Content uploaded by Shanthy A Bowman
Author content
All content in this area was uploaded by Shanthy A Bowman on May 10, 2019
Content may be subject to copyright.
A preview of the PDF is not available
Food Patterns Equivalents Database 2015-2016: Methodology and User Guide Acknowledgement of Reviewers
Abstract and Figures
Provides information on the methodology used to develop USDA Food Patterns Equivalents for the NHANES 2015-2016 dietary data
Figures - uploaded by Shanthy A Bowman
Author content
All figure content in this area was uploaded by Shanthy A Bowman
Content may be subject to copyright.
... Raw weight of beef was estimated, for later linkage with HGP concentrations measured in raw retail beef products, by dividing the beef ingredient's cooked weight by a moisture adjustment factor of 0.75 (assuming 25% of weight is lost as water during cooking). This factor is used by FNDDS to account for moisture loss during cooking when determining nutritional value [30,31]. The steps are described in Eq. (1) below. ...
Background
Endogenous and exogenous hormones may be present in beef. Human consumption of hormones has been linked to adverse health effects.
Objective
To estimate daily intake of hormonal growth promotants (HGP) from beef consumed by the US population.
Methods
We combined self-reported beef consumption information from a nationally-representative survey with concentrations of 12 HGP measured in 397 samples of retail beef/fat purchased in California. We defined typical, high, and maximum intake scenarios assuming self-reported consumed beef contained the mean, 95th percentile, and maximum concentrations of each HGP, respectively. We estimated distributions of usual (i.e., long-term) daily intake and short-term daily intake (µg/kg/day). We calculated the hazard quotient (HQ), or ratio of estimated intake to the World Health Organization’s acceptable daily intake (ADI) for the HGP.
Results
The highest estimated HQs were found for melengestrol acetate (MGA). For usual daily intake under the typical intake scenario, no HQ exceeded 0.02 (0.00047 µg MGA/kg/day). Under the maximum intake scenario, the highest HQ was 0.29 (0.0087 µg MGA/kg/day), corresponding to the 99th percentile of intake among young boys (ages 1–5). The highest short-term intake estimates for MGA under the maximum intake scenario were the 99th percentile of intake among young girls and boys, which equaled (HQ = 1.00) or exceeded (HQ = 1.29) the ADI for MGA, respectively.
Impact
Hormonal growth promotants (HGP) are used to increase beef production and have been linked to adverse reproductive effects. We estimated daily intake of MGA and several other HGP using US nationally-representative beef consumption data collected between 2015–2018 and HGP concentrations in retail beef. Estimated intake was highest for young children, but estimates were generally very low compared to current health-based intake limits. However, these limits are typically based on studies in adult animals, and further study of potential adverse effects during sensitive developmental periods, such as in early life, may be warranted to ensure recommended intake limits are health-protective.
... Dietary intake is first weighted across the two dietary recall days in order to provide more precise estimates of consumed portions [13]; then, it is disaggregated into ingredients using the Nutrient Database for Dietary Studies, which provides nutrient compositions for about 8700 foods reported in What We Eat In America, which are the dietary intake components of the NHANES [14]. Dietary data are then converted into the respective amounts of food pattern equivalents present in them [15] and assigned to a United States Department of Agriculture food pattern component listed in the Food Patterns Equivalent Database (FPED). The food pattern components are measured as cup equivalents of fruit, vegetables, and dairy; ounce equivalents of grains and protein foods; teaspoon equivalents of added sugars; gram equivalents of solid fats and oils; and the number of alcoholic drinks consumed [16]. ...
Cardiovascular disease and metabolic disorders are disproportionately prevalent among Hispanic and Latino adults in the United States. We extracted a posteriori dietary patterns (DPs) among a nationally representative sample of 2049 Hispanic adults using the 2013–2018 National Health and Nutrition Examination Survey. Three primary DPs and their tertiles were identified, and their associations with cardiometabolic outcomes were examined. Those with higher levels of the Solids Fats, Cheeses, Refined Carbohydrates DP were more likely younger, male, and Mexican American. Those with higher levels of the Vegetables DP were more likely female, higher income, and long-term immigrant residents. Those with higher levels of The Plant-Based DP tended to have higher education levels. Higher levels of the Solid Fats, Cheeses, Refined Carbohydrates DP level were positively associated with body mass index (Tertile 2, β: 1.07 [95%CI: 0.14, 1.99]) and negatively associated with lower high-density lipoprotein cholesterol (HDL-C) levels (Tertile 3, β: −4.53 [95%CI: −7.03, −2.03]). Higher levels of adherence to the Vegetables DP were negatively associated with body fat (Tertile 3, β: −1.57 [95%CI: −2.74, −0.39]) but also HDL-C (Tertile 2, β: −2.62 [95%CI: −4.79, −0.47]). The Plant-Based DP showed no associations with cardiometabolic outcomes. Future research and interventions should consider these associations as well as the sociodemographic differences within each DP.
