Elevated leucine/isoleucine levels increased the risk of T2D. In conclusion, multiple metabolic dysregulations are observed to occur before T2D onset, and the new biomarker panel can help to predict T2D risk.In a Chinese prospective cohort, 500 patients with new‐onset type 2 diabetes (T2D) within 4.61 years and 500 matched healthy participants are selected as case and control groups, and randomized into discovery and validation sets to discover the metabolite changes before T2D onset and the related diabetogenic loci. A serum metabolomics analysis reveals that 81 metabolites changed significantly before T2D onset. Based on binary logistic regression, eight metabolites are defined as a biomarker panel for T2D prediction. Pipecolinic acid, carnitine C14:0, epinephrine and phosphatidylethanolamine 34:2 are first found associated with future T2D. The addition of the biomarker panel to the clinical markers  significantly improves the predictive ability in the discovery and validation sets, respectively. By associating metabolomics with genomics, a significant correlation  within 4.61 years and 500 matched healthy participants are selected. A serum metabolomics analysis is performed and 81 metabolites are found to significantly change before T2D onset. The new biomarker panel helps to predict T2D. Gene FADS1, CHRM3, and WWOX are found to be associated with T2D‐related metabolites. Thus, early diagnosis and early intervention are very meaningful for patients. Although some clinical biomarkers are characteristic of diabetes , biomarkers for predicting diabetes are still urgently needed.Type 2 diabetes (T2D) is a prevalent, progressive chronic disease that increases the risk of heart disease and microvascular complications.1 The  In prospective nested case‐control studies, cases and controls are from the same cohort, and selection bias in effect estimation can be reduced. Recently, prospective nested case‐control studies have been used to reveal the changes in the metabolome antecedent to diabetes and to find predictors of future diabetes. In a European nested case–control study, several glycerophospholipids, sugar metabolites and a purine nucleotide were found to be significantly changed before the onset of T2D. In addition, alanine, phenylalanine, tyrosine, and palmitoylcarnitine were also found to be risk factors for incident T2D in two independent Chinese prospective cohort studies based on a targeted metabolomics method. In another Chinese cohort study, 38 lipids were found to be significantly associated with the risk of T2D. Then, the combination of six lipids and eight clinical parameters improved the predictive ability of the clinical parameters from 0.710 to 0.781 in the discovery set and from 0.698 to 0.722 in the validation set.Metabolomics, which focuses on endogenous small‐molecule metabolites, is becoming a reliable tool for discovering potential biomarkers and explaining the pathogenesis of diseases.2 In p These T2D‐related metabolites were used as intermediate molecular phenotypes in mGWAS to link genes and incident T2D. CPS1 (rs1047883), which is significantly associated with glycine, was shown to be related to the risk of T2D. The newly discovered genetic loci and their relationships with metabolites can help understand the complex pathogenesis of T2D. However, to the best of our knowledge, no mGWAS of T2D has been conducted in Chinese prospective nested case‐control cohorts so far.T2D is a metabolic disease affected by multiple genes and has a strong genetic predisposition. Therefore, the discovery of genetic loci associated with T2D may help understand the pathogenesis. Recently, a metabolome‐genome‐wide association study (mGWAS) was used to verify known gene loci and identify new related gene loci. In a nested case‐control study of a Korean population, nine metabolites were found to be significantly related to the occurrence of diabetes.6 Thes of the Chinese population. The case group of this study consisted of 500 participants who developed T2D after an average follow‐up of 4.61 ± 0.15 years, and each case was matched with a nondiabetic participant in the cohort to form the control group. Serum untargeted metabolomics analysis was performed to identify significantly differential metabolites before the onset of T2D, and a new combinational metabolite marker panel was established to predict T2D in advance. Then, metabolomics and genomics were integrated to reveal possible diabetogenic loci and causal relationships between metabolites and T2D.In this work, a prospective nested case‐control study was conducted in the Dongfeng‐Tongji cohort7 of t22.1Tablep < 0.05, false positive rate (FDR) < 0.1). The odds ratios (ORs) per standard deviation (SD) increment in clinical parameters were calculated by Cox regression  value less than 30% among quality control (QC) samples in the discovery set  and biosynthesis of unsaturated fatty acids  were the most significantly altered pathways before T2D onset.Serum samples of the discovery set were analyzed by a liquid chromatography‐mass spectrometry (LC‐MS)‐based metabolomics approach. A total of 206 metabolites were identified according to our in‐house database.8 Amond Figure . The mul2.3p = 0.002) for carnitine C14:0, 1.427  for leucine/isoleucine, 1.229  for FFA 20:4, 1.315  for epinephrine, and 1.341  for PE 34:2. Furthermore, before the onset of T2D, the levels of pipecolinic acid, 1,5‐anhydro‐D‐glucitol and LPC 18:2 decreased, and their baseline levels were negatively associated with future T2D risk. The multivariable‐adjusted ORs per SD increment were 0.731  for pipecolinic acid and 0.808  for 1,5‐anhydro‐D‐glucitol. For LPC 18:2, the unadjusted OR per SD increment was 0.690 , but it became insignificant after multivariable adjustment (p = 0.116).To distinguish the control group from the case group, we performed binary logistic regression on the 81 metabolites that were significantly different between the case group and control group in the discovery set. A combinational metabolic biomarker panel containing eight metabolites was established, including pipecolinic acid, 1,5‐anhydro‐D‐glucitol, LPC 18:2, carnitine C14:0, leucine/isoleucine, eicosatetraenoic acid (FFA 20:4), epinephrine and PE 34:2. For convenience, they are named combination metabolite biomarker (CMB). The change trends of these eight metabolites are shown in Figure p < 0.001) for CCB, 2.243  for CMB and 3.445  for the combination model (CMB + CCB)  . The ORs per SD increase in T2D were 3.028  Figure . The are) Figure .p values of the ORs were less than 0.001 .To validate these three predictive models, 208 pairs of cases and matched control samples were used as the validation set. Serum samples in the validation set were analyzed by the same LC‐MS‐based metabolomics approach, and 89.8% of the 206 identified metabolites had an RSD value of less than 30% in the QC samples . Rs174559 is in the region of FADS1 (fatty acid desaturase 1) gene. The alleles at rs174559 are alleles G and A, with A being the major allele and G being the minor allele  = 0.39). The minor allele was related to an increased level of FFA 20:4 (β = 0.206).On the basis of the above study, we conducted mGWAS between significantly changed metabolites at baseline and SNPs to find the potential locus related to incident T2D. The GWAS results of FFA 20:4 are shown in  As shown in Figure p = 5.40 × 10−7, β = 0.157, MAF = 0.42). Rs72487966 (SNP on chromosome 16) is located in the WW domain containing oxidoreductase (WWOX) gene region, which is a risk gene for T2D. As shown in Figure p = 2.92 × 10−6, β = 0.411, MAF = 0.03). We also attempted to conduct two‐sample Mendelian randomization (MR) analysis to evaluate whether there are causal relationships between pipecolinic acid or leucine/isoleucine and T2D. Based on the meta‐analyses using summary statistics of the Dongfeng‐Tongji cohort, and the publicly available results of the KORA cohort and the Twins UK cohort, two‐sample MR analysis of leucine/isoleucine levels was conducted, and the OR per SD genetically predicted difference was scaled using an inverse‐variance‐weighted method. As a result, the relationship between leucine/isoleucine and T2D was proven to be causal .The GWAS results of pipecolinic acid and leucine/isoleucine are also shown in Figure 9 As s3 and validated in our Chinese prospective cohort study. Four of them  were first reported to be significantly associated with future T2D risk in a prospective cohort study. The AUCs of CMB were 0.708 and 0.687 in the discovery and validation sets, respectively, indicating that CMB had an acceptable predictive ability for the onset of T2D. The combination of CMB and CCB significantly improved the predictive ability of CCB from 0.755 to 0.773 in the discovery set and from 0.772 to 0.797 in the validation set.In this nested Chinese prospective cohort, an untargeted ultra‐performance liquid chromatography‐mass spectrometry (UPLC‐MS)‐based metabolic profiling of serum samples was performed and integrated with genomics. To the best of our knowledge, this is one of the most comprehensive metabolomics studies conducted on Chinese participants without diabetes at baseline to reveal the significant metabolic changes before the onset of T2D and predict the risk of future T2D. According to our results, 206 metabolites were identified. Among them, 81 metabolites, including carnitines, amino acids, fatty acids, organic acids, and lipids were significantly changed before T2D onset. Eight metabolites related to future T2D risk were defined as a CMB for T2D prediction. Among them, four metabolites  were reported in other cohort studies15 and It was reported that the levels of phospholipids were significantly changed in diabetic patients. The significant changes of those phospholipids in our study hint that abnormal phospholipid metabolism appears before the onset of T2D. It is worth mentioning that as a member of CMB, a lower level of LPC 18:2 was found to be associated with future T2D risk in a European cohort, which was consistent with our results in the Chinese cohort. Furthermore, PE 34:0 was first reported to be associated with future T2D risk, and defined as a discriminant marker in our study.In this study, before the onset of T2D, the levels of most LPEs and LPCs were decreased, while the levels of PCs and PEs were increased in the case group Figure . LPCs an16 It p values less than 0.05). Among them, FFA 20:4 and carnitine C14:0 were also defined by binary logistic regression as predictors of T2D. According to published studies, the levels of FFAs and acylcarnitines are significantly increased in T2D patients. FFA 20:4 was reported to be positively associated with future T2D in a French prospective cohort. Baseline carnitine C14:0 was also analyzed in another Chinese prospective cohort of T2D, but neither significant differences between the case group and the control group nor a significant relationship with the risk of future T2D was found. Furthermore, we calculated the ratio of carnitine C4:0/carnitine C3:0, which can indicate the activity of short‐chain acyl‐CoA dehydrogenase (SCAD). SCAD is the rate‐limiting enzyme of electron transport, which is the initial reaction in mitochondrial β‐oxidation. In our case group, the ratio of carnitine C4:0/carnitine C3:0 was significantly increased (p < 0.001), indicating the activity of SCAD was decreased. This demonstrated that before the occurrence of T2D, β‐oxidation of fatty acids may have been impaired. Notably, it was reported that incomplete fatty acid β‐oxidation can contribute to insulin resistance, which plays an important role in the pathogenesis of T2D.FFAs are important energy substances that can be transferred into mitochondria for β‐oxidation with the help of carnitine. In our results, the levels of FFAs and acylcarnitines were significantly increased before T2D occurred. Most of them were significantly positively correlated with the development of T2D , which is in the region of FADS1 gene Figure ,B. Some 24 FADy Figure , a signip > 0.05). This is the first time that the baseline level of pipecolinic acid was found to be significantly related to future T2D risk. Furthermore, rs535514 was related to the pipecolinic acid level based on the mGWAS results, and rs535514 was in the regions of the CHRM3 gene .27 Thi amino acid metabolism is closely related to T2D. For example, aromatic amino acids and branched‐chain amino acids were reported to be correlated with insulin resistance. Furthermore, high levels of branched amino acids were confirmed as risk factors for incident diabetes in a Japanese cohort. Branched amino acids were also chosen as predictors of future diabetes. In our work, leucine/isoleucine was significantly positively related to future T2D risk and selected as a marker for predicting the occurrence of diabetes, which is consistent with the previously mentioned article. According to the mGWAS results, a