A multicentre study for clinical phenotype prediction in juvenile dermatomyositis: categorical principal component analysis-based hierarchical clustering.

INTRODUCTION

Juvenile dermatomyositis (JDM) is the most common inflammatory myopathy in childhood and exhibits a heterogeneous disease course. This study aimed to analyse and identify phenotypic clusters by examining the laboratory findings, nailfold capillaroscopy results, and myositis-specific autoantibodies (MSAs) in patients with JDM.

MATERIAL AND

METHODS

This retrospective cohort study included data from patients with JDM treated at the Paediatric Rheumatology Departments of 14 advanced health centres in Turkey. A categorical principal component analysis (CATPCA)-based hierarchical cluster analysis method was employed for clustering.

RESULTS

A total of 176 JDM patients were enrolled, and 5 phenotypic clusters were identified using 23 categorical variables. These clusters were interpreted as follows: Cluster A with severe muscle weakness and oesophageal involvement requiring intensive immunosuppressive treatment; Cluster B with amyopathic/hypomyopathic patients; Cluster C with skin manifestations and lung involvement; Cluster D with complicated skin manifestations; and Cluster E with classic JDM. The clinical and laboratory findings and treatments of these 5 clusters were compared. Fatigue, myalgia, photosensitivity, Raynaud's phenomenon, and the use of pulse glucocorticosteroids, intravenous immunoglobulin, and cyclophosphamide treatments differed between the groups (p < 0.001, p = 0.002, p = 0.015, p = 0.036, p = 0.002, p = 0.006, and p = 0.024, respectively). Myositis-specific autoantibodies results were available for 119 patients (65.3%). The most frequent MSAs were antinuclear matrix protein 2 (26.1%) and anti-transcription intermediary factor 1 (20.9%). However, no significant differences were found in MSAs or nailfold capillaroscopy findings.

CONCLUSIONS

We identified 5 clusters based on patient symptoms and findings. The identification of these 5 clusters can guide more effective treatment strategies in clinical practice. Additionally, these approaches may contribute to improving patients' quality of life and long-term outcomes by increasing the feasibility of individualised treatment.