Donna Rhodes

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.

Identifying sources of reporting error using measured food intake.

Objective:To investigate the magnitude and relative contribution of different sources of measurement errors present in the estimation of food intake via the 24-h recall technique.Design:We applied variance decomposition methods to the difference between data obtained from the USDAs Automated Multiple Pass Method (AMPM) 24-h recall technique and measured food intake (MFI) from a 16-week cafeteria-style feeding study. The average and the variance of biases, defined as the difference between AMPM and MFI, were analyzed by macronutrient content, subject and nine categories of foods.Subjects:Twelve healthy, lean men (age, 39±9 year; weight, 79.9±8.3 kg; and BMI, 24.1±1.4 kg/m2).Results:Mean food intakes for AMPM and MFI were not significantly different (no overall bias), but within-subject differences for energy (EI), protein, fat and carbohydrate intakes were 14, 18, 23 and 15% of daily intake, respectively. Mass (incorrect portion size) and deletion (subject did not report foods eaten) errors were each responsible for about one-third of the total error. Vegetables constituted 8% of EI but represented >25% of the error across macronutrients, whereas grains that contributed 32% of EI contributed only 12% of the error across macronutrients.Conclusions:Although the major sources of reporting error were mass and deletion errors, individual subjects differed widely in the magnitude and types of errors they made.

Urinary Malondialdehyde-Equivalents during ingestion of meat cooked at high or low temperatures

Excretion of malondialdehyde (MDA)-generating substances in the urine has been suggested as an indicator ofin vivo lipid peroxidation. However, MDA in the urine also reflects the amount of lipid peroxidation products consumed in the diet. We determined MDA as the thiobarbituric acid (TBA)-MDA complex in urine of 19 healthy adults (10 male and 9 female) fed large quantities (3.6–4.1 g/kg body weight) of ground beef cooked at a low or a high temperature. Subjects are a controlled diet with no alcohol or nutritional supplements. For 7 d they consumed ground beef cooked at 100°C for 20 min (low-temperature meat) followed by 7 d with meat fried at 250°C for 22 min (high-temperature meat). Prior to the study, subjects consumed their normal free choice diet with moderate amounts of meat. The concentration of MDA in urine at baseline was 2.1±0.3 μmol TBA-MDA equivalents/day (mean±SEM). After 7 d of low-temperature meat, urinary TBA-MDA equivalents increased to 23.1±1.4 μmol/d. Urinary TBA-MDA equivalents were consistently lower (6.9–8.0 μmol/d) 1, 2, 3, and 7 d after subjects changed to high-temperature meat. After 7 d of treatment, 97% of the MDA-equivalents in the meat was recovered in 24-h urine samples. The low temperature meat had 3–4 times more MDA than did the high-temperature meat. These data indicate that the amount of meat eaten and the cooking procedures used can dramatically alter urinary MDA. Dietary sources of MDA must be controlled if urinary MDA is to be used as an indicator of oxidative stress.

Diet Interviews of Subject Pairs: How Different Persons Recall Eating the Same Foods

OBJECTIVE To compare qualitative descriptions of the same food items eaten by different persons using 24-hour dietary recall interviews. DESIGN Eleven pairs of subjects were interviewed twice using 24-hour dietary recalls such that each member of the pair described the same days foods. Each pair shared a home and ate at least 2 meals together daily. After each interview, subjects were asked to identify the foods reported during the interview that they observed the other member of their pair consuming and to note when a particular food was the only item of that type available in the house. Qualitative descriptions of the foods were compared, differences in descriptions were noted, and calculations were made of the potential energy error produced if a subject erred in reporting a food item. SUBJECTS/SETTING Subjects were randomly selected from a database of persons who have participated in other studies at the Beltsville Human Nutrition Research Center. Ten pairs were husbands and wives and 1 pair was sisters. Each pair reported eating at least 2 meals per day together. Dietary recall interviews were done at the Research Center and were conducted by a trained dietitian in a quiet room free of distractions. RESULTS Discrepancies in qualitative food descriptions were identified for every subject pair interviewed. Men were found to be more likely to omit food items than women, snack items were more likely to be omitted than meal items, meat items were likely to be described inaccurately, and first interviews were likely to contain more errors than second interviews. APPLICATIONS/CONCLUSIONS This analysis shows which types of food items are most likely to be omitted or inaccurately described, and that dietetics professionals may improve the accuracy of dietary intake interviews by asking questions related to meat, milk, and snacks very carefully. The analysis also showed reductions in recall inconsistencies from the first recall to the second recall, suggesting that the learning associated with repeated interviews may be helpful in accurately identifying what a person consumes.

Difference between 24-h diet recall and urine excretion for assessing population sodium and potassium intake in adults aged 18–39 y

BACKGROUND Limited data are available on the accuracy of 24-h dietary recalls used to monitor US sodium and potassium intakes. OBJECTIVE We examined the difference in usual sodium and potassium intakes estimated from 24-h dietary recalls and urine collections. DESIGN We used data from a cross-sectional study in 402 participants aged 18-39 y (∼50% African American) in the Washington, DC, metropolitan area in 2011. We estimated means and percentiles of usual intakes of daily dietary sodium (dNa) and potassium (dK) and 24-h urine excretion of sodium (uNa) and potassium (uK). We examined Spearmans correlations and differences between estimates from dietary and urine measures. Multiple linear regressions were used to evaluate the factors associated with the difference between dietary and urine measures. RESULTS Mean differences between diet and urine estimates were higher in men [dNa - uNa (95% CI) = 936.8 (787.1, 1086.5) mg/d and dK - uK = 571.3 (448.3, 694.3) mg/d] than in women [dNa - uNa (95% CI) = 108.3 (11.1, 205.4) mg/d and dK - uK = 163.4 (85.3, 241.5 mg/d)]. Percentile distributions of diet and urine estimates for sodium and potassium differed for men. Spearmans correlations between measures were 0.16 for men and 0.25 for women for sodium and 0.39 for men and 0.29 for women for potassium. Urinary creatinine, total caloric intake, and percentages of nutrient intake from mixed dishes were independently and consistently associated with the differences between diet and urine estimates of sodium and potassium intake. For men, body mass index was also associated. Race was associated with differences in estimates of potassium intake. CONCLUSIONS Low correlations and differences between dietary and urinary sodium or potassium may be due to measurement error in one or both estimates. Future analyses using these methods to assess sodium and potassium intake in relation to health outcomes may consider stratifying by factors associated with the differences in estimates from these methods. This trial was registered at clinicaltrials.gov as NCT01631240.

