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）是一种免疫介导的肠道疾病，具有复杂的病理生理学，涉及遗传、环境、微生物、免疫和其他潜在因素。流行病学数据为了解 IBD 相关风险因素提供了重要见解，但受到混杂因素、偏差和数据质量的限制，特别是在涉及生命早期的风险因素时。多组学平台可同时提供大量变量的精细高通量数据，并可用于表征 IBD 等慢性疾病的分子途径和风险因素。在此，我们描述了可以增进我们对 IBD 风险因素和途径的理解的组学平台，以及 IBD 和其他相关疾病的可用组学数据。我们强调知识差距，并强调出生、高危人群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，用于组装高通量数据并得出临床相关性。