- Email: [email protected]
Identification of index nutrients for dietary assessment
Identification of Index Nutrients for Dietary Assessment R. Michael Jenkins1 and Helen A. Guthrie Department of Nutrition, The Pennsylvania State University, University Park, PA 16802
In order to identify a smaller number of nutrients than generally used to assess dietary adequacy, we examined the interrelationships among the micronutrients. We constructed a profile of adult food intake from data from the Nationwide Food Consumption Survey (NFCS) and converted this to a nutrient profile using a data base complete for 15 nutrients. By performing a factor analysis of nutrients in this profile we were able to identify four distinct nutrient factors. We then selected the best index nutrient from each factor. A combination of the four index nutrients-iron, vitamin B-6, calcium, and vitamin A-assured comparable intakes of the six additional nutrients - magnesium, phosphorus, vitamin C, riboflavin, vitamin B- 12, and thiamin-in more than 90070 of records from 3980 adult respondents in the NFCS. (JNE 16:15-18, 1984) ABSTRACT
As the number of essential nutrients for which there is sufficient information on which to base Recommended Dietary Allowances has increased, the task of the nutritionist in assessing dietary adequacy has become correspondingly complex. By 1980 there were RDAs for 17 such nutrients and recommendations for safe and adequate intakes for an additional 12 nutrients (1). Any attempt to simplify dietary assessment would be contingent on identifying relationships among various micronutrients so that a smaller number of nutrients might be used to make inferences and recommendations about a much larger number. Correlations among nutrients have been reported in a limited number of studies since 1946 (2-6). On the basis of the results of a correlation analysis in food, Pennington (7) selected seven index nutrients-vitamin B-6, magnesium, panthothenic acid, vitamin A, folacin, iron, and calcium - which she maintained were the best combination of nutrients to use for judging dietary adequacy. She postulated that if a diet met the suggested intakes for these seven index nutrients, and if a few simple dietary guidelines were followed, there was a high probability that all 45 essential nutrients included in her data base would be present in the diet in adequate amounts. Unfortunately, Pennington reached her conclusions based on data on the nutrient ·Current Address: School of Family and Consumer Studies, Kent State University, Kent, Ohio 44242.
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composItions of equal portions of 202 foods without considering the relative amounts used in a typical diet. Additionally, data on only three of her seven nutrients are available and complete for the majority of foods in the U.S. diet. In 1973 Guthrie et al. (8) used factor analysis to study 32 dietary, biochemical and anthropometric variables often considered in the assessment of nutritional status. Factor analysis refers to a variety of statistical techniques whose common objective is to reduce a number of variables to a few underlying factors. Because variables loading on the same factor are best correlated with that factor compared to the other factors in the solution, they measure the same trait or characteristic (9,10) . Guthrie et al. found that all of the dietary measures appeared in just two factors: the first containing vitamins and iron, and the second, macronutrients and calcium. Ror their study population of 419 preschool children, they found that a determination of energy or one of the energy yielding nutrients or both, and of either iron, thiamin, or niacin, was sufficient to permit an assessment of the total supplemental diet for children. An additional assessment for ascorbic acid was indicated for children who did not receive supplements. Guthrie and Guthrie (11) in 1976 repeated the 1973 procedure on a population of all ages and found that intakes of iron and vitamin A provided as much information as the determination of intakes of eight nutrients and energy. For nutrition surveillance purposes, they concluded that there
was no reduction in diagnostic potential using the specified two versus nine dietary components. The overall objective of these studies was not, however, to identify index nutrients, but to attempt to reduce the number of nutritional status indicators without sacrificing diagnostic accuracy. The analysis included biochemical and physical variables as well as dietary factors. Thus, the factor loadings were derived from correlations among all of the variables. If the dietary variables alone had been subjected to factor analysis, the resulting factor loadings might have been quite different. In addition, information on many more nutrients is now generally available, and it is important to know whether they load on the same factors or assess other descriptive parameters of dietary adequacy. The objective of our study was, therefore, to investigate the occurrence of micronutrients in freely selected adult diets and to determine if it was possible to reduce the number of nutrients that need to be considered for dietary assessment. METHODS Initially, we obtained a profile of food intake for adults and converted this to a nutrient profile using a data base providing information on 15 nutrients . This nutrient profile was in turn subjected to factor analysis to identify commonalities of covariation among nutrients. We then identified the best index nutrient for each factor identified and finally tested the extent to which the combination of index nutrients could be used to characterize the adequacy of freely selected diets. Development of the food profile. We developed a profile of food intake as based on reports of the adult population surveyed in the 1977-1978 Nationwide Food Consumption Study (NFCS). This involved determining the frequency with which food items listed in the food dictionary were selected by the 3318 individuals who provided threeday food intake records in February 1978. Data from anyone month period met our needs because food item selections in the JOURNAL OF NUTRITION EDUCATION
15
