We aimed to analyze the association between hypertension and deep vein thrombosis (DVT) after orthopedic surgery.Q statistic and I2 statistics. Finally, publication bias was evaluated by Egger’s test.Relevant studies were identified by a search of PubMed, Embase, China National Knowledge Infrastructure, Wanfang, the Chinese Biomedical Literature, and Weipu database until December 2015. The association between hypertension and DVT after orthopedic surgery was assessed by pooled odds ratios (ORs) and 95 % confidence intervals (CIs). Heterogeneity was evaluated by the Chi-square test based on Q = 38.41, P = 0.0008, and I2 = 60.9 %). Meta-analysis showed that hypertension was associated with DVT orthopedic surgery . No statistical evidence of publication bias was found among studies . The funnel plot was symmetry, and the results were reliable.A total of 16 articles with 68,955 males and 53,057 females were eventually identified. Studies yielded effects for homogeneous (Hypertension may promote DVT after orthopedic surgery, and may be an important risk factor of DVT occurrence. Hypertension is one of the major causes of disease burden all over the world . In 2000Deep vein thrombosis (DVT) is a systemic disease with a incidence of 67 per 100,000 of cases every year , 9 and oThus, in the current study, we performed a meta-analysis of available eligible studies to better elucidate the association between hypertension and DVT after orthopedic surgery.The paper did not involve any human or animal study, so the ethical approval was not required.http://www.ncbi.nlm.nih.gov/pubmed), Embase (http://www.embase.com), China National Knowledge Infrastructure , Wanfang (http://g.wanfangdata.com.cn/), the Chinese BioMedical Literature , and Weipu database (http://www.cqvip.com/) updated to December 2015 for all the publications on the association between hypertension and DVT. The search terms were hypertension or high blood pressure or HBP; deep vein thrombosis or thrombose veineuse profonde or DVT or deep venous thrombosis; orthopedic post-operation or orthopedic or orthopaedic and postoperative. Language restrictions were not used for the search.We searched electronic databases PubMed (Studies were included if they met the following criteria: (1) the observation group was patients with DVT after orthopedic surgery and the control group was patients without DVT after orthopedic surgery; (2) published on association between hypertension (blood pressure > 140/90 mm Hg) and DVT after orthopedic surgery in Chinese or English; (3) the number of patients with hypertension in the observation group and control group could be obtained. Studies were excluded if they were reviews, reports, or letters.With the standard protocol, two investigators independently extracted the following data from the included studies: the first author, publication year, study time and region, the number of patients in control group or observation group, the number of patients with hypertension, and the demographic characteristics . Disagreements were resolved through discussion or settled by a third reviewer.Q statistic [I2 statistics [P < 0.05 or I2 ≥ 50 %); otherwise, a fixed effects model was used [P value less than 0.05 was considered statistically significant.Meta-analysis was carried out using R 3.12 software. The odds ratio (OR) and its 95 % confidence interval (CI) were calculated for effect index. Heterogeneity test was evaluated by Chi-square based on tatistic  and I2 satistics . A randowas used . A sensiwas used . Publicawas used , 20. A PThe process of study selection was shown in Fig. Q = 38.41, P = 0.0008, and I2 = 60.9 %). Then, the random effects model was used for further analysis. Meta-analysis showed that hypertension was associated with DVT after orthopedic surgery . The funnel plot was symmetry, so there was no publication bias and the result was reliable .This is the first systematic review and meta-analysis of studies, to our knowledge, examining the correlation between hypertension and DVT after orthopedic surgery. Totally 16 articles with 68,955 males and 53,057 females were included in this meta-analysis. The results showed that hypertension might promote DVT after orthopedic surgery .The only risk factor of DVT in accordance with the conclusion from this meta-analysis is hypertension, which has already been verified previously. Several prospective studies have addressed the associations between hypertension and DVT. Patients with hypertension have been found with 2-fold increased likelihood of developing DVT . In the 2, and smoking were risk factors for DVT [Age is a high prevalence of asymptomatic DVT event which has been identified in patients over 80 years . A previ for DVT . In a re for DVT . Examini for DVT  found th for DVT . In addi for DVT .Some limitations of this study should be addressed. First, only published studies were included and publication bias might exist, although no significant bias was detected by Egger’s test. Second, significant heterogeneity across studies was presented in overall and subgroup analysis, which might influence the pooled results. Third, although there was no limitation for language, only Chinese population was included, which might lead to bias. Fourth, as limited researches included in this meta-analysis, association between DVT and age, BMI, or gender was not analyzed. Finally, the small sample size was still insufficient to obtain a conclusive result. However, larger and well-designed studies based on different populations are warranted to validate our results.In conclusion, this study showed that hypertension might promote DVT after orthopedic surgery, and it might be an important risk factor of DVT occurrence.


Dietary alpha-linolenic acid was inversely associated with PCa risk , which was dominated by a single study. Subgroup analyses indicated that blood eicosapentaenoic acid concentration and blood docosahexaenoic acid concentration were positively associated with aggressive PCa risk and nonaggressive PCa risk, respectively. Among studies with nested case-control study designs, a 0.2% increase in blood docosapentaenoic acid concentration was associated with a 3% reduced risk of PCa . In conclusion, different individual n-3 PUFA exposures may exhibit different or even opposite associations with PCa risk, and more prospective studies, especially those examining dietary n-3 PUFAs and PCa risk stratified by severity of cancer, are needed to confirm the results.Epidemiological studies have suggested inconsistent associations between omega-3 polyunsaturated fatty acids (n-3 PUFAs) and prostate cancer (PCa) risk. We performed a dose-response meta-analysis of prospective observational studies investigating both dietary intake and circulating n-3 PUFAs and PCa risk. PubMed and EMBASE prior to February 2014 were searched, and 16 publications were eligible. Blood concentration of docosahexaenoic acid, but not alpha-linolenic acid or eicosapentaenoic acid, showed marginal positive association with PCa risk (relative risk for 1% increase in blood docosahexaenoic acid concentration: 1.02; 95% confidence interval, 1.00–1.05; I ALA from plant food sources is partly converted to EPA and DPA in the body, whereas EPA, DPA, and DHA are categorized as marine n-3 PUFAs because they are largely from marine-based animal food sources .4 Besides dietary exposure of n-3 PUFAs, blood concentration of n-3 PUFAs should also be examined for its association with PCa, because blood biomarkers of n-3 PUFAs are objective, and the reliability does not depend on the accuracy of memories, awareness of fat intake, or willingness to report details of one’s diet.Prostate cancer (PCa) is the second most common cancer and the sixth leading cause of cancer-related death among men around the world, accounting for 14% of the total new cancer cases and 6% of the total cancer deaths in men in 2008.5–7 Results from observational studies, however, have been inconsistent. Several case-control and prospective cohort studies have suggested a positive association of both circulating ALA and dietary ALA with PCa risk.8–11 On the contrary, there are also multiple case-control or prospective cohort studies reporting negative associations or no association between ALA (circulating and dietary) and PCa risk.12–14 Moreover, accumulating prospective studies have suggested that long chain n-3 PUFAs showed inverse, null, or even positive association with PCa risk.15–18 Even systematic reviews and meta-analyses summarizing the relationship of n-3 PUFAs with PCa risk have yielded opposite conclusions, though none of them were dose-response meta-analyses of prospective studies quantifying the association between n-3 PUFAs (dietary or circulating) with PCa risk.19–22A considerable number of studies, including both animal and in vitro cell studies, have demonstrated that n-3 PUFAs are the most promising type of nutrients to inhibit or curtail carcinogenesis and reduce PCa risk.Since the most recently published systematic review, at least four new studies with large sample sizes have been published and made available. Therefore, we performed a dose-response meta-analysis to estimate the trend and quantify the association for both dietary intakes and blood concentration of individual n-3 PUFAs with PCa risk based on prospective studies only.23 for conducting and reporting meta-analysis of observational studies were followed in the present study. We searched two databases (PubMed and EMBASE) up to February 2014. The search strategy was based on the following title/abstract key words:  AND (‘prostate cancer’ OR ‘prostate neoplasms’) (The standard MOOSE criteriaplasms’) . We alsoLiterature search and data extraction were independently conducted by two investigators (YQ-F and JS-Z), and discrepancies were resolved by discussion with a third investigator (DL). Inclusion criteria included prospective study design ; exposure of interest (dietary n-3 PUFAs or blood n-3 PUFAs concentrations); endpoint ; and reporting of risk estimate  of PCa with corresponding 95% confidence intervals (CIs) for individual n-3 PUFA exposure. Retrospective or cross-sectional studies, animal or cell culture studies, reviews, editorials, and commentaries were all excluded from the present study.24Detailed data relating to participants , characteristics of study , risk estimates with the most adjustment and corresponding 95% CIs for each category of n-3 PUFA exposure were extracted. Relative risk (RR) was used for risk estimates, and odd ratio and hazard ratio were treated as RR directly as reported elsewhere.25 The criterion contains selection domain (4 points), comparability domain (2 points), and outcomes (for cohort studies) or exposures (for case-control studies) domain (3 points). Scores of 0–3, 4–6, and 7–9 were regarded as low, moderate, and high quality, respectively.We assessed study quality using the Newcastle-Ottawa criteria.17 reporting the unit for fatty acid intake category as grams/1000 kcal, was transformed to g/day assuming an average energy intake of 2000 kcal/day in this population.12 Any results stratified by stage or grade of PCa were treated as separate reports and combined first before being pooled into the overall meta-analysis. We did not carry out dose-response analysis for dietary individual n-3 PUFA and PCa subgroups , because very few studies (not more than three) reported the findings