Predicting time to dementia using a quantitative template of disease progression
Published in Alzheimer's and Dementia: Diagnosis, Assessment and Disease Monitoring, 2019
Abstract
Introduction: Characterization of longitudinal trajectories of biomarkers implicated in sporadic Alzheimer’s disease (AD) in decades before clinical diagnosis is important for disease prevention and monitoring.
Methods: We used a multivariate Bayesian model to temporally align 1369 Alzheimer’s disease Neuroimaging Initiative participants based on the similarity of their longitudinal biomarker measures and estimated a quantitative template of the temporal evolution of cerebrospinal fluid Aurn:x-wiley:23528729:dad2jdadm201901005:equation:dad2jdadm201901005-math-0001, p-urn:x-wiley:23528729:dad2jdadm201901005:equation:dad2jdadm201901005-math-0002, and t-tau and hippocampal volume, brain glucose metabolism, and cognitive measurements. We computed biomarker trajectories as a function of time to AD dementia and predicted AD dementia onset age in a disjoint sample.
Results: Quantitative template showed early changes in verbal memory, cerebrospinal fluid Aβ1–42 and p-tau181p, and hippocampal volume. Mean error in predicted AD dementia onset age was urn:x-wiley:23528729:dad2jdadm201901005:equation:dad2jdadm201901005-math-0003 years.
Discussion: Our method provides a quantitative approach for characterizing the natural history of AD starting at preclinical stages despite the lack of individual-level longitudinal data spanning the entire disease timeline.