BACKGROUND Inflammatory Bowel Disease (IBD) is a multifactorial chronic disease. Understanding only one aspect of IBD pathogenesis does not reflect the complex nature of IBD nor will it improve its clinical management. Therefore, it is vital to dissect the interactions between the different players in IBD pathogenesis in order to understand the biology of the disease and enhance its clinical outcomes. AIMS To provide an overview of the available omics data used to assess the potential mechanisms through which various players are contributing to IBD pathogenesis and propose a precision medicine model to fill the current knowledge gap in IBD. RESULTS Several studies have reported microbial dysbiosis, immune and metabolic dysregulation in IBD patients, however, this data is not sufficient to create signatures that can differentiate between the disease subtypes or between disease relapse and remission. CONCLUSIONS We summarized the current knowledge in the application of omics in IBD patients, and we showed that the current knowledge gap in IBD hinders the improvements of clinical decision for treatment as well as the prediction of disease relapse. We propose one way to fill this gap by implementing integrative analysis of various omics datasets generated from one patient at a single time point.

中文翻译：

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整合组学以更好地了解炎症性肠病：迈向个性化医疗的一步。

背景炎症性肠病(IBD)是一种多因素慢性疾病。仅了解 IBD 发病机制的一个方面并不能反映 IBD 的复杂性，也不会改善其临床管理。因此，剖析 IBD 发病机制中不同参与者之间的相互作用以了解该疾病的生物学并提高其临床结果至关重要。目的 概述用于评估各种参与者促成 IBD 发病机制的潜在机制的可用组学数据，并提出一个精准医学模型来填补当前 IBD 的知识空白。结果 几项研究报告了 IBD 患者的微生物菌群失调、免疫和代谢失调，然而，这些数据不足以创建可以区分疾病亚型或疾病复发和缓解的特征。结论 我们总结了目前在 IBD 患者中应用组学的知识，我们发现目前 IBD 的知识差距阻碍了临床治疗决策的改进以及疾病复发的预测。我们提出了一种方法来填补这一空白，即对单个患者在单个时间点生成的各种组学数据集进行综合分析。