OBJECTIVE: To define robust resilience metrics by leveraging CSF biomarkers of 
Alzheimer disease (AD) pathology within a latent variable framework and to 
demonstrate the ability of such metrics to predict slower rates of cognitive 
decline and protection against diagnostic conversion.
METHODS: Participants with normal cognition (n = 297) and mild cognitive 
impairment (n = 432) were drawn from the Alzheimer's Disease Neuroimaging 
Initiative. Resilience metrics were defined at baseline by examining the 
residuals when regressing brain aging outcomes (hippocampal volume and 
cognition) on CSF biomarkers. A positive residual reflected better outcomes than 
expected for a given level of pathology (high resilience). Residuals were 
integrated into a latent variable model of resilience and validated by testing 
their ability to independently predict diagnostic conversion, cognitive decline, 
and the rate of ventricular dilation.
RESULTS: Latent variables of resilience predicted a decreased risk of conversion 
(hazard ratio < 0.54, p < 0.0001), slower cognitive decline (β > 0.02, p < 
0.001), and slower rates of ventricular dilation (β < -4.7, p < 2 × 10-15). 
These results were significant even when analyses were restricted to clinically 
normal individuals. Furthermore, resilience metrics interacted with biomarker 
status such that biomarker-positive individuals with low resilience showed the 
greatest risk of subsequent decline.
CONCLUSIONS: Robust phenotypes of resilience calculated by leveraging AD 
biomarkers and baseline brain aging outcomes provide insight into which 
individuals are at greatest risk of short-term decline. Such comprehensive 
definitions of resilience are needed to further our understanding of the 
mechanisms that protect individuals from the clinical manifestation of AD 
dementia, especially among biomarker-positive individuals.