P=0.00086). 22q11.2 duplications represent the first putative protective mutation for schizophrenia.A number of large, rare copy number variants (CNVs) are deleterious for neurodevelopmental disorders, but large, rare, protective CNVs have not been reported for such phenotypes. Here we show in a CNV analysis of 47 005 individuals, the largest CNV analysis of schizophrenia to date, that large duplications (1.5–3.0 Mb) at 22q11.2—the reciprocal of the well-known, risk-inducing deletion of this locus—are substantially less common in schizophrenia cases than in the general population (0.014% vs 0.085%, OR=0.17,  There are several reports that the phenotypic spectrum of 22q11.2dup is wide, including apparently unaffected transmitting parents.12 As well as having a variety of physical manifestations, 22q11.2dup is reported to associate with ID and developmental delay in children13 and a wide range of psychiatric and behavioral abnormalities have been reported including attention-deficit hyperactivity disorder and autism, as well as other social and behavioral problems.14 The prevalence of the duplication in adults with psychiatric disorders has not been widely studied.Given the non-allelic homologous recombination mechanism by which deletions are produced, it is not surprising that reciprocal 22q11.2 duplications also arise in human populations,15 . Full details of CNV calling and quality control are provided in the In the present study, we have established the rate of 22q11.2dup in a discovery sample of 6882 schizophrenia cases and 11 255 controls. The schizophrenia cases were genotyped on Illumina HumanOmniExpress-12v1 or HumanOmniExpressExome-8v1 arrays, and have been described elsewhere15 . The con15 . CNVs weP=0.017). By way of contrast, we found reciprocal 22q11.2 deletions (a known strong risk factor) in 20 schizophrenia cases (0.29%) but in zero controls. No other CNV at any locus in the genome was found to be a putative protective factor at a nominal level of significance in this sample (data not shown).In our discovery sample, we found no 22q11.2 duplications in schizophrenia cases (0%) but 10 (0.089%) in controls . The age of onset for the schizophrenia cases carrying the 22q11.2dup was 34, 28 and 43 years. Two of these cases have a history of seizures and none of them had any additional known pathogenic CNVs. Of the controls with a 22q11.2dup for which we have psychiatric data (n=6 from MGS sample), none had histories compatible with schizophrenia, schizoaffective disorder or major affective disorder. All graduated from high school and several had higher education degrees .In order to critically evaluate the reduced frequency of 22q11.2dup in cases in additional cohorts, we obtained data from the largest available CNV data sets known to us, in total comprising 14 256 additional cases and 14 612 additional controls. In this independent cohort, 22q11.2dups were also significantly rarer in cases than controls . If the duplication resulted in phenotypes inconsistent with inclusion in studies, we would expect greater depletion in controls, which are typically screened for health more intensively than cases, consistent with the finding of no 22q11 deletions among our controls. Finally, in the genome-wide analysis of our discovery sample, we also obtained evidence to support the association of deletions at 1q21.1, NRXN1, 3q29, 15q11.2, 15q13.3, 17q12 and 22q11.2 and duplications at 1q21.1, Williams–Beuren syndrome region, Prader–Willi/Angleman syndrome region, 16p13.11 and 16p11.2.21 These CNVs have all been associated with a similar range of neurodevelopmental phenotypes and our findings therefore argue strongly against the conclusion that our methods of ascertainment precluded the inclusion of cases carrying 22q11.2dup.Our study therefore identifies as the first putative protective mutation for schizophrenia duplications of the genomic segment that, when deleted, is the most potent genetic risk factor for the disorder. We can discount on several grounds the alternative explanation that those with 22q11.2dup have such a severe neurodevelopmental phenotype as to preclude a diagnosis of schizophrenia. First, as noted above, unaffected carriers are observed frequently (0.1% of controls across a large number of studies of psychiatric and non-psychiatric phenotypes). Second, the highly variable phenotype observed in 22q11.2dup carriers is often milder than that seen with many other schizophrenia-associated CNVs, as evidenced by its inheritance from an apparently unaffected parent in 69–74% of cases.In all, 3 out of the 21 138 cases tested carried the 3 Mb 22q11.2 duplication, indicating that its putative protective effect is incomplete. However, our data suggest that its strength of effect may be strong, with an odds ratio less than 0.20 and an upper confidence limit of 0.56. The three case carriers had no additional pathogenic CNVs and we must assume that the development of schizophrenia resulted from other genetic or environmental factors. It was not possible to identify any particular characteristics of the cases carrying duplications but given their rarity power to do so was extremely limited.1 The present study in contrast provides a clear, opposite-direction dissociation between schizophrenia risk and both ID and autism spectrum disorder, both of which are more common in 22q11.2dup than in controls , suggests that the dosage-sensitive gene or genes might not just point the way to treatment, it may also hold clues to enhancing resilience among those who would generally be thought to be of elevated risk of the disorder.To our knowledge, 22q11.2dup is the first putative protective mutation for schizophrenia that has been described in the literature. Our study suggests the existence of one or more dosage-sensitive gene in the duplication with the capacity to reduce risk of schizophrenia with implications here for further studies aimed at identifying targets for treating the disorder. This finding is of additional interest as this mutation does not appear to be similarly protective against the other neurodevelopmental phenotypes with which schizophrenia-associated CNVs are frequently associated.controls . Neither22 The identification of the dosage-sensitive gene(s) at 22q11.2 and the implication of risk and protective mechanisms is therefore an important direction for research—particularly as pharmacological intervention might offer protection from schizophrenia. Our study was limited in its scope to implicate specific genes and possible mechanisms because CNVs at this locus affect multiple genes and the three duplications observed in cases of schizophrenia all involved the whole 3 Mb region. Further genetic studies on larger samples might inform this issue but it seems likely that a detailed understanding will only be provided by mechanistic studies in deletion and duplication carriers and in animal and cellular models.Although large CNVs associate to risk of many disorders, it is generally unknown whether such effects arise from alterations of gene dosage or from other mechanisms. The lack (to date) of point mutations that phenocopy the neurodevelopmental and psychiatric effects of large deletions has invited alternative hypotheses, such as large-scale disruptions of chromatin or chromosomal pairing. Our finding that the reciprocal deletion and duplication of the same locus have potent risk and potential protective effects, respectively, for schizophrenia supports the hypothesis that one or more genes at 22q11.2 are dosage sensitive.


