Predicting time to dementia using a quantitative template of disease progression

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 A ${\beta }_{1-42}$ , p- $\mathrm{ta}{u}_{181\text{p}}$ , 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 Aβ_1-42 and p-tau_181p, 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.