Department of Mechanical Engineering, University of California Berkeley, Berkeley, CA, USA.
Department of Global Health and Population, Harvard T.H. Chan School of Public Health, Boston, MA, USA.
J Pharmacokinet Pharmacodyn. 2018 Dec;45(6):829-845. doi: 10.1007/s10928-018-9611-z. Epub 2018 Nov 3.
Our objective is to develop a physiology-based model of insulin kinetics to understand how exercise alters insulin concentrations in those with type 1 diabetes (T1D). We reveal the relationship between the insulin absorption rate ([Formula: see text]) from subcutaneous tissue, the insulin delivery rate ([Formula: see text]) to skeletal muscle, and two physiological parameters that characterize the tissue: the perfusion rate (Q) and the capillary permeability surface area (PS), both of which increase during exercise because of capillary recruitment. We compare model predictions to experimental observations from two pump-wearing T1D cohorts [resting subjects ([Formula: see text]) and exercising subjects ([Formula: see text])] who were each given a mixed-meal tolerance test and a bolus of insulin. Using independently measured values of Q and PS from literature, the model predicts that during exercise insulin concentration increases by 30% in plasma and by 60% in skeletal muscle. Predictions reasonably agree with experimental observations from the two cohorts, without the need for parameter estimation by curve fitting. The insulin kinetics model suggests that the increase in surface area associated with exercise-induced capillary recruitment significantly increases [Formula: see text] and [Formula: see text], which explains why insulin concentrations in plasma and skeletal muscle increase during exercise, ultimately enhancing insulin-dependent glucose uptake. Preventing hypoglycemia is of paramount importance in determining the proper insulin dose during exercise. The presented model provides mechanistic insight into how exercise affects insulin kinetics, which could be useful in guiding the design of decision support systems and artificial pancreas control algorithms.
我们的目标是开发一种基于生理学的胰岛素动力学模型，以了解运动如何改变 1 型糖尿病（T1D）患者的胰岛素浓度。我们揭示了皮下组织中胰岛素吸收率([Formula: see text])、向骨骼肌输送胰岛素的速度([Formula: see text])，以及两个生理参数之间的关系，这两个参数描述了组织的特点：灌注率（Q）和毛细血管通透性表面积（PS），这两者在运动期间都会因毛细血管募集而增加。我们将模型预测与两个佩戴胰岛素泵的 T1D 队列的实验观察结果进行了比较[休息受试者([Formula: see text])和运动受试者([Formula: see text])]，他们都接受了混合餐耐量试验和胰岛素推注。使用文献中独立测量的 Q 和 PS 值，该模型预测在运动期间，血浆中的胰岛素浓度增加 30%，骨骼肌中的胰岛素浓度增加 60%。该预测与两个队列的实验观察结果基本一致，而无需通过曲线拟合进行参数估计。胰岛素动力学模型表明，与运动引起的毛细血管募集相关的表面积增加显著增加了[Formula: see text]和[Formula: see text]，这解释了为什么在运动期间血浆和骨骼肌中的胰岛素浓度增加，最终增强了胰岛素依赖性葡萄糖摄取。在确定运动期间适当的胰岛素剂量时，防止低血糖至关重要。所提出的模型提供了对运动如何影响胰岛素动力学的机制性见解，这可能有助于指导决策支持系统和人工胰腺控制算法的设计。
