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

Abstract

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.

Keywords: Cluster analysis; IgG4-RD CS; IgG4-related disease; Laboratory test; Organs involved.

Fig. 1

The flow scheme of statistical analysis, patients’ grouping, and comparisons among subgroups. PCA, principal components analysis; PCs, principal components; RI, IgG4-RD responder index (2012); IgG4-RD CS, IgG4-RD composite score

Fig. 2

Results of PCA based on 22 baseline variables. a Scree plot. Every hollow spot represents one principal component. Vertical axis shows eigenvalue of each spot. b–e Loading plot. Each baseline variable was visualized in 3 dimensions (b). Planes consisting of axis PC1 and PC2, PC2 and PC3, and PC3 and PC1 are colored green, light purple, and blue. LY.ab, absolute lymphocyte count; Ly.per, lymphocyte percentage; Eos.ab, absolute eosinophil count; Eos.per, Eosinophil percentage; total.org, the number of total organs involved; superficial.org, the number of superficial organs involved; internal.org, the number of internal organs involved

Fig. 3

Results of cluster analysis and differences among clusters. a Three clusters of patients identified by cluster analysis. Hierarchical statistical clustering of IgG4-RD patients. b–j Comparisons of baseline characteristics among three clusters of IgG4-RD patients. Inter.ratio, proportion of internal organs to total organs involved. *P value < 0.05; **P value < 0.01; ***P value < 0.001

Fig. 4

Comparisons of treatments and disease responses among subgroups. a–c Percent of the treatment regimens among different subgroups. d–f Disease responses were compared among subgroups. CR, complete response; PR, partial response; NC, no change. *P value < 0.05; **P value < 0.01