Asymptomatic Alzheimer disease: Defining resilience

Area of Science:

• Neuroscience
• Biomarkers
• Cognitive Aging

Background:

• Alzheimer's disease (AD) poses a significant challenge, necessitating novel approaches to understand protective factors.
• Resilience, the ability to withstand pathological processes, is crucial for mitigating AD's clinical impact.
• Current metrics may not fully capture the complex interplay between pathology and cognitive outcomes.

Purpose of the Study:

• To develop robust resilience metrics using cerebrospinal fluid (CSF) biomarkers for Alzheimer's disease (AD) pathology.
• To evaluate the predictive power of these metrics for cognitive decline and conversion to AD dementia.
• To identify individuals at higher risk of decline based on resilience and biomarker status.

Main Methods:

• Utilized data from 729 participants (normal cognition and mild cognitive impairment) from the Alzheimer's Disease Neuroimaging Initiative.
• Defined resilience metrics by analyzing residuals of brain aging outcomes (hippocampal volume, cognition) regressed on CSF biomarkers.
• Employed a latent variable framework to model resilience and validated its predictive ability against diagnostic conversion, cognitive decline, and ventricular dilation rates.

Main Results:

• Latent resilience variables significantly predicted decreased conversion risk (HR < 0.54, p < 0.0001).
• Resilience metrics indicated slower cognitive decline (β > 0.02, p < 0.001) and reduced ventricular dilation (β < -4.7, p < 2 × 10^-15).
• A significant interaction showed biomarker-positive individuals with low resilience faced the greatest decline risk.

Conclusions:

• Robust resilience phenotypes derived from AD biomarkers and brain aging offer insights into short-term decline risk.
• These comprehensive resilience definitions are vital for understanding protective mechanisms against AD dementia.
• Particular focus on biomarker-positive individuals with low resilience is warranted for targeted interventions.