Department of Electrical and Computer Engineering, Faculty of Engineering, McGill University, Montreal, Quebec, Canada.
Institut de Recherches Cliniques de Montréal, Montreal, Quebec, Canada.
Can J Diabetes. 2017 Oct;41(5):485-490. doi: 10.1016/j.jcjd.2017.08.004. Epub 2017 Aug 30.
A glucose clamp procedure is the most reliable way to quantify insulin pharmacokinetics and pharmacodynamics, but skilled and trained research personnel are required to frequently adjust the glucose infusion rate. A computer environment that simulates glucose clamp experiments can be used for efficient personnel training and development and testing of algorithms for automated glucose clamps.
We built 17 virtual healthy subjects (mean age, 25±6 years; mean body mass index, 22.2±3 kg/m), each comprising a mathematical model of glucose regulation and a unique set of parameters. Each virtual subject simulates plasma glucose and insulin concentrations in response to intravenous insulin and glucose infusions. Each virtual subject provides a unique response, and its parameters were estimated from combined intravenous glucose tolerance test-hyperinsulinemic-euglycemic clamp data using the Bayesian approach. The virtual subjects were validated by comparing their simulated predictions against data from 12 healthy individuals who underwent a hyperglycemic glucose clamp procedure.
Plasma glucose and insulin concentrations were predicted by the virtual subjects in response to glucose infusions determined by a trained research staff performing a simulated hyperglycemic clamp experiment. The total amount of glucose infusion was indifferent between the simulated and the real subjects (85±18 g vs. 83±23 g; p=NS) as well as plasma insulin levels (63±20 mU/L vs. 58±16 mU/L; p=NS).
The virtual subjects can reliably predict glucose needs and plasma insulin profiles during hyperglycemic glucose clamp conditions. These virtual subjects can be used to train personnel to make glucose infusion adjustments during clamp experiments.
葡萄糖钳夹程序是量化胰岛素药代动力学和药效动力学的最可靠方法，但需要熟练且经过培训的研究人员频繁调整葡萄糖输注率。模拟葡萄糖钳夹实验的计算机环境可用于高效的人员培训和开发，并测试自动化葡萄糖钳夹的算法。
我们构建了 17 个虚拟健康受试者（平均年龄 25±6 岁；平均体重指数 22.2±3kg/m²），每个受试者均包含葡萄糖调节的数学模型和一组独特的参数。每个虚拟受试者模拟对静脉内胰岛素和葡萄糖输注的血浆葡萄糖和胰岛素浓度的反应。每个虚拟受试者提供独特的反应，其参数是使用贝叶斯方法从联合静脉葡萄糖耐量试验-高胰岛素正常血糖钳夹数据中估计的。通过将虚拟受试者的模拟预测与 12 名接受高血糖葡萄糖钳夹程序的健康个体的数据进行比较，对虚拟受试者进行了验证。
通过受过训练的研究人员执行模拟高血糖钳夹实验来确定葡萄糖输注时，虚拟受试者预测了血浆葡萄糖和胰岛素浓度。模拟和真实受试者的葡萄糖输注总量无差异（85±18g 与 83±23g；p=NS），血浆胰岛素水平也无差异（63±20mU/L 与 58±16mU/L；p=NS）。
虚拟受试者可以可靠地预测高血糖葡萄糖钳夹条件下的葡萄糖需求和血浆胰岛素谱。这些虚拟受试者可用于培训人员在钳夹实验中进行葡萄糖输注调整。
