Access Type

Open Access Thesis

Date of Award

January 2012

Degree Type

Thesis

Degree Name

M.S.

Department

Nutrition and Food Science

First Advisor

Smiti Gupta

Abstract

ABSTRACT

THE USE OF METABOLOMICS TO INVESTIGATE BIOMARKERS PROFILES AS POTENTIAL EARLY RISK FACTORS FOR DEVELOPMENT OF TYPE II DIABETES

by

JENNELLE ARNEW

MAY 2012

Advisor: Dr. Smiti Gupta

Major: Nutrition and Food Science

Degree: Masters of Science

Type 2 Diabetes affects an estimated 17.5 million individuals in the United States and is considered to be one of the most pressing public health issues of our time. To date, the exact cause of type 2 diabetes remains unclear, however, it is considered to be an interplay between environmental and genetic factors. Emerging technologies for metabolomics analysis increases the capacity to detect the onset of disease or ideally, the pre-diseased state. Metabolomics has the potential to find early metabolic changes related to diabetes progression prior to many clinical symptoms. Although this technology is considered to be in its infancy many believe that metabolomics strategies can have an impact on the discovery of pathological biomarkers for diabetes progression. Given this emerging technology that is available, the increasing burden of diabetes, earlier identification of ¡®at risk¡¯ individuals is particularly important.

The objective of this study was to determine if metabolomics analytical techniques could identify differences in the metabolic profiles of persons at risk for developing diabetes. A heterogeneous non-diabetic sample of persons with and without risk factors for development of diabetes were recruited for this pilot study. A standard questionnaire was conducted to assess risk factors for diabetes. Fasting blood and urine samples were collected and frozen at -800 C. Bivariate correlations were determined to investigate the linear relationship between the risk factors. Urinary metabolite profiles were analyzed by proton nuclear magnetic resonance (1H NMR) spectra. The processed, digitized NMR spectral data was analyzed using multivariate data analysis software, SIMCA P+. Partial Least Squares Discriminant Analysis (PLS-DA) Score Plot showed a clear separation between the urinary metabolomic profiles of subject based on a BMI ¡Ý 27 kg/m2. PLS correlation plot showed a significant correlation between the urinary profiles between BMI and fasting blood sugar in this non-diabetic population. The data suggests and in agreement with the hypothesis, that 1H NMR was able to detect changes in the urinary profiles of a non-heterogeneous non-diabetic population with the greatest degree of discrimination based on BMI of 27 kg/m2. Based on current clinical practices, the identification of the metabolites causing discriminating in the urinary profiles based on obesity may be a relevant focus for predicting risk in a non-diabetic population.

This study was considered a pilot for a future project and was not designed to provide a high degree of precision. However, this project does provide greater insight that metabolomics is a sensitive tool that is able to differentiate between the metabolic profiles of human urinary profiles based on BMI, thus making it important to use the project characteristics for a larger trial.