Meta-analysis is increasingly used as a key source of evidence synthesis to inform clinical practice. The theory and statistical foundations of meta-analysis continually evolve, providing solutions to many new and challenging problems. In practice, most meta-analyses are performed in general statistical packages or dedicated meta-analysis programs.Herein, we introduce Meta-Analyst, a novel, powerful, intuitive, and free meta-analysis program for the meta-analysis of a variety of problems. Meta-Analyst is implemented in C# atop of the Microsoft .NET framework, and features a graphical user interface. The software performs several meta-analysis and meta-regression models for binary and continuous outcomes, as well as analyses for diagnostic and prognostic test studies in the frequentist and Bayesian frameworks. Moreover, Meta-Analyst includes a flexible tool to edit and customize generated meta-analysis graphs  and provides output in many formats . The software architecture employed allows for rapid changes to be made to either the Graphical User Interface (GUI) or to the analytic modules.We verified the numerical precision of Meta-Analyst by comparing its output with that from standard meta-analysis routines in Stata over a large database of 11,803 meta-analyses of binary outcome data, and 6,881 meta-analyses of continuous outcome data from the Cochrane Library of Systematic Reviews. Results from analyses of diagnostic and prognostic test studies have been verified in a limited number of meta-analyses versus MetaDisc and MetaTest. Bayesian statistical analyses use the OpenBUGS calculation engine .We have developed and validated a new program for conducting meta-analyses that combines the advantages of existing software for this task. Systematic reviews of randomized controlled trials or epidemiological studies have emerged as a key source of evidence across medical disciplines ,2. A cenhttp://tuftscaes.org/meta_analyst/ (last accessed 11/12/2009).Meta-analysis is usually performed using computer programs. Herein we present a new program for the Microsoft Windows operating system, Meta-Analyst, and report on its testing versus other widely used and accepted software. Meta-Analyst features an easy and intuitive graphical user interface and has a spreadsheet-based layout. The program was developed by the Tufts Evidence-based Practice Center under contract with the US Agency for Healthcare Research and Quality (AHRQ). It is available for use by the AHRQ-designated Evidence-based Practice Centers for performing meta-analyses in their evidence reports. Additionally, the software is now being made available to all interested investigators worldwide at no cost. The latest version can be obtained from Meta-analysis can be performed in various general statistical and numerical analysis environments , or in dedicated programs . A receTwo of the most popular dedicated meta-analysis packages are Comprehensive Meta-Analysis and MIX. The former is a commercial product, costing $1295 for a licence, while the latter is a free plug-in for the commercial Microsoft Excel package. Both feature intuitive, spreadsheet interfaces for data entry, and provide numerical and graphical output in standard formats. However, both implement only basic methods for the meta-analysis of binary and continuous data  and repd charts  documentThe design of Meta-Analyst is based on the Model-View-Control design pattern , which eFor Bayesian analyses, we invoke OpenBUGS  on the bUnlike other dedicated meta-analysis packages, Meta-Analyst integrates the capabilities to perform meta-analyses of binary or continuous outcomes and diagnostic or prognostic tests, combining the functionality of software such as MIX and Meta-DiSc. For each of these types of outcomes, we have implemented standard meta-analysis routines, as well as some more advanced ones. Table http://tuftscaes.org/meta_analyst/AppendixA.html, last accessed 11/12/2009). Because of the way we have interfaced Meta-Analyst with OpenBUGS  Meta-Analyst implements only one Bayesian model for each type of data .For detailed explanation of the statistical routines used, including handling of zero-cells, please see our methods document at: cumulative meta-analysis, leave-one-out sensitivity analysis and subgroup analysis. In cumulative meta-analysis one reorders the studies according to a covariate  and re-estimates the summary effect at each step, i.e., each time a new study is added. It is typically a graphical analysis that plots the aggregate overall estimate at each step [n studies in the meta-analysis at hand, plot n summary estimates, each corresponding to leaving one of the n studies out of the calculation. This plot illuminates influential studies, as when they are left out of the analysis, the overall estimate will be notably perturbed. Subgroup analysis is a tool for exploring the effects of a treatment on population subgroups, e.g., females older than 50 years old versus younger women. This is done by conducting separate meta-analyses on the respective subgroups and plotting overall estimates for both.Meta-Analyst automates ach step ,18. ThisMeta-Analyst generates different graphical output suitable to the data at hand. Figures The GUI comprises two tabs; one for data entry and editing and the other for displaying the results of analyses  and the outcomes will be re-computed automatically. While Figure covariates. Typically, covariates are used as explanatory variables in meta-regression analyses. To use covariates in the analyses a user has to activate them (by right clicking on the corresponding covariate name and choosing the respective option). When at least one covariate is activated, fixed and random effects meta-regression becomes available as an analytic option in the program's menus. If the covariate is excluded from the analyses, meta-regression is not available as an option. To perform meta-regression with several explanatory variables, the user simply activates the corresponding covariates.The user can also provide additional numeric and string variables that describe characteristics of the analyzed studies. By convention, user-added numeric variables are termed labels and are typically used to provide textual descriptions, or to specify subgroups for subgroup analyses. When a dataset contains at least one label, the program allows the user to perform subgroup analyses according to the categories defined by the selected label. The subgroups are automatically named according to the contents of the label. Labels are ignored in meta-regression analyses (though displayed in plots when pertinent). For example, suppose the user adds a label "country". Further suppose that studies 1 and 2 are labelled "United States" while studies 3 and 4 are labelled "India". Then a subgroup analysis performed using the "country" label will automatically plot the overall (pooled) effects for the studies that were labelled as being conducted in "India" (1 and 2) and the pooled estimate will also be plotted for those labelled as being conducted in the "United States" (3 and 4).User-added string variables are termed Studies can be included and excluded from a particular analysis by selecting/deselecting the corresponding checkbox in the first column. Once the data is entered, the outcome metric set and the studies and covariates desired to be excluded from the analysis (if any) are deselected, users can perform an analysis via the drop down 'Analysis' menu, at which point they will be prompted with the dialogue shown in Figure After the analysis runs to completion, the results will be displayed in the results tab Figure .The left-hand side of the results tab shows a tree populated with collapsible parent nodes for each analysis that has been run . Each of these parent nodes have child nodes corresponding to the various tabular and graphical outputs associated with the analysis. Clicking on one of these child nodes, e.g., "Forest Plot", scrolls the corresponding graphic into view.forest plot editor tool, as seen in Figure e. g., study sizes), and in which order, as well as the symbols used for point and overall estimates, the scale of the plot (minimum and maximum) values and more.All of the tables and graphics can be copied to the user's clipboard via a right-click menu (and subsequently pasted into other programs). Additionally, the tables and any text therein can be edited and formatted via an embedded editor. Moreover, the user can edit forest plots using the © PDF and Microsoft© Word -ready Rich Text Format report files are generated and saved. These include all of the tabular and graphical output. Finally, the graphics themselves are automatically output to separate image (.PNG) files, for use in other programs. Our aim was to provide the output in as many formats as possible, in order to provide flexibility to the user.In addition to the interactive output shown in Figures metan version 1.86 in Stata. We compared the output of the programs in 11,803 meta-analyses of binary outcomes and 6,881 meta-analyses of continuous outcomes from issue 4 of the Cochrane Library of Systematic Reviews, 2005. This database of meta-analyses was described elsewhere [To validate the computational results, we systematically tested Meta-Analyst vs. the results from lsewhere , and it rel = | ΘStata - ΘMA|/ΘStata. Θ is any of the numerical output of the program such as a summary effect size for each meta-analysis metric and method, its variance, and the Q, τ2 and I2 statistics (for random effects models). We aimed to identify differences that are beyond those introduced by machine (im)precision. If there are such differences, both the absolute and normalized difference between the two numbers will be relatively large. When the numbers in question are very large, the absolute difference might be relatively large (merely because of machine imprecision) whereas their normalized difference will be very small. Reciprocally, for small magnitudes the normalized difference can be relatively large , while the absolute difference is very small.Over the 11,803 analyses  for binary data and 6,881 analyses over continuous data, we recorded the minimum of the absolute and normalized differences between the outputs from Stata and Meta-Analyst, where the normalized difference is defined as Δ-6. For continuous data analyses, the maximum discrepancy was 7.4 × 10-5. These maximum discrepancies appeared in meta-analyses with extreme between study heterogeneity, and are ascribed to rounding errors .Over the binary set of meta-analyses, the maximum discrepancy was 2.9 × 10As previously discussed, Bayesian analyses are run through OpenBUGS, and so the output is as thoroughly tested as OpenBUGS.Testing for diagnostic test accuracy analyses is not as extensive, because we have not found a suitable reference scripting environment to test Meta-Analyst output against. However, the simple diagnostic test methods are based on weighted proportions , relative risks (likelihood ratios), odds ratios and regression . These methods use the same computational algorithms as those for binary data and so have been tested. Two diagnostic methods remain to be sufficiently tested; bivariate meta-analysis of sensitivity and specificity for diagnostic tests and random effects SROC. These analyses are flagged as not thoroughly checked when they are requested from the user. However, these will soon be reconfigured to run in OpenBUGS so that they will be validated as well.In order to attain widespread use, meta-analysis software must be easy to use. In particular, requiring that users learn an entire language to run their analyses will prohibit general adaptation of a program. Dedicated meta-analysis programs such as MIX, Comprehensive Meta-analysis, and MetaDiSc are appealing due to their small learning curve. On the other hand, by their very nature, such programs are less flexible than general statistical packages. For example, they have no scripting functionality, which precludes their use for large-scale empirical research or simulation studies. Further, they are not able to perform advanced analyses, such as bivariate diagnostic test meta-analyses, because they cannot maximize difficult likelihood functions, and they cannot be readily extended to include additional analytic options.Meta-Analyst mitigates several of the weaknesses inherent to dedicated meta-analysis packages. It incorporates their ease-of-use, while providing advanced analytic methods that can be implemented in packages such as Stata, R and SAS by a statistical programmer.http://www.github.com/bwallace/OpenMeta-analyst-.The current version of Meta-Analyst is made available free of charge to interested researchers. It runs on any version of Windows that is compatible with the .NET platform . We have already started development of a cross-platform completely open-source version of the software that uses the R statistical language, and will be readily modifiable and extendable by any interested party http://tuftscaes.org/meta_analyst/ (last accessed 11/12/2009). Meta-Analyst is made readily available to any scientist wishing to use it for non-commercial purposes, without any restriction .An installer file for the latest version of Meta-Analyst has been provided as an additional/supplemental file for the peer-reviewers [Additional File The authors declare that they have no competing interests.BW developed Meta-Analyst, porting some code to the program that was originally written by CS. TT and BW designed the testing of Meta-Analyst, and BW performed all analyses. All authors interpreted the results. BW wrote the first draft of the manuscript which was critically commented on by all other authors. All authors have read and approved the final manuscript.The pre-publication history for this paper can be accessed here:http://www.biomedcentral.com/1471-2288/9/80/prepubMeta-Analyst installer. This is a Windows installer for the Meta-Analyst software described in this manuscript.Click here for file


