25th National Nutrient Databank Conference
March 30, 2000! Orlando, Florida
Supplemental Food Composition Databases Vitamin K, Carotenoids, and FlavonoidsB Are They Needed?
J Dwyer, N McKeown, M Neuhouser, J Peterson
This symposium begins with a brief review by Dr. Dwyer on the utility of supplemental food composition databases for exploring diet disease interactions and other nutritional purposes. The most vexing problem is determining which of the many possible compounds are biologically active among all of the candidate compounds. The discussion then turns to a brief review of the similarities and differences between three supplemental food composition databases now being developed and highlighted in this symposium. Considerations include whether the substance in the database is a nutrient or not, the number of compounds involved, structures, analytic techniques used, degree of absorption, food sources and current status of the databases. Collaborations between public and private sector groups are necessary to accomplish the enormous amount of analytical and informatics work that must be done to complete these supplemental food composition databases, Dr. Nicola McKeown will discuss the Vitamin K supplemental database. Dr. Marian Neuhouser will discuss the carotenoid database. Julia Peterson, MS, will discuss the provisional food flavonoid database. The symposium concludes with a discussion.
Carotenoid Databases in Human Nutrition Research
Marian L. Neuhouser, Ph.D., RD. Fred Hutchinson Cancer Research Center
In the past 10-15 years there has been considerable interest in the association of the predominant dietary carotenoids (α-carotene, β-carotene, β-cryptoxanthin, lycopene, and lutein+zeaxanthin) with risk for chronic diseases including cardiovascular disease, age-related macular degeneration, and some cancers. The validity of these findings depends, in part, on the precision of the dietary data used to measure exposure assessment.
This presentation will discuss the USDA-NCC Carotenoid Database for U.S. Foods and its use in human nutrition research. Specifically, we will examine studies that have used this carotenoid database to estimate dietary intake, discuss which dietary assessment instruments are most appropriate for assessing dietary intake of carotenoids, cite strengths and weaknesses of the database, and mention gaps in assessment of carotenoid intake (i.e., supplements).
The development and use of the USDA provisional vitamin K nutrientdatabase for research.
Nicola McKeown and Sarah Booth. Vitamin K Program, Jean Mayer USDA Human Nutrition Research Center on Aging at Tufts University, 711 Washington St., Boston, MA 02111.
Recent research indicates that the role of vitamin K extends beyond blood coagulation, and may include regulation of bone mineralization and vascular calcification. Phylloquinone is the predominant dietary source of vitamin K in the US diet and is found primarily in green leafy vegetables, followed by plant oils. Among the plant oils, the highest concentrations of phylloquinone are found in canola, soybean and olive oils. Thus foods that are generally considered poor sources of phylloquinone, such as meat and root vegetables, may in fact be moderate sources based on the addition of these oils in cooking.
In 1994, the USDA released the first provisional table on the vitamin K content of foods. This database included previously published data obtained from methods using HPLC technology and incorporated analytical data generated from the vitamin K laboratory at the USDA HNRCA at Tufts University. Prior to this database, there were limited data on the phylloquinone intake among free-living populations. Furthermore, health care professionals were unable to provide solid dietary recommendations to patients taking oral anti coagulants.
Since the release of this database, several laboratories in the US and Europe have analyzed and published food composition data for phylloquinone. The Minnesota Nutrition Data System for Research (NDS-R) has incorporated the more recent published phylloquinone data into its database. We decided to compare the USDA provisional phylloquinone database (1994) and the phylloquinone data in the NDS-R data system (1998) with direct food analyses of metabolic diets that varied in phylloquinone content. By including more nationally representative phylloquinone data, the NDS-R updated system provided better estimates of the phylloquinone content of several metabolic diets compared to the older USDA database. However, both databases substantially overestimated the phylloquinone content of diets containing low amounts of phylloquinone. Given the potential importance of vitamin K in health, this study highlights the need to expand and update the USDA provisional table as has been successfully achieved by the NDS-R nutrient database. However, despite improvements in nutrient databases, direct food analysis and verification of sources of vitamin K food composition is important for confirmation of actual phylloquinone intake in metabolic studies.
