Inflammatory illness is associated with depression. Preclinical work has shown that chemokines are linked with peripheral–central crosstalk and may be important in mediating depressive behaviours. We sought to establish what evidence exists that differences in blood or cerebrospinal fluid chemokine concentration discriminate between individuals with depression and those without. Following PRISMA guidelines, we systematically searched Embase, PsycINFO and Medline databases. We included participants with physical illness for subgroup analysis, and excluded participants with comorbid psychiatric diagnoses. Seventy-three studies met the inclusion criteria for the meta-analysis. Individuals with depression had higher levels of blood CXCL4 and CXCL7 and lower levels of blood CCL4. Sensitivity analysis of studies with only physically healthy participants identified higher blood levels of CCL2, CCL3, CCL11, CXCL7 and CXCL8 and lower blood levels of CCL4. All other chemokines examined did not reveal significant differences . Analysis of the clinical utility of the effect size of plasma CXCL8 in healthy individuals found a negative predictive value 93.5%, given the population prevalence of depression of 10%. Overall, our meta-analysis finds evidence linking abnormalities of blood chemokines with depression in humans. Furthermore, we have demonstrated the possibility of classifying individuals with depression based on their inflammatory biomarker profile. Future research should explore putative mechanisms underlying this association, attempt to replicate existing findings in larger populations and aim to develop new diagnostic and therapeutic strategies. Yet, 30% of patients fail to respond to such anti-depressants and among responders only a third attain remission.2 This high rate of treatment failure likely reflects an incomplete understanding of the pathogenesis of depressive disorders. Efforts are focused on effective stratification of a heterogeneous population and novel therapeutic avenues.Depressive disorders are the third leading cause of years lost to disability worldwide, rising to second among 15–49 year olds. This burden has increased over the past 25 years.10 Broadly, this evidence stems from four main observations:A third of those with depression show elevated inflammatory cytokines, in the absence of medical illness.Inflammatory illnesses are associated with greater rates of depression than noninflammatory illnesses.Up to 40% of patients treated with cytokine therapy develop clinical depression.Higher baseline inflammation is associated with a lack of response to anti-depressant treatment.Over the past quarter century, an increasingly compelling body of evidence has emerged linking inflammation to depression.chemotactic cytokines (chemokines) as an important group of molecules in both the immune system, where they act to coordinate immune cells and attract them to sites of ongoing inflammation, and the nervous system. Evidence indicates they play a role in neuron–glia communication, synaptic transmission, neurogenesis, neurodevelopment and plasticity and this was recently extensively reviewed by Stuart et al.11 Because of their role in cellular migration and immune coordination, chemokines are prime candidates for linking peripheral and central inflammation and orchestrating neuroinflammatory crosstalk. D’Mello et al.12 and Wohleb et al.13 highlighted the role for chemokines, particularly CCL2, in these processes in their studies of monocytic migration. Despite being widely expressed in the noninflamed adult brain,14 it is only in recent years that their role in neuropsychiatric disease has been explored more extensively. Recent studies have implicated CCL2, CCL3 and CXCL10 in modifications of neuronal transmission and alterations in cognitive function,17 and the neuromodulatory functions of other chemokines have been reviewed elsewhere.19 Several chemokines, including CXCL8 and CXCL10, have been associated with alterations in neuroendocrine regulation and hypothalamic–pituitary–adrenal axis function.21 The CXCL12–CXCR4 axis has been repeatedly identified as having a role in neurogenesis.24 Finally, crosstalk between CX3CR1 and CX3CL1 appears to be a critical pathway for communication between microglia and neurons during both physiological and pathological states.25 Alterations in neurotransmitter and hypothalamic–pituitary–adrenal axis function and reductions in neurogenesis are consistently associated with depressive pathologies.29 As such, these data provide compelling evidence of a possible role for the chemokine molecular family in the pathogenesis of depression.Preclinical work has identified et al.30 recently undertook a meta-analysis in the field and encountered such issues that limited their interpretation to only two of the most commonly studied chemokines, CCL2 and CXCL8.Existing biomarker studies of chemokines in depression are often underpowered, of disparate methodologies and are frequently conflicting. Chemokine nomenclature is notoriously difficult despite standardisation into a unified format. Ensuring a complete coverage, especially of older literature, can be challenging. Eyre et al.30 remain. Aiming to be pragmatic, clinically relevant and allowing for the current state of the field, we set out to be as comprehensive as possible while still using high-quality meta-analytical techniques. We primarily achieved this aim through extensive search terms utilising as many possible variant chemokine names as possible.Encouragingly, even within the past few months the field continues to grow. However, the issues encountered by Eyre In this paper, we lay out the results of our meta-analysis and frame them within the context of the current animal and human literature on inflammatory disease and depression, providing evidence for the role of chemokines in depression and identifying gaps in the literature to inform future research.Do differences in blood or cerebrospinal fluid (CSF) chemokine concentration discriminate between those with and those without depression?Our primary outcome:Do differences in blood or CSF chemokine concentration discriminate between those with and those without depression only in a healthy subgroup, or only in a subgroup with physical illness?Are differences in blood chemokine concentration more apparent in plasma or serum?Are higher-quality studies more likely to identify subgroup differences?Our secondary outcomes:We (SPL and LN) searched Embase, PsycINFO and Medline up to 5 March 2016. We manually checked references cited in the systematically searched articles. In an effort to avoid publication bias we included non-English language studies and grey literature . We used a broad but highly structured search strategy based around the PICOS framework, our Population is humans, the Intervention/Exposure is depression, the Comparison is no depression, the Outcome is change in blood or CSF chemokines, and the Study design included any type of study design. Keywords for the search included various combinations of terms for depression, depressive disorders and depression research scales, and both historical and current chemokine names. Extensive use of wildcards, truncation and Boolean operators was implemented to allow for variant names . The full search strategy is available in the Studies were selected for data extraction and analysis based on the following inclusion criteria: (1) original research studies measuring blood or CSF chemokine concentrations in depressed and nondepressed subjects; (2) subjects met depression criteria based on a conventional psychiatric classification system or recognised depression rating scale (see search strategy); (3) control subjects did not meet said criteria for depression; (4) in an attempt to be pragmatic and increase real-world applicability, we included studies with participants suffering from physical comorbidity (any comorbidity with an established inflammatory component or sequelae) if the comorbidity was comparable and identifiable between cases and controls to allow for subgroup analysis (see characteristics of included studies table).in vitro and nonhuman studies; (3) studies looking at stimulated levels of cytokines as they reflect the consequences of immune challenge as opposed to basal immune activity; (4) studies including participants who had not yet reached puberty.We excluded studies based on the following criteria: (1) participants had a comorbid or additional psychiatric diagnosis that would exclude or confound depression as per conventional psychiatric nosological thinking ; (2) 31 if data were presented only in graphical format, we used Engauge data extraction software;32 if presented as raw data with chemokine concentration and depression rating scale score, participants were dichotomised based on established cutoff scores for ‘caseness’ for the relevant rating scale and mean(±s.d.) calculated; if different severities of depression were presented separately, means and s.d. were combined based on subgroup number. Such calculations were carried out within Microsoft Excel 2013. Disagreements regarding inclusion were settled by consensus.Two independent reviewers (SPL and LN) used a custom data extraction template adapted from the Cochrane checklist of items see . Non-EngThis meta-analysis was carried out according to Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. To assess quality we adapted the Newcastle–Ottawa Quality Assessment Scale for observational studies as recommended by the Cochrane Collaboration, together with the Cochrane common classification scheme for bias, with additional points for adjusting or matching important confounders identified from the literature , body mass index, age, sex and medications), whether the source was a peer-reviewed article or an abstract, and whether we used Engauge data extraction software or data approximation giving a total score out of 17 . Given t33) for analysis. To be conservative, we used weighted standardised mean differences (SMD) between controls and cases to calculate the effect size of each study given the heterogeneity of the assays used to measure the chemokines  and, for blood chemokines, that of the modality in which they were measured . Weighted SMD and 95% confidence intervals (CIs) were then calculated using a random effects inverse variance model. This meta-analytic method includes both within-study variance and between-studies variation in the estimate of the uncertainty of results. Unlike a fixed effects model, a random effects model assumes that the underlying true effects vary from one study to another. A random effects model is more conservative and is chosen if significant heterogeneity is expected.We used RevMan 5.3 .Additional sensitivity analyses were performed for chemokines that attained statistical significance to further explore heterogeneity. Where numbers allowed, we assessed: only studies that used a Diagnostic and Statistical Manual of Mental Disorders (DSM) Major Depressive Disorder (MDD) diagnosis, specific subtypes of illness, only articles, removing Fontenelle 201235 using the metafor package.36 The nonparametric (rank-based) trim and fill data augmentation technique was used within R using metafor to estimate the number of studies missing from a meta-analysis because of the suppression of the most extreme results on one side of the funnel plot. The method then augments the observed data so that the funnel plot is more symmetric and its effect on the results was assessed.The possibility of publication bias was tested by visual inspection of funnel plots and by testing for asymmetry using the Egger weighted regression test within Rn cases and n controls in R based on the SMD (Hegde’s g) derived from meta-analysis of plasma CXCL8 concentration in participants without comorbid physical illness, together with the number of cases and of controls, based on a known mean and s.d. of plasma CXCL8 in a normal healthy population  given the assumption of a normal distribution as stated by the central limit theorem using the code outlined by Fried and Kievit37  fold cross-validation, data are divided into 10 folds. A logistic classifier is built using n-1 folds, leaving out one fold. The built classifier is then tested on the left-out fold, and the classification accuracy is calculated. The above procedure is repeated n times by leaving out one-fold each time. Finally, the cross-validation classification accuracy is the average accuracy across all the left-out folds. In addition, we report the sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV) of CXCL8. Here, sensitivity measures the proportion of positives that are correctly identified as such; in this case the percentage of patients who are correctly identified as having depression. Specificity measures the proportion of negatives that are correctly identified, or the percentage of healthy controls who are correctly identified as controls. PPV represents the proportion of individuals with depression among all those who tested positive, and NPV represents the proportion of healthy controls among those who tested negative. The PPV and NPV are sensitive to the prevalence of the disease. These were calculated with a conservative estimate of 10% prevalence of MDD in the general population. These quantities are computed as follows:For the analysis, we estimated the classification accuracy using a logistic regression classifier with a 10-fold cross-validation procedure using Weka 3 Data Mining Software.109 . Evidence of asymmetry was present in the funnel plot (P=0.0071); despite this, trim and fill analysis did not impute any missing studies.A total of 21 studies including 4688 participants  were included in the CCL2 analysis. Blood CCL2 measurements were significantly