Helicobacter pylori infection, and prophylaxis against ulceration from nonsteroidal anti-inflammatory drugs. Other factors might therefore be involved in the pathogenesis of GIB. Patients with GIB have increasing nongastrointestinal comorbidity, so we investigated whether comorbidity itself increased the risk of GIB.The incidence of upper gastrointestinal bleeding (GIB) has not been reduced despite the decreasing incidence of peptic ulcers, strategies to eradicate We conducted a matched case-control study using linked primary and secondary care data collected in England from April 1, 1997 through August 31, 2010. Patients older than 15 years with nonvariceal GIB  were matched to 5 controls by age, sex, year, and practice . All available risk factors for GIB were extracted and modeled using conditional logistic regression. Adjusted associations with nongastrointestinal comorbidity, defined using the Charlson Index, were then tested and sequential population attributable fractions calculated.Comorbidity had a strong graded association with GIB; the adjusted odds ratio for a single comorbidity was 1.43  and for multiple or severe comorbidity was 2.26 (95% CI: 2.14%–2.38%). The additional population attributable fraction for comorbidity  was considerably larger than that for any other measured risk factor, including aspirin or nonsteroidal anti-inflammatory drug use .Nongastrointestinal comorbidity is an independent risk factor for GIB, and contributes to a greater proportion of patients with bleeding in the population than other recognized risk factors. These findings could help in the assessment of potential causes of GIB, and also explain why the incidence of GIB remains high in an aging population. See Covering the Cover synopsis on page 1327.H pylori eradication therapy, the burden of peptic ulcer disease has been decreasing.Helicobacter pylori infection, nonsteroidal anti-inflammatory medications (NSAIDs), and aspirin are believed to be the main causes of nonvariceal upper gastrointestinal bleeding,Historically, nongastrointestinal comorbidity was believed to be associated with stress ulcerationWe have therefore conducted a study aimed primarily at assessing whether comorbidity might have an important role in the etiology of upper GIB. To do this we have conducted a case-control study and formed a model fully corrected for known measured risk factors of upper GIB. We have then calculated the additional explanatory effect of adding comorbidity to our model to understand its effect on bleeding incidence in the general population.We conducted a matched case control study.To provide the detailed longitudinal data and necessary power for this study, we have used the recently linked English Hospital Episodes Statistics data (secondary care data) and General Practice Research Database (GPRD) (primary care data). Because of the comprehensive English primary care system, the population registered to the GPRD is representative of the general English population.International Classification of Diseases, 10th Revision codes K25–K28). The Read codes had high positive predictive values (>95%) for peptic ulcers and upper gastrointestinal complications when validated in English primary care routine records.We have previously published the codes and methods used to define upper gastrointestinal bleeds in this study.Each case was age (±5 years) and sex matched without replacement to 5 controls selected randomly who were alive at the time of the gastrointestinal bleed and registered to the same primary care practice. Controls were required to have been registered with the primary care practice for at least 3 months before the match date to be consistent with the definition for cases.Potential final common causal pathways of an upper gastrointestinal bleed were defined a priori for erosions/ulceration, varices, angiodysplasia, fistula/trauma and coagulopathy, and code lists derived for diagnoses and medications that might be associated with each pathway based on published literature . AlthougComorbidity was defined using the Charlson Index.Unadjusted odds ratios (ORs) were calculated for each exposure using conditional logistic regression to allow for the matched study design.Adjusted ORs for each exposure of interest were calculated with conditional logistic regression adjusting for all exposures in addition to age, PPI use, and previous gastrointestinal procedures. As calendar year, sex, and primary care practice were precisely matched on in the controls, it was not necessary to include them in the model. Comorbidity was added last, and its association with bleeding tested using a likelihood ratio test. The variance inflation factor (a measure of the increase in model variance due to correlation between variables) was calculated for each exposure of interest to assess the effect of correlation between variables. All exposures with a variance inflation factor >5 were excluded from the final conditional logistic regression model.Sequential (or extra) population attributable fractions (PAFs) were calculated for each exposure, using the prevalence among the cases and the respective coefficients from the conditional logistic regression model.Previous studies of risk factor medications, such as NSAIDs,Two additional sensitivity analyses were performed to assess the effect of potential under-reporting. First the analysis was restricted to those older than 65 years old and who were eligible for free prescriptions, to assess the effect of potential under-reporting of nonprescribed NSAID use. Secondly, multiple imputation was used to re-estimate the association with comorbidity by imputing missing values for alcohol and smoking status. Alcohol and smoking were categorised as binary exposures of excess alcohol or current smoking to fit the logistic regression imputation model. All previously extracted exposures were used in the imputation model with addition of the socioeconomic status, and 20 sets of imputations were calculated. Socioeconomic status was measured by the Index of Multiple Deprivation quintiles obtained from linked Office of National Statistics data.Finally, to assess the effect of using the aggregated and weighted Charlson Index, the model was re-estimated to assess the effect of the individual component comorbidities from the Charlson Index.There were 16,355 unique cases identified with a first nonvariceal bleed; 13,372 with specific code in Hospital Episodes Statistics, 10,938 with a specific code in GPRD, and 7955 with a specific code in both datasets. There were 16,304 (99.7%) cases matched to 5 controls each and only 8 cases (0.05%) were not matched to any controls. Median observed time before admission for cases was 7.4 years  compared with 7.5 years  for controls.P < .001 likelihood ratio test). There was strong evidence for an association between the nongastrointestinal Charlson Index and upper GIB after adjusting for all measured risk factors  was 20%, and this was higher in magnitude than for any other measured exposure . The nexThe PAF for comorbidity associated with peptic ulcer bleeds was slightly lower than that for nonulcer bleeds (18% vs 21%), with a higher contribution from previous peptic ulcer bleeds and aspirin and NSAIDs . In contThe crude ORs were re-estimated for medications after excluding cases with nonmedication risk factors and these are shown in Restricting the analysis to those older than 65 years old increased the proportion of cases attributable to the combined effect of all exposures from 48% to 63%, and reduced the additional proportion of cases attributable to nongastrointestinal comorbidity from 19.8% to 16.1%. Re-estimating the model using multiple imputation for missing alcohol and smoking status (modeled as binary exposures) slightly reduced the PAF associated with comorbidity from 22.9% to 22.4%, but when alcohol and smoking status were omitted from the model, the PAF was almost unaltered at 22.2%. Finally, the full model was re-estimated for each component of the Charlson Index . The conThis study has demonstrated that a combined measure of nongastrointestinal comorbidity is a significant independent predictor of upper GIB, even after accounting for all other recognized and measured risk factors. In addition, it explained a greater proportion of the burden of bleeding than any other risk factor in the population. The effect of this combined measure of nongastrointestinal comorbidity was far in excess of that which would be expected from its constituent diseases.The association of comorbidities with upper GIB has been studied previously, but only in smaller secondary care surveys with comorbidity as a confounder and not as the primary exposure. We searched PubMed using variants of comorbidity, etiology, causality, risk factors, and gastrointestinal hemorrhage; however, no studies were identified that set out to address the question of our article. Studies were most frequently designed to measure the association of a single medication while adjusting for any confounding by comorbidity.Two studies assessed a larger range of medications in cross-sectional hospital-based surveys.H pylori,Other studies assessed higher alcohol intake,Our study has a number of important strengths when compared with these previous works because we set out specifically to assess the degree to which nongastrointestinal comorbidity predicts nonvariceal upper GIB after removing the effects of all the available known risk factors in a much larger general population. In addition, we used a method of defining cases and exposures that utilized information from both primary and secondary care, thereby maximizing the evidence supporting each case while not excluding severe events.As our study was one of the first to assess the effect of the burden of comorbidity as a risk factor for upper GIB, no measure of comorbidity had been specifically validated for this purpose. We decided to use the Charlson Index because it is a well-validated score for measuring comorbidity in many different contexts. Other comorbidity scores that could be used, such as the Elixhauser Index or a simple counts of diagnoses, have been used and validated less frequently and in fewer contexts. In addition, some of the other scores also include other outcomes, such as financial cost, which are not necessarily a measure of the severity of disease. The Charlson Index was therefore selected as the most appropriate comorbidity score for our study.H pylori infection, NSAID use, alcohol, and smoking. In the case of H pylori, there was inevitable under-reporting because there was no population screening. However, if the under-reporting of H pylori infection was to explain our study's findings, it would have to be strongly associated with comorbidity, and the evidence for this is conflicting and underpowered.H pylori was minimal after adjustments for confounding.H pylori after adjustments for confounding was not surprising because H pylori will have been eradicated when found.We do need to consider alternative explanations for our observed association of comorbidity with upper GIB. A potential weakness of our study is the inevitably imperfect data on some recognized risk factors that might have caused us to underestimate their importance. The GPRD contains comprehensive recording of all available diagnoses and prescriptions. However, under-reporting is likely to have occurred for NSAID use might also have been under-reported, as NSAIDs can be bought over the counter from a pharmacy without a prescription, potentially explaining the low association between NSAIDs and bleeding in our study compared with a previous meta-analysis.We therefore believe that potential under-reporting of exposures does not explain the association we found between upper GIB and a general measure of comorbidity. This suggests that comorbidity itself, or other factors not included in our study that are associated with comorbidity, might be causing the association. It is possible that other medications not included in the study were responsible for some of this association, however, we are not aware of any additional prescribed or nonprescribed medication that would fulfill the requirements of common usage and a strong association with bleeding. Historically, nongastrointestinal comorbidity itself was commonly recognized as a risk factor for upper GIB.Our findings contrast with current beliefs that the main burden of bleeding in the general population comes from known iatrogenic causes, such as NSAIDs prescribed for analgesia or antiplatelet agents prescribed for cardiac and cerebrovascular disease,In conclusion, the largest measurable burden of upper gastrointestinal hemorrhage in this study was attributed to nongastrointestinal comorbidity. In a proportion of patients, a bleed is an indicator of the burden of their comorbidity, and recognizing this will help guide management, particularly in the absence of modifiable gastrointestinal risk factors. However, our finding also explains why the incidence of nonvariceal bleeding is likely to remain high in an aging population, thereby necessitating continued acute gastroenterology service provision.


