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IntroductionSustainable foods need to be nutrient-rich, affordable, environmentally friendly, and socially acceptable. Pulses, which include beans, lentils, chickpeas, and dried peas are a food group that can fit all those criteria.MethodsThese concepts were tested serially using nutrient profiling methods that focused on protein and were extended to include food prices, greenhouse gas emissions, and energy demand. The present sustainability analyses were based on the US Department of Agriculture (USDA) nutrient composition and food prices data. Environmental impact data came from life cycle assessments (LCA). First, the USDA Protein Foods Group was disaggregated into animal and plant proteins. Plant proteins were separated into pulses, soy products, and nuts and seeds.ResultsPulses were among the lowest-cost protein sources (per 100 g and per 100 kcal) and had the lowest greenhouse gas emissions GHGE and energy demand. Pulses were among the most sustainable foods when monetary and energy costs were expressed per 50 g of protein (equivalent to 100% DV). Pulses scored well on the Nutrient Rich Food (NRF9.3) nutrient profiling system and on the related Affordable Nutrition Index that assessed nutrient density per penny.DiscussionPulses are a source of low-cost plant-based protein and a variety of priority vitamins and minerals, have low carbon footprint and energy demand, and are a valued culinary ingredient across diverse regions and cultures. As dietary guidance turns to plant-based diets, pulses need to be integrated into the global sustainability framework.

Processed foods have been part of the American diet for decades, with key roles in providing a safe, available, affordable, and nutritious food supply. The USDA Food Guides beginning in 1916 and the US Dietary Guidelines for Americans (DGA) since 1980 have included various types of commonly consumed processed foods (e.g., heated, fermented, dried) as part of their recommendations. However, there are multiple classification systems based on “level” of food processing, and additional evidence is needed to establish the specific properties of foods classified as “highly” or “ultra”-processed (HPF/UPFs). Importantly, many foods are captured under HPF/UPF definitions, ranging from ready-to-eat fortified whole grain breakfast cereals to sugar-sweetened beverages and baked goods. The consequences of implementing dietary guidance to limit all intake of foods currently classified as HPF/UPF may require additional scrutiny to evaluate the impact on consumers’ ability to meet daily nutrient recommendations and to access affordable food, and ultimately, on health outcomes. Based on a meeting held by the Institute for the Advancement of Food and Nutrition Sciences in May 2023, this paper provides perspectives on the broad array of foods classified as HPF/UPFs based on processing and formulation, including contributions to nutrient intake and dietary patterns, food acceptability, and cost. Characteristics of foods classified as UPF/HPFs are considered, including the roles and safety approval of food additives and the effect of food processing on the food matrix. Finally, this paper identifies information gaps and research needs to better understand how the processing of food affects nutrition and health outcomes.

Introduction: This study investigates the associations between built environment features and 3-year BMI trajectories in children and adolescents. Methods: This retrospective cohort study utilized electronic health records of individuals aged 5–18 years living in King County, Washington, from 2005 to 2017. Built environment features such as residential density; counts of supermarkets, fast-food restaurants, and parks; and park area were measured using SmartMaps at 1,600-meter buffers. Linear mixed-effects models performed in 2022 tested whether built environment variables at baseline were associated with BMI change within age cohorts (5, 9, and 13 years), adjusting for sex, age, race/ethnicity, Medicaid, BMI, and residential property values (SES measure). Results: At 3-year follow-up, higher residential density was associated with lower BMI increase for girls across all age cohorts and for boys in age cohorts of 5 and 13 years but not for the age cohort of 9 years. Presence of fast food was associated with higher BMI increase for boys in the age cohort of 5 years and for girls in the age cohort of 9 years. There were no significant associations between BMI change and counts of parks, and park area was only significantly associated with BMI change among boys in the age cohort of 5 years. Conclusions: Higher residential density was associated with lower BMI increase in children and adolescents. The effect was small but may accumulate over the life course. Built environment factors have limited independent impact on 3-year BMI trajectories in children and adolescents.

