Data sharing in clinical trials: An experience with two large cancer screening trials.

Paul Pinsky of the US National Cancer Institute and colleagues describe the implementation and outcomes of web-based data sharing from the PLCO and NLST cancer screening trials.

Pooled results from 5 validation studies of dietary self-report instruments using recovery biomarkers for potassium and sodium intake.

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.

Application of a New Statistical Model for Measurement Error to the Evaluation of Dietary Self-report Instruments.

Most statistical methods that adjust analyses for dietary measurement error treat an individual's usual intake as a fixed quantity. However, usual intake, if defined as average intake over a few months, varies over time. We describe a model that accounts for such variation and for the proximity of biomarker measurements to self-reports within the framework of a meta-analysis, and apply it to the analysis of data on energy, protein, potassium, and sodium from a set of five large validation studies of dietary self-report instruments using recovery biomarkers as reference instruments. We show that this time-varying usual intake model fits the data better than the fixed usual intake assumption. Using this model, we estimated attenuation factors and correlations with true longer-term usual intake for single and multiple 24-hour dietary recalls (24HRs) and food frequency questionnaires (FFQs) and compared them with those obtained under the "fixed" method. Compared with the fixed method, the estimates using the time-varying model showed slightly larger values of the attenuation factor and correlation coefficient for FFQs and smaller values for 24HRs. In some cases, the difference between the fixed method estimate and the new estimate for multiple 24HRs was substantial. With the new method, while four 24HRs had higher estimated correlations with truth than a single FFQ for absolute intakes of protein, potassium, and sodium, for densities the correlations were approximately equal. Accounting for the time element in dietary validation is potentially important, and points toward the need for longer-term validation studies.

Pooled results from 5 validation studies of dietary self-report instruments using recovery biomarkers for energy and protein intake.

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.

A human model of selenium that integrates metabolism from selenite and selenomethionine.

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 Kinetics in Humans Change Following 2 Years of Supplementation With Selenomethionine.

Background: Selenium (Se) is a nutritionally essential trace element and health may be improved by increased Se intake. Previous kinetic studies have shown differences in metabolism of organic vs. inorganic forms of Se [e.g., higher absorption of selenomethionine (SeMet) than selenite (Sel), and more recycling of Se from SeMet than Sel]. However, the effects on Se metabolism after prolonged Se supplementation are not known. Objective: To determine how the metabolism and transport of Se changes in the whole-body in response to Se-supplementation by measuring Se kinetics before and after 2 years of Se supplementation with SeMet. Methods: We compared Se kinetics in humans [n = 31, aged 40 ± 3 y (mean ± SEM)] studied twice after oral tracer administration; initially (PK1), then after supplementation for 2 y with 200 µg/d of Se as selenomethionine (SeMet) (PK2). On each occasion, we administered two stable isotope tracers of Se orally: SeMet, the predominant food form, and selenite (Na276SeO3, or Sel), an inorganic form. Plasma and RBC were sampled for 4 mo; urine and feces were collected for the initial 12 d of each period. Samples were analyzed for tracers and total Se by isotope dilution GC-MS. Data were analyzed using a compartmental model, we published previously, to estimate fractional transfer between pools and pool masses in PK2. Results: We report that fractional absorption of SeMet or Sel do not change with SeMet supplementation and the amount of Se absorbed increased. The amount of Se excreted in urine increases but does not account for all the Se absorbed. As a result, there is a net incorporation of SeMet into various body pools. Nine of the 11 plasma pools doubled in PK2; two did not change. Differences in metabolism were observed for SeMet and Sel; RBC uptake increased 247% for SeMet, urinary excretion increased from two plasma pools for Sel and from two different pools for SeMet, and recycling to liver/tissues increased from one plasma pool for Sel and from two others for SeMet. One plasma pool increased more in males than females in PK2. Conclusions: Of 11 Se pools identified kinetically in human plasma, two did not increase in size after SeMet supplementation. These pools may be regulated and important during low Se intake.

Effects of selenomethionine supplementation on selenium status and thyroid hormone concentrations in healthy adults.

BACKGROUND: Selenium, a potential cancer prevention agent currently being tested against prostate cancer in the Selenium and Vitamin E Cancer Prevention Trial (SELECT), plays an integral role in thyroid metabolism. The effects of long-term selenium supplementation on thyroid hormone concentrations are unknown. OBJECTIVE: The objective was to investigate the effects of long-term selenium supplementation on thyroid hormone concentrations. DESIGN: Twenty-eight healthy adults took 200 microg selenomethionine/d for 28 mo. The thyroid hormones triiodothyronine (T3), thyroxine (T4), and thyrotropin (TSH) were measured in plasma for 4 mo before supplementation and quarterly during supplementation. The assay methods were changed midstudy; the results of the 2 methods were not comparable. Therefore, one analysis was conducted based on the results of the first method, and a second analysis was based on all of the data, adjusted for the change. Serial data collection permitted a test for trends rather than simply a difference between initial and final values. RESULTS: By 9 mo, mean (+/-SEM) plasma selenium concentrations had increased from 1.78 +/- 0.07 micromol/L at baseline to 2.85 +/- 0.11 micromol/L for men and from 1.64 +/- 0.04 to 3.32 +/- 0.1.2 micromol/L for women. T3 concentrations in men increased 5% per year (P = 0.01). T4 and TSH concentrations were unchanged. CONCLUSIONS: 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.