Dynamics and predictors of cognitive impairment along the disease course in multiple sclerosis

Background: The evolution and predictors of cognitive impairment (CI) in multiple sclerosis (MS) are poorly understood. We aimed to define the temporal dynamics of cognition throughout the disease course and identify clinical and neuroimaging measures that predict CI. (2) Methods: This paper feature...

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Detalles Bibliográficos
Autores: López Soley, Elisabet, Martinez-Heras, Eloy, Andorra, Magi, Solanes Font, Aleix, Radua, Joaquim, Montejo, Carmen, Alba Arbalat, Salut|||0000-0003-1662-2317, Sola Valls, Núria, Pulido-Valdeolivas, Irene, Sepúlveda, Maria, Romero Pinel, Lucia, Munteis Olivas, Elvira, Martínez Rodríguez, Jose Enrique, Blanco, Yolanda, Martínez Lapiscina, Elena H., Villoslada, Pablo, Saiz Hinarejos, Albert, Solana, Elisabeth, Llufriu, Sara
Tipo de recurso: artículo
Fecha de publicación:2021
País:España
Institución:Universitat Politècnica de Catalunya (UPC)
Repositorio:UPCommons. Portal del coneixement obert de la UPC
Idioma:inglés
OAI Identifier:oai:upcommons.upc.edu:2117/401744
Acceso en línea:https://hdl.handle.net/2117/401744
https://dx.doi.org/10.3390/jpm11111107
Access Level:acceso abierto
Palabra clave:Multiple sclerosis
Cognitive neuroscience
Cognition
Cognitive impairment
Neuroimaging
Longitudinal
Predictors
Esclerosi múltiple
Neurociència cognitiva
Cognició
Àrees temàtiques de la UPC::Ciències de la salut::Medicina
Descripción
Sumario:Background: The evolution and predictors of cognitive impairment (CI) in multiple sclerosis (MS) are poorly understood. We aimed to define the temporal dynamics of cognition throughout the disease course and identify clinical and neuroimaging measures that predict CI. (2) Methods: This paper features a longitudinal study with 212 patients who underwent several cognitive examinations at different time points. Dynamics of cognition were assessed using mixed-effects linear spline models. Machine learning techniques were used to identify which baseline demographic, clinical, and neuroimaging measures best predicted CI. (3) Results: In the first 5 years of MS, we detected an increase in the z-scores of global cognition, verbal memory, and information processing speed, which was followed by a decline in global cognition and memory (p < 0.05) between years 5 and 15. From 15 to 30 years of disease onset, cognitive decline continued, affecting global cognition and verbal memory. The baseline measures that best predicted CI were education, disease severity, lesion burden, and hippocampus and anterior cingulate cortex volume. (4) Conclusions: In MS, cognition deteriorates 5 years after disease onset, declining steadily over the next 25 years and more markedly affecting verbal memory. Education, disease severity, lesion burden, and volume of limbic structures predict future CI and may be helpful when identifying at-risk patients.

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