IntroductionNutrient profiling (NP) models designed to evaluate the healthfulness of plant-based foods ought to incorporate bioactive phytochemicals. Herbs and spices are one food group of current interest.MethodsTwo new versions of the well-established Nutrient Rich Food (NRF) index were applied to spices, herbs, vegetables, fruit, and other plant-based foods. Analyses used the US Department of Agriculture (USDA) SR-28 nutrient composition database merged with the USDA Expanded Flavonoid database 3.3. The NRF4.3 model was based on protein, fiber, potassium, and vitamin C. The NRFa11.3 model was based on micronutrients with reported antioxidant activity (vitamin C, vitamin E, selenium, copper, and zinc), carotenoids (alpha and beta carotene, beta-cryptoxanthin, lycopene, lutein/zeaxantin) and flavonoids. Saturated fat, added sugar, and sodium were nutrients to limit. The NRF algorithm was based on sums of percent daily values (%DVs) capped at 100%.ResultsThe NRF4.3 model awarded high scores to herbs and spices, cocoa powder, and nuts, but did not discriminate well among vegetables and fruit. The NRFa11.3 model performed better. Green leafy, red orange and cruciferous vegetables had the highest carotenoid content. Highest in flavonoids were cocoa powder, herbs and spices, and berries. Highest combined NRFa11.3 values were observed for herbs and spices, green leafy vegetables, cocoa, nuts, and red-orange and cruciferous vegetables.DiscussionFresh and dry herbs and spices, often ignored by NP models, were particularly nutrient-rich and may provide non-negligible amounts of key phytonutrients to the human diet.

Purpose: Rates of obesity are significantly higher for those living in a rural versus urban setting. High levels of stress and low levels of subjective well-being (SWB) have been linked to poor weight-related behaviors and outcomes, but it is unclear if these relationships differ as a function of rurality. This study investigated the extent to which living in a rural versus urban county (''rurality'') moderated associations between stress / subjective wellbeing (predictors) and diet quality, dietary intake of added sugars, physical activity, and BMI (outcomes). Methods: Participants were recruited from urban (n = 355) and rural (n = 347) counties in Washington State and self-reported psychological, demographic, and food frequency questionnaires while physical activity behavior was measured objectively. Findings: After controlling for relevant covariates, levels of stress were positively associated with added sugar intake for those living in the urban county while this relationship was non-significant for those residing in the rural county. Similarly, SWB was negatively associated with added sugar intake, but only for urban residents. County of residence was also found to moderate the relationship between SWB and BMI. Higher SWB was inversely associated with BMI for those living in the urban county while no relationship was observed for rural county residents. Conclusions: These findings support the hypothesis that the relationships between stress / SWB and weight function differentially based on the rurality of the residing county. This work adds to the growing body of literature highlighting the role stress and SWB play in the rural obesity disparity.

BackgroundNutrient profiling methods can be applied to individual foods or to composite meals. This article introduces a new method to assess the nutrient density of breakfast meals.ObjectiveThis study aimed to develop a new breakfast quality score (BQS), based on the nutrient standards previously published by the International Breakfast Research Initiative (IBRI) consortium.MethodsBQS was composed of three sub-scores derived from the weighted arithmetic mean of corresponding nutrient adequacy: an eLIMf sub-score (energy, saturated fat, free sugars, and sodium), a PF (protein and fiber) sub-score, and a VMn1 − 14 micronutrient sub-score, where n varied from 0 to 14. The effects of assigning different weights to the eLIMf, PF, and VMn were explored in four alternative models. The micronutrients were calcium, iron, potassium, magnesium, zinc, vitamin A, thiamin, riboflavin, niacin, vitamin B5, vitamin B6, vitamin B12, vitamin C, and vitamin D. Micronutrient permutations were used to develop alternate VMn1 − 14 sub-scores. The breakfast database used in this study came from all breakfasts declared as consumed by adults (>18 years old) in the French dietary survey INCA3. All models were tested with respect to the Nutrient Rich Food Index (NRF9.3). BQS sensitivity was tested using three prototype French breakfasts, for which improvements were made.ResultsThe correlations of the models with NRF9.3 improved when the VMn>3 sub-score (n > 3) was included alongside the PF and eLIMf sub-scores. The model with (PF+VMn) and eLIMf each accounting for 50% of the total score showed the highest correlations with NRF9.3 and was the preferred final score (i.e., BQS). BQS was sensitive to the changing quality of three prototype breakfasts defined as tartine, sandwich, and cereal.ConclusionThe proposed BQS was shown to valuably rank the nutritional density of breakfast meals against a set of nutrient recommendations. It includes nutrients to limit along with protein, fiber, and a variable number of micronutrients to encourage. The flexible VMn sub-score allows for the evaluation of breakfast quality even when nutrient composition data are limited.

