Predicting Time to Dementia Using a Quantitative Template of Disease Progression

Introduction: Characterization of longitudinal trajectories of biomarkers implicated in sporadic Alzheimer\u27s disease (AD) in decades prior to clinical diagnosis is important for disease prevention and monitoring. Methods: We 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) Aβ1-42, 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. Results: Quantitative template showed earliest changes in verbal memory, followed by CSF Aβ1-42, hippocampal volume, and p-tau181p. Mean error in predicted AD dementia onset age was \u3c 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