Flavonoid Food Composition DatabaseB A Brave New World
Julia Peterson, MS. Tufts University School of Nutrition Science & Policy and Jean Mayer USDA Human Nutrition Research Center on Aging at Tufts, Boston, MA
Flavonoids are the newest group of phytochemicals to be considered for a supplemental food composition database and the third non-nutrient phytochemical supplemental databases after carotenoids and dietary fibers. The development of a flavonoid food composition database opens a brave new world of research on compounds in food with possible beneficial health significance.
There are 26 classes and 4,000 or more naturally occurring flavonoid compounds. Of these, 6 classes and 250-350 flavonoids exist in foods. The compounds are chiefly distinguished from each other by their sugars. Only 20-30 flavonoid aglycones are common in foods. Most of the flavonoid compounds in foods are in 6 classes (anthocyanins, flavanones, flavones, flavonols, isoflavonoids, and flavans). The flavonoid composition of foods depend upon three factorsB 1) botanical family, genus and species, 2) part of the plant, and 3) processing. For example, citrus peel and juice differ in amount of flavonoids. Black tea, which is fermented, is quite different in flavonoid content from green tea, which is less processed. Food composition databases must therefore include information on botanical, anatomical and processing characteristics of foods.
The absorption of different flavonoids varies. Although too little is known at present to make statements about the relative absorption of most compounds or classes, the flavonoid class and compound do appear to affect absorption. The presence of sugars increases absorption. In some cases, as with the isoflavonoids, gut metabolism may be required for absorption and this rate may vary from one individual to another. The implication of this for databases is that flavonoid values cannot be easily adjusted for absorption.
The process of creating a flavonoid database involves nine stepsB
Identify all foods likely to contain component of interest
Collect and organize sources of existing analytical data from international scientific literature
Evaluate quality of each value for each compound of each food from various sources
Aggregate data, quality indices, and related documentation for each compound and food that is acceptable
Review quality indices for each value and source by food and component
Calculate means and other statistics for each food or component from pool of acceptable values
Assign USDA food codes, verify matching of food descriptions, botanical names, etc
Finalize new database
Use data to set priorities for new analyses, methods development, and applications concerning intake studies
In conclusion, much remains to be done in developing a flavonoid food compositiondatabase. A good isoflavone food composition table does now exist. We hope to have the first round of database development completed next year.
Uses of Nutrient Databases and Food Consumption Surveys in Support of Research at Procter & Gamble
Alison L. Eldridge, Ph.D., R.D. P&G Nutrition Science Institute
Nutrient databases and food consumption surveys play an important role in research conducted by scientists in the P&G Nutrition Science Institute. We rely on a variety of sources for nutrient data, including the USDA Nutrient Database for Standard Reference and various supplemental tables, the Continuing Survey of Food Intakes by Individuals, NHANES, commercially available food intake databases, market research data, and dietary assessment software. P&G uses national food survey databases to help understand consumer behaviors related to food and beverage consumption and thereby identify opportunities for products that help consumers better meet nutritional needs. We also use the survey databases to evaluate consumption patterns to better understand exposure to certain food ingredients. Exposure estimates are critical to safety evaluations for new and currently used food ingredients. We support database development for phytonutrients to explore diet and disease relationships with these emerging compounds. P&G also uses nutrient databases to evaluate how consumption of novel food ingredients, like olestra, affects dietary intake patterns. Finally, we use nutrient databases to plan menus and develop recipes for long- and short-term food intake studies.
