State and parameter estimation in dynamical metabolic models for personalized model-based management of diabetes

动态代谢模型中的状态和参数估计，用于基于模型的个性化糖尿病管理

• 批准号: 339175157
• 负责人: Professor Dr.-Ing. Christoph Ament
• 金额: --
• 依托单位: Hochschule Fulda
• 依托单位国家: 德国
• 项目类别: Research Grants
• 财政年份: 2017
• 资助国家: 德国
• 起止时间: 2016-12-31 至 2019-12-31
• 项目状态: 已结题
• 来源: https://gepris.dfg.de/gepris/projekt/339175157?language=en
• 关键词: State; parameter; estimation; dynamical; metabolic

In this research project a model-based procedure for personalized diagnosis of patients with diabetes mellitus is developed which estimates progress of blood glucose based on a model of glucose insulin metabolism. The model should be identified using established blood glucose measurements and novel sensors. Analysis of controllability and observability of states and parameters of that system follows. Required parameter sets shall be fitted to each individual in order to obtain better predictions superior to generic model approaches. Abnormal changes in the metabolic system, which could arise in diabetes mellitus, can be individually characterized by a change of these parameters. The gained methods can assist users in their diagnosis and treatment.Diabetes mellitus is a chronic metabolic disease. It results from the body's inability to produce and/ or use insulin. Regardless of the specific type of diabetes affected people need a lifelong insulin therapy.Therapy of diabetes tends to support the disturbed physiologic control of glucose insulin homeostasis by an artificial feedback control. As sensor a patient can measure glucose levels in blood or in subcutaneous tissue. As actuator insulin can be injected. Feedback control is established by the patient oneself after clinically instruction.Metabolic processes are, like technical systems, describable by the use of differential equations. Therefore, it can be assumed that diagnosis and treatment of diabetes mellitus benefits from a model-based proceeding.Previous approaches to system identification consider individual condition of patients only insufficient, because of huge variabilities in metabolic behavior between different persons. Also good standard models describe these processes only for mean values gathered from collectives. Actually physicians try to tackle these problems by developing 'personalized medicine'. That means for a model-based approach besides a fine-grained model, to design an efficient and personalized model identification system, which needs few measurement points. The focus is on novel, continuously measuring sensors which make it possible to capture the dynamics in the waveforms for the first time.It should be possible to derive diagnostic information that are much better in quality superior to considering only rigid limits.This can be done by adapting the insulin therapy based on the current condition of the patient or to optimize number of glucose measurements. Taking into account disturbances such as meals or physical activity a prediction of future blood glucose levels is possible.

在本研究项目中，开发了一种基于模型的糖尿病患者个性化诊断程序，该程序基于葡萄糖胰岛素代谢模型估计血糖的进展。应使用已建立的血糖测量和新型传感器来识别模型。分析该系统的状态和参数的可控性和可观性如下。所需的参数集应适合每个个体，以获得优于通用模型方法的更好预测上级。代谢系统的异常变化，这可能会出现在糖尿病，可以单独的特点是这些参数的变化。糖尿病是一种慢性代谢性疾病，其发病机制复杂，发病率高，发病机制复杂，发病机制复杂。它是由于身体无法产生和/或使用胰岛素。 无论哪种类型的糖尿病患者都需要终身胰岛素治疗，糖尿病的治疗往往通过人工反馈控制来支持葡萄糖胰岛素稳态的生理控制。作为传感器，患者可以测量血液或皮下组织中的葡萄糖水平。作为促动器，可以注射胰岛素。反馈控制是由病人在临床指导后自行建立的。代谢过程和技术系统一样，可以用微分方程来描述。因此，可以假设糖尿病的诊断和治疗受益于基于模型的过程。以前的系统识别方法只考虑患者的个体状况是不够的，因为不同人之间的代谢行为存在巨大的差异。此外，好的标准模型只描述了从集体收集的平均值的这些过程。实际上，医生试图通过开发“个性化医疗”来解决这些问题。这意味着除了细粒度模型之外，还需要设计一种基于模型的方法来设计一个高效的个性化模型识别系统，该系统需要很少的测量点。重点是新型的连续测量传感器，它可以首次捕获波形中的动态特性。与只考虑严格的限制相比，应该可以获得质量更好的诊断信息。这可以通过根据患者的当前状况调整胰岛素治疗或优化葡萄糖测量次数来实现。考虑到诸如进餐或体力活动的干扰，预测未来的血糖水平是可能的。