Background: The association between circulating vitamin D levels and risk of vitiligo was inconsistent among observational studies, and whether these observed associations were causal remained unclear. Therefore, we aimed to evaluate the effect of vitamin D on the risk of vitiigo using meta-analysis and Mendelian randomization (MR).Methods: At the meta-analysis stage, literature search was performed in PubMed and Web of Science to identify eligible observational studies examining the association of circulating 25-hydroxyvitamin D [25(OH)D] or 25-hydroxyvitamin D3 [25(OH)D3] levels with risk of vitiligo up to April 30, 2021. Standardized mean differences (SMDs) with 95% confidence intervals (CIs) of 25(OH)D and 25(OH)D3 in patients with vitiligo relative to controls were pooled. Then at the MR stage, genetic instruments for circulating 25(OH)D  and 25(OH)D3  levels were selected from a meta-analysis of genome-wide association studies (GWAS) of European descent, and summary statistics of vitiligo were obtained from a meta-analysis of three GWASs including 4,680 cases and 39,586 controls. We used inverse-variance weighted (IVW) as main method, followed by weighted-median and likelihood-based methods. Pleiotropic and outlier variants were assessed by MR-Egger regression and MR Pleiotropy RESidual Sum and Outlier (MR-PRESSO) test.Results: In the meta-analysis, patients with vitiligo had a lower level of circulating 25(OH)D compared with controls , while no statistically significant difference of 25(OH)D3 between vitiligo cases and controls was found . However, in the MR analyses, genetically predicted 25(OH)D  and 25(OH)D3 levels  had null associations with risk of vitiligo using the IVW method. Sensitivity analyses using alternative MR methods and instrumental variables (IV) sets obtained consistent results, and no evidence of pleiotropy or outliers was observed.Conclusion: Our study provided no convincing evidence for a causal effect of 25(OH)D or 25(OH)D3 levels on the risk of vitiligo. Further longitudinal and experimental studies, as well as functional studies are warranted to elucidate the role of vitamin D in the development of vitiligo. Vitiligo is an acquired pigmented skin disease caused by the loss of melanocyte function, which is characterized by localized or generalized complete depigmentation of skin mucosa. It can occur in all parts of the body, commonly in finger back, wrist, forearm, face, neck and around genitalia, affecting both men and women . Globall3 [25(OH)D3] is also widely accepted as an indicator of overall vitamin D status . Another case-control study . However, Saniee (P = 0.19). Similarly, Ustun et al. also found no statistically significant association  study utilizes genetic variants such as single nucleotide polymorphisms (SNPs), which act as instrumental variables (IVs) to estimate the potential causal relationship between exposure and outcome . Since g3 levels with risk of vitiligo, and then performed MR analysis to further investigate whether there was evidence for a causal relationship of circulating 25(OH)D and 25(OH)D3 levels with risk of vitiligo.Therefore, in the present study, we first performed a systematic review and meta-analysis of existing observational evidence on the association of circulating 25(OH)D or 25(OH)D3 levels with risk of vitiligo. The keywords included vitiligo AND (vitamin D OR 25-hydroxyvitamin D OR 25-dihydroxyvitamin D3 OR 25(OH)D OR 25(OH)D3 OR calcidiol).The overall design of the present study is shown in 3 between patients with vitiligo and controls, or provided sufficient data to calculate SMD and 95% CI; (iii) When two or more literatures recruited overlapped study population, the literature with the largest sample size was included. The exclusion criteria were: (i) Review or meta-analysis; (ii) Full text is unavailable; (iii) Data provided in the literature were insufficient.The inclusion criteria were: (i) The study was cross-sectional, case-control or cohort design; (ii) The original study reported standardized mean difference (SMD) and 95% confidence interval (95% CI) of circulating 25-hydroxyvitamin D or 25-hydroxyvitamin DRelevant information was extracted and crosschecked by two researchers independently (Liu K and Chen W), and discussed with the third researcher (Ye D) in case of disagreement. The extracted information included the first author, year of publication, region, type of study design, study period, sample size, gender and age of the study participants. Two researchers (Liu K and Chen W) independently scored the quality of the included articles using Newcastle Ottawa scale (NOS) . The sco3 between patients with vitiligo and controls were used to calculate SMDs and 95% CIs. If the study provided with median and range, we computed mean and SD using previously described formulas  of circulating levels of 25 (OH) D and 25 (OH) Dformulas , 19. If 3π)*lnOR . The hetg's test  and Eggeg's test  were useP-values < 0.05 were considered statistically significant.All statistical analyses were performed using STATA (version 14.0). Two-sided Genetic association data of vitiligo were obtained from a meta-analysis of three genome-wide association studies (GWAS) involving 4,680 vitiligo cases and 39,586 controls of European ancestry . A total3 were derived from a large-scale GWAS including 120,618 and 40,562 participants of European descent, respectively (r2 threshold < 0.1) associated with circulating 25(OH)D and 25(OH)D3 levels at genome-wide significance level (P < 5 × 10−8) were identified, respectively. None of the SNPs overlapped or were in linkage disequilibrium (r2 < 0.1) with known risk loci of vitiligo. The variance explained for total 25(OH)D by the ten lead SNPs was 3.95%, and the variance explained for 25(OH)D3 by the seven SNPs was 4.58%. The details of GWAS studies and datasets used in the present study are listed in IVs for 25(OH)D and 25(OH)Dectively . A totalF = R2 × (n – 2)/(1 – R2), in which R2 represents the variance explained by the IVs and n indicates the sample size . SNPs associated with other traits at genome-wide significance were excluded from the IVs and MR analyses were subsequently performed using the remaining SNPs.We further performed “leave-one-out” method as sensitivity analyses to test the reliability of the association of genetically predicted circulating 25(OH)D and 25(OH)DP-values < 0.05 were considered statistically significant.R (version 4.0.5) with packages “MendelianRandomization” and “MR-PRESSO” was used for MR analysis. 3 D, and 4 case-control studies and one cross-sectional study for 25(OH)D3. As assessed by NOS, the quality of the included studies was high in general. The basic information of the literature included is shown in The flowchart of literature inclusion and exclusion is shown in 3 , 44–46 wI2 = 95.5%, P < 0.001) and 25(OH)D3  with risk of vitiligo, respectively. Therefore, we used random-effects models to combine the effect sizes. As shown in P < 0.001). However, the differences of circulating 25(OH)D3 levels in cases and controls did not reach statistical significance .Statistically significant heterogeneity was observed among studies for the association of 25(OH)D , published in 2017 and before , and in Asian populations . However, meta-regression analysis did not find potential sources of heterogeneity from region, publication year, quality score, study type and sample size for 25(OH)D and 25(OH)D3 (P = 0.010) and Egger's test (P = 0.003) suggest evidence of potential publication bias in 25(OH)D, and the funnel plot was not symmetrical  and Egger's test (P = 0.966) did not suggest evidence of publication bias D and risk of vitiligo in different strata. The levels of 25(OH)D25(OH)D3 . The Begmetrical . For 25D and 278.07 for 25(OH)D3, suggesting the IVs were unlikely to suffer from weak instrument bias.P = 0.663) and 25(OH)D3 levels  were not associated with altered risk of vitiligo using the IVW method. Similar results were obtained using the weighted-median approach and maximum-likelihood method. The intercept from the MR-Egger regression analysis did not reach statistical significance , suggesting no apparent evidence of directional pleiotropy. Additionally, no outlier SNPs were detected using MR-PRESSO test, and the causal effect estimates of 25(OH)D (P = 0.529) and 25(OH)D3 (P = 0.215) with risk of vitiligo were not statistically significant.As shown in 3 with risk of vitiligo did not change substantially after excluding one single SNP at a time  or 25(OH)D3  with risk of vitiligo D and 25(OH)Dt a time . Then wevitiligo .3 on the risk of vitiligo by using meta-analysis and Mendelian randomization, however, we did not find convincing evidence to support a protective role of vitamin D supplementation on the risk of vitiligo.To date, vitiligo is a disease that cannot be completely cured. Patients often encounter psychological difficulties due to discrimination, such as shame, depression and anxiety, which usually lead to inferiority and social isolation , 48, andP = 0.0001). Our updated meta-analysis included five additional recent published studies with a sample size of 987 patients and 770 controls for 25(OH)D, 305 patients and 321 controls for 25(OH)D3, and therefore had increased statistical power. Similarly, we also found heterogeneity in the studies included in the meta-analysis. However, meta-regression did not suggest potential sources of heterogeneity, and subgroup analysis consistently revealed associations between circulating levels of 25(OH)D and risk of vitiligo in different strata. Publication bias was detected for the association between 25(OH)D and risk of vitiligo, suggesting that our results may be more based on positive results, resulting in overestimation of the summary effect. Previously, no meta-analysis has focused on the association between 25(OH)D3 and risk of vitiligo. In the present study, we found no statistical