血蛋白质学用于预测亚临床冠状动脉化在初级预防中的预测
Patrick Royer1, Elias Björnson2, Martin Adiels3
1Department of Molecular and Clinical Medicine, Institute of Medicine, Sahlgrenska Academy, Gothenburg University, Gothenburg, Sweden; Region Västra Götaland, Sahlgrenska University Hospital, Department of Clinical Physiology, Gothenburg, Sweden; Department of Critical Care, University Hospital of Martinique, Fort-de-France, France.
American heart journal
|February 7, 2024
概括
这项研究发现,在无症状个体中,血蛋白子集并没有改善冠状动脉化 (CAC) 的预测,超出了传统的风险因素. 蛋白质组没有为检测亚临床CAC增加价值.
科学领域:
- 心血管疾病的研究研究.
- 蛋白质组学和生物标志物发现发现.
- 机器学习在医疗保健中的应用
背景情况:
- 冠状动脉样硬化检测依赖于传统的危险因素.
- 高通量蛋白质组技术为新的生物标志物发现提供了潜力.
- 亚临床冠状动脉化 (CAC) 评估对于早期干预至关重要.
研究的目的:
- 评估血蛋白子集是否可以在无症状个体中检测CAC.
- 为了确定蛋白质生物标志物是否比CAC的传统风险因素更具有预测价值.
- 评估向蛋白质组学对预测亚临床CAC的有用性.
主要方法:
- 测量了2,710个人的1,342个血蛋白,使用近距离延伸试验.
- 训练有素的极端梯度增强机器学习模型来预测CAC.
- 在50-64岁的独立队列 (IGTM和SCAPIS) 上验证的模型.
主要成果:
- 最好的血蛋白子集 (44种蛋白质) 预测了CAC,其AUC为0.691.
- 这一表现并不优于单独的传统风险因素 (AUC = 0.710).
- 将蛋白质添加到传统的危险因素中并没有显著改善预测 (AUC = 0.705).
结论:
- 血蛋白子集可以预测亚临床CAC,但没有超过传统的风险因素.
- 这项研究没有发现有针对性的蛋白质组学可以改善超越已知方法的CAC预测的支持.
- 可能需要进一步的研究来确定冠状动脉样硬化的临床相关蛋白质生物标志物.
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