an informatics approach to flavonoid database development

JOURNAL OF FOOD COMPOSITION AND ANALYSIS (2000) 13, 441}454doi:10.1006/jfca.2000.0926Available online at http://www.idealibrary.com on

ORIGINAL RESEARCH

An Informatics Approach to Flavonoid Database Development

Julia Peterson and Johanna Dwyer

Tufts University School of Nutrition Science and Policy, Frances Stern Nutrition Center, New England MedicalCenter Hospital, USDA Jean Mayer Human Nutrition Research Center on Aging at Tufts University, 132 Curtis

Street, Medford, MA 02155, U.S.A.

Received July 13, 1999, and in revised form March 20, 2000

The association between consumption of fruits and vegetables and reduced chronic disease riskmay be due in part to their #avonoid content. A food #avonoid database describes six #avonoidclasses (anthocyanins, #avans, #avanones, #avones, #avonols, and iso#avonoids) in fruits,vegetables and beverages. Developing a #avonoid database requires the use of informatics andinvolves nine steps. The steps include identifying all foods likely to contain #avonoids, collectingand organizing sources of existing analytical data, and evaluating the quality of each value foreach compound from the various sources. The methodology for evaluating each set of analyticdata uses "ve criteria established by the United States Department of Agriculture's (USDA)Food Composition and Nutrient Data Laboratories: (1) analytical method, (2) analytical qualitycontrol, (3) number of samples, (4) sample handling, and (5) sampling plan. Data ratings ofacceptable quality for each component and food are aggregated. Quality indices for each foodvalue are further reviewed. Means and other statistics are calculated. The data are harmonizedand standardized by assigning appropriate USDA food codes, botanical names, chemical names,etc., and the new database is "nalized. Finally, the data are used to set priorities for new analyses,methods, and applications in intake and health outcome studies. The provisional #avonoiddatabase will highlight gaps in #avonoid food analyses and needs for additional analytical work.

( 2000 Academic Press

INTRODUCTION

Informatics, or information science, includes the collection, classi"cation, storage,retrieval, and dissemination of recorded knowledge. This article discusses an infor-matics approach to developing a nutrient database for #avonoids. It relies heavily onthe use of computerized searching, evaluation, and collation of data.

Epidemiological evidence suggests that consumption of a diet high in fruits andvegetables is associated with reduced incidence of coronary heart disease and somecancers (Hollman et al., 1996b). The association between consumption of fruits andvegetables and reduced chronic disease risk (Block, 1991; Szarka et al., 1994) may bedue in part to #avonoids* phytochemicals in fruits, vegetables, and related products(Stoner and Mukhtar, 1995).

High #avonoid intakes are associated with lowered risk of cardiovascular diseasein some studies. Studies in Finland (Knekt et al., 1996), in seven Europeancountries (Seven Countries Study) (Hertog et al., 1995), and in the Netherlands(Zutphen) (Hertog et al., 1993a) have examined the association between dietaryintakes of "ve #avonoids (the #avones apigenin and luteolin, and the #avonolskaempferol, myricetin, and quercetin) and coronary heart disease. Consumption ofthese #avonoids was inversely correlated with coronary heart disease mortality.Dietary intake of #avonoids was also associated with decreased risk of stroke in the

0889}1575/00/040441#14 $35.00/0 ( 2000 Academic Press

442 PETERSON AND DWYER

Zutphen Elderly study (Keli et al., 1996). In contrast, no associations between #avonoidintake and heart disease risk were found in a study done in Wales (Hertog et al., 1997)and in a study of male health professionals in the United States (Rimm et al., 1996).

Data on #avonoid intake and cancer risk are inconclusive (Barnes et al., 1996;Goldbohm et al., 1996). Cancer mortality from all causes did not appear to be a!ectedby #avonoid intake in the Seven Countries study (Hertog et al., 1995), but only a few#avonoids were studied. Anthocyanins, #avanones, #avans, and iso#avonoids werenot included in the estimates of #avonoid intakes used in this study (Hertog et al.,1995; Hollman et al., 1996c).

