  Nonlinear mixed effects models represent a powerful tool to simultaneously
analyze data from several individuals. In this study a compartmental model of
leucine kinetics is examined and extended with a stochastic differential
equation to model non-steady state concentrations of free leucine in the
plasma. Data obtained from tracer/tracee experiments for a group of healthy
control individuals and a group of individuals suffering from diabetes mellitus
type 2 are analyzed. We find that the interindividual variation of the model
parameters is much smaller for the nonlinear mixed effects models, compared to
traditional estimates obtained from each individual separately. Using the mixed
effects approach, the population parameters are estimated well also when only
half of the data are used for each individual. For a typical individual the
amount of free leucine is predicted to vary with a standard deviation of 8.9%
around a mean value during the experiment. Moreover, leucine degradation and
protein uptake of leucine is smaller, proteolysis larger, and the amount of
free leucine in the body is much larger for the diabetic individuals than the
control individuals. In conclusion nonlinear mixed effects models offers
improved estimates for model parameters in complex models based on
tracer/tracee data and may be a suitable tool to reduce data sampling in
clinical studies.