Meta-analyses evaluating the association between the serotonin transporter polymorphism (5-HTTLPR) with neuroticism and depression diagnosis as phenotypes have been inconclusive. We examined a gene–environment interaction on a cognitive vulnerability marker of depression, cognitive reactivity (CR) to sad mood. A total of 250 university students of European ancestry were genotyped for the 5-HTTLPR, including SNP rs25531, a polymorphism of the long allele. Association analysis was performed for neuroticism, CR and depression diagnosis (using a self-report measure). As an environmental pathogen, self-reported history of childhood emotional abuse was measured because of its strong relationship with depression. Participants with the homozygous low expressing genotype had high CR if they had experienced childhood emotional maltreatment but low CR if they did not have such experience. This interaction was strongest on the Rumination subscale of the CR measure. The interaction was not significant with neuroticism or depression diagnosis as outcome measures. Our results show that 5-HTTLPR is related to cognitive vulnerability to depression. Our findings provide evidence for a differential susceptibility genotype rather than a vulnerability genotype, possibly because of the relatively low levels of abuse in our sample. The selection of phenotype and environmental contributor is pivotal in investigating gene–environment interactions in psychiatric disorders. One of the most studied polymorphisms in psychiatry is in the promoter region of the serotonin transporter gene (5-HTTLPR). A recent meta-analysis concluded that there is no association between this polymorphism and depression, and also no interaction effect with stressful life events . The incStudies on the association of 5-HTTLPR with the personality trait of neuroticism have also produced inconsistent results (Methods). It reliably distinguishes between previously depressed and never-depressed groups and also correlates highly with CR as measured with a mood induction procedure (Cognitive reactivity (CR) to sad mood may be a more promising vulnerability measure of depression than neuroticism, and does not have the limitations of a categorical diagnosis of depression. The concept of CR is based on the differential activation theory, which states that conditioned associations that are formed between depressed mood states and dysfunctional cognitions remain intact during periods of remission . These crocedure . The LEIrocedure , which irocedure .emotional abuse (CEA). We investigated whether the association of 5-HTTLPR with CR would be stronger than with neuroticism or with depression diagnosis. We hypothesized that this relationship would be moderated by childhood abuse, in particular childhood We tested the Gene × Environment (G × E) hypothesis in 250 university students of European ancestry. The research was approved by the Ethics Committee of the Leiden University Medical Center in the Netherlands and all participants gave written informed consent before participating in the study.m NaCl, 10 mm EDTA, 10 mm Tris pH 8, 0.1 mg/ml proteinase K and 0.5% w/v SDS) until further processing.DNA was obtained using the Oragene Self-Collection Kit—DISC format ; 200 µl of saliva was collected in lysis buffer . DNA concentrations were quantified by OD260 measurement and by agarose gel electrophoresis. The average yield was approximately 4 µg of genomic DNA per sample.The region of interest from the serotonin transporter (5-HTT) gene was amplified by triplex polymerase chain reaction (PCR) using the following primers: a FAM-labelled primer HTTLPR-FWFAM 5′-TCCTCCGCTTTGGCGCCTCTTCC-3′, and a reverse primer HTTLPR-RV 5′-TGGGGGTTGCAGGGGAGATCCTG-3′. Typical PCR reactions contained between 10 and 100 ng of genomic DNA template, and 10 pmol of forward and reverse primer. PCR was carried out in the presence of 5% DMSO with 0.5 U of BioThermAB polymerase  in a total volume of 30 µl using the following cycling conditions: initial denaturation step of 5 min at 95°C, followed by 40 cycles of 30 seconds at 96°C, 30 seconds at 61°C, 60 seconds 72°C and a final extension step of 10 min at 72°C. After PCR, 5 µl of the sample was subjected to restriction digestion with the enzyme HpaII in a total volume of 20 µl. Restriction was incubated for 3 h at 37°C.One microlitre of PCR product before and after restriction digestion was mixed with LIZ-500 size standard and formamide and run in two separate lanes on an AB 3100 genetic analyser set up for genotyping with 50 cm capillaries. Results were analysed using Genescan software version 3.7 , and alleles were scored visually according to the following scheme: Uncut: S, 469 bp; L, 512 bp. Cut: Sg, 402 + 67 bp; Lg, 402 + 110 bp.n = 57); L′S′ (n = 114); L′L′ (n = 77). Genotype frequencies were consistent with Hardy–Weinberg Equilibrium χ2(1) = 1.55, P = 0.21.Genotype analysis failed for two participants, yielding 248 samples for association analysis. Genotype frequencies were as follows: SS, 16.9%; SLg, 5.2%; LgLg, 0.8%; LaLg, 8.9%; SL, 37.1%; LaLa, 31.1%. Participants were divided on the basis of the triallelic classification  into three genotype groups: S′S′ ; Acceptance/ Coping ; Aggression ; Control/Perfectionism ; Risk Aversion ; Rumination . Internal consistency (Cronbach's α) for the LEIDS-R total was 0.89, and ranged between 0.62 and 0.83 for the subscales.CR was measured using the LEIDS-R  has been examined in a sample of 39 individuals: Sensitivity = 100%; Specificity = 75%; Positive Predictive Value = 79%; Negative Predictive Value = 100%; overall κ = 0.75) , which is a screening measure for maltreatment histories in both clinical and non-referred groups . The emoα = 0.87.Current symptoms of anxiety and depression were used as a covariate. An authorized Dutch translation of the Hospital Anxiety and Depression Scale (HADS), a 14-item self-report screening scale for anxiety and depression, was used (z > 3) and heterogeneity of variance on the LEIDS-R total and subscales. Figures report untransformed values. We used analysis of variance (ANOVA) to detect main effects and interactions on continuous outcomes—Univariate ANOVA was used for the LEIDS-R and neuroticism total scores and a separate multivariate ANOVA for the LEIDS-R subscales, because of high correlations with the total score. Partial eta squared  and gender distribution . No between-group differences were found on depression diagnoses . There were also no differences between genotypes in current levels of anxiety  (P = 0.94), or depression  (P = 0.61), or total symptomatology  . Genotype groups also did not differ on CEA scores  .F2,242 = 3.21, P = 0.04) . Post hoc Tukey test showed that when CEA was low, the S′S′ genotype had significantly lower scores than the L′L′ genotype (P = 0.03). When CEA was high, differences between genotype groups failed to reach significance. Furthermore, CEA was associated with higher CR scores    ; no other significant differences were found. A similar interaction was marginally significant for the Risk Aversion subscale  (P < 0.05). We examined correlations between CEA and CR for each genotype separately. Within the S′S′ genotype, we found significant correlations between CEA and the LEIDS-R total score , and with the Rumination subscale . Within the S′L′ genotype, we found a significant correlation only with the total score . There were no significant correlations within the L′L′ genotype group.ANOVA yielded a significant interaction between genotype and emotional abuse on CR    (P = 0.009) but no main effect of genotype and no interaction (P > 0.58) (Nagelkerke R2 = 0.094) (We found no significant G × E interaction on neuroticism (0.54)   with or = 0.094) . To detebiallelic classification, observations were consistent with HWE: χ2(1) = 3.29, P = 0.07. After controlling for current symptoms of depression and anxiety , the same results were found as with the triallelic classification [G × E interaction: RUM:  LEIDS-R total: ]. G × E interactions fell short of statistical significance for the uncorrected analyses . However, correlations between emotional abuse and the LEIDS-R outcomes for each genotype separately remained significant, consistent with the results for the triallelic classification. G × E interactions were non-significant for the other outcomes.Genotype groups were re-classified as SS vs. SL vs. LL, not taking into account the rs25531 SNP. Using the habitual in nature. In vulnerable individuals, LEIDS-R scores remain high when symptoms are low, but the scores do tend to get higher with increasing levels of depression. In other words, the scores are partly mood-dependent, which makes it important to rule out variance explained by current state. The gene–environment interaction on the Rumination subscale of the LEIDS-R is noteworthy, as rumination is a robust vulnerability factor for (and characteristic of) depression  genotype with a low emotional abuse history had significantly lower CR scores than the other genotype groups. They also had (non-significantly) higher CR than other genotypes when emotional abuse in childhood was above threshold. After controlling for current levels of depression and anxiety symptoms, the G × E interactions become stronger and were replicated in the analysis using the biallelic classification. The LEIDS-R is designed to capture negative cognitive patterns of reactivity, which are pression . The sampression . FurtherThis pattern of a crossover interaction on CR supports the ‘differential susceptibility’ theory that states that individuals of a certain genetic make-up are not merely vulnerable to an adverse outcome but rather ‘susceptible’: they are more likely to suffer from an adverse environment but also benefit more from a supportive one , and abused individuals may have failed to recollect their maltreatment (i.e. mis-specification of E variable). Another limitation is that we were unable to use the established cut-off scores for dichotomizing CEA because of the relatively low mean scores in our sample. Future studies examining samples with higher exposure to maltreatment may detect genotype vulnerability effects of greater magnitude, especially in the Our sample was comprised of young adults, which is consistent with previous studies that successfully detected a gene–environment interaction on depression outcomes . Finally


The analytical performance and practicability of the Boehringer Mannheim (BM)/Hitachi 911 analysis system have been assessed in a multicentre evaluation, which involved six laboratories from European countries. Analytes commonly used in classical clinical chemistry were tested in a core programme, which mainly followed the ECCLS guidelines. In addition, a satellite programme covered other analytes, such as proteins, drugs and urine analytes. In total, the study comprised more than 100 000 data items collected over a three-month period. The evaluation was supported with ‘Computer Aided Evaluation’ (CAEv) and telecommunications.Acceptance criteria for the results were established at the beginning of the study. Nearly all of the analytes met the imprecision limits: within-run imprecision (as CVs) was 2% for enzyme and substrate assays, 1% for ISE methods and 5% for immunoassays; between-day imprecision was 3l% for enzyme and substrate assays, 2% for ISE methods and 10% for immunoassays.No relevant drift effects (systematic deviation ≥ 3%) were observed over eight hours. The methods were linear over a wide range. Sample-related and reagent-dependent carry-over can be reduced to a negligible amount by integration of a softwarecontrolled wash-step.Endogenous interferences were found for creatinine (Jaffé method) and uric acid assays (caused by bilirubin), for creatine kinase, creatine kinase MB isoform and γ-glutamyltransferase (caused by haemoglobin), and for immunoglobulin A (caused by lipaemia)Accuracy was checked by an interlaboratory survey, recovery studies in control materials and method comparison studies. The survey showed that, with the exception of cholesterol and iron in two laboratories, the recovery of analytes did not deviate by more than 5%. Sixty-six of the 77 method comparisons performed met the acceptance criteria. The deviations of the remaining 11 results could be explained by differences in either calibration, application or by the use of different methods.Practicability was assessed using a questionnaire which covered all of the important aspects of an analysis system in the clinical laboratory. Twelve groups of attributes out of 14 were rater higher for the BM/Hitachi 911 than for the present situation in the laboratories concerned. Especially high scores were given for the versatility group.The acceptance criteria for the analytical performance of the BM/Hitachi 911 analysis system were fulfilled in all laboratory segments with few exceptions. The practicability exceeded the requirements in most of the attributes. The results of the study confirmed the usefulness of the system as a consolidated workstation in small- to medium-sized clinical laboratories and in STAT laboratories, or as an instrument for special analytes like proteins and drugs, or for urinalysis in large laboratories.