The Food Industry=s Use of Nutrient Databases: An Unique Example
Rose Toblemann, General Mills
The Food Industry uses nutrient databases in a variety of ways throughout a product=s lifecycle. Research scientists early in the experimental design phase use nutrient data as a reference point when comparing nutrient values for specific ingredients. Nutrient data on ingredients and products is tracked and stored for evaluative purposes. These uses are fairly common across the industry. However General Mills has developed a unique food intake system in which the University of Minnesota=s (Univ. of MN) nutrient database is an important facet of the design.
Neilsen Panel Data annually collects food intake from 2,000 households in their National Eating Trends Survey. Each food recorded by the persons in the survey is given a code based on the individual characteristics including food category, form, flavor, preparation method and special nutritional attributes. A set of 113 nutrient values is assigned to each food from the Univ. of MN Nutrient Data System (NDS). For basic foods it is a simple one to one relationship. The more complex foods include mixed dishes, fortified foods or uncommonly consumed foods. For these foods a more in-depth process of developing a recipe, identifying common ingredients and researching time-sensitive formulas was utilized. The additional capabilities of the NDS system were vital to this project=s completion. The User Recipe feature, Food Groupings and variety of Output Reports are just a few examples of such unique features. Nutrient databases are the cornerstone to accurate food and nutrient intake data about a specific population. One of the key tools in dietary intake research is the nutrient database
National Food and Nutrient Analysis Program Update. Haytowitz, D.B. and Pehrsson, P.R. Nutrient Data Laboratory, USDA-ARS, Beltsville, Maryland.
In 1997 the Nutrient Data Laboratory began the National Food and Nutrient Analysis Program in cooperation with the National Heart Lung and Blood Institute and 17 other institutes and offices of the NIH to improve the quantity and quality of data in USDA food composition databases. Initially 1000 Key Foods and ingredients were identified for review, sampling and analysis. During the past year we sampled approximately 90 food items. As part of our collaboration with the Produce for Better Health Foundation, a major portion of the foods selected during the past year were fresh fruits, vegetables and nuts. As many as 50 were identified and sampled to develop a new database on the flavonoid content of foods. Samples generated through this effort will also be analyzed to update the traditional nutrients in the database for these foods. Work also began on sampling USDA Commodity Foods and foods consumed by American Indians as part of our effort to develop an ancillary database on the composition of foods consumed by this group. Details will also be presented on the foods analyzed, the evaluation of the results and quality control information, and the processing of the data through our databank system. We will also outline our plans for the next year.
Nutrient Data Laboratory=s Web Site: Meeting Consumer Needs
V. de Jesus, R. Cutrufelli, and D. Haytowitz. Nutrient Data Laboratory, USDA-ARS, Beltsville, Maryland.
The primary responsibility of the USDA Nutrient Data Laboratory (NDL) is to provide authoritative nutrient composition data for foods eaten in the United States. Historically, NDL has made this data available in the form of handbooks. Today, the NDL web site at www.nal.usda.gov/fnic/foodcomp is our primary vehicle for dissemination of nutrient composition data. The site includes downloadable datasets, tables, previous USDA food composition releases, and the widely used online search tool to access the USDA Nutrient Database for Standard Reference (SR). The search tool has made access to nutrient data easier by eliminating the need to download data files into a database program and allowing visitors without technical expertise to access the database. We aim to make data retrieval easier and also to extend the usefulness of our web site to additional audiences. Access of our web site is regularly monitored to determine the number of total web site hits per month, the number of visitors who access our site, and which domains (e.g. .gov and .com) access our site most frequently. Furthermore, Internet searches are utilized to determine the number of web sites that provide links to our site. Given this information, NDL can gain insights for improvements benefiting users of our web site. Questions can be answered such as whether visitors use our online database search rather than download the SR datasets or if use by corporate visitors outweigh use by educational visitors. When our users and audience can be identified, we can distinguish which types of data and what kinds of data access are most desired. We can then apply revisions to make the site and the data more accessible to these groups while, at the same time, adding features to appeal to the under represented.