Tom A Fuchs1, Curtis Wojcik2, Gregory E Wilding3, Jeta Pol2, Michael G Dwyer1, Bianca Weinstock-Guttman2, Robert Zivadinov4, Ralph Hb Benedict2. 1. Department of Neurology, Buffalo Neuroimaging Analysis Center, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, The State University of New York, Buffalo, NY, USA/Jacobs Multiple Sclerosis Center, Department of Neurology, School of Medicine and Biomedical Sciences, University at Buffalo, The State University of New York, Buffalo, NY, USA. 2. Jacobs Multiple Sclerosis Center, Department of Neurology, School of Medicine and Biomedical Sciences, University at Buffalo, The State University of New York, Buffalo, NY, USA. 3. Department of Biostatistics, University at Buffalo, The State University of New York, Buffalo, NY, USA. 4. Department of Neurology, Buffalo Neuroimaging Analysis Center, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, The State University of New York, Buffalo, NY, USA/Jacobs Multiple Sclerosis Center, Department of Neurology, School of Medicine and Biomedical Sciences, University at Buffalo, The State University of New York, Buffalo, NY, USA/Center for Biomedical Imaging, Clinical Translational Science Institute, University at Buffalo, The State University of New York, Buffalo, NY, USA.
Abstract
BACKGROUND: Many people with multiple sclerosis (MS) exhibit cognitive decline over several years. Baseline differences may put people at greater risk for such decline. OBJECTIVE: To characterize rates of longitudinal cognitive decline and investigate baseline clinical predictors. METHODS: We report a retrospective analysis of 531 MS patients whose data were gleaned from a multi-study database, aggregated over 16 years. Linear mixed effects modeling was applied to estimate the average rate of decline on Symbol Digit Modalities Test (SDMT) performance and to predict rates of decline using baseline clinical variables. RESULTS: Participants exhibited an average estimated decline of 0.22 SDMT raw-score points/year (95% confidence interval (CI) (-0.32, -0.12)). We observed a significant main effect of time from baseline (t = -2.78, p = 0.006), test form (t = 2.13, p = 0.034), disease course (t = 2.91, p = 0.004), age (t = -2.76, p = 0.006), sex (t = -2.71, p = 0.007), subjective cognitive impairment (t = -2.00, p = 0.046), premorbid verbal intelligence (t = 5.14, p < 0.001), and trait Conscientiousness (t = 2.69, p = 0.008). A significant interaction emerged for Conscientiousness and time from baseline (t = 2.57, p = 0.011). CONCLUSION: Higher baseline trait Conscientiousness predicts slower rates of longitudinal cognitive decline in MS. This relationship, the average rate of decline, and practice effects can inform future research and clinical treatment decisions.
BACKGROUND: Many people with multiple sclerosis (MS) exhibit cognitive decline over several years. Baseline differences may put people at greater risk for such decline. OBJECTIVE: To characterize rates of longitudinal cognitive decline and investigate baseline clinical predictors. METHODS: We report a retrospective analysis of 531 MS patients whose data were gleaned from a multi-study database, aggregated over 16 years. Linear mixed effects modeling was applied to estimate the average rate of decline on Symbol Digit Modalities Test (SDMT) performance and to predict rates of decline using baseline clinical variables. RESULTS:Participants exhibited an average estimated decline of 0.22 SDMT raw-score points/year (95% confidence interval (CI) (-0.32, -0.12)). We observed a significant main effect of time from baseline (t = -2.78, p = 0.006), test form (t = 2.13, p = 0.034), disease course (t = 2.91, p = 0.004), age (t = -2.76, p = 0.006), sex (t = -2.71, p = 0.007), subjective cognitive impairment (t = -2.00, p = 0.046), premorbid verbal intelligence (t = 5.14, p < 0.001), and trait Conscientiousness (t = 2.69, p = 0.008). A significant interaction emerged for Conscientiousness and time from baseline (t = 2.57, p = 0.011). CONCLUSION: Higher baseline trait Conscientiousness predicts slower rates of longitudinal cognitive decline in MS. This relationship, the average rate of decline, and practice effects can inform future research and clinical treatment decisions.
Authors: Tom A Fuchs; Michael G Jaworski; Margaret Youngs; Omar Abdel-Kerim; Curtis Wojcik; Bianca Weinstock-Guttman; Ralph H B Benedict Journal: Int J MS Care Date: 2021-07-09
Authors: Stijn Denissen; Oliver Y Chén; Johan De Mey; Maarten De Vos; Jeroen Van Schependom; Diana Maria Sima; Guy Nagels Journal: J Pers Med Date: 2021-12-11