Man Chiara Dalla, Micheletto Francesco, Lv Dayu, Breton Marc, Kovatchev Boris, Cobelli Claudio
Department of Information Engineering, University of Padova, Padova, Italy.
Center for Diabetes Technology, University of Virginia, Charlottesville, VA, USA.
J Diabetes Sci Technol. 2014 Jan;8(1):26-34. doi: 10.1177/1932296813514502. Epub 2014 Jan 1.
Recent studies have provided new insights into nonlinearities of insulin action in the hypoglycemic range and into glucagon kinetics as it relates to response to hypoglycemia. Based on these data, we developed a new version of the UVA/PADOVA Type 1 Diabetes Simulator, which was submitted to FDA in 2013 (S2013). The model of glucose kinetics in hypoglycemia has been improved, implementing the notion that insulin-dependent utilization increases nonlinearly when glucose decreases below a certain threshold. In addition, glucagon kinetics and secretion and action models have been incorporated into the simulator: glucagon kinetics is a single compartment; glucagon secretion is controlled by plasma insulin, plasma glucose below a certain threshold, and glucose rate of change; and plasma glucagon stimulates with some delay endogenous glucose production. A refined statistical strategy for virtual patient generation has been adopted as well. Finally, new rules for determining insulin to carbs ratio (CR) and correction factor (CF) of the virtual patients have been implemented to better comply with clinical definitions. S2013 shows a better performance in describing hypoglycemic events. In addition, the new virtual subjects span well the real type 1 diabetes mellitus population as demonstrated by good agreement between real and simulated distribution of patient-specific parameters, such as CR and CF. S2013 provides a more reliable framework for in silico trials, for testing glucose sensors and insulin augmented pump prediction methods, and for closed-loop single/dual hormone controller design, testing, and validation.
最近的研究为低血糖范围内胰岛素作用的非线性以及与低血糖反应相关的胰高血糖素动力学提供了新的见解。基于这些数据,我们开发了UVA/帕多瓦1型糖尿病模拟器的新版本,并于2013年提交给了美国食品药品监督管理局(S2013)。低血糖时葡萄糖动力学模型得到了改进,纳入了这样一种观点,即当葡萄糖降至特定阈值以下时,胰岛素依赖性利用呈非线性增加。此外,胰高血糖素动力学、分泌和作用模型已被纳入模拟器:胰高血糖素动力学为单室模型;胰高血糖素分泌受血浆胰岛素、低于特定阈值的血浆葡萄糖以及葡萄糖变化率控制;血浆胰高血糖素会延迟刺激内源性葡萄糖生成。还采用了一种改进的虚拟患者生成统计策略。最后,实施了确定虚拟患者胰岛素与碳水化合物比值(CR)和校正因子(CF)的新规则,以更好地符合临床定义。S2013在描述低血糖事件方面表现更佳。此外,新的虚拟受试者很好地涵盖了真实的1型糖尿病患者群体,这体现在患者特异性参数(如CR和CF)的实际分布与模拟分布之间具有良好的一致性。S2013为计算机模拟试验、测试葡萄糖传感器和胰岛素增强泵预测方法以及闭环单/双激素控制器的设计、测试和验证提供了一个更可靠的框架。