Statistical Functional Data Analysis Models of Glucose and Insulin Kinetics

描述(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的输送，使其符合患者的代谢需求。长期目标是开发一种机械人造胰腺，它结合了葡萄糖传感器、胰岛素泵和控制器，可以根据传感器的反馈自动调节胰岛素泵。对于一个成功的机械人工胰腺来说，合适的生理上合理的控制器算法是至关重要的。要开发这样的算法，必须有准确和有效的方法来分析葡萄糖传感器的输出和葡萄糖/胰岛素动力学模型。这样的模型还可以改善患者的高血糖和低血糖的预测，目前根据当前的血糖水平和预期的碳水化合物消耗量手动控制胰岛素递送率。该应用程序的总体目标是对在宾夕法尼亚州费城托马斯·杰斐逊大学人工胰腺中心对I型和II型糖尿病患者以及健康志愿者进行的五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不是当前标准的基于个体的血糖/胰岛素动态分析的统计模型，并通过比较其与真实数据的适合性来评估这些模型。现有的单人模型用一般的非线性微分方程组来描述葡萄糖动力学，并结合了许多潜在的(不可测量的)变量，这些变量描述了心脏或肝脏等内部生理空间中葡萄糖、胰岛素和胰升糖素的随时间变化的水平。拟议的研究将(1)开发由具有生理意义的葡萄糖和胰岛素动力学微分方程组定义的特定对象的混合效应模型；(2)扩展统计方法，将无限维回归项和随机效应纳入由非线性微分方程组定义的函数模型；(3)开发估计所建议的函数模型所需的计算算法和软件；(4)比较先前提出的各种模型在预测未来血糖值的准确性方面的性能，并考虑用于糖尿病患者和健康受试者的血糖和胰岛素动力学的新模型。与公众健康相关：标准的血糖控制方法，包括多剂量胰岛素治疗、胰岛素泵治疗和频繁使用血糖仪，都不足以使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造一种结合了葡萄糖传感器、胰岛素泵和控制器的机械人造胰腺。本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，这些模型将作为设计通过机械人工胰腺调节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.