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评估Th1 (IFN-γ⁺CD4⁺) /CD4⁺在快速进展的肌缩侧面硬化症中的预测潜力

Jiahui Zhu^1,2, Yuechen Zhang³, Shuqi Hu^1,2

¹Department of Neurology, Affiliated Hangzhou First People's Hospital, Westlake University School of Medicine, Hangzhou, Zhejiang, China.

Journal of neurology

|September 14, 2025

概括

升高的Th1/CD4+细胞表明肌缩侧面硬化症 (ALS) 快速进展的风险更高. 使用这些细胞的机器学习模型显示了对ALS疾病进展的强大预测能力.

科学领域:

神经免疫学神经免疫学
机器学习在医学中的应用
临床生物标志物临床生物标志物

背景情况:

Th1 (IFN-γ+CD4+) 细胞有助于ALS中神经炎症和神经元死亡.
外周免疫系统的参与在ALS病理生理学中至关重要.
快速进展的ALS (ALS) 需要预测生物标志物来及时干预.

研究的目的:

开发一种可解释的机器学习模型,用于预测快速进展的ALS.
为了研究血液Th1/CD4+细胞与ALS进展率之间的关联.
确定Th1/CD4+细胞作为ALS疾病严重程度的潜在生物标志物.

主要方法:

对564名零星ALS患者的分析,使用流细胞计量来定量免疫细胞.
应用多变量考克斯比例危险模型和LASSO回归.
开发一个包含Th1/CD4+细胞和其他特征的机器学习模型.

主要成果:

高Th1/CD4+细胞计数 (≥16.21) 与更快的ALS进展显著相关 (HR:1.90).
Th1/CD4+细胞与强迫生命能力下降相关 (r=0.11,P=0.01).
性能最好的机器学习模型 (Xgboost) 的AUC为0.804,G平均值为0.756.

结论:

Th1/CD4+细胞 (16.21截止值) 是快速ALS进展的独立风险因素.
开发的机器学习模型证明了对ALS进展的强大预测能力.
这项研究强调了免疫细胞分析在治疗ALS方面的潜力.

关键词:

肌缩性侧面硬化症 (AMLS) 是一种疾病.机器学习是机器学习.神经免疫学神经免疫学预测模型是一个预测模型.Th1 (IFN-γ+CD4+) /CD4+) 的时间.

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