Differences in Intakes of Select Nutrients by Urbanization Level in the United States Population 2 Years and Older, NHANES 2013-2018.

BACKGROUND: Differences in nutrient intakes by urbanization level in the Unites States is not well understood. OBJECTIVE: Describe, by urbanization level: 1) intake of protein, fiber, percent of energy from added sugars (AS) and saturated fat (SF), calcium, iron, potassium, sodium, and vitamin D; 2) the percent of the population meeting nutrient recommendations. METHODS: Twenty-four-hour dietary recalls from 23,107 participants aged 2 y and over from the 2013-2018 National Health and Nutrition Examination Surveys were analyzed. Usual intakes were estimated, and linear regression models adjusted for age, sex, race and Hispanic origin, and whether family income met the 130% threshold examined intake differences by urbanization levels-large urban areas (LUA), small to medium metro areas (SMMA), and rural areas (RA). RESULTS: A small percentage of the population met the nutrient recommendations, except for protein (92.8%) and iron (70.5%). A higher percentage of the population met recommendations than SMMA and RA for fiber (11.8% compared with 8.1% and 5.3%, P < 0.001), AS (40.2% compared with 33.4% and 31.3%, P < 0.001), SF (26.8% compared with 18.2% and 20.1%, P < 0.001), and potassium (31.5% compared with 25.5% and 22.0%, P < 0.001). Mean protein intake were also higher in LUA than RA (80.0 g compared with 77.7 g, P = 0.003) and fiber intake higher in LUA than SMMA (16.5 g compared with 15.9 g, P = 0.01) and RA (16.5 g compared with 15.2 g, P = 0.001). In addition, contributions to energy intake were lower in LUA than SMMA for AS (11.3% compared with 12.0%, P < 0.001) and SF (11.5% compared with 11.7%, P < 0.001), and for LUA than RA for AS (11.3% compared with 12.9%, P < 0.001) and SF (11.5% compared with 11.8%, P < 0.001). CONCLUSIONS: RA had some markers of poorer diet quality-lower protein and fiber intake and higher AS intake-compared with LUA, and these differences persisted in adjusted regression models. These results may inform public health efforts to address health disparities by urbanization levels in the Unites States.

Food Processing, According to the Nova Classification System, and Dietary Intake of US Infants and Toddlers.

BACKGROUND: High intake of ultra-processed foods (UPFs) is associated with increased risk of chronic disease; thus, it is important to understand how UPFs influence diet quality early in life. OBJECTIVES: We describe complementary foods and beverages (CFBs) according to the Nova Classification System of Food Processing for infants and toddlers in the United States and estimate how Nova groups and subgroups contribute to energy and select nutrients and food groups. METHODS: We used day 1 24-h recall from infants and toddlers aged 6-23 mo from the cross-sectional, nationally representative 2013-18 National Health and Nutrition Examination Survey (n = 1140). We estimated contributions of Nova groups and subgroups to energy and select nutrients and food groups consumed as CFBs (excluding human milk and formula) using the population ratio with weighted survey commands in SAS. RESULTS: For infants and toddlers in the United States, 42 ± 0.9% (mean ± standard error of the mean) of energy intake from CFBs came from unprocessed/minimally processed foods (U/MPFs) and 45 ± 0.8% from UPFs. U/MPFs contributed most to nutrient intakes (except iron, zinc, and sodium); ≥20% of all selected nutrients was from UPFs. UPFs contributed most to iron (75 ± 1.0%) and zinc (48 ± 1.3%); breakfast cereals were the top source. Most fruit, vegetables, and dairy were from U/MPFs. More than 80% of total grains, whole grains, refined grains, and added sugars were UPFs. CONCLUSIONS: U/MPFs support healthy dietary intake of infants and toddlers in the United States, whereas UPFs contribute meaningfully to nutrients and food groups to be encouraged (iron, zinc, and whole grains), as well as some that should be limited (added sugars and sodium). More research is needed to better understand the utility and sensitivities of using Nova for providing dietary guidance for infants and toddlers in the United States.

Identifying and Estimating Ultraprocessed Food Intake in the US NHANES According to the Nova Classification System of Food Processing.

