Kanderian Sami S, Weinzimer Stu, Voskanyan Gayane, Steil Garry M
Medtronic MiniMed, Northridge, California, USA.
J Diabetes Sci Technol. 2009 Sep 1;3(5):1047-57. doi: 10.1177/193229680900300508.
Algorithms for closed-loop insulin delivery can be designed and tuned empirically; however, a metabolic model that is predictive of clinical study results can potentially accelerate the process.
Using data from a previously conducted closed-loop insulin delivery study, existing models of meal carbohydrate appearance, insulin pharmacokinetics, and the effect on glucose metabolism were identified for each of the 10 subjects studied. Insulin's effects to increase glucose uptake and decrease endogenous glucose production were described by the Bergman minimal model, and compartmental models were used to describe the pharmacokinetics of subcutaneous insulin absorption and glucose appearance following meals. The composite model, comprised of only five equations and eight parameters, was identified with and without intraday variance in insulin sensitivity (S(I)), glucose effectiveness at zero insulin (GEZI), and endogenous glucose production (EGP) at zero insulin.
Substantial intraday variation in SI, GEZI and EGP was observed in 7 of 10 subjects (root mean square error in model fit greater than 25 mg/dl with fixed parameters and nadir and/or peak glucose levels differing more than 25 mg/dl from model predictions). With intraday variation in these three parameters, plasma glucose and insulin were well fit by the model (R(2) = 0.933 +/- 0.00971 [mean +/- standard error of the mean] ranging from 0.879-0.974 for glucose; R(2) = 0.879 +/- 0.0151, range 0.819-0.972 for insulin). Once subject parameters were identified, the original study could be reconstructed using only the initial glucose value and basal insulin rate at the time closed loop was initiated together with meal carbohydrate information (glucose, R(2) = 0.900 +/- 0.015; insulin delivery, R(2) = 0.640 +/- 0.034; and insulin concentration, R(2) = 0.717 +/- 0.041).
Metabolic models used in developing and comparing closed-loop insulin delivery algorithms will need to explicitly describe intraday variation in metabolic parameters, but the model itself need not be comprised by a large number of compartments or differential equations.
闭环胰岛素输注算法可通过经验设计和调整；然而，一个能预测临床研究结果的代谢模型可能会加速这一过程。
利用先前进行的闭环胰岛素输注研究的数据，为所研究的10名受试者中的每一位确定了现有的餐时碳水化合物出现模型、胰岛素药代动力学模型以及对葡萄糖代谢的影响模型。胰岛素增加葡萄糖摄取和减少内源性葡萄糖生成的作用由伯格曼最小模型描述，房室模型用于描述皮下胰岛素吸收和餐后葡萄糖出现的药代动力学。该复合模型仅由五个方程和八个参数组成，分别在有无胰岛素敏感性（S(I)）、零胰岛素时的葡萄糖效能（GEZI）和零胰岛素时的内源性葡萄糖生成（EGP）日内变化的情况下进行了识别。
在10名受试者中的7名观察到SI、GEZI和EGP存在显著的日内变化（固定参数时模型拟合的均方根误差大于25mg/dl，最低点和/或峰值血糖水平与模型预测值相差超过25mg/dl）。随着这三个参数的日内变化，模型对血浆葡萄糖和胰岛素的拟合良好（R(2)=0.933±0.00971[平均值±平均标准误差]，葡萄糖范围为0.879 - 0.974；R(2)=0.879±0.0151，胰岛素范围为0.819 - 0.972）。一旦确定了受试者参数，仅使用闭环开始时的初始血糖值、基础胰岛素输注率以及餐时碳水化合物信息，就可以重建原始研究（葡萄糖，R(2)=0.900±0.015；胰岛素输注，R(2)=0.640±0.034；胰岛素浓度，R(2)=0.717±0.041）。
用于开发和比较闭环胰岛素输注算法的代谢模型需要明确描述代谢参数的日内变化，但模型本身不必由大量房室或微分方程组成。
