Research Description
Type 2 diabetes (T2D) is a chronic disorder that affects the body’s ability to use and regulate blood glucose and over time it causes other health problems. Many people with prediabetes (1 in 3 adults) will develop diabetes. The prediabetes population has mixed causes and needs different interventions. For example, prediabetes can be caused by dysfunction in the liver or muscle. Efficient grouping and personalized intervention (e.g., food) can be both beneficial to patients and more cost-effective. However, there lacks extensive knowledge to separate patients into homogenous groups. An analogy is to separate birds into species. Seeing the whole bird, instead of the head helps. Seeing the bird alive, instead of a specimen, also helps. Similarly, our current view of prediabetes is incomplete and static. It relies on blood glucose, an important but only part of the distorted system (like the bird’s head). It focuses on one time point (like a specimen). The purpose of this study is to group patients with different dysfunctions based on their response to food and discover distinguishing markers of proteins and metabolites, in addition to glucose. In this project, the question of patient grouping is studied. Time-series data on proteins and metabolites after eating are collected. The data represent the body’s unique response to nutrients. Machine learning is used to uncover the hidden causes and group the population. This work will contribute to our knowledge of prediabetes and propose personalized interventions. It will improve the health condition of the huge prediabetes population.Research Profile
What area of diabetes research does your project cover? What role will this particular project play in preventing, treating and/or curing diabetes?My research focuses on metabolic change, particularly after diet, and its effect on the risk of diabetes and complications. This project will focus on discovering different subgroups of people with prediabetes and providing personalized interventions (e.g., food recommendations). The analysis will be based on our omics and continuous glucose mentoring (CGM) data. This project contributes to diabetes prevention and treatment by offering personalized food interventions and reducing risks of diabetes development and complications.
If a person with diabetes were to ask you how your project will help them in the future, how would you respond?Different people have different postprandial responses. Some people’s glucose spikes more on cookies and some more on bananas. Postprandial glucose response is associated with future risk in many complications and choosing a proper diet can be beneficial. My research also indicates the association between different diet responses and underlying metabolic functions (e.g., insulin resistance) and can help select personalized interventions.
Why important for you, personally, to become involved in diabetes research? What role will this award play?Diabetes research is interesting to me in both its impact and science. Diabetes and its complications affect a large population (1 in 3 adults with prediabetes) and cause extensive burden. This is a fascinating important question to solve. In addition, I come from the field of time-series dynamics and metabolomics, and the monitoring techniques in diabetes (e.g., CGM) provide one of the first examples of observing metabolic dynamic changes in the human body. This award provides a crucial opportunity for me to apply my computational skills to the analysis of CGM and omics data of prediabetes patients. It allows me to gain additional training in cohort studies and clinical omics data, prepares me for an independent investigator career, and expands my research to a broader research field of diabetes.
In what direction do you see the future of diabetes research going?Personalized intervention will be an important branch of diabetes prevention and treatment, and wearable (e.g., CGM) will play an important role. I expect there to be an expansion in analysis of CGM data, personalized diet intervention, and even real-time monitoring of other metabolites and proteins.