IEEE Trans Biomed Eng. 2024 Mar;71(3):977-986. doi: 10.1109/TBME.2023.3324206. Epub 2024 Feb 26.
Modeling the effect of meal composition on glucose excursion would help in designing decision support systems (DSS) for type 1 diabetes (T1D) management. In fact, macronutrients differently affect post-prandial gastric retention (GR), rate of appearance (R[Formula: see text]), and insulin sensitivity (S[Formula: see text]). Such variables can be estimated, in inpatient settings, from plasma glucose (G) and insulin (I) data using the Oral glucose Minimal Model (OMM) coupled with a physiological model of glucose transit through the gastrointestinal tract (reference OMM, R-OMM). Here, we present a model able to estimate those quantities in daily-life conditions, using minimally-invasive (MI) technologies, and validate it against the R-OMM.
Forty-seven individuals with T1D (weight =78±13 kg, age =42±10 yr) underwent three 23-hour visits, during which G and I were frequently sampled while wearing continuous glucose monitoring (CGM) and insulin pump (IP). Using a Bayesian Maximum A Posteriori estimator, R-OMM was identified from plasma G and I measurements, and MI-OMM was identified from CGM and IP data.
The MI-OMM fitted the CGM data well and provided precise parameter estimates. GR and R[Formula: see text] model parameters were not significantly different using the MI-OMM and R-OMM (p 0.05) and the correlation between the two S[Formula: see text] was satisfactory ( ρ =0.77).
The MI-OMM is usable to estimate GR, R[Formula: see text], and S[Formula: see text] from data collected in real-life conditions with minimally-invasive technologies.
Applying MI-OMM to datasets where meal compositions are available will allow modeling the effect of each macronutrient on GR, R[Formula: see text], and S[Formula: see text]. DSS could finally exploit this information to improve diabetes management.
模拟膳食成分对血糖波动的影响有助于设计 1 型糖尿病（T1D）管理的决策支持系统（DSS）。事实上，宏量营养素对餐后胃潴留（GR）、出现率（R[Formula: see text]）和胰岛素敏感性（S[Formula: see text]）有不同的影响。在住院环境中，可以使用口服葡萄糖最小模型（OMM）结合胃肠道葡萄糖转运的生理模型（参考 OMM，R-OMM），从血浆葡萄糖（G）和胰岛素（I）数据中估计这些变量。在这里，我们提出了一种能够在日常生活条件下使用微创（MI）技术估计这些量的模型，并将其与 R-OMM 进行验证。
47 名 T1D 患者（体重=78±13kg，年龄=42±10yr）接受了 3 次 23 小时的访问，在此期间，他们穿着连续血糖监测仪（CGM）和胰岛素泵（IP）频繁采样血糖和胰岛素。使用贝叶斯最大后验估计器，从血浆 G 和 I 测量中确定 R-OMM，并从 CGM 和 IP 数据中确定 MI-OMM。
MI-OMM 很好地拟合了 CGM 数据，并提供了精确的参数估计。使用 MI-OMM 和 R-OMM 时，GR 和 R[Formula: see text]模型参数没有显著差异（p>0.05），并且两者的 S[Formula: see text]相关性令人满意（ρ=0.77）。
MI-OMM 可用于从使用微创技术在真实生活条件下收集的数据中估计 GR、R[Formula: see text]和 S[Formula: see text]。应用 MI-OMM 到可以获得膳食成分的数据集将允许模拟每种宏量营养素对 GR、R[Formula: see text]和 S[Formula: see text]的影响。DSS 最终可以利用这些信息来改善糖尿病管理。
将 MI-OMM 应用于可以获得膳食成分的数据集将允许模拟每种宏量营养素对 GR、R[Formula: see text]和 S[Formula: see text]的影响。DSS 最终可以利用这些信息来改善糖尿病管理。
