Multigene analysis can discriminate between ulcerative colitis, Crohn's disease, and irritable bowel syndrome.

BACKGROUND & AIMS: Inflammatory bowel diseases (IBDs) and the irritable bowel syndrome (IBS) are heterogeneous disorders of the gastrointestinal tract and can profoundly affect the quality of life. Because many of the symptoms of IBD are similar to those of IBS, the former may be misdiagnosed. In addition, the 2 major forms of IBD, ulcerative colitis (UC) and Crohn's disease (CD), have overlapping nonspecific, pathologic features leading to difficulties in assessing colonic inflammation and hence the term IBD unclassified has been proposed. The aim of this study was to identify and assess the utility of a certain set of marker genes that could help to distinguish IBS from IBD, and further to discriminate between UC and CD. METHODS: Subtractive suppression hybridization was used to identify IBD-specific genes in colonic mucosal biopsy specimens. In quantitative polymerase chain reaction experiments, the differential expressions of identified genes then were analyzed using a classification algorithm and the possible clinical value of these marker genes was evaluated in a total of 301 patients in 3 stepwise studies. RESULTS: Seven marker genes were identified as differentially expressed in IBD, making it possible to discriminate between patients suffering from UC, CD, or IBS with area under the receiver-operating characteristic curves ranging from 0.915 to 0.999 (P < .0001) using the clinical diagnosis as gold standard. CONCLUSIONS: Expression profiling of relevant marker genes in colonic biopsy specimens from patients with IBD/IBS-like symptoms may enable swift and reliable determination of diagnosis, ultimately improving disease management.

A statistical methodology for drug-drug interaction surveillance.

Interaction between drug substances may yield excessive risk of adverse drug reactions (ADRs) when two drugs are taken in combination. Collections of individual case safety reports (ICSRs) related to suspected ADR incidents in clinical practice have proven to be very useful in post-marketing surveillance for pairwise drug--ADR associations, but have yet to reach their full potential for drug-drug interaction surveillance. In this paper, we implement and evaluate a shrinkage observed-to-expected ratio for exploratory analysis of suspected drug-drug interaction in ICSR data, based on comparison with an additive risk model. We argue that the limited success of previously proposed methods for drug-drug interaction detection based on ICSR data may be due to an underlying assumption that the absence of interaction is equivalent to having multiplicative risk factors. We provide empirical examples of established drug-drug interaction highlighted with our proposed approach that go undetected with logistic regression. A database wide screen for suspected drug-drug interaction in the entire WHO database is carried out to demonstrate the feasibility of the proposed approach. As always in the analysis of ICSRs, the clinical validity of hypotheses raised with the proposed method must be further reviewed and evaluated by subject matter experts.

Control of confounding through secondary samples.

The control of confounding is essential in many statistical problems, especially in those that attempt to estimate exposure effects. In some cases, in addition to the 'primary' sample, there is another 'secondary' sample which, though having no direct information about the exposure effect, contains information about the confounding factors. The purpose of this article is to study the influence of the secondary sample on likelihood inference for the exposure effect. In particular, we investigate the interplay between the efficiency improvement and the possible bias introduced by the secondary sample as a function of the degree of confounding in the primary sample and the sizes of the primary and secondary samples. In the case of weak confounding, the secondary sample can only little improve estimation of the exposure effect, whereas with strong confounding the secondary sample can be much more useful. On the other hand, it will be more important to consider possible biasing effects in the latter case. For illustration, we use a formal example of a generalized linear model and a real example with sparse data from a case-control study of the association between gastric cancer and HM-CAP/Band 120.

Statistical modeling in case-control real-time RT-PCR assays, for identification of differentially expressed genes in schizophrenia.

Aspects of experimental design, statistical modeling, and statistical inference in case-control real-time reverse transcription-polymerase chain reaction (RT-PCR) assays are discussed. The background is mRNA expression data from an investigation of genes previously suggested to be schizophrenia related. Real-time RT-PCR allows large samples of individuals. However, with more individuals than positions per plate, incomplete designs are required. A basic multivariate (for several genes jointly) random-effects analysis of covariance model, incorporating heterogeneity both between and within individuals, is formulated. The use of reference genes to form additional regressors is found to be highly efficient. Because regressions between and within individuals are usually different, it is important first to average over the intraindividual replicates. This has consequences for the influence of plate effects. Topics also discussed are testing for a significant mean disease effect, differential coregulation, and the difficulty of identifying genes affected in only a subgroup of cases.

Decrease of serotonin receptor 2C in schizophrenia brains identified by high-resolution mRNA expression analysis.

BACKGROUND: RNA expression profiling can provide hints for the selection of candidate susceptibility genes, for formulation of hypotheses about the development of a disease, and/or for selection of candidate gene targets for novel drug development. We measured messenger RNA expression levels of 16 candidate genes in brain samples from 55 schizophrenia patients and 55 controls. This is the largest sample so far used to identify genes differentially expressed in schizophrenia brains. METHODS: We used a sensitive real-time polymerase chain reaction methodology and a novel statistical approach, including the development of a linear model of analysis of covariance type. RESULTS: We found two genes differentially expressed: monoamine oxidase B was significantly increased in schizophrenia brain (p =.001), whereas one of the serotonin receptor genes, serotonin receptor 2C, was significantly decreased (p =.001). Other genes, previously proposed to be differentially expressed in schizophrenia brain, were invariant in our analysis. CONCLUSIONS: The differential expression of serotonin receptor 2C is particularly relevant for the development of new atypical antipsychotic drugs. The strategy presented here is useful to evaluate hypothesizes for the development of the disease proposed by other investigators.