Addressing Current Criticism Regarding the Value of Self-Report Dietary Data.

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.

The Dietary Patterns Methods Project: synthesis of findings across cohorts and relevance to dietary guidance.

The Dietary Patterns Methods Project (DPMP) was initiated in 2012 to strengthen research evidence on dietary indices, dietary patterns, and health for upcoming revisions of the Dietary Guidelines for Americans, given that the lack of consistent methodology has impeded development of consistent and reliable conclusions. DPMP investigators developed research questions and a standardized approach to index-based dietary analysis. This article presents a synthesis of findings across the cohorts. Standardized analyses were conducted in the NIH-AARP Diet and Health Study, the Multiethnic Cohort, and the Women's Health Initiative Observational Study (WHI-OS). Healthy Eating Index 2010, Alternative Healthy Eating Index 2010 (AHEI-2010), alternate Mediterranean Diet, and Dietary Approaches to Stop Hypertension (DASH) scores were examined across cohorts for correlations between pairs of indices; concordant classifications into index score quintiles; associations with all-cause, cardiovascular disease (CVD), and cancer mortality with the use of Cox proportional hazards models; and dietary intake of foods and nutrients corresponding to index quintiles. Across all cohorts in women and men, there was a high degree of correlation and consistent classifications between index pairs. Higher diet quality (top quintile) was significantly and consistently associated with an 11-28% reduced risk of death due to all causes, CVD, and cancer compared with the lowest quintile, independent of known confounders. This was true for all diet index-mortality associations, with the exception of AHEI-2010 and cancer mortality in WHI-OS women. In all cohorts, survival benefit was greater with a higher-quality diet, and relatively small intake differences distinguished the index quintiles. The reductions in mortality risk started at relatively lower levels of diet quality. Higher scores on each of the indices, signifying higher diet quality, were associated with marked reductions in mortality. Thus, the DPMP findings suggest that all 4 indices capture the essential components of a healthy diet.

Higher diet quality is associated with decreased risk of all-cause, cardiovascular disease, and cancer mortality among older adults.

Increased attention in dietary research and guidance has been focused on dietary patterns, rather than on single nutrients or food groups, because dietary components are consumed in combination and correlated with one another. However, the collective body of research on the topic has been hampered by the lack of consistency in methods used. We examined the relationships between 4 indices--the Healthy Eating Index-2010 (HEI-2010), the Alternative Healthy Eating Index-2010 (AHEI-2010), the alternate Mediterranean Diet (aMED), and Dietary Approaches to Stop Hypertension (DASH)--and all-cause, cardiovascular disease (CVD), and cancer mortality in the NIH-AARP Diet and Health Study (n = 492,823). Data from a 124-item food-frequency questionnaire were used to calculate scores; adjusted HRs and 95% CIs were estimated. We documented 86,419 deaths, including 23,502 CVD- and 29,415 cancer-specific deaths, during 15 y of follow-up. Higher index scores were associated with a 12-28% decreased risk of all-cause, CVD, and cancer mortality. Specifically, comparing the highest with the lowest quintile scores, adjusted HRs for all-cause mortality for men were as follows: HEI-2010 HR: 0.78 (95% CI: 0.76, 0.80), AHEI-2010 HR: 0.76 (95% CI: 0.74, 0.78), aMED HR: 0.77 (95% CI: 0.75, 0.79), and DASH HR: 0.83 (95% CI: 0.80, 0.85); for women, these were HEI-2010 HR: 0.77 (95% CI: 0.74, 0.80), AHEI-2010 HR: 0.76 (95% CI: 0.74, 0.79), aMED HR: 0.76 (95% CI: 0.73, 0.79), and DASH HR: 0.78 (95% CI: 0.75, 0.81). Similarly, high adherence on each index was protective for CVD and cancer mortality examined separately. These findings indicate that multiple scores reflect core tenets of a healthy diet that may lower the risk of mortality outcomes, including federal guidance as operationalized in the HEI-2010, Harvard's Healthy Eating Plate as captured in the AHEI-2010, a Mediterranean diet as adapted in an Americanized aMED, and the DASH Eating Plan as included in the DASH score.

The Healthy Eating Index-2010 is a valid and reliable measure of diet quality according to the 2010 Dietary Guidelines for Americans.

