[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.

FNDDS is a database that provides the nutrient values for foods and beverages reported in What We Eat in America, the dietary intake component of the National Health and Nutrition Examination Survey.

Dataset

This dataset was created by Samia Haque Tisha

Contents

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

*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.

Several USDA food composition databases, including the Food and Nutrient Database for Dietary Studies (FNDDS), Standard Reference (SR) Legacy, and the USDA Branded Food Products Database, have transitioned to FoodData Central, a new and harmonized USDA food and nutrient data system. FoodData Central also includes expanded nutrient content information as well as links to diverse data sources that offer related agricultural, environmental, food, health, dietary supplement, and other information. The new system is designed to strengthen the capacity for rigorous research and policy applications through its search capabilities, downloadable datasets, and detailed documentation. Application developers can incorporate the information into their applications and web sites through the application programming interface (API) REST access. The constantly changing and expanding food supply is a challenge to those who are interested in using food and nutrient data. Including diverse types of data in one data system gives researchers, policymakers, and other audiences a key resource for addressing vital nutrition and health issues. FoodData Central: Includes five distinct types of data containing information on food and nutrient profiles, each with a unique purpose: Foundation Foods; Experimental Foods; Standard Reference; Food and Nutrient Database for Dietary Studies; USDA Global Branded Food Products Database. Provides a broad snapshot in time of the nutrients and other components found in a wide variety of foods and food products. Presents data that come from a variety of sources and are updated as new information becomes available. Includes values that are derived through a variety of analytic and computational approaches, using state-of-the-art methodologies and transparent presentation. FoodData Central is managed by the Agricultural Research Service and hosted by the National Agricultural Library. Resources in this dataset: Resource Title: Website Pointer for FoodData Central. File Name: Web Page, url: https://fdc.nal.usda.gov/index.html Includes Search, Download data, API Guide, Data Type Documentation, and Help pages.

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.

The Access format has been discontinued as of October 2021. It is recommended to use the new file format, JSON, or to import CSV files from "Full Download of All Data Types" above to replace the tables within the Access format. The October 2022 files contain the most recent data. These files were fixed to include some missing fields and were re-released on 12/8/2020. The branded food and full download CSV archives from April 2020 to April 2022 have been re-released with corrections to the Nutrient ID column. SR Legacy is the final release of the Standard Reference data type. These data will not be updated. Earlier versions of Standard Reference (i.e., SR 17 through 28) are available on the Methods and Application of Food Composition Laboratory (MAFCL) website. The Food and Nutrient Database for Dietary Studies (FNDDS) is updated every two years, in conjunction with the two-year cycles of the National Health and Nutri-tion Examination Survey. The October 15, 2020 date represents the date on which FNDDS 2017-2018 was published and made available for download in FoodData Central. Earlier releases can be found on the Food Surveys Research Group website. The Full Download file contains files for all of the FoodData Central data types. Therefore, whenever a download for an individual data type is updated, the Full Download file also will be updated to include those new data. The Access file only contains the most recent version of products in the Branded Foods dataset. We recommend you use the API to access previous versions of Branded Food products. April 2020* Version 2 includes corrections made on May 1, 2020 to branded foods data.

Data Type Comparison Foundation Foods Experimental Foods Food and Nutrient Database for Dietary Studies (FNDDS) Branded Foods SR Legacy Definition Data and metadata on individual samples of commodity/commodity-derived minimally processed foods with insights into variability Data on food published in peer-reviewed journals supported by or in collaboration with USDA Data on nutrients and portion weights for foods and beverages reported in What We Eat in America, NHANES Data from labels of national and international branded foods collected by a public-private partnership Historic data on food components including nutrients derived from analyses, calculations, and published literature Data Source USDA: based on analytically derived values Researchers: based on scientific publications USDA: compiled based on values from FDC data types Manufacturers: based on food label information USDA: based on Standard Reference originally available via the USDA National Nutrient Database (NNDB) Update Frequency April and October of each year April and October of each year as data are available Every two years in concert with WWEIA, NHANES release Monthly Final release April 2018

Foundation Foods (lab-analyzed staples like meats, veggies, grains): – Includes macros (protein, fat, carbs) and sodium. SR Legacy (standard reference for common foods): ) – Covers calories, protein, fat, carbs, and sodium for everyday items.

API for Custom Exports: If you need filtered data (e.g., only high-calorie fast foods), use the free API at API Documentation to query and download in JSON/CSV.

Food Item,Calories (kcal),Protein (g),Fat (g),Carbs (g),Sodium (mg) Apple (raw),52,0.3,0.2,13.8,1 Hamburger (beef patty),250,26.1,17.8,0,75 "Pizza (cheese, slice)",266,11.4,10.4,33.0,640 Chicken breast (grilled),165,31.0,3.6,0,74

This data is updated regularly and sourced from lab analysis, making it reliable for "famous" items like fast-food staples or global dishes. If you need branded foods (e.g., McDonald's Big Mac), check the Branded Foods section on the site for exports.

The dataset is a collection of food and nutrient information sourced from the USDA FoodData Central, which is the U.S. Department of Agriculture's integrated data system.

The primary goal of this dataset is to provide a comprehensive, structured set of tables that link food items to their nutritional profiles. This is particularly useful for researchers, dietitians, and developers building health and nutrition applications.

Key Content & Structure

The dataset typically includes several interlinked CSV files that follow the USDA's data model:

food.csv

Contains general information about each food item.

Key Columns:
- fdc_id (unique identifier)
- description (name of the food)
- food_category_id
- data_type

nutrient.csv

A reference list of all nutrients measured.

Key Columns:
- id
- name (e.g., Protein, Vitamin C)
- unit_name (e.g., g, mg, kcal)

food_nutrient.csv

The core data table linking specific foods to their nutrient values.

