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close this book Food Composition Data: A User's Perspective (1987)
close this folder The uses of food composition data
close this folder Need for a standardized nutrient data base in epidemiologic studies
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Need for a standardized nutrient data base in epidemiologic studies

(introductory text)

Introduction
Limitations of diet-related epidemiologic studies
Factors influencing diet-related epidemiologic studies, using diet and colon cancer studies as an illustration
Some potential problems with incomplete and non-standardized nutrient data bases
Summary
References

ANN SORENSON

Office of Assistant Secretary of Health, Office of Disease Prevention and Health Promotion,
Department of Health and Human Services, Washington, D.C., USA

HYUN KYUNG MOON LEE and MARGARET F. GLONINGER

Department of Epidemiology, Graduate School of Public Health, University of Pittsburgh,
Pittsburgh, Pennsylvania, USA

Introduction

Introduction

The current concern in the area of nutrition, diet, and chronic diseases such as coronary heart disease, diabetes, hypertension, stroke, and cancer has stimulated an interest in detailed chemical data on foods, and subsequently called attention to some major deficiencies in the nutrient data bases available to support a variety of research activities in this field. This has become especially evident in epidemiologic studies charting the dietary differences between various populations which have markedly different incidences of chronic diseases thought to be associated with diet. Such studies have been able to show international differences in diet by broad, nutrient-food categorizations, but they are limited in assessing dietary risks because of a dearth of detailed information on the nutrient content of many of the foods consumed.

Current diet and disease studies require data on the human requirements or allowances for essential nutrients and quantified data on the ability of the food supply to provide these nutrients. In addition, other components of foodstuffs, including contaminants, intrinsic and extrinsic toxicants, and non-nutritive chemicals, should be identified and quantified to elucidate possible etiological relationships between diet and major public health problems.

Ideally diet and disease studies should take into account the synergism and inhibitory factors of nutrients with each other and with other environmental factors. Factors relating to bioavailability could be calculated and mathematical algorithms developed to adjust intake for other conversion factors related to gut metabolism. For example, the conversion factors for enhanced absorption of non-haem iron in the presence of ascorbic acid can be stored as part of the data system.

At present, no food composition data system exists that provides complete and systemic nutrient and non-nutrient information on food composition. Many foods commonly included in research studies have not been assayed. There are no values for some nutrients in some foods, and in other cases the existing food composition analyses are inadequate. Much of the problem stems from the complex and dynamic nature of human food supplies and the lack of reliable analytical chemical techniques for determining food composition for some food constituents.

Limitations of diet-related epidemiologic studies

Limitations of diet-related epidemiologic studies

Suitable and up-to-date food composition tables are practical tools for the identification of dietary problems and the planning of intervention programmes. Epidemiologic studies are largely dependent on food composition data bases because of the cost and impracticability of obtaining and assaying foods from the large number of free living subjects required for such studies. Therefore, food composition data bases should, whenever possible, give reliable representative data for indigenous foods reflecting the effect of growing conditions and treatment before consumption. They should include a wide variety of nutrients, making possible a comprehensive study of nutrient intake.

Advances in analytical chemical technology and the advent of high-speed computers have made feasible the processing of complex human diets. However, there is substantial criticism of diet-related epidemiologic research because the results of many studies have been weak, inconclusive, or equivocal, and at variance with animal models and in vitro evidence. Many problems with population-based diet studies relate to the following issues: (a) determining the strength of diet relationships to disease states which have multiple histologic and physiological characteristics; (b) identifying the significant dietary causal risk factors affecting the disease state; (c) having an incomplete or inappropriate nutrient data base to analyse data; (d) conducting studies with weak designs and limited technology; and (e) making inappropriate comparisons between study variables. Epidemiologic research related to diet and colon cancer can be used to illustrate how some of these problems can be influenced by food composition data, which in turn can influence the outcome of such studies. Colon cancer was selected as the example because it is a disease that has been strongly implicated with diet.