BACKGROUND: The degree of food processing may be an important dimension of diet in how it relates to health outcomes. A major challenge is standardizing food processing classification systems for commonly used datasets. OBJECTIVES: To standardize and increase transparency in its application, we describe the approach used to classify foods and beverages according to the Nova food processing classification in the 24-h dietary recalls from the 2001-2018 cycles of What We Eat in America (WWEIA), NHANES, and investigate variability and potential for Nova misclassification within WWEIA, NHANES 2017-2018 data via various sensitivity analyses. METHODS: First, we described how the Nova classification system was applied to the 2001-2018 WWEIA, NHANES data using the reference approach. Second, we calculated the percentage energy from Nova groups [1: unprocessed or minimally processed foods, 2: processed culinary ingredients, 3: processed foods, and 4: ultraprocessed foods (UPFs)] for the reference approach using day 1 dietary recall data from non-breastfed participants aged ≥1 y from the 2017-2018 WWEIA, NHANES. We then conducted 4 sensitivity analyses comparing potential alternative approaches (e.g., opting for more vs. less degree of processing for ambiguous items) to the reference approach, to assess how estimates differed. RESULTS: The energy contribution of UPFs using the reference approach was 58.2% ± 0.9% of the total energy; unprocessed or minimally processed foods contributed 27.6% ± 0.7%, processed culinary ingredients contributed 5.2% ± 0.1%, and processed foods contributed 9.0% ± 0.3%. In sensitivity analyses, the dietary energy contribution of UPFs ranged from 53.4% ± 0.8% to 60.1% ± 0.8% across alternative approaches. CONCLUSIONS: We present a reference approach for applying the Nova classification system to WWEIA, NHANES 2001-2018 data to promote standardization and comparability of future research. Alternative approaches are also described, with total energy from UPFs differing by ∼6% between approaches for 2017-2018 WWEIA, NHANES.

A Decade of Dietary Assessment Methodology Research at the National Institutes of Health, 2012-2021.

BACKGROUND: Assessment of individual and population-level dietary intake is critical for public health surveillance, epidemiology, and dietary intervention research. In recognition of that need, the National Insitutes of Health (NIH) has a history of funding research projects designed to support the development, implementation, and refinement of tools to assess dietary intake in humans. OBJECTIVES: This report provides data and information on NIH-funded dietary intake assessment methodological research over the period of 2012-2021. METHODS: Data were extracted from an internal NIH data system using the Research, Condition, and Disease Categorization (RCDC) spending category for Nutrition. Data were then examined to identify research focused on dietary assessment tools or methods to capture or analyze dietary intake. RESULTS: Over the decade of 2012-2021, NIH supported 46 grants and 2 large contracts specific to dietary assessment methods development. The top 6 Institutes and Offices funding dietary assessment methods research were identified. Most projects were limited to adults. Projects ranged from novel methods to capture dietary intake, and refinement of analytical methods, to biomarkers of dietary intake. One key contract supported the automated self-administered 24-h dietary assessment tool (ASA24), a widely used, free tool available to the research community for assessing dietary intake. CONCLUSIONS: NIH's support for dietary assessment methods development over this 10-y period was small but grew over time with an expanding number and variety of methods, data sources, and technological advancements in the assessment of dietary intake. NIH remains committed to supporting research seeking to advance the field of dietary assessment methods research.

Validation of Total Water Intake from the Automated Self-Administered 24-h Recall, 4-d Food Records, and a Food Frequency Questionnaire Using Doubly Labeled Water.

BACKGROUND: Although prior evidence indicates that water intake is important for health, the ability to accurately measure community-dwelling intake is limited. Only a few studies have evaluated self-reported water intake against an objective recovery biomarker. OBJECTIVES: The aim was to compare preformed water intakes (all sources including food) by multiple Automated Self-Administered 24-h recalls (ASA24s), food frequency questionnaires (FFQs), and 4-d food records (4DFRs) against a recovery biomarker, doubly labeled water (DLW), to assess measurement error. METHODS: Over 1 y, 1082 women and men (50%), aged 50 to 74 y, were asked to complete 6 ASA24s, 2 FFQs, 2 unweighted 4DFRs, and an administration of DLW (n = 686). Geometric means of water intake by self-report tools were compared with DLW. Attenuation factors and correlation coefficients between self-reported and the recovery biomarker (DLW) were estimated. RESULTS: Mean water intakes by DLW were 2777 mL/d (interquartile range, 2350 to 3331) in women and 3243 mL/d (interquartile range, 2720 to 3838) in men. Compared with DLW, water intake was underestimated by 18% to 31% on ASA24s and 43% to 44% on 4DFRs. Estimated geometric means from FFQs differed from DLW by -1% to +13%. For a single ASA24, FFQ, and 4DFR, attenuation factors were 0.28, 0.27, and 0.32 and correlation coefficients were 0.46, 0.48, and 0.49, respectively. Repeated use of 6 ASA24s, 2 FFQs, and 2 4DFRs improved attenuation factors to 0.43, 0.32, and 0.39 and correlation coefficients to 0.58, 0.53, and 0.54, respectively. CONCLUSIONS: FFQs may better estimate population means for usual water intake compared with ASA24 and 4DFR. Similar attenuation factors and correlation coefficients across all self-report tools indicate that researchers have 3 feasible options if the goal is understanding intake-disease relationships. The findings are useful for planning future nutrition studies that set policy priorities for populations and to understand the health impact of water. This trial was registered at clinicaltrials.gov as NCT03268577.