IntroductionFew studies have evaluated the sustainability of popular diet patterns in the US, which limits policy action and impedes consumer efficacy to make sustainable dietary changes. This study filled this gap by evaluating the relationship between diet quality, greenhouse gas emissions (GHGE), and diet cost for plant-based, restricted carbohydrate, low grain, low fat, and time restricted diet patterns.MethodsDietary data were retrieved from the National Health and Nutrition Examination Survey (2011–2018, n = 8,146) and linked with data on GHGE and food prices from publicly available databases. Diet quality was measured using the Healthy Eating Index-2015. The present study (1) compared the mean diet quality, GHGE, and diet cost between diet patterns, (2) evaluated the association of diet quality to GHGE and diet cost for each diet pattern, and (3) estimated the contribution of food sources to GHGE and diet cost for each diet pattern.ResultsHigher diet quality was associated with lower GHGE for the general population and for most diet patterns (p 0.05). Higher diet quality was associated with higher cost for the general population and for all dietary patterns (p 0.05). Protein foods, mostly beef, accounted for the largest share of GHGE (29–40%) and diet cost (28–47%) for all diet patterns except plant-based.DiscussionHigher diet quality was associated with lower GHGE but was often accompanied by higher diet cost. These sustainability trade-offs can help inform major policy discussions in the US and shed light on further research needs in the area of food systems transformation.

BackgroundThe Laspeyres price index is the ratio of the current cost of a market basket of commodities or food groups relative to base period prices.ObjectiveTo develop a nutrition-relevant version of the Laspeyres price index, using market baskets based on tertiles of the nutrient rich food (NRF9.3) nutrient density metric.MethodsNutrient composition data for 151 foods from the 2012 Mexico national health and nutrition survey (ENSANUT) were merged with food prices and price indices from the national institute of geography and statistics (INEGI). Nutrient Rich Food Index (NRF9.3) was the measure of nutrient density. May 2012 was the base period. Nutrient rich food price index (NRFPI) values were calculated for each tertile of NRF nutrient density scores for each month between June 2011 and March 2022.ResultsThe market basket of foods in the top tertile of NRF nutrient density scores cost more per 100 kcal and had higher NRFPI values compared to foods in the bottom tertile. Higher NRF9.3 scores were correlated with greater monthly inflation. The NRFPI for foods in the top tertile of NRF9.3 scores was marked by seasonal price spikes, and greater volatility compared to foods in the bottom tertile.ConclusionThe present adaptation of the Laspeyres Index used market baskets defined by nutrient density tertiles instead of commodity groups. This approach allows for easier tracking of the cost of nutrient dense foods and healthful diets across geographic regions and over time. Applied to Mexico food prices prior to and during the Covid-19 pandemic, the NRFPI was sensitive to time trends, seasonality, and price fluctuations. The new tool may be useful in monitoring the rising cost of healthy foods worldwide.

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.

Food and beverage companies reformulate packaged foods and to better align their products with public health policies and evolving consumer needs. The nutritional quality of packaged foodscan be tracked using nutrient profiling methods. The present study compared the nutritional quality of packaged foods launched globally between 2016 and 2018 and those launched in 2018–2020, as reported in the Mintel Global New Products Database. Nutrient profiling analyses showed that the nutrient composition of product categories shifted for almost 40% of newly launched products between 2016–2018 and 2018–2020. For example, pizzas that had been assigned to one nutritionally homogenous category in 2016–2018 separated in two nutritionally distinct subgroups in 2018–2020. The new products that were reduced in protein, saturated fat, and sodium were now nutritionally distinct from the traditional pizza offer. By 2018–2020 “best of category” products were significantly lower in sugar and sodium than before; however, no clear trend was observed for saturated fats, proteins, or fibers. The relative speed of product reformulation was category specific. This analysis of the Mintel Global New Products Database suggests that the WHO nutritional recommendations focusing on sugar and sodium reduction did have a positive impact on the composition of new packaged foods, whether through reformulation of existing products, launches of line extensions or new product development.

