Model-based compartmental analyses in nutrition research.

Abstract

Kinetic tracer studies have been used extensively in understanding digestion, absorption, and whole-body metabolism of nutrients. Optimal interpretation of changes in tracer levels over time and movement across body pools often requires sophisticated data analysis. The use of model-based compartmental analysis (MCA) can yield more detailed quantitative and predictive information concerning system dynamics, compared with direct stochastic approaches. With MCA, tracer and tracee data from both experimental and literature values are fit to a model that best approximates the system on the basis of experimental data at hand. The number of compartments of the model is determined by the shape of the curve fit to the tracee and tracer data and by literature information. On this basis, MCA can yield information about compartment numbers and sizes, fractional and net turnover, as well as catabolic and synthetic rates. PC-based MCA programs are now available. Whereas earlier editions required use of a programming language, the most recent versions being developed are completely menu driven. Model-based compartmental analyses thus represent important biotechnological advances permitting maximal interpretation of kinetic data in nutrition research.