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Reformulating packaged foods has the potential to improve the nutrient density of the global diet. The present perspective illustrates The Kraft Heinz Company’s approach to product (re)formulation to develop healthier product lines that are lower in saturated fats, total sugars, and sodium, and contain health promoting components. Here we present the rationale for The Kraft Heinz Company’s global nutrition targets used for the global innovation and renovation of foods and beverages. The global nutrition targets use a category specific approach to set maximum levels for the main nutrients of public health concern: saturated fat, total sugars and sodium, taking into account product characteristics (typical portion size, eating occasion, role in the diet, etc.) as well as regulatory, technological, sensory and safety constraints. Benchmarking examples illustrate how the nutrition targets are positioned within the United States, France, and Australia. These global nutrition targets serve as part of The Kraft Heinz Company’s environmental, social and governance nutrition commitments and demonstrates how the food industry is improving the nutritional value of packaged foods and beverages both now and into the future.

This study explored the characteristics of plant-based beverages (PBBs) that are marketed as "milks" in the United States. First, machine searches of product names and ingredients in the USDA Branded Food Products Database (BFPDB) yielded 641 nondairy PBBs that included almond, soy, coconut, cashew, other tree nut, flax/hemp, pea, and quinoa and rice "milks." The products varied in energy density and the majority of PBBs contained added salt (69%) and added sugar (53%). Scores on nutrient density metrics [Nutri-Score, Choices, and the Nutrient Rich Food index 7.3 (NRF7.3)] were higher for almond and pea products and lower for coconut PBBs, which contained saturated fat. Ingredient lists were searched further for added flavors, stabilizers, or preservatives said to be characteristic of the NOVA food classification system's ultra-processed group. Most PBBs (90.1%) and 95% of almond milks met the NOVA criteria for ultra-processed foods, because they were created from food components and contained multiple substances not used in normal cooking. Replacing milk and dairy products with plant-based alternatives will necessarily involve the use of ultra-processed foods.

Front-of-Pack Nutrition labels (FOPNLs) aim to improve consumers’ food purchases and prompt product reformulation by the food and beverage industry. Despite their widespread use, the effectiveness of FOPNL in achieving these goals is still a matter of debate. This review has gathered 65 original studies exploring the performances of four widely used FOPNLs (Multiple Traffic Light, Warning signs, Nutri-Score and Health Star Rating). Although FOPNLs have been associated with healthier food purchases, the magnitude of improvements was small and dependent on study settings. Any associated health effects were modeled rather than observed. None of the four FOPNLs clearly outperformed the other ones on any outcome. Few studies dealt with the impact of FOPNL on product reformulation. Some of those studies, but not all, found small reductions in energy, sodium, sugar and saturated fat content of foods in some food categories. Although global trends point to a small favorable effect of FOPNL, this conclusion is subject to caution since the evidence is inconsistent and comes from a wide variety of contexts and study designs. There remain numerous research gaps, notably with regard to the optimal characteristics of FOPNLs, the durability of FOPNL effects on consumer behaviors, and any possible unexpected consequences.

Low-calorie sweeteners (LCS) serve to replace added sugars in beverages and foods. The present goal was to explore any potential links between LCS use and cancer risk using the nationally representative National Health and Nutrition Examination Surveys 1988–2018 linked to 2019 Public-Use Linked Mortality Files. Analyses were based on dietary intakes from 1988–1994 NHANES (n = 15,948) and 1999–2018 NHANES (n = 48,754) linked to mortality data. The 1988–1994 NHANES separated aspartame from saccharin consumption; later data did not. LCS consumers were more likely to be older, female, non-Hispanic White, and with higher education and incomes compared to nonconsumers. LCS consumers were less likely to smoke and had higher HEI-2015 scores indicating higher-quality diets. In the cross-sectional NHANES data, LCS use was associated with higher BMI and higher prevalence of obesity and diabetes. There was no indication that aspartame, saccharin, or all LCS had any impact on overall cancer mortality. By using nonconsumers as the reference group, the hazard ratio (95th confidence interval, CI) group trend for tertiles of LCS use for 1988–1994 for aspartame was 1.00 (0.89–1.12), for saccharin 0.96 (0.79–1.10), and for 1988–2018 for all LCS was 0.92 (0.88–1.101). The null group trend effects were seen for analyses stratified by age/gender. The present analyses confirm past US-based reports that LCS use was associated with higher socioeconomic status, lower prevalence of smoking, and generally higher-quality diets. No association with cancer mortality was observed.

Plant-based milk alternatives and plant-based waters are of variable nutritional value. The present objective was to assess nutrient density of all plant-based beverages in the US Department of Agriculture Branded Food Products Database and determine whether plant-based milk alternatives met the proposed nutrient standards. Plant-based milk alternatives (n = 1042) were identified as almond, soy, coconut, cashew, other tree nut, flax/hemp, pea, and oat, quinoa, and rice products. Plant-based waters (n = 550) were coconut, aloe, tree, fruit, and plain. Machine searches of ingredient lists identified products with added sugars, salt, vitamins, and minerals. Plant-based milk alternatives were tested for compliance with previously developed nutrient standards. The Nutrient Rich Food Index (NRF5.3), two versions of Nutri-Score, and Choices International were the nutrient density metrics. Plant-based milk alternatives had mean energy density of 49 kcal/100 g, were low in protein (~1.1 g/100 g), often contained added sugars and salt, and tended to be fortified with calcium, vitamin A, vitamin D, and vitamin B12. Only 117 milk alternatives (11.2%) met nutrient standards and only 80 (7.7%) met the more stringent “best of class” standards for ≥2.8 g/100 g protein and

Affordable nutrient density is provided by low-cost and nutrient-rich foods. We explored nutrient density, cost, and NOVA category assignments within and across food groups in Brazil. The nutrient density of the foods (n = 591) was assessed using the Nutrient Rich Food Index (NRF9.3) based on protein, fiber, vitamin A (RAE), vitamin C, vitamin E (mg), Ca, Fe, K and Mg; and NRF6.3 score for priority nutrients: Ca, Fe, Zn, vitamin A, vitamin B12, and folate. Nutrients to limit (LIM) were saturated fat, added sugar, and sodium. Affordability was defined as the ratio of energy and/or nutrient density of foods and retail price per 100 kcal. Foods were classified as minimally processed (n = 106), processed (n = 188), ultra-processed (n = 286), and culinary ingredients (n = 11). Nutrient density was positively linked to per 100 kcal food cost. Ultra-processed foods (UPF) contained more energy, fat, sugar, and salt and had lower NRF scores compared to minimally processed (MPF) foods. UPF was also less expensive than MPF foods. Nutrient-rich foods below the median per 100 kcal costs included MPF foods, but also processed foods (PF) and UPF. Affordable nutrient-rich foods can be found in the different categories of the NOVA classification.

