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2 Overview and Methods This report focuses on fourteen micronutrientsâvitamin A, vita- min K, arsenic, boron, chromium, copper, iodine, iron, manganese, molybdenum, nickel, silicon, vanadium, and zinc. These micro- nutrients fall into two categories: (1) those known to have a benefi- cial role in human health and (2) those that lack sufficient evidence of their specific role in human health and lacking a reproducibly ob- served human indicator in response to their absence in the diet. The micronutrients that have a beneficial role in human health include vitamin A, vitamin K, chromium, copper, iodine, iron, man- ganese, molybdenum, and zinc. Vitamin A is required for normal vision, gene expression, cellular differentiation, morphogenesis, growth, and immune function. Vitamin K plays an essential role in the coagulation of blood. Chromium improves the efficiency of insulin in individuals with impaired glucose tolerance. Copper is associated with many metalloenzymes and is necessary for proper development of connective tissue, myelin, and melanin. Iodine pre- vents dwarfism, cretinism, and goiter. Iron, via hemoglobin and myoglobin, is necessary for the movement of oxygen from the air to the various tissues and the prevention of anemia. Manganese is asso- ciated with a number of metalloenzymes and is involved with the formation of bone and the metabolism of amino acids, lipids, and carbohydrates. Molybdenum is a cofactor of several enzymes, and a deficiency of these enzymes can result in neurological abnormalities and death. Zinc is associated with catalytic activity of more than 200 enzymes and regulatory proteins, including transcription factors. The micronutrients reviewed that lack a demonstrated role in 44
OVERVIEW AND METHODS 45 human health include arsenic, boron, nickel, silicon, and vanadium. Arsenic has been shown to have a role in methionine metabolism in rats, and a deprivation of arsenic has been associated with impaired growth in various animals. Embryonic defects have been demon- strated in boron-depleted trout. Abnormal metabolism of vitamin D and estrogen has been proposed as a related function for boron in humans. Nickel has been demonstrated to be essential for animals, and its deprivation in rats can result in retarded growth. Silicon is involved with the formation of bone and collagen in animals. Vana- dium has been shown to mimic insulin and stimulate cell prolifera- tion and differentiation in animals. METHODOLOGICAL CONSIDERATIONS Types of Data Used The scientific data for developing the Dietary Reference Intakes (DRIs) have essentially come from observational and experimental studies in humans. Observational studies include single-case and case-series reports and cross-sectional, cohort, and case-control stud- ies. Experimental studies include randomized and nonrandomized therapeutic or prevention trials and controlled dose-response, balance, turnover, and depletion-repletion physiological studies. Results from animal experiments are generally not applicable to the estab- lishment of DRIs, but selected animal studies are considered in the absence of human data. Animal Models Basic research using experimental animals affords considerable advantage in terms of control of nutrient exposures, environmental factors, and even genetics. In contrast, the relevance to free-living humans may be unclear. In addition, dose levels and routes of ad- ministration that are practical in animal experiments may differ greatly from those relevant to humans. Nevertheless, animal feed- ing experiments were sometimes included in the evidence reviewed to determine the ability to specify DRIs. Human Feeding Studies Controlled feeding studies, usually in a confined setting such as a metabolic ward, can yield valuable information on the relationship between nutrient consumption and health-related biomarkers.
