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Can microscopic ileitis in patients with clinically suspected inflammatory bowel disease predict the future?

ABSTRACT: BACKGROUND:The implication of microscopic ileitis finding in patients referred for ileocolonoscopy for clinically suspected inflammatory bowel disease (IBD) is not well defined, and its correlation with clinical outcome has not been fully studied. The current study aims to determine the prognostic yield of biopsies in this setting, and to evaluate the correlation of microscopic ileitis with long-term clinical outcome. METHODS:We reviewed endoscopic reports of patients referred to our department for ileocolonoscopy in the years 2010-2016, as part of a diagnostic work-up for suspected IBD. Patients whose ileocolonoscopies proved normal were included, provided that terminal ileum biopsies had been performed. Accordingly, patients were divided into groups classified as normal (normal or reactive changes) and microscopic ileitis (inflammation or ileitis of any severity). Both groups were followed prospectively to determine clinical outcome. RESULTS:A total of 439 patients met the inclusion criteria. Sixty-four (14.6%) showed inflammation on biopsy and were included in the microscopic ileitis group. Age range and gender figures did not differ significantly between the groups. Overall follow-up period was 6.1?±?2.3?years. Patients in the microscopic ileitis group were significantly associated with Crohn's diagnosis during the follow-up period compared with the normal group (19% vs 2%, OR?=?11.98, 95%CI?=?4.48-32.01; p?

SUBMITTER: Abu Baker F

PROVIDER: S-EPMC7059716 | biostudies-literature | 2020 Mar

REPOSITORIES: biostudies-literature

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Can microscopic ileitis in patients with clinically suspected inflammatory bowel disease predict the future?

Abu Baker Fadi F Z'cruz De La Garza Jesus Alonso JA Nafrin Smadar S Mari Amir A Suki Muhammed M Ovadia Baruch B Gal Oren O Kopelamn Yael Y

BMC gastroenterology 20200305 1

<h4>Background</h4>The implication of microscopic ileitis finding in patients referred for ileocolonoscopy for clinically suspected inflammatory bowel disease (IBD) is not well defined, and its correlation with clinical outcome has not been fully studied. The current study aims to determine the prognostic yield of biopsies in this setting, and to evaluate the correlation of microscopic ileitis with long-term clinical outcome.<h4>Methods</h4>We reviewed endoscopic reports of patients referred to ...[more]

PMID: 32138683

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Project description:Inflammatory bowel disease (IBD) is characterized by complex etiology and a disrupted colonic ecosystem. We provide a framework for the analysis of multi-omic data, which we apply to study the gut ecosystem in IBD. Specifically, we train and validate models using data on the metagenome, metatranscriptome, virome, and metabolome from the Human Microbiome Project 2 IBD multi-omic database, with 1,785 repeated samples from 130 individuals (103 cases and 27 controls). After splitting the participants into training and testing groups, we used mixed-effects least absolute shrinkage and selection operator regression to select features for each omic. These features, with demographic covariates, were used to generate separate single-omic prediction scores. All four single-omic scores were then combined into a final regression to assess the relative importance of the individual omics and the predictive benefits when considered together. We identified several species, pathways, and metabolites known to be associated with IBD risk, and we explored the connections between data sets. Individually, metabolomic and viromic scores were more predictive than metagenomics or metatranscriptomics, and when all four scores were combined, we predicted disease diagnosis with a Nagelkerke's R2 of 0.46 and an area under the curve of 0.80 (95% confidence interval: 0.63, 0.98). Our work supports that some single-omic models for complex traits are more predictive than others, that incorporating multiple omic data sets may improve prediction, and that each omic data type provides a combination of unique and redundant information. This modeling framework can be extended to other complex traits and multi-omic data sets.IMPORTANCEComplex traits are characterized by many biological and environmental factors, such that multi-omic data sets are well-positioned to help us understand their underlying etiologies. We applied a prediction framework across multiple omics (metagenomics, metatranscriptomics, metabolomics, and viromics) from the gut ecosystem to predict inflammatory bowel disease (IBD) diagnosis. The predicted scores from our models highlighted key features and allowed us to compare the relative utility of each omic data set in single-omic versus multi-omic models. Our results emphasized the importance of metabolomics and viromics over metagenomics and metatranscriptomics for predicting IBD status. The greater predictive capability of metabolomics and viromics is likely because these omics serve as markers of lifestyle factors such as diet. This study provides a modeling framework for multi-omic data, and our results show the utility of combining multiple omic data types to disentangle complex disease etiologies and biological signatures.

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Can microscopic ileitis in patients with clinically suspected inflammatory bowel disease predict the future?

Publications

Can microscopic ileitis in patients with clinically suspected inflammatory bowel disease predict the future?

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