Government agencies and private companies have supported the development of nutrient profiling (NP) systems to facilitate the selection of nutrient-dense foods by consumers, promote nutritious food development, and limit excessive advertising of products with low nutritional value. While most NP models were developed to assess individual foods, the Ajinomoto Group Nutrient Profiling System (ANPS) was developed to assess the overall nutritional value of cooked dishes that are culturally specific to Japan. Based on the national dietary recommendations and nutritional surveys, target values were created for 13 dish categories, while considering the combinations of meal units. For the ANPS, the four evaluating elements were protein and vegetables, which should be encouraged, and sodium and saturated fatty acids, which should be limited. The ANPS algorithm for dishes was the sum of the scores of individual elements, with a maximum of 10 points per serving. The sum of scores was then multiplied by 2.5 to convert to the 100-point scale. Convergent validity was tested using the nutrient-rich food index (NRF) score of 6.3. In total, 1,089 popular Japanese dishes were evaluated using the ANPS, and the median score of ANPS was 70.0 points (interquartile range, 55–78.8), and the average score was 67.7 (standard deviation, 16.5) points. Since salt intake is a major health risk in Japan, this tool was designed to evaluate sodium content with high sensitivity, and low-salt dishes significantly improved sodium and ANPS scores compared with regular dishes. The Pearson’s correlation coefficient between the total score of NRF 6.3 and ANPS in 1,089 dishes was r = 0.452 (p < 0.0001). This newly developed ANPS could be used to evaluate culture-specific cooked dishes per serving size. It can determine the nutritional values of dishes, with a high sensitivity to sodium content, a major Japanese nutritional issue. Further research is needed to determine the accuracy and usefulness of the ANPS as a system that would lead to changes in eating behavior nationwide.

The USDA Thrifty Food Plan (TFP) is an estimate of a lowest-cost healthy diet that meets dietary guidelines while respecting existing eating habits. In the US, the TFP provides the basis for federal food assistance. Included in the TFP are protein foods from both animal and plant sources. The present goal was to explore the place of fresh pork among protein foods in the revised TFP 2021. Our analyses used the same databases and the same quadratic programming (QP) methods as had been used by the USDA to develop the TFP 2021. Dietary intakes came from the National Health and Nutrition Examination Survey (NHANES 2015-16); nutrient composition data came from the Food and Nutrient Database for Dietary Studies (FNDDS 2015-16), and national food prices came from the 2021 TFP report. Amounts and prices were for foods as consumed. Our QP Model 1 used USDA modeling categories to replicate the TFP 2021. The non-poultry meat category was then separated into pork and beef. Model 2 examined whether the TFP 2021 algorithm would select pork or beef. Model 3 sought the lowest cost healthy diet, the same as the TFP 2021. Model 4 replaced beef and poultry with pork; whereas Model 5 replaced pork and poultry with beef. Weekly costs were calculated for a family of four and eight age-gender groups. All models met the nutrient requirements. The market basket cost for a family of four in Model 1 was USD 189.88, compared to the purchase price of USD 192.84 in the TFP 2021. In Model 2, fresh pork was selected preferentially over beef. The lowest-cost healthy food plan in Model 3 increased fresh pork to 3.4 lbs/week. Replacing beef and poultry with pork in Model 4 led to a modest decrease in the weekly cost. Replacing pork and poultry with beef in Model 5 led to a major increase in the weekly cost. We conclude, based on TFP-analogous modeling, that fresh pork is the preferred meat source, providing high-quality protein at a low cost. QP methods, as used in the TFP 2021, are a valuable tool for designing food plans that are affordable, acceptable, and nutrient-rich.

BackgroundStarchy vegetables, including white potatoes, are often categorized as “lower-quality” carbohydrate foods, along with refined grains, 100% fruit juices, sweetened beverages, and sugars, snacks and sweets. Among “higher-quality” carbohydrates are whole grains, non-starchy vegetables, legumes, and whole fruits.ObjectiveTo apply multiple nutrient profiling (NP) models of carbohydrate quality to foods containing >40% carbohydrate by dry weight in the USDA Food and Nutrient Database for Dietary Studies (FNDDS 2017-18).MethodsCarbohydrate foods in the FNDDS (n = 2423) were screened using four recent Carbohydrate Quality Indices (CQI) and a new Carbohydrate Food Quality Score (CFQS-4). Cereal products containing >25% whole grains by dry weight were classified as whole grain foods.ResultsBased on percent items meeting the criteria for 4 CQI scores, legumes, non-starchy and starchy vegetables, whole fruit, and whole grain foods qualified as “high quality” carbohydrate foods. Distribution of mean CFQS-4 values showed that starchy vegetables, including white potatoes placed closer to non-starchy vegetables and fruit than to candy and soda.ConclusionPublished a priori determinations of carbohydrate quality do not always correspond to published carbohydrate quality metrics. Based on CQI metrics, specifically designed to assess carbohydrate quality, starchy vegetables, including white potatoes, merit a category reassignment and a more prominent place in dietary guidance.

Many plant-based beverages (PBBs) in the US Department of Agriculture Branded Food Products Database serve as milk alternatives or replacements. Their collective nutrient content and fortification patterns need to be evaluated more fully. Listed ingredients for 641 PBBs were machine-searched for added vitamins, minerals, sugar and salt. The Nutrient Rich Food index NRF5.3 measured nutrient density. Except for soy milks, there was little consistency in nutrient density and micronutrient content across and within PBB product types. Industry-wide voluntary nutrient standards are one mechanism to bring more consistency to the expanding PBB category. Analysis of nutrient density and micronutrient content across plant-based milk alternatives shows inconsistencies pertinent to the definition of these products as a single food group.

Background: The global policy discourse on sustainability and health has called for dietary transformations that require diverse, concerted actions from governments and institutions. In this article, we highlight the need to examine sociocultural influences on food practices as precursors to food policy decisions. Discussion: Sociocultural food practices relate to ideas and materials that give rise to food choices and food patterns of a group. We begin with a discussion of how individuals experience, interpret, negotiate, and symbolize the food world around them. We examine primarily the ideational pathways, such as identity, gender, religion, and cultural prohibitions, and their influence on food practices. We then provide guiding questions, frameworks, and a brief overview of food choice values to support policy planning and design. Lastly, we explore how sociocultural change for sustainable or healthy diets is already happening through food movements, food lifestyles, and traditional diets.

The amounts of animal-sourced foods required to achieve a least-cost nutritious diet depend on the food prices prevalent in each country. Using linear programming, we determine least-cost dietary patterns in the United States and the constituent amounts of animal-sourced foods. We considered local foods and prices from 2009–2010, and the average energy and nutrient requirements of adults. Nutrient-adequate food patterns were estimated at US$1.98 per day and included animal and plant products. Limiting nutrients were α-linolenic acid, potassium, choline, and vitamins C, D, E and K. The prices of animal-based foods had to be increased by 2–11.5 times to be excluded from the modelled food pattern, with the least cost of a plant-only diet at US$3.61. Given relative food prices in the United States, we show that animal-based foods are needed to secure adequate nutrition at the lowest cost, underscoring the role of price and market mechanisms in the choice of nutrient-adequate, sustainable diets. Affordability of dietary patterns varies across countries, thereby affecting the levels of animal- and plant-based foods required to satisfy nutritional demands. Using an optimization model, Chungchunlam et al. reveal the components of the least-cost nutritious diet in the United States.

