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Recent reports have asserted that, because of energy underreporting, dietary self-report data suffer from measurement error so great that findings that rely on them are of no value. This commentary considers the amassed evidence that shows that self-report dietary intake data can successfully be used to inform dietary guidance and public health policy. Topics discussed include what is known and what can be done about the measurement error inherent in data collected by using self-report dietary assessment instruments and the extent and magnitude of underreporting energy compared with other nutrients and food groups. Also discussed is the overall impact of energy underreporting on dietary surveillance and nutritional epidemiology. In conclusion, 7 specific recommendations for collecting, analyzing, and interpreting self-report dietary data are provided: (1) continue to collect self-report dietary intake data because they contain valuable, rich, and critical information about foods and beverages consumed by populations that can be used to inform nutrition policy and assess diet-disease associations; (2) do not use self-reported energy intake as a measure of true energy intake; (3) do use self-reported energy intake for energy adjustment of other self-reported dietary constituents to improve risk estimation in studies of diet-health associations; (4) acknowledge the limitations of self-report dietary data and analyze and interpret them appropriately; (5) design studies and conduct analyses that allow adjustment for measurement error; (6) design new epidemiologic studies to collect dietary data from both short-term (recalls or food records) and long-term (food-frequency questionnaires) instruments on the entire study population to allow for maximizing the strengths of each instrument; and (7) continue to develop, evaluate, and further expand methods of dietary assessment, including dietary biomarkers and methods using new technologies. [ABSTRACT FROM AUTHOR]

The USDA's Healthy Eating Index-2005 (HEI-2005) is a tool to quantify the quality of diet consumed by individuals in the U.S. It comprises 12 components expressed as ratios of a food group or nutrient intake to energy intake. Components are scored on a scale from 0 to M, where M is 5, 10, or 20. Ideally, the HEI-2005 is calculated on the basis of the usual, or long-term average, dietary intake of an individual. In recent cycles of the NHANES, intake data have been collected via 24-h recalls for more than 1 d on most participants. We present here a statistical method to estimate a population's distribution of usual HEI-2005 component scores when 2 d of dietary information is available for a sample of individuals from the population. Distributions for the total population and for age-gender subgroups may be estimated. The method also yields an estimate of the population's mean total HEI-2005 score. Application of the method to NHANES data for 2001-2004 yielded estimated distributions for all 12 components; those of total vegetables (range 0-5), whole grains (range 0-5), and energy from solid fats, alcoholic beverages, and added sugars (range 0-20) are presented. The total population mean scores for these components were 3.21, 1.00, and 8.41, respectively. An estimated 30% of the total population had a score of <2.5 for total vegetables. This is the first time, to our knowledge, that estimated distributions of usual HEI-2005 component scores have been published. [ABSTRACT FROM AUTHOR]

Estimating the population distribution of the usual intake of a nutrient relative to that of another nutrient requires determination of individual-level ratios. If intake data are available on a per-day basis, as with 24-h dietary recalls, those ratios can be determined in 1 of 2 ways: as the usual ratio of intakes or the ratio of usual intakes. Each of these ratios has its own meaning and determination; the ratio of usual intakes is conceptually consistent with determinations obtained from FF0 data. We present a method for estimating the ratio of usual intakes that uses bivariate modeling of the 2 nutrient intakes in question. Application of the method to the NHANES data for the years 2001-2004 yielded estimated distributions for percent of usual energy intake from total fat, percent of usual energy intake from saturated fat, and usual sodium intake per 1000 kcal (4184 kJI of usual energy intake. Distributions for both the total population and forage-gender subgroups were estimated. Approximately 60% of adults 1>19 yl had a usual total fat intake that was within the recommended range of 20-35% of total energy, but only ∼34% had a usual saturated fat intake <10% of total energy. The results changed only minimally when the other definition of usual intake, the usual ratio of intakes, was adopted [ABSTRACT FROM AUTHOR]

Estimating the population distribution of the usual intake of a nutrient relative to that of another nutrient requires determination of individual-level ratios. If intake data are available on a per-day basis, as with 24-h dietary recalls, those ratios can be determined in 1 of 2 ways: as the usual ratio of intakes or the ratio of usual intakes. Each of these ratios has its own meaning and determination; the ratio of usual intakes is conceptually consistent with determinations obtained from FFQ data. We present a method for estimating the ratio of usual intakes that uses bivariate modeling of the 2 nutrient intakes in question. Application of the method to the NHANES data for the years 2001-2004 yielded estimated distributions for percent of usual energy intake from total fat, percent of usual energy intake from saturated fat, and usual sodium intake per 1000 kcal (4184 kJ) of usual energy intake. Distributions for both the total population and for age-gender subgroups were estimated. Approximately 60% of adults (>19 y) had a usual total fat intake that was within the recommended range of 20-35% of total energy, but only approximately 34% had a usual saturated fat intake <10% of total energy. The results changed only minimally when the other definition of usual intake, the usual ratio of intakes, was adopted. [ABSTRACT FROM AUTHOR]

