Computational Causal Modeling of the Dynamic Biomarker Cascade in Alzheimer’s Disease

Background The heterogeneity and complexity of Alzheimer’s disease (AD) lends itself to dynamic causal modeling, a computational systems biology approach to simulate complex systems. Methods We implemented a computational dynamic causal model (DCM) to test the common descriptive assumptions of biomarker evolution in AD. We modeled beta-amyloid, tau, neuro-degeneration and cognitive impairment as first order non-linear differential equations to include beta-amyloid dependent and non-dependent neurodegenerative cascades. We tested the DCM, by adjusting its parameters to simulate three specific simulation scenarios in early-onset autosomal dominant AD and late-onset AD, to determine whether computed biomarker trajectories agreed with current assumptions of AD progression. We also simulated the effects of anti-amyloid therapy in late-onset AD. Results The computational model of early-onset “pure AD” demonstrated the initial appearance of amyloid, followed by other biomarkers of tau and neurodegeneration, followed by the onset of cognitive decline as predicted by prior literature. The model also showed the ability to vary onset of cognitive decline based on cognitive reserve. Similarly, the late-onset AD computational models demonstrated the first appearance of amyloid-related or non-amyloid-related tauopathy, depending on the magnitude of age-related comorbid pathology, and also closely matched the cascade predicted by prior literature. Forward simulation of anti-amyloid therapy in symptomatic late-onset AD failed to demonstrate any benefit on cognitive decline, consistent with prior failed clinical trials in symptomatic patients. Conclusions To our knowledge, this is the first computational model of the dynamic biomarker cascade in autosomal dominant and late-onset AD. Such models, with refinement using actual molecular, clinical and genomic data, are an important tool towards developing a systems biology precision medicine approach for understanding and preventing AD.