled odds ratio of 1.19 (95% CI = 1.09–1.31) in MUHNW, 1.14 (95% CI = 1.06–1.22) in MHO, and 1.24 (95% CI = 1.19–1.29) in MUHO subjects. When analyses were stratified based on gender, associations remained significant for males. However, the elevated risk of CRC associated with MHO and MUHO was not significant in female participants.The individuals with metabolic abnormality, although at a normal weight, have an increased risk for CRC. Moreover, obesity is associated with CRC irrespective of metabolic status.The online version contains supplementary material available at 10.1186/s12885-021-09149-w. Colorectal cancer (CRC) is the third most common cancer in men and the second one in women; over 1.8 million new cases of CRC were recorded in 2018 , 4. The In the present meta-analysis, the Preferred Reporting Items for Systematic Reviews and Meta-Analyses 2020 (PRISMA) statement was followed to write and report data . This stA systematic literature search was conducted through three major databases including PubMed, Scopus, and Web of Science up to Jun 2021. The systematic search was supplemented by a screening of the reference lists of all eligible studies and reviews. The combination of the following controlled vocabulary term was searched:  AND  AND  AND . The primary search was not restricted to the language, ethnicity, or geographical region.All relevant studies considered the following criteria were included: 1) studies with prospective design ; 2) examined association of metabolically healthy obese (MHO), metabolically unhealthy normal weight (MUHNW) and metabolically unhealthy obese (MUHO) phenotypes of body size with the risk of CRC; 3) stratified participants according to metabolic status and BMI categories and had one reference group in the MHNW category; 4) reported the definition of being metabolically healthy; 5) studies which reported risk estimates (relative risk (RR) or hazard ratio (HR) or odds ratio (OR)) and the corresponding 95% confidence intervals (CIs) or sufficient data to calculate them. Letters, comments, reviews, short communication, case reports, book chapters, and studies conducted on animals all were excluded.The required data were extracted independently by two authors according to a standardized form for the following information: the first author’s name, year of publication, country of origin, sex, mean or range of age, sample size, and risk estimates with their 95% CIs, confounding factors adjusted for in the analyzes, and criteria used to define metabolically healthy status. If there was any discrepancy between the two authors the extracted data were compared with the original file. A quality assessment of included studies was carried out by two reviewers. To evaluate the methodological quality of eligible studies, Newcastle-Ottawa Scale (NOS)  was applp < 0.1) and calculation of the I2 statistic. Accordingly, heterogeneity was significant if Q statistic had p < 0.1 or if I2 > 50%. Low heterogeneity (≤ 25%), moderate heterogeneity (> 25 to 50%), and high heterogeneity (> 50%) were also evaluated. Data were combined via the random-effects model (REM) and fixed-effect models when appropriate. To find and attenuate potential sources of heterogeneity subgroup analysis by gender  was performed. The conclusiveness and robustness of results by excluding each of the studies from the pooled estimate and analyzing the rest of them were evaluated. The publication bias was evaluated through visual inspection of asymmetry, and Egger’s weighted regression test . Study characteristics and data were extracted to RevMan 5  and STATA version 17.0 .In this meta-analysis, estimated pooled ORs with 95% CIs were used to assess the strength of association between metabolic phenotypes of obesity and risk of CRC and the metabolically healthy normal weight (MHNW) was considered as the reference group. Statistical heterogeneity was assessed using the Chi-square test , whereas moderate heterogeneity was evident in other analyses (I2 = 50%). MUHNW  as a gold standard. However, if the participants with MHO or MUHO had higher lean mass than that of normal-weight participants, the observed associations for the risk of CRC would have been attenuated toward the null. Moreover, the cut-points used to define obesity were different in the included studies. However, this was done to capture ethnicity differences as some ethnic groups have shown a higher risk of weight-related diseases at lower BMI values. Additionally, studies used different guidelines to distinguish metabolic healthy and unhealthy individuals such as ATP III, and so on. Even in studies that used ATP III, some of them only relied on one or a few criteria of metabolic syndrome to determine metabolically abnormal individuals, whereas others considered all the six criteria provided by ATP III. Therefore, it might be difficult to make direct comparisons among these studies. Although reported findings were all conditioned on certain confounders, covariates have widely differed across studies. Measurements were also done at a baseline time point, which cannot capture body weight and metabolic change throughout the study. In previous studies, about 30 to 50% of MHO transitioned to a metabolically unhealthy state, whereas 25 to 30% of MUHO recovered their metabolic health –46. HoweIndividuals with metabolic abnormality, although at a normal weight, have an increased risk for CRC. Moreover, obesity is associated with CRC irrespective of metabolic status. Since the relationship between metabolic phenotypes of