Background: Since combining information from different domains could be useful to increase prediction accuracy over and above what can be achieved at the level of single category of markers, this study aimed to identify distinct and predominant subtypes, i.e., cognitive phenotypes, in people with multiple sclerosis (PwMS) considering both cognitive impairment and mood disorders.
Methods: A latent class analysis (LCA) was applied on data from 872 PwMS who were tested with Montreal Cognitive Assessment (MoCA), Symbol Digit Modalities Test (SDMT) and Hospital Anxiety and Depression Scale (HADS). Furthermore, the distribution of demographic (i.e., age, gender, years of education) and clinical characteristics (i.e., disease duration, disease course, disability level) was examined amongst the identified phenotypes.
Results: Based on model fit and parsimony criteria, LCA identified four cognitive phenotypes: 1) only memory difficulties (n = 247; 28.3%); 2) minor memory and language deficits with mood disorders (n = 185; 21.2%); 3) moderate memory, language and attention impairments (n = 164; 18.8%); 4) severe memory, language, attention, information processing and executive functions difficulties (n = 276; 31.7%).
Conclusions: Since less is known about the progressive deterioration of cognition in PwMS, a taxonomy of distinct subtypes that consider information from different clustered domains (i.e., cognition and mood) represents both a challenge and opportunity for an advanced understanding of cognitive impairments and development of tailored cognitive treatments in MS.
Keywords: Cognitive impairment; Cognitive phenotype; Latent class analysis; Mood disorders; Multiple sclerosis.
Copyright © 2021 The Authors. Published by Elsevier B.V. All rights reserved.