Dataset Information

[Imaging Techniques and Differential Diagnosis for Inflammatory Bowel Disease].

ABSTRACT: The two main types of inflammatory bowel disease (IBD) are Crohn's disease and ulcerative colitis. Currently, when IBD is suspected, CT enterography is widely used as an initial imaging test because it can evaluate both the bowel wall and the outside of the bowel, helping to differentiate IBD from other diseases. When IBD is suspected, it is necessary to distinguish between Crohn's disease and ulcerative colitis. In most cases this is not difficult; however, in some cases, it is difficult and such cases are called IBD-unclassified. CT findings are often non-specific for ulcerative colitis, making it difficult to differentiate it from other diseases using imaging alone. In contrast, characteristic CT findings for Crohn's disease are often helpful in diagnosis, although diseases, such as tuberculous enteritis can mimic Crohn's disease. Recently, mutations in the gene encoding a prostaglandin transporter called SLCO2A1 have been discovered as the cause of the disease in some patients with multiple ulcers and strictures, similar to Crohn's disease. Therefore, genetic testing is being used to make a differential diagnosis.

SUBMITTER: Song KD

PROVIDER: S-EPMC10265240 | biostudies-literature | 2023 May

REPOSITORIES: biostudies-literature

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Publications

[Imaging Techniques and Differential Diagnosis for Inflammatory Bowel Disease].

Song Kyoung Doo KD

Journal of the Korean Society of Radiology 20230509 3

The two main types of inflammatory bowel disease (IBD) are Crohn's disease and ulcerative colitis. Currently, when IBD is suspected, CT enterography is widely used as an initial imaging test because it can evaluate both the bowel wall and the outside of the bowel, helping to differentiate IBD from other diseases. When IBD is suspected, it is necessary to distinguish between Crohn's disease and ulcerative colitis. In most cases this is not difficult; however, in some cases, it is difficult and su ...[more]

PMID: 37325005

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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.

Dataset Information

[Imaging Techniques and Differential Diagnosis for Inflammatory Bowel Disease].

Publications

[Imaging Techniques and Differential Diagnosis for Inflammatory Bowel Disease].

Similar Datasets

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