Location: Nutrient Data Laboratory2016 Annual Report
The mission of the Nutrient Data Laboratory is "To develop authoritative food composition databases and state of the art methods to acquire, evaluate, compile, and disseminate composition data on foods and dietary supplements available in the United States." The following three objectives and nine sub-objectives provide the infrastructure for completing planned research over the next five years and the guiding principles for accomplishing the research with a clear, scientific focus. Objective 1. Develop and expand the USDA-ARS food composition databases to represent the dynamics of the U.S. food supply, including increased use of commercially packaged foods, restaurant foods, school foods, and ethnic foods. Sub-Objective 1.A. Update the USDA National Nutrient Database for Standard Reference (SR) to represent the dynamics of the current U.S. food supply, including increased availability and variety of commercially packaged, restaurant, school, and ethnic foods. Sub-Objective 1.B. Expand and update existing food yield and nutrient retention factor tables to reflect current food preparation methods and food products. Sub-Objective 1.C. Provide nutrient composition data for use in the national survey, What We Eat In America (WWEIA), NHANES. Sub-Objective 1.D. Monitor sodium and related nutrients in commercially processed and restaurant foods in the U.S. food supply. Objective 2. Develop authoritative food composition databases for non-nutritive components that may promote health; examples include isothiocyanates and other sulfur-containing compounds. Expand existing databases, including flavonoids, to include more foods, variability estimates, and other information (cultivar, weather, growing conditions, etc.), which impact the nutrient values. Sub-Objective 2.A. Expand and update accurate representative values for a number of bioactive compounds in raw, processed, and prepared foods in different Special Interest Databases (SID). Sub-Objective 2.B. Develop a new Special Interest Database (SID) on the content of sulfur-containing bioactive compounds in selected foods, with special emphasis on variability and factors, e.g., cultivar, location, and growing conditions, which potentially could influence variability. Sub-Objective 2.C. Determine the effect of various preparation methods on the content of various bioactive compounds in selected foods. Objective 3. Identify, evaluate, and develop new methods for the acquisition, evaluation, compilation, and dissemination of food composition data from diverse sources through modernization of existing and development of new, robust information technology. Sub-Objective 3.A. Provide easy-to-use, web-based mechanisms for data submission. Sub-Objective 3.B. Enhance dissemination routines in the National Nutrient Databank System (NDBS) via automated methods to expand the types of data formats available on NDL’s web site.
Multiple methods for obtaining data will be used (e.g., nationwide product sampling and analysis, collaborations [food manufacturers, agricultural scientists], and publicly available information). The National Food and Nutrient Analysis Program (NFNAP) generates high-quality, analytical data for U.S. foods and includes a rigorous scientific process to develop nationally representative estimates of means and variability, under USDA analytical oversight. The 5 aims are: a) identify/prioritize foods/nutrients for analysis; b) devise/implement nationally based sampling plan(s); c) analyze food samples; and d) review, compile, and disseminate data. Beef data will be evaluated by nutrient and cut and compared across primals (chuck, brisket, etc.) and cooking methods. New meat data processed through the National Data Bank System (NDBS) will support calculations of cooking yield and retention factors (means, variances, and associated 90% or 95% confidence intervals). NDL will identify SR foods to be added or updated; this list will be provided to FSRG to prioritize foods needed in WWEIA (e.g., commercially processed and restaurant foods to replace home recipes), ensuring WWEIA adequately represents respondent reports. For important new foods, NFNAP sampling may include analytical data available for the next survey-SR dataset; standardized NDBS imputation procedures will be used to calculate missing values. NDL analyzed each Sentinel Food (SF; 2010-2013) for all nutrients; they will be reanalyzed every 4-8 years depending on budget and priority using the Principal Axis Factoring (Factor Analysis): consumption frequency in WWEIA, NHANES, 2009-10; potential for reduction (New York City’s National Salt Reduction Initiative targets); and history of change in the market. NDL will obtain and disseminate data for sulfur-containing bioactive compounds, detailing other factors (cultivars, location, weather, growing conditions) which impact concentration and focusing on genus Brassica, Allium; samples will be obtained through NFNAP and analyzed by ARS-Food Composition and Methods Development Laboratory (FCMDL) and sources and magnitudes of variability studied. Food selected for analysis for non-nutritive components will be based on flavonoid content, lack of analytical data, and potential for developing retention factors for related foods. The proanthcyanidin database will be expanded using formulations (linear regression techniques) developed for SR, to provide values to FSRG for foods reported in WWEIA-NHANES. Standardized, user-friendly databases will be released; collaboration with ILSI North America/Agricultural Technology Innovation Partnership (ATIP) Foundation and the food industry will be explored to expand the number of brand name foods. ATIP will develop/manage a new portal to facilitate submission of food manufacturers’ brand name nutrient data, strengthening NDL database for policy makers, researchers and the public. In parallel, NDL will work with other partners to identify infrastructure improvements to the NDBS.
