  Alzheimer's disease (AD) is a prominent, worldwide, age-related
neurodegenerative disease that currently has no systemic treatment. Strong
evidence suggests that permeable amyloid-beta peptide (Abeta) oligomers,
astrogliosis and reactive astrocytosis cause neuronal damage in AD. A large
amount of Abeta is secreted by astrocytes, which contributes to the total Abeta
deposition in the brain. This suggests that astrocytes may also play a role in
AD, leading to increased attention to their dynamics and associated mechanisms.
Therefore, in the present study, we developed and evaluated novel stochastic
models for Abeta growth using ADNI data to predict the effect of astrocytes on
AD progression in a clinical trial. In the AD case, accurate prediction is
required for a successful clinical treatment plan. Given that AD studies are
observational in nature and involve routine patient visits, stochastic models
provide a suitable framework for modelling AD. Using the approximate Bayesian
computation (ABC) approach, the AD etiology may be modelled as a multi-state
disease process. As a result, we use this approach to examine the weak and
strong influence of astrocytes at multiple disease progression stages using
ADNI data from the baseline to 2-year visits for AD patients whose ages ranged
from 50 to 90 years. Based on ADNI data, we discovered that the strong
astrocyte effect (i.e., a higher concentration of astrocytes as compared to
Abeta) could help to lower or clear the growth of Abeta, which is a key to
slowing down AD progression.