In addition to their possible role in decreasing risks of some forms of cardiovasculardisease and cancer, there is growing interest in the health e!ects of speci"c classes of#avonoids. Anthocyanins are of particular interest because they are potent anti-oxidants (Wang et al., 1997). They may play a role in vision (Politzer, 1977; Timber-lake and Henry, 1988), either by their direct e!ect on lens proteins or by more indirecte!ects as local or systemic antioxidants (Detre et al., 1986; Miskulin et al., 1980;Robert et al., 1977). Anthocyanins may also have bene"cial e!ects in decreasing therisk or severity of diabetic retinopathy and other retinal pathologies (Scharrer andOber, 1981). However, these relationships are not yet well established. Few epi-demiological studies have been done on the association of #avonoid intakes with risksof other diseases (Serfaty and Magneron, 1997).

Figure 1 illustrates the chemical structures of the six classes of #avonoids (antho-cyanins, #avanones, #avones, iso#avones, #avonols, and #avans). All six classes havean intact &&C'' ring but di!er from each other in the substituents and the bond structureof the &&C'' ring. Anthocyanins are charged #avonoids that usually have attachedsugars. They are responsible for most of the red, blue and violet colors in fruits andvegetables. Flavanones are found in the citrus family. They usually contain sugarmoieties that contribute to their characteristic #avors. Flavones are most commonlyfound in leaves. Iso#avones, which di!er from #avones only in the placement of theattached benzene ring, are found in legumes, particularly soy. They are known fortheir estrogenic activity. Like the #avanones, #avonols commonly include sugars. Themost ubiquitous #avonoid in foods is quercetin, a #avonol. Flavans, the mostchemically complex #avonoids, include the catechins, procyanidins, thea#avins, andother polymeric #avonoids such as thearubigins.

Food composition information on #avonoids exists, but it is scattered in the literatureand is of variable quality. The data have not been assessed and high-quality studies havenot been compiled for use in food composition tables. Foods lacking in #avonoidcomposition data have not been identi"ed so that those data gaps can be remedied.

Dietary intakes of #avonoids are poorly and incompletely documented. In 1993,workers in the Netherlands measured intakes of "ve compounds (apigenin, kaemp-ferol, luteolin, myricetin, and quercetin) from two classes of #avonoids (#avones and#avonols) and estimated dietary intakes of #avonoids at 23 mg/day (Hertog et al.,1993b). An earlier review in 1976 that used a less-precise methodology (Kuhnau, 1976)and included "ve #avonoid classes (all except iso#avonoids) estimated dietary intakesof these #avonoids to be 1000 mg/day. There is a need for better estimates of the#avonoid content of foods so that more accurate estimates of dietary intakes of thesecompounds can be developed.

METHOD

Developing a #avonoid database requires an informatics-based approach that in-cludes identifying, locating, evaluating, aggregating, reviewing, harmonizing, and

FIGURE 1. Flavonoid structures and occurrence.The propanoid structure consists of two fused rings. The "rst &&A'' ring is aromatic. The second &&C'' ring is anoxygen-containing heterocyclic ring attached by a carbon}carbon bond to a third aromatic &&B'' ring. These#avonoids di!er from each other in their structure at the &&C'' ring. The #avans include bi#avans, catechins,proanthocyanidins and tannins. The bi#avan shown here is a procyanidin. (The #avonoids found in citrusrinds are called bio#avonoids.) Arrows indicate biosynthetic path.

FLAVONOID DATABASE DEVELOPMENT 443

compiling all the valid data available in the international food composition analyticalliterature. Figure 2 presents a schematic view of the database development. Table 1illustrates the process with current work on tea. The overall goal was to collect andevaluate existing data to determine where gaps exist and to set priorities for newanalyses.

Identify all Foods likely to Contain the Component of Interest

First, since all fresh fruits and vegetables, and beverages made from them, contain#avonoids, the foods to be studied must be prioritized. We used our knowledge of

FIGURE 2. Schematic view of #avonoid food composition database development.


#avonoids and botany and drew upon dietary surveys conducted in the U.S.A. todevelop lists of most frequently consumed foods rich in these compounds. The list of50 most commonly eaten fruits and vegetables from the USDA's Continuing Survey ofFood Intakes by Individuals (CSFII) survey of 1994}1996 was used to establisha prioritized list of foods to be searched (USDA, 1998). A few high-#avonoid foodssuch as cranberries, and the fruits, vegetables, and related beverages used in foodfrequency questionnaires for epidemiological studies were also included.