Many studies have assessed the association between ocular pseudoexfoliation syndrome (PEX) and vascular disease and produced controversial results. We performed a meta-analysis of epidemiologic studies to evaluate this relationship.Eligible studies that reported the incidence of vascular disease among PEX and control groups were identified via computer searches and reviewing the reference lists of the key articles. The summary odds ratio (OR) and 95% confidence interval (CI) were pooled using a random-effects model. Meta-regression to assess heterogeneity by several covariates and a subgroup analysis on study design and population were performed. Publication bias was tested by Begg's funnel plot and Egger's regression test.Sixteen eligible studies involving 8,533 PEX patients and 135,720 control patients were included in the meta-analysis. All studies were performed primarily in whites with a mean age between 54.7 and 77.1 years. The overall combined ORs for patients with PEX compared with the reference group were 1.72 (95% CI: 1.31 to 2.26) for any vascular disease, 1.61 (95% CI: 1.22 to 2.14) for coronary heart disease, 1.59 (95% CI: 1.12 to 2.23) for cerebrovascular disease, and 2.48 (95% CI: 1.30 to 4.72) for aortic aneurysm. There was evidence of statistical heterogeneity; however, subgroup and sensitivity analyses showed this result to be robust. No evidence of publication bias was observed.The overall current literature suggests that PEX was associated with increased risk of vascular disease. Because of the limitations of the included studies and meta-analysis, the findings need to be confirmed in future research via well-designed cohort studies. Pseudoexfoliation syndrome (PEX) is an age-related disorder characterized by the production and accumulation of an abnormal pseudoexfoliation fibrillar material in various ocular tissues Vascular disease is the leading cause of death worldwide. In recent decades, a number of epidemiological and experimental studies have assessed the association of PEX with vascular disease risk This study was conducted using a predefined protocol and in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) Statement and the Meta-Analysis of Observational Studies in Epidemiology (MOOSE) guideline  [23].The databases of PubMed, Embase, and Web of Knowledge were systematically searched for relevant articles published between 1966 and December 2013. Both medical search headings and open text fields were used to identify articles. No date or language restrictions were applied. The search terms for exposure were “pseudoexfoliation syndrome” and “exfoliative syndrome,” and the search terms for the outcomes were: “cardiovascular disease”, “coronary artery disease”, “myocardial infarction”, “heart attack”, “coronary heart disease”, “vascular disease”, “ischemic heart disease”, “ischaemic heart disease”, “stroke”, “transient ischemic attack”, “transient ischaemic attack”, “vascular accident”, “aneurysm”, and “cerebrovascular disease”. The search strategy was optimized for all consulted databases, taking into account the differences in the various controlled vocabularies as well as the differences of database-specific technical variations (e.g. the use of quotation marks). Once relevant articles were identified, their reference lists were searched for additional articles.A study was considered relevant if it reported quantitative estimates of the unadjusted and (or) multivariable adjusted  odds ratio (OR) with a corresponding 95% confidence interval (CI) for the log relative risk for vascular events. As few studies were eligible and as authors employed heterogeneous endpoints related to vascular disease, we defined a composite of major clinical vascular disease s as the primary endpoint for our meta-analysis. Vascular diseases include coronary heart disease , cerebrovascular disease , aortic aneurysm, and peripheral vascular disease. Unpublished papers, nonhuman studies, letters/case reports, studies enrolling <10 subjects or subjects age <18 years, editorials, reviews, studies lacking raw data, and studies lacking a suitable control group, and studies using inadequate case definition were excluded.Two reviewers independently extracted data using a standardized data-collection form, and any disagreements were discussed. The data collected included the first author, the year of publication, the study design, the population studied, the exposure and outcome evaluated, the number of cases and controls, the association measure, the point estimate and 95% CI, and any adjustment/stratification/matching variables. In studies with overlapping patients or controls, only the latest or the most complete were included.The qualities of included case-control studies were assessed independently by the same two investigators using the Newcastle-Ottawa Scale (NOS) The pooled ORs with 95% CIs were calculated with the method of DerSimonian and Laird using the assumptions of a random-effects model to provide a conservative estimate of the effect owing to potential population differences among the studies. Where possible, data for maximally adjusted risk estimates were extracted. In studies where no appropriate association measure was provided, if possible, the raw aggregated data  were used to estimate unadjusted associations.2 statistic. An I2 value of more than 50% was defined to represent obvious heterogeneity. For the Q statistic, a P value of <0.1 was considered statistically significant heterogeneity. To examine the magnitude of the combined OR in each stratum and its respective test of heterogeneity, subgroup analyses according to study design, geographic area, study quality, number of cases, and levels of adjustment were conducted. Meta-regression analysis was used to investigate the influence of these variables on study heterogeneity across strata. We further conducted a sensitivity analysis to explore possible explanations for heterogeneity and to examine the influence of various exclusion criteria on the overall risk estimate. We also investigated the influence of a single study on the overall risk estimate by omitting one study in each turn.Heterogeneity was assessed using the Q statistic and the IPotential publication bias was assessed by visual inspection of Begg funnel plots in which the log ORs were plotted against their SEs. We also performed the Begg rank correlation test and Egger linear regression test at the p<0.10 level of significance. All analyses were performed using Stata version 12.0 . A P-value <0.05 was considered statistically significant, except where otherwise specified.Upon the literature search and manual review for pertinent references, 511 citations were retrieved and evaluated for inclusion at the title and abstract review stage . From thThe main characteristics of the studies included in this analysis are provided in 2 = 83.3%). When we evaluated the relation of PEX with risk of CHD and angina, the associations were all statistically significant, with pooled OR at a 95% CI of 1.61 (1.22 to 2.14) and 1.71 (1.34 to 2.18), respectively. As for CVD, for four studies, the summary OR at a 95% CI was 1.59 (1.12 to 2.23), with no evidence of heterogeneity . With respect to aortic aneurysm, the pooled OR was 2.48 (95% CI: 1.30 to 4.72), with no significant heterogeneity .To explore further the heterogeneity among studies of PEX and vascular disease, we performed subgroup and sensitivity analyses. When a subgroup analysis was stratified by research design, source of control, sample method, number of cases, geographic region, age, gender, publication year, and adjustment variables, a positive relation between PEX and vascular disease risk was observed in all subgroups except in the POAG subgroup as control patients . There wVisual inspection of the Begg funnel plot did not identify substantial asymmetry. The Begg rank correlation test and the Egger linear regression test also indicated little evidence of publication bias among studies of PEX and vascular disease risk .Pseudoexfoliation syndrome is easily diagnosed by slit-lamp examination on anterior segment changes—whitish deposits on the pupillary margin or anterior side of the lens, along with heavy chamber angle pigmentation. For more than a decade, the scientific debate on the relationship between PEX and systemic vascular disease risk has remained unsettled The results from our subgroup and sensitivity analyses were quite similar and robust, and the associations were neither significantly modified by research design, source of control, sample method, number of cases, geographic region, age, gender, publication year, or adjustment variables nor substantially driven by any single study. A significant positive association was observed in all subgroups except in the subgroup using POAG as control. Although vascular risk may differ between PEX and POAG patients, the non-significant association in the subgroup using POAG as controls was likely due to the small number of studies (n = 3) and, hence, insufficient statistical power. As for individual studies, several studies differed from others in various aspects. For example, in the study by Mitchell et al The underlying mechanisms linking PEX to vascular disease are not fully understood, but several possible biologic mechanisms have been suggested. The accumulation of pseudoexfoliation material in various tissues seen with ageing is one of the proposed causal mechanisms Our analysis provides more precise estimates for vascular disease compared with prior observations by using a large pool of studies and using adjusted estimates. The current meta-analysis is also consistent with clinical reports showing an association between ocular PEX and hemodynamic parameters. Recently, Ulus et al Substantial heterogeneity was observed among studies of PEX and vascular disease risk, which was not surprising given the differences in the characteristics of populations, ascertainment of vascular disease, and adjustment for confounding factors. Nonetheless, we failed to find the major source of study heterogeneity in a sensitivity analysis. In a meta-regression analysis, the number of cases was statistically significant. Interestingly, there was still substantial heterogeneity among studies with the number of cases exceeding 100; an explanation might be that authors used different statistical approaches and adjusted for different potential confounding variables. Of note, seven out of sixteen studies calculated only crude ORs rather than a multivariable analysis controlling for relevant confounders. All of these factors could be relevant when considering the difference in findings. Further evaluation of effect modification is needed in larger studies or individual participant pooled analyses with more power to detect effect modification than our analyses based on study-level characteristics. In addition, since only a few studies were eligible, we pooled results from studies involving different designs and dissimilar definitions of vascular disease. However, nearly all of the papers that were finally included in this meta-analysis were ones that generally had a positive correlation between vascular disease and pseudoexfoliation syndrome.A major strength of our study is that a comprehensive and transparent literature search identified all relevant reports, and the methodological quality of included studies was assessed according to standard and detailed criteria. The inclusion of more than 100,000 control patients and 8,000 PEX cases greatly enhanced the statistical power to provide more precise and reliable risk estimates. Moreover, the association of PEX with vascular disease persists and remains statistically significant in subgroup analysis according to key characteristics and sensitivity analyses based on various exclusion criteria.Limitations in this study should also be emphasized. First, the majority involved retrospective data analyses, which increase the risk of bias associated with the recording of baseline data, the need for imputation, and potential selection bias. The appropriateness of the reference group is of paramount importance, and the ideal comparison group should be similar to the PEX in all ways except in their ocular signs. The ORs of vascular disease and PEX were least when compared to a population of patients with POAG, because POAG itself a risks factor for vascular disease. Individuals with PEX are more likely to seek medical attention and be screened for vascular disease; thus, the recorded increased incidence may be an artifact of more complete detection rather than PEX itself driving the effect. Second, the included studies are not equally designed, and obvious heterogeneity was detected across our study. Although we did not identify the sources of heterogeneity, our subgroup and sensitivity analyses suggest that the results were consistent and robust. Third, residual confounding is of concern. Uncontrolled or unmeasured risk factors potentially produce biases. Although subgroup analyses by adjustment for potential confounders did not materially alter the combined risk estimate, we still cannot rule out the possibility that residual confounding could affect the results because these factors do not explain all of the risk for vascular events. Fourth, because current data in relation to PEX and outcomes for vascular disease are sparse, we were unable to investigate each subtype of vascular disease. Fifth, we cannot eliminate publication bias, though neither Begg's test nor Egger's test suggests publication bias. Finally, most studies were conducted by European and North American populations, with predominately Caucasian participants. It is possible that the strength or existence of the association varies by ethnicity. Therefore, more studies especially well-designed cohort studies are warranty to confirm the findings in this analysis.On the basis of our findings, several questions arise. The first is whether PEX has a causal effect on vascular disease or is only a surrogate marker for other biological risk factors. To answer this question, several issues should be considered, including the use of a standardized vascular disease definition, the interval between the incidence of the two diseases, and adequate control for confounding factors. Second, by what exact mechanisms does PEX increase the risk of vascular disease? Expanding the number of newly recognized, potentially independent risk factors for vascular disease, such as inflammation (as measured by the C-reactive protein), vitamin D level, and hours of regular sleep may be advantageous The findings of this meta-analysis suggest that PEX is associated with an increased risk of vascular disease. Because of the limitations of the included studies and meta-analysis, subsequent well-designed prospective cohort studies that adequately control for confounding factors and detection bias are urgently needed to confirm our results. The underlying mechanisms that link PEX to vascular disease also deserve further investigations.Table S1PRISM Checklist.(DOC)Click here for additional data file.


