增长差异化因素15 预测心血管事件在周围动脉疾病中的发生
Ben Li1,2,3,4, Farah Shaikh1, Houssam Younes5
1Division of Vascular Surgery, St. Michael's Hospital, Unity Health Toronto, University of Toronto, Toronto, ON M5B 1W8, Canada.
Biomolecules
|July 29, 2025
概括
增长差异化因子15 (GDF15) 是外周动脉疾病 (PAD) 患者主要不良心血管事件 (MACE) 的强有力的预测因子. 这种应激反应性细胞因子,当与机器学习一起使用时,有助于识别高风险个体,以提供个性化的心血管护理.
科学领域:
- 心血管医学 心血管医学
- 生物标志物发现发现
- 医疗技术 医疗技术 医学技术
背景情况:
- 周围动脉疾病 (PAD) 显著增加了主要不良心血管事件 (MACE) 的风险,但可靠的预后生物标志物很少.
- 增长分化因子15 (GDF15),一种与炎症和动脉样硬化有关的细胞因子,在PAD风险分层方面具有潜力,但尚未得到充分研究.
研究的目的:
- 评估GDF15对预测PAD患者2年MACE的预后效用.
- 评估一个可解释的机器学习模型的性能,其中包括用于MACE预测的GDF15.
主要方法:
- 对1192名个体 (454名患有PAD) 的前性分析,在基线测量了血GDF15水平.
- 开发和验证一个极端梯度提升 (XGBoost) 机器学习模型来预测2年的MACE.
- 利用沙普利增量解释 (SHAP) 来实现模型解释性和确定关键预测特征.
主要成果:
- 与非PAD对照组相比,PAD患者的血GDF15水平中位数显著更高 (p < 0.001).
- 在XGBoost模型取得了强大的预测性能为2年的MACE (F1得分=0.83,召回=83.7%).
- SHAP分析发现GDF15是MACE最有影响力的预测因子,其表现优于传统的临床因素.
结论:
- GDF15是一种强大的预后生物标志物,用于预测PAD患者的2年MACE.
- 集成GDF15的可解释机器学习模型可以提升高风险个体的早期识别.
- 这种以生物标志物为导向的方法促进了个性化降低风险的策略,并改善了PAD的心血管结果.
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