Michael H Green1, Joanne Balmer Green1. 1. Department of Nutritional Sciences, College of Health and Human Development, The Pennsylvania State University, University Park, PA, USA.
Abstract
BACKGROUND: An optimal blood sampling time for application of the retinol isotope dilution (RID) method for predicting vitamin A total body stores (TBS) (i.e., vitamin A status) has not been established. OBJECTIVES: Objectives were to identify sampling times that provide accurate estimates of TBS by RID in groups and individuals by applying compartmental modeling to data for theoretical adults and children. METHODS: We selected previously generated hypothetical adults and children (20 per group) that had a wide range of assigned values for TBS and vitamin A kinetic parameters. We used the Simulation, Analysis and Modeling software to simulate individual kinetic responses; then we calculated geometric mean values for the RID equation coefficients and each individual's plasma retinol specific activity at various times, using those values to predict group mean and individual subject TBS. Predicted values for TBS were compared with assigned values. RESULTS: Accurate estimates of group mean TBS were obtained at all sampling times from 1 to 30 d in both adults and children. For individuals, correlations between RID-predicted TBS and assigned values increased with time in the adults (R2 = 0.80 at day 14, 0.96 at day 21, and 0.99 at day 28); a similar trend was observed for the children, with R2 = 0.82 at day 7 and increasing to 0.97 at days 21 and 28 (P < 0.001 for all comparisons). CONCLUSIONS: Although no single, unique time provided the most accurate prediction of TBS for all individuals within these groups, applying the RID method at 21 or 28 d yielded predictions that were within 25% of assigned values for 90% or 95% of adults, respectively; corresponding values for children were 80% from 10 to 20 d, and 85% at 21 and 28 d. For most subjects, early times (<14 d for adults and <10 d for children) provided less accurate predictions.
BACKGROUND: An optimal blood sampling time for application of the retinol isotope dilution (RID) method for predicting vitamin A total body stores (TBS) (i.e., vitamin A status) has not been established. OBJECTIVES: Objectives were to identify sampling times that provide accurate estimates of TBS by RID in groups and individuals by applying compartmental modeling to data for theoretical adults and children. METHODS: We selected previously generated hypothetical adults and children (20 per group) that had a wide range of assigned values for TBS and vitamin A kinetic parameters. We used the Simulation, Analysis and Modeling software to simulate individual kinetic responses; then we calculated geometric mean values for the RID equation coefficients and each individual's plasma retinol specific activity at various times, using those values to predict group mean and individual subject TBS. Predicted values for TBS were compared with assigned values. RESULTS: Accurate estimates of group mean TBS were obtained at all sampling times from 1 to 30 d in both adults and children. For individuals, correlations between RID-predicted TBS and assigned values increased with time in the adults (R2 = 0.80 at day 14, 0.96 at day 21, and 0.99 at day 28); a similar trend was observed for the children, with R2 = 0.82 at day 7 and increasing to 0.97 at days 21 and 28 (P < 0.001 for all comparisons). CONCLUSIONS: Although no single, unique time provided the most accurate prediction of TBS for all individuals within these groups, applying the RID method at 21 or 28 d yielded predictions that were within 25% of assigned values for 90% or 95% of adults, respectively; corresponding values for children were 80% from 10 to 20 d, and 85% at 21 and 28 d. For most subjects, early times (<14 d for adults and <10 d for children) provided less accurate predictions.
Authors: Veronica Lopez-Teros; Jennifer L Ford; Michael H Green; Brianda Monreal-Barraza; Lilian García-Miranda; Sherry A Tanumihardjo; Mauro E Valencia; Humberto Astiazaran-Garcia Journal: J Nutr Date: 2020-06-01 Impact factor: 4.798
Authors: Jennifer Lynn Ford; Joanne Balmer Green; Marjorie J Haskell; Shaikh M Ahmad; Dora Inés Mazariegos Cordero; Anthony Oxley; Reina Engle-Stone; Georg Lietz; Michael H Green Journal: J Nutr Date: 2020-02-01 Impact factor: 4.798
Authors: Veronica Lopez-Teros; Jennifer Lynn Ford; Michael H Green; Guangwen Tang; Michael A Grusak; Luis Quihui-Cota; Tawanda Muzhingi; Mariela Paz-Cassini; Humberto Astiazaran-Garcia Journal: J Nutr Date: 2017-09-20 Impact factor: 4.798
Authors: Christopher J Cifelli; Joanne B Green; Zhixu Wang; Shian Yin; Robert M Russell; Guangwen Tang; Michael H Green Journal: J Nutr Date: 2008-05 Impact factor: 4.798
Authors: Michael H Green; Jennifer Lynn Ford; Joanne Balmer Green; Philip Berry; Alan V Boddy; Anthony Oxley; Georg Lietz Journal: J Nutr Date: 2016-08-10 Impact factor: 4.798
Authors: Reina Engle-Stone; Jody C Miller; Maria Fatima Dolly Reario; Charles D Arnold; Ame Stormer; Eleanore Lafuente; Anthony Oxley; Mario V Capanzana; Carl Vincent D Cabanilla; Jennifer Lynn Ford; Adam Clark; Thirumalaisamy P Velavan; Kenneth H Brown; Georg Lietz; Marjorie J Haskell Journal: Curr Dev Nutr Date: 2022-07-25