eMB: Mouth to Mind: Leveraging network dynamical systems and software to understand diet, diabetes, dementia and modifiable risk factors delaying Alzheimer's disease

美国人正在输掉这场争夺头脑的战斗。肥胖和2型糖尿病会加速阿尔茨海默病(AD)等致命的、与年龄相关的神经退化的进展。事实上，到2050年，AD将影响到每个美国家庭；每7个65岁以上的美国人中就有一个患有AD，目前还没有明确有效的药物治疗机制。在缺乏治疗阿尔茨海默病的实际药物治疗策略的情况下，了解如何通过饮食和锻炼等可改变的危险因素延缓阿尔茨海默病，是至关重要的。美国国家科学基金会资助的数学家和德克萨斯理工大学的营养学家正在共同开发新的方法来研究肥胖和糖尿病中的可改变的风险因素是如何影响AD的，比如一个人选择的食物类型，或者一个人可以得到多少锻炼和睡眠。为了做到这一点，研究人员正在开发新的数学模型，将脑部炎症和新陈代谢压力等因素与AD进展联系起来，并开发必要的计算机软件，以解决从医学数据生成的大型、复杂的人类脑图上的这类问题。这项研究旨在确定哪些可改变的因素对推迟AD最重要，调查团队正在与农村社区领导人合作，将他们的结果直接带到美国家庭的生活中，并出现在餐桌上。该项目将为数学研究生和一名暑期本科生提供培训。此外，该项目将允许研究人员与德克萨斯A&Amp；M农业生活扩展服务公司合作，通过为德克萨斯人提供更好的生活，将这项研究的结果直接带到拉伯克和周边县未得到充分服务的人群。氧化应激、脑胰岛素敏感性和神经炎症是阿尔茨海默病(AD)病理的显著机制，已知与可改变的AD风险因素有关。什么可改变因素的中介作用延缓了AD的发生和发展？在人类受试者中调查阿尔茨海默病的可改变风险因素的详细研究面临重大的伦理、技术或经济障碍。为了避免这些挑战，研究团队将采用一种基于数学模型的灵活方法，这些模型描述了AD相关的淀粉样β蛋白和tau蛋白病理在复杂的人脑网络上的演变，这些网络来自医学数据。这个跨学科项目的目标有三个。首先，设计新的高维网络动力学系统(NDS)，在胰岛素稳态、氧化应激和神经炎症存在的情况下表达AD蛋白质病理的演变。第二，构建有效的计算软件，基于高性能的库，如PETSC和日规，通过可访问的高级编程接口实例化和求解大型NDS模型。第三，使用微分方程组、网络和数据科学的技术来分析NDS模型，并对来自患者神经成像数据的脑图进行大规模的AD病理计算模拟。结合这些研究成果，将加深对改变可改变的AD风险因素的有效性的理解，并为公众提供高层建议，以减轻他们一生中患AD的风险。这一奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

Americans are losing the battle for minds. Obesity and type 2 diabetes accelerate the progression of deadly, age-related neurodegeneration like Alzheimer's disease (AD). In fact, by 2050, AD will impact every American family; 1 in 7 Americans over 65 will have AD, and there is no clear, effective pharmaceutical treatment regime in sight. In the absence of practical pharmaceutical treatment strategies for AD, understanding how AD can be delayed through modifiable risk factors, such as diet and exercise, is of paramount importance. NSF-funded mathematicians and nutritional scientists from Texas Tech University are coming together to develop new methods for studying how the modifiable risk factors in obesity and diabetes, like the types of food one chooses to eat or how much exercise and sleep one can get, affect AD. To do this, the investigators are developing new mathematical models to relate factors like brain inflammation and metabolic stress to AD progression and producing the necessary computer software to solve these types of problems on large, complex human brain graphs generated from medical data. The research aims to identify what modifiable factors matter the most for delaying AD, and the team of investigators is partnering with rural community leaders to bring their results straight into the lives, and onto the dinner tables, of American families. This project will provide training for graduate mathematics students and one part-time summer undergraduate. Additionally, this project will allow the investigators to partner with Texas A&M AgriLife Extension Service to bring the results of this research directly to under-served populations in Lubbock and surrounding counties through Better Living for Texans.Oxidative stress, brain insulin sensitivity and neuroinflammation are salient mechanisms of Alzheimer's disease (AD) pathology and known associates of modifiable AD risk factors. What mediation of modifiable factors delay the pathogenesis and progression of AD? Detailed studies that investigate modifiable risk factors for AD in human subjects face significant ethical, technical or financial barriers. To avoid these challenges, the research team will employ an agile approach based on mathematical models that describe the evolution of AD-associated amyloid-beta and tau protein pathology on complex human brain networks generated from medical data. The goals of the interdisciplinary project are threefold. First, to devise novel, high-dimensional network dynamical systems (NDS) that express the evolution of AD protein pathology in the presence of perturbed insulin homeostasis, oxidative stress and neuroinflammation. Second, to construct effective computational software, based on high-performance libraries such as PETSc and SUNDIALS, that instantiates and solves large NDS models via an accessible, high-level programming interface. Third, to analyze the NDS models using techniques from the theory of differential equations, networks and data science in addition to performing large computational simulations of AD pathology on brain graphs derived from patient neuroimaging data. Combining these research outcomes will develop an understanding of the efficacy of altering modifiable AD risk factors and enable high-level recommendations for the public to mitigate the risk of developing AD in their lifetime.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.