Uncovering the multicausality of Alzheimer’s disease: A systems modeling approach

Abstract

AbstractBackgroundIn the onset and progression of Alzheimer’s disease (AD), many factors and processes are involved. Our understanding of the multicausal and complex nature of AD may benefit from computational modeling approaches that can be used for knowledge synthesis at the level of the whole system. We have developed a systems dynamics (SD) model of AD.MethodWe have applied the group model building methodology to develop from expert input and literature review a causal loop diagram (CLD) that graphically describes the relationships between important risk factors and proposed causal mechanisms in non‐familial AD from midlife onwards. This CLD was implemented computationally as a SD simulation model.ResultFor the resulting CLD, experts identified 38 variables and 144 connections between them. The experts modeled hypotheses about their relations with a multitude of important processes ranging from amyloid‐β accumulation and cerebral endothelial dysfunction, via physical activity and sleep to social functioning. The SD model was parametrized using empirical information taken from the Global Alzheimer’s Association Interactive Network (GAAIN) and is able to simulate preventive interventions on potentially modifiable risk factors.ConclusionThe CLD and SD model described and quantified hypotheses about relationships and feedback loops implicated in AD onset in a comprehensive way. Our approach showed the potential of systems‐oriented simulation models for studying the complex etiology of AD.