血浆蛋白和多基因风险评分在预测炎症性肠病方面具有互补作用。

Q2 Computer Science
Jakob Woerner, Thomas Westbrook, Seokho Jeong, Manu Shivakumar, Allison R Greenplate, Sokratis A Apostolidis, Seunggeun Lee, Yonghyun Nam, Dokyoon Kim
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引用次数: 0

摘要

炎症性肠病(IBD),包括克罗恩病(CD)和溃疡性结肠炎(UC),具有重要的遗传因素,而且由于环境因素的影响,发病率越来越高。目前的多基因风险评分(PRS)的预测能力有限,无法告知症状出现的时间。循环蛋白质组学分析为了解复杂疾病的炎症状态提供了一种新颖、非侵入性的方法,使蛋白质组风险评分(ProRS)成为可能。本研究利用英国生物库中 51,772 人的数据来评估 PRS 和 ProRS 对 IBD 风险预测的独特和综合贡献。我们为 CD 和 UC 开发了 ProRS 模型,评估了它们随时间变化的预测性能,并研究了整合 PRS 和 ProRS 以增强风险分层的益处。我们的研究结果首次证明,将基因和蛋白质组数据结合在一起可提高 IBD 发病率预测,其中 ProRS 可提供时效性预测,而 PRS 可提供额外的长期预测价值。我们还表明,ProRS 对高 PRS 的个体具有更好的预测效果。这种综合方法凸显了多组学数据在 IBD 精准医疗中的潜力。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Plasma protein-based and polygenic risk scores serve complementary roles in predicting inflammatory bowel disease.

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.

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