There is conflicting evidence for the association between alcohol consumption and common joint conditions such as Osteoarthritis (OA), which affects millions of people. We sought to determine the true association between alcohol intake and OA. We conducted a PRISMA systematic review and meta-analysis of observational studies that reported associations between alcohol consumption and OA. Pooled estimates of association were represented through odds ratios (ORs). Publication bias was assessed with Funnel and Galbraith plots, and risk of bias was assessed with the Newcastle Ottawa Scale. We included 29 studies and 25,192 subjects with OA and reported an OR between any alcohol consumption and OA of 0.79 (0.68–0.93), suggesting a protective effect. OR of weekly or more frequent use was 0.79 (0.65–0.97). When grouped by covariates, alcohol consumption was negatively associated with radiographic , hand  and knee OA , North American ethnicity and female gender. Subgroup analysis of unadjusted data resulted in an OR of 0.70 (0.55–0.89) but this disappeared upon analysis of studies with data adjusted for any covariate . Whilst our pooled analysis suggest that weekly or more frequent alcohol consumption was negatively associated with OA, this was not observed when adjusted for confounding factors. Reasons for this include selection bias and lack of longitudinal exposure and adjustment for confounding variables. Therefore, this meta-analysis provides evidence to dispel notions that alcohol use may be protective against OA.The online version contains supplementary material available at 10.1007/s00296-021-04844-0. Alcohol consumption is a well-known risk factor for various major health conditions such as cerebrovascular disease, cardiovascular disease, and cancer. Reportedly, nearly 3 million deaths worldwide can be attributed to alcohol consumption each year . Yet, thThis debate extends into musculoskeletal disorders. The evidence that links alcohol use and musculoskeletal pain is tenuous. While it seems that situational pain due to musculoskeletal disease can increase risk of alcohol abuse as a means of self-medication , 7, otheThere are studies that suggest alcohol consumption may be a protective factor in Osteoarthritis (OA) . This, hThe purpose of this meta-analysis of observational studies is to delineate the combined result between studies of the association between alcohol consumption and OA, and to determine whether subgroup analysis by subject demographics and diagnostic criteria can offer explanation to previously reported results. To our knowledge, this is the first meta-analysis to provide a pooled estimate of the association between alcohol consumption and risk of OA. Given the increasing global burden of OA and alcohol use , 21, undThis meta-analysis was conducted in line with a protocol that adhered to Meta-analysis Of Observational Studies in Epidemiology (MOOSE) recommendations . A literInclusion criteria were applied to all studies by each reviewer and disagreements were resolved through discussion. Titles and abstracts were screened for potential eligibility by three reviewers, and those satisfying inclusion criteria or where further information was required to determine eligibility, progressed to full-text review. Any disagreements were resolved through discussion and consensus. Eligibility criteria applied to titles and abstract included: full-text availability in the English language; the investigation of alcohol consumption as a potential risk factor for OA ; and a cohort, case–control, cross-sectional or randomised control trial (RCT) design. Subjects of all ethnicity or geographical habitation were included.Raw data allowing calculation of the quantitative association (in terms of odds ratio) between alcohol consumption (exposure) and diagnosis of OA.orReporting of either Relative Risk (RR), Odds Ratios (OR) or Hazard Ratio (HR) with confidence intervals.The inclusion of a comparison/control group of participants who abstain from alcoholic beverages.Reporting of participant gender and age.Methodology detailing study design including subject recruitment.Information detailing methodology of establishing diagnosis of OA.The eligibility criteria applied at full-text screen for inclusion in our meta-analysis were as follows:Studies that defined OA through clinical endpoints such as having had joint replacement surgery were excluded. Systematic and literature reviews were excluded but to maximise capture, the bibliographies of these articles were scanned. The study selection process is illustrated in Fig. The population demographics of each study were extracted for analysis. Variables of interest included the quantity of alcohol consumed over a given time, and other cohort information as shown in Table ORs and confidence intervals (CI) were extracted for meta-analysis. RRs and HRs with CI or standard errors (SE) were converted to ORs through calculation using the metafor package on R software. Both confounder-adjusted and unadjusted OR were included for pooled meta-analysis. All data analyses were conducted using R version 4.0.0 software. A meta-analysis of all studies was performed to generate a summary forest plot Fig. , stratifQ test and I2 statistic performed on R 4.0.0 using the metafor package and RevMan 5.3 software, respectively. To further determine evidence for publication bias, a Galbraith plot for Egger’s test was generated  and a random effects model was applied for these to account for variability in variance. A fixed-effects model was applied for subgroup analysis of covariates that reported a low degree of heterogeneity (I2 < 25%). Likewise, all meta-regression analyses were performed under a restricted maximum likelihood (REML) approach. Meta-regression plots with ORs as the dependent variable were generated to illustrate the results of the analysed covariates graphically  determined OA by radiographic diagnosis, with Kellgren–Lawrence (KL) grade 2 or greater as the diagnostic threshold (Supplementary Table 1). Eight studies evaluated self-reported OA, seven of which reported a previous clinician diagnosis of OA [Just under half of the studies , suggesting that alcohol has a protective effect .The analysis of cohort studies included ten studies, which did not show a statistically significant association with an OR of 0.90 (0.72–1.12). Within this group, two studies reported a statistically significant negative association , 48 and I2 = 95.0%). An overall odds ratio of 0.75 (0.59–0.94) was shown, suggesting that the negative association seen in the primary analysis of all 29 studies is likely attributed to data from cross-sectional studies.Sixteen cross-sectional designs were included in our meta-analysis. Within this subgroup, seven studies showed a statistically significant negative association between alcohol use and OA and one study showed a significant positive association. It is, therefore, not unexpected that a significant degree of heterogeneity was present (n = 6) that had majority male subjects showed no significant association. Conversely, a significant negative association  was observed in the analysis of the 23 studies with majority female subjects. Regressing the odds ratios by the average BMI of subjects did not reveal any significant associations  and included 9782 subjects. When analysing studies that combined both clinical and radiographic evidence as diagnostic criteria, no significant association was found .Only OA diagnosed radiographically showed a significantly negative association . This group also had the greatest number of studies  and site-unspecified OA  found significant negative associations. There were no significant associations reported in the pooled analysis of hip OA, spine OA or multi-site OA subgroups.n = 15 studies), monthly (n = 7), and undefined current drinkers (n = 9). Only the group that included weekly or more frequent drinkers, which encompassed 14,292 subjects . The other two categories did not identify any significant association. It was not possible to determine the upper limit of alcohol consumed in terms of ethanol units per time, as this was not detailed within nearly all of the studies.Owing to the differences in study design and heterogeneity among studies, the amount of alcohol use in the exposed subjects was further stratified into three categories: weekly or more frequent (n > 500 and n > 1000) was related to significantly negative associations between alcohol use and OA. Whereas stratifying for studies with cohort sizes less than 500 (n = 13) did not show a significant association. The negative association found in the primary analyses was present on subgroup analysis of studies that did not adjust for covariates  but did not remain after adjustment for any confounders .We found that a large study sample size . This was a more negative association than that of the primary analysis which reported an OR of 0.79 (0.68–0.93). When grouped by any adjustment, this negative association was no longer observed.These results suggest that any negative association seen in the summary analysis was probably due to a lack of confounder adjustment in individual studies. We found that there were several covariates which could explain this. When exploring cohort characteristics, a negative association was seen in weekly or more frequent drinkers, radiographically diagnosed OA, hand OA, knee OA, and site-unspecified OA. It was also seen in North American cohorts and cohorts with female-predominant subjects. When grouped by cohort size, studies that contributed over 500 or over 1000 subjects appeared to show a significant negative association. Consistent with results of the meta-analyses, our meta-regression analyses found negative associations with age, female gender, and study sample size.It also appeared that study design played a significant role. The subgroup analysis for cross-sectional studies supported the negative association observed in the primary analysis, whereas no association was seen when stratified by other study designs.I2 = 25% or lower was adopted for the application of a fixed-effects model in meta-analyses. Use of higher thresholds may erroneously report statistically significant associations [I2 test, a Q-statistic was calculated for our primary analysis.To our knowledge, this meta-analysis is the first to provide a pooled estimate of the association between alcohol consumption and risk of OA. Other meta-analyses have employed similar methodology to assess the association between alcohol consumption and rheumatoid arthritis , and betciations . To confThe frequency of alcohol consumption was categorised to determine whether any associations observed may be dose-related and specific to a pattern of use. This is important as referent cohorts are likely biased towards common habits of alcohol consumption, which tends to be more frequent than monthly use .In addition to this, we stratified OA by method of diagnosis. This may be informative as the use of alcohol could potentially interfere with clinical symptoms of OA and thereby obscure any association seen with clinical OA. On the contrary, radiographic assessment allows the degree of structural OA changes to be quantified. Interestingly, when grouped into such classifications, our analysis showed that only radiographic OA had a negative association with alcohol consumption. A potential explanation for this could be that individuals with symptomatic OA are more likely to consume alcohol as a means of analgesia .In our meta-analysis, we show that the reported negative associations between alcohol use and OA are likely untrue. In support of this, analysis of studies adjusted for confounders eradicated any significant associations and suggested that alcohol had no significant association with OA. We further attempted to reduce heterogeneity by assessing adjustment by different covariates but were not successful. Most of the heterogeneity seem to arise from the OA joint of interest, as hand OA and hip OA groups both demonstrated low heterogeneity alone and did not show a significant association.The basis for how alcohol affects OA can be primary or secondary. There is no clear consensus on what the primary biological effects may be. It is known that alcohol use can increase uric acid levels and thus increase the risk of inflammatory joint conditions such as gout . In OA, The majority of the meta-analyses displayed a substantial degree of heterogeneity, and so interpretations should be made with caution . As onlyEstimates of alcohol use were largely subjective and self-reported. Therefore, alcohol consumption is likely to be underestimated, especially in heavy use . This prIt is difficult to determine the most suitable inclusion criteria for the presence of OA. Structural changes can be reliably determined radiographically , whereasOur meta-analysis shows that previous reports of a protective effect of alcohol consumption on risk of osteoarthritis are likely to be inaccurate. The suggested protective effect is likely to have been observed due to lack of confounder adjustment. Alcohol use is a public health issue and with mounting global disease burden attributable to alcohol misuse  it is inSupplementary file1 (DOCX 31 KB)Below is the link to the electronic supplementary material.


