Predicting the course of Alzheimer’s progression

Health and Wellbeing and Population Dynamics and Urbanization in Africa

  • January 2020
  • General

Alzheimer’s disease is the most common neurodegenerative disease and is characterized by the accumulation of
amyloid-beta peptides leading to the formation of plaques and tau protein tangles in brain. These neuropathological
features precede cognitive impairment and Alzheimer’s dementia by many years. To better understand and predict
the course of disease from early-stage asymptomatic to late-stage dementia, it is critical to study the patterns of
progression of multiple markers. In particular, we aim to predict the likely future course of progression for individuals given only a single observation of their markers. Improved individual-level prediction may lead to improved clinical care and clinical trials. We propose a two-stage approach to modeling and predicting measures of cognition, function, brain imaging, fuid biomarkers, and diagnosis of individuals using multiple domains simultaneously. In the frst stage, joint (or multivariate) mixed-efects models are used to simultaneously model multiple markers over time.
In the second stage, random forests are used to predict categorical diagnoses (cognitively normal, mild cognitive
impairment, or dementia) from predictions of continuous markers based on the frst-stage model. The combination
of the two models allows one to leverage their key strengths in order to obtain improved accuracy. We characterize
the predictive accuracy of this two-stage approach using data from the Alzheimer’s Disease Neuroimaging Initiative.
The two-stage approach using a single joint mixed-efects model for all continuous outcomes yields better diagnostic classifcation accuracy compared to using separate univariate mixed-efects models for each of the continuous
outcomes. Overall prediction accuracy above 80% was achieved over a period of 2.5 years. The results further indicate that overall accuracy is improved when markers from multiple assessment domains, such as cognition, function, and brain imaging, are used in the prediction algorithm as compared to the use of markers from a single domain only.