SNP (rs72487966) in the WWOX gene was related to leucine/isoleucine , aromatic amino acids , lysine and methionine. According to previous studies,31 amie Figure . The WWOTo the best of our knowledge, this study reports correlations between pipecolinic acid and CHRM3, and between leucine/isoleucine and WWOX for the first time. The new association might provide new insights into the pathogenesis of T2D. However, the correlations were suggestive, and more validation studies are needed. The lower 1,5‐anhydro‐D‐glucitol level has been clinically used as a marker of the increase in blood glucose levels. According to our results, a significant increase in epinephrine and a significant decrease in 1,5‐anhydro‐D‐glucitol preexisted before the occurrence of T2D ) was provided by the Ethics and Human Subjects Committee of Tongji Medical College, Huazhong University of Science & Technology.The flowchart of the study is shown in  1) fasting blood glucose level higher than 7.0 mmol L−1; 2) hemoglobin A1c level higher than 6.5%; and 3) self‐reported diagnosis of T2D. Healthy subjects (n = 500) matched 1:1 by age and sex at baseline were selected as the control group. A total of 292 pairs of cases and matched control samples were randomly selected as the discovery set. The remaining 208 pairs of case and matched control samples were used as the validation set.After 4.61 ± 0.15 years of follow‐up, a total of 1039 eligible participants developed incidents in the Dongfeng‐Tongji cohort, among whom 500 new‐onset T2D subjects, excluding participants with cardiovascular disease or cancer, were selected as the case group. T2D was diagnosed if any of the following three criteria were met:36 1)  Differential metabolites between the groups with or without the onset of diabetes were identified in the discovery set, and the metabolites used as the CMB were selected from them. The optimal clinical parameters were selected as the CCB and combined with the CMB to create as a combination predictive model. The predictive ability of each model for T2D onset was assessed by the AUC. In addition, the predictive abilities of the three models were further evaluated in the validation set. Genotyping of SNPs of the participants was performed. Furthermore, a metabolome‐genome‐wide association study was performed to find associations between T2D‐related metabolites and gene loci. Mendelian randomization (MR) analysis was conducted to scale possible causal relationships.Untargeted UPLC‐MS‐based metabolic profiling of baseline fasting serum samples from the 1000 participants was performed. A Waters ACQUITY UPLC system  was used for the separation of metabolites. For positive ion mode analysis, an ACQUITY UPLC BEH C8  column and a TripleTOF 5600 mass spectrometer  were used. For the negative ion mode analysis, an ACQUITY UPLC BEH T3  column and a Q Exactive HF mass spectrometer  were used. The UPLC‐MS method was specifically designed for large‐scale metabolomics studies to assure the robustness and repeatability.37 Dif metabolites were identified by retention time, m/z value and MS/MS information. The peak areas of metabolites were adjusted by internal standards. The identified metabolites whose RSD below 30% in QCs were used for subsequent data analysis. Nonparametric tests were conducted by Multi‐Experiment Viewer software (version MEV 4.7.4) to identify significantly changed metabolites and clinical parameters  between the case and control groups. Pathway analysis of significantly changed metabolites was conducted by using MetaboAnalyst 4.0 . All of the following analyses were conducted by IBM SPSS Statistics for Windows . Odds ratios and 95% confidence intervals were calculated by the Cox regression model. Binary logistic regressions were carried out to define clinical biomarkers and metabolic biomarkers. Receiver operating characteristic (ROC) curves were plotted to show the ability to differentiate between the controls and cases. Detailed information on the genotyping of SNPs of participants and genotype–metabolite association analysis is shown in the Supporting Information. As a result, ≈81 million SNP markers were obtained. Genotype‐metabolite association tests were performed by an SE‐weighted meta‐analysis with METAL (https://genome.sph.umich.edu/wiki/METAL_Documentation). The mendelian randomization‐based website (http://www.mrbase.org/) was used to conduct two‐sample MR analysis.Detailed information on the preparation of serum samples and the LC‐MS methods for metabolomics analysis are shown in the Supporting Information. According to the in‐house database,8 metaThe authors declare no conflict of interest.Y.O. and G.Q. contributed equally to this work. G.X. and T.W. conceived and designed the study, interpreted the data, critically revised the manuscript for important intellectual content and approved the version to be published. Y.O. and G.Q. contributed equally to this work. Y.O. and G.Q. performed data acquisition and statistical analyses. Y.O. wrote the manuscript. X.Z. interpreted the data, and critically revised the manuscript for important intellectual content, and approved the version to be published. B.Z., D.F., X.Q., W.L., and X.L. contributed to statistical analyses. W.J., D.Y., and Q.W. contributed to data acquisition.Supporting InformationClick here for additional data file.


Preclinical data suggest that endogenous sex steroid hormones may be implicated in colorectal cancer (CRC) development, however, findings from epidemiological studies are conflicting. The aim of this systematic review and meta-analysis was to investigate the associations between endogenous concentrations of sex hormones and CRC risk. PubMed and Scopus were searched until June 2020 for prospective studies evaluating the association between pre-diagnostic plasma/serum concentrations of estradiol, testosterone and sex-hormone binding globulin (SHBG) and CRC risk. Summary relative risks (RRs) and 95% confidence intervals (CIs) were estimated using the inverse-variance weighted random-effects model based on the DerSimonian-Laird estimator. Eight studies were included in the meta-analysis after evaluating 3,859 non-duplicate records. Four of the eight studies had a nested case–control design, one study was a case-cohort and the rest three studies were cohort studies, and they included on average 295 cases (range:48–732) and 2,105 controls. No associations were found for endogenous sex steroid hormones in men or post-menopausal women with CRC risk, with evidence for substantial heterogeneity observed among women. Findings from this meta-analysis do not support presence of associations between pre-diagnostic concentrations of testosterone, estradiol and SHBG with incident CRC risk in men and post-menopausal women.The online version contains supplementary material available at 10.1007/s12672-021-00402-z. Colorectal cancer (CRC) is the third most frequent cancer in males and second in females worldwide, with almost one million new incident cases in 2018  and in sSummarizing the available evidence on the role of endogenous sex steroid hormones on CRC risk could assist in understanding better the hormonal perturbations associated with CRC development. Therefore, we conducted a systematic review and meta-analysis for the association of circulating endogenous concentrations of estradiol, testosterone and SHBG with CRC risk combining results from prospective epidemiological investigations conducted in men and women.We searched for eligible published studies in MEDLINE-PubMed and Elsevier’s Scopus databases up to 17 June, 2020 using the search algorithm described in Additional file 1: Table S1. All studies obtained from the literature searches were uploaded in the web-based application of rayyan, which was used to manage the study selection process . We cons1) study selection (maximum 4 points); 2) adequacy of the exposure or outcome for nested case–control and cohort studies, respectively (maximum 3 points); and 3) comparability of study groups (maximum 2 points). The studies were categorized as either high quality or low quality. The maximum score was 9, and a score of ≥ 6 indicated high methodological quality.Screening of eligible studies and data extraction were performed independently by two investigators (EB and CP), and data was re-checked for consistency by a third author (KT). We initially removed duplicate publications from the electronic databases, and screened the titles and abstracts for eligibility in a blinded manner using rayyan. After the primary screening, the full texts of eligible studies were retrieved, and the inclusion and exclusion criteria were assessed. The “Newcastle–Ottawa Scale”  was usedThe data extracted from each eligible study included the following: first authors’ name and year of publication, country, study design, duration of follow-up, population characteristics such as age and sex, number of cases and controls, exposure and method of assessment, association estimates per level of exposure and adjustment factors. In case of multivariable adjusted statistical models, we retained the most adjusted model.Our primary analysis was a dose–response meta-analysis model considering continuous association estimates by sex. In studies, where association estimates per exposure were presented as three or more categories, generalized least-square regression was used to estimate the linear trends and express the association as per unit increase, using the method proposed by Greenland & Longnecker . SpecifiP < 0.05 was considered statistically significant. The presence of heterogeneity was assessed using Cochran's Q test and the degree of heterogeneity was evaluated using the I2 metric. I2 values ≥ 50% indicated substantial heterogeneity [The inverse-variance weighted random-effects DerSimonian-Laird meta-analysis , 30 was ogeneity . Predictogeneity .As a secondary analysis, we also meta-analysed the extreme quartiles of the endogenous sex hormone concentrations. All analyses were performed in R v.4.0.2 .The initial search yielded 3,859 non-duplicate records and after further screening, eight studies were deemed eligible for inclusion in the meta-analysis, namely three prospective cohorts –24, one Table 2 = 0% in men; RR per 10 pg/ml = 1.06, 95%CI: 0.88–1.28, I2 = 53% in post-menopausal women) and testosterone . SHBG was also not significantly associated with CRC risk .Summary random- and fixed-effects RRs and 95% CIs, and heterogeneity statistics by sex are shown in Figs. 2 = 0%), while no significant associations were observed for estradiol or SHBG .In the comparisons of the top vs. bottom quartile of sex hormones concentrations, the meta-analyses showed a significant positive association between testosterone and CRC in post-menopausal women but only two studies were included  have revealed a significant role in tissue types other than the female reproductive tract, including the gastrointestinal system . There h2 = 76%). In the NYUWHS, there was a large amount of missing data (28.6%) and a high degree of laboratory error in the estradiol measurements (intra-batch coefficient of variation = 33.5%) [Possible explanations for these discrepancies include differences in the hormone assessment methods, variation in the number of cases and adjustments for confounders. Most included studies adjusted for several factors that may confound the sex hormone and CRC association such as adiposity , physica= 33.5%) , which m= 33.5%) .The strengths of this systematic review and meta-analysis include: (1) the prospective design and long follow-up period of the studies; (2) the inclusion of studies with populations largely free from exogenous administration of sex steroid hormones close to the time of assessment; (3) the inclusion of high-quality studies, as assessed with the “Newcastle–Ottawa Scale”.Several limitations should also be noted. First, due to the observational nature of the studies included, residual confounding cannot be ruled out . Second,In conclusion, the results of this systematic review and meta-analysis of prospective studies on the associations between endogenous sex hormone concentrations and CRC risk indicate that circulating concentrations of estradiol, testosterone and SHBG do not appear to be associated with risk of CRC in men and post-menopausal women. The observed lack of consistency in findings among the single studies suggests a need for more research in this area, with a particular focus on large prospective studies and reliable sex hormone assessments.Additional file 1: Table S1. PubMed and Scopus search strategies. Table S2. Quality assessment of the prospective cohort studies, based on the NEWCASTLE-OTTAWA scalea. Table S3. Quality assessment of the nested case-control studies, based on the NEWCASTLE-OTTAWA scalea. Figure S1. Top versus bottom meta-analysed of the endogenous sex hormone concentrations. (PDF 71 KB)Below is the link to the electronic supplementary material.