The G-403A polymorphism in RANTES gene may be involved in the development of coronary artery disease (CAD) through increasing RANTES-mediated leukocyte trafficking and activation. However, studies investigating the relationship between G-403A polymorphism and CAD yielded contradictory and inconclusive results. In order to shed some light on these inconsistent findings, a meta analysis was performed to clarify the role of G-403A polymorphism of RANTES gene in the susceptibility of CAD.A systemic literature search of PubMed and EMBASE was conducted from their inception to March 23, 2012, to retrieve related studies. In addition, Conference Proceedings Citation Index-Science was searched, authors of relevant studies were contacted, and reference lists of the included studies and their related citations in PubMed were reviewed for additional pertinent studies.2 = 65.9%; recessive model: OR = 1.140, 95% CI = 0.774–1.678, I2 = 53.1%; dominant model: OR = 1.000, 95% CI = 0.820–1.21), I2 = 62.6%; AA vs GG: OR = 1.141, 95% CI = 0.734–1.773, I2 = 61.2%; GA vs GG: OR = 0.993, 95% CI = 0.800–1.232, I2 = 64.6%). Subgroup analysis and meta regression indicated that ethnicity and genotyping method accounted for the significant heterogeneity among studies. In the stratified analysis by ethnic group, G-403A polymorphism was found to be associated with increased CAD risk in Caucasian population whereas its protective role was observed in Asian population in some but not all comparisons.A total of 8 eligible studies were identified, with a total of 4252 CAD cases and 2150 controls. There was no evidence of significant association between G-403A polymorphism and CAD risk in any genetic model or pairwise comparisons (additive model: OR = 1.046, 95% CI = 0.883–1.239, IData from the current meta-analysis do not support the existence of a relationship between G-403A polymorphism and the development of CAD, and large sample size study employing unified genotyping method is needed to further evaluate the influence of G-403A polymorphism on susceptibility of CAD. It has been widely accepted that atherosclerosis is an inflammatory disease mediated by intense immunological activity, a process characterized with the accumulation and activation of the inflammatory and immune cells within the vessel wall Regulated on activation, normal T cell expressed and secreted (RANTES), also known as CCL5, belongs to the CC chemokine family and has strong chemoattractant and activating properties for T-lymphocytes and macrophages PubMed, and EMBASE  were searched to identify potentially relevant studies. A search strategy combining both the Medical Subject Heading and text words was used. No language restriction was applied and non–English-language articles were translated before further analysis. The search strategy is listed in Studies were included for the meta-analysis if they satisfied the following criteria: 1) they evaluate the association between G-403A polymorphism in RANTES gene and CAD; 2) they included individuals with CAD and those without CAD; 3) the baseline characteristics were comparable between the two distinct population; and 4) the size of sample, source of control, distribution of alleles, and frequency of genotype were all reported or can be calculated using the data provided in studies.Two investigators independently extracted data from each study with a predefined review form and discrepancies were resolved by consensus of all investigators. Information extracted included: author, year of publication, study design, country of origin, gender composition, mean age, diagnosis of CAD, selection of control , sample size, ethnicity, and genotyping method.The quality of each study was assessed independently by two investigators using the Newcastle-Ottawa quality assessment scale Deviation from Hardy-Weinberg equilibrium (HWE) in the control group was examined using a two tailed chi-square test. Association between G-403A polymorphism and CAD were tested under the following three genetic models: additive, recessive, and dominant. In addition, contrast of genotype AA versus GG, and GA versus GG were also evaluated.2 test was used to quantify inconsistency. A Q statistic P value <0.10 or an I2 value≥50% was considered to represent significant statistical heterogeneity. Data were combined using a Mantel-Haenszel fixed effect method in the absence of heterogeneity; otherwise, DerSimonian and Laird random effect method was used.The statistical heterogeneity among studies was assessed with Q statistic, and IA sensitivity analysis was conducted, after excluding each of the studies in turn, to evaluate whether the results could have been affected significantly by a single study. To explore the potential source of heterogeneity among individual studies and test the effects of study characteristics and methodological quality on the overall effect estimates, subgroup analyses and meta regression were performed stratified by the following factors: source of controls , ethnicity (Caucasians or Asians), definition of cases (coronary angiography (CA) or non-CA), violation of HWE (yes or no), and genotyping methods.Potential publication bias was examined by the asymmetry linear regression of Egger's test and displayed as funnel plot of precision (standard error of log RR) against the treatment effect . Statistical analyses were undertaken using Stata version 12.0 .A total of 220 potentially relevant studies were identified from the following databases in our initial literature search: 66 from PubMed, 154 from EMBASE. Our hand search of the relevant reviews identified an additional citation. Our retrieving in CPCI-S and contacting with authors identified no additional studies. After screening the titles or abstracts, 211 studies were excluded because of duplicate data, other types of publications , or non-relevance research  . The fulAll studies included in the present meta analysis used a case-control design, with a total of 4252 CAD cases and 2150 controls. Characteristics of the included studies are summarized in Using the quality assessment instrument, case-control study was assigned a maximum of 4 points for selection of case and controls, a maximum of 2 points for comparability of cases and controls, and a maximum of 3 points for ascertainment of exposure. Consequently, the theoretical maximum sum score of 9 points could be obtained if a study fulfilled all of the aforementioned criteria in quality assessment. Based on the quality assessment scale for case control studies, two studies had the maximum score of 9 points, four scored 8 points, and two scored 7 points, with a mean score of 8 .A DerSimonian and Laird random effect method was employed in the estimation of overall odds ratio (OR) because of the significant heterogeneity in our initial data synthesis with fixed effect model. The results of meta analysis remained non-significant when each study was sequentially omitted from the analysis, indicating that the results of the present meta analysis were stable. The shape of funnel plots was symmetrical in the additive and dominant genetic model, as well as in contrast of GA versus GG, and the Egger’s test P value was 0.643, 0.519, and 0.429 respectively, suggesting no evidence of publication bias . However2-residual = 0%, Adj R2 = 100%).Since there was significant heterogeneity among individual studies, a univariable regression was conducted to explore the predefined possible source of heterogeneity under additive model. The results of univariable regression suggested that ethnicity and genotyping method were the significant source of heterogeneity , while the heterogeneity in the overall ORs could not be accounted for by source of controls, difference in cases definition or deviation from HWE . IncludiThe present meta-analysis, which involved 4252 CAD cases and 2150 controls, showed no evidence of an association between the G-403A polymorphism and CAD. Of the five overall ORs estimated under different genetic models and genotype contrasts, three showed a trend toward increased risk for CAD (OR>1), one showed an opposite trend toward decreased risk for CAD (OR<1), and the remaining one was a null value (OR = 1). Therefore it was not possible to draw any firm conclutions from these contradictory and not statistically significant results.2 = 0% for RFLP subgroup under both additive and dominant model) or only minor heterogeneity (I2 = 18.8% for no-RFLP subgroup under dominant model) was presented in these statistically significant results, due to the variation in sensitivity and accuracy of different genotyping method, choosing an appropriate one is critical in SNP analysis. RFLP was the most often used method for genotyping RATENT in the present meta analysis because of its relative simplicity. However, RFLP is not a direct method and its discriminatory power depends on the restriction enzymes’ success in achieving recognition and cleavage at restriction sites. Besides, the high reaction temperature needed for RFLP might decrease the sensitivity of assay. Other genotyping methods, such as SSCP, SNaPshot, Taqman and PCR-SSP, were also employed in the studies included in the present meta analysis. Although SSCP has been widely used for genotyping, it is only suitable for screening sequence variations because of its relative low sensitivity Significant heterogeneity existed among studies in all five comparisons, and subgroup analysis as well as meta regression confirmed that ethnicity and genotyping method variation account for all of the heterogeneity. Besides, subgroup analysis stratified by ethnicity, genotyping method, and departure from HWE provided significant and contradictory results in some, but not all, comparisons. G-403A polymorphism seemed to be a risk factor for the development of CAD in Caucasians ethnic group (under additive and dominant model), whereas its protective role in CAD was shown in Asians ethnic group , indicating that the role of G-403A polymorphism in the development of CAD may be ethnicity dependent. However, given the limited studies in Asians ethnic group, such implication needs further scientific justification based on large sample. Similarly, opposite results were also obtained among studies employing different genotyping method. In studies using RFLP method, G-403A polymorphism was shown to be a risk factor (under additive and dominant model) while in studies using non-RFLP method, it was shown to be protective for the development of CAD (under dominant model). Although no heterogeneity (IThe G-403A polymorphism is caused by the substitution of adenine for guanine in the promoter region of the RANTES gene. There has been evidence that the A-403 mutant can lead to increased transcription of RANTES There are some limitations with this meta-analysis that should be considered when interpreting the clinical significance of the results. A notable limitation is that all analyses were conducted at the study level because of the unavailability of individual patient data from the original studies. Therefore, our analysis was based on unadjusted estimates and classic CAD risk factors, including smoking, hypertension, diabetes, and dyslipidemia, can not be adjusted to provide more precise overall risk estimates. Besides, environmental factors such as lifestyle, diet, alcohol consumption, and stress were also unavailable and limited the evaluation of gene-environment interactions. Additionally, unpublished studies were not included in this meta analysis and both the asymmetric funnel plot and Egger’s test indicated the existence of publication bias in two of the five comparisons. Finally, it was likely that the statistically significant results observed in certain subgroup might be due to chance or confounding because of the limited studies included in that subgroup.In conclusion, there was no evidence in support of the hypothesis that the G-403A polymorphism had any influence on the susceptibility of CAD in the current meta-analysis. Although subgroup analysis and meta regression suggested possible different and statistically significant influence of this mutant on CAD in Caucasian and Asian population, further analysis with more homogeneous sample and unified genotyping method is warranted to clarify this potential impact.