Gestational diabetes mellitus (GDM) is a common disease during pregnancy. The association of vitamin D receptor (VDR) polymorphisms with GDM is still controversial. This study aimed to assess the associations between VDR polymorphisms and GDM risk.We searched Cochrane Library, PubMed, and Embase electronic database for all eligible studies published from Jan 1, 1980 to December 31, 2020 to conduct a Meta-analysis. We analyzed four VDR polymorphisms: BsmI (rs1544410), ApaI (rs7975232), TaqI (rs731236), and FokI (rs2228570). Inclusion Criteria: (1) The data can be evaluated; (2) case–control study; and (3) meeting the Hardy–Weinberg’s law. Exclusion criteria: (1) Insufficient or extractable data; (2) Severe publication bias in the data; and (3) duplicate publications. We eventually included 15 studies in seven articles, including 2207 cases and 2706 controls.We eventually included 15 studies in seven articles, including 2207 cases and 2706 controls. The data showed that ApaI (rs7975232) VDR gene polymorphism was related with the risk of GDM for the comparison of CC vs AA and recessive model in overall population and FokI (rs2228570) VDR gene polymorphism was associated with the risk of GDM for recessive model in overall population. BsmI (rs1544410) polymorphism was not related with the risk of GDM in overall population. However, in the analysis of subgroups grouped by race, BsmI (rs1544410) has certain correlations. And, the data suggested the TaqI (rs731236) polymorphism was not associated with GDM.Based on the meta-analysis, VDR ApaI (rs7975232) and FokI (rs2228570) polymorphisms increase susceptibility to GDM. In the future, it can be used to diagnose and screen molecular biomarkers for GDM patients. Gestational diabetes mellitus (GDM) is defined as glucose intolerance diagnosed during pregnancy . GDM is Vitamin D deficiency is associated with diabetes mellitus –8. VitamMany studies have researched the role of VDR gene polymorphisms in GDM. It is reported that VDR has four well-characterized di-allelic polymorphisms: BsmI , ApaI , TaqI , and FokI . However, the results of these studies are still uncertain –19. DiffWe identified the keywords “VDR” OR “vitamin D receptor” AND “polymorphism” OR “variant” OR “allele” OR “genotype” OR “gestational diabetes” OR “gestational diabetes mellitus” OR “GDM” to search the articles in Cochrane Library, PubMed, and Embase electronic database. All articles published until December 31, 2020. In addition, manually search the article's reference list for more literature. This article does not collect unpublished data. When multiple articles contain studies of the same population, complete studies were chosen in this study. The language of the publication is limited to English or at least an English abstract.Inclusion Criteria: (1) The data can be evaluated; (2) case–control study; and (3) meeting the Hardy–Weinberg’s law. Exclusion criteria: (1) Insufficient or extractable data; (2) Severe publication bias in the data; and (3) duplicate publications.The data was independently evaluated by two reviewers according to include and exclude criteria for these documents, discuss whether can be included in the meta-analysis. The difference was not resolved until the consensus of each item was reached. The following information was recorded for each study: author’s name, year of publication, country of origin, racial descent, source of the control population, genotyping methods, matched factors as well as adjusted factors, number of cases and controls.ORs (odds ratios) and 95% CIs were used to estimate the relationships between VDR gene polymorphism and GDM. For heterogeneity detection, we chose the P value to measure. If P < 0.05, we chose the random effect model, otherwise chose the fixed effect model. For publication bias we calculated Egger and Begg’ test, respectively . If P < 0.05, it was considered biased. Hardy–Weinberg’s law was detected in all control groups. This meta-analysis was performed using STATA .We found 186 records through a full search of the database. After several rounds of screening, 36 articles met our requirements. After two individuals independently evaluated the inclusion and exclusion criteria, 15 case–control studies in a total of seven articles were included in the study –19. We iWe identified 15 independent studies in seven eligible reports, including 2207 cases and 2706 controls. The main characteristics of all the studies included in our study were shown in Table Funnel plot for comparison of allele models for ApaI Fig. A, FokI B and BsmThe results showed that in the total population of CC vs AA and the recessive model, ApaI rs7975232) was associated with a higher GDM risk  was associated with a higher GDM risk in overall population  was not related to GDM risk in the general population. In the subgroup, a relationship with higher GDM risk was found in the Asian population allele model, the comparison of GA vs AA, the dominant model and the over-dominant model.  polymorphism of the VDR gene was not related to susceptibility to GDM  and FokI (rs2228570) polymorphisms increase susceptibility to GDM. In the future, it can be used to diagnose and screen molecular biomarkers for GDM patients. VDR BsmI (rs1544410) polymorphism was associated with GDM in Asian and African population. VDR TaqI (rs731236) polymorphism was not associated with GDM.


This meta-analysis was designed to reassess the prognostic and clinicopathologic values of the microRNA-125 family in GC patients. The miR-125 family  has been reported as being pivotal prognostic biomarkers of gastric cancer (GC). However, there is controversy about the role of the miR-125 family in predicting the progression of GC.The miR-125 family  has been reported as being pivotal prognostic biomarkers of gastric cancer (GC). However, there is controversy about the role of the miR-125 family in predicting the progression of GC. The electronic databases of PubMed, ISI Web of Science, Scopus, and Cochrane Library were systematically searched for relevant studies. Overall survival (OS) rate as the primary outcome from each study was extracted. The overall hazard ratio  and odds ratio (OR) with 95% confidence interval (CI) was calculated to evaluate the association between miR-125 family expression and prognosis and susceptibility to gastric cancer. The quality of evidence was evaluated using the Newcastle-Ottava Scale (NOS). The extracted data was combined based on the random-effects model. The low expression of miR-125 family predicts poor OS in GC patients. Thus, the miR-125 family may be helpful as a potential biomarker for the prognosis of gastric cancer. Cancer is a critical threat for human health, and in recent years, it has emerged as an important factor leading to death . Recent Today, several biomarkers are used to diagnose or predict disease, including miRNAs associated with cancer development , 11. miRSeveral studies have reported differential expression profiles of miRNAs in various cancers including lung, breast, and other cancers. Recently, miRNAs have been identified as important contributors in molecular mechanisms of GC tumorigenesis and progression, which may participate in the development of new therapeutic strategies for GC patients . For exaAccumulating reports have revealed the main role of the miR-125 family in the development of GC. Given the many contradictions in the prognostic value of the miR-125 family in GC patients, clarifying the prognostic role of this miRNA in GC will support the discovery of new therapeutic targets for GC. Therefore, in the present study, the data collected from studies was reassessed through a meta-analysis. Search strategy This meta-analysis was performed in accordance with the guidelines of the 2009 Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) statement . A systeInclusion and exclusion criteriaInclusion criteria comprised: 1) publications reporting expression levels of miR-125 family and prognostic value in GC patients compared to control subjects; 2) assessments of expression levels of the miR-125 family in tissue or blood samples; 3) Studies with a retrospective design comprising 30 patients or more; and 4) Studies presenting sufficient information to estimate the HR and OR and corresponding 95% CI. Exclusion criteria were: 1) Studies that did not report diagnostic or prognostic value of miR-125 family in gastric cancer; 2) Studies that lacked valuable and quality data; and 3) Duplicate studies, letters, and review publications.Data collectionData extracted from each selected eligible paper comprised the first author’s name, publication year, country, study design, clinical stage of cancer, sample type, mean age of participants, clinicopathological features, miR-125 family, number of patients with gastric cancer, miR-125 family cutoff, method for assessing miR-125 family, follow-up time, outcome of prognosis, HR, 95% confidence interval, and NOS score of every included study. Quality assessmentTwo independent investigators reviewed the full articles of all included studies and evaluated their quality based on the Newcastle–Ottawa scale (NOS) . The selStatistical analysisp <0.1, values <25%, 25%-50%, and >50% were set to indicate mild, moderate, and significant heterogeneity, respectively). The Begg’s rank correlation test and the Egger’s regression asymmetry test were also applied to evaluate any potential publication bias produced by the funnel plot  and odds ratio (OR) and their 95% CI were used to quantitatively determine the prognostic value of the miR-125 family in GC. In survival analyses, the hazard ratio (HR) is the ratio of hazard rates corresponding to the conditions described by two levels of an explanatory variable. An odds ratio (OR) is a measure of association between an exposure and an outcome. Heterogeneity among studies was assessed using the Q test and the Higgins I-square test  was used to assess expression levels of the miR-125 family in frozen samples. Of these 10 studies, 8 and 2 studies were performed in China and Japan, respectively. These articles were published between the years 2011 and 2020. Studies by Nishida et al., Dai et al., Cai et al., Cao et al., Li et al. and Zhang et al. measured the expression levels of miR-125a-5p in GC patients in comparison with controls , 33-36. Prognostic value of miR-125 family in GCp< 0.001) (p = 0.53) (p = 0.05). The combined data of the included studies demonstrated a reduction in the expression levels of the miR-125 family in GC patients and indicated a strong association between low expression levels of the miR-125 family and susceptibility to GC as well as poor overall survival with HR calculated for miR-125a   and miR- = 0.53) . Sensiti2 = 86%, p=0.00) was observed among studies with respect to demographic differences. Removing each study in turn did not change the overall effect size that ranged between 1.70 and 1.84. In addition, a significant heterogeneity  (Ten studies assessed odds ratio (OR) and 95% CI for the relationship between clinicopathological features such as liver metastasis, venous invasion, lymph node metastasis, and tumor progression. A random-effects model was applied to estimate effect size. The results revealed that liver metastasis   along wip = 0.17) (p = 0.28) (p = 0.51)  , venous  = 0.28) , and tum = 0.51)  were notPublication biasp = 0.72 and Egger's: p = 0.95) were not significant. On the other hand, the funnel plot of the study precision by effect size was symmetric, suggesting no significant publication bias . Meta-regression analysisp = 0.40), lymph node metastasis ([ = 0.22) (p = 0.18)   or liver = 0.18) . TherefomiRNAs are a member of small non-coding RNAs that regulate a wide array of biological processes including carcinogenesis . RecentlmiR-125 is a family of microRNAs that are involved in various cancer types . SeveralIn the next step, a significant heterogeneity was observed among studies. To explore the source of heterogeneity, subgroup analysis was performed based on the subgroups of liver and lymph node metastasis, TNM stage, and sex. At first, the prognostic value of miR-125a was estimated in subgroups of patients with or without liver and lymph node metastasis. The results indicated no significant difference in the prognostic value of miR-125a in patients with liver and lymph node metastasis compared to patients it. Furthermore, the prognostic value of miR-125a was analyzed in two subgroups of TNM stages I and II and TNM stages III and IV in GC patients. The pooled analysis for the two subgroups showed no significant difference in the prognostic value of miR-125a. Moreover, it was found that sex has no effect on the prognostic value of miR-125a in patients with GC. These results were confirmed by meta-regression analysis. Meta-regression analysis showed no significant association between the prognostic value of miR-125a and confounder variables of liver and lymph node metastasis and TNM stage in GC patients. Taken together, the present results confirm that the relationship between low miR-125a expression and poor survival rate is not dependent upon clinicopathological features. This study had several limitations. One was the significant heterogeneity among included studies. To explore the source of heterogeneity, subgroup analysis was performed. The relatively small sample size of primary studies involved in the subgroup analysis could be another limitation of the present meta-analysis. However, to validate these results, further studies are required with larger sample sizes. The present meta-analysis found that low miR-125a expression could be a biomarker in the prognosis of poor survival rate in GC patients. However, with respect to the small sample size, the results should be interpreted cautiously.The authors declare that they have no conflict of interest.


