BACKGROUND: Depression is a common mental health disorder. OBJECTIVE: The objective of this study was to explore the association between diet quality, as measured by the Healthy Eating Index (HEI-2020), and depression symptoms among US adolescents and young adults, 12-29 years. DESIGN: The study design was a cross sectional secondary data analysis. PARTICIPANTS: Data from the first 24-hour dietary recall for adolescents and young adults 12-29 years in the National Health and Nutrition Examination Survey 2015-March 2020 (n=4750) were analyzed. MAIN OUTCOME: Depression was defined as a score ≥10 from the Patient Health Questionnaire reflecting moderate or severe symptoms in the previous 2 weeks. STATISTICAL ANALYSES PERFORMED: HEI-2020 total (range 0-100) and component scores were calculated for those with and without depression. Predicted HEI-2020 scores were estimated from linear regression models, adjusting for age, gender, race and Hispanic origin, family income, and seeing a mental health professional in the past year. Statistical analyses accounted for the complex sample design. RESULTS: The prevalence of depression among adolescents and young adults was 7.8% (95% confidence interval (CI) 6.3-9.5%). Total HEI-2020 score was 45.9 (95% CI 45.0-46.7) out of 100 on a given day during 2015-March 2020. Among young people with depression, total HEI-2020 score was lower than among those without depression (41.7 versus 46.2, p<0.001). After adjustment for covariates, the difference was attenuated but remained significant. In adjusted analyses, HEI-2020 component scores were lower for those with depression compared to those without depression for the adequacy components: Total Fruits (1.4 vs. 1.7 p=0.03), Whole Fruits (1.2 vs. 1.7 p<0.01), Total Vegetables (2.1 vs. 2.6 p<0.01), Greens and Beans (0.8 vs. 1.2 p<0.01), and Total Protein foods (3.6 vs. 4.0 p=0.02) out of maximum scores of 5. There were no statistically significant differences observed for Whole Grains, Dairy, Seafood and Plant Proteins, Fatty Acids, Saturated Fats, Refined Grains, Sodium, or Added Sugars. CONCLUSION: Overall diet quality is low among young people. Diet quality, especially consumption of fruits, vegetables, and protein foods was lower among young people with depression compared to those without depression.

BACKGROUND: Differences in nutrient intakes by urbanization level in the U.S. 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: 24-hour dietary recalls from 23,107 participants aged 2 years 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% vs. 8.1% and 5.3%, p<0.001), AS (40.2% vs. 33.4% and 31.3%, p<0.001), SF (26.8% vs. 18.2% and 20.1%, p<0.001), and potassium (31.5% vs. 25.5% and 22.0%, p<0.001). Mean protein intake were also higher in LUA than RA (80.0g vs. 77.7g, p=0.003) and fiber intake higher in LUA than SMMA (16.5g vs. 15.9g, p=0.01) and RA (16.5g vs. 15.2g, p=0.001). Additionally, contributions to energy intake were lower in LUA than SMMA for AS (11.3% v 12.0%, p<0.001) and SF (11.5% v 11.7%, p<0.001), and for LUA than RA for AS (11.3% v 12.9%, p<0.001) and SF (11.5% v 11.8%, p<0.001). CONCLUSIONS: Rural areas had some markers of poorer diet quality-lower protein and fiber intake and higher AS intake-compared to large urban areas, and these differences persisted in adjusted regression models. These results may inform public health efforts to address health disparities by urbanization levels in the U.S.