higher in depressed subjects compared with controls , but not plasma. Analysis of studies with a low risk of bias did not retain statistical significance. Further details of analysis can be found in Sensitivity analyses demonstrated that in studies with physically healthy participants there was a significant difference in blood CCL2, similar to that observed across all studies . There was no obvious asymmetry in the funnel plot (P=0.5472).A total of 6 studies including 510 participants  were included in the CCL3 analysis. There was no significant difference between blood CCL3 measurements in depressed and control subjects . Neither plasma nor serum retained significance alone. Analysis of studies with a low risk of bias (n=4) also demonstrated a significant difference between groups . Further details of analysis can be found in Sensitivity analyses demonstrated that in studies with physically healthy participants there was a significant increase in blood CCL3 between depressed and control subjects . There was no obvious asymmetry in the funnel plot (P=0.5548).A total of 5 studies including 507 participants  were included in the CCL4 analysis. Blood CCL4 measurements were significantly lower in depressed subjects compared with controls . Comparisons of plasma and serum measurements revealed a significant difference in serum , but not plasma. All healthy studies for CCL4 were considered to have low risk of bias, and therefore further sensitivity analysis was not performed. Further details of analysis can be found in Sensitivity analyses demonstrated that in studies with physically healthy participants there was a significant difference in blood CCL4, similar to that observed across all studies . There was evidence of possible asymmetry in the funnel plot (P=0.4629).A total of 7 studies including 454 participants  were included in the CCL11 analysis. There was no significant difference between blood CCL11 measurements in depressed and control subjects . Moderatnel plot . HoweverP=0.0003). Neither plasma nor serum was significant alone. Analysis of studies with a low risk of bias also demonstrated a significant difference between groups . Further details of analysis can be found in Sensitivity analysis demonstrated that in studies with healthy participants there was a significant increase in blood CCL11 in depressed subjects compared with controls . The funnel plot showed evidence of asymmetry (P=0.3541).A total of 11 studies involving 792 participants were included in the blood CXCL4 meta-analysis. Blood CXCL4 measurements were significantly higher in depressed subjects compared with controls . Sensitivity analysis involving only studies with physically healthy participants or only those with a physical illness did not attain statistical significance. Neither plasma nor serum was significant alone. Sensitivity analysis involving studies with a low risk of bias did not retain statistical significance. Further details of analysis can be found in Sensitivity analysis using Karege 2005 serum values instead of plasma values did not alter overall significance or statistical heterogeneity but reduced the overall SMD . The funnel plot showed evidence of asymmetry (P=0.7837).A total of 11 studies involving 771 participants were included in the blood CXCL7 meta-analysis. Blood CXCL7 measurements were significantly higher in depressed subjects compared with controls . However, this was not true for studies with only physically ill participants. Furthermore, among studies with only healthy participants there was no evidence of statistical heterogeneity (I2=0% P=0.56). Comparisons of plasma and serum measurements revealed a significant difference in plasma , but not serum. Sensitivity analysis involving studies that demonstrated low risk of bias retained statistical significance for the healthy subgroup  and borderline statistical significance overall . Further details of analysis can be found in Sensitivity analysis involving only studies with physically healthy participants demonstrated a significant increase in blood CXCL7 . The funnel plot showed evidence of asymmetry (P=0.1069). Removing the potential outlier among the studies with participants with physical illness, Lebedeva 2014,73 retained overall statistical significance .A total of 40 studies involving 3788 participants were included in the blood CXCL8 meta-analysis. Blood CXCL8 measurements were significantly higher in depressed subjects compared with controls  but still with statistical heterogeneity (I2=75% P<0.00001). Comparisons of plasma and serum measurements revealed a significant difference in plasma , but not serum. Given the large number of studies, it was possible to conduct sensitivity analysis for studies involving only female participants; this retained statistical significance overall  and among healthy participants only . Sensitivity analysis involving studies that demonstrated low risk of bias did not retain statistical significance. Further details of analysis can be found in Sensitivity analysis involving only studies with physically healthy participants demonstrated a significant increase in blood CXCL8 , with receiver operating characteristic area under the curve of 64.5% ; sensitiP=0.03). Detailed results of additional sensitivity are summarised in Additional sensitivity analyses of CCL3, CCL4, CCL11, CXCL4 and CXCL7 did not affect the significance of results. For CCL2, significance of results was not affected except when including only studies that matched for body mass index or for circadian rhythm of blood sampling. For CXCL8, all additional sensitivity analyses except removing Fontenelle 2012 resulted in a loss of overall significance. CXCL8 studies using DSM MDD diagnoses or those matching for age retained significance for healthy participants. When looking at only cardiovascular illness, CXCL8 meta-analysis attained statistical significance  when measured in the blood discriminate between those with and without depression.Chemokine ligands and receptors are expressed throughout both the developing and mature central nervous system,Specifically, significant increases were seen in CCL2, CXCL4, CXCL7 and CXCL8 and a decrease in CCL4 when comparing depressed and nondepressed. Examining our secondary outcomes, including only healthy participants showed additional increases in CCL3 and CCL11, whereas CXCL4 lost significance. There were no significant differences in chemokine levels when restricting our analysis to only those with physical comorbidities, implying that additional inflammatory change associated with depression is masked by the raised profile associated with the underlying physical disease. However, when looking at cardiovascular illness alone CXCL8 attained statistical significance. Perhaps, this finding is related to the reduction in heterogeneity with analysing only one illness subtype. Analysis of studies measuring in plasma versus serum did not reveal any particular pattern, suggesting there was no specific advantage in using plasma rather than serum in terms of differences detected. Finally, when examining studies with a low risk of bias we found that effects tended to be ameliorated or lose significance, suggesting that higher-risk studies are greater contributors to the effect sizes observed.et al.30 and Köhler et al.,112 significant increases in CCL2 found in our study are similar. It is worth noting, however, that significance for CCL2 seen in Köhler et al.112 did not remain after exclusion of a study by Shen et al.113 We excluded this study before meta-analysis because of its high risk of bias and our inability to contact the authors. In contrast to either Eyre et al.30 or Köhler et al.,112 we did note increases in CXCL8. In addition, when including studies with only a low risk of bias, neither CCL2 nor CXCL8 were significantly different in our meta-analysis, suggesting that less methodologically rigorous studies may be contributing to the differences we identified.On comparing our findings with those observed in other meta-analyses by Eyre et al.112 did not identify alterations in CCL3, our study indicated significant increases that were retained even when considering only those studies with a lower risk of bias. Another possible explanation for the differences in CCL2, CCL3 and CXCL8 findings between our study and the other meta-analyses is that we identified a greater number of studies for inclusion, possibly because of our more complete search strategy. Finally, it is worth highlighting that we were able to analyse an increased number of chemokines in this study, compared with previous meta-analyses. Although this may be because of our broad inclusion criteria, it highlights the need for comprehensive search strategies when examining chemokine literature and the strengths of using the multiple variant names of these molecules when searching databases.Although Köhler 114 We propose that accuracy would be likely to improve with a multivariate approach, utilising a number of blood and mood measures. In addition, replicating our CXCL8 meta-analysis using the complementary methodological approach suggested by Fried and Kievit36 allows for a more accurate interpretation of our results and adds to the validity of our findings.Our analysis suggests that the classification accuracy, receiver operating characteristic area under the curve, sensitivity and specificity of plasma CXCL8 in discriminating depressed subjects from controls is significantly better than 50% (chance). Because of the low specificity of depressive mood symptoms, identification of a test with high NPV that can discriminate MDD from other conditions that have similar symptomatology could have potential clinical utility. With a realistic population prevalence of major depressive disorder of 10%, we found a high NPV of 93.71%. This univariate test compares favourably with a 97.59% NPV established via meta-analysis of major depression screening instruments in primary care.et al.115 showed that monocytes traffic to the brain and congregate perivascularly in association with CCL2. Similarly, Pantazatos et al.116 used RNA-sequencing on post-mortem brain tissue (DLPFC-BA9) from MDD suicide (MDD-S) and compared with MDD non-suicide and controls. The greatest differences between MDD with and without suicide  were in molecular pathways that related to microglia and the immune system, in particular in genes for CCL2 and CCL4. The greatest difference between MDD and controls was seen in the chemokine CXCL8. Pantazatos et al.116 have highlighted the issue of chemokine pleiotropy and this is germane to the apparently counterintuitive finding of reduced levels of CCL4. In their post-mortem tissue samples, Pantazatos et al.116 also noted lower expression of CCL2 in the brains of MDD-S—in contrast with Torres-Platas et al.115 who found raised levels in MDD-S. However, the data in the literature varies here, with measurements in different tissue compartments and different patient groups contributing to sometimes opposing findings. Interestingly, studies that have noted lower CXCL8, CCL2 and CCL4 have all been in groups exhibiting suicidal behaviour.119Several human studies point to relevant role for chemokines in the biology of depression. In the brains  of depressed suicides compared with sudden death matched controls, Torres-Platas There are a number of limitations to this systematic review and meta-analysis. We found that there was a reasonable risk of publication bias for a number of analyses, particularly when looking at funnel plots. Overall, a risk of reporting bias was present. However, by contacting authors directly for data we hope to have overcome the main issues associated with this. Strengths are that by including multiple databases and following PRISMA guidelines, it is unlikely that relevant studies were missed. In addition, two independent reviewers assessed the quality and bias of included studies.In terms of individual studies, the quality was highly variable with notable differences in controlling for confounders, the mixture of plasma and serum used to measure the chemokines and the lack of parsing of subtypes of depression, for example, melancholic vs nonmelancholic.If the relationships between chemokine biology and the neurobiology of depression are to be fully understood, then a number of steps require to be followed. Longitudinal studies of peripheral blood measures of chemokines would allow more than the currently available single time snapshots and help to unpick a state versus trait relationship with inflammatory biomarkers. Such designs would also allow correlation with age groups, symptom types and Research Domain Criteria profiles along with relationships, if any, with treatment status.More fundamental mechanistic research is essential if we are to truly understand the role that chemokines play in the context of sickness behaviour and longer-term models of inflammation in preclinical models. Much of the work to date has focussed on the role that chemokines play in normal neurodevelopment. The extent to which chemokines play their part in orchestrating the systemic inflammatory response is strongly indicative of an important role in how this inflammatory response operates within the central nervous system thus affecting behaviour. Furthermore, unpicking inflammatory versus neuromodulatory functions and the relationship between them will provide further insight into the pathophysiological mechanisms of depressive disorders.In this systematic review and meta-analysis of the available literature, a number of chemokines were found to be altered in depressed individuals. These chemokines are involved in both neurobiological functions and with leukocyte trafficking and recruitment known to be associated with depression.


