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

Research output: Contribution to journalArticle

Nicolai Franzmeier, Nikolaos Koutsouleris, Tammie Benzinger, Alison Goate, Celeste M. Karch, Anne M. Fagan, Eric McDade, Marco Duering, Martin Dichgans, Johannes Levin, Brian A. Gordon, Yen Ying Lim, Colin L. Masters, Martin Rossor, Nick C. Fox, Antoinette O'Connor, Jasmeer Chhatwal, Stephen Salloway, Adrian Danek, Jason Hassenstab & 4 more Peter R. Schofield, John C. Morris, Randall J. Bateman, Michael Ewers

Original languageEnglish
Pages (from-to)501-511
Number of pages11
JournalAlzheimer's & Dementia
Volume16
Issue number3
DOIs
PublishedMar 2020

King's Authors

Abstract

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.

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