Objective: To examine associations between neighborhood built environment (BE) variables, residential property values, and longitudinal 1- and 2-year changes in body mass index (BMI). Methods: The Seattle Obesity Study III was a prospective cohort study of adults with geocoded residential addresses, conducted in King, Pierce, and Yakima Counties in Washington State. Measured heights and weights were obtained at baseline (n = 879), year 1 (n = 727), and year 2 (n = 679). Tax parcel residential property values served as proxies for individual socioeconomic status. Residential unit and road intersection density were captured using Euclidean-based SmartMaps at 800 m buffers. Counts of supermarket (0 versus. 1+) and fast-food restaurant availability (0, 1–3, 4+) were measured using network based SmartMaps at 1600 m buffers. Density measures and residential property values were categorized into tertiles. Linear mixed-effects models tested whether baseline BE variables and property values were associated with differential changes in BMI at year 1 or year 2, adjusting for age, gender, race/ethnicity, education, home ownership, and county of residence. These associations were then tested for potential disparities by age group, gender, race/ethnicity, and education. Results: Road intersection density, access to food sources, and residential property values were inversely associated with BMI at baseline. At year 1, participants in the 3rd tertile of density metrics and with 4+ fast-food restaurants nearby showed less BMI gain compared to those in the 1st tertile or with 0 restaurants. At year 2, higher residential property values were predictive of lower BMI gain. There was evidence of differential associations by age group, gender, and education but not race/ethnicity. Conclusion: Inverse associations between BE metrics and residential property values at baseline demonstrated mixed associations with 1- and 2-year BMI change. More work is needed to understand how individual-level sociodemographic factors moderate associations between the BE, property values, and BMI change.

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.

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.

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.

Summary Background In‐person assessments of physical activity (PA) and body weight can be burdensome for participants and cost prohibitive for researchers. This study examined self‐reported PA and weight accuracy and identified patterns of misreporting in a diverse sample. Methods King, Pierce and Yakima county residents, aged 21–59 years (n = 728), self‐reported their moderate‐to‐vigorous PA (MVPA) and weight, in kilograms. Self‐reports were compared with minutes of bout‐level MVPA, from 3 days of accelerometer data, and measured weights. Regression models examined characteristics associated with underreporting and overreporting of MVPA and weight, the potential bias introduced using each measure and the relation between perceived and measured PA and weight. Results MVPA underreporting was higher among males and college educated participants; however, there was no differential MVPA overreporting. Weight underreporting was higher among males, those age 40–49 years and persons with obesity. Weight overreporting was higher among Hispanic participants and those reporting stress, unhappiness and fair or poor health. The estimated PA–obesity relation was similar using measured and self‐reported PA but not self‐reported weight. Perceived PA and weight predicted measured values. Conclusion Self‐reported PA and weight may be useful should objective measurement be infeasible; however, though population‐specific adjustment for differential reporting should be considered.

Abstract Background Dietary Guidelines for Americans 2015–20 recommend choosing water in place of sugar-sweetened beverages (SSB). This study examined water consumption patterns and trends among children and adults in the US. Methods Dietary intake data for 7453 children (4-18y) and 15,263 adults (>19y) came from two 24 h dietary recalls in three cycles of the National Health and Nutrition Examination Survey (NHANES 2011–2016). Water was categorized as tap or bottled (plain). Other beverages were assigned to 15 categories. Water and other beverage intakes (in mL/d) were analyzed by sociodemographic variables and sourcing location. Consumption time trends from 2011 to 2016 were also examined. Total water intakes from water, other beverages and moisture from foods (mL/d) were compared to Dietary Reference Intakes (DRI) for water. Results Total dietary water (2718 mL/d) came from water (1066 mL/d), other beverages (1036 mL/d) and from food moisture (618 mL/d). Whereas total water intakes remained stable, a significant decline in SSB from 2011 to 2016 was fully offset by an increase in the consumption of plain water. The main sources of water were tap at home (288 mL/d), tap away from home (301 mL/d), and bottled water from stores (339 mL/d). Water and other beverage consumption patterns varied with age, incomes and race/ethnicity. Higher tap water consumption was associated with higher incomes, but bottled water was not. Non-Hispanic whites consumed most tap water (781 mL/d) whereas Mexican Americans consumed most bottled water (605 mL/d). Only about 40% of the NHANES sample on average followed US recommendations for adequate water intakes. Conclusion The present results suggest that while total water intakes among children and adults have stayed constant, drinking water, tap and bottled, has been replacing SSB in the US 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.

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 (N 1665) 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 and χ2 tests 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.

