心血管医学中的临床蛋白质组学:当前的能力,局限性和未来方向
Bhawana Singh1, Oleg A Karpov2, Manuel Mayr3
1National Heart and Lung Institute, Faculty of Medicine, Imperial College London, London, United Kingdom.
Atherosclerosis
|January 15, 2026
概括
高通量蛋白质组学平台加速生物标志物发现,但面临数据质量问题. 提高测量可靠性对于临床翻译和个性化医学至关重要,超越风险预测,了解疾病机制.
科学领域:
- 蛋白质组学是指蛋白质组学.
- 多个omics的多个omics.
- 流行病学 流行病学
- 生物标志物发现发现
背景情况:
- 在大型生物库中使用商业蛋白质组学平台 (如Olink, SomaLogic).
- 蛋白质组学中的数据质量问题经常被忽视,阻碍了临床翻译.
- 人工智能 (AI) 越来越多地应用于多领域的数据.
研究的目的:
- 审查目前用于临床使用的蛋白质组学平台的局限性.
- 概述改善蛋白质组学数据质量的策略.
- 探索蛋白质组学作为"液体健康检查"的潜力.
主要方法:
- 文献综述的重点是动脉样硬化心血管疾病 (ASCVD) 中的AI,多组和蛋白组.
主要成果:
- 人工智能驱动的多态学可以增强对ASCVD和衰老等多因素疾病的理解.
- 商业蛋白质组学平台加速生物标志物发现,但存在翻译挑战.
- 平台 (Olink, SomaLogic) 之间的不同结果和诸如表位效应等问题使因果推理复杂化.
- 新兴的技术,如单细胞和空间蛋白质组学,为疾病异质性提供了更深入的见解.
结论:
- 蛋白质组学可以阐明疾病机制,并帮助药物重新使用超出风险预测范围.
- 可靠的量化对于血蛋白质组学来指导患者护理至关重要.
- 蛋白质组学的临床实用性取决于测量质量,而不仅仅是数量.
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