Trends in diet quality and associated comprehensive environmental impacts in the United States, 2001 to 2018: a serial cross-sectional study.

BackgroundWhile the admittedly limited number of epidemiological findings on the association between diet-related greenhouse gas emissions (GHGE) and diet quality are not always consistent, potential influence of bias in the estimation of diet-related GHGE caused by misreporting of energy intake (EI) has not been investigated. This cross-sectional study evaluated diet-related GHGE in the UK and their association with diet quality, taking account of EI under-reporting.MethodsDietary data used were from the National Diet and Nutrition Survey rolling programme 2008/2009–2013/2014, in which 4-day food diaries were collected from 3502 adults aged ≥19 years. Diet-related GHGE were estimated based on 133 food groups, using GHGE values from various secondary sources. Diet quality was assessed by the healthy diet indicator (HDI), Mediterranean diet score (MDS) and Dietary Approaches to Stop Hypertension (DASH) score. EI misreporting was assessed as reported EI divided by estimated energy requirement (EI:EER).ResultsMean value of daily GHGE was 5.7 kg carbon dioxide equivalents (CO2eq), which is consistent with those reported from a number of national representative samples in other European countries. Mean EI:EER was 0.74. Assuming that all the dietary variables were misreported in proportion to the misreporting of EI, the mean value of the misreporting-adjusted diet-related GHGE was 8.2 kg CO2eq/d. In the entire population, after adjustment for potential confounders (i.e., age, sex, ethnicity, socioeconomic classification, smoking status and physical activity), diet-related GHGE were inversely associated with HDI and DASH score but not with MDS. However, with further adjustment for EI:EER, diet-related GHGE showed inverse associations with all three measures of diet quality. Similar associations were observed when only under-reporters (EI:EER < 0.70; n = 1578) were analysed. Conversely, in the analysis including only plausible reporters (EI:EER 0.70–1.43; n = 1895), diet-related GHGE showed inverse associations with all diet quality measures irrespective of adjustment.ConclusionsWith taking account of EI under-reporting, this study showed inverse associations between diet-related GHGE and diet quality not only in the entire sample but also in the separate analyses of plausible reporters and under-reporters, as well as potential underreporting of diet-related GHGE.

The majority of adult diets in the United States do not meet recommendations for consumption of whole grains (3), putting these individuals at increased risk for diet-related chronic diseases (10). Poor diet quality, inadequate consumption of whole grains, and chronic diseases are particularly prevalent in the impoverished rural South (1,7,9,12). Hence, dietary interventions designed to address nutrition inadequacies and thereby improve diet quality could lower chronic disease prevalence in this region of the country. Assessing a population’s diet quality is a necessary step for determining nutrition inadequacies and subsequently designing effective dietary interventions that address these problems. The Healthy Eating Index–2005 (HEI-2005) is a tool that assesses diet quality in terms of adherence to the 2005 Dietary Guidelines for Americans (DGA). Whole grains are among the 2005 and 2010 DGA key recommendations for food groups to encourage (10,11). However, little research has been conducted to determine the potential impact of whole grain for refined grain substitutions on diet quality. Hence, the objective of this study was to use simulation modeling to determine the effects of substituting familiar, more healthful whole grain foods for less healthy, refined grain foods on diet quality and total energy intake in lower Mississippi Delta (LMD) adults. Analyses were performed using data from the Foods of Our Delta Study (FOODS), a cross-sectional telephone survey of residents in a 36-county LMD region that was conducted from January to June 2000 (2). Dietary intake data were collected using the U.S. Department of Agriculture’s (USDA) 24-hour dietary recall multiple pass methodology with the assistance of a foods measurement guide that had been mailed to study participants. Dietary intake data were collected for 1,751 adults (18 years of age and older) using a single 24-hour recall. However, only plausible (500 ≤ intake kcal ≤ 6,000) 24-hour recalls were used for this study (13). The HEI-2005, a scoring method designed to measure adherence to the 2005 DGA (5), was used to measure diet quality. Briefly, the HEI-2005 is composed of 12 components corresponding to total fruit, whole fruit, total vegetables, dark green and orange vegetables and legumes, total grains, whole grains, milk, meat and beans, oils, saturated fat, sodium, and kcal from solid fats, alcoholic beverages, and added sugars (SoFAAS). The total score, calculated as the sum of the component scores, has a maximum value of 100. For each component, higher scores reflect better adherence to DGA recommendations corresponding to that component. All foods selected as substitutions were deemed familiar foods based on their inclusion in the FOODS data set. White breads, rolls, biscuits, and spaghetti were replaced with their whole wheat counterparts, and white rice was replaced with brown rice. To simulate the effects of substituting 25, 50, and 100% of targeted foods with their replacements on HEI-2005 total and component scores, the targeted items’ nutrient profiles were reduced by the respective amounts for the analyses and the replacement items’ nutrient profiles were inserted at the corresponding level. The nutrient profiles for the replacement foods were extracted from the FOODS data set. All statistical analyses were performed using SAS software (version 9.2, SAS Institute) and SUDAAN software (version 10.0.1, Research Triangle Institute). SAS survey and SUDAAN procedures were used to compute frequencies, means, and their associated 95% confidence intervals. These procedures are tailored to account for the complex sampling design used in FOODS. Hence, the results are weighted and should be considered representative of the LMD adult population. The population ratio method was used to compute mean HEI-2005 scores and corresponding 95% confidence intervals using jackknife variances for the overall population. It is the least biased way to estimate a mean HEI-2005 score for a population (4). Sixty-two of the 1,751 LMD respondents were excluded due to implausible dietary records, resulting in a total sample size of 1,689 adults. The sample was composed of 48% males, 43% African Americans, 21% < 30 years of age, and 23% ≥ 60 years of age. Approximately one-fourth (23%) of the sample’s household income was under $15,000 per year and 23% of the sample had less than a high school education. Almost one-fourth (24%) of the respondents were current smokers and 18% were former smokers. Based on self-reported heights and weights, 67% of the sample was either overweight (25.0 ≤ BMI ≤ 29.9) or obese (BMI ≥ 40.0). The mean daily energy intake for the LMD adult respondents was 2,010 kcal. 1 Corresponding author. USDA Agricultural Research Service. E-mail: jessica.thomson@ars.usda.gov. 2 Department of Medicine, University of Illinois at Chicago. Affiliated with USDA Agricultural Research Service at the time this work was completed.