46 DIETARY REFERENCE INTAKES Much of the understanding of human nutrient requirements to pre- vent deficiencies is based on studies of this type. Studies in which the subjects are confined allow for close control of both intake and activities. Complete collections of nutrient losses through urine and feces are possible, as are recurring sampling of biological materials such as blood. Nutrient balance studies measure nutrient status in relation to intake. Depletion-repletion studies, by contrast, measure nutrient status while subjects are maintained on diets containing marginally low or deficient levels of a nutrient; then the deficit is corrected with measured amounts of that nutrient. Unfortunately, these two types of studies have several limitations: typically they are limited in time to a few days or weeks, and so longer-term outcomes cannot be measured with the same level of accuracy. In addition, subjects may be confined, and findings are therefore not always generalizable to free-living individuals. Finally, the time and expense involved in such studies usually limit the number of subjects and the number of doses or intake levels that can be tested. In spite of these limitations, feeding studies play an important role in understanding nutrient needs and metabolism. Such data were considered in the DRI process and were given particular atten- tion in the absence of reliable data to directly relate nutrient intake to disease risk. Observational Studies In comparison to human feeding studies, observational epidemio- logical studies are frequently of direct relevance to free-living hu- mans, but they lack the controlled setting. Hence they are useful in establishing evidence of an association between the consumption of a nutrient and disease risk but are limited in their ability to ascribe a causal relationship. A judgment of causality may be supported by a consistency of association among studies in diverse populations, and it may be strengthened by the use of laboratory-based tools to measure exposures and confounding factors, rather than other means of data collection, such as personal interviews. In recent years, rapid advances in laboratory technology have made possible the increased use of biomarkers of exposure, susceptibility, and dis- ease outcome in âmolecularâ epidemiological research. For exam- ple, one area of great potential in advancing current knowledge of the effects of diet on health is the study of genetic markers of dis- ease susceptibility (especially polymorphisms in genes encoding metabolizing enzymes) in relation to dietary exposures. This devel- opment is expected to provide more accurate assessments of the
OVERVIEW AND METHODS 47 risk associated with different levels of intake of both nutrients and nonnutritive food constituents. While analytic epidemiological studies (studies that relate expo- sure to disease outcomes in individuals) have provided convincing evidence of an associative relationship between selected nondietary exposures and disease risk, there are a number of other factors that limit study reliability in research relating nutrient intakes to disease risk. First, the variation in nutrient intake may be rather limited in populations selected for study. This feature alone may yield modest relative risk trends across intake categories in the population, even if the nutrient is an important factor in explaining large disease rate variations among populations. A second factor, one that gives rise to particular concerns about confounding, is the human dietâs complex mixture of foods and nutrients that includes many substances that may be highly correlated. Third, many cohort and case-control studies have relied on self- reports of diet, typically food records, 24-hour recalls, or diet history questionnaires. Repeated application of such instruments to the same individuals show considerable variation in nutrient consump- tion estimates from one time period to another with correlations often in the 0.3 to 0.7 range (e.g., Willett et al., 1985). In addition, there may be systematic bias in nutrient consumption estimates from self-report as the reporting of food intakes and portion sizes may depend on individual characteristics such as body mass, ethnicity, and age. For example, total energy consumption may tend to be substantially underreported (30 to 50 percent) among obese per- sons, with little or no underreporting among lean persons (Heitmann and Lissner, 1995). Such systematic bias, in conjunction with random measurement error and limited intake range, has the potential to greatly impact analytic epidemiological studies based on self-reported dietary habits. Note that cohort studies using objective (biomarker) measures of nutrient intake may have an important advantage in the avoidance of systematic bias, though important sources of bias (e.g., confounding) may remain. Randomized Clinical Trials By randomly allocating subjects to the (nutrient) exposure of in- terest, clinical trials eliminate the confounding that may be intro- duced in observational studies by self-selection. The unique strength of randomized trials is that, if the sample is large enough, the study groups will be similar with respect not only to those confounding variables known to the investigators, but also to any unknown fac-
