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European Journal of Clinical Nutrition (1997) 51, 443±448
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Diet composition and body mass index in pre-school children
PSW Davies1,2
1
School of Human Movement Studies, Faculty of Health, Queensland University of Technology, Kelvin Grove Campus, Red Hill,
Queensland, 4059, Australia; and 2Infant and Child Nutrition Group, Dunn Nutrition Unit, Downhams Lane, Milton Road, Cambridge
CB4 1XJ, UK
Objective: To investigate the relationship between diet composition and body mass index (BMI) in pre-school
children.
Design: Reanalysis of data from the National Diet and Nutrition Survey of children aged 1.5±4.5 y. Height and
weight of children were used to calculate Body Mass Index (BMI) and BMI standard deviation scores. Dietary
intake data were used to calculate percentage of total energy intake derived from fat, carbohydrate and protein.
These data were then divided into quintiles. The data were then analysed in order to assess if there was any
relationship between the diet composition and BMI.
Setting: Community based project throughout Great Britain.
Subjects: 1444 children aged 1.5±4.5 y.
Measurements: Diet composition was assessed in terms of percentage energy derived from fat, protein and
carbohydrate following a four day weighed intake carried out by the parents or carers of the child. Body size was
assessed by measuring BMI and calculating the standard deviation score relative to UK reference data.
Results: There were no trends apparent using ANOVA and multiple regression that indicated that diet
composition was related to body size.
Conclusions: In a large cohort of pre-school children we are unable to con®rm the recent ®ndings in much
smaller samples that diet composition affects body size. Other factors such as energy intake per se and levels of
habitual physical activity might have a more important bearing on BMI in pre-school children.
Descriptors: children, body mass index, obesity, diet composition
Introduction
It is accepted that obesity is a hazard to health. Moreover,
obesity is now prevalent enough to be considered as one of
the most important public health and medical problems of
recent years (Prentice & Jebb, 1995; Garrow, 1992; ). The
last of these three publications highlights the UK Government's aim to reduce the percentages of men and women
aged 16±60 y who are obese by at least 25% for men and at
least 33% for women by 2005, that is to reduce the
prevalence from 8%±6% in men and from 12%±8% in
women.
A number of studies have related obesity in adulthood to
obesity in childhood (Poskitt & Cole, 1977; Eid, 1970;
Durnin & McKillop, 1978; Rolland-Cachera et al, 1987),
and the conclusion that can be drawn from these investigations is that many overweight or obese children become
overweight or obese adults. Thus the aetiology of obesity in
childhood is important to the study and implications of
obesity in later life. The accumulation of excess body fat is
dependant on a change in the fundamental energy balance
equation, namely energy intake ˆ energy expenditure ‡
energy stored.
An increase in energy stored, as fat, can lead to obesity,
and a number of mechanisms can contribute to an increase
in stored energy. Some work in this ®eld (Johnson et al,
1956; Hampton et al, 1967; Baecke et al, 1983) suggested
that obese children did not consume more calories than
their leaner peers and this led to suggestions that a reduced
Correspondence: Dr PSW Davies.
Received 17 December 1996; revised 5 March 1997; accepted 14 March
1997
basal metabolic rate was a cause of obesity. However,
subsequent studies (Southgate, 1986) have suggested that
there is no fundamental reduction in basal metabolic rate
which is responsible for the development of obesity. This
area is still controversial with recent data suggesting a role
for a low resting metabolic rate in the development of
obesity (Astrup et al, 1995).
Attention has also focused on the energy intake side of
the energy balance equation. A very straightforward, but
possibly simplistic argument can be put forward which
reasons that, if energy intake is in excess of energy
expenditure and hence requirements, energy stored will
increase and hence there will be an increase in body fat
potentially leading to obesity. This argument however fails
to consider that the composition of the diet might have an
in¯uence on the metabolism of energy by the body. There
is now mounting evidence that diet composition is related
to body size.