Food environment research is increasingly gaining prominence in low- and middle-income countries (LMICs). However, in the absence of a systematic review of the literature, little is known about the emerging body of evidence from these settings. This systematic scoping review aims to address this gap. A systematic search of 6 databases was conducted in December 2017 and retrieved 920 records. In total, 70 peer-reviewed articles met the eligibility criteria and were included. Collectively, articles spanned 22 LMICs, including upper-middle-income countries (n = 49, 70%) and lower-middle-income countries (n = 18, 26%). No articles included low-income countries. Articles featured quantitative (n = 45, 64%), qualitative (n = 17, 24%), and mixed-method designs (n = 11, 8%). Studies analyzed the food environment at national, community, school, and household scales. Twenty-three articles (55%) assessed associations between food environment exposures and outcomes of interest, including diets (n = 14), nutrition status (n = 13), and health (n = 1). Food availability was associated with dietary outcomes at the community and school scales across multiple LMICs, although associations varied by vendor type. Evidence regarding associations between the food environment and nutrition and health outcomes was inconclusive. The paucity of evidence from high-quality studies is a severe limitation, highlighting the critical need for improved study designs and standardized methods and metrics. Future food environment research must address low-income and lower-middle-income countries, and include the full spectrum of dietary, nutrition, and health outcomes. Improving the quality of food environment research will be critical to the design of feasible, appropriate, and effective interventions to improve public health nutrition in LMICs.

The category of “ultraprocessed” foods in the NOVA food classification scheme is ostensibly based on industrial processing. We compared NOVA category assignments with the preexisting family of Nutrient Rich Food (NRF) indices, first developed in 2004. The NRF indices are composed of 2 subscores: the positive NR based on protein, fiber, and vitamins and minerals, and the negative LIM subscore based on saturated fat, added sugars, and sodium. The 378 foods that were components of the widely used Fred Hutchinson Cancer Center food frequency questionnaire were assigned to NOVA categories and scored using multiple NRF indices. Contrary to published claims, NOVA was largely based on the foods' content of saturated fat, added sugars, and sodium. There were strong similarities between NOVA categories and NRF scores that were largely driven by the nutrients to limit. Nutrient density led to higher increased NRF scores but had less impact on NOVA categories. As a result, the NOVA scheme misclassified some nutrient-rich foods. We conclude that the NOVA classification scheme adds little to the preexisting nutrient profiling models. The purported links between NOVA categories and health outcomes could have been obtained using preexisting NRFn.3 nutrient density metrics.

Nutrient profiling (NP) models, intended to capture the full nutritional value of plant-based foods, ought to incorporate bioactive phytochemicals, including flavonoids, in addition to standard nutrients. The well-established Nutrient Rich Food (NRF9.3) score is based on 9 nutrients to encourage (protein, fiber, vitamins A, C, D and calcium, iron, potassium, magnesium) and 3 nutrients to limit (saturated fat, added sugar, sodium). The new category-specific NRF9f.3 score kept the same algorithm based on sums of percent daily values (%DVs) but swapped vitamin D for total flavonoids from the USDA database. NRF9f.3 was applied to the USDA fruit group categories, comparing nutrient density of citrus fruit, citrus juice, dried fruit, raw and cooked fruit, berries, fruit mixtures, fruit salads, non-citrus fruit juice, and fruit nectars. Adding total flavonoids to NRF9f.3 allowed for a recalibration of fruit total nutritional value. Citrus fruits and juices had significantly higher flavanones, berries had significantly higher anthocyanidins, and dried fruit and berries had significantly higher flavan-3-ols, than other fruits (all p < 0.05). Citrus fruit, citrus juice and berries had significantly higher NRFf9.3 scores than all other fruit subcategories (p < 0,05), but were not different from each other. The more innovative NP models are both category specific and make effective use of new nutrient composition databases. NRF9.3 when applied to the fruit group discriminates primarily on fiber, vitamin C, and added sugar content. Incorporating flavonoid and polyphenol data modernizes NP models to better capture nutrient density of plant foods that can aid in dietary guidance and policy development to improve diversity and nutritional value of the diet.

There is a growing global consensus among food system experts that diets and how we source our foods must change. The sustainable nutrition community continues exploring the environmental impact and dietary value of foods. Packaged foods have been largely ignored within the dialogue, and if they are addressed, existing frameworks tend to label them all as "ultraprocessed" and uniformly discourage their consumption. This approach lacks the nuance needed to holistically evaluate packaged foods within recommended dietary patterns. Additionally, there is considerable diversity of opinion within the literature on these topics, especially on how best to improve nutrition security in populations most at risk of diet-related chronic disease. In support of addressing these challenges, 8 sustainability and nutrition experts were convened by Clif BarCompany for a facilitated discussion on the urgent need to drive adoption of healthy, sustainable diets; the crucial role that certain packaged foods can play in helping make such diets achievable and accessible; and the need for actionable guidance around how to recommend and choose packaged foods that consider human, societal, and planetary health. This article summarizes the meeting discussion, which informed the development of a proposed framework based on guiding principles for defining sustainable, nutritious packaged foods across key nutrition, environmental, economic, and sociocultural well-being indicators. Although additional research is needed to substantiate specific metrics in order to operationalize the framework, it is intended to be a foundation from which to build and refine as science and measurement capabilities advance, and an important step toward broader adoption of healthy, sustainable diets.

The ongoing nutrition transition in lower- and middle-income countries (LMIC) in South East Asia may have a positive impact on protein nutrition. This study assessed the diversity of plant and animal protein food sources in relation to essential amino acid (EAA) adequacy in a population-based sample (N1665) in Indonesia. Dietary intakes from in-person 24 h recalls provided data on energy and protein intakes (in g/d) from plants (grains, legumes), meat, poultry and fish, and eggs and dairy. Protein diversity scores were based on the number of protein food sources over 24 h. EAA scores were the ratio of amino acid intakes to recommended values. Protein diversity and EAA scores were then compared across multiple socio-demographic indices. Analysis of variance andχ2tests were used to test for differences among groups. Energy intakes were 1678 kcal/d for men and 1435 kcal/d for women. Average protein intakes (and prevalence of inadequacy) were 59⋅4 g/d (41⋅7 %) for men and 51⋅5 g/d (51⋅1 %) for women. In regression analyses, higher protein diversity scores were associated with higher protein intakes, more animal protein and less plant protein and with higher EAA scores. Lower protein diversity scores were associated with lower intakes of lysine, leucine and valine relative to requirements, as well as with lower EAA, rural settings, less wealth and less modernisation. Greater diversity of animal protein food sources, observed among groups of higher socio-economic status, was linked to better amino acid adequacy and protein nutrition.