48 DIETARY REFERENCE INTAKES tors that might be related to risk of the disease. Thus, randomized trials achieve a degree of control of confounding that is simply not possible with any observational design strategy, and thus they allow for the testing of small effects that are beyond the ability of observa- tional studies to detect reliably. Although randomized controlled trials represent the accepted standard for studies of nutrient consumption in relation to human health, they too possess important limitations. Specifically, persons agreeing to be randomized may be a select subset of the population of interest, thus limiting the generalization of trial results. For prac- tical reasons, only a small number of nutrients or nutrient combina- tions at a single intake level are generally studied in a randomized trial (although a few intervention trials to compare specific dietary patterns have been initiated in recent years). In addition, the follow- up period will typically be short relative to the preceding time period of nutrient consumption that may be relevant to the health out- comes under study, particularly if chronic disease endpoints are sought. Also, dietary intervention or supplementation trials tend to be costly and logistically difficult, and the maintenance of interven- tion adherence can be a particular challenge. Because of the many complexities in conducting studies among free-living human populations and the attendant potential for bias and confounding, it is the totality of the evidence from both obser- vational and intervention studies, appropriately weighted, that must form the basis for conclusions about causal relationships between particular exposures and disease outcomes. Weighing the Evidence As a principle, only studies published in peer-reviewed journals have been used in this report. However, studies published in other scientific journals or readily available reports were considered if they appeared to provide important information not documented elsewhere. To the extent possible, original scientific studies have been used to derive the DRIs. On the basis of a thorough review of the scientific literature, clinical, functional, and biochemical indica- tors of nutritional adequacy and excess were identified for each nutrient. The quality of the study was considered in weighing the evidence. The characteristics examined included the study design and the representativeness of the study population; the validity, reliability, and precision of the methods used for measuring intake and indica- tors of adequacy or excess; the control of biases and confounding
OVERVIEW AND METHODS 49 factors; and the power of the study to demonstrate a given differ- ence or correlation. Publications solely expressing opinions were not used in setting DRIs. The assessment acknowledged the inher- ent reliability of each type of study design as described above, and it applied standard criteria concerning the strength and dose-response and temporal pattern of estimated nutrient-disease or adverse effect associations, the consistency of associations among studies of various types, and the specificity and biological plausibility of the suggested relationships (Hill, 1971). For example, biological plausibility would not be sufficient in the presence of a weak association and lack of evidence that exposure preceded the effect. Data were examined to determine whether similar estimates of the requirement resulted from the use of different indicators and different types of studies. For a single nutrient, the criterion for setting the Estimated Average Requirement (EAR) may differ from one life stage group to another because the critical function or the risk of disease may be different. When no or very poor data were available for a given life stage group, extrapolation was made from the EAR or Adequate Intake (AI) set for another group; explicit and logical assumptions on relative requirements were made. Be- cause EARs can be used for multiple purposes, they were estab- lished whenever sufficient supporting data were available. Data Limitations Although the reference values are based on data, the data were often scanty or drawn from studies that had limitations in address- ing the various questions that confronted the Panel. Therefore, many of the questions raised about the requirements for and rec- ommended intakes of these nutrients cannot be answered fully be- cause of inadequacies in the present database. Apart from studies of overt deficiency diseases, there is a dearth of studies that address specific effects of inadequate intakes on specific indicators of health status, and thus a research agenda is proposed (see Chapter 15). For many of these nutrients, estimated requirements are based on factorial, balance, and biochemical indicator data because there is little information relating health status indicators to functional suf- ficiency or insufficiency. Thus, after careful review and analysis of the evidence, including examination of the extent of congruent findings, scientific judg- ment was used to determine the basis for establishing the values. The reasoning used is described for each nutrient in Chapters 4 through 13.