Collect and Organize Sources of Existing Analytical Data and Related Reportsfrom the International ¸iterature

The taxonomic names, genus and species, of the targeted food items were identi"ed.Searching for all the foods in a genus was more e$cient than searching for each foodindividually. Common plant and food names and botanical synonyms are employedusing information provided by the Germplasm Resources Information Network(GRIN) (http://www.ars-grin.gov/npgs/tax/index.html) (USDA, 1999). Dialog(http://www.dialog.com/), a company that provides access to international botanical,chemical, food, medical, nutrition, and pharmaceutical databases, was used to per-form the searches.

The food item search by genera, plant and food names was then cross-searched for#avonoid compounds and classes using the Food Science and Technology Abstracts(FSTA) database (International Food Information Service, 2000). The searches werethen reviewed and appropriate articles were collected. Searches focused on tea and themost easily accessible articles, and then expanded to include other fruits and veg-etables and sources available by the interlibrary loan.

;sing Five ;SDA Assessment Categories, Assign Ratings to Each <alue for EachComponent in Each Food from the <arious Sources

From each article that the search yielded, the data were evaluated. Key chemicalanalytical techniques were de"ned and the quality of the analytical methods was

TABLE 1

Example of application of the informatics method to tea

Step Description

Identifyrelevant foods

Tea is a major beverage in many countries, is found on Food FrequencyQuestionnaires, and is a major source of #avonoids in the diet

Locate citations Dialog access; Food Science Technology Abstracts database; search results usingGermplasm Resources Information Network botanical, Latin, and common names andsynonyms

Flavonoids terms 6177 citationsTea genus terms 172 Camellia

38 TheaTea terms 4578 Tea

909 TeasCollection of tea terms 4691Cross #avonoid and tea terms 594 articles/citations

Out of the 594 citations, 100}150 articles were chosen for further study. Black and greentea #avonoid data are available and some data on herbal teas also exist

Evaluate method Out of 70 articles already obtained, 19 articles have useable data. 51 have no data at allor no useable data (but do have useful information on methods or tea chemistry). Dataon catechins (#avans) in black and green teas predominate. Some data are also availableon #avonols and #avones. Only studies of commercial tea using a water extract of dryteas or brewed tea are abstracted. Tea #avonoid studies of organic (non-water) extractsare not appropriate for food analyses destined for humans. Catechins and thea#avinshave been isolated and characterized. Thearubigins have not been isolated andcharacterized. Evaluation of quantitation methods for thearubigins needs specialattention

Aggregate data All details from the article are placed into the &&warehouse database''. These includedescription of the tea and #avonoids, the amount of each #avonoid for each tea, and thedata quality ratings

Review data Archive unacceptable data. Out of 19 articles, only one article was rejected at this point.Use the acceptable data for the "nal database

Calculatestatistics

Calculate means for each individual #avonoid, total for each class, ranges for otherstatistics, and con"dence code

Harmonize Add USDA food code(s), make botanical names and chemical names consistent

Finalize Besides the statistical and qualitative review of the data, tea experts are asked to checkthe material for completeness and e$cacy. A method for computing the #avonoidcontent of brewed tea from #avonoid data on dry tea will be developed

Use Apply tea #avonoid data to diet data. Determine what tea data need to be updated orrevised

Problems Need more data on brewed tea and on herbal teas. Need to have thearubigins isolatedand characterized. Need paper on di!erences in quantity between water and methanolic#avonoid extracts of dry tea


assessed using criteria for evaluating the quality of food composition data developedand used by Beecher, Holden, Mangels et al. in compiling USDA food compositiontables for selenium (Holden et al., 1987) and carotenoids (Mangels et al., 1993). Themethodology of each type of analysis was examined to develop computer schematicsfor assessing the quality of the analytical data and an expert panel was used to checkthe judgements.

