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Background: Progressive multifocal leukoencephalopathy (PML) can in rare cases occur in natalizumab-treated patients with high serum anti-JCPyV antibodies, hypothetically due to excessive blockade of immune cell migration. Objective: Immune cell recruitment to the central nervous system (CNS) was assessed in relapsing-remitting multiple sclerosis (RRMS) patients stratified by low versus high anti-JCPyV antibody titers as indicator for PML risk. Methods: Cerebrospinal fluid (CSF) cell counts of 145 RRMS patients were quantified by flow cytometry. Generalized linear models were employed to assess influence of age, sex, disease duration, Expanded Disability Status Scale (EDSS), clinical/radiological activity, current steroid or natalizumab treatment, as well as anti-JCPyV serology on CSF cell subset counts. Results: While clinical/radiological activity was associated with increased CD4, natural killer (NK), B and plasma cell counts, natalizumab therapy reduced all subpopulations except monocytes. With and without natalizumab therapy, patients with high anti-JCPyV serum titers presented with increased CSF T-cell counts compared to patients with low anti-JCPyV serum titers. In contrast, PML patients assessed before (n = 2) or at diagnosis (n = 5) presented with comparably low CD8 and B-cell counts, which increased after plasma exchange (n = 4). Conclusion: High anti-JCPyV indices, which could be indicative of increased viral activity, are associated with elevated immune cell recruitment to the CNS. Its excessive impairment in conjunction with viral activity could predispose for PML development. [ABSTRACT FROM AUTHOR]

Although CSF analysis routinely enables the diagnosis of neurological diseases, it is mainly used for the gross distinction between infectious, autoimmune inflammatory, and degenerative disorders of the CNS. To investigate, whether a multi-dimensional cellular blood and CSF characterization can support the diagnosis of clinically similar neurological diseases, we analysed 546 patients with autoimmune neuroinflammatory, degenerative, or vascular conditions in a cross-sectional retrospective study. By combining feature selection with dimensionality reduction and machine learning approaches we identified pan-disease parameters that were altered across all autoimmune neuroinflammatory CNS diseases and differentiated them from other neurological conditions and inter-autoimmunity classifiers that subdifferentiate variants of CNS-directed autoimmunity. Pan-disease as well as diseases-specific changes formed a continuum, reflecting clinical disease evolution. A validation cohort of 231 independent patients confirmed that combining multiple parameters into composite scores can assist the classification of neurological patients. Overall, we showed that the integrated analysis of blood and CSF parameters improves the differential diagnosis of neurological diseases, thereby facilitating early treatment decisions. [ABSTRACT FROM AUTHOR]