Improving the nutrient density of processed foods is one way to bring the global food supply closer to the WHO Sustainable Development Goals. Nutrient profiling (NP) has emerged as the preferred method of monitoring the progress toward product innovation and reformulation. This paper presents PepsiCo Nutrition Criteria (PNC), a new internal NP model that was designed to guide and monitor improvements in nutrient density and overall nutritional quality of foods and beverages. The new PNC NP model assigns food products into four classes of increasing nutritional value, based on the content of nutrients to limit, along with nutrients and ingredients to encourage. The nutrient standards used for category assignment followed those developed by global dietary authorities. Standards are proposed for calories, sodium, added sugars, saturated, and industrially produced trans fats. Also included are minimum values for food groups to encourage, low-fat dairy, and for country-specific gap nutrients. Internal use of the NP model has spurred product changes that are consistent with WHO goals for industry transparency. An audited review of company products showed that 48% met added sugar, 65% met sodium, and 71% met saturated fat goals. By the end of 2020, in the top 26 regions in which products are sold, 48% of the total sales volume of global beverages had 100 kcal or less from added sugars per 355 ml serving representing 80% of beverage volume and over 90% of food volume sold globally. The PNC NP model is not consumer-facing but is specifically intended for internal use to motivate stepwise and incremental product innovation and reformulation. Transparent and published NP models further WHO goals of engaging industry stakeholders in the (re)formulation of processed foods and beverages consistent with public health goals.

Background: Plant-based beverages (PBB) that are marketed as alternatives to cow milk are gaining in popularity worldwide. Nutrient quality of PBB can be highly variable.Objective: To develop a set of voluntary or mandatory nutrient standards for the PBB product category in order to assist innovation and guide product development and reformulation.Methods: The present goal was to develop standards for PBB energy content, minimum protein content and quality, maximum content for added fat, sugar, and salt, and to suggest fortification levels for selected vitamins and minerals. The standards were based on dietary recommendations and guidelines and current practices of federal agencies in the US.Results: The proposed energy and nutrient content for PBB milk alternatives are maximum 85–100 kcal energy per 100 g; a minimum for 2.2/100 g of high-quality protein, low content of saturated fat (

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.

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.

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

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.

Abstract Background Patterns of beverage consumption among children and adolescents can be indicative of food choices and total diet quality. Methods Analyses of beverage consumption patterns among 8119 children aged 4–19 y were based on the first 24-h recall of the National Health and Nutrition Examination Survey (2009–14 NHANES). Four pre-defined beverage patterns were: 1) milk pattern; 2) 100% juice pattern; 3) milk and 100% juice pattern; and 4) other caloric beverages. Food- and nutrient-based diet quality measures included the Healthy Eating Index 2010. Results Most children drank other caloric beverages, as opposed to milk (17.8%), 100% juice (5.6%), or milk and 100% juice (13.5%). Drinkers of milk and 100% juice had diets that did not differ from each other in total calories, total and added sugars, fiber, or vitamin E. Milk drinkers consumed more dairy and had higher intakes of calcium, potassium, vitamin A and vitamin D as compared to all other patterns. Juice drinkers consumed more total fruit, same amounts of whole fruit, and had higher intakes of vitamin C as compared to the other consumption patterns. Drinkers of both milk and 100% juice had the highest HEI 2010 scores of all the consumption patterns. Conclusions Beverage consumption patterns built around milk and/or 100% juice were relatively uncommon. Promoting the drinking of milk and 100% juice, in preference to other caloric beverages, may be an effective strategy to improve children’s diet quality. Restricting milk and 100% juice consumption may encourage the selection of other caloric beverages.

Abstract Background The UK government has announced a tax on sugar-sweetened beverages. The aim of this study was to assess consumption patterns for plain drinking water relative to sugary beverages among UK children. Methods Dietary intake data for 845 children aged 4–13 years came from the nationally representative cross-sectional National Diet and Nutrition Survey, 2008–2011. Beverage categories were drinking water (tap or bottled), milk, 100% fruit juices, soda, fruit drinks, tea, coffee, sports drinks, flavored waters, and liquid supplements. Consumption patterns were examined by age group, gender, household incomes, time and location of consumption, region and seasonality. Total water consumption from drinking water, beverages, and foods, and the water-to-calorie ratios (L/kcal) were compared to the EFSA (European Food Safety Authority) adequate intake standards. Results Total water intake (1338 ml/d) came from plain water (19%), beverages (48%), and food moisture (33%). Plain drinking water provided 258 g/d (241 g/d for children aged 4–8 years; 274 g/d for 9–13 years), mostly (83.8%) from tap. Water and beverages supplied 901 g /d of water. Tap water consumption increased with income and was highest in the South of England. The consumption of bottled water, soda, tea and coffee increased with age, whereas milk consumption declined. About 88.7% of children did not meet EFSA adequate intake standards. The daily water shortfall ranged from 322 ml/d to 659 ml/d. Water-to-calorie ratio was 0.845 L/1000 kcal short of desirable levels of 1.0–1.5 L/1000 kcal. Conclusion Total water intake were at 74.8% of EFSA reference values. Drinking water consumption among children in the UK was well below US and French estimates.