P = 1.940E-06). Rs16879552 was significantly associated with HSCR in per-allele , additive and recessive model, except for dominant model. Stratified analysis by ethnicity showed that rs7835688 and rs16879552 were only causative for Asians, but not risk locus for Caucasians. Furthermore, pooled data based on segment length indicated that individuals with rs7835688 experienced a significantly higher risk for short-segment HSCR in all genotypes; but rs16879552 was only found to be associated with long-segment HSCR/ total colonic aganglionosis at the allele level.Substantial resources have been devoted to evaluate the relationship between NRG1 variants rs7835688 and rs16879552 and Hirschsprung’s Disease (HSCR) but no consistency exists. This meta-analysis aimed to assess the association between the two SNPs and HSCR. PubMed, EMBASE, and Chinese Biological Medicine databases were searched for studies potentially eligible up to March, 2017. The summary odds ratios (ORs) with 95% CIs were calculated from different genetic models. Nine case-control studies (8 for both and 1 for rs16879552 only) involving 1984 HSCR patients and 4220 controls were identified. The combined results showed a significant association between HSCR risk and rs7835688 in all genetic models (per-allele model: OR = 1.66, 95% CI = 1.35–2.05;  According to the extent of the affected bowel, HSCR can be classified as short segment , long-segment  or total colonic aganglionosis . The incidence of the disease has a significant racial and gender variation, and the highest morbidity is found among Asians 2. Moreover, HSCR can be either sporadic or familial.As a congenital malformation of the lower gastrointestinal tract, Hirschsprung’s disease (HSCR) can be attributed to the migration of the neural crest cells (NCCs) been disrupted during embryonic development. This disorder leads to an absence of enteric ganglia in the submucosal and myenteric plexuses along a variable length of the gut which produces a functional intestinal obstruction4, EDNRB5, END36, GDNF7, PHOX2B9 and SOX1010 have been found to be responsible for HSCR, implying that this disease has a complicated genetic etiology. In 2009, a genome-wide association study (GWAS) identified a new HSCR causative gene–NRG1, which was first confirmed as a susceptibility locus for HSCR in Chinese11. Within the NRG1 region, the single nucleotide polymorphisms (SNPs) rs7835688 (G > C) and rs16879552 (T > C) showed the strongest overall associations with HSCR, yielding odds ratios (OR) of 1.98  and 1.68 , respectively, under an additive model. As is known, NRG1 and the ErbB family of tyrosine kinase receptors are vital molecular regulators for the NCCs’ development12. Loss-of-function of ErbB2 signaling in the colonic epithelial cells could lead to postnatal colonic aganglionosis in mice, for the maintenance of the enteric nervous system (ENS) is dependent on the survival factors induced by NRG1–ErbB2 interaction13.Several genes, such as RET16, but the results are still controversial due to the inconsistency among these studies. Recently, a research consisting of 115 HSCR patients and 117 unaffected controls in Han Chinese reported that there was no evidence of genetic association between HSCR and the two SNPs, at either allele or genotype level17. This is partially inconsistent to another study conducted by our team18. Our results demonstrated that individuals with the risk allele of rs7835688 C had an increased risk of HSCR at both allele and genotype level, whereas no genetic interaction was found between HSCR and rs16879552 under all genotypes.Up to now, several case control studies have been conducted to investigate the association between rs7835688 and rs16879552 variants and HSCR riskTo our knowledge, no quantitative reviews or meta-analysis of the literature on the association between rs7835688 and rs16879552 and HSCR have been conducted up to now. Besides, meta-analysis could reduce the risk of random error and obtain a precise prediction for the major effect through combining data from all eligible researches. In view of the accumulated data, we performed this meta-analysis to provide the evidence for the implication of NRG1 rs7835688 and rs16879552 polymorphisms in the HSCR susceptibility.19. A total of 108 papers were identified after an initial search strategy from the databases. After the removal of 59 duplicate articles, 49 articles were considered of potential relevance. In total, 16 articles were retrieved for full-text review, 9 of which met our inclusion criteria23. Of these 8 articles for rs783568823 and 9 articles for rs1687955223 were included in the final analysis. The Table 22. As for rs16879552, 4 studies22 were satisfied with the HWE except one18. For the rest 4 studies23, the authors pointed out that the genotype distribution in the controls for rs7835688 and rs16879552 didn’t violate HWE, but the exact data was not given. Of all of the studies included, 7 studies involved Asians22, 2 studies investigated Caucasians23. All studies followed a case–control design, 2 of them were GWAS20; 5 studies used population-based controls23, and 4 studies used hospital-based controls21. The quality score of the included studies ranged from 9 to 11 , followed by the Asian (28.0% cases vs 20.8% controls) population. The distribution of the rs7835688 genotypes and alleles is presented in Table P = 5.170E-03; I2 = 80.5%, P = 3.977E-04)  , and recessive model  . After analyzing the relationship between the C allele and the risk of HSCR, we found no significant association between rs16879552 polymorphism and HSCR under dominant model of CC + CT vs TT    to 0.0% (P = 0.477). However, the pooled OR after removing this study was 1.85 , which was not deviated from the total estimate substantially. Furthermore, after exclusion of the article  deviating from HWE in the controls for rs1687955218, the result of the relationship was not influenced significantly  nor the Egger’s tests  supported the existence of publication bias  of NRG1 were found to be causal mutations for HSCR in Caucasian population23. Immunocytochemistry illustrated a different distribution of the NRG1 proteins in the cytoplasmic organelles between wildtype and mutants  in COS7 cell line. Besides, all three mutants showed a substantial lower protein expression. The results suggested that NRG1 would be associated with HSCR not only in Asian but also in Caucasian population. In contrast to the previous reports, aberrant high expression of NRG1 in aganglionic bowel of HSCR patients was observed in another study, but how this discrepancy happened was not clear27. Mounting evidence have vindicated the role of NRG1 in the HSCR pathology, however, the underlying mechanisms were still largely unknown. Further research into the pathogenesis of HSCR is needed.After been identified as a HSCR causative locus, several functional studies have been initiated to explore the genotype-phenotype association between NRG1 and HSCR. Garcia-Barcelo and colleagues found that the expression of NRG1 was decreased in the aganglionic bowelI2 = 0, P > 0.10). But quite on the contrary, we just identified a marginal association between rs16879552 and L-HSCR/TCA only in allelic association analysis. It is necessary to point out that the HSCR type-based subgroup analysis was limited to Asians due to no available data was provided in the other two studies about Caucasians23. The sensitivity analysis demonstrated that the literature by Kapoor et al.16 was the main source of heterogeneity for rs7835688. The heterogeneity was significantly decreased (I2 = 0.0%) after excluding this study. Nevertheless, the summary OR did not changed essentially, supporting the stability of the pooled results.The ethnicity, type of HSCR and sex distribution might serve as confounders to influence the effect size, so we stratified data from the included studies to evaluate the association between NRG1 variants and HSCR risk in confounder-matched groups. In ethnicity-based studies, our results showed that rs7835688 and rs16879552 related to HSCR appeared to be Asian-specific. Moreover, when the data were stratified by segment length, a robust association was found between rs7835688 and risk of S-HSCR, in all genotypes with no heterogeneity  and rs16879552 C .The polymorphic variance of NRG1 could also be attributed to the gender difference. Unfortunately, only one of the studies in our research provided detailed information of genotype distributions in males and femalesAs a meta-analysis, some intrinsic limitations need to be acknowledged. First, significant heterogeneity was observed across studies for the association between the two SNPs and HSCR risk, which might result from differences in study quality, study populations, and ratios of the subgroups . However, despite moderate to high heterogeneity existed for the overall effect, in the ethnicity- and segment length- based subgroup analysis low heterogeneity was detected in most of the genotypes. Second, most of the study subjects came from Asian ancestry, and the Caucasian subgroup was very limited in our meta-analysis. Thus, potential publication bias and selective bias may have occurred. Third, residual confounding is still possible since HSCR is a multifactor malformation, gene-environment and gene-gene interactions should be considered. Finally, the sample size of L-HSCR/TCA in this meta-analysis was not big enough to reach a strong statistical power for making a definite conclusion about the risk of rs7835688 and rs16879552 for these patients.28. Second, no significant publication bias was detected in this meta-analysis, and the results were proved to be stable by the sensitivity analysis. Furthermore, this meta-analysis was in line with our previous fine mapping of the two SNPs by showing that rs7835688 played a role in predisposition to S-HSCR18.Despite the limitations, we believe that our meta-analysis have provided accumulated and useful evidence for the role of NRG1 in HSCR. First, the sample size of each single study in our meta-analysis was not large enough to achieve a definite association between the NRG1 polymorphisms (rs7835688 and rs16879552) and HSCR risk, but the pooled OR calculated from the 8 or 9 studies significantly increased the statistical power. This is essential in genetic association studies to obtain adequate statistic powerOur analysis provides substantial evidence that NRG1 rs7835688 and rs16879552 are significantly associated with increased risk of HSCR. This finding expands the number of confirmed HSCR susceptibility loci. The NRG1 locus may represent another pathway in the pathogenesis of HSCR and could lead to insights regarding ways to modify the risk of HSCR.We searched the PubMed, EMBASE, and Chinese Biological Medicine data bases until March, 2017, using the search terms [“NRG1” OR “NRG 1” OR “neuregulin 1” OR “neuregulin-1” OR “neuregulin1”] and [“Hirschsprung’s Disease” OR “Hirschsprung Disease” OR “HSCR” OR “HD”] to identify eligible studies that investigating the association between NRG1 SNPs and HSCR risk. In addition, the reference lists of the selected articles were hand checked to find other relevant publications that might be missed in the initial search strategy. We imposed no language or year restrictions on the search strategy.Two of us independently assessed the retrieved studies (M.J. and C.-L.L.). Potentially relevant studies were selected based on the following inclusion criteria: (1) studies could be defined as case–control or cohort study; (2) studies in which the diagnosis of HSCR was clear ; (3) studies had examined the associations between the NRG1 SNPs (rs7835688 or rs16879552) and HSCR; (4) the genotype data in case and control groups could be collected; (5) the cases and controls were recruited from a population with the same ethnic background. Studies with duplicated data or no available data were excluded. In the case of different articles related to the same patient population, only the reports with the highest number of cases were included.P values for Hardy-Weinberg equilibrium (HWE) in controls. We contacted the authors of included studies if additional raw data were needed.Two reviewers (G.