Preprandial ghrelin is not affected by macronutrient intake, energy intake or energy expenditure

BackgroundGhrelin, a peptide secreted by endocrine cells in the gastrointestinal tract, is a hormone purported to have a significant effect on food intake and energy balance in humans. The influence of factors related to energy balance on ghrelin, such as daily energy expenditure, energy intake, and macronutrient intake, have not been reported. Secondly, the effect of ghrelin on food intake has not been quantified under free-living conditions over a prolonged period of time. To investigate these effects, 12 men were provided with an ad libitum cafeteria-style diet for 16 weeks. The macronutrient composition of the diets were covertly modified with drinks containing 2.1 MJ of predominantly carbohydrate (Hi-CHO), protein (Hi-PRO), or fat (Hi-FAT). Total energy expenditure was measured for seven days on two separate occasions (doubly labeled water and physical activity logs).ResultsPreprandial ghrelin concentrations were not affected by macronutrient intake, energy expenditure or energy intake (all P > 0.05). In turn, daily energy intake was significantly influenced by energy expenditure, but not ghrelin.ConclusionPreprandial ghrelin does not appear to be influenced by macronutrient composition, energy intake, or energy expenditure. Similarly, ghrelin does not appear to affect acute or chronic energy intake under free-living conditions.

Assessing U.S. Sodium Intake through Dietary Data and Urine Biomarkers

Sodium intake is related to blood pressure, an established risk factor for heart disease and stroke. Reducing intake may save billions in United States health care dollars annually. Efforts targeting sodium reductions make accurate monitoring vital, yet limited information exists on the accuracy of the current data to assess sodium intake in the United States population. In this symposium, new findings were presented on the accuracy of estimating population 24-h urinary excretion of sodium from spot urine specimens or sodium intake from 24-h dietary recalls. Differences in accuracy by sex, BMI, and race were apparent as well as by timing of spot urine collections. Although some published equations appear promising for estimating group means, others are biased. Individual estimates of sodium intake were highly variable and adjustment for within-individual variation in intake is required for estimating population prevalence or percentiles. Estimates indicated United States sodium intake remains high.

Sodium Intake Among Persons Aged ≥2 Years — United States, 2013–2014

High sodium consumption can increase hypertension, a major risk factor for cardiovascular diseases (1). Reducing sodium intake can lower blood pressure, and sodium reduction in the U.S. population of 40% over 10 years might save at least 280,000 lives (2). Average sodium intake in the United States remains in excess of Healthy People 2020 objectives,* and monitoring sources of sodium in the U.S. population can help focus sodium reduction measures (3,4). Data from 2013-2014 What We Eat in America (WWEIA), the dietary intake portion of the National Health and Nutrition Examination Survey (NHANES),† were analyzed to determine the ranked percentage sodium contribution of selected food categories and sources of sodium intake from all reported foods and beverages, both overall and by demographic subgroups. These latest data include updated food codes and separate estimates for intake among non-Hispanic Asians.§ In 2013-2014, 70% of dietary sodium consumed by persons in the United States came from 25 food categories; breads were the top contributor, accounting for 6% of sodium consumed. A majority of sodium consumed was from food obtained at stores; however, sodium density (mg/1,000 kcal) was highest in food obtained at restaurants. A variety of commonly consumed foods contributes to U.S. sodium intake, emphasizing the importance of sodium reduction across the food supply (4).

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.

Evaluation of the 24-Hour Recall as a Reference Instrument for Calibrating Other Self-Report Instruments in Nutritional Cohort Studies: Evidence From the Validation Studies Pooling Project

Calibrating dietary self-report instruments is recommended as a way to adjust for measurement error when estimating diet-disease associations. Because biomarkers available for calibration are limited, most investigators use self-reports (e.g., 24-hour recalls (24HRs)) as the reference instrument. We evaluated the performance of 24HRs as reference instruments for calibrating food frequency questionnaires (FFQs), using data from the Validation Studies Pooling Project, comprising 5 large validation studies using recovery biomarkers. Using 24HRs as reference instruments, we estimated attenuation factors, correlations with truth, and calibration equations for FFQ-reported intakes of energy and for protein, potassium, and sodium and their densities, and we compared them with values derived using biomarkers. Based on 24HRs, FFQ attenuation factors were substantially overestimated for energy and sodium intakes, less for protein and potassium, and minimally for nutrient densities. FFQ correlations with truth, based on 24HRs, were substantially overestimated for all dietary components. Calibration equations did not capture dependencies on body mass index. We also compared predicted bias in estimated relative risks adjusted using 24HRs as reference instruments with bias when making no adjustment. In disease models with energy and 1 or more nutrient intakes, predicted bias in estimated nutrient relative risks was reduced on average, but bias in the energy risk coefficient was unchanged.