Growing body of evidence suggest the association between SNP − 11377 C > G and SNP + 276 G > T polymorphisms of adiponectin gene with type 2 diabetes (T2D). However, these findings have not been conclusive and consistent. The present study quantitatively evaluates the data on the association between DIPOQ − 11377C/G, and + 276G/T polymorphisms and risk of T2D through a meta-analysis.A systematic search was performed in the PubMed, Web of science, Scopus and Cochrane library databases to extract published studies according to the inclusion criteria. Among the 741 studies, 391 of them were screened as full text and 31 studies were finally included in the meta-analysis. Analysis of data was performed using random-effects model. Odds ratios (ORs) with 95% confidence intervals (CIs) were used to analyze the strength of association. Subgroup and meta-regression analyses were performed to identify the potential source of heterogeneity.P = 0.14), CG  and GG  ADIPO − 11377 polymorphism with increased risk of T2D. In addition, the results revealed a trend toward an increased risk of T2D for the SNP + 276 TT genotype  as compared with the GT and GG genotypes. Subgroup analysis by ethnicity indicated significant association between the TT genotype of the SNP + 276 and increased risk of T2D among Europeans. Met-regression demonstrated significant association between the GT genotype of + 276 polymorphism with risk of T2D in male individuals .The pooled analysis showed that there was no statistically significant association between genotypes of CC  plays an important role in the development of type 2 diabetes and previous studies regarding the association between ADIPOQ polymorphisms and T2D risk reported conflicting results.We performed a meta-analysis to assess the association between two ADIPOQ polymorphisms  and risk for T2D.Odds ratios (ORs) with corresponding 95% confidence intervals (95% CIs) were pooled to assess the association between four aforementioned polymorphisms and susceptibility to T2D.Overall, there was an association between ADIPOQ +276 G > T with increased T2D risk.Subgroup by ethnicity suggested significant association between +276G/T polymorphism and T2D risk among European ethnicities.Our findings evidence a positive association between ADIPOQ +276 G > T polymorphism with increased risk of T2D in male individuals with European ethnicity. Type 2 diabetes (T2D), a metabolic disorder with severe complications, is a growing public health problem all over the world . AccordiThis study was outlined according to the guidelines of the 2009 Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) statement . To findAccording to the topic of this study, inclusion criteria included: (1) observational  studies which evaluated the association between the polymorphisms of SNP − 11377 and SNP + 276 of adiponectin gene in patients with T2D and healthy controls, (2) studies providing relevant information with topic such as genotype frequency for assessment of odds ratio (ORs) and 95% confidence intervals (95% CI), (3) studies with human subjects, (4) the control group with healthy individuals. The following items were excluded from the study: (1) animal and in vitro studies, (2) observational studies were not related to the association between the polymorphism of adiponectin gene and T2D, (3) conference abstracts, reviews, case reports, or editorials; (4) lack of sufficient information on adiponectin gene polymorphisms and susceptibility to T2D, and (5) studies with insufficient details of study methodology.To find the original articles associated with this subject, titles and abstracts of all retrieved studies were screened separately by two reviewers to identify the relevant articles. According to the predefined inclusion and exclusion criteria, the articles were selected for the meta-analysis.Selected articles were reviewed and data were independently retrieved by two researches using a standardized electronic form. The following information was extracted: (1) first author name, (2) publication year, (3) study location (country), (4) study designs which were classified into case–control and cohort, (5) genotyping methods, (6) ethnicity, (7) target population which was categorized as Asian, American, African and European, (8) sample sizes of case and controls , (9) genotype and/or allele frequencies in case and control groups, (10) P-value of Hardy–Weinberg equilibrium (HWE) and (11) Newcastle Ottawa Scale (NOS) score. Above data were extracted for two polymorphisms of SNP − 11377 and SNP + 276 separately.The quality of all eligible studies was assessed by two researches using the Newcastle Ottawa Scale (NOS) . The NOS2 < 50%, the fixed-effects model (the Mantel–Haenszel method) was selected to pool the data. Otherwise, the random effects model (the DerSimonian and Laird method) was used. Potential publication bias was calculated using Begg's funnel plot, the funnel plot of the study precision (inverse standard error) by effect size (log OR) and Egger’s weighted regression tests . The analysis of moderator variables showed that they were not the main cause of heterogeneity in the included studies, however sex may modify this association.Random-effects meta-regression showed no significant association between genotypes of SNPs of − 11377 with potential confounding factors such as male , female , age  and genotyping method  gene polymorphisms (SNP − 11377 C > G and SNP + 276 G > T) and risk of T2D among included studies. There are many fundamental mechanisms involved in T2D pathogenesis. However, adiponectin gene polymorphisms have been associated with T2D. In this regard, several studies demonstrated a significant difference in the risk of T2D among individuals with genotypes of adiponectin gene , 49. TheWe performed subgroup analyses according to ethnicity, study design and sex. Subgroup analyses by ethnicity and sex suggested significant association between + 276 G > T polymorphism and T2D risk among male individuals in European population compared to other ethnicities and female individuals. Moreover, we did not find any correlation between T2D risk and ADIPOQ − 11377 C > G polymorphism in the whole population. The obtained results in this study were similar to several previous studies with populations of different ethnicities , 51, whiIn order to find the observed heterogeneity, subgroup analyses were performed. Firstly, we analyzed the association between the genotypes  of + 276 G > T with risk of T2D by subgroup of ethnicity . The results showed no significant association between genotypes of − 11377 C > G and the risk of T2D, whereas, the TT genotype of + 276 G > C in European population is significantly associated with increased risk of T2D. In the next step, we analyzed this association in subgroups of sex and study design. The pooled results from two SNPs indicated no significant correlation between these SNPs and the risk of T2D in subgroups stratified to sex and study design. For confirming these results, meta-regression was conducted. We found a strong association between susceptibility to T2D and sex in the GT genotype of + 276 G > T. Our meta-analysis data have suggested that males are significantly susceptibility to T2D. This provides some evidence that the association between + 276 (TT and GT) and T2D might be mediated by sex. Therefore, sex may modify this association.In 2011, Hara et al. have reported that SNP + 276 GG genotype of ADIPOQ gene was associated with T2D in the Japanese population . This reOur meta-analysis has several advantages. This study included the most recent published articles on the association between the two SNPs of adiponectin gene and T2D. To enhance the power of strategy search, we used precise inclusion and exclusion criteria and a predefined standard sheet for data extraction. Moreover, to find observed heterogeneity among included studies, we performed subgroup and meta-regression analyses according to the moderator variables of sex, ethnicity, study design and genotyping methods. A comprehensive quality assessment of included studies using NOS checklist detected that the most of the studies had moderate to high quality. In addition, having the high number of studies with large sample size has raised the statistical power of this study.The limitations of this meta-analysis are that our search was restricted to published studies with English language, which potentially might lead to publication bias. With respect to significant heterogeneity among included studies, the findings of the present study should be interpreted with caution. Finally, the number of studies for subgroup analyses was relatively small, thus, further studies are needed for sex, study design and ethnicity to identify the precise relationship between these two SNPs and increased risk of T2D.Regarding to the role of adiponectin level in the control of T2D, this meta-analysis of available studies suggests a strong significant association between the TT genotype of SNP + 276 G > T of adiponectin gene and increased risk of T2D in European population. Sex may modify this association. However, further studies with the higher quality are needed for confirming this association.