Factors influencing diet-related epidemiologic studies, using diet and colon cancer studies as an illustration

Factors influencing diet-related epidemiologic studies, using diet and colon cancer studies as an illustration

Searching on the key words "diet or dietary" and "colon cancer," "colonic neoplasms," or "sigmoid neoplasms," a MEDLINE literature search yielded 166 citations dating back to 1980. Twenty-six or 16 per cent of these studies were population-based or epidemiologic in nature. Thirty-three population-based studies reported after 1977 were identified by cross-referencing colon cancer with dietary risk factors. These studies have been summarized in table 1. The studies have been grouped according to the most commonly cited dietary risk/protective factors: dietary fibre, fat/meat, beer/alcohol, and cruciferous vegetables. The headings in table 1 list major components of epidemiological studies, each of which can effect the outcome of the study. The major types of study design as seen in the table are: ecological and food disappearance studies, retrospective (case-control) studies, cross-sectional surveys, and prospective (cohort) studies. In addition to choosing the appropriate study design, the investigator must also decide how to collect dietary information.

Though there are many variations of each, there are four basic dietary data collection tools: diet diaries, diet recalls, diet histories, and food frequencies. If data on specific food or foodgroup intake or availability is obtained for individuals or groups. the information can be transformed into nutrient intake by interfacing the food intake data of study respondents with a food composition data base.

Each technique has inherent strengths and weaknesses. Retrospective data collection methods are subject to respondent memory bias while diary methods tend to distort usual intake patterns. In addition these standard methods measure different aspects of dietary intake. Therefore there will be differences in study outcome depending on the food-intake datacollection instrument chosen. (Notice that all four intake tools were employed in the studies reported in table!.)

The type of food or nutrient data base selected is dependent on the study design, the data collection method, the study objectives, and the endpoints to be measured. However, a lack of standardized definitions of dietary study variables has been a major weakness in interpreting study outcomes. Definition has presented problems for developing standardized food names as well as for food composition tables. For example, dietary fibre, the first risk factor listed in table 1, is a complex of a number of physically and chemically different entities found in foods. They include cellulose, hemicellulose, lignins, pectins, and gums, and the ratio of these materials varies in fibre-containing foods. Until recently, data bases reported only crude fibre values, in which food samples were subjected to strong acid and then alkali solutions. These values are not equivalent to dietary fibre, which is the residue of undigested food.

The last column in the table describes the outcome or risk-factor association found in the studies. Drawing correct conclusions from the data concerning the strength of association of study variables and the attributable risk for diseases is dependent on choosing appropriate statistical tests. In addition one must control for confounding variables and adjust for covariables. Unlike other clinical or laboratory studies, epidemiological studies are based mainly on relative rather than absolute differences of risk factors between exposed and unexposed groups. However, these studies lose power if real differences exist in the nutrient content of foods consumed by different population groups. This problem is analogous to regressing to the mean by not utilizing significant differences in food composition consumed by study populations. Increasing the power of a study is important, since the influence of diet is often obscured by stronger overriding etiological factors encountered in multi-etiological chronic disease studies. Also, epidemiologic methods and techniques are sometimes inadequate or inappropriate for the evaluation of diet and disease relationships, especially if one assumes that nutrient variables are independent of other dietary or environmental factors. Furthermore, much of the confusion in outcomes of diet-related epidemiologic research may stem from inappropriately comparing studies that differ in design, analytical techniques, or food composition data bases.

Some potential problems with incomplete and non-standardized nutrient data bases

Some potential problems with incomplete and non-standardized nutrient data bases

Food composition data is required on many different levels of complexity and in forms that are readily computerized. In addition, users of such nutrient information need computerized composition data in a variety of formats that are not readily compatible with currently available data tapes. Thus, many epidemiologists are developing specialized data bases, usually by modifying or adding to the USDA tapes.

Table 1. Selected epidemiologic studies of dietary factors and colon cancer reported after 1977

Ref no. Year
pubIished
Place of
study
Type of
study
Study
method
Source of
data base
Risk factor
association

Dietary fibre

12 1977 Finland,
Denmark
Cross-sectional Dietary record D10 +
23 1977 India Cross-sectional Food frequency - +
5 1978 USA Case-control Food frequency - +
21 1978 Utah, USA Ecological study Food consumption - 0
22 1978 Scandinavia Cross-sectional Dietary record D7 +
37 1978 South Africa Ecological study - - +
20 1979 Many countries Ecological study Food consumption D11 0
27 1979 4-country Ecological study Food consumption D11 +
1 1979 UK Ecological study Food consumption D4 0
15 1979 Scandinavia Prospective study Food frequency D1 0
29 1979 Israel Case-control Food frequency - +
13 1979 Canada Case-control Diet History D7 0
3 1980 Kenya Prospective study Food frequency - +
26 1980 Australia Migrant study Food consumption - +
35 1981 Israel Case-control Food frequency D1, D2, D3 +
16 1982 Denmark, Finland Cross-sectional Dietary record D4, D5, D6 +
6 1982 4-country Cross-sectional Dietary record D4, D5, D5 +
28 1983 Canada Case-control Food frequency D7 +
33 1983 3-country Cross-sectional Diet history D5, D9 +
31 1984 7-country Ecological study Food consumption D11 +
19 1984 SDA in USA Ecological study Dietary record D7 +

