Lin Li, Sun Yu, Wang Xiaoqi, Su Li, Wang Xiaoni, Han Ying
Department of Neurology, XuanWu Hospital of Capital Medical University, Beijing, China.
Department of Psychiatry, University of Cambridge, Cambridge, United Kingdom.
Front Aging Neurosci. 2021 Feb 24;13:610755. doi: 10.3389/fnagi.2021.610755. eCollection 2021.
To define resilience metrics for cognitive decline based on plasma and cerebrospinal fluid (CSF) amyloid-β (Aβ) and examine the demographic, genetic, and neuroimaging factors associated with interindividual differences among metrics of resilience and to demonstrate the ability of such metrics to predict the diagnostic conversion to mild cognitive impairment (MCI). In this study, cognitively normal (CN) participants with Aβ-positive were included from the Sino Longitudinal Study on Cognitive Decline (SILCODE, = 100) and Alzheimer's Disease Neuroimaging Initiative (ADNI, = 144). Using a latent variable model of data, metrics of resilience [brain resilience (BR), cognitive resilience (CR), and global resilience (GR)] were defined based on the plasma Aβ and CSF Aβ. Linear regression analyses were applied to investigate the association between characteristics of individuals (age, sex, educational level, genetic, and neuroimaging factors) and their resilience. The plausibility of these metrics was tested using linear mixed-effects models and Cox regression models in longitudinal analyses. We also compared the effectiveness of these metrics with conventional metrics in predicting the clinical progression. Although individuals in the ADNI cohort were older (74.68 [5.65] vs. 65.38 [4.66], < 0.001) and had higher educational levels (16.3 [2.6] vs. 12.6 [2.8], < 0.001) than those in the SILCODE cohort, similar loadings between resilience and its indicators were found within both models. BR and GR were mainly associated with age, women, and brain volume in both cohorts. Prediction models showed that higher CR and GR were related to better cognitive performance, and specifically, all types of resilience to CSF Aβ could predict longitudinal cognitive decline. Different phenotypes of resilience depending on cognition and brain volumes were associated with different factors. Such comprehensive resilience provided insight into the mechanisms of susceptibility for Alzheimer's disease (AD) at the individual level, and interindividual differences in resilience had the potential to predict the disease progression in CN people.
基于血浆和脑脊液（CSF）淀粉样蛋白-β（Aβ）定义认知衰退的恢复力指标，研究与恢复力指标个体差异相关的人口统计学、遗传学和神经影像学因素，并证明这些指标预测向轻度认知障碍（MCI）诊断转化的能力。在本研究中，从中国认知衰退纵向研究（SILCODE，n = 100）和阿尔茨海默病神经影像学倡议（ADNI，n = 144）中纳入Aβ阳性的认知正常（CN）参与者。使用数据的潜在变量模型，基于血浆Aβ和脑脊液Aβ定义恢复力指标[脑恢复力（BR）、认知恢复力（CR）和整体恢复力（GR）]。应用线性回归分析研究个体特征（年龄、性别、教育水平、遗传学和神经影像学因素）与其恢复力之间的关联。在纵向分析中使用线性混合效应模型和Cox回归模型测试这些指标的合理性。我们还比较了这些指标与传统指标在预测临床进展方面的有效性。尽管ADNI队列中的个体比SILCODE队列中的个体年龄更大（74.68 [5.65] 对65.38 [4.66]，P < 0.001）且教育水平更高（16.3 [2.6] 对12.6 [2.8]，P < 0.001），但在两个模型中均发现恢复力与其指标之间的负荷相似。在两个队列中，BR和GR主要与年龄、女性和脑容量相关。预测模型表明，较高的CR和GR与更好的认知表现相关，具体而言，对脑脊液Aβ的所有类型恢复力都可以预测纵向认知衰退。根据认知和脑容量不同的恢复力表型与不同因素相关。这种综合恢复力在个体水平上为阿尔茨海默病（AD）易感性机制提供了见解，恢复力的个体差异有可能预测CN人群的疾病进展。