Background: Cancer-derived extracellular vesicles (EVs) are regarded to have significant function in most steps during cancer progression. This meta-analysis aims to investigate the accuracy of EVs as a biomarker in cancer diagnosis.Methods: The diagnostic efficacy of EVs for different cancers was assessed using pooled sensitivity and specificity, diagnostic odds ratio (DOR), and overall area under the curve (AUC) of the summary receiver operating characteristic (SROC). The positive likelihood ratio (PLR) and negative likelihood ratio (NLR) were verified to estimate the diagnostic efficacy of EV at a clinical level.Results: In all, 6,183 cancer patients and 2,437 healthy controls from 75 eligible studies reported in 42 publications were included in the study. The overall pooled sensitivity, specificity, PLR, NLR, and DOR were 0.62 (95% CI: 0.60–0.63), 0.76 (95% CI: 0.75–0.78), 3.07 (95% CI: 2.52–3.75), 0.34 (95% CI: 0.28–0.41), and 10.98 (95% CI: 7.53–16.00), respectively. Similarly, the AUC of the SROC was 0.88, indicating a high conservation of EVs as an early diagnostic marker. Furthermore, subgroup analysis suggested that the use of small EVs as a biomarker was more accurate in serum-based samples of nervous system cancer (p < 0.001). As a result, ultracentrifugation and quantification and size determination methods, such as Western blotting and ELISA were the most reliable identification methods for EV detection. We also indicated that increased secretion of EVs made them a capable biomarker for diagnosing cancer in elderly European individuals.Conclusions: Our study provides evidence that EVs are a promising non-invasive biomarker for cancer diagnosis. Well-designed cohort studies should be conducted to warrant the clinical diagnostic value of EVs. Cancer is still the second-leading cause of death worldwide, with an estimated 10 million cancer-related deaths reported in 2020  have gradually attracted wide attention in recent years  principle. The selection of clinical outcomes after using EVs as an early cancer biomarker is detailed in P-value; and (v) studies including cancer patients with additional systematic disorders and co-morbidities such as inflammations and/or autoimmune diseases.The current meta-analysis was conducted in accordance with the PICOs principle. Studies that met the following criteria were considered eligible: (i) cancer was confirmed by immunohistochemical or histochemical analysis; (ii) levels of EVs in tissue, plasma, serum, or other body fluids were measured; and (iii) relationship between EV secretion level and the diagnosis and prognosis of cancer, such as sensitivity and specificity, were reported in case-control study or could be measured from the provided data. The following studies were excluded: (i) non-English articles; (ii) reviews studies, conference abstracts, meetings, comments, letters, or experiments on cell line and animal models (iii); duplicate articles or continued studies of previous publications. (iv) studies with unqualified key data such as true positive (TP), false positive (FP), false negative (FN), true negative (TN), odds ratios (ORs) with 95% confidence intervals (CIs), and inadequate Information from all eligible studies was separately recorded by two investigators (S. L. and Y. L.). The following key component data were collected from the included studies: first author's name, publication year, country origin, continent, cancer type, cancer stage, cancer category, specimen type, patient age group, therapy type, sampling size, EV purification and identification, detection methods, EV biomarkers, and true and false positives and negatives. Any inconsistency or disagreement in the research process was resolved through a debate and consultations. If a consensus could not be reached, a third investigator (S.I.) resolved these disagreements based on the original data. We also sent e-mails to the corresponding authors of qualified studies and asked for the original data, as well as any missing or additional information, required for our meta-analysis. The systematic review and meta-analysis were conducted according to the recommendations of the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines (46). In case of controversial judgement, the diagnostic accuracy of each study was evaluated independently by three authors using the Quality Assessment of Diagnostic Accuracy Studies 2 (QUADAS-2) tool, which consists of four domains: patient selection, index text, reference standard, and flow timing (47). Any discrepancies were resolved through a discussion. Each assessment was subjected to seven questions that were answered with “yes,” “no,” or “unclear.” The answer of “yes” indicated that a study's risk of bias was low, whereas “no” and “unclear” indicated a high risk of bias.Q test and Higgins I2 statistic from non-threshold effect  or median (range), including a description of qualitative variables such as numbers and percentages. The diagnosis accuracy of included studies was presented on the summary receiver operating characteristic (SROC) curve. Subgroup analysis was conducted to determine the source of heterogeneity. Publication bias was evaluated by a funnel plot and Egger's regression test. A value of “Pr > |z|” <0.05 was considered to indicate potential publication bias. All the reported p values were two-sided, and a p < 0.05 was considered to indicate statistical significance.The systematic meta-analysis was conducted by using the R software version 4.0.1  and package “mada” version 0.5.9. Pooled sensitivity, pooled specificity, negative likelihood ratio (NLR), positive likelihood ratio (PLR), and diagnostic odds ratio (DOR) were calculated with corresponding 95% CIs to evaluate the diagnostic value of EVs. Heterogeneity of studies in meta-analysis refers to the variation in study outcomes between studies , respiratory system cancer (n = 8), endocrine system cancer (n = 5), urinary system cancer (n = 5), and nervous system cancer (n = 2). We did not find any study between 2019 and 2021 according to the inclusion/exclusion criteria and PICOs principle. The studies were conducted mostly in Asia  and Europe , and four studies were conducted in America (10%), with no study conducted in the African continent. Among these studies, EV secretion levels were measured in serum samples (n = 22), plasma samples (n = 12), urine samples (n = 6), saliva sample (n = 1), and ascites sample (n = 1). The major characteristics of EVs in the included studies are shown in The main characteristics of all included literature are presented in The main results of this meta-analysis with regard to the relation between EV secretion and cancer risk are shown in p = 0.004). The SROC curve for the included studies is indicated in The effect of heterogeneity on the diagnostic threshold was evaluated based on the Spearman correlation coefficient. p > 0.05). Therefore, based on these covariates, the pooled sensitivity, specificity, and AUC for important sub-analysis parameters were measured. The results showed higher accuracy in American than in Asian and European populations, with a sensitivity of 0.79 (95% CI: 0.72–0.84), specificity of 0.74 (95% CI: 0.64–0.83), and AUC of 0.822  compared with the other four types, with a sensitivity of 0.85 and specificity of 0.80. With regard to specimen type, EVs showed a higher diagnostic accuracy for cancer detection in urine samples (with an 0.79 sensitivity and 0.82 specificity) than in plasma (with an 0.50 sensitivity and 0.72 specificity) and serum (with an 0.66 sensitivity and 0.77 specificity) samples, with an AUC of 0.78 , those sedimenting at speeds lower than 20,000 × g into medium EVs , and those sedimenting at 2,000 × g into large EVs   showed higher sensitivity and specificity than kit-based isolation methods . The kit-based isolation methods depend on the optimization step for rapidly isolating EVs from the tissue culture media  and the high level of heterogeneity (I2 = 69%) remained (t = 1.16 and p = 0.164). Hence, no noticeable evidence for significant publication bias in our meta-analysis was observed, which indicates that our meta-analysis results are stable and credible.Funnel plots and Egger's test were performed to estimate publication bias. These were conducted by precluding a single study at a time and significant differences between events and hypothesis were observed  Tobias,. The triremained . The resIt is well-established that cancer cell-derived EVs released into a tumor microenvironment and circulation can promote tumor progression and metastasis by inducing matrix remodeling, angiogenesis, inflammation, and metastatic niche formation  enriched in EVs that can serve as biomarkers for cancer  are more accurate diagnostic biomarkers than plasma or urine-based EVs. Recently, several studies have reported that the biological fluid source of exRNA is associated with the histological grade of cancer. Surely, most clinical studies have evaluated EVs as cancer biomarkers in whole blood, urine, and cerebrospinal fluids rather than in tissue. For example, serum miR-34a levels were associated with the last stage of breast cancer and serum-based tumor markers were the most effective screening tool for the detection of metastatic breast cancer  effect size. In theory, this will yield an unbiased estimate of the effect size. Too, major advantage of this approach is finding the best estimate of the unbiased effect size in intuitive visual display , Sichuan Province, China grants (No. 18080) that were awarded to SI.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.


