Classification of metabolic patients using dynamic variables

The severity of metabolic syndrome is usually determined according to static variables (blood glucose, insulin level, body mass index etc.) The most important classification is dynamic and prognostic classification which can be used to determine the ability to decrease elevated metabolite and hormone levels or to lose weight. Different mathematical approaches were used to determine these phenomena: 1. Mathematical modelling e.g. (Bergman minimal model or glycation model). 2. Predictive calculations using multiple regression (using static and dynamic parameters to determine weight loss in obesity treatment). 3. One day starvation test (finding very variable hormone and metabolic changes in obese patients). We can conclude There are 3 types of metabolic parameters: A. Static (basic) description, B. Functional (actual) description, C. Dynamic-stability describing variables. Mathematical modelling is a complicated method needing many blood samples. It is very invasive for patients and it is difficult to be repeated. Predictive importance can have also repeated measured metabolic data which are able to classify the stability (fixation) of metabolic state. Some basic parameters and simple dynamic tests like one day starvation test can be used in prognostic classification of patients who are able to change their fixed metabolic state.