[Note: Integrated as part of FoodData Central, April 2019.] USDA's Food and Nutrient Database for Dietary Studies (FNDDS) is a database that is used to convert food and beverages consumed in What We Eat In America (WWEIA), National Health and Nutrition Examination Survey (NHANES) into gram amounts and to determine their nutrient values. Because FNDDS is used to generate the nutrient intake data files for WWEIA, NHANES, it is not required to estimate nutrient intakes from the survey. FNDDS is made available for researchers using WWEIA, NHANES to review the nutrient profiles for specific foods and beverages as well as their associated portions and recipes. Such detailed information makes it possible for researchers to conduct enhanced analysis of dietary intakes. FNDDS can also be used in other dietary studies to code foods/beverages and amounts eaten and to calculate the amounts of nutrients/food components in those items.
FNDDS is released every two-years in conjunction with the WWEIA, NHANES dietary data release. The FNDDS is available for free download from the FSRG website. Resources in this dataset:Resource Title: Website Pointer to Food and Nutrient Database for Dietary Studies. File Name: Web Page, url: https://www.ars.usda.gov/northeast-area/beltsville-md-bhnrc/beltsville-human-nutrition-research-center/food-surveys-research-group/docs/fndds/ USDA's Food and Nutrient Database for Dietary Studies (FNDDS) is a database that is used to convert food and beverages consumed in What We Eat In America (WWEIA), National Health and Nutrition Examination Survey (NHANES) into gram amounts and to determine their nutrient values.

The dataset, Survey-SR, provides the nutrient data for assessing dietary intakes from the national survey What We Eat In America, National Health and Nutrition Examination Survey (WWEIA, NHANES). Historically, USDA databases have been used for national nutrition monitoring (1). Currently, the Food and Nutrient Database for Dietary Studies (FNDDS) (2), is used by Food Surveys Research Group, ARS, to process dietary intake data from WWEIA, NHANES. Nutrient values for FNDDS are based on Survey-SR. Survey-SR was referred to as the "Primary Data Set" in older publications. Early versions of the dataset were composed mainly of commodity-type items such as wheat flour, sugar, milk, etc. However, with increased consumption of commercial processed and restaurant foods and changes in how national nutrition monitoring data are used (1), many commercial processed and restaurant items have been added to Survey-SR. The current version, Survey-SR 2013-2014, is mainly based on the USDA National Nutrient Database for Standard Reference (SR) 28 (2) and contains sixty-six nutrientseach for 3,404 foods. These nutrient data will be used for assessing intake data from WWEIA, NHANES 2013-2014. Nutrient profiles were added for 265 new foods and updated for about 500 foods from the version used for the previous survey (WWEIA, NHANES 2011-12). New foods added include mainly commercially processed foods such as several gluten-free products, milk substitutes, sauces and condiments such as sriracha, pesto and wasabi, Greek yogurt, breakfast cereals, low-sodium meat products, whole grain pastas and baked products, and several beverages including bottled tea and coffee, coconut water, malt beverages, hard cider, fruit-flavored drinks, fortified fruit juices and fruit and/or vegetable smoothies. Several school lunch pizzas and chicken products, fast-food sandwiches, and new beef cuts were also added, as they are now reported more frequently by survey respondents. Nutrient profiles were updated for several commonly consumed foods such as cheddar, mozzarella and American cheese, ground beef, butter, and catsup. The changes in nutrient values may be due to reformulations in products, changes in the market shares of brands, or more accurate data. Examples of more accurate data include analytical data, market share data, and data from a nationally representative sample. Resources in this dataset:Resource Title: USDA National Nutrient Database for Standard Reference Dataset for What We Eat In America, NHANES 2013-14 (Survey SR 2013-14). File Name: SurveySR_2013_14 (1).zipResource Description: Access database downloaded on November 16, 2017. US Department of Agriculture, Agricultural Research Service, Nutrient Data Laboratory. USDA National Nutrient Database for Standard Reference Dataset for What We Eat In America, NHANES (Survey-SR), October 2015. Resource Title: Data Dictionary. File Name: SurveySR_DD.pdf

