Ready-to-eat cereal consumption: its relationship with BMI and nutrient intake of children aged 4 to 12 years

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Ready-to-eat cereal consumption: Its relationship with BMI and nutrient intake of children aged 4 to 12 years ANN M. ALBERTSON, MS, RD; G. HARVEY ANDERSON, PhD; SUSAN J. CROCKETT, PhD, RD, FADA; MICHAEL T. GOEBEL

ABSTRACT Objective To examine the relationship between ready-to-eat cereal consumption habits and body mass index of a sample of children aged 4 to 12 years. Design Fourteen-day self-reported food diary records were obtained from a sample of 2,000 American households from February 1998 through February 1999. Height and weight of the family members were also self-reported. Subjects/setting The sample population of 603 children, aged 4 to 12 years, was broken into tertiles based on cereal consumption over the 14 days: (three or fewer, four to seven, or eight or more servings). Statistical analysis Logistic regression and analysis of variance were used to determine associations between frequency of ready-to-eat cereal consumption and body mass index or nutrient intakes. Results More than 90% of children aged 4 to 12 years consumed ready-to-eat cereal at least once in the two-week collection period. Within tertiles of consumption, children in the upper tertile had lower mean body mass indexes than those in the lowest tertile consistently across all age groups (P⬍.01). Additionally, the proportion of children aged 4 to 12 years who were at risk for overweight/overweight was significantly lower in the upper tertile of cereal consumption (P⬍.05). Children in the upper tertile also had lower fat intakes and higher intakes of many micronutrients. Applications The consumption of ready-to-eat cereals at breakfast should be encouraged as a component of an eating pattern that promotes the maintenance of healthful body weights and nutrient intakes in children. J Am Diet Assoc. 2003;103:1613-1619.

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dramatic increase in the prevalence of obesity has occurred in the United States in recent years (1). Rapid increases in childhood obesity rates have also been reported. Increases have been seen in both male and female populations, and across socioeconomic, racial, and ethnic groups. In the United States, close to one in three children is either at risk for overweight or is overweight (2). Overweight children often become overweight adults (3). Prevention is recognized to be the best solution to the problem, so the rising prevalence of overweight in children is a major concern. The etiology of obesity is undefined and no doubt of complex origins (4). One major obvious environmental variable is diet. To determine the dietary factors influencing body mass index (BMI), eating patterns, food selection, and macronutrient composition of diets have been examined. However, the majority of data available are based on survey intake information of one or A. M. Albertson is a senior nutrition research scientist, S. J. Crockett is director of nutrition, and M. T. Goebel is a statistical programmer with The Bell Institute of Health and Nutrition, General Mills, Inc, Minnapolis, MN. G. H. Anderson is a professor of nutrition with the Department of Nutrition Sciences, University of Toronto,Toronto, Ontario. Address correspondence to: Ann M. Albertson, MS, RD, Senior Nutrition Research Scientist, The Bell Institute of Health and Nutrition, General Mills, Inc, 9000 Plymouth Ave N, Minneapolis, MN 55427. E-mail: [email protected] Copyright © 2003 by the American Dietetic Association. 0002-8223/03/10312-0003$30.00/0 doi: 10.1016/j.jada.2003.09.020 Journal of THE AMERICAN DIETETIC ASSOCIATION / 1613

