Identifying clinical subgroups in IgG4-related disease patients using cluster analysis and IgG4-RD composite score

• Published in: Arthritis research & therapy Vol. 22; no. 1; pp. 7 - 13
• Main Authors: Li, Jieqiong, Peng, Yu, Zhang, Yuelun, Zhang, Panpan, Liu, Zheng, Lu, Hui, Peng, Linyi, Zhu, Liang, Xue, Huadan, Zhao, Yan, Zeng, Xiaofeng, Fei, Yunyun, Zhang, Wen
• Format: Journal Article
• Language: English
• Published: England BioMed Central Ltd 10.01.2020; BioMed Central; BMC
• Subjects: Adolescent; Adult; Aged; Aged, 80 and over; Allergy; Arthritis; Blood tests; Child; Cluster Analysis; Diagnosis; Diseases; Eigenvalues; Female; Health aspects; Humans; IgG4-RD CS; IgG4-related disease; Immunoglobulin G; Immunoglobulin G4-Related Disease - classification; Immunoglobulin G4-Related Disease - immunology; Immunoglobulin G4-Related Disease - pathology; Immunoglobulins; Immunologic deficiency syndromes; Laboratories; Laboratory test; Lymphocytes; Male; Medical research; Medical tests; Middle Aged; Organs involved; Patients; Phenotypes; Regression analysis; Statistical analysis; Young Adult; China; Cluster analysis; Organs involved; Laboratory test; IgG4-RD CS; IgG4-related disease
• ISSN: 1478-6362; 1478-6354; 1478-6362
• DOI: 10.1186/s13075-019-2090-9

To explore the clinical patterns of patients with IgG4-related disease (IgG4-RD) based on laboratory tests and the number of organs involved. 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. 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. 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.