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• W2899192510 abstract "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.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 β1-42 , 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.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.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." @default.
• W2899192510 created "2018-11-09" @default.
• W2899192510 creator A5036011059 @default.
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• W2899192510 date "2019-02-28" @default.
• W2899192510 modified "2023-10-16" @default.
• W2899192510 title "Predicting time to dementia using a quantitative template of disease progression" @default.
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• W2899192510 doi "https://doi.org/10.1016/j.dadm.2019.01.005" @default.
• W2899192510 hasPubMedCentralId "https://www.ncbi.nlm.nih.gov/pmc/articles/6396328" @default.
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