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Cognitive Profiles of Aging in Multiple Sclerosis

BACKGROUND: Increasingly favorable mortality prognosis in multiple sclerosis (MS) raises questions regarding MS-specific cognitive aging and the presence of comorbidities such as Alzheimer’s disease (AD). OBJECTIVE: To assess elderly with MS (EwMS) and age-matched healthy controls (HCs) using both M...

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Autores principales: Jakimovski, Dejan, Weinstock-Guttman, Bianca, Roy, Shumita, Jaworski, Michael, Hancock, Laura, Nizinski, Alissa, Srinivasan, Pavitra, Fuchs, Tom A., Szigeti, Kinga, Zivadinov, Robert, Benedict, Ralph H. B.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6524468/
https://www.ncbi.nlm.nih.gov/pubmed/31133845
http://dx.doi.org/10.3389/fnagi.2019.00105
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author Jakimovski, Dejan
Weinstock-Guttman, Bianca
Roy, Shumita
Jaworski, Michael
Hancock, Laura
Nizinski, Alissa
Srinivasan, Pavitra
Fuchs, Tom A.
Szigeti, Kinga
Zivadinov, Robert
Benedict, Ralph H. B.
author_facet Jakimovski, Dejan
Weinstock-Guttman, Bianca
Roy, Shumita
Jaworski, Michael
Hancock, Laura
Nizinski, Alissa
Srinivasan, Pavitra
Fuchs, Tom A.
Szigeti, Kinga
Zivadinov, Robert
Benedict, Ralph H. B.
author_sort Jakimovski, Dejan
collection PubMed
description BACKGROUND: Increasingly favorable mortality prognosis in multiple sclerosis (MS) raises questions regarding MS-specific cognitive aging and the presence of comorbidities such as Alzheimer’s disease (AD). OBJECTIVE: To assess elderly with MS (EwMS) and age-matched healthy controls (HCs) using both MS- and AD-specific psychometrics. METHODS: EwMS (n = 104) and 56 HCs were assessed on a broad spectrum of language, visual-spatial processing, memory, processing speed, and executive function tests. Using logistic regression analysis, we examined cognitive performance differences between the EwMS and HC groups. Cognitive impairment (CI) was defined using a -1.5 SD threshold relative to age and education years-matched HCs, in two cognitive domains. RESULTS: CI was observed in 47.1% of EwMS with differences most often seen on tests emphasizing cognitive processing speed as measured by Symbol Digit Modalities Test (SDMT) (d = 0.9, p < 0.001) and verbal fluency (both category-based d = 0.87, p < 0.001; letter-based d = 0.67, p < 0.001). After adjusting for age, sex and years of education, MS/HC diagnosis was best predicted (R(2) = 0.27) by differences in category-based verbal fluency (Wald = 9.935, p = 0.002) and SDMT (Wald = 13.937, p < 0.001). CONCLUSION: This study confirms the common hallmark of slowed cognitive processing speed in MS among elderly patients. Defective verbal fluency, less often observed in younger cohorts, may represent emerging cognitive pathology due to other etiologies.
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spelling pubmed-65244682019-05-27 Cognitive Profiles of Aging in Multiple Sclerosis Jakimovski, Dejan Weinstock-Guttman, Bianca Roy, Shumita Jaworski, Michael Hancock, Laura Nizinski, Alissa Srinivasan, Pavitra Fuchs, Tom A. Szigeti, Kinga Zivadinov, Robert Benedict, Ralph H. B. Front Aging Neurosci Neuroscience BACKGROUND: Increasingly favorable mortality prognosis in multiple sclerosis (MS) raises questions regarding MS-specific cognitive aging and the presence of comorbidities such as Alzheimer’s disease (AD). OBJECTIVE: To assess elderly with MS (EwMS) and age-matched healthy controls (HCs) using both MS- and AD-specific psychometrics. METHODS: EwMS (n = 104) and 56 HCs were assessed on a broad spectrum of language, visual-spatial processing, memory, processing speed, and executive function tests. Using logistic regression analysis, we examined cognitive performance differences between the EwMS and HC groups. Cognitive impairment (CI) was defined using a -1.5 SD threshold relative to age and education years-matched HCs, in two cognitive domains. RESULTS: CI was observed in 47.1% of EwMS with differences most often seen on tests emphasizing cognitive processing speed as measured by Symbol Digit Modalities Test (SDMT) (d = 0.9, p < 0.001) and verbal fluency (both category-based d = 0.87, p < 0.001; letter-based d = 0.67, p < 0.001). After adjusting for age, sex and years of education, MS/HC diagnosis was best predicted (R(2) = 0.27) by differences in category-based verbal fluency (Wald = 9.935, p = 0.002) and SDMT (Wald = 13.937, p < 0.001). CONCLUSION: This study confirms the common hallmark of slowed cognitive processing speed in MS among elderly patients. Defective verbal fluency, less often observed in younger cohorts, may represent emerging cognitive pathology due to other etiologies. Frontiers Media S.A. 2019-05-10 /pmc/articles/PMC6524468/ /pubmed/31133845 http://dx.doi.org/10.3389/fnagi.2019.00105 Text en Copyright © 2019 Jakimovski, Weinstock-Guttman, Roy, Jaworski, Hancock, Nizinski, Srinivasan, Fuchs, Szigeti, Zivadinov and Benedict. http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Neuroscience
Jakimovski, Dejan
Weinstock-Guttman, Bianca
Roy, Shumita
Jaworski, Michael
Hancock, Laura
Nizinski, Alissa
Srinivasan, Pavitra
Fuchs, Tom A.
Szigeti, Kinga
Zivadinov, Robert
Benedict, Ralph H. B.
Cognitive Profiles of Aging in Multiple Sclerosis
title Cognitive Profiles of Aging in Multiple Sclerosis
title_full Cognitive Profiles of Aging in Multiple Sclerosis
title_fullStr Cognitive Profiles of Aging in Multiple Sclerosis
title_full_unstemmed Cognitive Profiles of Aging in Multiple Sclerosis
title_short Cognitive Profiles of Aging in Multiple Sclerosis
title_sort cognitive profiles of aging in multiple sclerosis
topic Neuroscience
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6524468/
https://www.ncbi.nlm.nih.gov/pubmed/31133845
http://dx.doi.org/10.3389/fnagi.2019.00105
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