Michelle E Farrell1, Shu Jiang1, Aaron P Schultz1, Michael J Properzi1, Julie C Price1, J Alex Becker1, Heidi I L Jacobs1, Bernard J Hanseeuw1, Dorene M Rentz1, Victor L Villemagne1, Kathryn V Papp1, Elizabeth C Mormino1, Rebecca A Betensky1, Keith A Johnson1, Reisa A Sperling1, Rachel F Buckley2. 1. From the Departments of Neurology (M.E.F., S.J., A.P.S., M.J.P., D.M.R., K.V.P., R.A.B., K.A.J., R.A.S., R.F.B.) and Radiology (J.C.P., J.A.B., H.I.L.J., B.J.H., K.A.J.), Massachusetts General Hospital, Harvard Medical School; Department of Biostatistics (S.J., R.A.B.), Harvard T.H. Chan School of Public Health, Boston, MA; Division of Public Health Sciences (S.J.), Department of Surgery, Washington University School of Medicine in St. Louis, MO; Faculty of Health (H.I.L.J.), Medicine and Life Sciences, School for Mental Health and Neuroscience, Alzheimer Centre Limburg, Maastricht University, the Netherlands; Cliniques Universitaires Saint-Luc (B.J.H.), Université Catholique de Louvain, Brussels, Belgium; Center for Alzheimer Research and Treatment (D.M.R., K.V.P., R.A.S., R.F.B.), Brigham and Women's Hospital, Boston, MA; Department of Molecular Imaging & Therapy (V.L.V.), Austin Health, Melbourne, Australia; Department of Neuroscience (E.C.M.), Stanford University, Palo Alto, CA; Department of Biostatistics (R.A.B.), New York University School of Global Public Health, NY; Center for Alzheimer Research and Treatment, Brigham and Women's Hospital, Boston, MA; and Melbourne School of Psychological Sciences (R.F.B.), University of Melbourne, Australia. 2. From the Departments of Neurology (M.E.F., S.J., A.P.S., M.J.P., D.M.R., K.V.P., R.A.B., K.A.J., R.A.S., R.F.B.) and Radiology (J.C.P., J.A.B., H.I.L.J., B.J.H., K.A.J.), Massachusetts General Hospital, Harvard Medical School; Department of Biostatistics (S.J., R.A.B.), Harvard T.H. Chan School of Public Health, Boston, MA; Division of Public Health Sciences (S.J.), Department of Surgery, Washington University School of Medicine in St. Louis, MO; Faculty of Health (H.I.L.J.), Medicine and Life Sciences, School for Mental Health and Neuroscience, Alzheimer Centre Limburg, Maastricht University, the Netherlands; Cliniques Universitaires Saint-Luc (B.J.H.), Université Catholique de Louvain, Brussels, Belgium; Center for Alzheimer Research and Treatment (D.M.R., K.V.P., R.A.S., R.F.B.), Brigham and Women's Hospital, Boston, MA; Department of Molecular Imaging & Therapy (V.L.V.), Austin Health, Melbourne, Australia; Department of Neuroscience (E.C.M.), Stanford University, Palo Alto, CA; Department of Biostatistics (R.A.B.), New York University School of Global Public Health, NY; Center for Alzheimer Research and Treatment, Brigham and Women's Hospital, Boston, MA; and Melbourne School of Psychological Sciences (R.F.B.), University of Melbourne, Australia. rfbuckley@mgh.harvard.edu.
Abstract
INTRODUCTION: As clinical trials move toward earlier intervention, we sought to redefine the β-amyloid (Aβ)-PET threshold based on the lowest point in a baseline distribution that robustly predicts future Aβ accumulation and cognitive decline in 3 independent samples of clinically normal individuals. METHODS: Sequential Aβ cutoffs were tested to identify the lowest cutoff associated with future change in cognition (Preclinical Alzheimer Cognitive Composite [PACC]) and Aβ-PET in clinically normal participants from the Harvard Aging Brain Study (n = 342), Australian Imaging, Biomarker and Lifestyle study of aging (n = 157), and Alzheimer's Disease Neuroimaging Initiative (n = 356). RESULTS: Within samples, cutoffs derived from future Aβ-PET accumulation and PACC decline converged on the same inflection point, beyond which trajectories diverged from normal. Across samples, optimal cutoffs fell within a short range (Centiloid 15-18.5). DISCUSSION: These optimized thresholds can help to inform future research and clinical trials targeting early Aβ. Threshold convergence raises the possibility of contemporaneous early changes in Aβ and cognition. CLASSIFICATION OF EVIDENCE: This study provides Class II evidence that among clinically normal individuals a specific Aβ-PET threshold is predictive of cognitive decline.
INTRODUCTION: As clinical trials move toward earlier intervention, we sought to redefine the β-amyloid (Aβ)-PET threshold based on the lowest point in a baseline distribution that robustly predicts future Aβ accumulation and cognitive decline in 3 independent samples of clinically normal individuals. METHODS: Sequential Aβ cutoffs were tested to identify the lowest cutoff associated with future change in cognition (Preclinical Alzheimer Cognitive Composite [PACC]) and Aβ-PET in clinically normal participants from the Harvard Aging Brain Study (n = 342), Australian Imaging, Biomarker and Lifestyle study of aging (n = 157), and Alzheimer's Disease Neuroimaging Initiative (n = 356). RESULTS: Within samples, cutoffs derived from future Aβ-PET accumulation and PACC decline converged on the same inflection point, beyond which trajectories diverged from normal. Across samples, optimal cutoffs fell within a short range (Centiloid 15-18.5). DISCUSSION: These optimized thresholds can help to inform future research and clinical trials targeting early Aβ. Threshold convergence raises the possibility of contemporaneous early changes in Aβ and cognition. CLASSIFICATION OF EVIDENCE: This study provides Class II evidence that among clinically normal individuals a specific Aβ-PET threshold is predictive of cognitive decline.
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