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BackgroundThe low FODMAP diet (LFD) leads to clinical response in 50%–80% of patients with irritable bowel syndrome (IBS). It is unclear why only some patients respond.AimsTo determine if differences in baseline faecal microbiota or faecal and urine metabolite profiles may separate clinical responders to the diet from non-responders allowing predictive algorithms to be proposed.MethodsWe recruited adults fulfilling Rome III criteria for IBS to a blinded randomised controlled trial. Patients were randomised to sham diet with a placebo supplement (control) or LFD supplemented with either placebo (LFD) or 1.8 g/d B-galactooligosaccharide (LFD/B-GOS), for 4 weeks. Clinical response was defined as adequate symptom relief at 4 weeks after the intervention (global symptom question). Differences between responders and non-responders in faecal microbiota (FISH, 16S rRNA sequencing) and faecal (gas–liquid chromatography, gas-chromatography mass-spectrometry) and urine (1H NMR) metabolites were analysed.ResultsAt 4 weeks, clinical response differed across the 3groups with adequate symptom relief of 30% (7/23) in controls, 50% (11/22) in the LFD group and 67% (16/24) in the LFD/B-GOS group (p = 0.048). In the control and the LFD/B-GOS groups, microbiota and metabolites did not separate responders from non-responders. In the LFD group, higher baseline faecal propionate (sensitivity 91%, specificity 89%) and cyclohexanecarboxylic acid esters (sensitivity 80%, specificity 78%), and urine metabolite profile (Q2 0.296 vs. randomised −0.175) predicted clinical response.ConclusionsBaseline faecal and urine metabolites may predict response to the LFD.

Objective: Thiopurines are widely used as maintenance therapy in inflammatory bowel disease (IBD) but the evidence base for their use is sparse and their role increasingly questioned. Using the largest series reported to date, we assessed the long-term effectiveness of thiopurines in ulcerative colitis (UC) and Crohn’s disease (CD), including their impact on need for surgery. Design: Outcomes were assessed in 11 928 patients (4968 UC, 6960 CD) in the UK IBD BioResource initiated on thiopurine monotherapy with the intention of maintaining medically induced remission. Effectiveness was assessed retrospectively using patient-level data and a definition that required avoidance of escalation to biological therapy or surgery while on thiopurines. Analyses included overall effectiveness, time-to-event analysis for treatment escalation and comparison of surgery rates in patients tolerant or intolerant of thiopurines. Results: Using 68 132 patient-years of exposure, thiopurine monotherapy appeared effective for the duration of treatment in 2617/4968 (52.7%) patients with UC compared with 2378/6960 (34.2%) patients with CD (p

ObjectiveManagement of acute severe ulcerative colitis (ASUC) during the novel coronavirus 2019 (COVID-19) pandemic presents significant dilemmas. We aimed to provide COVID-19-specific guidance using current British Society of Gastroenterology (BSG) guidelines as a reference point.DesignWe convened a RAND appropriateness panel comprising 14 gastroenterologists and an IBD nurse consultant supplemented by surgical and COVID-19 experts. Panellists rated the appropriateness of interventions for ASUC in the context of severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) infection. Median scores and disagreement index (DI) were calculated. Results were discussed at a moderated meeting prior to a second survey.ResultsPanellists recommended that patients with ASUC should be isolated throughout theirhospital stay and should have a SARS-CoV-2 swab performed on admission. Patientswith a positive swab should be discussed with COVID-19 specialists.As per BSG guidance, intravenous hydrocortisone was considered appropriate asinitial management; only in patients with COVID-19 pneumonia was their use deemeduncertain. In patients requiring rescue therapy, infliximab with continuing steroidswas recommended. Delaying colectomy because of COVID-19 was deemedinappropriate.Steroid tapering as per BSG guidance, was deemed appropriate for all patients apartfrom those with COVID-19 pneumonia in whom a 4-6-week taper was preferred. Post-ASUC maintenance therapy was dependent on SARS-CoV-2 status but, in general,biologics were more likely to be deemed appropriate than azathioprine or tofacitinib.Panellists deemed prophylactic anticoagulation post-discharge to be appropriate inpatients with a positive SARS-CoV-2 swab.ConclusionWe have suggested COVID-19-specific adaptations to the BSG ASUC guideline using a RAND Panel.

