ArticleArthritis research & therapy2020
Identifying clinical subgroups in IgG4-related disease patients using cluster analysis and IgG4-RD composite score.
Article in Arthritis research & therapy, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- Implication of allergy and atopy in IgG4-related disease.The World Allergy Organization journal · 2023Review
- Retroperitoneal fibrosis, a rare entity with urorenal and vascular subtypes - preliminary data.Renal failure · 2022Article
- Correlation between serologic parameters and disease activity of IgG4-related disease: Differences between patients with normal and elevated serum IgG4 concentrations.Frontiers in immunology · 2022Article
- Longitudinal analysis of symptom-based clustering in patients with primary Sjogren's syndrome: a prospective cohort study with a 5-year follow-up period.Journal of translational medicine · 2021Article
- Peripheral B-Cell Immunophenotyping Identifies Heterogeneity in IgG4-Related Disease.Frontiers in immunology · 2021Article
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13 authors.
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Abstract
backgroundTo explore the clinical patterns of patients with IgG4-related disease (IgG4-RD) based on laboratory tests and the number of organs involved.
methodsTwenty-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.
resultsPCA 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.
conclusionOur 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.
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