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Daily food intake in relation to dietary energy density in the free-living environment: a prospective analysis of children born atdifferent risk of obesity13

Tanja VE Kral, Albert J Stunkard, Robert I Berkowitz, Virginia A Stallings, Danielle D Brown, and Myles S Faith

ABSTRACT

Background: Young children adjust their short-term intake in re-

sponse to variations in energy density (ED; kcal/g) from preloads in

laboratory studies. It remains unknown whether this compensation

also occurs under free-living conditions.

Objective: The aims of the study were to test whether children aged

3–6 y regulate their habitual daily food (g) and energy (kcal) intakes

in relation to ED andwhether compensationdiffers forchildren born

at different risk of obesity.

Design: Participants were children born at high risk (n 22) orlow

risk (n 27) of obesity on the basis of maternal prepregnancy body

mass index (BMI; in kg/m2). Daily ED, food intake, and energy

intake were assessed from 3-d food records that either included or

excluded beverages. Intakeregulation was explored by relating chil-

dren’sdailyfoodand energyintakes to ED and, more importantly,by

examining residual scores derived by regressing daily food intake

on ED.

Results: For both risk groups, daily food intake was inversely cor-

related with ED (P 0.05), whereas daily energy intake was not

significantly correlated withED at most ages (P0.05).In analyses

that excluded beverages, mean residual scores significantly in-creased from 3 to 6 y of age in high-risk children, which indicates

relative overconsumption, but decreased in low-risk children, which

indicates relative underconsumption (risk group time interaction,

P 0.005).

Conclusions: Children adjusted their daily food intake in relationto

ED, which suggests caloric compensation under free-living condi-

tions. Compensation ability may deteriorate with age in a manner

that favors relative food overconsumption among obesity-prone

children. Am J Clin Nutr 2007;86:41–7.

KEY WORDS Energy density, daily energy intake, daily food

intake, obesity risk status, children, caloric compensation

INTRODUCTION

The prevalence of childhood obesity is on the rise worldwide

(1–10), and the dietary patterns contributing to this increase are

poorly understood. One dietary factor that has received less at-

tention in the study of pediatric obesity is dietary energy density

(ED), which is defined as calories per weight of food (kcal/g).

Controlled laboratory-based feeding studies found that experi-

mentally increasing the ED of foods significantly increases

short-term energy intake in adults (11–14). Specifically, across

conditions of ED, subjects ate a similar weight of food, thereby

increasing their energy intake during a single meal. Thissuggests

that under laboratory conditions, as well as in the free-living

environment (15), adults regulate the amount of food they con-

sume (by weight or volume) to a greater extent than the calories

they consume.

Studies of children thatexperimentally altered the ED of foods

tested children’s ability to compensate for energy (16 –23). En-ergy compensationrefersto theadjustmentof food intakeduring

an ad libitum meal in response to variations in the ED from a

preload (ie, a fixedamount of food or liquidconsumed beforethe

meal). As reviewed by Birch and Fisher (24), most laboratory

studies have shown that young children compensate for energy

from a preload, although compensation is usually partial and

shows considerable interindividual variability. In addition, the

effect of compensation ability on longer-term intake regulation

(ie, beyond a single laboratory meal) remains poorly understood.

Laboratory protocols have the advantage of assessing chil-

dren’s food intake objectively. However, they generally limit the

food choices, which, in turn, may create conditions that alter the

subjects’ habitual eating behavior. Children may compensatereasonably well forenergy during a singlemeal under controlled

conditions but not necessarily outside of the laboratory. It is

important to assess children’s eating behavior in relation to di-

etary ED under free-living conditions when they have access to

an amplearrayof foods that vary in ED. It is of particular interest

to further elucidate the nature of children’s intake regulation.