Vincristine is a drug that is part of the treatment for many children with cancer. Its main side-effect is vincristine-induced peripheral neuropathy (VIPN), which often presents as tingling, pain, and lack of strength in the hands and feet. It is not yet possible to predict which children will suffer from VIPN. In this review, we report on all genetic variations that are associated with VIPN. We found that variations in genes related to vincristine transport, cell structure, hereditary nerve disease, and genes without a previously known connection to vincristine or VIPN are related to VIPN. Variations in genes involved in vincristine breakdown are not significantly associated with VIPN. In conclusion, genetic variations affect a child’s tendency to develop VIPN. In the future, this information might be used to predict the risk of VIPN and adapt treatment on this.Vincristine-induced peripheral neuropathy (VIPN) is a debilitating side-effect of vincristine. It remains a challenge to predict which patients will suffer from VIPN. Pharmacogenomics may explain an individuals’ susceptibility to side-effects. In this systematic review and meta-analysis, we describe the influence of pharmacogenomic parameters on the development of VIPN in children with cancer. PubMed, Embase and Web of Science were searched. In total, 1597 records were identified and 21 studies were included. A random-effects meta-analysis was performed for the influence of CYP3A5 expression on the development of VIPN. Single-nucleotide polymorphisms (SNPs) in transporter-, metabolism-, cytoskeleton-, and hereditary neuropathy-associated genes and SNPs in genes previously unrelated to vincristine or neuropathy were associated with VIPN. CYP3A5 expression status was not significantly associated with VIPN. The comparison and interpretation of the results of the included studies was limited due to heterogeneity in the study population, treatment protocol and assessment methods and definitions of VIPN. Independent replication is essential to validate the clinical significance of the reported associations. Future research should aim for prospective VIPN assessment in both a discovery and a replication cohort. Ultimately, the goal would be to screen patients upfront to determine optimal vincristine dosage with regards to efficacy and risk of VIPN. Vincristine is an important chemotherapeutic agent that is commonly used in treatment for pediatric cancers. It is approved by the United States Food and Drug Administration (FDA) for the treatment of acute lymphoblastic leukemia (ALL), Hodgkin and non-Hodgkin lymphoma, neuroblastoma, rhabdomyosarcoma, low-grade glioma and nephroblastoma. Furthermore, off-label uses include the treatment of Ewing sarcoma and medulloblastoma ,2. The m2 as opposed to 1.5 mg/m2) . Fu. Fu13]. Four studies assessed the influence of SNP rs924607 in CEP72 on the development of CTCAE or WHO grade 2−4 VIPN ,50,51. IGWAS or EWAS demonstrated significant associations between VIPN and eight SNPs in genes previously not associated with neuropathy, vincristine mechanism of action or metabolism . All stuGutierrez-Camino et al. found two SNPs in miRNA to be associated with VIPN, of which one miRNA could be related to the axon-guidance pathway, whereas the other could not be related to any known vincristine- or neurotoxicity-related pathway  48]..48].Several studies assessed the influence of covariates such as cumulative vincristine dosage, treatment protocol, and patient characteristics on their results, but these covariates did not have a significant influence on the reported associations . Only th2 = 50%, τ2 = 0.33). The study by Kayilioğlu et al., which could not be included in the meta-analysis due to the unavailability of appropriate data, did not find a significant association either   .This systematic review shows that pharmacogenomic parameters have a significant influence on VIPN in children with cancer and show potential for clinical relevance. Several SNPs in genes related to vincristine metabolism, hereditary neuropathy, the cytoskeleton and microtubules have been associated with VIPN. Furthermore, population-based GWAS and EWAS identified significant interactions with SNPs in genes previously unrelated to VIPN or vincristine. Our meta-analysis showed that CYP3A5 expression status was not a significant risk factor for VIPN.Several significant associations were found between SNPs in the ABC family of genes . These genes code for transmembrane proteins that mediate vincristine efflux across cell membranes; variations may thus contribute to different vincristine levels and therefore VIPN  54,55].,55.54,552) and conventional dosage (1.5 mg/m2) of vincristine during the continuation phase of treatment [Furthermore, SNPs in cytoskeleton-associated genes were associated with VIPN . Vincrisreatment . Recruitreatment ,50.In addition, we assessed the effect of CYP3A5 expression status on VIPN in a meta-analysis and found an overall pooled effect of 0.69 (95% CI 0.38−1.26) . Two stuThe comparison and interpretation of the results of the included studies is limited due to heterogeneity in the study population, treatment protocol and varying assessment methods and definitions of VIPN. Firstly, cumulative vincristine dosage varied between 6.5 and 97.5 mg across studies. Since cumulative vincristine dosage likely is an independent risk factor for VIPN, it could be of influence when establishing a relationship between a pharmacogenomic parameter and VIPN ,3. HowevIndependent replication is essential to validate the clinical significance of reported associations ,34. ThisThe strength of this systematic review and meta-analysis is the inclusion of all studies assessing the relationship between pharmacogenomic parameters and VIPN. No restrictions were applied regarding patient or study characteristics. Furthermore, we followed the PRISMA guidelines and two independent authors performed screening, data extraction and risk of bias assessment. A standardized risk of bias assessment was performed with the validated EPHPP tool . The weaFrom this systematic review, we can conclude that the following pharmacogenomic parameters have a significant influence on VIPN in children with cancer: SNPs in ABCB1, ABCC1, ABCC2, CYP3A4, GSTM1, VDR, ACTG1, CAPG, CEP72, MAPT, SYNE2, TUBB2B, SLC5A7, BAHD1, COCH, chromosome 12/chemerin, ETAA1, MRPL4, MTNR1B, NDUFAF6, TMEM215 and in three miRNAs. Our meta-analysis shows that CYP3A5 expression does not result in a heightened susceptibility of VIPN. To actualize the potential of pharmacogenomic testing, future research should prospectively assess VIPN with a sensitive measurement tool in both a discovery and replication cohort. Ultimately, the goal would be to develop an individualized protocol based on a patients’ genotype, taking all risk and protective genes into account, and subsequently give patients a dosage that limits the risk of VIPN while maintaining highest possible therapeutic efficacy. Dosage reductions or cessation of treatment, or for some patients even standardized dose capping, would no longer be necessary.