TABLE 2

USDA generic description of food composition data evaluation criteria applied to tea articles (Holden et al., 1987; Mangels et al. 1993)

Relative scores

Criteria categories 3 2 1 0

Analyticalmethod

Published documentation withvalidation for foods analyzed,including use of appropriate re-ference material with results withinacceptable range or 95}105%recoveries on similar food and useof some other method or laboratoryon same sample with excellentagreement; acceptable repeatability;exemplary processing of sample;detailed identi"cation of analyte(s)

Some documentation; incompletevalidation studies, including90}110% recoveries on similarfoods or use of some other methodor laboratory on same sample withgood agreement; acceptablerepeatability; adequate identi-"cation of analyte(s)

Some documentation; minimalvalidation; including 80}120%recoveries on foods similar tosample or use of some other methodor laboratory on related food withacceptable agreement; acceptablerepeatability; minimally acceptableprocessing of sample; limitedidenti"cation of analyte(s)

No documentation of methodno reference or inaccessiblereference given; no validationstudies or failure to achieveacceptable results with referencematerial, lack of acceptablerepeatability, recovery ((80 or'120%), or comparisonmethod or laboratory;inadequate processing ofsample; inadequate identi"ca-tion of analyte(s)

Applicationto tea

Not possible, no outside standardsavailable

HPLC methods generally Spectrophotometric methodsgenerally

Asian articles sometimes1,articles with methanolicextraction of dry tea not used

Analyticalquality control

Optimum accuracy and precisionof method monitored and indicatedexplicitly by data

Documentation of assessment ofboth accuracy and precision ofmethod; acceptable accuracy andprecision

Some description of minimallyacceptable accuracy and/orprecision

No documentation of accuracyor precision; unacceptableaccuracy and/or precision

Applicationto tea

Not possible, no outside standardsavailable

HPLC methods generally Spectrophotometric methodsgenerally

Asian articles sometimes1

No. of samples Extensive ('10, S.D., S.E.,raw data)

Adequate (3}10) Limited (1, 2 not speci"ed)

Applicationto tea

Very common in tea articles Usually in tea articles Rare in tea articles

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Sample handling Complete documentation ofprocedures including analysis ofedible portion only, validation ofhomogenization method, details offood preparation, and storage andmoisture changes monitored

Pertinent procedures documentedincluding analysis of edible portiononly; procedures seem reasonablebut some details not reported

Limited description of proceduresincluding evidence of analysis ofedible portion only

Totally inappropriate proceduresor no documentation of criteriapertinent to food analyzed

Applicationto tea

Sometimes in tea articles Common in tea articles Rare in tea articles Very rare in tea articles

Sampling plan Multiple geographical samplingwith description of and statisticalbasis for sampling, samplerepresentative of brands/varieties consumed orcommercially used

At least two geographical regionssampled; sample is representative

One geographical area sampled;sample is representative ofwhat some eat

Not described or sampleis not representative

Applicationto tea

Sometimes in tea articles Very common in tea articles Rare in tea articles Articles with non-commercialtea not used

1During translation, method details may have been omitted that would give the data a higher score.

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During the quality assessment process the chemical analysis of each food value wasscored on a four-point scale (0"least, 3"highest quality) for data quality. A rating of3 for analytical method required standard reference material characterized by an outsideauthority (such as the National Institutes of Standards and Technology). A rating of 3 inanalytical quality control required a consistent, regular monitoring of the execution of theanalytical methods cited. In contrast, a rating of 0 for the analytical method was givenwhen the laboratory had not validated the method, or there was inadequate identi"cationof methods used or of the analyte(s). For analytical quality control, a rating of 0 was givenwhen there was no documentation of the monitoring of accuracy or precision, or whenunacceptable accuracy or precision was provided in the documentation.

Standardized methodology for scoring number of samples, sample handling, samp-ling plan, analytical method and analytical quality control based on this generalschema was developed cooperatively with the USDA for the #avonoids studied. Theauthors consulted with USDA scientists to determine and apply the new algorithms asthey were developed.

Each food value was rated within each of the following categories: analyticalmethod, analytical quality control, number of samples, sample handling, and samp-ling plan. A composite rating (known as a quality index) was calculated as the averageof all "ve ratings for each value from each source.