stratified by stage of cancer. We performed a 2-stage random-effects dose-response meta-analysis first to examine a potential curvilinear association between dietary n-3 PUFA exposure and risk of PCa, using restricted cubic splines with three knots at fixed percentiles  of dietary n-3 PUFA distribution.26 The median or mean value of n-3 PUFA exposure in each category was used as the corresponding dose of exposure. If the median or mean value was not available, the midpoint of the upper and lower boundary was considered the dose of each category. If the highest category was open-ended, we assumed the category to be of the same width as the closest category. For only one study, the median value (or average or range) of the fatty acids in each category was not reported in the publication, we obtained the data by contacting the authors.27 A P-value for non-linearity was calculated by testing the null hypothesis that the coefficient of the second spline was equal to zero. If the non-linear association between a specific individual n-3 PUFA and PCa risk was found to be non-significant, the trend estimation from the correlated estimates for log RR across categories of individual dietary n-3 PUFA exposure, assuming a linear relationship, was performed, and the RR with 95% CI for a specific increment of individual n-3 PUFA intake was computed using generalized least squares regression, as described by Greenland and Longnecker28 and Orsini and colleagues.29For studies evaluating the association between dietary n-3 PUFAs and PCa risk, dose-response analysis was only conducted among studies with exposure units reported as or transformable to g/day; we did not conduct dose-response analysis for studies reporting units as %energy/day due to the limited number of studies. One study,26 The dose-response analysis was separately conducted for aggressive, non-aggressive, and total PCa risk. For one study, the reported PCa risks were stratified by grade or stage of cancer and we obtained the RRs for total PCa by contacting the authors.14 As described previously, we first performed a 2-stage random-effects dose-response meta-analysis to examine a potential non-linear association between any individual blood n-3 PUFA concentration and PCa risk, using restricted cubic splines with three knots at fixed percentiles  of n-3 PUFA distribution.26 If significant non-linear association between a specific individual n-3 PUFA and PCa was not found, the trend estimation, assuming a linear relationship, was performed, and the RR with 95% CI for a specific increment of individual n-3 PUFA concentration was computed using generalized least squares regression, as described above. For only one study without sufficient information to estimate the distribution, we used variance-weighted least squares regression for the dose-response estimation.30For studies examining the association between blood n-3 PUFA concentration and PCa risk, all reported the concentration of blood n-3 PUFA as ‘% total fatty acid’, and some of the studies also examined the findings stratified by grade or stage of cancer. We defined low-grade (Gleason scores ≤7) and localized tumors as nonaggressive, while treating high-grade (Gleason scores >7) and advanced tumor cases as aggressive, according to the classification described by Orsini.2 statistic. An I2 statistic <25% indicated low heterogeneity, 25%–75% indicated moderate heterogeneity, and >75% indicated high heterogeneity. Subgroup analyses were conducted to examine sources of study heterogeneity and the influence of potential residual confounding factors, such as age, BMI, alcohol consumption, and education. If the number of reports in each subgroup was very limited, we did not estimate dose-response trends for these subgroups. Sensitivity analysis was conducted by omitting one study at a time and examining the influence of each individual study on the pooled RR. Publication bias was evaluated by visual inspection of a funnel plot and use of Begg’s regression test (significant at P < 0.05). Stata version 12  was used for all statistical analyses.All dose-response analyses were separately conducted for ALA, EPA, DHA, and DPA. Study heterogeneity was assessed using the I13–15,17,31–36 5 were from Europe,8,16,18,30,37 and 1 was from Australia.27 Furthermore, 14 articles reported the association of marine n-3 PUFA exposure with PCa risk, and 14 articles described the association of alpha-linolenic acid with PCa risk.Through full-text examination of 87 potential publications, we identified 16 eligible publications, including 35 828 PCa cases and 840 242 participants, from 14 independent prospective cohort studies  between dietary ALA intake and PCa risk . The summary RR of PCa for an increase of 0.5 g/day was 0.99 , and no heterogeneity was observed (I2 = 0%); however, this result should be interpreted with caution because one study dominated the results  was examined  . For PCa stratified by grade or stage of cancer, we did not observe a significant linear association  between blood ALA concentration and aggressive PCa from 6 eligible studies. For non-aggressive PCa, of which only 4 studies were included, we found a statistically significant non-linear association with blood ALA (P < 0.01), indicating a protective role of ALA . A significant curvilinear association was observed (P < 0.01) between dietary DHA exposure and PCa risk, which indicated that increased DHA intake was associated with higher risk of PCa    . The trend estimation analysis indicated a borderline significant positive association with PCa risk per 1% increase of blood DHA concentration (P = 0.11) but not per 0.5% increase of EPA concentration (P = 0.32)  and for blood EPA concentration with aggressive PCa risk (P < 0.01), both indicating increased PCa risk with higher concentration of DHA or EPA (Table Nine studies) Figure . HoweverP = 0.21)   per 0.5 g/day increments. However, exclusion of the study17 dominating the overall outcomes did make the pooled RR estimate non-significant. Therefore, the significant inverse association between ALA intake and PCa risk should be interpreted with caution. Sensitivity analysis also indicated that omitting any individual study did not substantially change the significance of the non-linear association with PCa risk for dietary DHA intakes and did not change the pooled RR estimates for dietary EPA intakes. However, much lower heterogeneity was observed for dietary EPA (I2 = 12% vs I2 = 36%) after omitting the only study30 for which variance-weighted least squares regression was used in the dose-response estimation. Subgroup dose-response analyses were not conducted for dietary n-3 PUFA intakes, but only for circulating n-3 PUFA concentrations, because the number of studies reporting dietary n-3 PUFAs in each subgroup was too small (not more than three) and the according exposure categories were very limited.For dietary ALA exposure, sensitive analysis indicated that excluding the only study37 contributed much of the weight in the continuous analysis, exclusion of this study did not change the pooled RR estimate . Sensitivity analysis showed that after omitting the study30 for which trend estimation was carried out using variance-weighted least squares regression as described above, the pooled RR of PCa was statistically significant for every 1% increase in blood DHA concentration , and the heterogeneity decreased from I2 = 26% to I2 = 16%. However, for blood EPA, exclusion of any individual study did not materially change the pooled estimates.For circulating ALA exposure, even though one studyP = 0.04) or among studies conducted in Europe (P = 0.02) (Table P < 0.01)  Table . For bloPnonlinearity = 0.88) and 1.02  were observed for every 1% increase in DHA concentration and every 0.5% increase in EPA concentration, respectively (Table 2 = 0%) (Table 2 = 43.5%) for every 0.2% increase in blood DPA concentration among studies with nested case-control designs. Moreover, the heterogeneity between these two subgroups  was also significant (P < 0.01), which implied that study design may be a source of heterogeneity  used in the included studies were validated by dietary records and were reported to have moderate to strong correlations between blood n-3 PUFA biomarkers and their dietary intakes assessed by FFQ, perfect correlations are unrealistic.40 and the complex metabolism of PUFAs, which may include beta-oxidation, storage in adipose tissues, incorporation into cell membranes, and bio-conversion to longer chain omega-3 PUFAs,41 may help explain the small heterogeneity of effects between dietary ALA and blood ALA. In addition, other potential reasons may help us explain the heterogeneity in the results of the present and previous studies. First, for the different original observational studies included, ALA can come from different dietary sources, such as vegetables and meat, which might lead to different types of confounding.42 Second, the quality of studies included in a meta-analysis plays a critical role in achieving reliable conclusions. As prospective studies are less prone to bias and could produce better evidence than retrospective studies, systematic reviews based on prospective studies may obtain inconsistent conclusions compared to systematic reviews that include retrospective studies. Even though blood ALA concentration showed no significant association with PCa risk, low ALA was reported as a factor for the preferred synthesis of AA from LA, instead of EPA from ALA, because competition exists between n-3 and n-6 for the respective synthesis of EPA and AA.4 Additionally, since the conversion rate from ALA to EPA and DPA in the body is very limited, dietary ALA is still the main source of circulating ALA.For ALA, previous studies did not report a conclusive association, and even meta-analyses have reported opposite conclusions (positively or negatively associated with PCa risk). Our present dose-response meta-analysis did not conservatively support a protective role of ALA, as the marginally significant negative association between PCa risk and per 0.5 g/day increment of dietary ALA was dominated by a single larger study and blood ALA concentration also showed no significant association with aggressive, non-aggressive, or total PCa risk. Higher dietary intakes of ALA did not result in higher physiologic levels of ALA in prostatic tissue43For long chain n-3 PUFAs, which are present in fatty fish and fish oil, the median or average quantity of estimated exposure in extreme categories are listed in 34,44 Nevertheless, one of the studies was not based on prospective studies, and the other one did not include two recently published prospective studies with large sample sizes. In our study, the dose-response analysis using a random-effect model, which conservatively takes into account the heterogeneity between included studies, only found a borderline significant positive association between blood concentration of DHA and PCa risk. Moreover, this association was more significant after omitting a study that did not provide sufficient information regarding the distribution of cases used for trend estimation. On stratifying PCas by grade or stage, a borderline significant positive linear association was observed only for blood DHA with aggressive PCa risk. Nevertheless, both circulating DHA and EPA were positively associated with PCa risk in studies with shorter