使用蛋白质体标记物增强预测心房动风险:与临床和多基因风险得分进行比较分析
Mengyi Liu1, Yuanyuan Zhang1, Ziliang Ye1
1Division of Nephrology, Nanfang Hospital, Southern Medical University; National Clinical Research Center for Kidney Disease; State Key Laboratory of Organ Failure Research; Guangdong Provincial Institute of Nephrology; Guangdong Provincial Key Laboratory of Renal Failure Research, Guangzhou, Guangdong, China.
Heart (British Cardiac Society)
|September 5, 2024
概括
一个新的47蛋白风险评分,包括NT-proBNP,显著改善了对心房动 (AF) 风险的预测. 这种蛋白质组方法的表现优于AF的传统临床和多基因风险评分.
科学领域:
- 心血管研究的心血管研究.
- 蛋白质组学是指蛋白质组学.
- 发现生物标志物的发现.
背景情况:
- 预测心房动 (AF) 风险对心血管健康至关重要.
- 蛋白质生物标志物对AF风险分层的有用性尚未得到充分证实.
- 现有的临床和多基因风险评分在预测事件AF方面存在局限性.
研究的目的:
- 开发和验证一种新的基于蛋白质的风险评分,用于预测事件AF.
- 将蛋白质风险评分的预测性能与已建立的风险模型进行比较.
- 评估蛋白质组数据在增强AF风险评估中的作用.
主要方法:
- 在36129名英国生物库参与者中分析了2923种血蛋白,使用了Olink Explore 3072试验.
- 在培训组 (70%) 中开发 47 蛋白质风险得分,并在测试组 (30%) 中验证.
- 蛋白质风险评分的预测准确度与HARMS2-AF模型和多基因风险评分的比较.
主要成果:
- 开发了一种47蛋白质风险评分,其中脑性尿素 (NT-proBNP) 的N端前激素是最强的预测因素.
- 与HARMS2-AF (0.751) 和多基因风险得分 (0.748) 相比,蛋白质风险得分显示出更好的预测性能 (C统计:0.802).
- 单独NT-proBNP也显示出强大的预测值 (C-统计:0.785),表现优于传统得分.
结论:
- 基于蛋白质的风险评分,特别是当包括NT-proBNP时,可以更好地预测AF风险.
- 蛋白质组生物标志物具有改善AF风险分层的巨大潜力.
- 这项研究突出了个性化心血管风险评估的有希望的新途径.
关键词:
在心房动的情况下,心房动.
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