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Franzmeier, Nicolai; Koutsouleris, Nikolaos; Benzinger, Tammie; Goate, Alison; Karch, Celeste M.; Fagan, Anne M.; McDade, Eric; Duering, Marco; Dichgans, Martin; 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: Alzheimers & 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 (R2 = 24%) and memory (R2 = 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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