NFCS varied minimally from month to month (12), and the number of individuals reporting in one month was adequate for our proposed analysis. Food items were categorized into 227 minor food subgroups based on similarities in nutrient qualities and use in the diet. We eliminated 65 of these subgroups; 27 contained non-adult foods (infant formulas and baby foods) or non-nutritive food items such as black coffee, artificial sweeteners, and seasonings; and 38 were selected fewer than six times. The table of frequencies for the remaining 162 minor food subgroups showed usages varying up to 9100 "mentions" for the white bread and rolls subgroup. These steps generated a weighted profile of food intake by adults based on the frequency of selections of each minor food subgroup. Development of the nutrient profile. Because we were interested in a nutrient rather than a food profile of a typical diet, we had to identify the most complete and accurate food composition data base available so that we could translate food selections into nutrient terms. For this, the U.S. Department of Agriculture (USDA) provided us with a copy of the Expanded Nutrient Data Base (ENDB) (Note 1), which contained the nutrient composition of 439 commonly selected food items. Although this number of food items was somewhat restrictive, the data on each item was complete and its accuracy was backed by the USDA. Other food composition tables suffered from incomplete data, imputed values, or poor documentation of values for some nutrients. The next step involved selecting, from the ENDB, food items that most closely represented the nutritional composition of each of the 162 minor food subgroups. Because of the limited choices of food items from the ENDB, we had to use some representative food items more than once. For example, the nutritive value of beef loin was used to represent several minor subgroups including beef steak without bone, beef slices or chunks, as well as game. We also had to combine some minor food subgroups with similar nutrient profiles, for example, table fats, cooking fats, and vegetable oils, and use the most representative food item, in this case, regular margarine. We made the best available substitutions, reflecting the nutrient composition of these different minor food subgroups. The result of the combination process was that 69 minor food subgroups were reduced to 25 different 16
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groups. This reduction decreased the original 162 minor food subgroups to 118, each represented by a food item from the ENDB. Because some ENDB items were used to represent more than one minor food subgroup, the final analysis involved 85 different food items, representing the 118 minor food subgroups. Because the ENDB gives nutritive values in 100 gram portions, we had to convert the typical portion size used in the NFCS data for the 118 minor food subgroups to multiples of 100 gram portions. We then used this information to generate a matrix of the nutrient values of each of the representative food items, and subsequently, a nutrient equivalent of the weighted profile of food intake. Selection of index nutrients. We generated a correlation matrix of the 15 nutrients used to characterize the food intake of an adult population. The correlation matrix involved the following nutrients: carbohydrate, protein, calcium, phosphorus, vitamin A, thiamin, riboflavin, niacin, vitamin B-6, vitamin B-12, vitamin C, folacin, iron, magnesium and zinc. Although we were interested in the micronutrient interrelationships, the correlation matrix included carbohydrate and protein to aid in the subsequent identification of the factors . We then performed a principal axis factor analysis with an orthogonal (varimax) rotation (10). Once we had identified the factors, our next step was to identify the index nutrients for each factor. To do this, we used individual nutrient consumption records, which allowed us to determine whether the relationships identified by the factor analysis held true for actual individual food consumption data. For this we used an adult popUlation from the NFCS, one from which we deleted pregnant and lactating women, vegetarians, and consumers of alcohol. We systematically subdivided this population of 3980 adults into five samples (designated A to E), each containing 7% persons. Unfortunately, the nutrient analyses of the diets of the participants in the NFCS contained information for only 10 micronutrients: calcium, iron, magnesium, phosphorus, vitamin A, thiamin, riboflavin, vitamin B-6, vitamin B-12, and vitamin C. Although values for niacin intake were included in the data, we did not use niacin because these values did not represent the niacin equivalents in which requirements are expressed. Consequently, we were unable to assess the predictability of zinc,
niacin, and folacin, the other three micronutrients included in our original factor analysis based on the ENDB. Although we had not intended to define "adequate intakes" of nutrients, we had to establish some standard upon which to base an assessment of predictability that was common across age and sex. We set as the Standard of Adequacy for each of the micronutrients the highest Recommended Dietary Allowance for any age group in our population, which was generally that for the adult male because adolescents were not represented in our population. To determine the best index nutrient for each factor, we first identified those individual records with 1000/0 of the Standard of a given nutrient. Then we examined these records to determine the intakes of the other nutrients loading on the same factor. For example, if a factor had but two nutrients loading on it, nutrients A and B, we determined the number of records with 100070 of the Standard for nutrient A. Then from those records we tabulated the percent of the diets also having 1000/0 of the Standard for nutrient B. We repeated the process, determining the percent of records with 1000/0 of the Standard for nutrient Band the percent of those also having 1000/0 of the Standard for nutrient A. We considered one nutrient that ensured the higher percentage of the other nutrient the better index nutrient for that factor. We followed this procedure for each factor and identified the best (better) index nutrient from each. Finally, to determine whether the index nutrients would be sensitive indicators of micronutrient intake, we examined records having from at least 1000/0 of the Standard for all indicator nutrients to at least 100/0. For each of these levels we computed the percent of records having intakes of the other six nutrients in at least an equivalent percentage of the Standard. RESULTS The results of the first factor analysis procedure were interpretable from a practical nutritional standpoint, except that vitamin A and vitamin B-12 appeared on the same factor. We subsequently determined that the extremely high concentrations of these two nutrients in liver caused them to load together, even though the minor food subgroup, organ meats and mixtures, containing liver was not frequently selected. The entire minor food subgroup was selected 205 times, and represented less than 0.0020/0 of the 120,000 food item selections by the VOLUME
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Factor loadings from factor analysis of nutrients in a profile of 84 foods representative of the food consumption of adults in the U.S. population.