Recent genetic association studies have implicated several candidate susceptibility variants for schizophrenia among general populations. Rs1344706, an intronic SNP within ZNF804A, was identified as one of the most compelling candidate risk SNPs for schizophrenia in Europeans through genome-wide association studies (GWASs) and replications as well as large-scale meta-analyses. However, in Han Chinese, the results for rs1344706 are inconsistent, and whether rs1344706 is an authentic risk SNP for schizophrenia in Han Chinese is inconclusive. Here, we conducted a systematic meta-analysis of rs1344706 with schizophrenia in Chinese population by combining all available case-control samples (N = 12), including a total of 8,982 cases and 12,342 controls. The results of our meta-analysis were not able to confirm an association of rs1344706 A-allele with schizophrenia . Such absence of association was further confirmed by the non-superiority test (p = 0.0003), suggesting that rs1344706 is not a risk SNP for schizophrenia in Han Chinese. Detailed examinations of individual samples revealed potential sampling bias in previous replication studies in Han Chinese. The absence of rs1344706 association in Han Chinese suggest a potential genetic heterogeneity in the susceptibility of schizophrenia on this locus and also demonstrate the difficulties in replicating genome-wide association findings of schizophrenia across different ethnic populations. Schizophrenia is one of the most severe complex psychiatric disorders, and a lifetime prevalence of schizophrenia is estimated at 0.70% to 1.10% in general populations worldwide −11, odds ratio = 1.12) −12, odds ratio = 1.16) −9, odds ratio = 1.25) Schizophrenia is widely acknowledged to be a polygenic disorder, involving both common variants with low effect size and rare variants with high effect size −11), and the result was more strengthened when bipolar disorder samples were added into the meta-analysis (p = 4.1×10−13) although the effect size is relatively small  Rs1344706 was firstly identified by a GWAS of a sample from the UK, in combination with a follow-up replication study among different world populations The significant association of rs1344706 was firstly replicated in a small Han Chinese sample  To test whether rs1344706 is a real risk SNP for schizophrenia in Chinese population, in the present study we conducted a comprehensive meta-analysis by combining all available Han Chinese case-control samples .), a total of nine studies in Chinese population were identified and included in the meta-analysis Using our literature search approaches  was used to detect the presence of potential publication bias. Begg’s funnel plot Figure 2We extracted the data for mean age and gender ratio from each study Table 1,To further test if the inconsistent association of rs1344706 in Chinese was caused by other factors, e.g. geography, we first ascertained the sample collection area of each study according to information provided in their published papers and categorized these area into Northern, Central and Southern China, and conducted regression analysis with the odds ratios. The result was not significant (p = 0.47). Furthermore, we obtained the latitude of each sample collection area and tested their associations with the odds ratios for rs1344706, and still no significant associations were observed (p = 0.40).Figure S1–A) on our total sample size using the following assumptions: 8,982 patients with schizophrenia and 12,342 controls, two-tailed α = 0.05, and the frequency of A-allele in Chinese (0.5206). Given the inconsistent odds ratios of rs1344706 for schizophrenia in Han Chinese, we used two commonly observed odds ratios in genetic association studies, 1.10 and 1.20. The present sample size revealed a >92% power of detecting a significant association of allele given an odds ratio of 1.10 (corresponding to a “weak” gene effect). When an odds ratio of 1.20 was assumed, i.e. a “weak to moderate” gene effect, our sample size showed >99% power to detect significance (α<0.05).Before performing the meta-analysis, we conducted a power analysis , and meta-analysis (Z-score test) of the combined samples was assessed with the random-effect model. By combining the twelve samples in the meta-analysis, we found that rs1344706 was not associated with schizophrenia . Furthermore, we noticed that when the Xi’an sample was removed from the meta-analysis, the estimates of the p-value for the heterogeneity test increased from 0.002 to 0.03, suggesting that the genetic heterogeneity was mainly caused by the Xi’an sample, and when the Xi’an sample was removed from the meta-analysis, the p-value remained non-significant .‘Leave-one-out’ sensitivity analysis to determine whether the meta-analysis results were driven by any sample data and therefore suggestive of a ‘winner’s curse’ phenomenon Non-superiority test was conducted to confirm the absence of association between rs1344706 and schizophrenia in Chinese. The null hypothesis is that the frequency of A-allele at rs1344706 in schizophrenia patients is greater by 3% than that in controls. The 3% was set based on the reported differences of rs1344706 A-allele between schizophrenia patients and healthy controls in the discovery UK GWAS The non-superiority p-values are presented −11), though again, the sample mostly consisted of European subjects Recently, O’Donovan et al. undertook a GWAS with 479 UK schizophrenia cases and 2,937 controls in tandem with a follow-up replication study of 16,726 additional subjects, and found that rs1344706 in ZNF804A is significantly associated with schizophrenia We conducted a meta-analysis combining all available Han Chinese case-control samples to test this association in a non-European population and found that rs1344706 was not associated with schizophrenia in the combined samples, and the effect size of rs1344706 for schizophrenia was smaller in Chinese than in Europeans  −7) Figure S1–B). In addition, our sample size was much larger than the samples from ICCSS  Figure S1–C), in which all three found significant associations for rs1344706 whereas we did not. These comparisons further suggest that the non-significant association for rs1344706 in our meta-analysis was not caused by the sample size, but is likely due to the relatively small effect size of this SNP in Han Chinese.We further compared the sample size between our meta-analysis  and the studies in Europeans, and found that our total sample size was larger than the discovery GWA+replication studies on rs1344706 in O’Donovan et al.  and small-to-moderate effect size (odds ratio = 1.20). However, if we use the observed odds ratio (1.06) in Chinese, the present sample size only showed 55.4% power of detecting significant association. Alternatively, given the observed odds ratio (1.06) is authentic, the sample size would have to be increased to more than 24,786 cases and 24,786 controls in order for the sample to have a >90% power of detecting a significant association (a<0.05). Based on these assumptions, studies on rs1344706 in small Chinese case-control samples are unlikely to observe significant results. That said, there are three previous studies reporting significant associations of rs1344706 in small samples In the meta-analysis of the twelve Chinese case-control samples, it should be noted that we did not perform population stratification analysis in the combined samples. However, among these twelve individual samples, there is no obvious population stratification in the Shanghai The data presented in our analysis is limited, and consequently we are cautious in the interpretation of our results or making any definitive conclusions. In this study, we only tested rs1344706, and the other SNPs in ZNF804A were not studied in most of the analyzed Chinese samples. However, in a previous study, we performed a relatively systematic analysis of the ZNF804A region (up to 111 SNPs) in several Han Chinese samples , which we did include in the present study and found that most of the common SNPs in ZNF804A were not associated with schizophrenia in Chinese populations Through our meta-analysis of all the available Han Chinese case-control samples, we did not find evidence for association of rs1344706 with schizophrenia. This is not unexpected, given that the effect size of rs1344706 to schizophrenia risk in Chinese is smaller than that found in Europeans. Moreover, because a non-significant difference test cannot be interpreted as acceptance of the null hypothesis, the equivalence-based method that provides the possibility of observing a lack of association by chance was conducted to avoid the false-negative results in this study. The p-value of the non-superiority test for rs1344706 is 0.0003 in the combined Chinese samples, supporting the absence of association between rs1344706 and schizophrenia. Interestingly, rs1344706 is not the only SNP showing large differences in associations with schizophrenia between Han Chinese and Europeans, and similar situations were also observed for some other GWAS-identified SNPs in Europeans including NRGN rs12807809, RELN rs7341475, and CNNM2 rs7914558 Collectively, the current data to date does not support speculations that rs1344706 is a risk SNP for schizophrenia in Chinese, and the failures of replicating rs1344706 in the present sample suggest a potential genetic heterogeneity of schizophrenia susceptibility on this locus, likely resulting from large differences in linkage disequilibrium patterns of this genomic region covering rs1344706 between European and Han Chinese, as demonstrated in our previous study The research protocol was approved by the internal review board of Kunming Institute of Zoology, Chinese Academy of Sciences. We firstly considered all the studies listed for ZNF804A on the SZGene database Eligible studies in the meta-analysis must meet the following criteria: (1) be case-control studies, case-only and family-based studies were excluded; (2) contain at least 100 cases and 100 controls; (3) case status being defined as having diagnosis of schizophrenia according to the DSM-IV or ICD-10 criterion assessed by established psychiatric interviews, with control subjects having no history of mental disorder, other neurological disorder, alcohol dependence, or drug dependence; (4) studies where the samples have no overlap with the other identified studies; (5) rs1344706 was genotyped and in Hardy-Weinberg equilibrium in healthy controls (p>0.05).For each candidate gene study, the following data was extracted: (1) author(s) and year of publication; (2) methods, including study design, sample size, sample collection area, definition of case status, and genotyping method; (3) sample characteristics, i.e., gender ratio and mean age; and (4) data for rs1344706 . All the required data were available in the published studies or from supplementary information, except for one study that contained no information on the mean age and genotyping method For the GWASs, we recruited the following data: (1) author(s) and year of publication; (2) methods; and (3) sample characteristics. However, if rs1344706 was not available from the main text or supplementary information of these GWASs, we contacted the corresponding authors to request access to the data. The authors of two GWASs provided the data for rs1344706 Data from each study was extracted independently by two investigators (ML and HZ), using a standardized data extraction form. In cases of disagreement of study inclusion, a third investigator was involved (XJL). Disagreement over eligibility of a study was resolved by discussion until a consensus was reached.Publication bias was assessed visually using a funnel plot and tested with Egger’s regression test A random effects regression of odds ratio with mean age and the proportion of males as covariates was performed with the use of SPSS 16.0  to determine whether these covariates could influence odds ratios for rs1344706. We also analyzed whether the sample collection area could impact the observation of odds ratios.Power analysis was performed by the Power and Sample Size Program software Meta-analysis examined the allelic association of the rs1344706 A-allele with the risk of schizophrenia relative to the C-allele . Allele frequencies and counts were extracted for cases and controls. For some studies, allele frequencies and counts were calculated from the available data http://ims.cochrane.org/revman/download/revman-4). The heterogeneity between individual studies was tested using the Cochran’s (Q) χ2 test, which is a weighted sum of the squares of the deviations of individual odds ratio estimates from the overall estimate. When the odds ratios are homogeneous, Q follows a χ2 distribution with degrees of freedom. If PQ <0.10, the heterogeneity is considered statistically significant. Inconsistency across studies was quantified with the I2 metric (I2 = Q-d.f./Q), which can be interpreted as the percentage of total variation across several studies due to heterogeneity. I2 takes values between 0 and 100%, with higher values denoting a greater degree of heterogeneity . In the presence of heterogeneity among individual studies, we used random-effects models to combine the sample and to calculate the odds ratio and the corresponding 95% confidence interval (CI); otherwise, a fixed-effect mode was used. We used a forest plot to graphically present the pooled odds ratios and the 95% CIs. Each study was represented by a square in the plot, and the weight of each study was also shown. P<0.05 was considered statistically significant.To combine the individual studies, we conducted meta-analyses using Review Manager 4.2.2 , using the command ‘equip’. Detailed descriptions of the nonsuperiority test can be found in previous studies The nonChecklist S1PRISMA Checklist.(DOC)Click here for additional data file.Flow Chart S1PRISMA Flow Chart.(DOC)Click here for additional data file.Figure S1Power analysis for the studied samples.(DOC)Click here for additional data file.Table S1Studies referring ZNF804A and schizophrenia in Han Chinese, but not meet the inclusion criteria for the meta-analysis.(DOC)Click here for additional data file.Table S2‘Leave-one-out’ sensitivity analysis for meta-analysis.(DOC)Click here for additional data file.