follow-up duration (less than 7.4 years), while circulating DHA and EPA were not associated with PCa risk in studies with longer follow-up periods (more than 7.4 years). Two reasons may help explain this heterogeneity: first, in order to avoid the high cost of fatty acid assays, most of the included nested case-control and case-cohort studies did not measure n-3 PUFA levels immediately after taking blood samples but instead stored the samples until the cases were diagnosed and matched controls were selected. As a result, longer follow-up means longer storage time, which may influence the precision of n-3 PUFA measurement; second, for assessment of dietary n-3 PUFA, different follow-up times may produce different levels of bias because the fatty acids database may not be regularly updated and other confounding constituents in the source of n-3 PUFA may influence the effects of n-3 PUFA in the long run.45Compared with the protective effects of long chain n-3 PUFAs reported in animal or in vitro studies, especially regarding EPA and DHA, results from epidemiological studies have been grossly inconsistent. At least two previous meta-analyses concluded that the blood levels of DHA and EPA were positively associated with PCa risk.45 may influence or even reverse the effects of marine n-3 PUFA consumption. Therefore, more prospective studies are warranted to investigate the impact of the n-3:n-6 ratio on cancers.For dietary DHA exposure, we observed a significant non-linear association with PCa risk, which indicated a higher PCa risk with increasing intake of DHA. The discrepancies between the results of the present study and the protective effects reported in animal or in vitro studies may be explained by several reasons. First, the blood levels of marine n-3 PUFA in the population studied may be too low to produce observable protective effects, assuming that the protective effects of n-3 PUFA in preclinical studies of animal and cell culture models were true. Second, consumption of the main dietary source of DHA, fish, is very strongly related to socio-economic status and, more generally, to health-conscious behaviors. Undergoing screening for PCa is not only a very health-conscious behavior but is also known to increase the risk of being diagnosed with PCa (as is true of any screening method). Therefore, this bias may be involved in the positive association between DHA and PCa risk. Third, the background diet, which may contain n-6 PUFA or even potential carcinogenic substances, such as pesticides and heavy metals accumulated in fatty fish,46–48 In the present study, the pooled RR from 5 nested case-control studies indicated a significant inverse association between blood DPA concentration and total PCa risk. However, including another case-cohort study34 or stratifying the PCa by grade or stage eliminated this significant association. Therefore, more prospective studies examining the association between circulating or dietary DPA and PCa risk are needed.It is worth mentioning that even though DPA is present in smaller quantities than DHA and EPA in human blood, this fatty acid was reported to exhibit potent activities, such as reducing platelet aggregation, reducing age-related oxidation, inhibiting angiogenesis, and reducing inflammation, which may be involved in prostate carcinogenesis.49 Our subgroup analysis also indicates that BMI is an important confounding factor, because the positive association for both blood DHA and blood EPA with PCa risk is more evident in studies that adjusted for BMI. However, only two included studies assessed the association between blood DHA and PCa risk separately for localized and advanced PCa, which prevented us from performing further analysis on this interaction. Therefore, more studies assessing this confounding factor are needed, and the precise mechanisms for this interaction remain unclear. In addition, the adjustment for alcohol consumption also highlighted the positive association between blood DHA or blood EPA exposure and PCa risk, indicating that alcohol consumption may be involved in the incidence of PCa. However, several recent meta-analyses provided no consistent evidence on the association between alcohol drinking and PCa,50–52 and more studies are needed.One recent meta-analysis of prospective studies suggests that high BMI may be protective against localized PCa, while it was a risk factor for advanced PCa.The present dose-response meta-analysis has several strengths. First, the large sample size and extensive information allowed us to quantitatively assess the dose-response association between ALA or other individual marine n-3 PUFA exposures and PCa risk, thus making it more powerful than any individual study. Second, the prospective nature of the included studies avoided the influence of recall and selection bias. Third, we systematically reviewed and assessed the summarized association between PCa with different types of individual n-3 PUFAs, including EPA, DHA, DPA, and ALA. Last, both blood level and dietary intakes of n-3 PUFAs were assessed for the association with PCa risk. These data give the most comprehensive view of the association between n-3 fatty acids and PCa risk based on current evidence.The meta-analysis also has several limitations. First, when separately examining the findings for aggressive and non-aggressive PCa, available data on dietary intake of n-3 PUFAs is rather limited. Therefore, dose-response analysis for this aspect was not performed. In addition, subgroup analyses were not conducted for dietary n-3 PUFA exposure, because not more than three studies were eligible in each subgroup. Therefore, future prospective studies are needed for the detailed analysis of the association between dietary n-3 PUFA intake and risk of PCa. Second, the blood n-3 PUFA concentrations reflected exposure for only a short time period; therefore, this measurement may be subject to substantial random error. Last, possible language bias could occur because we excluded articles not in English.20 that high ALA intake or high ALA concentration is associated with increased PCa risk. However, both higher dietary DHA intakes and blood DHA concentrations were found to be associated with elevated risk of PCa, and each 1% increase in DHA concentration was associated with a 2% higher risk of PCa. When examining the findings stratified by the grade and stage of cancer, we observed that blood EPA concentrations and blood DHA concentrations were positively associated with aggressive PCa risk and nonaggressive PCa risk, respectively. Subgroup analyses indicated a 2% lower PCa risk per 1% increase in DPA concentration in studies with nested case-control study designs and suggested that study design, BMI, and alcohol consumption are important confounding factors. In general, different individual n-3 PUFAs may exhibit different or even opposite associations with PCa risk, and studies with larger sample sizes and longer follow-up times are needed to confirm the results.The present systematic review indicates that a significant negative association exists between ALA exposure and PCa risk, though the results were dominated by a single study. This finding does not support previous reportseTable 1. eTable 2. eTable 3. eTable 4. eTable 5. eTable 6. eTable 7. eTable 8.


RAD51 gene has been a subject of great interest, which is evidenced by at least 28 case-control studies and eight meta-analyses undertaken on this polymorphism till now. We undertook a meta-analysis on RAD51 135G > C data for 21236 cases and 19407 controls pooled from 28 studies on breast cancer in women. Pooled data analysis suggested a significant association of the substitution with breast cancer in the recessive model (GG + GC versus CC) and in the co-dominant models comparing GG versus CC and GC versus CC. Analysis of the results suggested that ‘CC’ genotype is a significant breast cancer risk factor in comparison to ‘GG’ and ‘GC’ genotypes. We also undertook pooled analyses on different ethnic groups and found that ‘CC’ was a strong risk factor in Caucasians, but not in East-Asians and populations of mixed ethnicity. In conclusion, the RAD51 135G > C substitution in the homozygous form (CC) increases the risk of breast cancer in an ethnic-specific manner.RAD51 is a homolog of bacterial RecA protein, which plays an important role in preserving stability of the genome. RAD51 interacts with BRCA1 and BRCA2 for homologous recombination repair. A functional polymorphism (135G > C) in the  BRCA1 and BRCA2, play a role in the homologous recombination pathway (HR) for repairing the double strand breaks (DSB) in DNABreast cancer (BC) is the most prevalent cancer amongst women worldwide. It represents high incidence rates in North America and Western European countries in comparison to Asian or African populationsRAD51 gene, changing a guanine to cytosine at position 135 in its 5’ untranslated region, has been accused of modulating breast cancer risk by altering gene transcriptionRAD51 is a homolog of bacterial RecA protein, which plays an important role in preserving stability of the genomeRAD51 135G > C polymorphism and the risk of breast cancer in different populations. But, inconsistent results, even within the same population, have hampered the consensus building regarding the impact of this polymorphism on breast cancer risk. For example, Sliwinski et al. did not find any association between RAD51 135G > C polymorphism and breast cancer risk, which Synoweic et al. found in a Polish population10RAD51 135G > C polymorphism on breast cancer risk.Many epidemiological studies have been carried out to examine the association between RAD51 135G > C with breast cancer). A total of thirty-five studies were then considered for inclusion. Seven studies were excluded as they either lacked data on RAD51 135G > C substitution312131415Eighty-two studies were retrieved as a result of a literature search. Forty-seven were excluded as they were not relevant to the aim of our study . However, the recessive model of analysis, (GG + GC) vs. CC, showed significant association between the polymorphism and BC risk  . Another co-dominant model, GC vs. CC also showed an association of RAD51 135G > C polymorphism with BC risk . It can be concluded from the above that double substitution (CC) is a highly significant risk factor against homozygous common (GG) and heterozygous genotypes (GC); however, heterozygous does not appear to be a significant risk factor against homozygous common genotype.The meta-analysis results have been summarized in = 0.008) . In the Since there was significant heterogeneity in the outcome and the inference of the studies, we undertook further analysis on groups based on ethnicity. We stratified all studies into three groups depending upon their ethnic affinity. ‘Mixed’ groups included studies where the authors had mentioned the mixed nature of study samples. Pooled analysis of each group was undertaken using all genetic models of analysis: dominant, recessive, and co-dominant.RAD51 CC genotype and breast cancer risk in Caucasians  (RAD51 genotypes and breast cancer was seen (P = 0.064)  , Fig. 3.