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Resilience to Plasma and Cerebrospinal Fluid Amyloid-β in Cognitively Normal Individuals: Findings From Two Cohort Studies

  • Lin, Li1
  • Sun, Yu1
  • Wang, Xiaoqi1
  • Su, Li2
  • Wang, Xiaoni1
  • Han, Ying1, 3, 4
  • 1 Department of Neurology, XuanWu Hospital of Capital Medical University, Beijing , (China)
  • 2 Department of Psychiatry, University of Cambridge, Cambridge , (United Kingdom)
  • 3 Center of Alzheimer's Disease, Beijing Institute for Brain Disorders, Beijing , (China)
  • 4 National Clinical Research Center for Geriatric Disorders, Beijing , (China)
Published Article
Frontiers in Aging Neuroscience
Frontiers Media SA
Publication Date
Feb 24, 2021
DOI: 10.3389/fnagi.2021.610755
  • Neuroscience
  • Original Research


Objective: 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). Methods: In this study, cognitively normal (CN) participants with Aβ-positive were included from the Sino Longitudinal Study on Cognitive Decline (SILCODE, n = 100) and Alzheimer's Disease Neuroimaging Initiative (ADNI, n = 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. Results: Although individuals in the ADNI cohort were older (74.68 [5.65] vs. 65.38 [4.66], p < 0.001) and had higher educational levels (16.3 [2.6] vs. 12.6 [2.8], p < 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. Conclusion: 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.

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