Predicting time to dementia using a quantitative template of disease progression

Abstract

IntroductionCharacterization of longitudinal trajectories of biomarkers implicated in sporadic Alzheimers disease (AD) in decades prior to clinical diagnosis is important for disease prevention and monitoring.\n\nMethodsWe used a multivariate Bayesian model to temporally align 1369 AD Neuroimaging Initiative participants based on the similarity of their longitudinal biomarker measures and estimated a quantitative template of the temporal evolution cerebrospinal fluid (CSF) A{beta}1-42, p-tau181p, and t-tau, 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.\n\nResultsQuantitative template showed early changes in verbal memory, CSF A{beta}1-42 and p-tau181p, and hippocampal volume. Mean error in predicted AD dementia onset age was < 1.5 years.\n\nDiscussionOur 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.