Although there is strong evidence of an association between general adiposity and incidence of heart failure, previous systematic reviews and meta-analyses have not reliably assessed the association of heart failure risk with other aspects of body composition (such as body fat distribution or lean mass), or between body composition and risk of heart failure subtypes. We aim to conduct a systematic review and meta-analysis of prospective studies to address these uncertainties, and inform efforts to prevent and treat heart failure.2 statistic. Subgroup analyses will be by age, sex, ethnicity and heart failure subtypes. Publication bias in the meta-analysis will be assessed using Egger’s test and funnel plots.The Preferred Reporting Items for Systematic Reviews and Meta-Analyses for Protocols statement was used as a template for this protocol. A systematic search of Medline, Embase and Global Health from database inception to present will be conducted to identify prospective studies reporting on the associations between major measures of body composition  and risk of heart failure. Article screening and selection will be performed by two reviewers independently, and disagreements will be adjudicated by consensus or by a third reviewer. Data from eligible articles will be extracted, and article quality will be assessed using the Newcastle-Ottawa Scale. Relative risks (and 95% CIs) will be pooled in a fixed effect meta-analysis, if there is no prohibitive heterogeneity of studies as assessed using the Cochrane Q statistic and IThis work is secondary analyses on published data and ethical approval is not required. We plan to publish results in an open-access peer-reviewed journal, present it at international and national conferences, and share the findings on social media.CRD42020224584. Heart failure (HF) is the pathophysiological endpoint of many cardiac diseases. There are about 65 million new cases of HF globally, and despite improvements in HF care over the last few decades, 5-year mortality continues to exceed 50% in most settings.There is increasing evidence of the importance of adiposity, body fat distribution and lean (or ‘fat-free’) mass (collectively referred to here as ‘body composition’) to risk of cardiovascular disease.2) and overweight or obese ranges (BMI>25 kg/m2) at higher risk than those in the normal range (18.5–25 kg/m2).2; above this level, 10 kg/m2 higher BMI was associated with about 50% higher risk of HF mortality.21Body mass index  is the most commonly used anthropometric measure of general adiposity. However, BMI is imprecise, it does not differentiate between body fat and lean mass and does not account for different distributions of body fat.The relationship between HF risk and measures of central adiposity, such as waist circumference or waist–hip ratio, has been less well studied than for BMI.10 12In addition to general adiposity and body fat distribution, there is emerging evidence that lean body mass (measured using bioelectric impedance or whole-body imaging) is associated with cardiovascular risk.The subtyping of HF based on left ventricular ejection fraction (LVEF) into those with preserved, midrange, or reduced ejection fraction, requires cardiac echocardiography or cardiac MRI. To date, few studies have achieved sufficient scale to reliably describe the association of body composition and HF ejection fraction subtypes. Obesity has been shown to be a risk factor for HFpEF in some observational studies but data on HFrEF and HFmEF are scarce.34 35In summary, although there is strong evidence of an association of obesity (as measured by BMI) with incidence and mortality from HF, previous systematic reviews and meta-analyses have not reliably assessed the association of HF risk with body fat distribution and lean mass, or between any of the measure of body composition and risk of HF LVEF subtypes or HF aetiologies. This is in part because such studies require measures of bioelectric impedance, whole-body imaging or measures of cardiac function that it has not been feasible to include in large-scale studies until recently. We aim to conduct a systematic review and meta-analysis of prospective studies to address these uncertainties, and inform efforts to prevent and treat HF.The primary objective of this study is to determine the associations of HF incidence with body composition. The secondary objective is to determine the extent to which these associations vary by age, sex, ethnicity, HF LVEF subtypes and HF aetiologies.What is the association between general adiposity  and HF incidence?What is the association between central adiposity  and HF incidence?What is the relationship between lean body mass and HF incidence?To what extent do these associations between body composition and HF incidence vary by age, sex, ethnicity, HF LVEF subtypes and HF aetiologies?This study will be a systematic review and meta-analysis of prospective studies which report the association between body composition measures and HF risk. We followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses-Protocols (PRISMA-P) review guidelines to draft our protocol  studies other than prospective studies, such as case-control studies (except nested case-control studies), review articles, conference abstracts, editorials, letters to the editor and cross-sectional studies; (2) studies which do not report effect sizes of associations between the selected measures of body composition and HF incidence; (3) studies conducted in participants with established HF; (4) studies with incomplete data  which could not be obtained after reasonable request from the corresponding author of such studies; (5) studies with too few HF events for reliable analyses (defined here as 20 events or fewer).We will conduct a systematic search of Medline, Embase and Global Health databases for articles published from the inception of each of the databases to present using the search strategy developed in conjunction with the information specialist (NR). The MeSH terms were chosen from the thesaurus used for indexing the subject headings. We also checked the search strategy used in previous systematic reviews.Abstracts from articles identified from the database searches will be imported into EndNote reference manager to allow duplicate citations to be deleted. The remaining abstracts will then be uploaded to ‘Covidence systematic review’ a webpage portal which allows collaborations between two or more reviewers for article screening and selection, based on the study inclusion and exclusion criteria.40Data will be collected from eligible articles using predesigned data extraction form. We will develop and pilot-test the data collection form before the start of the study and extract the following variables from each of the eligible articles: name of first author, year of publication, country of study, year of baseline survey, mean follow-up time, selection criteria for study participants, baseline characteristics , measures of body composition at baseline, date of resurvey (if performed), number of new cases of HF , LVEF among patients with HF, HF subtype(s), HF aetiologies, details of statistical analyses performed  estimates and 95% CIs).Quality of included studies will be assessed using the Newcastle-Ottawa Scale (NOS) which has been validated for use in systematic reviews.41 422 statistic. We will consider I2>50% as a significant level of heterogeneity.Descriptive characteristics of included studies will be reported, together with tables of the RRs extracted from each study. For the systematic review, findings from each study will be discussed in qualitative analyses. For meta-analyses, the RR estimates and 95% CIs for the association between different body composition measures and HF events will be extracted from each study and pooled using a fixed effects model. We will abstract the risk estimates from the most fully adjusted model except where such models adjusted for potential intermediate risk factors in a further step. In such cases, we will use models which did not include such intermediate factors. Where studies only reported RR in subgroups, we will use fixed-effect meta-analysis to generate an overall study-level RR so that each study will be represented only once in the main analysis. Where possible, subgroup analyses will be performed for each body composition measure and HF outcome, by age, sex, ethnicity, HF LVEF subtypes and HF aetiologies . Risk estimates and their 95% CI will be rescaled to 1-SD increase where appropriate to facilitate comparison across studies. Sensitivity analysis will be performed by restricting analyses to studies with a low risk of bias. In sensitivity analyses, we will test for the effect of (1) including studies which reported on HF aetiologies  and HF LVEF subtypes ; (2) excluding studies which used national databases as opposed to prospective cohort analysis; (3) excluding individual studies to investigate extreme results. Meta-analyses of reported associations will be presented using forest plots. Heterogeneity between studies will be determined using Q statistic and IThis systematic review will provide answers on the biological relationship between different body composition indices and incident HF risk. This will be invaluable for risk prediction of different HF types in clinical practice. It will also shed light on the independent and additive role of whole-body imaging to conventional anthropometric indices in the risk stratification of patients in primary care. This is especially important given the increased prevalence of overweight and obesity globally in the adult workforce.


