A Novel Approach Applying CGM Metrics to Identify a Prediabetic State

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DESCRIPTION (provided by applicant): The proposed investigation will examine indexes of glycemic variability using advanced Continuous Glucose Monitoring (CGM) metrics in first degree relatives of Type 1 diabetic (T1D) probands to capture new information relevant to T1D pathogenesis and prediction. We propose to utilize participants who are currently enrolled in the TrialNet Natural History Study (Living Biobank). These subjects are longitudinally followed and deeply phenotyped with respect to multiple islet-related autoantibodies, HLA genotyping, metabolic assessment and many other risk factors. To the best of our knowledge, an integrated approach providing a complete and accurate assessment of glycemic excursions during the preclinical state of T1D has not been applied in TrialNet participants prior to the development and diagnosis of full-blown T1D. We believe the use of a combination of CGM-based metrics to assess indexes of glycemic variability will assist the identification of first-degree relatives likly to progress to the clinical onset of T1D. We hypothesize that first-degree relatives of T1D patients with ongoing immunological abnormalities (presence of 2 or e3 islet autoantibodies: insulin, GAD65, IA-2 and islet cell antibodies [ICA]) exhibit wide interstitial glucose variations detected by CGM as compared to subjects with the presence of 1 islet autoantibody. Such excursions may also occur in relatives with immunologic abnormalities, normal fasting glycemic values and normal Oral Glucose Tolerance Test (OGTT) and would otherwise go undetected. We have recently reported the development of the Continuous Glucose Monitoring - Graphical User Interface for Diabetes Evaluation (CGM-GUIDE) that provides a superior assessment of a patient's blood glucose excursions. We have assembled an unprecedented team of leading experts in: 1. Formulating practice guidelines for determining settings where patients are most likely to benefit from the use of CGM; 2. The field of mathematical modeling and CGM metrics development; and 3. Expertise in metabolic abnormalities and TrialNet-supported clinical trials for T1D. The proposed study should ultimately be useful as a new tool in combination with other immunologic as well as mechanistic biomarkers to improve prediction, understand the pathogenesis and ultimately to more accurately evaluate the response to treatment in future intervention trials aimed at preventing type 1 diabetes.