Bio-Behavioral Monitoring and Control of IDDM

在过去的几年里，我们已经用数学方法制定了优化血糖控制的临床问题，而不增加严重低血糖（SH）的风险，并成功地开发了糖尿病特异性的数学方法，量化控制1型糖尿病（T1DM）的各个方面。我们的成果包括：1)低BG指数——迄今为止最好的SH预测指标，占随后6个月SH发作的46%；2)预测50%即将到来的中度/重度低血糖，3)胰岛素-葡萄糖动力学网络模型占高胰岛素钳夹期间95%的血糖（BG）波动。我们发表了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执行新的数据收集，目的是创建理论模型和先进的信息处理方法，以评估患者在几个层面的代谢控制。第一阶段（1- 2年级）将量化T1DM患者行为和生理之间的相互作用。我们将设计、测试和验证一种新的生物行为模型，并附带Palm pilot型计算机的行为评估协议，该模型将收集关于低血糖/高血糖的意识和检测以及自我治疗判断/行为的现场数据。第二阶段（2-3年）是一项实地研究，将开发和验证三种算法，使用自我监测BG （SMBG）数据来估计HbA1c、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的效用不仅在很大程度上取决于其频率，而且取决于测量时间的想法。第三阶段（3-4年）是一项在普通临床研究中心进行的研究，将通过网络模型调查高胰岛素钳夹和受控医院条件下的BG动态。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。在这个项目结束后，我们将建立理论模型和定量方法，将：1。评估导致T1DM控制不良的特殊行为（以高HbA1c和/或SH风险增加为代表），从而促进T1DM患者的行为训练；2. 处理和解释两个基本水平的SMBG数据，估计HbAt和长期低血糖风险；3. 3 .当检测到低血糖的高风险时，提供增强的SMBG和即将到来的低BG监测；对连续血糖监测数据进行动态解读，预测30分钟内血糖波动，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.