50 DIETARY REFERENCE INTAKES Method for Determining the Adequate Intake for Infants The AI for young infants is generally taken to be the average intake by full-term infants who are born to healthy, well-nourished mothers and who are exclusively fed human milk. The extent to which intake of a nutrient from human milk may exceed the actual requirements of infants is not known, and ethics of experimentation preclude testing the levels known to be potentially inadequate. Using the infant exclu- sively fed human milk as a model is in keeping with the basis for earlier recommendations for intake (e.g., Health Canada, 1990; IOM, 1991). It also supports the recommendation that exclusive intake of human milk is the preferred method of feeding for normal full-term infants for the first 4 to 6 months of life. This recommendation has been made by the Canadian Paediatric Society (Health Canada, 1990), the American Academy of Pediatrics (AAP, 1997), the Institute of Medi- cine (IOM, 1991), and many other expert groups, even though most U.S. babies no longer receive human milk by age 6 months. In general, this report does not cover possible variations in physi- ological need during the first month after birth or the variations in intake of nutrients from human milk that result from differences in milk volume and nutrient concentration during early lactation. In keeping with the decision made by the Standing Committee on the Scientific Evaluation of Dietary Reference Intakes, specific DRIs to meet the needs of formula-fed infants have not been proposed in this report. The use of formula introduces a large number of com- plex issues, one of which is the bioavailability of different forms of the nutrient in different formula types. Ages 0 through 6 Months To derive the AI for infants ages 0 through 6 months, the mean intake of a nutrient was calculated based on (1) the average con- centration of the nutrient from 2 to 6 months of lactation using consensus values from several reported studies, if possible, and (2) an average volume of milk intake of 0.78 L/day. This volume was re- ported from studies that used test weighing of full-term infants. In this procedure, the infant is weighed before and after each feeding (Butte et al., 1984; Chandra, 1984; Hofvander et al., 1982; Neville et al., 1988). Because there is variation in both the composition of milk and the volume consumed, the computed value represents the mean. It is expected that infants will consume increased volumes of human milk during growth spurts.
OVERVIEW AND METHODS 51 Ages 7 through 12 Months Except for iron and zinc, during the period of infant growth and gradual weaning to a mixed diet of human milk and solid foods from ages 7 through 12 months, there is no evidence for markedly different nutrient needs. The AI can be derived for this age group by calculating the sum of (1) the content of the nutrient provided by 0.6 L/day of human milk, which is the average volume of milk reported from studies of infants receiving human milk in this age category (Heinig et al., 1993) and (2) that provided by the usual intakes of complementary weaning foods consumed by infants in this age category. Such an approach is in keeping with the current recommendations of the Canadian Paediatric Society (Health Canada, 1990), the American Academy of Pediatrics (AAP, 1997), and the Institute of Medicine (IOM, 1991) for continued feeding of infants with human milk through 9 to 12 months of age with appropriate introduction of solid foods. The amounts of vitamin A, copper, iron, and zinc consumed from complementary foods were determined by using Third National Health and Nutrition Examination Survey data, and they are discussed in the nutrient chapters. For some of the nutrients, two other approaches were considered as well: (1) extrapolation downward from the EAR for young adults by adjusting for metabolic or total body size and growth and adding a factor for variability and (2) extrapolation upward from the AI for infants ages 0 through 6 months by using the same type of adjust- ment. Both of these methods are described below. The results of the methods are compared in the process of setting the AI. Human milk does not provide sufficient levels of iron and zinc for proper growth and development of the older infant. Because facto- rial data were available for iron and zinc in the older infants, an EAR for iron and zinc has been established for infants ages 7 through 12 months. Method for Extrapolating Data from Adults to Infants and Children Setting the EAR or AI for Children For vitamin A, chromium, copper, iodine, and molybdenum, data were not available to set the EAR and Recommended Dietary Allow- ance (RDA) or an AI for children ages 1 year and older and adoles- cents. Therefore, the EAR or AI has been extrapolated down by
52 DIETARY REFERENCE INTAKES using a consistent basic method. The method relies on at least four assumptions: 1. Maintenance needs for vitamin A, chromium, copper, iodine, and molybdenum, expressed with respect to metabolic body weight ([kilogram of body weight]0.75), are the same for adults and chil- dren. Scaling requirements to the 0.75 power of body mass adjusts for metabolic differences demonstrated to be related to body weight, as described by Kleiber (1947) and explored further by West et al. (1997). By this scaling, a child weighing 22 kg would require 42 percent of what an adult weighing 70 kg would requireâa higher percentage than that represented by actual weight. If there is a lack of evidence demonstrating an association between metabolic rate and nutrient requirement, needs are estimated directly proportional to total body weight. 2. The EAR for adults is an estimate of maintenance needs. 3. The percentage of extra vitamin A, chromium, copper, and molybdenum needed for growth is similar to the percentage of extra protein needed for growth. 4. On average, total needs do not differ substantially for males and females until age 14, when reference weights differ. The formula for the extrapolation is EARchild = EARadult Ã F, where F = (Weightchild/Weightadult)0.75 Ã (1 + growth factor). Refer- ence weights from Table 1-1 are used. If the EAR differs for men and women, the reference weight used for adults differs in the equation by gender; otherwise, the average for men and women is used. The approximate proportional increase in protein require- ments for growth (FAO/WHO/UNA, 1985) is used as an estimate of the growth factor as shown in Table 2-1. If only an AI has been set for adults, it is substituted for the EAR in the above formula, and an AI is calculated; no RDA will be set. Setting the RDA for Children To account for variability in requirements because of growth rates and other factors, a 10 percent coefficient of variation (CV) for the requirement is assumed for children just as for adults unless data are available to support another value, as described in Chapter 1.