= 0.064) . In the = 0.064) , Fig. 5.2 = 51.94, Pheterogeneity = 0, OR = 1.773, P = 0), stood firm . Classic fail safe ‘N’ was 28 , suggesting that an addition of 28 null studies would be needed to bring the P value to a non-significant range. In the same manner, Orwin’s fail safe ‘N’ was 32, suggesting that the number of studies required to bring the observed odds ratio over 0.999 would be 32. Since the chances of missing so many studies for twenty-eight pooled studies are negligible, we concluded that the results are stable and the pooled analysis was not biased. Our observation of symmetric funnel plots and non-significant statistical tests confirmed the absence of bias .RAD51 135G > C polymorphism has been subjected to meta-analyses eight times, but with different outcomes. This has led to an interesting scenario of variation across the case-control studies and pooled analyses. Interestingly, similar to the issue with the case-control studies, the inference in the meta-analyses on RAD51 135G > C varies from one extreme of association to another extreme of no association. Yu et al. conducted a meta-analysis on twelve studies (7065 cases and 6981 controls) and suggested that RAD51 135G > C might not modify BC risk in non- BRCA1 and BRCA2 mutation carrierset al. on nine studies  suggested an association of this polymorphism with breast cancer risket al. performed a meta-analysis on seventeen studies  and found that RAD51 135 G> C polymorphism may be a protective factor against breast canceret al. conducted a meta-analysis on fourteen studies  and observed that RAD51 variant 135C homozygote is associated with an elevated risk of breast cancer among BRCA2 mutation carriers, but not in BRCA1 mutation carrierset al. conducted a meta-analysis on twenty-one studies, including 12,860 cases and 11,169 controls and observed that CC genotype was associated with a high risk of breast cancer as compared to its other variantsMeta-analysis is a tool to develop consensus when the results across the case-control studies vary. However, it is interesting to note that the RAD51 135G > C polymorphism and breast cancer risk. We used robust methods and undertook a sensitivity analysis by excluding the studies that deviated from the HW equillibrium. The results suggested that women carrying CC genotypes are at an increased risk of breast cancer as compared to those with other genotypes (GG or GC). On the basis of available biological evidence, the plausible mechanism of increased risk is its effect on the stability of mRNA and in turn the efficiency of translation. The production of altered protein as a result of this mutation influences the functionality of a multi-protein DNA repair complex consisting of BRCA1, BRCA2 and RAD51The present meta-analysis included 21236 cases and 19407 controls from twenty-eight studies on RAD51 135G > C polymorphism with the risk of breast cancer. Brooks et al. observed that white women were more susceptible to the disease as compared to Asian women (P <0.0001). 37.4% of the black women had at least one copy of the variant allele RAD51 135C while among non-Jewish white, Jewish white and other ethnic populations, the frequencies were 15.9%, 9.6% and 17.3%, respectivelyet al. observed that genetic contrast in cell cycle and DNA repair genes actively contributed to the risk of breast cancer. A group of betel quid chewing cases showed an elevated risk of cancer, which was linked to betel quid carcinogens, minor alleles of BRCA2 mutation and C allele of the RAD51 geneet al. suggested that in Caucasians, the G/C polymorphism of the RAD51 gene may not directly correlate with the progression of the disease and hence, it is not a useful markeret al. found no statistically significant difference in the genotype frequency of RAD51 ‘C’ between cases and controls in a population of mixed ethnicity (P = 0.96)RAD51 135G > C polymorphism with the risk of breast cancer significantly. The substitution associated with breast cancer in Caucasians, but not in East Asians and populations (study groups) having mixed ethnicity.Literature review suggests that there are contradictory conclusions regarding the association of RAD51 135G > C locus increases the risk of breast cancer significantly. Nevertheless, heterozygous substitution may not raise the risk considerably. Further, the risk is strongly affected by ethnicity of the study population, as group-wise analysis suggested strong impact of this polymorphism in Caucasians, but not in East Asians and populations of mixed ethnicities. A large sample size and the absence of publication bias further strengthened our conclusions. We conclude that RAD51 135G > C substitution may serve as a useful marker for screening of breast cancer risk; Nevertheless, its use may be restricted to the Caucasian populations. Since about 65% of the studies included in this meta-analysis were undertaken on Caucasian subjects, further studies on East Asians may be required to confidently rule out the association of RAD51 135G > C substitution with breast cancer in these populations. Since in the case of mixed ethnicity groups, one of the analysis models (fixed effect) suggested a significant correlation, reporting of genotype details ethnicity-wise is encouraged to help pooled analysis reach appropriate conclusions.In conclusion, homozygous substitution (CC) at the www.sciencedirect.com)’ and ‘Pubmed (www.pubmed.com)’ databases up to the 31st of October 2014 as the publication date, using the following keywords: ‘RAD 51’, ‘polymorphism’ and ‘breast cancer’ in various combinations. All the studies were checked for their references for identification of other relevant studies. The studies published in English only were considered for further screening. We did not specify a minimum sample size as the criterion for inclusion of a study in the analysis. In the case of multiple studies from a research group, the study with the largest sample size was chosen to avoid over-representation or duplication of data.Relevant studies were chosen by searching ‘Google Scholar (scholar.google.co.in)’ ‘ScienceDirect  Each study was an independent case-control study. iii) The statistical methods and the purpose of all the studies were similar. iv) The given information was enough to calculate the odds ratio. v) SNP genotyping had been undertaken using standard genotyping techniques. vi) Patients within the study had been recruited in accordance with the standard diagnostic parameters.The inclusion criteria comprised of the following: i) The studies looking for correlation of RAD51 135G > C substitution with breast cancer risk. iii) The study had reviewed the literature and not presented new data. iv) The raw data were unavailable in the article and the authors did not respond to three requests by e-mail. v) The study had been expanded to include more number of samples at a later stage.The exclusion criteria included: i) The study was not a case-control study. ii) The study did not aim to look for correlation of RAD51 135G > C polymorphism and breast cancer risk was evaluated by odds ratio (OR) with the corresponding 95% confidence interval (CI). The pooled OR is computed by the fixed effects model (the Mantel- Haenszel method) when there was a lack of heterogeneity between studies, otherwise, the random effects model (the Der Simonian and Laird method) is preferred2224252627282930313233343536373839404142The effectiveness of the association between The OR and 95% CI were calculated using different genetic models: dominant, co-dominant and recessive. Bonferroni’s correction for multiple testing was not considered given a limited number of tests that were pre-hypothesized. Stratification based on ethnicity  was employed for group-wise analyses. Genotype data of the control groups were studied for fitness in the Hardy Weinberg Equilibrium (HWE). Publication bias was assessed using Egger’s linear regression test that was followed by visual inspection of the funnel plot. All statistical tests were performed using the Comprehensive Meta Analysis software (version 2).How to cite this article: Sekhar, D. et al. RAD51 135G > C substitution increases breast cancer risk in an ethnic-specific manner: a meta-analysis on 21236 cases and 19407 controls. Sci. Rep.5, 11588; doi: 10.1038/srep11588 (2015).


Additionally, the SRR for an increment of one live birth was 1.00 . These non-significant associations were observed in all subgroups as stratified by the number of gastric cases, follow-up years, geographic location, menopausal status, anatomic subsite of gastric cancer, and adjustment for potential confounders, as well as in sensitivity analyses. Our meta-analysis found no significant association between parity and gastric cancer risk. However, further studies should be conducted to validate our findings and could provide more detailed results by stratifying their findings by Lauren’s subtype, histology, and anatomic site, as well as fully adjusting for potential confounding factors.We performed this meta-analysis of epidemiological studies to comprehensively assess the association between parity and gastric cancer risk, because previous studies have shown conflicting results regarding this topic. Relevant prospective studies were identified by searching the following databases: PubMed, EMBASE, and Web of Science, and random-effects models were used to estimate summary relative risks (SRRs) and 95% confidence intervals (CIs). Our search yielded 10 prospective cohort studies involving a total of 6624 gastric cancer cases and 5,559,695 non-cases. The SRRs for ever parity  Helicobacter pylori (H. pylori) infection456In 2012, gastric cancer was the fourth most common type of cancer and the third leading cause of cancer mortality among both men and women worldwidePregnant women have markedly elevated serum levels of certain hormones, including estrogens9104121381516171819202122The detailed procedures used for searching and screening articles are outlined in 81516171819202122Characteristics of the 10 selected studies are shown in Information regarding the quality assessment of studies is provided in 81619218161921vs. nulliparous yielded a SRR of 0.96 (95%CI = 0.87–1.05), without heterogeneity (I2 = 0%) (P = 0.159) failed to show evidence of publication bias, and similar non-significant results were observed in all stratified analyses performed based on study characteristics. Additionally, the results of a meta-regression showed no evidence of significant heterogeneity between subgroup analyses (Seven prospective studies investigated the relationship between ever parity and the risk of gastric cancer. A comparison of ever parity I2 = 0%) . Both a analyses .vs. the lowest parity number yielded a SRR of 1.03 (95%CI = 0.94–1.13), without heterogeneity (I2 = 1.6%) (Ten prospective studies investigated the relationship between parity number and the risk for gastric cancer. A comparison of the highest  = 1.6%) . Similar = 1.6%) .I2 = 18.6%) (P for nonlinearity = 0.093) , with low heterogeneity (= 18.6%) . There w= 0.093) . Similar= 0.093) .vs. never parity performed by excluding one study at a time showed that the SRR for gastric cancer ranged from 0.93  when Bahmanyar et al.I2 = 0%), when Persson et al.vs. low parity number, the SRR for gastric cancer ranged from 0.99  when Chang et al.I2 = 11.7%) when Bahmanyar et al.I2 = 0%) when Chang et al.I2 = 22.7%) when Green et al.vs. lowest parity number was restricted to studies included in the dose-response analysis of parity number, the SRR was 1.02 , which is similar to that in the original analysis. Furthermore, we excluded three studies which reported their risk estimates based