In conventional epidemiology confounding of the exposure of interest with lifestyle or socioeconomic factors, and reverse causation whereby disease status influences exposure rather than vice versa, may invalidate causal interpretations of observed associations. Conversely, genetic variants should not be related to the confounding factors that distort associations in conventional observational epidemiological studies. Furthermore, disease onset will not influence genotype. Therefore, it has been suggested that genetic variants that are known to be associated with a modifiable (nongenetic) risk factor can be used to help determine the causal effect of this modifiable risk factor on disease outcomes. This approach, mendelian randomization, is increasingly being applied within epidemiological studies. However, there is debate about the underlying premise that associations between genotypes and disease outcomes are not confounded by other risk factors. We examined the extent to which genetic variants, on the one hand, and nongenetic environmental exposures or phenotypic characteristics on the other, tend to be associated with each other, to assess the degree of confounding that would exist in conventional epidemiological studies compared with mendelian randomization studies.n = 4,560 correlations) being significant at the p < 0.01 level . Similar findings were observed for other levels of significance. In contrast, genetic variants showed no greater association with each other, or with the 96 behavioural, socioeconomic, and physiological factors, than would be expected by chance.We estimated pairwise correlations between nongenetic baseline variables and genetic variables in a cross-sectional study comparing the number of correlations that were statistically significant at the 5%, 1%, and 0.01% level  with the number expected by chance if all variables were in fact uncorrelated, using a two-sided binomial exact test. We demonstrate that behavioural, socioeconomic, and physiological factors are strongly interrelated, with 45% of all possible pairwise associations between 96 nongenetic characteristics  are associated with the risk of a particular disease. The link between smoking and lung cancer was discovered in this way. Once the modifiable factors associated with a disease are established as causal factors, individuals can reduce their risk of developing that disease by avoiding causative factors or by increasing their exposure to protective factors. Unfortunately, modifiable factors that are associated with risk of a disease in observational studies sometimes turn out not to cause or prevent disease. For example, higher intake of vitamins C and E apparently protected people against heart problems in observational studies, but taking these vitamins did not show any protection against heart disease in randomized controlled trials . One explanation for this type of discrepancy is known as confounding—the distortion of the effect of one factor by the presence of another that is associated both with the exposure under study and with the disease outcome. So in this example, people who took vitamin supplements might have also have exercised more than people who did not take supplements and it could have been the exercise rather than the supplements that was protective against heart disease.It isn't always possible to check the results of observational studies in randomized controlled trials so epidemiologists have developed other ways to minimize confounding. One approach is known as mendelian randomization. Several gene variants have been identified that affect risk factors. For example, variants in a gene called APOE affect the level of cholesterol in an individual's blood, a risk factor for heart disease. People inherit gene variants randomly from their parents to build up their own unique genotype . Consequently, a study that examines the associations between a gene variant and a disease can indicate whether the risk factor affected by that gene variant causes the disease. There should be no confounding in this type of study, the argument goes, because different genetic variants should not be associated with each other or with nongenetic variables that typically confound directly assessed associations between risk factors and disease. But is this true? In this study, the researchers have tested whether nongenetic risk factors are confounded by each other and also whether genetic variants are confounded by nongenetic risk factors and also by other genetic variantsUsing data collected in the British Women's Heart and Health Study, the researchers calculated how many pairs of nongenetic variables  were significantly correlated with each other. That is, the number of pairs of nongenetic variables in which a high correlation between both variables occurred in more study participants than expected by chance. They compared this number with the number of correlations that would occur by chance if all the variables were totally independent. When the researchers assumed that 1 in 100 combinations of pairs of variables would have been correlated by chance, the ratio of observed to expected significant correlations was seen 45 times more frequently than would be expected by chance. When the researchers repeated this exercise with genetic variants, the ratio of observed to expected significant correlations was 1.58, a figure not significantly different from 1. Similarly, the ratio of observed to expected significant correlations when pairwise combinations between genetic and nongenetic variants were considered was 1.22.These findings have two main implications. First, the large excess of observed over expected associations among the nongenetic variables indicates that many nongenetic modifiable factors occur in clusters—for example, people with healthy diets often have other healthy habits. Researchers doing observational studies always try to adjust for confounding but this result suggests that this adjustment will be hard to do, in part because it will not always be clear which factors are confounders. Second, the lack of a large excess of observed over expected associations among the genetic variables  indicates that little confounding is likely to occur in studies that use mendelian randomization. In other words, this approach is a valid way to identify which environmentally modifiable risk factors cause human disease.http://dx.doi.org/10.1371/journal.pmed.0040352.Please access these Web sites via the online version of this summary at epidemiology and on mendelian randomization .Wikipedia has pages on Epidemiology for the Uninitiated is a primer from the British Medical Journalthe British Women's Heart and Health StudyInformation is available on  Observational epidemiology has had notable successes, but also high-profile failures, in that it has identified many modifiable exposures apparently increasing or decreasing disease risk that have been revealed by randomized controlled trials to be noncausal . The expOne approach to such problems in observational epidemiology is mendelian randomization . The basSuch mendelian randomization studies have been conducted within the cardiovascular and cancer fields. This approach has provided evidence that alcohol intake increases the risk of esophageal cancer , and thaDespite theoretical reasons why genetic variants should be largely unrelated to many exposures or phenotypic characteristics, it has been suggested that genetic association studies in general, and mendelian randomization approaches in particular , are susPopulation substructure would result in confounding of the genotype–outcome association if subgroups exist within a population that have different genetic histories and different disease risks (for genetic or other reasons) as this would generate misleading associations between genetic variants and phenotypes . The importance of population stratification has generated a vigorous debate, but it appears that if basic precautions are applied with respect to the ethnicity and population of origin of study sample members, and appropriate analytical strategies are applied, then bias should generally be small –24.Thus, we suggest that conventional observational epidemiology is particularly prone to confounding because nongenetic characteristics are highly associated with each other, perhaps even more so than is generally acknowledged. At the same time, mendelian randomization studies can exploit the general lack of associations between one genetic variant and other genetic variants, and between genetic and nongenetic variables, to provide an unconfounded estimate of the association between factors that the genetic variant directly influences and disease outcomes. As discuData from the British Women's Heart and Health Study were used. Full details of the selection of participants and measurements, including DNA extraction and genotyping, have been previously reported . Brieflyp-values (of the order of 0.0001 or below) are recommended to avoid false-positive claims. By using three different values of statistical significance, we were able to determine the extent to which our results were driven by the level of significance chosen.We estimated pairwise correlations between 96 nongenetic baseline variables in the study that were continuous, binary, or ordered categorical  decided a priori whether they considered that any of the nongenetic variables were measuring the same underlying characteristic or phenotype  or whether there were subgroups that were constituents of an overall variable . In order to explore the extent to which the use of genetic variants as indicators of exposure levels is valid for measuring the unconfounded associations of a nongenetic risk factor with outcomes, we examined the correlations between each of 23 genetic variants and each of the 96 nongenetic characteristics. In these analyses, we also compared observed with expected statistically significant correlations at α = 5%, 1%, and 0.01% using a two-sided binomial exact test. Results presented for these associations were not age adjusted since genetic variants