Fat and/or meat

12 1977 Finland, Denmark Cross-sectional Dietary record D10  
5 1978 USA Case-control Food frequency    
40 1978 Buffalo, USA Case-control Food frequency - 0
21 1978 Utah, USA Ecological study Food consumption - 0
22 1978 Scandinavia Cross-sectional Dietary record    
27 1979 4-country Ecological study Food consumption D11 0
1 1979 UK Ecological study Food consumption D4  
20 1979 Many countries Ecological study Food consumption D11 0
40 1979 Many countries Ecological study Food consumption D11  
9 1980 Japan Case-control Diet history - 0
13 1980 Canada Case-control Diet history D7  
26 1980 Australia Migrant study Food consumption    
32 1980 USA Ecological study Food consumption    
35 1981 Israel Case-control Food frequency D1, D2, D3  
18 1981 Hawaii, USA Cross-sectional Food frequency D7 0
16 1982 Denmark, Finland Cross-sectional Dietary record D4, D5, D6 0
17 1982 UK Ecological study Food consumption - 0
24 1983 Greece Case-control Food frequency    
28 1983 Canada Case-control Food frequency D7  
4 1984 USA Cross-sectional Dietary record D7  
36 1984 Hawaii, USA Prospective study 24-hour recall - -

Beer and/or alcohol

7 1977 USA Ecological study Food consumption    
22 1978 Scandinavia Ecological study Dietary record    
27 1979 4-country Ecological study Food consumption    
1 1979 UK Ecological study Food consumption D4 0
15 1979 Scandinavia Prospective study Food frequency D10 0
11 1980 Hawaii, USA Cross-sectional Food frequency - 0
24 1983 Greece Case-control Food frequency - 0
28 1983 Canada Case-control Food frequency D7 0
14 1983 SDA in Denmark Prospective study Food consumption    
30 1984 Nebraska, USA Case-control Diet history D7, D8  

Cruciferous vegetables

8 1978 Buffalo, USA Case-control Food frequency - +
9 1980 Japan Case-control Diet history - +
24 1983 Greece Case-control Food frequency - +
28 1983 Canada Case-control Food frequency D7 0

a. Risk factor: "-" = harmful effect; "0" = no effect; i`+" = protective effect.

Sources of data bases:

D1. Y. Guggenheim, N. Kaufman, and A. Reshaf, Food Composition Tables (Ministries of Health and Culture, Government School of Home Economics and Nutrition, Romema, Jerusalem, 1980).

D2. R. M. Freely, P. E. Criner, and B. K. Watt, "Cholesterol Content of Foods," J. Am. Diet. Assoc., 61: 134148 (1972).

D3. R. M. Narayana and M. Polacchi, Food Composition Table for Use in East Asia, part 2 (NIAMDD; NIH, DHWS, Bethesda, Md., 1972), pp. 298 301.

D4. D. A. T. Southgate, "Dietary Fibre: Analysis and Food Sources," A.J.C.N., Suppl. 31: s107-s110 (1978).

D5. R. A. McCance and E. M. Widdowson, eds., The Composition of Foods, 4th ed. (HMSO, London, 1978).

D6. W. P. T. James and O. Theander, eds., Analysis of Dietary Fiber in Foods (Marcel Dekker, New York, 1981).

D7. US Department of Agriculture, "Composition of Foods: Raw, Processed, Prepared," Agriculture Handbook No. 8 (Science and Education Administration, USDA, Washington, D.C., 1968; expansion, 1972).

D8. US Department of Agriculture, Nutritive Value of American Foods in Common Units, Agriculture Handbook No. 456 (US Government Printing Office, Washington, D.C., 1975).

D9. Consumer and Food Economics Institute, Nutrition Value of Foods (USDA' Washington, D.C., 1971).

D10. Laboratory analysis.

D11. FAO, Food Balance Sheets (FAO, Rome, 1977, 1980).