Specifically, do children regulate the number of calories they

consume on a daily basis (25), or do they regulate the amount or

the volume of food they ingest, as the data in adults suggest

(26–28)? Also,are there differencesin intake regulation between

children who are born with a different predisposition to obesity

and, if so, how does this affect their ability to compensate? It is

possible that obesity-promoting genes or family environmentsinfluence compensation ability in children. It has also been

1 From the University of Pennsylvania, Philadelphia, PA (TVEK, AJS,

RIB, VAS, DDB, and MSF), and The Children’s Hospital of Philadelphia,

Philadelphia, PA (RIB, VAS, and MSF).2 Supported by NIH grant DK068899 and the General Clinical Research

Center (grant RR00240) of the Children’s Hospital of Philadelphia.3 Address reprint requests to TVEKral, Center forWeight and Eating Disor-

ders, University of Pennsylvania School of Medicine, 3535 Market Street, 3rd

Floor, Philadelphia, PA 19104. E-mail: [email protected] January 18, 2007.

Accepted for publication March 16, 2007.

41 Am J Clin Nutr 2007;86:41–7. Printed in USA. © 2007 American Society for Nutrition

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suggestedthat calories consumed from beverages areless well

regulated than are those from solid foods (29, 30). Thus, it is

possible that the results for children’s intake regulation may

differ when liquids (ie, beverages) are included or excluded

from the analyses.

A first aim of this study was to examine the interrelation

between daily ED (kcal/g),daily foodintake (g),and daily energy

intake (kcal) among children born at high or low risk of obesity.

We predicted that children would reduce their daily food intake

as a function of the ED of their diet, thereby regulating daily

energy intake. A second aim was to examine children’s daily

food intake in relation to their predicted daily food intake for a

given level of daily energy density and to assess potential

changes in children’s compensation ability over time (from ages

3 to 6 y). We predictedthat children’s daily food intakes, relative

to their predicted ones, would differas a function of their obesity

risk status. We also predicted that compensation ability would

deteriorate over time in both risk groups. All analyses were

completed including or excluding all beverages.

SUBJECTS AND METHODS

Subjects

Thesubjects in this report were part of an ongoing longitudinal

study of growth and development in early life that has been

conducted at the University of Pennsylvania and the Children’s

Hospital of Philadelphia. The children in this study were born at

eitherlow or high risk of obesity (31). Thechildren’s obesity risk

status was based on maternal prepregnancy body mass index

(BMI; in kg/m2). Infants born to mothers with a prepregnancy

BMIlessthan the33rdpercentile (meanBMI of 19.51.1) were

classified as being at low risk of obesity (n 37). Children born

to mothers with a prepregnancy BMI greater than the 66th per-centile (meanBMI of 30.3 4.2) were classified at being at high

risk of obesity (n35). Children were enrolled in thestudy at the

ageof 3 mo,and their growthand development were followed up

to year 12. Allchildrenin thestudywere white.Further details of

parental and subject characteristics and the study design were

reported previously (31–35). The present report is based on a

subsample of thiscohort (low-risk: n 27; high-risk: n 22)for

whom 3-dweighed-food records were availableat 3, 4, 5, and6 y

of age. Written informed consent was obtained from the parents.

The protocol was approved by the institutional review boards of

the University of Pennsylvania and the Children’s Hospital of

Philadelphia.

Dietary assessment

Each year, within 2 wk of each child’s birthday, the primary

caretakers of children in both risk groupswere asked to complete

3-d weighed-food records (2 weekdays, 1 weekend day). Care-

takerswere provided electronic food scalesto preweigh allfoods

and beverages (exceptwater) consumedby the child and to weigh

all leftovers. Food records were analyzed by research nutrition-

ists at the General Clinical Research Center at the Children’s

Hospital of Philadelphia by using the Food Processor Nutrition

Analysis software (ESHA Research, Salem, OR). Only those

food records that were completed for 2 d were included in

the present analyses. Two- and three-day food records were

available for the following numbers of children at ages 3, 4, 5,

and 6 y: 45 (40 three-day records and 5 two-day records); 48 (45

three-day records and 3 two-day records); 42 (38 three-day

records and 4 two-day records); and 42 (39 three-day records

and 3 two-day records), respectively. One three-day food record

was not included in the analyses because the child was fed infant

formula almost exclusively. Likewise, 2 one-day records were

not included because the children were either sick or fell asleep

before dinner.

