Objective

This work aimed to estimate population-level insulin sensitivity (S_I) from 2-hour oral glucose tolerance tests (OGTT) with less than 7 samples.

Research design and methods

The current methodology combines the OGTT mathematical model developed by Dalla Man et al., with nonlinear multilevel (NLML) statistical model to estimate population-level insulin sensitivity (S_I) from sparsely sampled datasets (3 or 4 samples per subject obtained in 120 min).

To validate our novel methodology of population S_I estimation, we simulated 50 virtual subjects. We simulated 10 observations per subject over 240 minutes. After estimating their S_I using the OGTT model, the virtual subjects were split into two groups, subjects with S_I above the average and ones with below average. Subsequently, the simulated data were analyzed using statistical software and employing a t-test. The mean estimates of population S_I for the two groups of virtual subjects and their respective 95% CI were compared to the estimates obtained with our novel NLML group S_I estimates obtained using the 3 and 4 time points per subject.

To further validate the performance of the novel NLML model, a set of 34 prediabetic and 30 diabetic subjects with T2D was used. As outlined above for the in-silico subjects, differences between the prediabetic and T2D subjects in regard to S_I was assessed using the classical two-stage approach (individual S_I estimation followed by statistical comparison of the two groups). The average estimates obtained with the classical two-stage approach were compared to the group estimated obtained with the NLML approach using 3 (0, 60, and 120 minutes) points per subject obtained in 120 minutes.

Results

Unique and identifiable individual estimates of S_I were obtained for all virtual subjects. In comparison to the subjects with above average S_I (n=25), the subjects with simulated below average S_I (n=25) exhibited significantly lower insulin sensitivity (P<0.001). Our novel NLML population model confirmed these findings (4-point OGTT: P<0.001; 3-point OGTT: P<0.001). In a similar fashion to the one outlined for the virtual subjects, the median insulin sensitivities estimated with the classical two-stage approach were different between the prediabetic (n=34) and T2D subjects (n=32, P=0.004). Using 3 points per subject, our novel NLML model confirmed these findings (P<0.001).

Conclusions

The population estimates of S_I from OGTT data is an effective tool to assess population insulin sensitivity and assess differences that may not be possible when calculating individual S_I or when less than 7 samples are available.

中文翻译：

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来自稀疏采样的口服葡萄糖耐量试验的人群胰岛素敏感性。

客观的

这项工作的目的是估计群体水平的胰岛素敏感性（小号_我）从2小时口服葡萄糖耐量试验（OGTT）具有小于7个样品。

研究设计和方法

目前的方法合成由达拉曼等人开发的OGTT的数学模型，具有非线性多级（NLML）统计模型来估计群体水平的胰岛素敏感性（小号_我从稀疏采样数据集）（在120分钟获得的每个受试者3个或4个样本）。

为了验证我们的人口S _I估计的新方法，我们模拟了 50 个虚拟对象。我们在 240 分钟内模拟了每个受试者的 10 次观察。在使用 OGTT 模型估计他们的S _I后，虚拟受试者被分成两组，S _I高于平均水平的受试者和低于平均水平的受试者。随后，使用统计软件并采用 t 检验分析模拟数据。将两组虚拟受试者的总体S _I的平均估计值及其各自的 95% CI 与我们使用每个受试者的 3 和 4 个时间点获得的新型 NLML 组S _I估计值进行比较。

为了进一步验证新型 NLML 模型的性能，使用了一组 34 名糖尿病前期和 30 名患有 T2D 的糖尿病受试者。如上所述，对于in-silico受试者，糖尿病前期和 T2D 受试者在S _I方面的差异使用经典的两阶段方法（个体S _I估计，然后对两组进行统计比较）进行评估。使用经典两阶段方法获得的平均估计值与使用 NLML 方法获得的组估计值进行比较，每个受试者在 120 分钟内获得 3（0、60 和 120 分钟）点。

结果

所有虚拟对象都获得了S _{I 的}独特且可识别的个体估计。相比于与受试者高于平均小号_余（N = 25）中，用模拟低于平均水平的受试者小号_余（N = 25）显示出较低的显著胰岛素敏感性（P <0.001）。我们的新型 NLML 群体模型证实了这些发现（4 点 OGTT：P<0.001；3 点 OGTT：P<0.001）。与为虚拟受试者概述的方式类似，用经典的两阶段方法估计的中位胰岛素敏感性在糖尿病前期 (n=34) 和 T2D 受试者 (n=32，P=0.004) 之间不同。我们新的 NLML 模型对每个受试者使用 3 分，证实了这些发现（P <0.001）。

结论

根据OGTT 数据对S _I的总体估计是评估总体胰岛素敏感性和评估差异的有效工具，这些差异在计算个体S _I或可用样本少于 7时可能无法实现。