In March 2020, the COVID-19 pandemic led to restricted vocational (Voc-PA) and recreational physical activity (Rec-PA) outside of the home. We conducted a nation-wide survey in the United States (US) during the mitigation peak of the pandemic (June 2020) to assess health-related changes from the previous year. A diet quality (DQ) assessment tool weighted the relative healthfulness of eating occasions from foods prepared-at-home (Home) and away-from-home (Away). Previously-validated instruments assessed PA and demographic variables; height/weight were self-reported to calculate body mass index (BMI). T-tests explored longitudinal, between-sex, and obesity status differences in DQ, PA, and BMI; Pearson correlations explored associations. Of 1648 respondents, 814 valid responses (56.8% female, 81.7% white) were analyzed. Overall and Home DQ was higher for females than males in 2020 (p < 0.001 for both). Respondents increased DQ from 2019 to 2020, primarily from Away (p < 0.001 for both sexes). Total Rec-PA and Voc-PA was higher in males (p = 0.002, p < 0.001) than females in 2020; females reported higher other PA (p = 0.001). Change in BMI was inversely associated with change in both DQ and PA (p < 0.001 for both). In this sample of US adults, early adaptations to the COVID-19 pandemic included improved DQ and BMI. Whether these short-term improvements were maintained warrant further investigation.