Metabolomic Profiling of an Ultraprocessed Dietary Pattern in a Domiciled Randomized Controlled Crossover Feeding Trial.

BACKGROUND: Objective markers of ultraprocessed foods (UPF) may improve the assessment of UPF intake and provide insight into how UPF influences health. OBJECTIVES: To identify metabolites that differed between dietary patterns (DPs) high in or void of UPF according to Nova classification. METHODS: In a randomized, crossover, controlled-feeding trial (clinicaltrials.govNCT03407053), 20 domiciled healthy participants (mean ± standard deviation: age 31 ± 7 y, body mass index [kg/m2] 22 ± 11.6) consumed ad libitum a UPF-DP (80% UPF) and an unprocessed DP (UN-DP; 0% UPF) for 2 wk each. Metabolites were measured using liquid chromatography with tandem mass spectrometry in ethylenediaminetetraacetic acid plasma, collected at week 2 and 24-h, and spot urine, collected at weeks 1 and 2, of each DP. Linear mixed models, adjusted for energy intake, were used to identify metabolites that differed between DPs. RESULTS: After multiple comparisons correction, 257 out of 993 plasma and 606 out of 1279 24-h urine metabolites differed between UPF-DP and UN-DP. Overall, 21 known and 9 unknown metabolites differed between DPs across all time points and biospecimen types. Six metabolites were higher (4-hydroxy-L-glutamic acid, N-acetylaminooctanoic acid, 2-methoxyhydroquinone sulfate, 4-ethylphenylsulfate, 4-vinylphenol sulfate, and acesulfame) and 14 were lower following the UPF-DP; pimelic acid, was lower in plasma but higher in urine following the UPF-DP. CONCLUSIONS: Consuming a DP high in, compared with 1 void of, UPF has a measurable impact on the short-term human metabolome. Observed differential metabolites could serve as candidate biomarkers of UPF intake or metabolic response in larger samples with varying UPF-DPs. This trial was registered at clinicaltrials.gov as NCT03407053 and NCT03878108.

Nonfood Prebiotic, Probiotic, and Synbiotic Use Has Increased in US Adults and Children From 1999 to 2018.

BACKGROUND & AIMS: Public interest in pre-, pro-, and synbiotic products is increasing because of interactions between gut microbiota and human health. Our aim was to describe nonfood (from dietary supplements or medication) pre-, pro-, and synbiotic use by US adults and children and reported reasons. METHODS: Using data from the National Health and Nutrition Examination Survey (NHANES), we text-mined dietary supplement and prescription medication labels and ingredients to identify pre-, pro-, and synbiotic products used in the past 30 days. We describe trends in use from 1999 to 2018 (n = 101,199) and prevalence in 2015-2016 and 2017-2018 (n = 19,215) by age groups, sex, ethnicity/race, education, income, self-reported diet and health quality, and prescription gastrointestinal medication use stratified by children (<19 years) and adults (19+ years). RESULTS: Nonfood pre-, pro-, and synbiotic use increased up to 3-fold in recent cycles. Prevalence of use for all ages for prebiotics was 2.4% (95% confidence interval [CI], 2.0-2.9), for probiotics was 4.5% (95% CI, 3.5-5.6), and for synbiotics was 1.1% (95% CI, 0.8-1.5). Use was highest among older adults (8.8% [95% CI, 5.4-13.3] among those aged 60-69 years for probiotics), non-Hispanic Whites, those with higher educational attainment and income, those with more favorable self-reported diet or health quality, and those with concurrent prescription gastrointestinal medication use. The top reasons for use were for digestive health and to promote/maintain general health. Less than 30% reported using these products based on a health care provider's recommendation. CONCLUSIONS: One in 20 US adults or children use nonfood pre-, pro-, or synbiotic products, and use has sharply increased in recent years. Most individuals voluntarily take these products for general digestive or overall health reasons.

A Standardized Assessment of Processed Red Meat and Processed Poultry Intake in the US Population Aged ≥2 Years Using NHANES.

