Journal article

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

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 Show all

Alzheimer's and Dementia | WILEY | Published : 2020


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 estim..

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Awarded by Dominantly Inherited Alzheimer's Network (DIAN) - National Institute on Aging (NIA)

Awarded by Alzheimer Forschung Initiative (AFI)

Awarded by European Commission

Awarded by ADNI (National Institutes of Health)

Awarded by DOD ADNI (Department of Defense award)

Funding Acknowledgements

Data collection and sharing for this project was supported by the Dominantly Inherited Alzheimer's Network (DIAN, UF1AG032438) funded by the National Institute on Aging (NIA), the German Center for Neurodegenerative Diseases (DZNE), Raul Carrea Institute for Neurological Research (FLENI), partial support by the Research and Development Grants for Dementia from Japan Agency for Medical Research and Development, AMED, and the Korea Health Technology R&D Project through the Korea Health Industry Development Institute (KHIDI).This manuscript has been reviewed by DIAN Study investigators for scientific content and consistency of data interpretation with previous DIAN Study publications. We acknowledge the altruism of the participants and their families and contributions of the DIAN research and support staff at each of the participating sites for their contributions to this study. The study was funded by grants from the Alzheimer Forschung Initiative (AFI, Grant 15035 to Michael Ewers) and European Commission (Grant 334259 to Michael Ewers). ADNI data collection and sharing for this project was funded by the ADNI (National Institutes of Health Grant U01 AG024904) and DOD ADNI (Department of Defense award number W81XWH-12-2-0012). ADNI is funded by the National Institute on Aging, the National Institute of Biomedical Imaging, and Bioengineering, and through contributions from the following: AbbVie; Alzheimer's Association; Alzheimer's Drug Discovery Foundation; Araclon Biotech; BioClinica, Inc.; Biogen; Bristol-Myers Squibb Company; CereSpir, Inc.; Cogstate; Eisai, Inc.; Elan Pharmaceuticals, Inc.; Eli Lilly and Company; EuroImmun; F. Hoffmann-La Roche Ltd and its affiliated company Genentech, Inc.; Fujirebio; GE Healthcare; IXICO Ltd.; Janssen Alzheimer Immunotherapy Research & Development, LLC; Johnson & Johnson Pharmaceutical Research & Development LLC; Lumosity; Lundbeck; Merck & Co., Inc.; Meso Scale Diagnostics, LLC; NeuroRx Research; Neurotrack Technologies; Novartis Pharmaceuticals Corporation; Pfizer Inc.; Piramal Imaging; Servier; Takeda Pharmaceutical Company; and Transition Therapeutics. The Canadian Institutes of Health Research is providing funds to support ADNI clinical sites in Canada. Private-sector contributions are facilitated by the Foundation for the National Institutes of Health ( acknowledges support from the MRC, the NIHR UCLH Biomedical Research Centre, and the UK Dementia Research Institute at UCL.