Modeling and modulating insulin delivery in automated insulin delivery systems to accommodate for meal compositions

在自动胰岛素输送系统中建模和调节胰岛素输送以适应膳食成分

• 批准号: 10170344
• 负责人: Laya Ekhlaspour
• 金额: $ 19.12万
• 依托单位: STANFORD UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-07-01 至 2022-02-13
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10170344
• 关键词: Address; Affect; Algorithms; Applications Grants; Artificial Pancreas; Blood Glucose; Bolus Infusion; Carbohydrates; Caring; Clinical; Clinical Investigator; Clinical Research; Clinical Trials; Data; Data Set; Development; Diabetes Mellitus; Dietary Fats; Dietary Proteins; Dose; Fatty acid glycerol esters; Food; Gastric Emptying; Gastroparesis; Glucose; Goals; Hour; Hyperglycemia; Hypoglycemia; Individual; Insulin; Insulin Resistance; Insulin-Dependent Diabetes Mellitus; Knowledge; Macronutrients Nutrition; Measures; Mentorship; Modeling; Nutrient; Observational Study; Patients; Pattern; Process; Proteins; Pump; Randomized Controlled Trials; Reactive hypoglycemia; Research; Research Personnel; Research Training; Risk; Structure; System; Time; Work; absorption; base; blood glucose regulation; comorbidity; diabetes management; experience; experimental study; glucose monitor; glycemic control; improved; in silico; predictive modeling; rate of change; real time model; response

PROJECT SUMMARY The candidate, Laya Ekhlaspour, MD, is dedicated to advancing diabetes management by decreasing the burden of diabetes care. This proposal will provide a structured clinical research training experience with formal mentorship that will enable Dr. Ekhlaspour to become an independent clinical researcher with expertise in closed loop systems. Currently, carbohydrates are considered the primary and major determinant affecting postprandial glucose control and the insulin bolus is based solely on the carbohydrate content of the meal along with the blood glucose at the time of the bolus. Given the known benefits of normalizing postprandial glucose excursions, the goal of this research is to specifically address problems related to providing adequate insulin coverage for meals that have variable fat and protein content that might result in both early hypoglycemia and prolonged hyperglycemia, when automated insulin systems are used. This proposal will take advantage of existing data collected during an observational study at the Barbara Davis Center, and the feasibility closed-loop trials at Stanford. In order to characterize the effect of macronutrient food content on postprandial glucose levels, a retrospective review of meals with known glucose, protein and fat content combined with CGM and insulin values in both open loop and closed loop situations will be conducted. This analysis will provide data to allow modeling for insulin requirements with meals of variable patterns of postprandial glycemic levels. It will also determine whether this modeling will allow for setting the percent of insulin required upfront and how long the insulin delivery needs to be extended in pumps which allow for extended meal boluses. The result of this analysis can be used in closed-loop control real-time modeling of meal boluses when there is a real-time adaptation to food absorption patterns, which will contribute to eventual fully closed-loop glucose control. The developed model will be based on assessing the hourly glucose and insulin requirements which could be integrated into prandial dosing algorithms in closed loop systems in order to optimize postprandial glycemic control. The long-term goal is to have a fully-closed loop algorithm, which will recognize the need for additional insulin with a high fat or protein meal based on the CGM postprandial tracing and insulin requirements during closed-loop control. This will reduce the burden of diabetes management significantly because the patient does not have to announce a meal. The proposed studies will provide the preliminary meal data for modeling of meal-responses that will account for both the carbohydrate, protein and fat content of the meal without user input. The following step will be to validate this model through in silico experiments and then conduct a randomized, controlled trial of the implementation of a potential refined meal algorithm that could handle postprandial glucose levels without an extended bolus as part of a competitive R01 grant application.

项目摘要 候选人Laya Ekhlaspour，医学博士，致力于通过减少 糖尿病护理的负担。该提案将提供结构化的临床研究培训经验， 正式的指导将使Ekhlaspour博士成为一名具有专业知识的独立临床研究人员 在闭环系统中。目前，碳水化合物被认为是主要的和主要的决定因素， 餐后血糖控制和胰岛素推注仅基于膳食的碳水化合物含量 沿着推注时的血糖。考虑到餐后饮食正常化的好处 葡萄糖波动，这项研究的目标是专门解决有关的问题，提供足够的 脂肪和蛋白质含量不同的膳食的胰岛素覆盖率， 低血糖和长期高血糖，当使用自动胰岛素系统时。 该提案将利用芭芭拉戴维斯的一项观察性研究期间收集的现有数据 中心，以及斯坦福大学的可行性闭环试验。为了表征大量营养素的作用， 食物含量对餐后血糖水平的影响，对已知葡萄糖、蛋白质和 在开环和闭环情况下，脂肪含量与CGM和胰岛素值的组合将 进行。该分析将提供数据，以允许对不同膳食的胰岛素需求进行建模。 餐后血糖水平的模式。它还将确定此建模是否允许设置 预先所需胰岛素的百分比以及胰岛素泵中胰岛素输注需要延长多长时间， 允许延长餐时大剂量。分析结果可用于实时闭环控制 当存在对食物吸收模式的实时适应时， 有助于最终的完全闭环葡萄糖控制。开发的模型将基于评估 每小时的葡萄糖和胰岛素需求，可以整合到封闭的餐时给药算法中， 循环系统，以优化餐后血糖控制。长远目标是要有一个全封闭的 循环算法，该算法将根据患者的血糖水平识别是否需要在高脂肪或蛋白质膳食中额外注射胰岛素。 闭环控制期间的CGM餐后跟踪和胰岛素需求。这将减轻 糖尿病管理显著，因为患者不必宣布吃饭。 拟议的研究将为膳食反应建模提供初步的膳食数据， 对于膳食的碳水化合物、蛋白质和脂肪含量，无需用户输入。下一步将是 通过计算机模拟实验验证该模型，然后进行随机对照试验， 实现了一种潜在的精细膳食算法，该算法可以处理餐后葡萄糖水平， 作为竞争性R 01拨款申请的一部分，延长大剂量。