For example, almost every researcher who begins nutrition-related clinical or population-based studies begins by finding and purchasing a data base that must then he modified (usually by a review of the literature) for the specific foods or nutrients under study. However, the uncoordinated creation of such data bases makes it virtually impossible to compare nutritional studies that utilize different data bases even when those data bases are relatively well known and documented. In addition, the repeated modification of existing USDA tapes duplicates effort and increases costs that could be minimized by having an available standardized data system.

Table 2. Selected nutrient composition of several varieties of cabbage reported by two different food composition tables (amount per 100 g)

  Kcal Fibre
(g)
Fat
(g)
Vit. A
(IU)
Vit. C
(mg)

Bowes and Church

Headed 24 0.8 0.2 130 57
Red 31 1.0 0.2 40 61
Savoy 24 4.6 0.2 200 55
Chinese 14 0.6 0.1 150 25
Spoon 16 0.6 0.2 3,100 25

USDA

Headed 24 0.8 0.18 126 47
Red 27 1.0 0.26 40 57
Savoy 27 0.8 1.0 1,000 31
Chinese 13 0.6 0.20 3,000 45
Spoon 16 0.6 0.20 1,200 27

Sources: A. dc P. Bowes and C. F. Church, eds., Food Value of Portion Commonly Used, 12th ea., rev. C. F. and H. N. Church (J. B. Lippincott, Philadelphia, Pa., 1975); US Department of Agriculture, "Composition of Foods: Raw, Processed, Prepared`" Agricultural Handbook No. 8-11 (Science and Education Administration USDA, Washington, D.C., 1984).

With reference to the colon cancer literature, ten different nutrient data base sources were cited in the studies listed in table 1. The variability of data can be demonstrated by the differences in data on the nutrients contained in foods in even well-known food composition tables, as shown in table 2. Here, reported vitamin A levels show differences between source A and B for savoy, Chinese, and spoon (pi-tsai) cabbage.

Epidemiological investigations could also be improved if the foods chosen for nutrient analysis were representative of those foods consumed by the study population. For example, the colon cancer studies identified in table 1 were conducted in numerous regions all over the world. However, because there has been no systematic sampling frame, it is difficult to determine how well the values in food composition tables represent various regional and national food supplies. Note that the nutrient content of the varieties of cabbage indigenous to various world regions may differ. Consider the differences in fibre and vitamin A content for the four different kinds of cabbage shown in table 2. There is more than a fourfold difference in the fibre content between the savoy cabbage and the Chinese and spoon varieties, while the vitamin A values ranged from 40IU per l00g for red cabbage to 3100IU per l00g for spoon cabbage. Thus, food sampling is a key issue in developing food composition tables, especially since the world supply is constantly expanding and the product on offer changing. Sampling should include new strains of edible plants and animals [10]. At present, none of the major food composition tables are based on sampling that is representative of the foods offered to consumers in defined geographic regions. Instead, data is compiled from food industry, government and independent laboratories, and from the scientific and technical literature, with each covering a different geographic area [2, 10, 25, 34, 38, 39]. The current practice is to weight the averaged analytical values of foods that are similar but not identical. Weighting schemes reflect geographic production of samples, and seasonal availability or production figures [381. However, without a representative sample of the food at the retail level, weighted models for many foods must remain empirical. Systematic sampling is also required to determine the variance of nutrients in foods consumed by specific regional populations.

As shown in table 3, food sampling variance differs from one nutrient to another and from food to food. For example, note that the standard error for the mean is large for the iron content of apples selected from the retail food supply of Utah, but is small for the fibre content. A statistically significant nutrient difference in consumption may be observed between two groups, but these differences are not meaningful if the difference is less than the food sampling variance.

In addition to regional, seasonal, and maturational variations and differences between various parts of a foodstuff, variability in reported food composition data may also be caused by differences in analytical method. The new methodological advances in the field of nutrient analysis, including widespread use of radio-immunoassay (RIA), radiobioassay, fluorometry, atomic absorption, neutron activation, high performance liquid chromatography (HPLC), stable isotope electrophoresis, and auto analysis techniques, among others, are creating masses of new data which need to be rapidly incorporated into existing data bases if these are to be kept current and relevant.

However, there are differences in reported food composition data due to intra- and interlaboratory variance even when samples are assayed by the same analytical techniques. Such analytical errors could bias study outcomes. The difference in the fibre content of foods analysed by three different analytical methods (shown in table 4) illustrates the point. Note that crude fibre values generally underestimate dietary fibre as measured by the newer assays. Neutral detergent fibre values from two sources, Van Soest and Mahoney, show interlaboratory variation. It should be noted that assays were performed on different food samples. However, inter-laboratory differences between different food samples are less than those observed between different analytic methods. Such differences point out the need for suitable standard reference materials that can be distributed to laboratories as part of the quality-control process.