For example, there are numerous animal studies (Hill,
1990; Hill et al, 1989; Hill et al, 1983; Triscari et al, 1985;
Lucas et al, 1989; Herberg et al, 1974; Jen, 1988; Levin et
al, 1986; Oscai et al, 1984; Oscai et al, 1987) that show that
a high fat diet induces obesity. Some such studies that used
iso-calori®c diets differing in fat and carbohydrate content
offer compelling evidence that diet composition is of
importance in the aetiology of obesity, at least in animal
models. A number of studies in adult humans (Dreon et al,
1988; Kromhout et al, 1988; Romieu et al, 1988; Miller et
al, 1990; Tucker & Kano, 1992) have suggested that diets
high in fat caused body fat levels to increase and diets high
in carbohydrate are associated with lower levels of body
fat. Carefully controlled studies that manipulate the diet
also lend weight to the argument that diet composition is
Diet composition and BMI in pre-school children
PSW Davies
444
important in weight maintenance and can in¯uence body
composition (Lissner et al, 1987; Kendall et al, 1991;
Prewitt et al, 1991).
It has been postulated that in adults a high fat diet leads
to the deposition of body fat due to the ef®ciency of the
conversion of dietary fat into stored triglyceride. The
metabolic cost of such a conversion is in the order of 3%
of energy intake whereas the cost of storing dietary carbohydrate as body fat requires the expenditure of 23% of the
ingested energy (Flatt, 1985). Again, in adults it has been
suggested that resting metabolic rate and the thermic effect
of food differ according to diet composition, in that there is
more potential for a thermogenesis effect following a high
carbohydrate low fat meal (Sims & Danforth, 1987).
Some animal work (Landsberg & Young, 1983) has
shown that sympathetic nervous activity is increased following a high fat meal and such changes might in¯uence
some of the changes in energy output and adaptive thermogenesis that follow with changes in daily intake. Differences in enzyme response to diet composition have been
implicated in the relationship between diet composition and
body composition. Speci®cally, high levels of dietary fat
have been found to be directly related to levels and activity
of lipoprotein lipase, the major rate limiting enzyme for the
conversion of dietary lipids in stored lipid.
Finally, there is evidence to suggest that in some
individuals there is a blunted post meal response to high
fat intakes, and that reduced post meal fat oxidation leads to
an increase in body size and potential obesity.
To date however, there has been limited investigation of
the effect of those possible phenomena in children (Gazzaniga & Burns, 1993; Rolland-Cachera et al, 1995; Oretega
et al, 1995). In the ®rst of these studies a signi®cant
positive correlation was found between percentage energy
intake derived from fat and percentage body fat in a group
of 48 children aged 9±11 y. However, in a subgroup of
obese children (n ˆ 18), the correlation was not signi®cant.
The second study found that the only nutrient intake in
early childhood that was correlated with later BMI was
percentage energy intake derived from protein. Again this
study was of relatively few children (n ˆ 112). Finally, in a
cohort of sixty four adolescents divided into obese and nonobese groups based on their BMI despite there being
difference in energy intake per se, the obese group derived
a greater proportion of their energy from protein and fat.
Thus there currently con¯icting reports from studies using
small sample sizes. Nevertheless the phenomenon of diet
composition being related to body size would be of major
importance if proved to be true. The recently completed
National Diet and Nutrition Survey (NDNS) of children
aged 1.5±4.5 y (Gregory et al, 1995) provides the opportunity to investigate this potential phenomenon in a large
group of pre-school children.
Subjects and methods
Subjects
The NDNS required that a nationally representative sample
of pre-school children be recruited. The initial aim was to
recruit 1500 children evenly distributed across the age
range 1.5±4.5y. The sample was selected using a multistage random probability design, with postal sector as ®rst
stage units. The small user Post Code Address ®le was used
as the sampling frame. The frame was strati®ed by region.
A total of 100 postal sectors were selected. From each
postal sector 280 addresses were randomly selected. To
identify households which contained an eligible child,
namely a child aged between 1.5 and 4.5 y, each address
was sent a sift form which asked for details of the sex and
date of birth of all people living in the household. From
returns, households containing an eligible child were identi®ed. Only one eligible child from each household was
selected.
