Statistical Disease Progression Modeling in Alzheimer Disease

Area of Science:

• Neurology
• Biostatistics
• Cognitive Science

Background:

• Alzheimer's disease (AD) diagnosis and staging rely heavily on cognitive symptoms, yet cognitive decline is influenced by pre-disease capabilities.
• Current cognitive assessments may not accurately stage AD patients due to varying baseline cognitive function.
• Longitudinal cognitive assessments combined with biomarkers are crucial for understanding disease dynamics and identifying predictive markers.

Purpose of the Study:

• To develop and apply statistical disease progression models for improved staging of Alzheimer's disease patients.
• To differentiate between disease progression and baseline cognitive ability using longitudinal data.
• To provide a framework for understanding disease progression, biomarkers, and treatment effects.

Main Methods:

• Utilized non-linear mixed-effects disease progression models to analyze longitudinal cognitive scores.
• Modeled disease stage, baseline cognition, and individual cognitive changes as latent variables.
• Employed maximum-likelihood estimation to separate disease progression from baseline cognition.

Main Results:

• Estimated a 15-year cognitive decline timeline in Alzheimer's disease, from early deficits to severe dementia.
• Demonstrated how modeling latent factors enhances understanding of disease progression, biomarkers, and treatment effects.
• The model provides a data-driven method for staging patients based on cognitive trajectories.

Conclusions:

• The developed framework allows direct interpretation of factors influencing cognitive decline in Alzheimer's disease.
• Results highlight the utility of biomarkers for patient staging and offer new perspectives on cognitive decline.
• Findings suggest alternative explanations for accelerated cognitive decline in highly educated patients and those on symptomatic treatments.