The Healthy Eating Index (HEI), a measure of diet quality, was updated to reflect the 2010 Dietary Guidelines for Americans and the accompanying USDA Food Patterns. To assess the validity and reliability of the HEI-2010, exemplary menus were scored and 2 24-h dietary recalls from individuals aged ≥2 y from the 2003-2004 NHANES were used to estimate multivariate usual intake distributions and assess whether the HEI-2010 1) has a distribution wide enough to detect meaningful differences in diet quality among individuals, 2) distinguishes between groups with known differences in diet quality by using t tests, 3) measures diet quality independently of energy intake by using Pearson correlation coefficients, 4) has >1 underlying dimension by using principal components analysis (PCA), and 5) is internally consistent by calculating Cronbach's coefficient α. HEI-2010 scores were at or near the maximum levels for the exemplary menus. The distribution of scores among the population was wide (5th percentile = 31.7; 95th percentile = 70.4). As predicted, men's diet quality (mean HEI-2010 total score = 49.8) was poorer than women's (52.7), younger adults' diet quality (45.4) was poorer than older adults' (56.1), and smokers' diet quality (45.7) was poorer than nonsmokers' (53.3) (P < 0.01). Low correlations with energy were observed for HEI-2010 total and component scores (|r| ≤ 0.21). Cronbach's coefficient α was 0.68, supporting the reliability of the HEI-2010 total score as an indicator of overall diet quality. Nonetheless, PCA indicated multiple underlying dimensions, highlighting the fact that the component scores are equally as important as the total. A comparable reevaluation of the HEI-2005 yielded similar results. This study supports the validity and the reliability of both versions of the HEI.

Fast-food menu offerings vary in dietary quality, but are consistently poor.

OBJECTIVE: To evaluate five popular fast-food chains' menus in relation to dietary guidance. DESIGN: Menus posted on chains' websites were coded using the Food and Nutrient Database for Dietary Studies and MyPyramid Equivalents Database to enable Healthy Eating Index-2005 (HEI-2005) scores to be assigned. Dollar or value and kids' menus and sets of items promoted as healthy or nutritious were also assessed. SETTING: Five popular fast-food chains in the USA. SUBJECTS: Not applicable. RESULTS: Full menus scored lower than 50 out of 100 possible points on the HEI-2005. Scores for Total Fruit, Whole Grains and Sodium were particularly dismal. Compared with full menus, scores on dollar or value menus were 3 points higher on average, whereas kids' menus scored 10 points higher on average. Three chains marketed subsets of items as healthy or nutritious; these scored 17 points higher on average compared with the full menus. No menu or subset of menu items received a score higher than 72 out of 100 points. CONCLUSIONS: The poor quality of fast-food menus is a concern in light of increasing away-from-home eating, aggressive marketing to children and minorities, and the tendency for fast-food restaurants to be located in low-income and minority areas. The addition of fruits, vegetables and legumes; replacement of refined with whole grains; and reformulation of offerings high in sodium, solid fats and added sugars are potential strategies to improve fast-food offerings. The HEI may be a useful metric for ongoing monitoring of fast-food menus.

Knowledge of energy balance guidelines and associated clinical care practices: the U.S. National Survey of Energy Balance Related Care among Primary Care Physicians.

OBJECTIVE: To assess primary care physicians' (PCPs) knowledge of energy balance related guidelines and the association with sociodemographic characteristics and clinical care practices. METHOD: As part of the 2008 U.S. nationally representative National Survey of Energy Balance Related Care among Primary Care Physicians (EB-PCP), 1776 PCPs from four specialties who treated adults (n=1060) or children and adolescents (n=716) completed surveys on sociodemographic information, knowledge of energy balance guidelines, and clinical care practices. RESULTS: EB-PCP response rate was 64.5%. For PCPs treating children, knowledge of guidelines for healthy BMI percentile, physical activity, and fruit and vegetables intake was 36.5%, 27.0%, and 62.9%, respectively. For PCPs treating adults, knowledge of guidelines for overweight, obesity, physical activity, and fruit and vegetables intake was 81.4%, 81.3%, 70.9%, and 63.5%, respectively. Generally, younger, female physicians were more likely to exhibit correct knowledge. Knowledge of weight-related guidelines was associated with assessment of body mass index (BMI) and use of BMI-for-age growth charts. CONCLUSION: Knowledge of energy balance guidelines among PCPs treating children is low, among PCPs treating adults it appeared high for overweight and obesity-related clinical guidelines and moderate for physical activity and diet, and was mostly unrelated to clinical practices among all PCPs.

