Theses and Dissertations
This collection includes both ASU Theses and Dissertations, submitted by graduate students, and the Barrett, Honors College theses submitted by undergraduate students.
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- All Subjects: metabolomics
Description
Metabolomics focuses on the study of metabolic changes occurring in varioussystems and utilizes quantitative and semi-quantitative measurements of multiple metabolites in parallel. Mass spectrometry (MS) is the most ubiquitous platform in this field, as it provides superior sensitivity regarding measurements of complex metabolic profiles in biological systems. When combined with MS, multivariate statistics and advanced machine learning algorithms provide myriad opportunities for bioinformatics insights beyond simple univariate data comparisons. In this dissertation, the application of MS-based metabolomics is introduced with an emphasis on biomarker discovery for human disease detection. To advance disease diagnosis using MS-based metabolomics, numerous statistical techniques have been implemented in this research including principal component analysis, factor analysis, partial least squares-discriminant analysis (PLS-DA), orthogonal PLS-DA, random forest, receiver operating characteristic analysis, as well as functional pathway/enzyme enrichment analyses. These approaches are highly useful for improving classification sensitivity and specificity related to disease-induced biological variation and can help identify useful biomarkers and potential therapeutic targets. It is also shown that MS-based metabolomics can distinguish between clinical and prodromal disease as well as similar diseases with related symptoms, which may assist in clinical staging and differential diagnosis, respectively. Additionally, MS-based metabolomics is shown to be promising for the early and accurate detection of diseases, thereby improving patient outcomes, and advancing clinical care. Herein, the application of MS methods and chemometric statistics to the diagnosis of breast cancer, coccidioidomycosis (Valley fever), and senile dementia (Alzheimer's disease) are presented and discussed. In addition to presenting original research, previous efforts in biomarker discovery will be synthesized and appraised. A Comment will be offered regarding the state of the science, specifically addressing the inefficient model of repetitive biomarker discovery and the need for increased translational efforts necessary to consolidate metabolomics findings and formalize purported metabolic markers as laboratory developed tests. Various factors impeding the translational throughput of metabolomics findings will be carefully considered with respect to study design, statistical analysis, and regulation of biomedical diagnostics. Importantly, this dissertation will offer critical insights to advance metabolomics from a scientific field to a practical one including targeted detection, enhanced quantitation, and direct-to-consumer considerations.
ContributorsJasbi, Paniz (Author) / Johnston, Carol S (Thesis advisor) / Gu, Haiwei (Thesis advisor) / Lake, Douglas F (Committee member) / Sweazea, Karen (Committee member) / Tasevska, Natasha (Committee member) / Arizona State University (Publisher)
Created2022
Description
In the United States, two-thirds of adults are considered hypertensive orprehypertensive. In addition, chronic illness, such as hypertension, cardiovascular disease, and type II diabetes, results in $3.5 trillion in annual healthcare cost and is the primary cause of disability and death. As a result, many individuals seek cheaper and simpler alternatives to combat their conditions. In this exploratory analysis, a study assessing nitrate intake and its effects on vascular function in 39 young adult males was investigated for underlying metabolic variations through a liquid chromatography – mass spectrometry-based large-scale targeted metabolomics approach. A two-way repeated measures ANOVA was used, and 18 significant metabolites were discovered across the time, treatment, and time & treatment groups, including prostaglandin E2 (p<0.001), stearic acid (p=0.002), caprylic acid (p=0.016), pentadecanoic acid (p=0.027), and heptadecanoic acid (p=0.005). In addition, log-transformed principal component analysis and orthogonal partial least squares – discriminant analysis models demonstrated distinct separation among the treatment, control, and time variables. Moreover, pathway and enrichment analyses validated the effect of nitrate intake on the metabolite sets and its possible function in fatty acid oxidation. This better understanding of altered metabolic pathways may help explicate the benefits of nitrate on vascular function and reveal any unknown mechanisms of its supplementation.
ContributorsPatterson, Jeffrey (Author) / Gu, Haiwei (Thesis advisor) / Johnston, Carol (Committee member) / Sweazea, Karen (Committee member) / Arizona State University (Publisher)
Created2020