Bio-Behavioral Monitoring and Control of IDDM

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During the last the years, we have formulated mathematically the clinical problem of optimization of glycemic control without increasing the risk of severe hypoglycemia (SH), and successfully developed diabetes- specific mathematical methods quantifying aspects of the control of Type 1 Diabetes (T1DM). Our accomplishments include: 1) the Low BG Index - the best predictor of SH to date, accounting for 46% of SH episodes in the subsequent 6 months; 2) prediction of 50% of upcoming moderate/severe hypoglycemia, and 3) a network model of insulin- glucose dynamics accounting for 95% of blood glucose (BG) fluctuations during a hyperinsulinemic clamp. We published 15 scientific papers. Based on these advances we propose a four-year study that will utilize existing data and will perform new data collections with the goal to create theoretical models and advanced information processing methods evaluating patients' metabolic control at several levels. Phase 1 (Years 1- 2) will quantify interactions between behavior and physiology in T1DM. We will design, test, and validate a new bio-behavioral model, accompanied by a behavior-assessment protocol for Palm Pilot-type computers, that will collect field data on awareness and detection of hypoglycemia/hyperglycemia, and self-treatment judgment/behavior. Phase 2 (Years 2-3) is a field study that will develop and validate three algorithms using self-monitoring BG (SMBG) data to estimate HbA1c, long-term risk for SH, and risk for imminent (within 24 hours) hypoglycemia. We will design and test a new Enhanced SMBG sampling procedure, based on the idea that the utility of SMBG greatly depends not only on its frequency, but also on the timing of measurement. Phase 3 (Years 3-4) is a study, performed at the General Clinical Research Center that will investigate, through a network model, BG dynamics during both hyperinsulinemic clamp and controlled hospital conditions. Specifically, we will investigate functional relationships between insulin and glucose, physical activity and heart rate, and heart rate variability and hormonal counterregulation. Upon the conclusion of this project, we will have developed theoretical models and quantitative methods that will: 1. Provide assessment of idiosyncratic behaviors leading to poor control of T1DM (as represented by high HbA1c and/or increased risk for SH), thus facilitating behavioral training of patients with T1DM; 2. Process and interpret SMBG data at two basic levels, estimation of HbAt, and long-term risk of hypoglycemia; 3. Offer Enhanced SMBG and monitoring of upcoming low BG, whenever high risk of hypoglycemia is detected, and 4. Provide dynamic interpretation of continuous BG monitoring data with the goal to forecast the BG fluctuations, and especially the risk for hypoglycemia, within 30 minutes.