Clinical subsets of juvenile dermatomyositis classified by myositis-specific autoantibodies: Experience at a single center in Japan.
Naomi IwataHaruna NakasekoToaki KohaguraRyuhei YasuokaNaoki AbeShinji KawabeShiro SugiuraYoshinao MuroPublished in: Modern rheumatology (2018)
Objectives: This study investigated the association between myositis-specific autoantibodies (MSAs) and clinical subsets of juvenile dermatomyositis (JDM) in Japanese patients. Methods: Twenty-one patients at a single center who developed initial or relapsed JDM from 2011 to 2016 were analyzed. Serum concentrations of MSAs against TIF1-γ, MDA5, NXP2, Mi-2, ARS, and SAE were measured by enzyme-linked immunosorbent assays. Clinical symptoms and laboratory data were obtained from clinical records. Clinical characteristics were compared in patients with autoantibodies against TIF1-γ, MDA5, and NXP2. Results: Of the 21 patients, 20 (95.2%) were positive for one or more MSAs, including nine (42.9%), five (23.8%), six (28.6%), and one (4.8%) positive for anti-TIF1-γ, anti-MDA5, anti-NXP2, and anti-Mi-2 autoantibodies. No patient was positive for anti-ARS or anti-SAE autoantibodies. The frequency of diffuse cutaneous lesions was higher in patients with anti-TIF1-γ autoantibodies. Anti-MDA5 autoantibody-positive patients had features of interstitial lung disease on chest computed tomography. Severe muscle damage at disease onset was significantly associated with positivity for anti-NXP2 autoantibodies. Conclusion: Similar to findings in Western countries, the clinical characteristics of JDM in Japanese may differ for each type of MSAs.
Keyphrases
- interstitial lung disease
- systemic lupus erythematosus
- computed tomography
- systemic sclerosis
- end stage renal disease
- magnetic resonance imaging
- chronic kidney disease
- rheumatoid arthritis
- machine learning
- magnetic resonance
- oxidative stress
- breast cancer cells
- case report
- signaling pathway
- mass spectrometry
- deep learning
- high resolution
- physical activity
- high grade
- hodgkin lymphoma
- big data
- myasthenia gravis