The possible association of human papillomavirus (HPV) and bladder cancer has been controversial. Older findings suggest a significant association between the virus and bladder cancer. The aim of this study was to evaluate the data from the last ten years to estimate the prevalence of the virus in bladder cancer patients and to assess the association between the virus and cancer.A search of major databases was conducted to retrieve published English language studies between January 2011 and March 2021. In the present study overall prevalence of the virus in bladder cancer patients was estimated along with the prevalence of subgroups. Also, the possible associations between the prevalence of the virus and bladder cancer and the possible impact of variables in the geographical area and the type of sample were measured by comprehensive meta-analysis software .Unlike previous studies, despite the relatively high prevalence of the virus [pooled prevalence: 14.3% (95% CI 8.9–22.2%)] no significant association was found between HPV and bladder cancer . No significant association was found between geographical area (except Asia) and type of sample with bladder cancer.Given the significant prevalence, despite the insignificance of the association between virus and cancer, it seems that more studies with case–control design are needed to elucidate this association. Bladder cancer is the tenth most common cancer globally, with 573,000 new cases and 213,000 deaths each year . HPV is The present meta-analysis study reviewed articles over 10 years ago about the potential association of HPV in bladder cancer, besides the latest global prevalence of the virus in bladder cancer patients, also in this study, we discuss about the effects of various factors, including geographical distribution and type of sample used for virus detection.This study was conducted based on the PRISMA  protocols. Main electronic databases include Medline (PubMed), Scopus, Web of Science, and Google Scholar were systematically searched by two investigators independently (A.K. and M.R.) for related articles published from January 2011 to March 2021. The used search keywords and terms were “HPV or human papillomavirus” AND “bladder cancer”. Additionally, the references of all original and review articles were hand-searched to finding other relevant studies.Eligible studies were analyzed by abstract and title. First, all the articles were imported into the Endnotes software, and in the next step, duplicated articles were then removed. The following inclusion criteria were applied for recruited publications in this study: (1) articles with case–control, cross-sectional, and cohort design; (2) published studies in the English language; (3) the papers with full-text, and (4) papers published from January 2011 to March 2021 with a digital object identifier (DOI); In addition, we also excluded studies with the following reasons: (1) Research on animals and in vitro; (2) all types of review papers, letters, comments, case reports, and case series.Data from all retrieved studies were extracted by two authors and double checked. If there were any discrepancies of opinion or disagreement between the two investigators, they would reach a consensus by consensus and discussion with the lead investigator (A.K.). We extracted the following variables from articles: first author’s last name, year of publication, country, total numbers of cases and controls, diagnosis methods, and type of sample.We used the Joanna Briggs Institute (JBI) checklist for the quality assessment of the included articles , which cIn the present systematic review and meta-analysis, we estimated the pooled odd ratio and prevalence of HPV in bladder cancer patients based on the random effect model (REM) with a 95% confidence interval (CI). The REM application lets for a distribution of true effect sizes between studies. Prevalence was also estimated based on subgroups of geographical area (continents), diagnostic method, cancer grade, sample type, virus genotype, HPV type (high risk and low risk), and gender of the subjects. In addition to the overall odd ratio, the geographic area and type of samples with bladder cancer association were calculated. The I2 statistic was performed to evaluate the studies' heterogeneity, and the publication bias was assessed by Egger’s regression test, and p-value < 0.05 was considered statistically significant; funnel plot represents the publication bias between studies. We applied the comprehensive meta-analysis software  for all analytical steps.In the initial literature search strategy, a total of 1326 studies were identified from international electronic databases. In addition, 3 relevant papers were identified when searching for reference lists manually. Altogether, 659 duplicate studies were excluded, after the screening of 670 articles by title and abstract, which resulted in the removal of 620 articles. The remaining 51 articles were assessed for eligibility by the full-text review. After screening the full texts of the included articles, 24 studies were excluded based on reasons described in Fig. Twenty-seven datasets with a total of 2954 bladder cancer patients from eighteen countries were included in this study. The publication date of the studies was from January 2011 to March 2021. Twenty-seven records were selected for further analysis, of which 11 were from Asia, 1 study from Oceania, 4 studies from America, 3 studies from Europe, and 1 study from Africa. The characteristics of the selected studies of this analysis are summarized in Table The total number of bladder cancer patients included in this meta-analysis was 2954 from adults based on the results of 27 datasets. The pooled prevalence of HPV infection among bladder cancer patients was 14.3% (95% CI 8.9–22.2%) based on a random-effects meta-analysis. In sub-group analysis by continent, the maximum and minimum prevalence of HPV infection among bladder cancer patients were found in Africa and Oceania, respectively . Sub-group analysis based on HPV types showed that high-risk types had the highest prevalence , while low-risk types showed a lower prevalence . Details of the Pooled prevalence of HPV and bladder cancer risk for the subgroups are presented in Table In 12 studies, the meta-analysis showed that HPV was not associated with bladder cancer, as shown in Fig. I2 statistics revealed significant heterogeneity among the included studies .Egger's regression test results for publication bias calculation showed the statistically significant (P < 0.000), this can be due to the low number of studies and/or studied subjects, the very high and/or low number of positive cases in some studies, the difference between diagnostic methods and the difference between operators skills Fig. . The resBladder cancer is the tenth most common cancer in the world, with high morbidity and mortality rate. In addition to factors involved in cancer progressions such as gender, genetics, cigarette smoking, and exposure to polycyclic aromatic compounds, viral infections such as the human papilloma virus (HPV) have been introduced to be effective in the bladder cancer etiology and/or development . The assSeveral studies have surveyed the prevalence of high-risk and low-risk types of virus and their involvement in the development of bladder cancer , 43–45. However, we could not found any significant association between bladder cancer and HPV in our study; the results of our study are in line with various studies conducted by different groups in which no significant association was found between HPV infection and bladder cancer , 45–52 IThe present study has faced limitations, such as the geographical limitations of the reports, so that reports were not available from some countries and continents according to the inclusion criteria. Other limitations included studies published in local languages.Despite the results of previous meta-analysis studies that reported the etiological role of HPV in bladder cancer, we conclude there is no significant association between HPV infection and bladder cancer. However, according to the analysis of studies of the last ten years, a relatively high prevalence of the virus was observed, which raises the possibility that with the increase of studies in this field, more comprehensive results will be obtained. Furthermore, the geographical area has a potential impact on the association between HPV and bladder cancer as a high prevalence of HPV infection was observed in Asia.


An estimated 125 million workers are exposed to asbestos worldwide. Asbestos is classified by the International Agency for Research on Cancer as a Group 1 carcinogen. The association between occupational asbestos exposure and kidney cancer is not well established however. This study aimed to determine the mortality and incidence of kidney cancer in workers who have been exposed to asbestos. We performed a systematic review and meta-analysis to evaluate the association between occupational asbestos exposure and kidney cancer.Medline, EMBASE, and Web of Science were searched according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines for articles on occupational asbestos exposure and kidney cancer. The studies reported the standardized mortality ratio (SMR) or standardized incidence ratio (SIR) of kidney cancer in workers exposed to asbestos. SMRs or SIRs with its 95% confidence interval (CI) were pooled using a fixed-effect model.PQ = 0.09, I2 = 24.87%). The overall pooled-SIR of kidney cancer was 0.95 (95% CI: 0.79–1.11), with no significant heterogeneity . Subgroup analysis did not find any increased association with occupational asbestos exposure. There was no evidence of publication bias with Egger’s test P values of 0.08 for mortality studies and 0.99 for incidence studies.Forty-nine cohort studies involving 335 492 workers were selected for analysis. These studies included 468 kidney cancer deaths and 160 incident cases. The overall pooled-SMR of kidney cancer was 0.95 (95% CI: 0.86–1.05), with no significant heterogeneity (This systematic review and meta-analysis did not show evidence of association between occupational asbestos exposure and kidney cancer mortality or incidence. This systematic review and meta-analysis was necessary as there remains uncertainty about whether asbestos causes kidney cancer. This meta-analysis has the largest population of asbestos workers to date (335 492 participants) with 468 kidney cancer deaths and 160 incident kidney cancer cases. Mortality and incidence of kidney cancer were not found to be elevated overall, or among subgroups with occupational asbestos exposure. However, weaknesses in the individual epidemiological studies may limit the ability to identify associations, and longer follow-up may be required to detect kidney cancer risk.The World Health Organization estimates that there are about 125 million people exposed to asbestos in the workplace globally . Work-reThe 2012 International Agency for Research on Cancer Monograph 100C  states tRenal cell cancer represents about 85% of kidney