BACKGROUND: Standardized methods are needed to investigate intake patterns of processed meat subtypes, considering health concerns surrounding processed meat intake. OBJECTIVES: The objectives of this study were to create a standardized method of disaggregating processed meat into processed red meat and processed poultry and describe intake patterns of the US population aged ≥2 y. METHODS: Two researchers independently manually disaggregated processed meat from the Food Patterns Equivalents Database into processed red meat and processed poultry based on available information from the Foods and Nutrient Database for Dietary Studies. We created an SAS program (called Processed Meat Categories) to mimic the manual coding. We used the program to describe intake patterns and trends over time of processed red meat and processed poultry using 24-h recalls from 2007-2008 through 2017-2018 NHANES data with SAS survey-weighted procedures for complex surveys. RESULTS: The SAS program had high agreement with the manual code (Pearson concordance correlation ≥0.95). Of the US population aged ≥2 y, 46.8% (95% CI: 45.3, 48.2%) reported consuming any processed meat, 42.5% (95% CI: 41.0, 43.9%) reported consuming processed red meat, and 11.3% (95% CI: 10.2, 12.4%) reported consuming processed poultry. Most [74.1 ± 0.13% (SEM)] processed meat reported was red meat compared with poultry, and 32.1 ± 0.01% of total red meat and 13.7 ± 0.01% of total poultry reported were processed. Prevalence of processed poultry intake increased from 9.5% (95% CI: 8.9, 10.1%) in 2007-2010 to 11.3% (95% CI: 10.2, 12.4%) in 2015-2018 (P < 0.0001), but mean intake amount did not change. Prevalence of processed red meat intake did not change over time, but mean intake decreased from 0.8 ± 0.03 ounce-equivalents in 2007-2010 to 0.7 ± 0.02 ounce-equivalents (P = 0.0058) in 2015-2018. CONCLUSIONS: The Processed Meat Categories SAS program is a tool available for researchers to standardize estimates of processed meat subtypes for future dietary patterns research. Intake of total processed meat did not change in the United States, but intake amount of processed red meat decreased and the prevalence of processed poultry consumers increased.

Trends in Consumption of Ultraprocessed Foods Among US Youths Aged 2-19 Years, 1999-2018.

Importance: The childhood obesity rate has been steadily rising among US youths during the past 2 decades. Increasing evidence links consumption of ultraprocessed foods to excessive calorie consumption and weight gain, but trends in the consumption of ultraprocessed foods among US youths have not been well characterized. Objective: To characterize trends in the consumption of ultraprocessed foods among US youths. Design, Setting, and Participants: Serial cross-sectional analysis using 24-hour dietary recall data from a nationally representative sample of US youths aged 2-19 years (n = 33 795) from 10 cycles of the National Health and Nutrition Examination Survey (NHANES) from 1999-2000 to 2017-2018. Exposures: Secular time. Main Outcomes and Measures: Percentage of total energy consumed from ultraprocessed foods as defined by NOVA, an established food classification system that categorizes food according to the degree of food processing. Results: Dietary intake from youths were analyzed (weighted mean age, 10.7 years; 49.1% were girls). From 1999 to 2018, the estimated percentage of total energy from consumption of ultraprocessed foods increased from 61.4% to 67.0% (difference, 5.6% [95% CI, 3.5% to 7.7%]; P < .001 for trend), whereas the percentage of total energy from consumption of unprocessed or minimally processed foods decreased from 28.8% to 23.5% (difference, -5.3% [95% CI, -7.5% to -3.2%]; P < .001 for trend). Among the subgroups of ultraprocessed foods, the estimated percentage of energy from consumption of ready-to-heat and -eat mixed dishes increased from 2.2% to 11.2% (difference, 8.9% [95% CI, 7.7% to 10.2%]) and from consumption of sweet snacks and sweets increased from 10.7% to 12.9% (difference, 2.3% [95% CI, 1.0% to 3.6%]), but the estimated percentage of energy decreased for sugar-sweetened beverages from 10.8% to 5.3% (difference, -5.5% [95% CI, -6.5% to -4.5%]) and for processed fats and oils, condiments, and sauces from 7.1% to 4.0% (difference, -3.1% [95% CI, -3.7% to -2.6%]) (all P < .05 for trend). There was a significantly larger increase in the estimated percentage of energy from consumption of ultraprocessed foods among non-Hispanic Black youths (from 62.2% to 72.5%; difference, 10.3% [95% CI, 6.8% to 13.8%]) and Mexican American youths (from 55.8% to 63.5%; difference, 7.6% [95% CI, 4.4% to 10.9%]) than the increase among non-Hispanic White youths (from 63.4% to 68.6%; difference, 5.2% [95% CI, 2.1% to 8.3%]) (P = .04 for trends). Conclusions and Relevance: Based on the NHANES cycles from 1999 to 2018, the estimated proportion of energy intake from consumption of ultraprocessed foods has increased among youths in the US and has consistently comprised the majority of their total energy intake.