Objective 1A, Expanding food composition databases (SR): To focus efforts on the database modernization project, an interim release was made, instead of a full release of SR. The interim release included updated and new data for baby foods and 8 foods re-sampled, as part of the sodium monitoring project. Sixty-eight foods were sampled and analyzed under NFNAP; for each food, up to 3 brand-name items were purchased. These included: rice (wild, basmati and jasmine); new popular juices; infant/toddler foods; sausages/processed meats; kale and grape tomatoes; coffee beverages (Starbucks, Dunkin Donuts); several Red Lobster menu items; hummus, sunflower seeds and dry-roasted almonds. NDL partnered with the USA Pea & Lentil Council to obtain samples of chickpeas, lentils and dried peas for analysis and with National Frozen Raspberry Association for frozen raspberry products; data will be released in SR 29. Objective 1C, Expanding nutrient composition data for Survey: New and updated data were provided to FSRG to determine nutrient intakes of survey respondents in WWEIA. Objective 1D, Sodium monitoring: As part of an interagency with the CDC, monitoring plan, 125 popular, sodium contributing commercially processed/restaurant foods (Sentinel Foods) were tracked to assess the changes in the sodium content of the U.S. food supply. Related nutrients (total sugar, potassium, total and saturated fat, total dietary fiber) that may change as food manufacturers reformulate were also monitored. NDL developed a new monitoring database (2013-2015) of label nutrient data for Sentinel Foods for major national and store brands (obtained from manufacturers, restaurant chains, through websites or labels), to be updated yearly. Nine Sentinel Foods were sampled in FY15, as were 27 new sodium-contributing foods. Changes were reviewed and will be presented at the annual American Public Health Conference (October 2016). NDL conducted several post-hoc analysis using data obtained from the sodium monitoring project e.g., comparing sodium, potassium, total and saturated fat, total dietary fiber and total sugar content by brand type (national versus private brands); results showed no systematic differences. This is the first comparisons of nutrient content in the same foods of different brand types in the U.S.; therefore, private-label brand products due to their lower costs, have the potential to favorably influence intakes of nutrients. Results were submitted to the Journal of Academy of Nutrition and Dietetics. NDL also studied differences by census region in total fat, saturated fat, and sodium contents as represented by ~1,000 labels in about 75 foods (Northeast, West, Midwest, and South). No significant differences were observed, suggesting lack of regional variability among labels for similar foods in the US. NDL conducted a comparative analysis of fast-food sandwiches and burgers from major fast-food outlets i.e., Subway, McDonald's, Burger King, Wendy's, and Chick-Fil-A, which were sampled nationwide and analyzed. Large variations in sodium levels by brand and type were found, suggesting the importance of including brand-specific information for these foods for dietary assessment and national nutrition monitoring. A similar review was conducted for selected commercial baby and toddler foods. These analyses provide several insights for the ARS food composition databases, including the need to monitor private-label brands and brand specific information for commercial foods where nutrient differences exist. Results from the latter three studies were presented at the National Nutrient Databank Conference, May 2016. Finally, NDL collaborated with CDC on 2 manuscripts using dietary intake data from the WWEIA, NHANES to: 1) identify major food sources and eating occasions contributing to sodium intake among US school-aged children; and 2) model predicted changes in US daily average sodium intake and the prevalence of excess sodium intake using the New York City’s and Health Canada standards for commercially-processed and prepared foods. The manuscripts were submitted to the Journal of Academy of Nutrition and Dietetics and American Journal of Clinical Nutrition, respectively. Objective 2A, Special Interest Databases for bioactive compounds: Updates were made to the USDA Database for the Flavonoid Content of Selected Foods and the USDA Database for the Isoflavone Content of Selected Foods. These resulted in an update to the USDA's Expanded Flavonoid Database for the Assessment of Dietary Intakes. Five international and U.S. laboratories participated in the NDL-led inter-laboratory method evaluation for measuring vitamin D and 25(OH)D in animal-based foods and dietary supplements. This pilot study provided methods information and data for six materials in two separate trials. Objective 2B, Special Interest Database for sulfur containing compounds: Data for glucosinolates from 170 research articles published in peer-reviewed scientific journals were evaluated for data quality. Pilot studies were conducted by working with FCMDL to compare different sample preparation and analytical procedures to quantify