Predicting time to dementia using a quantitative template of disease progression

Alzheimer’s & Dementia: Diagnosis, Assessment & Disease Monitoring

Bruno M. Jedynak¹  Alzheimer's Disease Neuroimaging Initiative²  Murat Bilgel² 

[1] Dept. of Mathematics and StatisticsPortland State UniversityPortlandORUSA;Laboratory of Behavioral Neuroscience, National Institute on Aging, National Institutes of HealthBaltimoreMDUSA;

关键词: Alzheimer;    Dementia;    Onset;    Prediction;    Longitudinal;    Biomarkers;   

DOI  :  10.1016/j.dadm.2019.01.005

来源: DOAJ

【 摘 要 】

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.

【 授权许可】

Unknown