-Q.C. and D.-H.Y.) extracted the data from all eligible articles independently, according to the inclusion and exclusion criteria. Any disagreements were resolved by discussion between the two reviewers. The following data were extracted: name of first author, year of publication, country, ethnicity of the subjects, source of control, the genotyping method, sample size, frequency of NRG1 genotypes in the cases and controls; and the et al.29, which was based on both genetic issues and traditional epidemiologic considerations. The checklist contained 7 aspects: representativeness of cases, representativeness of controls, ascertainment of HSCR, ascertainment of controls, genotyping examination, Hardy-Weinberg equilibrium and association assessment. Total scores ranged from zero (worst) to 13 (best). Details of each item were outlined in Table Two reviewers (M.J. and L.Y.) assessed the quality of the studies independently with a checklist modified from Thakkinstian P value of HWE was not given, it was assessed by Chi-square test to analyze the genotype distribution in the control groups. In addition, we used the Cochrane Q statistic and the inconsistency index (I2) to evaluate the heterogeneity among the retrieved studies; P value < 0.10 or I2 > 50% was considered statistically significant for the heterogeneity30. If heterogeneity existed, we selected the random-effect model (the Dersimonian and Laird method) to calculate the pooled OR. Otherwise, the fixed effects model (the Mantel-Haenszel method) should be used if no obvious heterogeneity was detected31. Sensitivity analysis was performed by excluding individual studies to assess the stability of the overall OR. The publication bias was assessed using both Egger’s test and Begg’s test32. The visual inspection of funnel plots was also used to show the extent of publication bias33. Additionally, subgroup analyses were conducted by ethnicity and segment length (S-HSCR or L-HSCR/TCA). The statistical analysis was performed with STATA software version 12.0 . Except for heterogeneity, P value of < 0.05 (two tailed) was considered to be significant statistically in this report.ORs with 95% confidence intervals (CI) were calculated for determining the strength of the relationship between rs7835688 and rs16879552 and HSCR. The pooled ORs for rs7835688 and rs16879552 were estimated under four genetic models, namely, per-allele model (C vs G or C vs T), an additive/homozygous model (CC vs GG or CC vs TT), a dominant model (CC + CG vs GG or CC + CT vs TT) and a recessive model (CC vs CG + GG or CC vs CT + TT), respectively. If the Supplementary Information


Many studies investigated the association between alcohol drinking and gastric cancer risk, but the results were controversial. We performed a meta-analysis of observational studies to explore the association.Q test and I2 analyses were used to evaluate the heterogeneity. Meta-regression, subgroup, sensitivity and publication bias analyses were also performed.We searched PubMed to identify the relevant studies that reported the association between alcohol drinking and gastric cancer risk up to December 31, 2016. We pooled relative risks (RRs) in random effects model and performed dose-response analysis to quantify the association. Cochran P < 0.001), and a nonlinear association was further observed. Subgroup analysis showed that alcohol drinking significantly associated with the risk of gastric noncardia cancer , but not with the risk of gastric cardia cancer . Notably, the pooled RRs of high vs low analyses were 1.13  for beer drinking, 1.22  for liquor drinking, and 0.99  for wine drinking.75 studies were included in our study. The pooled RR of high vs low total alcohol drinking was 1.25 (95% CI, 1.15–1.37, Our meta-analysis found a nonlinear association between alcohol drinking and gastric cancer risk, and heavy drinking level was strongly related to gastric cancer risk. Beer and liquor had significant positive associations with gastric cancer risk, while wine drinking would not increase gastric cancer risk. These results need to be verified in future research. Gastric cancer is the fifth most common cancer and the third most common cause of death from cancer worldwide . In receAlcohol was classified as the first class carcinogen by IARC . The preHelicobacter pylori  and corresponding 95% confidence intervals (CIs). When several publications reported the same study, we selected the publication with the most complete information. Two authors (WPL and LFN) assessed independently all the articles for potentially eligible studies. We first reviewed all the identified titles and abstracts, and then full-texts for the articles which met the inclusion criteria or for which eligibility was uncertain. Inconsistencies were adjudicated by discussion and consensus.For each study, we extracted the characteristics of the studies , the characteristics of the study populations , outcome measurements , and the main confounders. For each study, multivariate-adjusted risk estimates were used whenever available, otherwise, the unadjusted RRs were extracted.Stratification by drinking amount was used to identify the highest and lowest exposure level if available, otherwise drinking frequency was used. The drinkers and nondrinkers were identified as the highest to lowest intake if the studies only compared the two groups. Alcohol drinking levels were generally defined as ‘light drinker’ (1 drink per drinking day), ‘moderate drinker’ (2 drinks) and ‘heavy drinker’ (3+ drinks). However, the definitions of how many grams of ethanol contained in one drink is different in each country. Thus, estimation of ethanol intake is complicated. In our present study, we used 12.5 grams of ethanol as the standard measurement of one drink as many previous studies described , 11, 19.Newcastle-Ottawa Scale was used to perform study quality assessment . The higTwo authors (GBC and XFT) performed the data extraction and quality assessment independently and cross-checked. Disagreements were resolved by team discussion.The measure of interest was the RR [or the odds ratios (OR) in case-control studies and the hazard ratio (HR) in the cohort studies]. To quantify the association between alcohol drinking and gastric cancer risk, we used a random-effects model to calculate summary relative risks, considering within- and between-study variability . For stuP values for nonlinearity were calculated by testing the null hypothesis that the coefficients of the second spline were equal to zero [We performed dose-response meta-analysis using the method provided by Greenland and Longnecker  and Orsi to zero .P values were less than .10.Heterogeneity among studies was assessed with the Cochran Q (heterogeneity chi-squared) and I-square statistics, with I-square > 50% representing significant heterogeneity . To findP-value < .05 was considered statistically significant.All the analyses were performed with STATA . 490 references were generated from PubMed with the search strategy, of which 393 were excluded as non-relevant studies after scanning the titles and abstracts. Figure  For the Quality assessment scores are summarized in P < 0.001) with significant heterogeneity   (73 studies (56 case-control studies and 17 cohort studies) were included in the pooled analysis of high vs low total alcohol drinking and gastric cancer risk. The pooled RR was 1.25  .P = 0.005) with significant heterogeneity  (for nonlinearity = 0.022)  were included in dose-response meta-analysis of total alcohol drinking. The pooled RR for an 12.5 g/day increment of ethanol was 1.04  . A curviP = 0.012) without significant heterogeneity   were included in the analysis of high vs low beer drinking and gastric cancer risk. The pooled RR was 1.13  Figure .P = 0.025) without significant heterogeneity  (for nonlinearity = 0.035)  were included in the dose-response meta-analysis. The pooled RR for an 12.5 g/day increment of ethanol intake was 1.07  . There w) Figure .P = 0.005) with significant heterogeneity   were included in the analysis of high vs low liquor drinking and gastric cancer risk. The pooled RR was 1.22  Figure .P = 0.296) without significant heterogeneity  (for nonlinearity = 0.269)  were included in the dose-response meta-analysis. The pooled RR for an 12.5 g/day increment of ethanol was 1.03  . There w) Figure P = 0.857) with significant heterogeneity  were included in the analysis of high vs low wine drinking and gastric cancer risk. The pooled RR was 0.99  Figure .P = 0.769) with significant heterogeneity  (for nonlinearity = 0.242)  were included in the dose-response meta-analysis. The pooled RR for an 12.5 g/day increment of ethanol was 0.99  . A linea) Figure To find the source of the heterogeneity, we performed meta-regression and subgroup analyses for total alcohol drinking Table . No variP = 0.033; n = 18 studies), but not with the risk of gastric cardia cancer   for an increment of 12.5g/d of ethanol intake, and the pooled RR of gastric noncardia cancer was 1.02   .Sensitivity analysis further approved the stability of the pooled results by omitting one study at a time. The pooled RRs of the remaining studies were stable, with a range from 1.24  to 1.28 .P = 0.004) with significant heterogeneity , which yielded to consistent results of all the studies included.Considering the effects of the excluded studies from dose-response analysis for insufficient data, we repeated the high vs low total alcohol intake analysis restricted to the studies included in dose-response analysis. The pooled RR was 1.20  was associated with a 4% increased risk of gastric cancer. A nonlinear association was further found between total alcohol drinking and gastric cancer risk, and the risk increased quickly in heavy alcohol drinking level. Furthermore, alcohol drinking had a significant positive association with gastric noncardia cancer, but not with gastric cardia cancer. Different categories of alcoholic beverages had different effects on gastric cancer risk, that is, beer and liquor drinking had significant positive association with gastric cancer risk, whereas wine drinking had not increased gastric cancer risk.H pylori [The present meta-analysis showed that heavy alcohol drinking may increase the risk of gastric cancer, instead of light and moderate alcohol drinking. Based on the previous studies, we inferred that ethanol may be a two-side sword. On the one hand, ethanol and its metabolites, especially acetaldehyde, have carcinogenic and mutagenic effects by modifying DNA via generation of DNA adducts, and inducing oxidative stress and functional genetic variants of alcohol-metabolizing enzymes . EthanolH pylori . Light aH pylori , which mH pylori . It shouH pylori –34.vitro study [Most notably, our meta-analysis demonstrated that different categories of alcohol drinking had different effects on gastric cancer risk. Beer and liquor drinking had a significant positive association with gastric cancer risk, whereas wine drinking could not increase gastric cancer risk. In the dose-response meta-analyses, beer consumption showed a nonlinear positive association with gastric cancer risk, while liquor showed a linear association with cancer risk, even though the positive trend was not significant in statistically. Compared with beer and wine, the ethanol concentration of liquor was much higher. Besides, non-alcoholic components in alcohol drinking may also play important roles on gastric cancer risk, for example, nitrosamine  in beer was considered