The effects of processing may significantly alter the nutrient content of foods. Processing includes harvesting, mechanical and heat treating, packaging, and storage procedures. Processing food products together also alters the nutrient content of the products. For example, deep frying potatoes in vegetable oil increases the fat content of the product as eaten. Table 5 shows the effects of boiling on selected cruciferous vegetables. Although calories remain constant, vitamins A and C decrease with cooking.

Cruciferous vegetables have characteristics as a group that appear to be protective for some types of cancer, including bowel cancer. Note, however, that the nutrient content of these vegetables is quite different. Thus, using cruciferous vegetables as a class reduces the quantitative power of a study unless the proportions of the individual vegetable consumed are known; one should therefore document manipulations of collapsing of data in a data base used for specific studies.

Going one step further, the potential protection of crucifers may be conferred by the nonnutrient compounds, aromatic isothiocyanates. There is no quantitative information about the concentration of these compounds in foods, but there is no reason to believe that their levels in foods are any more constant than those of essential nutrients. The study of the relationship of diet to health and disease may therefore

Table 3. Selected examples: Nutrient composition of foods in Utah (amount per 100 edible material). Retail food sample variance

Food and description Index number Watera
(g)
Fat
(g)
Protein
(g)
Neutral detergent fibre
(g)
Iron
(mg)
Copper
(mg)
Zinc
(mg)
Mn
(mg)
Ash
(g)
Apples 13 87.9 0.72 0.21 1.1 433 58 13 117 0.20
Raw,commercialvarieties:   ±0.75 ±0.35 ±0.04 ±0.08 ±188 ±13 ±16 ±50 ±0.03
not pared   (87.1-88.7) (0.33-1.1) (0.16-0.25) (0.94-1.2) (197-717) (43-78) (0.00-40) (40-175) (0.16-0.25)
Apple sauce,canned 29 77.0(4) 0.49 0.14 0.71 522 55 55 55 0.17
Sweetened   ±1.53 ±0.92 ±0.01 ±0.08 ±257 ±11 ±18 ±4.2 ±0.3
    (74.9-78.6) (0.00 1.87) (0.13-0.17) (0.62-0.81) (160- 761) (40-60) (31-74) (50-60) (0.12-0.21)
Apricots 30 85.2 0.39 1.86 1.32 1,220 164 245 175 1.03
Raw   ± 5.46 ±0.38 ±1.50 ±0.33 ±510 ±115 ±262 ± 43 ±0.35
    (77.4 89.9) (0.09-1.01) (1.08-4.54) (0.77-1.59) (137-1,990) (68-358) (73-608) (135-217) (0.66 138)
Apriots   72.9 0.56 0.94 0.81 678 114 190 90 0.29
Canned,heavy syrup:   ± 2.46 ±0.43 ±0.19 ±0.20 ±351 ± 36 ± 46 ±49 ±0.17
drained solids   (69.9-75.9) (0.00-1.2) (0.62-1.1) (0.61-1.1) (476-1,300) (69-158) (147-258) (27-156) (0.00-0.46)
Asparagus 52 94.0(4) 0.64 2.14 1.06 1,830 96 404 175 0.76
Canned spears:   ±1.3 ±0.12 ±0.29 ±0.08 ±1,570 ±23 ± 45 ±132 ±0.54
green,regular peck:   (92.7-95.2) (0.49-0.80) (1.85-2.63) (0.99-1.43) (601-3,960) (68-117) (340-477) (97-408) (0.28-1.25)
drained solids                    
Asparagus 63 92.4(4) 0.41 2.95 1.1 638 170 556 172 0.60
Frozen spears:   ±0.31 ±0.15 ±0.21 ±0.23 ±131 ± 10 ±114 ± 35 ±0.08
cooked, boiled, drained   (91.9-92.6) (0.29-0.64) (2.68-3.17) (0.82-1.4) (546-830) (161-180) (431-707) (131-214) (0.53-0.73)
Bacon, cured 126 14.9 44.0 31.6   2,180 360 3,140 103 7.50
Cooked,broiledor fried,   ± 5.33 ± 5.91 ± 6.83   ±640 ± 78 ±574 ± 18 ±1.64
drained   (11.1-24.3) (34.9-49.4) (25.6-42.3)   (1,500- (245-458) (2,430- (77-119) (5.11-9.72)
            2,960)   4,010)    
Bananas, raw 141 77.7(6) 0.70 0.96 1.1 382 130 155 152 0.74
Common   ±3.00 ±0.66 ±0.17 ±0.48 ± 38 ± 20 ± 18 ± 44 ±0.07
    (73.9-82.1) (0.22-1.9) (0.73-1.1) (0.68-1.8) (346-452) (111-167) (131-184) (98-208) (0.61-0.84)

a. Data reported as mean, standard error of the mean (SEM) and the range.

