在患有同时存在高血压和阻塞性睡眠呼吸暂停的患者中,用于长期预后风险评估的多种机器学习驱动的代谢框架:来自多中心队列研究的见解
Qiong Xu1, Yanan Xu2,3, Yijun Wang4
1Department of Cardiology, The First Affiliated Hospital of Bengbu Medical University, Bengbu, Anhui, China.
Frontiers in physiology
|March 12, 2026
概括
机器学习模型在患有高血压和阻塞性睡眠呼吸暂停 (OSA) 的患者中准确预测主要的不良心血管和脑血管事件 (MACCE). 新的基于甘油三糖 (TyG) 的指数改善了对这些高风险个体的风险评估.
科学领域:
- 心血管医学 心血管医学
- 代谢综合征是代谢综合征的一种.
- 睡眠医学 睡眠医学
- 医疗保健中的机器学习
背景情况:
- 像身体质量指数 (BMI) 这样的传统肥胖指数在直接测量肥胖方面存在局限性.
- 高血压和阻塞性睡眠呼吸暂停 (OSA) 是心血管和脑血管事件 (MACCE) 的重要危险因素.
- 需要在患有同时存在高血压和OSA的患者中改善MACCE的预测模型.
研究的目的:
- 开发和验证用于预测长期MACCE风险的机器学习 (ML) 模型.
- 为了研究基于新型甘油三糖 (TyG) 的新型代谢指数与人类测量相结合的实用性.
- 确定高血压和OSA患者队列中MACCE的关键预测因子.
主要方法:
- 包括708名患有高血压和OSA的患者.
- 计算多个代谢指数,包括TyG-BMI,TyG-NC,TyG-WC,TyG-NHtR和TyG-WHtR.
- 9个ML模型 (XGBoost,随机森林等) 的开发和比较. 对于MACCEs预测,XGBoost被选为优越的性能 (AUC 0.898).
主要成果:
- 确定MACCE的关键预测因素包括男性性别,年龄,TyG,TyG-BMI,HbA1c,FPG,甘油三,肌素,纤维素和呼吸暂停指数 (AHI).
- 在XGBoost模型展示了MACCEs的优秀预测性能.
- SHAP分析提供了关于每个变量对MACCE风险的贡献的见解.
结论:
- 机器学习技术与多维数据评估相结合,提高了对代谢结果的预测.
- 该研究支持早期识别患有OSA和高血压的高风险患者,需要专注的预防策略.
- 基于TyG的新型指数在改善心血管和脑血管事件的风险分层方面表现有前途.
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