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USDA’s Food Patterns Equivalents Database (FPED) converts the foods and beverages in the Food and Nutrient Database for Dietary Studies to the 37 USDA Food Patterns components. The FPED was formerly known as the MyPyramid Equivalents Database. The FPED serves as a unique research tool to evaluate food and beverage intakes of Americans with respect to the 2015-2020 Dietary Guidelines for Americans recommendations. The Food Patterns 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. In addition to the SAS datasets, the FPED release includes: (1) the Food Patterns Equivalents Ingredient Database (FPID) that includes the 37 USDA Food Patterns components per 100 grams of each unique ingredient used in the FNDDS; and (2) listings of gram weights for one cup equivalents of fruits, vegetables, dairy, and legumes used in the FPED. Resources in this dataset:Resource Title: Food Patterns Equivalents Database. File Name: Web Page, url: https://www.ars.usda.gov/northeast-area/beltsville-md-bhnrc/beltsville-human-nutrition-research-center/food-surveys-research-group/docs/fped-overview/ Food Patterns Equivalents Database (FPED) converts the foods and beverages in the Food and Nutrient Database for Dietary Studies to the 37 USDA Food Patterns components. The FPED serves as a unique research tool to evaluate food and beverage intakes of Americans with respect to the 2015-2020 Dietary Guidelines for Americans recommendations.

SuperTracker was an online tool offered by USDA (2011-2018) that helped users track diet, physical activity and weight. SuperTracker provided a personalized plan based on the 2015-2020 Dietary Guidelines for Americans for what you should eat and drink and guided users to making better choices. This dataset includes the SuperTracker source code (latest update April 2018), including: front end application, database schema, documentation, deployment scripts and a ReadMe.txt file that provides high level instructions for the source code. Database connection strings and actual data are not included. The full foods database spreadsheet is attached as well; these foods are based on the Food and Nutrient Database for Dietary Studies (FNDDS), and the Food Patterns Equivalents Database (FPED), both from the USDA/ARS Food Surveys Research Group.

It is important to note that the code is based on 2015-2020 Dietary Guidelines for Americans and will not be updated to reflect future guidance. In addition, the food database is based on FNDDS from 2011-2012 (FNDDS 6.0) and FPED from 2011-2012 and will not be updated with future data releases.

*Indicates robust test was used, as some evidence of heteroskedascity was present (p<0.10 Breusch-Pagan test for heteroskedasticity). Data are means and standard deviations by United States Department of Agriculture (USDA) lunch regulation subgroup.1USDA Food and Nutrient Database for Dietary Studies 2.0.2Economic Research Service.3Higher scores indicative of higher nutrient density.4Higher values represent greater amounts of 6 target nutrients per cost.5Data for beans and peas are not repeated here.6P-value of difference for each outcome does not include beans or peas and is based on a sample size of 86 foods.

This database was developed with support from the Office of Dietary Supplements, National Institutes of Health for flavonoid intake studies. The database is a useful tool for flavonoid intake and health outcome studies for any population globally. It contains data for 29 individual flavonoid compounds in six subclasses of flavonoids for every food in a subset of 2,926 food items which provide the basis for the Food and Nutrient Database for Dietary Studies (FNDDS 4.1). Proanthocyanidins data are not included at the present time. For flavonoid intake data for the U.S. population based on NHANES 2007-08, please refer to the Food Surveys Research Group website. Resources in this dataset:Resource Title: READ ME - USDA’s Expanded Flavonoid Database for the Assessment of Dietary Intakes Documentation and User Guide. File Name: FDB-EXP.pdfResource Description: Information regarding documentation, development of the database, limitations, format, and references.Resource Software Recommended: Adobe Acrobat Reader,url: http://www.adobe.com/prodindex/acrobat/readstep.html Resource Title: Data Dictionary. File Name: FDB_EXP_DD.pdfResource Title: FDB-EXP_R01-1.accdb. File Name: FDB-EXP_R01-1.accdb_.zipResource Description: This file contains USDA's Expanded Flavonoid Database for the Assessment of Dietary Intakes imported into a MS Access database version 2007 or later. The file structure is the same as that of the USDA National Nutrient Database for Standard Reference.

