Statistical Functional Data Analysis Models of Glucose and Insulin Kinetics

描述（由申请人提供）：对于I型糖尿病患者，挑战是调节外源性胰岛素递送，使其与患者的代谢需求相匹配。长期目标是开发一种机械人工胰腺，它结合了葡萄糖传感器，胰岛素泵和控制器，以允许基于传感器反馈自动调节胰岛素泵。适当的生理合理的算法控制器是成功的机械人工胰腺的关键。为了开发这样的算法，必须具有分析葡萄糖传感器输出和葡萄糖/胰岛素动力学模型的准确且经验证的方法。这样的模型还可以改善对患者的高血糖和低血糖的预测，现在根据当前血糖水平和碳水化合物的预期消耗手动控制胰岛素递送速率。本申请的总体目标是对在托马斯杰斐逊大学（费城，PA）人工胰腺中心对I型和II型糖尿病受试者以及健康志愿者进行的5项临床研究中收集的数据进行二次分析。将在功能数据分析的框架内开发基于人群而非当前标准的基于个体的葡萄糖/胰岛素动力学分析的统计模型，并通过比较其与真实的数据的拟合度进行评价。用于单个个体的现有模型根据一般非线性微分方程的系统描述葡萄糖动力学，并且结合了许多潜在（不可测量）变量，这些变量描述了内部生理区室（例如心脏或肝脏）中葡萄糖、胰岛素和胰高血糖素的时间依赖性水平。拟定研究将（1）开发由葡萄糖和胰岛素动力学的生理学意义微分方程系统定义的受试者特定混合效应模型;（2）扩展统计方法，将无穷维回归项和随机效应纳入由非线性微分方程系统定义的函数模型中;（3）开发估算拟议功能模型所需的计算算法和软件;（四）比较各种先前提出的模型在未来血糖值的预测准确性方面的性能，并考虑葡萄糖和胰岛素动力学的新模型在糖尿病患者和健康受试者中。公共卫生相关性：血糖控制的标准方法，包括多剂量胰岛素治疗、胰岛素泵治疗和频繁使用血糖仪，不足以使I型糖尿病患者在低血糖风险的情况下实现接近正常的血糖控制。I型糖尿病的最终治疗目标是创建一个结合葡萄糖传感器，胰岛素泵和控制器的机械人工胰腺。该项目的重点是开发和验证葡萄糖/胰岛素动力学的统计模型，这将作为设计用于调节机械人工胰腺的胰岛素输送的计算算法的基础。

DESCRIPTION (provided by applicant): For Type I diabetics, the challenge is to regulate exogenous insulin delivery so that it matches metabolic needs of the patient. The long-term goal is to develop a mechanical artificial pancreas that combines a glucose sensor, an insulin pump, and a controller to allow automated regulation of the insulin pump based on the sensor feedback. Appropriate physiologically justified algorithms for the controller are crucial for a successful mechanical artificial pancreas. To develop such algorithms, one must have accurate and validated methods of analyzing glucose sensors output and models for glucose/insulin dynamics. Such models may also improve prediction of hyper- and hypoglycemia in patients now manually controlling the insulin delivery rate depending on the current blood glucose level and expected consumption of carbohydrates. The overall goal of this application is to perform secondary analyses of the data collected in five clinical studies conducted in subjects with Type I and Type II diabetes as well as healthy volunteers at the Artificial Pancreas Center of Thomas Jefferson University, Philadelphia, PA. Statistical models for population-based rather than currently standard individual-based analysis of glucose/insulin dynamics will be developed in the framework of the functional data analysis and evaluated by comparing their fit to the real data. The existing models for single individuals describe glucose kinetics in terms of the systems of generally non- linear differential equations and incorporate numerous latent (immeasurable) variables describing the time-dependent levels of glucose, insulin and glucagon in internal physiological compartments such as heart or liver. The proposed studies will (1) develop subject-specific mixed effects models defined by the systems of physiologically meaningful differential equations for glucose and insulin kinetic; (2) extend statistical methodology to incorporate infinite dimensional regression terms and random effects into the functional models defined by the systems of non-linear differential equations; (3) develop computational algorithms and software necessary to estimate proposed functional models; (4) compare performance of various previously proposed models in terms of prediction accuracy of future blood glucose values and consider new models for glucose and insulin dynamics in diabetics and in healthy subjects. PUBLIC HEALTH RELEVANCE: The standard methods of blood glucose control, including multiple dose insulin therapy, insulin pump therapy, and frequent use of blood glucose meters are not sufficient tools to enable people with type I diabetes to achieve near-normal glucose control with a low risk for hypoglycemia. The ultimate treatment goal for type I diabetes is the creation of a mechanical artificial pancreas combining a glucose sensor, insulin pump, and controller. This project focuses on developing and validating statistical models for glucose/insulin dynamics, which would serve as a foundation for designing computational algorithms for regulating the insulin delivery by the mechanical artificial pancreas.