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Abstract
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.
GLOSSARY
- Aβ=
- β-amyloid;
- AD=
- Alzheimer disease;
- ADNI=
- Alzheimer's Disease Neuroimaging Initiative;
- ADNI-GO=
- Alzheimer's Disease Neuroimaging Initiative-Grand Opportunity;
- ICV=
- intracranial volume;
- MCI=
- mild cognitive impairment;
- NC=
- normal cognition;
- PLS=
- partial least squares
Footnotes
Interested investigative teams can contact Dr. Hohman for access to the calculated resilience phenotypes.
Data used in preparation of this article were obtained from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database (adni.loni.usc.edu). As such, the investigators within the ADNI contributed to the design and implementation of ADNI and/or provided data but did not participate in analysis or writing of this report. A complete listing of ADNI investigators can be found in the coinvestigators list at Neurology.org.
Go to Neurology.org for full disclosures. Funding information and disclosures deemed relevant by the authors, if any, are provided at the end of the article.
Supplemental data at Neurology.org
- Received March 2, 2016.
- Accepted in final form September 7, 2016.
- © 2016 American Academy of Neurology
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