Table 4. Fibre content of various foods by different analytic methods (amount per 100g)

  Crude fibre (g)a Dietary fibre (g)c Neutral detergent fibre (g)c
      1 2
All bran cereal 7.80 26.7 32.98  
Whole wheat bread 1.60 8.50 1.55 2.60
Apple 0.40 3.71 0.89 1.10
Broccoli 1.50 4.10 1.34 1.42
Cabbage 0.80 2.83 1.11 1.12
Potato 0.50 3.51 2.33 0.67

a. US Department of Agriculture, "Composition of Foods: Crude Fiber,"Agriculture Handbook No. 8 (Science and Education Administration, USDA, Washington, D.C., 1983).
b. D. A. T. Southgate, "Dietary Fiber," J. Hum. Nutr., 30: 303 (1976).
c. Neutral detergent fibre: (1) P. J. Van Soest, "Fiber Analysis Table: By the Amylase Modification,"A.J.C.N., 31: s281-s284 (1978): (2) A. W. Mahoney, S. K. Collinge, B. H. Byland, and A. W. Sorenson, Nutrient Composition of Foods Contained from Retail Outlets in Utah, Utah Agricultural Experiment Station, Research Report, 53 (Utah State University, 1980).

Table 5. Selected nutrient composition of cruciferous vegetablesa by different cooking methods (amount per 100 g)

Type

Raw

Cooked (boiled, drained)

  Kcal Fibre
(g)
Fat
(g)
Vit. A
(IU)
Vit. C
(m)
Kcal Fibre
(g)
Fat
(g)
Vit. A
(IU)
Vit. C
(m)
Cabbage (common) 24 0.80 0.18 126 47 21 0.60 0.25 86 24
Cauliflower 24 0.85 0.18 16 72 24 0.82 0.17 14 55
Brussels sprouts 43 1.51 0.30 883 85 39 1.37 0.51 719 62
Broccoli 28 1.11 0.35 1,542 93 29 1.20 0.28 1,409 63

a. Colon cancer protection may be conferred by a non-nutrient(s) component of food, aromatic isothiocyanates. There is no quantitative information on the proposed protective agent.

Source: US Department of Agriculture, "Composition of Foods: Raw, Processed, Prepared," Agriculture Handbook No. 8-11 (Science and Education Administration, USDA, Washington, D.C., (1984). require accurate information on the non-nutrient as well as the nutrient components of food. However, there is very little non-nutrient food composition data available.

In addition to non-nutrient data, future epidemiologic research will demand more information on subunits of nutrients, including data on biologically active forms of compounds found in food with chemically or physically different components. Pyridoxal/pyridoxine dietary fibre and carotenoids and retinoids are examples of difficult biological forms of nutrients.

A major hindrance to epidemiologic studies are data sets with missing values. Even though a computerized system is designed to update and expand food composition data, there will always be incomplete nutrient or food information which will necessitate users' judgement for dealing with missing data. Epidemiologists, like other users, are forced to fill in "zeros" in data bases with imputed values. Estimates of missing values may come from data on similar items, recipe calculations, or even values based on educated guesses. Raw values are often substituted for food usully consumed cooked, for example in relation to meat. And sometimes a food or food group is used as a surrogate for the nutrient content of diets: milk has been used to estimate retinoid values of diets while selected fruit and vegetables have been used as estimators of beta-carotene. Decisions regarding inputting missing values would be better made on standardized criteria developed by panels of experts in the fields of nutrition and data base management.

Summary

Summary

Some illustrative problems related to nutrient data bases that have the potential to affect the outcomes of epidemiological research have been presented here. This list is by no means exhaustive of all problems that are encountered in this kind of diet related research. In general, additional food composition data will improve the power of many epidemiologic research projects while standardization and careful documentation of data bases will allow more appropriate comparisons between studies. Bioavailability, nutrient (and non-nutrient) interactions, and the influence of environmental factors on food composition all have an impact on the outcome of diet-related epidemiologic investigations. These factors represent new challenges in nutrient data base management.