cancers, and renal pelvis cancer comprising the rest of kidney cancer cases  guidelines (Two reviewers (C.P. and K.P.) independently reviewed all the articles and extracted the data.We searched on Medline, EMBASE, and Web of Science up to 31 December 2019. Search terms were the following:‘Asbestos’, ‘Chrysotile’, ‘Amphiboles’, ‘Occupational exposure’, ‘Work exposure’, ‘Cancer’, ‘Kidney cancer’, ‘Renal cancer’, ‘Renal cell carcinoma’, ‘Neoplasm’, ‘Malignancy’, ‘Mortality’, and ‘Cohort study’. The reference lists of relevant publications were also reviewed manually to identify additional studies.Selection of studies was based on a clearly defined set of inclusion and exclusion criteria. If studies involving the same cohort had been published previously, only the most recent study was included.To be eligible for inclusion, studies had to meet the following criteria:Cohort study with published data.Contained information to estimate the relation between asbestos exposure and kidney cancer risk (effect size) in terms of relative risk, hazard ratio, SMR, or SIR.Included workers with predominant exposure to asbestos, such as asbestos cement, textile, and mining workers.The studies were excluded if they met the following criteria:Overlapping articles or duplicate data.Animal studies.Meta-analyses or review articles.No kidney cancer outcomes.Studies which included asbestos exposure that did not occur in the workplace.Data containing name of first author, publication year, geographic location of cohort, industry type, asbestos type, exposure assessment, period of employment, cohort size, follow-up period, total person-years of observation, SMR or SIR, and 95% CIs for kidney cancer, observed and expected kidney cancer cases or deaths were identified. Data were extracted and transferred to Microsoft Excel by two authors (C.P. and K.P.) for each eligible study. All disagreements arising from this process were resolved by discussion.C.P. and K.P. assessed the risk bias of each eligible study using the Newcastle–Ottawa Scale (NOS) tool  and 9 incidence studies . Ninety-two percent of the total cohort participants were male workers.Out of 49 studies, 38 studies reported SMR, 10 studies reported SIR, and 1 study reported the hazard ratio. Studies were conducted in Europe, North America, Asia, and Australia. The main industries represented by the studies included the mining, textile, cement, and insulation industries. Other asbestos workers included those working in the shipyards, ship breaking industry, rail industry, asbestos plants, and friction material manufacturing.The follow-up period for these studies ranged from 10 to 69 years for the mortality studies  and ranged from 8 to 47 years for the incidence studies .There was a total of 468 kidney cancer deaths among the 38 mortality studies. SMR estimates reported by the individual studies ranged from 0 to 5.00.There was a total of 160 incident kidney cancer cases among the 11 incidence studies. SIR estimates reported by the individual studies ranged from 0.48 to 1.85.Authors collected smoking data for 22 of the mortality studies, and 8 of the incidence studies.Annals of Work Exposures and Health online edition). The reasons for exclusion of studies are shown in Annals of Work Exposures and Health online edition).The main characteristics of the included mortality and incidence studies are shown in Q = 45.26, PQ = 0.09, I2 = 24.87).The pooled-SMR based on the fixed-effects model for kidney cancer among workers with occupational asbestos exposure was 0.95 (95% CI: 0.86–1.05), which is shown as a Forest Plot in The random-effects model yielded a similar effect size of 0.89 (95% CI: 0.73–1.05).Three mortality studies with zero observed and/or expected events were removed from the meta-analysis . The random-effects model yielded a similar effect size of 0.95 (95% CI: 0.78–1.11).The pooled-SIR based on the fixed-effects model for kidney cancer was 0.95 (95% CI: 0.79–1.11), which is shown as a Forest Plot in There was no association between asbestos type and kidney cancer. The SMRs for Chrysotile and Amphibole exposure were 0.73 (95% CI: 0.46–1.01) and 0.65 (95% CI: 0.25–1.06), respectively. Workers who were exposed to undefined asbestos fibres had an SMR of 1.01 (95% CI: 0.90–1.11).There was no significant association shown between workers in the mining, cement, or mixed industries, and kidney cancer SMRs. The kidney cancer SMR of those who worked in the textile industry had a marginally statistically significant pooled-SMR of 0.47 (95% CI: 0.04–0.91).There was no statistically significantly association between the kidney cancer SMRs in the various subgroups and cohort size, follow-up period, person-years follow-up, NOS rating and geographic location.There was no association between types of asbestos fibre exposure and kidney cancer SIRs. Workers who were exposed to chrysotile and amphibole fibres had SIRs of 0.93 (95% CI: 0.47–1.4) and 0.83 (95% CI: 0.57–1.09), respectively.There was no association between working in the mining, cement, and mixed industries, and kidney cancer SIR, with SIRs of 1.10 (95% CI: 0.60–1.60), 0.93 (95% CI: 0.62–1.23), and 1.03 (95% CI: 0.81–1.26), respectively. In comparison, workers in two studies representing the shipyard and ship breaking industries had a marginally lower SIR of 0.53 (95% CI: 0.09–0.98).Other study characteristics such as cohort size, follow-up period, person-years follow-up, and geographic location did not have any statistically significant association with kidney cancer incidence.Annals of Work Exposures and Health online edition).There were six studies that provided exposure assessment data specific to kidney cancer outcomes, including five mortality studies and one incidence study. The exposure assessment across all six studies was of varying quality, graded according to the characteristics proposed by Annals of Work Exposures and Health online edition). This showed that studies with good exposure assessment specific to kidney cancer outcomes, also did not demonstrate an association between occupational asbestos exposure and kidney cancer outcomes.The pooled-SMR for the four mortality studies with adequate exposure assessment data was 0.65 (95% CI: 0.30–1.00) as shown in Annals of Work Exposures and Health online edition). The pooled-SMR was marginally statistically significant after leaving out the Harding study [SMR: 0.88 (95% CI: 0.78–0.98)] .Sensitivity analysis showed that the pooled-SMRs were relatively robust to the exclusion of any one study from the overall meta-analysis and did not change by more than 10% .Sensitivity analysis of the incidence studies showed that the pooled-SIRs did not significantly change after excluding studies one by one. This suggested stability to the pooled-SIR results (see Begg’s funnel plots shown in P = 0.08) nor among the incidence studies .Additionally, Egger’s regression test did not show evidence of publication bias among the mortality studies  and pooled-SIR . For the incidence studies, SIRs from 10 studies and a hazard ratio from 1 study were pooled. The SIR and hazard ratio are generated through multivariable regression models and reflect the risk of the outcome in prospective data, hence combining the two is unlikely to produce heterogeneity.There is a high survival rate among people with kidney cancer  were higher compared with smaller cohort sizes (<5000 workers), with narrower CIs. This is consistent with increased precision of results with an increased sample size. Higher pooled estimates with an increased precision of the estimate size were also observed for longer person-years of follow-up (≥100 000 person-years) compared with shorter person-years of follow-up (<100 000 person-years), which is consistent with kidney cancer being a disease associated with advanced age.PQ = 0.74, I2 = 0.00%)  reported 2 kidney cancer deaths for 498 insulation workers 35 years after onset of exposure and reported that the SMR for these workers was 5.56 (95% CI: 1.80–12.96). The study did not find any kidney cancer deaths for workers with less than 35 years after onset of exposure. et al., 1979; Poor exposure assessment can result in a bias to the null (Furthermore, subgroup analysis according to the various NOS ratings of the individual studies did not show any association with kidney cancer outcomes. We concluded that neither the quality of exposure assessment nor the overall quality of the individual cohort studies affected the final result of this meta-analysis.There were potential weaknesses in ascertaining the occupational history for workers in some studies which involved answering self-reporting questionnaires. This may have led to inaccurate and inconsistent description of their work duties and quantification of asbestos exposure. Furthermore, there was a lack of information about workers’ subsequent occupation after leaving the asbestos industry for which there may have been exposure to other chemical hazards.The comparison population for the included studies was the general population from which the workers were derived. It is possible that the control population included asbestos exposed individuals, thus potentially causing a bias towards the null.These asbestos exposed workers were at higher risk of developing other cancers such as lung cancer and mesothelioma with a relatively short lag time between disease manifestation and death compared with kidney cancer. This may have resulted in decreased detection of kidney cancer cases, which may potentially bias the overall results towards the null.Risk of bias assessment was conducted using the NOS which has potential limitations. These limitations include subjectivity in interpreting the NOS criteria which may affect inter-rater reliability (This meta-analysis showed a lack of association between occupational asbestos exposure and kidney cancer risk. The summary risk estimates for both the mortality and incidence studies were not elevated. There was no clear dose–response relationship. These results are consistent with the results from the previous meta-analysis by While there is biological plausibility from animal studies of occupational asbestos exposure causing kidney cancer, this meta-analysis does not provide evidence to support this association. The inherent weaknesses of the individual studies including the lack of exposure and dose–response data, prohibited the establishment of occupational asbestos exposure as a potential cause of kidney cancer.Further research is required including characterization of occupational exposure levels at an individual level. Longer-term follow-up may also be required to detect any potential kidney cancer risk and improve the precision of outcome measures. Data about known confounders for kidney cancer such as smoking, and obesity should also be incorporated into future research.wxaa114_suppl_Supplementary_Tables_S1_S2_S3Click here for additional data file.wxaa114_suppl_Supplementary_Tables_S1_S2_S3_ReviewClick here for additional data file.wxaa114_suppl_Supplementary_Figures_S5_S6_S7Click here for additional data file.


