A multi-scale digital twin for adiposity-driven insulin resistance in humans: diet and drug effects

Abstract

AbstractAimsThe increased prevalence of insulin resistance is one of the major health risks in society today. Insulin resistance involves both short-term dynamics, such as altered meal responses, and long-term dynamics, such as development of type 2 diabetes. Insulin resistance also occurs on different physiological levels, ranging from disease phenotypes to organ-organ communication and intracellular signaling. To better understand the progression of insulin resistance, an analysis method is needed that can combine different timescales and physiological levels. One such method is digital twins, consisting of combined mechanistic multi-scale and multi-level mathematical models. We have previously developed a multi-level model for short-term glucose homeostasis and intracellular insulin signaling, and there exists long-term weight regulation models. However, no one has combined these kinds of models into an interconnected, multi-level and multi-timescale digital twin model. Herein, we present a first such multi-scale digital twin for the progression of insulin resistance in humans.MethodsThe model is based on ordinary differential equations representing biochemical and physiological processes, in which unknown parameters were fitted to data using a MATLAB toolbox.ResultsThe connected twin correctly predicts independent data from a weight increase study, both for weight-changes, for fasting plasma insulin and glucose levels, as well as for intracellular insulin signaling. Similarly, the model can predict independent weight-change data in a weight loss study, involving diet and the weight loss drug topiramate. These independent validation tests are confirmed by a chi-square test (𝑉(𝜃) = 4.8 < 21 = 𝜒2𝑐𝑢𝑚,𝑖𝑛𝑣(12,0.05)). In both these cases, the model can also predict non-measured variables, such as activity of intracellular intermediaries, glucose tolerance responses, and organ fluxes.ConclusionsWe present a first multi-level and multi-timescale model, describing dynamics on the whole-body, organ and cellular levels, ranging from minutes to years. This model constitutes the basis for a new digital twin technology, which in the future could potentially be used to aid medical pedagogics and increase motivation and compliance and thus aid in prevention and treatment of insulin resistance.