BACKGROUND To explore the clinical patterns of patients with IgG4-related disease (IgG4-RD) based on laboratory tests and the number of organs involved. METHODS Twenty-two baseline variables were obtained from 154 patients with IgG4-RD. Based on principal component analysis (PCA), patients with IgG4-RD were classified into different subgroups using cluster analysis. Additionally, IgG4-RD composite score (IgG4-RD CS) as a comprehensive score was calculated for each patient by principal component evaluation. Multiple linear regression was used to establish the "IgG4-RD CS" prediction model for the comprehensive assessment of IgG4-RD. To evaluate the value of the IgG4-RD CS in the assessment of disease severity, patients in different IgG4-RD CS groups and in different IgG4-RD responder index (RI) groups were compared. RESULTS PCA indicated that the 22 baseline variables of IgG4-RD patients mainly consisted of inflammation, high serum IgG4, multi-organ involvement, and allergy-related phenotypes. Cluster analysis classified patients into three groups: cluster 1, inflammation and immunoglobulin-dominant group; cluster 2, internal organs-dominant group; and cluster 3, inflammation and immunoglobulin-low with superficial organs-dominant group. Moreover, there were significant differences in serum and clinical characteristics among subgroups based on the CS and RI scores. IgG4-RD CS had a similar ability to assess disease severity as RI. The "IgG4-RD CS" prediction model was established using four independent variables including lymphocyte count, eosinophil count, IgG levels, and the total number of involved organs. CONCLUSION Our study indicated that newly diagnosed IgG4-RD patients could be divided into three subgroups. We also showed that the IgG4-RD CS had the potential to be complementary to the RI score, which can help assess disease severity.

中文翻译：

---

使用聚类分析和IgG4-RD综合评分确定IgG4相关疾病患者的临床亚组。

背景技术根据实验室检查和涉及的器官数目，探讨IgG4相关疾病（IgG4-RD）患者的临床模式。方法从154名IgG4-RD患者中获得22个基线变量。基于主成分分析（PCA），使用聚类分析将IgG4-RD患者分为不同的亚组。此外，通过主成分评估为每位患者计算出IgG4-RD综合评分（IgG4-RD CS）作为综合评分。使用多元线性回归建立“ IgG4-RD CS”预测模型，以全面评估IgG4-RD。为了评估IgG4-RD CS在疾病严重程度评估中的价值，比较了不同IgG4-RD CS组和不同IgG4-RD反应指数（RI）组的患者。结果PCA提示IgG4-RD患者的22个基线变量主要包括炎症，血清IgG4高，多器官受累以及与过敏相关的表型。聚类分析将患者分为三组：聚类1，炎症和免疫球蛋白显着组；第2类，内部器官为主的群体；簇3，炎症和免疫球蛋白低，以浅表器官为主。此外，根据CS和RI评分，亚组之间的血清和临床特征存在显着差异。IgG4-RD CS具有与RI类似的评估疾病严重程度的能力。“ IgG4-RD CS”预测模型是使用四个独立变量建立的，包括淋巴细胞计数，嗜酸性粒细胞计数，IgG水平和受累器官总数。结论我们的研究表明，新诊断的IgG4-RD患者可分为三个亚组。我们还表明，IgG4-RD CS具有与RI评分互补的潜力，可以帮助评估疾病的严重程度。