Woerner Jakob, Westbrook Thomas, Jeong Seokho, Shivakumar Manu, Greenplate Allison R, Apostolidis Sokratis A, Lee Seunggeun, Nam Yonghyun, Kim Dokyoon
Genomics and Computational Biology Graduate Group, University of Pennsylvania, Philadelphia, PA, USA.
Department of Biostatistics, Epidemiology and Informatics, University of Pennsylvania, Philadelphia, PA, USA.
Pac Symp Biocomput. 2025;30:522-534. doi: 10.1142/9789819807024_0037.
Inflammatory bowel disease (IBD), encompassing Crohn's disease (CD) and ulcerative colitis (UC), has a significant genetic component and is increasingly prevalent due to environmental factors. Current polygenic risk scores (PRS) have limited predictive power and cannot inform time of symptom onset. Circulating proteomics profiling offers a novel, non-invasive approach for understanding the inflammatory state of complex diseases, enabling the creation of proteomic risk scores (ProRS). This study utilizes data from 51,772 individuals in the UK Biobank to evaluate the unique and combined contributions of PRS and ProRS to IBD risk prediction. We developed ProRS models for CD and UC, assessed their predictive performance over time, and examined the benefits of integrating PRS and ProRS for enhanced risk stratification. Our findings are the first to demonstrate that combining genetic and proteomic data improves IBD incidence prediction, with ProRS providing time-sensitive predictions and PRS offering additional long-term predictive value. We also show that the ProRS achieves better predictive performance among individuals with high PRS. This integrated approach highlights the potential for multi-omic data in precision medicine for IBD.
炎症性肠病(IBD),包括克罗恩病(CD)和溃疡性结肠炎(UC),具有显著的遗传成分,并且由于环境因素而日益普遍。当前的多基因风险评分(PRS)预测能力有限,无法告知症状发作时间。循环蛋白质组学分析为理解复杂疾病的炎症状态提供了一种新颖的、非侵入性的方法,能够创建蛋白质组风险评分(ProRS)。本研究利用英国生物银行中51772名个体的数据,评估PRS和ProRS对IBD风险预测的独特贡献和联合贡献。我们开发了针对CD和UC的ProRS模型,随时间评估其预测性能,并研究整合PRS和ProRS以加强风险分层的益处。我们的研究结果首次证明,结合遗传和蛋白质组数据可改善IBD发病率预测,ProRS提供时间敏感型预测,而PRS提供额外的长期预测价值。我们还表明,ProRS在高PRS个体中实现了更好的预测性能。这种综合方法凸显了多组学数据在IBD精准医学中的潜力。