Ashraf Ghulam Md, Chatzichronis Stylianos, Alexiou Athanasios, Firdousi Gazala, Kamal Mohammad Amjad, Ganash Magdah
Pre-clinical Research Unit, King Fahd Medical Research Center, King Abdulaziz University, Jeddah, Saudi Arabia.
Department of Medical Laboratory Technology, Faculty of Applied Medical Sciences, King Abdulaziz University, Jeddah, Saudi Arabia.
Front Aging Neurosci. 2022 Jul 11;14:893018. doi: 10.3389/fnagi.2022.893018. eCollection 2022.
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.
阿尔茨海默病仍然是一种无法治愈的疾病，在全球范围内具有重大的社会和经济影响。然而，美国食品药品监督管理局（FDA）新批准的药物和非药物技术可能提供有效的疾病治疗方法。此外，人们普遍认为，使用先进的计算工具对阿尔茨海默病进行早期诊断甚至预后评估，可能会提供一种引人注目的替代管理方式。此外，多项研究已经深入探讨了线粒体动力学在阿尔茨海默病发展中的作用。结合各种与饮食和肥胖相关的疾病，线粒体生物能量学可能与神经退行性变有关。考虑到由于特定风险因素或生物标志物导致的阿尔茨海默病发展或进展的概率预期，我们设计了一个贝叶斯模型，以阐述饮食诱导的肥胖伴线粒体功能受损和行为改变的影响。所应用的概率基于全球范围内的临床试验，并且不断进行更新和重新定义。所提出的多参数模型基于统一概率结合了各种数据类型。该程序在1000名患者的样本中以均匀分布模拟所有变量。首先，程序初始化变量年龄（30 - 95岁）、四种不同的饮食类型（“高脂肪高糖饮食”、“饥饿饮食”、“高碳水化合物饮食”、“热量限制饮食”）以及与前驱期或混合型阿尔茨海默病相关的因素（三磷酸腺苷（ATP）、线粒体融合蛋白1（MFN1）、线粒体融合蛋白2（MFN2）、动力相关蛋白1（DRP1）、线粒体分裂因子1（FIS1）、糖尿病、氧化应激、高血压、肥胖、抑郁和身体活动）。除了与线粒体动力学相关的已知蛋白质外，我们的模型还包括年龄、高血压、氧化应激、肥胖、抑郁和身体活动等风险因素，这些因素与前驱期阿尔茨海默病有关。结果是与饮食选择和线粒体动力学参数相关的随机个体ID对应的疾病进展概率。所提出的模型和编程代码可针对不同参数和值进行调整。该程序用Python编码并执行，完全免费提供用于研究目的以及测试饮食类型与阿尔茨海默病进展之间关于各种风险因素和生物标志物的相关性。