Assessment of diet
The parent or carer of the child was asked to keep a
weighed record of all food and drink consumed by the
child, both in and out of the home, over four consecutive
days including a Saturday and Sunday. Parents were given
a set of calibrated Soehnle Quanta scales and two recording
documents; one for food eaten within the home and another
for use when foods could not be weighed, that is foods
eaten away from the home. Instructions on how to complete
the four day record were left with the parent or carer along
with instructions on how to weigh and record leftovers, and
food that was spilt or not eaten which could not be reweighed.
Folowing coding of the diaries by ®eldworkers, the
dietary record was linked to a nutrient database and nutrient
intakes calculated from quantity of food consumed. The
nutrient database which was compiled by the Ministry of
Agriculture, Fisheries and Food, holds information on 54
nutrients for each food coded. The quantity of food energy
derived from fat, carbohydrate and protein was expressed
both in grams per day and as a percentage of total energy
intake.
Before accepting completed four day records, a number
of checks were carried out. Firstly, checks were carried out
to identify cases where the weight of a speci®c food
consumed was outside of a speci®ed expected range. Any
such cases were individually evaluated and errors corrected. Secondly, checks were carried out to identify
cases where any speci®c nutrient was outside of an
expected range. In the case of most nutrients a maximum
value was the only one that could be speci®ed. Ranges were
provided by the Ministry of Agriculture, Fisheries and
Food.
This method for evaluating dietary intake has been
previously assessed in the same age group via a comparison
between estimates of energy intake from a 4 d weighed
intake with measures of total energy expenditure using the
doubly labelled water technique (Davies et al, 1994). The
mean measurements of energy intake and expenditure were
extremely close. The average difference of only 3% provided suf®cient con®dence in the weighed intake methodology for it to be used in the major nationwide survey
from which the data in this present study derives.
Assessment of body size
The nature of the design of the NDNS with many areas and
®eldworkers restricted the extent of the anthropometry that
could be undertaken. Nevertheless, height and weight were
recorded where possible. In children aged 2 y or more,
stature was measured using a portable modi®ed digital
telescopic stadiometer designed by the Of®ce of Population
Censuses and Surveys (Gregory et al, 1995). In younger
children supine length was measured using the same
equipment. Measurements were taken to the nearest millimetre. Body weight was measured with the child wearing
minimal clothing using Soehnle Quantratonic digital personal weighing scales. The measurement was recorded to
100 g.
Diet composition and BMI in pre-school children
PSW Davies
A summary of the dietary intake data is shown in Table
2. There was a signi®cant difference between mean energy
intake in the boys and girls (t ˆ 4.92, P < 0.01). Also the
girls consumed more energy from protein and less from
carbohydrate than the boys (t ˆ 2.27, P < 0.05; t ˆ 2.22,
P < 0.05) respectively. There was no signi®cant difference
in percentage energy from fat between the boys and girls.
The boys consumed signi®cantly more carbohydrate,
protein and fat in absolute terms than the girls.
In a similar analysis to that of Bolton±Smith and
Woodward (1994) the intake data for each sex has been
divided into ®fths. In each ®fth therefore there are approximately 145 children. Figures 1 and 2 show the mean BMI
SDS 2 s.e. of mean of the children that fell within each of
the intake ®fths for percentage energy derived from carbohydrate, fat and protein. In the boys the mean BMI SDS in
every ®fth is above 0 but this simply re¯ects the fact that
mean BMI SDS for the entire cohort of boys is 0.30.
Similarly for the entire cohort of girls the mean BMI
SDS is 70.14 and subsequently, the mean BMI SDS
values in the different ®fths for the girls are also generally
negative. Analysis of variance revealed no difference
across quintiles in mean BMI SDS, in either boys or girls
and it is clear that there are no obvious trends for BMI SDS
within the analysis of carbohydrate, fat and protein intake.
Nevertheless, it has been suggested (Saimon & Flatt, 1985)
that within groups determined by the percentage energy
derived from different nutrients while the mean BMI SDS
may be similar the distribution of BMI SDS becomes more
positively skewed as the percentage energy from fat
increases. In order to investigate this we have calculated
the percentage of children whose BMI SDS is above 1.5
(about the 93rd percentile) in each ®fth of intake. These
results are shown in Table 3. Again, no trend is apparent
with between about 6% and 13% of children having BMI
SDS greater than 1.5 within the different ®fths of intake.