on mortality resulting from gastric cancer. However, even after excluding those studies, the results remained statistically non-significant (data not shown).Our sensitivity analysis of ever parity The findings from this meta-analysis of prospective studies only marginally suggested that parity might help protect against gastric cancer. To the best of our knowledge, this is the most comprehensive and quantitative assessment of the aforementioned association conducted using high quality evidence. Additionally, these non-significant results were consistent with those in subgroup analyses stratified by study characteristics and adjustments for potential confounders.vs. diffuse) and gastric cancers located at different anatomic sites (cardia vs. non-cardia)248151517Increasing evidence has suggested that there are differences in risk factors associated with developing different Lauren’s subtypes of gastric cancer  instead of merely presenting results representing a summary of all different categories of parity . Third, although the outcomes, the incidence of gastric cancer, were identified via record linkage to the cancer registries, the criteria and procedure of diagnosis of this disease might be slightly different among these studies. For example, three included studies151716et al.et al.et al.This study also has several limitations that should be mentioned. First, this meta-analysis was based on the observational designs of the included prospective studies, and confounding is a concern in all observational studies. All of the included studies except oneIn summary, our meta-analysis of prospective studies found no association between parity and the risk for developing gastric cancer. The reliability of our finding is increased by the fact that evidence from prospective studies is generally considered to be more reliable than evidence from retrospective studies. However, with regards to public health recommendations, our findings are complicated by the fact that parity may have both beneficial and adverse effects with regards to other diseases. Further studies of parity in relation to other cancers as well as overall cancer risk and mortality are needed to better assess the risk-benefit of exposure to pregnancy-related hormones.Two independent investigators (T-TG and Q-JW) systematically searched PubMed (MEDLINE), EMBASE, and Web of Science for relevant prospective studies published starting from the time of each database’s inception to May 30, 2015. The following keywords were used for searching: (parity OR pregnancy OR livebirth OR reproductive OR reproduction) AND (stomach OR gastric) AND (cancer OR tumor OR carcinoma OR neoplasm). This search strategy was similar to that used in our previous studies9101125Two independent investigators (JC and T-TG) performed the study selection and exclusion procedures. Studies included in this analysis were required to satisfy the following criteria: (i) utilized a prospective study design; (ii) evaluated the association between parity and the risk for developing gastric cancer; (iii) presented estimates of relative risk (RR) or hazard ratio (HR) with 95% confidence intervals (CIs) or data necessary to calculate those parameters. If several publications involved overlapping patients, the study with the most patients was included.The following types of studies were excluded from our meta analysis: (i) randomized controlled trials, case-control studies, retrospective studies, reviews without original data, ecological studies, editorials, and case reports; (ii) studies reported with risk estimates that could not be summarized .Data were extracted using a data extraction form and entered into a database by a single investigator (Q-JW). Next, an independent investigator (JC) checked the data, and all differences were resolved by a third investigator (T-TG). The following information was extracted for each included study: last name of the first author, publication year, geographic location of the study, number of cases/size of cohort, follow-up years, exposure and outcome assessment, categories of exposure, and study-specific adjusted estimates with their 95%CIs (including adjusted confounder information if applicable). If there were multiple estimates for the association, we used the estimate adjusted for the most appropriate confounding variables. This was similar to the method used in other studies39404142The Newcastle-Ottawa Scale (NOS)253940414243444647et al.1517181920vs. nulliparous, and studies15We used the counting method proposed by Hamling et al.et al.P value was calculated by showed that these two regression coefficients simultaneously equaled zero. The P value for nonlinearity was calculated by showing that the coefficient of the second spline equaled zero. The details of this method for calculating P values have been previously described53To examine associations between parity and gastric cancer risk, the summarized relative risks (SRRs) with their 95% CIs were estimated by summarizing the risk estimates of each study using random effect models that considered both within- and between-study variationsThe following information was required and used to conduct the dose-response meta-analysis: 1) the distribution of cases and non-cases, and the risk estimates with the variance estimates for at least three quantitative exposure categories; 2) the median or mean level of these exposures in each category 815161718192021I2 metric was used to evaluate between-study heterogeneity. Values for I2 range between 0% and 100%, and represent the ratio of between-study variance divided by the sum of the within-study and between-study variancesvs. <250), number of follow-up years (≥10 vs. <10), geographic location , patient menopausal status , anatomic subsite of the gastric cancer (cardia vs. non-cardia), and adjustments made for potential confounders including body mass index, cigarette smoking, alcohol consumption, and socioeconomic status. Heterogeneity between subgroups was evaluated by meta-regression25394156P-value of 0.05 was used to determine whether significant publication bias existed. Furthermore, sensitivity analyses were conducted by deleting each study in turn to examine the influence of individual data on the overall estimate. All statistical analyses were performed using Stata .The How to cite this article: Chen, J. et al. Parity and gastric cancer risk: a systematic review and dose-response meta-analysis of prospective cohort studies. Sci. Rep.6, 18766; doi: 10.1038/srep18766 (2016).


Apolipoprotein E (ApoE) gene has been reported to be associated with migraine and tension-type headache (TTH), but the results are conflicting. This study aimed to evaluate the association of ApoE with migraine by a meta-analysis.MEDLINE, ISI Web of Knowledge, The Cochrane Central Register of Controlled Trials, and EMBASE databases were searched to identify eligible studies published in English from 2000 to 2014. Data were extracted using standardized forms. The association was assessed by relative risk (RR) with 95 % confidence intervals (CIs) using a fixed or random effects model.Four studies, comprising 649 migraineurs, 229 TTH subjects and 975 controls, met all the criteria and were included in the meta-analysis. No significant difference was found comparing genotypic and allelic frequencies in the case of migraineurs versus controls and TTH subjects versus controls. Only when migraineurs and TTH subjects were considered as a whole group, ApoE4 was found to increase the relative risk of headache by 1.48 , compared to controls.ApoE ε4 allele is not associated with migraine susceptibility, but is positively related to headache (including migraine and TTH). Migraine is a common neurovascular disorder, characterized by recurrent episodes of headache and the dysfunction of the autonomic nervous system, affecting 10–20 % of the population . In partApolipoprotein E (ApoE) is a 299 amino acid protein, encoded by a gene located on chromosome 19cen-q13.2 , 7. ApoETherefore, to define further the disease risk associated with APOE polymorphism, in this study we performed a meta-analysis of all related studies that evaluated allelic and genotypic frequencies of ApoE polymorphism in migraine.Eligible studies were identified by searching on web-based databases  with no language restrictions. Search terms were “apolipoprotein E”, “APOE”, “ɛ2”, “ɛ3”, “ɛ4”, “APOE2”, “APOE3”, “APOE4”, “migraine”, “headache” and “Polymorphism”. Potentially relevant studies were retrieved and reviewed by 2 reviewers. All case–control studies with extractable data were included. Included studies were published as full-length articles in peer-reviewed journals.Studies were included for meta-analysis if they met the following criteria: (1) the association between APOE and migraine was examined by using a population-based, case–control design; (2) migraine was diagnosed using an international standard; (3) genotype or allele frequencies were reported in both cases and controls; (4) the genotype frequencies in control groups were consistent with Hardy-Weinberg equilibrium.2 test (I2 = (Q− df) / Q × 100 %. I2 values of above 25 %, 50 %, and 75 % were taken as indicators of mild, modest, and high heterogeneities, respectively. When the effects were assumed to be homogeneous , the fixed-effects model was used; otherwise, the random-effects model was more appropriate. The meta-analyses were performed using Stata Version 12.0 software .Heterogeneity across the eligible studies was tested using Q-test, and it was considered statistically significant when P < 0.1. Heterogeneity was also quantified with IAs a result of our literature search, a total of four published articles reported on the relationship between APOE SNP and migraine and met the inclusion criteria. Rainero et al. divided the subjects into three subgroups: migraine with aura; migraine without aura; and mixed headaches (migraine associated with tension-type headache) . Stuart Total 649 migraineurs, 229 TTH subjects and 975 controls were genotyped for APOE polymorphism. In the four studies, the distribution of the genotypes in control groups was in Hardy–Weinberg equilibrium (P < 0.05).2 = 74.1 %). This significance disappeared with random effect . While E4 gene increased the relative risk of headache by1.48 , they did not show any effect when migraineurs and TTH subjects were taken as a reference category  plays an essential role in the pathogenesis of both migraine and TTH , 15. MorThere are some limitations in our study. First, since migraine is a complex and heterogeneous disorder with a wide clinical spectrum, patients with different clinical phenotypes may not sufficiently capture this variability and may result in misclassification. Second, we only chose three genotypes in our study, despite various genotypes of APOE. Third, the lack of individual data has restricted further adjustments for the subgroup analysis for TTH. Fourth, the analysis was performed on a relatively small number of retrospective case–control studies, and the subjects included in these studies are also limited. Further population-based studies with larger sample size are necessary to confirm our conclusions.In summary, the evidence from the present meta-analysis showed that APOE4 is not associated with migraine or TTH, but is related to headache (including migraine and TTH), suggesting a nonspecific role for the risk of headache.