... Due to the unavailability of some specific food items in NHANES dietary survey, we modified our food/drink diary survey to estimate the added sugar intake using a systemic methodology [13]. We determined added sugar in our modified dietary survey for added food items using the definition of added sugars as "sugars that are added to foods as an ingredient during preparation, processing, or at the table; added sugars do not include naturally occurring sugars such as lactose present in milk and fructose present in whole or cut fruit and 100% fruit juice" [14]. The online dietary survey was conducted to collect information about serving sizes (gram, and teaspoon), times (times, days, week and month) and type of intake (food/drink) recorded in the food diary. ...
Diet is a contributor to the pathogenesis of many non-communicable diseases. Among contributors to poor diet is high added sugar consumption, which is unfortunately on the rise nowadays. The recommended sugar intake by The American Heart Association (AHA) is 24g/day and 36g/day for women and men, respectively. The study’s aim is to assess added sugar intake among adults in Saudi Arabia. A cross-sectional study design was used via an online survey among adults in Saudi Arabia using convenience sampling, and social media platforms were used to collect the data. The authors conducted descriptive statistics to present demographic variables using Chi-square χ2 tests for categorical and t-tests for continuous variables. All statistical tests used a 95% confidence interval with a two-sided P-value <0.05 as significance level. A total of 1163 respondents were included in the study. The study has shown an overall added sugar intake average of 73 g/day. There was a significant difference in means of overall added sugar intake across genders for the age group 18–30 and the age group >60. Equivalently, there was a statistically significant difference in means of added sugar intake food across gender (P-value 0.008). Females tended to consume more added sugar in their food than males. The highest consumption was in the Northern region (123.71 g/day), followed by the Southern region (98.52 g/day), the Western region (86.14 g/day), and lastly, the Central and Eastern regions (66.95 and 62.02 g/day, respectively). The total added sugar intake of added sugar is extremely high in Saudi Arabia. Poor dietary habits lead to many adverse health consequences, including obesity and diabetes. Healthcare providers and public health officials are highly encouraged to shed light on added sugar consumption and create opportunities to promote healthy dietary patterns. The Saudi population is recommended to abide by the added sugar dietary recommendations to avoid future chronic medical conditions.
... The nutrient content of foods consumed each day, including the intake of total calories, carbohydrates, fats, and proteins, was determined [19]. As data on the total and added sugar content are not yet available in the FCTs, the total and added sugar intake was determined using the U.S. Department of Agriculture (USDA) National Nutrient Database for Standard Reference, Release 28 (SR28), and the Food Patterns Equivalents Database (FPED) 2015-2016 [20,21]. The amount of total and added sugars consumed in each food was determined by assigning values to items with exact food description matches, and imputing independent estimates for items without an exact match; this was carried out by two investigators (Y.L. and L.W.). ...