Obesity is a worldwide epidemic that has been associated with a plurality of diseases in observational studies. The aim of this study was to summarize the evidence from Mendelian randomization (MR) studies of the association between body mass index (BMI) and chronic diseases.n = 218,792 individuals).PubMed and Embase were searched for MR studies on adult BMI in relation to major chronic diseases, including diabetes mellitus; diseases of the circulatory, respiratory, digestive, musculoskeletal, and nervous systems; and neoplasms. A meta-analysis was performed for each disease by using results from published MR studies and corresponding de novo analyses based on summary-level genetic data from the FinnGen consortium , uterus, kidney, and bladder. In contrast, genetically predicted higher adult BMI was associated with a decreased risk of Dupuytren’s disease, osteoporosis, and breast, prostate, and non-melanoma cancer, and not associated with Alzheimer’s disease, amyotrophic lateral sclerosis, or Parkinson’s disease.The totality of the evidence from MR studies supports a causal role of excess adiposity in a plurality of chronic diseases. Hence, continued efforts to reduce the prevalence of overweight and obesity are a major public health goal.The online version contains supplementary material available at 10.1186/s12916-021-02188-x. Obesity is a worldwide epidemic that has been associated with an increased risk of a plurality of chronic diseases in traditional observational studies –6. TheseThe aim of this study was to conduct a systematic review and meta-analyses of MR studies to determine the causal role of excess adiposity in chronic diseases. Meta-analyses were performed using results from published MR studies and complemented with results from de novo MR analyses of pertinent disease outcomes in the FinnGen consortium.A search in the PubMed and Embase databases using the query “(Mendelian randomization) AND (body mass index OR overweight OR obesity or adiposity)” was performed on October 3, 2021. Eligible for inclusion were original articles that reported estimates from an MR analysis of genetically predicted adulthood BMI in relation to one or more chronic diseases in the following disease groups: diabetes mellitus (type 1 or type 2); disease of the circulatory, respiratory, digestive, musculoskeletal, or nervous system; or site-specific cancer. When more than one study was published on the same outcome and study population, the study based on the largest number of cases or the largest number of genetic variants (if the sample size was the same) was included. No restriction based on the number of cases was imposed.OR]) with 95% confidence interval (CI) for the BMI-outcome association from the primary analysis. The study quality was assessed by adapting a modified version of the Strengthening the Reporting of Mendelian Randomization Studies (STROBE-MR) Guidelines [Data were extracted and entered in predefined tables by one author (SCL) and independently reviewed by another author (SB). From each MR study, the following information was extracted: the last name of the first author and year of publication; the number of single-nucleotide polymorphisms (SNPs) used as instrumental variables in the analysis, the source for the SNPs, and the exposure unit; consortium, study, or studies from which the SNP-outcome association estimates were obtained; the number of cases and non-cases or the total number of participants; ancestry of the study population; and the relative risk estimate  increase in genetically predicted BMI. For studies using another unit , the estimate was rescaled to 1 SD increase in BMI. Meta-analysis was performed for each outcome using results of published MR studies and de novo MR analyses of summary-level genetic data from the R5 release of the FinnGen consortium  [R2 < 0.001) associated with BMI at P < 5 × 10−8 in a genome-wide association meta-analysis of the Genetic Investigation of ANthropometric Traits consortium and the UK Biobank  [I2 statistic [In most studies, the relative risk estimate was expressed per 1 standard deviation  . For theviduals)  were obtviduals) . All BMItatistic . Values The PubMed and Embase searches resulted in 1469 unique hits of which 116 articles reported results from an MR study of BMI in relation to one or more of the pertinent disease outcomes. An overview of study selection is presented in Fig. FTO). Most recent MR studies used a strict linkage disequilibrium cut-off (R2 < 0.001) to select independent SNPs as instrumental variables for BMI, but some studies selected all conditional independent SNPs identified in the genome-wide association study. The majority of MR studies were based on outcome data from one or few studies , a large-scale genetic consortium, or the UK Biobank. For several outcomes, two or more studies were published based on outcome data from the same source population . One two-sample MR study did not indicate the statistical method used for the primary analysis [The results from the evaluation of study quality are provided in Additional file analysis , and eiganalysis , 40, 41.The number of MR studies based on independent study samples for each disease category was two for diabetes mellitus , 8, 13 fOR of 2.03 (95% CI 1.88–2.19) , followed by heart failure  and hypertension . The associations were weaker for all stroke types, with ORs ranging from 1.16 (95% CI 1.10–1.23) for ischemic stroke to 1.21 (95% CI 1.02–1.44) for intracerebral hemorrhage. Excluding the study based on a Chinese population had a minor impact on the results for peripheral artery disease . High heterogeneity between studies was only observed in the analyses of aortic valve stenosis, atrial fibrillation, and hypertension, but this was due to different magnitude of positive associations rather than a lack of association in one of the studies  for asthma and 1.65 (95% CI 1.47–1.85) for COPD, with no heterogeneity among studies.MR studies on diseases of the respiratory system were scarce, with results reported only for asthma and chronic obstructive pulmonary disease (COPD) mortality . Results for gallstone disease remained essentially unchanged after removing the study based on a Chinese population  for Dupuytren’s disease, 1.92 (95% CI 1.60–2.30) for gout, 1.55 (95% CI 1.43–1.69) for osteoarthritis, and 1.27 (95% CI 1.17–1.39) for rheumatoid arthritis. There was high heterogeneity between studies on Dupuytren’s disease and moderate heterogeneity between studies on gout and rheumatoid arthritis. MR analysis of osteoporosis in the FinnGen consortium showed an OR of 0.81 (95% CI 0.65–0.99) per 1 SD increase in genetically predicted BMI  but not Alzheimer’s disease or amyotrophic lateral sclerosis  but not in the Parkinson’s disease genome-wide association study . The combined OR of Parkinson’s disease was 0.90 (95% CI 0.79–1.02).Genetically predicted BMI was associated with multiple sclerosis , uterus , ovary, kidney, and bladder, but with a decreased risk of breast, prostate, and non-melanoma skin cancer Fig. . There wThis contemporary meta-analysis of MR studies of genetically predicted BMI in relation to 56 chronic diseases provides evidence in support of causal associations of excess adiposity with increased risk of type 2 diabetes mellitus, 14 circulatory system diseases, asthma, chronic obstructive pulmonary disease, five digestive system diseases, three musculoskeletal system diseases, multiple sclerosis, and cancers of the digestive system (six cancer sites), uterus, kidney, and bladder. In contrast, MR evidence indicates that high BMI is associated with a decreased risk of breast, prostate, and non-melanoma skin cancer, Dupuytren’s disease, and osteoporosis, and not likely associated with risk of Alzheimer disease, amyotrophic lateral sclerosis, or Parkinson’s disease.OR 1.32, 95% CI 1.06–1.64 per SD score increase in BMI based on 32 SNPs) [This meta-analysis of MR studies found consistent associations of higher genetically predicted BMI and increased risk of type 2 diabetes mellitus and cardiovascular diseases. However, conflicting results were found for adulthood BMI in relation to type 1 diabetes mellitus. The inconsistent results might be related to different ages at onset of type 1 diabetes mellitus in the UK Biobank and FinnGen populations or that genetic instruments for adulthood BMI rather than childhood BMI were used. An MR study of genetically predicted childhood BMI showed a positive association with childhood-onset (<17 years) type 1 diabetes mellitus (32 SNPs) . Excess 32 SNPs) –65. An M32 SNPs) .Higher genetically predicted BMI was associated with increased risk of several diseases of the respiratory, digestive, and musculoskeletal systems, including asthma, gallbladder disease, diverticular disease, nonalcoholic fatty liver disease, gout, osteoarthritis, and rheumatoid arthritis. The associations may be related to an obesity-related reduction in lung volume (for asthma), joint loading (for osteoarthritis), and alterations in microbiota composition, inflammatory mediators, and hormone levels. In contrast, higher genetically predicted BMI was associated with a modest lower risk of osteoporosis, possibly explained by mechanical stresses mediated through gravitational action. This finding confirms previous MR studies that have shown a positive association between genetically predicted BMI and bone mineral density , 67. GenThis meta-analysis of MR studies provided evidence that excess adiposity increases the risk of multiple sclerosis but not Alzheimer’s disease, amyotrophic lateral sclerosis, or Parkinson’s disease. If anything, a suggestive inverse association was observed between genetically predicted BMI and Parkinson’s disease. This finding is consistent with the results of a previous meta-analysis that found that being underweight was associated with an increased risk of Parkinson’s disease .The opposite direction of the associations of genetically predicted BMI with different cancers suggests different causal pathways for BMI and various cancers. The increased risk of cancers of the digestive system, uterus, kidney, and bladder may be mediated by alterations in insulin signaling, growth factors, adipose tissue-derived inflammation, and hormone levels. Higher genetically predicted BMI has been shown to relate to lower serum testosterone levels , and tesHeterogeneity was observed between estimates from individual studies in analyses of genetically predicted BMI and several disease outcomes . The detected heterogeneity was mainly caused by the different magnitude of the associations across studies and may be related to different genetic instruments used or to different study populations with different characteristics.A strength of MR studies is that confounding and reverse causation bias are reduced as BMI is proxied by genetic variants that generally are not related to self-selected behaviors and environmental exposures and are not modified by disease development. The validity of MR findings relies on the absence of pleiotropy . Researchers of most MR studies included in the meta-analysis performed sensitivity analyses and found limited evidence that the associations were biased by pleiotropy. Another limitation in MR studies of obesity and other harmful exposures in relation to late-onset diseases is competing risk bias, which is a potential type of survival bias. It cannot be ruled out that this bias might have affected the results in some of the studies. A further shortcoming is that most MR studies comprised individuals of European ancestry and therefore cannot infer causality of the role of excess adiposity in chronic diseases in non-European populations.The totality of the evidence from published and de novo Mendelian randomization analyses supports a causal role of excess adiposity in a plurality of chronic diseases. Hence, continued efforts to reduce the prevalence of overweight and obesity are a major public health goal.Additional file 1: Tables S1-S2. Table S1 – Study quality assessment. Table S2 – Mendelian randomization studies included in the meta-analyses of genetically predicted body mass index in relation to diabetes mellitus, diseases of the circulatory, respiratory, digestive, musculoskeletal, and nervous systems, and neoplasms.


