Annals of clinical and translational neurology · 2026
Understanding Further the Phenotypic Spectrum of Central Nervous System Inflammatory Demyelinating Disorders Using Unsupervised Clustering.
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Gulec Bade, Everest Elif, Tutuncu Melih, Bilge Ugur, Saip Sabahattin, Uygunoglu Ugur, Siva Aksel
Аннотация
BACKGROUND: Central nervous system (CNS) inflammatory demyelinating syndromes, including multiple sclerosis (MS), aquaporin-4 antibody-positive neuromyelitis optica spectrum disorder (AQP4 + NMOSD), and myelin oligodendrocyte glycoprotein (MOG) antibody-associated disease (MOGAD), occasionally overlap. Some patients remain double-seronegative, showing atypical features that challenge current classifications.
OBJECTIVE: To better characterize the phenotypic spectrum of antibody-negative atypical inflammatory demyelinating disorders (AIDD) using unsupervised clustering.
METHODS: We retrospectively analyzed 316 patients (MS = 164, AQP4 + NMOSD = 36, double-seronegative NMOSD = 21, MOGAD = 15, AIDD = 80) followed between 2010 and 2023. Principal component analysis and k-means clustering were applied to AIDD cases using clinical, demographic, and radiological data.
RESULTS: AIDD patients had lower disability and fewer corpus callosum and posterior fossa lesions than MS and NMOSD. Three clusters emerged: (1) myelitis-predominant with unmatched CSF oligoclonal bands and longitudinally extensive spinal lesions, (2) brainstem-dominant with recurrent brainstem attacks, and (3) optic neuritis-dominant with recurrent LEON meeting MS dissemination criteria. Treatment patterns differed; rituximab was most frequent.
CONCLUSION: Double-seronegative AIDD represents a heterogeneous clinical spectrum. Unsupervised clustering provides a data-driven framework for refining phenotypic classification and may support biomarker and therapeutic development in antibody-negative CNS demyelination.
OBJECTIVE: To better characterize the phenotypic spectrum of antibody-negative atypical inflammatory demyelinating disorders (AIDD) using unsupervised clustering.
METHODS: We retrospectively analyzed 316 patients (MS = 164, AQP4 + NMOSD = 36, double-seronegative NMOSD = 21, MOGAD = 15, AIDD = 80) followed between 2010 and 2023. Principal component analysis and k-means clustering were applied to AIDD cases using clinical, demographic, and radiological data.
RESULTS: AIDD patients had lower disability and fewer corpus callosum and posterior fossa lesions than MS and NMOSD. Three clusters emerged: (1) myelitis-predominant with unmatched CSF oligoclonal bands and longitudinally extensive spinal lesions, (2) brainstem-dominant with recurrent brainstem attacks, and (3) optic neuritis-dominant with recurrent LEON meeting MS dissemination criteria. Treatment patterns differed; rituximab was most frequent.
CONCLUSION: Double-seronegative AIDD represents a heterogeneous clinical spectrum. Unsupervised clustering provides a data-driven framework for refining phenotypic classification and may support biomarker and therapeutic development in antibody-negative CNS demyelination.