To characterise the influence of the fat free mass on the metabolite profile in serum samples from participants of the population-based KORA  S4 study.Analyses were based on metabolite profile from 965 participants of the S4 and 890 weight-stable subjects of its seven-year follow-up study (KORA F4). 190 different serum metabolites were quantified in a targeted approach including amino acids, acylcarnitines, phosphatidylcholines (PCs), sphingomyelins and hexose. Associations between metabolite concentrations and the fat free mass index (FFMI) were analysed using adjusted linear regression models. To draw conclusions on enzymatic reactions, intra-metabolite class ratios were explored. Pairwise relationships among metabolites were investigated and illustrated by means of Gaussian graphical models (GGMs).−16–8.95×10−06) with higher FFMI were increasing concentrations of the branched chained amino acids (BCAAs), ratios of BCAAs to glucogenic amino acids, and carnitine concentrations. For various PCs, a decrease in chain length or in saturation of the fatty acid moieties could be observed with increasing FFMI, as well as an overall shift from acyl-alkyl PCs to diacyl PCs. These findings were reproduced in KORA F4. The established GGMs supported the regression results and provided a comprehensive picture of the relationships between metabolites. In a sub-analysis, most of the discovered associations did not exist in obese subjects in contrast to non-obese subjects, possibly indicating derangements in skeletal muscle metabolism.We found 339 significant associations between FFMI and various metabolites in KORA S4. Among the most prominent associations (p-values 4.75×10A set of serum metabolites strongly associated with FFMI was identified and a network explaining the relationships among metabolites was established. These results offer a novel and more complete picture of the FFMI effects on serum metabolites in a data-driven network. It is a predictor of basal metabolic rate and energy turn-over during physical activity. In addition, it has recently been identified as an endocrine organ; producing and releasing myokines which exhibit various biological effects on the muscle tissue itself and beyond So far, with an enormous effort over 4000 serum and plasma metabolites belonging to more than 50 different chemical classes have been identified, validated and characterised in the Serum Metabolome Database In this study, we took a targeted quantitative metabolomics approach to identify fat free mass/muscle mass related changes on human metabolism. To this end, the associations between FFMI and up to 190 serum metabolite concentrations including amino acids, acylcarnitines, phosphatidylcholines (PCs), sphingomyelins as well as hexose and biogenic amines measured by means of two different kits, were investigated in participants of the population-based studies KORA S4 and its seven-year follow-up KORA F4. As the biogenic amines are not part of the KORA F4 metabolomics dataset, the KORA S4 results were not discussed. However, they are shown in the The study was approved by the ethics committee of the Bavarian Medical Association. Written informed consent was obtained from each participant in accordance with institutional requirements and the Declaration of Helsinki Principles.The present analyses are based on subsamples of the metabolically characterised participants of the KORA S4 study and its seven-year follow-up KORA F4 study. KORA  is a research platform performing population-based surveys and subsequent follow-ups in the region of Augsburg in Southern Germany and has been described extensively In both studies, fasting serum samples for metabolic analysis were collected during study centre visits. For KORA S4, the blood drawing occurred after a period of overnight-fasting (minimum of 8 hours) using S-Monovette® serum tubes . Tubes were inverted two to three times, spent five minutes on the universal shaker  before being allowed to rest for 40 minutes at 4°C for total coagulation. Later on, tubes were centrifuged for 15 minutes at 2,660 g, serum was separated and filled into synthetic straws which were stored in liquid nitrogen (−196°C) until analysis. The collection and procedure for the KORA F4 samples has previously been described Metabolic characterisation of the KORA F4 serum samples was done in 2009 in three batches of approximately 1000 samples at three different time points with a recalibration of the equipment in between; whereas the complete KORA S4 set was characterised in 2011 in one batch.IDQ™ p150 kit and p180 kit , respectively. The assays allow simultaneous quantification of 163  or 186  metabolites out of 10 µl serum in each case. The AbsoluteIDQ™ p150 kit has previously been described in detail IQ™ software package, which is an integral part of the AbsoluteIDQ™ kits. Internal standards serve as reference for the calculation of metabolite concentrations. The methods of the AbsoluteIDQ™ p150 and p180 kits have been proven to be in conformance with the FDA Guideline The targeted metabolomics approach for KORA F4 and S4 was based on ESI-(LC-) MS/MS measurements by the Absolutex:y), 15 sphingomyelins (SMx:y), 77 phosphatidylcholines  and 15 lyso-phosphatidylcholines (lysoPCs). The lipid side chain composition is abbreviated as Cx:y, with x denoting the number of carbons in the side chain and y denoting the number of double-bonds. Kit p180  includes 21 amino acids (19 proteinogenic + citrulline + ornithine), hexose, free carnitine, 39 acylcarnitines, 15 sphingomyelins, 90 phosphatidylcholines (14 lysoPC and 76 PCs) as well as 19 biogenic amines. The overlap of both kits is 159 metabolites. Full biochemical names and abbreviations are provided in In total, up to 190 different metabolites were quantified. Kit p150  contains 14 amino acids (13 proteinogenic + ornithine), hexose (sum of hexoses – about 90–95% glucose), free carnitine and 40 acylcarnitines . One further metabolite was excluded as the number of missing values exceeded 5%. In the second step, the dataset was controlled for outliers. A subject’s metabolite concentration was defined as an outlier if the concentration was greater or less than the mean ± five standard deviations of the particular metabolite over the whole population. All subjects having more than three independent outlying metabolite concentrations were excluded from the dataset. An outlier was defined as independent if the correlation with all other outliers was less than 70%. If there were three or less independent outliers, only the data points were removed. All missing values were imputed with the R package “mice” which uses a linear regression approach. For the quality control of the KORA S4 metabolite dataset, a new coefficient of variation was calculated using the reference blood which was on all KORA S4 plates. The same quality criteria were applied to the KORA S4 sample, resulting in 20 metabolites being excluded from the dataset. This left us with 151 metabolites for the KORA F4 dataset and 166 metabolites for KORA S4, with an overlap of 141 metabolites. As the metabolic profile in KORA F4 samples was assessed in three batches a so called batch variable was included in all analyses of the F4 metabolomics dataset in order to avoid possible effects due to technical issues or different time points of analyses. This step was not necessary for the KORA S4 dataset.2), body fat mass index (body fat mass in kg/(height in m)2) and the appendicular skeletal muscle mass index 2.Height and weight were measured to the nearest 0.1 cm and 0.1 kg, respectively. Waist circumference was measured to the closest 0.1 cm at the smallest position between the lower rip and the upper margin of the iliac crest. Hip size was determined exactly to 0.1 cm as the widest circumference measured between the upper margin of the iliac crest and the crotch. For the assessment of body composition, two bioelectrical impedance analysis measurements of resistance (R), reactance (Xc) and the phase angle (α) were taken between the dominant hand wrist and dorsum and the dominant foot angle and dorsum (placement of the electrodes) by means of a body impedance analyser  while subjects were spreading their arms and legs and lying in a relaxed and supine position on a nonconductive surface with 50 kHz. Fat free mass, fat mass, and the appendicular skeletal muscle mass were then calculated by means of Kyle’s equations In KORA F4, genotyping was done by means of Affymetrix Human SNP Array 6.0. HapMap CEU version 22 was used as population reference and as reference for the imputation of the genotyped SNPs with IMPUTE v0.4.2. The complete procedure has been described in more detail −04 for KORA S4 and a p-value of 3.5×10−04 for KORA F4 were considered statistically significant at α = 5%. For an association with a metabolite ratio to be regarded as statistically significant, an additional criterion (the p-gain) had to be fulfilled next to a significant p-value. The p-gain is defined as the fold decrease in the p-value of association for the pair of metabolites compared to the lowest of the two p-values for the single metabolites The descriptive data is presented as mean and standard deviation for the continuous variables and as absolute quantities and percentages for the discrete