Department of Clinical Studies - New Bolton Center, School of Veterinary Medicine, University of Pennsylvania, Kennett Square, PA, United States.
Agriculture Research Division, Department of Economic Development Jobs Transport and Resources, Ellinbank Centre, Ellinbank, VIC 3821, Australia.
Comput Methods Programs Biomed. 2020 Jul;191:105353. doi: 10.1016/j.cmpb.2020.105353. Epub 2020 Feb 11.
Kinetic non-linear metabolic models are used extensively in medical research and increasingly for clinical diagnostic purposes. An example of such a model is the Glucose Minimal Model by Bergman and colleagues [1]. This model is similar to pharmacokinetic/pharmacodynamic models in that like pharmacokinetic/pharmacodynamic models, it is based on a small number of fairly simple ordinary differential equations and it aims to determine how the changing concentration of one blood constituent influences the concentration of another constituent. Although such models may appear prima facie, to be relatively simple, they have gained a reputation of being difficult to fit to data, especially in a consistent and repeatable fashion. Consequently, researchers and clinicians have generally relied on dedicated software packages to do this type of modeling. This article describes the use of statistical and spreadsheet software for fitting the Glucose Minimal Model to data from an insulin modified intravenous glucose tolerance test (IM-IVGTT). A novel aspect of the modeling is that the differential equations that are normally used to describe insulin action and the disposition of plasma glucose are first solved and expressed in their explicit forms so as to facilitate the estimation of Glucose Minimal Model parameters using the nonlinear (nl) optimization procedure within statistical and spreadsheet software. The most important clinical parameter obtained from the Glucose Minimal Model is insulin sensitivity (S). Using IM-IVGTT data from 42 horses in one experiment and 48 horses in a second experiment, we demonstrate that estimates of S derived from the Glucose Minimal Model fitted to data using STATA and Excel, are highly concordant with S estimates obtained using the industry standard software, MinMod Millennium. This work demonstrates that there is potential for statistical and spreadsheet software to be applied to a wide range of kinetic non-linear modeling problems.
动力学非线性代谢模型在医学研究中得到广泛应用，并且越来越多地用于临床诊断目的。伯格曼（Bergman）及其同事的葡萄糖最小模型（Glucose Minimal Model）就是此类模型的一个例子[1]。该模型类似于药代动力学/药效动力学模型，因为与药代动力学/药效动力学模型一样，它基于少数相当简单的常微分方程，旨在确定血液中一种成分的浓度变化如何影响另一种成分的浓度。尽管此类模型乍一看似乎相对简单，但它们却以难以拟合数据而著称，尤其是难以以一致且可重复的方式进行拟合。因此，研究人员和临床医生通常依靠专用软件包来进行此类建模。本文介绍了如何使用统计和电子表格软件拟合葡萄糖最小模型，以适应胰岛素改良静脉葡萄糖耐量试验（IM-IVGTT）的数据。建模的一个新颖方面是，通常用于描述胰岛素作用和血浆葡萄糖分布的微分方程首先以显式形式求解并表示，以便使用统计和电子表格软件中的非线性（nl）优化过程来方便地估计葡萄糖最小模型参数。从葡萄糖最小模型获得的最重要的临床参数是胰岛素敏感性（S）。使用一个实验中的 42 匹马和第二个实验中的 48 匹马的 IM-IVGTT 数据，我们证明了使用 STATA 和 Excel 拟合数据的葡萄糖最小模型得出的 S 估计值与使用行业标准软件 MinMod Millennium 获得的 S 估计值高度一致。这项工作表明，统计和电子表格软件有可能应用于广泛的动力学非线性建模问题。
