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

Objective: To define resilience metrics for cognitive decline based on plasma and CSF amyloid-β (Aβ) and to 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 MCI. Methods: In this study, cognitively normal 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 to data, metrics of resilience (brain, cognitive and global resilience) were defined, and a linear regression analysis was 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 clinical progression. Results: Although individuals in ADNI were older (74.68[5.65] vs 65.38[4.66], p<0.001) and had higher educational level (16.3[2.6] vs 12.6[2.8], p<0.001) than those in SILCODE, similar loadings between resilience and its indicators were found within models of SILCODE and ADNI. Brain resilience and global resilience were mainly associated with age, women, and brain volume in both datasets. Prediction models showed that higher cognitive resilience and global resilience 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 AD at the individual level and interindividual differences in resilience had the potential to predict disease progression.