The causes of childhood leukemia as the most common malignancy in children are vastly unknown.The aim of this study is to evaluate the relationship between maternal birth characteristics with environmental exposures in childhood leukemia.This is a case-control study which consists of children younger than 18 years old suffering from leukemia who reside at Fars Province of Iran. Patients were individually matched with variables such as age, sex and residence region. In order to evaluate the relationships between each variable and the risk of leukemia, odds ratio(OR) and 95% confidence interval (CI) were estimated using conditional logistic regression.Statistically, the association between risk of childhood leukemia with birth order , pet ownership  and history of leukemia in first and second degree relatives  was significant. However, there was no significant association between daycare attendance, history of miscarriage, number of siblings and history of mother's diagnostic radiology tests with risk of childhood leukemia.Although no definite etiologic factor for acute childhood leukemia has been clearly defined, the contribution of environmental risk factors in the context of genetic predisposition are strongly elucidated. Leukemia is the most common type of childhood malignancy which accounts for approximately 30% of cancer cases in children younger than 15 years old.[3] It isCertain inherited diseases like fanconi anemia, bloom syndrome, ataxia telangiectasia, Schwachman syndrome and neurofibromatosis are associated with a higher risk of developing leukemia. An identThe infectious etiology hypothesis for childhood leukemia may be supported by socioeconomic and seasonal variations in the incidence of childhood leukemia as well as the clustering of leukemia cases.10] Seve Seve10] Knowledge of these particular risk factors can be helpful in alleviating potentially harmful exposures and reducing the risk of diseases. The objective of this study was to examine the relationship between maternal, birth characteristics and environmental exposures with childhood leukemia.In this case-control study, children younger than 18 years old suffering from leukemia including acute lymphoblastic leukemia(ALL), acute myeloid leukemia(AML), and chronic myeloid leukemia (CML) who reside at Fars Province, Southern Iran were included. Their disease had been diagnosed by bone marrow aspiration and flowcytometry between 2005 to 2009 as they were undergoing chemotherapy at Pediatric Hematology-Oncology Ward in a referral Hospital, Shiraz, Southern Iran. The controls chosen by random sampling were individually matched to the cases by caliper matching method with respect to age , sex and residence region. The information which was gathered through an interview with one of the parents included child's birth order, mother's age at the time of childbirth, child's birth interval from the last birth, number of siblings, history of miscarriage, history of leukemia in the immediate and secondary family members, history of mother's diagnostic radiography during pregnancy, child's day care attendance, father's occupation one year before child's birth, level of parents education, history of pet ownership and type of delivery. The birth weight of cases and control were recorded from their vaccination cards. Informed written consent was taken from the parents before entry to the study. In order to examine the relationship between each variable and risk of leukemia, odds ratio (OR) and 95% confidence interval (CI) were estimated using conditional logistic regression. Chi-Square test was used for data analysis. P value less than 0.05 was considered significant. The data were analyzed with SPSS software .Approximately 163 children younger than the age of 18 who suffered from leukemia were eligible for the study. The demographic characteristics of both cases and controls are shown in More cases compared to controls (36 vs 22) had more than 5 years interval from the last birth, but this association was not statistically significant (p=0.46), neither maternal age over 35 years old . Children with family history of leukemia were at a higher risk of developing this malignancy . Also, the research revealed significant association between pet ownership and risk of childhood leukemia .However, no significant association was identified between daycare attendance, miscarriage, number of siblings, and mother’s diagnostic radiography during pregnancy with increased risk of childhood leukemia .Cancers are assumed to be a multi-factorial disease which occur when the genetic and environmental factors interact in a multistage sequence. Leukemia as the most common childhood cancer is particularly subjected to this rule. Although, the recent increase in the childhood ALL cases may be partly explained by more accurate diagnostic practice and better reporting scheme, the role of new environmental risk factors must be more clearly elucidated.The male predominance of leukemia cases in our study was previously reported by other researchers.12] The  The 12] The role of infectious agents in developing leukemia was initially proposed by Greaves. It was rSeveral studies have described an association between increased birth weight and childhood ALL. Researchers believe it is due to the high rate of cell proliferation and risk of malignant transformation.23]24][[24][23][[[24][23]In contrast to what Dockerty and colleagues described, this stuBross and Gibson reported in 1970, that the exposure to pets could be a factor of the development of leukemia. They discovered the increase risk of leukemia among children exposed to sick cats to two-folds. This wasBased on our findings, there is a positive significant link between the history of leukemia among first and second degree relatives, comparable to Ripert's study. SiblingsChild miscarriage and risk of childhood leukemia have been pursued in several studies and have reached contradictory results. While some studies similar to this study did not show any significant associations,19] othe othe19] There are inconsistent results regarding fetal irradiation from prenatal exposure to diagnostic radiographs and risk of childhood leukemia. While Doll and Wakeford estimated a relatively 6% increased risk of cancer per GY,35 results from other studies like ours have not supported this association.37]38] [38] 37][ [38] 37]In this study, we attempted to follow a comparable accuracy principle by informality in data gathering in both case and control groups. However, like other case-control studies, our research has been subjected to error due to biased information, especially in regard to variables dependent to recall.As with much other human diseases, both genetic and environmental factors have been implicated in the etiology of childhood leukemia. Data to support the exact causative factors are often inconsistent and somehow contradictory. This study presented an existence of a link between increased birth order, pet ownership and history of leukemia among first and second degree relatives to be significant. The contradiction presented in different studies regarding different variables is partly explained by the influence of genetic predisposition and leukomogenic translocations. Furthermore, the multi-center matched case-control studies are recommended to clarify the ambiguous aspects of this complex subject.


