Translational potential of metabolomics on animal models of inflammatory bowel disease: a systematic critical review

Research output: Contribution to journalReviewpeer-review

In the development of inflammatory bowel disease (IBD), the gut microbiota has been established as a key factor. Recently, metabolomics has become important for understanding the functional relevance of gut microbial changes in disease. Animal models for IBD enable the study of factors involved in disease development. However, results from animal studies may not represent the human situation. The aim of this study was to investigate whether results from metabolomics studies on animal models for IBD were similar to those from studies on IBD patients. Medline and Embase were searched for relevant studies up to May 2017. The Covidence systematic review software was used for study screening, and quality assessment was conducted for all included studies. Data howed a convergence of ~17% for metabolites differentiated between IBD and controls in human and animal studies with amino acids being the most differentiated metabolite subclass. The acute dextran sodium sulfate model appeared as a good model for analysis of systemic metabolites in IBD, but analytical platform, age, and biological sample type did not show clear correlations with any significant metabolites. In conclusion, this systematic review highlights the variation in metabolomics results, and emphasizes the importance of expanding the applied detection methods to ensure greater coverage and convergence between the various different patient phenotypes and animal models of inflammatory bowel disease.

Original languageEnglish
Article number3856
JournalInternational Journal of Molecular Sciences
Volume21
Issue number11
Number of pages28
ISSN1661-6596
DOIs
Publication statusPublished - 2020

    Research areas

  • Animal models, Inflammatory bowel disease, Metabolomics, Systematic review

Number of downloads are based on statistics from Google Scholar and www.ku.dk


No data available

ID: 243339321