Department of Inflammation & Immunity, Lerner Research Institute, Cleveland, OH 44195, USA.
Department of Gastroenterology, Hepatology and Nutrition, Digestive Disease and Surgery Institute, Cleveland Clinic, Cleveland, OH 44195, USA.
Int J Mol Sci. 2023 Oct 5;24(19):14912. doi: 10.3390/ijms241914912.
The recent advent of sophisticated technologies like sequencing and mass spectroscopy platforms combined with artificial intelligence-powered analytic tools has initiated a new era of "big data" research in various complex diseases of still-undetermined cause and mechanisms. The investigation of these diseases was, until recently, limited to traditional in vitro and in vivo biological experimentation, but a clear switch to in silico methodologies is now under way. This review tries to provide a comprehensive assessment of state-of-the-art knowledge on omes, omics and multi-omics in inflammatory bowel disease (IBD). The notion and importance of omes, omics and multi-omics in both health and complex diseases like IBD is introduced, followed by a discussion of the various omics believed to be relevant to IBD pathogenesis, and how multi-omics "big data" can generate new insights translatable into useful clinical tools in IBD such as biomarker identification, prediction of remission and relapse, response to therapy, and precision medicine. The pitfalls and limitations of current IBD multi-omics studies are critically analyzed, revealing that, regardless of the types of omes being analyzed, the majority of current reports are still based on simple associations of descriptive retrospective data from cross-sectional patient cohorts rather than more powerful longitudinally collected prospective datasets. Given this limitation, some suggestions are provided on how IBD multi-omics data may be optimized for greater clinical and therapeutic benefit. The review concludes by forecasting the upcoming incorporation of multi-omics analyses in the routine management of IBD.
近年来，测序和质谱平台等复杂技术与人工智能驱动的分析工具相结合，开创了“大数据”研究的新时代，用于研究病因和发病机制尚未确定的各种复杂疾病。这些疾病的研究直到最近还局限于传统的体外和体内生物学实验，但现在显然正在转向计算机模拟方法。这篇综述试图全面评估炎症性肠病（IBD）中omes、omics 和多组学的最新知识。本文首先介绍了 omes、omics 和多组学在健康和 IBD 等复杂疾病中的概念和重要性，然后讨论了被认为与 IBD 发病机制相关的各种 omics，并探讨了多组学“大数据”如何为 IBD 产生新的见解，转化为有用的临床工具，如生物标志物鉴定、缓解和复发预测、治疗反应和精准医疗。本文批判性地分析了当前 IBD 多组学研究的缺陷和局限性，结果表明，无论分析的 omes 类型如何，大多数当前报告仍然基于来自横断面患者队列的描述性回顾性数据的简单关联，而不是更强大的纵向收集前瞻性数据集。鉴于这一局限性，就如何优化 IBD 多组学数据以获得更大的临床和治疗效益提出了一些建议。最后，本文预测多组学分析将在 IBD 的常规管理中得到应用。