as carcinogenic for gastric cancer , 36, whero study . The benGastric cancer can be classified as cardia and noncardia subtypes according to the anatomic site. Some authors have investigated that the two subtypes are quite different in single-nucleotide polymorphism , causatiTo date, the present meta-analysis provides the most complete and recent evidence on the association between alcohol drinking and gastric cancer risk. First, we included as many related studies as possible, 75 studies with more than2 million of participants. The substantial included population strengthened the reliability of the results. Second, our meta-analysis may be the first to explore the dose-response association of different types of alcohol drinking on gastric cancer risk, and concluded that beer and liquor could increase gastric cancer risk, but wine would not. Third, we performed more relevant methods to improve the accuracy of the results, for example, both linear and non-linear dose-response analyses were applied to quantitate the association between alcohol drinking and gastric cancer risk.H pylori infection. Although we applied the adjusted measurements whenever available, the confounding effects could not be excluded completely. Majority of included studies were case-control, which may induce more recall and selection biases.However, there were several limitations in our meta-analysis. First, as the included studies were all observational, the residual confounders are inevitable, such as cigarette smoking, lacking of fruit or vegetable consumption and Second, for the absence of sufficient data, some studies were excluded from the dose-response analysis, which may weaken the strength of the conclusions . HoweverThird, we applied the uniform standard of 1 drink as 12.5 g of ethanol and the uniform concentration of each type of beverage to calculate the doses, which may cause measurement errors to some extent.Fourth, significant heterogeneity was observed in high vs low analysis of total alcohol drinking. To find the source of heterogeneity, we performed meta-regression analysis, but no covariate was significantly associated with the heterogeneity in both univariate and multivariate meta-regression analyses. Sensitivity analysis also verified the stability of the pooled results by omitting one study at a time.P for difference = 0.020), and the association was strengthened by studies with low quality scores. This indicated that our results may be exaggerated in some degree, but it should be noted that the pooled risk estimates of high-quality studies yielded similar results to the original analysis.Fifth, the subgroup analysis showed significant difference between high and low quality studies  was associated with a 4% increased risk of gastric cancer. Heavy alcohol drinking level, beer and liquor drinking were associated with gastric cancer risk significantly, whereas light, moderate, and wine drinking would not increase gastric cancer risk.In conclusion, our meta-analysis found a nonlinear positive association between alcohol drinking and gastric cancer risk, and the risk increased quickly in heavy alcohol drinking level. Alcohol drinking could increase the risk of gastric noncardia cancer significantly, instead of gastric cardia cancer. Beer and liquor had significant positive associations with gastric cancer risk, while wine drinking would not increase gastric cancer risk. However, we should explain the results with cautions for all the limitations, and further research would be needed to confirm our conclusions.


Gastric cancer (GC) is one of the most frequently diagnosed digestive tract cancers and carries a high risk of mortality. Acetaldehyde (AA), a carcinogenic intermediate of ethanol metabolism contributes to the risk of GC. The accumulation of AA largely depends on the activity of the major metabolic enzymes, alcohol dehydrogenase and aldehyde dehydrogenase encoded by the ADH  and ALDH2 genes, respectively. This study aimed to evaluate the association between genetic variants in these genes and GC risk in West Bengal, India.We enrolled 105 GC patients (cases), and their corresponding sex, age and ethnicity was matched to 108 normal individuals (controls). Genotyping for ADH1A (rs1230025), ADH1B , ADH1C (rs698) and ALDH2  was performed using DNA sequencing and RFLP.p = 0.002) for GC. Significant association was also observed between rs671 and incidence of GC (p = 0.003). Moreover, smokers having the Lys allele of rs671 had a 7-fold increased risk of acquiring the disease .Genotype and allele frequency analysis of these SNPs revealed that G allele of rs17033 is a risk allele  contains supplementary material, which is available to authorized users. Helicobacter pylori may predispose an individual to GC, but smoking, alcohol, diet, genetics and epigenetic factors may also contribute to disease risk . In our study, multivariable logistic analyses revealed that the ADH1B rs17033 GG genotype (dominant model) was associated with GC risk. This, however, was found to be insignificant after FDR adjustment. Interestingly, the important functional polymorphism of ADH1B, rs1229984 was not associated with the disease in our study. On the other hand Asian populations, particularly the northeast Asians , mainly harbor the ADH1B*47His allele (rs1229984 A). Similarly, in West Asian countries such as Iran and Turkey, where esophageal squamous cell carcinoma (ESCC) diagnoses are comparatively high, a corresponding high frequency of the ADH1B*47His allele is found. We detected one His (A) allele in our control group, the allele frequency was 0%, which is quite similar to South Asians (A: 2%) but differed significantly from East Asians (A: 70%) [1000 genomes project]. Therefore, geography and ethnic differences may be the probable reason behind the low frequency of rs1229984 polymorphism in our population as well as the lack of association with cancer risk. According to 1000 genomes project, the allele frequencies of rs698 in South Asians were A: 75%, G: 25%, which was quite similar to our result; however, the allele frequency was much different compared to East Asians and Europeans . A meta-analysis performed on 35 case-control studies indicate that theADH1C Ile350Val (rs698) polymorphism may contribute to cancer risk among Africans and Asians . Interestingly, smokers having the T allele of rs4147536 showed a decreased risk of GC . However, as no previous studies have linked the ADH1B SNPs rs3811802 and rs4147536 with GC risk, confirmation of a correlative link between these SNPs and GC warrants further study.A previous study has established the functional effect of the SNP rs1229982 in the proximal promoter region of 1.4-fold . This in1.4-fold . Howeverpulation . In agreALDH2 gene variants and GC have yielded conflicting results ; ALDH2 GA: 30–40%, ALDH2 AA: 2.5–5% [ALDH2 and drinking and smoking status indicated that rs671 and smoking synergistically increase risk of GC. We found that smokers having Lys allele of rs671 had a 7-fold increased risk of GC further validating previous reports [The major enzyme responsible for the elimination of AA is aldehyde dehydrogenase 2 . Studies results , 40. A sl intake . Blood Aectively . Homozyg: 2.5–5%  and has : 2.5–5% , the gen reports . In addiALDH2 gene, rs886205, is also controversial. While a study on a Polish population reported that alcohol consuming individuals with the G allele had an increased risk of GC [ALDH2 gene have been strongly linked to the intestinal subtype of GC [ALDH2 rs886205 and rs16941667 might be strongly correlated with an increased risk of GC [ALDH2  and GC risk. The prognostic importance of the minor alleles of rs17033, rs4147536 and rs671 has been evaluated by Kaplan-Meier method. We found that the G allele of rs17033 was associated with the overall survival of GC patients.The link between cancer and another common functional variant in the sk of GC , Duell esk of GC . In our The limitation of our study is the small sample size. In India, the incidence of gastric cancer (GC) varies across different registries. A higher incidence has been reported in the South compared to the North. The highest rate of GC cases is reported from the North Eastern state of Mizoram . But theWe conducted the first study regarding the associations between ADH1A, ADH1B, ADH1C and ALDH2 genes polymorphisms and the risk of GC from West Bengal, India. Our results indicate that rs17033 of ADH1B gene and rs671 of ALDH2 gene could be useful susceptibility molecular biomarkers for GC in our study population. Moreover, the combined effect of Glu504Lys (rs671) of ALDH2 with smoking significantly increases the risk of GC. In smokers, T allele of an intronic SNP, rs4147536 of ADH1B was shown to be associated with decreased risk of GC in our study population. Out results, though preliminary, suggest that it may be possible to identify genetic markers predisposing individuals to GC.Additional file 1: Data S1.Gastric cancer patient report, Description of data- participant questionnaire used in the study. (DOCX 16 kb)Additional file 2: Table S1.Primers using for amplification of SNPs of ADH1A, ADH1B, ADH1C and ALDH2 gene, Description of data- list all primers used in the study. (DOCX 14 kb)Additional file 3: Data S2.Description of data- raw data of all the participants in the study. (CSV 11 kb)


ACE I/D polymorphism on the risk of DR. Results—we found a significant association between the ACE I/D polymorphism and the risk of DR for all genetic model . In stratified analysis by ethnicity and DM type, we further found that the Asian group with T2DM showed a significant association for all genetic models . Conclusion—our study suggested that the ACE I/D polymorphism may contribute to DR development, especially in the Asian group with type 2 diabetes mellitus (T2DM). Prospective and more genome-wide association studies (GWAS) are needed to clarify the real role of the ACE gene in determining susceptibility to DR.Aims—to address the inconclusive findings of the association of angiotensin-converting enzyme (ACE) insertion/deletion (I/D) polymorphism on risk of diabetic retinopathy (DR), a meta-analysis was conducted. Methods—we conducted a meta-analysis on 4252 DR cases and 5916 controls from 40 published studies by searching electronic databases and reference lists of relevant articles. A random-effects or fixed-effects model was used to estimate the overall and stratification effect sizes on  Diabetic retinopathy(DR) is the premier cause of vision loss in adults aged 20–74 years . From 19ACE I/D contributes to the development of DR and numerous studies have addressed the role of the variation in the complex etiology of DR.The angiotensin-converting enzyme (ACE) gene, plays an critical role in modulating vascular tone through hydrolyzing angiotensin I to vasoconstrictory peptide angiotensin II, which seems to be particularly biologically and clinically relevant to diabetes . A numbeACE I/D polymorphism and DR [ACE I/D polymorphism to the risk of developing DR.Numbers of molecular epidemiological studies have been performed to examine the relationship between the m and DR ,53,54,55m and DR ,57, theyThis study was reported according to the Meta-analysis of Observational Studies in Epidemiology (MOOSE) guidelines and Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) for reporting systematic reviews and meta-analyses. Study selection, data extraction, and quality assessment were completed independently by two investigators. Disagreement was resolved through discussion. If the discussion did not lead to a consensus, Professor Wu made the final decision.ACE I/D polymorphism in cases with diabetes retinopathy, and (i) in diseased controls (subjects with diabetes and free of DR) or (ii) in healthy controls, were attempted to be included in the meta-analysis. Cases were type 1 or 2 diabetic