obesity and cancer risk has not been extensively investigated by systematic reviews and meta-analyses, the current study offers novel insights into the joint effect of obesity and metabolic abnormality on colorectal cancer risk, which could potentially inform public health practice to keep metabolic healthy, even with normal weight. To uncover the etiological characteristics of metabolic phenotypes an important step forward may be to include different and alternative definitions/criteria of metabolic status for comparison purposes.Additional file 1: Supplemental Figure 1: Sensitivity analysis in studies investigating the association of MUHNW phenotype, compared with individuals with MHNW, with odds of CRC.Additional file 2: Supplemental Figure 2: Sensitivity analysis in studies investigating the association of MHO phenotype, compared with individuals with MHNW, with odds of CRC.Additional file 3: Supplemental Figure 3: Sensitivity analysis in studies investigating the association of MUHO phenotype, compared with individuals with MHNW, with odds of CRC.Additional file 4: Supplemental Table 1. The risk of bias of included studies.Additional file 5: Supplemental Table 2. Risk of bias summary: review authors’ judgements about each risk of bias item for each included study.


Several studies have evaluated the association of cadmium exposure with the risk of gestational diabetes mellitus (GDM). However, the findings among these studies have been inconsistent. To further investigate the relationship, we carried out a meta-analysis to clarify the relationship between cadmium exposure and GDM risk.Five databases  were searched for eligible studies until September 09, 2021. The quality of eligible studies was evaluated using the Newcastle–Ottawa quality assessment scale (NOS). The summary odds ratios (ORs) and 95% confidence intervals (CIs) were calculated by random-effects models due to high heterogeneity. Sensitivity analysis was performed to explore the robustness of the results. Publication bias was evaluated by Egger's test and Begg's test. We also conducted meta-regression analysis and subgroup analysis to assess the potential sources of heterogeneity.P = 0.206). No publication bias was found in Begg's and Egger's tests . Meta-regression suggested that publication year was the potentially heterogeneous source (P = 0.034). Subgroup analysis of publication year showed that the OR of studies before the year of 2016 was 4.05 , and prospective cohort studies showed a borderline increased GDM risk .A total of 10 studies with 32,000 participants related to our issue were included. Comparing the highest vs. lowest categories of cadmium exposure, no significant association was observed between cadmium exposure and the risk of GDM (OR = 1.16, 95% CI = 0.92–1.46, and Our results indicated no significant association between cadmium exposure and GDM risk. Further high-quality prospective studies, especially those using standard analytic methods for cadmium exposure, are warranted to confirm the results. Gestational diabetes mellitus (GDM) is the initial occurrence of abnormal glucose tolerance during pregnancy and has a worldwide prevalence ranging from 1 to >30%, indicating that GDM is a relatively prevalent disease during pregnancy . MoreoveEpidemiological studies have confirmed a series of risk factors contributing to GDM, such as advanced maternal age, geography, ethnicity, and lifestyle factors. However, emerging risk factors, such as environmental pollutants and endocrine disruptors, have also attracted more attention , 5. As aConsidering the increased risk of diabetes resulting from cadmium, we hypothesized that cadmium exposure may also increase the risk of GDM by similar mechanisms. Several studies found significant evidence to prove the relationship between cadmium and the risk of GDM. However, others found negative results, which may be due to differences in study populations, regions, sample types, and study methods. To explore the association more deeply, we conducted a meta-analysis to synthesize the data from existing studies and to analyze the likely relationship between cadmium and the risk of GDM.A systematic search for published studies that reported the relationship between cadmium and GDM risk was conducted using the following five databases from the date of database inceptions to September 09, 2021: Scopus, PubMed, Web of Science, Cochrane, and Chinese National Knowledge Infrastructure (CNKI). As for PubMed, the search was designed to seek articles that reported the effect of cadmium on GDM risk in English databases by combining MeSH terms and free-text terms:  OR Diabetes, Pregnancy-Induced) OR Diabetes, Pregnancy Induced) OR Pregnancy-Induced Diabetes) OR Gestational Diabetes) OR Diabetes Mellitus, Gestational) OR Gestational Diabetes Mellitus) AND  OR cadmium). In other English databases, the keywords used for the search were  OR Pregnancy-Induced Diabetes) OR Gestational Diabetes Mellitus) AND . Regarding the CNKI, we used the terms gestational diabetes mellitus, metals, and cadmium in Chinese and then combined the terms “gestational diabetes mellitus” and “metals or cadmium” to search for potential articles. The search strategy of each database is presented in The participants, interventions, comparison, outcomes, and study design (PICOS) strategy was used to guide our eligibility criteria . StudiesTwo investigators independently extracted the following information regarding the eligible studies: (1) First author and timing of publication; (2) time