Many previous studies supported that shift of dietary choice is necessary to reduce environmental impact and achieve a healthy diet. However, inconsistent results have been shown for the association between the environmental impact of diet and its nutritional adequacy. The aim of this study was to investigate the association between environmental indicator including greenhouse gas emissions (GHGE) and energy use (EU) and diet quality using several diet quality scores among Japanese. Dietary data were obtained from a cross-sectional study including 392 healthy adults (196 women and 196 men, aged 20–69 y) living in 20 areas of 47 prefectures, in which four non-consecutive-day diet record was collected from February to March in 2013. Diet-related GHGE and EU were estimated using the Global Link Input-Output model. Diet quality was assessed with the adherence to the Japanese Food Guide Spinning Top (JFG score), its modified version (modified JFG score), Nutrient-rich Dietary index (NRD9.3.), the Mediterranean diet score (MDS) and the Dietary Approaches to Stop Hypertension (DASH) score. Participants were categorized into sex-specific tertiles according to energy-adjusted GHGE or EU then mixed for the analysis. Using the PROC GLM procedure, linear regression models were constructed to examine the association of environmental indicators with diet quality and intakes of foods and nutrition. Diet-related GHGE and EU were positively associated with NRF.9.3 score and MDS. Diet-related EU was also positively associated with DASH score. However, there was no significant association observed in either JFG score or the modified JFG score with both diet-related GHGE and EU. Diet-related GHGE and EU were both negatively associated with intakes of well-milled rice, fats and oils, and sugar-sweetened beverage, and positively associated with intakes of vegetables, mushroom, fish and seafood, tea and coffee and seasoning, protein, and several micronutrients. A positive association between GHGE or EU, and NRF.9.3 or MSD was seemed to be due to higher intake of vegetables, fish and seafood, meat, vitamin A, iron, potassium in high GHGE or EU group than low GHGE or EU group. The results suggested that achieving low GHGE or EU diet might not lead to higher nutritional quality among Japanese. On the other hand, it may be possible that diet quality scores used in this study could not appropriately evaluate nutritional adequacy of the diet for the Japanese population as previously suggested.

Introduction: Prior studies of diet quality in the United States (US) have focused on national estimates and trends. Examining geographic variations in diet quality over time can provide actionable and targeted policy insights. Hypothesis: Diet quality is heterogeneously distributed across US geographical locations with higher diet quality in urban and western and eastern regions. Methods: We included a nationally representative sample of US adults aged 20 years or older with data on 24-hour dietary recall and geocode (N=19,607) from six cycles (2007-2018) of the National Health and Nutrition Examination Survey (NHANES). Exposures are geographic location by rurality, level of urbanization (large, medium, and small), and US census region and calendar year. Outcomes are diet quality measured by the American Heart Association dietary primary (range 0-50; based on total fruits and vegetables, whole grains, fish and shellfish, sugar-sweetened beverage, and sodium); secondary scores (range 0-80; adding nuts/seeds/legumes, processed meat, and saturated fat); the Healthy Eating Index (HEI) 2015 (range 0-50); major dietary components; and poor diet quality. Poor diet was defined according to the AHA secondary score as less than 40% adherence (score &lt;32); intermediate, as 40% to 79.9% adherence (score 32-63.9); and ideal, as at least 80% adherence (score ≥64, respectively). Results: Among rural vs. urban residents, the primary AHA, secondary AHA, and HEI-2015 scores in 2017-2018 were 19.5 (95% CI, 18.9-20.1) vs. 20.6 (95% CI, 20.4-30.0); 35.1 (95% CI, 34.3-35.8) vs. 37.1 (95% CI, 36.7-37.6), and 51.8 (95% CI, 51.0-52.7) vs. 53.9 (95% CI, 53.9-54.4), respectively. The corresponding proportions having poor diet quality for rural vs. urban were 43.1% (95% CI, 40.8%-45.5%) vs. 36.2% (95% CI, 34.8%-37.5%). By region, the AHA secondary score was lowest (34.9; 95% CI, 34.1-35.6) for the South. Similar result patterns were found for the AHA primary and HEI-2015 scores. The proportion of adults having poor diet quality was highest in the South (43.2%; 95% CI, 40.8%-45.6%). Trends in diet quality remained stable from 2007 to 2018. Conclusions: Significant differences in diet quality by rurality, levels of urbanization, and region exist for adults in the US. These differences in diet quality have remained stable from 2007 to 2018. Geographic differences in diet quality—and the consistency of these differences over time—seem to be important in the development of targeted food policy strategies to address regional disparities.

Can United States Adults Accurately Assess Their Diet Quality?

Evaluation of time trends in dietary quality and their relation to disease burden provides essential feedback for policy making. We used an index titled the Alternate Healthy Eating Index 2010 to evaluate trends in dietary quality among 33,885 US adults. From 1999 to 2012 the index increased from 39.9 to 48.2 (perfect score = 110). Gaps in performance on the index across socioeconomic groups persisted or widened. Using data relating index scores to health outcomes in two large cohorts, we estimated that the improvements in dietary quality from 1999 to 2012 prevented 1.1 million premature deaths. Also, this improvement in diet quality resulted in 8.6 percent fewer cardiovascular disease cases, 1.3 percent fewer cancer cases, and 12.6 percent fewer type 2 diabetes cases. Although the steady improvement in dietary quality likely accounted for substantial reductions in disease burden from 1999 to 2012, overall dietary quality in the United States remains poor. Policy initiatives are needed to ensure further improvements.

