Department of Biomedical Engineering, IMT, Linköping University, Linköping, Sweden.
Center for Medical Image Science and Visualisation, Linköping University, Linköping, Sweden.
PLoS Comput Biol. 2022 Sep 12;18(9):e1010469. doi: 10.1371/journal.pcbi.1010469. eCollection 2022 Sep.
Today, there is great interest in diets proposing new combinations of macronutrient compositions and fasting schedules. Unfortunately, there is little consensus regarding the impact of these different diets, since available studies measure different sets of variables in different populations, thus only providing partial, non-connected insights. We lack an approach for integrating all such partial insights into a useful and interconnected big picture. Herein, we present such an integrating tool. The tool uses a novel mathematical model that describes mechanisms regulating diet response and fasting metabolic fluxes, both for organ-organ crosstalk, and inside the liver. The tool can mechanistically explain and integrate data from several clinical studies, and correctly predict new independent data, including data from a new study. Using this model, we can predict non-measured variables, e.g. hepatic glycogen and gluconeogenesis, in response to fasting and different diets. Furthermore, we exemplify how such metabolic responses can be successfully adapted to a specific individual's sex, weight, height, as well as to the individual's historical data on metabolite dynamics. This tool enables an offline digital twin technology.
如今，人们对提出新的宏量营养素组合和禁食方案的饮食方案非常感兴趣。不幸的是，由于现有研究在不同人群中测量了不同的变量集，因此对于这些不同饮食的影响几乎没有共识，这仅提供了部分、不相关的见解。我们缺乏一种将所有这些部分见解整合到一个有用且相互关联的整体的方法。在此，我们提出了这样一种整合工具。该工具使用了一种新的数学模型，该模型描述了调节饮食反应和禁食代谢通量的机制，包括器官间串扰和肝脏内部的机制。该工具可以从几个临床研究中解释和整合数据，并正确预测新的独立数据，包括来自一项新研究的数据。使用该模型，我们可以预测禁食和不同饮食对未测量的变量（例如肝糖原和糖异生）的反应。此外，我们举例说明了如何成功地将这种代谢反应适应于特定个体的性别、体重、身高，以及个体的代谢物动力学历史数据。该工具实现了离线数字孪生技术。
