Abstract
BACKGROUND: Public health nutritionists need accurate and feasible methods to assess vitamin A status and to evaluate efficacy of interventions, especially in children. The application of population-based designs to tracer kinetic data is an effective approach that reduces sample burden for each child. OBJECTIVES: Objectives of the study were to use theoretical data to validate a population-based (super-child) approach for estimating group mean vitamin A total body stores (TBS) and retinol kinetics in children and to use population-based data to improve individual TBS predictions using retinol isotope dilution (RID). METHODS: We generated plasma retinol kinetic data from 6 h to 56 d for 50 theoretical children with high vitamin A intakes, assigning values within physiologically reasonable ranges for state variables and kinetic parameters ("known values"). Mean data sets for all subjects at extensive (n = 36) and reduced (n = 11) sampling times, plus 5 data sets for reduced numbers (5/time, except all at 4 d) and times, were analyzed using Simulation, Analysis and Modeling software. Results were compared with known values; population RID coefficients were used to calculate TBS for individuals. RESULTS: For extensive and reduced data sets including all subjects, population TBS predictions were within 1% of the known value. For 5 data sets reflecting numbers and times being used in ongoing super-child studies, predictions were within 1-17% of the known group value. Using RID equation coefficients from population modeling, TBS predictions at 4 d were within 25% of the known value for 66-80% of subjects and reflected the range of assigned values; when ranked, predicted and assigned values were significantly correlated (R(s) = 0.93, P < 0.0001). Results indicate that 7 d may be better than 4 d for applying RID in children. For all data sets, predictions for kinetic parameters reflected the range of known values. CONCLUSION: The population-based (super-child) approach provides a feasible experimental design for quantifying retinol kinetics, accurately estimating group mean TBS, and predicting TBS for individuals reasonably well.