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PRACA ORYGINALNA
A multicentre study for clinical phenotype prediction in juvenile dermatomyositis: categorical principal component analysis-based hierarchical clustering
1
Department of Pediatric Rheumatology, Faculty of Medicine, Dokuz Eylul University, Izmir, Turkey
2
Department of Pediatric Rheumatology, Faculty of Medicine, Hacettepe University, Ankara, Turkey
3
Department of Pediatric Rheumatology, Ankara Etlik Integrated Health Campus, Ankara, Turkey
4
Department of Pediatric Rheumatology, Faculty of Medicine, Istanbul University, Istanbul, Turkey
5
Department of Pediatric Rheumatology, Istanbul University Cerrahpasa Medical School, Istanbul, Turkey
6
Department of Pediatric Rheumatology, Umraniye Research and Training Hospital, University of Health Sciences, Istanbul, Turkey
7
Department of Pediatric Rheumatology, Faculty of Medicine, Uludag University, Bursa, Turkey
8
Department of Pediatric Rheumatology, Faculty of Medicine, Gazi University, Ankara, Turkey
9
Department of Pediatric Rheumatology, Faculty of Medicine, Kocaeli University, Kocaeli, Turkey
10
Department of Pediatric Nephrology and Rheumatology, Faculty of Medicine, Karadeniz Technical University, Trabzon, Turkey
11
Department of Pediatric Rheumatology, Malatya Training and Research Hospital, Malatya, Turkey
12
Department of Pediatric Rheumatology, Istanbul Medeniyet University, Goztepe Prof. Dr. Suleyman Yalcın City Hospital, Istanbul, Turkey
13
Department of Pediatric Rheumatology, Faculty of Medicine, Pamukkale University, Izmir, Turkey
14
Department of Pediatric Rheumatology, University of Health Sciences Dr. Behcet Uz Child Disease and Pediatric Surgery Training and Research Hospital, Izmir, Turkey
15
Department of Biostatistics and Medical Informatics, Dokuz Eylul University Faculty of Medicine, Izmir, Turkey
Data nadesłania: 30-12-2024
Data ostatniej rewizji: 28-05-2025
Data akceptacji: 26-08-2025
Data publikacji: 23-04-2026
Reumatologia 2026;64(2):83-93
SŁOWA KLUCZOWE
DZIEDZINY
STRESZCZENIE
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.
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.
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