Sugar intake has been linked to the global rise in diabetes. However, the unique diabetogenic effect of sugar, independent of weight gain, remains controversial. This study aimed to investigate the associations between total and added sugar intake and diabetes status, and to test whether the sugar-diabetes associations were moderated or mediated by the body mass index (BMI). We performed a nationwide cross-sectional study on 12,889 Chinese adults who were enrolled in the China Health and Nutrition Survey (CHNS) 2011. The data for the total and added sugar intake were measured using three consecutive 24 h recalls, and determined based on the U.S. Department of Agriculture (USDA) National Nutrient Database for Standard Reference, Release 28 (SR28), the Food Patterns Equivalents Database (FPED) 2015-2016, and the labeled ingredients and nutrient contents. A multivariable logistic regression model was used to analyze the associations between the total and added sugar intake and diabetes. A nutrient density model was used to adjust for the total energy intake. A mediation analysis for significant sugar-diabetes associations shown in multivariable logistic analysis (p < 0.05), and a subgroup analysis according to the BMI category were performed, to examine the mediating and moderating effects of the BMI on the sugar-diabetes association, respectively. We included 12,800 individuals, with a mean age of 50.5, in the final analysis. The means of the total and added sugar intake, total sugar (%E), and added sugar (%E) were 28.2 ± 0.2 g/d, 5.0 ± 0.1 g/d, 6.0 ± 0.0%, and 1.0 ± 0.0%, respectively. The overall prevalence of self-reported physician-diagnosed diabetes was 4.0%. A significant association between the total sugar intake and an increased risk of diabetes was found (odds ratio [OR] =1.008, 95% CI 1.001-1.016). The mediation analysis showed a significant mediation effect through the BMI of the effect of the total sugar on diabetes status (p < 0.001), where 11.7% (95% CI: 4.7-35.7%) of the effect of the total sugar on diabetes was mediated through the BMI. The total sugar intake had a significant direct effect on diabetes around the BMI (estimated coefficient = 0.0004, p < 0.001). The overall total-sugar-intake-diabetes association remained significant in normal-weight participants in the subgroup analysis (OR =1.012, 1.000-1.024). In conclusion, although the BMI moderated and mediated the association between the total sugar intake and diabetes, the total sugar still showed some unique weight-independent diabetogenic effects. Our findings call for efforts to prevent and control diabetes by reducing sugar intake, and losing weight appropriately.
Background
The Planetary Health Diet Index (PHDI) measures adherence to the sustainable dietary guidance proposed by the EAT-Lancet Commission on Food, Planet, Health. To justify incorporating sustainable dietary guidance such as the PHDI in the US, the index needs to be compared to health-focused dietary recommendations already in use. The objectives of this study were to compare the how the Planetary Health Diet Index (PHDI), the Healthy Eating Index-2015 (HEI-2015) and Dietary Approaches to Stop Hypertension (DASH) relate to cardiometabolic risk factors.
Methods and findings
Participants from the National Health and Nutrition Examination Survey (2015–2018) were assigned a score for each dietary index. We examined disparities in dietary quality for each index. We used linear and logistic regression to assess the association of standardized dietary index values with waist circumference, blood pressure, HDL-C, fasting plasma glucose (FPG) and triglycerides (TG). We also dichotomized the cardiometabolic indicators using the cutoffs for the Metabolic Syndrome and used logistic regression to assess the relationship of the standardized dietary index values with binary cardiometabolic risk factors. We observed diet quality disparities for populations that were Black, Hispanic, low-income, and low-education. Higher diet quality was associated with improved continuous and binary cardiometabolic risk factors, although higher PHDI was not associated with high FPG and was the only index associated with lower TG. These patterns remained consistent in sensitivity analyses.
Conclusions
Sustainability-focused dietary recommendations such as the PHDI have similar cross-sectional associations with cardiometabolic risk as HEI-2015 or DASH. Health-focused dietary guidelines such as the forthcoming 2025–2030 Dietary Guidelines for Americans can consider the environmental impact of diet and still promote cardiometabolic health.
The purpose of the study is to assess the impact of partial meat replacement with walnuts using a dose–escalation approach on nutrient intake and diet quality in the usual US diet. Food modeling was implemented using the nationally representative 2015–2018 National Health and Examination Survey (NHANES), with a focus on non-nut consumers, which included 2707 children and adolescents and 5190 adults. Walnuts replaced meat in a dose-escalating manner (0.5, 1, 1.5, and 2 oz walnuts per day replaced 1, 2, 3, and 4 oz meat, respectively). Diet quality was estimated using the population ratio method of the 2015 Healthy Eating Index. The usual intake of nutrients was estimated using the National Cancer Institute method. Significant differences were determined using non-overlapping 95% confidence intervals. The partial replacement of meat with walnuts demonstrated significant increases in the mean intake of fiber, magnesium, and omega-3 fatty acids and significant decreases in cholesterol and vitamin B12 in the modeled diets for children, adolescents, and adults. Additionally, the partial replacement of meat with walnuts improved overall diet quality. Walnut consumption at 1–2 oz as a replacement for some meat may improve nutrient intake and diet quality across age groups.
