High serum neurofilament light chain levels correlate with brain atrophy and physical disability in multiple sclerosis

Serum neurofilament light chain (sNfL) is a promising biomarker of neuroaxonal damage in persons with multiple sclerosis (pwMS). In cross‐sectional studies, sNfL has been associated with disease activity and brain magnetic resonance imaging (MRI) changes; however, it is still unclear to what extent in particular high sNfL levels impact on subsequent disease evolution.

[1]  F. Barkhof,et al.  Serum neurofilament as a predictor of 10-year grey matter atrophy and clinical disability in multiple sclerosis: a longitudinal study , 2022, Journal of Neurology, Neurosurgery, and Psychiatry.

[2]  D. Arnold,et al.  Prognostic Value of Serum Neurofilament Light Chain for Disease Activity and Worsening in Patients With Relapsing Multiple Sclerosis: Results From the Phase 3 ASCLEPIOS I and II Trials , 2022, Frontiers in Immunology.

[3]  D. Conen,et al.  Serum neurofilament light chain for individual prognostication of disease activity in people with multiple sclerosis: a retrospective modelling and validation study , 2022, The Lancet Neurology.

[4]  V. Fleischer,et al.  NfL predicts relapse-free progression in a longitudinal multiple sclerosis cohort study , 2021, EBioMedicine.

[5]  Á. J. Cruz-Gómez,et al.  Cortical Thickness and Serum NfL Explain Cognitive Dysfunction in Newly Diagnosed Patients With Multiple Sclerosis , 2021, Neurology: Neuroimmunology & Neuroinflammation.

[6]  F. Zipp,et al.  The potential of serum neurofilament as biomarker for multiple sclerosis , 2021, Brain : a journal of neurology.

[7]  C. Granziera,et al.  Chronic White Matter Inflammation and Serum Neurofilament Levels in Multiple Sclerosis , 2021, Neurology.

[8]  D. Arnold,et al.  Temporal profile of serum neurofilament light in multiple sclerosis: Implications for patient monitoring , 2020, Multiple sclerosis.

[9]  L. Airas,et al.  High serum neurofilament associates with diffuse white matter damage in MS , 2020, Neurology: Neuroimmunology & Neuroinflammation.

[10]  Jeffrey A. Cohen,et al.  Long-term prognostic value of longitudinal measurements of blood neurofilament levels , 2020, Neurology: Neuroimmunology & Neuroinflammation.

[11]  S. Fereshtehnejad,et al.  Serum neurofilament light chain predicts long term clinical outcomes in multiple sclerosis , 2020, Scientific Reports.

[12]  F. Paul,et al.  Clinical implications of serum neurofilament in newly diagnosed MS patients: A longitudinal multicentre cohort study , 2020, EBioMedicine.

[13]  L. Kappos,et al.  Monitoring of radiologic disease activity by serum neurofilaments in MS , 2020, Neurology: Neuroimmunology & Neuroinflammation.

[14]  C. Enzinger,et al.  Serum neurofilament light levels in normal aging and their association with morphologic brain changes , 2020, Nature Communications.

[15]  L. Kappos,et al.  Neurofilament light levels are associated with long-term outcomes in multiple sclerosis , 2019, Multiple sclerosis.

[16]  Christian Gaser,et al.  Prognostic value of white matter lesion shrinking in early multiple sclerosis: An intuitive or naïve notion? , 2019, Brain and behavior.

[17]  H. Reichmann,et al.  Profiling individual clinical responses by high-frequency serum neurofilament assessment in MS , 2019, Neurology: Neuroimmunology & Neuroinflammation.

[18]  L. Kappos,et al.  Neurofilament light chain serum levels correlate with 10‐year MRI outcomes in multiple sclerosis , 2018, Annals of clinical and translational neurology.

[19]  Ludwig Kappos,et al.  Neurofilaments as biomarkers in neurological disorders , 2018, Nature Reviews Neurology.

[20]  Ludwig Kappos,et al.  Serum neurofilament as a predictor of disease worsening and brain and spinal cord atrophy in multiple sclerosis , 2018, Brain : a journal of neurology.

[21]  David H. Miller,et al.  Diagnosis of multiple sclerosis: 2017 revisions of the McDonald criteria , 2017, The Lancet Neurology.

[22]  M. Rovaris,et al.  Long-term disability progression in primary progressive multiple sclerosis: a 15-year study , 2017, Brain : a journal of neurology.

[23]  Nicholas C. Firth,et al.  Progression of regional grey matter atrophy in multiple sclerosis , 2017, bioRxiv.

[24]  M. Battaglini,et al.  Deep grey matter volume loss drives disability worsening in multiple sclerosis , 2017, bioRxiv.

[25]  Ludwig Kappos,et al.  Serum Neurofilament light: A biomarker of neuronal damage in multiple sclerosis , 2017, Annals of neurology.

[26]  G Tedeschi,et al.  Regional cortical thinning in multiple sclerosis and its relation with cognitive impairment: A multicenter study , 2016, Multiple sclerosis.

[27]  W. Brück,et al.  The topograpy of demyelination and neurodegeneration in the multiple sclerosis brain , 2016, Brain : a journal of neurology.

[28]  C. Enzinger,et al.  Periventricular lesions correlate with cortical thinning in multiple sclerosis , 2015, Annals of neurology.

[29]  F. Jacques Defining the clinical course of multiple sclerosis: The 2013 revisions , 2015, Neurology.

[30]  F. Fazekas,et al.  Consensus definitions and application guidelines for control groups in cerebrospinal fluid biomarker studies in multiple sclerosis , 2013, Multiple sclerosis.

[31]  Bernhard Hemmer,et al.  An automated tool for detection of FLAIR-hyperintense white-matter lesions in Multiple Sclerosis , 2012, NeuroImage.

[32]  Jeffrey A. Cohen,et al.  Diagnostic criteria for multiple sclerosis: 2010 Revisions to the McDonald criteria , 2011, Annals of neurology.

[33]  J. Fleming,et al.  A consensus protocol for the standardization of cerebrospinal fluid collection and biobanking , 2009, Neurology.

[34]  S Ropele,et al.  Quantitative assessment of brain iron by R2* relaxometry in patients with clinically isolated syndrome and relapsing–remitting multiple sclerosis , 2009, Multiple sclerosis.

[35]  S. Reingold,et al.  Diagnostic criteria for multiple sclerosis: 2005 revisions to the “McDonald Criteria” , 2005, Annals of neurology.

[36]  Mark W. Woolrich,et al.  Advances in functional and structural MR image analysis and implementation as FSL , 2004, NeuroImage.

[37]  Stephen M. Smith,et al.  Accurate, Robust, and Automated Longitudinal and Cross-Sectional Brain Change Analysis , 2002, NeuroImage.

[38]  A. Compston,et al.  Recommended diagnostic criteria for multiple sclerosis: Guidelines from the international panel on the diagnosis of multiple sclerosis , 2001, Annals of neurology.

[39]  J. Kurtzke Rating neurologic impairment in multiple sclerosis , 1983, Neurology.

[40]  F. Quintana,et al.  [Immunopathology of multiple sclerosis]. , 2014, Medicina.