Dietary Supplement Use among Infants and Toddlers Aged <24 Months in the United States, NHANES 2007-2014.

BACKGROUND: Limited nationally representative data are available on dietary supplement (DS) use and resulting nutrient exposures among infants and toddlers. OBJECTIVE: This study evaluated DS use among US infants and toddlers to characterize DS use, estimate nutrient intake from DSs, and assess trends in DS use over time. METHODS: Using nationally representative data from NHANES (2007-2014) and trends over time (1999-2014), we estimated prevalence of DS use and types of products used for US infants and toddlers aged <2 y (n = 2823). We estimated median daily intakes of vitamins and minerals consumed via DSs for all participants aged <2 y, by age groups (0-11.9 mo and 12.0-23.9 mo), and by feeding practices for infants 0-5.9 mo. RESULTS: Overall, 18.2% (95% CI: 16.2%, 20.3%) of infants and toddlers used ≥1 DS in the past 30 d. Use was lower among infants (0-5.9 mo: 14.6%; 95% CI: 11.5%, 18.1%; 6-11.9 mo: 11.6%; 95% CI: 8.8%, 15.0%) than among toddlers (12-23.9 mo: 23.3%; 95% CI: 20.4%, 26.3%). The most commonly reported DSs were vitamin D and multivitamin infant drops for those <12 mo, and chewable multivitamin products for toddlers (12-23.9 mo). The nutrients most frequently consumed from DSs were vitamins D, A, C, and E for those <2 y; for infants <6 mo, a higher percentage of those fed breast milk than those fed formula consumed these nutrients via DSs. DS use remained steady for infants (6-11.9 mo) and toddlers from 1999-2002 to 2011-2014, but increased from 7% to 20% for infants aged 0-5.9 mo. CONCLUSIONS: One in 5 infants and toddlers aged <2 y use ≥1 DS. Future studies should examine total nutrient intake from foods, beverages, and DSs to evaluate nutrient adequacy overall and by nutrient source.

Federal Monitoring of Dietary Supplement Use in the Resident, Civilian, Noninstitutionalized US Population, National Health and Nutrition Examination Survey.

Objective: This review summarizes the current and previous data on dietary supplement (DS) use collected from participants in the National Health and Nutrition Examination Survey (NHANES), describes the NHANES dietary supplement database used to compute nutrient intakes from DSs, discussed recent developments and future direction, and describes many examples to demonstrate the utility of these data in informing nutrition research and policy. Background and History: Since 1971, NHANES, has been collecting information on the use of DSs from participants. These data are critical to national nutrition surveillance and have been used to characterize usage patterns, examine trends over time, assess the percentage of the population meeting or exceeding nutrient recommendations, and to help elucidate the sources contributing nutrients to the diet of the US population. Rationale: Over half of adults and about one-third of children in the United States use at least one dietary supplement in the past 30 days. Dietary supplements contribute to the dietary intake of nutrients and bioactive compounds in the US and therefore need to be assessed when monitoring nutritional status of the population and when studying diet-health associations. Recent Developments: With the recent development and availability of the Dietary Supplement Label Database (DSLD), a comprehensive DS database that will eventually contain labels for all products marketed in the US, NHANES DS data will be more easily linked to product information to estimate nutrient intake from DS. Future Directions: Over time, NHANES has both expanded and improved collection methods. The continued understanding of sources of error in collection methods will continue to be explored and is critical to improved accuracy. Conclusions: NHANES provides a rich source of nationally representative data on the usage of dietary supplements in the US.

The Dietary Supplement Label Database: Recent Developments and Applications.

Objective: To describe the history, key features, recent enhancements, and common applications of the Dietary Supplement Label Database (DSLD). Background and History: Although many Americans use dietary supplements, databases of dietary supplements sold in the United States have not been widely available. The DSLD, an easily accessible public-use database was created in 2008 to provide information on dietary supplement composition for use by researchers and consumers. Rationale: Accessing current information easily and quickly is crucial for documenting exposures to dietary supplements because they contain nutrients and other bioactive ingredients that may have beneficial or adverse effects on human health. This manuscript details recent developments with the DSLD to achieve this goal and provides examples of how the DSLD has been used. Recent Developments: With periodic updates to track changes in product composition and capture new products entering the market, the DSLD currently contains more than 71,000 dietary supplement labels. Following usability testing with consumer and researcher user groups completed in 2016, improvements to the DSLD interface were made. As of 2017, both a desktop and mobile device version are now available. Since its inception in 2008, the use of the DSLD has included research, exposure monitoring, and other purposes by users in the public and private sectors. Future Directions: Further refinement of the user interface and search features to facilitate ease of use for stakeholders is planned. Conclusions: The DSLD can be used to track changes in product composition and capture new products entering the market. With over 71,000 DS labels it is a unique resource that policymakers, researchers, clinicians, and consumers may find valuable for multiple applications.