Meta-analyses are necessary to synthesize data obtained from primary research, and in many situations reviews of observational studies are the only available alternative. General purpose statistical packages can meta-analyze data, but usually require external macros or coding. Commercial specialist software is available, but may be expensive and focused in a particular type of primary data. Most available softwares have limitations in dealing with descriptive data, and the graphical display of summary statistics such as incidence and prevalence is unsatisfactory. Analyses can be conducted using Microsoft Excel, but there was no previous guide available.We constructed a step-by-step guide to perform a meta-analysis in a Microsoft Excel spreadsheet, using either fixed-effect or random-effects models. We have also developed a second spreadsheet capable of producing customized forest plots.It is possible to conduct a meta-analysis using only Microsoft Excel. More important, to our knowledge this is the first description of a method for producing a statistically adequate but graphically appealing forest plot summarizing descriptive data, using widely available software. Meta-analyses and systematic reviews are necessary to synthesize the ever-growing data obtained from primary research. Performing a search on Pubmed limiting to the type of article, the Mesh term "meta-analysis" will wield 4223 results in 2010 only. Although reviews of interventional studies, especially clinical trials, provide the best evidence, there are several situations in which observational studies are the only alternative. Meta-analyses of these studies are becoming more common, particularly after publication of the MOOSE statement [General purpose statistical packages such as SPSS, Stata, SAS, and R can be used to perform meta-analyses, but it is not their primary function and hence they all require external macros or coding. These can be downloaded, but are not always easy for the researcher to understand or customize. Additionally, the first three programs do not have free access, with prices ranging from $250 to over $30,000 depending on version and country. R is a very resourceful open source package, but its use in health is still limited, due mostly to the need of programming instead of a point-and-click interface.There are some software packages specifically developed to conduct meta-analyses. RevMan  is a fre2 can be computed with basic arithmetic operations. Borestein et al [Another option would be to analyze data using directly Microsoft Excel. Although it has a purchase cost, it is usually already installed in most computers, bundled with Microsoft Office package. Most researchers would be uncomfortable entering all the formulas themselves, since they may seem complex at first. However, if the calculations are done in steps, statistics like Q and Iin et al  cites thin et al .The method described here was designed on a laptop with Intel Core Duo 2.2 GHz processor, 4 GB RAM, running Windows Seven 64 bit and Microsoft Office Excel 2007. The spreadsheets were later tested on Excel 2003, with no differences found in either the calculations or graphs.es to follow textbooks standard.The outcome of meta-analyses is the effect summary. However, some reviews may only aim in combining rates or prevalences; technically these cannot be called "effects", since there is nothing "causing" it, and the correct term would be single group summary. We will refer to both these estimates simply as "outcome" in order to avoid confusion, and maintain only the abbreviation as Since we have established that the limitation of the existing software packages is handling descriptive data, we will be using rates in our example so that the difference in the final forest plot is more overt. The data could be the prevalence of smoking in a country or the incidence of myocardial infarction in high risk patients. We chose to use theoretical numbers so we could openly distribute the spreadsheets, test particular formulas and compare results obtained with other software. All formulas are presented in traditional equations and also in excel format.Steps 1 and 2 always require adjustments according to study type and outcome. Columns in light grey in spreadsheet 1 are the ones to be adapted, while columns in dark grey do not require any modification regardless of study type . The necessary adjustments can be easily found on methodological books -10.Cell B14 should be filled with the number of studies being analyzed. There are annotations on the spreadsheet that pop up when the mouse pointer is upon selected cells, so the downloaded file can be used without constant consultation of the full article. The explanation for the formulas and detailing of steps are not present on the spreadsheet though. A recently published paper by Schriger et al  reviewed1. Calculating the outcome D3 = B3/C3 in Excel. It is the same from D3 to D12, and copy and paste will automatically adjust the cell numbers. This copying and pasting should be done for steps 1 through 6 and in step 9 B.1.In our example we have the number of events and the number of subjects in columns B and C, so we can simply compute the rate in column D as 2. Calculating Standard Error (SE)E3 = D3/SQRT(D3*C3).All SE can be derived from the formula 3. Computing variance (Var)2. In Excel, F3 = E3^2.This formula is simple: Var = SE4. Computing individual study weights (w)G3 = 1/F3 in Excel.We must weight each study with the inverse of its variance, so weighted effect size (w*es)5. Computing each H3 = G3*D3This is computed multiplying each effect size by the study weight. If we are not using any corrections on the weight  this equation will result again in the study size for some types of studies. In excel, this will be 2 and w2)6. Other necessary variables (w*esI3 = G3*(D3 ^ 2) and J3 = G3 ^ 2.We will need two other variables in order to calculate the Q statistics (columns I and J of spreadsheet 1). In excel this will be G14 = SUM (G3:G12), H14 = SUM (H3:H12), I14 = SUM (I3:I12), J14 = SUM (J3:J12)Now we need to sum all values of each variable. In our spreadsheet they are in line 14, labeled "Sums": 7. Calculating QThe Q test measures heterogeneity among studies, and works like a t test. It is calculated as the weighted sum of squared differences between individual study effects and the pooled effect across studies, with the weights being those used in the pooling method. Q is distributed as a chi-square statistic with k (number of studies) minus 1 degrees of freedom. Our null hypothesis is that all studies are equal. To test that, we need to calculate Q and compare it against a table of critical values. If our calculated Q is lower than that of the table's, than we fail to reject the null hypothesis (and hence the studies are similar).B17 = I14 - ((H14 ^ 2)/G14) since we already have all the sums.The formula is 28. Calculating I2 was proposed as a method to quantify heterogeneity, and it is expressed in percentage of the total variability in a set of effect sizes due to true heterogeneity, that is, to between-studies variability. The formula is B18 = ((B17 - B15)/B17)*100.The I9. Deciding on effect summary 2 is 49%, so we have moderate heterogeneity [If heterogeneity is low, we can use a fixed effect model, that assumes the effect size is the same in our parameter population, and differences in studies are just from sampling error. However, if we think our sample populations may differ from each other, we can use a random effects model. Many researchers will choose this model even if heterogeneity is low. In our example, Q is higher than 16.919, the critical value for 9 degrees of freedom found in a chi-square distribution, and Iogeneity . We mustA. Fixed effects ModelB20 = (H14/G14). The standard error is B21 = RAIZ (1/G14). With the B22 = B20 - (1.96*B21) and C22 = B20 - (1.96*B21). In our example we will not use these results.Our effect summary is B. Random effects modelv) that represents this.Since we are assuming that variability is not only due to sampling error, but also to variability in the population of effects, in this model the weight of each study will be adjusted with a constant . Now, applying the formula, M16 = (B17 - B15)/(G14 - (J14/G14)).B1. The formula is L3 = 1/((E3 ^ 2)+$M $16). We need the $ to fix cell M16, or else it will change when we copy the equation to cells L4 to L12.B2. Once we have the constant, we can calculate new weight for each study, using v. The results are in columns M, N and O. Applying the Q and I2 formulas we have now an acceptable Q and low heterogeneity. We calculate our effect summary as B3. Now we repeat steps 5 to 8, but using our new weight WF20 = M14/L14, F21 = SQRT (1/L14), F22 = F20 - (1.96*F21) and G22 = F20+(1.96*F21). The confidence intervals are broader than the ones calculated with fixed effect model, however, little change in the effect summary is expected.In excel: Analyzing these numbers in CMA we achieved exactly the same results. - [Additional files Columns A-G have the studies information. The user can insert each study effect size and confidence interval directly into columns D, F and G if he has the data. In our example we copied the calculations from spreadsheet 1, and also the values of the random effects model effect summary.1. Make sure the information is the way we want it displayed. In our example, we wanted the rates in percentages, so column I = column D*100.J2 = I2 - (100*F2) and K2 = I2 + (100* F2). Again, we multiply by 100 to have it in percentage.2. We usually read the lower and upper confidence interval as a value, but excel understands it as a difference to the mean. This is key to obtain a proper forest plot. These values are 3. In order to have each study in a different line, we will assign ordinal numbers to the studies. Our effect summary must be number 1 if we want it in the bottom of the graph. This is done manually in column H of our spreadsheet.4. We are ready to build the graph. Insert > Graph > Scatter Plot. X values will be column I, lines 2-12, and Y values column H, lines 2-12.5. We must now add the error bars. In Excel 2007 this is done in the Layout tab, clicking the "Error Bar" button on the right side. In Excel 2003 we must right click on the data series (points on the graph) and click "format data series", then chose the "X error bar" tab. In this window we mark the option "personalized values", and then assign columns J and K, lines 2 to 12, to the lower and upper value.6. To insert the line marking the summary effect value we will add another data series. First we manually build this data set in the spreadsheet. Then right click on the graph > Select Data. Click on "add", and chose X values as column C, lines 15 to 26, and Y values as columns B, lines 15 to 26. A new set of points will appear on the graph. Right-click on any of the new dots and select "format data series". Then we will choose "no marker" and "solid line" on the Marker Options and Line Color tabs.7. We can now format the X axis, right-clicking on it. In our example we want it to begin on 10 and end on 28, interval of 2 units. It is not our case, but if the researcher is dealing with relative data, then "logarithmic scale" must be marked.8. The graph is ready. The user can format colors, outlines, shadows and sizes. In our example we changed the summary effect to a diamond shape. This is done by selecting only one dot (double click) and then right clicking it.9. For presentation we recommend copying and pasting the graph over a table with study information Figure .We have constructed a guide to aid researchers interested in meta-analyzing data using a spreadsheet. To the best of our knowledge there is no prior step-by-step approach, but it should be noted that all formulas and methodology were previously publicly available.The main limitation of analyzing data in a spreadsheet is the potential for errors by typing incorrect formulas. We believe that a step-by-step approach as those presented in this article with all formulas already incorporated in the excel format can help minimize this possibility. The guide presented also does not handle advanced analyses such as multiple regression. However, this is not frequently used in summarizing descriptive data. All sensitivity analysis must be done manually, including and excluding each study of the effect summary calculations, but this limitation is also present in other softwares.Microsoft Excel is part of the Microsoft Office Package, and therefore it is not free of costs. However, for those who already have the package, this use of Excel could amplify its utility offering an alternative for customizing the graphic presentation of the forest plot.The main limitation of the forest plot is that all studies are represented by squares of the same size, instead of proportional to study weight. We