parameters. Sports activity was defined by the amount of regular leisure time exercise per week; more than one hour equals active; less equals inactive. The KORA S4 and F4 samples were analysed as independent cross-sectional studies. Besides the absolute metabolite concentrations, all pairs of intra-metabolite class ratios (n = 4629 for KORA S4 and n = 4518 for KORA F4) were part of the metabolomics datasets. Associations between metabolite concentrations or metabolite ratios and the FFMI were assessed by means of linear regression models. First, metabolite variables (absolute concentrations and ratios) were standardised with an inverse log-normal transformation to allow for comparison of the estimates derived from the linear regression models. Then, linear regression models were applied with the metabolite variables as dependent variables and the FFMI as explanatory variable. The models for the KORA S4 metabolomics data were adjusted for sex and age, whereas the models for KORA F4 were adjusted for age, sex, and batch. To control for multiple testing and with regard to the dependencies among the various metabolites and ratios, a p-value of 3.12×10Different sub-analyses were performed. First, a different main explanatory variable was used. Instead of FFMI, the appendicular skeletal muscle mass index was included in the linear regression models with the same adjustments. Second, stratified analyses were conducted with age, sex, sports activity and BMI as potential confounders or effect modifiers.In addition to the linear regression analyses, we investigated the relationships between metabolites as well as the propagation of FFMI effects through the metabolic network by means of Gaussian graphical modelling. In order to obtain a GGM, the partial correlation coefficients between all pairs of metabolites were calculated −06 for S4 and 5.3×10−06 for F4 was considered as statistically significant at α = 5%.The associations between serum metabolite concentrations or ratios and SNPs which are found to be associated with anthropometric characteristic were analysed using linear models with the assumption of additive genetic effects. For the metabolite concentrations of the genotyped KORA S4 subjects (n = 668), models were adjusted for age and sex. For the analysis with KORA F4 metabolic data (n = 890), adjustments were made for age, sex and batch. A p-value of 4.73×10http://www.r-project.org]. The GGMs were visualised by the yEd graph editor version 3.6.1.1 .All statistical analyses were performed with the R software version 2.12.0 [R Development Core Team, 2010, With higher FFMI, increasing serum concentrations of the branched chained amino acids (BCAAs) valine, isoleucine, and leucine as well as of the sum of BCAAs were observed. Furthermore, other serum amino acids increased with higher FFMI, including the glucogenic amino acid alanine, and the aromatic amino acids tyrosine and phenylalanine. With respect to metabolite ratios, strong positive associations were found between FFMI and the ratios of isoleucine/glycine and leucine/glycine. In addition, the ratio of all BCAAs/all glucogenic amino acids  was positively related to FFMI, supporting the notion of increasing BCAAs concentrations in relationship to glucogenic amino acids in serum samples of subjects with higher FFMI. Inspecting the GGM results , we alsoSerum concentrations of free carnitine and short-chain odd-numbered acylcarnitines, such as propionylcarnitine (C3) and valerylcarnitine (C5), were found to be positively associated with FFMI while octadecanoylcarnitine (C18) decreased with increasing FFMI. This is also reflected in the results for the ratios of C18 to C0, C3, or C5. These findings in S4 are supported by the KORA F4 results. The acylcarnitines form a separate group within the partial correlation networks and a particularly strong correlation exists between the metabolites C0 and C3.The group of phosphatidylcholines consists of different PC diacyl (aa), PC acyl-alkyl (ae), and lysoPC acyl (a) compounds. Numerous associations between FFMI and PCs or PC ratios could be observed. An increase in FFMI was associated with (i) a higher serum concentration of PC aa in relation to PC ae, (ii) a decrease in chain length of the fatty acid residues, and (iii) a decrease in saturation  of the fatty acid moieties. Significant results for single PC compounds were all negatively associated with FFMI, such as PC ae C42:3 or PC ae C36:2, lysoPC a C18:2 or lysoPC a C18:1; the only exception is PC aa C38:3 which increased with higher FFMI. Ratios within subgroups  demonstrated that increasing FFMI is associated with a shift towards shorter fatty acids and fatty acids with more double bonds. Examples are ratios of PC aa C38:3/PC aa C42:6, PC aa C38:3/PC aa C42:2, PC aa C38:3/PC aa C42:0, PC ae C36:4/PC ae C40:1 or lysoPC a C14:0/lysoPC a C18:2. This is most likely a consequence of the associations observed for the single compounds . Ratios of PC aa to PC ae were significantly associated with FFMI, e.g., PC aa C38:3/PC ae C42:3 or PC aa C38:3/PC ae 42:2.There are two very large groups of PCs which were identified in the network analyses . PC aa CThese findings are also supported by the results obtained in the KORA F4 sample.With higher FFMI an increased concentration of sphingomyelins (SM) as compared to hydroxysphingomyelins (SM (OH)) was observed. This is reflected e.g., by the ratios of SM (OH) C16:1/SM C18:1, SM (OH) C16:1/SM C18:0 or SM (OH) C22:2/SM C18:1. Also, the results of SM ratios, such as SM C16:0/SM C16:1 or SM 16:0/SM C18:1, demonstrate a decrease in saturation of the fatty acids with increasing FFMI. The same associations are found in KORA F4. The GGM results show that almost all SM are interrelated and the strong relationship between SM (OH) C16:1 and SM (OH) C14:1 is present in both GGMs .2 did not improve as compared to the FFMI-based results. Therefore, FFMI remained as primary explanatory variable.For both, KORA S4 and F4, the results for the appendicular skeletal muscle mass index were largely comparable to the results with FFMI (data not shown). However, the p-values in the linear regression models did not decrease and also the adjusted RIn stratified analyses, very similar results could be observed for men and women. With respect to sports activity, there was only a slight difference between both groups (active versus inactive participants) for the acylcarnitine metabolites. Most of the statistically significant associations with FFMI and the acylcarnitines were not present in the inactive group. Regarding age , there were no distinct differences between groups.With respect to the associations between FFMI and the metabolites in obese and non-obese subjects in KORA S4 we did observe significant differences. Most associations described for the whole population and also noted for the non-obese subgroup are not present in obese subjects .No significant associations were found between the selected genetic variants  and the In this population-based study, strong associations between FFMI and serum metabolite concentrations were found in KORA S4 and reproduced in KORA F4. With higher FFMI, BCAA serum concentrations and the ratio of BCAAs to glucogenic amino acids increased. Free carnitine levels were also positively associated with FFMI, and for the various PCs we found a decrease in chain length and/or saturation of the fatty acid residues in combination with higher concentrations of PC aa in expense of PC ae and lysoPC. In obese subjects of KORA S4, however, these associations were lacking.The human serum metabolome is currently characterised in many studies using different analytic approaches In contrast to a study with physical activity intervention, it was not clear how strong associations between FFMI and serum concentrations of amino acids, acylcarnitines, glycerophospholipids, and sphingolipids could be in a cross-sectional study with collection of serum samples after short-term fasting of at least eight hours. However, our results fit well to biological pathways that are related to skeletal muscle metabolism, namely amino acid metabolism and fatty acid metabolism (reflected by acylcarnitines and glycerophospholipids).BCAAs are described as a preferred substrates for muscle tissue, and BCAA supplementation can suppress protein degradation Several other studies reported that acylcarnitines are also found in circulation Our phospholipid results point towards a decrease in chain length and degree of saturation of the lipid side chains. The concentrations of lysoPC and PC ae decrease with increasing FFMI and PC aa C38:3 is the only PC positively associated with higher FFMI. Considering the GGM results, PC aa C38:3 is central in one cluster of PC metabolites, while the other cluster consists of long chain PCs. Overall, our results implicate that FFMI is associated with lower plasma concentrations of very long chain PCs (GGM cluster 2), while the concentration of PC aa C38:3 increased. Thus, a higher FFMI might indicate a higher activity of enzymes involved in fatty acid oxidation, especially in the oxidation of very-long chain fatty acids. In addition, an altered activity of enzymes involved in chain elongation (ELOVL) or desaturation (FADS) could contribute to these findings. Earlier studies using metabolomics and