Department of Rheumatology, Peking Union Medical College Hospital, Chinese Academy of Medical Science and Peking Union Medical College, National Clinical Research Center for Dermatologic and Immunologic Diseases, State Key Laboratory of Complex Severe and Rare Diseases, Beijing, China.
Department of Rheumatology, Peking Union Medical College Hospital, Chinese Academy of Medical Science and Peking Union Medical College, National Clinical Research Center for Dermatologic and Immunologic Diseases, State Key Laboratory of Complex Severe and Rare Diseases, Beijing, China; Department of Rheumatology, Beijing Tongren Hospital, Capital Medical University, Beijing, China.
Clin Immunol. 2023 Jul;252:109301. doi: 10.1016/j.clim.2023.109301. Epub 2023 Mar 21.
IgG4-related disease (IgG4-RD) is a chronic immune-mediated disease with heterogeneity. In this study, we used machine-learning approaches to characterize the immune cell profiles and to identify the heterogeneity of IgG4-RD. The XGBoost model discriminated IgG4-RD from HCs with an area under the receiver operating characteristic curve of 0.963 in the testing set. There were two clusters of IgG4-RD by k-means clustering of immunological profiles. Cluster 1 featured higher proportions of memory CD4T cell and were at higher risk of unfavorable prognosis in the follow-up, while cluster 2 featured higher proportions of naïve CD4T cell. In the multivariate logistic regression, cluster 2 was shown to be a protective factor (OR 0.30, 95% CI 0.10-0.91, P = 0.011). Therefore, peripheral immunophenotyping might potentially stratify patients with IgG4-RD and predict those patients with a higher risk of relapse at early time.
IgG4 相关疾病（IgG4-RD）是一种具有异质性的慢性免疫介导性疾病。在本研究中，我们使用机器学习方法来描述免疫细胞特征，并确定 IgG4-RD 的异质性。XGBoost 模型在测试集中区分 IgG4-RD 和 HC 的受试者工作特征曲线下面积为 0.963。通过免疫特征的 k-均值聚类，将 IgG4-RD 分为两个聚类。聚类 1 表现为记忆 CD4T 细胞比例较高，随访中不良预后风险较高，而聚类 2 表现为幼稚 CD4T 细胞比例较高。在多变量逻辑回归中，聚类 2 是一个保护因素（OR 0.30，95%CI 0.10-0.91，P=0.011）。因此，外周免疫表型分析可能有助于对 IgG4-RD 患者进行分层，并在早期预测那些具有更高复发风险的患者。