OVERVIEW AND METHODS 53 TABLE 2-1 Estimated Growth Factor, by Age Group Age Group Growth Factor 7 moâ3 y 0.30 4â8 y 0.15 9â13 y 0.15 14â18 y Males 0.15 Females 0.0 SOURCE: Proportional increase in protein requirements for growth from FAO/WHO/ UNA (1985) used to estimate the growth factor. Setting the Tolerable Upper Intake Level for Children When data are not available to set the Tolerable Upper Intake Level (UL) for children, the UL for adults is extrapolated down using the reference body weights in Table 1-1: ULchild = ULadult Ã Weightadult/Weightchild. Method for Extrapolating Data from Young to Older Infants Using the metabolic weight ratio method to extrapolate data from young to older infants involves metabolic scaling but does not in- clude an adjustment for growth because it is based on a value for a growing infant. To extrapolate from the AI for infants ages 0 through 6 months to an AI for infants ages 7 through 12 months, the following formula is used: AI7â12 mo = AI0â6 mo Ã F, where F = (Weight7â12 mo/Weight0â6 mo)0.75. Methods for Determining Increased Needs for Pregnancy It is known that the placenta actively transports certain nutrients from the mother to the fetus against a concentration gradient (Hytten and Leitch, 1971). However, for many nutrients, experi- mental data that could be used to set an EAR and RDA or an AI for pregnancy are lacking. In these cases, the potential increased need for these nutrients during pregnancy is based on theoretical consid-
54 DIETARY REFERENCE INTAKES erations, including obligatory fetal transfer, if data are available, and on increased maternal needs related to increases in energy or protein metabolism, as applicable. For chromium, manganese, and molybdenum, the AI or EAR is determined by extrapolating up according to the additional weight gained during pregnancy. Carmichael et al. (1997) reported that the median weight gain of 7,002 women who had good pregnancy outcomes was 16 kg. No consistent relationship between maternal age and weight gain was observed in six studies of U.S. women (IOM, 1990). Therefore, 16 kg is added to the reference weight for nonpregnant adolescent girls and women for extrapolation. Methods for Determining Increased Needs for Lactation It is assumed that the total nutrient requirement for lactating women equals the requirement for nonpregnant, nonlactating women of similar age plus an increment to cover the amount needed for milk production. To allow for inefficiencies in the use of certain nutrients, the increment may be greater than the amount of the nutrient contained in the milk produced. Details are provided in each nutrient chapter. ESTIMATES OF NUTRIENT INTAKE Reliable and valid methods of food composition analysis are crucial in determining the intake of a nutrient needed to meet a requirement. For nutrients such as chromium, analytic methods to determine the content of the nutrient in food have serious limita- tions. Methodological Considerations The quality of nutrient intake data varies widely across studies. The most valid intake data are those collected from the metabolic study protocols in which all food is provided by the researchers, amounts consumed are measured accurately, and the nutrient com- position of the food is determined by reliable and valid laboratory analyses. Such protocols are usually possible with only a few sub- jects. Thus, in many studies, intake data are self-reported (e.g., through 24-hour recalls of food intake, diet records, or food fre- quency questionnaires). Potential sources of error in self-reported intake data include over- or underreporting of portion sizes and frequency of intake, omis-