glucosinolates in vegetables. Additional data were obtained by collaborating with FCMDL on selected vegetables. Objective 2C, Effect of processing/preparation on bioactive content: Samples of broccoli, collard greens, kale, onion, and red cabbage were purchased from 3 different supermarket chains. FCMDL prepared (using NDL protocols) and analyzed the samples in raw and cooked forms (boiled, steamed, microwaved) for selected flavonoids and glucosinolates to determine retention factors of these compounds after preparation. Preliminary results for broccoli and red cabbage suggested higher apparent retention of flavonoids in broccoli by microwave cooking than steaming or boiling, while anthocyanidin cyanidins in red cabbage were retained better by steaming than boiling. Glucosinolates are generally well retained after cooking due to deactivation of enzyme myrosinase. Retention factors obtained from these studies would be used to determine the flavonoid contents of cooked multi-ingredient foods through recipe calculations, when analytical values are not available. As many vegetables are consumed as cooked forms, the data will provide more accurate information in calculating dietary intake of these bioactive compounds, and to help interpreting their health benefits from epidemiological studies. Objective 3A, Information technology modernization for web-based data submission: Food industry data were initially received (2014) through the Public-Private Partnership utilizing an industry-standard format; 244 food products were demonstrated to industry partners. In May 2016, data for 354 foods were publically released after data accuracy issues were addressed as the first installment of the USDA Branded Food Products Database (BFPDB) and incorporated into the SR online search program as a separate, connected database. In summer 2016, data for over 75,000 foods were received from industry partners through a University of Maryland data pipeline (developed in the past year) and are being evaluated for a September 2016 release. These data will include GS1 or Label Insights data, per a USDA-ILSI NA (International Life Sciences Institute North America) contract. Data exchange and ongoing discussions between USDA and ILSI NA have provided information enabling NDL to better design modifications to the NDL’s database management system. Additionally, development of an independent NDL data portal to receive analytical data received through NFNAP and other external sources is underway (McWest Corporation contract). This portal will automate formatting and importing of these data into the ARS FooDS under development, which will then be available to the public through the search program hosted at National Agricultural Library. Initial work has begun on ARS FooDS, the comprehensive food composition database which brings all BHNRC databases (SR, special interest database, FNDDS, chemometric data from FCMDL, etc.) into one master database system. This system will allow interconnectivity among the databases, links to external, related databases, and improved provider and user functionality. Objective 3B, Enhanced NDBS data dissemination systems: NDL implemented NDBS enhancements e.g., software programs to reformat data from external sources (as part of the data portal), data processing bypasses, and an automated linear programming tool/ Formulations Program (FP) for generating complete nutrient profiles for multi-ingredient, commercially processed foods. This tool uses nutrient and ingredient information from food labels to develop nutrient values where data are not available. The McWest Corporation is programming a new FP using industry standard software packages and project management practices. Versions using Agile process were delivered and were tested by NDL. An validation study of FP (in progress) determines how effectively the FP calculates with precision (to analytical value) a calculated nutrient value and then can be used to determine nutritional significance in relationship of foods/diet to health outcomes. The new technology incorporates a high degree of automation and improved user interface, dramatically reducing the need for hands-on processes by eliminating the need for manual data entry. In addition, a thesaurus, and new statistical and data analysis techniques are under development. These efforts are being extended to other components of NDBS (in progress). NDL has acquired 4 major industry databases through collaborations with FDA (Mintel, Label Insights and Labelbase) and ERS (IRI market share data) at no cost. They will improve specificity and currency of commercial products in SR, e.g., by providing market shares of different brands and pro-active mechanisms for monitoring changes in these foods.
1. Maintaining Currency of Food Composition Database (SR). An SR interim release was made available to update data for baby foods (also provided to survey) and on 8 foods re-sampled for the sodium monitoring project. This permits SR to represent the dynamics of the current U.S. food supply, including increased availability and variety of commercially packaged, restaurant, school, and ethnic foods.
