Agrawal Manasi, Allin Kristine H, Petralia Francesca, Colombel Jean-Frederic, Jess Tine
The Henry D. Janowitz Division of Gastroenterology, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
Center for Molecular Prediction of Inflammatory Bowel Disease, Department of Clinical Medicine, Aalborg University, Copenhagen, Denmark.
Nat Rev Gastroenterol Hepatol. 2022 Jun;19(6):399-409. doi: 10.1038/s41575-022-00593-y. Epub 2022 Mar 17.
Inflammatory bowel disease (IBD) is an immune-mediated disease of the intestinal tract, with complex pathophysiology involving genetic, environmental, microbiome, immunological and potentially other factors. Epidemiological data have provided important insights into risk factors associated with IBD, but are limited by confounding, biases and data quality, especially when pertaining to risk factors in early life. Multiomics platforms provide granular high-throughput data on numerous variables simultaneously and can be leveraged to characterize molecular pathways and risk factors for chronic diseases, such as IBD. Herein, we describe omics platforms that can advance our understanding of IBD risk factors and pathways, and available omics data on IBD and other relevant diseases. We highlight knowledge gaps and emphasize the importance of birth, at-risk and pre-diagnostic cohorts, and neonatal blood spots in omics analyses in IBD. Finally, we discuss network analysis, a powerful bioinformatics tool to assemble high-throughput data and derive clinical relevance.
炎症性肠病(IBD)是一种肠道免疫介导性疾病,其病理生理学复杂,涉及遗传、环境、微生物群、免疫及其他潜在因素。流行病学数据为与IBD相关的风险因素提供了重要见解,但受混杂因素、偏差和数据质量的限制,尤其是涉及早期生活中的风险因素时。多组学平台可同时提供关于众多变量的详细高通量数据,可用于表征诸如IBD等慢性疾病的分子途径和风险因素。在此,我们描述了能够增进我们对IBD风险因素和途径理解的组学平台,以及关于IBD和其他相关疾病的现有组学数据。我们强调了知识空白,并强调了出生队列、高危队列和诊断前队列以及新生儿血斑在IBD组学分析中的重要性。最后,我们讨论网络分析,这是一种强大的生物信息学工具,可整合高通量数据并得出临床相关性。