Estimating the population distribution of the usual intake of a nutrient relative to that of another nutrient requires determination of individual-level ratios. If intake data are available on a per-day basis, as with 24-h dietary recalls, those ratios can be determined in 1 of 2 ways: as the usual ratio of intakes or the ratio of usual intakes. Each of these ratios has its own meaning and determination; the ratio of usual intakes is conceptually consistent with determinations obtained from FF0 data. We present a method for estimating the ratio of usual intakes that uses bivariate modeling of the 2 nutrient intakes in question. Application of the method to the NHANES data for the years 2001-2004 yielded estimated distributions for percent of usual energy intake from total fat, percent of usual energy intake from saturated fat, and usual sodium intake per 1000 kcal (4184 kJI of usual energy intake. Distributions for both the total population and forage-gender subgroups were estimated. Approximately 60% of adults 1>19 yl had a usual total fat intake that was within the recommended range of 20-35% of total energy, but only ∼34% had a usual saturated fat intake <10% of total energy. The results changed only minimally when the other definition of usual intake, the usual ratio of intakes, was adopted [ABSTRACT FROM AUTHOR]

The USDA Healthy Eating Index-2005 (HEI-2005) is a tool to quantify and evaluate the quality of diet consumed by the U.S. population. It comprises 12 components, expressed as ratios of a food group or nutrient to energy intake. The components are scored on a scale from 0 to M, where M is 5, 10, or 20. Ideally, the HEI-2005 is calculated on the basis of the usual dietary intake of an individual. Intake data, collected via a 24-h recall, are often available for only 1 d for each individual. In this article, we examine how best to estimate a population's mean usual HEI-2005 component and total scores when 1 d of dietary information is available for a sample of individuals from the population. Three methods are considered: the mean of individual scores, the score of the mean of individual ratios, and the score of the ratio of total food group or nutrient intake to total energy intake, which we call the population ratio. We investigate via computer simulation which method is the least biased. The simulations are based on statistical modeling of the distributions of intakes reported by 738 women participating in the Eating at America's Table Study. The results show that overall, the score of the population ratio is the preferred method. We therefore recommend that the quality of the U.S. population's diet be assessed and monitored using this method. [ABSTRACT FROM AUTHOR]

There are reasons to suspect that dietary changes through adult life may modify risk for some cancers. We examined the association of recent and past dietary habits and changes in dietary intake over time with ovarian cancer risk. Long-term nutritional assessment was performed retrospectively in 631 incident cases of invasive epithelial ovarian cancer and in 1174 matched controls (matched by age +/- 2 y, country of origin, and period of immigration) as part of a nationwide case-control study of ovarian cancer conducted between the years of 1994 and 1996 in Israel. Using a 2-step quantified Food Frequency Questionnaire, participants were first asked about their consumption of food items 1 y prior to the interview, and then whether their consumption had changed over time. The time of the change and consumption level before the change were recorded, allowing reconstruction of daily intakes for several time points. The reported mean percentage of animal fat intake decreased by 1.3% in cases but by 1.9% in controls (P for difference = 0.003). Conditional multivariate logistic regression was used to estimate odds ratios adjusted for total energy, parity, and oral contraceptive use. Substituting nonanimal fat in preference to animal fat over a relatively short term (between 2 and 7 y prior to interview) decreased the risk of ovarian cancer [OR = 0.65/100 kcal (418.4 kJ), 95% CI = 0.50 - 0.85]. Our results suggest that substitution of nonanimal for animal fat during adult life might reduce the risk of ovarian cancer, but this requires confirmation in prospective studies. [ABSTRACT FROM AUTHOR]

