Characterizing heterogeneity in Alzheimer's disease progression: a semiparametric model

The progression of Alzheimer's disease (AD), a leading cause of dementia worldwide, is known for its variability and complexity, challenging the conventional methods of monitoring and predicting disease trajectories. This study introduces a semiparametric modeling approach to analyze longitudinal cognitive and imaging data. We studied two different outcome variables from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database: the Alzheimer's Disease Assessment Scale-Cognitive Subscale 13 (ADAS13) scores and ventricular volumes \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${(\text{mm}}<^>{3})$$\end{document}. Unlike traditional linear mixed effects models, semiparametric models do not assume a linear AD progression over time. Semiparametric models offer the advantage of capturing the non-linear features of AD progression, such as cognitive decline and neurodegeneration, represented by changes in ADAS13 scores and ventricular enlargement, respectively. By integrating regression splines and mixed modeling techniques, we provide a nuanced understanding of AD progression that captures the heterogeneity of disease trajectories. Our analysis reveals variations in the timing and degree of cognitive decline and neurodegeneration among AD patients, underlining the need for personalized approaches for monitoring and managing AD. This study's findings contribute to the modeling of AD progression and offer potential implications for interventions and prognostic assessments in clinical and research settings.