OVERVIEW AND METHODS 55 sion of foods, and inaccuracies related to the use of food composi- tion tables (IOM, 2000; Lichtman et al., 1992; Mertz et al., 1991). In addition, because a high percentage of the food consumed in the United States and Canada is not prepared from scratch in the home, errors can occur due to a lack of information on how a food was manufactured, prepared, and served. Therefore, the values reported by nationwide surveys or studies that rely on self-report are often inaccurate and possibly biased, with a greater tendency to under- estimate actual intake (IOM, 2000). Adjusting for Day-to-Day Variation Because of day-to-day variation in dietary intakes, the distribution of 1-day (or 2-day) intakes for a group is wider than the distribution of usual intakes even though the mean of the intakes may be the same (for further elaboration, see Chapter 14). To reduce this prob- lem, statistical adjustments have been developed (NRC, 1986; Nuss- er et al., 1996) that require at least 2 days of dietary data from a representative subsample of the population of interest. However, no accepted method is available to adjust for the underreporting of intake, which may average as much as 20 percent for energy (Mertz et al., 1991). DIETARY INTAKES IN THE UNITED STATES AND CANADA Sources of Dietary Intake Data The major sources of current dietary intake data for the U.S. pop- ulation are the Third National Health and Nutrition Examination Survey (NHANES III), which was conducted from 1988 to 1994 by the U.S. Department of Health and Human Services, and the Con- tinuing Survey of Food Intakes by Individuals (CSFII), which was conducted by the U.S. Department of Agriculture (USDA) from 1994 to 1996. NHANES III examined 30,000 subjects aged 2 months and older. A single 24-hour diet recall was collected for all subjects. A second recall was collected for a 5 percent nonrandom subsam- ple to allow adjustment of intake estimates for day-to-day variation. The 1994 to 1996 CSFII collected two nonconsecutive 24-hour re- calls from approximately 16,000 subjects of all ages. Both surveys used the food composition database developed by USDA to calcu- late nutrient intakes (Perloff et al., 1990) and were adjusted by the method of Nusser et al. (1996). For boron, which is not included in the USDA food composition database, the Boron Nutrient Data-
56 DIETARY REFERENCE INTAKES base (Rainey et al., 1999) was used to calculate boron intakes from these surveys. National survey data for Canada are not currently available, but data have been collected in QuÃ©bec and Nova Scotia. The extent to which these data are applicable nationwide is not known. The Food and Drug Administration (FDA) Total Diet Study was used for estimating the intakes for many of the micronutrients re- viewed that were not covered by NHANES III and CFSII. The FDA Total Diet Study utilized a number of FDA Market Basket Surveys collected between the third quarter of 1991 and the first quarter of 1997. An updated food map was developed with use of a total of 306 core foods to map the USDA food consumption survey data for 1994 to 1996. The micronutrient contents of the 306 core foods were determined by FDA, USDA CFSII Code Book, Standard Refer- ence 12, or literature published by individual laboratories. The in- take data were not adjusted for day-to-day variation, and therefore do not represent usual intakes. Appendix C provides the mean and the fifth through ninety-ninth percentiles of dietary intakes of vitamin A, vitamin K, boron, copper, iron, and zinc from NHANES III, adjusted by methods described by the National Research Council (NRC, 1986) and by Feinleib et al. (1993) and adjusted for day-to-day variation by the method of Nusser et al. (1996). TABLE 2-2 Percentage of Persons Taking Vitamin and Mineral Supplements, by Sex and Age: National Health Interview Survey, United States, 1986 Women Men Vitamin/Mineral All Adults All Adults Supplement Taken 18+ y 18â44 y 45â64 y 65+ y 18+ y Vitamin A 25.9 26.3 26.3 24.4 19.8 Chromium 9.4 9.9 9.1 8.7 7.6 Copper 15.2 15.3 14.7 15.6 13.1 Iodine 15.3 15.7 14.3 15.5 12.6 Iron 23.1 24.5 22.0 20.7 16.0 Manganese 12.4 12.3 12.4 12.7 10.1 Zinc 17.2 17.0 17.2 17.9 14.5 NOTE: The high use of supplements by pregnant women is not reflected in this table. SOURCE: Moss et al. (1989).