should not be associated with age . When multiple single nucleotide polymorphisms (SNPs) were deliberately genotyped to form common haplotypes, based on existing literature about such haplotypes, only one of these SNPs (selected at random) was included in the analyses  would be expected to be associated by chance at the 5% significance level (α = 0.05). However, 2,447 (54%) of the correlations were significant at the α = 0.05 level, giving an observed to expected (O:E) ratio of 11, 0.000001 . At the 0.000001 . At the p < 0.01 level. At both α = 0.05 and α = 0.01, the median magnitude of the statistically significant age-adjusted partial correlation coefficients was 0.08 . At α = 0.0001, the median magnitude of the statistically significant, age-adjusted partial correlation coefficients was 0.11 .p < 0.05 level 12.7 would be expected to be associated by chance and 14 were observed to be associated at this level . At the p < 0.01 level there were four observed associations compared to 2.53 expected, O:E = 1.6, p = 0.33.The 23 genetic characteristics gave 253 possible pairwise correlations. At the APO_AV was associated with 11 nongenetic characteristics at α = 0.05 , giving an O:E ratio of 2.3, p for difference O:E = 0.009. The number of significant associations of this variant with nongenetic characteristics at α = 0.01 was no greater than expected. The hepatic-lipase genetic variant was associated with ten nongenetic characteristics at α = 0.05  giving an O:E of 2.1, p for difference O:E = 0.02. This variant was also associated with four of these nongenetic characteristics at α = 0.01 , giving an O:E ratio at this level of significance of 2.5, p for difference O:E = 0.02.When we examined the association of each individual SNP with all 96 nongenetic factors, the observed pairwise correlations were similar to expected, with the exception of four variants at the α = 0.05 level and two variants at α = 0.01  and 2. Ap for difference O:E = 0.02. This variant was also associated with four of these characteristics at α = 0.01 ; O:E ratio 2.5, p for difference O:E = 0.02. Finally, variants in TNFA were associated with 11 nongenetic characteristics at α = 0.05 , giving an O:E of 2.3, p for difference O:E = 0.009, with the number of associations of this variant with nongenetic characteristics at α = 0.01 being no greater than expected.The variant in the lipoprotein lipase gene was associated with ten nongenetic characteristics at α = 0.05 , giving an O:E ratio of 2.1, n = 0.0096) of the nongenetic variants. However, variation in the lactase gene was associated with mean outdoor temperature and rainfall in the area and month of the participants birth; variation in CETP was associated with high-density lipoprotein cholesterol; and variants in LPL were associated with triglyceride levels. The remaining 20 variants were not associated with any of the nongenetic characteristics at p ≤ 0.0001 (unpublished data).At α = 0.0001, each variant would be expected to be significantly associated with none  and α = 0.01 , and higher than expected at α = 0.0001 .Considering all 23 SNPs and 96 nongenetic factors , the number of expected significant correlations by chance would be 110, 22.1, and 0.221 at α = 0.05, 0.01, and 0.0001, respectively. We observed values similar to this at α = 0.05  and the 0.0001 level . This illustrates the considerable difficulty of determining which associations are valid and potentially causal from a background of highly correlated factors, reflecting that behavioural, socioeconomic, and physiological characteristics tend to cluster. This tendency will mean that there will often be high levels of confounding when studying any single factor in relation to an outcome. Given the complexity of such confounding, even after formal statistical adjustment, a lack of data for some confounders, and measurement error in assessed confounders will leave considerable scope for residual confounding .p < 0.01 level in our study, and our study being unexceptional with regard to the levels of confounding that will be found in observational investigations, it is clear that the large majority of associations that exist in observational databases will not reach publication. We suggest that those that do achieve publication will reflect apparent biological plausibility  as protective against cardiovascular disease and cancer, with the findings of randomized controlled trials of these dietary factors ,27. Inderiterion ) and theriterion —but not riterion  that renriterion .p ≤ 0.01 and p ≤ 0.05 of 0.08, and it might be considered that such weak associations are unlikely to be important sources of confounding. However, so many associated nongenetic variables, even with weak correlations, can present a very important potential for residual confounding. For example, we have previously demonstrated how 15 socioeconomic and behavioural risk factors, each with weak but statistically independent (at p ≤ 0.05) associations with both vitamin C levels and coronary heart disease (CHD), could together account for an apparent strong protective effect (odds ratio = 0.60 comparing top to bottom quarter of vitamin C distribution) of vitamin C on CHD [The magnitudes of most of the significant correlations between nongenetic characteristics were small see , with a C on CHD .The independence of genetic and environmental factors is of importance in other domains of genetic epidemiology, in addition to that of mendelian randomization. First, case-only studies necessarily assume the independence of genetic and environmental factors in their basic rationale ,34. SecoThree of the authors decided a priori which baseline characteristics were likely to be biologically closely related to each other or likely to be measuring the same underlying characteristic and did not include such variables in the overall correlations. Other investigators might have come up with somewhat different grouping of variables. However, the very high proportion of statistically significant associations at all three levels of significance and the similar findings with sensitivity analyses using different nongenetic characteristics  suggest that our findings are likely to be replicated even with different opinions about which baseline nongenetic variables should be included in the analyses (provided this selection of nongenetic variables was done a priori within any given dataset).We also deliberately chose only one genetic variant when we had typed several within a gene; this selection ensured there is no association caused by linkage disequilibrium due to close physical proximity of variants. It is possible that pleiotropy or population stratification could generate associations between genetic variants and nongenetic factors, but we do not see strong evidence of this in our study population of United Kingdom (UK) women, very largely of white European origin.MTHFR variant and the SNP that marks the lactase persistence trait, two well-known and widely studied variants with clear biological correlates. The number of associations found with phenotypic variables should, therefore, be higher for our SNPs than for a group of SNPs selected without reference to known function. Four of the chosen variants  were associated with more phenotypes than expected at either the 0.05 or 0.01 significance level. It is possible that these variants are involved in such a wide range of biological processes that the observations are causal. However, these “positive” findings, particularly those at the 0.05 level, may well simply represent the play of chance and be nonreplicable in future studies. In support of our general hypothesis that in mendelian randomization studies, genetic variants are seldom confounded by phenotypic factors [The genetic polymorphisms that we investigated were those that had been assayed in this cohort study. The variants that we have typed to date are those that we (or study collaborators) wish to use in mendelian randomization studies or to replicate previous association studies. Thus, these variants have all been selected on the grounds that there was some evidence that they relate to biological differences between individuals for phenotypes or disease outcomes that we have assessed in this cohort. Therefore, they are a group of variants that will tend to be related to phenotypic differences. Our variants include, for example, the C→T677  factors , overallp-value threshold for genetic association studies (p ≤ 0.0001), only four (0.18%) out of 2,208 associations of 23 genetic variants with 96 nongenetic variants were statistically significant. Although this is greater than the number (0.22) expected by chance, the proportion of statistically significant associations of genotype with nongenetic characteristics is considerably smaller than the proportion of significant associations between nongenetic characteristics (0.18% versus 30%) at this level of significance. It is difficult to believe that all or a substantial proportion of the 1,378 statistically significant associations (at p ≤ 0.0001) between two nongenetic characteristics are truly causal, whereas the four associations of genetic variants with nongenetic factor associations at this level of significance may well be real. The association of variants in lactase with mean outdoor temperature and rainfall for the area and month of birth of the participant is likely to reflect the established population stratification for this variant [CETP with high-density lipoprotein cholesterol [LPL with triglycerides [At a more realistic  variant ,40 Sincelesterol –43; and ycerides —reflect Our findings provide reassuring evidence that utilising genetic variants in mendelian randomization studies is generally a legitimate strategy. Furthermore, statistical methods that assume independence of genetic and environmental factors are also legitimate in many circumstances –38. Our Table S1(38 KB DOC)Click here for additional data file.Table S2(27 KB DOC)Click here for additional data file.Table S3(46 KB DOC)Click here for additional data file.Text S1(35 KB DOC)Click here for additional data file.