BACKGROUND: Research suggests that the effects of saturated fat (SF) on health differ depending on its food source. Dairy SF has been associated with lower cardiovascular disease (CVD) risk, while meat SF is linked to a higher CVD risk. OBJECTIVES: To estimate the contribution to total intake of SF of, (1) 5 food groups - dairy, meats, seafood, plants, and "other", and (2) the top 10 specific food category sources in the US population overall and by sociodemographic subgroup. METHODS: Analysis included data from 11,798 participants in the 2017-March 2020 NHANES aged 2+ years. Grams of SF from the food sources, expressed as a percentage of the total grams of SF consumed, were estimated using the population ratio method. RESULTS: Mean daily intake of SF was 28.1g (95% CI: 27.6-28.6g), comprising 11.9% (95% CI: 11.7-12.1%) of total energy intake. Dairy contributed 28.4% of SF, followed by meats (22.1%), plant sources (7.5%), fish and seafood (1.2%), and the remaining foods (41.6%). Youth had higher SF intake from dairy than adults (p<0.001), while non-Hispanic Whites had higher intake than non-Hispanic Blacks (NHB) (p<0.001) and Hispanics (p=0.016). Adults had higher SF intake from meats than youth (p=0.002), males more than females (p<0.001), and NHB more than non-Hispanic Asians (P=0.016) and Hispanics (P<0.001). The top 10 specific sources of SF were unprocessed red meats, sweet bakery products, cured meats, milk, cheese, pizza, unprocessed poultry, Mexican mixed dishes, eggs, and combined fruits and vegetables. CONCLUSIONS: Although dairy contributed about 30% of SF compared to about 20% for total meat, the top specific food category source of SF was unprocessed red meats, which ranked in the top 2 food category sources of SF for most subgroups. These findings may be useful for further research to examine the relationship between the different sources of SF and health outcomes.

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 US are not well understood. OBJECTIVE: The aim of this analysis was to describe the contribution of foods and beverages from six outlets - grocery stores, convenience stores, full-service restaurants, quick-service restaurants, schools, and other outlets - to total energy intake and Healthy Eating Index (HEI)-2015 scores in the US population, by urbanization level (non-metropolitan statistical areas (MSA), small to medium MSA, and large MSA). DESIGN: Data were from the National Health and Nutrition Examination Survey 2013-2018. Dietary intake from one 24-hour 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 total energy intake and HEI-2015 total and component scores by urbanization level. RESULTS: During 2013-2018, foods and beverages from grocery stores, quick-service, and full-service restaurants provided 62.1%, 15.1% and 8.5% of energy intake, respectively. The percent of energy intake from full- and quick-service restaurants increased with increasing urbanization level. HEI-2015 total scores increased with 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). CONCLUSION: 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.

BACKGROUND: In 2009, the US Department of Agriculture Food and Nutrition Service's Special Supplemental Nutrition Program for Women, Infants, and Children (WIC) food packages were revised to include more whole fruits, vegetables, whole grains, and lower-fat milk. OBJECTIVE: To describe trends over time in the consumption of fruits (total and whole), vegetables, whole grains, milk (whole, reduced fat, low-fat or nonfat (LFNF), and flavored), and added sugars, including breakfast cereals, by WIC participation status (current WIC recipient, WIC income-eligible nonrecipient, and WIC income-ineligible nonrecipient). METHODS: Dietary intakes on a given day for 1- to 4-y-old children (n = 5568) from the 2005-2018 National Health and Nutrition Examination Survey (NHANES) were analyzed to examine trends in the percentage of individuals consuming and amounts consumed over time using linear regression adjusted for age, sex, and race and Hispanic origin. RESULTS: From 2005 through 2018, the percentage of WIC recipients or WIC income-eligible nonrecipients consuming fruits and vegetables on a given day did not change, but the percentage of fruit consumed as whole fruit increased significantly among WIC recipients (36.4%-62.1%), but not among income-eligible nonrecipients. Among the WIC recipients, the percentage of consumption (5.5%-29.3%), the amount of LFNF milk servings consumed (0.1-0.4 cups), and the percentage of the total milk consumed as LFNF milk (4.8%-27%) significantly increased from 2005 to 2018. Conversely, the percentage of energy (12.3%-10.8%) and servings (11.4-10.6 teaspoons) from added sugars declined significantly. Among WIC-eligible nonrecipients, the servings of whole grains increased significantly, whereas servings and percentage of energy from added sugars declined significantly. CONCLUSIONS: From 2005 through 2018, changes in dietary patterns for WIC recipients did not always mirror those of US children of the same age. The percentage of fruit consumed as whole fruit, and the percentage and quantity of milk consumed as LFNF milk increased significantly among WIC recipients, but not among income-eligible nonrecipients. J Nutr 20XX;xx:xx-xx.

