Children Health Issues – Library Research Assignment

Eating Patterns and Obesity in Children
The Bogalusa Heart Study
Theresa A. Nicklas, DrPH, LN, Su-Jau Yang, MS, Tom Baranowski, PhD, Issa Zakeri, PhD, Gerald Berenson, MD

Background: Childhood obesity is a growing public health problem. This study examined the association
between eating patterns and overweight status in children who participated in the Bogalusa
Heart Study.

Methods: A single 24-hour dietary recall was collected on a cross-sectional sample of 1562 children
aged 10 years (65% Euro-American [EA], 35% African American [AA]) over a 21-year
period. Overweight was defined as body mass index greater than the 85th percentile using
Centers for Disease Control and Prevention reference standards. Multivariate logistic
regression was used to investigate the association between eating patterns and overweight.

Results: Consumption of sweetened beverages (58% soft drinks, 20% fruit flavor drinks, 19% tea,
and 3% coffee) (p�0.001); sweets (desserts, candy, and sweetened beverages) (p�0.001);
meats (mixed meats, poultry, seafood, eggs, pork, and beef) (p�0.051); and total
consumption of low-quality foods (p�0.01) were positively associated with overweight
status. Total amount of food consumed, specifically from snacks, was positively associated
with overweight status (p�0.05). There was a lack of congruency in the types of eating
patterns associated with overweight status across four ethnic– gender groups. The percent
variance explained from the eating pattern– overweight models was very small. The
interaction of ethnicity and gender was significantly associated with overweight status (p�0.001).
The odds of being overweight for EA males were 1.2 times higher than for AA females.

Conclusions: These results demonstrate that numerous eating patterns were associated with overweight
status, yet the odds of being overweight were very small. Additional studies are needed to
confirm these findings in a longitudinal sample having multiple days of assessment.
(Am J Prev Med 2003;25(1):9 –16) © 2003 American Journal of Preventive Medicine

Introduction

Obesity among children has increased dramati-cally over the past 3 decades.1,2 Today anestimated one in four children in the United
States is at risk of overweight (body mass index [BMI]
�85th percentile), while 11% are overweight (BMI
�95th percentile). Obese children tend to become
obese adults.3–5 Further, obesity in early life is associ-
ated with several risk factors for coronary heart dis-
ease6,7 and is predictive of coronary heart disease,8

hypertension,9 and diabetes9 in adulthood.
U.S. society has been increasingly characterized as

“obesogenic.”10 Although obesity has a strong genetic
background,11 environmental factors are commonly
considered to be the underlying cause of the increase

in obesity by promoting or exacerbating the prob-
lem.11,12 The dietary causes of obesity are complex and
poorly understood.13 While individual nutrients have
been implicated in obesity,14 –18 few attempts have been
made to identify eating patterns in this regard. Several
studies have shown an association between BMI and
restaurant food consumption,19 –21 soft drink consump-
tion,22,23 increased portion sizes,24 meal patterns and
meal frequency,25–28 diet quality,29 and diet diversity.20

However, most of these studies were conducted with
adults, with very little reported on the eating pattern–
obesity relationship in children. Moreover, several of
the adult findings have yet to be replicated with other
populations.

Secular increases in relative weight and adiposity
have been documented among children in Bogalusa,
Louisiana, over 2 decades.3,30,31 The prevalence of
overweight among these children in 1973–1974 in-
creased approximately twofold by 1994, with the largest
increases observed among 19- to 24-year-olds. Further-
more, the annual increases in relative weight and
obesity from 1983 through 1994 were 50% greater than
those between 1973 and 1982.30 During the 1970s and

From the Children’s Nutrition Research Center, Department of
Pediatrics, Baylor College of Medicine (Nicklas, Yang, Baranowski,
Zakeri), Houston, Texas; and Tulane Center for Cardiovascular
Health, Tulane School of Public Health and Tropical Medicine
(Berenson), New Orleans, Louisiana

Address correspondence to: Theresa A. Nicklas, DrPH, LN, Chil-
dren’s Nutrition Research Center, Department of Pediatrics, Baylor
College of Medicine, 1100 Bates Street, Houston TX 77030. E-mail:
[email protected].

