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Observational Study
. 2020 Nov;16(11):1524-1533.
doi: 10.1002/alz.12140. Epub 2020 Jul 30.

AD risk score for the early phases of disease based on unsupervised machine learning

Zheyu Wang  1   2 , Zhuojun Tang  1 , Yuxin Zhu  1   2 , Corinne Pettigrew  3 , Anja Soldan  3 , Alden Gross  4   5 , Marilyn Albert  3
Affiliations
Observational Study

AD risk score for the early phases of disease based on unsupervised machine learning

Zheyu Wang et al. Alzheimers Dement. 2020 Nov.

Abstract

Introduction: Identifying cognitively normal individuals at high risk for progression to symptomatic Alzheimer's disease (AD) is critical for early intervention.

Methods: An AD risk score was derived using unsupervised machine learning. The score was developed using data from 226 cognitively normal individuals and included cerebrospinal fluid, magnetic resonance imaging, and cognitive measures, and validated in an independent cohort.

Results: Higher baseline AD progression risk scores (hazard ratio = 2.70, P < 0.001) were associated with greater risks of progression to clinical symptoms of mild cognitive impairment (MCI). Baseline scores had an area under the curve of 0.83 (95% confidence interval: 0.75 to 0.91) for identifying subjects who progressed to MCI/dementia within 5 years. The validation procedure, using data from the Alzheimer's Disease Neuroimaging Initiative, demonstrated accuracy of prediction across the AD spectrum.

Discussion: The derived risk score provides high predictive accuracy for identifying which individuals with normal cognition are likely to show clinical decline due to AD within 5 years.

Keywords: Alzheimer's disease; cognitive testing; latent variable; machine learning; multidomain biomarkers; progression; risk score; unsupervised learning.

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Conflict of interest statement

Conflict of Interest: Dr. Albert is a consultant to Eli Lilly. All other authors have no conflict of interest.

Figures

Figure 1:
Figure 1:
An illustrative graph of the unsupervised machine learning model. MRI markers are entorhinal cortex thickness, entorhinal cortex volume and hippocampus volume. Cognitive tests included DSST, LM and Paired Associates.
Figure 2:
Figure 2:
Trajectory of the AD risk score by age among different diagnostic groups in the BIOCARD study (N=226). The thin lines reflect fitted individual trajectories, and the thick lines are group trajectories.
See this image and copyright information in PMC

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