- Alzheimer's disease -- Risk factors,
- Biochemical markers
Introduction: Characterization of longitudinal trajectories of biomarkers implicated in sporadic Alzheimer's disease (AD) in decades prior to clinical diagnosis is important for disease prevention and monitoring.
Methods: We used a multivariate Bayesian model to temporally align 1369 AD Neuroimaging Initiative participants based on the similarity of their longitudinal biomarker measures and estimated a quantitative template of the temporal evolution cerebrospinal fluid (CSF) Aβ1-42, p-tau181p, and t-tau, 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 earliest changes in verbal memory, followed by CSF Aβ1-42, hippocampal volume, and p-tau181p. 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.
Available at: http://works.bepress.com/bruno-jedynak/27/
Copyrighted 2019 The Authors. Published by Elsevier Inc. on behalf of the Alzheimer’s Association. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/ 4.0/).
Article is available online at: https://doi.org/10.1016/j.dadm.2019.01.005