References

References

1. S. Gingham, R. R. Williams, J. J. Cole, and W. P. T. James, "Dietary Fibre and Regional Large-bowel Cancer Mortality in Britain," Br. J. Cancer, 40: 456-463 (1979).

2. R. R. Butrum and S. E. Gebhardt, "Nutrient Data Bank: Computer-based Management of Nutrient Values in Foods," J. Am. Oil Chemist' Soc., 53 (1976).

3. J. F. Calder, M. W. Wachira, T. Van Sant, M. S. Malik, and R. N. Bowry, "Diverticular Disease, Carcinoma of the Colon and Diet in Urban and Rural Kenyan Africans," Diagn. Imaging, 49: 23-28 (1980).

4. B. M. Calkins, D. J. Whittaker, P. P. Nair, A. A. Rider, and N. Turjman, "Diet, Nutrition Intake, and Metabolism in Populations at High and Low Risk for Colon Cancer: Nutrient Intake," A. J. C. N., 40: 896 905 (1984).

5. L. B. Dales, G. D. Friedman, H. K. Ury, S. Grossman, and S. R. Williams, "A Case-control Study of Relationships of Diet and Other Traits to Colorectal Cancer in American Blacks," Am. J. Epidemiol., 109: 132-144 (1979).

6. H. N. Englyst, S. A. gingham, H. S. Wiggins, et al., "Nonstarch Polysaccharide Consumption in Four Scandinavian Populations," Nutr. Cancer, 4: 50-59 (1982).

7. J. E. Enstrom, "Colorectal Cancer and Beer Drinking," Br. J. Cancer, 35: 674 (1977).

8. S. Graham, H. Payal, M. Swanson, A. Mittleman, and G. Wilkinson, "Diet in the Epidemiology of Cancer of the Colon and Rectum," J. Natl. Cancer Inst., 61: 709-714 ( 1978).

9. W. Haenszel, H. B. Locke, and M. Segi, "A Case-control Study of Large Bowel Cancer in Japan," J.N.C.I., 64: 17-22 (1980).

10. R. G. Hansen, B. W. Wyse and A. W. Sorenson, Nutritional Quality Index for Food (AVI, Westport, Conn., 1979).

11. M. W. Hinds, L. N. Kolonel, J. Lee, and T. Hiroshata, "Associations between Cancer Incidence and Alcohol/Cigarette Consumption among Five Ethnic Groups in Hawaii," Br. J. Cancer, 41: 929 940 (1980).

12. International Agency for Research on Cancer Intestinal Microecology Group, "Dietary Fiber, Transittime, Fecal Bacteria, Steroids, and Colon Cancer in Two Scandinavia Populations," Lancet, 2: 207-211 (1977).

13. M. Jain, G. M. Cook. F. G. Davish, M. G. Grace, G. R. Howe, and A. B. Miller, "A Case-control Study of Diet and Colo-rectal Cancer," Int. J. Cancer, 26: 757-768 (1980).

14. O. M. Jensen, "Cancer Risk among Danish Male Seventh Day Adventists and Other Temperance Society Members,"J.N.C.I., 70: 1011-1014 (1983).

15. O. M. Jensen and R. MacLennan, "Dietary Factors and Colorectal Cancer in Scandinavia," Isr. J. Med. Sci., 15: 329 334 (1979).

16. O. M. Jensen, R. MacLennan, and J. Wahrendort, "Diet, Bowel Function, Fecal Characteristics, and Large Bowel Cancer in Denmark and Finland." Nutr. Cancer, 4: 5-19 (1982).

17. L. J. Kinlen, "Meat and Fat Consumption and Cancer Mortality: A Study of Strict Religious Orders in Britain," Lancet, 1: 946-949 (1982).

18. L. K. Kolonel, J. H. Hankin, A. M. Nomura, and S. Y. Chu, "Dietary Fat Intake and Cancer Incidence among Five Ethnic Groups in Hawaii," Cancer Res., 41: 3727-3728 (1981).

19. P. A. Kurup, N. Jayakumari, M. Indira, et al., "Diet, Nutrition Intake, and Metabolism in Population at High and Low Risk for Colon Cancer: Composition, Intake, and Excretion of Fiber Constituents," A. J. C.N., 40: 942-946 (1984).

