New computational tool reveals subtypes of metabolic syndrome in mice

Scientists at TU/e have developed a new computational model that accurately predicts the gradual, long-term progression of metabolic syndrome in mice. This disease affects a lot of people. The model also identified two previously unknown subtypes of the disease. It was created by biomedical engineer Yvonne Rozendaal TU/e and colleagues, and was presented in PLOS Computational Biology earlier this month.

Metabolic syndrome is a collection of several factors: obesity, insulin resistance, elevated fat levels in the blood, and high blood pressure. A person with metabolic syndrome faces increased risk of cardiovascular disease, type 2 diabetes, and 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 fat 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.”