Predicting sporadic Alzheimer's disease progression via inherited Alzheimer's disease-informed machine-learning.

Autor: Franzmeier N; Institute for Stroke and Dementia Research, Klinikum der Universität München, Ludwig-Maximilians-Universität LMU, Munich, Germany., Koutsouleris N; Department of Psychiatry and Psychotherapy, Ludwig-Maximilians-Universität LMU, Munich, Germany., Benzinger T; Department of Radiology, Washington University in St. Louis, St. Louis, Missouri, USA.; Knight Alzheimer's Disease Research Center, Washington University in St. Louis, St. Louis, Missouri, USA., Goate A; Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, New York, USA.; Ronald M. Loeb Center for Alzheimer's Disease, Department of Neuroscience, Icahn School of Medicine at Mount Sinai, New York, New York, USA., Karch CM; Knight Alzheimer's Disease Research Center, Washington University in St. Louis, St. Louis, Missouri, USA.; Hope Center for Neurological Disorders, Washington University in St. Louis, St. Louis, Missouri, USA.; Department of Psychiatry, Washington University in St. Louis, St. Louis, Missouri, USA., Fagan AM; Knight Alzheimer's Disease Research Center, Washington University in St. Louis, St. Louis, Missouri, USA.; Hope Center for Neurological Disorders, Washington University in St. Louis, St. Louis, Missouri, USA.; Department of Neurology, Washington University in St. Louis, St. Louis, Missouri, USA., McDade E; Knight Alzheimer's Disease Research Center, Washington University in St. Louis, St. Louis, Missouri, USA.; Department of Neurology, Washington University in St. Louis, St. Louis, Missouri, USA., Duering M; Institute for Stroke and Dementia Research, Klinikum der Universität München, Ludwig-Maximilians-Universität LMU, Munich, Germany., Dichgans M; Institute for Stroke and Dementia Research, Klinikum der Universität München, Ludwig-Maximilians-Universität LMU, Munich, Germany.; Munich Cluster for Systems Neurology, Munich, Germany.; German Center for Neurodegenerative Diseases (DZNE), Munich, Germany., Levin J; Munich Cluster for Systems Neurology, Munich, Germany.; German Center for Neurodegenerative Diseases (DZNE), Munich, Germany.; Department of Neurology, Ludwig-Maximilians-Universität München, Munich, Germany., Gordon BA; Knight Alzheimer's Disease Research Center, Washington University in St. Louis, St. Louis, Missouri, USA.; Mallinckrodt Institute of Radiology, Washington University, St. Louis, Missouri, USA.; Department of Psychological and Brain Sciences, Washington University, St. Louis, Missouri, USA., Lim YY; The Florey Institute, The University of Melbourne, Parkville, Victoria, Australia., Masters CL; The Florey Institute, The University of Melbourne, Parkville, Victoria, Australia., Rossor M; Dementia Research Centre, University College London, Queen Square, London, UK., Fox NC; Dementia Research Centre, University College London, Queen Square, London, UK., O'Connor A; Dementia Research Centre, University College London, Queen Square, London, UK., Chhatwal J; Massachusetts General Hospital, Department of Neurology, Harvard Medical School, Boston, Massachusetts, USA., Salloway S; Department of Neurology, Warren Alpert Medical School of Brown University, Providence, Rhode Island, USA., Danek A; Department of Neurology, Ludwig-Maximilians-Universität München, Munich, Germany., Hassenstab J; Knight Alzheimer's Disease Research Center, Washington University in St. Louis, St. Louis, Missouri, USA.; Department of Neurology, Washington University in St. Louis, St. Louis, Missouri, USA.; Department of Psychological and Brain Sciences, Washington University, St. Louis, Missouri, USA., Schofield PR; Neuroscience Research Australia, Randwick, New South Wales, Australia.; School of Medical Sciences, University of New South Wales, Sydney, New South Wales, Australia., Morris JC; Knight Alzheimer's Disease Research Center, Washington University in St. Louis, St. Louis, Missouri, USA.; Department of Psychiatry, Washington University in St. Louis, St. Louis, Missouri, USA.; Department of Neurology, Washington University in St. Louis, St. Louis, Missouri, USA., Bateman RJ; Knight Alzheimer's Disease Research Center, Washington University in St. Louis, St. Louis, Missouri, USA.; Department of Neurology, Washington University in St. Louis, St. Louis, Missouri, USA., Ewers M; Institute for Stroke and Dementia Research, Klinikum der Universität München, Ludwig-Maximilians-Universität LMU, Munich, Germany.
Jazyk: angličtina
Zdroj: Alzheimer's & dementia : the journal of the Alzheimer's Association [Alzheimers Dement] 2020 Mar; Vol. 16 (3), pp. 501-511. Date of Electronic Publication: 2020 Feb 11.
DOI: 10.1002/alz.12032
Abstrakt: Introduction: Developing cross-validated multi-biomarker models for the prediction of the rate of cognitive decline in Alzheimer's disease (AD) is a critical yet unmet clinical challenge.
Methods: We applied support vector regression to AD biomarkers derived from cerebrospinal fluid, structural magnetic resonance imaging (MRI), amyloid-PET and fluorodeoxyglucose positron-emission tomography (FDG-PET) to predict rates of cognitive decline. Prediction models were trained in autosomal-dominant Alzheimer's disease (ADAD, n = 121) and subsequently cross-validated in sporadic prodromal AD (n = 216). The sample size needed to detect treatment effects when using model-based risk enrichment was estimated.
Results: A model combining all biomarker modalities and established in ADAD predicted the 4-year rate of decline in global cognition (R 2 = 24%) and memory (R 2 = 25%) in sporadic AD. Model-based risk-enrichment reduced the sample size required for detecting simulated intervention effects by 50%-75%.
Discussion: Our independently validated machine-learning model predicted cognitive decline in sporadic prodromal AD and may substantially reduce sample size needed in clinical trials in AD.
(© 2020 The Authors. Alzheimer's & Dementia published by Wiley Periodicals, Inc. on behalf of Alzheimer's Association.)
Databáze: MEDLINE
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