genetic data could demonstrated that genetic effects (mediated by the expression of enzymes) are reflected in the serum metabolic profile, thus lending credibility to the approach of identifying enzyme activities by means of serum metabolites or ratios of serum metabolites In addition, a higher FFMI was found to be associated with an increased concentration of sphingomyelins as compared to hydroxysphingomyelins. Recently, Wang-Sattler et al. IDQ™ kits p150 and p180. These two kits are not identical (190 different metabolites were measured with an overlap of 141) and the measurement method of the amino acids slightly differs. However, this technology allows the automated quantification of hundreds of metabolites for many samples at a time which is very helpful for future studies The participants of our studies represent random samples of the underlying population. In such a population with a heterogeneous metabolic makeup influenced by different environmental factors, genetic predispositions, and lifestyles, the interpretation of metabolic profiling is quite ambitious The bioelectrical impedance analysis measurements did only take place in KORA S4. For the analyses of associations between FFMI and the metabolite concentrations in KORA F4 only the results of the bioelectrical impedance analysis measurements at baseline examination (KORA S4) were available. Assuming that the participants’ body composition is fairly stable in weight-stable subjects, the KORA F4 based analyses were restricted to the weight-stable subjects in order to account for this.For the current analysis, subjects with a diagnosis of diabetes or hypertension or medication for both diseases were excluded. However, obese subjects are more likely to develop derangements in glucose or lipid metabolism as compared to normal-weight subjects which are in a subclinical state and have not led to a clinical diagnosis yet. An analysis of serum glucose (hexose) and triacylglycerol indicated significantly different means between non-obese and obese subjects. In non-obese subjects, mean ± SD serum glucose concentration was 5.18±0.76 mmol/l and mean serum triacylglycerol concentration was 1.4±0.81 mmol/l; the corresponding values in obese subjects were 5.6±1.11 mmol/l and 1.70±1.05 mmol/l, respectively. However, the higher glucose and triacylglycerol concentrations in obese do not indicate a catabolic status and thus are not sufficient to explain the missing association between FFMI and serum metabolites.2. In addition, the FFMI is independent of height Stratified analyses by gender, age, and sports activity were conducted in order to detect potential confounders and effect modifiers. Similar results were found for men and women separately. P-values increased, as the sample size within the strata (slightly more for men) was smaller, but there was no improvement of the model fit, represented by the adjusted RThe GGMs present low levels of linkage between the metabolites which is in line with findings from Krumsiek et al. We found strong associations between serum amino acids, acylcarnitines, and glycerophospholipids with the FFMI in a population-based sample. These findings were stable as they could be reproduced in a follow-up study of this population. Most interestingly, such associations were largely missing in obese subjects. The latter finding supports the hypothesis that a sedentary lifestyle associated with accumulation of fat tissue may be accompanied by a derangement in skeletal muscle metabolism, especially a limited inducibility of fatty acid oxidation.Figure S1Gaussian graphical model of serum metabolite concentrations of KORA F4. Each node represents a metabolite, whereas edges represent significant partial correlations. Nodes were coloured according to the β-estimate and the p-value from the linear models .(TIF)Click here for additional data file.Table S1Full biochemical names, abbreviation, mean ± standard deviation of all metabolite concentrations measured in µmol/l with the Biocrates AbsoluteIDQ kits p150  and p180 .(DOC)Click here for additional data file.Table S2Metabolic traits significantly associated with fat free mass index in linear regression models adjusted for age, and sex  in the KORA S4 sample.(DOC)Click here for additional data file.Table S3Metabolic traits significantly associated with fat free mass index in linear regression models adjusted for age, sex and batch  in the KORA F4 weight-stable sample.(DOC)Click here for additional data file.Table S4List of selected SNP, which were significantly associated in genome wide association studies with anthropometric characteristic as outcomes and tested in the present study for associations with metabolomics data.(DOC)Click here for additional data file.


The association between TP53 R72P and/or MDM2 SNP309 polymorphisms and hepatocellular carcinoma (HCC) risk has been widely reported, but results were inconsistent. To clarify the effects of these polymorphisms on HCC risk, an updated meta-analysis of all available studies was conducted.Eligible articles were identified by search of databases including PubMed, Cochrane Library, EMBASE and Chinese Biomedical Literature database (CBM) for the period up to July 2013. Data were extracted by two independent authors and pooled odds ratio (OR) with 95% confidence interval (CI) was calculated. Metaregression and subgroup analyses were performed to identify the source of heterogeneity.Finally, a total of 10 studies including 2,243 cases and 3,615 controls were available for MDM2 SNP309 polymorphism and 14 studies containing 4,855 cases and 6,630 controls were included for TP53 R72P polymorphism. With respect to MDM2 SNP309 polymorphism, significantly increased HCC risk was found in the overall population. In subgroup analysis by ethnicity and hepatitis virus infection status, significantly increased HCC risk was found in Asians, Caucasians, Africans, and HCV positive patients. With respect to TP53 R72P polymorphism, no significant association with HCC risk was observed in the overall and subgroup analyses. In the MDM2 SNP309–TP53 R72P interaction analysis, we found that subjects with MDM2 309TT and TP53 Pro/Pro genotype, MDM2 309 TG and TP53 Arg/Pro genotype, and MDM2 309 GG and TP53 Pro/Pro genotype were associated with significantly increased risk of developing HCC as compared with the reference MDM2 309TT and TP53 Arg/Arg genotype.We concluded that MDM2 SNP309 polymorphism may play an important role in the carcinogenesis of HCC. In addition, our findings further suggest that the combination of MDM2 SNP 309 and TP53 Arg72Pro genotypes confers higher risk to develop HCC. Further large and well-designed studies are needed to confirm this association. Liver cancer, which consists predominantly of hepatocellular carcinoma (HCC), was the sixth most common cancer worldwide and the third most common cause of cancer mortality in 2008 TP53 is a tumour suppressor that plays an important role in cell cycle regulation and the maintenance of genome integrity Over the last two decades, a number of case–control studies have been conducted to investigate the associations between TP53 R72P and MDM2 SNP309 polymorphisms and HCC risk, but the results remain controversial and inconclusive. With respect to MDM2 SNP309 polymorphism, a meta-analysis by Ma et al. This study was performed according to the proposal of Meta-analysis of Observational Studies in Epidemiology group (MOOSE) Studies included in the meta-analysis were required to meet the following criteria: (1) Case–control studies which evaluated the association between TP53 R72P and/or MDM2 SNP309 polymorphisms and HCC risk; (2) used an unrelated case–control design; (3) had an odds ratio (OR) with 95% confidence interval (CI) or other available data for estimating OR (95% CI); and (4) control population did not contain malignant tumor patients. Conference abstracts, case reports, editorials, review articles, and letters were excluded.Two investigators (Xue Qin and Qiliu Peng) independently extracted data from the eligible studies. Data extracted from eligible studies included the first author’s name, publication date, country of origin, ethnicity, genotyping method, matching criteria, source of control, HCC confirmation, QC when genotyping, total numbers of cases and controls and genotype frequencies of cases and controls. The hepatitis virus infection status was additionally recorded for the stratified analysis. Two investigators checked the data extraction results and reached consensus on all of the data extracted.Methodological quality was independently assessed by two reviewers (Xue Qin and Qiliu Peng), according to a set of predefined criteria  based onSummary odds ratios (ORs) and corresponding 95% confidence intervals (CIs) were estimated for each polymorphism in different comparison models, including allelic contrast, additive genetic models, recessive genetic model, and dominant genetic model.Q test and 2I statistics were used to assess the statistical heterogeneity among studies Q test was QP <0.1 or 2I≥50%, indicating the presence of heterogeneity, a random-effects model (the DerSimonian and Laird method) was used to estimate the summary ORs Q test was QP ≥0.1 and 2I<50%, indicating the absence of heterogeneity, the fixed-effects model (the Mantel–Haenszel