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two days. Hence, the survey data are not amenable to providing an examination of longer-term dietary patterns. The Bell Institute of Health and Nutrition Dietary Intake Study contains data on food consumption patterns. It provides the opportunity to determine the affect of food consumption patterns on nutrient intake, but also because height and weight are recorded, it also allows an examination of the relationship between food consumption patterns and BMI. One dietary pattern that is promoted as providing positive nutrition benefits is the consumption of breakfast. Breakfast eaters have higher nutrient intakes and lower-fat diets than nonbreakfast eaters (5-8). In children, the consumption of breakfast is also associated with better cognitive function in school (9). Ready-to-eat (RTE) cereal is a prevalent food in the diet of American children. The majority of cereal is consumed at breakfast and is a significant nutrient source in the diets of American children (10). Information about the relationship of breakfast eating and its composition to childhood obesity is lacking, however. Therefore, this research investigated the relationship between RTE cereal consumption and BMI of school-aged children (aged 4 to 12 years) using a 14-day food intake methodology. METHODS To determine the impact of food consumption patterns on nutrient intakes, a unique methodology utilizing a 14-day food diary data was developed at the General Mills Bell Institute of Health and Nutrition. This methodology combines 14-day food diary data with portion size data from the US Department of Agriculture’s Continuing Survey of Food Intakes by Individuals and nutrient data from the University of Minnesota’s Nutrition Data System for Research (NDS-R) (version 29, University of Minnesota Nutrition Coordinating Center, 1998, Minneapolis, MN). The resulting integrated database is housed and was analyzed using SAS (2000, SAS Institute, Cary, NC). The dynamic software system allows users to categorize the population based on “usual” consumption of food categories, specific foods, and/or specific brands of foods and determines dietary differences vs their “nonconsuming” counterparts. Food Consumption Data The food industry has traditionally used detailed food records to track the consumption of specific branded food items, monitor the growth of food categories, and provide insight into consumer purchasing and behavior. One supplier of this data is The NPD Group, a marketing information company that has a National Eating Trends service (NET). NET has been continuously tracking the eating habits of Americans since 1980. The annual sample consists of 2,000 households representing approximately 5,000 persons. This study utilized data collected by The NPD Group from February 1998 through January 1999. The panel is demographically and geographically balanced to US Census Bureau statistics each year at the household level. The sample is divided into 52 subsamples and each week a group of nearly 60 households begin recording all the foods and beverages consumed by all household members. Reporting is distributed evenly throughout the year to be sensitive to seasonal eating habits. Each household maintains a daily eating diary for two weeks. The person most responsible for meal preparation is instructed to record the name and brand of each food and beverage con1614 / December 2003 Volume 103 Number 12

sumed by any member of the household, including all additives, ingredients, and cooking aids. The diary consists of separate sections for each meal and snack situation, and collects food names, flavor descriptors, brand names, package types, product forms, appliances used in preparation, and any special nutritional attributes, among other details. The same information is collected on ingredient and additive items used to create dishes or meals in the home. At the end of each day, the recorder is instructed to mail the daily diary to The NPD Group’s offices. After all 14 daily diaries are received from a household they are coded and made ready for data processing. Portion-Size Data NET panelists record the foods and beverages consumed by household members but not the quantities of each food. This procedure is standard for panel surveys to minimize recorder burden and thus increase reliability. Portion-size data were estimated from the Continuing Survey of Food Intakes by Individuals 1989-91 and 1994-96 (11,12), and were aggregated, collapsed for like-foods to strengthen cell sizes, and smoothed to eliminate outliers. Age- and gender-specific mean serving weights were thereby determined for more than 800 food types; these portions were subsequently assigned to each food recorded and coded in the NET diary. Nutrient Data Nutrient intakes were estimated according to previously reported procedures (13-15). Briefly, the nutrient values for foods recorded and coded in the NET diary were determined using the recipe component of the NDS-R. This system is a highly accurate and comprehensive nutrient calculation system that contains complete values for 113 nutrients for more than 18,000 foods, including many brand-name products. Each food or recipe was entered into NDS-R for 100 g of that food and closely matched to the description provided in the NET diary, including any special nutritional attributes (ie, low fat, fat free, low cholesterol, low sodium, or reduced sodium). If special attributes existed, special recipes were added to the nutrient database to reflect these foods. Each food was assigned to one of more than 100 food groups, which makes analysis by specific food group possible. For this study, estimated mean daily intake values for the following nutrients were reported: carbohydrate, sugar, fat, saturated fat, protein, cholesterol, sodium, dietary fiber, vitamin A, vitamin E, vitamin C, thiamin, riboflavin, niacin, vitamin B-6, folate, calcium, magnesium, iron, zinc, and energy (kilocalories). Additionally, the percentage of children below their Estimated Average Requirement (EAR) (16) was calculated for the total sample and for children in each of the tertiles of cereal consumption. BMI Individual, self-reported heights and weights were recorded in the diary by each respondent and used to calculate BMI using the formula: BMI⫽weight (lb)⫼height (in)2⫻704.5. The BMI was compared against 2000 Centers for Disease Control and Prevention age- and sex-specific growth charts to determine if the child is at risk of being overweight. The statistical definition of the at-risk of overweight population is at or above the 85th percentile, but less than the 95th percentile of BMI for age from these charts. Overweight is defined as at or above the 95th percentile of BMI from these charts (17). Children who did not