Table 1
Factor 1
Factor 3
Factor 2
Nutrient
Factor Loadings
Nutrient
Factor Loadings
protein
0.89 0.89 0.87 0.86 0.74
calcium
0.96 0.90 0.85 0.76 0.76
niacin vitamin
B-6
zinc iron
riboflavin phosphorus vitamin
B-12
magnesium
Nutrient
Factor 4
Factor Loadings
0.91 0.61 0.38
folacin vitamin C vitamin A
Nutrient
Factor Loadings
0.67 0.66
carbohydrate thiamin
Organ meats were eliminated.
Table 2
Range of percentage of diet records with intakes of predicted nutrients at the same or greater Standard of Adequacya as the most limiting of the four index nutrients.
Percent of Standard of Adequacy of the Most Limiting Index Nutrient Predicted Nutrient
100
90
80
70
60
50
40
30
20
10
(l64)b
(274) 78-95 100 89-100 100 96-100 96-100
(470) 88-95 100 90-98 100 97-100 97-99
(790) 90-95 100 94-97 96-100 96-100 98-100
(1259) 95-97 100 93-96 100 97-99 98-99
(1894) 97-98 100 94-96 100 99 99-100
(2565) 98-99 100 95-98 100 98-99 99-100
(3245) 99-100 100 94-96 99-100 99 99-100
(3704) 99-100 100 97-98 100
(3929) 100 100 99 100 99 100
75-91 c 100 90-97 97-100 97-100 96-100
magnesium phosphorus vitamin C riboflavin vitamin B-12 thiamin
99
99-100
alOO"7o Standard of Adequacy (SA) is the highest Recommended Dietary Allowance for any age group in the adult population and was as follows: For the index nutrients; calcium, 800 mg; iron, 18 mg; vitamin A, 5000 IV; and vitamin B-6, 2.2 mg; for the other nutrients in the analysis; magnesium, 350 mg; phosphorus, 800 mg; vitamin C, 60 mg; riboflavin, 1.6 mg; vitamin B-12, 3 ug; and thiamin, 1.4 mg. bNumber in parentheses indicates the number of individuals whose intake of index nutrients met the Percent of Standard of Adequacy. The population was comprised of 3980 adults in the Nationwide Food Consumption Survey. CRange of number of records with intakes of predicted nutrients were based on five samples drawn systematically from the popUlation.
total population. Because we could not justify using either nutrient as an index for the other unless liver was included in the diet, and because liver is not a frequently selected item, we repeated the factor analysis procedure on the data omitting the organ meats and mixtures minor food subgroup. Table 1 shows the factor analysis results. Each of the factors can be associated with a food group: factor 1 - protein-rich foods, factor 2 - milk, factor 3 - fruit and vegetables, factor 4 - bread and cereal. This pattern suggested that we could choose four nutrients, one from each factor, to characterize the nutrient quality of the diet. Factor 1 contained two micronutrients, vitamin B':"6 and iron, that were included in our data base on individual nutrient intakes and could, therefore, serve as possible index nutrients. We found that when iron was present at 1000/0 of the Standard, only 65% of the records had comparable intakes of vitamin B-6. When vitamin B-6, as an indicator, was present in the diet in amounts greater than or equal to recommended amounts, 68% of those records also had VOLUME
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comparable intakes of iron. Because each nutrient was a comparable indicator of the other, we included both nutrients in our preliminary list of index nutrients. We used the same approach with the other three factors. For factor 2, calcium did an excellent job of predicting comparable levels of intake for phosphorus (97%), riboflavin (88%), and vitamin B-12 (95%); but was not as effective an indicator of magnesium (51%). Vitamin A was the best predictor from factor 3, assuring comparable intake of vitamin C in 90% of the records with adequate intakes of vitamin A; whereas vitamin C intake would only predict comparable intakes of vitamin A in 73% of the records. Because thiamin was the only nutrient from factor 4, we included it as the fifth nutrient in our preliminary list of index nutrients. Overall results showed that the best indicators were vitamin B-6, iron, calcium, vitamin A, and thiamin. However, including thiamin contributed little additional predictability because 95% of the records having 100% of the Standard for iron also
had 100% of the Standard for thiamin. Therefore, we eliminated thiamin from the list of index nutrients and used the four remaining index nutrients in combination for the next stage of the analysis. Analysis of the records of the five samples showed that with the single exception of magnesium, intakes of the other micro nutrients were at least as adequate as the most limiting of the index nutrients in 90% of the records analyzed (Table 2). As we decreased the percent of the Standard, more records were included in the analysis. Even magnesium intakes were more closely reflected by the index nutrients at their lower levels, with approximately 90% of the records having magnesium present at a percent of Standard comparable to that of the index nutrients when they were at 50 to 80% of the Standard. DISCUSSION Although in a randomly selected population of adults the adequacy of intake of the four index nutrients identified in this study JOURNAL OF NUTRITION EDUCATION
