Master of Science
Date of Defense
Alzheimer Disease (AD) is difficult to diagnose by using genetic testing or other traditional methods. Unlike diseases with simple genetic risk components, there exists no single marker determining as to whether someone will develop AD. Furthermore, AD is highly heterogeneous and different subgroups of individuals develop the disease due to differing factors. Traditional diagnostic methods using perceivable cognitive deficiencies are often too little too late due to the brain having suffered damage from decades of disease progression. In order to observe AD at early stages prior to the observation of cognitive deficiencies, biomarkers with greater accuracy are required. By using the non-scalar, bidirectional correlation measure, Duo, we overcame the problem of AD’s heterogeneity by creating a bidirectional network. By using this method, we identified key communities of synchronized proteins that are significantly associated with AD. We found that low levels of IP10 and MIG in the cerebrospinal fluid (CSF) may be a protective factor, whereas high values in the CSF appeared as a risk factor. High levels of Clusterin and Sortilin are also found to be risk factors in the cerebrospinal fluid. Additionally, low levels of Testosterone, FSH and LH in the blood plasma show a protective factor in men, whereas high levels of GH, LH, and FSH exhibit a risk factor in women. With these initial findings from a cohort of individuals, we seek to replicate the process on independent datasets, ultimately facilitating the development of methods for revealing preclinical AD and to further understand the pathogenesis of AD.
Lane, Matthew J., "Network Exploration of Correlated Multivariate Protein Data for Alzheimer's Disease Association" (2017). Theses. 306.
Applied Statistics Commons, Bioinformatics Commons, Biostatistics Commons, Computational Neuroscience Commons, Multivariate Analysis Commons, Nervous System Diseases Commons, Numerical Analysis and Scientific Computing Commons, Statistical Models Commons