Diet-related environmental impacts must be reduced to mitigate climate change. Although many sustainable diets have been proposed, the human and planetary impacts of following such diets have not been tested. The aim of this study was to assess health and environmental outcomes related to following whole-diet sustainable dietary advice. The MyPlanetDiet RCT was a 12-week single-blinded, parallel study testing the impacts of a more sustainable diet. Participants were randomly assigned to receive personalized advice based on a sustainable diet (intervention) or based on current healthy eating guidelines (control). Dietary assessments, fasted anthropometry, and fasted serum samples were collected at baseline and end point. The primary outcome was change in diet-related greenhouse gas emissions (GHGE) measured in kilograms carbon dioxide equivalents per day (kgCO2-eq/d). Secondary outcomes included changes in diet quality, macronutrient and food group intakes, diet-related water footprint, and health biomarkers. Data were analyzed using 2-way mixed analysis of covariance. Study participants (n = 292) decreased diet-related GHGE over time (P < 0.001) with a significant time × group interaction between control (from 6.5 ± 0.2 to 5.7 ± 0.2 kgCO2-eq/d) and intervention groups (7.1 ± 0.2 to 4.8 ± 0.1 kgCO2-eq/d; P < 0.001). Diet quality increased in control (from 44.2 ± 0.8 to 52.9 ± 0.9) and intervention (from 44.7 ± 0.8 to 53.0 ± 0.9) groups (P < 0.001). Participants decreased red meat intakes (control: from 34.2 ± 2.9 to 25.7 ± 2.4 g/d; intervention: from 42.7 ± 3.4 to 12.8 ± 1.9 g/d; P < 0.001) and increased plant-based food intakes including beans, peas, and lentils (control: from 15.4 ± 1.9 to 18.3 ± 2.1 g/d; intervention: from 18.4 ± 2.1 to 49.2 ± 4.3 g/d; P < 0.001), fruit (control: from 164.8 ± 12.3 to 264.5 ± 13.9 g/d; intervention: from 188.5 ± 14.2 to 233.7 ± 13.5 g/d; P < 0.001), and vegetables (control: from 148.1 ± 6.5 to 163.1 ± 7.3 g/d; intervention: from 161.3 ± 5.9 to 201.9 ± 8.0 g/d; P < 0.001). No changes in anthropometry, serum biochemistry, or diet-related water footprint were observed. Personalized sustainable dietary advice led to healthier diets and lower diet-related GHGE with no short-term negative health effects. This trial was registered with clinicaltrials.gov as NCT05253547 (https://clinicaltrials.gov/study/NCT05253547).

Water Insecurity Indicators Are Associated with Lower Diet and Beverage Quality in a National Survey of Lower-Income United States Adults

Diet quality or macronutrient composition of total daily sodium intake (dNa) <2300 mg/day in the United States (US) is unknown. Using data from 2011–2014 NHANES (National Health and Nutrition Examination Survey), we examined 24-h dietary recalls (n = 10,142) from adults aged ≥18 years and investigated how diet composition and quality are associated with dNa. Diet quality was assessed using components of macronutrients and Healthy Eating Index 2010 (HEI-2010). Associations were tested using linear regression analysis adjusted for total energy (kcal), age, gender, and race/ethnicity. One-day dNa in the lower quartiles were more likely reported among women, older adults (≥65 years old), and lower quartiles of total energy (kcal) (p-values ≤ 0.001). With increasing dNa, there was an increase in the mean protein, fiber, and total fat densities, while total carbohydrates densities decreased. As dNa increased, meat protein, refined grains, dairy, and total vegetables, greens and beans densities increased; while total fruit and whole fruit densities decreased. Modified HEI-2010 total score (total score without sodium component) increased as dNa increased (adjusted coefficient: 0.11, 95% confidence interval = 0.07, 0.15). Although diet quality, based on modified HEI-2010 total score, increased on days with greater dNa, there is much room for improvement with mean diet quality of about half of the optimal level.