9Am J Prev Med 2003;25(1) 0749-3797/03/$–see front matter
© 2003 American Journal of Preventive Medicine • Published by Elsevier Inc. doi:10.1016/S0749-3797(03)00098-9

1980s, the average increase in the body weight of
children was 2.5 kg, without a significant increase in
height. During the 1980s and 1990s, the increase was 5
kg. In this article, we assess the extent to which eating
patterns (e.g., food consumption and meal patterns)
are related to childhood obesity in the Bogalusa Heart
Study.

Materials and Methods
Population

The Bogalusa Heart Study, which began in 1973, is a
long-term epidemiologic study designed to examine
the early natural history of heart disease in a well-
defined biracial (African-American [AA] and Euro-
American [EA]) pediatric population. Dietary intake
and anthropometric data were obtained on 1562 fifth-
grade students (10-year-olds) in the Bogalusa, Louisi-
ana, school system between 1973 and 1994, and then
the seven cross-sectional surveys were combined. Sam-
ple sizes varied slightly across the surveys; ethnicity
(65% EA, 35% AA) and gender (51% female, 49%
male) distributions were similar for each survey year of
10-year-olds and reflective of the total population.32,33

Dietary Methodology and Nutrient Database

The 24-hour dietary recall method was used in inter-
viewing the children.34,35 Quality controls included:
(1) a standardized protocol that specified exact tech-
niques for interviewing, recording, and calculating results;
(2) standardized graduated food models for quantifica-
tion of foods and beverages consumed; (3) a product
identification notebook for probing of snack consump-
tion; (4) school lunch assessment to identify all school
lunch recipes, preparation methods, and average por-
tion sizes of menu items reflected in each 24-hour
recall36; (5) follow-up telephone calls to parents to
obtain information on brand names, recipes, and prep-
aration methods of meals served at home; (6) products
researched in the field to obtain updated information
on their ingredients and weights, primarily snack foods,
candy, and fast foods; and (7) the Moore Extended
Nutrients (MENu), formerly known as the Extended
Table of Nutrient Values, for nutrient composition.37

All interviewers participated in rigorous training ses-
sions and pilot studies before the field surveys to
minimize interviewer effects. One 24-hour dietary recall
was collected on each study participant. Duplicate
recalls were obtained from 10% random subsamples of
each study population to assess interviewer variabili-
ty.38,39 The timeframe of the 24-hour recall period
included everything the child consumed from the time
he/she woke up until the time of the interview and
everything after the interview time on the previous day
until the time the child went to bed. The same dietary-
recall interview protocol was followed for all surveys.

Nutrient Database

MENu is a nutrient database that includes more than
5000 core foods and recipes, with values for 97 dietary
components.37 The data bank is a flexible system
permitting continuous updates of existing values and
additions of new single or composite foods. Periodic
updates are made to MENu to reflect nutrient changes
in food products. Nutrient values were obtained from
U.S. Department of Agriculture data, other published
references, manufacturers’ information, and recipe cal-
culation by ingredients. The database includes brand
names of foods, school and family recipes, and foods
commonly consumed by children.

For each survey period, the 24-hour recalls were
analyzed with MENu. The version of MENu used for
that analysis was saved on a tape. Analyses reflected data
retrieved from stored information files specific to each
time period.

Food Groups

The food-grouping scheme was designed for all foods
or entries (core and recipe) appearing in MENu. Food
types were identified for groups (e.g., cheese, as a
major ingredient, was included in a food group list).
Twenty-one major food groups were established, based
on similar source characteristics (e.g., “fruit and fruit
juices” formed one major group; “rice, biscuits, and
cereals” were included in the breads and grains catego-
ry). Composite food items, such as recipes, were as-
signed to food groups according to primary ingredi-
ents. If no single type of food (other than water)
accounted for at least 60% of the weight, the item was
classified as a mixed food. Examples of foods included
in the food groups have been documented previously.40

Four food groups were deleted from the analyses due to
small sample sizes (i.e., formula, vitamins, veal/lamb,
alcohol), resulting in 17 food groups.

Four larger food categories were created and used in
the analyses: FJV (fruit, fruit juices, vegetables); meats
(mixed meats, poultry, seafood, eggs, pork, and beef);
sweets (desserts, candy, and sweetened beverages); and
dairy (milk and cheese). High- and low-quality food
groups were also created. The high-quality food group
reflected foods consumed at least once from meats,
dairy, breads/grains, fruits/fruit juices, and vegetable
groups. The low-quality food groups reflected foods
that were consumed at least once from salty snacks,
candy, desserts, fats/oils, and sweetened beverage
groups.

