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Ananthakrishnan A.N.,Massachusetts General Hospital | Ananthakrishnan A.N.,Harvard University | Guzman-Perez R.,Informatics | Gainer V.,Informatics | And 7 more authors.
Alimentary Pharmacology and Therapeutics | Year: 2012

Background The increasing incidence of Clostridium difficile (C. difficile) infection (CDI) among patients with inflammatory bowel disease is well recognised. However, most studies have focused on demonstrating that CDI is associated with adverse outcomes in IBD patients. Few have attempted to identify predictors of severe outcomes associated with CDI among IBD patients. Aim To identify clinical and laboratory factors that predict severe outcomes associated with CDI in IBD patients. Methods From a multi-institution EMR database, we identified all hospitalised patients with at least one diagnosis code for C. difficile from among those with a diagnosis of Crohn's disease or ulcerative colitis. Our primary outcome was time to total colectomy or death with follow-up censored at 180 days after CDI. Cox proportional hazards models were used to identify predictors of the primary outcome from among demographic, disease-related, laboratory and medication variables. Results A total of 294 patients with CDI-IBD were included in our study. Of these, 58 patients (20%) met our primary outcome (45 deaths, 13 colectomy) at a median of 31 days. On multivariate analysis, serum albumin <3 g/dL (HR 5.75, 95% CI 1.34-24.56), haemoglobin below 9 g/dL (HR 5.29, 95% CI 1.58-17.69) and creatinine above 1.5 mg/dL (HR 1.98, 95% CI 1.04-3.79) were independent predictors of our primary outcome. Examining laboratory parameters as continuous variables or shortening our primary outcome to include events within 90 days yielded similar results. Conclusion Serum albumin below 3 g/dL, haemoglobin below 9 g/dL and serum creatinine above 1.5 mg/dL were independent predictors of severe outcomes in hospitalised IBD patients with Clostridium difficile infection. © 2012 Blackwell Publishing Ltd.


Jacobsen J.C.,Massachusetts General Hospital | Gregory G.C.,Massachusetts General Hospital | Woda J.M.,Massachusetts General Hospital | Woda J.M.,Athersys | And 11 more authors.
Human Molecular Genetics | Year: 2011

Huntington's disease is initiated by the expression of a CAG repeat-encoded polyglutamine region in full-length huntingtin, with dominant effects that vary continuously with CAG size. The mechanism could involve a simple gain of function or a more complex gain of function coupled to a loss of function (e.g. dominant negative-graded loss of function). To distinguish these alternatives, we compared genome-wide gene expression changes correlated with CAG size across an allelic series of heterozygous CAG knock-in mouse embryonic stem (ES) cell lines (HdhQ20/7, HdhQ50/7, HdhQ91/7, HdhQ111/7), to genes differentially expressed between Hdhex4/5/ex4/5 huntingtin null and wild-type (HdhQ7/7) parental ES cells. The set of 73 genes whose expression varied continuously with CAG length had minimal overlap with the 754-member huntingtin-null gene set but the two were not completely unconnected. Rather, the 172 CAG length-correlated pathways and 238 huntingtin-null significant pathways clustered into 13 shared categories at the network level. A closer examination of the energy metabolism and the lipid/sterol/lipoprotein metabolism categories revealed that CAG length-correlated genes and huntingtin-null-altered genes either were different members of the same pathways or were in unique, but interconnected pathways. Thus, varying the polyglutamine size in full-length huntingtin produced gene expression changes that were distinct from, but related to, the effects of lack of huntingtin. These findings support a simple gain-of-function mechanism acting through a property of the full-length huntingtin protein and point to CAG-correlative approaches to discover its effects. Moreover, for therapeutic strategies based on huntingtin suppression, our data high-light processes that may be more sensitive to the disease trigger than to decreased huntingtin levels. © The Author 2011. Published by Oxford University Press. All rights reserved.