differences in circulating levels of 25(OH)D3 between cases and controls. However, the number of included studies was relatively small, more studies are warranted for obtaining stable results. Additionally, owing to the limitations inherent in observational studies, high heterogeneity among studies may be due to differences in the study design, characteristics of the study population such as age and ethnicity, use of drugs and so on. Finally, all of the included studies were cross-sectional studies and case-control studies with relatively small study sample sizes, which is weak in demonstrating causality, since it is difficult to judge the time sequence of exposure and disease D compared with controls. Consistently, a meta-analysis conducted by Zhang et al.  in 2018  disease . Therefo3 levels with risk of vitiligo. MR approach offers great opportunities to the etiological research of diseases, provided that the following three assumptions are satisfied (3 levels achieving genome-wide significance level (P < 5 × 10−8) as IVs, which was identified from the largest GWAS to date. The second assumption is that the IVs must not be associated with potential confounders of the exposure-outcome association. Since genotypes are randomly allocated during gamete formation, MR analyses using genetic variants as IVs to a large extent solve the problem of confounding in conventional observational studies. The third assumption is that the IVs influence the outcome only through the risk factor. To ensure this, we excluded potential pleiotropic SNPs and retained those solely associated with 25(OH)D and 25(OH)D3 levels in the MR sensitivity analyses to evaluate the robustness of our results. We also performed different MR methods to test for potential pleiotropy. We did not observe evidence of directional pleiotropy for the causal association between 25(OH)D, 25(OH)D3 and risk of vitiligo in any of the above MR approaches. However, the estimate of MR studies reflect the effects of lifelong interference, whereas that of observational studies reflects more acute effects.In order to overcome the limitations inherent in conventional observational studies, we adopted a two-sample MR method which used genetic variants as IVs of exposures to further investigate the relationship of 25(OH)D and 25(OH)Datisfied . The fir3, respectively. Besides, the number of reported cases in the outcome dataset is relatively small, which could affect the statistical power in MR study. However, it is the largest sample size in the outcome database available at present, which needs more vitiligo GWASs with larger sample size to explore the association. Finally, because our MR analyses were restricted to participants of European ancestry, it is unclear whether our findings can be extrapolated to other study populations. Therefore, further prospective studies and functional studies in vivo and in vitro are needed to elucidate the exact role of 25(OH)D and 25(OH)D3 in the occurrence of vitiligo.The limitations of the present study should be noted. First, the data for the meta-analysis were from observational studies, in which there could be potential confounding factors in the baseline characteristics of the selected population. The statistical ability of the small sample is limited, thus the well-designed and large sample studies are necessary to validate the findings. Second, heterogeneity in meta-analysis is a potential problem that affects the results of statistical analysis. In the present study, subgroup analysis and meta-regression did not indicate sources account for the heterogeneity observed in the study. In addition, publication bias was found in the results of 25(OH)D, which indicated that the results of meta-analysis were affected by publication bias. Third, the variance explained by IVs is limited, with 3.95% total 25(OH)D and 4.58% 25(OH)D3 level and risk of vitiligo. The findings suggested there is no convincing evidence that vitamin D may help to prevent vitiligo.Our study showed that although there seemed to be an inverse association between vitiligo and 25(OH)D level based on meta-analysis of observational studies, MR analysis pointed to a lack of causal association. In addition, meta-analysis and MR study did not provide evidence of an associations between 25(OH)DThe original contributions presented in the study are included in the article/JS performed the literature review, conducted data analysis, interpreted findings, and drafted the manuscript. KL and WC carried on data analysis and interpreted findings of meta-analysis. BL and HY mainly conducted on the data collation of the Mendelian randomization study. JS, LL, and XS can take responsibility for statistical reports, tables, and figures of the data analysis. YM and DY directed analytic strategy, supervised the study from conception to completion and revised drafts of the manuscript. All authors have read and approved the final manuscript.This work was supported by grants from the National Natural Science Foundation of China (81973663), the Natural Science Foundation of Zhejiang Province (LQ21H260001 and LQ20H260008), the Talent Project of Zhejiang Association for Science and Technology (2018YCGC003), the Foundation of Zhejiang Chinese Medical University (2020ZG01 and 2020ZG16), the Research Project of Zhejiang Chinese Medical University (2021JKZKTS004A).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.


Objective: Several epidemiological studies have linked tomato products consumption with prostate cancer risk; however, the findings yielded inconsistent results. The aim of the present meta-analysis is to summary the evidence on this association based on eligible cohort studies.Materials and Methods: A comprehensive literature search of articles was performed in March 2021 using PubMed, ISI Web of Science, and Scopus databases. A random-effects model was used to calculate the combined relative risks (RRs) and their corresponding 95% confidence intervals (CIs). Heterogeneity across studies was assessed using Cochran's Q statistic and the I2 score.Results: A total of 10 prospective studies were finally included in our meta-analysis. There was no evidence of a significant association between tomato products consumption and prostate cancer risk . Subgroup meta-analyses were performed by tomato types, geographical region, publication year, study quality and number of cases. No significant associations were observed in any subgroups . No significant publication bias was observed using Begg's test (P = 0.602) or Egger's test (P = 0.957).Conclusion: The results of this meta-analysis indicated that tomato consumption was not related with the risk of prostate cancer. Further prospective large-scale cohort studies are still warranted to verify our findings. Prostate cancer is the second most common cancer diagnosis made in men and the fifth leading cause of death worldwide. According to GLOBOCAN 2018 estimates, about 1.27 million new cases of prostate cancer occurred worldwide in 2018, with higher prevalence in the developed world compared with that in the developing countries . SeveralDietary tomato or lycopene intake has been shown beneficial for multiple health outcomes in humans . The assLiterature search was performed in March 2021 using PubMed, ISI Web of Science, and Scopus databases by two independent reviewers (JL and DK) with the following search algorithm: (tomato or tomatoes or lycopene or vegetable or vegetables or diet or nutrition) and (prostate cancer or prostatic cancer or prostate neoplasm or prostatic neoplasm) and . The reference lists of retrieved articles and reviews were also examined to identify any additional relevant studies. This systematic review and meta-analysis was designed, performed, and reported according to the standards of quality for reporting meta-analyses .iv) the risk estimates with their corresponding 95% confidence intervals (CIs) were provided. If multiple studies used data from the same population, the study with the largest sample size was included in this meta-analysis. There were no restrictions on publication language, publication date or publication status.An included study met all the following criteria: (i) the risk factor was consumption of tomato or tomato products; (ii) the outcome was the incidence of prostate cancer; (iii) study design was cohort, case-cohort, nested case-control or clinical trial; and  by two independent reviewers (JL and DK). NOS is a tool used for assessing the quality of non-randomized studies included in a systematic review and/or meta-analyses. NOS contains eight items within three domain and the highest score is 9. A study with score from 7–9 was considered as high quality.Methodological quality of each included study was assessed using Newcastle-Ottawa Scale . The following data were collected from each study: first author's surname, publication year, country where the study was performed, study design, age, number of cases, method of exposure measurement, method of outcome assessment, fully-adjusted risk estimates with their corresponding 95% CIs, and adjusted variables in the statistical analysis. If data were not reported in the primary study, the items were designated “not applicable.” Any discrepancies were resolved by consensus.Q statistic (significant level set at 0.1) . A sensitivity analysis was conducted by excluding each study in turn and repeated the meta-analysis to assess the impact of each included study on the summary risk estimate. Publication bias was evaluated using Begg's test  . The I2 g's test  and Eggeg's test .P-value for non-linearity was calculated by testing the null hypothesis that the coefficient of the second spline was equal to zero .Dose–response meta-analysis was performed with the method described by Greenland et al.  and Orsi to zero . All stan = 89), based on same population (n = 2), and the outcome was mortality (n = 