did not feel this could overshadow all other formatting possibilities, since study weight can also be estimated by the confidence interval width.In conclusion, it is possible to meta-analyze data using a Microsoft Excel spreadsheet, using either fixed effect or random effects model. The main advantages of this approach are the understanding of the complete process and formulas, and the use of widely available software. It is also possible and simple to make a forest plot using excel. Since displaying results in a graphically appealing but also statistically correct way is usually a problem to most researchers, we believe the method presented here could be of great use. Figure Project name: Meta-analyses and Forest Plots using a Microsoft Excel spreadsheet: step-by-step guide focusing on descriptive data analysis;Project home page: none;Operating systems: any OS supporting Microsoft Excel;Programming language: not-applicable;Other requirements: Microsoft Excel 2003 or higher;License: Creative Commons Attribution 3.0 Unported (CC BY 3.0);Restrictions to use by non-academics: noneThe spreadsheets mentioned and the CMA files used for comparison of statistics are available as complementary material.The authors declare that they have no competing interests.JLN conceived the article, designed the spreadsheets, and drafted the manuscript. LBM and SCF revised the manuscript and approved the final version.Meta-analyses and forest plots in MS Excel. This file contains both spreadsheets developed.Click here for fileCMA calculations fixed effect. This is a portable document format (pdf) of the calculations performed by the software Comprehensive Meta-Analysis, when calculating the effect summary using fixed effect model. It is provided so readers may compare the calculations and results obtained using Microsoft Excel spreadsheet and the commercial software.Click here for fileCMA calculations random effects. This is a portable document format (pdf) of the calculations performed by the software Comprehensive Meta-Analysis, when calculating the effect summary using random effects model. It is provided so readers may compare the calculations and results obtained using Microsoft Excel spreadsheet and the commercial software.Click here for file


MTHFR 677C>T polymorphism is a genetic alteration in an enzyme involved in folate metabolism, but its effect on host susceptibility to cervical cancer is still uncertain. The aim of this study was to investigate the association between MTHFR 677C>T polymorphism and cervical cancer by performing a meta-analysis.Pubmed, Embase, Web of Science, and the Chinese Biomedical Database (CBM) databases were searched for case–control studies investigating the association between MTHFR 677C>T polymorphism and cervical cancer. Odds ratios (OR) and 95% confidence intervals (95%CI) were used to assess this possible association.2 = 0.0%). The odds for the homozygous TT genotype were greater in cervical cancer cases than in controls in East Asians, which also implied an association between the homozygous TT genotype and cervical cancer in East Asians . Both subgroup analyses and meta-regression analyses suggested ethnicity was the major source of heterogeneity. Publication bias was not evident.11 studies with a total of 1898 cervical cancer cases and 2678 controls were included. Meta-analyses of a total 11 studies showed no association between MTHFR 677C>T polymorphism and cervical cancer using all five genetic models . However, subgroup analyses showed the odds of the homozygous TT genotype were much less in cervical cancer cases than in controls in Europeans, which implied an association between the homozygous TT genotype and cervical cancer in Europeans (For TT versus CC, fixed-effects OR = 0.65, 95%CI 0.45-0.93, P = 0.020, IThis meta-analysis supports an association between MTHFR 677C>T polymorphism and cervical cancer, and the effect of this association may be race specific. Further studies with large sample sizes and careful design are needed to identify this association more comprehensively. Cervical cancer is the third most commonly diagnosed cancer and the fourth leading cause of cancer death in women worldwide [1]. Global cervical cancer incidence increased from 378,000 cases per year in 1980 to 454,000 cases per year in 2010, and killed 200,000 women in 2010. Of these, 46,000 were found in developing countries in the 15–49 year old age bracket [2]. Though there have been many advances in the classification, diagnosis and treatment of cervical cancer, it is still a public health concern [2]. The mechanism of cervical carcinogenesis remains unclear, and multiple environmental and lifestyle factors may increase the risk of developing cervical cancer, including human papilloma virus (HPV) . However, not all of those subjects exposed to these risk factors develop cervical cancer, which suggests genetic factors may also play an important role in the host's susceptibility to the disease.Methylenetetrahydrofolate reductase (MTHFR) converts 5, 10-methylenetetrahydrofolate to 5-methyltetrahydrofolate and directs the homeostasis between DNA synthesis and methylation [5]. The MTHFR 677C>T (rs1801133) polymorphism changes the amino acid 222 from Alanine to Valine and decreases the MTHFR enzyme activity . Individuals with the MTHFR 677C>T homozygous TT have only 25% of MTHFR enzyme activity, which reduces plasma folate levels and elevates plasma homocysteine levels. This homozygous TT genotype may confer elevated plasma homocysteine levels, lifelong DNA hypomethylation, and increased risk of cancer . However, the homozygous TT genotype also can cause a greater availability of 5,10-methylenetetrahydrofolate and thymidine, increasing purine synthesis and leading to smaller probability of DNA strand breakage . Thus, the MTHFR 677C>T TT genotype may therefore confer both an increased as well as decreased risk of cancer . Many case–control studies have been published to assess the association between MTHFR 677C>T polymorphism and cervical cancer, but the available evidence for this genetic association is still weak, owing to disagreements among the conclusions from those studies [12-18]. Small genetic association studies have various designs, different methodologies and insufficient power, and fail to demonstrate a strong correlation, while combining data from all eligible studies by meta-analysis has the advantage of reducing random error and obtaining precise estimates for potential genetic associations . To shed some light on this possible association between MTHFR 677C>T polymorphism and cervical cancer, we presented herein the results of a meta-analysis of published data. We followed the Meta-analysis of Observational Studies in Epidemiology (MOOSE) consensus statement during stages of design, implementation, and reporting of this meta-analysis [21].We conducted a comprehensive search in the Pubmed, Embase, Web of Science, and Chinese Biomedical Database (CBM) databases from their inception through August 22, 2011 and updated on May 6, 2012. We combined search terms for MTHFR 677C>T polymorphism and cervical cancer. Search terms included: ; and . There was no language limitation. All references cited in the included studies were also reviewed to identify additional published articles not indexed in common databases.Eligibility criteria included the following: 1) case–control design with genotyping of individuals with and without cervical cancer 2) cervical cancer confirmed histologically or pathologically 3) sufficient reported genotypic frequencies in both cases and controls for estimating an odds ratio (OR) with a 95% confidence interval (95%CI) 4) the genotype distribution among the control population consistent with Hardy-Weinberg Equilibrium (HWE). In studies with overlapping cases or controls, only the largest study with extractable data was included in the meta-analysis. Studies without relevant data or about cervical intraepithelial neoplasia (CIN) were excluded. Studies investigating cancer progression, severity, phenotype modification, response to treatment, or survival were excluded from this review. Family-based association studies were also excluded due to differing study designs.Two investigators independently extracted data, and disagreements were resolved through consensus. Standardized abstraction sheets were employed for recording data from individual studies. Data retrieved from these studies included the following: author, year of publication, study design, study population, ethnicity of the study population , demographics, genotyping method, adjustment for known confounding variables , and the genotype distribution of MTHFR 677C>T polymorphism in the cases and controls.Quality assessment of case–control studies in this meta-analysis was performed using the Newcastle Ottawa scale (NOS) as recommended by the Cochrane Non-Randomized Studies Methods Working Group [22-24]. This instrument was developed to assess the quality of nonrandomized studies, specifically cohort and case–control studies. Based on the NOS, case–control studies were judged based on three broad perspectives: selection of study groups (1 criterion), comparability of study groups (4 criteria), and ascertainment of outcome of interest (3 criteria). Given the variability in quality of observational studies found on our initial literature search, we considered studies that met 5 or more of the NOS criteria as high quality [22-24].2 statistic were calculated . I2 values of 25%, 50%, and 75% were used as evidence of low, moderate, and high heterogeneity, respectively [27]. The random-effects model was used to pool the data when moderate heterogeneity existed; however, the data were not pooled if high heterogeneity existed . In addition, the fixed-effects model was used to pool the data when low heterogeneity existed . For additional analyses, the cases and controls were subgrouped by ethnicity. Ethnicity was firstly categorized into Caucasians, East Asians, Africans, and others according to racial classifications for genetic studies . Caucasians were further categorized into Europeans and Indians . East Asians mainly included China, Korea, and Japan. Because characteristics of participants were not consistent between studies, we further conducted meta-regression analysis to explore possible explanations for heterogeneity [29]. To validate the credibility of outcomes in this meta-analysis, sensitivity analysis was performed by sequential omission of individual studies or by omitting studies without high quality [32]. Potential publication bias was assessed by visual inspection of the Begg’s funnel plot, and a symmetric plot suggested low risk of possible publication bias [33]. In addition, we also performed the Egger linear regression test at the P < 0.10 level of significance to assess the publication bias [34]. All analyses were performed using STATA version 12.0 . A P value < 0.05 was considered statistically significant, except where specified.We performed a meta-analysis of the association between MTHFR 677C>T polymorphism and cervical cancer under the allele contrast (T versus C), homozygote (TT versus CC), heterozygote (CT versus CC), recessive (TT versus CT and CC), and dominant (TT and CT versus CC) models. We calculated the pooled OR with its corresponding 95%CI to assess this possible association. The significance of the pooled OR was determined by the Z test, and a P value of less than 0.05 was considered significant. In our study, two models of meta-analysis for dichotomous outcomes were utilized: the random-effects model and the fixed-effects model . The random-effects model was performed, using the DerSimonian and Laird’s method [26], while the fixed-effects model was conducted using the Mantel-Haenszel’s method [25]. To assess the between-study heterogeneity more precisely, both the chi-square based Q statistic test and the IA flow diagram illustrating the study selection process is shown in FigureHWE values were more than 0.05, TableTableTable2 = 0.0%)  , which further indentified the association between the homozygous TT genotype and cervical cancer in East Asians. In addition, in the subgroup analysis of Europeans, there was no heterogeneity in any genetic models , the funnel plot’s shape was symmetrical, suggesting no presence of publication bias Figure; in addiMany case–control studies have been published to assess the association between MTHFR 677C>T polymorphism and cervical cancer, but the available evidence is still weak owing to disagreements among those studies [12-18]. Also, no meta-analysis assessing the association between MTHFR 677C>T polymorphism and cervical cancer has been performed. Thus, there is a need to perform a meta-analysis of published data investigating the association between MTHFR 677C>T polymorphism and cervical cancer to shed light on the contradictory