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Need for a standardized nutrient data base in
epidemiologic studies
Epidemiological uses of food composition data in
the european context
NCI food data needs: impact on coding systems
Food composition -a key to dietary appraisal and
improvement in the United States
Using food composition data to communicate
nutrition to the consumer
Nutrient composition data uses and needs of food
companies
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
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.
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.
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.