OVERVIEW AND METHODS 57 Appendix D provides comparable information from the 1994â 1996 CSFII for boron, copper, iron, and zinc. Appendix E gives the mean and first through ninety-ninth percentiles of dietary intakes of vitamin K, arsenic, copper, iodine, iron, manganese, nickel, sili- con, and zinc from the FDA Total Diet Study. Appendix F provides means and selected percentiles of dietary intakes of vitamin A, iron, and zinc for individuals in QuÃ©bec and Nova Scotia. Sources of Supplement Intake Data Although subjects in the CSFII (1994â1996) were asked about the use of dietary supplements, quantitative information was not col- lected. Data on supplement intake obtained from NHANES III were reported as a part of total intake of vitamin K, copper, iron, and zinc (Appendix C). Intake, based on supplement intake alone for vitamin A, boron, chromium, iodine, manganese, molybdenum, nickel, silicon, and vanadium, is also reported in Appendix C. NHANES III data on overall prevalence of supplement use are also available (LSRO/FASEB, 1995). In 1986, the National Health Inter- view Survey queried 11,558 adults and 1,877 children on their in- take of supplements during the previous 2 weeks (Moss et al., 1989). The composition of the supplement was obtained directly from the product label whenever possible. Table 2-2 shows the percentage of Mineral w Men All Adults 65+ y 18+ y 18â44 y 45â64 y 65+ y 24.4 19.8 19.6 20.5 19.4 8.7 7.6 7.9 6.4 8.5 15.6 13.1 13.2 12.9 12.7 15.5 12.6 12.6 12.7 12.6 20.7 16.0 15.8 16.3 16.4 12.7 10.1 9.9 9.6 11.4 17.9 14.5 14.3 15.1 14.6 this table.
58 DIETARY REFERENCE INTAKES adults, by age, taking at least one of the micronutrients reviewed in this report. Food Sources For some nutrients, two types of information are provided about food sources: identification of the foods that are the major contrib- utors of the nutrients to diets in the United States and the foods that contain the highest amounts of the nutrient. The determina- tion of foods that are major contributors depends on both nutrient content of a food and the total consumption of the food (amount and frequency). Therefore, a food that has a relatively low concen- tration of the nutrient might still be a large contributor to total intake if that food is consumed in relatively large amounts. SUMMARY General methods for examining and interpreting the evidence on requirements for nutrients are presented in this chapter, with spe- cial attention given to infants, children, and pregnant and lactating women, methodological problems, and dietary intake data. Rele- vant detail is provided in the nutrient chapters. REFERENCES AAP (American Academy of Pediatrics). 1997. Breastfeeding and the use of human milk. Pediatrics 100:1035â1039. Butte NF, Garza C, Smith EO, Nichols BL. 1984. Human milk intake and growth in exclusively breast-fed infants. J Pediatr 104:187â195. Carmichael S, Abrams B, Selvin S. 1997. The pattern of maternal weight gain in women with good pregnancy outcomes. Am J Public Health 87:1984â1988. Chandra RK. 1984. Physical growth of exclusively breast-fed infants. Nutr Res 2:275â 276. FAO/WHO/UNA (Food and Agriculture Organization of the United Nations/ World Health Organization/United Nations). 1985. Energy and Protein Require- ments Report of a Joint FAO/WHO/UNA Expert Consultation. Technical Report Series, No. 724. Geneva: WHO. Feinleib M, Rifkind B, Sempos C, Johnson C, Bachorik P, Lippel K, Carroll M, Ingster-Moore L, Murphy R. 1993. Methodological issues in the measurement of cardiovascular risk factors: Within-person variability in selected serum lipid measuresâResults from the Third National Health and Nutrition Survey (NHANES III). Can J Cardiol 9:87Dâ88D. Health Canada. 1990. Nutrition Recommendations. The Report of the Scientific Review Committee 1990. Ottawa: Canadian Government Publishing Centre.