The eating patterns selected for this study included
food consumption patterns, total gram amount of
food/beverages consumed by meal period, total eating
episodes, number of meals and snacks consumed, and
total gram amount of high- and low-quality foods.
These eating patterns were selected based on an exten-

10 American Journal of Preventive Medicine, Volume 25, Number 1

sive review of the literature41 and the eating patterns
that could actually be extracted from the 24-hour
dietary recall.

Measure of Adiposity

Trained examiners followed rigid protocols that
changed little over time.32 Briefly, height was measured
twice to the nearest 0.1 cm on a standard board, and
weight was measured twice to the nearest 0.1 kg by
using a balance-beam metric scale. For both weight and
height, the two readings were averaged. The children
were clothed in only a hospital gown, underpants, and
socks. BMI (kg/m2) was used as a measure of adiposity.
The age- and gender-specific Centers for Disease Con-
trol and Prevention (CDC) reference standards42 were
used to classify children who were normal weight (BMI
�85th percentile); at risk of overweight (BMI �85th
and �95th percentile); and overweight (BMI �95th
percentile).

Statistical Analysis
Prevalence of Overweight

In the analysis, children at risk for overweight (BMI
�85th percentile to �95th percentile) and those who
were overweight (BMI �95th percentile) were com-
bined to reflect the overweight group. The number and
percentage of 10-year-olds who were overweight were
identified by study year, ethnicity, gender, or ethnicity
by gender groups. The Cochran–Armitage trend test
was applied to examine the trend of the proportions of
overweight 10-year-olds over a 21-year period. Trends in
height among normal-weight and overweight 10-year-
olds were examined separately by ANOVA over the
same period.

Association Between Eating Patterns and
Overweight Status

Data from the seven surveys were analyzed together to
investigate the association between eating patterns and
being overweight. The association was evaluated by
logistic regression analysis via the PROC LOGISTIC
procedure of SAS (version 8.0, SAS Institute Inc., Cary,
North Carolina, 1999). In each logistic regression
model, being overweight was used as a dependent
variable and eating patterns were used as independent
variables. The collinearity among independent vari-
ables was checked first, using the PROC REG proce-
dure of SAS with options VIF and COLLINOINT,
before logistic regression was carried out. Multivariate
models were conducted for overall (N�1562) as well as
separately for each ethnicity– gender group (n�497 for
EA male; n�513, EA female; n�273, AA male; and
n�279, AA female) because ethnicity– gender interac-
tion had significant effects on being overweight. Each

model for overall effects included total calorie intake,
age, study year, ethnicity, gender, and ethnicity �
gender interaction to control for their effects on being
overweight. Likewise, the models for each ethnicity–
gender group were adjusted for total calorie intake,
age, and study year. An association was defined if the
unity was not in the 95% confidence interval (CI) of an
odds ratio (OR). The OR presented in Tables 1 and 2
were calculated depending on the type of eating pat-
tern construct. If the eating pattern was measured as
consumption in grams, then the OR was calculated as
the ratio of odds of being overweight for participants
with the mean amount of increased consumption in
grams, compared to participants with mean gram con-
sumption. The mean gram consumption was the aver-
age amount of food/beverage consumed for each of
the food groups. This average amount consumed re-
flected the average “serving size” of each food group.
For example, the odds of being overweight for a
10-year-old who consumed two average servings of a
sweetened beverage (2�399 g) would be 1.33 times
higher than a 10-year-old who consumed only an aver-
age serving (399 g). If the eating pattern was not
measured in grams, such as the number of eating
episodes, then the OR was calculated as the ratio of
odds of being overweight for a 10-year-old who had
“n�1” eating episodes, compared to that for a 10-year-
old with n eating episodes “(n�0).” For example, the
odds of being overweight for a 10-year-old, African-
American girl who had three meals was 0.56 times lower
than for those who had only two meals.

