Enabling Fully Automated Closed Loop Control in Type 1 Diabetes Through an Artificial Intelligence Meal Detection Algorithm and Pramlintide

通过人工智能膳食检测算法和普兰林肽实现 1 型糖尿病的全自动闭环控制

基本信息

批准号：
10647759
负责人：
Peter G Jacobs
金额：
$ 56.13万
依托单位：
OREGON HEALTH & SCIENCE UNIVERSITY
依托单位国家：
美国
项目类别：
财政年份：
2021
资助国家：
美国
起止时间：
2021-08-20 至 2025-06-30
项目状态：
未结题

项目摘要

Summary People with type 1 diabetes (T1D) have autoimmune destruction of beta cells resulting in insufficient insulin to maintain normal glucose levels, thereby requiring exogenous insulin treatment, typically delivered subcutaneously either continuously infused through an insulin pump or by injection multiple times throughout the day. There are now commercial closed loop systems available that enable automated delivery of fast- acting insulin in response to wireless continuous glucose monitoring (CGM) sensors that inform an automated control algorithm to calculate and deliver insulin via a pump. Unfortunately, current commercial closed loop systems are hybrid systems that require users to announce meals to the system, and these hybrid systems do not control glucose well following meals. People oftentimes forget to announce their meals or misestimate their carbohydrate intake, thereby leading to poor overall postprandial glucose control. The primary benefit of hybrid closed loop systems has been during the overnight period when meals are not consumed. In a normal working beta cell, the hormone amylin is co-secreted with insulin in response to meals to help suppress glucagon production and delay gastric emptying, thereby reducing postprandial glucose increases. Pramlintide is an analog of the endogenous hormone amylin. Our commercial partner, Adocia, has developed a coformulation of insulin and pramlintide. In this project, we will develop a dual hormone (insulin and pramlintide), fully automated closed loop system with a meal detection algorithm that will enable substantial improvements in postprandial glycemic control for people with T1D while minimizing patient burden by not requiring a meal announcement to the system. Our group has previously developed a multi hormone closed loop system (insulin and glucagon) and we have developed models of insulin, pramlintide, glucagon, and carbohydrate kinetics and dynamics that will be integrated into a new insulin+pramlintide closed loop model predictive control (MPC) system (Aim 1). We have also developed a meal detection algorithm that will be integrated with this MPC control algorithm to dose insulin and pramlintide shortly after a meal is detected, thereby eliminating the need for the user to announce this meal to the system (Aim 1). We will use our in silico simulator of glucose metabolism to identify the optimal dosing amount and timing when insulin and pramlintide are delivered in response to the meal detection algorithm (Aim 1). Next, we will evaluate the optimal insulin and pramlintide dosing therapies identified in Aim 1 and evaluate the top 4 strategies during an in-clinic meal test in a 4-arm randomized crossover study in 14 people with T1D (Study 1a, Aim 2). We will then evaluate the optimal insulin and pramlintide dosing therapy identified in Study 1a and compare with various insulin-only hybrid and automated therapies in an in-clinic randomized crossover study (Study 1b, Aim2). Finally, in Aim 3, we will evaluate the optimal insulin and pramlintide dosing therapy determined in Aim 2 and compare it with the Tandem Diabetes Control-IQ commercial hybrid closed loop system.

概括患有1型糖尿病（T1D）的人会自动免疫β细胞，导致胰岛素不足维持正常的葡萄糖水平，因此需要外源胰岛素治疗，通常会递送皮下持续通过胰岛素泵连续注入或多次注射一天。现在有商业闭环系统可实现快速交付响应无线连续葡萄糖监测（CGM）传感器的作用胰岛素，该传感器告知自动化控制算法通过泵计算和输送胰岛素。不幸的是，当前的商业闭环系统是混合系统，需要用户向系统宣布餐点，这些混合动力系统确实进餐后不能很好地控制葡萄糖。人们通常会忘记宣布他们的饭菜或误会碳水化合物的摄入量，从而导致餐后葡萄糖总体控制差。混合动力的主要好处封闭的循环系统在不消耗餐点的过夜期间。在正常工作中 β细胞，激素氨基蛋白与胰岛素共归因于餐食，以帮助抑制胰高血糖素生产和延迟胃排空，从而减少餐后葡萄糖增加。 Pramlintide是一个内源激素淀粉素的类似物。我们的商业合作伙伴Adocia已发展胰岛素和pramlintide。在这个项目中，我们将开发一种双激素（胰岛素和pramlintide），完全使用餐检测算法的自动闭环系统，该算法将大大改进 T1D患者的餐后血糖控制，同时不需要用餐来最大程度地减少患者负担通知系统。我们的小组以前已经开发了多激素闭环系统（胰岛素和胰高血糖素），并且我们开发了胰岛素，pramlintide，胰高血糖素和碳水化合物的模型动力学和动力学将集成到新的胰岛素+pramlintide闭环模型预测控制（MPC）系统（AIM 1）。我们还开发了一种将与该MPC对照算法检测到饭后不久剂量胰岛素和pramlintide，从而消除了用户需要向系统宣布这顿饭（AIM 1）。我们将在葡萄糖的硅硅中使用我们的代谢以确定胰岛素和pramlintide的最佳剂量量和时间安排对餐检测算法的响应（AIM 1）。接下来，我们将评估最佳胰岛素和pramlintide 在AIM 1中鉴定出的剂量疗法，并在4臂的临床餐内测试中评估前4个策略在14人患有T1D的人中的随机跨界研究（研究1A，AIM 2）。然后，我们将评估最佳胰岛素和研究1a中确定的pramlintide剂量疗法，并与各种仅胰岛素杂种和在临界内随机分频研究中的自动疗法（研究1B，AIM2）。最后，在AIM 3中，我们将评估在AIM 2中确定的最佳胰岛素和pramlintide剂量疗法，并将其与串联糖尿病控制-IQ商业混合闭环系统。