We reexamined the current practice in estimating the distribution of usual dietary nutrient intakes from population surveys when using self-report dietary instruments, particularly the 24-h recall (24HR), in light of the new data from the Observing Protein and Energy Nutrition Study. In this study, reference biomarkers for energy (doubly labeled water) and protein [urinary nitrogen (UN)], together with multiple FFQs and 24HRs, were administered to 484 healthy volunteers. By using the reference biomarkers to estimate the distributions for energy and protein, the data confirmed previous reports that FFQs generally do not give an accurate impression of the distribution of usual dietary intake. The traditional method applied to 24HRs performed poorly because of underestimating the mean and overestimating the SD of the usual energy and protein intake distributions, and, although the National Research Council and the Iowa State University methods generally give better estimates of the shape of the distribution, they did not improve the estimates of the mean (10-15% underestimation for energy and 6-7% underestimation for protein). Results for urinary potassium, a putative biomarker for potassium intake, and reported potassium intake did not display this underestimation and may reflect either differential underreporting of foods or inadequacy of the potassium biomarker. A large controlled feeding study is required to validate conclusively the potassium biomarker. For energy intake, adjusting its 24HR-based distribution by using the UN biomarker appeared to capture the usual intake distribution quite accurately. Incorporating UN assessments into nutritional surveys, therefore, deserves serious consideration. [ABSTRACT FROM AUTHOR]

A response from the authors of the article "Addressing Current Criticism Regarding the Value of Self-Report Dietary Data" in the 2015 issue is presented.

Background: Recently, we confirmed 24-h urinary sucrose plus fructose (24 uSF) as a predictive biomarker of total sugar intake. However, the collection of 24-h urine samples has limited feasibility in population studies., Objective: We investigated the utility of the urinary sucrose plus fructose (uSF) biomarker measured in spot urine as a measure of 24 uSF biomarker and total sugar intake., Methods: Hundred participants, 18-70 y of age, from the Phoenix Metropolitan Area completed a 15-d feeding study. For 2 of the 8 collected 24-h urine samples, each spot urine sample was collected in a separate container. We considered 4 timed voids of the day [morning (AM) void: first void 08:30-12:30; afternoon (PM) void: first void 12:31-17:30; evening (EVE) void: first void 17:31-12:00; and next-day (ND) void: first void 04:00-12:00]. We investigated the performance of uSF from 1 void, and uSF combined from 2 and 3 voids as a measure of 24 uSF and sugar intake., Results: The biomarker averaged from PM/EVE void strongly correlated with 24 uSF (partial r = 0.75). The 24 uSF predicted from the PM/EVE combination was significantly associated with observed sugar intake and was selected for building the calibrated biomarker equation (marginal R 2 = 0.36). Spot urine-based calibrated biomarker, ie, biomarker-estimated sugar intake was moderately correlated with the 15-d mean-observed sugar intake (r = 0.50)., Conclusions: uSF measured from a PM and EVE void may be used to generate biomarker-based sugar intake estimate when collecting 24-h urine samples is not feasible, pending external validation., (Copyright © 2023 American Society for Nutrition. Published by Elsevier Inc. All rights reserved.)

The Jewish majority and Arab minority populations in Israel exhibit disparities in nutrition-related chronic diseases, but comparative, population-based dietary studies are lacking. We evaluated ethnic differences in dietary patterns in a population-based, cross-sectional study of Arab and Jewish urban adults (n = 1104; age 25-74 y). Dietary intake was assessed with an interviewer-administered, quantified FFQ. We used principal-component analysis to identify 4 major dietary patterns: Ethnic, Healthy, Fish and Meat Dishes, and Middle Eastern Snacks and Fast Food. The Ethnic and Healthy patterns exhibited major ethnic differences. Participants in the top Ethnic intake tertile (97% Arab) had modified Mediterranean-style Arabic dietary habits, whereas those in the bottom Ethnic tertile (98% Jewish) had central/northern European-style dietary habits. The Arab participants with less strongly ethnicity-associated dietary habits were younger [OR for 10-y decrease = 1.42 (95% CI: 1.21-1.68)] and male [OR = 2.23 (95% CI: 1.53-3.25)]. Jews with less strongly ethnicity-associated dietary habits were less recent immigrants [OR = 8.97 (95% CI: 5.05-15.92)], older [OR for 10-y decrease = 0.80 (95% CI: 0.69-0.92)], had post-secondary education [OR = 2.04 (95% CI: 1.06-3.94)], and reported other healthy lifestyle behaviors. In relation to the Healthy pattern, Arabs were less likely than Jews to be in the top intake tertile, but the magnitude of the difference was less in diabetic participants. Participants reporting other healthy lifestyle behaviors were more likely to have a high intake of the Healthy pattern. Substantial differences were found between Arabs and Jews in dietary patterns and suggest a need for culturally congruent dietary interventions to address nutrition-related chronic disease disparities.