These data represent the mass quantity (in grams) of each ingredient in the Food Commodity Intake Database (n=484) in each food in the Food and Nutrient Database for Dietary Studies from 2001-2018 (n=8,684), totaling 93,681 ingredient-food combinations. Of the total number of FNDDS foods included, 1,584 (18%) were imputed according to the methods described in Conrad et al. and 7,099 (82%) were not imputed. All of the non-imputed data were created by the US Environmental Protection Agency and are in the public domain.

These data represent the mass quantity (in grams) of each ingredient in the Food Commodity Intake Database (n=484) in each food in the Food and Nutrient Database for Dietary Studies from 2001-2018 (n=8,684), totaling 93,681 ingredient-food combinations. Of the total number of FNDDS foods included, 1,584 (18%) were imputed according to the methods described in Conrad et al. and 7,099 (82%) were not imputed. All of the non-imputed data were created by the US Environmental Protection Agency and are in the public domain. Date Submitted: 2022-05-11

BackgroundThe healthiest way to prevent metabolic syndrome (MetS) is through behavioral and nutritional adjustments. We examined the relationship between total flavonoids intake, flavonoid subclasses, and clinically manifest MetS.MethodsA cross-sectional analysis was conducted among 28,719 individuals from the National Health and Nutrition Examination Survey (NHANES) and Food and Nutrient Database for Dietary Studies (FNDDS) 2007–2011 and 2017–2018. Two 24-h reviews were conducted to determine flavonoids intake and subclasses. The link between flavonoids intake and MetS was investigated using a multivariate logistic regression model.ResultsQ2 and Q3 of total flavonoids intake were associated with 20 and 19% lower risk of incident MetS after adjusting age and sex. Anthocyanidins and flavanones intake in Q2 and Q3 substantially reduced the MetS risk compared to Q1. MetS risk decreased steadily as the total intake of flavonoids increased to 237.67 mg/d. Flavanones and anthocyanidins also displayed V-shaped relationship curves (34.37 and 23.13 mg/d).ConclusionMetS was adversely linked with total flavonoids intake, flavanones, and anthocyanidins. Moreover, the most effective doses of total flavonoids, flavanones, and anthocyanidins were 237.67, 34.37, and 23.13 mg/d, respectively, potentially preventing MetS.

ObjectiveThis study aims to explore the association between niacin intake and stroke within a diverse, multi-ethnic population.MethodsA stringent set of inclusion and exclusion criteria led to the enrollment of 39,721 participants from the National Health and Nutrition Examination Survey (NHANES). Two interviews were conducted to recall dietary intake, and the USDA’s Food and Nutrient Database for Dietary Studies (FNDDS) was utilized to calculate niacin intake based on dietary recall results. Weighted multivariate logistic regression was employed to examine the correlation between niacin and stroke, with a simultaneous exploration of potential nonlinear relationships using restricted cubic spline (RCS) regression.ResultsA comprehensive analysis of baseline data revealed that patients with stroke history had lower niacin intake levels. Both RCS analysis and multivariate logistic regression indicated a negative nonlinear association between niacin intake and stroke. The dose-response relationship exhibited a non-linear pattern within the range of dietary niacin intake. Prior to the inflection point (21.8 mg) in the non-linear correlation between niacin intake and stroke risk, there exists a marked decline in the risk of stroke as niacin intake increases. Following the inflection point, the deceleration in the decreasing trend of stroke risk with increasing niacin intake becomes evident. The inflection points exhibit variations across diverse populations.ConclusionThis investigation establishes a negative nonlinear association between niacin intake and stroke in the broader American population.

