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New computational tool predicts progression of metabolic syndrome in mice

Modeling approach identifies two disease subtypes and reveals their underlying metabolic differences

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IMAGE: The study published by Rozendaal et al. makes use of a genetically modified animal model of which its metabolic system resembles that of the human. They have created a computational... view more 

Credit: Rozendaal et al.

Scientists have developed a new computational model that accurately predicts the gradual, long-term progression of metabolic syndrome in mice. The model, created by Yvonne Rozendaal of Eindhoven University of Technology in the Netherlands and colleagues, is presented in PLOS Computational Biology.

Metabolic syndrome is a collection of several factors: obesity, insulin resistance, elevated lipid levels in the blood, and high blood pressure. A person with metabolic syndrome faces increased risk of cardiovascular disease, type 2 diabetes, and non-alcoholic fatty liver disease. Computational modeling of metabolic syndrome can provide new insights into its development, but previous modeling efforts have not fully captured the gradual progression and complexity of the disease.

In the new study, Rozendaal and colleagues developed a new computational model that describes glucose, lipid, and cholesterol metabolism - central factors in metabolic syndrome. A previously developed simulation method was applied to the model, allowing for accurate prediction of gradual, long-term development of the disease. The scientists then ran the model using data from real-world experiments in which mice were fed diets that resulted in development of metabolic syndrome.

The researchers found that their modeling approach correctly predicted progression of metabolic syndrome in the mice, as well as development of comorbidities, such as fatty liver disease. The model also uncovered the unexpected existence of two disease subtypes in the mice: those with elevated lipid levels and those without. It correctly predicted underlying metabolic differences that could explain the two subtypes, which were confirmed with experimental data.

"Our model is an important step in understanding the development of metabolic syndrome, offering new opportunities to identify strategies to prevent the disease and its comorbidities," Rozendaal says. "Our framework can also be applied to study long-term development of other complex, progressive diseases."

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In your coverage please use this URL to provide access to the freely available article in PLOS Computational Biology: http://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1006145

Citation: Rozendaal YJW, Wang Y, Paalvast Y, Tambyrajah LL, Li Z, Willems van Dijk K, et al. (2018) In vivo and in silico dynamics of the development of Metabolic Syndrome. PLoS Comput Biol 14(6): e1006145. https://doi.org/10.1371/journal.pcbi.1006145

Funding: This study was supported by the European Union's Research and Innovation programme (https://ec.europa.eu/research/health/index.cfm); grant FP7-HEALTH-305707: "A systems biology approach to RESOLVE the molecular pathology of two hallmarks of patients with metabolic syndrome and its co-morbidities; hypertriglyceridemia and low HDL-cholesterol". YW is supported by a VENI grant from NWO-ZonMW (91617027). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Competing Interests: The authors have declared that no competing interests exist.

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