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have a recorded height and weight were excluded from the analysis (n⫽170). Data Tabulation To be included in the study, a minimum of seven days of food collection was required, however no child had less than that number. Ninety-two percent of children had complete diaries with food intake data for all 14 days. The number of times RTE cereal was consumed in 14 days was recorded for each of the 603 children. The children were categorized into tertiles based on consumption during their 14-day data collection period. For subjects with incomplete diaries, cereal consumption was normalized to 14 days by multiplying the rate of RTE consumption per day by 14. STATISTICAL ANALYSIS Analysis of variance was used to determine if BMI differed among the cereal consumption tertiles in each of three age groups, 4 to 6 years, 7 to 9 years, and 10 to 12 years, as well as all ages (4 to 12 years) combined. Pairwise post hoc t tests were performed where differences were found among the tertiles. Logistic regression was used to analyze the association between cereal consumption pattern and risk for overweight in each of the three age groups and for all ages. The contrasts were examined between the possible pairs of cereal tertiles using the Wald ␹2 test. Comparisons were made using analysis of variance on intakes of 21 key nutrients among cereal consumption tertiles with post hoc comparisons. An ␣ level of 0.01 was used for analysis of variance analyses except where otherwise noted. All analyses were performed using the Statistical Analysis System (version 8.0, 2000, SAS Institute, Cary, NC). RESULTS The sample of 2,000 households (approximately 5,000 subjects, including 603 children aged 4 to 12 years) collected from February 1998 to February 1999 that was used in this study closely approximates the US census data for age and race (Table 1). The 603 children were categorized according to the child’s age and cereal consumption pattern (Table 2). Intake ranged from zero to more than 15 servings in 14 days (Figure). There was a statistically significant inverse relationship between BMI and frequency of RTE cereal consumption (P⬍.01) within each age group as well as for the total sample (Table 2). Children aged 4 to 12 years who consumed eight or more servings of RTE cereal in two weeks had significantly lower BMI compared to the children who consumed two or fewer servings during a two-week period (P⬍.0001) (Table 2). A significant inverse relationship also exists between the population at risk for being overweight and frequency of cereal consumption (P⬍.01) (Table 3). The proportion of children aged 4 to 12 years at risk for overweight or overweight according to Centers for Disease Control and Prevention standards (17) is 33.67% or roughly one in three. When children aged four to 12 ate RTE cereal eight or more times in two weeks that risk lowers to 21.3% or nearly one in five. Conversely, when children ate RTE cereal zero to three times in two weeks their risk for overweight increases to 47.4%, nearly one in two. This inverse trend is consistent across each of the age groups (4 to 6 years, 7 to 9 years, and 10 to 12 years). Frequent cereal eaters (eight or more servings during two weeks) in the 7- to 9-year-

Table 1 Sample demographics compared with 1998 US census data Demographic All persons Age (years) 0-12 4-6 7-9 10-12 13-17 18-34 35-44 45-54 55-64 65⫹ Families Income ⬍$12,500 $12,500-$19,999 $20,000-$29,999 $30,000-$39,999 $40,000⫹ Household size 2 Members 3-4 Members 5⫹ Members Age of female head of household (years) ⬍35 35-44 45-54 55⫹ Female employment Employed Not Employed Race White Nonwhite Nonfamilies Income ⬍$7,500 $7,500-$14,999 $15,000-$24,999 $25,000⫹ Sex Female Male Age of female head of household (years) ⬍35 35-54 55⫹ Age of male head of household (years) ⬍35 35-54 55⫹

Census (%)

Sample (%)

