Computational modeling of cholesterol metabolism

The clustering of cardiovascular risk factors such as obesity, insulin resistance, and high plasma lipids is commonly reffered to as metabolic syndrome. Metabolic syndrome in humans can be simulated by feeding mice that have a humanized lipid profile a high fat diet. By integrating this data into a computer model, predictions can be made about how metabolic pathways must change in activity over time. This dissertation describes how such a method may be used to gain new insights about what determines individual susceptibility towards developing metabolic syndrome. It was found that, at least in mice, a higher concentration of unabsorbed fat in faeces is accompanied by mitigation of diet-induced obesity, lower plasma lipids, and less insulin resistance. Mice with reduced susceptibility to the metabolic syndrome were characterized by both a lower rate of bile salt production and changes in the bile acid profile. By subsequently manipulating bile acid production pharmacologically, we could change bile acid profiles in prone mice to the profile otherwise observed in non-prone mice. Upon this pharmacological intervention, body weight and plasma lipids are largely normalized. Since these mice have a human lipid profile, drugs with the same mechanism may likely have a similar effect on people, thus instilling hope that a new and effective class of drugs for people who are overweight and have high plasma triglyceride and cholesterol levels may soon become available