| Summary: | 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.
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