Using Short-Term Dietary Intake Data to Address Research Questions Related to Usual Dietary Intake among Populations and Subpopulations: Assumptions, Statistical Techniques, and Considerations.

Many research questions focused on characterizing usual, or long-term average, dietary intake of populations and subpopulations rely on short-term intake data. The objective of this paper is to review key assumptions, statistical techniques, and considerations underpinning the use of short-term dietary intake data to make inference about usual dietary intake. The focus is on measurement error and strategies to mitigate its effects on estimated characteristics of population-level usual intake, with attention to relevant analytic issues such as accounting for survey design. Key assumptions are that short-term assessments are subject to random error only (i.e., unbiased for individual usual intake) and that some aspects of the error structure apply to all respondents, allowing estimation of this error structure in data sets with only a few repeat measures per person. Under these assumptions, a single 24-hour dietary recall per person can be used to estimate group mean intake; and with as little as one repeat on a subsample and with more complex statistical techniques, other characteristics of distributions of usual intake, such as percentiles, can be estimated. Related considerations include the number of days of data available, skewness of intake distributions, whether the dietary components of interest are consumed nearly daily by nearly everyone or episodically, the number of correlated dietary components of interest, time-varying nuisance effects related to day of week and season, and variance estimation and inference. Appropriate application of assumptions and recommended statistical techniques allows researchers to address a range of research questions, though it is imperative to acknowledge systematic error (bias) in short-term data and its implications for conclusions.

Comparing 3 dietary pattern methods--cluster analysis, factor analysis, and index analysis--With colorectal cancer risk: The NIH-AARP Diet and Health Study.

The authors compared dietary pattern methods-cluster analysis, factor analysis, and index analysis-with colorectal cancer risk in the National Institutes of Health (NIH)-AARP Diet and Health Study (n = 492,306). Data from a 124-item food frequency questionnaire (1995-1996) were used to identify 4 clusters for men (3 clusters for women), 3 factors, and 4 indexes. Comparisons were made with adjusted relative risks and 95% confidence intervals, distributions of individuals in clusters by quintile of factor and index scores, and health behavior characteristics. During 5 years of follow-up through 2000, 3,110 colorectal cancer cases were ascertained. In men, the vegetables and fruits cluster, the fruits and vegetables factor, the fat-reduced/diet foods factor, and all indexes were associated with reduced risk; the meat and potatoes factor was associated with increased risk. In women, reduced risk was found with the Healthy Eating Index-2005 and increased risk with the meat and potatoes factor. For men, beneficial health characteristics were seen with all fruit/vegetable patterns, diet foods patterns, and indexes, while poorer health characteristics were found with meat patterns. For women, findings were similar except that poorer health characteristics were seen with diet foods patterns. Similarities were found across methods, suggesting basic qualities of healthy diets. Nonetheless, findings vary because each method answers a different question.

Americans do not meet federal dietary recommendations.

A longstanding goal of dietary surveillance has been to estimate the proportion of the population with intakes above or below a target, such as a recommended level of intake. However, until now, statistical methods for assessing the alignment of food intakes with recommendations have been lacking. The purposes of this study were to demonstrate the National Cancer Institute's method of estimating the distribution of usual intake of foods and determine the proportion of the U.S. population who does not meet federal dietary recommendations. Data were obtained from the 2001-2004 NHANES for 16,338 persons, aged 2 y and older. Quantities of foods reported on 24-h recalls were translated into amounts of various food groups using the MyPyramid Equivalents Database. Usual dietary intake distributions were modeled, accounting for sequence effect, weekend/weekday effect, sex, age, poverty income ratio, and race/ethnicity. The majority of the population did not meet recommendations for all of the nutrient-rich food groups, except total grains and meat and beans. Concomitantly, overconsumption of energy from solid fats, added sugars, and alcoholic beverages ("empty calories") was ubiquitous. Over 80% of persons age ≥ 71 y and over 90% of all other sex-age groups had intakes of empty calories that exceeded the discretionary calorie allowances. In conclusion, nearly the entire U.S. population consumes a diet that is not on par with recommendations. These findings add another piece to the rather disturbing picture that is emerging of a nation's diet in crisis.

The population distribution of ratios of usual intakes of dietary components that are consumed every day can be estimated from repeated 24-hour recalls.

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.

A population's distribution of Healthy Eating Index-2005 component scores can be estimated when more than one 24-hour recall is available.

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 >or=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.

A mixed-effects model approach for estimating the distribution of usual intake of nutrients: the NCI method.