Existing metrics of carbohydrate food quality have been based, for the most part, on favorable fiber- and free sugar-to-carbohydrate ratios. In these metrics, higher nutritional quality carbohydrate foods are defined as those with >10% fiber and

BackgroundNutrient profiling (NP) models that are used to assess the nutrient density of foods can be based on a combination of key nutrients and desirable food groups.ObjectiveTo compare the diagnostic accuracy of a new balanced hybrid nutrient density score (bHNDS) to Nutri-Score and Health Star Rating (HSR) front-of-pack systems using receiver operating characteristic (ROC) curve analyses. The diet-level bHNDS was first validated against Healthy Eating Index (HEI-2015) using data from the 2017–18 National Health and Nutrition Examination Survey (2017–18 NHANES). Food-level bHNDS values were then compared to both the Nutri-Score and HSR using ROC curve analyses.ResultsThe bHNDS was based on 6 nutrients to encourage (protein, fiber, calcium, iron, potassium, and vitamin D); 5 food groups to encourage (whole grains, nuts and seeds, dairy, vegetables, and fruit), and 3 nutrients (saturated fat, added sugar, and sodium) to limit. The algorithm balanced components to encourage against those to limit. Diet-level bHNDS values correlated well with HEI-2015 (r = 0.67; p < 0.001). Food-level correlations with both Nutri-Score (r = 0.60) and with HSR (r = 0.58) were significant (both p < 0.001). ROC estimates of the Area Under the Curve (AUC) showed high agreement between bHNDS values and optimal Nutri-Score and HSR ratings (>0.90 in most cases). ROC analysis identified those bHNDS cut-off points that were predictive of A-grade Nutri-Score or 5-star HSR. Those cut-off points were highly category-specific.ConclusionThe new bHNDS model showed high agreement with two front-of-pack labeling systems. Cross-model comparisons based on ROC curve analyses are the first step toward harmonization of proliferating NP methods that aim to “diagnose” high nutrient-density foods.

Carbohydrate foods (≥40% energy from carbohydrates) are the main source of energy in the US diet. In contrast to national-level dietary guidance, many regularly consumed carbohydrate foods are low in fiber and whole grains but high in added sugar, sodium, and/or saturated fat. Given the important contribution of higher-quality carbohydrate foods to affordable healthy diets, new metrics are needed to convey the concept of carbohydrate quality to policymakers, food industry stakeholders, health professionals, and consumers. The recently developed Carbohydrate Food Quality Scoring System is well aligned with multiple key healthy messages on nutrients of public health concern from the 2020–2025 Dietary Guidelines for Americans. Two models are described in a previously published paper: one for all non-grain carbohydrate-rich foods (e.g., fruits, vegetables, legumes) known as the Carbohydrate Food Quality Score-4 (CFQS-4), and one for grain foods only known as the Carbohydrate Food Quality Score-5 (CFQS-5). These CFQS models provide a new tool that can guide policy, programs, and people towards improved carbohydrate food choices. Specifically, the CFQS models represent a way to unify and reconcile diverse ways to describe different types of carbohydrate-rich foods (e.g., refined vs. whole, starchy vs. non-starchy, dark green vs. red/orange) and make for more useful and informative messaging that better aligns with a food’s nutritional and/or health contributions. The present paper’s aims are to show that the CFQS models can inform future dietary guidelines and help support carbohydrate food recommendations with other health messages aimed at promoting foods that are nutrient-dense, fiber-rich, and low in added sugar.

Background The coronavirus disease 2019 (COVID-19) pandemic profoundly affected food systems including food security. Understanding how the COVID-19 pandemic impacted food security is important to provide support and identify long-term impacts and needs. Objective The National Food Access and COVID research Team (NFACT) was formed to assess food security over different US study sites throughout the pandemic, using common instruments and measurements. This study presents results from 18 study sites across 15 states and nationally over the first year of the COVID-19 pandemic. Methods A validated survey instrument was developed and implemented in whole or part through an online survey of adults across the sites throughout the first year of the pandemic, representing 22 separate surveys. Sampling methods for each study site were convenience, representative, or high-risk targeted. Food security was measured using the USDA 6-item module. Food security prevalence was analyzed using ANOVA by sampling method to assess statistically significant differences. Results Respondents (n = 27,168) indicate higher prevalence of food insecurity (low or very low food security) since the COVID-19 pandemic, compared with before the pandemic. In nearly all study sites, there is a higher prevalence of food insecurity among Black, Indigenous, and People of Color (BIPOC), households with children, and those with job disruptions. The findings demonstrate lingering food insecurity, with high prevalence over time in sites with repeat cross-sectional surveys. There are no statistically significant differences between convenience and representative surveys, but a statistically higher prevalence of food insecurity among high-risk compared with convenience surveys. Conclusions This comprehensive study demonstrates a higher prevalence of food insecurity in the first year of the COVID-19 pandemic. These impacts were prevalent for certain demographic groups, and most pronounced for surveys targeting high-risk populations. Results especially document the continued high levels of food insecurity, as well as the variability in estimates due to the survey implementation method., Multi-site assessment demonstrates widespread food insecurity during COVID-19, especially for households with children, job loss, and Black, Indigenous, People of Color across multiple survey methods.

Typical diets include an assortment of unprocessed, processed, and ultra-processed foods, along with culinary ingredients. Linear programming (LP) can be used to generate nutritionally adequate food patterns that meet pre-defined nutrient guidelines. The present LP models were set to satisfy 22 nutrient standards, while minimizing deviation from the mean observed diet of the Seattle Obesity Study (SOS III) sample. Component foods from the Fred Hutch food frequency questionnaire comprised the market basket. LP models generated optimized 2000 kcal food patterns by selecting from all foods, unprocessed foods only, ultra-processed foods only, or some other combination. Optimized patterns created using all foods contained less fat, sugar, and salt, and more vegetables compared to the SOS III mean. Ultra-processed foods were the main sources of added sugar, saturated fat and sodium. Ultra-processed foods also contributed most vitamin E, thiamin, niacin, folate, and calcium, and were the main sources of plant protein. LP models failed to create optimal diets using unprocessed foods only and ultra-processed foods only: no mathematical solution was obtained. Relaxing the vitamin D criterion led to optimized diets based on unprocessed or ultra-processed foods only. However, food patterns created using unprocessed foods were significantly more expensive compared to those created using foods in the ultra-processed category. This work demonstrates that foods from all NOVA categories can contribute to a nutritionally adequate diet.

BackgroundPlant-based diets in lower-income countries are often associated with inadequate protein nutrition and adverse health outcomes.ObjectiveTo examine the diversity of protein food sources, in both animal and plant, across diverse socio-demographic groups in Indonesia as compared to Malaysia.DesignThe SCRiPT (Socio Cultural Research in Protein Transition) study was based on population-based samples recruited in Indonesia (N = 1665) and in Malaysia (N = 1604). Data from 24-h in-person dietary recalls in each country were used to construct the frequency counts of protein sources by food group. Protein sources were defined as fish, poultry, red meat (beef, pork, and mutton), eggs, dairy, and plants (cereals, pulses, and tubers). The percent reported frequencies for animal and plant proteins were compared across socio-demographic strata and by country. Analyses were based on one-way Anovas and general linear model regressions adjusting for covariates.ResultsAnimal protein frequency counts were 34% of total in Indonesia, but 50% in Malaysia's. Higher reported consumption frequencies for poultry and red meat in both countries were associated with urban living, greater modernization, and higher socioeconomic status, with stronger social gradients observed in Indonesia. Reported fish consumption was higher in Indonesia than in Malaysia. Fish was more likely to be listed by rural island populations in Indonesia and was associated with lower education and incomes. Consumption frequencies for plant-based proteins were associated with lower socio-economic status in Indonesia and in Malaysia.ConclusionsMore affluent groups in both countries reported higher frequencies for meat, eggs, and dairy as opposed to fish. Greater economic development in Southeast (SE) Asia is associated with more animal protein, particularly from poultry, which may displace fish, the traditional source of high quality protein for the region.