method) was used The p values were two-sided. To ensure the reliability and the accuracy of the results, two authors entered the data into the statistical software programs independently with the same results.Sensitivity analysis was performed by sequential omission of individual studies. For each polymorphism, publication bias was evaluated using Begg’s funnel plot and Egger’s regression asymmetry test Based on our search criteria, 25 studies relevant to the role of TP53 R72P and/or MDM2 SNP309 on HCC susceptibility were identified. Six of these articles were excluded including 3 publications containing overlapping data 2>50.0%, or PQ <0.10), thus, the random-effects model was used to pool the results. The results of pooling all studies showed that the MDM2 SNP309 polymorphism was associated with increased HCC risk in all genetic models , thus the random-effects model was used to pool the results. The meta-analysis results showed that the TP53 R72P polymorphism was not associated with increased HCC risk in the overall populations. In the stratified analyses by ethnicity and hepatitis virus infection status, statistically significant associations were also not observed in all subgroups  in the overall populations, which indicated statistically significant heterogeneity among studies. To explore the sources of heterogeneity, we performed metaregression and subgroup analyses. Metaregression analysis of data showed that the ethnicity and Quality scores were the major sources which contributed to heterogeneity . The Genotyping methods, HCC confirmation, Source of control, and QC when genotyping were not effect modifiers. Subsequently, we performed subgroup analyses by ethnicity and hepatitis virus infection status. However, heterogeneity still existed in all the above genetic comparison models in Asians and HBV positive subgroup . Metaregression analysis showed that the Quality scores was the major source heterogeneity . The Ethnicity, Genotyping methods, HCC confirmation, Source of control, and QC when genotyping were not effect modifiers. Galbraith plots analysis indicated that the studies by Jiang et al. 2I values decreased obviously and QP values were greater than 0.10 after excluding the two studies Jiang et al. For the TP53 R72P polymorphism, significant between-study heterogeneity was also observed in the pooling analyses of total available studies . For the MDM2 SNP309 polymorphism, sensitivity analysis was further performed by omitting the studies by Leu et al. Begg’s funnel plot and Egger’s test were performed to access the publication bias in this meta-analysis. Funnel plot shapes did not reveal obvious evidence of asymmetry, and all the p values of Egger’s tests were more than 0.05 for both MDM2 SNP309 and TP53 R72P polymorphisms, providing statistical evidence of the funnel plots’ symmetry . The resPrevious studies investigating the associations between MDM2 SNP309 and/or TP53 R72P polymorphisms and HCC risk have provided inconsistent results, and most of those studies involved no more than a few hundred HCC cases, which is too few to assess any genetic effects reliably. Meta-analysis has been recognized as an important tool to more precisely define the effect of selected genetic polymorphisms on the risk for disease and to identify potentially important sources of between-study heterogeneity. Hence, we performed this meta-analysis including all available studies to provide the most comprehensive assessment of the associations between the MDM2 SNP309 and TP53 R72P polymorphisms and HCC risk. Our results suggested that the MDM2 SNP309 polymorphism was significantly associated with increased HCC risk in the overall populations, different ethnic subgroups, and HCV positive patients. However, our data did not support a genetic association between the TP53 R72P polymorphism and HCC risk. In the MDM2 SNP309–TP53 R72P interaction analysis, we found that individuals with MDM2 309TT and TP53 Pro/Pro genotype, MDM2 309 TG and TP53 Arg/Pro genotype, and MDM2 309 GG and TP53 Pro/Pro genotype had significantly increased HCC risk compared to those with the reference MDM2 309TT and TP53 Arg/Arg genotype.Acting as a tumor suppressor, TP53 could lead to cell growth arrest and/or apoptosis in response to DNA damage and other cellular stresses MDM2 is one of the central nodes in the TP53 pathway. The proper regulation of MDM2 levels has been shown to be vital for TP53 tumor suppression, and even a modest change in levels could affect the TP53 pathway and, subsequently, cancer development in mouse models HBV belongs to a family of DNA viruses called hepadnaviruses. The oncogenic potential of HBV has been attributed to its ability to integrate into host cellular DNA, which, may activate neighboring cellular genes directly to offer a selective growth advantage to the liver cells. In addition, production of hepatitis B x (HBx) protein can act as a transactivator on various cellular genes for cell growth and tumorigenesis Previous research has demonstrated an interaction between MDM2 and TP53 at the molecular level QP values for MDM2 SNP 309 and TP53 Arg72Pro polymorphisms were all less than 0.10, and 2I values were larger than 50.0%). To find the sources of heterogeneity, we performed subgroup analyses and. Subgroup analyses showed that the heterogeneity was still significant in Asians and HBV positive patients, while it was removed in the other subgroups, indicating that heterogeneity might result from the inconsistency of effects across those included studies from Asian population and HBV positive patients. Metaregression analysis showed that the ethnicity and Quality scores were the major sources of heterogeneity for MDM2 SNP 309 polymorphism and the Quality scores was the major source heterogeneity for TP53 Arg72Pro polymorphism. Subsequently, we performed Galbraith plots to further investigate the heterogeneity. For the MDM2 SNP 309 polymorphism, Galbraith plots spotted 2 studies 2I values decreased lower than 50% and QP values were larger than 0.10 in all genetic comparison models in the overall populations, Asians, and HBV positive subgroup. However, the summary ORs for the MDM2 SNP309 and TP53 Arg72Pro polymorphisms in different comparison models in the overall population and subgroup analyses were not material change by omitting the studies spotted as outliers, indicating that our results were robust and reliable.Heterogeneity is a potential problem when interpreting the results of a meta-analysis, and finding the sources of heterogeneity is one of the most important goals of meta-analysis Some possible limitations in this meta-analysis should be acknowledged. First, the overall outcomes were based on individual unadjusted ORs, whereas a more precise evaluation should be adjusted by potentially suspected factors including age, gender, smoking status, and environmental factors. Second, the controls were not uniformly defined. Although most of the controls were selected mainly from healthy populations, some had benign disease such as liver cirrhosis, HBsAg positive and so on. Therefore, non-differential misclassification bias was possible because these studies may have included the control populations who have different risks of developing HCC. Third, the number of studies included in the meta-analysis for African population was relatively small and there was only one study in the African group, which may lead to low statistical power and generated fluctuate estimation. Finally, gene–environment interactions were not addressed in this meta-analysis due to the lack of sufficient data. As is generally accepted, aside from genetic factors and chronic infection with HBV or HCV, exposure to aflatoxin B1, liver cirrhosis, and habitual alcohol abuse are major risk factors for HCC; however, we could not perform subgroup analyses based on these environmental exposures owing to the limited reported information on such associations in those included studies.Despite these limitations, this meta-analysis suggests that the MDM2 SNP 309 polymorphism but not TP53 Arg72Pro variant is associated with increased risk of HCC. In addition, our findings further suggest that the combination of MDM2 SNP 309 and TP53 Arg72Pro genotypes confers higher risk to develop HCC. However, it is necessary to conduct large sample studies using standardized unbiased genotyping methods, homogeneous HCC patients and well-matched controls. Moreover, gene–environment interactions should also be considered in the analysis. Such studies taking these factors into account may eventually lead to better, comprehensive understanding of the association between these polymorphisms and HCC risk.Figure S1Flow diagram of included studies for this meta-analysis.(TIF)Click here for additional data file.Figure S2Funnel plot analysis to detect publication bias. Each point represents a separate study for the indicated association. A Funnel plot for MDM2 SNP309 polymorphism in the overall analysis (recessive model GG vs. TG+TT: P = 0.180); B Funnel plot for TP53 Arg72Pro polymorphism in the overall analysis (recessive model ProPro vs. ArgPro+ArgArg: P = 0.114).(TIF)Click here for additional data file.Checklist S1Checklist of items to include when reporting a systematic review or meta-analysis.(DOC)Click here for additional data file.