The SNP309 polymorphism (T-G) in the promoter of MDM2 gene has been reported to be associated with enhanced MDM2 expression and tumor development. Studies investigating the association between MDM2 SNP309 polymorphism and endometrial cancer risk reported conflicting results. We performed a meta-analysis of all available studies to explore this association.All studies published up to August 2013 on the association between MDM2 SNP309 polymorphism and endometrial cancer risk were identified by searching electronic databases PubMed, Web of Science, EMBASE, and Chinese Biomedical Literature database (CBM). The association between the MDM2 SNP309 polymorphism and endometrial cancer risk was assessed by odds ratios (ORs) together with their 95% confidence intervals (CIs).P < 0.001; GG vs. TG + TT: OR = 1.726, 95% CI 1.251–2.380, P = 0.001; GG + TG vs. TT: OR = 1.169, 95% CI 1.048–1.304, P = 0.005). In subgroup analysis by ethnicity and HWE in controls, significant increase of endometrial cancer risks were observed in Caucasians and studies consistent with HWE. In subgroup analysis according to study quality, significant associations were observed in both high quality studies and low quality studies.Eight case–control studies with 2069 endometrial cancer cases and 4546 controls were identified. Overall, significant increase of endometrial cancer risk was found when all studies were pooled in the meta-analysis (GG vs. TT: OR = 1.464, 95% CI 1.246–1.721, This meta-analysis suggests that MDM2 SNP309 polymorphism contributes to endometrial cancer susceptibility, especially in Caucasian populations. Further large and well-designed studies are needed to confirm this association. Endometrial cancer is one of the most common gynecologic cancers in developed countries ,2. AlthoThe murine double minute-2 (MDM2) is a key negative regulator of the P53 tumor suppressor pathway which has been suggested to be implicated in a variety of cancers . EvidencOver the last two decades, a number of molecular epidemiological studies have been conducted to investigate the association between the MDM2 SNP309 polymorphism and endometrial cancer risk, but the results remain inconsistent. Several studies have previously suggested that the MDM2 SNP309 polymorphism was associated with an increased risk of endometrial cancer -13. HoweWe conducted a comprehensive literature search in PubMed, Web of Science, EMBASE, and Chinese Biomedical Literature (CBM) databases up to August 01, 2013 using the following search strategy:  and . There was no restriction on time period, sample size, population, language, or type of report. All eligible studies were retrieved and their references were checked for other relevant studies. The literature retrieval was performed in duplication by two independent investigators (Qiliu Peng and Cuiju Mo).Studies included in the meta-analysis were required to meet the following criteria: (1) Case–control studies which evaluated the association between MDM2 SNP309 polymorphism and endometrial cancer risk; (2) used an unrelated case–control design; (3) had an odds ratio (OR) with 95% confidence interval (CI) or other available data for estimating OR (95% CI); and (4) the control population did not contain malignant tumor patients. Conference abstracts, case reports, editorials, review articles, and letters were excluded. When multiple publications reported on the same or overlapping data, we chose the most recent or largest population. When a study reported the results on different subpopulations, we treated it as separate studies in the meta-analysis.Two reviewers (Qiliu Peng and Cuiju Mo) independently reviewed and extracted data from all eligible studies. Data extracted from eligible studies included the first author, year of publication, country of origin, ethnicity, genotyping method, matching criteria, source of control, endometrial cancer confirmation criteria, total number of cases and controls and genotype frequencies of cases and controls. Ethnic backgrounds were categorized as Caucasian and Asian. To ensure the accuracy of the extracted information, the two investigators checked the data extraction results and reached consensus on all of the data extracted.Methodological quality was independently assessed by two reviewers (Qiliu Peng and Cuiju Mo), according to a set of predefined criteria , recessive model (GG vs. TG + TT), and dominant model (GG + TG vs. TT).The strength of the association between MDM2 SNP309 polymorphism and endometrial cancer risk was assessed by odds ratios (ORs) with 95% confidence intervals (CIs). The significance of the pooled OR was determined by Z test and a 2 based Q test and I2 statistics were used to assess the heterogeneity among studies [Q test was PQ < 0.1 or I2 ≥ 50%, indicating the presence of heterogeneity, a random-effects model (the DerSimonian and Laird method) was used to estimate the summary ORs [Q test was PQ ≥ 0.1 and I2 < 50%, indicating the absence of heterogeneity, the fixed-effects model (the Mantel–Haenszel method) was used [The χ studies ,22. If tmary ORs ; otherwiwas used . To expl2 test. All analyses were performed using Stata software, version 12.0 .Sensitivity analysis was performed by sequential omission of individual studies. Publication bias was evaluated using a funnel plot and Egger’s regression asymmetry test . The disWith our search criterion, 35 individual records were found, but only ten full-text publications were preliminarily identified for further detailed evaluation. According to the exclusion criteria, three publications were excluded including one publication containing overlapped data , one wasP < 0.001, Figure P = 0.001; GG + TG vs. TT: OR = 1.169, 95% CI 1.048–1.304, P = 0.005). In subgroup analysis by ethnicity, significant increased endometrial cancer risk was found in Caucasians  but not in Asians. In stratified analysis by HWE in controls, significant increased endometrial cancer risk was also observed in studies consistent with HWE . When stratified by study quality, significant associations were found in both high quality studies and low quality studies.The results of the association between MDM2 SNP309 polymorphism and endometrial cancer risk were shown in Table PQ < 0.001; Table PQ < 0.001), and studies consistent with HWE (GG vs. GT + TT: PQ < 0.001). To further investigate the heterogeneity, we performed Galbraith plots analysis to identify the outliers which might contribute to the heterogeneity. Our results showed that the study Zajac et al. [I2 values decreased obviously and PQ values were greater than 0.10 after excluding the study Zajac et al. [PQ = 0.241), Caucasians (GG vs. GT + TT: PQ = 0.179), and studies consistent with HWE (GG vs. GT + TT: PQ = 0.260). However, the significance of the summary ORs for MDM2 SNP309 polymorphism in the overall population and subgroup analyses were not influenced by omitting the study by Zajac et al. [Statistical significant heterogeneity among studies was observed in the association analysis between the MDM2 SNP309 polymorphism and endometrial cancer risk in the overall populations  with a region of the MDM2 promoter ,28. SNP3In the subgroup analysis by ethnicity, significantly increased endometrial cancer risk was found in Caucasians. However, no significant association was found in Asians. It might not be uncommon for the same polymorphism playing different roles in cancer susceptibility among different ethnic populations, because cancer is a complicated multi-genetic disease, and different genetic backgrounds may contribute to the discrepancy. Nevertheless, owing to the limited number of relevant studies among Asian populations included in this meta-analysis, the observed negative association between MDM2 SNP309 polymorphism and endometrial cancer risk in Asians is likely to be caused by chance because study with small sample sizes may have insufficient statistical power to detect a slight effect or may have generated a fluctuated risk estimate. Currently there were only two studies ,15 on MDTo clarify an association between genetic polymorphisms and cancer risk, the quality of the study design is of great importance. In addition to controls that should be in HWE, strict definitions of the study population, appropriate materials used to assess genotype as well as sufficient statistical power are required. Of the eigh eligible studies, three were considered as low quality studies ,13,18 anIt seemed that selection bias could have played a role because the genotype distribution of the MDM2 SNP309 polymorphism among control subjects disobeyed the law of HWE in one of the included studies . It is wPQ value was less than 0.001). To find the sources of heterogeneity, we performed metaregression and subgroup analyses. Metaregression analysis of data showed that the ethnicity, study quality, and HWE status were the sources of heterogeneity. Subgroup analyses stratified by ethnicity, study quality, and HWE status showed that the heterogeneity was still significant in Caucasians and studies consistent with HWE. To further investigate the heterogeneity, Galbraith plots analysis was performed to identify the outliers which might contribute most to the heterogeneity. Our results showed that the study of Zajac et al. [I2 values decreased lower than 50% and PQ values were larger than 0.10 after excluding the studies of Zajac et al. [Heterogeneity is a potential problem when interpreting the results of a meta-analysis, and finding the sources of heterogeneity is one of the most important goals of meta-analysis . In the c et al.  was the c et al.  in the rc et al.  might beSome limitations of this meta-analysis should be addressed. First, in subgroup analysis by ethnicity, the included studies regarded only Asians and Caucasians. Data concerning other ethnicities such as Africans were not found. Thus, additional studies are warranted to evaluate the effect of this functional polymorphism on endometrial cancer risk in different ethnicities, especially in Africans. Second, our results were based on unadjusted estimates. We did not perform the analysis adjusted for other covariates such as age, obesity, drinking and smoking status, menopausal status, use of contraceptives, environment factors, and so on, because of the unavailable original data of the eligible studies.In conclusion, this meta-analysis suggests that the MDM2 SNP309 polymorphism may be associated with increased risk of developing endometrial cancer particularly among Caucasians. However, it is necessary to conduct large sample studies using standardized unbiased genotyping methods, homogeneous endometrial cancer patients, and well-matched controls. Moreover, gene–gene and gene–environment interactions should also be considered in the analysis. Such studies taking these factors into account may eventually lead to our better, comprehensive understanding of the association between the MDM2 SNP309 polymorphism and endometrial cancer risk.Written informed consent was obtained from the patient for the publication of this report and any accompanying images.HWE: Hardy–Weinberg equilibrium; MDM2: Murine double minute 2; SNP: Single nucleotide polymorphism; OR: Odds ratio; CI: Confidence interval.The authors do not have any potential competing interests.PQL, QAP, LXJ and MCJ conceived and designed the study, CZP, SJZ, WJR, ZLM, YS, QX, and LS participated in selecting study, extracting data, performing the statistical analysis and drafting the manuscript. PQL has been involved in revising the manuscript critically for important intellectual content. All authors read and approved the final manuscript.Scale for Quality Assessment.Click here for file