Carbohydrate-containing crops provide the bulk of dietary energy worldwide. In addition to their various carbohydrate forms (sugars, starches, fibers) and ratios, these foods may also contain varying amounts and combinations of proteins, fats, vitamins, minerals, phytochemicals, prebiotics, and anti-nutritional factors that may impact diet quality and health. Currently, there is no standardized or unified way to assess the quality of carbohydrate foods for the overall purpose of improving diet quality and health outcomes, creating an urgent need for the development of metrics and tools to better define and classify high-quality carbohydrate foods. The present report is based on a series of expert panel meetings and a scoping review of the literature focused on carbohydrate quality indicators and metrics produced over the last 10 years. The report outlines various approaches to assessing food quality, and proposes next steps and principles for developing improved metrics for assessing carbohydrate food quality. The expert panel concluded that a composite metric based on nutrient profiling methods featuring inputs such as carbohydrate–fiber–sugar ratios, micronutrients, and/or food group classification could provide useful and informative measures for guiding researchers, policymakers, industry, and consumers towards a better understanding of carbohydrate food quality and overall healthier diets. The identification of higher quality carbohydrate foods could improve evidence-based public health policies and programming—such as the 2025–2030 Dietary Guidelines for Americans.

Fruit and vegetables (FV) have been a cornerstone of healthy dietary recommendations; the 2015-2020 U.S. Dietary Guidelines for Americans recommend that FV constitute one-half of the plate at each meal. FV include a diverse collection of plant foods that vary in their energy, nutrient, and dietary bioactive contents. FV have potential health-promoting effects beyond providing basic nutrition needs in humans, including their role in reducing inflammation and their potential preventive effects on various chronic disease states leading to decreases in years lost due to premature mortality and years lived with disability/morbidity. Current global intakes of FV are well below recommendations. Given the importance of FV for health, public policies that promote dietary interventions to help increase FV intake are warranted. This externally commissioned expert comprehensive narrative, umbrella review summarizes up-to-date clinical and observational evidence on current intakes of FV, discusses the available evidence on the potential health benefits of FV, and offers implementation strategies to help ensure that public health messaging is reflective of current science. This review demonstrates that FV provide benefits beyond helping to achieve basic nutrient requirements in humans. The scientific evidence for providing public health recommendations to increase FV consumption for prevention of disease is strong. Current evidence suggests that FV have the strongest effects in relation to prevention of CVDs, noting a nonlinear threshold effect of 800 g per day (i.e., about 5 servings a day). A growing body of clinical evidence (mostly small RCTs) demonstrates effects of specific FV on certain chronic disease states; however, more research on the role of individual FV for specific disease prevention strategies is still needed in many areas. Data from the systematic reviews and mostly observational studies cited in this report also support intake of certain types of FV, particularly cruciferous vegetables, dark-green leafy vegetables, citrus fruits, and dark-colored berries, which have superior effects on biomarkers, surrogate endpoints, and outcomes of chronic disease.

Background: Hybrid nutrient density scores are based on both nutrients and selected food groups. Objective: To compare the new hybrid nutrient-rich food NRFh 4:3:3 score to other nutrient-rich food (NRF) scores, energy density, and energy cost and to model the impact on the Healthy Eating Index (HEI-2015) of partially replacing less nutrient-rich with more nutrient-rich foods. Methods: Analyses were based on 5870 foods and beverages in the Food and Nutrient Database for Dietary Studies and on 24 h dietary recalls from the National Health and Nutrition Examination Survey (NHANES 2013–16). The NRFh 4:3:3 model was based on four nutrients to encourage (protein fiber, potassium, MUFA + PUFA), three food groups to encourage (dairy, fruit, whole grains), and three nutrients to limit (saturated fat, added sugar, sodium). Ratings generated by NRFh 4:3:3 and by other NRF models were correlated with score components, energy density (kcal/100 g), and energy cost (USD/100 kcal). The impact on HEI-2015 of replacing foods in the lowest nutrient density tertile (T1) with top tertile (T3) foods at 10%, 20%, 30%, and 100% equicaloric replacement was modeled using NHANES 2013–16 dietary data by population subgroups. Results: The NRFh 4:3:3 model awarded higher scores to foods containing dairy, fruit, and whole grains and proportionately lower scores to vegetables when compared to the NRF 9.3 model. Higher NRF and NRFh nutrient density scores were linked to lower energy density and higher energy cost, however, both correlations were lower for the NRFh 4:3:3. Isocaloric replacement of bottom tertile with top tertile foods as rated by both models led to significantly higher HEI-2105 values, based on complete (100%) and on partial (10–30%) replacement. Conclusion: The new NRFh 4:3:3 model provides the basis for developing new metrics of affordable nutrient density. The model identified “best value” food categories that were both affordable and nutrient-rich. Total and partial replacement of low nutrient density with high nutrient density foods was associated with higher HEI-2015 scores, suggesting that even partial inclusion of more nutrient dense foods in the diet may have an important impact on total diet quality.

Nutrient profiling systems, initially designed to promote healthy food choices at the point of sale, can also provide the scientific basis for innovation and product reformulation by the food industry. This work presents a new profiling system to help define feasible nutrient targets for reformulation of packaged foods. The focus is on five key nutrients for which the World Health Organisation (WHO) has set population-level goals: sugar, saturated fat, sodium, fiber, and protein. The methodology uses Mintel’s Global New Products Database of packaged foods to (1) identify nutrients relevant to each food category (2) sort products into sub-categories defined by a unique nutritional signature, and (3) develop standards for “best of class” products. For instance, if targeted to be amongst the best 15% of the global food supply, pizza must have less than 4.0 g/100 g saturated fat, less than 520 mg/100 g total sodium and more than 9.8 g/100 g protein. Fiber and sugar are not identified as relevant nutrients for the pizza category and no targets are provided.