Previous studies had shown that trends in diet quality between children and adults may vary but lack quantitative comparisons. We aimed to compare diet quality and its trends between US children and adults in this research. Children aged 2 to 18 and adults aged 19 to 59 years old in the US were enrolled the serial cross-sectional analysis of National Health and Nutrition Examination Survey (NHANES) cycles from 1999 to 2018. Diet quality was assessed using the Healthy Eating Index-2015 (HEI-2015), and trends were analyzed by joinpoint regression model. This study included 31988 children and 34317 adults. From 1999 to 2018, there was a trend-change among 5 children's components trends (including total fruits in 2011-2012, whole fruits in 2005-2006, greens and beans in 2013-2014, dairy in 2013-2014, and total protein foods in 2013-2014, p for joinpoint <0.05 for each) and overall trend in 2013-2014, whereas no significant trend-change in adults' trend. The trends of overall HEI-2015 between children (average annual percent change 0.3%; 95% CI: -0.1% to 0.8%) and adults (0.3%; 95%CI: 0.0% to 0.6%) showed no significant difference in parallelism (p for parallelism=0.60), but a significant difference in coincidence (intercept -7.7±3.7 among children; -2.3±2.5 among adults; p for coincidence <0.05). Children had a different trend with more trend-changes in diet quality compared with adults, and the diet quality of children was worse than that of adults during 1999-2018 in the US.

New Findings from the Feeding Infants and Toddlers Study: Data to Inform Action

Background: Cross-sectional evidence suggests individuals overestimate their consumption of healthful foods. However, how perceived diet quality (PDQ) relates to diet quality calculated from standard methods is unclear. Examining study participants' misperceptions of either diet quality, and/or improvements in diet quality, may enhance our understanding of factors that influence the effectiveness of dietary interventions. Methods: The Healthy Eating Index (HEI-2015) was calculated pre- and post-intervention from dietary recalls in this randomized weight loss trial. Participant self-perception of pre-intervention and post-intervention diet quality was rated on a scale of 0 to 100 at the 12-month post-intervention time point. Agreement between perceived and calculated diet quality was examined using Bland-Altman method. Good agreement between measures was considered scores within 6 points. Results: Participants with complete HEI and PDQ data (n=116) was similar to the total sample and mostly female (79%), white (84%), and with median (q1, q3) age of 51.5 (35.5, 58.5) years. A quarter of participants had good agreement between HEI and PDQ scores at 12 months with differences between scores ranging from -44.8 to 29.9 points. Most of the disagreement arose from PDQ scores being higher (mean [std] 67.6 [18.5]) than HEI scores (mean [std] 56.4 [13.6]). Only 12% of participants had good agreement between change in HEI and change in PDQ scores with differences ranging from -68.4 to 39.6 points. Again, disagreement mainly arose from higher perceived improvement in diet quality (mean=18.6 points) compared to improvement in HEI scores (mean=1.3 points). Discussion: The diet quality of weight loss-seeking adults is less than ideal with little evidence of improvement; however, the majority of participants appear to perceive their diet quality and improvements in diet quality as better than measured. Future studies might consider the effect of better aligning participants’ perceptions with reality.

Trends in the Prevalence, Awareness, Treatment and Control of High Low Density Lipoprotein-Cholesterol Among United States Adults From 1999–2000 Through 2009–2010

This perspective examines the utility of the glycemic index (GI) as a carbohydrate quality indicator to improve Dietary Guidelines for Americans (DGA) adherence and diet quality. Achieving affordable, high-quality dietary patterns can address multiple nutrition and health priorities. Carbohydrate-containing foods make important energy, macronutrient, micronutrient, phytochemical, and bioactive contributions to dietary patterns, thus improving carbohydrate food quality may improve diet quality. Following DGA guidance helps meet nutrient needs, achieve good health, and reduce risk for diet-related non-communicable diseases in healthy people, yet adherence by Americans is low. A simple indicator that identifies high-quality carbohydrate foods and improves food choice may improve DGA adherence, but there is no consensus on a definition. The GI is a measure of the ability of the available carbohydrate in a food to increase blood glucose. The GI is well established in research literature and popular resources, and some have called for including the GI on food labels and in food-based dietary guidelines. The GI has increased understanding about physiological responses to carbohydrate-containing foods, yet its role in food-based dietary guidance and diet quality is unresolved. A one-dimensional indicator like the GI runs the risk of being interpreted to mean foods are “good” or “bad,” and it does not characterize the multiple contributions of carbohydrate-containing foods to diet quality, including nutrient density, a core concept in the DGA. New ways to define and communicate carbohydrate food quality shown to help improve adherence to high-quality dietary patterns such as described in the DGA would benefit public health.