BackgroundThe increasing influence of overactive bladder (OAB) on physical as well as mental health of individuals is becoming more pronounced annually, as evidenced by the urge urinary incontinence and nocturia. Symptoms in OAB patients may be influenced by inflammation and oxidative stress. Flavonoids are recognized as significant anti-inflammatory and antioxidant agents, which are commonly available in fruits, tea, vegetables, etc. Previous research has demonstrated the therapeutic potential of flavonoids and their subclasses in treating inflammation, and oxidative stress. Despite this, there remains a paucity of research exploring the potential correlation between flavonoid consumption, specifically within distinct subclasses, and OAB. Thus, our study aims to investigate the relationship between flavonoid intake and OAB to identify possible dietary interventions for OAB management.MethodsWe utilized the survey data from the National Health and Nutrition Examination Survey (NHANES) and the USDA Food and Nutrient Database for Dietary Studies (FNDDS) to investigate the relationship between dietary intake of total and subclass flavonoids and the risk of OAB based on 13,063 qualified American adults. The dietary flavonoid intake was estimated from two 24-h dietary recalls. Weighted multivariate logistic regression model, quantile-based g-computation, restricted cubic spline model, and stratified analysis were used to explore the association between flavonoid intake and OAB, respectively.ResultsThe participants diagnosed with OAB exhibited a higher percentage of being female, older, Non-Hispanic Black, unmarried, former drinkers, having a lower annual household income, lower poverty to income ratio, lower educational attainment, and a higher likelihood of being obese and smokers. Upon adjusting for confounding factors, the weighted logistic regression models revealed that the third quartile of consumption of anthocyanidin and the second quartile of consumption of flavone were significantly associated with the reduced odds of OAB, while total flavonoid consumption did not show a significant correlation with the risk of OAB. The quantile-based g-computation model indicated that flavone, anthocyanidin and flavonol were the primary contributors to the observed negative correlation. Furthermore, the restricted cubic spline models demonstrated a J-shaped non-linear exposure-response association between anthocyanidin intake and the risk of OAB (Pnonlinear = 0.00164). The stratified and interaction analyses revealed that the relationship between anthocyanidin intake and the risk of OAB was significantly influenced by age (Pinteraction = 0.01) and education level (Pinteraction = 0.01), while the relationship between flavone intake and the risk of OAB was found to vary by race (Pinteraction = 0.02) and duration of physical activity (Pinteraction = 0.05).ConclusionOur research suggests that consuming a diet rich in flavonoid subclass anthocyanidin and flavone is associated with a reduced risk of OAB, potentially offering clinical significance in the prevention of OAB development. This underscores the importance of dietary adjustments in the management of OAB symptoms.

BackgroundFlavonoids are a class of plant chemicals known to have health-promoting properties, including six subclasses. Anthocyanin is one of the subclasses that have anti-inflammatory and antioxidant activities. However, the relationship between flavonoid subclass intake and the risk of non-alcoholic fatty liver disease (NAFLD) and liver fibrosis has not been verified in representative samples of the United States.MethodsThis is a cross-sectional study based on the data from the National Health and Nutrition Examination Survey (NHANES) and the Food and Nutrient Database for Dietary Studies (FNDDS) in 2017–2018. The intake of flavonoid subclasses of the participants was obtained from two 24 h dietary recalls. The NAFLD and liver fibrosis were defined based on the international consensus criteria. The relationship between flavonoid subclass intake and NAFLD and liver fibrosis was evaluated using a multivariate logistic regression model corrected for multiple confounding factors. Subgroup analysis, trend tests, interaction tests and restricted cubic spline were carried out to further explore this relationship. In addition, we also explored the relationship between anthocyanin and liver serum biomarkers, dietary total energy intake and healthy eating index (HEI)-2015 scores.ResultsA total of 2,288 participants were included in the analysis. The intake of anthocyanin was significantly negatively associated with the risk of NAFLD, but not other flavonoid subclasses. A higher anthocyanin intake was significantly associated with a lower risk of NAFLD (quartile 4, OR 0.470, 95% CI 0.275–0.803). The results of subgroup analysis showed that the protective effect of dietary anthocyanin intake on NAFLD was more pronounced in participants of non-Hispanic whites, with hypertension and without diabetes (P for interaction