Confounding can be thought of as mixing the effect of exposure on the risk of disease with a third factor which distorts the measure of association such as risk ratio or odds ratio. This bias arises because of complex functional relationship of confounder with both exposure and disease (outcome). In this article, we provided a conceptual framework review of confounding issues in epidemiologic studies, in particular in observational studies and nonrandomized experimental studies. We have shown in 2 by 2 tables with analytical examples how the index of association will be distorted when confounding is present. The criteria, source of confounding and several points in confounding issues have been addressed. The advantages and disadvantages of several strategies for control of confounding have been discussed.  Many epidemiologic studies are planned to examine the causal association of exposure with the outcome of interest using non-experimental  or experimental data . By defiIn observational studies, there is always a possibility to influence such extraneous variables on the outcome of interest because of lack of comparability of two groups at baseline , 8, 9. IDefinition of Confounding Mixing the effect of exposure (or treatment) on occurrence of disease (or outcome) with a third factor  happens when the third factor is an independent risk factor for disease and also it should have association with exposure independently , 5. SuchExamples of Mixing Effect with Confounding Let us assume a cohort study was designed to determine the association between physical activity and myocardial infarction (MI). Age can be considered as a confounder that distorts the magnitude of this association since the distribution of age may differ between those with and without physical activities and the group with physical activities may be younger thus, age is associated physically (condition 1). The younger subjects may have a lower risk of MI for both with and without physical activities. Thus, age is inherently a risk factor for MI (condition 2). In addition, the effect of age is not on a pathway relation of physical activities and MI (condition 3). So, the lack of control of the effect of age, the apparent association between physical activities and MI is confounded by age. In this example, the question will arise in the distortion of this association to which direction occurs. Since the physical active group is younger and the younger people has a lower risk of MI, consequently, the mixing effect of age with physical activities leads to the exaggeration of the inverse association between physical activities and MI. In this example, we call age as a positive confounder. In the context of this example, gender also can be considered as confounding since the level of physical activities differs between men and women, and men have a greater risk for MI both with and without physical activities. The lack of control for gender leads to the dilution of the inverse association between physical activity and MI. Thus, gender is a negative confounder. In analytical epidemiologic studies, age and gender are associated with several lifestyles, physical and chemical exposures and they are also risk factors for disease (or outcome) of interest. Thus, their rules as confounder variables should be taken into account. Crude and Specific Effect: Examples of 2 by 2 TablesIn this section, with several hypothetical examples, we have shown how collapsing data over different stratum of confounding factor can distort the risk ratio in analysis 2 by 2 tables.Example 1. We have shown an example that the lack of control for confounding (collapsing data in a single 2 by 2 table) produces a false association in  As Example 2. This example shows that confounder partially distorts the association. For example, in a cohort study, the effect of smoking on occurrence of heart disease, the results of crude data (collapsing on stratum of confounder) is shown in As Example 3. This example shows that the mixing effect of confounder with exposure distorts the real effect toward the null value i.e. the real effect will be diluted by confounding. In a cohort study, to determine the association between air pollution and pulmonary disease the results of collapsing data were presented in As Several Points on Confounding Characteristics: We draw the attention of researchers and clinicians to the following points: First, confounder variables must be independently associated with disease (or outcome) of interest (i.e. risk factor for disease) for both the exposed and the non-exposed groups. If a covariate is associated with disease in exposed group not for non-exposed, this association might be triggered by exposure only. If this is the case, this variable is no longer a confounder. For example, if an association between sugar consumption and MI only was present in smokers not for non-smokers, sugar consumption would not be considered as confounding factor. Second, confounding factors must not be an intermediate variable in causal pathway relationship between exposure and disease (or outcome). For example, if the association of high density lipoprotein (HDL) with alcohol consumption and MI was in an intermediate causal pathway between alcohol consumption and MI, then HDL could not be considered as confounding and its effect should not be controlled in statistical analysis , the effect of interest might be over or underestimated . Fifth, Sources of confounding: There are several sources for confounding:Susceptibility bias: This bias occurs, because of the presence of susceptible factors at baseline (before exposure). Thus, the risk of outcome in exposed group is substantially greater than the non-exposed group at baseline  and it is a serious bias in non-experimental studies of medication effect  that can affect on outcome  analysis has become a popular method to determine the accuracy of quantitative (or rating) test results and the area under curve (AUC) has been used as measurement of accuracy with meaningful interpretation  in the design of diagnostic studies or analysis for the evaluation of accuracy of diagnostic test leads accuracy index erroneous distortion (i.e. over or underestimated). This occurs when covariate is associated with both test results and the true state of disease. Janes and Pepe 2011) graphically showed that the overall ROC curve and corresponding AUC substantially differed from stratum specific ROC curve and their AUC when confounding was present  concentration, the influence of patient age has been revealed . NeverthStrategies for Control of Confounding: Control for confounding has an important role on the validity of analytical epidemiologic studies and this depends to what extent the confounding variables were considered and correctly measured. It is necessary that researchers have enough evidences of presence of such variables in the stage of study design and to collect their data in order to be able to control them in their analysis.To avoid confounding is to obtain a reference population for which to be comparable with study group. Such reference population may not be possible in practice. Thus investigators attempt to construct such population based on the study design for control of confounding. Several methods have been proposed in the stage of design and analysis for prevention of confounding.Control of Confounding in DesignRestrictionRestriction is an effective approach for prevention of known risk factor of confounding. For example, if gender imbalance confounds study results, restriction on specific gender (e.g. women) would no longer be a confounder. Nonetheless, restriction on many confounding factors can reduce the number of subjects in the study and the generalizability of results as well. In addition, when a factor is restricted in the design, its effect as a risk factor for disease can not be assessed.MatchingMatching refers to applying a restriction in the selection of reference group in which it should have similarities with some characteristics with the study groups. This can be done with individual matching and group matching. For example, gender imbalance is not found, the proportion of women is similar in two compared groups by gender matching. This requires many reference groups of candidates to meet the matching criteria. In addition, matching becomes difficult if one attempts to match reference group and study group with several confounders.Although, matching reduces confounding bias in epidemiology, the advantage of matching is not only for control of confounding that can be achieved in analysis without matching. While the advantage is to achieve a greater efficiency in terms of amount of information obtained , 6. In cThus, applying matching in study design requires a conditional analysis for validity of results in case-control studies. While such analysis is not required for cohort studies because the procedure of matching differs in the study design, in cohort study, non-exposed and exposed are matched and matching factors are independent from the outcome of interest that might occur sometimes in the future. Moreover, the effect of matching factor can not be assessed as a risk factor for disease . The addRandomizationRandomized clinical trials (RCT) are the gold standard for establishing causality in clinical research. Randomized treatment allocation prevents imbalance covariates in the study design. To a large extent, it ensures that patients allocated in treatment groups be similar with respect to baseline characteristics. In particular, random allocation deals with confounding which is unknown and it inherently produces comparable groups if sufficient number of subjects is attained in the study , 21. HowBlock randomization (stratified randomization) with respect to covariate has a great assurance to prevent imbalance covariate. This type of randomization can be applied only for explicitly defined covariate not for unknown covariate. For example, if gender is covariate, stratified randomization by gender potentially prevents imbalance gender. A possible solution in dealing with non-compliance problem is to use intention to treat analysis in which the comparison is carried out by treatment assignment rather than treatment received .Control of Confounding in AnalysisIn observational study, control for all potential confounders may be impossible in design. Thus the investigator should have planned to adjust the effect of different covariates that can be considered as potential confounders in the stage of analysis. For this purpose, it is required that all potential confounders are measured correctly in the design. Obviously, those that were not measured and also unknown confounders could not be adjusted in analysis. Stratification and regression model are the two approaches for the control of confounding in analysis . StratifThis method of control for confounding is easy to understand and the data can be shown in 2 by 2 tables; stratum specific effect can easily be compared with crude effect when collapsing data over different strata in a single table as we have shown in our three examples earlier. Moreover, the stratum specific effect can explore whether the covariate is a modifier. Nevertheless, this method can deal with few confounding factors because of paucity of sample size within each cell and thus the statistical inference becomes unreliable. A more powerful method for control of confounding is using multiple regression models that allow us to adjust the effect of several confounding factors simultaneously without loss of information. The development of several statistical software helps the practitioner to adjust simultaneously several confounding variables. For binary outcome, multiple logistic regression models are commonly used for the adjustment of categorical and continuous covariates in epidemiology for both cohort and case-control studies . The coeConsequently, the exponential coefficient that corresponds to exposure is the adjusted OR given all potential confounders to be into account in the regression model. For the continuous outcome, the multiple linear regression models are used for adjustment and the regression coefficients represent the adjusted mean difference of binary exposure and the adjusted increment change in mean of outcome for continuous covariate. For the censored data in survival analysis, Cox regression model is a proper method for the adjustment several covariates and the regression coefficients represent the adjusted log hazard ratio (or risk ratio) and their exponential corresponded coefficient of exposure is the adjusted RR in prognostic studies .Although, statistical models are able to smoothen the sparse data and estimate the adjusted effect of interest, nonetheless, no approach can solve sparse data problem properly . The lacLimitation for Control of Confounding One limitation for the control of confounding in observational studies is that the data of all potential confounders may not be available since these studies usually use the data that have already been available in the patients’ records. Thus, there are unknown confounders that were not measured. Another limitation is that the measurement errors often occur in the collection of data of confounding because of the soft instrument that is applied in data collection. Or even there is a possibility of misclassification for binary confounder. Therefore, the residual confounding (i.e. uncontrolled confounding) almost threatens the study results in observational studies -30. In aThe proper measuring of potential confounding factors besides exposure and outcome in study design helps the statistician to control them in analysis. In some situations, in particular confounding by indication in non- randomized interventional studies, measuring for indication variables for a particular drug therapy as a proxy and adjusting in analysis help in a way that such confounding is partially adjusted. A proper control for confounding bias does not assure the validity of the study unless the other sources of bias such as information bias, selection bias and also random errors (chance) are excluded. Additionally, a proper design for assurance of causality inference is necessary.