Finally results of a multiple regression with BMI SDS as
the dependant variable and per cent energy from protein,
carbohydrate and fat as independent variables are shown in
Table 4. Again there is no signi®cant association between
Height and weight together allow the calculation of BMI
(weight/height2). BMI is a good index of body size and has
been used for many years to assess the level of overweight
and underweight in adults (Khosla & Lowe, 1967; Garrow
& Webster, 1985) and in children (Cole et al, 1981;
Rolland-Cachera et al, 1982; Matteis et al, 1993). However, in childhood as mean BMI changes markedly with
age, rising rapidly in infancy, then falling to a nadir at
about 6 y of age and then rising again through to adulthood,
BMI needs to be assessed using age-related references and
such data has been recently published (Cole et al, 1995).
These new data also allow for any individual's BMI to be
converted to a standard deviation score (SDS) thus adjusting for age and sex simultaneously.
Measurements of dietary intake, height and weight were
available on 1444 children aged 1.5±4.5 y. The body mass
index SDS was calculated for each child.
Results
Some physical characteristics of the 1444 children are
shown in Table 1. The boys were signi®cantly taller than
the girls (t ˆ 2.06, P < 0.05) and signi®cantly heavier
(t ˆ 4.22, P < 0.01). Both BMI and BMI SDS were signi®cantly greater in the boys than in the girls (t ˆ 4.50,
P < 0.01) and (t ˆ 7.30, P < 0.01) respectively.
Table 1
Some characteristics of the children studied
Males (n ˆ 721)
Age (y)
Height (m)
Weight (kg)
BMI (kg/m2)
BMI SDS
Females (n ˆ 723)
Mean
s.d.
Mean
s.d.
2.97
0.950
14.87
16.4
0.30
0.82
0.074*
2.59**
1.6***
1.12****
2.98
0.942
14.30
16.0
70.14
0.83
0.076
2.49
1.5
1.13
* t ˆ 2.06, P < 0.05; ** t ˆ 4.22,
**** t ˆ 7.30, P < 0.01.
P < 0.01;
*** t ˆ 4.50,
P < 0.01;
Table 2 The mean and standard deviation of energy intake and macronutrient intake in the
boys and girls in the survey
Males (n ˆ 721)
Energy intake (kcal/d)
Carbohydrate intake (g/d)
Energy from carbohydrate (%)
Protein intake (g/d)
Energy from protein (%)
Fat intake (g/d)
Energy from fat (%)
Females (n ˆ 723)
Mean
s.d.
Mean
s.d.
1184
162.9
51.6
37.6
12.8
47.0
35.6
258*
40**
6.2
10.3***
2.6****
13.1*****
5.3******
1118
151.5
50.9
36.5
13.1
44.9
36.0
248
37
6.1
10.3
2.5
12.7
5.2
* t ˆ 4.92, P < 0.01; ** t ˆ 5.60, P < 0.001; *** t ˆ 2.06, P < 0.05; **** t ˆ 2.27, P < 0.05;
***** t ˆ 3.03, P < 0.01; ****** t ˆ 2.22, P < 0.05.
Table 3
The percentage of children whose BMI SDS was greater than 1.5 within quintiles of macronutrient intakes
Fifths of % energy intake from
Carbohydrate
Protein
Fat
1
13.1
11.1
10.4
Boys
Girls
Percentage of BMI SDS > 1.5
Percentage of BMI SDS > 1.5
2
7.6
10.3
11.0
3
13.1
11.1
11.8
4
11.8
11.8
12.5
5
11.1
12.5
11.1
1
6.3
8.3
11.8
2
6.9
8.3
8.3
3
6.9
9.0
11.1
4
5.6
9.0
6.3
5
9.7
7.6
4.9
445
Diet composition and BMI in pre-school children
PSW Davies
446
Table 4 Results of multiple regression analysis with BMI SDS being the dependant variable and percentage energy from protein, fat and carbohydrate
being the independent variables
Boys
Predictor
Constant
% energy from carbohydrate
% energy from protein
% energy from fat
Girls
Coef®cient
Standard deviation
t-ratio
P
Coef®cient
Standard deviation
t-ratio
P
78.31
0.081
0.103
0.086
52.30
0.522
0.521
0.524
70.16
0.16
0.20
0.16
0.87
0.87
0.84
0.87
1.56
70.018
0.028
70.031
47.24
0.472
0.470
0.474
0.03
70.04
0.06
70.07
0.97
0.97
0.95
0.95
Figure 1 The mean two standard errors of BMI SDS for the boys
within quintiles for macronutrient intake.
percentage energy intake from any nutrient group and BMI
SDS.