BACKGROUND: High energy intake from non-nutrient dense sources correlates with poorer diet quality. OBJECTIVES: To, 1) estimate total energy intake, and energy from solid fats and added sugars, and combined (SOFAS), and identify their top food category sources for ages 2-18 years in 2015-2018, and 2) describe trends over time in 2009-2018. DESIGN: Data were from the National Health and Nutrition Examination Survey. Pairwise differences were examined using univariate t statistics (2015-2018, n=5,038), and trends by age, and over time (2009-2018, n=14,038) examined using orthogonal polynomials. RESULTS: In 2015-2018, SOFAS contributed (mean [SE], 30.0% [0.3%]) of total energy. Solid fats 16.1% [0.2%] and added sugars 13.8% [0.2%] each contributed >10%. The contribution of added sugars increased with age from 11.1% (2-3 years) to 14.4% (14-18 years), and was higher for all other race/Hispanic origins than Non-Hispanic Asians. Top five sources of energy were sweet bakery products, savory snacks, pizza, other mixed dishes, and unflavored milk, and for SOFAS also included soft drinks, other desserts, candy and snack bars. Total energy did not change between 2009-2018, but energy from SOFAS, and servings of solid fats, and added sugars declined. The contribution of unflavored milk to total energy declined for all ages and most race/Hispanic origins. Fruit drinks (all ages) and soft drinks (9-18 years) remained among top added sugars sources despite declines. The contribution of sweet bakery products to energy from SOFAS increased for most ages, and candy and snack bars to energy from added sugars. CONCLUSIONS: In 2015-2018, SOFAS contributed over 30% of total energy for ages 2-18 years, which doubled the Dietary Guidelines for Americans' recommended limit of 15%. Top five sources of total energy were similar to those of solid fats, and those of SOFAs similar to those of added sugars. These results may inform public health efforts for improving diet quality.

BACKGROUND: The 2020-2025 Dietary Guidelines for Americans (DGAs) recommend intake of a variety of vegetables, including dark green, red and orange, starchy, and other vegetables. OBJECTIVES: This study aims to describe sociodemographic differences in the contribution of different categories of vegetables, and the form in which they are consumed, i.e., discrete vegetables, mixed dishes, and other foods such as savory snacks to total vegetables intake on a given day. DESIGN: This is a cross-sectional, secondary analysis of the 2017-2018 National Health and Nutrition Examination Survey (NHANES). PARTICIPANTS: /Setting: This study included the data of 7122 persons aged 2 years with reliable day 1 24-hour dietary recalls. MAIN OUTCOME MEASURES: Serving equivalents of vegetables from 20 discrete categories of vegetables, and from mixed dishes and other foods as a percentage of total vegetables. STATISTICAL ANALYSES: Pairwise differences by age, sex, and race and Hispanic origin, and family income were examined using univariate t statistics, and trends by age and income examined using orthogonal polynomials. RESULTS: Mean serving equivalents of vegetables was 1.4 cups. The serving equivalents increased with age among youth, was higher among non-Hispanic Asian (NHA) persons than other subgroups and increased with increasing family income. Overall, discrete vegetables contributed 55.2% of total vegetable intake and the contribution increased with age in adults, and with increasing family income. The top five discrete vegetable contributors were other vegetables and combinations, French fries and other fried white potatoes, lettuce and lettuce salads, mashed potatoes and white potato mixtures, and baked or boiled white potatoes. Non-starchy discrete vegetables contributed more to total vegetables for adults (37.6%) than youth (28.0%), and the contribution increased with increasing family income. On the other hand, the contribution of mixed dishes and other foods decreased with increasing family income. CONCLUSIONS: Discrete vegetables only contributed 55.2% of total vegetables intake, and the top sources were not varied, three of them potato-based, which may explain the reported low vegetables intake, relative to the DGAs. More than one-third of vegetables consumed were non-starchy discrete vegetables, many of which are high in vitamins. Non-starchy discrete vegetable intake was higher in adults than youth and increased with family income.