Dietary Alterations in Impaired Mitochondrial Dynamics Due to Neurodegeneration

Alzheimer's disease is still an incurable disease with significant social and economic impact globally. Nevertheless, newly FDA-approved drugs and non-pharmacological techniques may offer efficient disease treatments. Furthermore, it is widely accepted that early diagnosis or even prognosis of Alzheimer's disease using advanced computational tools could offer a compelling alternative way of management. In addition, several studies have presented an insight into the role of mitochondrial dynamics in Alzheimer's development. In combination with diverse dietary and obesity-related diseases, mitochondrial bioenergetics may be linked to neurodegeneration. Considering the probabilistic expectations of Alzheimer's disease development or progression due to specific risk factors or biomarkers, we designed a Bayesian model to formulate the impact of diet-induced obesity with an impaired mitochondrial function and altered behavior. The applied probabilities are based on clinical trials globally and are continuously subject to updating and redefinition. The proposed multiparametric model combines various data types based on uniform probabilities. The program simulates all the variables with a uniform distribution in a sample of 1000 patients. First, the program initializes the variable age (30-95) and the four different diet types ("HFO_diet," "Starvation," "HL_diet," "CR") along with the factors that are related to prodromal or mixed AD (ATP, MFN1, MFN2, DRP1, FIS1, Diabetes, Oxidative_Stress, Hypertension, Obesity, Depression, and Physical_activity). Besides the known proteins related to mitochondrial dynamics, our model includes risk factors like Age, Hypertension, Oxidative Stress, Obesity, Depression, and Physical Activity, which are associated with Prodromal Alzheimer's. The outcome is the disease progression probability corresponding to a random individual ID related to diet choices and mitochondrial dynamics parameters. The proposed model and the programming code are adjustable to different parameters and values. The program is coded and executed in Python and is fully and freely available for research purposes and testing the correlation between diet type and Alzheimer's disease progression regarding various risk factors and biomarkers.