Poultry meats, in particular chicken, have high rates of consumption globally. Poultry is the most consumed type of meat in the United States (US), with chicken being the most common type of poultry consumed. The amounts of chicken and total poultry consumed in the US have more than tripled over the last six decades. This narrative review describes nutritional profiles of commonly consumed chicken/poultry products, consumption trends, and dietary recommendations in the US. Overviews of the scientific literature pertaining to associations between, and effects of consuming chicken/poultry on, body weight and body composition, cardiovascular disease (CVD), and type II diabetes mellitus (T2DM) are provided. Limited evidence from randomized controlled trials indicates the consumption of lean unprocessed chicken as a primary dietary protein source has either beneficial or neutral effects on body weight and body composition and risk factors for CVD and T2DM. Apparently, zero randomized controlled feeding trials have specifically assessed the effects of consuming processed chicken/poultry on these health outcomes. Evidence from observational studies is less consistent, likely due to confounding factors such as a lack of a description of and distinctions among types of chicken/poultry products, amounts consumed, and cooking and preservation methods. New experimental and observational research on the impacts of consuming chicken/poultry, especially processed versions, on cardiometabolic health is sorely needed.
Background:
Dairy consumption is related to chronic disease risk, yet the measurement of dairy consumption has largely relied upon self-report. Untargeted metabolomics allows for the identification of objective markers of dietary intake.
Objectives:
We aimed to identify associations between dietary intake of dairy (total dairy, low-fat dairy, and high-fat dairy) and serum metabolites in two independent study populations of U.S. adults.
Methods:
Dietary intake was assessed with food frequency questionnaires. Multivariable linear regression models were used to estimate cross-sectional associations between dietary intake of dairy and 360 serum metabolites analyzed in two subgroups of the Atherosclerosis Risk in Communities study (ARIC; n= 3776). Results from the two subgroups were meta-analyzed using fixed effects meta-analysis. Significant meta-analyzed associations in the ARIC study were then tested in the Bogalusa Heart Study (BHS; n= 785).
Results:
In the ARIC study and BHS, respectively, mean age was 54 and 48 years, 61% and 29% were Black, and mean dairy intake was 1.7 and 1.3 servings/day. Twenty-nine significant associations between dietary intake of dairy and serum metabolites were identified in the ARIC study (total dairy, n=14; low-fat dairy, n=10; high-fat dairy, n=5). Three associations were also significant in BHS: myristate (14:0) was associated with high-fat dairy and pantothenate was associated with total dairy and low-fat dairy, but 23 of the 27 associations significant in the ARIC study and tested in BHS were not associated with dairy in BHS.
Conclusions:
We identified metabolomic associations with dietary intake of dairy, including three associations found in two independent cohort studies. These results suggest that myristate (14:0) and pantothenate (vitamin B5) are candidate biomarkers of dairy consumption.
Evidence-based dietary advice regarding meats (including beef), requires accurate assessment of beef and other red meat intakes across life stages. Beef intake is subject to misclassification due to the use of broad categories such as “red and processed meat”. In the current study, intake trends for total beef (i.e., any beef type) and specific beef types (fresh lean, ground, processed) among Americans participating in the National Health and Nutrition Examination Survey (NHANES) 2001–2018 (n = 74,461) were characterized and usual intake was assessed using NHANES 2011–2018 (n = 30,679). The usual intake amounts of beef were compared to those of relevant protein food subgroups modeled in the Healthy U.S.-Style Dietary Pattern (HDP) reported in the 2020–2025 Dietary Guidelines for Americans (DGA). Total per capita beef consumption declined an average of 12 g (p < 0.0001) for ages 2–18 years and 5.7 g (p = 0.0004) for ages 19–59 years per 2-yr NHANES cycle, over the 18-year timeframe, while remaining unchanged for Americans aged 60+ years. On a per capita basis, Americans aged 2 years and older consumed 42.2 g (1.5 ounces) of total beef per day. Fresh lean beef per capita consumption was 33.4 g (1.2 ounces) per day. Per capita intake was similar across all age groups and below the daily HDP modeled amount of 3.7 ounce equivalents for the “Meats, Poultry, Eggs” (MPE) subgroup, while approximately 75% of beef consumers’ intakes of total beef was within HDP modeling. Evidence from intake trends suggests beef is not overconsumed by the majority of Americans but rather within the amounts for MPE and red meat modeled in the HDP of the DGA at the 2000-calorie level.