OVERVIEW AND METHODS 59 Heinig MJ, Nommsen LA, Peerson JM, Lonnerdal B, Dewey KG. 1993. Energy and protein intakes of breast-fed and formula-fed infants during the first year of life and their association with growth velocity: The DARLING Study. Am J Clin Nutr 58:152â161. Heitmann BL, Lissner L. 1995. Dietary underreporting by obese individualsâIs it specific or non-specific? Br Med J 311:986â989. Hill AB. 1971. Principles of Medical Statistics, 9th ed. New York: Oxford University Press. Hofvander Y, Hagman U, Hillervik C, Sjolin S. 1982. The amount of milk con- sumed by 1â3 months old breast- or bottle-fed infants. Acta Paediatr Scand 71:953â958. Hytten FE, Leitch I. 1971. The Physiology of Human Pregnancy, 2nd ed. Oxford: Blackwell Scientific. IOM (Institute of Medicine). 1990. Nutrition During Pregnancy. Washington, DC: National Academy Press. IOM. 1991. Nutrition During Lactation. Washington, DC: National Academy Press. IOM 2000. Dietary Reference Intakes: Applications in Dietary Assessment. Washington, DC: National Academy Press. Kleiber M. 1947. Body size and metabolic rate. Physiol Rev 27:511â541. Lichtman SW, Pisarska K, Berman ER, Pestone M, Dowling H, Offenbacher E, Weisel H, Heshka S, Matthews DE, Heymsfield SB. 1992. Discrepancy between self-reported and actual caloric intake and exercise in obese subjects. N Engl J Med 327:1893â1898. LSRO/FASEB (Life Sciences Research Office/Federation of American Societies for Experimental Biology). 1995. Third Report on Nutrition Monitoring in the United States. Washington, DC: US Government Printing Office. Mertz W, Tsui JC, Judd JT, Reiser S, Hallfrisch J, Morris ER, Steele PD, Lashley E. 1991. What are people really eating? The relation between energy intake de- rived from estimated diet records and intake determined to maintain body weight. Am J Clin Nutr 54:291â295. Moss AJ, Levy AS, Kim I, Park YK. 1989. Use of Vitamin and Mineral Supplements in the United States: Current Users, Types of Products, and Nutrients. Advance Data, Vital and Health Statistics of the National Center for Health Statistics, Number 174. Hyattsville, MD: National Center for Health Statistics. Neville MC, Keller R, Seacat J, Lutes V, Neifert M, Casey C, Allen J, Archer P. 1988. Studies in human lactation: Milk volumes in lactating women during the onset of lactation and full lactation. Am J Clin Nutr 48:1375â1386. NRC (National Research Council). 1986. Nutrient Adequacy. Assessment Using Food Consumption Surveys. Washington, DC: National Academy Press. Nusser SM, Carriquiry AL, Dodd KW, Fuller WA. 1996. A semiparametric transfor- mation approach to estimating usual daily intake distributions. J Am Stat Assoc 91:1440â1449. Perloff BP, Rizek RL, Haytowitz DB, Reid PR. 1990. Dietary intake methodology. II. USDAâs Nutrient Data Base for Nationwide Dietary Intake Surveys. J Nutr 120:1530â1534. Rainey CJ, Nyquist LA, Christensen RA, Strong PL, Culver BD, Coughlin JR. 1999. Daily boron intake from the American diet. J Am Diet Assoc 99:335â340. West GB, Brown JH, Enquist BJ. 1997. A general model for the origin of allometric scaling laws in biology. Science 276:122â126. Willett WC, Sampson L, Stampfer MJ, Rosner B, Bain C, Witschi J, Hennekens CH, Speizer FE. 1985. Reproducibility and validity of a semiquantitative food fre- quency questionnaire. Am J Epidemiol 122:51â65.