Hyssopus cuspidatus Boriss from Xinjiang has great value. This article deals with the detailed pharmacognostic evaluation of the crude drug H. cuspidatus Boriss.It is reported that the plant H. cuspidatus Boriss from Xinjiang, China, was extracted by the method of hydrodistillation and the chemical composition of the essential oil was analyzed by gas chromatography-mass spectrometry (GC–MS).The essential oil of The yield of essential oil based on the dry weight of the plant was 0.6%(w/w). Fifty compounds accounting for 99.42% of the total oil were identified. The major components were oxygenated terpenes (66.33%), monoterpenes (26.14%), oxygenated sesquiterpenes (1.25%), and octane (1.85%).H. cuspidatus Boriss and can possibly aid pharmacognostic and taxonomic species identification.Oxygenated terpenes were the main group of the compounds. The physicochemical parameters presented in this article may be proposed as parameters to establish the authenticity of  Hyssopus cuspidatus Boriss has various uses in the Xinjiang medicinal system, but there are few high-quality human trials researching these uses. It has been used traditionally as an antispasmodic for its antispasmodic action,[It is commonly known that c action, expectorH. cuspidatus Boriss and its oil are mainly used in the treatment of respiratory diseases. Hippocrates recommended using hyssop to treat bronchitis. Today, H. cuspidatus Boriss is used in the treatment of nasal congestion and mild irritations of the respiratory tract. The essential oil contains pinocamphone and isopinocamphone, which have neurotoxic effects.[ effects.3H. cuspidatus Boriss has two native and one cultivated species in China, which is one of the aromatic endemic plants in the Xinjiang Province, of China. This plant, with the common local name of Shenxiangcao or Uygur’s name zufa, has been of interest to Uygur traditional medicine, especially in Arletai. Infusion obtained from the aerial parts of H. cuspidatus Boriss is used traditionally for the following purposes: carminative and antispasmodic, in the treatment of cough, bronchitis, colds and chronic catarrh, and also for its tonic effects on the digestive, urinary, nervous, and bronchial systems.[ systems.5H. cuspidatus Boriss officinalis have previously been done, but not for H. cuspidatus Boriss.The literature survey showed that the evaluation of the physicochemical properties and the essential oil composition of H. cuspidatus Boriss.In spite of the numerous medicinal uses attributed to this plant, pharmacognosy information about this plant has not been published. Hence, the present investigation is an attempt in this direction, including the determination of physicochemical constants, the preliminary phytochemical screening, physicochemical analysis, and essential oil composition analysis of the water extract of H. cuspidatus Boriss were collected locally from the Arletai mountain of Xinjiang Province, China. Voucher specimens were deposited in the Traditional Chinese Medicine College Museum of Chinese herbal samples of Xinjiang Medical University.The aerial parts of Solvents, namely, petroleum ether, chloroform, ethanol (95%), methanol, formic acid, and reagents, namely, ammonia, iodine, ferric chloride, acetic acid, nitric acid, sulfuric acid, silicowolframic acid, hydrochloric acid, bromocresol green, α-naphthol, ninhydrin, gelatin, and so on, were purchased from Tianjin Fu-Yu Meticulous Chemical Reagent Company, China.Gas chromatography-mass spectrometry (GC–MS) analyses were carried out by using Shimadzu QP-2010 GC–MS system.H. cuspidatus Boriss by qualitative chemical tests, the entire plant extract was obtained by using petroleum ether, ethanol (95%), and water.The entire plant was ground to obtain a fine powder, which has been put into experiments. Preliminary phytochemical screening was carried out by using standard procedures according to the official method prescribed. To detecet al.[Physicochemical analyses to obtain the percentage of ash values and extractive values were performed according to the official methods prescribed in the book of extraction and abstraction of active ingredient from plant drug. Fluorescet al.The sample (100 g) was steam distilled with a Clevenger-type apparatus for 6 h. The oil was collected and dried over anhydrous sodium sulfate, then stored at 4°C for analysis.GC–MS analyses were carried out by using a Shimadzu QP-2010 GC–MS system, operated in the EI mode at 70 eV with scanning from 41 to 450 amu at 0.5 s, using a DB-5  capillary column. The temperature program was 40°C –250°C at a rate of 5°C/min. Injector and transfer line temperatures were 250°C, the ion source temperature was 200°C. Helium was used as the carrier gas, flow rate 1 mL/min. Split ratio was 1:100.6–C22) internal standards. The mass spectra and relative retention indices (RI) were compared with those of commercial (NIST 05 and NIST 05 s). Area percentages were obtained from the TIC response without using an internal standard.The identification of the components was made by comparison of their retention time with respect to the n-alkane series , acid-insoluble ash value is 0.28% (w/w), most of the total ash is soluble in acid, so acid-insoluble ash value is very low. Meanwhile, the water-soluble ash value is 1.56% (w/w), the water-soluble extractive is very high in this plant, extractive value is 11.47% (w/w). But ethanol-soluble extractive value and ether-soluble extractive value is low, 2.50% (w/w) and 1.79% (w/w), respectively. The results are presented in Tables H. cuspidatus Boriss is presented in The result of fluorescence analysis of the entire plant power of H. cuspidatus Boriss were reduced to coarse powder and the oil was isolated by hydrodistillation using a Clevenger-type apparatus for 6 h. The oil was subsequently dried over anhydrous sodium sulfate. The volatile light orange yellow oil (0.6% w/w) was analyzed by GC–MS [The air-dried aerial parts of by GC–MS . The com


Pneumonia remains the leading cause of death in young children globally and improved diagnostics are needed to better identify cases and reduce case fatality. Metabolomics, a rapidly evolving field aimed at characterizing metabolites in biofluids, has the potential to improve diagnostics in a range of diseases. The objective of this pilot study is to apply metabolomic analysis to childhood pneumonia to explore its potential to improve pneumonia diagnosis in a high-burden setting.+ and Random Forests (RF). ‘Unsupervised’ (blinded) data were analyzed by Principal Component Analysis (PCA), while ‘supervised’ (unblinded) analysis was by Partial Least Squares-Discriminant Analysis (PLS-DA) and Orthogonal Projection to Latent Structures (OPLS). Potential markers were extracted from S-plots constructed following analysis with OPLS, and markers were chosen based on their contribution to the variation and correlation within the data set. The dataset was additionally analyzed with the machine-learning algorithm RF in order to address issues of model overfitting and markers were selected based on their variable importance ranking. Unsupervised PCA analysis revealed good separation of pneumonia and control groups, with even clearer separation of the groups with PLS-DA and OPLS analysis. Statistically significant differences (p<0.05) between groups were seen with the following metabolites: uric acid, hypoxanthine and glutamic acid were higher in plasma from cases, while L-tryptophan and adenosine-5′-diphosphate (ADP) were lower; uric acid and L-histidine were lower in urine from cases. The key limitation of this study is its small size.Eleven children with World Health Organization (WHO)-defined severe pneumonia of non-homogeneous aetiology were selected in The Gambia, West Africa, along with community controls. Metabolomic analysis of matched plasma and urine samples was undertaken using Ultra Performance Liquid Chromatography (UPLC) coupled to Time-of-Flight Mass Spectrometry (TOFMS). Biomarker extraction was done using SIMCA-PMetabolomic analysis clearly distinguished severe pneumonia patients from community controls. The metabolites identified are important for the host response to infection through antioxidant, inflammatory and antimicrobial pathways, and energy metabolism. Larger studies are needed to determine whether these findings are pneumonia-specific and to distinguish organism-specific responses. Metabolomics has considerable potential to improve diagnostics for childhood pneumonia. Pneumonia is the biggest single cause of death in children, accounting for around 20% of 10 million deaths under the age of 5 years every year globally, 70% of these occurring in sub-Saharan Africa Case management will remain a key strategy in reducing the mortality of pneumonia, and other infectious diseases, even if current vaccines fulfill their promise. Better diagnostics will be needed to improve case management, the more so as the introduction of conjugate vaccines worldwide changes the aetiology and epidemiology of pneumonia Metabolomics is a rapidly evolving field that aims to identify and quantify the concentration changes of all the metabolites  in a biofluid  or model system. This approach has been used successfully to identify biomarkers following exposure to ionizing radiation This paper describes the application of metabolomic methods in a pilot study to characterize children with and without severe pneumonia. The objective is to obtain preliminary data to assess whether metabolomic analysis might be able to distinguish these groups and hence have potential diagnostic application. It is also hoped that this data might provide pointers for the future exploration of disease mechanisms in childhood pneumonia.The Gambia is a geographically long and narrow sub-Saharan African country, extending 400 km inland from the West African coast along the Gambia River. It has a population of 1.4 million, over 40% of which is less than 15 years of age (2003 census) Cases are children aged between 2 and 59 months, originating from within the study area, presenting to the Medical Research Council (MRC) hospital in Fajara, the Royal Victoria Teaching Hospital in Banjul (RVTH), Fajikunda Health Centre, Serekunda Health Centre, or Brikama Health Centre with severe or very severe pneumonia defined clinically by modified World Health Organization (WHO) criteria. Severe pneumonia is defined as cough or difficulty breathing, plus any of lower chest wall indrawing, nasal flaring, or an oxygen saturation of <90% on pulse oximetry (the latter defining very severe pneumonia). Children with a cough of two or more weeks, those with severe anemia (Hb <6 g/dL) and those with confirmed wheeze at recruitment are excluded. Community controls without WHO-defined pneumonia, matched by neighborhood age and sex, are recruited for cases in a 1∶1 ratio.Streptococcus pneumoniae and Haemophilus influenzae specific primers  and 16SrRNA primers with sequencing of the gel electrophoresis bands for identification of other bacterial species. The culture and molecular methods used have been described elsewhere Blood from cases was collected for culture and a full blood count, and urine was collected where practicable. Percutaneous lung aspiration for culture was performed where defined safety criteria were met and written informed consent was given. Blood and lung aspirate samples were also subjected to molecular analysis for pathogen detection using + = 311.0814 m/z and [M−H]− = 309.0658m/z) at a concentration of 300 pg/mL in 50% acetonitrile at a rate of 40 mL/min, were used for accurate mass measurements.Urine and plasma samples were analyzed using Ultra Performance Liquid Chromatography (UPLC) coupled to Time-of-Flight Mass Spectrometry (TOFMS) from Waters . In particular, urine samples were deproteinized by 1∶5 dilution in 50% acetonitrile with 2 mM debrisoquine sulfate and 30 mM 4-nitrobenzoic acid as internal controls. Plasma samples were deproteinized by 1∶40 dilution in 66% acetonitrile containing 2 mM debrisoquine sulfate and 30 mM 4-nitrobenzoic acid as internal controls. Following centrifugation at 13,000× g, 5 mL of the supernatant were injected into the UPLC-TOFMS. A Waters Acquity UPLC BEH C18 2.1×50 mm column packed with 1.7 mm beads was used to separate the molecules in the biofluids set at 40°C for the urine and 60°C for plasma. The mobile phase flow rate was set at 0.5 mL/min. The gradient mobile phase consisted of water with 0.1% formic acid (A) and acetonitrile containing 0.1% formic acid (B). A 10 min urine sample run consisted of 0.5 min of 99% (A), 3.5 min of 20% (B), 4 min of 95% (B), 1 min of 99% (B), and finally 1 min of 99% (A). A 10 min plasma sample run consisted of 0.5 min of 100% of (A), 3.5 min of 60% of (B), 5 min of 100% of (B), and finally 1 min of 100% of (A). Mass spectrometry and accurate mass acquisition was performed with a Waters QTOF Premier®  operating at either positive-ion (ESI+) or negative-ion (ESI-) electrospray ionization mode. The capillary voltage was set to 3200 V and the sampling cone voltage to 45 V. The desolvation gas flow was set to 800 L/h and the temperature was set to 350°C. The cone gas flow was set to 25 L/h for plasma and 15 L/h for urine and the temperature was set to 130°C. Intermittent injections of sulfadimethoxine as a lock mass  and the machine-learning algorithm Random Forests (RF). For SIMCA-P+ analysis, all centroid data were Pareto scaled, which increases the importance of low abundance ions without