Recent dietary guidelines have become more food-based, as opposed to purely nutrient-based. By contrast, assessing the impact of dietary changes on chronic disease risk continues to rely on single-nutrient substitutions. To assess the real-world implications of a nutrient-for-nutrient swap, this study examined dietary nutrient density and healthy diet scores following removal of food sources of dairy fat from diets of 15,260 individuals age ≥4 y in the National Health and Nutrition Examination Survey (NHANES 2013–2016). Those foods were then replaced with foods containing a comparable amount of non-dairy polyunsaturated fatty acids (PUFA) and monounsaturated fatty acids (MUFA). The present food-level substitution model was based on 576 diverse eating patterns of US population subgroups. Diet quality measures were the Nutrient Rich Food (NRF 9.3) Index and the 2015-Healthy Eating Index (HEI-2015). Removing 5% of dietary energy from dairy fat led to lower levels of multiple micronutrients and to lower NRF 9.3 scores. These deficits were not remedied by the modeled replacements. Although swapping dairy fat for foods containing non-dairy MUFA/PUFA did alter the fatty acid ratios, the resulting food patterns were still significantly lower in some key micronutrients. Nutrient-based dietary guidance is prone to ignore the complexity of food patterns and the recommended dietary change may have unintended nutritional consequences.

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.

Background: The goal of the Frequency of Eating and Satiety Hormones (FRESH) Study is to understand the relationship between eating frequency (EF) and biomarkers of appetite and disease risk. This report gives the study rationale and design. Methods: The FRESH study was conducted in n = 50 overweight and obese, but otherwise healthy, male and female adults aged 18–50 years. The protocol included four in-person clinic visits for protocol instruction, blood draws, anthropometry, and meal testing; all other activities were done at home. Participants completed two 21-day phases in random order with a two-week washout between phases. One phase was high EF (6 eating occasions/day) and the other was low EF (3 eating occasions/day). Each phase specified time of day for each eating occasion. Participants prepared their own meals throughout the study using study-provided individualized, structured meal plans ensuring that calories, macronutrients and micronutrients were identical during both study phases. Fasting blood was collected before and after each phase to test intervention effects on the biomarkers. At the end of each phase participants also completed extended appetite testing with meals prepared by the study clinic. Results: Participants were recruited using print, radio, and digital ads. 60 participants consented to enroll; 10 dropped out due to work or school scheduling conflicts and 50 (target sample size) completed the study. Compliance was assessed by completion of daily on-line meal plan checklists. Conclusions: The FRESH study will provide data on whether higher vs. lower daily EF in the context of constant energy and nutrient intake may be harmful or beneficial based on intervention effects on biomarkers of health and disease risk. Keywords: Eating frequency, Appetite, Randomized controlled trial, Cross-over design

Background: The NOVA food classification scheme divides foods into ultra-processed, processed, unprocessed, and culinary ingredients. Ultra-processed foods contribute >60% of energy to diets in the US.Objective: To characterize ultra-processed foods by energy density, nutrient density, and monetary cost.Methods: The 384 component foods of Fred Hutch (FHCRC) food frequency questionnaire (FFQ), were assigned to 4 NOVA categories and to 7 USDA MyPyramid food groups. Energy density was kcal/g. Nutrient density was measured using the Nutrient Rich Food index NRF9.3. Food prices were collected in local supermarkets from 2004 to 2016. Analyses examined time trends in food prices by NOVA category and by USDA food group.Results: The ultra-processed classification captured mostly grains (91%), fats and sweets (73%), dairy (71%), and beans, nuts and seeds (70%), but only 36% of meat, poultry and fish, 26% of vegetables, and 20% of fruit. Compared to unprocessed foods, ultra-processed foods had lower nutrient density (NRF9.3 per 100 kcal: 21.2 vs. 108.5),higher energy density (mean (SD): 2.2 vs. 1.10 in kcal/g), and lower per calorie cost (0.55 vs. 1.45 in $/100 kcal). Ultra-processed foods did not increase in price as much as unprocessed foods over the 12 year period.Conclusion: Ultra-processed foods tend to be energy-dense, low-cost, and nutrient-poor. Low energy cost could be one mechanism linking ultra-processed foods with negative health outcomes. Food-based Dietary Guidelines may need to address food processing in relation to economic aspects of food choice.

