Bio-Behavioral Monitoring and Control of IDDM

在过去的几年中，我们已经在数学上制定了优化血糖控制而不增加严重低血糖（SH）风险的临床问题，并成功开发了量化1型糖尿病（T1 DM）控制方面的糖尿病特异性数学方法。我们的成就包括：1）低血糖指数-迄今为止SH的最佳预测因子，占随后6个月SH发作的46%; 2）预测50%即将发生的中度/重度低血糖，以及3）胰岛素-葡萄糖动力学的网络模型，占高胰岛素钳夹期间血糖（BG）波动的95%。发表了15篇科学论文。基于这些进展，我们提出了一项为期四年的研究，该研究将利用现有数据，并将进行新的数据收集，目标是创建理论模型和先进的信息处理方法，在多个层面上评估患者的代谢控制。第1阶段（1- 2年）将量化T1 DM患者行为和生理之间的相互作用。我们将设计，测试和验证一个新的生物行为模型，伴随着Palm Pilot型计算机的行为评估协议，将收集低血糖/高血糖的意识和检测，以及自我治疗判断/行为的现场数据。II期（第2-3年）是一项现场研究，将使用自我监测BG（SMBG）数据开发和验证三种算法，以估计HbA 1c、SH的长期风险和即将发生（24小时内）低血糖的风险。我们将设计和测试一种新的增强SMBG采样程序，基于SMBG的效用不仅取决于其频率，而且还取决于测量的时间。III期（3-4年）是一项在综合临床研究中心进行的研究，将通过网络模型研究高胰岛素钳夹和受控医院条件下的BG动力学。具体来说，我们将研究胰岛素和葡萄糖，体力活动和心率，心率变异性和激素反调节之间的功能关系。在本项目结束后，我们将开发理论模型和定量方法，将：1。评估导致T1 DM控制不良的特异质行为（表现为高HbA 1c和/或SH风险增加），从而促进T1 DM患者的行为训练; 2.在两个基本水平上处理和解释SMBG数据，HbAt的估计和低血糖的长期风险; 3.提供增强SMBG和监测即将到来的低血糖，只要检测到低血糖的高风险，和4。提供连续血糖监测数据的动态解释，目的是在30分钟内预测血糖波动，尤其是低血糖风险。

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.