Aggregate the Data, Ratings, and Related Documentation for Each Component and Food

Data from selected articles were entered into the &&warehouse'' database, where allpossible information from each article or other sources was kept for future reference.The &&warehouse'' database included all the following information if available: identi-"er, the food item, speci"cs (preparation, processing, sampling plan), genus, species,cultivar, variety, color, the #avonoid, its class descriptor (#avone, aglycone, etc.),amount of #avonoid, type of data (mean, individual), number of samples, statistics,geographic location, harvest year, year published, and notes. Flavonoid amountswere calculated as aglycones in mg/100 g from ppm, mg/kg, or other measures.Conversions using speci"c gravity or appropriate conventions were used for liquids.The data quality rating was calculated for each food by summing the scores for eachof the individual evaluation criteria and dividing by "ve (analytical method,analytical quality control, number of samples, sample handling, and samplingplan).

For Each <alue and Source by Food and Component, Acceptable <alues are Separatedfrom ;nacceptable <alues by New Cut-o+ Points. Archive ;nacceptable <aluesand Related Documentation

Food composition data with acceptable qualitative scores were utilized in the "nalcalculation of means, estimate of variance, ranges, and the con"dence code from allanalyses of a single food item. The cut-o! was that all criteria had a minimal score of1 and at least one criterion had a score of 2 or better. Our experience indicated thatthe quality of the data was low, that analytical methods and reporting neededimprovement, and that databases which base such ratings could be regarded as onlyprovisional.

Calculate Means for Each Food and Component from the Pool of Acceptable <alues,and other Statistics as Appropriate

The mean, range, an overall con"dence score and other statistics were calculated fromthe pool of acceptable values. The algorithm for calculating the &&Con"dence Code''


was developed cooperatively with experts at the USDA utilizing the quality indices foreach food.

Assign ;SDA Food Codes, <erify Matching of Food Descriptors, Names, and RelatedDetails

Once the data were collected, evaluated, and aggregated by similar food components,the next step in the database development process was to assign USDA food codes, toidentify and match data entries with USDA food codes, to verify food names,including common, scienti"c and taxonomic names, and to check related details.

Finalize the New Database

The data were then reviewed to ensure that only items with satisfactory quality indicesand statistics were included. The review committee included a statistician to evaluatethe treatment of discrepant values, the use of weighted averages and the use ofweighted means based on the quality of the data. All decisions were documented. Inaddition to the statistical and qualitative review of the data, experts for each group ofrelated foods were asked to check the material for completeness and e$cacy.

;se the Data to Set Priorities for New Analyses, Develop New Methods, andApplications Involving Studies of Intakes and Health Outcomes

When complete, the #avonoid database will be applied to dietary data to meet userneeds. For example, we will incorporate the #avonoid data into a food frequencyquestionnaire for use in a case-control study of #avonoids and various cancers. Valuesfor single food items on such a questionnaire are straightforward. Mixed food itemsrequire estimates of the proportion of each food that is included in recipes or in thecategory.

DISCUSSION

Signi"cant problems were encountered in the development of this #avonoid database.

Quality and Amount of Existing Flavonoid Data

There is a paucity of analyses in certain #avonoid categories, such as anthocyanins,and in certain food groups such as melons, squashes, nuts, and "nished products, suchas breads and sodas. Details are missing and quality control information is sparse inmany existing studies.

<ariability in Flavonoid Content

Variability in #avonoid content is considerable due to species and cultivar, ecologicalfactors (e.g., geographic location, climate, soil conditions, and weather) and analyticalmethods. In general, spectrographic methods give higher values for #avonoid contentthan chromatographic methods.

¸osses in Processing

Estimates of losses of #avonoids during home and commercial processes and prepara-tion are needed but are often lacking. In home preparations, peeling, skinning and leaf


selection may also remove or reduce the #avonoid content. Flavonoids are relativelystable, heat-resistant compounds, but leaching, oxygen, and moderate degrees ofacidity during preparation cause some #avonoid losses (Franke et al., 1994; Hertog etal., 1992, 1993c; Pierpoint, 1986).

In commercial food processing, #avonoids often cause enzymatic browning. Inapple juice, it is controlled and used as part of processing. In tomato and grainprocessing, it is avoided by peeling, stripping and pearling. Fruit juice processing,particularly of citrus fruits, may increase the #avonoid content because the extractionprocess releases #avonoids from the rind of the fruit (Pierpoint, 1986). Su$cient#avonoids survive for the production of jams to use them in detecting adulterants inapple, apricot, peach, plum, sour orange, and strawberry jams (TomaH s-Lorente et al.,1992).

