NSF Award
2325276
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.<br/><br/>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.<br/><br/>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.
