Paediatric Rheumatology
Identification of 4 subgroups in juvenile dermatomyositis by principal component analysis-based cluster analysis
J. Zhang1, Y. Xue2, X. Liu3, W. Kuang4, X. Tan5, C. Li6, C. Li7
- Department of Rheumatology, Beijing Children’s Hospital, Capital Medical University, National Centre for Children’s Health, Beijing,China.
- Department of Rheumatology, Beijing Children’s Hospital, Capital Medical University, National Centre for Children’s Health, Beijing,China.
- Nutrition Research Unit, Beijing Paediatric Research Institute, Beijing Children’s Hospital, Capital Medical University, Beijing, China.
- Department of Rheumatology, Beijing Children’s Hospital, Capital Medical University, National Centre for Children’s Health, Beijing,China.
- Department of Rheumatology, Beijing Children’s Hospital, Capital Medical University, National Centre for Children’s Health, Beijing,China.
- Department of Rheumatology, Beijing Children’s Hospital, Capital Medical University, National Centre for Children’s Health, Beijing,China.
- Department of Rheumatology, Beijing Children’s Hospital, Capital Medical University, National Centre for Children’s Health, Beijing,China. licaifengbch@sina.com
CER14284
2022 Vol.40, N°2
PI 0443, PF 0449
Paediatric Rheumatology
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PMID: 34251318 [PubMed]
Received: 01/12/2020
Accepted : 17/02/2021
In Press: 22/06/2021
Published: 25/02/2022
Abstract
OBJECTIVES:
Juvenile dermatomyositis (JDM) is an autoimmune disease characterised by a great heterogeneity in its clinical manifestations. In this study, we aimed to investigate the association between different clinical subtypes, laboratory data, and myositis antibodies of JDM.
METHODS:
A total of 132 JDM patients were enrolled and their medical records were retrospectively reviewed and autoantibodies tested. Twenty-one variables, including clinical manifestations and laboratory findings, were selected for analysis. We selected principal component analysis (PCA) as a pre-processing method for cluster analysis to convert the 21 original variables into independent principal components. We then conducted a PCA-based cluster analysis in order to analyse the association between patient clusters and the clinical data, laboratory data, and myositis autoantibodies.
RESULTS:
We identified 4 distinct JDM subgroups by PCA-based cluster analysis, namely: cluster A, JDM patients with arthralgia and intense inflammation; cluster B, JDM patients with clinical manifestations of vasculitis; cluster C, hypermyopathic JDM patients; and cluster D, JDM patients with skin involvement. There were significant differences between the 4 groups in serum alkaline phosphatase levels, usage of aggressive immunosuppressive therapy, and autoantibody expression of anti-mi2, anti-MDA5, anti-Jo1, and anti-PM-Scl100.
CONCLUSIONS:
We conducted cluster analysis of a cohort of JDM patients and identified 4 subgroups that represented diverse characteristics in the distribution of laboratory data and myositis autoantibodies, indicating that multidimensional assessment of clinical manifestations is highly valuable and urgently needed in JDM patients. These subgroups may contribute to individualised treatments and improved JDM patient prognosis.