Data S1. Amplicon Sequencing Data and Metadata for Microbiome Samples

This file contains two sheets:

Sheet 1: Sample IDs and Amplicon Sequencing Accession Numbers for human microbiome samples. A table listing sample IDs paired with their corresponding accession numbers for the amplicon sequencing files of human microbiome samples.

Sheet 2: Sample Metadata and Amplicon Sequencing Accession Numbers for mouse microbiome samples. Detailed metadata for each sample, including sample ID, experiment number, sample collection day, mouse number, antibiotic and diet treatments administered to the mice, and the NCBI Sequence Read Archive (SRA) accession number for the corresponding sequencing data.

Data S2. Detailed Nutritional Intake Data for Anonymized Patients

This file contains comprehensive, anonymized data on patient dietary intake. Columns include:

pid: Patient ID.

Meal: Meal category (e.g., breakfast, lunch, dinner).

Food_NSC: Food name.

fdrt: Diet entry day, relative to transplant.

Unit: Unit of measurement for food quantity (e.g., grams, ounces).

Por_eaten: Portion of food consumed.

Food_code, description: Food code and corresponding description from the Food and Nutrient Database for Dietary Studies (FNDDS).

Total calories, weight: Caloric content and weight of the consumed portion.

Individual macronutrients (grams): Gram weight of each macronutrient (e.g., protein, fat, sugar, fiber as well as carbohydrate that excludes sugar and fiber) in the consumed portion.

Dehydrated weight: Total weight of the consumed portion minus the water weight.

Data S3. Summarized Food Group Intake and Clinical Variables for Bayesian Modeling

This file provides summarized dietary intake data and relevant clinical variables used in the Bayesian model. Columns include:

sdrt: Stool sample collection day, relative to transplant.

fg_egg ... fg_veggie: Average intake (in grams) of foods belonging to nine broad food groups (e.g., eggs, vegetables) during the two days preceding stool sample collection.

intensity: Intensity of the conditioning regimen.

empirical: Binary indicator (yes/no) of patient exposure to specific antibiotics (piperacillin/tazobactam, carbapenems, cefepime, linezolid, oral vancomycin, and metronidazole) in the two days prior to stool sample collection.

simpson_reciprocal: Alpha diversity of the stool sample, calculated using the Simpson reciprocal index.

TPN, EN: Binary indicators (yes/no) of patient receiving total parenteral nutrition (TPN) or enteral nutrition (EN) in the two days prior to stool sample collection.

timebin: Time interval of stool sample collection, categorized by week relative to transplant.

Data S4. Medication Exposure Overlapping With Stool Samples

This file contains a record of all medication exposures that occurred during the 48-hour period before each stool sample was collected. This window was chosen to investigate the potential impact of recent medication use on the stool microbiome. The table includes the following columns:

sampleid: A unique identifier assigned to each stool sample.

pid: Patient ID.

sdrt: Stool sample collection day, relative to transplant.

class: The pharmacological class of the administered medication (e.g., "quinolones", "beta-lactamase inhibitors", etc.).

drug_name_clean: The name of the medication (e.g., "ciprofloxacin", "vancomycin").

route_clean: The route of administration (e.g., "IV", "oral").

drug_category_for_this_study: A study-specific categorization of the medication, based on its potential impact on the gut microbiome. The categories are:

• broad_spectrum: Broad-spectrum antibiotics, as classified in this study: piperacillin/tazobactam, carbapenems, cefepime, linezolid, oral vancomycin, and metronidazole

• fluoroquinolones: Fluoroquinolone antibiotics (ciprofloxacin or levofloxacin).

• other_antibacterial: Antibacterial medications not classified as broad-spectrum or fluoroquinolones.

