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隐性自身抗体轴与缺血性中风中的血管脆弱性相关:综合统计和机器学习分析

Tomohiro Sugiyama^1,2, Yoichi Yoshida^1,2, Takaki Hiwasa¹

¹Department of Neurological Surgery, Graduate School of Medicine, Chiba University, Chiba 260-8670, Japan.

International journal of molecular sciences

|March 14, 2026

概括

血清自身抗体可能提供关于中风血管风险的见解,即使它们不会显著改善预测模型. 这些生物标志物捕获血管和免疫应激的生物信号,可能有助于了解中风易感性.

科学领域:

心血管研究研究心血管研究
免疫学免疫学免疫学
生物标志物发现发现

背景情况:

缺血性中风是全球死亡和残疾的主要原因.
识别高血管风险的个体需要改进策略.
血清自身抗体在预测中风风险中的作用尚未完全理解.

研究的目的:

调查血清特定自身抗体 (抗PDCD11,抗DNAJC2,抗PAI-1) 对缺血性中风的预测价值.
探索这些自身抗体的综合生物学意义,超出已确定的临床风险因素.
评估机器学习模型的性能,包括用于预测中风风险的抗体标记物.

主要方法:

分析了833名参与者 (中风患者和对照组).
血清自身抗体的量化和逻辑回归和机器学习模型的构建.
使用交叉验证,校准和重新分类指数评估模型性能.
使用PCA,无监督聚类和SHAP分析探索生物结构.

主要成果:

临床模型显示出出色的中风区分 (AUC~0.92).
添加抗体标志物并没有显著改善整体模型性能或重新分类.
SHAP分析表明抗体标志物是有影响力的贡献者,PCA揭示了与中风风险相关的主导抗体成分.
无监督聚类确定了一个高风险子组,其抗体水平较高.

结论:

潜在的抗体轴与中风中的血管脆弱性有关.
自体抗体分析捕获了血管和免疫应激的综合生物信号.
虽然不会提高全球预测,但自身抗体可能会改善中风易感性的生物特征,需要潜在的验证.

关键词:

这是自抗体的自抗体.生物标志物的整合.免疫激活系统的激活缺血性中风中风机器学习是机器学习.精准医学是一门精准医学.主要组件分析的主要组件分析随机的森林随机的森林风险分层的风险分层.血管脆弱性的血管脆弱性

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