Americans waste about a pound of food per day. Some of this is represented by inedible food waste at the household level. Our objective was to estimate inedible food waste in relation to diet quality and participant socio-economic status (SES). Seattle Obesity Study III participants (n = 747) completed the Fred Hutch Food Frequency Questionnaire (FFQ) and socio-demographic and food expenditure surveys. Education and geo-coded tax-parcel residential property values were measures of SES. Inedible food waste was calculated from diet records. Retail prices of FFQ component foods (n = 378) were used to estimate individual-level diet costs. The NOVA classification was used to identify ultra-processed foods. Multivariable linear regressions tested associations between inedible food waste, SES, food spending, Nutrient Rich Food (NRF9.3) and Healthy Eating Index (HEI-2015) scores. Inedible food waste was estimated at 78.7 g/d, mostly from unprocessed vegetables (32.8 g), fruit (30.5 g) and meat, poultry, and fish (15.4 g). Greater inedible food waste was associated with higher HEI-2015 and NRF9.3 scores, higher food expenditures and lower percent energy from ultra-processed foods. In multivariable models, more inedible food waste was associated with higher food expenditures, education and residential property values. Higher consumption of unprocessed foods were associated with more inedible food waste and higher diet costs. Geo-located estimates of inedible food waste can provide a proxy index of neighborhood diet quality.

Healthy eating patterns, as described by dietary guidelines, typically favor whole grains, low-fat dairy, vegetables, fruit, legumes, and nuts and seeds. Nutrient-profiling (NP) models capture nutrient density of individual foods and can inform healthier food choices. Although whole grains are prominently featured in most dietary guidelines, they are not included in most NP models. Healthy foods, as identified by most NP models, are those that contain limited amounts of energy, saturated fat, total or added sugar, and sodium. As global dietary guidance turns to foods and food groups as opposed to individual nutrients, future nutrient-density metrics may need to do the same. Potential methods to incorporate whole grains into the overall concept of nutrient density and into selected NP models are outlined in this review. Incorporating whole grains into the Nutri-Score, Health Star Rating, or the Nutrient Rich Food index will require further analyses of dietary nutrient density in relation to health outcomes across diverse population subgroups. We present the rationale for how the inclusion of whole grains in NP models can assist in the implementation of dietary guidance.

Food processing and food (re)formulation can contribute to achieving sustainable healthy diets. Distinct from product formulation, the main purpose of food processing is to provide a stable and resilient supply of safe, shelf-stable, and affordable foods. Although efforts at reformulating processed foods have focused on removing excess added fat, sugar, and salt, product formulation can also take the form of voluntary fortification with protein, fiber, and micronutrients to improve dietary nutrient density and address population health needs. Advances in food technology have also led to the addition of desirable ingredients, including plant-based proteins and fermentation products, to processed foods. Among continuing challenges to product (re)formulation are the need to ensure product safety, maintain sensory appeal, control product cost, assure consumer acceptance, and manage the environmental footprint across the value chain. Voluntary (re)formulation of processed foods by the food industry can help improve diet quality and food security for all.

Background: Sustainable healthy diets are those dietary patterns that promote all dimensions of individuals’ health and well-being; have low environmental pressure and impact; are accessible, affordable, safe, and equitable; and are culturally acceptable. The food environment, defined as the interface between the wider food system and consumer’s food acquisition and consumption, is critical for ensuring equitable access to foods that are healthy, safe, affordable, and appealing. Discussion: Current food environments are creating inequities, and sustainable healthy foods are generally more accessible for those of higher socioeconomic status. The physical, economic, and policy components of the food environment can all be acted on to promote sustainable healthy diets. Physical spaces can be modified to improve relative availability (ie, proximity) of food outlets that carry nutritious foods in low-income communities; to address economic access certain actions may improve affordability, such as fortification, preventing food loss through supply chain improvements; and commodity specific vouchers for fruits, vegetables, and legumes. Other policy actions that address accessibility to sustainable healthy foods are comprehensive marketing restrictions and easy-to-understand front-of-pack nutrition labels. While shaping food environments will require concerted action from all stakeholders, governments and private sector bear significant responsibility for ensuring equitable access to sustainable healthy diets.

Methods to assess nutrient density of foods, commonly known as nutrient profiling (NP), typically include protein as a component. In this study, the goal was to apply a correction for protein quality by food source to selected NP algorithms. Analyses of 378 component foods of the Fred Hutch food frequency questionnaire showed that animal-source foods (ie, meat, eggs, and dairy) along with some soy products and nuts were the only foods that provided > 20% of the daily value (DV) of protein per 100 g or per 100 kcal. Most beans, pulses, legumes, grains, and vegetables provided

Nutrient profiling (NP) models aim to assess the nutritional quality of individual foods, according to their energy content and nutrient composition. NP models, initially created to prevent obesity in high-income countries, have tended to penalize dietary energy by giving lower ratings to foods containing excessive calories, fat, sugar, and salt. Energy-driven NP models may need to be reconceptualized for use in low- and middle-income countries (LMIC) where hunger, undernutrition, and micronutrient deficiencies continue to be issues of public health concern. Consistent with the position of the WHO that the purpose of NP methods is to address an identified public health problem, NP models intended for use in LMIC ought to address inadequate intakes of vitamin A, B vitamins, folate, calcium, iron, iodine, and zinc and the frequent lack of high-quality protein. Those models of nutrient density that feature beneficial nutrients (high-quality protein, vitamins, minerals, and trace elements) may be better suited to LMIC needs than are some current NP models that are wholly based around nutrients to limit. NP models intended for LMIC and global use will also need to take food fortification into account. The challenge for LMIC public health agencies is how to balance the future risk of excess “empty” calories against the continuing danger of inadequate nutrients and micronutrient deficiencies that persist at the population level.

Background: Choosing water in place of sugar-sweetened beverages (SSB) can reduce added sugars while maintaining adequate hydration. The present goal was to examine 2011–16 time trends in SSB vs. water consumption across US population subgroups.Methods: Dietary intake data for 22,716 persons aged >4 years came from two 24-h dietary recalls in successive cycles of the National Health and Examination Survey (NHANES 2011–16). Water intakes (in mL/d) from plain water (tap and bottled) and from beverages (SSB and not-SSB) were the principal outcome variables. Intakes were analyzed by age group, income to poverty ratio (IPR), and race/ethnicity. Time trends by demographics were also examined.Results: SSB and water intakes followed distinct social gradients. Most SSB was consumed by Non-Hispanic Black and lower-income groups. Most tap water was consumed by Non-Hispanic White and higher-income groups. During 2011–16, water from SSB declined from 322 to 262 mL/d (p < 0.005), whereas plain water increased (1,011–1,144 mL/d) (p < 0.05). Groups aged 30 years that increased drinking water (p < 0.001). Non-Hispanic White groups reduced SSB and increased tap water consumption. Non-Hispanic Black and lower income groups reduced SSB and increased bottled water, not tap.Conclusion: The opposing time trends in SSB and water consumption were not uniform across age groups or sociodemographic strata. Only the non-Hispanic White population reduced SSB and showed a corresponding increase in tap water. Lower-income and minority groups consumed relatively little plain drinking water from the tap.

The diet of Western societies is regarded as overly high in refined sugars and fat. Among the chief sources of dietary fat are meat, fats and cooking oils, and dairy products, including milk, cheese, and frozen desserts. Added sugars, mainly sucrose and fructose, are provided by discretionary table use, sweetened beverages, bakery goods, and confectionery products. Scientific literature on the nature of food preferences has been dominated by reports of cravings for carbohydrate-rich foods. Laboratory studies on taste responsiveness have generally focused on the perception of sweetness and the development of preferences for sweet taste. Relatively few studies have explored sensory perception of fats in sweetened foods, or the role fats play in determining food acceptance. The study of fat in food is considerably more complicated than the study of sugar solutions. Fats provide the characteristic texture, flavor, and aroma of many foods and endow foods with a variety of textural characteristics.