The SRR for incident periodontitis was 1.24 , comparing individuals with diabetes to individuals without diabetes. There were no significant differences between subgroups after stratification for risk of bias. The findings show a positive bidirectional association between periodontal disease and diabetes mellitus, and thus, underline the need for screening of patients with periodontitis regarding diabetes mellitus and vice versa. The main limitation of the study is the high unexplained heterogeneity between the studies including the different assessment methods of the disease diagnosis.Periodontal disease has been reported to be associated with diabetes mellitus. However, the direction of the association and the influence of bias are not clear. Thus, the aim of this systematic review and meta-analysis was to summarize the existing evidence on the bidirectional prospective association between periodontal disease and diabetes mellitus by accounting for the risk of bias of the original studies. The literature search was conducted on the electronic data sources PubMed and Web of Science up to February 9th, 2021. We included observational studies, which investigated the prospective association between diabetes mellitus and periodontal disease or vice versa. The risk of bias of the primary studies was evaluated by applying the Quality in Prognosis Studies (QUIPS) tool. Random effects models were used to calculate summary relative risk (SRR) with 95% CI. Subgroup analyses were applied to investigate heterogeneity and the robustness of the finding. In total, 15 studies were included . The SRR for incident diabetes mellitus was 1.26 (95% CI 1.12, 1.41; I Individuals with diabetes are at higher risk of developing further health-related complications and disorders, including cardiovascular disease2, retinopathy3, nephropathy4 and neuropathy5. In addition, there is indication, that patients with diabetes suffer more often from dental disease (e.g. periodontitis) compared to individuals without diabetes6. Periodontitis is a widespread disease, whose most severe form afflicted about 743 million people worldwide in 2010, but an underestimation is expected7. The disease is characterized by a chronic inflammation of the entire periodontium that can irreparably destruct the tooth-surrounding tissue and result in the resorption of the alveolar bone. Consequences such as gingival bleeding, increased tooth mobility and tooth loss can be expected8. Recently, a meta-analysis summarized findings on glucose disturbance, including diabetes, and periodontal disease and indicated a positive association between these two factors9. However, in this meta-analysis, studies with different exposures and outcomes were mixed. For example, the authors combined studies on diabetes, prediabetes and diabetes severity11. In addition, the outcome was a mixture of periodontal disease and progression of the disease12. Moreover, there is indication that periodontal disease is a risk factor for diabetes mellitus13. Both conditions are driven by inflammatory processes, which might be a possible explanation for this bidirectional association14. To draw clear conclusions on these associations, a systematic review and meta-analysis is needed that considers methodological challenges when combining the existing data from primary studies. First, the time sequence of exposure and outcome needs to be taken into account to obtain the direction of the association. Second, the measurement of periodontal disease differs between the studies. In observational studies, periodontal disease has been assessed as self-reported periodontitis, clinical measurements attained from oral examinations , periodontal pocket depth (PPD)), or established scores, such as the Community Periodontal Index , or Russell’s Periodontal Index , respectively. Further, the risk of bias of the primary studies, including selection bias, information bias and confounding, should be considered by applying an appropriate tool15 when interpreting the data.Diabetes mellitus is one of the most common chronic diseases worldwide and current estimates assume that 463 million adults were affected in 2019. The prevalence is projected to increase by almost 50% over the next years, and it is estimated that there will be about 700 million people living with diabetes by 2045Thus, our aim was to conduct a systematic review and meta-analyses of the existing evidence of observational studies, which investigate prospectively the bidirectional association between periodontal disease and diabetes mellitus. In addition, we accounted for the risk of bias of the original studies, especially the assessment of periodontal disease in our meta-analyses.16. A protocol has been registered in PROSPERO (https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=118829).The systematic literature search was conducted according to the preferred reporting items for systematic reviews and meta-analyses (PRISMA 2020) guidelinesThe literature search was conducted on PubMed and Web of Science up to February 9th, 2021 to identify studies analyzing the association either between diabetes mellitus and periodontal disease, or the other direction, respectively. We used a combination of predefined search terms developed for each database without applying filters or restriction of language and calendar date Table . FurtherStudies were included, if: (1) they investigated the association between either periodontal disease and diabetes mellitus, or the other direction, respectively, (2) the association was investigated prospectively , (3) reported risk ratios (relative risk (RR), odds ratio (OR), hazard ratio (HR)) with corresponding 95% confidence intervals (CI) and 4) the papers were published following a peer-reviewed process.17, as caries is another leading cause for tooth loss18. Thus, we decided to exclude tooth loss as exposure/outcome, to minimize bias.Studies were excluded, if (1) they were animal studies, cross-sectional studies, case–control studies, conference abstracts or reviews, (2) they only reported crude estimates, (3) the study population was not relevant (including pregnant women or children and adolescents), (4) they focused on hyperglycemia (Fasting plasma glucose (FPG) > 110 ml/dg or haemoglobin A1c (HbA1c) > 6%) as exposure or outcome, (5) they focused on the progression of the particular disease and (6) when studies investigated potential indicators for periodontal diseases, such as tooth loss. In more detail, several studies investigated tooth loss as an indicator of periodontal disease,because assessing the number of missing teeth is less difficult and less time-consuming. However, tooth loss is not a reliable marker for periodontitisIf different studies reported on similar data (same exposure and outcome), we selected the study with the largest number of participants and cases.The extraction of the data from the studies was conducted by one investigator (JS or SS) and checked by two other investigators (JB or MN). Each inconsistency was debated until agreement was reached. The following data were extracted from each eligible study: the last name of the first author, the name of the study and the country in which it was conducted, the study design (prospective and retrospective cohort studies), the publication year, follow-up time, number of participants and cases, definition and assessment of exposure and outcome, the exposure categories and number of cases and non-cases for each category, the RRs and 95% (CI) and the variables adjusted for in the analysis.15 by at least two independent investigators  and discrepancies were discussed with another investigator (SS) and resolved by discussion. It includes the following 6 domains: study participation, study attrition, prognostic factor measurements, outcome measurements, study confounding and statistical analysis, and reporting. In each domain, the studies were evaluated for their reliability and eligibility and each domain was rated as low, moderate or high risk of bias. Finally, an overall risk of bias for each primary study was determined, whereby the domains prognostic factor and outcome measurements were assigned a higher weight, as we considered these domains to be of decisive importance. Thus, if a study was rated with high risk in one of these domains, the overall risk of bias was also judged as high. In this context, assessment of periodontal disease was judged as low risk of bias if clinical measurements of CAL or PPD were obtained, or ICD-codes based on the previously valid classification system for periodontal disease by Armitage (according to CAL) were used19. Further, we considered the CPI, introduced by the World Health Organization (WHO), as appropriate method, since it is faster and more reproducible in large epidemiological studies. The extend of PPD is the main factor for establishing the code and thus provides an indication for CAL20. A self-reported diagnosis that is validated by a dentist, classification by the PI and ICD-codes from a health insurance database, without further specification, were judged with moderate risk of bias. Self-reported diagnosis without validation is rated with high risk. The assessment of the diabetes mellitus diagnosis was classified with low risk of bias, if one of the following diagnostic tests was conducted, in accordance with the criteria of the American Diabetes Association: values of HbA1c or of plasma glucose 21. If a study included a self-reported diagnosis, validated by a physician or on ICD-codes from a health insurance database, it was considered to be at moderate risk. Diagnosis based on self-reports without validation were rated with high risk. For the domain study confounding, we defined the following confounders as important, for which a study should have at least been adjusted: age, sex, body mass index (BMI)/overweight, smoking status and socio-economic status. This domain could not be rated higher than moderate risk of bias, because residual confounding cannot be completely excluded in observational studies. In detail, the signaling questions for the QUIPS tool can be found in Table The risk of bias of the primary studies was evaluated by applying the Quality In Prognosis Studies (QUIPS) tool22. In addition, we conducted dose–response meta-analyses for exposure on periodontitis, if it was measured as continuous variables (CPI or PPD), and studies provided findings (RRs and 95% CIs) for at least three quantified categories. For this, we calculated the study-specific slopes and corresponding 95% CIs from the natural logarithm of the reported RRs and 95% CIs across the exposure categories23. The shapes of the relationships were evaluated by using restricted cubic spline regression models, and a likelihood ratio test was used to test for non-linearity24.Meta-analyses were conducted for the following associations: (i) periodontal disease as exposure and incidence of diabetes mellitus as outcome, (ii) diabetes mellitus as exposure and incidence of periodontal disease as outcome. Summary relative risks (SRR) and 95% CIs for these associations were calculated by using random effects meta-analysis by DerSimonian and Laird2, tau2, and 95% prediction intervals (95% PIs), and was investigated in subgroups analyses. Subgroup meta-analyses were conducted by total risk of bias, risk of bias for the exposure and outcome domains , sex, geographic locations, type of diabetes, duration of follow-up, number of cases, smoking status, and by adjustment of the original studies for potentials confounders: education, smoking status, overweight, fruit and vegetable intake, alcohol intake, physical activity, other chronic diseases. Differences were tested by using meta-regression25. Small study effects and publication bias were assessed by using Egger’s test and by visual inspection of funnel plots26, if more than 10 studies were included in the meta-analysis27. Potential publication bias was indicated by asymmetry of the funnel plot and a p value < 0.01 for Egger’s test. All statistical analyses were conducted using Stata version 15.1 software .Heterogeneity was evaluated by applying IWe evaluated the certainty of evidence for each association using the updated Grading of Recommendations Assessment, Development and Evaluations (GRADE). The GRADE tool covers the following aspects: the study design, the risk of bias of the primary studies, imprecision of the findings, inconsistency between the primary studies, indirectness in the primary studies, publication bias, the magnitude of effect of the pooled findings, indication for dose–response relations and the impact of residual confounding. The certainty of evidence can be evaluated as high, moderate, low or very low. A high certainty of evidence means that it is very likely that the effect estimate lies close to the true effect, whereas a very low certainty of evidence means that it is very likely that the inclusion of future studies will change the estimate.In total, 9384 studies were initially identified, and after removing 2427 duplicates and excluding 6411 articles by title and abstract, 546 were investigated in a full-text analysis Figure . Finally35. Four studies achieved a low, three of them a moderate, and three a high overall risk of bias as Table . The ass2:71%, Tau2: 0.014, 95% PI: 0.92, 1.71), when comparing individuals with periodontitis to individuals without 41. The mean follow-up period was 11.1 years (range 5–20 years). The overall risk of bias was low in one study, moderate in four studies, and high in two studies , comparing individuals with diabetes to individuals without diabetes .There was no significant heterogeneity between subgroups after stratification by overall risk of bias, risk of bias in the exposure and outcome domains, sex, geographical location, type of diabetes, duration of follow-up, number of included cases, adjustment for important confounders  Table –S8. Publ9. In contrast, the meta-analysis by Ziukaite et al. focused on the other direction of the association and reported a 27%  higher prevalence of diabetes for patients with periodontitis42, which is comparable to our findings. Risk of bias was high in the previous meta-analyses because different study designs, different definitions of exposure and outcomes were combined. In our meta-analysis, we considered both directions separately, included only prospective studies and did not mix disease status with pre-disease status or progression of the diseases, respectively.This systematic review and meta-analysis of 15 cohort studies showed that there was a positive bidirectional association between both periodontal disease and diabetes mellitus with a moderate certainty of evidence. For patients with diabetes, the data indicated a 24%  increase in the incidence of periodontal disease. For patients with periodontitis, the relative risk of developing diabetes mellitus was elevated by 26% . These results coincide with those of the meta-analysis by Nascimento et al., where an 86%  increased relative risk of periodontitis for individuals with diabetes was found. The fact that the relative risk is higher than in our analysis may be due to the fact that different degrees of severity of diabetes were taken into account as exposure and the outcome was not only incidence of periodontitis but also progression of the disease and its marker42, our meta-analyses did not show a significant difference here. However, subgroup meta-analyses relied on small numbers of studies and more studies with accurate assessment methods are needed. Although it was rarely applied among the studies, assessment via CAL has been considered the gold standard for the classification of chronic periodontitis19. But with this method measurement errors may occur especially in the initial phase of periodontitis43, for example, if the manual probe is applied with incorrect force, it can be advanced into intact attachment fibers44. It should also be taken into account that these measurements are associated with a standard deviation of 0.8–1.07 mm, even for experienced investigators45. Another applied assessment method is the determination of the PPD. While the assessment is more simple than the CAL, it has been shown that this method used alone, can lead to an underestimation of cases46 because, especially with increasing age, the extent of PPD no longer correlates with CAL47. In addition, there are studies that used indices such as Russell's PI28, or the CPI37 to classify the disease. Periodontitis diagnosis based on the PI is critical because this index is only visually assessed and does not include clinical measurements, and it includes gingivitis as an early form of periodontitis48. Although the CPI is characterized by its reproducibility and simplification49, it is not considered sufficient to describe the extent of periodontal disease50. In summary, the assessment and definition of periodontal disease vary widely across studies and there are no consistent thresholds for CAL/PPD and numbers of affected teeth to determine whether the disease is present or not. The Division of Oral Health at the Centers for Disease Control and Prevention in collaboration with the American Academy of Periodontology has provided a definition that combines measurements of CAL and PPD to assess periodontal disease to avoid misinterpretation of the periodontal status48. In order to make clear statements about the association between periodontitis and diabetes mellitus, it should be ensured in the future studies that established assessment of periodontitis is applied, and thus, enables comparability between studies.In the risk of bias assessment of the individual studies, we particularly focused on the assessment and definition of periodontal disease and diabetes. In general, the diagnosis of a disease was defined by self- reports, clinical examinations or a combination of both. While a meta-analysis showed that the prevalence for diabetes was much lower for self-reported periodontitis compared to clinical periodontal measurements51. Poor glycemic control in patients with diabetes has been shown to raise the level of systemic inflammation markers, e.g. interleukin-1ß, in the gingival crevicular fluid of a periodontal pocket14, which is associated with the onset and severity of periodontal disease52. On the other hand, it has been show that gram-negative bacteria in the periodontal pockets elevate serum inflammatory markers such as c-reactive protein53. This can induce hyperinflammatory immune cells and promote the release of proinflammatory cytokines, which lead to insulin resistance54.There are many possible explanations for the observed bidirectional associations between periodontitis and diabetes, which are related to inflammatory processes. For example, on the one hand, untreated diabetes mellitus, both type 1 or 2 diabetes, lead to metabolic disorders caused by hyperglycemiaStrengths of our meta-analysis is the bidirectional investigation and the dose–response meta-analysis between periodontal disease and diabetes mellitus by including only prospective studies and accounting for the major risk of bias sources, namely assessment of exposures and outcomes, and other sources of bias e.g. selection of participants and confounding. However, there are also limitations in our meta-analysis. First, as already described in detail, risk of bias regarding the assessment of periodontitis could not be ruled out in all of the studies. To account for this, we conducted subgroup analysis and did not observe significant differences. However, these analyses were based on small numbers of studies, and should be treated with caution. Second, most of the included studies did not clearly differentiate between type 1 and type 2 diabetes. In the subgroup meta-analysis by type of diabetes, we did not observe substantial differences, but again, the number of studies in the subgroups were small. Third, residual confounding cannot be excluded because of the observational study design of the included studies. Fourth, the included studies did not report on repeated measurements and thus, we could not account for changes of the disease status . Fifth, we did not search for grey literature, because we prefer to include only peer-reviewed studies. Last, we identified high heterogeneity between studies, which may arise from different methodological aspects e.g. different assessment methods of periodontal disease and/or diabetes. However, we conducted several subgroups analyses and meta-regression for total risk of bias and risk of bias of exposure and outcome assessment, further factors such as geographic location, and methodological aspects  and heterogeneity remained unchanged.55. It has been shown, that 40.7% of the dental patients without a diagnosis of diabetes (< 45 years) had HbA1c values around 5,7% or higher56, thus, screening for diabetes mellitus in the dental office is as important.Our comprehensive meta-analysis can help investigators to plan and conduct further research regarding bidirectional associations between periodontal disease and diabetes mellitus and can help for decision making in the clinical context. Our findings support existing guidelines for physicians and dentists regarding the screening of patients with diabetes, which recommend that every new confirmed patient with diabetes mellitus, should be informed that there is an increased risk of developing periodontal disease and that glycemic control is more difficult in this case. Thus, every initial examination should include a periodontal evaluationIn conclusion, there was a bidirectional association between periodontal disease and diabetes mellitus, even after stratifying for major risk of bias. However, only few studies with low risk of bias were available. To strengthen these findings more studies with valid assessment of periodontal diseases and diabetes are needed. The findings support current guidelines that patients with periodontitis should be screened for diabetes mellitus, and that patients with diabetes mellitus should be informed about their higher risk of developing periodontal diseases.Supplementary Information.


Background: Although numerous epidemiological studies have investigated the association between −174G/C(rs1800795) polymorphism in the interleukin-6 (IL-6) gene-stimulatory region and the risk of ischemic stroke (IS), they failed to reach a unified conclusion. The true relationship between −174G/C(rs1800795) polymorphism and IS remains controversial and unclear. Therefore, in this meta-analysis, we aimed to analyze more precisely the association between −174G/C(rs1800795) single-nucleotide polymorphism (SNP) of IL-6 gene and IS in a larger pooled population.Methods: A comprehensive literature search was performed in PubMed, Web of Science, and the Cochrane Central Register of Controlled Trials until June 30, 2021. A fixed or random-effects model was utilized based on heterogeneity between studies. The odds ratios (ORs) and 95% confidence intervals (Cis) were calculated in the models of allele comparison (G vs. C), homozygote comparison (GG vs. CC) and (GC vs. CC), dominant (GG vs. GC + CC), hyper dominant (GG + CC vs. GC), and recessive (GG + GC vs. CC) to determine the strength of associations.Results: This meta-analysis included 13 case-control studies in 35 articles with 5,548 individuals. Overall, no significant associations between IL-6 −174G/C(rs1800795) and IS were identified . In the subgroup analyses by ethnicity or HWE P-value, there was a statistically significant association between IL-6 −174G/C(rs1800795) polymorphisms and IS in the alleles model; , homozygote model  and , dominant model , and recessive model  of the European population and in the dominant model  of the Asian population. No statistical significance was identified in both six models of HWE p ≥ 0.2 group (both P ≥ 0.05).Conclusion: This meta-analysis revealed no correlation between IL-6 −174G/C(rs1800795) polymorphism and IS, whereas the subgroup analysis indicated that the relationship between IL-6 −174G/C(rs1800795) polymorphism and IS susceptibility varied significantly according to ethnicity and geography. Stroke is characterized by high rates of morbidity, mortality, recurrence, and disability. Examples of strokes include ischemic strokes (IS) and hemorrhagic strokes (HS). IS accounts for 70-90% of the incidence of strokes . It can The pathogenesis of ischemic stroke (IS) is complex and unclear. Inflammatory factors have been demonstrated to play a fundamental role in the physiology, etiology, and pathology of IS and other brain injury forms . MoreoveThe brain's response to ischemic injury can be regarded as an acute and long-term inflammatory process, characterized by rapid polarization of microglia, production of pro-inflammatory cytokines, and presentation of various leukocyte types into ischemic brain tissue, leading to IS occurrence and development . This waTherefore, functional polymorphisms of inflammatory genes may influence IS incidence and outcome. The contribution of IL-6 genetic polymorphism to the change in IS IL-6 levels was also reported . A part In this study, a meta-analysis was performed to better evaluate the relationship between IL-6 −174G/C(rs1800795) polymorphism and IS in a larger clustered population.PubMed, Web of Science, and the Cochrane Central Register of Controlled Trials databases for papers associating IL-6 −174G/C polymorphism and IS available by June 30, 2021, without language restrictions using the following search terms:We searched  AND  AND ” OR “Polymorphisms (Genetics)”) AND (“174” OR “rs1800795”).Citations selected from this initial search were screened and assessed for eligibility according to the following inclusion criteria: (1) case-control design, (2) investigation of the association of IL-6 −174G/C polymorphism and susceptibility to IS, (3) providing sufficient data about the genotype frequencies of IL-6 genetic polymorphisms for calculating the value of odds ratio (OR) and 95% CI, and (4) full-text articles. Studies were excluded if one of the following criteria was fulfilled: (1) unrelated to IL-6 polymorphisms or IS (2) included reviews/comments/letters, (3) included case reports or case series, and (4) lack reusable data. If duplicate reports by the same authors or the same group of patients were found, the team included only the most complete study for pooled analyses.Following PRISMA guidance , the folNewcastle-Ottawa Scale (NOS). NOS uses a star rating system to assess quality and studies scores ranging from 0 to 9 stars  using  9 stars , 16. TweP-value < 0.05 was considered statistically significant. The meta-analysis was performed using Stata (version 16). Statistical heterogeneity among studies was estimated using the Q-test and I2 statistics. Heterogeneity was acceptable as long as I2 ≤ 50%. A random-effects model was used to estimate the pooled log ORs and 95% CIs as heterogeneity reached a P < 0.1 or I2 > 50%.The strength of the association between IL-6 −174G/C(rs1800795) gene polymorphisms and IS risk was measured by ORs with 95% CIs for allele comparison (G vs. C), homozygote comparison (GG vs. CC) and (GC vs. CC), dominant (GG vs. GC + CC), hyper dominant (GG + CC vs. GC), and recessive (GG + GC vs. CC) models. The deviation of HWE in the control group was tested by the goodness-of-fit chi-square test. A P-value, and quality score. These studies, which were enrolled from 2003 to 2016, encompassed 2,654 cases and 2,894 controls. Regarding patient's ethnicity, the 13 patients were from Italy (2), India (2), Australia (3), China (1), Ireland (1), Spain (1), Poland (1), Turkey (1), and Croatia (1). The study by Balcerzyk et al.  polymorphisms and IS in the allele model , homozygote model  and , dominant model , and recessive model  of European and in the allele model , homozygote model  and , dominant model , and recessive model  of HWE P-value < 0.2.In subgroup analyses by ethnicity or HWE In this study, 13 independent studies were evaluated with 5,548 individuals, including 2,654 cases and 2,894 controls. Overall, this meta-analysis showed that in a large population, no association exists between −174G/C(rs1800795) and IS. Our results are consistent with Jin et al., but our number of studies is about twice that included in their survey after removing duplicate data. Moreover, we conducted a subgroup analysis on ethnicity and HWE values , demonstGiven that animal and clinical trials demonstrated IS patients have elevated levels of pro-inflammatory cytokine IL-6, numerous studies were conducted to demonstrate the relationship between SNP −174G/C(rs1800795) in the promoter region of the IL-6 gene and IS but with different conclusions , 28, 29.Moreover, Tong et al. revealed that IL-6 polymorphism at −174 is unlikely to significantly contribute to susceptibility or affect IS progression in either Han or Uyghur populations . On the P < 0.05, but no significant difference was found after removing this study, and we conducted a subgroup meta-analysis. It should be noted that the small sample size of included studies may limit the generalizability of our findings. Finally, another potential limitation is that our meta-analysis was performed at a study level, implying the reuse of data provided in papers. As a result, it is evident that there is a publication bias between the studies included. Although we used additional methods to prove that this impact is negligible, we cannot completely ignore the potential impact of publication bias on our research results.This current study has a few limitations. First, we have included a study of IS in children. However, after excluding this study, the meta-analysis also showed no statistically significant difference. Second, we included a study in which HWE There is a high degree of heterogeneity in our meta-analysis. Using different methods to assess the potential sources of heterogeneity indicates that heterogeneity can almost be explained by national and ethnic differences. After careful reading and summarizing the articles, we found that the possibility of clinical heterogeneity is small. Due to slight differences in gene detection technology, there may be methodological heterogeneity. These results may indicate that our heterogeneity is mainly attributed to statistical and methodological heterogeneity. Our research findings require careful consideration, and larger-scale experiments are required to verify these findings.In conclusion, although our study indicates that IL-6 −174G/C(rs1800795) polymorphism does not correlate with IS susceptibility, the subgroup analysis revealed that IL-6 −174G/C(rs1800795) polymorphism is linked to IS susceptibility in the European population sample. It also indirectly confirms that there are racial differences in IS susceptibility. However, further studies using a larger sample from multiple centers may enhance our study results.The original contributions presented in the study are included in the article/supplementary material, further inquiries can be directed to the corresponding author/s.JC and X-LC wrote this paper. FL revised this paper. All authors contributed to the article and approved the submitted version.This work was funded by Key Project of National Key R&D Program for Modernization of Traditional Chinese Medicine 2018 (No. 2018YFC1707402) and Business Construction of National Clinical Research Base of Traditional Chinese Medicine in 2016 (No. JDZX2015141).The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


Hum. Behav. 4, 423–434. (doi:10.1038/s41562-019-0787-z)) compared effect size estimates derived from these two different methods for 15 different psychological phenomena. The authors reported that, for the same phenomenon, the meta-analytic estimate tended to be about three times larger than the MLR estimate. These results are a specific example of a broader question: What is the relationship between meta-analysis and MLR estimates? Kvarven et al. suggested that their results undermine the value of meta-analysis. By contrast, we argue that both meta-analysis and MLR are informative, and that the discrepancy between the two estimates that they observed is in fact still largely unexplained. Informed by re-analyses of Kvarven et al.’s data and by other empirical evidence, we discuss possible sources of this discrepancy and argue that understanding the relationship between estimates obtained from these two methods is an important puzzle for future meta-scientific research.What is the best way to estimate the size of important effects? Should we aggregate across disparate findings using statistical meta-analysis, or instead run large, multi-laboratory replications (MLR)? A recent paper by Kvarven, Strømland and Johannesson (Kvarven  Such estimates can be used for the development and testing of quantitative models, leading to more robust theories . Furtherpost hoc analytic decision-making; , p = 0.003; Indeed, examining KSJ’s data indicate that there is a strong relationship between effect size estimates from the MLR and the meta-analyses: phenomena with larger meta-analytic estimates tend to also have larger estimates in the MLR , as in many of KSJ’s comparisons. However, in a series of four subsequent replications, Lewis & Frank [d = 0.71, which was more than four times that of the first replication study (d = 0.17), although the estimate remained smaller than that of the original study. The estimated heterogeneity across the five replication studies, τ2 (0.04), was within the range found in the sample of meta-analyses reported by KSJ . Notably, we observed this effect size variability in a case where the experiment was motivated by a formal model [Lewis & Frank  conducte & Frank  identifial model , and wheImportantly, when effects are heterogeneous, a comparison of the meta-analytic mean to the MLR mean—KSJ’s primary comparison—does not, on its own, adequately characterize the evidence for a true difference between the two . Compariand variability due to noise in individual studies.To test this idea, we examined where each MLR mean would fall in the distribution of effects in the corresponding meta-analysis. A naive approach to this question would simply look at the variability of effects. However, this approach would overstate variability in true effects because it incorporates both variability in the true effects measured by different studies To address this issue, we used statistical methods that estimate the distribution of population effects in a meta-analysis while accounting for statistical error in the point estimates ,19. To daverage effect sizes were typically larger in meta-analyses versus MLRs, often a sizeable minority of the effects in the meta-analyses were smaller than the corresponding MLR estimate. This finding indicates a smaller discrepancy than is apparent when comparing only the means.We thus estimated that, across the meta-analyses that KSJ analysed, a median of 20% of the population effects in the meta-analysed studies were at least as small as the corresponding MLR mean estimate  of the assumptions of standard methods that might in principle have caused them to adjust inadequately [To help adjudicate between these two possibilities, we conducted a re-analysis that uses a different approach for assessing publication bias, called ‘sensitivity analysis’ . In contirection . Heuristequately .p-value treated as continuous, rather than dichotomized at α = 0.05). This model of publication bias is identical to that assumed by the three-parameter selection model1 used by KSJ [For our sensitivity analysis, we assume a model of publication bias in which statistically significant positive studies (affirmative studies) are more likely to be published than non-significant and/or negative studies (non-affirmative studies), and there is no further selection based on the size of the point estimate or on characteristics associated with the point estimate  would be essentially a random sample of those from the larger, underlying population of non-affirmative studies, of which most were not published. A bias-corrected meta-analytic estimate could therefore be obtained by up-weighting the contribution of each non-affirmative study in the meta-analysis by fivefold to counteract the publication process’ fivefold favouring of affirmative studies.infinitely more likely to be published than non-affirmative studies. That is, worst-case publication bias would effectively favour affirmative studies by an infinite ratio, so a worst-case estimate can be obtained by meta-analysing only the non-affirmative studies that are included in the meta-analyses and simply discarding the affirmative studies. Intuitively, this method works because such an analysis is effectively equivalent to up-weighting each non-affirmative study by a factor of infinity. Such a worst-case analysis does not require actual estimation of the ratio by which the publication process favours affirmative studies.Since the degree of publication bias is not exactly known in practice, the sensitivity analyses can also estimate the meta-analytic mean under a hypothetical ‘worst-case’ publication bias scenario , in whicd = 0.39 and d = 0.17, respectively. Thus, the worst-case estimates were on average 32% as large as their corresponding naive estimates, with a mean absolute difference of d = 0.25. Nevertheless, for the majority of such meta-analyses (62%), even worst-case publication bias of this nature could not attenuate the meta-analytic estimate to match that of the MLR. That is, even selecting only the non-significant findings for the meta-analysis led to a meta-analytic estimate larger than the MLR estimate! Also, for all but one of these meta-analyses (i.e. 92%), worst-case publication bias could not attenuate the meta-analytic estimate all the way to the null . Across the meta-analyses, the mean naive and worst-case estimates were the null .It therefore appears somewhat implausible that publication bias, no matter how severe, could entirely explain the discrepancy between meta-analytic and MLR effect size estimates. This suggests that KSJ’s failure to eliminate the discrepancy in effect sizes derived from the two methods was not due entirely to limitations of publication bias correcting statistical methods; rather, there are additional causes of the discrepancy.. 5post hoc explanation for direct replication failures [What, then, are the other possible causes of the discrepancy? One possibility is that the phenomena being studied may be sensitive to the details of the experimental materials and methods, and especially to how these interact with the specific populations being assessed. This kind of method- and context-sensitivity has frequently been invoked as a failures , but dirfailures ,42,43. Ffailures , we founFurther, subtle methodological choices made by individual studies within a meta-analysis may interact with the population of participants in those experiments.  efforts to ensure intervention fidelity, thereby increasing effect sizes. Such differences in fidelity would not constitute investigator bias; instead, under such circumstances, the interventions themselves would effectively be different (e.g. because participants received greater encouragement to engage). Such differences could be due to experimenter expertise, though meta-scientific attempts to find effects of expertise on replication success have been unsuccessful . More pl. 6Building good scientific theories relies on having precise estimates of effect sizes, but the best way to obtain these estimates is not obvious. Both meta-analysis and MLRs provide methods for estimating the effect size of important phenomena by aggregating evidence across multiple studies. KSJ present the first systematic comparison of these two methods and show that effect sizes derived from meta-analyses are puzzlingly larger than those derived from MLRs. We demonstrate that meta-analytic effect sizes are related to MLR estimates, but there is still a remaining discrepancy between the two methods. Further, our analyses suggest that effect size heterogeneity and publication bias may contribute to—but are unlikely to account fully for—this discrepancy. Speculative possibilities for the remaining discrepancy include that MLRs obtain smaller effect sizes because of standardization of methods across laboratories  and because of the potential for differential effort to ensure intervention fidelity comparing MLRs and original literature. Understanding the source of the discrepancy between effect sizes estimated from meta-analyses and those from MLRs is an important, complex question for future meta-scientific research.