20 4.4 4.5 4.3 8 24 17 13 8 11

20 5.8 5.0 4.3 7 19 17 14 10 12

9 9 13 12 57

10 9 15 13 53

42 44 14

43 43 14

26 28 20 26

25 27 22 26

59 41

56 44

84 16

87 13

14 22 20 45

14 23 20 44

55 45

54 46

18 24 58

20 33 47

33 40 27

23 37 40

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Table 2 Mean body mass index (BMI) by cereal consumption tertiles (N⫽603) Age group

Cereal consumption ≤3 Servings BMI

4-6 years 7-9 years 10-12 years 4-12 years

≥8 Servings BMI

4-7 Servings BMI

Total BMI

P

MeanⴞSD

n

MeanⴞSD

n

MeanⴞSD

n

MeanⴞSD

n

18.2⫾5.0x 18.7⫾5.9x 21.0⫾4.3x 19.3⫾5.2x

59 56 58 173

16.8⫾3.6xy 17.6⫾3.6xy 19.4⫾4.8xy 17.9⫾4.2y

72 60 59 191

15.9⫾2.8y 16.1⫾2.7y 18.1⫾3.5y 16.7⫾3.1z

90 74 75 239

16.8⫾3.8 17.3⫾4.3 19.4⫾4.4 17.8⫾4.3

221 190 192 603

.0019 .0027 .0004 ⬍.0001

Means within the same row with the same letter are not significantly different (P⬍.01).

Table 3 Proportion of children aged 4 to 12 years at risk for overweighta by cereal consumption tertiles (N⫽603) Age group

Cereal consumption ≤3 Servings

4-6 years 7-9 years 10-12 years 4-12 years

≥8 Servings

4-7 Servings

Total

P

% at risk for Overweight

n

% at risk for Overweight

n

% at risk for Overweight

n

% at risk for Overweight

n

47.5x 50.0x 44.8x 47.4x

59 56 58 173

34.7xy 38.3xy 37.3xy 36.7y

72 60 59 191

25.6y 16.2y 21.3y 21.3y

90 74 75 239

34.4 33.2 33.3 33.7

221 190 192 603

SD⫽standard deviation. Proportions within the same row with the same letter are not statistically significantly different (P⬍.05). Based on 2000 Centers for Disease Control and Prevention definitions (17).

a

FIG. The distribution of ready-to-eat (RTE) cereal consumption over a 14-day collection period.

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.02 ⬍.001 .011 ⬍.001

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Table 4 Mean daily nutrient intake of children aged 4 to 12 years by cereal consumption tertiles (N⫽603) Nutrient

Cereal consumption ≤3 servings (nⴝ173)

≥8 servings (nⴝ239)

4-7 servings (nⴝ191)

P

4™™™™™™™™™™™™™™™™™™™™™™™™™™™™™™™™™™™™ Mean⫾standard deviation ™™™™™™™™™™™™™™™™™™™™™™™™™™™™™™™™™3 1726⫾454 1693⫾437 1,681⫾414 .57 219.4⫾59.1 218.5⫾60.8 227.4⫾61.1 .24 108.5⫾37.0 105.6⫾38.3 113.0⫾38.9 .13 x xy y 65.9 ⫾20.0 61.7 ⫾17.1 ⬍.001 68.7 ⫾20.9 24.0⫾7.4 23.4⫾7.7 22.5⫾7.1 .11 62.2⫾17.7 61.2⫾15.7 69.9⫾14.1 .33 x x y 197⫾78 170 ⫾61 ⬍.001 216 ⫾105 2966⫾818 2929⫾745 2854⫾720 .31 11.2⫾3.5 11.2⫾3.8 11.6⫾3.5 .35 x y z 631.7 ⫾211.1 738.3 ⫾208.0 ⬍.001 559.5 ⫾238.3 7.4⫾2.5 7.0⫾2.2 7.0⫾2.5 .21 83.9⫾44.2 81.3⫾39.7 93.5⫾47.4 .011 x y z 1.5 ⫾0.4 1.7 ⫾0.4 ⬍.001 1.4 ⫾0.4 1.8y⫾0.5 2.0z⫾0.5 ⬍.001 1.6x⫾0.5 x y z 18.1 ⫾4.2 19.6 ⫾4.0 ⬍.001 16.7 ⫾4.6 x y z 1.5 ⫾0.3 1.7 ⫾0.4 ⬍.001 1.3 ⫾0.4 x y z 212.3 ⫾59.1 265.5 ⫾68.4 ⬍.001 188.9 ⫾68.2 806.6xy⫾275.4 877.3y⫾296.4 ⬍.001 784.4x⫾314.4 200.9⫾57.3 200.9⫾53.8 213.9⫾53.5 .021 12.1y⫾2.9 13.9z⫾3.4 ⬍.001 10.6x⫾2.9 x y z 8.9 ⫾2.3 10.3 ⫾2.5 ⬍.001 8.2 ⫾2.4

