代谢学数据通过SCORE2算法改善了10年心血管风险预测,用于没有心血管疾病或糖尿病的普通人群
Ruijie Xie1,2, Sha Sha1,2, Lei Peng1,2
1Division of Clinical Epidemiology and Aging Research, German Cancer Research Center, Im Neuenheimer Feld 581, 69120 Heidelberg, Germany.
European journal of preventive cardiology
|April 24, 2025
概括
将新陈代谢生物标志物添加到SCORE2模型中显著改善了心血管风险预测. 核磁共振 (NMR) 代谢学在预测主要不良心血管事件 (MACE) 的临床应用方面显示出前景.
科学领域:
- 心血管疾病研究研究
- 代谢学 代谢学 代谢学
- 生物标志物发现发现
背景情况:
- 代谢生物标志物对心血管风险的预测价值尚未得到充分证实.
- SCORE2模型 (2021) 估计了在没有先前心血管疾病或糖尿病的欧洲人口中10年心血管风险.
研究的目的:
- 评估是否将代谢生物标志物添加到SCORE2模型中可以提高对重大心血管不良事件 (MACE) 10年风险的预测.
- 为了在大型基于人口的队列中评估这一点.
主要方法:
- 使用了来自英国生物库 (187,039名参与者) 和德国ESTHER队列 (5,578名参与者) 的数据.
- 使用核磁共振 (NMR) 光谱测量了249种代谢物.
- 采用LASSO回归来进行代谢物选择,并使用Harrell的C指数评估预测性表现.
主要成果:
- 确定了13个关键的代谢生物标志物 (注意到性别特异性变异),以改善MACE预测.
- 将这些生物标志物添加到SCORE2模型中,在内部 (0.691到0.710) 和外部 (0.673到0.688) 验证队伍中显著提高了C指数.
- 甘氨酸蛋白乙作为一个关键的炎症生物标志物出现,有助于改善预测.
结论:
- 与SCORE2模型集成的代谢生物标志物大大提高了10年心血管风险预测.
- 由于成本降低和标准化,NMR代谢组学已经准备好临床实施.
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