findings. This meta-analysis investigating the association between MTHFR 677C>T polymorphism and cervical cancer was based on 11 studies, which gave a larger set of data for detecting significant differences. Meta-analyses of these 11 studies showed that there was no association between MTHFR 677C>T polymorphism and cervical cancer under any of the five genetic models . However, subgroup analyses showed the odds of finding a homozygous TT genotype in subjects was much less in cases than controls in Europeans while the odds of finding the homozygous TT genotype was greater in cases than controls in East Asians, which implied an association between homozygous TT genotype and cervical cancer. Meta-regression analyses suggested ethnicity was the major source of heterogeneity in this meta-analysis. Publication bias was not evident in this meta-analysis. Thus, these findings support an association between MTHFR 677C>T polymorphism and cervical cancer, and there may be a race-specific effect in this association.et al. and Jin et al. reported no association between liver and pancreatic cancers and MTHFR 677C>T polymorphism . Zhang et al. reported the MTHFR 677C>T homozygous TT genotype may be associated with gastric cancer among Asians but not in Europeans [51]. However, Taioli et al. found that the MTHFR 677C>T homozygous TT genotype was associated with colorectal cancer among both Asians and Europeans [50]. Zacho et al. reported an association between MTHFR 677C>T the homozygous TT genotype and any site cancer in Asians but not in Europeans [11]. Our meta-analysis suggests the occurrence of the MTHFR 677C>T homozygous TT genotype is much less common in cases than controls in Europeans, but is greater in cases than controls in East Asians, which implies an association between MTHFR 677C>T polymorphism and cervical cancer. Thus, MTHFR 677C>T polymorphism may exert different effects in different kinds of cancer, and there might be race-specific effect in those associations. Several large-scale meta-analyses combining data from multiple studies have been published investigating the association between MTHFR 677C>T polymorphism and various cancers such as gastric cancer, lung cancer, breast cancer, colorectal cancer, and liver cancer Figure . To explore the possible sources for the high heterogeneity in present meta-analysis, we performed two steps including subgroup analysis and meta-regression analysis. By subgroup analysis and meta-regression analysis, we found ethnicity was the major source of the high heterogeneity in our meta-analysis, which could be explained by the race-specific effect of MTHFR 677C>T polymorphism on susceptibility to cervical cancer. However, ethnicity didn’t explain all heterogeneity in this meta-analysis, and no other sources were found by meta-regression analysis.MTHFR regulates the metabolism of folate, and it is an important factor in DNA methylation and synthesis. Both heterozygous and homozygous variants of MTHFR 677C>T polymorphism have reduced MTHFR enzyme activity compared with the homozygous normal wild-type genotype. Reduction of MTHFR enzyme activity can increase the pool of 5, 10-methylene-THF at the expense of the pool of 5-methyl-THF and impair DNA methylation. Because DNA methylation plays a critical role in regulation of gene expression and maintenance of genomic stability, the aberrations in normal methylation patterns caused by MTHFR 677C>T polymorphism might further result in the development of cervical cancer by impairing the DNA methylation. Thus, there is also biological evidence for the association between MTHFR 677C>T polymorphism and cervical cancer. Our meta-analysis further provides epidemiological evidence for the association above. In addition, there is another common polymorphism in the MTHFR gene known as MTHFR 1298A>C polymorphism. MTHFR 1298A>C polymorphism is also associated with reduced levels of MTHFR enzyme and related to hyperhomocysteinemia, and it may also be associated with the development of cervical cancer [53]. However, there are only two case–control studies published to assess the association between MTHFR 1298A>C polymorphism and cervical cancer , and neither find an association. This lack of association may result from the limited sample size reflected in those two studies, and further studies with larger sample sizes are needed to identify this possible association.Our analysis has several limitations that should be considered when interpreting the findings. Firstly, the main limitation of the meta-analysis was the inclusion of only a few studies with relatively small sample sizes and poor validation. There were only four studies from East Asian and three studies from Europeans. In addition, though we used two methods to assess the publication bias, these two methods may be limited in effectiveness for detecting the risk of publication bias, especially with the limited number of included studies. Thus, more studies with large sample sizes and careful design are needed to further identify this association more comprehensively. Secondly, our main analysis was based on unadjusted estimates owing to the lack of available data. However, a more precise analysis could be performed if adjusted estimates were available in all studies [54]. Furthermore, variability in the study designs and the selection of controls was revealed in present meta-analysis. Though we found that ethnicity was one major source of the high heterogeneity and could explain part of the high heterogeneity, no other sources were found. To get results from meta-analysis of homogeneous studies, more studies with homogeneously constructed design are needed in the future. Thirdly, the association between MTHFR 677C>T polymorphism and cervical cancer may be affected by the different histological types of cervical cancer. However, little data on this aspect were reported in the included studies, and we were unable to make subgroup analyses by histological type. Further studies are needed to identify this association in different histological types of cervical cancer. Finally, gene-gene and gene-environmental factors interactions were not fully addressed in this meta-analysis due to the lack of sufficient data. Previous study suggested an association between methylation of CpG sites in the HPV genome (particularly in L2 and L1) and diagnosis of CIN3, as compared to viral clearance, and the MTHFR gene may have a potential effect on cervical cancer by impairing DNA methylation not only in the host genes but also on the viral genes [55]. Thus, the association between MTHFR 677C>T polymorphism and cervical cancer could be affected by host HPV status, and there is high probability of gene-HPV interaction effects. In addition, several other gene polymorphisms are also associated with cervical cancer, and there may be gene-gene interactions . Future studies may further assess the possible gene-gene and gene-environmental interactions in the association between MTHFR 677C>T polymorphism and cervical cancer.In conclusion, our meta-analysis supports an association between MTHFR 677C>T polymorphism and cervical cancer, and there may be a race-specific effect in this association. In addition, further studies with larger sample sizes and careful design are needed to identify this association more comprehensively.None of the authors have any conflict of interests to declare.ZD, MQ and GJ carried out the meta-analysis study, drafted the manuscript and involved in revising the manuscript critically for important intellectual content. SS and WJ participated in the design of the study and revised the manuscript. GL carried out the meta-analysis study and drafted the manuscript. LZ participated in the design of the study, drafted the manuscript and revised the manuscript. All authors read and approve the final manuscript.No external funding was either sought or obtained for this study.The pre-publication history for this paper can be accessed here:http://www.biomedcentral.com/1471-2407/12/467/prepub


CCKAR) gene variation rs1800857 and language lateralization assessed using the dichotic listening task. Individuals carrying the schizophrenia risk allele C of this polymorphism showed a marked reduction of the typical left-hemispheric dominance for language processing. Since the cholecystokinin A receptor is involved in dopamine release in the central nervous system, these findings suggest that genetic variation in this receptor may modulate language lateralization due to its impact on dopaminergic pathways.Schizophrenia is a psychiatric disorder associated with atypical handedness and language lateralization. However, the molecular mechanisms underlying these functional changes are still poorly understood. Therefore, the present study was aimed at investigating whether variation in schizophrenia-related genes modulates individual lateralization patterns. To this end, we genotyped 16 single nucleotide polymorphisms that have previously been linked to schizophrenia on a meta-analysis level in a sample of 444 genetically unrelated healthy participants and examined the association of these polymorphisms with handedness, footedness and language lateralization. We found a significant association of the cholecystokinin-A receptor ( Schizophrenia is a complex psychiatric disorder that is likely to be caused by a combination of environmental and genetic factors CCKAR) gene which has been shown to be related to these symptoms However, despite the multitude of studies reporting atypical handedness and language lateralization in schizophrenic patients, the molecular mechanisms underlying these functional changes are still poorly understood. Therefore, the aim of the present study was to examine the role of schizophrenia susceptibility genes for handedness and language lateralization. To this end, we genotyped 16 single nucleotide polymorphisms (SNPs) that have previously been linked to schizophrenia on a meta-analysis level (see section 2.2 Genotyping and Four-hundred and forty-four (n = 444) healthy participants of Caucasian descent for at least two generations participated in the present study (257 women and 187 men). Participants had no history of any psychiatric or neurological diseases as assessed by experienced psychologists applying a psychiatric screening  and were genetically unrelated. The mean age was 23.71 (range: 18–34 years). Participants were mainly university students, and all of them were native German speakers. In order to ensure that all participants had normal hearing capability (a prerequisite for the dichotic listening task) audiometric screening was performed before the start of the experiment. Hearing thresholds were screened at 750, 1500 and 3000 Hz using a MA 25 audiometer . Only individuals with hearing thresholds below 30 dB for all frequencies and inter-aural differences below 15 dB were included in the cohort.All participants gave written informed consent and were treated in accordance with the declaration of Helsinki. The study was approved by the ethics committee of the medical faculty of the Ruhr-University Bochum.http://www.szgene.org/ April 2010) which lists all genetic loci associated with schizophrenia. All polymorphisms included in the present study had at least one nominally significant meta-analysis result in SZGene. The following SNPs were selected: CCKAR rs1800857, DAOA rs2391191, DAOA rs778293, DRD1 rs4532, DRD4 rs1800955, DISC1 rs3737597, GABRB2 rs6556547, MDGA1 rs11759115, NOTCH4 rs3131296, NRGN rs12807809, PGBD1 rs13211507, PLXNA2 rs841865, RELN rs7341475, TCF4 rs9960767, TPH1 rs1800532, ZBTB42 rs3803300. Further methodological details and primer sequences are available upon request.DNA was isolated from exfoliated cells that were brushed from the oral mucosa of the participants. DNA isolation was conducted using a QIAamp DNA mini Kit  according to the protocol provided by Qiagen. SNP's were genotyped using polymerase chain reaction (PCR) and differential enzymatic analysis was performed using the PCR restriction fragment length polymorphism method. Candidate SNPs were chosen from the SZGene database  that were simultaneously presented (one syllable to each ear) using headphones. The syllables were matched for voice-onset time and had a standardized mean duration of 350 ms. They were presented at 80 dB, and the inter-stimulus interval was two seconds. Before beginning the task, participants were instructed to indicate as quickly and accurately as possibly which syllable they heard best on each trial. To this end, participants had to press one of six keys labeled with the six syllables on a customized response box. The task started with two practice runs of 12 trials each, which were excluded from later analysis. Subsequently participants performed four test blocks of 30 trials each, with each of the 30 possible dichotic combinations of the syllable pairs