subjects with background, simple, advanced, or proliferative DR. The control group consisted of two subgroups, the first was the diseased control group, which consisted of subjects with diabetes and which were free of diabetic retinopathy disease, i.e., diabetes nephropathy and myocardial infarct, and the second group was the healthy controls, which was made up of subjects without any diseases.All studies that determined the genotype distribution of ACE I/D polymorphism and diabetic retinopathy risk, and restricted attention to the studies that satisfied all of the following criteria: studies related to the ACE polymorphism were determined regardless of sample size and study design ; each genotype frequency was reported, and there was sufficient information for extraction of data; if studies had partly overlapped subjects, only the one with a larger and/or the latest sample size was selected for the analysis. Additional studies were identified by hands-on searches from references of original studies or review articles on this topic. According to these criteria, we finally included 40 papers in our meta-analysis.Studies were firstly identified by searching the electronic literature PubMed for relevant reports in English and CNKI for papers in Chinese . We chose articles which were conducted among human subjects. Eligible studies were then identified by further searching the studies published to date on the association between ACE I/D genotypes. The frequencies of the alleles and the genotypic distributions were extracted or calculated for both cases and controls. We defined that diabetic patients without retinopathy and/or matched healthy persons constituted the control group, and patients with DR were the case group. We merged the original data into the control group or case group if the study did not provide corresponding data. For some studies without sufficient information for extraction of data, we tried to contact the studies’ authors by sending emails to request data missing from their articles. In addition, it was tested whether the distribution of genotypes in the controls was consistent with the Hardy-Weinberg equilibrium (HWE) for each study, and calculated the frequency of the minor allele for ACE I/D polymorphism. Two investigators independently extracted data and reached a consensus on all of the items. Data extracted from these articles included the first author’s name, year of publication, study design, ethnicity of the study population, type of DM, clinical characteristics, and the number of cases and controls for The Newcastle-Ottawa scale (NOS) method was used to assess the observational included studies. The NOS is composed of three parts (8 entries): selection, comparability, and exposure. A quality item is given only one star for the study in selection and exposure, and a quality item is given, at most, two stars for the study in comparability. It is a semi-quantitative scale, and a score of 0–9 stars is assigned to each study. Studies whose scores were more than 6 stars were considered to be of relatively high quality . The scoACE I/D polymorphism and the risk of DR for each study by odds ratio (OR), with 95% confidence intervals (95% CI). For all studies, we calculated the ORs for the: (i) separate pairwise comparisons; (ii) allele contrast; (iii) recessive model; and (iv) dominant model. In addition, we conducted stratification analysis by ethnicity and DM type. A sensitivity analysis, which examines the effect of excluding specific studies, was also performed [p < 0.05).The meta-analysis evaluated the relationship between the erformed . Our met2-based Q statistic test was used to assess the heterogeneity, and it was considered significant for p < 0.05. Heterogeneity was quantified with the I2 metric, which is independent of the number of studies in the meta-analysis. I2 takes values between 0% and 100%, with higher values denoting a greater degree of heterogeneity (I2 > 50% was considered significant) [p values were two-sided.The χificant) . We usedificant) . In addiificant) ,63. All The study selection process is shown in ACE I/D polymorphism allele/genotype prevalence are shown in The selected study characteristics from the studies included in the meta-analysis are provided in Data from 40 articles that investigated the association between the ACE I/D polymorphism and DR risk were included in the meta-analysis. The overall frequency (%) of minor D allele frequency (MAF) was 0.47/0.46 for cases and controls. The frequency of the MAF for each study polymorphism on controls is shown in ACE I/D polymorphism with DR is presented in The estimation of the relationship of As shown in In a stratified analysis by ethnicity and DM type, we further detected that the Asian group, T2DM group, and Asian group with T2DM all showed significant associations for all genetic models . However, we did not find any significant effects for different genetic models in other subgroup. Further sensitivity analysis for HWE did not alter the pattern of results in both overall analysis and subgroup analysis.p = 0.78 for dominant model and p = 0.39 for recessive model), DM type (p = 0.59 for dominant model and p = 0.9 for recessive model), HWE (p = 0.26 for dominant model and p = 0.77 for recessive model), and study design (p = 0.06 for dominant model and p = 0.24 for recessive model).From The potential presence of publication bias was estimated by using a funnel plot of the evaluation of log-odds ratio for the genotype DD+ ID versus II against the reciprocal of its standard error . As showACE I/D polymorphism also influences ACE tissue concentrations [Why some diabetics develop retinopathy, whereas others do not, despite having long-term hyperglycemia, remains an undetermined question. Because known environmental factors do not fully explain this, researchers have sought the answer in the genetic background of the host . The rentrations . Numeroutrations ,57.ACE I/D polymorphism on DR risk, but also found a significant relationship in the T2DM group. From the stratification analysis by ethnicity and DM type, we found that the ACE I/D polymorphism was significantly associated with DR risk in the T2DM and Asian groups, especially in the Asian group with T2DM. These findings may indicate that genetic factors may have more impact on the Asian population with T2DM, rather than on other subgroups like the T1DM and Non-Asian population.It is worth emphasizing that our current meta-analysis obtained several critically different conclusions from the previous reports ,57. In ZACE I/D to the risk of DR, which can provide better ability to detect smaller effect sizes. Its strength was based on the accumulation of published data, giving greater information to detect significant differences. In order to estimate the power of the study, we used the Power and Precision 4 software to conduct the power calculation by respectively accumulating the frequency of ACE D allele in case and control groups from all studies, and the result showed the power of our study is 80.2%.We conducted a comprehensive meta-analysis on 40 published studies with 4252 diabetic retinopathy cases and 5916 controls relating the variant of the In this study, the effect of separate pairwise comparisons, allele contrast, and the dominant and recessive genetic models were evaluated. Substratification analysis by DM type andethnicity, and sensitivity analysis for studies not in HWE, was performed. In addition, we further evaluated the source of heterogeneity and the publication bias of included literatures.1c, blood pressure, total serum cholesterol, control of diabetes, and body mass index, all of which modulate the development of DR [Despite this, we still have some limits. In the meta-analysis, non-English/Chinese, non-indexed, and non-published studies literature was not reviewed, thus, some bias might be introduced ; only thnt of DR . Furthernt of DR . TherefoACE I/D polymorphism, especially in the Asian group with T2DM. Prospective and more genome-wide association studies (GWAS) are needed to clarify the real role of the ACE gene in determining susceptibility to DR.In conclusion, the present meta-analysis finds an association between DR and


Research on a possible causal association between alcohol consumption and risk of prostate cancer is inconclusive. Recent studies on associations between alcohol consumption and other health outcomes suggest these are influenced by drinker misclassification errors and other study quality characteristics. The influence of these factors on estimates of the relationship between alcohol consumption and prostate cancer has not been previously investigated.PubMed and Web of Science searches were made for case–control and cohort studies of alcohol consumption and prostate cancer morbidity and mortality (ICD–10: C61) up to December 2014. Studies were coded for drinker misclassification errors, quality of alcohol measures, extent of control for confounding and other study characteristics. Mixed models were used to estimate relative risk (RR) of morbidity or mortality from prostate cancer due to alcohol consumption with study level controls for selection bias and confounding.P < 0.001), medium , high  and higher  volume drinkers compared to abstainers. There was a significant dose–response relationship for current drinkers (Ptrend < 0.01). Studies free from misclassification errors produced the highest risk estimates for drinkers versus abstainers in adjusted models .A total of 340 studies were identified of which 27 satisfied inclusion criteria providing 126 estimates for different alcohol exposures. Adjusted RR estimates indicated a significantly increased risk of prostate cancer among low . This relationship is stronger in the relatively few studies free of former drinker misclassification error. Given the high prevalence of prostate cancer in the developed world, the public health implications of these findings are significant. Prostate cancer may need to be incorporated into future estimates of the burden of disease alongside other cancers  and be integrated into public health strategies for reducing alcohol related disease.The online version of this article (doi:10.1186/s12885-016-2891-z) contains supplementary material, which is available to authorized users. Prostate cancer is the development of cancer in the prostate, a walnut–sized gland in men that surrounds the top of the urethra and which produces seminal fluid . Its groThe risk factors for prostate cancer that can be considered established include age, race/ethnicity and family history . Many obOver the past few decades there have been several reviews and meta–analyses conducted to examine the association of prostate cancer with alcohol consumption , 21–25. The objectives of the present meta–analysis were: (i) to investigate the relationship between prostate cancer and alcohol consumption; and (ii) to examine whether estimates of this relationship may have been biased by drinker misclassification errors and other study characteristics.The criteria for inclusion were: (i) case–control and cohort studies evaluating the relationship between alcohol consumption and prostate cancer; (ii) original articles published in English up till December 2014; (iii) articles that reported findings in odds ratio, hazard ratio, incidence ratio or standardized mortality ratio; and (iv) articles reporting at least three levels of alcohol consumption with drinking amounts, including the reference level. Articles with no abstainer group or a lowest drinking level greater than 0.33g/d were excluded. Additionally, studies reporting total alcohol consumption were included while studies based on consumption of specific beverages only such as wine, whiskey, vodka, sake or hard liquors were excluded. When the results of the study were published more than once or if the