Predicting time to dementia using a quantitative template of disease progression

Abstract

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 Aβ1−42, p‐tau181p, 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 <1.5 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.

Keywords

• Alzheimer
• Dementia
• Onset
• Prediction
• Longitudinal
• Biomarkers