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Brummer, Tobias [VerfasserIn]; Muthuraman, Muthuraman [VerfasserIn]; Steffen, Falk [VerfasserIn]; Uphaus, Timo [VerfasserIn]; Minch, Lena [VerfasserIn]; Person, Maren [VerfasserIn]; Zipp, Frauke [VerfasserIn]; Groppa, Sergiu [VerfasserIn]; Bittner, Stefan [VerfasserIn]; Fleischer, Vinzenz [VerfasserIn]

Improved prediction of early cognitive impairment in multiple sclerosis combining blood and imaging biomarkers

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  • Medientyp: E-Artikel
  • Titel: Improved prediction of early cognitive impairment in multiple sclerosis combining blood and imaging biomarkers
  • Beteiligte: Brummer, Tobias [VerfasserIn]; Muthuraman, Muthuraman [VerfasserIn]; Steffen, Falk [VerfasserIn]; Uphaus, Timo [VerfasserIn]; Minch, Lena [VerfasserIn]; Person, Maren [VerfasserIn]; Zipp, Frauke [VerfasserIn]; Groppa, Sergiu [VerfasserIn]; Bittner, Stefan [VerfasserIn]; Fleischer, Vinzenz [VerfasserIn]
  • Erschienen: Augsburg University Publication Server (OPUS), 2022
  • Sprache: Englisch
  • DOI: https://doi.org/10.1093/braincomms/fcac153
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  • Anmerkungen: Diese Datenquelle enthält auch Bestandsnachweise, die nicht zu einem Volltext führen.
  • Beschreibung: Disability in multiple sclerosis is generally classified by sensory and motor symptoms, yet cognitive impairment has been identified as a frequent manifestation already in the early disease stages. Imaging- and more recently blood-based biomarkers have become increasingly important for understanding cognitive decline associated with multiple sclerosis. Thus, we sought to determine the prognostic utility of serum neurofilament light chain levels alone and in combination with MRI markers by examining their ability to predict cognitive impairment in early multiple sclerosis. A comprehensive and detailed assessment of 152 early multiple sclerosis patients (Expanded Disability Status Scale: 1.3 ± 1.2, mean age: 33.0 ± 10.0 years) was performed, which included serum neurofilament light chain measurement, MRI markers (i.e. T2-hyperintense lesion volume and grey matter volume) acquisition and completion of a set of cognitive tests (Symbol Digits Modalities Test, Paced Auditory Serial Addition Test, Verbal Learning and Memory Test) and mood questionnaires (Hospital Anxiety and Depression scale, Fatigue Scale for Motor and Cognitive Functions). Support vector regression, a branch of unsupervised machine learning, was applied to test serum neurofilament light chain and combination models of biomarkers for the prediction of neuropsychological test performance. The support vector regression results were validated in a replication cohort of 101 early multiple sclerosis patients (Expanded Disability Status Scale: 1.1 ± 1.2, mean age: 34.4 ± 10.6 years). Higher serum neurofilament light chain levels were associated with worse Symbol Digits Modalities Test scores after adjusting for age, sex Expanded Disability Status Scale, disease duration and disease-modifying therapy (B = −0.561; SE = 0.192; P = 0.004; 95% CI = −0.940 to −0.182). Besides this association, serum neurofilament light chain levels were not linked to any other cognitive or mood measures (all P-values > 0.05). The tripartite combination of serum neurofilament ...
  • Zugangsstatus: Freier Zugang