Background: Documenting geographic disparities in dietary behaviors can help inform public health interventions at the local level. Objective: To study and visualize socioeconomic gradient in soda and salad consumption using a geo-localized measure of socioeconomic status in contrast to more traditional measures. Methods: Geo-localized dietary intake data came from the Seattle Obesity Study I, a population-based sample of King County adults (n=1099). Socio-demographic data and soda and salad consumption frequencies (times/week) were obtained by 20-min telephone survey. Food frequency questionnaire (FFQ) data were used to construct Healthy Eating Index (HEI) scores. Individual residential property values obtained from the King County tax assessor. Multivariable linear regressions examined socioeconomic gradient in the frequency of soda and salad consumption by residential property values, the primary independent variable, in comparison to annual household incomes and educational attainment, with adjustment for age, gender, and race/ethnicity. Geographic disparities in soda and salad consumption by property value metric were illustrated at the census block level using modeled predicted marginal means. Results: Among all three socioeconomic indicators (income, education and residential property values), residential property values captured strongest gradient in soda and salad consumption. Higher quintiles of residential property values were associated with lower soda and higher salad consumption. Respondents living in the highest quintile of property values −1.04 fewer sodas per week (95% CI= −1.87, −0.21) and 0.89 more salads per week (95% CI= 0.36, 1.42), adjusting for sociodemographic covariates. Residential property values illustrated geographic disparities in soda and salad consumption at the census-block level. Conclusion: Geo-localized disparities in food consumption patterns by neighborhood can inform current discourse on the socioeconomic determinants of health, while providing a useful tool for targeted interventions at the local level. Keywords: Diet, Residential property values, Geographic information systems, Socioeconomic status

The US Department of Agriculture (USDA) has identified three Healthy Food Patterns as ways to implement the 2015–2020 Dietary Guidelines for Americans. We estimated the daily cost of the Healthy Vegetarian, US-Style, and Mediterranean Food Patterns (at 2,000 kcal/d) using national food prices adjusted for inflation. We also estimated the cost of existing dietary intakes in $/2,000 kcal/d for persons ≥2 years in the National Health and Nutrition Examination Survey (NHANES 2013–2014) using the same national food prices. The Nutrient Rich Food index (NRF9.3) was used as a measure of diet quality. Compared to existing diets, the USDA Healthy Food Patterns were higher in protein, fiber, vitamins, and minerals; were lower in solid fats, sugars, and sodium, and had higher diet quality scores. However, they also cost more. The cost of existing diets in NHANES was $5.47/d for Hispanics, $5.48/d for African-Americans, $5.94/d for Whites and $6.57/d for Asians. By contrast, the recommended US-Style Pattern cost $8.27/d, the Vegetarian Pattern cost $5.90/d, and the Mediterranean Pattern cost $8.73/d. Further, the Healthy Food Patterns featured some of the recommended food groups in unrealistic amounts. To ensure that the US Dietary Guidelines are both feasible and relevant to minority health, economic modeling studies should accompany government-issued dietary advice.

This study analyzed the impact of area residential property values, an objective measure of socioeconomic status (SES), on self-rated health (SRH) in Seattle, Washington and Paris, France. This study brings forth a valuable comparison of SRH between cities that have contrasting urban forms, population compositions, residential segregation, food systems and transportation modes. The SOS (Seattle Obesity Study) was based on a representative sample of 1394 adult residents of Seattle and King County in the United States. The RECORD Study (Residential Environment and Coronary Heart Disease) was based on 7131 adult residents of Paris and its suburbs in France. Socio-demographics, SRH and body weights were obtained from telephone surveys (SOS) and in-person interviews (RECORD). All home addresses were geocoded using ArcGIS 9.3.1 (ESRI, Redlands, CA). Residential property values were obtained from tax records (Seattle) and from real estate sales (Paris). Binary logistic regression models were used to test the associations among demographic and SES variables and SRH. Higher area property values significantly associated with better SRH, adjusting for age, gender, individual education, incomes, and BMI. The associations were significant for both cities. A one-unit increase in body mass index (BMI) was more detrimental to SRH in Seattle than in Paris. In both cities, higher area residential property values were related to a significantly lower obesity risk and better SRH. Ranked residential property values can be useful for health and weight studies, including those involving social inequalities and cross-country comparisons. Keywords: Socioeconomic status, Health disparities, Adult population, BMI

