Stochastic models for the onset and disease course of multiple sclerosis

Abstract

ObjectiveExact causes and mechanisms regulating the onset and progression in many chronic diseases, including multiple sclerosis (MS), remain uncertain. Until now, the potential role of random process based on stochastic models in the temporal course of chronic diseases remains largely unevaluated. Therefore, the present study investigated the applicability of stochastic models for the onset and disease course of MS. MethodsStochastic models with random temporal process in disease activity, underlying clinical relapse and/or subclinical brain atrophy, were developed. The models incorporated parameters regarding the distribution of temporal changes in disease activity and the drift constant. ResultsBy adjusting the parameters (temporal change dispersion and drift constant) and the threshold for the onset of disease, the stochastic disease progression models could reproduce various types of subsequent disease course, such as clinically isolated syndrome (monophasic), relapsing-remitting MS, primary-progressive MS, and secondary-progressive MS. Furthermore, the disease prevalence and distribution of onset age could be also reproduced with stochastic models by adjusting the parameters. The models could further explain why approximately half of the patients with relapsing-remitting MS will eventually experience a transition to secondary-progressive MS. ConclusionStochastic models with random temporal changes in disease activity could reproduce the characteristic onset age distribution and disease course forms in MS. Further studies by using real-world data to underscore the significance of random process in the occurrence and progression of MS are warranted.