Dietary outcome measures

The following 3 dietary outcome measures were computed

from the3-d weighed-foodrecords:dailyED (kcal/g), daily food

intake (g),and daily energy intake (kcal).All 3 outcomevariables

were computed either to include or exclude all beverages. The

daily dietary ED was computed by dividing the total daily calo-

ries by the total amount of food and beverages (ie, weight in

grams) consumed (36). Daily food andenergy intakes were com-

puted as the sums of the individual weights and calories con-

sumed from foods and beverages on a given day. All dietary

outcome measureswere averaged across the total number of daysof completed records included in the analyses. A detailed de-

scription of the children’s daily energy density and daily energy

intake is provided elsewhere (37).

Statistical analysis

Descriptive statistics are presented as means SEMs. To test

aim one, a Pearson correlation analysis examined relations be-

tween daily ED and daily energy and food intakes. Dietary ED

was correlated first with daily food intake and second with daily

energy intake within each risk group for each year. These corre-

lation analyses were completed by using intakes that included or

excluded all beverages.

Because thecorrelationstested in aimone could have been due

in part to thefunctional relation between these variables(38), we

proceeded by using an analysis of residuals to further examine

children’s intake regulation. Thus, to test the second aim of the

study, an analysis of residuals was conducted in which, for each

age, daily food intake was regressed onto daily ED and the

residual scores were saved. The residual is that part of daily food

intake that is uncorrelated with daily ED. Residuals are the dif-

ferences between an observed value of the response variable and

the value predicted by the model. In our model, the residuals

represent the vertical difference between the regression line (or

the predicted value) and the actual (or observed) data. The re-

sidual represents the difference between an individual’s actual

food intake and what their food intake would be expected to befor a given level of ED based on this sample. Thus, the residuals

allow us to break down the sample into children who eat more or

less than would be expected. Positive residuals represent greater

food consumption than would be expected (ie, overconsump-

tion), whereas negative residuals represent less food consump-

tion than expected (ie, underconsumption). A residual of zero

represents what the model would predict food intake to be for a

given ED value on the basis of this sample; thus, the deviation

from the perfect prediction is zero.

A 2 (risk group) 4 (age) mixed linear model analysis of

variance (ANOVA) tested for (linear) differences in the mean

residual food scores as a function of risk group and child age.

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Risk group was a between-subjects variable with 2 levels (low-

risk or high-risk), and child age was a within-subjects variable

with 4 levels (3, 4, 5, or 6 y). A main effect of risk group would

imply thatthe tendencyto overconsume fooddiffersfor high-risk and low-risk children. Because of the repeated-measures struc-

ture of the data, the model used restricted maximum likelihood

estimates and a compound symmetry error structure. Significant

main effects or interactions were followed up by pair-wise com-

parisons, as well as contrasts, to test forpotential lineareffectsof

time across all 4 ages and linear time by risk group interactions.

Again, these analyses were completed by using intakes that in-

cluded or excluded all beverages.

The ANOVA model to test aim 2 was run with and without

child BMI z score as a covariate to ensure that any risk group

differences in residual scores were not merely due to differences

in children’s weight status. We identified 2 children in the low-

risk group who, at age 4 y, qualified as statistical outliers perTukey’s criteria (39) with regard to BMI z score within their

respective risk group.We comment in theResultssectionthatthe

outcomes did not change when the outliers were removed.

We adjusted the level by using the Bonferroni correction

(ie, 0.05/ n, where n equals the number of comparisons in

a given analysis). We note associations that were significant

both at the conventional 0.05 level as well as when using

the Bonferroni correction because this correction can be

overly stringent (40). We used the same approach in previous

analyses of this cohort (33).

Thedata were analyzed by using SPSS software (version 12.0;

SPSS Inc, Chicago, IL) and SAS software (version 9.1; SAS

Institute, Inc, Cary, NC). For all analyses, P values 0.05 were

considered statistically significant.

RESULTS

Child characteristics

The number of low-risk children included in the present study

at ages 3, 4, 5, and 6 y were 23 (13 boys, 10 girls), 27 (14 boys,

13 girls), 23 (11 boys, 12 girls), and 22 (8 boys, 14 girls); the

number of high-risk children were 22 (12 boys, 10 girls), 21 (9

boys, 12 girls), 19 (10 boys, 9 girls), and 20 (11 boys, 9 girls),

respectively. The mean (SEM) BMI z scores for high-risk

children were 0.4 0.3, 0.4 0.3, 0.5 0.4, and 0.2 0.3,

and those for low-risk children were 0.4 0.2, 0.1 0.1,

0.1 0.2, and0.3 0.2 at ages 3, 4, 5, and 6 y, respectively.