Background:Colorectal cancer remains a leading cause of mortality and morbidity. The UK Bowel Cancer Screening Programme (BCSP) has demonstrated that detection of colorectal cancer at an earlier stage and identification of advanced pre-malignant adenomas reduces mortality and morbidity.Aim:To assess the utility of volatile organic compounds as a biomarker for colorectal neoplasia.Methods:Faeces were collected from symptomatic patients and people participating in the UK BCSP, prior to colonoscopy. Headspace extraction followed by gas chromatography mass spectrometry was performed on faeces to identify volatile organic compounds. Logistic regression modelling and 10-fold cross-validation were used to test potential biomarkers.Results:One hundred and thirty-seven participants were included (mean age 64 years [range 22-85], 54% were male): 60 had no neoplasia, 56 had adenomatous polyp(s) and 21 had adenocarcinoma. Propan-2-ol was significantly more abundant in the cancer samples (P < 0.0001, q = 0.004) with an area under ROC (AUROC) curve of 0.76. When combined with 3-methylbutanoic acid the AUROC curve was 0.82, sensitivity 87.9% (95% CI 0.87-0.99) and specificity 84.6% (95% CI 0.65-1.0). Logistic regression analysis using the presence/absence of specific volatile organic compounds, identified a three volatile organic compound panel (propan-2-ol, hexan-2-one and ethyl 3- methyl- butanoate) to have an AUROC of 0.73, with a person six times more likely to have cancer if all three volatile organic compounds were present (P < 0.0001).Conclusions:Volatile organic compound analysis may have a superior diagnostic ability for the identification of colorectal adenocarcinoma, when compared to other faecal biomarkers, including those currently employed in UK. Clinical trial details: National Research Ethics Service Committee South West - Central Bristol (REC reference 14/SW/1162) with R&D approval from University of Liverpool and Broadgreen University Hospital Trust (UoL 001098).

BACKGROUND/AIMS: Current models of Crohn's disease (CD) describe an inappropriate immune response to gut microbiota in genetically susceptible individuals. NOD2 variants are strongly associated with development of CD, and NOD2 is part of the innate immune response to bacteria. This study aimed to identify differences in fecal microbiota in CD patients and non-IBD controls stratified by NOD2 genotype. METHODS: Patients with CD and non-IBD controls of known NOD2 genotype were identified from patients in previous UK IBD genetics studies and the Cambridge bioresource (genotyped/phenotyped volunteers). Individuals with known CD-associated NOD2 mutations were matched to those with wild-type genotype. We obtained fecal samples from patients in clinical remission with low fecal calprotectin (

Data assumptions made for the ZICE trial. Descriptions of how the adherence and outcome data were derived for the ZICE study. Sensitivity analysis exploring the impact of missing data on the interpretation of the SMM analysis in the ZICE trial. (DOCX 20 kb)

Frontiers in immunology 10, 2168 (2019). doi:10.3389/fimmu.2019.02168, Published by Frontiers Media, Lausanne

BACKGROUND/AIMS: Current models of Crohn's disease (CD) describe an inappropriate immune response to gut microbiota in genetically susceptible individuals. NOD2 variants are strongly associated with development of CD, and NOD2 is part of the innate immune response to bacteria. This study aimed to identify differences in fecal microbiota in CD patients and non-IBD controls stratified by NOD2 genotype. METHODS: Patients with CD and non-IBD controls of known NOD2 genotype were identified from patients in previous UK IBD genetics studies and the Cambridge bioresource (genotyped/phenotyped volunteers). Individuals with known CD-associated NOD2 mutations were matched to those with wild-type genotype. We obtained fecal samples from patients in clinical remission with low fecal calprotectin (

The COVID-19 pandemic is putting unprecedented pressures on healthcare systems globally. Early insights have been made possible by rapid sharing of data from China and Italy. In the UK, we have rapidly mobilised inflammatory bowel disease (IBD) centres in order that preparations can be made to protect our patients and the clinical services they rely on. This is a novel coronavirus; much is unknown as to how it will affect people with IBD. We also lack information about the impact of different immunosuppressive medications. To address this uncertainty, the British Society of Gastroenterology (BSG) COVID-19 IBD Working Group has used the best available data and expert opinion to generate a risk grid that groups patients into highest, moderate and lowest risk categories. This grid allows patients to be instructed to follow the UK government’s advice for shielding, stringent and standard advice regarding social distancing, respectively. Further considerations are given to service provision, medical and surgical therapy, endoscopy, imaging and clinical trials.

ObjectiveManagement of acute severe UC (ASUC) during the novel COVID-19 pandemic presents significant dilemmas. We aimed to provide COVID-19-specific guidance using current British Society of Gastroenterology (BSG) guidelines as a reference point.DesignWe convened a RAND appropriateness panel comprising 14 gastroenterologists and an IBD nurse consultant supplemented by surgical and COVID-19 experts. Panellists rated the appropriateness of interventions for ASUC in the context of severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) infection. Median scores and disagreement index (DI) were calculated. Results were discussed at a moderated meeting prior to a second survey.ResultsPanellists recommended that patients with ASUC should be isolated throughout their hospital stay and should have a SARS-CoV-2 swab performed on admission. Patients with a positive swab should be discussed with COVID-19 specialists. As per BSG guidance, intravenous hydrocortisone was considered appropriate as initial management; only in patients with COVID-19 pneumonia was its use deemed uncertain. In patients requiring rescue therapy, infliximab with continuing steroids was recommended. Delaying colectomy because of COVID-19 was deemed inappropriate. Steroid tapering as per BSG guidance was deemed appropriate for all patients apart from those with COVID-19 pneumonia in whom a 4–6 week taper was preferred. Post-ASUC maintenance therapy was dependent on SARS-CoV-2 status but, in general, biologics were more likely to be deemed appropriate than azathioprine or tofacitinib. Panellists deemed prophylactic anticoagulation postdischarge to be appropriate in patients with a positive SARS-CoV-2 swab.ConclusionWe have suggested COVID-19-specific adaptations to the BSG ASUC guideline using a RAND panel.

