通过可解释的Clinlabomics模型识别代谢参数作为高尿血和缺血性中风并发症的关键指标
1Department of Clinical Laboratory Medicine, Suining Central Hospital, Suining, Sichuan, China.
Frontiers in endocrinology
|January 29, 2026
概括
这项研究确定了与缺血性中风 (HUA-IS) 共病性高尿血症的关键代谢指标. 开发的Clinlabomics模型准确预测HUA-IS风险,有助于临床评估.
科学领域:
- 生物医学研究的研究.
- 临床诊断 临床诊断 临床诊断
- 代谢健康 代谢健康
背景情况:
- 缺血性中风 (IS) 与高尿血 (HUA) 结合,与不良结果有关,但根本机制尚未完全理解.
- 确定共享的病理生理特征和开发HUA-IS并发症的预测模型对于患者管理至关重要.
研究的目的:
- 调查与HUA-IS并发症相关的代谢参数.
- 开发和验证一个可解释的Clinlabomics模型来评估HUA-IS风险.
主要方法:
- 对2,164名IS患者和2,459名健康对照 (HC) 的回顾性分析.
- 倾向分数匹配 (PSM) 创建了可比的组,用于分析十个代谢参数.
- 机器学习算法,包括LASSO回归和递归分区和回归树 (rpart),用于构建和验证Clinlabomics模型.
主要成果:
- 多变量逻辑回归确定了血无氧化指数 (AIP) 作为一个显著的风险因素 (OR = 2.74).
- 高水平的甘油三指数 (TyG),甘油三指数 (TG),AIP和脂蛋白组合指数 (LCI) 与增加的并发症风险有关.
- 基于rpart的Clinlabomics模型表现出高性能,在训练组中达到0.987的AUC,在测试组中达到0.955,在验证组中达到0.957.
结论:
- TyG,TG,AIP和LCI是HUA-IS并发症中的关键代谢参数.
- 开发的Clinlabomics模型显示了对HUA-IS的准确风险评估的巨大潜力.
- 这些发现强调了监测IS和HUA患者的特定代谢指标的重要性.
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