蛋白质组签名作为动脉样硬化负担的生物标志物
Lanyue Zhang1, Murad Omarov1, LingLing Xu1
1Institute for Stroke and Dementia Research (ISD), LMU University Hospital, LMU Munich, Munich, Germany.
新的血蛋白质组签名可以识别动脉样硬化负担并预测心血管事件. 这些基于血液的生物标志物为早期疾病检测和预防提供了可扩展的成像替代方案.
科学领域:
- 心血管研究研究心血管研究
- 蛋白质组学是指蛋白质组学.
- 生物标志物发现发现
背景情况:
- 动脉样硬化是一种沉默的,渐进的疾病,往往是晚诊断.
- 目前的方法缺乏可靠的循环生物标志物来量化动脉样硬化负担.
- 图像技术是有效的,但可能不是普遍可用的.
研究的目的:
- 定义反映动脉样硬化系统负担的血蛋白质组签名.
- 评估这些特征对未来心血管事件的预测能力.
- 探索蛋白质组学作为可扩展的成像替代方案,用于亚临床动脉样硬化检测.
主要方法:
- 机器学习 (CatBoost) 应用于来自44788名英国生物库参与者的血蛋白质 (Olink Explore 3072).
- 衍生出四种不同的蛋白质组签名 (整个蛋白质组,遗传,机械,动脉).
- 在外部队列 (KORA S4,KORA-Age1) 中验证了签名,并评估了主要不良心血管事件的预测.
主要成果:
- 四个蛋白质组签名强有力的歧视已知动脉样硬化 (ROC-AUC高达0.92) 个体.
- 签名预测了无症状个体未来的主要不良心血管事件 (HR/SD增加:1.70),改善了风险预测超出SCORE2.
- 签名水平与疾病负担相关,并在验证队列中预测了心肌梗塞和中风.
结论:
- 蛋白质组特征有效捕捉动脉样硬化负担,并提高无症状个体的心血管风险预测.
- 血蛋白质组学为识别亚临床动脉样硬化提供了一个可扩展和可访问的工具.
- 这些发现支持在心血管疾病预防策略中使用蛋白质组签名.
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