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reflected the adequacy of intake of six other nutrients, the data used to identify these index nutrients was restrictive in the number of food items included. Because of this limitation, we used 85 food items to represent 162 minor food subgroups. This may have caused some loss of accuracy, but whether and to what extent this occurred is impossible to determine without complete food composition data on a larger number of food items. In selecting the population to identify the index nutrients, we eliminated vegetarians, pregnant and lactating women, and consumers of alcohol from the larger NFCS population. Thus, the relationships might change slightly if these excluded groups were included or were studied separately. We eliminated these groups in developing a data base for a larger study of which this was a part. The possibility of using these selected index nutrients as predictors of other micronutrients in other populations, such as children, adolescents, or vegetarians, should be tested on appropriate data. Our results differ from those of Guthrie and Guthrie (1976) in that we identified four
HUMAN The beneficial effects of resistance factors in breast milk are well known, and a recent study has presented evidence for the presence of yet another such factor that may provide protection against the intestinal parasite Giardia lamblia. This parasite is a major cause of enteric disease in the U.S. and is especially prevalent in children, in whom it can cause failure to thrive. Some Giardia lamblia infections disappear within a few days although symptoms of others continue for years despite the presence of antibodies. This indicates that nonimmune factors may be effective in combating the 18
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index nutrients and they identified only two. Those two, iron and vitamin A were, however, included in our four. Our four index nutrients included those which Pennington (7) selected using another procedure. Her list also included folacin and pantothenic acid , nutrients that we could not investigate because of insufficient data, and magnesium, which we found to be fairly well predicted by calcium, the index nutrient we selected from the milk factor. If the index nutrients identified here hold for other population segments, their use would simplify dietary intake analysis as a tool for nutritional assessment. Using the intake of a few nutrients as an index for the intake of other nutrients would reduce the cost of the dietary component of nutrition surveillance and nutritional diagnosis. This would represent a response to policymakers who are challenging nutrition scientists to develop ways of reducing the number of tests required to identify vulnerable individuals and groups, to assess the dietary adequacy of a population, and to determine the impact of intervention programs on target populations. 0
MILK
KILLS
PARASITIC
NOTES
I United Stares Department of Agriculture. P rivileged com munication.
LITERAT URE CITED
1 National Academy of Sciences. N ational Researc h Council. Food and Nutrition Board . Recommended dietary allo wances, 9th ed, Washington, D.C.: National Academy of Sciences, 1980, 185 pp . 2 Lawrence, J. M., B. L. Herringwn , a nd L. A. Ma ynard. T he nicotinic acid, biotin, and pathot henic add content of cow's milk. Journal of N utrition 32:73-91, 1946. 3 Calhoun, W. K., W. O. Bechtel, a nd W. B. Bradley. The vitamin content of wheat. Hour, and bread, Cereal Chemistry 35 :350-59, 1958. 4 Murphy, E. M., P. C. Koons, and L. Page. Vita min content of T ype A school lunches. Journal of the American Dietelie A ssociation 55:372-78, 1969. 5 Murphy, E. M., L. Page. and P. C. Koons. Lipid components of Type A school lunches. Journal oj the American Diefefic Association 56:504-9, 1970. 6 Murphy, E. M., L. Page, and B. K. Watt . Major minera l elements in Type A school lunches. Journal of the American Dietetic Association 58:115- 21, 1970. 7 Pennington. 1. T. Dietary N utrient Guide. Westport, CT: AVI Publishing Co. , 1976, pp. 40-44. 8 Gut hrie, H. A., G. M. Owen, and G. M. Guth rie. FaclOr a nalysis o f measures of nut ritional stat us of preschool children. American Journal of Clinical Nutrition 26:497- 502, 1973 . 9 Kim, J. and C. W. Mueller. Introduction 10 factor analysis. Beverly Hills, CA : Sage P Ublications, 1978, 80 pp. 10 Ki m, J. and C. W. Mu eUer. Factor allalysis. Beverl y HiUs, CA: Sage Publicat ions, 1978, 88 pp. II Guthrie, H. A., and G. M. Guthrie. Factor analysis o fnutritional status data from Ten State Nutrition surveys. American Journal of Clinical Nutrition 29: 1238-41 , 1976. 12 Crocetti, A. F., and H. A. Guthrie. Eating Behavior and Assoc ia ted Nu t ri e nt Q ua lit y o f Diets. U.S.D. A . Reporl 53-22U49· 192, Washington, D.C., 1982, p. 2.
PROTOZOA
disease. Gillin and Reiner (Scienc e 221:1290-92, 1983) have suggested that, if present, such factors are most likely to be in the upper small intestine, the site colonized by the parasite. The nonimmune factors may be produced by intestinal tissue, secreted into the intestine, or ingested. Noting that infant mice were protected from Giardia muris by milk from previously infected mice, researchers tested the effect of human milk on Giardia lamblia. Human milk, even at fairly low concentrations, killed the parasite but neither cow's milk nor goat's milk had this effect. The researchers
conducted a number of tests t o determine the Giardia-cidal component of human milk and concluded that bile salt-stimulated lipase was probably responsible for the Giardia-cidal activity but that other factors might also be involved. They emphasized the need for studies to clarify the mechanism whereby human milk exerts its Giardia- cidal effe ct and to determine whether breast-fed children have a lower incidence of giardiasis than non-breast-fed children.
