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探索免疫学决定因素并构建一种用于诊断痛风的预测模型:对免疫生物标志物的洞察

Huanhuan Zeng¹, Liqiang Zheng¹, Dan Wu¹

¹Department of Rheumatology and Immunology, People's Hospital of Chongqing Liangjiang New Area, Chongqing, 401121, People's Republic of China.

Journal of inflammation research

|October 29, 2025

概括

这项研究揭示了痛风患者的独特免疫炎症特征,确定了准确预测疾病的关键生物标志物. 机器学习模型在痛风的分类中表现出高精度,有助于潜在的早期诊断.

科学领域:

免疫学免疫学免疫学
病变的发生和发病.
生物标志物生物标志物

背景情况:

免疫-炎症机制在痛风的发病过程中至关重要.
特定的免疫学特征及其在痛风中的预测价值尚未得到充分理解.

研究的目的:

在痛风患者中识别出不同的免疫学特征.
开发和验证用于使用免疫生物标志物的痛风预测的机器学习模型.

主要方法:

130名参与者的横截面研究 (65名痛风患者,65名对照).
使用LASSO回归和使用XGBoost的模型构建进行变量选择.
使用SHAP,PCA和外部数据进行模型解释性和验证.

主要成果:

痛风患者的炎症标志物 (CRP,hs-CRP,IL-1β,IL-6,NLRP3) 和免疫失调 (T细胞比率改变,单细胞扩张) 增加.
确定了两个免疫学亚型.
一个具有七个生物标志物的XGBoost模型实现了完美的内部验证 (AUC=1.0) 和高的外部验证 (AUC=0.977),其中hs-CRP和IL-1β作为关键预测因素.

结论:

痛风的特点是特定的免疫-炎症特征.
使用免疫生物标志物的机器学习模型显示出高精度和痛风分类的概括性.
这种方法可以促进临床实践中的早期查,诊断和免疫学亚型.

关键词:

拉索回归法 (Lasso Regression) 是一种回归法.一个XGBoost模型.痛风是一种痛风.免疫生物标志物免疫生物标志物免疫学亚型的划分这是一种炎症炎症炎症炎症.预测模型是一个预测模型.主要组件分析的主要组件分析随机的森林随机的森林

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