1). A total of 10 prospective studies (n = 1), North America (n = 7), Asia (n = 1), and Oceania (n = 1). Exposure data was collected by self-administrated questionnaire, except for the Diallo et al.'s study . There was significant heterogeneity across included studies .Multivariable adjusted RRs with their CIs for each included study and for the combination of all studies are presented in P > 0.05).Subgroup meta-analyses were performed by tomato types, geographical region, publication year, study quality and number of cases. No significant associations were observed in any subgroups .The impact of individual study on the summary RR was assessed by repeating the meta-analysis after removing each study in turn. The study-specific RRs ranged from a low of 0.88 (95% CI 0.78–1.00) to a high of 0.93 (95% CI 0.80–1.07) by removing the study by Diallo et al.  and the P-values for non-linear assessment were >0.05. The pooled RRs for a 20 g/day increase in tomato intake were 0.99 (95% CI 0.97–1.01), 0.99 (95% CI 0.97–1.01), and 0.98 (95% CI 0.96–1.00), respectively . The mostly recent meta-analysis published in 2018 (P = 0.579). An early meta-analysis based on three prospective studies also found no significant association between raw or cooked tomato intake and prostate cancer risk (The findings of our study were not completely consistent with the previous meta-analyses , 29, 30. in 2018 , the RR One of the main mechanisms that has been proposed to explain the favorable effect of tomato intake on cancer prevention involves lycopene, which are found in high amounts in tomato products . A recenOur meta-analysis has several strengths. First, the present study had large sample size and thus enhanced the statistical power. Second, this meta-analysis only included cohort studies, which avoided the recall and selection biases. Third, based on the NOS scores, the methodological quality of the included studies was generally high. Finally, no obvious publication bias was observed across studies, indicating that the results were less likely to prone to biases.Several limitations should also be acknowledged. First, the number of eligible studies was relatively limited and most of the included studies were performed in USA, which limited the generality of the findings of our study. Second, a certain degree of heterogeneity existed, which may distort the summary risk estimate. Finally, the types of tomato product and the cut-off points were various among the included studies, which might also impact the current analysis.In conclusion, the results of this study indicated that tomato consumption was not related with the risk of prostate cancer. Further prospective large-scale cohort studies are still warranted to verify our findings.The original contributions presented in the study are included in the article/supplementary material, further inquiries can be directed to the corresponding author/s.JL and DK contributed to the conception or design of the work. JL, DK, and QH contributed to the acquisition, analysis, or interpretation of data for the work. JL and DK drafted the manuscript. QH critically revised the manuscript. All authors gave final approval and agree to be accountable for all aspects of work ensuring integrity and accuracy.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.


Purpose: Cytochrome P450 (CYP) is involved in the metabolism of statins; CYP3A5 is the main enzyme responsible for lipophilic statin metabolism. However, the evidence of the association between CYP3A5*3 polymorphism and the risk of statin-induced adverse events remains unclear. Therefore, this study aimed to perform a systematic review and meta-analysis to investigate the relationship between the CYP3A5*3 polymorphism and the risk of statin-induced adverse events. Methods: The PubMed, Web of Science, and EMBASE databases were searched for qualified studies published until August 2020. Observational studies that included the association between statin-induced adverse events and the CYP3A5*3 polymorphism were reviewed. The odds ratios (ORs) and 95% confidence intervals (CIs) were evaluated to assess the strength of the relationship. The Mantel–Haenszel method was used to provide the pooled ORs. Heterogeneity was estimated with I2 statistics and publication bias was determined by Begg’s and Egger’s test of the funnel plot. Data analysis was performed using Review Manager (version 5.4) and R Studio (version 3.6). Results: In total, data from 8 studies involving 1614 patients were included in this meta-analysis. The CYP3A5*3 polymorphism was found to be associated with the risk of statin-induced adverse events . For myopathy, the pooled OR was 1.30 (95% CI: 0.96–1.75). The subgroup analysis of statin-induced myopathy revealed a trend, which did not achieve statistical significance. Conclusions: This meta-analysis demonstrated that the CYP3A5*3 polymorphism affected statin-induced adverse event risk. Therefore, CYP3A5 genotyping may be useful to predict statin toxicity. Statins, 3-hydroxy-3-methylglutaryl coenzyme A (HMG-CoA) reductase inhibitors , are oneThe most common adverse events of statins include constipation, abdominal pain, nausea, dyspepsia, muscle pain, headache, and skin rash . AlthougSLCO1B1) gene and risk of statin-induced adverse events [SLCO1B1 genotype [Advanced age, female sex, Asian ethnicity, pre-existing liver and kidney diseases, high-dose statin therapy, and drug interactions are known to be risk factors for statin intolerance . In addie events ,11. The genotype . NeverthCYP3A5*3 is one of the functionally important single-nucleotide polymorphisms (SNPs) with high allele frequency  [CYP3A5*3 may influence the risk of statin-induced adverse events. Therefore, we conducted a systematic review and meta-analysis to assess the genetic association between the CYP3A5*3 polymorphism and the risk of statin-induced adverse events.As most statins are primarily metabolized by two members of the cytochrome P450 superfamily, CYP3A4 and CYP3A5  Supplem, and CYPctively) ,15, it iThis meta-analysis was conducted according to the checklist outlined in the Preferred Reporting Items for Systematic Reviews and Meta-Analyses . Two revCYP3A5*3 polymorphism with statin-induced adverse events; (ii) they were based on observational design ; and (iii) they provided sufficient information to calculate odds ratios (ORs) and 95% confidence intervals (CIs). Studies were excluded if they were (i) not original articles ; (ii) in vitro or animal studies; or (iii) studies on healthy subjects. In case of overlapping data, only the most recent and comprehensive data were included in the meta-analysis.Studies were included if (i) they evaluated the association of the Two reviewers independently extracted data, and discrepancies were resolved by consensus. Extracted data included the following information: the name of the first author, publication year, country, study design, number of cases and controls, mean age, percentage of males, case definition, statins included in the analysis, and the genotyping method. Articles were assessed by two investigators based on the Newcastle–Ottawa Scale (NOS) evaluating studies in the three categories of selection, comparability, and outcome assessment . The scoCYP3A5*3 polymorphism and the risk of statin-induced adverse events. The Mantel–Haenszel method and Z-test were used to evaluate the significance of the pooled OR [2I statistic. Given the high heterogeneity (I2 > 75%), a random-effects model was applied [The meta-analysis was performed using Review Manager . ORs and 95% CIs were used to identify the relationship between the ooled OR . A P-val applied .Subgroup analysis was performed in patients with statin-induced myopathy. Both Begg’s rank correlation test and Egger’s regression test of the funnel plot were performed using R Studio software  to detect publication bias ,21. To en = 3), pharmacokinetic studies (n = 3), studies evaluating efficacy only (n = 27), studies with healthy control groups (n = 1), studies on other genotypes (n = 3), studies unable to extract data (n = 1), and overlapping studies (n = 1). Ultimately, eight articles were selected for this systematic review [A detailed flow chart of the study selection process is presented in c review ,27,28,29The characteristics of the included studies are presented in CYP3A5*3 polymorphism and the risk of statin-induced adverse events. A statistically significant association was found between the CYP3A5*3 polymorphism and the risk of statin-induced adverse events  and myopathy   . The senCYP3A5*3 polymorphism increased the risk of statin-induced adverse events. Subgroup analysis showed that CYP3A5*3 tended to increase the risk of statin-induced myopathy without statistical significance, possibly because of insufficient power due to the small sample size. No significant publication bias was detected, and sensitivity analysis provided stable results except for excluding Shek et al. [CYP3A5*3 and statin-induced adverse event risk was high in the study of Shek et al. [In summary, our results showed that the k et al. . As the k et al.  and thisCYP3A5*3 on myopathy was similar to the overall one, although it showed a marginally statistical significance. The failure to achieve statistical significance in the subgroup analysis with myopathy was attributable to two studies, which had opposite trends [CYP3A5*3 and liver injury. A study by Shek et al. [CYP3A5*3 polymorphism increased the risk of hepatotoxicity , whereas a study by Fukunaga et al. [Statins can induce a wide range of adverse events from mild gastrointestinal symptoms to autoimmune disorders; however, statin-induced adverse events are often dose dependent ,31. Moste trends ,28. Therk et al.  revealeda et al.  did not CYP3A5*3 is the most prevalent variant allele related to reduced enzyme activity by alternative splicing and protein truncation of the CYP3A5 protein [CYP3A5 gene could be the leading genetic contributor to inter-individual differences in CYP3A-dependent drug clearance and responses.Several studies investigated the effect of CYP3A inhibitors  on the adverse events of statins ,33. Simi protein , the CYPCYP3A5*3 polymorphism and drug responses. A meta-analysis on the association between CYP3A5*3 and tacrolimus response in kidney transplant recipients revealed that CYP3A5*1 allele carriers had a higher risk of acute rejection and chronic nephrotoxicity than CYP3A5*3/*3 carriers. The plasma concentration divided by the daily dose per body weight was significantly lower among CYP3A5*1 allele carriers compared with CYP3A5*3/*3 carriers [CYP3A5*3 polymorphism was found to be associated with the cyclosporine dose-adjusted concentration. Patients carrying the CYP3A5*3/*3 genotype required a lower dose of cyclosporine to reach target levels than CYP3A5*1 allele carriers [Several meta-analyses have been performed to examine the association between carriers . Similarcarriers CYP3A5*3/*3 was associated with decreased statin clearance, along with increased statin exposure and decreased clearance [CYP3A5*3 reported that CYP3A5*1 carriers had significantly less effective responses than those with CYP3A5*3/*3 during lipophilic statin therapy [Lipophilic statins, such as atorvastatin, lovastatin, and simvastatin, are known to be metabolized primarily by CYP3A . In linelearance . In addi therapy .CYP3A5*3 polymorphism significantly increased statin-induced myopathy. With respect to the association between rosuvastatin and CYP3A5, the Genetic Effects On STATins (GEOSTAT-1) study reported a significantly enhanced response to rosuvastatin in patients with variant genotypes of CYP3A5 [Studies by Liu  and Ramaf CYP3A5 .In addition to genetic factors, several factors can affect drug metabolism, such as age, sex, nutritional conditions, comorbidities, and concomitant medications . Such noCYP3A5*3 polymorphism and the risk of statin-induced adverse events.This study has some limitations. First, the number of studies was relatively small, making it difficult to achieve sufficient statistical power for subgroup analysis. Second, there was clinical heterogeneity, including the definition of adverse events. Third, we could not adjust the statin type, dose, or the concomitant medications, which could affect the occurrence of statin-induced adverse events. Despite those limitations, to our knowledge, this is the first meta-analysis to reveal an association between the CYP3A5*3 polymorphism increased the statin-induced adverse event risk. This finding suggests that CYP3A5 genotyping can help individualized statin therapy. Further studies are needed to evaluate the utility of CYP3A5 genotyping in statin therapy.This study showed that the


Meta-analysis methods are used to synthesize results of multiple studies on the same topic. The most frequently used statistical model in meta-analysis is the random-effects model containing parameters for the overall effect, between-study variance in primary study’s true effect size, and random effects for the study-specific effects. We propose Bayesian hypothesis testing and estimation methods using the marginalized random-effects meta-analysis (MAREMA) model where the study-specific true effects are regarded as nuisance parameters which are integrated out of the model. We propose using a flat prior distribution on the overall effect size in case of estimation and a proper unit information prior for the overall effect size in case of hypothesis testing. For the between-study variance , a proper uniform prior is placed on the proportion of total variance that can be attributed to between-study variability. Bayes factors are used for hypothesis testing that allow testing point and one-sided hypotheses. The proposed methodology has several attractive properties. First, the proposed MAREMA model encompasses models with a zero, negative, and positive between-study variance, which enables testing a zero between-study variance as it is not a boundary problem. Second, the methodology is suitable for default Bayesian meta-analyses as it requires no prior information about the unknown parameters. Third, the proposed Bayes factors can even be used in the extreme case when only two studies are available because Bayes factors are not based on large sample theory. We illustrate the developed methods by applying it to two meta-analyses and introduce easy-to-use software in the R package BFpack to compute the proposed Bayes factors. The rapidly expanding scientific literature calls for methods to synthesize research such as a systematic review. Meta-analysis is an important part of a systematic review and refers to applying statistical methods to combine findings of different studies on the same topic. The first step of a meta-analysis is to obtain a standardized effect size  for each included study and these effect sizes are subsequently combined by means of meta-analysis methods to summarize the included studies. Meta-analysis is nowadays seen as the gold standard for research synthesis  model rather than the random-effects model. In this MAREMA model, the study-specific true effects are regarded as nuisance parameters and integrated out of the probability density function. The elimination of nuisance parameters via integration is common in integrated likelihood methods , and ha et al., ; 2019, a et al., .The proposed MAREMA model encompasses three important meta-analysis models. First, the equal-effect model is included if the between-study variance is equal to zero. Second, the random-effects model is included when the between-study variance is positive, which implies that the differences between studies’ effect sizes cannot be fully explained by sampling error. Third, the random-effects model in case of a negative between-study variance is also included. A negative between-study variance indicates that the differences between studies’ effect sizes are smaller than expected based on sampling error. A negative between-study variance may yield relevant insights for meta-analysts because it may indicate that the assumption of independence of primary studies in a meta-analysis is violated or that the computation of the effect sizes of the primary studies is incorrect  uniform prior to compute Bayes factors  or is larger than zero . Moreover, the proposed Bayes factors also enable testing multiple hypotheses simultaneously . Another attractive property is that the quantification of relative evidence between hypotheses is exact even in the extreme case of only two studies in a meta-analysis as Bayes factors do not rely on large sample theory.The proposed Bayes factors enable testing point and one-sided hypotheses. Examples of a point and one-sided hypothesis are testing whether the overall effect size in a meta-analysis equals zero  true effect size, μ is the overall true effect size, τ2 is the between-study variance in true effect size, and i th study, which is conventionally estimated in practice and then assumed to be known in the analysis. The random-effects model simplifies to an equal-effect model if τ2 = 0 because all studies then share a common true effect size. Note that other distributions for the random effects than the normal distribution have been proposed  that is routinely reported in multilevel analysis. This ICC indicates the proportion of total variance that can be attributed to taking into account the dependence of observations within the level 2 units  withρmin is the smallest possible value of the parameter ρ given the observed data and is always negative. Note that the special case ρ = 0  does not lie on the boundary of the parameter space, which enables testing the hypothesis of homogeneous true effect size.Next, we reparameterize the MAREMA model in Eq. U refers to the uniform distribution. Flat priors are used for estimation to minimize the impact of the priors on the results. We illustrate Bayesian estimation under the MAREMA model by analyzing data of two meta-analyses. The first meta-analysis is by Ho & Lee, (g) and a positive effect size indicates that EMDR therapy is more efficacious than CBT. The second meta-analysis is by Whittaker et al.,, .Parameter estimates of the MAREMA model and frequentist meta-analysis of the meta-analysis by Ho & Lee,  were comμ under the MAREMA model were approximately zero whereas the frequentist meta-analytic estimate for μ was slightly larger for the meta-analysis by Whittaker et al.,, , respectively.We propose testing hypotheses for both y is a vector of the observed effect size yi and m1 and m2 are the marginal likelihoods under H1 and H2, respectively. For example, B12 = 1 indicates that both hypotheses are equally supported by the data whereas B12 = 20 indicates that there is 20 times more support in the data for H1 relative to H2.Bayes factors are used for testing the proposed hypotheses  contains the amount of information of a single study  because the number of studies in a meta-analysis k is multiplied by the variance of 1 is a column vector of ones and π(μ|ρ) depends on ρ because τ2 is included in the variance-covariance matrix Σρ. Note that the prior π(μ|ρ) is different from the prior used for Bayesian estimation under the MAREMA model in Section “π(μ|ρ) used for testing is a proper prior. The proper prior π(ρ) is the same prior distribution as we proposed for estimation under the MAREMA model.In Bayesian hypothesis testing for the overall effect size, it is not recommended to use an arbitrarily vague prior to avoid Bartlett’s paradox  is baseSchwarz, . This clμ where ρ is left unconstrained. The dashed line refers to the prior distribution of the unconstrained hypothesis in Eq. H0 : μ = 0. The solid and dotted lines refer to the prior distributions for the one-sided hypotheses H1 : μ < 0 and H2 : μ > 0, respectively. Their heights are twice as large