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In order to identify a smaller number of nutrients than generally used to assess dietary adequacy, we examined the interrelationships among the micronutrients. We constructed a profile of adult food intake from data from the Nationwide Food Consumption Survey (NFCS) and converted this to a nutrient profile using a data base complete for 15 nutrients. By performing a factor analysis of nutrients in this profile we were able to identify four distinct nutrient factors. We then selected the best index nutrient from each factor. A combination of the four index nutrients-iron, vitamin B-6, calcium, and vitamin A-assured comparable intakes of the six additional nutrients - magnesium, phosphorus, vitamin C, riboflavin, vitamin B- 12, and thiamin-in more than 90070 of records from 3980 adult respondents in the NFCS. (JNE 16:15-18, 1984) ABSTRACT
As the number of essential nutrients for which there is sufficient information on which to base Recommended Dietary Allowances has increased, the task of the nutritionist in assessing dietary adequacy has become correspondingly complex. By 1980 there were RDAs for 17 such nutrients and recommendations for safe and adequate intakes for an additional 12 nutrients (1). Any attempt to simplify dietary assessment would be contingent on identifying relationships among various micronutrients so that a smaller number of nutrients might be used to make inferences and recommendations about a much larger number. Correlations among nutrients have been reported in a limited number of studies since 1946 (2-6). On the basis of the results of a correlation analysis in food, Pennington (7) selected seven index nutrients-vitamin B-6, magnesium, panthothenic acid, vitamin A, folacin, iron, and calcium - which she maintained were the best combination of nutrients to use for judging dietary adequacy. She postulated that if a diet met the suggested intakes for these seven index nutrients, and if a few simple dietary guidelines were followed, there was a high probability that all 45 essential nutrients included in her data base would be present in the diet in adequate amounts. Unfortunately, Pennington reached her conclusions based on data on the nutrient ·Current Address: School of Family and Consumer Studies, Kent State University, Kent, Ohio 44242.
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composItions of equal portions of 202 foods without considering the relative amounts used in a typical diet. Additionally, data on only three of her seven nutrients are available and complete for the majority of foods in the U.S. diet. In 1973 Guthrie et al. (8) used factor analysis to study 32 dietary, biochemical and anthropometric variables often considered in the assessment of nutritional status. Factor analysis refers to a variety of statistical techniques whose common objective is to reduce a number of variables to a few underlying factors. Because variables loading on the same factor are best correlated with that factor compared to the other factors in the solution, they measure the same trait or characteristic (9,10) . Guthrie et al. found that all of the dietary measures appeared in just two factors: the first containing vitamins and iron, and the second, macronutrients and calcium. Ror their study population of 419 preschool children, they found that a determination of energy or one of the energy yielding nutrients or both, and of either iron, thiamin, or niacin, was sufficient to permit an assessment of the total supplemental diet for children. An additional assessment for ascorbic acid was indicated for children who did not receive supplements. Guthrie and Guthrie (11) in 1976 repeated the 1973 procedure on a population of all ages and found that intakes of iron and vitamin A provided as much information as the determination of intakes of eight nutrients and energy. For nutrition surveillance purposes, they concluded that there
was no reduction in diagnostic potential using the specified two versus nine dietary components. The overall objective of these studies was not, however, to identify index nutrients, but to attempt to reduce the number of nutritional status indicators without sacrificing diagnostic accuracy. The analysis included biochemical and physical variables as well as dietary factors. Thus, the factor loadings were derived from correlations among all of the variables. If the dietary variables alone had been subjected to factor analysis, the resulting factor loadings might have been quite different. In addition, information on many more nutrients is now generally available, and it is important to know whether they load on the same factors or assess other descriptive parameters of dietary adequacy. The objective of our study was, therefore, to investigate the occurrence of micronutrients in freely selected adult diets and to determine if it was possible to reduce the number of nutrients that need to be considered for dietary assessment. METHODS Initially, we obtained a profile of food intake for adults and converted this to a nutrient profile using a data base providing information on 15 nutrients . This nutrient profile was in turn subjected to factor analysis to identify commonalities of covariation among nutrients. We then identified the best index nutrient for each factor identified and finally tested the extent to which the combination of index nutrients could be used to characterize the adequacy of freely selected diets. Development of the food profile. We developed a profile of food intake as based on reports of the adult population surveyed in the 1977-1978 Nationwide Food Consumption Study (NFCS). This involved determining the frequency with which food items listed in the food dictionary were selected by the 3318 individuals who provided threeday food intake records in February 1978. Data from anyone month period met our needs because food item selections in the JOURNAL OF NUTRITION EDUCATION
15
