Biomarker discovery using NMR and MS-based metabolomics: Applications to diabetes
Abstract
The emerging field of “metabolomics,” in which a large number of small molecule metabolites from body fluids or tissues are detected quantitatively in a single step, promises immense potential for early diagnosis, therapy monitoring and for understanding the pathogenesis of many diseases. Metabolomics methods are mostly focused on the information rich analytical techniques of nuclear magnetic resonance (NMR) spectroscopy and mass spectrometry (MS). Analysis of the data from these high-resolution methods using advanced chemometric approaches provides a powerful platform for translational and clinical research as well as diagnostic applications. This work mainly focuses on developing sensitive and specific biomarker discovery using NMR and MS-based metabolomics approach. This work is divided into two parts: methodology development and application to diabetes. In the first part, statistical analytical methods were developed to enhance the detection accuracy and detection scope of metabolite biomarkers: a statistical method was developed to evaluate the appropriateness of current data processing and analysis methods used for NMR-based metabolomics to minimize the false discovery rate of potential biomarkers; and a new analytical approach with combined headspace SPME-GC/MS analysis with 1H NMR for metabolic profiling of human body fluids. This approach provides a markedly improved ability for characterizing subtle metabolite-based pathophysiological differences which are exemplified in the reported gender study. The second part of this thesis focuses on establishing new metabolite biomarkers for understanding diabetes from a system biology standpoint. Among the various types of diabetes including type 1, type 2 and gestational diabetes, we started our investigation with characterization of insulin deficiency in type 1 diabetes. We studied the insulin deficiency-induced metabolic disturbances in both an animal model and clinical subjects. For the clinical study, the insulin-treatment effect on the type 1 diabetic patients was assessed. The results suggest that type 1 diabetes could induce significant disturbances to many metabolic pathways such as glucose metabolism, TCA cycle carboxylic acids, proteins/amino acids metabolism and fatty acid metabolism. Therapeutic interventions such as insulin can effectively, although still not perfectly, adjust the disturbed metabolism back to normal. We have also explored diabetes in Zucker fatty rats, a commonly used type 2 diabetes animal model. Specifically, we investigated the effect of green tea and its formulation in ameliorating the characteristic type 2 diabetes insulin insensitivity. Two new hypotheses were generated that green tea can noticeably enhance the body’s metabolic control and that green tea formulation with ascorbic acid has a potential synergistic effect in alleviating acidosis. As shown by this work, metabolite profiling provides important information to help understand systems biology. Advanced methods in NMR, MS, and statistics are useful in identifying and quantifying selected components in complex biological samples. Implications for the future include improved chemical-based early disease detection, therapy feedback, disease monitoring, and personalized therapies.
Degree
Ph.D.
Advisors
Raftery, Purdue University.
Subject Area
Analytical chemistry
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