as the unconstrained prior to ensure that the distributions integrate to 1. The right panel shows prior distributions for testing hypotheses regarding ρ. The dashed, solid, and dotted lines refer to the prior distributions of the unconstrained (Hu), left-sided (H1 : ρ < 0), and right-sided (H2 : ρ > 0) hypotheses whereas the asterisk refers to the point mass for the hypothesis of no between-study variance (H0 : ρ = 0).The unconstrained prior in Eq. The marginal likelihoods of the different hypotheses differ with respect to the prior distributions. Hence, the marginal likelihoods of the different hypotheses can be computed by using different prior distributions in combination with adjusting the limits of integration.H1 : μ < 0 with ρ unconstrained can be written as a function of the marginal likelihood under the unconstrained model,For example, the marginal likelihood for the one-sided hypothesis f is the likelihood function of the MAREMA model in Eq. mu(y) is the marginal likelihood under the unconstrained model, P and P(μ < 0|Hu) are the posterior and prior model probabilities for μ < 0 under the hypothesis Hu, and the prior under H1 can be written as a truncation of the unconstrained priorI(⋅) is the indicator function. Note here that μ is centered around 0. The posterior probability in Eq. μ < 0. Interested readers are referred to Appendix μ and ρ based on a Gibbs sampler.The R package BFpack  contains functions for computing Bayes factors for a large set of statistical models . As an argument the main function “BF” needs a fitted modeling object which defines under which model Bayes factors need to be computed. To execute the Bayes factor test under the MAREMA model using the “BF” function, the function needs as argument an object returned by fitting a random-effects meta-analysis model with the metafor package. The metafor package is popular software for conducting meta-analysis that can be used for any effect size measure and requires the meta-analyst to supply the observed effect sizes of the primary studies and the corresponding sampling variances (or standard errors). Hence, researchers familiar with the metafor package can readily compute the Bayes factors that we propose using the function BF. The “BF” function also returns unconstrained estimates of μ and uniform prior on ρ.μ and ρ listed in Eqs. https://osf.io/ejfsv/.We compute Bayes factors using the MAREMA model for the two examples introduced in Section “?? ??”. The MAREMA model was fitted to the two meta-analyses using the proposed unit-information prior on μ are presented in Table H2 with H1 is the largest of the tested hypotheses, which implies that the hypothesis H2 : μ > 0 is 15.810 times more likely than the hypothesis H1 : μ < 0. Moreover, the Bayes factor comparing H2 with H0 : μ = 0 equaled 3.779 implying that μ in this meta-analysis is likely larger than zero. Also the posterior probabilities (last row in Table H2) was most likely after observing the data with a posterior probability of 0.753. The two-tailed frequentist hypothesis test of H0 : μ = 0 was not statistically significant using a significance level of 0.05 .The Bayes factors and posterior model probabilities for hypotheses on ρ are shown in the first columns of Table H0 : ρ = 0 with H1 : ρ < 0 and H2 : ρ > 0 indicated that H0 is approximately four and five times more likely, respectively. These results were also corroborated by the posterior probability that was the largest for H0 (with 0.689). This indicates that there was most evidence for an equal-effect model. The commonly used Q-test for testing whether the studies are homogeneous in a frequentist meta-analysis was not statistically significant (Q(9) = 9.417,p = 0.4). Note here that a nonsignificant result does not imply evidence for the null, as p values cannot be used for quantifying evidence for the null . To conclude, the EMDR treatment was observed to be on average more efficacious than the CBT treatment and effects are homogeneous across studies. However, due to the uncertainty in the posterior probabilities and the parameter estimates (see Section “?? ??”), more studies are required in order to draw more definite conclusions.The Bayes factors and posterior model probabilities for hypotheses on μ for the meta-analysis by Whittaker et al.,, . This absence of strong evidence was likely also caused by this meta-analysis only consisting of three studies. The posterior model probabilities (last row Table H0 was most likely (probability is 0.537). Application of a two-tailed frequentist hypothesis test resulted in a nonsignificant result, and thus H0 could not be rejected .Bayes factors for testing hypotheses on et al.,,  are showH0 : ρ = 0 received 10.958 more evidence than H1 : ρ < 0 and 2.901 more evidence than H2 : ρ > 0. Moreover, the hypothesis H2 : ρ > 0 was 3.778 more likely than H1 : ρ < 0. This corroborated the posterior model probabilities indicating that the effect sizes were most likely to be either homogeneous (with a probability 0.696) or heterogeneous (with a probability 0.240). Interestingly, the frequentist Q-test was statistically significant (Q(2) = 6.24,p = 0.044. This may be due to the fact that the significance tests rely on large sample theory, which may not be realistic here given there are only three studies. Using the posterior probabilities to get conditional error probabilities, there is a probability of 0.210 + 0.254 = 0.464 that we would be wrong when concluding that the hypothesis of no effect is true, and a probability of 0.064 + 0.240 = 0.304 that the hypothesis of homogeneous effects is true given the observed data. Thus (as expected) more data would be needed in order to receive more pronounced evidence about which hypothesis is likely to be true.The hypothesis The main goals of meta-analyses are estimating the overall effect and the heterogeneity in effect size as well as drawing inferences for these parameters. This paper proposes novel Bayesian estimation and hypothesis testing methods to achieve these goals. Our approach is novel compared to alternative Bayesian meta-analysis methods because the framework builds on the MAREMA model where the (nuisance) study-specific effects are integrated out. This MAREMA model encompasses both an equal-effect and a random-effects meta-analytic model, and also encompasses a model which assumes that there is less variance than under the equal-effect and random-effects models.Another major contribution is that we place a uniform prior distribution on the proportion of the variance that can be explained by between-study heterogeneity in true effect size rather than on the between-study variance or standard deviation in true effect size directly, as is usually done in Bayesian meta-analyses Berry, . This rep values tend to overestimate the evidence against a null hypothesis  may be negative. This implies that an equal-effect meta-analysis model (where ρ = 0) or a random effects model (where ρ > 0) may not be appropriate, and might cause bias in the results. A negative ρ may also indicate a violation of the assumptions of the meta-analysis model, which is “corrected” by allowing ρ to be negative. For example, the reported within-study sampling variances ρ, and how it affects the results. A good starting point would be  or hypotheses testing a parameter to another hypothesized value than zero. For example, meta-analysts may want to test point hypotheses using the rules of thumb for a small, medium, and large effect size as defined for many common effect size measures . Point ρ is available this could be translated to an informative stretched beta prior distribution (following, (Mulder & Fox, We proposed a framework for Bayesian hypothesis testing and estimation using minimally informative default prior distributions, but our methodology can readily be extended to other prior distributions. Informative priors can be specified to incorporate external information about the heterogeneity in true effect size or overall effect from research in comparable fields. This may provide better estimates and statistical inferences especially if the number of primary studies in the meta-analysis is small. For example, if prior information about the heterogeneity parameter Meta-analysts are generally not only interested in estimating and drawing inferences about the overall effect size and the between-study variance, but they are also interested in studying whether systematic differences between primary studies can explain the between-study variance. In case of large between-study variance, examining whether systematic differences between studies exist might even be more insightful than focusing on the overall effect size (Rouder et al., Future research may also focus on extending our methods to more advanced meta-analysis models such as multilevel meta-analysis (van den Noortgate & Onghena, Another topic for future research is studying to what extent the proposed estimation and Bayes factor test under the MAREMA model are affected by publication bias. Publication bias implies that especially studies with statistically significant findings are more likely to be published than studies with non-significant findings. Consequently, studies with non-significant findings are more difficult to locate and are less likely to be included in a meta-analysis. Due to publication bias, effect sizes of primary studies and, in turn, also the overall effect size of a meta-analysis are most likely positively biased (Kraemer et al., To conclude, we have proposed Bayesian hypothesis testing and estimation using the MAREMA model. The proposed Bayes factors allow testing point and one-sided hypotheses for both the overall effect and heterogeneity in true effect size. We hope that our methods together with the easy-to-use software included in the R package BFpack enables researchers to frequently use Bayesian methods in their meta-analyses.