period and location of the study; (3) study design; (4) sample type; (5) time of sample collection; (6) sample size; (7) diagnostic criteria for GDM; (8) concentration of cadmium; (9) covariates adjusted; and (10) OR/RR and 95% CI. The OR/RR and 95% CI were extracted from the adjusted models. When divergence occurred, the two investigators asked the mentor. Among the eligible studies, a study by Tomoko Oguri and his colleagues included nulliparous and parous women. As a result, we included them as two studies in our meta-analysis. Jia et al. used the prevalence ratios (PRs) to describe the risk. Based on the previous study , we alsoQ-test and inconsistency index (I2). Pooled OR and 95% CI were calculated using the fixed-effects model if the heterogeneity test was not significant (P ≥ 0.05). Otherwise, the random-effects model was adopted. Sensitivity analyses were performed by excluding studies one after the other to estimate the stability of the pooled findings. Begg's and Egger's tests were performed to evaluate potential publication bias. Multivariate meta-regression analysis was applied to assess the possible sources of heterogeneity. Subgroup analysis was performed by publication year, geographic region, study design, diagnosis criteria, sample type, sample size, NOS score, and time of collection. All statistical analyses were two-sided and carried out by StataSE 12.0 . P < 0.05 indicated statistical significance.Heterogeneity among the eligible studies was measured by Cochran's After searching the mentioned databases, 1,208 articles were identified as eligible in total, and 66 were excluded after finding duplicates. A total of 1,142 articles were then further screened, and 1,114 were excluded for different reasons, such as unrelated context, meeting abstract, or animal studies. The full texts of the remaining 28 articles were screened further. Among them, 1 news article, 1 retraction, 3 reviews, and 4 animal experiments were excluded. A total of 6 studies did not mention the ORs or RRs, and 1 study focused on mixed exposure. One study was then excluded for unclear statistics . We alsoAmong the 10 studies, six were cohort studies, while four were case-control studies. Seven studies were conducted in China, one in the USA, one in Japan, and one in Canada. Five of them measured the concentrations of cadmium in urine samples, three in blood samples, one in meconium samples, and one in hair samples. The quality scores of all the included studies ranged from 7 to 9 and were regarded as high quality. The eligible studies were published from 2015 to 2021, and 32,000 pregnant women were enrolled in total. The characteristics and NOS scores of the 10 studies are described in The pooled OR of cadmium exposure was 1.16  in the random-effects model due to the high heterogeneity, revealing that cadmium exposure had no significant association with the increased risk of GDM .To evaluate whether the findings was changed by excluding each study, a sensitivity analysis was performed. As shown in PEgger = 0.316, PBegg = 0.213, Egger's test and Begg's test were used to calculate the publication bias of these studies. We found no publication bias according to the results (P = 0.034). However, the source of heterogeneity was not observed among other factors .We performed meta-regression analysis to assess the sources of heterogeneity, including publication year, geographic region, design, sample type, time of sample collection, sample size, and NOS score. As shown in P < 0.001). When stratified by study design, we found a borderline increased risk of gestational diabetes due to cadmium exposure in cohort studies . Subgroup analyses by geographic region, diagnosis criteria, sample type, sample size, NOS score, and time of collection were not statistically significant  and different chemokines in various cell types . TNF actOur meta-analysis has several strengths. First, our meta-analysis is the first to explore the relationship between the risk of GDM and cadmium exposure. Second, the included studies were limited to English papers and Chinese papers, the latter of which could be more objective and representative for elaborating the potential effect of cadmium on GDM. Third, sub-analyses were conducted to further explore the associations and determine the source of heterogeneity, and the null results may have been due to the limited number of participants. Nevertheless, our study has several limitations. The small sample size may have resulted in random errors in our meta-analysis due to the relatively less attention given to GDM and cadmium exposure. Three studies had NOS scores ≤ 7, indicating potential defects in methods or study design, which may have affected the accuracy of our meta-analysis. Third, the accuracy of the analysis was affected by the high heterogeneity and inconsistent adjusted confounding factors.In conclusion, we found no statistically significant association between cadmium exposure and the risk of GDM. More studies with larger sample sizes and detailed experiments should be conducted to further explore the associations and identify the role of cadmium exposure in the mechanism of GDM.The original contributions presented in the study are included in the article/YL and KX designed the study. YL and TL searched for and evaluate the included studies. ZS and KX contributed to the funding acquisition and manuscript editing. YL and JX wrote the manuscript. KX analyzed data. All authors contributed to the article and approved the submitted version.This research was supported by the National Natural Science Foundation of China (81971410 and 81571458), Postdoctoral Research Funding Program of Jiangsu Province (2020Z146), and State Key Laboratory of Reproductive Medicine (SKLRM-UC201901 and SKLRM-UC201902).The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


