Addition of Vitamin A Intake Data during Compartmental Modeling of Retinol Kinetics in Theoretical Humans Leads to Accurate Prediction of Vitamin A Total Body Stores and Kinetic Parameters in Studies of Reasonable Duration

Abstract

ABSTRACT Background Mathematical modeling of theoretical data has been used to validate experimental protocols and methods in several fields. Objectives We hypothesized that adding dietary vitamin A intake data as an input during compartmental modeling of retinol kinetics would lead to accurate prediction of vitamin A total body stores (TBS) at 2 specified study lengths and would reduce study duration required to accurately define the system. Methods We generated reference values for state variables (including TBS and intake) and kinetic parameters for 12 theoretical individuals (4 each of children, younger adults, and older adults) based on modeling plasma retinol tracer data for 365 d. We compared TBS predictions using data to 28 d (children) or 56 d (adults) without and with intake included in the model to reference values for each subject. Then, by truncating data sets from 365 d, we determined the shortest study duration required to accurately define the system without and with inclusion of vitamin A intake. Results Reference values for TBS ranged from 30 to 3023 µmol. Study durations of 28 and 56 d were sufficient to accurately predict TBS for 6 of the 12 subjects without intake; adding intake resulted in accurate predictions of TBS for all individuals. When intake was not included as a modeling input, durations of 35–310 d were required to define the system; inclusion of intake data substantially reduced the time required to 10–42 d. Conclusions Inclusion of vitamin A intake as additional data input when modeling vitamin A kinetics allows investigators to accurately predict TBS and define the vitamin A system in studies of reasonable length (4 wk in children and 8 wk in adults). Because it is generally possible to obtain estimates/measures of intake, including such data increases confidence in model predictions while also making studies more feasible.