Energy (kcal) Carbohydrate (g) Total sugar (g) Fat (g) Saturated fat (g) Protein (g) Cholesterol (mg) Sodium (mg) Dietary fiber (g) Vitamin A (mcg rae) Vitamin E (mg ␣-tocopherol) Vitamin C (mg) Thiamin (mg) Riboflavin (mg) Niacin (mg) Vitamin B-6 (mg) Folate (mcg) Calcium (mg) Magnesium (mg) Iron (mg) Zinc (mg)

Means within the same row with the same letter are not significantly different (P⬍.01).

Table 5 Percent of children aged 4 to 12 years not meeting their Estimated Average Requirement (EAR) by cereal consumption tertiles (N⫽603) Nutrient

Vitamin A (mcg rae) Vitamin E (mg ␣-tocopherol) Vitamin C (mg) Thiamin (mg) Riboflavin (mg) Niacin (mg) Vitamin B-6 (mg) Folate (mcg) Magnesium (mg) Iron (mg) Zinc (mg)

Cereal consumption ≤3 Servings (nⴝ173)

4-7 Servings (nⴝ191)

≥8 Servings (nⴝ239)

Total (nⴝ603)

P

14.4x 54.9 7.5 1.2 0.6 1.7 2.9 59.0x 19.1 0.6 9.8x

3.7y 58.6 4.2 0 0 0 0 42.4y 14.1 0 2.1B

0.4y 57.7 2.1 0 0 0 0 8.8z 9.2 0 0.8y

5.5 57.2 4.3 0.3 0.2 0.5 0.8 33.8 13.6 0.2 3.8

⬍.0001 .77 .03 NA NA NA NA ⬍.001 .02 NA ⬍.001

Means within the same row with the same letter are not significantly different (P⬍.01).

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old age group had a one in six risk for being overweight vs a one in two risk in the infrequent cereal-eaters group. Although energy intake was not statistically significantly different across cereal consumption tertiles, there were differences in intakes of fat and cholesterol as well as vitamin A, vitamin B-6, thiamin, riboflavin, niacin, folate, calcium, iron, and zinc (Table 4). As seen with BMI, there was a significant inverse relationship with frequency of RTE cereal consumption and daily fat intake (P⬍.01) and daily cholesterol intake (P⬍.01). Intakes of vitamin A, vitamin B-6, thiamin, riboflavin, niacin, and folate intakes increased from the low cereal tertile to the upper tertile. Calcium, iron, and zinc intake also increased from the low cereal tertile to the upper tertile. The percent of the population consuming below 100% of their EARs (18) was also examined for the children in the three tertiles of cereal consumption. Sizable proportions of children did not meet their EARs for vitamin E (57%) and folate (34%); however, folate intakes were not measured on dietary folate equivalents because data was not available in the nutrient database. Proportions of children not meeting their EARs for vitamin A, folate, and zinc were highest in the low tertile of cereal consumption (P⬍.01) (Table 5). DISCUSSION Children who consumed RTE cereal most frequently had the most appropriate age-related BMI, were least likely to be at risk for overweight, and had the most positive nutrient intake profiles. The positive relationship between RTE cereal consumption and nutrient intakes can be explained, at least in point, by the nutrient fortification and low fat content of the cereals. The relationship with BMI, however, is more difficult to explain. Several explanations can be proposed for the association between cereal consumption and body weight. First, RTE cereal consumption may be a marker for other healthful lifestyle factors practiced by the children and possibly the other household members or caregivers. Similar to these results, adults (men and women aged 35 to 64 years) who consumed RTE cereal at least every other day had more healthful BMIs and were less likely to be overweight or obese (19,20). Second, calcium intakes were higher for the high cereal consumers who also had the more appropriate body weight (Table 4). It has been demonstrated that increased consumption of dairy calcium is related to lower BMI in adults (21), and a similar effect has recently been demonstrated in children (22,23). Because RTE cereal is most frequently consumed with milk, which is a good source of calcium, it is possible that the proposed mechanism (24) is a factor in contributing to the better intake regulation of frequent cereal eaters. At the time of collection, breakfast cereals were not routinely fortified with calcium. Calcium intake of frequent cereal eaters would be expected to increase with current calcium fortification levels of many RTE cereals. Third, the children who ate cereal most frequently were children who most often ate breakfast. Thus, the association may also reflect on eating patterns that are more favorable for the regulation of body weight. For example, more frequent breakfast eating has been associated with lower BMI in adults (25,26) and lower fat intakes (27). Because RTE cereals are traditionally lower-fat foods than other breakfast alternatives, fat intakes have been shown to be lower for both adults and children consuming RTE cereal most frequently (28,29). High-fat diets have been associated with 1618 / December 2003 Volume 103 Number 12