In a meta-analysis with multiple end points of interests that are correlated between or within studies, multivariate approach to meta-analysis has a potential to produce more precise estimates of effects by exploiting the correlation structure between end points. However, under random-effects assumption the multivariate estimation is more complex (as it involves estimation of more parameters simultaneously) than univariate estimation, and sometimes can produce unrealistic parameter estimates. Usefulness of multivariate approach to meta-analysis of the effects of a genetic variant on two or more correlated traits is not well understood in the area of genetic association studies. In such studies, genetic variants are expected to roughly maintain Hardy-Weinberg equilibrium within studies, and also their effects on complex traits are generally very small to modest and could be heterogeneous across studies for genuine reasons. We carried out extensive simulation to explore the comparative performance of multivariate approach with most commonly used univariate inverse-variance weighted approach under random-effects assumption in various realistic meta-analytic scenarios of genetic association studies of correlated end points. We evaluated the performance with respect to relative mean bias percentage, and root mean square error (RMSE) of the estimate and coverage probability of corresponding 95% confidence interval of the effect for each end point. Our simulation results suggest that multivariate approach performs similarly or better than univariate method when correlations between end points within or between studies are at least moderate and between-study variation is similar or larger than average within-study variation for meta-analyses of 10 or more genetic studies. Multivariate approach produces estimates with smaller bias and RMSE especially for the end point that has randomly or informatively missing summary data in some individual studies, when the missing data in the endpoint are imputed with null effects and quite large variance. In genetic association studies of complex traits, estimation of the average effects of genetic variants on one or multiple quantitative phenotypic traits such as systolic blood pressure (SBP), diastolic blood pressure (DBP), blood triglycerides level (TG), low density lipoprotein (LDL) and high density lipoprotein (HDL) levels, etc. could be of interest. If two or more of these traits are measured in the same set of individuals, they may be correlated as they could be simultaneously influenced by the same gene(s) (pleiotropic effects) and/or environment  in the same individuals ,2. HenceMultivariate approach could be used to jointly synthesize such correlated end points. . It can exploit the between and/or within-study correlation structure to yield more efficient or precise estimates while univariate approach ignores such correlation structure ,4. It hap, of multiple endpoints increases, estimation problem under multivariate random-effect meta-analysis becomes more complex because the effective number of parameters to be estimated is p(p + 1)/2. For example, when p = 3, a 3-variate meta-analysis requires the estimation of a total of six between study variances and correlation parameters simultaneously while a univariate meta-analysis requires estimation of just one between-study variance parameter at a time. Therefore, even when the end points are highly correlated, the use of multivariate approach can be prohibitive or may offer no clear advantage especially when number of studies is small or between-study variances are smaller compared to within-study variances. Despite advantages in theory, recent studies summarizing the empirical meta-analysis studies found that the improvement on the bias or precision of the pooled estimates is not remarkable from multivariate analysis compared to univariate in most applications [However, there are some practical issues with the use of multivariate approach in meta-analysis. First, for a small meta-analysis or for situation where between-study variation is relatively small compared to within-study variation, the multivariate method often estimates the between-study correlation at the boundary of parameter space (−1 or +1) ,8. This ications ,10,11. Fications .Given the above-discussed promises and issues of multivariate meta-analysis, it is not clear when its application may be preferable  to univariate analysis in the setting of genetic association studies such as candidate genes studies, genome-wide association studies (GWASs) or their replication and validation studies. Minor allele frequency (MAF), and genotypic distribution that maintains Hardy-Weinberg Equilibrium (HWE) are important characteristics of such studies. Also, the effects of the most genetic variants on complex traits are very small to moderate. Another important consideration is potentially high degree of heterogeneity in genetic effects . Besidesp (2-variate and 3-variate end points), 2) degrees of between-study correlation, 3) degrees of within-study correlation, 4) levels of heterogeneity, 5) average size of individual study, 6) size of meta-analysis. Each of these scenarios were analyzed under four different aggregate (summary) data availability scenarios: a) all aggregate data are available, b) all aggregate data except estimates of within-study correlations are available, hence are ignored in the meta-analysis, c) aggregate data for some studies are missing at random for end point 2, and d) aggregate data for some studies are missing informatively for end point 2. We evaluated the performance with respect to mean bias, relative mean bias percentage and root mean square error of the pooled estimate of effect and coverage probability of the 95% confidence interval of the effect for each end point via extensive simulation.In this study, we compared the performance of univariate (separate) vs. multivariate (joint) meta-analysis under random-effects (RE) assumption. When heterogeneity exists  allele in a genotype of a single nucleotide polymorphism (SNP) at a specific DNA locus in an individual, and the effects could be we the average increase/decrease in the traits values per one copy increase in X (under additive model of inheritance). Such a meta-analysis could be performed using univariate or multivariate approach.In a meta-analysis of genetic association studies, suppose we are interested in the estimation of overall (average) effects of some factor ith study , suppose jth phenotype  from the kth subject , and xik is the corresponding value of x. Then their relationship in the original study or in a meta-analysis when individual participant data (IPD) are available can be modeled as (can include other covariates as well)βij is the true effect of x on the jth end point (phenotype) and i. In a univariate random-effects (RE) meta-analysis, we are interested in estimating βj, the average of βij's of x on jth phenotype, from m studies. For the observed effect (estimate), j . If fixed-effect (FE) of x is assumed t is the p-dimensional true effects of x jointly on all p phenotypes and Ψi is the p × p residual covariance matrix in study i. In random-effect multivariate meta-analysis, we are interested in simultaneously estimating the average joint effects vector β = t of x on p phenotypes in overall population from m studies. Let Si be covariance matrix of Yi in study i. Under RE model, let’s assume that βi∼MVNp and its estimates Yi|βi∼MVNpβ in study i. Then, we can use the marginal distribution of the joint estimate in study i is Yi∼MVNpβ, Σ + S(i) to estimate the parameter Σ and then compute the estimate of β. Here, Σ, the between-study covariance matrix represents population variation in the studies’ true underlying effects, while Si, the within-study covariance matrix represents variation in the ith study’s results due to repeated sampling or chance. For instance, in bivariate problem:βi1 and βi2 across studies, and Yi1 and Yi2 within study i, respectively. The between-study correlation, ρb, is the correlation between βi1 and βi2 across studies (or populations) and the within-study correlation, ρwi, is the correlation between Yi1 and Yi2 within study i.Multivariate meta-analysis is the generalization of univariate meta-analysis when Si  is assumed to be known in all studies . However, in real meta-analysis it is typically estimated from individual participant data, if accessible, by fitting the ρwi’s might not be available in the corresponding published studies for the reasons: some of the published studies might report the aggregate data on ρwi’s might have to be inferred or estimated indirectly for multivariate meta-analysis [Σ  is estimated before computing the estimate of β . But, in univariate analysis only one parameter jth endpoint before computing the estimate of βj. Restricted maximum likelihood (REML) method is commonly used for estimation of Σ, assuming multivariate normality of random effects ). REML generally produces smaller variance estimates within the realistic parameter space compared to the method of maximum likelihood [Σ [β and its variance are obtained asΣ = 0 is assumed.In a meta-analysis, the within-study covariance matrix analysis  or diffeemployed . For a Rkelihood ,4. Howevkelihood  or a metihood [Σ . Then thWe first generated the IPD data for a range of scenarios varying the study level parameters such as average sample sizes, number of studies, etc. and estimated the summary data  within a study) in each study to ensure that we pool realistic summary data typical of genetic association studies. We then pooled them over all studies, thus performing a two-stage IPD meta-analysis. Estimating aggregate data by generating IPD in individual studies (rather than directly sampling aggregate data using some distributions) has another advantage that it also allows us to vary study level parameters such as sizes of individual studies, and MAF of a genetic variant across studies and maintain the Hardy-Weinberg Equilibrium (HWE) within each study, etc. This in turn allows us to assess the impact of such study level parameters on the performance of methods. However, we also compared the performance of two approaches by directly generating (sampling) aggregate data from some reasonable distributions.X , with two traits (p = 2) and three traits (p = 3). We considered the minor allele frequency (MAF), f = 0.20 at the locus. We considered different scenarios with a set of number of studies (m), and total meta-analysis size (N), as m = 5 and N = 5000; m = 5 and N = 10000; m = 10 and N = 10000; m = 15 and N = 20000; m = 30 and N = 30000, with the average study size, n = N/m. To approximate the practical situation where all studies will not be of equal size and the distribution of minor allele will not be the same across all populations, we considered variable study size (Ni) around n and slightly variable MAF (fi) around f across studies. The distribution of X maintained HWE at p-value ≥ 0.001 in HWE test in each study.In the first stage of IPD meta-analysis, we considered a meta-analytic problem of estimating the effect of βi = t were simulated from Np, where β = t for p = 2 and β = t for p = 3. We considered small to modest genetic effect size βj for trait j from a pool = {0.10,0.15,0.20,0.25,0.30,0.40}. For instance, β1 = 0.10 and β2 = 0.10 in a scenario with p = 2, and β1 = 0.20, β2 = 0.30 and β3 = 0.30 in another scenario with p = 3 was considered. Since the vast majority of causal SNPs might contribute only little and only a few of them contribute considerably to heritability of a quantitative complex trait, this pool of βj represents a reasonable spectrum of heritability, f = 0.20 and small to modest effect  of the estimate of βj in a study with the average size n = N/m and MAF distribution strictly under HWE for f = 0.20 (see j) for the between-study heterogeneity, I2 = 25%,50%, and 75%, respectively. Here, Σ are obtained as ρbjj, from a pool {0.20,0.50,0.60,0.70,0.75}.The study-wise effect vector sk model ). For sukth subject  with genotype x = xik  from study i  for p = 2 or p = 3 scenario as in [αi is a p × 1 vector of intercepts (baseline effects on j traits when x = 0), βi is the p × 1 vector of the true effect of x and Ψi is a p × p residual variance matrix with p diagonal elements as the error variances t for p = 2 and αi = t for p = 3 for all x and m, and let x genotypes, p traits and m studies to ensure the identifiability of the model for comparison purpose in our simulation [Ψi was constructed.).In the first stage of IPD meta-analysis, we simulated IPD trait values io as in ,22,23yimulation ,22,23.  