Parkinson's disease (PD) is characterized by memory loss and multiple cognitive disorders caused primarily by neurodegeneration. However, the preventative effects of the mitochondrial A10398G DNA polymorphism remain controversial. This meta‐analysis comprehensively assessed evidence on the influence of the mitochondrial DNA A10398G variant on PD development.The PubMed, EMBASE, EBSCO, Springer Link, and Web of Science databases were searched from inception to May 31, 2020. We used a pooled model with random effects to explore the effect of A10398G on the development of PD. Stata MP version 14.0 was used to calculate the odds ratios and 95% confidence intervals (CIs) from the eligible studies to assess the impact of mitochondrial DNA A10398G on PD development.p = 0.111); however, a significant association between the mutation and PD was observed in the Caucasian population . A neutral effect was observed in the Asian population .The overall survey of the populations showed no significant association between mitochondrial DNA A10398G polymorphism  and PD  is characterized by memory loss and multiple cognitive disorders caused primarily by neurodegeneration. Neurodegeneration is the major cause of symptoms. A previous genome‐wide association study of PD identified many genetic variants in genes including PARKIN, GBA, LRRK2, and SNCA associated with PD. Moreover, mutations in mitochondria‐related genes, including PARKIN, PARK6, POLG, DJ, and 12S rRNA are also implicated in PD. While most of these genes are nuclear, the 12S rRNA gene is mitochondrial. Moreover, mutations in mitochondria‐related genes, including PARKIN, PARK6, POLG, DJ, and 12S rRNA are also implicated in PD.Parkinson's disease (PD) is characterized by memory loss and multiple cognitive disorders.PARKIN and LRRK2 account for 10% of PD cases. Thus, different genes are important for PD development in most cases. Some genes are more likely than others to influence the risk of an individual developing PD. This genetic heterogeneity may provide conflicting information and confusing data in research studies.Mitochondria play a critical role in providing cellular energy, metabolism regulation, cell death, etc. Various diseases may be caused by mitochondrial dysfunction. Owing to mitochondrial dysfunction associated with PD, sporadic PD patients may have respiratory chain enzyme deficiencies in the substantia nigra (SN).Sharing a point mutation with a single‐nucleotide polymorphism (SNP),p < 0.0001) for the U, K, J, and T haplogroups . van der Walt attributed this observation to the A10398G SNP polymorphism. Pyle et al. suggested that other SNPs might be causative, as this SNP was also found in other haplogroup backgrounds.van der Walt et al.While several recent studies concluded that the mitochondrial A10398G DNA polymorphism prevented PD,22.1Two investigators independently searched the PubMed, EMBASE, EBSCO, Springer Link, and Web of Science databases for relevant literature published through May 2020 using the following keywords: mitochondrial DNA or A10398G, gene polymorphism or variant, and familial Parkinson's disease or familial PD. Human case‐control studies set to be searched as references.2.2The case‐control studies included in this analysis fulfilled four inclusion criteria: (1) explored the effect of mitochondrial DNA polymorphism A10398G on PD, (2) provided odds ratios (ORs) and 95% confidence intervals (CIs) from sufficient allele data, (3) investigated equal numbers of cases and controls, and (4) were written in English and had available full texts.The exclusion criteria were: (1) case reports, editorials, review articles, or meta‐analyses; and (2) investigations of mitochondrial DNA polymorphism A10398G without sufficient data.2.3Two investigators (IST and an anonymous investigator) independently assessed eligible articles and collected the required data. We also discussed with a third reviewer (second anonymous investigator) to resolve disagreement between the two investigators. We extracted and presented the eligible studies as the surname of the first author followed by et al., and recorded the publication year, population ethnicity, and the number of cases and controls. The quality of the eligible studies was evaluated independently by IST and the anonymous investigators according to the Newcastle–Ottawa Scale (NOS).2.4This was a literature‐based study; thus, no ethical approval was required.2.5p < 0.05. Q‐ and I2 tests were used to examine the heterogeneity between studies.p > 0.05).Stata MP, version 14.0 (Stata Corporation) was used to analyze the extracted data. To assess the impact of the relationship between the variant and PD sensitivity, OR and CI were calculated. Statistically significant differences were defined as 3Figure 3.12 value indicated the presence of heterogeneity in the allele model under the random‐effects assumption . The results of a subgroup analysis stratified by Caucasian  and Asian  populations showed no significant relationship between the mitochondrial A10398G DNA polymorphism and PD  in the pooled populations. However, individually, we observed a significant relationship between this polymorphism and PD in the Caucasian population  but not in the Asian population . Figure Table 3.2p = 0.474).In the allele model, the sensitivity tests for integrated OR and CI showed no significant changes when removing each study individually. The OR of the meta‐analysis also remained stable Figure . Figure 4Endurance training is a powerful tool to improve health and performance. Physical activity is an effective intervention for many diseases.PD is one of the most common neurodegenerative disorders due to the characteristic loss of dopaminergic neurons in the SN. Although the genetic factors involved in the development of PD have been identified, some etiological factors of PD remain ambiguous. Previous studies showed that mitochondrial dysfunction triggers PD development.p = 0.013).p = 0.242) in the Asian population, consistent with the findings of the included studies. Meanwhile, compared to the A allele, the G allele of the mitochondrial A10398G DNA polymorphism exhibited a protective function against PD  in the Caucasian population.Among included case‐control studies, seven and three investigated Caucasian and Asian populations, respectively. Among 271 cases and 230 controls in the Spanish population, Huerta et al.Due to the amino acid replacement from Thr to Ala, which leads to a transition from A to G, the A10398G ND3 SNP may encode one subunit constituting complex I.The results in the Caucasian population differed from those of the Asian population due to the different distributions of the A10398G polymorphism. A previous study showed a lower frequency of the 10398G mitochondrial DNA allele in two Asian populations compared to that of a Caucasian population.Randomized clinical trials provide research inference with the highest level of evidence. However, some factors, including environmental and socioeconomic factors, may not be completely excluded to strictly control for their impact in epidemiologic research. Regardless of how perfect the design and measurement of epidemiologic research are, it is impossible to exclude potential and unmeasured confounders. Hence, although the research designs may be similar, they produce different conclusions, eventually leading to controversy. Meta‐analyses are commonly used to address controversial conclusions from relevant studies on similar issues. After considering the weight (random‐effects model), the pooled model provides greater objective research results. Recently, Mendelian randomization was used to modify potential and unmeasured confounders to obtain true causal inferences. Billingsley et al. used this method to identify 14 mitochondrial function‐associated genes linked with the risk of PD.This meta‐analysis has some limitations. First, PD development may also be influenced by the interactions of genetic‐genetic or genetic‐environmental factors. Second, the small sample sizes may have limited the reliability of the studies. In particular, only three studies included an Asian population. Furthermore, due to insufficient data, other possible variables associated with A10398G and PD, including age of onset, sex, and smoking, were not assessed.5The mitochondrial A10398G DNA polymorphism plays a protective role in the risk of PD development in the Caucasian population. These results require validation in well‐constructed studies with larger sample sizes.The author declared no competing interests.The author constructed all works of the final manuscript. I‐Shiang Tzeng involved in the conceptualization, formal analysis, methodology, supervision, writing original draft, reviewing and editing. I‐Shiang Tzeng and anonymous investigators involved in the data curation.Table S1‐S2Click here for additional data file.