NFCS varied minimally from month to month (12), and the number of individuals reporting in one month was adequate for our proposed analysis. Food items were categorized into 227 minor food subgroups based on similarities in nutrient qualities and use in the diet. We eliminated 65 of these subgroups; 27 contained non-adult foods (infant formulas and baby foods) or non-nutritive food items such as black coffee, artificial sweeteners, and seasonings; and 38 were selected fewer than six times. The table of frequencies for the remaining 162 minor food subgroups showed usages varying up to 9100 "mentions" for the white bread and rolls subgroup. These steps generated a weighted profile of food intake by adults based on the frequency of selections of each minor food subgroup. Development of the nutrient profile. Because we were interested in a nutrient rather than a food profile of a typical diet, we had to identify the most complete and accurate food composition data base available so that we could translate food selections into nutrient terms. For this, the U.S. Department of Agriculture (USDA) provided us with a copy of the Expanded Nutrient Data Base (ENDB) (Note 1), which contained the nutrient composition of 439 commonly selected food items. Although this number of food items was somewhat restrictive, the data on each item was complete and its accuracy was backed by the USDA. Other food composition tables suffered from incomplete data, imputed values, or poor documentation of values for some nutrients. The next step involved selecting, from the ENDB, food items that most closely represented the nutritional composition of each of the 162 minor food subgroups. Because of the limited choices of food items from the ENDB, we had to use some representative food items more than once. For example, the nutritive value of beef loin was used to represent several minor subgroups including beef steak without bone, beef slices or chunks, as well as game. We also had to combine some minor food subgroups with similar nutrient profiles, for example, table fats, cooking fats, and vegetable oils, and use the most representative food item, in this case, regular margarine. We made the best available substitutions, reflecting the nutrient composition of these different minor food subgroups. The result of the combination process was that 69 minor food subgroups were reduced to 25 different 16
JOURNAL OF NUTRITION EDUCATION
groups. This reduction decreased the original 162 minor food subgroups to 118, each represented by a food item from the ENDB. Because some ENDB items were used to represent more than one minor food subgroup, the final analysis involved 85 different food items, representing the 118 minor food subgroups. Because the ENDB gives nutritive values in 100 gram portions, we had to convert the typical portion size used in the NFCS data for the 118 minor food subgroups to multiples of 100 gram portions. We then used this information to generate a matrix of the nutrient values of each of the representative food items, and subsequently, a nutrient equivalent of the weighted profile of food intake. Selection of index nutrients. We generated a correlation matrix of the 15 nutrients used to characterize the food intake of an adult population. The correlation matrix involved the following nutrients: carbohydrate, protein, calcium, phosphorus, vitamin A, thiamin, riboflavin, niacin, vitamin B-6, vitamin B-12, vitamin C, folacin, iron, magnesium and zinc. Although we were interested in the micronutrient interrelationships, the correlation matrix included carbohydrate and protein to aid in the subsequent identification of the factors . We then performed a principal axis factor analysis with an orthogonal (varimax) rotation (10). Once we had identified the factors, our next step was to identify the index nutrients for each factor. To do this, we used individual nutrient consumption records, which allowed us to determine whether the relationships identified by the factor analysis held true for actual individual food consumption data. For this we used an adult popUlation from the NFCS, one from which we deleted pregnant and lactating women, vegetarians, and consumers of alcohol. We systematically subdivided this population of 3980 adults into five samples (designated A to E), each containing 7% persons. Unfortunately, the nutrient analyses of the diets of the participants in the NFCS contained information for only 10 micronutrients: calcium, iron, magnesium, phosphorus, vitamin A, thiamin, riboflavin, vitamin B-6, vitamin B-12, and vitamin C. Although values for niacin intake were included in the data, we did not use niacin because these values did not represent the niacin equivalents in which requirements are expressed. Consequently, we were unable to assess the predictability of zinc,
niacin, and folacin, the other three micronutrients included in our original factor analysis based on the ENDB. Although we had not intended to define "adequate intakes" of nutrients, we had to establish some standard upon which to base an assessment of predictability that was common across age and sex. We set as the Standard of Adequacy for each of the micronutrients the highest Recommended Dietary Allowance for any age group in our population, which was generally that for the adult male because adolescents were not represented in our population. To determine the best index nutrient for each factor, we first identified those individual records with 1000/0 of the Standard of a given nutrient. Then we examined these records to determine the intakes of the other nutrients loading on the same factor. For example, if a factor had but two nutrients loading on it, nutrients A and B, we determined the number of records with 100070 of the Standard for nutrient A. Then from those records we tabulated the percent of the diets also having 1000/0 of the Standard for nutrient B. We repeated the process, determining the percent of records with 1000/0 of the Standard for nutrient Band the percent of those also having 1000/0 of the Standard for nutrient A. We considered one nutrient that ensured the higher percentage of the other nutrient the better index nutrient for that factor. We followed this procedure for each factor and identified the best (better) index nutrient from each. Finally, to determine whether the index nutrients would be sensitive indicators of micronutrient intake, we examined records having from at least 1000/0 of the Standard for all indicator nutrients to at least 100/0. For each of these levels we computed the percent of records having intakes of the other six nutrients in at least an equivalent percentage of the Standard. RESULTS The results of the first factor analysis procedure were interpretable from a practical nutritional standpoint, except that vitamin A and vitamin B-12 appeared on the same factor. We subsequently determined that the extremely high concentrations of these two nutrients in liver caused them to load together, even though the minor food subgroup, organ meats and mixtures, containing liver was not frequently selected. The entire minor food subgroup was selected 205 times, and represented less than 0.0020/0 of the 120,000 food item selections by the VOLUME
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Factor loadings from factor analysis of nutrients in a profile of 84 foods representative of the food consumption of adults in the U.S. population.
