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Franzmeier, Nicolai; Koutsouleris, Nikolaos; Benzinger, Tammie; Goate, Alison; Karch, Celeste M.; Fagan, Anne M.; McDade, Eric; Duering, Marco; Dichgans, Martin ORCID logoORCID: https://orcid.org/0000-0002-0654-387X; Levin, Johannes; Gordon, Brian A.; Lim, Yen Ying; Masters, Colin L.; Rossor, Martin; Fox, Nick C.; O'Connor, Antoinette; Chhatwal, Jasmeer; Salloway, Stephen; Danek, Adrian; Hassenstab, Jason; Schofield, Peter R.; Morris, John C.; Bateman, Randall J. and Ewers, Michael (2020): Predicting sporadic Alzheimer's disease progression via inherited Alzheimer's disease-informed machine-learning. In: Alzheimer's and Dementia, Vol. 16, No. 3: pp. 501-511 [PDF, 2MB]


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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