Type 2 deiodinase (Dio2) is a selenoenzyme that is mainly expressed in the endoplasmic reticulum of the central nervous system, brown adipose tissue, and placenta and is responsible for outer ring deiodination of thyroxine (T4) to form biologically active triiodothyronine (T3). The Thr92Ala polymorphism of Dio2 has been found to be a potential risk factor for various diseases beyond the hypothalamus-pituitary-thyroid (HPT) axis.  We searched the relevant studies in the PubMed, Embase, and Cochrane Library databases and Google Scholar. A systematic review and meta-analysis of studies on the Thr92Ala polymorphism and metabolic parameters beyond the HPT axis  were performed. p = 0.04) and higher fasting glucose levels , p = 0.04). However, fasting insulin levels, plasma lipid levels, and hypertension risk showed a nonsignificant association with the Thr92Ala polymorphism.  Six eligible studies that analyzed the relationship between the Thr92Ala polymorphism and metabolic parameters beyond the thyroid were identified. All selected studies excluded patients with thyroid dysfunction, and diabetic patients were also excluded when fasting glucose and fasting insulin levels were meta-analyzed. The Thr92Ala polymorphism was found to be a significant risk factor for higher BMI ,  Compared with euthyroid noncarriers (Thr/Thr), euthyroid Ala92-Dio2 carriers showed increased BMI levels, and Ala92-Dio2 carriers also had higher fasting plasma glucose levels than matched euthyroid nondiabetic noncarriers. Thyroid hormones are indispensable for maintaining the normal physiological function of various systems during the lifetime  and mainThe Thr92Ala polymorphism of Dio2 means a Thr92-to-Ala substitution caused by a SNP rs225014) that is prevalent in 12%-36% of the population  that is . TherefoIn addition to acting as a potential risk factor for abnormal thyroid function and abnormal therapeutic effects of thyroid medications, Thr92Ala might also correlate with several diseases beyond the hypothalamus-pituitary-thyroid (HPT) axis. It has been hypothesized that Thr92Ala is significantly associated with metabolic disorders such as type 2 diabetes , insulinThe purpose of the present study was to explore the association between Thr92Ala and metabolic phenotypes such as BMI, fasting glycemic traits, plasma lipid levels, and hypertension risk to provide more evidence to support the association between the polymorphism or activity of Dio2 and metabolic disorders beyond the HPT axis.Two investigators independently conducted the literature search in the PubMed, Embase, and Cochrane Library databases and Google Scholar in sequence until 8 December 2020. The keywords “Thr92Ala,” “rs225014,” “Dio2 A/G,” and “deiodinase polymorphism” were used. Moreover, the references of all initially included articles, relevant reviews, and meta-analyses were also manually screened by full-text examination. We used PubMed as the main retrieval tool, supplemented by other tools mentioned above and the reference lists of relevant papers. The searching formula on PubMed was as follows: (((Thr92Ala[Title/Abstract]) OR (rs225014[Title/Abstract])) OR (Dio2 A/G[Title/Abstract])) OR (deiodinase polymorphism[Title/Abstract]).The overall process of searching the literature is shown in Articles were excluded if they met one of the following criteria: (1) studies that did not detect Thr92Ala (rs225014) specifically, (2) studies on pregnant women, and (3) studies conducted among patients with particular diseases (such as diabetes).As shown in Quality assessment was conducted by two persons in parallel with the Newcastle-Ottawa scale (NOS) for nonrandomized controlled trials (RCTs) . DisagreData from the final included studies were retrieved and annotated. The inverse variance method and Mantel-Haenszel method were applied when analyzing continuous variables and dichotomous variables, respectively. For continuous variables such as BMI and fasting glucose levels, the differences between different genotypes are presented in the form of standardized mean differences (SMDs). Moreover, dichotomous variables such as the hypertension prevalence of each genotype were also displayed, and the corresponding odds ratio (OR) was meta-analyzed.Q test and I2 test were applied to estimate the heterogeneity of the included studies, and 75%, 50%, and 25% were identified as high, moderate, and low levels, respectively. A random-effects model was selected if the included studies showed moderate or high heterogeneity; otherwise, a fixed-effects model was selected. Sensitivity analysis was performed by sequential removal of each study. Meta-analysis was conducted using Review Manager (RevMan) [Computer program]. Version 5.4.1, The Cochrane Collaboration, 2020.The chi-squared-based As shown in p = 0.04).Four studies were included in the meta-analysis between Thr92Ala and BMI –22. In tAs shown in Figures p = 0.04)  for Ala/Ala vs. Thr/Thr and SMD 0.99  for Thr/Ala vs. Thr/Thr). However, the overall fasting glucose levels of carriers were significantly higher than those of noncarriers ,  = 0.04) .p = 0.06), indicating that the fasting insulin level of carrier subjects was comparable to that of noncarrier subjects.However, the results of fasting insulin levels were somewhat different from those of fasting glucose levels . There wp value: p for TC = 0.25, p for TG = 0.91, and p for HDL = 0.60).Two studies were included when the difference in plasma lipid levels between different genotypes was analyzed , 20. As p = 0.45).Two articles (three studies) were meta-analyzed with regard to the impact of the Thr92Ala polymorphism on hypertension risk , 23. We In the present meta-analysis, we found for the first time that the Dio2 Thr92Ala polymorphism could lead to a significant increase in BMI among euthyroid participants. Furthermore, for subjects without diabetes or thyroid dysfunction, the polymorphism could significantly induce elevated fasting glucose levels. However, serum fasting insulin levels, serum lipid levels, and hypertension risk showed no significant difference between the different genotypes.As Dio2 is the key enzyme that determines the ratio of T3/T4 in local tissues, its activity is essential for maintaining the physiological metabolic function of specific sites, such as the central nervous system, brown adipose tissue, and placenta. Previous studies have confirmed that if the threonine at position 92 of Dio2 is replaced by alanine, its catalytic activity will be reduced . This inThere are currently several studies on the associations between the Thr92Ala polymorphism and BMI or obesity. From a basic perspective, a decrease in Dio2 activity would decrease the production of Dio2-generated T3 in various organs, inhibiting energy utilization and probably inducing a higher BMI. However, positive conclusions have rarely been drawn in epidemiological studies. In a case-control study conducted by Heemstra et al., it was demonstrated that among patients with Hashimoto thyroiditis, mean BMI was significantly elevated with the increase in Ala92 frequency . HoweverAmong a series of metabolic disorders beyond the HPT axis, studies on the Thr92Ala polymorphism and type 2 diabetes are currently the most common. It has been indicated that a reduction in T3 production in skeletal muscle or the liver might induce local hypothyroidism and inhibit glucose disposal. In addition, the decreased level of Dio2-generated T3 might also inhibit the transcription of GLUT4 in insulin-sensitive tissues, such as adipose tissue or skeletal muscle, which could lead to insulin resistance . We founIn the present meta-analysis, we did not conclude a significant association between the Thr92Ala polymorphism and plasma lipid levels. Since there was no previous study that specifically analyzed the relationship between the two, the selected studies did not exclude subjects who took lipid-lowering treatments or had a personal history of dyslipidemia, which might have a certain bias on the results. In addition to the studies included in the present meta-analysis , 20, we Regarding the association between the Thr92Ala polymorphism and blood pressure value or hypertension risk, previous studies have reported positive conclusions. Studies have found a larger proportion of Ala92 carriers among hypertensive subjects than among normotensive subjects, and the Thr92Ala polymorphism could be regarded as a significant risk factor for hypertension after adjusting for confounding factors, especially in Black populations . This stHeterogeneity is one of the unavoidable problems in meta-analysis, and this study is no exception. It is worth noting that this meta-analysis covers six countries or regions, including Slovakia, the United States, and South Korea. Therefore, geographic and ethnic heterogeneity may have an impact on the conclusions. There is inherent heterogeneity in the genetic background of the subjects. In addition, the diagnostic criteria for thyroid diseases are also heterogeneous. Although all studies have excluded patients with thyroid disease, only Butler et al. , MentuccThe limitations of the present meta-analysis are as follows. First, the elevation of various metabolic parameters beyond the thyroid is actually determined by multiple factors, and the proportion of genetic factors is still unknown. Second, we did not consider the impact of lipid-lowering treatments or personal history of dyslipidemia when analyzing plasma lipid levels, which might bias the final conclusion. Third, as mentioned above, the relevant studies have a relatively small sample size, and the heterogeneities of ethnicity, excluding criteria for thyroid patients, and reference interval for TSH all might induce the bias. More research is needed since genetic association research should recruit a large population to ensure convincible conclusions.To the authors' knowledge, the present meta-analysis is the first study that clearly demonstrated the significant positive association between the Thr92Ala polymorphism and BMI among euthyroid subjects. For nondiabetic euthyroid subjects, the Thr92Ala polymorphism could also induce a higher fasting glucose level. However, fasting insulin levels, serum lipid levels, and hypertension risk showed no significant association with the Thr92Ala polymorphism.


