Investigations of a compartmental model for leucine kinetics using nonlinear mixed effects models with ordinary and stochastic differential equations

TL;DR

This study applies nonlinear mixed effects models, including stochastic differential equations, to analyze leucine kinetics data, revealing reduced interindividual variability and differences between healthy and diabetic groups, with implications for clinical data collection.

Contribution

The paper introduces a stochastic differential equation extension to nonlinear mixed effects models for leucine kinetics, improving parameter estimation and reducing data requirements.

Findings

Interindividual variation of parameters is smaller with mixed effects models.

Population parameters are accurately estimated with only half the data.

Diabetic individuals show altered leucine metabolism compared to controls.

Abstract

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.