being applied once per test block. Thus, overall the task consisted of 120 test trials. To control for possible handedness effects, two of the test blocks were answered with the left and the other two with the right hand, in counter-balanced order. Moreover, to minimize effects of possible aural differences between the left and the right headphone channels, the headphones were reversed for two of the test blocks in counter-balanced order. In analogy to the LQ calculated for limb preferences, a dichotic laterality index LI;  was calchttp://ihg.gsf.de/cgi-bin/hw/hwa1.pl).All SNPs conformed to Hardy-Weinberg equilibrium , with the exception of the DISC1 rs3737597 and the DRD4 rs1800955 (both p's<0.05) as calculated using the online version of DeFinetti  and a mean footedness LQ of 43.48 (+/−2.01), both indicating that most participants preferred to use their right hand or foot. The mean dichotic listening LI was 39.53 (+/−1.55), indicating that on average participants had a right ear advantage, reflecting left hemispheric language dominance. The statistical analyses were performed assuming a dominant effect for each polymorphism. Thus, the heterozygous and the rarely observed homozygous genotypes were combined for statistical analysis. LQs for handedness and footedness and LIs for the dichotic listening task were analyzed using independent-samples two-sided t-tests see .CCKAR rs1800857 SNP, the combined TC/CC group (n = 113) had a lower dichotic listening LI (37.32+/−1.86) than individuals with the TT genotype (n = 329)  = −3.39; p = 0.0008). For the PLXNA2 rs841865 SNP the homozygous GG group (n = 311) had a lower dichotic listening LI (37.12+/−1.86) than the combined AA/AG group (n = 131)  = 2.47; p = 0.01) and for the TCF4 rs9960767 SNP the combined AC/CC group (n = 57) had a lower dichotic listening LI (30.77+/−3.85) than the homozygous AA group (n = 386)  = 2.23; p = 0.03). A footedness effect was observed for the TPH1 rs1800532 SNP. Here, the homozygous CC group (n = 160) had a lower LQ (37.19+/−3.84) than the combined CA/AA group (n = 283)  = 2.44; p = 0.02).Overall, four effects reached significance at the p<0.05 level. For the CCKAR rs1800857 SNP for dichotic listening was the only effect that had a p-value below p = 0.001325  this effect was further investigated using a 2×2×2×2×2×2 repeated-measures' analyses of variance (ANOVA) with the three within-subjects factor ear , response hand , headphones position  as well as the three between-subjects factors genotype , handedness  and gender . Overall, the participants showed a clear right ear advantage  as indicated by a main effect of ear  = 53.07; p<0.000001; η2 = 0.11). Moreover, the key interaction ear by genotype  = 16.87; p = 0.000048; η2 = 0.037), indicating that the combined TC/CC group had a reduced right ear advantage  compared to individuals with the TT genotype . Moreover, the interaction ear by handedness reached significance  = 10.48; p = 0.0013; η2 = 0.037), reflecting the well-known fact that left-handers are more likely to show a atypical right-hemispheric language dominance than right-handers are. All other main effects and interactions failed to reach significance, showing that the specific effect of CCKAR gene variation on performance in the dichotic listening task was neither affected by gender or handedness, nor by headphone position or the hand used to react.Since the effect of the type see  reached CCKAR gene and language lateralization assessed with the dichotic listening task. While we observed the frequently reported right-ear advantage in the dichotic listening task CCKAR, since we found a significant effect of the rs1800857 polymorphism in CCKAR on language lateralization. The polymorphism rs1800857 is located in intron 1 near the intron 1/exon 2 boundary (IVS1-5T>C) and could therefore have an impact on the mRNA splice acceptor site sequence. Yet so far, functional studies found no effect on the splicing efficiency of exon 2 in the CCKAR gene The aim of the present study was to investigate the relevance of sequence variations in different schizophrenia-related genes for language lateralization and limb preferences. The analysis of 16 schizophrenia-related polymorphisms revealed an association between the In line with our hypothesis that those genotypes which have previously been shown to be related to an increased risk of schizophrenia should be linked to reduced or reversed lateralization, individuals carrying at least one schizophrenia risk allele C CCKAR (located at 4p15.1 - p15.2) encodes the cholecystokinin A receptor which binds non-sulfated members of the cholecystokinin family of peptides CCKAR an interesting candidate gene for schizophrenia risk, since a theory about the origins of schizophrenia postulated that it is the result of excessive dopaminergic activity in the brain CCKAR had been linked to schizophrenia risk and especially positive psychotic symptoms CCKAR and auditory hallucinations might explain why we found an effect of CCKAR on language lateralization. Moreover, the dopaminergic system has been linked to hemispheric asymmetries, with administration of the dopamine antagonist chlorpromazine leading to a reduction of emotional lateralization CCKAR rs1800857 polymorphism is not clear at present, our results nevertheless suggest that variation in CCKAR may modulate language lateralization potentially by influencing dopaminergic pathways.Functionally, CCKAR and language lateralization, the effect size of the ear by genotype interaction indicated that only about 3.7% of the variance in the dichotic listening data could be explained by genetic variation. This rather low amount of explained variance and the fact that there was no association of the CCKAR polymorphism with handedness make an interesting implication regarding the inheritance of language lateralization and handedness. In contrast to theories that assume a common monogenic background for these two traits GRIN2B variation rs1806201 and language lateralization with a variance explanation of 2% PLXNA2 as well as the rs9960767 SNP within TCF4 represent promising candidate polymorphisms for future studies. Moreover, our results indicate that the SNP rs1800532 within TPH1 is a promising candidate polymorphism for the investigation of the genetic basis of footedness, while no effects regarding handedness have been observed.While we found a significant association between variation in CCKAR gene modulates language lateralization, with the schizophrenia risk allele C of the rs1800857 polymorphism being related to reduced functional asymmetry. Replication in independent healthy cohorts, and especially in schizophrenic patients with and without auditory hallucinations are needed in order to further validate the present results and would be particularly helpful in order to understand the complex relationship of CCKAR, dopamine and language lateralization.In summary, the present study shows that variation in the


Alpha-actinins are an ancient family of actin-binding proteins that play structural and regulatory roles in cytoskeletal organization. In skeletal muscle, α-actinin-3 protein is an important structural component of the Z disc, where it anchors actin thin filaments, helping to maintain the myofibrillar array. A common nonsense polymorphism in codon 577 of the ACTN3 gene (R577X) results in α-actinin-3 deficiency in XX homozygotes. Based on knowledge about the role of ACTN3 R557X polymorphism in skeletal muscle function, we postulated that the genetic polymorphism of ACTN3 could also improve sprint and power ability.We compared genotypic and allelic frequencies of the ACTN3 R557X polymorphism in two groups of men of the same Caucasian descent: 158 power-orientated athletes and 254 volunteers not involved in competitive sport.The genotype distribution in the group of power-oriented athletes showed significant differences (P=0.008) compared to controls. However, among the investigated subgroups of athletes, only the difference of ACTN3 R577X genotype between sprinters and controls reached statistical significance (P=0.041). The frequencies of the ACTN3 577X allele  were significantly different in all athletes compared to controls.Our results support the hypothesis that the ACTN3 577XX allele may have some beneficial effect on sprint-power performance, because the ACTN3 XX genotype is significantly reduced in Polish power-oriented athletes compared to controls. This finding seems to be in agreement with previously reported case-control studies. However, ACTN3 polymorphism as a genetic marker for sport talent identification should be interpreted with great caution. MSTN), angiotensin converting enzyme gene (ACE), peroxisome proliferator-activated receptor-γ coactivator 1α (PGC-1α), hypoxia-inducible factor-1 (HIF-1) and others (ACTN3), which encodes the protein a-actinin-3.Twin studies indicate that muscle function phenotypes are highly heritable  and varid others . Therefod others . One of Alpha-actinins are an ancient family of actin-binding proteins  that plaACTN3 R577X (dbSNP rs1815739) that results in absence of α-actinin-3 in more than one billion people worldwide, despite the ACTN3 gene being highly conserved during human evolution. Though this genetic variation is not associated with any known disease phenotype, the α-actinin-3-deficient XX genotype is believed to preclude top-level athletic performance in ‘pure’ power and sprint sports .The first evidence that a mononucleotide difference in DNA sequence was associated with power ability referred to the R577X polymorphism of the ACTN3 may have a beneficial effect on skeletal muscle function .As a control group, samples were prepared from 254 unrelated volunteers . The athletes and controls were all Caucasian to ensure there was no ethnicity skew and to overcome any potential problems of population stratification.The buccal cells donated by the subjects were collected in Resuspension Solution  with use of Sterile Foam Tipped Applicators . DNA was extracted from the buccal cells using GenElute Mammalian Genomic DNA Miniprep Kit  according to the producer protocol.Dde I endonuclease  in a condition recommended by the supplier  were similar to those reported in previous studies on Caucasian populations  compared to controls. However, among the investigated subgroups of athletes, only the difference of ACTN3 R577X genotype between sprinters and controls reached statistical significance (P=0.041)ulations . The disACTN3 577X allele  were significantly different in all athletes group compared to controls. This trend was worse when comparing each subgroups of investigated athletes to controls separately . Furthermore, none of the top sprinters harboured the XX genotype.The association between the RR genotype of athletes . The comP=0.017) and genotypes (P=0.016) between elite power-orientated athletes and the control group . It is worth to mention that all of the investigated Olympic/European- level sprinters had at least one 577R allele of ACTN3.Another study suggested that the presence of α-actinin-3 protein had a positive effect on power performance. A report by Papadimitriou et al. (2008) showed statistically significant differences in the frequencies of alleles  reported a case of a Spanish elite long jumper (two times Olympian) whose genotype for the ACTN3 gene is XX. The similar finding was made by However, the role of the ACTN3 R577X allele could be one of the factors influencing power-oriented sport disciplines. Such findings have important implications for understanding of molecular mechanisms underlying the predisposition to high power potential and support the hypothesis that the presence of α-actinin-3 has a beneficial effect on the function of skeletal muscle in generating forceful contractions at high velocity  statistically significant differences in genotype distributions were only demonstrated in sprinters. This fact can be explained by the specificity of performed movements and big loads, resulting in a greater risk of injury in sprinters than in swimmers or weightlifters. Presumably, that is the reason why having at least one ACTN3 R577 allele may also have a beneficial role in swimmers and weightlifters. Unfortunately, the relatively small size of subgroups in this study can cause some ambiguity in the obtained results. For example, the frequency of allele ACTN3 577X in swimmers (54) was 31.48 % and was statistically insignificant (p= 0.085), although, it was only 0.79 % lower than the frequency of this allele in all the examined athletes (158) which proved to be highly statistically significant (p= 0.005)). Therefore, our conclusions should be supported with more experimental studies on ACTN3 polymorphisms in elite athletes.On the other hand, the differences in the genotype distribution between the subgroups were not statistically significant. This fact suggests that the