Objective
Continuous glucose monitors (CGM) are small, minimally invasive, and widely adopted by people with and without diabetes. With increasing use of CGM, it is essential to develop a simple and easy-to-understand glucose metric that would help researchers and people at higher risk of developing T1D predicting progression of type 1 diabetes promptly.
The main objective of this research proposal is to develop and validate a novel CGM-based metric (called CGM-dynamic index, CDI) to identify stages of type 1 diabetes and progression from stage 2 (slightly abnormal blood sugar) to stage 3 type 1 diabetes. We hypothesize that CDI will accurately identify the various stages of T1D, and it will predict progression from stage 2 to stage 3 T1D earlier than current method, oral glucose tolerance test.
We will also test this novel metric, CDI, to predict response to teplizumab, a recently approved therapy by the FDA to delay the development of type 1 diabetes.
This novel marker will open the door for precision medicine to understand who is likely to progress and develop type 1 diabetes or who is likely to respond to preventing therapy like teplizumab.
Background Rationale
Type 1 diabetes is a chronic disease requiring life-long insulin therapy.Type 1 diabetes is broadly divided into three stages. The first stage is where an individual develops antibodies against the pancreatic beta cells, cells that produce insulin. Stage 2 is characterized by development of some degree of abnormal glucose readings but not high enough to call it diabetes. Finally, with loss of in a large number of beta cells, a person develops high blood sugar and stage 3 or clinical type 1 diabetes. The current way to define stage 2 is either by drawing blood after drinking glucose (called OGTT) or by a test called HbA1c, which is a measure of an average of 2 to 3 months of blood sugar. The OGTT is time-consuming as it takes 2-3 hours of a person’s time, requires multiple blood draws, and is not always accurate enough to diagnose stage 2 type 1 diabetes. Similarly, HbA1c may not be accurate in deciding if someone is in stage 2 since as subtle changes in sugar may not change HbA1c. Continuous glucose monitors (CGM) have proved the ability to track blood sugar fluctuations, trends, and patterns over time compared to just one or few time points of blood sugar during OGTT. Therefore, we propose to develop a mathematical prediction model using CGM based glucose profile and changes in the profile overtime. We expect that our mathematical model would define various stages of type 1 diabetes accurately and will predict who is likely to progress to clinical type 1 diabetes (stage 3).
Description of Project
With the approval of teplizumab, the first drug to delay development of type 1 diabetes, now there is hope and momentum in delaying and preventing type 1 diabetes. Prevention of type 1 diabetes depends on accurately staging the type 1 diabetes. Type 1 diabetes is broadly divided into three stages. The first stage is where an individual develops antibodies against the pancreatic beta cells, cells that produce insulin. Stage 2 is characterized by development of some degree of abnormal glucose readings but not high enough to call it diabetes. Finally, with loss of in a large number of beta cells, a person develops high blood sugar and stage 3 or clinical type 1 diabetes.
Prevention therapies such as teplizumab are FDA-approved for use during stage 2 diabetes. The current way to define stage 2 is either by drawing blood after drinking glucose (called OGTT) or by a test called HbA1c, which is a measure of an average of 2 to 3 months of blood sugar. The OGTT is time-consuming as it takes 2-3 hours of a person’s time, requires multiple blood draws, and is not always accurate enough to diagnose stage 2 type 1 diabetes. Similarly, HbA1c may not be accurate in deciding if someone is in stage 2 since as subtle changes in sugar may not change HbA1c.
Continuous glucose monitors (CGMs) are used often for care of persons with and without diabetes. Considering limitations of currently used blood tests to define stage 2 type 1 diabetes, we are proposing research to develop a CGM-based metric (called CGM-dynamic index, CDI) which can assess the changes in blood sugars over time to accurately define stage 2 diabetes. Moreover, we anticipate that CDI will also be able to predict development of type 1 diabetes (stage 3) accurately and earlier than currently used blood tests such as OGTT and HbA1c. Developing a simple and easy way to understand glucose pattern would help researchers and people at higher risk of developing diabetes to use measures for delaying or preventing type 1 diabetes.
Anticipated Outcome
At the end of this research, we expect to develop a novel CGM-based metric called CGM-dynamic index (CDI) that can define and predict the progression of one stage to another stage of type 1 diabetes. The CDI would be a simple numeric value (like HbA1c or glucose management indicator, GMI) that will differentiate individuals with normal glucose profile, abnormal glucose profile (high risk), and presence of diabetes. Based on changes in CDI over time, it would be easier to predict progression to diabetes and response to a drug that may delay or prevent type 1 diabetes.
Relevance to T1D
The progression towards clinical type 1 diabetes can be categorized into three stages; the first stage is characterized by the presence of ≥ 2 islet autoantibodies with normoglycemia, the second stage progresses to dysglycemia (i.e., at-risk), and finally, the third stage is defined by the onset of symptomatic (i.e., clinical) type 1 diabetes. Therefore, the presence of autoantibodies is related to the immunological risk of developing diabetes in the future and is a key biomarker of the pathogenic processes leading to clinical diagnosis. This, and also other, biomarkers could be used to screen a much broader population besides individuals at increased genetic risk of type 1 diabetes (e.g., first-degree relatives).
Early identification and screening of individuals at increased type 1 diabetes risk can reduce the rates of diabetic ketoacidosis (DKA) at diagnosis, improve the quality of glycemic control, and reduce other future poor health outcomes, and complications. Overall, as monitoring of high-risk individuals in natural history studies markedly reduces DKA rates at diagnosis, research participation in these studies is critical to finding means of preventing or delaying type 1 diabetes and justifies the development of efficient screening methods to identify individuals at high type 1 diabetes risk which appears influenced by immunological and genetic factors.
Therefore, we propose this research project aimed at developing and validating a novel CGM-based dynamic index (CDI) for precise identification of stage 2 type 1 diabetes and progression from stage 2 (dysglycemia) to stage 3 disease, tracking the progression of diabetes over time, and identify healthy individuals who may progress to onset of type 1 diabetes.