higher BMI in adults (30), suggesting that lower fat intake in the daily diet may also make a contribution to more favorable energy balance and hence more favorable BMI. Consumption of RTE cereal has been shown to improve not only macronutrient intake but also micronutrient intakes and dietary fiber (28,31,32). Similarly, the frequent cereal consumers in this study had significantly higher intakes of the B vitamins, iron, zinc, and calcium and were most likely to meet their recommended levels of these nutrients. Higher intakes of these nutrients in particular are characteristic of a breakfast including nutrient-fortified RTE cereals eaten with milk. The improved nutrient intake profile appears to be largely related to the consumption of RTE cereal, the foods it could be replacing at breakfast, and a pattern for healthful eating throughout the day (27). In our study, energy intake was not correlated with BMI, but this is not surprising given the small sample size and assumptions required in making estimates of intake. Often with survey data, the instrument is not sensitive enough to detect small energy intake differences. Similarly, it may be argued that the nutrient intake data do not describe precisely the actual intakes of the children and therefore the adequacy or inadequacy of their diets. This study has certain inherent limitations that need to be recognized. The food records were self-reported. However, The NPD Group trains panelists to fully describe food intakes on a daily basis. Food dairies are returned each day and NPD Group conducts follow-up to help ensure complete records. Height and weight were also self-reported, but this method is known to be accurate even when used to calculate BMI (33). Nutrient intake data were derived from records of foods consumed combined with estimates of serving size taken from survey data. The latter assumes that an average serving size applies to all persons of same age and gender in the sample, which clearly results in errors of the estimates for individual persons. However, when applied to the total sample, it can be expected that mean intakes approximate estimates of intake provided by dietary survey data. Although no direct comparison has been made in this sample population, the average intakes in these children (Table 4) are similar to that reported in other large, population-based surveys (12,34). Although this lack of proof of validity can be seen as a weakness it should be noted that differences in the mean comparisons across tertiles of RTE cereal consumption cannot be explained by an error in accuracy of the estimate of total intake, an error that would be present across all tertiles. Thus, the conclusion remains that the most frequent consumers of RTE cereals have better nutrient intakes than those who consumed RTE cereals least frequently. A strong element of this survey design is the recording of 14 days of foods consumed. Thus, it is possible to determine additional associations between other food patterns and categories with the BMI of children, as well as adults, and this will lead to continued research in this area.

APPLICATIONS Although regular cereal consumption in itself may not ensure healthful BMI, it may be an indication of a pattern of healthful eating that includes regular breakfast consumption and contributes to age-appropriate energy intakes. Thus, the consumption of RTE cereals at breakfast should be encouraged as a

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