Table 1
Factor 1
Factor 3
Factor 2
Nutrient
Factor Loadings
Nutrient
Factor Loadings
protein
0.89 0.89 0.87 0.86 0.74
calcium
0.96 0.90 0.85 0.76 0.76
niacin vitamin
B-6
zinc iron
riboflavin phosphorus vitamin
B-12
magnesium
Nutrient
Factor 4
Factor Loadings
0.91 0.61 0.38
folacin vitamin C vitamin A
Nutrient
Factor Loadings
0.67 0.66
carbohydrate thiamin
Organ meats were eliminated.
Table 2
Range of percentage of diet records with intakes of predicted nutrients at the same or greater Standard of Adequacya as the most limiting of the four index nutrients.
Percent of Standard of Adequacy of the Most Limiting Index Nutrient Predicted Nutrient
100
90
80
70
60
50
40
30
20
10
(l64)b
(274) 78-95 100 89-100 100 96-100 96-100
(470) 88-95 100 90-98 100 97-100 97-99
(790) 90-95 100 94-97 96-100 96-100 98-100
(1259) 95-97 100 93-96 100 97-99 98-99
(1894) 97-98 100 94-96 100 99 99-100
(2565) 98-99 100 95-98 100 98-99 99-100
(3245) 99-100 100 94-96 99-100 99 99-100
(3704) 99-100 100 97-98 100
(3929) 100 100 99 100 99 100
75-91 c 100 90-97 97-100 97-100 96-100
magnesium phosphorus vitamin C riboflavin vitamin B-12 thiamin
99
99-100
alOO"7o Standard of Adequacy (SA) is the highest Recommended Dietary Allowance for any age group in the adult population and was as follows: For the index nutrients; calcium, 800 mg; iron, 18 mg; vitamin A, 5000 IV; and vitamin B-6, 2.2 mg; for the other nutrients in the analysis; magnesium, 350 mg; phosphorus, 800 mg; vitamin C, 60 mg; riboflavin, 1.6 mg; vitamin B-12, 3 ug; and thiamin, 1.4 mg. bNumber in parentheses indicates the number of individuals whose intake of index nutrients met the Percent of Standard of Adequacy. The population was comprised of 3980 adults in the Nationwide Food Consumption Survey. CRange of number of records with intakes of predicted nutrients were based on five samples drawn systematically from the popUlation.
total population. Because we could not justify using either nutrient as an index for the other unless liver was included in the diet, and because liver is not a frequently selected item, we repeated the factor analysis procedure on the data omitting the organ meats and mixtures minor food subgroup. Table 1 shows the factor analysis results. Each of the factors can be associated with a food group: factor 1 - protein-rich foods, factor 2 - milk, factor 3 - fruit and vegetables, factor 4 - bread and cereal. This pattern suggested that we could choose four nutrients, one from each factor, to characterize the nutrient quality of the diet. Factor 1 contained two micronutrients, vitamin B':"6 and iron, that were included in our data base on individual nutrient intakes and could, therefore, serve as possible index nutrients. We found that when iron was present at 1000/0 of the Standard, only 65% of the records had comparable intakes of vitamin B-6. When vitamin B-6, as an indicator, was present in the diet in amounts greater than or equal to recommended amounts, 68% of those records also had VOLUME
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1984
comparable intakes of iron. Because each nutrient was a comparable indicator of the other, we included both nutrients in our preliminary list of index nutrients. We used the same approach with the other three factors. For factor 2, calcium did an excellent job of predicting comparable levels of intake for phosphorus (97%), riboflavin (88%), and vitamin B-12 (95%); but was not as effective an indicator of magnesium (51%). Vitamin A was the best predictor from factor 3, assuring comparable intake of vitamin C in 90% of the records with adequate intakes of vitamin A; whereas vitamin C intake would only predict comparable intakes of vitamin A in 73% of the records. Because thiamin was the only nutrient from factor 4, we included it as the fifth nutrient in our preliminary list of index nutrients. Overall results showed that the best indicators were vitamin B-6, iron, calcium, vitamin A, and thiamin. However, including thiamin contributed little additional predictability because 95% of the records having 100% of the Standard for iron also
had 100% of the Standard for thiamin. Therefore, we eliminated thiamin from the list of index nutrients and used the four remaining index nutrients in combination for the next stage of the analysis. Analysis of the records of the five samples showed that with the single exception of magnesium, intakes of the other micro nutrients were at least as adequate as the most limiting of the index nutrients in 90% of the records analyzed (Table 2). As we decreased the percent of the Standard, more records were included in the analysis. Even magnesium intakes were more closely reflected by the index nutrients at their lower levels, with approximately 90% of the records having magnesium present at a percent of Standard comparable to that of the index nutrients when they were at 50 to 80% of the Standard. DISCUSSION Although in a randomly selected population of adults the adequacy of intake of the four index nutrients identified in this study JOURNAL OF NUTRITION EDUCATION
17