It is of interest to estimate the distribution of usual nutrient intake for a population from repeat 24-h dietary recall assessments. A mixed effects model and quantile estimation procedure, developed at the National Cancer Institute (NCI), may be used for this purpose. The model incorporates a Box-Cox parameter and covariates to estimate usual daily intake of nutrients; model parameters are estimated via quasi-Newton optimization of a likelihood approximated by the adaptive Gaussian quadrature. The parameter estimates are used in a Monte Carlo approach to generate empirical quantiles; standard errors are estimated by bootstrap. The NCI method is illustrated and compared with current estimation methods, including the individual mean and the semi-parametric method developed at the Iowa State University (ISU), using data from a random sample and computer simulations. Both the NCI and ISU methods for nutrients are superior to the distribution of individual means. For simple (no covariate) models, quantile estimates are similar between the NCI and ISU methods. The bootstrap approach used by the NCI method to estimate standard errors of quantiles appears preferable to Taylor linearization. One major advantage of the NCI method is its ability to provide estimates for subpopulations through the incorporation of covariates into the model. The NCI method may be used for estimating the distribution of usual nutrient intake for populations and subpopulations as part of a unified framework of estimation of usual intake of dietary constituents.

Modeling data with excess zeros and measurement error: application to evaluating relationships between episodically consumed foods and health outcomes.

Dietary assessment of episodically consumed foods gives rise to nonnegative data that have excess zeros and measurement error. Tooze et al. (2006, Journal of the American Dietetic Association 106, 1575-1587) describe a general statistical approach (National Cancer Institute method) for modeling such food intakes reported on two or more 24-hour recalls (24HRs) and demonstrate its use to estimate the distribution of the food's usual intake in the general population. In this article, we propose an extension of this method to predict individual usual intake of such foods and to evaluate the relationships of usual intakes with health outcomes. Following the regression calibration approach for measurement error correction, individual usual intake is generally predicted as the conditional mean intake given 24HR-reported intake and other covariates in the health model. One feature of the proposed method is that additional covariates potentially related to usual intake may be used to increase the precision of estimates of usual intake and of diet-health outcome associations. Applying the method to data from the Eating at America's Table Study, we quantify the increased precision obtained from including reported frequency of intake on a food frequency questionnaire (FFQ) as a covariate in the calibration model. We then demonstrate the method in evaluating the linear relationship between log blood mercury levels and fish intake in women by using data from the National Health and Nutrition Examination Survey, and show increased precision when including the FFQ information. Finally, we present simulation results evaluating the performance of the proposed method in this context.

A population's mean Healthy Eating Index-2005 scores are best estimated by the score of the population ratio when one 24-hour recall is available.

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.

A comparison of food-based recommendations and nutrient values of three food guides: USDA's MyPyramid, NHLBI's Dietary Approaches to Stop Hypertension Eating Plan, and Harvard's Healthy Eating Pyramid.

The purpose of this research was to compare food-based recommendations and nutrient values of three food guides: the US Department of Agriculture's MyPyramid; the National Heart, Lung, and Blood Institute's Dietary Approaches to Stop Hypertension Eating Plan, and Harvard University's Healthy Eating Pyramid. Estimates of nutrient values associated with following each of the food guides at the 2,000-calorie level were made using a composite approach. This approach calculates population-weighted nutrient composites for each food group and subgroup, assuming average choices within food groups. Nutrient estimates were compared to the Dietary Reference Intakes and other goals and limits. Recommendations were similar regarding almost all food groups for both the type and amount of foods. Primary differences were seen in the types of vegetables and protein sources recommended and the amount of dairy products and total oil recommended. Overall nutrient values were also similar for most nutrients, except vitamin A, vitamin E, and calcium. These food guides were derived from different types of nutrition research, yet they share consistent messages: eat more fruits, vegetables, legumes, and whole grains; eat less added sugar and saturated fat; and emphasize plant oils.

Development of the Healthy Eating Index-2005.