Key Columns:
- fdc_id
- nutrient_id
- amount (usually measured per 100g of the food item)

food_category.csv

Defines the broad groups for food items.

Examples:
- Fruits and Fruit Juices
- Vegetables
- Dairy and Egg Products

Data Types Included

The data is often categorized into distinct types:

• Foundation Foods: Heavily researched, minimally processed foods with detailed metadata.
• SR Legacy: Historic data from the National Nutrient Database for Standard Reference.
• Branded Foods: Data for commercial food products provided by food brands.
• FNDDS: Data used for the "What We Eat in America" survey.

Common Use Cases

• Nutritional Analysis: Calculating the macro and micronutrient content of meals.
• Machine Learning: Building recommendation engines or caloric estimation models.
• Dietary Research: Studying the relationships between food intake and health outcomes.

Mean nutrient content per 100 g of pizza food codes in the FNDDS 2011–12 database, by NNPS standards a.

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.pdf Resource 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. Date Submitted: 2022-04-20

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.

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).

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.

How much do fruits and vegetables cost? The USDA, Economic Research Service (ERS) estimated average prices for more than 150 commonly consumed fresh and processed fruits and vegetables. Reported estimates include each product's average retail price and price per edible cup equivalent (the unit of measurement for Federal recommendations for fruit and vegetable consumption). Average retail prices are reported per pound or per pint. For many fruits and vegetables, a 1-cup equivalent equals the weight of enough edible food to fill a measuring cup. ERS calculated average prices at retail stores using 2013, 2016, and 2020 retail scanner data from Circana (formerly Information Resources Inc. [IRI]). A selection of retail establishments—grocery stores, supermarkets, supercenters, convenience stores, drug stores, and liquor stores—across the U.S. provides Circana with weekly retail sales data (revenue and quantity). https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F8355503%2Fdc9ad38e1e746e44375d304c6a0e4920%2Fveggies.PNG?generation=1697529320644249&alt=media" alt=""> ERS reports average prices per edible cup equivalent to inform policymakers and nutritionists about how much money it costs U.S. households to eat a sufficient quantity and variety of fruits and vegetables. Every five years the Departments of Agriculture and Health and Human Services release a new version of the Dietary Guidelines for Americans with information about how individuals can achieve a healthy diet. However, the average consumer falls short in meeting these recommendations. Many people consume too many calories from refined grains, solid fats, and added sugars, and do not eat enough whole grains, fruits, and vegetables. ERS fruit and vegetable prices are updated periodically to coincide with the release of each version of the Dietary Guidelines for Americans. When generating estimates using 2013, 2016, 2020 data, ERS researchers priced similar fruit and vegetable products each year. However, because of different methods for coding the underlying Circana data, the entry of new products into the market, the exit of old products from the market, and other factors, the data are not suitable for making year-to-year comparisons. These data should not be used for making inferences about price changes over time.

For data on retail food price trends, see the ERS Food Price Outlook (FPO). The FPO provides food price data and forecasts changes in the Consumer Price Index (CPI) and Producer Price Index (PPI) for food.

For additional data on food costs, see the ERS Purchase to Plate (PP-Suite). The PP-Suite reports a U.S. household’s costs to consume other categories of foods in addition to fruits and vegetables, such as meats, seafood, and cereal and bakery products. Food groupings in the PP-Suite are based on the USDA Food and Nutrient Database for Dietary Studies (FNDDS). This allows users to import price estimates for foods found in USDA dietary survey data. FNDDS food groupings are broader than the specific food products priced for constructing this data product. They also include both conventional and organic products. For example, the PP-Suite average price to consume broccoli purchased raw is the average price paid for organic and conventional heads, crowns, and florets. By contrast, this data product distinguishes and separately reports the average costs to consume conventional raw broccoli purchased as heads and florets.

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This content has been updated - view the USDA's Expanded Flavonoid Database for the Assessment of Dietary Intakes, Release 1.1 - December 2015 at https://doi.org/10.15482/USDA.ADC/1324677 for version 1.1 data, or visit the USDA Special Interest Database on Flavonoids dataset at https://doi.org/10.15482/USDA.ADC/1178142 for links to the most current data.

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: FDB-EXP.accdb. File Name: FDB-EXP.zipResource Description: (Local copy of the Access Database file - 10/26/2016)

This file contains USDA's Expanded Flavonoid Database for the Assessment of Dietary Intakes imported into a MS Access database. It includes relationships between files. You need MS Access 2007 to use this file. The file structure is the same as that of the USDA National Nutrient Database for Standard Reference.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: (Local copy of the PDF file - 10/26/2016)

Information regarding documentation, development of the database, limitations, format, and references.Resource Title: Data Dictionary. File Name: FDB-EXP-DD_2.pdf

Additional file 3 : Table S1. Foods included in analysis. NHANES, National Health and Nutrition Examination Survey; CPI, Consumer Price Index; FoodAPS, Food Acquisition and Purchase Survey. 1Approximately 80% of FoodAPS codes come from the USDA Food and Nutrient Database for Dietary Studes (FNDDS), and the remaining codes are manually assigned by FoodAPS staff (FoodAPS). 2Leading digits in each 8-digit food code. 3All codes begin with the prefix “CUUR0000S”. 4Leading digits in each 8- or 10-digit food code. 5Includes crackers and other grain-based snacks. 6Includes French toast and other sweet grain-based foods. 7Includes imitation milk, flavored milk and milk drinks, evaporated and condensed milk, and dry and powdered milk. 8Includes dairy-based sauces. 9Includes lamb, goat, and game. 10Includes sandwiches made from all meat and seafood sources, luncheon meats, and burgers. 11Includes butter and margarine. 12Includes water, alcohol, and beverage concentrates.