Results
Trends in Obesity Status

The percentage of children with a BMI in the �50th
percentile significantly decreased (p�0.0001) from
55% in 1973 to 34% in 1994. In contrast, there was a
twofold increase (p�0.0001) in the percentage of chil-
dren with a BMI �85th and �95th percentile and a
five-fold increase (p�0.0001) in the percentage of
children with a BMI �95th percentile (4% to 20%)
over 2 decades. Mean height (cm) significantly in-
creased (p�0.05) for 10-year-old children with a BMI
�50th percentile. In all survey years for children with a
BMI �85th percentile, mean height was significantly
(p�0.0001) higher than those children with a BMI
�85th percentile.

Percentage of Overweight 10-Year-Olds by
Ethnicity and Gender

All surveys were combined and divided into two weight-
status groups (based on BMI): normal weight (�85th
percentile), and overweight (�85th percentile) (Table

Am J Prev Med 2003;25(1) 11

3).43 The overall percentage of overweight among
10-year-olds was 24%, with 76% being normal weight.
The percentage of overweight children by ethnicity
(approximately 24%) and gender (approximately
24%) was equally distributed. There was a significant
(p�0.0001) difference in the ethnic � gender distri-
bution of overweight status. EA males had the highest
prevalence of overweight (27%) and AA males had
the lowest prevalence (19%). The interaction of

ethnicity and gender was significantly associated with
overweight status (p�0.01). Among the four ethnici-
ty– gender groups, the likelihood to be overweight
for EA males was 1.2 times higher than AA females at
10 years of age (data not shown). The percentage of
10-year-olds overweight in Bogalusa (24%) was slightly
higher than among 6- to 11-year-olds in the National
Health and Nutrition Examination Survey (22%), partic-
ularly for EA males.

Table 1. The association between eating-pattern variables and overweight status

Eating-pattern variable OR (95% CI) Mean g Example of food weight (g)

Food groups consumption Ia,b (R2�0.05)
Fats/oils 0.98 (0.91–1.00) 22.0 1/8 c. oil (25.8)
Fruits/fruit juices 0.97 (0.87–1.07) 135.0 1 apple (129.0)
Vegetables 0.98 (0.86–1.12) 161.0 1/8 c. broccoli (23.3)
Breads/grains 1.03 (0.85–1.24) 187.0 1 c. cereal (37.6)
Mixed meats 1.05 (0.96–1.14) 60.0 1 slice of 12� pizza (66.4)
Desserts 0.98 (0.87–1.10) 70.0 1/8 c. ice cream (16.6)
Candy 0.93 (0.82–1.06) 40.0 Snickers Fun Bar (23.7)
Sweetened beverages 1.33 (1.12–1.57)**** 399.0 8 oz. Coke (235.2)
Poultry 1.01 (0.95–1.07) 31.0 1 fried chicken wing (32.0)
Salty snacks 0.95 (0.88–1.02) 12.0 1 bag potato chips (32.0)
Seafood 1.00 (0.97–1.02) 9.0 1 fish stick (6.0)
Condiments 1.00 (0.95–1.05) 7.0 1 Tb mayonnaise (13.8)
Eggs 1.00 (0.95–1.04) 11.0 1 medium egg (44.0)
Milk 1.08 (0.87–1.33) 409.0 1 c. whole milk (244.0)
Pork 1.03 (0.98–1.09) 23.0 1 slice bacon (6.3)
Cheese 1.01 (0.97–1.06) 22.0 1 slice cheese (18.9)
Beef 1.06 (0.99–1.15) 47.0 1 hotdog (22.0)
Food groups consumption IIb,c (R2�0.05)
Fats/oils 0.99 (0.92–1.06)
FJV 0.96 (0.81–1.13)
Breads/grains 1.05 (0.87–1.25)
Meats 1.21 (1.00–1.46)*
Sweets 1.38 (1.12–1.71)*
Salty snacks 0.96 (0.89–1.03)
Condiments 1.00 (0.95–1.05)
Dairy 1.12 (0.91–1.39)
Grams from high- and low-quality foods (R2�0.04)
Gram of high-quality foodsd 1.19 (0.78–1.80)
Gram of low-quality foodse 1.35 (1.08–1.68)*
Gram amountc

Total (R2�0.04) 1.77 (1.02–3.08)*
From breakfast (R2�0.04) 0.96 (0.79–1.16)
From lunch (R2�0.04) 1.14 (0.87–1.49)
From dinner (R2�0.04) 1.26 (0.96–1.63)
From snacks (R2�0.04) 1.24 (1.02–1.50)*
Eating episodef