Background: Dietary guidelines have shifted emphasis from single nutrients to food patterns, food groups, and dietary ingredients. Nutrient profiling models need to do the same.Methods: Dietary intake data for 23,643 persons aged >2 years came from the 2011–2016 National Health and Examination Survey (NHANES 2011-16). Healthy Eating Index HEI-2015 was the diet quality measure. The new Nutrient Rich Food hybrid score (NRFh) was based on three subscores. The subscore based on x nutrients to encourage was defined as NRx. The subscore based on y MyPlate food groups to encourage was MPy. The negative subscore based on z nutrients to limit was LIMz. The final algorithm was NRFh(x.y.z) = NRx + MPy − LIMz. The selection of NRFh model components from among 16 nutrients and five food groups was based on regression analyses.Results: We conducted a total of 2,162,720 iterative regression analyses against HEI-2015 diet quality scores. NRF scores based on 16 nutrients accounted for up to 66% of the variance, whereas scores based on 5 MP food groups accounted for 50%. The new NRFh3:4:3 score with six nutrients and four food groups (fiber, potassium, PUFA+MUFA; whole grains, dairy, fruit, nuts and seeds; saturated fat, added sugar, sodium) explained 72%. The new NRFh4:3:3 score with seven nutrients and three food groups (protein, fiber, potassium, PUFA+MUFA; whole grain, dairy, fruit; saturated fat, added sugar, sodium;) also explained 72%. In both NRFh models, regressions remained significant for each population subgroup examined.Conclusion: The NRFh3:4:3 and NRFh4:3:3 models correlated well with HEI-2015 scores, a measure of diet quality that tracks compliance with Dietary Guidelines. Hybrid NP models based on nutrients and food groups could become part of dietary guidance.

OBJECTIVES: Young US adults consume inadequate amounts of recommended food groups, including fruit, vegetables, and whole grains but little is known about their fluid consumption. The present study aimed to evaluate if this population is meeting the recommendations for adequate fluid intake issued by National Academy of Medicine (NAM). METHODS: Beverage consumption data for 2160 adults aged 18–24y came from two 24 h dietary recalls in the three most recent cycles of the United States National Health and Nutrition Examination Survey (NHANES 2011–16). Water intakes (in mL/d) from drinking water, other beverages and moisture from foods were analyzed in relation to the current NAM Adequate Intake (AI) thresholds (3700 mL/d for males and 2700 mL/d for females). The proportion of young adults adhering to the AIs for total water intake was assessed using the National Cancer Institute method for estimating usual intake distributions. Additional analyses examined adherence by gender, income-to-poverty ratio and race/ethnicity. RESULTS: Mean water intakes were 2713 mL/d (3003 mL/d for men and 2382 mL/d for women). Of total water, 1191 mL/d (44%) came from drinking water, tap and bottled; 947 mL/d (35%) from non-water beverages; and 575 mL/d (21%) from foods. Only 35.3% of young adults met the AI recommendations. Female young adults were significantly more likely to meet the recommendations than males (37.8% vs. 29.6%; P = 0.04). Compared to the other race/ethnicity groups, non-Hispanic white young adults were most likely to meet recommendations (41.5%). Compared to non-Hispanic white young adults, non-Hispanic black young adults were least likely (18.7%, P

This article is based on a session at ASN 2019 entitled "Addressing the Four Domains of Sustainable Food Systems Science (Health, Economics, Society and the Environment): What Will It Take to Harmonize the Evidence to Advance the Field?" A summary of presentations is included. The presentations addressed the 4 principal domains of sustainability defined as nutrition/health, economics, environment, and society and the ways in which they are represented in current research. The session also introduced metrics and measures that are specific to each domain. Participants discussed next steps to move toward consensus and collaboration among scientific communities, especially those of health/nutrition science and environmental science. Food systems may need to be restructured to ensure that the global food supply provides adequate calories and nutrients at an affordable cost. Finally, the session addressed strategies to implement research concepts and move toward policies that encourage consumers to choose healthy diets from sustainable food systems.

This study explored consumption patterns of 100% orange juice by socio-demographics among US children and adults. Dietary intakes data for 15,983 persons aged >2 y came from the nationally representative National Health and Nutrition Examination Survey (NHANES 2013–2016). The What We Eat in America nutrient composition database was merged with the USDA Expanded Flavonoid Database to assess flavonoid intakes. Diet quality measures were the Healthy Eating Index (HEI-2015) and Nutrient Rich Food (NRF9.3) Index. Orange juice consumption accounted for a mean of 14 kcal/d and varied with age, incomes, and race/ethnicity. Orange juice consumption was associated with higher intakes of bioactive flavonoids, lower added sugars, and higher-quality diets overall. Diets of consumers were higher in vitamin C, potassium, calcium, vitamin D (adults), flavanones, and total flavonoids (children) as compared to non-consumers. Consumers had significantly higher HEI-2015 and NRF9.3 scores and lower body mass index values (adults). However, only 15.9% of the NHANES sample consumed any orange juice at all; of these 11.8% had

Global food systems will face unprecedented challenges in the coming years. They will need to meet the nutritional needs of a growing population and feed an expanding demand for proteins. This is against a backdrop of increasing environmental challenges (water resources, climate change, soil health) and the need to improve farming livelihoods. Collaborative efforts by a variety of stakeholders are needed to ensure that future generations have access to healthy and sustainable diets. Food will play an increasingly important role in the global discourse on health. These topics were explored during Nestle's second international conference on 'Planting Seeds for the Future of Food: The Agriculture, Nutrition and Sustainability Nexus', which took place in July 2017. This article discusses some of the key issues from the perspective of three major stakeholder groups, namely farming/agriculture, the food industry and consumers. © 2018 The Authors. Journal of The Science of Food and Agriculture published by John Wiley & Sons Ltd on behalf of Society of Chemical Industry.

Nutrient profiling (NP) models rate the nutritional quality of individual foods, based on their nutrient composition. Their goal is to identify nutrient-rich foods, generally defined as those that contain more nutrients than calories and are low in fat, sugar and salt. NP models have provided the scientific basis for evaluating nutrition and health claims and regulating marketing and advertising to children. The food industry has used NP methods to reformulate product portfolios. To help define what we mean by healthy foods, NP models need to be based on published nutrition standards, mandated serving sizes and open-source nutrient composition databases. Specifically, the development and testing of NP models for public health should follow the seven decision steps outlined by the European Food Safety Authority. Consistent with this scheme, the nutrient-rich food (NRF) family of indices was based on a variable number of qualifying nutrients (from six to fifteen) and on three disqualifying nutrients (saturated fat, added sugar, sodium). The selection of nutrients and daily reference amounts followed nutrient standards for the USA. The base of calculation was 418·4 kJ (100 kcal), in preference to 100 g, or serving sizes. The NRF algorithms, based on unweighted sums of percent daily values, subtracted negative (LIM) from positive (NRn) subscores (NRn – LIM). NRF model performance was tested with respect to energy density and independent measures of a healthy diet. Whereas past uses of NP modelling have been regulatory or educational, voluntary product reformulation by the food industry may have most impact on public health.

