In vivo and in silico dynamics of the development of Metabolic Syndrome

Abstract

The Metabolic Syndrome (MetS) is a complex, multifactorial disorder that develops slowly over time presenting itself with large differences among MetS patients. We applied a systems biology approach to describe and predict the onset and progressive development of MetS, in a study that combined in vivo and in silico models. A new data-driven, physiological model (MINGLeD: Model INtegrating Glucose and Lipid Dynamics) was developed, describing glucose, lipid and cholesterol metabolism. Since classic kinetic models cannot describe slowly progressing disorders, a simulation method (ADAPT) was used to describe longitudinal dynamics and to predict metabolic concentrations and fluxes. This approach yielded a novel model that can describe long-term MetS development and progression. This model was integrated with longitudinal in vivo data that was obtained from male APOE*3-Leiden.CETP mice fed a high-fat, high-cholesterol diet for three months and that developed MetS as reflected by classical symptoms including obesity and glucose intolerance. Two distinct subgroups were identified: those who developed dyslipidemia, and those who did not. The combination of MINGLeD with ADAPT could correctly predict both phenotypes, without making any prior assumptions about changes in kinetic rates or metabolic regulation. Modeling and flux trajectory analysis revealed that differences in liver fluxes and dietary cholesterol absorption could explain this occurrence of the two different phenotypes. In individual mice with dyslipidemia dietary cholesterol absorption and hepatic turnover of metabolites, including lipid fluxes, were higher compared to those without dyslipidemia. Predicted differences were also observed in gene expression data, and consistent with the emergence of insulin resistance and hepatic steatosis, two well-known MetS co-morbidities. Whereas MINGLeD specifically models the metabolic derangements underlying MetS, the simulation method ADAPT is generic and can be applied to other diseases where dynamic modeling and longitudinal data are available.

Computational modelling of energy balance in individuals with Metabolic Syndrome

“…The previously published computational model describing the metabolic system in both healthy and Metabolic Syndrome conditions (Model Integrating Glucose and Lipid Dynamics; MINGLeD) [27] is schematically displayed in Fig. 1.…”

“…Energy expenditure takes place in both hepatic (indicated by the blue arrow) and peripheral (indicated by the red arrow) compartments. This model scheme was adapted with permission from [27]. This multi-compartment framework encompasses pathways in dietary absorption, hepatic, peripheral, and intestinal lipid metabolism, hepatic, and plasma lipoprotein metabolism and plasma, hepatic, and peripheral carbohydrate metabolism.…”

“…The model was previously calibrated to data derived from APOE3L.CETP mice which respond in a human-like manner [37, 38] to a high-fat diet supplemented with cholesterol, thereby inducing MetS. This data set [27] comprises monthly samples of plasma metabolite pool sizes and body weight and composition over the course of 3 months and was used for calibration of the model using maximum likelihood estimation. Here we will specifically utilize the N = 1000 model realizations subset representing the onset and progression dyslipidemic MetS [27].…”

“…Previously, we have developed a computational modelling framework describing the progressive and heterogeneous development of MetS [27]. This study yielded an extensive library of N = 1000 different model realizations.…”

The Digital Mouse: why computational modelling of mouse models of disease can improve translation

“…For bone biomechanics and mechano-biology [16], [17] detailed models can be constructed by combining data from in vivo high-resolution imaging [18] with single-cell “omics” technologies [19]. Mechanistic models and simulation techniques to predict disease onset and progression in mice have been developed [20], and were applied to predict the effect of pharmacological interventions in pre-clinical studies with a longitudinal design [21].…”