To overcome the frequently debated crisis of confidence, replicating studies is becoming increasingly more common. Multiple frequentist and Bayesian measures have been proposed to evaluate whether a replication is successful, but little is known about which method best captures replication success. This study is one of the first attempts to compare a number of quantitative measures of replication success with respect to their ability to draw the correct inference when the underlying truth is known, while taking publication bias into account. Our results show that Bayesian metrics seem to slightly outperform frequentist metrics across the board. Generally, meta-analytic approaches seem to slightly outperform metrics that evaluate single studies, except in the scenario of extreme publication bias, where this pattern reverses. The focus of the present study is on close replications, which ‘aim to recreate a study as closely as possible, so that ideally the only differences between the two are the inevitable ones (e.g. different participants)’ , p. 218.A variety of methods, both frequentist and Bayesian, has been proposed to quantify replication success. Some of them have been widely used in various large replication projects across scientific fields such as psychology , economiWith a plethora of available quantitative measures of replication success, it might be difficult for replication researchers to decide which indicator(s) they should use to quantify replication success. Therefore, it is important to gain more insight into how these metrics function.In one of the few studies that examines features of methods of quantifying replication success, Schauer & Hedges  focused p < 0.05; e.g. )05; e.g. ). On the05; e.g. . Given t05; e.g. , researc05; e.g. ). This b05; e.g. ). Howevep-value, for which no analytical solutions are readily available. In our paper, we therefore resort to simulation to examine the properties of these metrics. In addition, contrary to Schauer & Hedges ) to ±0.0135 (i.e. 1.96 × √[0.05 × 0.95/1000]) for no versus maximal publication bias, respectively. These margins are for proportions, hence for percentages we have ±0.3% and ±1.4%, respectively. Findings in p-value, the false positive rate shows the largest decrease when moving from 100% to 75% publication bias, followed by a smaller and more gradual decrease for the remaining decreasing publication bias levels. On the other hand, the true positive rate does not show such a large drop when moving from the 100% to the 75% publication bias scenario.Several general observations can be made when comparing the replication success metrics with one another across the five different publication bias levels. These should, however, be made taking into account uncertainty margins around the observations. Rough estimates of 95% uncertainty marginsThe significance metric and the JZS BF seem to have similar results. The false positive rate is approximately 5 to 6% when publication bias is 100%, while this rate is about 2 to 3% for the remaining publication bias levels. The true positive rate for these two metrics varies between approximately 95% for the lower publication bias levels and 98% for the higher publication bias levels.The Small Telescopes has much higher false positive rates compared with the remaining metrics, which varies from 24% when publication bias is 0% to 51.7% when publication bias is 100%. Although the true positive rate shows a little decrease with decreasing levels of publication bias, this rate is almost 100% across all publication bias scenarios.The classical meta-analysis has a high false positive rate (40.5%) when publication bias is 100%, while this rate is about 6 to 11% for the remaining publication bias levels. The true positive rate for this metric varies between approximately 98% for the lower publication bias levels and 100% for the higher publication bias levels.Looking at the Bayesian meta-analysis, the false positive rate is 29.4% when publication bias is 100%, while this rate is about 4 to 7% for the remaining publication bias levels. The true positive rate varies between approximately 97% for the lower publication bias levels and 100% for the higher publication bias levels.p-value metrics. The true positive rate is quite high (97.6%) and comparable to that for the significance, JZS BF and sceptical p-value metrics, but lower than for the Small Telescopes and meta-analysis metrics.As explained above, for the replication BF, results were only computed for the 100% publication bias level. Looking at the replication BF, the false positive rate is somewhat high (9.8%). It is clearly lower than that for the Small Telescopes and meta-analysis metrics, but clearly higher than that for the significance, JZS BF and sceptical p-value, the false positive rate is 4.7% when publication bias is 100%, while this rate is about 0.3 to 0.8% for the remaining publication bias levels. The true positive rate varies between approximately 89% for the lower publication bias levels and 98% for the higher publication bias levels.For the sceptical 2 did qualitatively worse in terms of a high rate of false positives compared with the remaining metrics. However, it might be difficult to compare the significance metric, the (replication) BF, the (Bayesian) meta-analysis and the sceptical p-value with one another based on Overall, these findings show that the Small Telescopesp-value. In figures Specifically, for each of the five publication bias level scenarios, the proportion of true positives is plotted against the proportion of false positives for a wide range of threshold values in figures p-value, the alpha levels varied from 0.0001 to 0.5 in increments of 0.0001 (i.e. 5000 thresholds). For the Bayesian methods, the BF thresholds varied from 1/10 to 200 in increments of 1/25 (i.e. 4998 thresholds). We do realize that the majority of thresholds we included in our study are not used in practice, and in the case of the Bayesian methods, even provide evidence for the null hypothesis. However, the aim of the present examination is not to compare individual thresholds with one another, but to compare the different quantitative measures of replication success to one another across the board. For instance, if the curve for the JZS BF is entirely above and to the left of the curve for the replication BF, that indicates that for any threshold for the replication BF, there exists a threshold for the JZS BF that has a higher true positive rate and a lower false positive rate and is therefore strictly better in terms of quantifying replication success. To provide the reader with a handhold, we marked commonly used thresholds  in the figures with specific symbols.For the frequentist methods and the sceptical Several general observations can be made when comparing the five different figures. These should again be made taking into account our rough estimates of uncertainty margins around the observations . The findings show the extent to which working with more conservative thresholds for statistical evidence reduces the false positive rates at the expense of reducing the true positive rates.p-value seems to outperform all other metrics in the panel; however, the curves for the sceptical p-value and the JZS BF appear to cross each other : the sceptical p-value seems to perform better than the BF for the more liberal thresholds in the top left, whereas the BF seems to perform slightly better for the more conservative thresholds. Furthermore, the JZS BF seems to outperform the significance metric, the (Bayesian) meta-analysis and the replication BF, though the differences between the JZS BF and the significance metric are quite small. Although the classical and Bayesian meta-analyses seem to have a similar performance, the Bayesian meta-analysis lies just slightly more to the top and more to the left of the frequentist alternative. Both meta-analyses seem to perform slightly worse than the significance method for the more conservative thresholds. The replication BF clearly performs worse with respect to true and false positive rates than the sceptical p-value, the JZS BF and the significance metric, with differences clearly exceeding the uncertainty margins. Although the replication BF performs worse than both meta-analyses, the curve for the replication BF on the one hand and the curves for the meta-analytical techniques on the other hand appear to cross each other . Specifically, the replication BF performs better than the classical meta-analysis, but similar to the Bayesian meta-analysis for the very conservative thresholds.When looking at the top left in p-value, the JZS BF and the significance metric. Just like for the 100% publication bias scenario, these differences are fairly small. Note that the replication BF is designed for scenarios where the original result is significant. Therefore, findings for the replication BF are only presented for the scenario of 100% publication bias.Looking at p-value. This means that the Bayesian meta-analysis has the best performance, followed by the classical meta-analysis, the JZS BF, the sceptical p-value (the latter two seem to have a very similar performance in the top left of the figure) and the significance metric.When publication bias is 50%, we find a similar pattern to the 75% publication bias scenario , except p-value and the significance metric (the latter two have a very similar performance).Quite consistent with the previous two publication bias levels, p-value. Bayesian meta-analysis has the best performance, followed by the classical meta-analysis, the default BF, the significance metric and the sceptical p-value, with the differences between the methods again being relatively small.Finally, and an equal rate of false positives compared with the significance metric at threshold 0.01.To provide one, fairly arbitrary, example of a comparison between two methods for specific thresholds: inspection of each figure shows that the green triangle (corresponding to a JZS BF with a threshold of 3) is right above the black circle  in each of the five figures. This indicates that regardless of the level of publication bias, the JZS BF with a threshold of 3 obtains a higher rate of true positives (approximately 0.3–0.5%) In sum, Bayesian metrics seem to slightly outperform frequentist metrics across the board. Generally, meta-analytic approaches seem to slightly outperform metrics that evaluate single studies, except in the scenario of 100% publication bias, where this pattern reverses.. 4p-value.In the present study, we simulated original and replication studies to evaluate and compare a number of quantitative measures of replication success in terms of detecting real and spurious effects across different publication bias levels. We included the following replication success metrics that make claims about the existence of a true underlying population effect: the traditional significance test metric, the Small Telescopes, the (Bayesian) meta-analysis, the default BF, the replication BF and the sceptical p-value and the JZS BF seem to have the best performance with respect to true and false positives when one assumes that only significant original studies achieve publication. In the scenarios where we assumed that at least 25% of the non-significant original studies achieve publication, the Bayesian meta-analysis seems to outperform the remaining replication success metrics. However, it is important to note that the differences in true and false positive rates between the significance metric, the JZS BF, the (Bayesian) meta-analysis and the sceptical p-value were quite small. The replication BF performed slightly worse compared with the significance metric, the JZS BF, the (Bayesian) meta-analysis and the sceptical p-value. The Small Telescopes did worse compared with the other metrics; it seemed to be too liberal.The take-home message when surveying the results of all simulations together is that the sceptical 3Replication success is an interesting concept; when conducting the present study we also learned that replication success is a difficult concept. We used replication success in this study in its binary form (i.e. success/failure). However, one could argue that the extent to which the findings of two studies are consistent with each other is a matter of degree, and binary success/failure inferences might be overly simplistic. Additionally, a success/failure inference based only on consideration of the quantitative results of a study ignores other important factors such as study design. In our study, the original and the replication studies were exactly the same; the only differences between the two were the samples  and the sample sizes . However, in daily science, a replication classified as ‘failed’ might not necessarily mean that the tested theory is wrong. In fact, it has been argued that the failure to replicate results might reflect the presence of hidden moderators, which refers to contextual differences between the original study and the replication attempt (e.g. ). FinallSeveral caveats need to be discussed. Not all available quantitative measures of replication success were included; some of them did not fit in the design of the present study. For example, Anderson & Maxwell  suggest Furthermore, it should be noted that some of the methodological decisions were made for the sake of the simplicity of the present study. The first one is that we included only two distributions for the underlying true population effect sizes, namely one distribution where the mean was 0.5 , and one distribution where the mean was 0 (for the spurious effects). Even though we used distributions in our simulations (meaning that there were various true underlying effect sizes), rather than fixed underlying true effect sizes, and incorporated what we believe to be a realistic range of effect sizes, we cannot exclude the possibility that different choices for our effect size distributions would have led to different results. Additionally, we would like to emphasize that all procedures in this study are being evaluated on a criterion that we would like them to perform well on (separating meaningful from spurious true effects), but not on a criterion they was necessarily designed to perform well on.p-value. It could be argued that the mechanism behind the publication process is more complex than this. Several other, more gradual, biasing mechanisms are discussed by Guan & Vanderkerckhove [Another factor that might affect our results is the operationalization we used for publication bias. We included five constant probabilities of publishing non-significant results, regardless of the size of the erckhove .Furthermore, in the present study, we simulated that all replication studies were published, so there was no publication bias for the replication attempts. This may sound like an unrealistic scenario, since journals mostly seem to accept studies that are novel, meaning they prefer original studies over replications (e.g. ). HoweveFinally, it is important to note that our results might depend on how we defined the criteria for each metric. We do not think that our results would be qualitatively different if we would have defined the replication success criteria differently, but we cannot rule this out.p-value performed particularly well under scenarios of high publication bias.Allowing for these caveats, this study is one of the first attempts to compare different quantitative measures of replication success in terms of detecting real and spurious effects. Even though the differences between the majority of the methods were small, the JZS BF and the Bayesian meta-analysis had higher true positives and/or lower false positives than their frequentist counterparts, irrespective of the publication bias level. The sceptical Click here for additional data file.