Student t tests showed that, at each of the years, none of these

BMI z scores were significantly different between risk groups. More detailed analyses of anthropometric measures

for this subsample (37) as well as the full cohort (34) are

provided elsewhere.

Daily food intake, energy intake, and energy density

Themean daily food intake(g), daily energyintake (kcal), and

daily energy density (kcal/g) for children in both risk groups at

ages 3, 4, 5, and 6 y are shown in Table 1. These data are

displayed for descriptive purposes only. A detailed discussion of

theseresultscan be found ina previously publishedpaper by Kral

et al (37) that investigated the changes in dietary energy density

in children in this cohort over time.

TABLE 1

Daily energy intake, food intake, and energy density stratified by age and risk group1

Daily intake and risk group Age 3 y Age 4 y Age 5 y Age 6 y

Energy intake, food only (kcal)2

Low risk 898 47 939 55 1064 52 1159 45

High risk 847 51 965 57 1177 70 1393 673

Energy intake, food and beverages (kcal)4

Low risk 1145 49 1219 62 1311 58 1435 43

High risk 1189 59 1253 61 1493 84 1687 69

Food intake, food only (g)5

Low risk 443 25 451 30 466 25 525 31

High risk 395 24 447 24 526 34 620 36

Food intake, food and beverages (g)6

Low risk 948 45 1018 58 980 54 1104 42

High risk 1062 74 1097 58 1210 90 1262 51

Energy density, food only (kcal/g)7

Low risk 2.14 0.10 2.23 0.08 2.37 0.09 2.31 0.08

High risk 2.23 0.09 2.24 0.11 2.36 0.11 2.34 0.10

Energy density, food and beverages (kcal/g)8

Low risk 1.26 0.06 1.27 0.05 1.39 0.06 1.35 0.06

High risk 1.21 0.07 1.19 0.05 1.30 0.07 1.39 0.09

1 All values are x SEM. These data were part of a previous publication by Kral et al (37).2 2 4 ANOVA indicated a significant (linear) risk group time interaction (P 0.02).3 Significantly different from the low-risk group, P 0.05.4 2 4 ANOVA indicated a significant main and linear effect of time (P 0.0001) but no significant risk group time interaction (P 0.19).5 2 4 ANOVA indicated a significant risk group time interaction (P 0.02).6 2 4 ANOVA indicated a significant linear effect of time (P 0.0001) but no risk group time interaction (P 0.40).7 2 4 ANOVA indicated a significant linear effect of time (P 0.03) but no risk group time interaction (P 0.98).8 2 4 ANOVA indicated a significant linear effect of time (P 0.002) but no risk group time interaction (P 0.58).

DAILY INTAKE IN RELATION TO ENERGY DENSITY 43

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Relation between daily food intake and energy density

and between daily energy intake and energy density

All beverages excluded

Daily food intake was significantly inversely correlated with

daily ED for the low-risk group at all ages and for the high-risk

group at ages 5 and 6 y (P 0.05; Table 2). The inverse relation

was borderline significant in the high-risk group at 3 y of age

(P 0.06).This indicatesthat children who consumed a diet that

was relatively higher in ED tended to eat less food on a daily

basis than did children who consumed a diet that was relatively

lower in ED. Two of these 8 associations remained significant,

and 2 were borderline significant, when applying a Bonferroni-

adjusted (0.05/8 0.006).

Daily energy intake was not significantly correlated with daily

ED for the low-risk group at all ages and for the high-risk group

at ages 3, 5, and 6 y (Table 2). This indicates that across a range

of dietary ED, daily caloric intake did not vary significantly as afunction of ED during these years.

All beverages included

Daily food intake was significantly inversely correlated with

daily ED for both the low-risk and the high-risk group at all ages

(P 0.05; Table 3). Six of these 8 associations remained sig-

nificant when applying a Bonferroni-adjusted (0.05/8

0.006). Daily energy intake, however, was not significantly cor-

related with daily ED for either group at any age (Table 3).