Total (R2 � 0.04) 0.97 (0.90–1.05)
No. of meals (R2 � 0.04) 0.91 (0.72–1.15)
No. of snacks (R2 � 0.04) 0.98 (0.90–1.05)

*p�0.051; *p�0.05; *p�0.01; ****p�0.001.
aFood group consumption I includes all individual food groups.
bOdds ratio � risk of being overweight if increasing mean gram consumption.
cFood group consumption II includes the four larger food group categories (FIV, dairy, meats, and sweets) and four individual food groups
(fats/oils, breads/grains, salty snacks, condiments).
dHigh-quality foods: fruits/fruit juices, vegetables, breads/grains, meats, dairy.
eLow-quality foods: fats/oils, sweets, salty snacks.
fOdds ratio � risk of being overweight if having one more eating episode.
CI, confidence interval; dairy, milk and cheese; FJV, fruits/fruit juices and vegetables; g, grams; meats, mixed meats, poultry, seafood, eggs, pork,
and beef; OR, odds ratio; sweets, desserts, candy, and sweetened beverages.

12 American Journal of Preventive Medicine, Volume 25, Number 1

Association Between Eating Patterns and
Overweight Status
Total gram amount of food/beverage consumed, par-
ticularly from snacks (p�0.05), and total gram con-
sumption of low-quality foods (p�0.01) were positively
associated with overweight status (Table 1). Consump-
tion of sweets (p�0.001), specifically sweetened bever-

ages (p�0.001), and meats (p�0.051) was positively
associated with overweight status.

Despite these significant associations, the percentage
of variance explained by the model was very small
(Table 1). In the food group consumption model, only
5% of the variance was explained, of which sweetened
beverages alone explained 1%. The combined food

Table 2. The association between eating-pattern variables and overweight status by ethnicity– gender groups

Eating pattern
EA male
OR (95% CI)

EA female
OR (95% CI)

AA male
OR (95% CI)

AA female
OR (95% CI)

Food groups consumption Ia,b R2�0.08 R2�0.10 R2�0.17 R2�0.13
Fats/oils 0.97 (0.85–1.10) 1.00 (0.83–1.19) 0.93 (0.71–1.22) 1.06 (0.86–1.32)
Fruits/fruit juices 1.03 (0.88–1.20) 1.10 (0.92–1.31) 0.97 (0.69–1.41) 0.55 (0.38–0.79)*
Vegetables 0.98 (0.77–1.24) 1.09 (0.87–1.36) 1.05 (0.74–1.49) 0.75 (0.51–1.09)
Breads/grains 1.20 (0.86–1.67) 0.90 (0.62–1.30) 0.62 (0.33–1.16) 1.03 (0.60–1.79)
Mixed meats 1.12 (0.95–1.31) 0.93 (0.78–1.12) 1.06 (0.82–1.37) 0.97 (0.78–1.19)
Desserts 0.89 (0.73–1.09) 1.08 (0.86–1.35) 0.89 (0.65–1.22) 0.89 (0.66–1.21)
Candy 0.94 (0.76–1.18) 0.78 (0.60–1.01) 0.79 (0.51–1.23) 1.00 (0.73–1.35)
Sweetened beverages 1.68 (1.21–2.33)* 1.53 (1.05–2.22)* 1.02 (0.72–1.46) 0.92 (0.65–1.30)
Poultry 0.99 (0.89–1.09) 1.04 (0.94–1.16) 0.97 (0.76–1.23) 0.99 (0.84–1.16)
Salty snacks 0.98 (0.88–1.09) 0.92 (0.80–1.05) 1.15 (0.94–1.42) 0.84 (0.66–1.06)
Seafood 0.97 (0.92–1.02) 1.07 (1.01–1.13)* 0.73 (0.48–1.11) 1.03 (0.95–1.11)
Condiments 1.02 (0.93–1.12) 0.99 (0.90–1.08) 1.02 (0.86–1.22) 0.89 (0.74–1.07)
Eggs 0.97 (0.88–1.06) 1.07 (0.99–1.17) 0.99 (0.85–1.14) 0.91 (0.79–1.05)
Milk 0.96 (0.64–1.46) 1.19 (0.82–1.73) 1.25 (0.68–2.30) 0.93 (0.55–1.59)
Pork 1.04 (0.94–1.14) 1.00 (0.89–1.12) 1.16 (0.99–1.35) 0.98 (0.84–1.13)
Cheese 1.02 (0.95–1.11) 1.04 (0.97–1.13) 0.95 (0.82–1.09) 0.90 (0.77–1.06)
Beef 1.08 (0.92–1.25) 1.06 (0.91–1.24) 1.11 (0.89–1.37) 1.02 (0.88–1.18)
Food groups consumption IIb,c R2�0.05 R2�0.08 R2�0.12 R2�0.11
Fats/oils 0.98 (0.86–1.11) 1.00 (0.84–1.19) 0.94 (0.75–1.17) 1.08 (0.88–1.32)
FJVd 1.05 (0.81–1.36) 1.23 (0.93–1.62) 1.05 (0.64–1.75) 0.43 (0.26–0.73)*
Breads/grains 1.25 (0.92–1.70) 0.93 (0.66–1.31) 0.70 (0.41–1.19) 1.15 (0.48–2.74)
Meatsd 1.26 (0.89–1.78) 1.25 (0.88–1.78) 1.49 (0.87–2.56) 0.98 (0.63–1.52)
Sweetsd 1.65 (1.12–2.44)* 1.65 (1.06–2.57)* 1.01 (0.63–1.59) 0.93 (0.59–1.46)
Salty snacks 0.99 (0.89–1.10) 0.91 (0.80–1.05) 1.16 (0.96–1.42) 0.85 (0.67–1.07)
Condiments 1.01 (0.92–1.11) 1.00 (0.92–1.08) 0.99 (0.84–1.18) 0.93 (0.79–1.10)
Dairyd 1.08 (0.73–1.62) 1.30 (0.90–1.89) 1.27 (0.72–2.26) 0.91 (0.55–1.53)
Gram from high-and