same dataset was used multiple times, only the most recent or more complete data were included in analyses. The primary outcomes of interest were mortality and/or morbidity from prostate cancer (ICD–9: 185 or ICD–10: C61) .While published and peer reviewed cohort or case–control studies were included in the review, all other article types including narrative reviews, letters, editorials, commentaries, unpublished manuscripts, dissertations, government reports, books and book chapters, conference proceedings, meeting abstracts, lectures and address, and consensus development statement including guideline statements, were excluded.The systematic review follows the Preferred Reporting Items for Systematic Reviews and Meta–Analyses (PRISMA) guidelines . We idenTwo reviewers trained and supervised by the PI read the titles and/or abstracts of all the citations retrieved from the electronic database searches and removed all citations that were clearly not related to studies of the relationship between prostate cancer and alcohol consumption. The screening further involved abstract review. Full–text articles were obtained for all abstracts except for those that clearly did not meet eligibility criteria. The investigators were consulted in the event of any disagreement. Two of the investigators independently evaluated all studies selected for inclusion. The initial search identified a total of 340 studies of which 27 studies , 28–48 sTwo reviewers independently reviewed all eligible papers to extract and code data from all studies fulfilling the inclusion criteria, and any disagreements were resolved by discussion with the investigators. Each study was coded with reference to a standardized code–book (available from authors on request) and under the supervision of investigators. The coding of all variables in the meta–dataset was double–checked by the first two authors. The data to be extracted were: (1) outcome, mortality or morbidity of prostate cancer; (2) measures of alcohol consumption; (3) study characteristics; (4) types of misclassification error of alcohol measure; and (5) controlled variables in individual studies.A multitude of different approaches are used for assessing alcohol consumption in this literature . ProblemThe primary exposure variable was level of daily alcohol consumption in grams of ethanol assessed at baseline and compared with a reference group of variously defined “non–drinkers” or “abstainers”. When studies did not define the grams of alcohol per unit or drink, we used 8 g/unit for the UK; 10 g/drink for Australia, Austria, France, Greece, Hungary, Ireland, Netherlands, New Zealand, Poland, Spain, Sweden; 11 g/drink for Finland; 12 g/drink for Denmark, Germany, Italy, South Africa and Switzerland; 13.45 g/drink for Canada; 14 g/drink for US; 12.5 g/drink for China, 19.75 g/drink for Japan and 12 g/drink for other countries , 51. We Studies were classified according to the presence or absence of two types of potential abstainer group bias: (i) including former drinkers and/or (ii) including occasional drinkers in the abstainer reference category. Studies were coded as having former drinker bias if a) results were not reported separately for former drinkers and b) there was no mention of removing former drinkers from the abstainer reference group. Following Fillmore et al. , lifetimRR is relative risk, OR is odds ratio and P0 is the incidence of outcome of interest in the non–exposed group.Where studies only reported mortality or incidence rates, these were converted to RR estimates . Otherwi2 statistic [Publication bias was assessed through visual inspection of the funnel plot of log–RR of morbidity or mortality of prostate cancer due to alcohol consumption against the inverse standard error of log–RR  and Eggetatistic . As no htatistic . We alsot-test in multivariate linear regression analysis [We used the fixed effects models to estimate the weighted RRs of prostate cancer for any alcohol use and by drinking groups while adjusting for the potential effects of study–level covariates , 61–63. analysis .We investigated the potential modification and confounding effects of study–level covariates using bivariate analysis of RR of prostate cancer morbidity or mortality and any alcohol consumption . AccordiP–value of bivariate tests of the log–RR of each covariate, and correlations with other covariates. Using all 27 studies, any variable whose bivariate test had a P–value <0.10 was considered as a candidate for the multivariate regression analyses of the log–RR of prostate cancer morbidity or mortality [P–value from the bivariate test was included in the multivariate regression analyses. Abstainer bias was the main interest of the present study and thus its potential confounding effect was adjusted for in the pooled analysis (Table P < 0.001) and it was not included in the final models.The covariates above were selected for control in multivariate regression analyses on empirical grounds based on the ortality , 66. If is Table  and furtis Table . On the is Table  and 4. Ais Table  and 4. HIn multivariate regression analysis, the dependent variable was the natural log of the RR estimated using the rate ratio, hazard ratio or odds ratio of each drinking group in relation to the abstainer category. All analyses were weighted by the inverse of the estimated variance of the natural log RR. Variance was estimated from reported standard errors or confidence intervals. The weights for each individual study were created using the inverse variance weight scheme used in fixed regression analysis in order to obtain maximum precision for the main results of the meta–analysis  and suchStudies with large or small estimates and/or variance can be highly influential. Univariate analysis , 67, 68 P values or 95% CIs. All statistical analyses were performed using SAS 9.3 and the SAS PROC MIXED procedure was used to model the log–transformed RR [All significance tests assumed two–tailed ormed RR .The study funders had no role in study design, data collection, analysis or interpretation, report preparation and the decision to publish. All authors had full access to all the data and had final responsibility for the decision to submit for publication.There were 126 risk estimates available for different alcohol exposures across the 27 selected studies. Table P > 0.05 for each drinking category). Similarly, there was no significant heterogeneity detected using the Q statistic in either the pooled or individual drinking category estimates (P > 0.05 in each case). Compared to the “abstainers” (a heterogeneous group defined differently in different studies due to presence or absence of misclassification errors), being a drinker at any level was associated with increased risk of prostate cancer . Risk of prostate cancer was significantly raised for low  and higher volume drinkers  but not other drinking categories. In unadjusted analysis, a significant dose–response relationship in the RR was observed among active drinkers .Table P = 0.0005) but not significant for low volume drinking versus non–drinking (t–test P = 0.1432)  and so meta-analyses are presented on a pool of both US and non-US studies. When tests with low volume alcohol exposure alone were conducted  for the comparison between studies free of misclassification errors and those with just former drinker error. Two other variables were identified in bivariate analyses as potential confounders of the risk relationship between alcohol consumption and prostate cancer morbidity or mortality: (i) whether the US–non–US study (P = 0.0019) and (ii) whether a study controlled for smoking status (P = 0.0838). The misclassification error variable was included as covariates in the pooled (un–stratified) multivariate regression analysis given previous research highlighting their importance. Table P = 0.0001), medium , high  and higher volume drinkers . There was also still a significant dose–response relation between risk of prostate cancer and alcohol consumption for current drinkers in adjusted analysis . Figure We next examined whether study characteristics either significantly modified or potentially confounded the risk relationships between alcohol consumption and prostate cancer morbidity or mortality outcomes. The weighted RR estimate for any drinking versus non–drinking is significantly higher for US than non–US studies (t–test P = 0.0143) and those with only former drinker bias having the lowest . A similar pattern of results was evident for higher levels of alcohol consumption and, also, for estimates of prostate cancer risk with any level of current alcohol consumption for which only the error–free studies show a significant risk for drinking regardless of whether adjustment is made whether studies controlled for US-non-US study or smoking. Sensitivity analysis found that inclusion or exclusion of the study design variable in the models made no difference to the estimates. These results are basically consistent with the pooled analysis in suggesting an increased risk even for low volume drinking but also indicate the importance of misclassification errors as a potential cause of bias. In particular, inclusion of former drinkers in the abstaining reference group appears to reduce the risk estimates.Given the previous literature indicating the potential for misclassification errors to bias risk estimates, visual inspection of the Fig. Meta–analyses of cohort and case–control studies were conducted to investigate (i) the role of alcohol consumption as a potential risk factor for prostate cancer and, (ii) whether this relationship was significantly influenced by key study characteristics and potential biases, in particular according to whether former and/or occasional drinkers were misclassified as abstainers. Unique among published meta-analyses –23, 25, Alcohol is a known carcinogen causing a variety of human cancers  via diffSeveral limitations with our meta–analysis must be acknowledged. Our meta–analysis was based on 27 studies including 126 risk estimates. This sample is relatively small when conducting multivariate regression to control for study level characteristics that might confound the relationship between prostate cancer and alcohol consumption. Furthermore, adjustment for study level characteristics such as whether smoking status was controlled is of course not as precise as controlling for this variable at the individual level within a study. Inevitably, uncontrolled confounding from unmeasured or imprecisely measured variables will be present both within and between studies. Control for smoking status, for example, can be done in many ways and some studies did not distinguish former smokers from lifetime non–smokers. Our analysis showed a statistically significantly higher risk of prostate cancer due to any drinking in the studies conducted in the US than in other countries. However, this effect disappeared when controls for other covariates such as drinker bias and between-study variation were introduced. This finding might be affected by the relatively small number of studies available for analysis and/or other unmeasured confounders and modifiers. The issue should be revisited in future meta-analyses when more studies are available. While there were relatively few studies available for analysis, the required number of subjects per variable for linear regression is much smaller than in logistic regression and a minimum of even two subjects per variable would not bias the estimate in linear regression analysis . The greIn summary, the RR of prostate cancer morbidity or mortality significantly increased at low volume alcohol consumption levels (>1.3 g and <24g per day) compared to abstinence and a statistically significant a dose–response relationship was observed for the first time in a meta–analysis. The level of increased risk observed for low volume drinkers was relatively modest in the pooled analysis (8%), but was as high as 23% in the studies free of misclassification error. In either scenario, with a condition as prevalent as prostate cancer in developed countries, the public health implications of the findings are significant, so we suggest these are practically significant levels of risk for prevention purposes. Different forms of misclassification bias may differentially affect risk estimates, particularly the common practice of including former drinkers in the abstainer reference group may reduce risk estimates. Prostate cancer may need to be incorporated in future estimates of the burden of disease alongside other cancers  and be integrated into public health strategies for reducing alcohol related disease. We recommend that future prospective studies on alcohol and disease seek to avoid biasing risk estimates by misclassifying either (i) former or occasional drinkers as abstainers, or (ii) occasional drinkers as low volume drinkers. Following Liang and Chikritzhs , we also