Bioavailability

To be useful in human studies food #avonoid content must be coupled with the dataon bioavailability, including absorption, metabolism, and excretion of metabolites.Little is known about the absorption of #avonoids after they are eaten and these dataare unavailable for many of these compounds (Graefe et al., 1999; Hollman andKatan, 1998, 1999; Wiseman, 1999).

The least is known about anthocyanins. Recent study on human absorption of redwine and elderberry anthocyanins demonstrated the presence of anthocyanins and&&anthocyanin-like'' compounds in urine, 1}3 h after consumption (Cao and Prior,1999; Lapidot et al., 1998). Catechins, members of the #avan class that have beenstudied most extensively, are readily absorbed and peak in plasma about 1}2 h afteringestion (Lee et al., 1995; Nakagawa et al., 1997; Unno et al., 1996; Yang et al., 1999).Flavanones, #avones, and #avonols peak in plasma 1}3 h after ingestion (Ameer et al.,1996; Aziz et al., 1998; Bourne and Rice-Evans, 1998; Conquer et al., 1998; Cova et al.,1992; deVries et al., 1998; Hollman et al., 1996a; Ishii et al., 1997; Lee and Reidenberg,1998; Manach et al., 1998; McAnis et al., 1999; Nielsen et al., 1999; Shimoi et al., 1998).Their sugar moieties may facilitate uptake from the gut, since most meals take at least4}6 h to reach the large intestine (Hollman et al., 1995; 1997a, b; Mauri et al., 1999).Iso#avone absorption is best documented. Iso#avonoid presence in plasma peaks at6.5 h post prandial (Lu and Anderson, 1998; Morton et al., 1997; Setchell, 1998;Setchell et al., 1998). Colonic hydrolysis appears necessary for the absorption of theseclasses of #avonoid (Day et al., 1998; Kelly et al., 1995; Xu et al., 1995).

Priorities for New Analyses

Priorities must be set for food #avonoid composition analyses in the future.Commonly eaten foods indicate that additional analyses may be needed. Second, onecan choose foods that are thought to be especially rich sources of #avonoids as thefocus of analysis. Priority setting that employs estimated concentration of the com-ponent in commonly eaten foods helps single out major food contributors to dietaryintakes.

Financial Resources for Analyses

Database development is an iterative process; provisional databases should be up-dated as new values are generated. For some components, improvements in analyticalmethods may necessitate new analyses. Lack of funds has limited #avonoid analyses of


foods, database development, and applications of food #avonoid composition.Partnerships between government, voluntary organizations, and industry can help toovercome these barriers in the future.

CONCLUSION

We are collaborating with the USDA and others in the development of a provisional#avonoid database. It will help to identify gaps in food #avonoid analyses and directe!orts toward the most fruitful analyses of additional foods. The work is greatlyassisted by advances in analytical chemistry, in information science, and in computersoftware and hardware. The #avonoid database will become one of the special-purpose databases that are developed and maintained by United States Departmentof Agriculture on its U.S. National Nutrient Databank website.

ACKNOWLEDGEMENTS

This project was funded with Federal funds from the U.S. Department of Agriculture Research Serviceunder contract no. 53-3K06-01 and the National Institute on Aging grant AG 13457-01. The contents ofthis publication do not necessarily re#ect the views or policies of the U.S. Department of Agriculture, nordoes mention of trade names, commercial products, or organizations imply endorsement by the U.S.government. We thank the following collaborators and sources of funding: Massachusetts Department ofPublic Health's Breast Cancer Research Grants Program; Tea Council of the U.S.A.; Tufts UniversitySchool of Nutrition Science and Policy; University of Athens, School of Public Health; Jean Mayer USDAHNRC on Aging at Tufts University; Joanne Holden MS, USDA Nutrient Data Laboratory at Beltsville,MD; Gary Beecher Ph.D., USDA Food Composition Laboratory at Beltsville, MD; Will Rand Ph.D., TuftsUniversity School of Medicine.

The authors express their appreciation to Joanne Holden for her review and comments on drafts of thismanuscript.

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an informatics approach to flavonoid database development

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