Document Type

Post-Print

Publication Date

11-2-2018

Subjects

Alzheimer's disease -- Risk factors, Biochemical markers

Abstract

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.

Description

The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. This article is a US Government work. It is not subject to copyright under 17 USC 105 and is also made available for use under a CCO License and can be found online at: https://doi.org/10.1101/458273.

DOI

10.1101/458273

Persistent Identifier

https://archives.pdx.edu/ds/psu/26577

Included in

Mathematics Commons

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