Modeling Alzheimer's disease progression using the disease system analysis approach

Area of Science:

• Neuroscience
• Biostatistics
• Computational Biology

Background:

• Alzheimer's disease (AD) progression involves complex homeostatic and deterioration processes.
• Understanding these mechanisms is crucial for developing effective therapeutic strategies.

Purpose of the Study:

• To develop and evaluate a novel mechanistic model for Alzheimer's disease progression.
• To explore the roles of homeostatic mechanisms and disease-specific deterioration in AD.
• To identify key factors influencing cognitive decline trajectories in AD patients.

Main Methods:

• Utilized longitudinal AD Assessment Scale-cognitive subscale (ADAS-cog) data from 926 Alzheimer's disease patients in placebo-controlled trials.
• Developed and compared mechanistic models based on disease system analysis, focusing on deterioration and compensatory regulatory responses.
• Employed covariate analysis to assess the impact of baseline factors on cognitive trajectories.

Main Results:

• A mechanistic model incorporating a time-varying deterioration rate of ADAS-cog outperformed a model based on time-varying homeostatic control.
• Baseline Mini-Mental State Examination scores, education, age, and apolipoprotein ɛ4 genotype significantly influenced predicted ADAS-cog change trajectories.
• The findings suggest that the rate of cognitive deterioration is a primary driver in AD progression.

Conclusions:

• The developed mechanistic model provides a better fit for understanding Alzheimer's disease progression compared to models solely focused on homeostatic control.
• Key demographic and genetic factors significantly modulate the course of cognitive decline in Alzheimer's disease.
• This research offers insights into the dynamics of AD progression, aiding in the development of predictive models and therapeutic interventions.