2. Sodium monitoring. ARS researchers at Beltsville, Maryland, published sodium levels of popular commercially processed and restaurant foods based on nationwide sampling and chemical analyses on their website and in a peer reviewed journal. These foods will serve as indicators for assessing changes in the sodium content over time as food industry reformulates in response to public health efforts to reduce sodium in the US food supply. In addition, they transferred to CDC a new monitoring database (2013-2015) of nutrient data from manufacturers/restaurant chains, either obtained directly from them or through websites or labels for Sentinel Foods for major national and store brands.
3. Bioactive research. Updates were made to the USDA Database for the Flavonoid Content of Selected Foods and the USDA Database for the Isoflavone Content of Selected Foods to enter data correction and completeness. Additional data are constantly acquired through literature search and working with collaborators or industry partners (e.g. Ocean Spray) for future updates. These resulted in an update to the USDA's Expanded Flavonoid Database for the Assessment of Dietary Intakes, and NDL played an important role to make sure the data were calculated and expressed correctly. These data are used by national and international researchers and as the base for other databases.
4. Iodine research. Iodine research was initiated with the Office of Dietary Supplements and the Food and Drug Administration. Two of 14 manuscripts with Nutrient Data Laboratory (NDL) authors and reflecting 2014 NIH-hosted roundtables are in press for September 2016 release. NDL has outlined the plan to jointly analyze iodine-containing foods in the Food and Drug Administration (FDA) Total Diet Study database and the USDA National Nutrient Database for Standard Reference. Both databases are used widely by researchers and nutrition policy makers. With the increase in commercially packaged foods (containing non iodized salt), the issue of deficiency, especially among women of reproductive age, is being revisited in the United States.
5. New technology. NDL has enhanced National Data Bank System (NDBS) data dissemination systems, automated data processing modules for data import and export, and an automated linear programming tool or Formulations Program (FP) for generating complete nutrient profiles for multi-ingredient, commercially processed foods. This system incorporates a high degree of automation and improved user interface, dramatically reducing the need for hands-on processes by eliminating the need for manual data entry by obtaining ingredient information and label data from external sources, i.e. the manufacturers’ data portal, the development of which is also underway at the University of Maryland with USDA. Import of specialty market databases have improved specificity and currency of commercial products in SR, for example, by providing market shares of different brands and pro-active mechanisms for monitoring changes in these foods. Cumulatively, this allows for more current and accurate data for the user and the ability to use the data more efficiently in studies of diet and health outcome.
Continued tracking nutrient content of foods consumed by ethnic groups consistent with oversampling in the National Health and Nutrition Examination Survey, What We Eat in America. Continued tracking nutrient content of high sodium foods which affect subpopulations at risk or with hypertension. Continued tracking nutrient content of baby foods for the B24 project (1-24 months, aligned with 2020 Dietary Guidelines for Americans.
Ahuja, J.K., Wasswa-Kintu, S., Daniel, M., Thomas, R.G., Haytowitz, D.B., Showell, B.A., Nickle, M.S., Roseland, J.M., Pehrsson, P.R., Cogswell, M.E., Gunn, J. 2015. Sodium content of popular commercially processed and restaurant foods in the United States. Preventive Medicine Reports. 2:962-967.
Maalouf, J., Cogswell, M.E., Yuan, K., Martin, C.L., Gillespie, C., Ahuja, J.K., Pehrsson, P.R. 2015. Sodium content of foods contributing to sodium intake: A comparison between selected foods from the CDC Packaged Food Database and the USDA National Nutrient Database for Standard Reference. Procedia Food Science. 4:114-124.
Cogswell, M.E., Yuan, K., Gunn, J.P., Gillespie, C., Sliwa, S., Galuska, D.A., Moshfegh, A.J., Rhodes, D.G., Ahuja, J.K., Pehrsson, P.R., Merritt, R., Bowman, B.A. 2014. Sodium intake among U.S. school-aged children - United States, 2009-2010. Electronic Publication. 63(36):789-797.
Quader, Z.S., Gillespie, C., Sliwa, S.A., Mugavero, K., Gunn, J.P., Ahuja, J.K., Pehrsson, P.R., Moshfegh, A.J., Burdg, J.P., Cogswell, M.E. 2016. Sodium intake among U.S. school-age children: National Health and Nutrition Examination Survey, 2011-2012. Journal of the Academy of Nutrition and Dietetics. 64(22):4531-4535.