low-quality foods
R2�0.05 R2 � 0.07 R2�0.09 R2�0.06

High-quality foodsd 1.44 (0.70–2.95) 1.89 (0.90–3.96) 1.06 (0.34–3.33) 0.34 (0.12–1.04)
Low-quality foodse 1.69 (1.12–2.53)* 1.60 (1.01–2.53)* 1.00 (0.62–1.63) 0.89 (0.56–1.42)
Gram amount2 R2�0.05 R2�0.07 R2�0.09 R2�0.06
Total 3.17 (1.20–8.41)*

R2�0.05
2.97 (1.03–8.52)*
R2�0.07

1.03 (0.25–4.30)
R2�0.11

0.33 (0.09–1.22)
R2�0.07

From breakfast 1.14 (0.81–1.60) 1.18 (0.83–1.71) 0.67 (0.40–1.10) 0.62 (0.42–0.93)*
From lunch 1.20 (0.75–1.93) 1.38 (0.86–2.23) 1.03 (0.48–2.23) 0.91 (0.47–1.77)
From dinner 1.82 (1.13–2.91)* 1.24 (0.72–2.14) 0.81 (0.43–1.54) 0.95 (0.54–1.67)
From snacks 1.39 (1.00–1.95) 1.43 (0.96–2.14) 1.34 (0.81–2.20) 0.68 (0.41–1.13)
Eating episodef R2�0.04 R2�0.06 R2�0.09 R2�0.06
Total 0.90 (0.79–1.03) 1.07 (0.93–1.24) 1.04 (0.85–1.28) 0.91 (0.76–1.08)

R2�0.04 R2�0.06 R2�01.4 R2�0.07
No. of meals 0.97 (0.63–1.50) 1.23 (0.80–1.89) 0.70 (0.38–1.33) 0.56 (0.33–0.95)*
No. of snacks 0.90 (0.78–1.03) 1.06 (0.91–1.23) 1.07 (0.87–1.31) 0.93 (0.78–1.12)

*p�0.05; *p�0.01.
aFood group consumption I: individual food group consumption as eating pattern variables.
bOdds ratio � risk of being overweight if increasing mean gram consumption.
cFood group consumption II includes the four larger food group categories (FIV, dairy, meats, sweets) and four individual food groups (fats/oils,
breads/grains, salty snacks, condiments).
dHigh-quality foods: fruits/fruit juices, vegetables, breads/grains, meats, dairy.
eLow-quality foods: fats/oils, sweets, salty snacks.
fOdds ratio � risk of being overweight if having one more eating episode.
AA, African American; CI, confidence interval; EA, Euro-American; FJV, fruits/fruit juices and vegetables; meats, mixed meats, poultry, seafood,
eggs, pork, and beef; OR, odds ratio; sweets, desserts, candy, and sweetened beverages; dairy, milk and cheese.