were simultaneously obtained by fitting multivariate linear regression of x in each study.The study-wise estimates Simulation methods and scenarios for two-stage IPD meta-analysis are summarized in Yi’s and Si’s across all studies in each scenario performing both multivariate and univariate meta-analyses under random-effects assumption. For each combination of parameters, we considered four scenarios related to the availability of aggregate data: 1) complete data scenario, 2) complete data scenario but complete data scenario, we utilized all summary data including complete data scenario with ignoring , we set j≠j′ pairs) and re-meta-analyzed under MV framework. This allows us to assess the impact of ignoring within-study correlation when they are missing (not reported) in some or all studies and investigators choose to ignore such correlation rather than inferring them indirectly [missing at random (MAR) scenario, we assumed that about 30%  of studies had randomly missing summary data for end point 2 and chose those studies randomly. Random missing is likely in a meta-analysis of genetic studies if investigators in some studies do not consider estimating and reporting the risk of a genetic variant on some trait(s) that are of interest in the meta-analysis. Under sing informatively (MIF) scenario, we assumed that about 30% (m′ = 0.3m) of the studies had informatively missing (for some reason) summary data for end point 2. This is a typical scenario representing ‘publication bias’ in a meta-analysis of genetic studies, where investigators might not report or journal might not publish insignificant genetic association of a variants with some trait in some studies, whereas significant genetic association in any direction (irrespective whether it is protective or risk) is still more likely to be reported and published. Under this scenario, we identified the first m′ smallest m studies for end point 2 and considered them to be missing.In the second stage of IPD meta-analysis, we meta-analyzed the estimated summary data directly  or usingdirectly  in multim′ missing studies , we considered yl2 = 0, yl2j′ = 0 for j′ ≠ 2  using metafor package in R language. The estimates of Σ in MV meta-analysis and j  [R = 5000 times (number of replications).  ,25. We rj . We also compared the percentage of times We compared the performance of MV and UV approaches with respect to of mean bias, relative mean biases percentage (% bias), and RMSE in of each of '|>.9995 ,18. We aYi and Si) in each study . For this, we considered bivariate case with β = t. We used the same Σ  and average I2 = 50% as in two-stage IPD meta-analysis for the similar scenario. To facilitate the direct generation of realistic Si, we relied on the distribution of summary estimates  and Si1, Si2) across studies) observed in the analysis of IPD data. t from its marginal distribution Yi∼N2 provided in Additionally, we compared the performance of multivariate and univariate approaches via simulation in a few specific scenarios by directly generating (sampling) aggregate data  at which multivariate approaches are thought to offer no clear benefit, are also presented.Comparative performance of multivariate and univariate RE meta-analytic methods in certain key scenarios based on estimation of summary data through IPD data generation and analysis are presented on Tables ρb’s were estimated at the parameter boundary m or N or I2 increased , where RMSE of The percentage of times analysis , which id Tables . Also, tρw ≥ 0.3 resulted in the higher percentage of ρw, ignoring βj’s.Ignoring d Tables –5. Also,Mean bias on s Tables . Howeverispersed  Figs resI2 ≤ 50%) by both approaches for m ≥ 10, where UV tended to underestimate more severely and more frequently produced the estimates at the parameter boundary  in individual replicated data sets   were the same.There was no bias on I2 ≥ 50%), MV approach performed similar or better   at p = 2 and I2 = 50%). These figures also show how smaller, similar or larger the m or weaker ρb or greater I2, estimation of ρb or Tables related) –S5 Figs.However, such estimations in the parameter boundaries did not result in higher mean biases or RMSE of pooled estimates in MV than in UV analysis Tables . The avem and moderate to large within- or between- study correlation. For example, for p = 2,m = 15,ρb = 0.75,ρw = 0.75, β1 = β2 = .1, and j = 1,2) under MIF scenario, MV produced much smaller mean bias . Reason might be that MV requires estimating more parameters (including between-study correlation in between-study variance matrix) simultaneously than univariate one. The estimation of between-study correlation at the parameter boundary was quite often for small or moderate m or I2, in which univariate approach much severely and frequently underestimated the between study variances (as seen in m = 10), and consequently produced smaller standard errors of the pooled estimates. Also, there were only 3 studies with randomly missing summary data when m = 10, which might not be sufficient to produce noticeable benefit of MV over UV approach for such moderate sized meta-analysis. UV analysis offering in general similar or slightly higher coverage for randomly missing end point for small or moderate m might be because it relies on fewer available studies, consequently, producing wider confidence interval . However, for larger meta-analysis (m ≥ 15) with moderate to large heterogeneity (I2 ≥ 50%), such estimation problem were minimal and MV estimates were in general similar or better  for the randomly missing end point.We compared the performance of multivariate and univariate approaches to meta-analysis of genetic association studies for the correlated traits via simulation. When all summary data were available from individual studies, MV offered no clear advantage. Also, MV did not offer noticeable advantage even when summary data for some end points were missing randomly  for modm ≥ 15 with I2 ≥ 50%, where the relative mean bias, RMSE and the coverage probability for missing end point were better, confirming the previous finding in clinical studies setting [β2 > 0 (and would be downwardly biased if β2 < 0 was considered) for the missing end point, a phenomenon known as 'publication bias', in univariate analysis. But, multivariate analysis that assigned null effect for missing summary data  might have borrowed the strength of correlation structure to bring otherwise upwardly biased pooled estimate somewhat towards 0, thus decreasing the degree of both mean bias and RMSE, hence somewhat correcting the impact of publication bias. Despite producing wider confidence interval with using fewer studies, UV method might still have lower coverage than MV method for informatively missing end point, perhaps because the pooled estimate in UV analysis was usually much more biased.The biggest advantage of MV method is seen for informatively missing end point for  setting . For infρb ≥ 0.5). However, we noticed that I2 = 50% in complete data scenario as seen in m ≤ 10 or I2 = 25% at which A previous study  suggestem ≥ 10, and preferably m ≥ 15 studies) with large between-study heterogeneity (I2 ≥ 50%) and moderate to large correlations (|ρw| ≥ 0.5 or |ρb| ≥ 0.5) for an end point with missing summary data in some studies (irrespective of whether it was randomly or informatively missing). However, these results are yet to be seen in real genetic data applications. Also, in real meta-analysis of genetic data, IPD data might not be accessible in one or more studies. Therefore, considering additional data (un)availability scenarios might provide further insights about the performance of these approaches in various real data applications. Comparing these as well as other emerging techniques under univariate and multivariate meta-analysis frameworks in various scenarios mimicking real data applications will be even more helpful for genetic and clinical investigators when they are interested in meta-analyzing two or more correlated end points from genetic association studies.Despite the complexities of the model and parameters estimation, multivariate approach in general can be useful in moderate to large meta-analysis Click here for additional data file.S2 Figρb = 0.75, ρw = 0.5. Symbols and abbreviations: N, total subjects; m, number of studies, β2 and τ2, average effect and between-study standard deviation of true study-wise effects for end point 2, respectively; I2 = degree of between-study heterogeneity; ρb and ρw, true between-and within-study correlations, respectively; MAF, minor allele frequency; SE, standard error; MV, multivariate approach; UV, univariate approach. aSummary data for end point 2 from 3 least significant studies were missing (either not reported or unpublished).Scenario: (TIFF)Click here for additional data file.S3 Figρb = 0.75, ρw = 0.5. Symbols and abbreviations: N, total subjects; m, number of studies, βj and τj, average effect and between-study standard deviation of true study-wise effects for end point j, respectively; I2 = degree of between-study heterogeneity; ρb and ρw, true between-and within-study correlations, respectively; MAF, minor allele frequency; SE, standard error; MV, multivariate approach; UV, univariate approach.Scenario: (TIFF)Click here for additional data file.S4 Figρb = 0.75, ρw = 0.5. Symbols and abbreviations: N, total subjects; m, number of studies, βj and τj, average effect and between-study standard deviation of true study-wise effects for end point j, respectively; I2 = degree of between-study heterogeneity; ρb and ρw, true between-and within-study correlations, respectively; MAF, minor allele frequency; SE, standard error; MV, multivariate approach; UV, univariate approach. aSummary data for end point 2 from 3 studies were missing randomly.Scenario: (TIFF)Click here for additional data file.S5 Figρb = 0.75, ρw = 0.5. Symbols and abbreviations: N, total subjects; m, number of studies, βj and τj, average effect and between-study standard deviation of true study-wise effects for end point j, respectively; I2 = degree of between-study heterogeneity; ρb and ρw, true between-and within-study correlations, respectively; MAF, minor allele frequency; SE, standard error; MV, multivariate approach; UV, univariate approach. aSummary data for end point 2 from 3 least significant studies were missing (either not reported or unpublished).Scenario: (TIFF)Click here for additional data file.S1 File(DOCX)Click here for additional data file.S2 File(DOCX)Click here for additional data file.