Disparities in plain, tap and bottled water consumption among US adults: National Health and Nutrition Examination Survey (NHANES) 2007-2014.

OBJECTIVE: Differences in bottled v. tap water intake may provide insights into health disparities, like risk of dental caries and inadequate hydration. We examined differences in plain, tap and bottled water consumption among US adults by sociodemographic characteristics. DESIGN: Cross-sectional analysis. We used 24 h dietary recall data to test differences in percentage consuming the water sources and mean intake between groups using Wald tests and multiple logistic and linear regression models. SETTING: National Health and Nutrition Examination Survey (NHANES), 2007-2014. SUBJECTS: A nationally representative sample of 20 676 adults aged ≥20 years. RESULTS: In 2011-2014, 81·4 (se 0·6) % of adults drank plain water (sum of tap and bottled), 55·2 (se 1·4) % drank tap water and 33·4 (se 1·4) % drank bottled water on a given day. Adjusting for covariates, non-Hispanic (NH) Black and Hispanic adults had 0·44 (95 % CI 0·37, 0·53) and 0·55 (95 % CI 0·45, 0·66) times the odds of consuming tap water, and consumed B=-330 (se 45) ml and B=-180 (se 45) ml less tap water than NH White adults, respectively. NH Black, Hispanic and adults born outside the fifty US states or Washington, DC had 2·20 (95 % CI 1·79, 2·69), 2·37 (95 % CI 1·91, 2·94) and 1·46 (95 % CI 1·19, 1·79) times the odds of consuming bottled water than their NH White and US-born counterparts. In 2007-2010, water filtration was associated with higher odds of drinking plain and tap water. CONCLUSIONS: While most US adults consumed plain water, the source (i.e. tap or bottled) and amount differed by race/Hispanic origin, nativity status and education. Water filters may increase tap water consumption.

Differential associations between television viewing, computer use, and adiposity by age, gender, and race/ethnicity in United States youth: A cross-sectional NHANES analysis.

BACKGROUND: Time spent on screens and adiposity change rapidly from childhood to adolescence, with differences by gender and race/ethnicity. OBJECTIVE: Apply time-varying effect models (TVEMs) to a nationally representative sample of youth to identify the age ranges when the cross-sectional associations between television viewing, computer use, and adiposity are significant. METHODS: Data from 8 to 15-year-olds (n = 3593) from the National Health and Nutrition Examination Survey (2011-2018) were extracted. TVEMs estimated the associations between television viewing, computer use, and fat mass index as dynamic functions of the participants' age, stratified by gender and race/ethnicity. RESULTS: TVEMs revealed age-specific statistically significant associations that differed by gender and race/ethnicity. Notably, computer use was related to higher adiposity in non-Hispanic White females aged 9.3-11.4 years (slope ß-range: 0.1-0.2) and in non-Hispanic Black females older than 14.8 years (ß-range: 0.1-0.5). In males, these age windows were 13.5-15.0 years (non-Hispanic White, ß-range: 0.1-0.2), 11.4-13.0 years (non-Hispanic Black, ß-range: 0.1-0.14), and older than 13.0 years (Hispanic, ß-range: 0.1-0.4). CONCLUSIONS: More research during the specific age ranges in the demographic subgroups identified here could increase our understanding of tailored interventions in youth.

Dietary contributions of food outlets by urbanization level in the US population aged 2 years and older-NHANES 2013-2018.