giving importance to noise. ‘Unsupervised’  data were analyzed by Principal Component Analysis (PCA), while ‘supervised’  analysis was by Partial Least Squares-Discriminant Analysis (PLS-DA) and Orthogonal Projection to Latent Structures (OPLS). Potential markers were extracted from S-plots constructed following analysis with OPLS, and markers were chosen based on their contribution to the variation and correlation within the data set.Mass chromatograms and spectra were acquired with the software MassLynx (Waters) in centroid format and markers were extracted with the software MarkerLynx (Waters). Urine samples were normalized to their respective creatinine relative peak area of [M+H]In order to address issues concerning overfitting of the data, which is common with large datasets containing relatively small numbers of samples, Random Forests analysis was performed through R, a programming language that allows for statistical processing Selected ions that showed variable differences between the Control and Pneumonia groups were chosen for further analysis and identification. Fifteen ions from either urine or plasma samples were chosen based on the S-plot and RF significance ranking and exhibited statistically significant p-values (p<0.05) based on analysis of the means of the data through two-tailed t-test. Searches for the identity of the metabolites were conducted through the publicly available online database “Madison Metabolomics Consortium Database” (MMCD) with tol (ppm) equal to twenty and ions were validated through tandem mass spectrometry (MS/MS) against pure chemicals . Chemicals of the highest available purity were either diluted in 50% acetonitrile for urine samples or 66% acetonitrile for plasma samples and fragmented with ramping collision energy of 5–30 eV. MS/MS spectra of the pure chemicals were compared to the biological sample MS/MS spectra for the masses in question.3/mm3 (median 13.4) compared to 3.9–11.0 (median 7.2) in community controls. Culture of blood (n = 11) or lung aspirate (n = 3) identified a pathogen in just one case (S. pneumoniae) and blood cultures were negative in all controls. Molecular analyses of plasma and lung aspirate identified an organism in seven out of eleven cases: S. pneumoniae in five, H. influenzae in one and S. pneumoniae and H. influenzae in the other. Five of the eleven asymptomatic community controls also had organisms identified .Among the eleven children with clinically defined severe pneumonia (the cases), five were female and six were male, and six of the eleven were under two years old while five were two years or older. Ten had radiographic changes of pneumonia, while the other had a normal radiograph. The length of illness at presentation ranged from 1–7 days (median 3 days) and four children had reportedly received antibiotics before presentation. The total white blood count of cases ranged from 7.8 to 67.7×10The urine samples were analyzed in both positive and negative ionization modes with the UPLS-TOFMS. Unsupervised PCA analysis revealed a good separation of the two groups when investigating the first two principal components for the results of positive ionization mode . HoweverRF analysis, utilizing the whole sample set of ions, showed distinct clustering of the two groups in positive mode; however, three of the pneumonia samples were misclassified as belonging in the control group. The overall classification accuracy of the sample set was 86.4%. The negative mode data had an overall classification accuracy of 95.5%, with only one sample of the pneumonia group misclassified as belonging in the controls. A heatmap was additionally constructed based on the top fifty ranked metabolites of the positive mode data, revealing patterns of differential levels of urinary metabolites between controls and pneumonia samples .+ = 169.0352 m/z and retention time of 0.3281 min was identified as uric acid, which is involved in the metabolism of purines , ion number 9 , ion number 10 , ion number 11 , and ion number 12  were tested against L-glutamine, alanyl-glycine (Ala-Gly), adipic acid, and D-glutamine for ion 8, hydantoin for ion 9, b-nicotinamide mononucleotide for ion 10, L-cystine for ion 11, and b-thymidine for ion 12. Ions 3–7 and 15  , ion number 14 , and ion number 15  were tested against 2′-deoxyadenosine and muramic acid for ion 13, cytosine and pyrrole-2-carboxylic acid for ion 14, and L-glutamine, Ala-Gly, adipic acid, and D-glutamine for ion 15. Ions 3–6, 14 and 15 Click here for additional data file.Figure S2Scores plots generated through SIMCA-P+ based on the metabolic profiles of plasma samples from controls and pneumonia patients. A, B, and C panels show the PCA, PLS-DA and OPLS scores plots from positive ionization mode plasma samples, respectively. D and E show the PLS-DA and OPLS scores plots from the negative ionization mode.(0.37 MB TIF)Click here for additional data file.Figure S3PLS-DA scores plots examining the existence of sex differences based on metabolic profiles between control and pneumonia groups. Panels A (ESI+) and B (ESI-) based on urinary metabolic profiles clearly depict that possible differences based on sex could exist in the pneumonia group. In panels C (ESI+) of plasma samples separation of profiles based on sex is still evident, whereas in panel D (ESI-) of plasma samples the separation is not existent.(0.38 MB TIF)Click here for additional data file.


This study aimed to analyze the possible resemblance or difference in outcome in a case-control study of quality of life for IBS patients compared to controls free from the disease, when a matching procedure for age and sex was applied for the control group compared to when all participating subjects were included in the control group. The main result was that almost the same and identical results were found irrespective of whether matching or not matching was applied in this epidemiological case-control study. The matching procedure however, slightly diminished the statistical power of the results. Most frequently matching is used in case-control studies but it can also be used in cohort studies. The matching procedure is often directed towards classical background factors such as sex and age. If data collection from subjects is expensive, it is desirable to optimize the amount of information obtained per subject. This could be done by matching controls with cases [Matching is not uncommon in epidemiological studies and refers to the selection of unexposed subjects’ th cases ,2. The mth cases , cancersth cases –6, pediath cases , gastroeth cases –10, and th cases .Generally, to match controls with cases is an often applied device to control for confounding in studies. This is especially relevant when there is a substantial difference in the occurrence of possible confounders between cases and controls. A confounder could be defined as a third variable that is both associated to the independent and the dependent variable. The existence of a confounder introduces a bias since an observed effect could be attributed to the confounder rather than the studied independent variable. A misconception concerning matching is that its purpose is to raise the validity of the study. However, the role of matching is not to increase validity, but rather to increase the efficiency of a study. The improvement in efficiency by matching is generally small unless the variable is a strong confounder .In a systematic literature review of techniques used to measure influences of confounding in observational studies of health effects of drug therapies 29 studies were analysed. Of these, almost all used regression techniques as the main method to control for confounding. Some studies also used stratification and only four used matching to address confounding . In mostSo the fundamental methodological question with general implications for the design of epidemiological studies is if one should match or not match controls with cases. What methodological benefits or drawbacks could be made by matching procedures? The aim of this study was to analyze the possible resemblance or difference in results and outcome in a case-control study when a matching procedure for age and sex was applied for the control group compared to when all participating subjects were included in the control group.2.2.1.The data analyzed in this study is based on a case-control design measuring quality of life for individuals in the age-group 18–65 years diagnosed during a 5-year period with the gastrointestinal disorder IBS (ICD-10 K-58 p). The IBS patients were identified from computer-based medical records from three Swedish primary health care centers in the city of Linköping  located in the south east of Sweden. The primary health care centers covered in total a catchment population of 40,000 inhabitants and hosted practically all general practitioner consultations in their respective geographical areas.2.2An analysis of the computerized medical records at the primary health care centers identified 487 IBS patients in the age-group 18–65 years. The collection of baseline data from the IBS patients has been described elsewhere ,16. Thro2.3.A postal questionnaire was sent to both the cases and controls. The main part of this questionnaire was the generic health-related quality of life measure Short Form 36 (SF-36). This well established instrument includes eight multi-item scales measuring the extent to which an individual’s health limits his or her physical, emotional and social functioning. All questions were asked in respect to the previous four weeks. Responses in the SF-36 were transferred to a standard scale, ranging from 0 (the worst possible score) to 100 (the best possible score) . The que2.4.The matching technique applied in this study was based on the variables age and sex. Three controls per IBS case were randomly selected and matched by age and sex from the control population . For the un-matched analyze, the final study population comprised the number of responders of the postal questionnaire, n = 347 IBS cases and n = 2,727 controls.2.5.2 for background variables and for the SF-36 scale by ANOVA tests, 95% confidence intervals were also calculated. A p-value of p < 0.05 was considered statistically significant. The study was approved by the Research Ethics Committee at Linkoping University, Sweden.All data were stored in a common database and statistically analyzed using the SPSS version 15.0 program . The significance of differences between cases and the controls were calculated by chi3.The proportion of females was significantly (p < 0.0001) higher for the IBS cases and also accordingly among the matched controls in comparison with the un-matched controls from the general population. For the other background variables like age-groups, civil status and educational level there were no significant difference between cases, matched controls or un-matched controls, see In In 4.A main finding in this study was that almost the same and identical main results were found irrespectively of matching or not matching was applied. However, the resemblance between matched and un-matched females was more in concordance than the same results for males. This is most likely an effect of increased statistical power related to population size when the larger un-matched group was used as comparison. A principal way in epidemiological studies to increase precision, reduce random error and increase the statistical power of a study is to enlarge the number of subjects .The role of matching in epidemiological research is somewhat controversial. Many epidemiologists routinely match on age and sex, even when they are not regarded as confounders nor extremely distributed. This practice is questionable since a matched case-control study nevertheless often requires complex statistical analysis. Further the matching procedure might reduce the statistical power in the study, which also was shown in the results of this study. But, on the other hand if the distribution of the matching factor in the case group is distributed in an extremely way, matching could be a reasonable option. A general perception of matching procedures in epidemiological studies is that it controls for confounding. Another way to control for the influence of confounding factors like age and sex than matching is to apply multivariate analysis . The mul5.A possible study limitation is that the matched and un-matched data used in this study was already collected so the analyze was based on a watching technique. In this study, females constitute two third (n = 251) of all n = 347 identified cases of IBS, thus in this respect the males are a minority group. The overload of females with IBS in this study is quite in common with other epidemiological findings of this type of gastrointestinal disease –19. The 6.This study shows that almost the same and identical results were found irrespective of matching or not matching was applied in this epidemiological case-control study. The matching procedure however, slightly diminished the statistical power of the results, but only for males since this group only represented one third of all the cases. This tendency that the statistical power will be reduced when matching procedures is applied is a circumstance that talks against matching in epidemiological studies. In most epidemiological studies the procedure of multivariate analysis is instead preferable to handle confounding situations in the analysis.


