Eichenlaub Manuel M, Hattersley John G, Gannon Mary C, Nuttall Frank Q, Khovanova Natasha A
School of Engineering, University of Warwick, Coventry CV4 7AL, UK; Coventry NIHR CRF Human Metabolic Research Unit, University Hospitals Coventry and Warwickshire NHS Trust, Coventry CV2 2DX, UK.
Veterans Affairs Medical Center/University of Minnesota Medical School, Minneapolis, MN, USA.
Comput Methods Programs Biomed. 2021 Mar;200:105911. doi: 10.1016/j.cmpb.2020.105911. Epub 2020 Dec 22.
The oral minimal model (OMM) of glucose dynamics is a prominent method for assessing postprandial glucose metabolism. The model yields estimates of insulin sensitivity and the meal-related appearance of glucose from insulin and glucose data after an oral glucose challenge. Despite its success, the OMM approach has several weaknesses that this paper addresses.
A novel procedure introducing three methodological adaptations to the OMM approach is proposed. These are: (1) the use of a fully Bayesian and efficient method for parameter estimation, (2) the model identification from non-fasting conditions using a generalised model formulation and (3) the introduction of a novel function to represent the meal-related glucose appearance based on two superimposed components utilising a modified structure of the log-normal distribution. The proposed modelling procedure is applied to glucose and insulin data from subjects with normal glucose tolerance consuming three consecutive meals in intervals of four hours.
It is shown that the glucose effectiveness parameter of the OMM is, contrary to previous results, structurally globally identifiable. In comparison to results from existing studies that use the conventional identification procedure, the proposed approach yields an equivalent level of model fit and a similar precision of insulin sensitivity estimates. Furthermore, the new procedure shows no deterioration of model fit when data from non-fasting conditions are used. In comparison to the conventional, piecewise linear function of glucose appearance, the novel log-normally based function provides an improved model fit in the first 30 min of the response and thus a more realistic estimation of glucose appearance during this period. The identification procedure is implemented in freely accesible MATLAB and Python software packages.
We propose an improved and freely available method for the identification of the OMM which could become the future standardard for the oral minimal modelling method of glucose dynamics.
葡萄糖动力学的口服最小模型（OMM）是评估餐后葡萄糖代谢的一种重要方法。该模型通过口服葡萄糖耐量试验后的胰岛素和葡萄糖数据，得出胰岛素敏感性以及与餐相关的葡萄糖出现量的估计值。尽管取得了成功，但OMM方法存在一些本文要解决的弱点。
提出了一种对OMM方法进行三项方法学改进的新程序。这些改进包括：（1）使用完全贝叶斯且高效的参数估计方法；（2）使用广义模型公式从非空腹条件下进行模型识别；（3）引入一种基于对数正态分布的修改结构的两个叠加成分来表示与餐相关的葡萄糖出现的新函数。将所提出的建模程序应用于葡萄糖耐量正常的受试者间隔四小时连续进食三餐后的葡萄糖和胰岛素数据。
结果表明，与先前结果相反，OMM的葡萄糖效能参数在结构上是全局可识别的。与使用传统识别程序的现有研究结果相比，所提出的方法产生了同等水平的模型拟合和相似的胰岛素敏感性估计精度。此外，当使用非空腹条件下的数据时，新程序的模型拟合没有恶化。与传统的分段线性葡萄糖出现函数相比，基于对数正态的新函数在反应的前30分钟提供了更好的模型拟合，从而在此期间对葡萄糖出现进行了更现实的估计。识别程序在可免费访问的MATLAB和Python软件包中实现。
我们提出了一种改进的、可免费获得的OMM识别方法，该方法可能成为未来葡萄糖动力学口服最小建模方法的标准。
