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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    β   1  −  42, p‐   ta   u   181  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.

Predicting time to dementia using a quantitative template of disease progression