• not_antibacterial: Medications not expected to have a direct antibacterial effect.

Additional File: Filled-out STORMS checklist

This file is a filled-out STORMS checklist for the manuscript. It is version 1.03, downloaded from 10.5281/zenodo.5703116. The STORMS checklist is a standardized checklist for microbiome studies, published in the journal Nature Medicine (https://www.nature.com/articles/s41591-021-01552-x).

IntroductionDietary assessment is important for understanding nutritional status. Traditional methods of monitoring food intake through self-report such as diet diaries, 24-hour dietary recall, and food frequency questionnaires may be subject to errors and can be time-consuming for the user.MethodsThis paper presents a semi-automatic dietary assessment tool we developed - a desktop application called Image to Nutrients (I2N) - to process sensor-detected eating events and images captured during these eating events by a wearable sensor. I2N has the capacity to offer multiple food and nutrient databases (e.g., USDA-SR, FNDDS, USDA Global Branded Food Products Database) for annotating eating episodes and food items. I2N estimates energy intake, nutritional content, and the amount consumed. The components of I2N are three-fold: 1) sensor-guided image review, 2) annotation of food images for nutritional analysis, and 3) access to multiple food databases. Two studies were used to evaluate the feasibility and usefulness of I2N: 1) a US-based study with 30 participants and a total of 60 days of data and 2) a Ghana-based study with 41 participants and a total of 41 days of data).ResultsIn both studies, a total of 314 eating episodes were annotated using at least three food databases. Using I2N’s sensor-guided image review, the number of images that needed to be reviewed was reduced by 93% and 85% for the two studies, respectively, compared to reviewing all the images.DiscussionI2N is a unique tool that allows for simultaneous viewing of food images, sensor-guided image review, and access to multiple databases in one tool, making nutritional analysis of food images efficient. The tool is flexible, allowing for nutritional analysis of images if sensor signals aren’t available.

BackgroundMetabolic associated fatty liver disease (MAFLD) formerly known as non-alcoholic fatty liver disease (NAFLD) is the most common chronic liver disease. Flavonoid is considered a promising candidate for metabolic disease prevention although few studies have explored the relationship between flavonoid intake and MAFLD.PurposeTo assess the relationship between flavonoid intake and MAFLD prevalence in the U.S. adult population.Materials and methodsThe data of this cross-sectional study was obtained from National Health and Nutrition Examination Survey (NHANES) and Food and Nutrient Database for Dietary Studies (FNDDS) 2017–2018. Flavonoid and subclasses intake was assessed by two 24h recalls. MAFLD was diagnosed according to the consensus definitions. Multivariate logistic regression model was performed to examine the association between flavonoid intake and MAFLD with adjustments for confounders.ResultsA total of 4,431 participants were included in this cross-sectional analysis. MAFLD had a weighted prevalence of 41.93% and was not associated with total flavonoid intake. A higher anthocyanin and isoflavone intake, on the other hand, was associated with a lower prevalence of MAFLD. The protective effect of higher anthocyanin intake was significant among male, Non-Hispanic White, and Non-Hispanic Asia participants. Higher isoflavone intake was associated with a lower risk of MAFLD in participants of younger (age < 50), Non-Hispanic Black, Non-Hispanic Asia, and higher HEI-2015 scores compared with the lowest quartile of isoflavone intake. Stratified analysis showed that compared with the lowest quartile of anthocyanin intake, the effect of anthocyanin intake on MAFLD varied by racial groups (Pinteraction = 0.02). A positive correlation existed between HDL and anthocyanidin intake (P = 0.03), whereas a negative correlation existed between FPG and isoflavone intake (P = 0.02).ConclusionMAFLD was adversely linked with flavonoid subclasses, anthocyanin and isoflavone. This modifiable lifestyle provides a potential opportunity to prevent MAFLD. These findings promote future research into the links and mechanisms between anthocyanin and isoflavone intake and MAFLD.