The objective was to develop the Food Intakes Converted to Retail Commodities Database 2003–04 (FICRCD) that converts foods in What We Eat In America (WWEIA), the dietary component of the National Health and Nutrition Examination Survey (NHANES) 2003–04. There are two major steps in the FICRCD development: (1) foods consumed in the surveys are separated into their ingredients and assigned to an appropriate retail commodity category, and (2) conversion factors are applied to adjust for food preparation and processing losses and convert foods consumed back to their retail commodity levels. There are eight major retail‐level commodity categories in the FICRCD: Dairy Products; Fats and Oils; Fruits; Grains; Meat, Poultry, Fish and Eggs; Nuts; Caloric Sweeteners; and Vegetables. Because each major category has several components, FICRCD has a total of 65 retail‐level commodities. In the WWEIA, NHANES 2003–04, tomatoes, potatoes, and leafy vegetables contributed 31, 29, and 6 percent, respectively to the Total Vegetables Commodity; oranges, apples, bananas and melons contributed 41, 20, 8, and 5 percent, respectively to the Total Fruit Commodity; and wheat flour, 75 percent to the Grains Commodity. FICRCD is useful for identifying disparities among socio‐economic groups in commodity consumption and consequently for nutrition and agricultural policy development. Funded by ARS and ERS, USDA
To identify food selections in each MyPyramid food group or subgroup reflective of typical consumption patterns by Americans, and the nutrient intake that can be expected from consuming a specified amount of these foods from each group, in a low-fat and no-added-sugars form.
An analytical process to identify food consumption choices within each food group and subgroup using national food consumption surveys, and to identify the expected nutrient content of each group using food composition databases.
Relative consumption of foods within each food group; nutrient content for each food group and subgroup (energy plus 27 nutrients).
Disaggregated foods from consumption surveys into component ingredients. Combined similar ingredients into "item clusters" and determined relative consumption of each. Calculated a consumption-weighted nutrient profile for each food group.
Consumption-weighted food intake selections and nutrient profiles were developed for all MyPyramid food groups and subgroups.
This analytical process derived food group and subgroup composites which estimate typical food choices within each MyPyramid food group. These were used to assess the adequacy of the MyPyramid food intake patterns as they were being iteratively developed.
Department of Health and Human Services
U.S. Department of Agriculture, Agricultural Research Service, Beltsville Human
Nutrition Research Center, Food Surveys Research Group, Beltsville, Maryland, and
U.S. Department of Health and Human Services, Centers for Disease Control and
Prevention, National Center for Health Statistics (Hyattsville, Maryland). What We Eat
in America, NHANES 2015-2016. Dietary Data. Available at:
https://wwwn.cdc.gov/nchs/nhanes/search/datapage.aspx?Component=Dietary&Cy
cleBeginYear=2015. Accessed date August 30, 2018.
CNPP-19). U.S. Department of Agriculture, Center for Nutrition Policy and Promotion
- A Carlson
- M Lino
- W-Y Juan
- K Hanson
- P P Basiotis
Carlson A, Lino M, Juan W-Y, Hanson K, and Basiotis PP. 2007. Thrifty Food Plan, 2006.
(CNPP-19). U.S. Department of Agriculture, Center for Nutrition Policy and Promotion.
Available at:
http://www.cnpp.usda.gov/sites/default/files/usda_food_plans_cost_of_food/TFP2
006Report.pdf. Accessed date August 30, 2018.
Estimation of Grain Equivalents in Foods: A Critical Review of Methodology. 36 th National Nutrient Databank Conference
- S Bowman
- J Friday
- J Clemens
- A Moshfegh
Bowman S, Friday J, Clemens J, Moshfegh A. 2011. Estimation of Grain Equivalents in
Foods: A Critical Review of Methodology. 36 th National Nutrient Databank Conference,
April 8, 2011, Bethesda, MD.
Department of Agriculture
U.S. Department of Agriculture, Agricultural Research Service, Beltsville, MD, and U.S.
Department of Agriculture, Economic Research Service, Washington, D.C. Available at:
http://www.ars.usda.gov/nea/bhnrc/fsrg. Accessed date August 30, 2018.