Residual analysis

All beverages excluded

Mean residual food intake scores for each risk group by child

age are displayed in Figure 1. The 2 (risk group) 4 (age)

ANOVA indicated a significant risk group age category in-

teraction (P 0.005) and a linear risk group age (as a contin-

uousvariable) interaction (P0.0005). Thisinteractionwas still

significant when applying a Bonferroni-adjusted alpha in the

respective ANOVA models (0.05/3 0.02). Specifically, mean

residualscores increasedbetweenages3 and6 y amonghigh-risk

children, reflecting a greater tendency to overconsume food rel-

ative to predicted intake, but decreasedamong low-risk children,

reflecting a greater tendency to underconsume food relative to

predicted intake. A pairwisefollow-up comparison indicated that

the mean residual food intake score was significantly greater in

the high-risk children than in the low-risk children at age 6 y

(residual 53.4 137.0 versus 48.5 105.1; P 0.01).The risk group age interaction remained significant (P

0.02) when BMI z score was added to the model as a covariate.

The main effect of BMI z score was significant in this model

(P 0.04), indicating that heavier children tended to overcon-

sume food (relative to their predicted intake) compared with

thinner children. After removal of the 2 statistical outliers for

BMI z score from the model, the main effect of BMI z score was

not significant (P 0.06); however, the risk group age

interaction remained significant (P 0.02). We note that this

TABLE 2

Pearson correlation coefficients summarizing the relation between dietary energy density and daily food and energy intakes (excluding beverages) by risk

group and child age

Risk group Daily intake

Pearson’s r with daily energy density (kcal/g)1

Age 3 y Age 4 y Age 5 y Age 6 y

Low risk Food intake, food only (g) 0.552 0.492 0.503 0.702,4

High risk Food intake, food only (g) 0.405 0.23 0.652,4 0.513

Low risk Energy intake, food only (kcal) 0.31 0.10 0.30 0.01

High risk Energy intake, food only (kcal) 0.21 0.503 0.13 0.29

1 With the use of Fisher r -to- z transformations and a 2-tailed significance test, none of the correlation coefficients was significantly different within each

age group or across age groups.2 Correlation significant at 0.01 level.3 Correlation significant at 0.05 level.4 P 0.006 (Bonferroni correction, 0.05/8).5 P 0.06.

TABLE 3

Pearson correlation coefficients summarizing the relations between dietary energy density and daily food and energy intakes (including beverages) by risk

group and child age

Risk group Daily intake

Pearson’s r with daily energy density (kcal/g)1

Age 3 y Age 4 y Age 5 y Age 6 y

Low risk Food intake, food and beverages (g) 0.582,3 0.454 0.642,3 0.782,3

High risk Food intake, food and beverages (g) 0.692,3 0.494 0.622,3 0.612,3

Low risk Energy intake, food and beverages (kcal) 0.28 0.13 0.05 0.26

High risk Energy intake, food and beverages (kcal) 0.002 0.40 0.13 0.42

1 With the use of Fisher r -to- z transformations and a 2-tailed significance test, none of the correlation coefficients was significantly different within each

age group or across age groups.2 Correlation significant at 0.01 level.3 P 0.006 (Bonferroni correction, 0.05/8).4 Correlation significant at 0.05 level.

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interaction effect approached but did not reach significance

when using the Bonferroni-adjusted (0.05/4 0.01).

All beverages included

Mean residual food intake scores for each risk group by child

age are displayed in Figure 2. The 2 (risk group) 4 (age)

ANOVA indicated a trend for a main effect of risk group (P

0.07), which suggests a tendency for high-risk children to over-

consume, relative to their predicted intake, compared with

low-risk children across all 4 y. The risk group age inter-

action was not significant (P 0.55), nor was the linear risk

group age interaction (P 0.38). None of the main effects

or the interaction was significant when using a Bonferroni-

adjusted (0.05/3 0.02).

There was a main effect of BMI z score when added to the

model as a covariate (P 0.0001), whereas the main effect of

risk group (P 0.21) and the risk group age interaction (P

0.76) were not significant. After removal of the 2 outliers, the

main effect of BMI z score remained significant (P 0.0001);

the maineffect of riskgroup remainednonsignificant (P0.19),

as did the risk group age interaction (P 0.72).