Foot orthoses have been demonstrated to be effective in the management of a range of conditions, but there is debate as to the benefits of customised foot orthoses over less expensive, prefabricated devices.In a randomised, cross-over trial, 15 flat-footed participants aged between 18 and 45 years were provided with semi-rigid, customised orthoses and semi-rigid, contoured, prefabricated orthoses. Pressures and forces were measured using an in-shoe system with subjects wearing shoes alone, wearing customised orthoses, and again when wearing contoured prefabricated orthoses. Two weeks acclimatisation was included between cross-over of therapy. Repeated measures ANOVA models with post-hoc, pair-wise comparisons were used to test for differences.When compared to wearing shoes alone, wearing either the customised orthoses or the prefabricated orthoses was associated with increases in force and force time integrals in the midfoot region. Peak and maximum mean pressure and pressure-time, and force-time integrals were reduced in both the medial and lateral forefoot. There were, however, no significant differences between the customised orthoses and the prefabricated orthoses at any site.There was a similar change in loading with both the semi-rigid customised and the semi-rigid prefabricated orthoses when compared to the shoe alone condition. However, while customised devices offered minor differences over prefabricated orthoses in some variables, these were not statistically significant. The results suggest that there may be only minor differences in the effects on plantar pressures between the customised and the less expensive prefabricated orthoses tested in this study, however further research is warranted. Functional foot orthoses are reportedly helpful to patients with a range of lower limb musculoskeletal problems -5 and arIn clinical practice, a range of customised, and less expensive prefabricated orthoses are prescribed . CustomiCustomised semi-rigid orthoses have been shown to cause different mechanical effects than the cheapest prefabricated alternative, single-plane wedges. It is not appropriate therefore, to consider single-plane wedging comparable to provision of customised orthoses in clinical practice . Single-The aim of this exploratory study was to evaluate differences in the magnitude and timing of plantar pressures and forces occurring with the use of semi-rigid, cast functional foot orthoses and semi-rigid, contoured, prefabricated orthoses.The study was conducted at the University of Western Sydney between October 2002 and July 2003, with ethical approval granted by the University of Western Sydney Human Ethics Committee. Fifteen participants, aged 18–45 and with a flat foot type were recruited through the polyclinic via a poster campaign. The sample size in the study provides greater than 80% power to detect a difference between the orthoses in the force time integral of 29 N.s, incorporating a standard deviation of 27 N.s and an alpha level of 0.05 .All participants met the inclusion criteria of a relaxed calcaneal stance position of > 5° valgus, plus a Foot Posture Index score of greater than eight from a maximum score of 16  and a sc3 ethyl vinyl acetate (EVA), machined as appropriate to the clinician's prescription, and a thin vinyl top cover was added.To allocate orthosis type, sealed-envelope randomisation was employed, with intervention cross over. Following enrolment into the study, the participants underwent a standardised prone casting protocol to obtain neutral impression casts . The cus®) supplied by the same laboratory  and each device incorporated a 4° varus rearfoot post. Materials used for the manufacture of the prefabricated orthoses were the same as used for the customised devices. Comparisons of the two devices are presented in additional file The prefabricated devices were a contoured device made to a standardised last rather than a custom plaster mould. The prefabricated devices were a commercially available brand . This system has been described in the literature -26 and hParticipants were assessed using a standardised protocol. Gait speed and cadence were recorded using a stopwatch and metronome while participants walked for three minutes until a comfortable gait speed and cadence were established. Subsequent analyses used this standardised cadence and gait speed for all measures to ensure parity between conditions. All measures were made using the Pedar insoles of appropriate size fitted to a pair of lightweight canvas Dunlop Volley sneakers , from which the inner sole/linings had been stripped to create a lightweight shoe, assumed to have only a minimal effect on foot function. Measures were undertaken with participants wearing either the standard shoe only or the standard shoe and the appropriate test device.Baseline pressure and force measures were obtained prior to issue of the trial devices to avoid cumulative adaptive affects that might have occurred once orthosis wearing commenced. Participants were then randomised according to a computer generated randomisation protocol, to wear one type of orthosis for at least two-weeks prior to returning for plantar pressure and force measures. Participants then crossed over to the alternative orthosis and repeated the two-week run-in period before returning for further measurement.Participants walked for three laps of a nine meter walkway at a controlled gait speed and cadence . Pedar dth metatarsophalangeal joints), and hallux. Data from the lateral digits yielded low values with high variance. Because of the potential for error and limited importance of the lateral digit mask area, this mask was excluded from the subsequent analysis. Variables of interest were extracted from each of the five mask areas. It is not yet known precisely which measures of force and pressure are most meaningful in the clinical setting so, for completeness, a broad range was described in full. These included pressure , force , spatial (contact area) and temporal  variables. The integrals of force and pressure were also investigated.Data were compared for five mask regions Figure  correspoComparisons were made between the group mean values of the three conditions: (i) the patient in the shoe only (control) condition; (ii) while wearing the prefabricated orthoses and; (iii) while wearing the customised orthoses. Preliminary plotting and tests were undertaken to explore suitability of the data for parametric analysis. PP plots were examined and the data were interpreted against Mauchly's Test for Sphericity and found to be suitable. Repeated measures ANOVA models were used to determine the significance of the within-subject effects in each of the models, and post-hoc pair-wise comparisons were made using the Bonferroni adjustment for multiple comparisons.Force and pressure measurements for each of the mask areas are presented in detail in Tables Although the differences were not significant, the customised orthoses compared to the prefabricated devices produced decreased loading at the heel by up to 12% and increased the contact area of the midfoot  – Figure There were no significant differences between the customised and the prefabricated insoles in any of the variables measured at the heel Table . The cusNeither device had a profound effect on the heel contact area or force, with change in these variables limited to 4%. The effect of the customised and prefabricated devices did not differ significantly for any variable.Both types of device alter midfoot loading considerably, with maximum force increased by 23% (prefabricated orthoses) to 29% (customised orthoses) over the control condition , although as the prefabricated devices also demonstrated a 33% increase in midfoot contact area, the additional benefits of customisation may be limited. In the forefoot, both types of device produced similar systematic changes compared with the control, suggesting that for forefoot complaints, the mechanical effects of prefabricated and customised devices might be comparable.We note that the prefabricated orthosis used in our study was considerably less expensive than the customised device. Formal recommendations on cost effectiveness can only be made, however, on data from quality health-economic studies, in which the burden of disease and any alleviation associated with the interventions are evaluated in detail. Nevertheless, as the preparation and manufacturing are different between the two devices, it is appropriate to explore the issue of costs associated with both orthoses. The diagnostic assessment and follow-up protocols are similar for both the customised and the prefabricated orthoses. However, there are some differences in the costs incurred during the pre-manufacture stage. Customised devices require appropriate measurements  and extra time, materials and expertise to take the required neutral plaster cast. These costs are not incurred with prefabricated orthoses. Further costs are incurred at the manufacturing laboratory by the production of a positive cast and this is reflected in the cost to the practitioner. In this study the purchase price of the customised devices ($AUS 89.95) was two and a quarter times that of the prefabricated devices ($AUS 39.95). The labour, materials and laboratory costs incurred in this study indicate that at the point of issue, customised devices were 3.5 times more costly, and for the entire episode of care, 2.5 times more costly than the prefabricated devices.In common with similar studies, there are protocol issues that also warrant further discussion. Participants in the study wore standard shoes during data capture but were free to wear their own footwear in the intervening periods. Footwear is known to influence lower limb function variably  and the Also warranting consideration is the choice of the 14 day acclimatisation period. It is known that the process of acclimatising to wearing orthoses in the early stages includes both mechanical and more complex neurophysiological adaptations . Same-daTwo statistical issues warrant discussion when considering the results of this study. On the one hand, this was an exploratory study which aimed to describe the differences in loading that occur over the plantar surface of the foot in response to orthotic therapy. The results of inferential tests (ANOVAs) have been reported to indicate which variables demonstrated differences that are more likely to be of statistical significance. However, because it was an exploratory study, in which we did not pre-specify a primary hypothesis or hypotheses, many inferential (significance) tests were performed. There are important drawbacks in this approach, the most important being the increased possibility of Type I statistical errors (where insignificant effects are deemed significant because of cumulative probabilities associated with the conduct of multiple hypothesis tests). In an exploratory study such as this, the statistical significance of differences should be interpreted by the reader only as an indicator of those differences that are most likely to be of relevance. Such exploratory studies are hypothesis generating and usually lead to further study if significant findings emerge. If significant findings are found, the appropriate course of action is to then design a further study that focuses on those variables, thus reducing the number of hypothesis tests and chance of Type 1 error. With this in mind, further research would be beneficial that specifically targeted the most relevant of the variables explored in this study.On the other hand, even though we attempted to recruit sufficient participants into the study – via an a priori sample size calculation – to have appropriate statistical power to detect important differences, the sample size at 15 was still relatively small. Accordingly, for