Background: Triglyceride-glucose (TyG) index is a recently proposed surrogate indicator of insulin resistance. Previous studies evaluating the association between TyG index and hypertension risk in general adult population showed inconsistent results. We performed a meta-analysis to systematically evaluate this association.Methods: Observational studies, which evaluated the independent association between TyG index and hypertension in the general adult population, were identified by systematic search of PubMed, Embase, Web of Science, Wanfang data, and Chinese National Knowledge Infrastructure databases. A random-effect model, which incorporated the potential intra-study heterogeneity, was used for the meta-analysis.Results: Eight observational studies including 200,044 participants were included. Results showed that compared with those with the lowest category of TyG index, subjects with the highest category of TyG index were associated with higher odds of hypertension . Sensitivity analysis by excluding one dataset at a time showed consistent result . Results of univariate meta-regression analysis showed that differences in sample size, mean age, male proportion, mean body mass index, and study quality score among the included studies did not have significant influence on the association between TyG index and hypertension , suggesting that differences in these characteristics may not be the major source of heterogeneity. Subgroup analyses showed that study characteristics such as study design, participant ethnicity, age, or sex of the participants did not significantly affect the association .Conclusions: Higher TyG index may be associated with higher odds of hypertension in general adult population. Large-scale prospective cohort studies are needed to validate these findings, and further studies are needed to elucidate the potential pathophysiological mechanisms underlying the association between TyG index and hypertension. Currently, hypertension remains an important cause of morbidity and mortality of global population , particuThe meta-analysis was performed in accordance with the MOOSE   and CochStudies were identified via systematic search of electronic databases of PubMed, Embase, Web of Science, Wanfang data, and Chinese National Knowledge Infrastructure (CNKI) databases via the following terms: (1) “TyG index” OR “triglyceride-glucose index” OR “triglyceride and glucose index”; and (2) “hypertension” OR “blood pressure” OR “hypertensive.” The search was limited to human studies published in English or Chinese. The reference lists of related original and review articles were also analyzed using a manual approach. The final literature search was performed on March 10, 2021.The inclusion criteria for the studies were: (1) observational studies published as full-length articles; (2) included general adult population; (3) evaluated the association between TyG index and hypertension; and (4) reported the relative risk for this association after adjustment of potential confounding factors. TyG index was calculated as ln [TG (mg/dl) × FPG (mg/dl)/2] . DiagnosLiterature search, data extraction, and quality assessment of the included studies were performed by two authors (YW and WY) independently according to the predefined inclusion criteria. Discrepancies were resolved by consensus. The extracted data included: (1) name of first author, publication year, and country where the study was performed; (2) study design characteristics; (3) participant characteristics, including health status, sample size, age, sex, and body mass index (BMI); (4) patterns for Lp (a) analysis and cutoff values; (5) follow-up durations for cohort studies; (6) definitions of hypertension and methods for outcome validation; and (7) confounding factors adjusted in the multivariate analyses. The quality of each study was evaluated using the Newcastle-Ottawa Scale , which rP values, and were logarithmically transformed to stabilize variance and normalize the distribution  and STATA software for the meta-analysis and statistics.We used risk ratios (RRs) and their corresponding 95% confidence intervals (CIs) as the general measure for the association between TyG index and hypertension in general adult population. For all of the included studies, TyG index was analyzed as categorized variables. Accordingly, RRs of hypertension in adults with the highest TyG index level compared with those with the lowest TyG index level were extracted. Data of RRs and their corresponding stand errors (SEs) were calculated from 95% CIs or ribution . The Coc studies . A signi studies . Sensiti studies . A univa studies . Besides studies  and Begg studies . A P < 0The process of database search is summarized in 2. For the analysis of the association between TyG index and odds of hypertension, comparisons were performed between participants with highest and lowest quintiles in two studies . These results suggested that differences in these characteristics may not be the major source of heterogeneity. Moreover, subgroup analyses showed consistent association in prospective studies  and cross-sectional studies  and non-Chinese  and ≥50 years  and women .The funnel plots regarding the association between serum