Background and objective

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.

Methods

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.

Results

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.

Conclusions

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.

中文翻译：

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非空腹条件下葡萄糖动力学的口服最小模型中的贝叶斯参数估计，使用新的葡萄糖外观函数

背景和目标

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重要方法。该模型从口服葡萄糖激发后的胰岛素和葡萄糖数据中得出胰岛素敏感性和膳食相关葡萄糖外观的估计值。尽管获得了成功，但是OMM方法仍存在本文要解决的一些缺点。

方法

提出了一种对OMM方法引入三种方法学调整的新颖过程。它们是：（1）使用完全贝叶斯有效的方法进行参数估计，（2）使用广义模型公式从非禁食条件中识别模型，以及（3）引入一种新颖的功能来表示膳食-基于两个重叠成分的对数葡萄糖外观，利用对数正态分布的修改结构。拟议的建模程序应用于来自正常葡萄糖耐量受试者的血糖和胰岛素数据，这些受试者在四个小时的间隔内连续三餐进餐。

结果

结果表明，与以前的结果相反，OMM的葡萄糖有效性参数在结构上是全球可识别的。与使用传统识别程序的现有研究结果相比，所提出的方法可产生同等水平的模型拟合和相似的胰岛素敏感性估算精度。此外，当使用非禁食条件下的数据时，新程序不会显示模型拟合的恶化。与常规的葡萄糖出现的分段线性函数相比，新颖的基于对数正态的函数在响应的前30分钟内提供了改进的模型拟合，因此在此期间可以更现实地估计葡萄糖的出现。识别过程在可免费访问的MATLAB和Python软件包中实现。

结论

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的葡萄糖动力学口服最小建模方法的标准。