some variables our data may be at risk of Type 2 statistical error where we concluded there were no statistically significant differences, even though there may have been clinically important differences but the sample size was insufficient to detect them. With the above two statistical issues in mind it would be (i) worthwhile confirming our significant findings with further studies, and (ii) ensure these studies have sufficiently large sample sizes using appropriate a priori sample size calculations.Finally, in this exploratory study, we have evaluated the mechanical effects of two different types of devices, similar in material, with the main difference between the two the customisation process. Objective results from a study of the mechanical effects are important, but further studies incorporating a range of the broader, patient-reported factors such as symptom relief and comfort are also needed to further inform the debate.In this study both customised and prefabricated orthoses altered plantar loading in a shod foot compared to wearing a shoe without an orthosis. The customised device demonstrated minor differences over the prefabricated orthosis in some variables, but in no case were the differences statistically significant. This is in contrast to the significant differences between customised orthoses and single plane wedges evaluated in earlier studies, suggesting that the contouring of the arch of an orthotic device is influential, whether it is derived from a custom cast, or from the generic last used to form a prefabricated device.While these data indicate that customised and prefabricated orthoses alter the plantar loading profile during walking, further research is required to ascertain whether one device affords a greater mechanical effect than the other. While previous work has suggested that single plane prefabricated orthoses cannot be considered a mechanical surrogate for custom orthoses, contoured prefabricated devices may address some of the shortcomings of single-plane devices without incurring the attendant costs of customisation.KBL is a Deputy Editor of Journal of Foot and Ankle Research. It is journal policy that editors are removed from the peer review and editorial decision-making processes for papers they have co-authored.ACR designed the study, secured funding for the study, supervised data collection, performed the data analysis and wrote the manuscript. KBL assisted with the design of the study, securing funding and writing of the manuscript. AMK assisted with the design of the study, securing funding and writing of the manuscript. All authors have read and approved the final manuscript.Characteristics of the customised and prefabricated orthoses. The data compare the prescription and physical characteristics of the two types of device.Click here for file


Females with a lifetime diagnosis of major mood disorder  investigated during the luteal phase of their menstrual cycle and in a condition of clinical well-being showed higher blood serum concentrations of progesterone and allopregnanolone compared to healthy controls. Women with BD presented even higher levels than those affected by MDD. This study attempted to verify, in line with a dimensional approach, if the possible differences in neurohormonal levels may be directly linked to some syndromal clusters (dimensions) of the mood spectrum disorders indipendently of diagnosis.Premenstrual concentrations of allopregnanolone, THDOC, progesterone, and cortisol were measured in 3 groups of women: 17 BD and 14 MDD outpatients, and 16 control subjects. Psychiatric evaluation was performed with the SCID-I interview and the SCI-MOODS-SR questionnaire. The correlation between steroid levels and mood disorder syndromal cluster (SCI-MOODS-SR domains and sub-domains) was evaluated by means of analysis of main components with Varimax rotation and Kaiser's normalization .Analysis of the main components evidenced the presence of 3 components: 1) mania, 2) depression both with mixed component 3) steroid + manic cognitivity and suicidal ideas.Levels of allopregnanolone and progesterone do not correlate with the association of the depressive and manic syndromes, but rather with mixed symptomatological aspects, and in particular with cognitive manic and depressive  dimensions. Further studies should be carried out to confirm these findings. Clinical and preclinical studies have suggested that fluctuations in the peripheral and brain concentrations of progesterone and its metabolites 3α-hydroxy-5α-pregnan-20-one  and 3α,21-dihydroxy-5α-pregnan-20-one (THDOC) might play an important role in certain pathological conditions characterized by emotional or affective disturbances, including major depression, anxiety disorders, and schizophrenia progesterone  and was found to be ~90%. Quantitative analysis of steroids was performed by radioimmunoassay with specific antibodies and respective tritiated standards as described previously ,13. AntiPsychiatric assessment of all subjects was performed by a psychiatrist trained in the use of the Structured Clinical Interview for DSM-IV Axis I Disorders (SCID-I) . ClinicaData were analyzed with the SPSS-SP 12.0 software package . The correlation between steroid levels, mood disorders (SCI-MOODS-SR domains and sub-domains) and premenstrual symptoms was evaluated by means of analysis of main components with Varimax rotation and Kaiser's normalization .The plasma concentrations of cortisol, progesterone, 3α,5α-THPROG, and 3α,5α-THDOC in patients with BD, patients with MDD, and control subjects, as presented in the previous paper [The analysis of main components performed taking as items frequency of response to SCI-MOODS-SR domains and cortisol concentrations, allows 3 components accounting for a total of 70.3% of variance to be identified. Table D1-28, Depressed Mood, (0.92); D58-67, Depressive Energy, (0.86); D81-98, Depressive Cognitivity, (0.75); D150-161, Rhythmicity, (0.72); D99-108, Depressive Cognitivity, (0.67); D138-149, Rhythmicity, (0.67); D68-80, Manic Energy, (0.44); D29-57 (0.39); component 2  aggregates SCI-MOODS-SR items: D109-124, Manic Cognitivity, (0.79); D68-80, Manic Energy, (0.74); D132-137, Rhythmicity, (0.73); D29-57, Manic Mood (0.70); D125-131, Manic Cognitivity, (0.69); D81-98, Depressive Cognitivity, (0.45); D150-161, Rhythmicity, (0.37); D138-149, Rhythmicity, (0.36); and component 3  aggregates the level of cortisol (0.91) with SCI- MOODS-SR item: D99-108, Depressive Cognitivity, (0.43).The analysis of main components performed taking as items frequency of response to SCI-MOODS-SR domains and progesterone concentrations, allows 3 components accounting for a total of 71.2% of variance to be identified. Table The analysis of main components performed taking as items frequency of response to SCI-MOODS-SR domains and allopregnanolone concentrations, allows 3 components accounting for a total of 71.3% of variance to be identified. Table The analysis of main components performed taking as items frequency of response to SCI-MOODS-SR domains and THDOC concentrations, allows 3 components accounting for a total of 70.4% of variance to be identified. Table Table Analysis of the main components at all domains of the SCI-MOODS-SR rating scale, together with the levels of cortisol, allopregnanolone, progesterone and THDOC  underlines 3 components, of which the first 2, pertaining solely to SCI-MOODS-SR items, remain substantially homogeneous throughout the four analyses performed for each specific steroid.The first component is the most representative in terms of general variability and refers to depressive symptoms  but some manic components are also correlated to it . The second component refers to manic symptoms and aggregates with SCI-MOODS-SR domains D68-80 Manic Energy; D132-137 Rhythmicity; D125-131, D109-124 Manic Cognitivity; D29-57 Manic Mood; D81-98 Depressive Cognitivity .The presence of components not associated with "pure" manic or depressive syndromes seems to confirm the validity of a unitary approach in the mood spectrum suggested by the findings of a study by Cassano and coll. . This stIntroduction of the "neuroactive steroid" item does not modify the expected "core" profiles for mood disorders, depressive component 1 and manic component 2, both with a "mixed" component, but rather introduces a new exclusive component. Ultimately, depression or mania are the main syndromic components which correlate with neurosteroid concentrations, although manic components such as delusions of grandeur and, to a lesser but statistically significant degree, suicidal thoughts may also concur. The latter profile is clearly distinguishable for progesterone and allopregnanolone, cortisol is characterised by a tendency to correlate with manic components  and THDOC correlates with manic ideas alone, rendering interpretation of this finding rather complicated. Should the hypothesis of a relationship between blood serum concentrations and clinical improvment of depressive symptomatology be confirmed, it may provide an explanation for the association with manic ideas, although it would not justify the association  with depressive ideas, particularly with the series of SCI-MOODS-SR items concerned with suicidal thoughts.Similar to antidepressants, neurosteroids may possibly associate with improvement of syndromic components of "pure" forms of depression, but not with improvement of "mixed"states in which components such as suicidal ideas and delusions of grandeur frequently co-exist .With regard to blood serum concentrations of cortisol, the possible existence of two distinct psychoneuroendocrinological profiles for depressive disorders has been hypothesized , the firThe findings of the present study do not provide evidence to support this hypothesis; however, on assuming the presence of marked suicidal ideas in melancholy depression, it is feasible to maintain that a higher frequency of increased cortisol levels may have determined an association between this steroid and suicidal ideas. Moreover, atypical forms of depression would appear to be more frequent among subjects with bipolar disorders  and unliThe SCI-MOODS is an instrument that explores lifetime symptoms, while the neurosteroids concentrations are cross-sectional. As a consequence, the neurohormonal levels found performing the study 7 to 10 days before menstruation (a single observation per woman) are linked with syndromal clusters of lifetime mood spectrum. A study testing more than a single menstrual phase during a longer observational period should be carried out.Levels of allopregnanolone and progesterone do not correlate with association of the depressive and manic syndromes, but rather with "mixed" symptomatological aspects, and in particular with cognitive manic and depressive  dimensions.The specific symptomatological areas involved may indeed suggest the involvement of an endogenous mechanism similar, at least in part, to that implicated for antidepressive drugs. The data obtained in our study do not appear to exclude the possibility that an increase in neurosteroid levels may be related to improvement of the clinical picture.Further studies should be carried out to confirm these findings and to ascertain whether fluctuations observed in neurosteroid levels may be in some way related to the cyclicity of the bipolar spectrum, particularly to the prevailing of several syndromic components. To this regard, studies aimed at further investigating the role of neurosteroids in mood disorders could lead to the opening of new therapeutic frontiers.MCH, LDO and MGC conceived of the study, and participated in its design and coordination. CS and MGC participated in the design of the study and performed the statistical analysis. MCH, LDO and MGC drafted the manuscript. All authors read and approved the final manuscript.