We pooled data from 5 large validation studies of dietary self-report instruments that used recovery biomarkers as references to clarify the measurement properties of food frequency questionnaires (FFQs) and 24-hour recalls. The studies were conducted in widely differing US adult populations from 1999 to 2009. We report on total energy, protein, and protein density intakes. Results were similar across sexes, but there was heterogeneity across studies. Using a FFQ, the average correlation coefficients for reported versus true intakes for energy, protein, and protein density were 0.21, 0.29, and 0.41, respectively. Using a single 24-hour recall, the coefficients were 0.26, 0.40, and 0.36, respectively, for the same nutrients and rose to 0.31, 0.49, and 0.46 when three 24-hour recalls were averaged. The average rate of under-reporting of energy intake was 28% with a FFQ and 15% with a single 24-hour recall, but the percentages were lower for protein. Personal characteristics related to under-reporting were body mass index, educational level, and age. Calibration equations for true intake that included personal characteristics provided improved prediction. This project establishes that FFQs have stronger correlations with truth for protein density than for absolute protein intake, that the use of multiple 24-hour recalls substantially increases the correlations when compared with a single 24-hour recall, and that body mass index strongly predicts under-reporting of energy and protein intakes.

We pooled data from 5 large validation studies (1999-2009) of dietary self-report instruments that used recovery biomarkers as referents, to assess food frequency questionnaires (FFQs) and 24-hour recalls (24HRs). Here we report on total potassium and sodium intakes, their densities, and their ratio. Results were similar by sex but were heterogeneous across studies. For potassium, potassium density, sodium, sodium density, and sodium:potassium ratio, average correlation coefficients for the correlation of reported intake with true intake on the FFQs were 0.37, 0.47, 0.16, 0.32, and 0.49, respectively. For the same nutrients measured with a single 24HR, they were 0.47, 0.46, 0.32, 0.31, and 0.46, respectively, rising to 0.56, 0.53, 0.41, 0.38, and 0.60 for the average of three 24HRs. Average underreporting was 5%-6% with an FFQ and 0%-4% with a single 24HR for potassium but was 28%-39% and 4%-13%, respectively, for sodium. Higher body mass index was related to underreporting of sodium. Calibration equations for true intake that included personal characteristics provided improved prediction, except for sodium density. In summary, self-reports capture potassium intake quite well but sodium intake less well. Using densities improves the measurement of potassium and sodium on an FFQ. Sodium:potassium ratio is measured much better than sodium itself on both FFQs and 24HRs.

Selenium (Se) metabolism is affected by its chemical form in foods and by its incorporation (specific vs. nonspecific) into multiple proteins. Modeling Se kinetics may clarify the impact of form on metabolism. Although the kinetics of Se forms have been compared in different participants, or the same participants at different times, direct comparisons of their respective metabolism in the same participants have not been made. The aim of this study was to simultaneously compare kinetics of absorbed Se from inorganic selenite (Sel) and organic selenomethionine (SeMet) in healthy participants (n = 31). After oral administration of stable isotopic tracers of each form, urine and feces were collected for 12 d and blood was sampled over 4 mo. Tracer enrichment was determined by isotope-dilution-GC-MS. Using WinSAAM, a compartmental model was fitted to the data. Within 30 min of ingestion, Se from both forms entered a common pool, and metabolism was similar for several days before diverging. Slowly turning-over pools were required in tissues and plasma for Se derived from SeMet to account for its 3-times-higher incorporation into RBC compared with Se from Sel; these presumably represent nonspecific incorporation of SeMet into proteins. Pool sizes and transport rates were determined and compared by form and gender. The final model consisted of 11 plasma pools, 2 pools and a delay in RBC, and extravascular pools for recycling of Se back into plasma. This model will be used to evaluate changes in Se metabolism following long-term (2 y) Se supplementation.

Selenium supplementation produced no clinically significant changes in thyroid hormone concentrations. A small but statistically significant increase in T3 concentrations was noted in men, with no corresponding decreases in TSH. A subset of SELECT subjects might be monitored periodically for changes during long-term selenium supplementation.