Franzmeier, N;
Koutsouleris, N;
Benzinger, T;
Goate, A;
Karch, CM;
Fagan, AM;
McDade, E;
... Ewers, M; + view all
(2020)
Predicting sporadic Alzheimer's disease progression via inherited Alzheimer's disease-informed machine-learning.
Alzheimer's and Dementia
, 16
(3)
pp. 501-511.
10.1002/alz.12032.
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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 (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.
| Type: | Article |
|---|---|
| Title: | Predicting sporadic Alzheimer's disease progression via inherited Alzheimer's disease-informed machine-learning |
| Location: | United States |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1002/alz.12032 |
| Publisher version: | https://doi.org/10.1002/alz.12032 |
| Language: | English |
| Additional information: | This is an open access article under the terms of the Creative Commons Attribution-NonCommercial License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited and is not used for commercial purposes. |
| Keywords: | Alzheimer's disease, MRI, PET, autosomal-dominant Alzheimer's disease, biomarkers, machine learning, progression prediction, risk enrichment |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Brain Sciences UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Brain Sciences > UCL Queen Square Institute of Neurology UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Brain Sciences > UCL Queen Square Institute of Neurology > Neurodegenerative Diseases |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10091798 |
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