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.
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.
Mult Scler. 2020 Feb;26(2):245-252. doi: 10.1177/1352458518820272. Epub 2019 Jan 7.
Many people with multiple sclerosis (MS) exhibit cognitive decline over several years. Baseline differences may put people at greater risk for such decline.
To characterize rates of longitudinal cognitive decline and investigate baseline clinical predictors.
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.
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 ( = -2.78, = 0.006), test form ( = 2.13, = 0.034), disease course ( = 2.91, = 0.004), age ( = -2.76, = 0.006), sex ( = -2.71, = 0.007), subjective cognitive impairment ( = -2.00, = 0.046), premorbid verbal intelligence ( = 5.14, < 0.001), and trait Conscientiousness ( = 2.69, = 0.008). A significant interaction emerged for Conscientiousness and time from baseline ( = 2.57, = 0.011).
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.
许多多发性硬化症（MS）患者在几年内会出现认知能力下降。基线差异可能使人们面临更大的下降风险。
描述纵向认知能力下降的速度，并研究基线临床预测因素。
我们报告了对 531 名 MS 患者数据的回顾性分析，这些数据来自一个多研究数据库，汇总了 16 年的数据。应用线性混合效应模型来估计符号数字模态测验（SDMT）表现的平均下降速度，并使用基线临床变量预测下降速度。
参与者的 SDMT 原始分数平均估计下降了 0.22 个标准差/年（95%置信区间（CI）（-0.32，-0.12））。我们观察到基线时间（ = -2.78， = 0.006）、测试形式（ = 2.13， = 0.034）、疾病病程（ = 2.91， = 0.004）、年龄（ = -2.76， = 0.006）、性别（ = -2.71， = 0.007）、主观认知障碍（ = -2.00， = 0.046）、术前言语智力（ = 5.14， < 0.001）和特质尽责性（ = 2.69， = 0.008）有显著的主效应。尽责性和基线时间之间存在显著的交互作用（ = 2.57， = 0.011）。
较高的基线特质尽责性预示着 MS 患者纵向认知能力下降的速度较慢。这种关系、平均下降速度和练习效应可以为未来的研究和临床治疗决策提供信息。