BACKGROUND: Differences in food access, availability, affordability, and dietary intake are influenced by the food environment, which includes outlets where foods are obtained. These differences between food outlets within rural and urban food environments in the United States are not well understood. OBJECTIVES: The aim of this analysis is to describe the contribution of foods and beverages from 6 outlets-grocery stores, convenience stores, full-service restaurants, quick-service restaurants, schools, and other outlets-to the total energy intake and Healthy Eating Index (HEI)-2015 scores in the United States population, by urbanization level (nonmetropolitan statistical areas [MSAs], small-to-medium MSAs, and large MSAs). METHODS: Data from the National Health and Nutrition Examination Survey 2013-2018 were used. Dietary intake from one 24-h dietary recall was analyzed by the outlet where a food or beverage was obtained and by urbanization. Linear regression, adjusted for sex, age, race and Hispanic origin, and family income, was used to predict the contribution of each food outlet to the total energy intake and HEI-2015 total and component scores by urbanization level. RESULTS: During 2013-2018, foods and beverages from grocery stores and quick-service and full-service restaurants contributed to 62.1%, 15.1%, and 8.5% of the energy intake, respectively. The percentage of energy intake from full- and quick-service restaurants increased with increasing urbanization level. HEI-2015 total scores increased with the increasing urbanization level overall (48.1 non-MSAs, 49.2 small-to-medium MSAs, and 51.3 large MSAs) for grocery stores (46.7 non-MSAs, 48.0 small-to-medium MSAs, and 50.6 large MSAs) and for quick-service restaurants (35.8 non-MSAs, 36.3 small-to-medium MSAs, and 37.5 large MSAs). CONCLUSIONS: Grocery stores and restaurants were the largest contributors of energy intake in urban and rural areas. Diet quality improved with increasing urbanization overall and for grocery stores and quick-service restaurants.

Healthy Eating Index-2020: Review and Update Process to Reflect the Dietary Guidelines for Americans,2020-2025.

The purpose of this review is to share the process for reviewing, updating, and developing the most recent version of the Healthy Eating Index-2020 (HEI-2020) for ages 2 and older, following the release of the Dietary Guidelines for Americans (DGA), 2020-2025. The overall review process included: 1) gathering information from the updated DGA, experts, and federal stakeholders; 2) considering substantive changes and needs for new development, keeping in mind the HEI's key features and guiding principles, the US Department of Agriculture's Dietary Patterns that serve as the foundation for the HEI, and scoring considerations; and 3) completing evaluation analyses, including the examination of content validity. The review process led to the development of the HEI-2020; a separate HEI-Toddlers-2020 was developed for ages 12 through 23 months. The 13 components and scoring standards of the HEI-2020 fully align with the HEI-2015, although the index was renamed to clarify that it aligned with the most recent 2020-2025 DGA. As the evidence informing the DGA continues to evolve, various aspects of the HEI may need to evolve in the future as well. Further methodological research is encouraged to add to the scientific evidence base on dietary patterns, to examine needs specific to each life stage, and to model optimal trajectories of healthy dietary patterns over the lifespan.

Continuity, Considerations, and Future Directions for the Healthy Eating Index-Toddlers-2020.

The Dietary Guidelines for Americans, 2020-2025 includes guidance for infants and toddlers aged birth to 24 months. To assess alignment with this new guidance, the Healthy Eating Index (HEI)-Toddlers-2020 was developed for toddlers 12 through 23 months old. This monograph focuses on the continuity, considerations, and future directions of this new index for toddlers in the context of evolving dietary guidance. There is considerable continuity between the HEI-Toddlers-2020 and previous versions of the HEI. The same process, guiding principles, and features (with caveats) are repeated in the new index. However, there are unique considerations for measurement, analysis, and interpretation for the HEI-Toddlers-2020 that this article addresses, while identifying future directions for the HEI-Toddlers-2020. The continued evolution of dietary guidance for infants, toddlers, and young children will provide additional opportunities for index-based metrics: considering inclusion of multidimensional layers in dietary patterns, defining a healthy eating trajectory, bridging healthy eating across different life stages, and communicating the constructs of balance among dietary constituents.

Development of the Healthy Eating Index-Toddlers-2020.

The Healthy Eating Index-Toddlers-2020 (HEI-Toddlers-2020) is a measure for assessing how well a set of foods aligns with new guidance in the Dietary Guidelines for Americans, 2020-2025 (DGA) for toddlers aged 12 through 23 months. This new tool was developed using consistent features and the guiding principles of the HEI. The HEI-Toddlers-2020, like HEI-2020, has 13 components reflecting all constituents of dietary intake, except for human milk or infant formula. These components include the following: Total Fruits, Whole Fruits, Total Vegetables, Greens and Beans, Whole Grains, Dairy, Total Protein Foods, Seafood and Plant Proteins, Fatty Acids, Refined Grains, Sodium, Added Sugars, and Saturated Fats. Healthy dietary patterns for toddlers have unique considerations reflected in the scoring standards for Added Sugars and Saturated Fats. Toddlers have lower energy intake relative to high nutrient needs and added sugars should be avoided. Another distinctive difference is that there is no recommendation to limit saturated fats to <10% of energy intake in this age group; however, saturated fats cannot be unlimited without displacing the energy available to achieve other food group and subgroup goals. Calculations using the HEI-Toddlers-2020, like the HEI-2020, result in a total score and a set of individual component scores that reveal a dietary pattern. The release of a HEI-Toddlers-2020 will enable assessment of diet quality that aligns with the DGA and support additional methodological research to examine needs specific to each life stage and how to model trajectories of healthy dietary patterns.