Am J Prev Med 2003;25(1) 13

ategories of meats and sweets (1%) explained very little of
the variance in obesity. Similarly, the percentage of vari-
ance explained by total gram amount of low-quality foods
consumed and the total gram amount of foods/beverages
consumed (particularly from lunch and dinner), the
number of snacking episodes was equally low.

Association Between Eating Patterns and
Overweight Status by Ethnicity and Gender

The association of eating patterns and obesity varied by
ethnicity and gender (Table 2). For EA males, con-
sumption of sweets (p�0.05), especially sweetened bev-
erages (p�0.01); total gram amount of food/beverages
consumed (p�0.05), particularly from the dinner meal
(p�0.05); and total gram amount of low-quality foods
consumed (p�0.05) were positively associated with
obesity. For EA females, consumption of seafood
(p�0.05) and sweets (p�0.05), particularly sweetened
beverages (p�0.05), were positively associated with
obesity. Total gram amount of food consumed
(p�0.05), particularly low-quality foods (p �0.05), was
positively associated with obesity. For AA females, con-
sumption of fruits/fruit juices (p �0.01); FJV (p�0.01);
total gram amount of breakfast consumed (p�0.05);
and total number of meals consumed (p�0.05) were
negatively associated with obesity. Overall, the percent
of variance explained (range 4% to 8%) by the model
was low for all ethnic � gender groups.

Discussion

The present investigation adds to previous studies,
using dietary intake data to establish the association of
eating patterns with overweight status in 10-year-old
children. The prevalence of overweight among the
children was 24%, which is comparable to the national
average,1,2 with the highest prevalence shown among

EA males. Overall, several eating patterns were posi-
tively associated with overweight status: consumption of
sweetened beverages, sweets, and meats, and total gram
consumption of low-quality foods. Total gram amount
of food/beverages consumed, particularly from snacks,
was also positively associated. Other studies have shown
an association between soft drink consumption,22,23

low-quality foods,29 and overweight status, while other
studies have not.44,45 In the present study, soft drink
consumption accounted for 50% of the sweetened
beverages consumed, with the remaining percentage
reflecting consumption of tea or coffee with sugar and
fruit drinks. When only soft drink consumption is
considered, the positive association with overweight
status still existed but the percent of variance explained
was very low (1%). The positive association between
sweets and overweight status resulted from consump-
tion of sweetened beverages and not desserts or candy.

The association between total gram amount of bev-
erage/food consumed and overweight status was not
surprising, particularly since the amount of time chil-
dren are spending in physical activity has de-
creased.46,47 A previous study of these same Bogalusa
children showed that the mean total gram amount of
food/beverages consumed increased from 1973 to 1994
(first author, unpublished observations, 2003). If phys-
ical activity is decreasing48 –51 and the total gram
amount of food consumed has increased, it is reason-
able to find an association between total gram amount
consumed and overweight status.

A particularly interesting finding was the lack of
congruency in the types of eating patterns associated
with overweight status across the four ethnic– gender
groups. Data suggest that the eating patterns associated
with obesity may in fact vary by ethnicity and gender.
This initial finding needs to be confirmed in other

Table 3. Percentage of overweight 10-year-olds by ethnicity and gender

Weight status Bogalusa
NHANES II
and NHES III

Normal
n (%)

Overweight
n (%)

Overweight
n (%)

Ethnicity
Euro-American 768 (76) 242 (24) —
African American 427 (77) 125 (23) —

Gender
Male 584 (76) 186 (24) 467 (23)
Female 611 (77) 181 (23) 458 (21)

Ethnicity and gendera

Euro-American, male 364 (73) 133 (27) 267 (21)
Euro-American, female 404 (79) 109 (21) 270 (22)
African American, male …

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