Multicomponent methods: evaluation of new and traditional soft tissue mineral models by in vivo neutron activation analysis,,
Open Access
- 1 November 2002
- journal article
- research article
- Published by Elsevier in The American Journal of Clinical Nutrition
- Vol. 76 (5) , 968-974
- https://doi.org/10.1093/ajcn/76.5.968
Abstract
Background: Practical and accurate methods for quantifying the soft tissue mineral component of multicomponent fat-estimation models are needed. Objectives: The aims were to develop a new complete model for estimating soft tissue minerals based on measured total body water (TBW) and extracellular water (ECW) and a simplified new model based on TBW measurements only and to compare these estimates with those determined with 2 traditional models (ie, the BroΩek and Selinger models) and with criterion estimates based on in vivo neutron activation (IVNA) analysis. Design: The subjects were 156 healthy adults and 50 patients with AIDS. Total body potassium, sodium, chlorine, and calcium were measured by IVNA; TBW by 3H2O or D2O dilution; ECW by bromide dilution; and bone mineral by dual-energy X-ray absorptiometry. Results: The mean (± SD) mass of total-body soft tissue minerals in healthy adults was 467 ± 62 g with the IVNA model, 492 ± 62 g with the new model, and 487 ± 59 g with the simplified new model. Compared with the IVNA model, the complete and simplified new models overestimated soft tissue minerals by 5.4% and 4.6% (both P < 0.001), respectively. In contrast, the BroΩek and Selinger models overestimated overall mean soft tissue minerals by 35% and 99% (both P < 0.001), respectively. Overall results for soft tissue mineral prediction with the 2 new models were less satisfactory for the patients with AIDS, although the results were better than those with the traditional models. Conclusions: The physiologically formulated complete new model for estimating soft tissue minerals provides the opportunity to upgrade the accuracy of current multicomponent models for estimating total body fat.Keywords
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