序列式多组学分析识别了长期COVID的临床表型和预测生物标志物
Kaiming Wang1, Mobin Khoramjoo2, Karthik Srinivasan3
1Division of Cardiology, Department of Medicine, University of Alberta, Edmonton, AB, Canada; Mazankowski Alberta Heart Institute, University of Alberta, Edmonton, AB, Canada; Cumming School of Medicine, University of Calgary, Calgary, AB, Canada.
长期COVID (COVID-19的后急性后果) 涉及持续的炎症和代谢变化. 一个新的模型以83%的准确度预测不良结果,有助于长期COVID治疗的开发.
科学领域:
- 免疫学 免疫学 免疫学
- 代谢学 代谢学 代谢学
- 系统生物学 系统生物学
背景情况:
- COVID-19 (PASC) 或长期COVID的后急性后续症状呈现出多种多系统症状.
- 了解康复期间长期COVID的生物学基础对于有效的治疗策略至关重要.
研究的目的:
- 在急性COVID-19感染期间和之后全面描述血中的分子变化.
- 确定与长期COVID发展和不良结果相关的生物过程和生物标志物.
主要方法:
- 在急性感染期间和感染后6个月内从117个人收集了血样本.
- 使用了多组学方法 (细胞因子,蛋白质组,代谢组) 和网络分析.
- 开发了一个使用机器学习预测临床结果的预后模型.
主要成果:
- 在康复期间确定了持续的炎症,血小板脱粒和细胞激活.
- 揭示了关键代谢途径的调节失调,包括阿尔金因生物合成,氨酸,氨酸代谢和TCA循环.
- 开发了一种20分子预后模型,以83%的准确度 (AUC 0.96) 预测不良结果.
结论:
- 与急性感染相比,从COVID-19中康复的过程涉及不同的生物过程.
- 持续的炎症和代谢变化与长期的COVID有关.
- 预后模型为早期识别高风险患者提供了潜力,并指导了针对性治疗的开发.
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