Background: The US Food and Drug Administration has modified its regulations on nutrient content claims by considering healthy dietary ingredients as well as nutrients. Objective: To assess the relation between dairy and fruit as main ingredients in children's snacks and the Nutrient Rich Food (NRF8.3) nutrient density score. Methods: Commonly consumed children's snacks in the United States, Canada, France, and the United Kingdom (n = 261) were assigned into USDA What We Eat in America (WWEI) categories. Nutrient composition data came from industry websites, open-source government databases (USDA Standard Reference SR28; CIQUAL), and back-of-pack food labels. Nutrient density was calculated using the Nutrient Rich Food Index NRF8.3. Snacks with dairy or fruit as the first listed ingredient (n = 115) were compared to those that listed neither (n = 146). Snacks that contained fruits-vegetables-nuts (FVN) (n = 88) were compared to those that did not (n = 173). Results: NRF8.3 scores were higher for snacks listing dairy or fruit as main ingredients. Dairy or fruit when listed as the first ingredient were associated with higher percent daily values of protein, fiber, calcium, vitamin A, vitamin C, and vitamin D, lower saturated fat content and a 30-point increment in NRF8.3 scores. The presence of FVN was associated with a 22-point increment in NRF8.3 scores. Conclusion: The correspondence between back-of-pack food ingredients and the nutrient based NRF8.3 scores suggests that ingredients can also be used to communicate the nutritional value of foods to the consumer. Dairy and fruit, when listed as first ingredients, were an important component of the NRF8.3 nutrient density score.

Dietary Guidelines for Americans 2015&ndash, 20 recommend replacing sugar sweetened beverages (SSBs) with plain water in order to promote adequate hydration while reducing added sugar intake. This study explored how water intakes from water, beverages, and foods are distributed across the day. The dietary intake data for 7453 children (4&ndash, 18 y) and 15,263 adults (&gt, 19 y) came from the National Health and Nutrition Examination Survey (NHANES 2011&ndash, 2016). Water was categorized as tap or bottled. Beverages were assigned to 15 categories. Water intakes (in mL/d) from water, beverages, and food moisture showed significant differences by age group, meal occasion, and time of day. Plain water was consumed in the morning, mostly in the course of a morning snack and between 06:00 and 12:00. Milk and juices were consumed at breakfast whereas SSBs were mostly consumed at lunch, dinner, and in the afternoon. Children consumed milk and juices, mostly in the morning. Adults consumed coffee and tea in the morning, SSBs in the afternoon, and alcohol in the evening. Relatively little drinking water was consumed with lunch or after 21:00. Dietary strategies to replace caloric beverages with plain water need to build on existing drinking habits by age group and meal type.

Flavonoid-rich tea offers an alternative to sugar-sweetened beverages. The present analyses, based on 2 24-hour dietary recalls for 17,506 persons aged &ge, 9 years old in the 2011&ndash, 2016 National Health and Nutrition Examination Survey database (NHANES 2011&ndash, 2016), explored tea consumption patterns in relation to demographics, diet quality, cardiovascular disease (CVD) biomarkers (lipids and blood pressure), and body weight. Beverage categories were unsweetened tea, other tea (herbal and presweetened tea), coffee, milk, 100% juice, water and other high-calorie (HC) and low-calorie (LC) beverages. Tea consumption (18.5% of the sample) was highest among older adults (51&ndash, 70 years old), non-Hispanic Asians and Whites, and those with college education and higher incomes. The effects of age, gender, education, income, and race/ethnicity were all significant (p &lt, 0.001 for all). Adult tea consumers had diets with more protein, fiber, potassium, iron, and magnesium, and less added sugars and alcohol. Their diets contained fewer HC beverages and coffee but had more total and citrus fruit, more total dark green and orange vegetables, and more seafood, eggs, soy and milk. Tea consumers had higher Healthy Eating Index (HEI-2015) and higher Nutrient-Rich Foods (NRF9.3) nutrient density scores. Few children drank tea and no differences in diet quality between consumers and non-consumers were observed. Adult tea consumers had slightly higher high-density lipoprotein (HDL) cholesterol and lower body mass index (BMI) values. Tea consumption was associated with higher socioeconomic status and better diets.

International audience; Background: The American Academy of Pediatrics (AAP) has recommended placing limits on the consumption of milk and 100% juice by children. Methods: Consumption data for 9,069 children aged 2-19 years came from three cycles of the nationally representative National Health and Nutrition Examination Survey (NHANES 2011-2016). Beverages were classified into 100% juices, milk (whole, reduced fat, and skim), caloric sugar sweetened beverages (SSB), low calorie beverages (LCB), and drinking water. The Healthy Eating Index 2015 and Nutrient Rich Food Index NRF9.3 were two measures of diet quality. Analyses examined consumption patterns for milk and 100% juice in relation to diet quality, AAP recommendations, and BMI z-scores across time and for different age groups. Results: Intakes of milk and 100% juice declined sharply with age, whereas SSB and water increased. Top quartiles of HEI 2015 and NRF9.3 diet quality scores were associated with higher intakes of water, milk, and 100% juice and with lower intakes of SSB. Lower-income groups drank less skim milk and water and more whole milk and SSB. Only 30% of the children consumed any 100% juice. There was no association between the consumption of milk or 100% juice and BMI z-scores for any age group. Conclusions: Top quartiles of diet quality were associated with more milk, 100% juice, and water, and less SSB. Higher quality diets were associated with lower compliance with the AAP 100% juice recommendations. Compliance with the AAP 100% juice recommendations was not associated with lower body weights. Attempts to limit the consumption of milk and 100% juice by children might have the unintended consequence of increasing consumption of SSB and may have limited value for obesity prevention.

The quality of dietary patterns can be optimized using a mathematical technique known as linear programming (LP). LP methods have rarely been applied to individual meals. The present LP models optimized the breakfast meal for those participants in the nationally representative National Health and Nutrition Examination Survey 2011&ndash, 2014 who ate breakfast (n = 11,565). The Nutrient Rich Food Index (NRF9.3) was a measure of diet quality. Breakfasts in the bottom tertile of NRF9.3 scores (T1) were LP-modeled to meet nutrient requirements without deviating too much from current eating habits. Separate LP models were run for children and for adults. The LP-modeled breakfasts resembled the existing ones in the top tertile of NRF9.3 scores (T3), but were more nutrient-rich. Favoring fruit, cereals, and dairy, the LP-modeled breakfasts had less meat, added sugars and fats, but more whole fruit and 100% juices, more whole grains, and more milk and yogurt. LP modeling methods can build on existing dietary patterns to construct food-based dietary guidelines and identify individual meals and/or snacks that need improvement.