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Transition From Juvenile Dermatomyositis to Spondyloarthritis: A Novel Overlapping Inflammatory Phenotype
Austen Grooms1,2, Ioannis Karageorgiou2, Kerry Mychaliska1
1Department of Pediatrics, William Beaumont University Hospital, Royal Oak, Michigan.
Objective:
Juvenile dermatomyositis (JDM) is a rare childhood inflammatory myopathy, whereas spondyloarthritis (SpA) is an inflammatory arthropathy characterized by enthesitis and peripheral or axial involvement. We describe a series of patients diagnosed with JDM in childhood who later fulfilled classification criteria for SpA, a sequential phenotype that has not been well characterized.
Methods:
This institutional review board-exempt retrospective study (2024-180) reviewed demographics, clinical features, laboratory tests, imaging, treatments, and outcomes in a convenience sample of patients who met criteria for JDM before age 18 and subsequently fulfilled the Assessment of Spondyloarthritis International Society (ASAS) or Classification for Psoriatic Arthritis (CASPAR) criteria. Patients with SpA preceding JDM were excluded. Descriptive statistics were used to explore potential predictors of SpA development.
Results:
Seven patients met the inclusion criteria; 57% were female and 86% were White. The median age at JDM diagnosis was 8 years (interquartile range [IQR] 5-10 years). The median interval time to SpA diagnosis was 7 years (IQR 5-14 years). All tested patients had elevated neopterin and magnetic resonance imaging evidence of myositis; 71% experienced complications before SpA onset. All met ASAS criteria for peripheral SpA, and 29% met CASPAR criteria. HLA-B27 was positive in 20% of those tested. Most (83%) responded to standard SpA therapies; one required rituximab and azathioprine for calcinosis.
Conclusion:
This preliminary small study highlights a possible evolution from JDM, an adaptive immune disease, to SpA, an innate-driven illness. These findings suggest potential predictors of disease overlap and warrant future studies to clarify mechanisms and inform predictive models.
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