There is inconsistency in how we operationalise dropout in clinical research, with studies defining it based on time, number of sessions, therapeutic goals, an identified 'end' of therapy, or the therapist's judgement. The effect of different definitions is poorly understood, but meta-analyses have indicated that they may produce varying rates of dropout and relate to heterogenous client groups. This study aimed to investigate the effect of different definitions of dropout on associated rates, predictors and outcomes in one sample. Analysis involved data from 249 clients  who received individual outpatient CBT-E. Each participant was classified as a completer or non-completer according to four definitions: before 30 days (10%), before 10 sessions (26%), before prescribed sessions (56%), and non-mutual termination (40%). Analyses indicated that dropout classifications were significantly associated but had poor to moderate agreement. Three definitions were associated with at least one predictor variable and significant variables changed across definitions. Therapeutic outcome was only associated with therapist-judged dropout. Results indicate that inconsistent definitions may prevent accurate interpretation of the dropout literature, and may impede efforts to increase treatment completion. Therapist-judged dropout appeared to be the most useful definition, and we recommend it be the standard definition of dropout.Understanding and Treating Eating Pathology stream of the 2013 ANZAED Conference.This abstract was presented in the


Migraine is a common disorder that often coexists with depression. While a functional polymorphism in methyleneterahydrofolate reductase gene (MTHFR C677T) has been implicated in depression; the evidence to support an association of MTHFR with migraine has been inconclusive. We aim to investigate the effect of this variant on propensity for migraine and to perform a systematic review and meta-analysis of studies of MTHFR and migraine to date.Individuals with migraine (n = 447) were selected from the Depression Case Control (DeCC) study to investigate the association between migraine and MTHFR C677T single nucleotide polymorphism (SNP) rs1801133 using an additive model compared to non-migraineurs adjusting for depression status. A meta-analysis was performed and included 15 studies of MTHFR and migraine.MTHFR C677T polymorphism was associated with migraine with aura (MA)  that remained significant after adjusting for age, sex and depression status. A meta-analysis of 15 case-control studies showed that T allele homozygosity is significantly associated with MA  and total migraine , but not migraine without aura . In studies of non-Caucasian population, the TT genotype was associated with total migraine , whereas in studies of Caucasians this variant was associated with MA only .MTHFR C677T is associated with MA in individuals selected for depression study. A meta-analysis of 15 studies supports this association and demonstrated effects across ethnic groups. Migraine is a common primary headache disorder characterised by recurrent headaches associated with gastrointestinal, neurological and autonomic symptoms . MigrainA number of studies have found a significant association between the TT genotype and migraine -14; espeThe MTHFR C677T polymorphism has been reported to be associated with depression ; while oTo our knowledge previous studies have investigated this gene variant in either cases of depression alone, or migraine alone without controlling for the presence of the other condition. We hypothesize that the MTHFR C677T polymorphism is a migraine gene and the association with depression is due to the presence of migraine in the individuals studied. In this study, we investigate the association between MTHFR C677T polymorphism and migraine in a sample of cases of depression and healthy controls. A meta-analysis of studies of this variant and migraine is also reported.The study subjects were recruited for a genetic case-control association study of recurrent major depression, the Depression Case Control study (DeCC). The sample selection and recruitment have been described in detail . BrieflyAll depression cases were given a face to face diagnostic interview, the Schedule for Clinical Assessment in Neuropsychiatry (SCAN) , adminisFor the lifetime diagnosis of migraine the structured migraine interview (SMI)  based onhttp://www.kbioscience.co.uk), blinded to the phenotype status. Full details of the genotyping methods and primers design were previously described )  and total migraine , but not with MO   in the MTHFR gene and MA in a well-characterized sample of depressed cases compared to psychiatrically healthy controls. Previous reports have shown an association between this SNP and depression. In fact, the association between MTHFR polymorphism and MA remained significant after adjusting for depression suggesting that this association is unique to MA. No statistically significant association was seen between this SNP and depression or MO in this study. The association between MTHFR C677T polymorphism and MA was stronger in women, however remained significantly associated with MA after controlling for sex, age and depression. Several studies suggested an association between MTHFR C677T polymorphism and migraine as described earlier. Similarly, our meta-analysis of 15 case-control studies showed that the T allele is significantly associated with MA and total migraine, the association with total migraine is most likely accounted for by the presence of migraine with aura. Conversely other studies found the TT genotype protective for MA -29. A me01133 in In this depression case control study, we found migraine to be significantly associated with excess T allele and TT genotype of the MTHFR C677T polymorphism. This association was specific only to MA. Broad migraine  was not significantly different than non-migraine headaches in their T allele and TT genotype distribution. This trend was also shown in previous studies in different populations as described earlier. The results were not always consistent however and other studies reported negative findings. A relatively large study failed to show an association between migraine and MTHFR C677T polymorphism . Such coOur results showed that excess T allele and homozygous TT genotype of the MTHFR gene C677T polymorphism is associated with increased risk for MA regardless of depression status. Our case control study showed that individuals carrying this genetic variant are 30% more likely to have migraine with aura and meta-analysis strengthened this observation and reported 42% increase in risk for such individuals, however it is important to keep in context that this 42% risk increase for MA is only in individuals carrying this genetic variant, the number of which is relatively small compared to the general population. The importance of our findings can be focused into 2 main areas:st studies of the MTHFR C677T variant are common especially in psychiatric and cardiovascular fields. These studies, we encourage, must take into consideration the common co occurrence of migraine in these disorders and the strong and reproducible association between this variant and migraine with aura. It is advisable therefore to screen and adjust for migraine in these studies to avoid spurious associations.1nd individuals carrying this variant  are at 42% increased risk for migraine with aura. In the light of increasing interests in personalised medicine, adding folate supplements to individuals at risk  may mitigate future development of migraine with aura.2The main strengths of our study are the use of validated diagnostic tool to identify migraine and the use of direct interviewing of all participants. Our study participants are also relatively homogenous  thus reducing population stratification that can have a confounding effect. There are however certain weaknesses to our study. The study recruitment was designed to select depressed cases, which may have led to ascertainment bias. However migraine was systematically screened for in all participants and we have adjusted for depression status in our analysis. In addition our sample size did not allow for subgroup analysis of each headache type and therefore we are not able to report whether there is any association between this SNP and other types of primary headache disorders in addition to MA such as tension type headache.The current study adds to the growing evidence for the role MTHFR C677T gene variant in migraine with aura. Our meta-analysis showed a significant association between this genetic variant and MA in Caucasian population and overall migraine in non-Caucasian population, although the effect in this case is very likely to be due to MA than migraine in general. Heterogeneity of studies remains a significant cause of conflicting results and ethnic variations in genotype-phenotype association must be examined closely.MA: migraine with aura; MO: migraine without aura; MTHFR: methylene tetrahydrofolate reductase; SNP: single nucleotide polymorphismThe authors declare that they have no competing interests.st draft. With AM. ZS also performed literature search for meta-analysis. DG and SCW performed all DNA related lab experiments. NC, LJ, AK and MO are investigators on the depression case control study, designed the original study, were responsible for the study conduct and recruitment, supervision of study personnel and contributed to the manuscript. AM performed literature search for the mea-analysis and run the analysis for this part of the study, he also contributed to the manuscript writing. PM and AF are the depression case control study PIs and the supervisors of the 1st author; they guided the study with significant input into the design of the migraine study and selection of gene variant, they also contributed to the writing of the manuscript and provided the resources for the migraine study. All authors read and approved the final manuscript.ZS conceived the migraine study, designed the migraine interviews, collected data, analyzed data and wrote the 1The pre-publication history for this paper can be accessed here:http://www.biomedcentral.com/1471-2377/11/66/prepub