We performed several meta-analyses, using a random-effects model, and, when there was no evidence of heterogeneity, a fixed-effects model. We distinguished between all migrants, African-Europeans and Asian-Europeans.We retrieved five population-based surveys and two health care record studies. The latter included one incidence study, the remainder were prevalence studies. The meta-analysis of all studies yielded a pooled OR, adjusted for age and sex, of 1.73 (95% CI 1.42–2.11) for dementia in all migrant groups. However, the pooled OR of population surveys  was significantly higher than that for the health care record studies . The pooled ORs for African-Europeans and Asian-Europeans, based on population surveys, were 2.54 (95% CI 1.70–3.80) and 5.36 (95% CI 2.78–10.31), respectively.The discrepancy between health care record studies and population surveys suggests that many migrants remain undiagnosed. Migrants from Asia and Africa seem to be at significantly increased risk of dementia in Europe. Since the prevalence rates in their countries of origin are generally not higher than those for natives in Europe, there may be a parallel with the epidemiology of schizophrenia. Since the world population is ageing, age-associated diseases as dementia are expected to increase likewise. At the same time, population diversity is changing markedly, as since the 1950s emigration from several non-western countries to Europe has risen distinctly. First-generation migrants from non-western countries in Europe have now reached the age at which age-associated diseases may become manifest and several studies on the risk of dementia among migrant groups have been published.Studies conducted in the USA concerned ethnic minorities. According to a meta-analysis, the incidence rate of Alzheimer's disease is 64%  higher among African-Americans than among Caucasian-Americans .The final version of the study protocol was registered with PROSPERO on 11 February 2019. PRISMA guidelines were followed  terms were used.In order to be considered for the meta-analysis, studies were required (I) to report on incidence or prevalence rate of dementia for migrant groups or ethnic minorities in Europe, with a risk ratio  and a 95% confidence interval ; (II) to adjust the risk ratio for differences in age (or to provide data that make this adjustment possible) and (III) to have been published in an English-language, peer-reviewed scientific journal. Reports on the frequency of cognitive decline or cognitive impairment not based on operational criteria (e.g. DSM- or ICD-criteria) and reports that failed to specify the pertinent migrant or ethnic group were excluded.For details of the study selection, see online Supplementary Methods and Supplementary Fig. S1Two authors (MvS and JPS) rated the quality of prevalence surveys independently, using the quality assessment of prevalence studies as suggested by Boyle . The quaWe replaced the criterion as to ‘whether special features of the sampling design were accounted for in the analysis’ by the criterion age adjustment: were the results adjusted for age differences between groups? Quality was rated using scores from 1–10, with higher numbers indicating higher quality. In case of disagreement, consensus was reached through discussion. For details of the quality assessment, see online Supplementary Table S1.Two authors (MvS and JPS) extracted the effect sizes independently. They recorded information about diagnosis , type of diagnostic criteria, ethnicity and/or country of birth, sex, adjustment for age, for sex and/or other adjustments. In case of any discrepancy, consensus was reached by discussion.Since the majority of the participants in the included studies were foreign-born (see below), we use the term ‘migrants’. Only one study specified subtypes of dementia  African-European, i.e. sub-Saharan African, African-Caribbean, African-Surinamese; (2) Asian-European, i.e. from the Indian subcontinent, Chinese, Asian-Surinamese; (3) White-European, i.e. migrants from the European continent; (4) from the Maghreb/Middle-East, i.e. from Morocco or Turkey; (5) other ethnicity and (6) unspecified ethnicity. The terms ‘African-Surinamese’ and ‘Asian-Surinamese’ require an explanation. The Netherlands have received many immigrants from Surinam. The population of Surinam is ethnically diverse and the two largest groups are the African-Surinamese and the Asian-Surinamese. The latter migrated in the 19Individuals of African ancestry were described as ‘Blacks’, ‘Black Africans’, ‘Black Caribbeans’, ‘Black Other’ or ‘Surinamese-Creole’ (i.e. African-Surinamese). Since they share a genetic background, in a first analysis we grouped them into one category. For the same reason, we added the Asian-Surinamese to those from the Indian subcontinent and China.However, since the migration history of Africans who moved from the Caribbean or Surinam to Europe differs from that of individuals from sub-Saharan Africa, we also computed a separate effect estimate for the first group. There were too few effect estimates for the second group to allow a proper comparison between the two groups.The age-groups examined differed by the study. Some studies published data for 5- or 10-year strata, other studies reported age-adjusted effect estimates for large age-bands (e.g. 55 years old and over). We used effect estimates adjusted for age and sex or calculated such effect estimates.Using the information provided on numerators and denominators we calculated ORs for the migrant groups examined by Parlevliet et al. . We consLivingston et al.  providedAs for the meta-analyses, we calculated pooled ORs, adjusted for age and sex. First, we calculated the pooled OR for the development of dementia among all migrants in Europe . We also calculated separate pooled ORs for population surveys and for health care record studies . Second, we calculated the pooled OR for developing dementia among African-Europeans . Finally, we calculated the pooled OR for developing dementia among Asian-Europeans .Qw, was calculated to test whether the studies could be considered to share one common population effect size. When this was not the case, a random-effect model was used. Such a model, before it estimates the overall effect estimate and its variance, weighs each study both by the variance of its individual effect size and by the between study variance. Thus, the influence of studies that produce outliers is mitigated.The analyses were performed using SPSS version 23 and MetaWin version 2. First, a fixed effects model was used and a heterogeneity statistic, n = 3), purely descriptive studies (n = 12), studies performed outside Europe or published in another language than English (n = 9), and studies describing associations or diagnostic instruments (n = 14). One article was excluded, because it used the same sample as a previous publication.After screening 5937 publications, seven articles were retained for the review and meta-analysis. Online Supplementary Fig. S1 shows the PRISMA flow-diagram. We excluded studies from which no effect estimate could be derived .A funnel plot of the population-based surveys failed to show evidence of publication bias (To our knowledge, this is the first systematic review and meta-analysis of studies that compared the risk of dementia for migrant populations to that for the local reference populations in Europe. Seven studies have been conducted in only three countries. There was a marked discrepancy between results obtained from population-based surveys and health care record studies. Although the findings were heterogeneous, the results strongly suggest that migrants from Asia and Africa are at a significantly increased risk of developing dementia.A strength of this meta-analysis is the high quality of some prevalence surveys. Most samples were highly representative of a well-defined target population. Parlevliet et al.  used theOur meta-analysis has several limitations. First, the number of studies is small and investigations from South- or Eastern-Europe are lacking. Second, since the classification of migrants varies across studies, our division of migrants into sub-groups is necessarily somewhat arbitrary. Third, the cultural validity of most screening instruments was uncertain. Finally, the response rates of four population surveys were below 70%. The results of the survey with a higher response rate, however, also reported a significant excess of dementia among people from the Caribbean or continental Africa (Livingston et al., Our findings for African-Europeans are in line with previous reports of increased risks of developing dementia among African-Caribbeans in the UK (Adelman et al., A plausible interpretation of the discrepancy between population-based surveys and health care record studies implies that migrants tend to avoid the services or remain undiagnosed. However, this is as yet uncertain, because population-based surveys might over-estimate the risk for migrants.v. early dementia, this distinction can be difficult to determine consistently across studies and is likely to be influenced by contextual expectations and levels of support. An older person with a given level of cognitive impairment may be more likely to show impaired activities in daily living in an unsupported urban western setting than in a more rural, traditional setting with high levels of support from extended family. Moreover, the expectations of normal function for a person of that age may be lower.For an interpretation of the high risks of dementia for migrants in Europe it is important to be informed about the risk of dementia in the countries of origin. However, when one compares the pertinent risks, it is important to bear in mind that a diagnosis of dementia requires both cognitive impairment and impaired activities in daily living as a result. For people with borderline mild cognitive impairment Despite this caveat, it is worthwhile to consider the World Alzheimer Report (Alzheimer's Disease International, Consequently, the available evidence suggests that the increased risks of dementia among migrants from Asia and Africa in Europe are not explained by correspondingly high rates in their countries of birth (see also Prince et al., The first factors to consider are atherosclerosis and atherosclerosis risk factors, which are not only associated with the risk of vascular dementia, but also with that of Alzheimer's disease (Casserly & Topol, A second factor that may play a role is a diminished cognitive reserve. The cognitive reserve hypothesis posits that higher levels of education and participation in socially or mentally stimulating activities reduce the risk of dementia. This hypothesis is supported by a large number of studies (e.g., Ott et al., This is relevant for the present findings. In 1991 in the Netherlands, for instance, the proportions of male and female migrants from Morocco aged 50–64 years who had received no education at all were 88 and 97%, respectively. These figures were 40% and 83% for their Turkish counterparts (Central Bureau for Statistics, However, if the relatively low prevalence rates reported for Asia and Africa are valid (Alzheimer's Disease International, Consequently, we should consider the possibility that one mechanism by which migration from a developing country increases the risk of dementia is a decline in social status. Interestingly, Marmot  proposedHowever, there may be other mechanisms whereby low status confers an increased risk of dementia. A study of mice reported that chronic social defeat stress led to a reduced expression of the endothelial cell tight junction protein Claudin-5. This impaired the integrity of the blood-brain barrier in the nucleus accumbens, with resulting infiltration of interleukin-6 into vessel walls and brain parenchyma (Menard et al., Of note, epidemiological studies of migrants from developing countries have also shown an increased risk of schizophrenia (Selten, van der Ven, & Termorshuizen, We conclude that the evidence presented here suggests that migrants from Asia and Africa are at an increased risk of developing dementia and that they are less likely to reach the services. Since migrants from these continents are also at an increased risk of schizophrenia, there may be a parallel with the epidemiology of this disorder. The findings, which may provide more insight into the aetiology or pathogenesis of dementia, need to be confirmed by further studies.