20. K. Liu, J. Stamler, D. Moss, D. Garside, V. Persky, and L. Soltero, "Dietary Cholesterol, Fat, and Fiber, and Colon-cancer Mortality," Lancet, 2: 782-785 (1979).

21. J. L. Lyon and A. W. Sorenson, ' Colon Cancer in a Low-risk Population," A.J.C.N., 31: s227-s230 (1978).

22. R. MacLennan, O. M. Jensen, J. Mosbech, and H. Vuori, "Diet, Transit Time, Stool Weight, and Colon Cancer in Two Scandinavian Populations, A.l.C.N., 31: s239-s242 (1978).

23. S. L. Malhotra, "Dietary Factors in a Study of Colon Cancer from Cancer Registry," Med. Hypotheses, 3: 122-126 (1977).

24. O. Manousos, N. E. Day, D. Trichopoulos, F. Gerovassilis, A. Tzonou, and A. Polychronopoulai, "Diet and Colorectal Cancer: A Case-control Study in Greece," Int. J. Cancer, 32: 1-5(1983).

25. R. A. McCance and E. M. Widdowson, eds., The Composition of Foods, 4th ed. (HMSO, London, 1978).

26. A. J. McMichael, M. B. McCall, J. M. Hartshorne, and T. L. Wooding, "Patterns of Gastrointestinal Cancer in European Migrants to Australia: The Role of Dietary Change," Int. J. Cancer, 25: 431-437 (1980).

27. A. J. McMichael, J. D. Potter, and B. S. Hetzel, "Time Trends in Colo-rectal Cancer Mortality in relation to Food and Alcohol Consumption: US. United Kingdom, Australia, and New Zealand," Int. J. Epidemiol., 8: 295-303 (1979).

28. A. B. Miller, G. R. Howe, M. Jain, K. L. P. Craib, and L. Harrison, Food Items and Food Groups as Risk Factors in a Case-control Study of Diet and Colo-rectal Cancer," Int. J. Cancer, 32: 155-161 (1983).

29. B. Modan, "Patterns of Gastrointestinal Neoplasms in Israel," ls. J. Med. Sci., 15: 301-304 (1979).

30. L. W. Pickle, M. H. Greene, R. G. Ziegler, A. Toledo, R. Hoover, H. T. Lynch, and R. F. Fraumeni, Jr., "Colorectal Cancer in Rural Nebraska," Cancer Res., 44: 363 369 (1984)

31. J. W. Powles and D. R. R. Williams, "Trends in Bowel Cancer in Selected Countries in relation to Wartime Changes in Flour Milling," Nutr. Cancer, 6: 40-48 (1984).

32. R. W. Rawson, "The Total Environment in the Epidemiology of Neoplastic Disease: The Obvious "Ain't Necessarily So," Cancer Incidence in Defined Populations, Banbury Report, no. 4 (Cold Spring Harbor Laboratory, Cold Spring Harbor, N.Y., 1980), pp. 109-119.

33. B. S. Reddy, G. Ekelund, M. Bohe, A. Engle, and L. Domellot, "Metabolic Epidemiology of Colon Cancer: Dietary Pattern and Fecal Sterol Concentrations of Three Populations," Nutr. Cancer, 5: 3440 (1983).

34. R. L. Rizek, B. P. Perloff, and L. P. Posati. "USDA's Nutrient Data Bank," Food Tech. in Australia, 33: 3 (1981).

35. P. Rozen, S. M. Hellerstein, and C. Horwitz, "The Low Incidence of Colorectal Cancer in a High-risk Population," Cancer, 48: 2692-2695 (1981).

36. G. N. Stemmermann, A. M. Y. Nomura, and L. K. Heilbrum, 'Dietary Fat and the Risk of Colorectal Cancer," Cancer Res., 44: 4633 4637 (1984).

37. A. R. P. Walker, "The Relationship between Bowel Cancer and Fiber Content in the Diet," A.J.C.N., 31: s248-s251 (1978).

38. B. K. Watt, "Tables of Food Composition: Uses and Limitations," Contemp. Nutr., 5: 2 ( 1 980).

39. B. K. Watt and A. L. Merrill, Composition of Foods: Raw, Processed, Prepared (USDA, Washington, D.C., 1963).

40. R. Zaldivar, W. H. Wetterstrand, and G. L. Ghai, "Relative Frequency of Mammary, Colonic, Rectal, and Pancreatic Cancer in a Large Autopsy Series," Zb/. Bakt. B, 169: 474-481 (1979)