HIV modifies, or not, any association between diabetes and active tuberculosis.To determine current evidence for the association between diabetes and active tuberculosis in Africa, and how EMBASE, Global Health and MEDLINE databases. Studies were eligible for inclusion if they explored the association between diabetes mellitus prevalence and active tuberculosis incidence or prevalence, used a comparison group, were conducted in an African population and adjusted the analysis for at least age. Study characteristics were compared, and risk of bias was assessed. The range of effect estimates was determined for the primary association and for effect modification by HIV.We conducted a systematic review by searching the HIV as a potential effect modifier. All studies were case–control studies, including a combined total of 1958 tuberculosis cases and 2111 non‐tuberculosis controls. Diabetes diagnostic methods and analysis strategies varied between studies. Individual study adjusted odds ratios of active tuberculosis for the effect of diabetes mellitus (unstratified) ranged from 0.88 (95% CI 0.17–4.58) to 10.7 (95% CI 4.5–26.0). Individual study P‐values for HIV interaction ranged from 0.01 to 0.83. Quantitative synthesis of individual study data was not performed due to heterogeneity between studies.Three eligible studies were identified: two investigated the primary association and two investigated HIV on this association. Existing data are disparate. More regional research is needed to guide policy and practice on the care and control of tuberculosis and diabetes in Africa.Few data currently exist on the association between diabetes and active tuberculosis in Africa, and on the effect of  An ecological longitudinal study covering the years 2000 and 2012 studied the global relationship between the prevalence of DM and the incidence of TB to evaluate their coexistence worldwide r = 0.17, P = 0.013). A non‐significant inverse relationship was found for the African region (r = −0.27).An association between diabetes mellitus (DM) and active tuberculosis (TB) has been established: systematic reviews and meta‐analyses of studies exploring the relationship suggest that the incidence of active TB is two to three times higher in those with DM than those without DM Whilst there is no reason to suspect the underlying pathophysiology of the association should differ between ethnographic populations, the context of the association in Africa could be different from the rest of the world and consequently lead to a different overall association. Other risk factors for tuberculosis could act as effect modifiers on the association. The most notable difference relating to tuberculosis risk factors between Africa and elsewhere is the high prevalence of HIV in Africa, which is more than five times higher than in any other world region The dual effect of diabetes and HIV on the risk of developing TB disease or on its clinical evolution is unclear. It could be that the effect of hyperglycaemia on TB risk is relatively small in HIV‐positive individuals compared with its effect in HIV‐negative individuals, as the greatly increased risk of TB among HIV‐positive individuals could diminish any additional increased risk from hyperglycaemia. On the other hand, the effect of hyperglycaemia might be exacerbated in the presence of HIV infection if the contributions of each to increased TB risk are synergistic.The prevalence and incidence of tuberculosis remain high in many parts of Africa The EMBASE, Global Health and PubMed databases were searched. Studies that investigated the relationship between the prevalence of diabetes and the incidence or prevalence of active tuberculosis, included a comparison group, were conducted in an African population and adjusted for age were eligible for inclusion. As age has the potential to be a major confounder of the association, studies that did not adjust for at least age in the analysis, or present data that enabled this analysis, were not eligible. Reference lists of identified eligible papers were additionally hand‐searched to identify further potentially relevant studies.® database was searched for publications from 1946 until June 2016, including Epub Ahead of Print, In‐Process, Other Non‐Indexed Citations and Ovid MEDLINE® Daily. The search terms and strategies used were deliberately broad to increase the likelihood of all relevant studies being identified. Searches were restricted to human studies. No restriction on language was made. The following MESH and text search terms were used as follows:Diabetes mellitus.mp. Hyperglycaemia.mp. 1 OR 2Tuberculosis.mp. Africa.mp. 3 AND 4 AND 5The EMBASE Classic+EMBASE database was searched for publications from 1947 until June 2016, the Global Health database was searched for publications from 1910 until June 2016, and the Ovid MEDLINEDeduplication of papers identified by the searches was performed using the Ovid database platform. The titles and abstracts were screened for eligibility, and the full texts of articles identified as potentially relevant were examined. All studies meeting the eligibility criteria were included for exploration of the first aim, to determine the association between the prevalence of diabetes and the incidence or prevalence of active tuberculosis in Africa. Studies that met the eligibility criteria and presented the primary age‐adjusted association stratified by HIV were also included in assessment of the second aim, to determine how HIV modifies, or not, any association between the prevalence of diabetes and the incidence or prevalence of tuberculosis.It is possible that studies relevant to the second aim could take place in non‐African populations, and so to ensure all relevant papers were identified, a second search was performed using the same databases and the same search strategy, except no restriction on location was made and the following MESH and text search terms were used: Diabetes mellitus OR hyperglycaemia AND tuberculosis AND HIV. Studies were eligible only if they investigated the primary association between DM prevalence and TB incidence/prevalence and stratified the analysis by HIV. Studies that investigated the prevalence of diabetes among TB patients stratified by HIV but did not include a non‐TB population were not eligible for inclusion because it is not possible to assess for effect modification without data from a control population for the primary association.P‐values for interaction was also determined.Individual study data were extracted from reports using data collection tables, to identify individual study characteristics, risk of bias and results. When necessary, study authors were contacted for clarification of study data. Study characteristics sought were period of data collection, study design, study setting, study size and HIV prevalence among study participants. The risk of bias for individual studies was assessed by ascertaining study definitions for the exposure variable, outcome variable and comparison group and determining the variables adjusted for in the analysis. Qualitative description was used to synthesise the risk of bias results. The principal summary measures sought were the odds ratios or risk ratios for the association between DM and TB, overall and stratified by HIV. The range of effect estimates was determined for the primary association and stratified by HIV. The range of The study protocol was not eligible for registration in current prospective registers of systematic reviews protocol because the review does not investigate an intervention or strategy to prevent, diagnose, treat or monitor a health condition; rather, it investigates observational associations.The database searches for studies investigating the primary association in Africa identified 314 potential papers after deduplication . Faurholt‐Jepsen et al. also found evidence for a stronger association among HIV uninfected individuals but only when adjusting for alpha‐1‐acid glycoprotein (P = 0.01). Quantitative synthesis of individual study data was not performed due to heterogeneity between studies.The individual study effects of HIV on the association between DM and TB were different depending on the definition of diabetes and the factors adjusted for in the analysis Table . BoillatThis systematic review identified only three eligible studies conducted in African populations. Only two of these studies presented data on the adjusted association between DM and TB. One found no evidence of association and the other found evidence of a positive association only when diabetes was measured in cases around the time of TB treatment initiation. There was no evidence of association when DM was measured in TB cases after receiving TB treatment, suggesting that the initial association seen was due to an increase in stress‐induced hyperglycaemia among newly diagnosed TB cases rather than due to true diabetes.Only two studies stratified their analysis by HIV. The results depended on the analysis strategy and method of DM diagnosis used. There was little evidence of association among either HIV‐infected or uninfected individuals when DM was measured in cases after TB treatment.The results seen in this systematic review are in keeping with the idea that the association between DM and active TB may be different among African populations, but existing data are currently too sparse to be conclusive. It remains possible that HIV could modify the association, but again, there are currently insufficient data to be certain.A limitation at the individual study level was the different criteria used for diabetes diagnosis in each study, both for the methods and the glycaemic cut‐offs used to determine diabetes. This made comparison between studies problematic. Conformity with WHO diagnostic criteria and presenting separate rather than combined analyses for each method of diagnosis used would mitigate this limitation.The results of this systematic review, and any future repeat review, have relevance to healthcare policymakers, academics and practitioners in Africa. The global collaborative framework for the care and control of tuberculosis and diabetes produced in 2011 by the World Health Organization and the International Union Against Tuberculosis and Lung Disease had few contributory studies from Africa Few data currently exist on the association between diabetes and active tuberculosis in Africa, or on the effect of HIV on this association. Exploration of diabetes diagnosed both at the time of TB diagnosis and after TB treatment is valuable to distinguish between diabetes and stress‐induced hyperglycaemia secondary to infection with TB. More regional research is needed to guide policy and practice on the care and control of tuberculosis and diabetes in Africa.