A Bayesian semi-parametric model for learning biomarker trajectories and changepoints in the preclinical phase of Alzheimer’s disease

• Published in: Biometrics Vol. 80; no. 2
• Main Authors: Wang, Kunbo, Hua, William, Wang, MeiCheng, Xu, Yanxun
• Format: Journal Article
• Language: English
• Published: England Oxford University Press 27.03.2024
• Subjects: Alzheimer Disease; Bayes Theorem; Biomarkers; Biometric Practice; Cognitive Dysfunction; Computer Simulation; Disease Progression; Humans; Longitudinal Studies; Models, Statistical; tau Proteins; Bayesian semiparametrics; BIOCARD study; Alzheimer’s disease; left truncation; changepoint detection
• ISSN: 0006-341X; 1541-0420; 1541-0420
• DOI: 10.1093/biomtc/ujae048

It has become consensus that mild cognitive impairment (MCI), one of the early symptoms onset of Alzheimer’s disease (AD), may appear 10 or more years after the emergence of neuropathological abnormalities. Therefore, understanding the progression of AD biomarkers and uncovering when brain alterations begin in the preclinical stage, while patients are still cognitively normal, are crucial for effective early detection and therapeutic development. In this paper, we develop a Bayesian semiparametric framework that jointly models the longitudinal trajectory of the AD biomarker with a changepoint relative to the occurrence of symptoms onset, which is subject to left truncation and right censoring, in a heterogeneous population. Furthermore, unlike most existing methods assuming that everyone in the considered population will eventually develop the disease, our approach accounts for the possibility that some individuals may never experience MCI or AD, even after a long follow-up time. We evaluate the proposed model through simulation studies and demonstrate its clinical utility by examining an important AD biomarker, ptau181, using a dataset from the Biomarkers of Cognitive Decline Among Normal Individuals (BIOCARD) study.