DISCUSSION

The main finding of the present study was that across a range

of ED, children born at high or low risk of obesity adjusted the

daily amount of food they consumed on the basis of the ED of

their diet. Children who consumed a diet that was higher in ED

consumed less food daily than did children whose diet was lower

in ED. These findings held true regardless of whether beverages

were included or excluded. This is the first longitudinal study to

show that children aged 3–6 y also adjusttheirdaily intakeunder

free-living conditions. These findings thus confirm and extend

previous research conducted under laboratory conditions.

Theprospectivenature of this designalso allowed us to test for

changes in energy regulation ability over time. It has been sug-gested that developmental differences in energy compensation

ability mayexist in children(24). Preschoolchildren, on average,

show compensation (24); however, Anderson et al (41) found no

effect on food intake when varying the ED of preloads in 9–10-

y-old children. These findings indirectly suggest developmental

changes in compensation ability; however, this phenomenon has

never been documented in long-term prospective studies that

track children during growth.

With theuseof a novelapproachof residualscoreanalysis, this

present study assessed changes in compensation ability over the

courseof 4 y. Theresults indicated different patterns of residuals

between high- and low-risk children, which suggests that famil-

ial predisposition to obesity may partially operate through errorsin compensation. This supports theories and experimental stud-

ies regarding energy compensation ability and the development

of pediatric obesity (22–24, 42). However, it should be pointed

out that the results from the residual analysis are sample-

dependent and therefore may not apply to other cohorts of chil-

dren. Themain finding of theresidualanalysiswas that high-risk

children tended to overconsume, whereas low-risk children

tended to underconsume, relative to their predicted intake, al-

though the results differeddepending on whether beverages were

included in the analyses. For analyses that excluded beverages,

the risk group differences gradually emerged over time, with

high-risk children not showing a tendency to overconsume until

after 4 y of age. This is consistent with the notion that compen-

sation ability gradually deteriorates during early childhood, pos-

sibly starting during the years corresponding to adiposity re-

bound (43), rather then being compromised consistently and as

early as 3 y of age. This gradual decline in compensation ability

was not seen when beverages were included in the analyses (see

below). Another key finding was that the risk group differences

remained significant even when the analyses were controlled for

BMI z score, which suggests that the errors in compensation

cannot be fully explained by differences in weight status. Again,

this was not the case when beverages were included in the anal-

yses (see below).

When beverages were included in the residual score analyses,

the results indicated a trend (P 0.07) for high-risk children to

-80

-60

-40

-20

0

20

40

60

80

100

Age (y)

U C

< - - - - - - - - - - - - - - - > O C

*

3 4 5 6

FIGURE 1. Mean residual food intake (food only) scores by risk group

(, low risk; f, high risk) and child age. Residual food intake scores weregenerated by regressing daily food intake (excluding all beverages) onto

daily energy density (excluding all beverages). These residuals represent thedifference between children’s actual food intake and their predicted foodintake for a given energy density based on this sample. UC, relative under-

consumption;OC, relative overconsumption.There was a significant(linear)risk group age interaction (P 0.005) for residual scores. *Significantlydifferent from the low-risk group, P 0.01.

-140

-90

-40

10

60

110

160

Age (y)

U

C

< - - - - - - - - - - - - - - - >

O C

3 4 5 6

FIGURE 2. Meanresidual food intake(foodandbeverages) scoresby risk group (, low risk;f, high risk) and child age. Residual food intake scores

were generated by regressing the daily food intake (including all beverages)ontodaily energydensity(includingall beverages). Theseresidualsrepresentthe difference between children’s actual food and beverage intakes and their

predicted food and beverage intakes fora given energy density based on thissample.UC, relative underconsumption;OC, relative overconsumption. Therisk group age interaction for residual scores was not significant ( P 0.55), nor was the linear risk group age interaction (P 0.38).


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overconsume and low-risk children to underconsume across all

4 y. That is, errors in compensation did not appear to gradually

emergeovertimebutmayhavebeenpresentasearlyas3yofage.