Abstract Background The built environment (BE) is said to influence local obesity rates. Few studies have explored causal pathways between home-neighborhood BE variables and health outcomes such as obesity. Such pathways are likely to involve both physical activity and diet. Methods The Seattle Obesity Study (SOS II) was a longitudinal cohort of 440 adult residents of King Co, WA. Home addresses were geocoded. Home-neighborhood BE measures were framed as counts and densities of food sources and physical activity locations. Tax parcel property values were obtained from County tax assessor. Healthy Eating Index (HEI 2010) scores were constructed using data from food frequency questionnaires. Physical activity (PA) was obtained by self-report. Weights and heights were measured at baseline and following 12 months’ exposure. Multivariable regressions examined the associations among BE measures at baseline, health behaviors (HEI-2010 and physical activity) at baseline, and health outcome both cross-sectionally and longitudinally. Results None of the conventional neighborhood BE metrics were associated either with diet quality, or with meeting PA guidelines. Only higher property values did predict better diets and more physical activity. Better diets and more physical activity were associated with lower obesity prevalence at baseline and 12 mo, but did not predict weight change. Conclusion Any links between the BE and health outcomes critically depend on establishing appropriate behavioral pathways. In this study, home-centric BE measures, were not related to physical activity or to diet. Further studies will need to consider a broader range of BE attributes that may be related to diets and health.

As participants at the Ecosystem Inception Meeting convened by the Global Dairy Platform and held in Chicago in June 2016, we have identified some concepts as central to the study of food systems science. Following the definition developed by the Food and Agriculture Organization for sustainable diets, the food supply needs to provide foods that are healthy and safe, affordable, culturally acceptable, and with low impact on the environment. Therefore, the four main domains of sustainable food systems science can be described as health, economics, society, and the environment. Food systems science needs to embrace and engage with all relevant allied disciplines that may include environmental health sciences, epidemiology, geography, history, sociology, anthropology, business, and political science. Research and training in food systems science, both domestic and international, would benefit from a set of competencies, from more extensive research networks, and from more public–private engagement. This document builds on major advances in the area of food system research, training, and practice, already achieved by individuals, institutions, foundations, and local and national governments.

To assess differences between GPS and self-reported measures of location, we examined visits to fast food restaurants and supermarkets using a spatiotemporal framework. Data came from 446 participants who responded to a survey, filled out travel diaries of places visited, and wore a GPS receiver for seven consecutive days. Provided by Public Health Seattle King County, addresses from food permit data were matched to King County tax assessor parcels in a GIS. A three-step process was used to verify travel-diary reported visits using GPS records: (1) GPS records were temporally matched if their timestamps were within the time window created by the arrival and departure times reported in the travel diary; (2) the temporally matched GPS records were then spatially matched if they were located in a food establishment parcel of the same type reported in the diary; (3) the travel diary visit was then GPS-sensed if the name of food establishment in the parcel matched the one reported in the travel diary. To account for errors in reporting arrival and departure times, GPS records were temporally matched to three time windows: the exact time, +/- 10 minutes, and +/- 30 minutes. One third of the participants reported 273 visits to fast food restaurants; 88% reported 1,102 visits to supermarkets. Of these, 77.3 percent of the fast food and 78.6 percent supermarket visits were GPS-sensed using the +/-10-minute time window. At this time window, the mean travel-diary reported fast food visit duration was 14.5 minutes (SD 20.2), 1.7 minutes longer than the GPS-sensed visit. For supermarkets, the reported visit duration was 23.7 minutes (SD 18.9), 3.4 minutes longer than the GPS-sensed visit. Travel diaries provide reasonably accurate information on the locations and brand names of fast food restaurants and supermarkets participants report visiting.