reflected the adequacy of intake of six other nutrients, the data used to identify these index nutrients was restrictive in the number of food items included. Because of this limitation, we used 85 food items to represent 162 minor food subgroups. This may have caused some loss of accuracy, but whether and to what extent this occurred is impossible to determine without complete food composition data on a larger number of food items. In selecting the population to identify the index nutrients, we eliminated vegetarians, pregnant and lactating women, and consumers of alcohol from the larger NFCS population. Thus, the relationships might change slightly if these excluded groups were included or were studied separately. We eliminated these groups in developing a data base for a larger study of which this was a part. The possibility of using these selected index nutrients as predictors of other micronutrients in other populations, such as children, adolescents, or vegetarians, should be tested on appropriate data. Our results differ from those of Guthrie and Guthrie (1976) in that we identified four
HUMAN The beneficial effects of resistance factors in breast milk are well known, and a recent study has presented evidence for the presence of yet another such factor that may provide protection against the intestinal parasite Giardia lamblia. This parasite is a major cause of enteric disease in the U.S. and is especially prevalent in children, in whom it can cause failure to thrive. Some Giardia lamblia infections disappear within a few days although symptoms of others continue for years despite the presence of antibodies. This indicates that nonimmune factors may be effective in combating the 18
JOURNAL OF NUTRITION EDUCATION
index nutrients and they identified only two. Those two, iron and vitamin A were, however, included in our four. Our four index nutrients included those which Pennington (7) selected using another procedure. Her list also included folacin and pantothenic acid , nutrients that we could not investigate because of insufficient data, and magnesium, which we found to be fairly well predicted by calcium, the index nutrient we selected from the milk factor. If the index nutrients identified here hold for other population segments, their use would simplify dietary intake analysis as a tool for nutritional assessment. Using the intake of a few nutrients as an index for the intake of other nutrients would reduce the cost of the dietary component of nutrition surveillance and nutritional diagnosis. This would represent a response to policymakers who are challenging nutrition scientists to develop ways of reducing the number of tests required to identify vulnerable individuals and groups, to assess the dietary adequacy of a population, and to determine the impact of intervention programs on target populations. 0
MILK
KILLS
PARASITIC
NOTES
I United Stares Department of Agriculture. P rivileged com munication.
LITERAT URE CITED
1 National Academy of Sciences. N ational Researc h Council. Food and Nutrition Board . Recommended dietary allo wances, 9th ed, Washington, D.C.: National Academy of Sciences, 1980, 185 pp . 2 Lawrence, J. M., B. L. Herringwn , a nd L. A. Ma ynard. T he nicotinic acid, biotin, and pathot henic add content of cow's milk. Journal of N utrition 32:73-91, 1946. 3 Calhoun, W. K., W. O. Bechtel, a nd W. B. Bradley. The vitamin content of wheat. Hour, and bread, Cereal Chemistry 35 :350-59, 1958. 4 Murphy, E. M., P. C. Koons, and L. Page. Vita min content of T ype A school lunches. Journal of the American Dietelie A ssociation 55:372-78, 1969. 5 Murphy, E. M., L. Page. and P. C. Koons. Lipid components of Type A school lunches. Journal oj the American Diefefic Association 56:504-9, 1970. 6 Murphy, E. M., L. Page, and B. K. Watt . Major minera l elements in Type A school lunches. Journal of the American Dietetic Association 58:115- 21, 1970. 7 Pennington. 1. T. Dietary N utrient Guide. Westport, CT: AVI Publishing Co. , 1976, pp. 40-44. 8 Gut hrie, H. A., G. M. Owen, and G. M. Guth rie. FaclOr a nalysis o f measures of nut ritional stat us of preschool children. American Journal of Clinical Nutrition 26:497- 502, 1973 . 9 Kim, J. and C. W. Mueller. Introduction 10 factor analysis. Beverly Hills, CA : Sage P Ublications, 1978, 80 pp. 10 Ki m, J. and C. W. Mu eUer. Factor allalysis. Beverl y HiUs, CA: Sage Publicat ions, 1978, 88 pp. II Guthrie, H. A., and G. M. Guthrie. Factor analysis o fnutritional status data from Ten State Nutrition surveys. American Journal of Clinical Nutrition 29: 1238-41 , 1976. 12 Crocetti, A. F., and H. A. Guthrie. Eating Behavior and Assoc ia ted Nu t ri e nt Q ua lit y o f Diets. U.S.D. A . Reporl 53-22U49· 192, Washington, D.C., 1982, p. 2.
PROTOZOA
disease. Gillin and Reiner (Scienc e 221:1290-92, 1983) have suggested that, if present, such factors are most likely to be in the upper small intestine, the site colonized by the parasite. The nonimmune factors may be produced by intestinal tissue, secreted into the intestine, or ingested. Noting that infant mice were protected from Giardia muris by milk from previously infected mice, researchers tested the effect of human milk on Giardia lamblia. Human milk, even at fairly low concentrations, killed the parasite but neither cow's milk nor goat's milk had this effect. The researchers
conducted a number of tests t o determine the Giardia-cidal component of human milk and concluded that bile salt-stimulated lipase was probably responsible for the Giardia-cidal activity but that other factors might also be involved. They emphasized the need for studies to clarify the mechanism whereby human milk exerts its Giardia- cidal effe ct and to determine whether breast-fed children have a lower incidence of giardiasis than non-breast-fed children.
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