The Healthy Eating Index (HEI) is a measure of diet quality as specified by Federal dietary guidance, and publication of the Dietary Guidelines for Americans 2005 necessitated its revision. An interagency working group based the HEI-2005 on the food patterns found in My-Pyramid. Diets that meet the least restrictive of the food-group recommendations, expressed on a per 1,000 calorie basis, receive maximum scores for the nine adequacy components of the index: total fruit (5 points), whole fruit (5 points), total vegetables (5 points), dark green and orange vegetables and legumes (5 points), total grains (5 points), whole grains (5 points), milk (10 points), meat and beans (10 points), and oils (10 points). Lesser amounts are pro-rated linearly. Population probability densities were examined when setting the standards for minimum and maximum scores for the three moderation components: saturated fat (10 points), sodium (10 points), and calories from solid fats, alcoholic beverages (ie, beer, wine, and distilled spirits), and added sugars (20 points). Calories from solid fats, alcoholic beverages, and added sugars is a proxy for the discretionary calorie allowance. The 2005 Dietary Guideline for saturated fat and the Adequate Intake and Tolerable Upper Intake Level for sodium, expressed per 1,000 calories, were used when setting the standards for those components. Intakes between the maximum and minimum standards are pro-rated. The HEI-2005 is a measure of diet quality as described by the key diet-related recommendations of the 2005 Dietary Guidelines. It has a variety of potential uses, including monitoring the diet quality of the US population and subpopulations, evaluation of interventions, and research.

Evaluation of the Healthy Eating Index-2005.

BACKGROUND: The Healthy Eating Index (HEI), a measure of diet quality as specified by federal dietary guidance, was revised to conform to the Dietary Guidelines for Americans 2005. The HEI has several components, the scores of which are totaled. OBJECTIVE: The validity and reliability of the HEI-2005 were evaluated. DESIGN: Validity was assessed by answering four questions: Does the HEI-2005 1) give maximum scores to menus developed by experts; 2) distinguish between groups with known differences in diet quality-smokers and nonsmokers; 3) measure diet quality independently of energy intake, a proxy for diet quantity; and 4) have more than one underlying dimension? The relevant type of reliability, internal consistency, was also assessed. SUBJECTS: Twenty-four-hour recalls from 8,650 participants, aged 2 years and older, in the National Health and Nutrition Examination Survey, 2001-2002 were analyzed to answer questions 2 to 4. Results were weighted to consider sample design and nonresponse. STATISTICAL ANALYSES: T tests determined differences in scores between smokers and nonsmokers. Pearson correlation coefficients determined the relationship between energy intake and scores. Principal components analysis determined the number of factors that comprise the HEI-2005. Cronbach's coefficient alpha tested internal consistency. RESULTS: HEI-2005 scores are at or very near the maximum levels for all sets of exemplary menus with one exception; the Harvard menus scored low on the milk component because these menus intentionally include only small amounts of milk products. Nine of 12 component scores were lower for smokers than nonsmokers. The correlations of component scores were virtually independent of energy intake (< I.22I). Multiple factors underlie the HEI-2005. Coefficient alpha was .43. The alpha value for all tests was .01. CONCLUSIONS: The HEI-2005 is a valid measure of diet quality. Potential uses include population monitoring, evaluation of interventions, and research. The individual component scores provide essential information in addition to that provided by the total score.

Sources of food group intakes among the US population, 2001-2002.

BACKGROUND: Food guides are typically built around a system of food groups. Accordingly, the US Department of Agriculture's MyPyramid includes both food groups and subgroups, as well as an allowance for discretionary calories, in its guidance. OBJECTIVE: To identify the major dietary contributors to food group intake in the US population. METHODS: This cross-sectional study used 2001-2002 National Health and Nutrition Examination Survey data to determine weighted population proportions for the contribution of each subgroup to its MyPyramid food group (ie, proportion), and the contribution of specific foods to the subgroups oils, solid fats, and added sugars (ie, major contributors). Food codes associated with each food were sorted into 96 categories, termed specific foods, and were linked to the MyPyramid Equivalents Database to obtain food group equivalents. RESULTS: In regard to proportion, dark green vegetables (6%), orange vegetables (5%), and legumes (6%) fell well short of recommended levels. Intake of whole grains (10% of total) was far below the recommendation that at least half of all grains be whole. In regard to major contributors, top sources of oils were potato chips, salad dressing, and nuts/seeds; major contributors of solid fats were grain-based desserts, cheese, and sausages. Sweetened carbonated beverages provided 37% of added sugars. CONCLUSIONS: Americans do not, in general, consume the most nutrient-dense forms of basic food groups, instead consuming foods that are high in solid fats and added sugars. The main culprits-the foods that contribute most to discrepancies between recommendations and actual intake-are sweetened carbonated beverages and other sweetened beverages, grain-based desserts, nonskim dairy products, and fatty meats.