Background: Androgen deprivation therapy (ADT) suppresses the production of androgen, and ADT is broadly used for intermediate or higher risk disease including advanced and metastatic cancer. ADT is associated with numerous adverse effects derived from the pharmacological properties. Previous meta-analysis on fracture risk among ADT users possessed limited data without further subgroup analysis. Risk estimation of updated real-world evidence on ADT-related fracture remains unknown.Objectives: To assess the risk of fracture and fracture requiring hospitalization associated with ADT among prostate cancer population on different disease conditions, treatment regimen, dosage level, fracture sites.Methods: The Cochrane Library, PubMed, and Embase databases were systematically screened for eligible cohort studies published from inception to March 2020. Two authors independently reviewed all the included studies. The risks of any fracture and of fracture requiring hospitalization were assessed using a random-effects model, following by leave-one-out, stratified, and sensitivity analyses. The Grading of Recommendations Assessments, Development and Evaluations (GRADE) system was used to grade the certainty of evidence.Results: Sixteen eligible studies were included, and total population was 519,168 men. ADT use is associated with increasing fracture risk  and fracture requiring hospitalization . Stratified analysis revealed that high-dose ADT results in an elevated risk of fracture with little statistical heterogeneity, whereas sensitivity analysis restricted to adjust for additional factors indicated increased fracture risks for patients with unknown stage prostate cancer or with no restriction on age with minimal heterogeneity. The GRADE level of evidence was moderate for any fracture and low for fracture requiring hospitalization.Conclusion: Cumulative evidence supports the association of elevated fracture risk with ADT among patients with prostate cancer, including those with different disease conditions, treatment regimens, dose levels, and fracture sites. Further prospective trials with intact information on potential risk factors on fracture under ADT use are warranted to identify the risky population. Prostate cancer is the most common cancer among men, and remains the second leading cause of death in the United States in 2020 . StandarADT is an effective treatment for prostate cancer , includiFractures are an important public health issue resulting in a heavy health care burden and affecting an individual’s quality of life . FurtherBecause of the limited predictive validity of BMD values on fracture and incomplete data from previous meta-analyses, more comprehensive systematic reviews are warranted to fill this knowledge gap. Therefore, we quantitatively assessed all eligible studies focusing on the effect of ADT on fracture risk to obtain complete information.This systematic review was conducted in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses PRISMA;  and MetaWe followed the participants(P), intervention(I), comparators(C), and outcomes(O) for study selection under the PICO framework. The proposed clinical question of the present systematic review and meta-analysis is as followed: does having used ADT (compared to no use) increase the risk of fracture among prostate cancer population ?We performed a comprehensive literature search of the Cochrane Library, PubMed, and Embase databases. The searching process has been conducted on 24th, April 2020. Literature reviewed was dated from inception through March 2020. The following search query, comprising a combination of keywords, was used in the 3 databases derived from previous PICO question: “prostate cancer AND  AND fracture” . Two indStudies had to be cohort studies published in English and from all countries. Hazard ratio (HR), odds ratio (OR), or relative risk (RR) along with corresponding 95% confidence interval (CI) were extracted. If the study population was limited to patients with localized prostate cancer, we grouped the results as pertaining to localized prostate cancer. We considered the results of studies without a clear delineation of prostate cancer stage or metastatic prostate cancer to pertain to unknown stage prostate cancer. If the results of multiple included studies were derived from the same database, we selected the study with the longest duration. If there was lacking of primary outcome on total fracture risk, stratified results from separate dosage intensities were combined. We excluded researches that focused on the risk of BMD change and osteoporosis among ADT users. Studies lacking any requisite data were also excluded.Two authors independently used a self-developed form to record data. The form documented the study source, study design, patient characteristics, exposure assessment, treatment characteristics, outcome categories, outcome measures, definitions of prostate cancer and fracture, adjusted factors, and conflicts of interest. Discrepancies regarding these data were resolved through consensus.The same authors assessed quality using the Newcastle-Ottawa scale (NOS) and Grading of Recommendations Assessments, Development and Evaluations (GRADE) assessment. The NOS is used to assess methodological quality in observational studies and is a validated 8-item tool that characterizes participant selection (4 items), comparability of populations (1 item), and outcome assessment (3 items). Low quality was defined as an NOS score of <7, and high quality was defined as an NOS score of ≥7 . MoreoveThe primary outcome was any fracture risk under ADT use. Leave-one-out sensitivity analysis was used to determine the robustness of the overall findings through sequential elimination of each trial. Stratified analyses were conducted based on disease condition, treatment regimen, dosage, age, and fracture site to identify relevant subgroups and investigate potential sources of heterogeneity among the studies. Sensitivity analysis was used to exclude studies with ≤4 adjusting factors for stratified groups comprising >3 studies. Secondary analyses were performed to evaluate the risk of fracture requiring hospitalization from ADT. Analyses were performed with Review Manager . Dichotomous outcomes are reported as ORs, and time-to-event outcomes are reported as HRs, each with a 95% CI. HRs and ORs were determined through the inverse variance method. Because the absolute risk of fracture is low, RRs or HRs from cohort studies were used to estimate the OR .I2 test. Forest plots were constructed to evaluate statistical heterogeneity. Statistic heterogeneity was assessed for each outcome with the I2 method. We considered I2 values of 25–49%, 50–74%, and ≥75% to represent low, moderate, and high heterogeneity, respectively  was examined only when more than 10 studies were included in the analysis of the primary outcomes.Fixed- or random-effects models were applied on calculating effect size and 95% CI on the concept of between-study heterogeneity. The heterogeneity derived from separated studies was quantified using ectively . When In  = 519,168 individuals)   were incThe characteristics of the 16 studies included in the meta-analysis are summarized in I2 = 83%), and this result was accompanied by significant heterogeneity  or unknown stage prostate cancer , with high heterogeneity. Diminished heterogeneity was observed among individuals with fracture risk with unknown stage prostate cancer under the restriction that studies must adjust for ≥4 factors ; however, all studies focusing on the patients with localized prostate cancer have been adjusted for ≥4 factors without further conduction of sensitivity analysis. Second, we explored the effects irrespective treatment regimen. Stratified analysis of LHRH agonist revealed an elevated fracture risk with limited information on antiandrogen or orchiectomy. In sensitivity analysis, the fracture risk among patients treated with LHRH agonists remained consistent, without a significant reduction in heterogeneity . Third, stratified analyses indicated an increase in fracture risk with increased dosage, and the ORs for low, medium, and high dosage were 1.08, 1.20, and 1.54, respectively; all three dose groups exhibited low heterogeneity (I2 = 0%). No sensitivity analysis was conducted for this stratification because all eligible studies were adjusted for more than 4 factors. Fourth, we analyzed only those individuals aged >65 years  and a group with no age restriction . Elevation in heterogeneity was noted on >65-year-old population  while lower heterogeneity has been noted on the population without age restriction (I2 = 0%). Finally, we restricted the fracture risk upon ADT administration to only that of the hip and discovered an elevated fracture risk  with consistently elevated heterogeneity , and the overall effect size remained significant  after trim-and-fill correction for missing data.Quality assessment of the cohort studies revealed that 6 studies had low quality (6 points) and 10 studies had high quality  and disease-related  factors, RT dose and RP, the purpose of ADT intervention , and antiresorptive medication use were not well documented in every study. In addition, most cohort studies did not adequately indicate the RT dose or divided radical surgery into different groups because of database limitations, potentially resulting in heterogeneity in the results, even after sensitivity analysis. Second, after RP, patients receiving ADT for localized disease were prone to disease progression to the advanced stage, biochemical failure, and micrometastasis, potentially resulting in a selection bias. Therefore, the certainty of evidence remains low to moderate because of the risk of bias and inconsistency among the included studies. Finally, data on fractures treated with LHRH antagonists and second-generation antiandrogen remain scarce. Further studies are required to confirm the differential risks of these agents on fracture.This study reports that ADT is significantly associated with an elevated risk of fracture among patients with localized and unknown stage prostate cancer. The elevated risk is positively correlated with the duration of ADT. Furthermore, analyses stratified by age and ADT regimen revealed similar risks. However, different treatment indications and disease populations potentially contribute to significant heterogeneity in outcomes. Well-designed prospective trials with intact information of risk factors on fracture are warranted to overcome these limitations.