This interpretation, if true, would suggest that errors in compen-

sation ability for (food and) beverages are established by 3 y of

age and, therefore, that any deterioration in compensation ability

may have occurred before the age of 3 y. This finding raises the

possibility that infancy and the first 2 y of life may be critical

periods for the development of compensationability for food andbeverages (20, 21, 44, 45) and therefore may be important peri-

ods for early-life interventions. To more convincingly identify

critical periods of time when children’s ability to compensate

may start to deteriorate, however, studies are needed that exam-

ine (or track) the stability of compensation indexes. Findings

from the current study are also consistent with studies reporting

that excess beverage consumption (especially from fruit juice) is

associated with excess weight gain in preschool-age children

(46,47). Hunger and thirst mechanisms arebelievedto be distinct

entities, because the consumption of beverages primarily acts

on thirst and not hunger mechanisms (48). It is possible that

liquids, which are thought of as having a generally lower

satiating efficiency (49), may thus further weaken children’scompensation ability.

Another unique finding of the residual score analyses that

included beverages was that the risk group differences no longer

approached statistical significance when BMI z score was added

as a covariate. BMI z score was consistently associated with

greater residual scores across analyses, which suggests that er-

rors in compensation ability for beverages (and food) may be

affected by child weight status and the determinants thereof.

Heavier childrenmay be morelikely to exceed their energy needs

through consumption of beverages (46). Despite these findings,

it should be noted that beverages, because of their low energy

density, addweight butfewer calories to thedaily total,and, thus,

by computation, disproportionately influence dietary ED.Hence, residual analyses that include beverages may need to be

interpreted with more caution.

The result of this investigation agrees with findings from 2

recent experimental studies (22, 23) that showed that energy

compensation in children 1) is often incomplete and shows great

interindividual variability and 2) tends to decline with age. It is

possible that children, especially those who are genetically pre-

disposed to obesity, may become more responsive to environ-

mental influences (such as the portion size of foods) as they

become older. A study conducted by Rolls et al (50) showed that

when presented with larger portions of food, 5-y-old children,

but not 3-y-old children, significantly increased their intakes

relative to when they were presented with smaller portions of

food. The authors suggested that as children age they become

increasingly responsive to environmental, social, and cultural

factors that all are likely to affect their intake. To what extent

these factors increase children’s vulnerability to overeat may

depend on parental obesity, a line of research that hasyet to be

further explored.

The strengthsof the present study include 1) theunique sample

of children born at different risk of obesity and who were pro-

spectively studied and 2) the use of extensive training and elec-

tronic food scales to measure the weights of the foods that the

children consumed. The study might well be extended in several

ways. First, itwouldbe desirableto examine ability toadjust food

intake in relation to dietary ED in a larger cohort of children who

are ethnically diverse and who show greater variability in their

weight status and age than did the children in the current cohort.

Thus, the results from this study are limited to the relatively

narrow study sample, namely, healthy, full-term, white children,

and may not be generalizable to children as a whole. Second, it

would be desirable to obtain intake data over a longer period of

time at each age to assess children’s habitual diet. Third, the

validity of the data may be increased by complementing self-

reported intakes, which may be subject to measurement or re-porting errors by mothers, with measured intake data in the feed-

inglaboratory. Fourth, futureresearchshould also collect data of

energy expenditure to be able to relate children’s intake data to

their energy needs. Last, the present design, unlike a classic twin

design or other more genetically sensitive designs, could not

formallytest the respective influence of genetic and environmen-

tal influences on compensation ability.

In conclusion, the results of the present study suggest that

children between the ages of 3 and 6 y have the ability to adjust

their daily food intake on the basis of the ED of their diet to

maintain a certain daily energy intake in the free-living environ-

ment. Energy compensation ability, however, may differentiate

children who were born at high-risk or at low-risk of obesity.Future research is needed that separates the effects of food and

beverages on children’s compensation ability.

We thank theGeneral Clinical Research Center of theChildren’s Hospital

of Philadelphia andthe staff of theInfantGrowth Study fortheirsupport.We

also thank Michael J Rovine for statistical consulting on the study.

The contributions of the authors were as follows—TVEK: contributed to

thestudydesign, data acquisition andanalysis, andwritingof themanuscript;

AJS, RIB, VAS, and MSF: were responsible for the study concept, acquisi-

tion of the data, writing of the manuscript, and obtaining funding; DDB:

contributed to the data analysis. None of the authors had any conflicts of

interest.

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