Timothy J Hohman1, Donald G McLaren2, Elizabeth C Mormino2, Katherine A Gifford2, David J Libon2, Angela L Jefferson2. 1. From the Vanderbilt Memory & Alzheimer's Center (T.J.H., K.A.G., A.L.J.), Vanderbilt University Medical Center, Nashville, TN; Biospective Inc (D.G.M.), Montreal, Quebec, Canada; Department of Neurology (E.C.M.), Massachusetts General Hospital, Harvard Medical School, Boston; and Department of Geriatric and Gerontology (D.J.L.), New Jersey Institute for Successful Aging and Department of Psychology, Rowan University School of Osteopathic Medicine, Stratford. Timothy.J.Hohman@Vanderbilt.edu. 2. From the Vanderbilt Memory & Alzheimer's Center (T.J.H., K.A.G., A.L.J.), Vanderbilt University Medical Center, Nashville, TN; Biospective Inc (D.G.M.), Montreal, Quebec, Canada; Department of Neurology (E.C.M.), Massachusetts General Hospital, Harvard Medical School, Boston; and Department of Geriatric and Gerontology (D.J.L.), New Jersey Institute for Successful Aging and Department of Psychology, Rowan University School of Osteopathic Medicine, Stratford.
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.
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.
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