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

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.

• Authors: 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, Peter R Schofield, John C Morris, Randall J Bateman, Alzheimer's disease neuroimaging initiative (ADNI), Dominantly Inherited Alzheimer Network (DIAN), Michael Ewers
• Journal: Alzheimer's & dementia : the journal of the Alzheimer's Association (Alzheimers Dement) Vol. 16 Issue 3 Pg. 501-511 (03 2020) ISSN: 1552-5279 [Electronic] United States
• PMID: 32043733 (Publication Type: Journal Article, Research Support, N.I.H., Extramural, Research Support, Non-U.S. Gov't, Research Support, U.S. Gov't, Non-P.H.S.)
• Copyright: © 2020 The Authors. Alzheimer's & Dementia published by Wiley Periodicals, Inc. on behalf of Alzheimer's Association.

Chemical References

• Biomarkers

Topics

• Adult
• Alzheimer Disease (genetics, pathology)
• Biomarkers (cerebrospinal fluid)
• Cognitive Dysfunction (genetics, pathology)
• Disease Progression
• Female
• Humans
• Machine Learning
• Magnetic Resonance Imaging
• Male
• Positron-Emission Tomography