Evaluation of the Healthy Eating Index-Toddlers-2020.

BACKGROUND: With the addition of new guidance for children from birth to 24 months in the Dietary Guidelines for Americans, 2020-2025 (DGA), a Healthy Eating Index (HEI) was developed for toddlers. OBJECTIVE: To evaluate the psychometric properties of the HEI-Toddlers-2020, 5 analyses relevant to construct and concurrent validity and 2 related to reliability were examined. DESIGN: Twenty-four-hour diet recall data from the cross-sectional National Health and Nutrition Examination Survey (2011-2018) were used. In addition, exemplary menus were analyzed. PARTICIPANTS/SETTING: The main analytic sample included toddlers aged 12 through 23 months (n = 838), with additional analyses of toddlers aged 12 through 35 months (n = 1,717) from the United States. Included participants had valid diet recalls and available weight-for-age data. MAIN OUTCOME MEASURES: Outcomes measures included HEI-Toddlers-2020 total and component scores on menus, population distributions, and correlations. STATISTICAL ANALYSES: HEI total and component scores were calculated using menus from the American Academy of Pediatrics and Healthy Eating Research. Score means and distributions were estimated using a Markov Chain Monte Carlo approach with National Health and Nutrition Examination Survey data (2011-2018). Principal component analysis explored dimensions and Pearson correlations examined components, energy, and Cronbach α. In addition, HEI-Toddlers-2020 and HEI-2020 scores were compared for identical intakes at age 24 months. RESULTS: For validity, exemplary menus received high scores with the HEI-Toddlers-2020. The mean ± SE total HEI-Toddlers-2020 score for toddlers aged 12 through 23 months was 62.9 ± 0.78 and ranged from 40.1 to 84.4 (1st to 99th percentile). Correlation between diet quality and diet quantity was low (-0.15); the scree plot revealed multiple factors. In addition, total scores for identical intakes were approximately 1.5 points higher for HEI-Toddlers-2020 compared with HEI-2020 (difference range for component scores, -4.97 to 4.89). For reliability, most of the intercorrelations among components were low to moderate (0 to 0.49), with a few exceptions among related components. Cronbach α was .48. These results indicate that the index is multidimensional, with no single component driving the total score, and no unnecessary components that are highly correlated with another component. CONCLUSIONS: The results demonstrated evidence supportive of validity and reliability. The HEI-Toddlers-2020 can be used to assess alignment with the DGA for toddlers.

Perspective: A Research Roadmap about Ultra-Processed Foods and Human Health for the United States Food System: Proceedings from an Interdisciplinary, Multi-Stakeholder Workshop.

Our objective was to convene interdisciplinary experts from government, academia, and industry to develop a Research Roadmap to identify research priorities about processed food intake and risk for obesity and cardiometabolic diseases (CMD) among United States populations. We convened attendees at various career stages with diverse viewpoints in the field. We held a "Food Processing Primer" to build foundational knowledge of how and why foods are processed, followed by presentations about how processed foods may affect energy intake, obesity, and CMD risk. Breakout groups discussed potential mechanistic and confounding explanations for associations between processed foods and obesity and CMD risk. Facilitators created research questions (RQs) based on key themes from discussions. Different breakout groups convened to discuss what is known and unknown for each RQ and to develop sub-RQs to address gaps. Workshop attendees focused on ultra-processed foods (UPFs; Nova Group 4) because the preponderance of evidence is based on this classification system. Yet, heterogeneity and subjectivity in UPF classification was a challenge for RQ development. The 6 RQs were: 1) What objective methods or measures could further categorize UPFs, considering food processing, formulation, and the interaction of the two? 2) How can exposure assessment of UPF intake be improved? 3) Does UPF intake influence risk for obesity or CMDs, independent of diet quality? 4) What, if any, attributes of UPFs influence ingestive behavior and contribute to excess energy intake? 5) What, if any, attributes of UPFs contribute to clinically meaningful metabolic responses? 6) What, if any, external environmental factors lead people to consume high amounts of UPFs? Uncertainty and complexity around UPF intake warrant further complementary and interdisciplinary causal, mechanistic, and methodological research related to obesity and CMD risk to understand the utility of applying classification by degree of processing to foods in the United States.