TyG index and hypertension are shown in In this meta-analysis of observational studies, we found that compared with those with the lowest category of TyG index, adults with the highest category of TyG index were independently associated with higher odds of hypertension. Besides, consistent results were obtained in subgroup analyses according to the study design, ethnicity, age, and sex of the participants. Taken together, these findings suggested that higher TyG index may be associated with higher odds of hypertension in general adult population. Although these findings should be validated in large-scale prospective cohort studies, results of our study suggest that TyG index, an easily obtained indicator of insulin resistance, could be applied as a predictor of hypertension risk in general adult population. Moreover, the potential pathophysiological mechanisms underlying the association between TyG index and hypertension deserve further investigation.To the best of our knowledge, our study is the first meta-analysis which evaluated the association between TyG index and hypertension in community-derived general population. The strengths of our meta-analysis include the following: First, only studies with multivariate analysis were included, which therefore could provide an independent association between TyG index and hypertension. In addition, sensitivity analysis was performed to evaluate the stability of the results, which confirmed that the association between TyG index and hypertension was not primarily driven by either of the included studies. Finally, multiple predefined subgroup analyses were applied to evaluate the robustness of the findings, which showed that the association between TyG index and hypertension was not affected by the differences in study design, ethnicity, age, or gender of the participants. Our findings may reflect the pathogenetic association between insulin resistance and hypertension. Previous studies showed that insulin resistance is characterized of low-degree systematic inflammation, which may cause endothelial dysfunction, one of the initial pathogenic processes underlying arterial hypertension , 27. BesAn important clinical implication of our study is that TyG index may be applied as an indicator to reflect the risk of hypertension in general adult population . CompareP values all > 0.10), suggesting that differences in these characteristics may not be the major source of heterogeneity. Besides, our predefined subgroup analyses also did not support that the varying results of the included studies could be explained by differences in study design, patient ethnicity, age, or sex. Currently, we are unable to determine the possible reasons of inconsistent results among the included studies. However, difference in some other related factors  may be responsible for the inconsistent results among the studies on the association between triglyceride-glucose index and hypertension. Large-scale prospective cohort studies are warranted to validate our findings and determine the potential influences of patient characteristics on the association between TyG index and hypertension risk. Besides, combining the results of studies with cross-sectional and cohort design could also contribute to the heterogeneity, although the results of subgroup analysis according to the study design showed no statistical significance. In addition, it remains unknown whether the association between TyG index and hypertension is linear because we only included categorized data. In fact, to the best of our knowledge, only one study showed that TyG index as a continuous variable remained to be associated with hypertension in elderly individuals from China (Some limitations should be noticed when the results of the meta-analysis are interpreted. First, the datasets available for the meta-analysis were limited, and the results for the subgroup analyses should be interpreted with caution due to the small number of datasets and participants included. Second, although only studies with multivariate analyses were included, we could not exclude the possibility of unadjusted residual factors that may confound the association between TyG index and hypertension, such as dietary factor of the included populations and the concurrent medications used. Moreover, significant heterogeneity was observed among the included studies. However, results of univariate meta-regression analysis showed that differences in sample size, mean age, male proportion, mean BMI, and study quality score among the included studies did not have significant influence on the association between TyG index and hypertension (om China . MoreoveIn conclusion, current evidence from observational studies suggests that higher TyG index may be associated with higher odds of hypertension in general adult population. Large-scale prospective cohort studies are needed to validate these findings, and further studies are needed to elucidate the potential pathophysiological mechanisms underlying the association between TyG index and hypertension.The original contributions presented in the study are included in the article/supplementary material, further inquiries can be directed to the corresponding authors.YW and XJ designed the study. YW and WY performed the literature search, data extraction, quality evaluation, and wrote the manuscript. All authors performed the statistical analyses, reviewed and revised the manuscript, and approved the manuscript for submission.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.