Author's School

School of Medicine


Author's Department/Program

Movement Science


English (en)

Date of Award

Spring 5-15-2023

Degree Type


Degree Name

Doctor of Philosophy (PhD)

Chair and Committee

Bettina Mittendorfer

Committee Members

Gretchen A Meyer, Catherine E Lang, Dominic N Reeds, Yikyung Park


In people with obesity, insulin resistance of glucose and fatty acid metabolism is common and it is thought that insulin resistance of fatty acid metabolism is involved in causing insulin resistance of glucose metabolism and subsequently hyperglycemia through a mechanism referred to as “lipotoxicity”. However, in many individuals with obesity and insulin resistance fasting plasma glucose and free fatty acid (FFA) concentrations are not elevated. It is widely believed that pancreatic beta-cells detect the need to increase insulin secretion in order to compensate for the impaired insulin action caused by insulin resistance in these people, whereas beta-cell dysfunction results in an imbalance between insulin secretion and insulin action and consequently a rise in plasma glucose and fatty acid concentrations. However, there is much debate about the independent effects of obesity and insulin resistance on beta-cell function because of equivocal results from different studies. Some of the differences in results could be due to differences in the methods used to evaluate beta-cell function, in particular the reliance on certain indices of beta-cell function. Understanding the interrelationships among obesity, insulin resistance and beta-cell function is essential to dissect the pathogenesis and development of obesity-related diseases.

In Chapter 1 (Introduction), I provide a background of current knowledge and gaps of knowledge that we addressed.

In Chapter 2, I describe the results from a study we conducted to examine the independent effects of obesity and insulin resistance on plasma FFA concentration and kinetics (appearance rate and clearance rate). Our study assessed FFA kinetics during basal (overnight fasted) conditions and during a hyperinsulinemic-euglycemic clamp procedure (HECP) in 14 lean people and 46 people with obesity by using [13C]palmitate tracer infusion. Insulin-stimulated muscle glucose uptake rate was evaluated by dynamic PET-imaging of skeletal muscles after [18F]-labeled fluorodeoxyglucose (FDG) injection. We found that basal plasma FFA concentration and FFA appearance rate in plasma were higher in the obese compared with the lean group and insulin infusion during the HECP decreased FFA concentration and appearance rate several-fold in both the lean and obese groups. There was no significant correlation between plasma FFA concentration and muscle insulin sensitivity or whole-body insulin sensitivity. However, plasma FFA clearance was accelerated in participants with obesity and correlated negatively with muscle insulin sensitivity without a difference between the lean and obese groups. Furthermore, insulin infusion increased FFA clearance and the increase was greater in obese than lean participants. Therefore, we conclude that obesity and insulin resistance independently affect FFA kinetics, determining plasma FFA concentration.

In Chapter 3, we evaluated differences in beta-cell function among normoglycemic lean people and people with obesity and different glycemic status: i) normal fasting glucose and normal glucose tolerance (NFG-NGT), ii) normal fasting glucose and impaired glucose tolerance (NFG-IGT), iii) impaired fasting glucose and impaired glucose tolerance (IFG-IGT), and iv) type 2 diabetes (T2D). We evaluated insulin secretion rate in relationship to plasma glucose (glucose-stimulated insulin secretion rate, GSIS) and by using commonly used measurements of beta-cell function. Our results showed that GSIS was statistically significantly: i) greater in the participants with obesity (OB) with NFG-NGT and with NFG-IGT groups compared with the lean group without a difference between the OB-NFG-NGT and OB-NFG-IGT group; ii) lower in the OB-IFG-IGT group compared with the OB-NFG-NGT and OB-NFG-IGT groups, and iii) lower in the T2D group compared with all other groups. Results obtained with beta-cell function indices were conflicting and did not depict accurately the statistical differences in the ISR-glucose relationship among the groups.

In Chapter 4, we evaluated the independent effects of obesity and insulin resistance on the plasma metabolome. We conducted PCA analysis and found metabolomic profiles were different between lean people and people with obesity, with these differences becoming more pronounced after insulin infusion. The interpretation of “metabolomics” results at a deeper level, including individual metabolites and subgroups of metabolites (i.e., those related to carbohydrate or amino acid metabolism) is difficult because of the large number of metabolites assessed. We developed “MetaOR” (, an accessible and specialized R Shiny application tailored for clinical researchers to investigate the effects of obesity and insulin resistance on the metabolome. MetaOR incorporates interactive visualization tools and user-friendly interfaces, enabling users to explore principal component analysis (PCA) and the relationships between insulin resistance, obesity, and metabolites without the burden of programming. The application features two main functionalities: PCA of the metabolome based on user-selected pathways and sub-pathways, and scatterplots and regression analyses examining the effect of insulin resistance and obesity on single metabolites. We validated the application using data obtained from ongoing studies involving 18 lean individuals and 47 individuals with obesity. Through a case study, we successfully replicated findings in Chapter 1 showing the effects of obesity and insulin resistance on free fatty acid metabolism.

In summary, the results of this dissertation further elucidate biological mechanisms underlying obesity, insulin resistance, and metabolic dysfunction. The development of user-friendly web applications equips researchers and clinicians with invaluable tools to evaluate metabolic health and potential interventions, ultimately advancing metabolic research and personalized healthcare strategies for metabolic diseases.


Available for download on Saturday, May 16, 2026