Metabolomics is an emerging field, which focuses on the study of small molecules (metabolites) and their chemical processes. Metabolomics data are highly dimensional, with p>>n where p is the number of variables and n is the sample size. Variable selection is therefore a key step in metabolomics studies. There are three categories of variable selection, such as filter, wrapper and embedded methods. Common univariate filter methods such as the t-test and ANOVA (analysis of variance) have been often used in the literature to identify important metabolites for a given clinical problem. A challenge in metabolomics research is that metabolite variables tend to be highly correlated. Multivariate approaches that take into account the correlation among variables, such as PCA (principal component analysis), have been applied to reduce the dimensionality of metabolite datasets. The correlation-sharing t-test method (corT) is a filter method that also considers the correlation among variables, but to my knowledge it has only been applied to genomic data. Penalized regression, and in particular the embedded method Lasso, has also been applied for variable selection with the aim of minimising the problem of overfitting that often affects prediction models in this area. In this thesis I presented a literature review on variable selection methods and classification methods applied to metabolomics data. I proposed an extended version of the variable selection method corT, which I name adjusted correlation-sharing t-test (adjcorT). Simulation studies were carried out to compare the performance of several variable selection methods (T, corT, adjcorT and Lasso) using logistic regression for data classification. Simulations assumed a set of 200 variables of which 2 variables were discriminators. A range of sample sizes (n=50, 76, 100, 300, 500, 1000, 2000 and 20000) and of different correlation values among the discriminant variables (\rho=-0.8, -0.5, -0.2, 0, 0.2, 0.5, 0.8) were considered to explore the effect that sample size and correlation have on the classification accuracy of each method. These methods were also applied to metabolomics datasets, including data from patients with colorectal cancer (aimed at discriminating between non-cancer vs colorectal cancer groups, and healthy control vs adenoma groups) as well as, kidney disease and infant sepsis datasets. R code was developed to analyse the datasets. Cross validation, with data split into two sets (80% for training and 20% for validation) was used to compare the performance of the variable selection methods using classification accuracy, sensitivity, specificity and area under ROC. Results from the simulation studies indicate that for small sample sizes (n=50, 76), T, corT, adjcorT and Lasso often failed to select the two discriminatory variables. For example, for \rho=0.5 and n=50, only 3%, 12%, 11% and 0% of the times the two discriminatory variables were selected. Nevertheless, the detection rates for adjcorT and Lasso improved for negative strong correlations (Table 4.3). These results are consistent with the better performance in classification accuracy observed for adjcorT and Lasso for negative strong correlations ( -0.5 ≤ \rho < -1.0; Table 4.4). As the sample size increased towards n=300, all methods increased their ability to select the two discriminatory variables, with Lasso underperforming for positive strong correlations and corT underperforming for moderate and strong negative correlations. These differences can explain the dissimilarities observed across methods in classification accuracy for sample sizes n=300, 500 and 1000; with Lasso showing poorer performance than T, corT and adjcorT for positive strong correlations, and corT showing poorer performance than T, corT and adjcorT for moderate and strong negative correlations (Tables 4.5 and 4.6). As the sample size increases, T, adjcorT and Lasso offered a similar level of accuracy but corT still underperforms for moderate and strong negative correlations and larger sample sizes (Table 4.7). In the clinical applications, corT and adjcorT show a similar level of classification accuracy, possibly due to the positive correlation that exists among most metabolites. For non-cancer and cancer discrimination, the method T showed the worst classification accuracy followed by Lasso. Methods corT and adjcorT achieved the best level of discrimination although this was still low (AUC of 0.60; Table 5.3). For healthy control and adenoma discrimination however, methods corT and adjcorT showed the lowest AUC, followed by the T method. Lasso achieved the best level of discrimination, although this remained low (AUC of 0.65; Table 5.8). For the discrimination between bacterial and non-bacterial sepsis cases, Lasso exhibited a better performance that the other variable selection methods with 83.1% classification accuracy (Table 5.13). Lasso also offered the best level of discrimination between healthy controls and kidney disease (AUC=0.90, Table 5.21), although the four methods showed a comparable performance (AUCs=0.86 and 0.87 were achieved with the T and with the corT and adjcorT methods respectively). My work based on simulations shows that adjcorT offers a flexible approach for variable selection aimed at clinical classification, especially for datasets involving negative correlations between discriminators for medium and large samples where adjcorT consistently shows a better performance than corT. These findings were however not reproduced by the analyses on real data. I believe this is possibly due to the lack of negative correlations among metabolites in the datasets considered. Both adjcorT and corT are filter variable selection methods. Given that adjcorT showed a better performance compared to corT for negative correlations and a similar performance for positive correlations across all sample sizes investigated, adjcorT is expected to offer advantages compared to corT as a variable selection method for the analysis of some metabolomics data.