Sinnott J.A.,Harvard University | Dai W.,Harvard University | Liao K.P.,Brigham and Womens Hospital | Shaw S.Y.,Massachusetts General Hospital | And 11 more authors.
Human Genetics | Year: 2014

To reduce costs and improve clinical relevance of genetic studies, there has been increasing interest in performing such studies in hospital-based cohorts by linking phenotypes extracted from electronic medical records (EMRs) to genotypes assessed in routinely collected medical samples. A fundamental difficulty in implementing such studies is extracting accurate information about disease outcomes and important clinical covariates from large numbers of EMRs. Recently, numerous algorithms have been developed to infer phenotypes by combining information from multiple structured and unstructured variables extracted from EMRs. Although these algorithms are quite accurate, they typically do not provide perfect classification due to the difficulty in inferring meaning from the text. Some algorithms can produce for each patient a probability that the patient is a disease case. This probability can be thresholded to define case–control status, and this estimated case–control status has been used to replicate known genetic associations in EMR-based studies. However, using the estimated disease status in place of true disease status results in outcome misclassification, which can diminish test power and bias odds ratio estimates. We propose to instead directly model the algorithm-derived probability of being a case. We demonstrate how our approach improves test power and effect estimation in simulation studies, and we describe its performance in a study of rheumatoid arthritis. Our work provides an easily implemented solution to a major practical challenge that arises in the use of EMR data, which can facilitate the use of EMR infrastructure for more powerful, cost-effective, and diverse genetic studies. © 2014, Springer-Verlag Berlin Heidelberg.


Fossale E.,Massachusetts General Hospital | Seong I.S.,Massachusetts General Hospital | Coser K.R.,Massachusetts General Hospital | Shioda T.,Massachusetts General Hospital | And 6 more authors.
Human Molecular Genetics | Year: 2011

Huntington's disease (HD) involves marked early neurodegeneration in the striatum, whereas the cerebellum is relatively spared despite the ubiquitous expression of full-length mutant huntingtin, implying that inherent tissue-specific differences determine susceptibility to the HD CAG mutation. To understand this tissue specificity, we compared early mutant huntingtin-induced gene expression changes in striatum to those in cerebellum in young Hdh CAG knock-in mice, prior to onset of evident pathological alterations. Endogenous levels of full-length mutant huntingtin caused qualitatively similar, but quantitatively different gene expression changes in the two brain regions. Importantly, the quantitatively different responses in the striatum and cerebellum in mutant mice were well accounted for by the intrinsic molecular differences in gene expression between the striatum and cerebellum in wild-type animals. Tissue-specific gene expression changes in response to the HD mutation, therefore, appear to reflect the different inherent capacities of these tissues to buffer qualitatively similar effects of mutant huntingtin. These findings highlight a role for intrinsic quantitative tissue differences in contributing to HD pathogenesis, and likely to other neurodegenerative disorders exhibiting tissue-specificity, thereby guiding the search for effective therapeutic interventions. © The Author 2011. Published by Oxford University Press. All rights reserved.


Galkina E.I.,Massachusetts General Hospital | Shin A.,Massachusetts General Hospital | Coser K.R.,Massachusetts General Hospital | Shioda T.,Massachusetts General Hospital | And 8 more authors.
PLoS ONE | Year: 2014

Background: The length of the huntingtin (HTT) CAG repeat is strongly correlated with both age at onset of Huntington's disease (HD) symptoms and age at death of HD patients. Dichotomous analysis comparing HD to controls is widely used to study the effects of HTT CAG repeat expansion. However, a potentially more powerful approach is a continuous analysis strategy that takes advantage of all of the different CAG lengths, to capture effects that are expected to be critical to HD pathogenesis. Methodology/Principal Findings: We used continuous and dichotomous approaches to analyze microarray gene expression data from 107 human control and HD lymphoblastoid cell lines. Of all probes found to be significant in a continuous analysis by CAG length, only 21.4% were so identified by a dichotomous comparison of HD versus controls. Moreover, of probes significant by dichotomous analysis, only 33.2% were also significant in the continuous analysis. Simulations revealed that the dichotomous approach would require substantially more than 107 samples to either detect 80% of the CAG-length correlated changes revealed by continuous analysis or to reduce the rate of significant differences that are not CAG length-correlated to 20% (n = 133 or n = 206, respectively). Given the superior power of the continuous approach, we calculated the correlation structure between HTT CAG repeat lengths and gene expression levels and created a freely available searchable website, "HD CAGnome," that allows users to examine continuous relationships between HTT CAG and expression levels of ∼20,000 human genes. Conclusions/Significance: Our results reveal limitations of dichotomous approaches compared to the power of continuous analysis to study a disease where human genotype-phenotype relationships strongly support a role for a continuum of CAG length-dependent changes. The compendium of HTT CAG length-gene expression level relationships found at the HD CAGnome now provides convenient routes for discovery of candidates influenced by the HD mutation. © 2014 Galkina et al.

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