Discussions
The NDNS of children aged 1.5±4.5 y, although designed
speci®cally to be a survey, offers the ability to investigate
Figure 2 The mean two standard errors of BMI SDS for the girls
within quintiles for macronutrient intake.
important nutritional questions because of the size and
extent of the data collected. One such question is the
extent to which diet composition in¯uences body size
in this age group. In this study of 1444 children aged 1.5±
4.5 y we have failed to ®nd a relationship between diet
composition and body size as measured by BMI.
Diet composition and BMI in pre-school children
PSW Davies
Diet composition was assessed via a four day weighed
intake. While such an approach can be subject to error and
bias, previous work in the same age group has shown that
estimates of energy intake via a four day weighed intake
and measurement of total energy expenditure using doubly
labelled water differed by only 37 kcal/d on average
(Davies et al, 1994). The four day weighed intake was
carried out by the parents (usually the mother) after
extensive instruction by a highly trained ®eld worker.
The most up to date databases were used to convert food
intake into nutrient intakes and hence we are con®dent that
the data obtained by this method is of the highest quality.
Body size was assessed using BMI. There are other
techniques that might assess body composition with greater
accuracy than BMI, however many of the techniques do not
lend themselves to large scale, community based studies.
Nevertheless using the recently published reference values
(Cole et al, 1995) for BMI and by adjusting for age and sex
simultaneously by the use of SD scores enables the best
possible estimate of body size in such large scale studies.
The coef®cient of variation of fat, carbohydrate and
protein in grams/day was 28%, 25% and 27% respectively
in the girls, and 28%, 26% and 25% in the boys, indicating
considerable variation in intakes within the cohort. For
example, there was almost a three times variation in fat
intake between the 5th percentile and 95th percentile value
in both sexes.
BMI also varied considerably with the cohort, with the
coef®cient of variation being 10% and 9% in the boys and
girls respectively. Some of this variation will be due to
changes in the distribution of BMI with age, however when
aged and sex are adjusted for by the use of BMI SDS values
the range of such values are from 74.9 to 6.0, again
indicating much variation, as would be expected, in the
entire cohort. Thus it is unlikely that small ranges in data
collected has masked any relationship between BMI and
diet composition in this group. It was unknown whether or
not the effect of considerable variation in dietary intake
would manifest itself as differences in body size in preschool children, indeed that was one of the major aims of
the current study. While we have not been able to demonstrate a relationship between diet composition and body
size in this group, other smaller studies have found a
relationship in older children (Gazzaniga & Burns, 1993;
Rolland-Cachera et al, 1995; Oretega et al, 1995) and
adults (Dreon et al, 1988; Kromhout et al, 1988; Romieu
et al, 1988; Miller et al, 1990; Tucker & Kano, 1992). It is
possible that if diet composition does in¯uence body size
there is a time course over which the in¯uence takes place
and that the group of children studied here (1.5±4.5 y) was
too young to reveal such a relationship. This would mean
however that there may be a need to target education on
good eating habits to parents and carers of younger children
in order to prevent the potentially detrimental changes to
body habitus occurring in later childhood and adulthood.
Other factors such as energy intake per se and lack of
habitual activity may have a more important short term
effect on BMI of pre-school children. Also, the limitations
of a cross-sectional study such as the one reported here
should be remembered. Longitudinal studies might reveal
that dietary habits in terms of diet composition might
predict changes in body mass index and body composition,
especially if the relationship does develop over time as
previously suggested.
There are other techniques that might assess body
composition with a greater accuracy than BMI however
many of these techniques do not lend themselves to large
scale community based studies. However using the recently
published reference values for BMI and adjusting for age
and sex by the use of SD scores enables the best possible
estimate of body size and body composition in such large
scale studies.
In a large cohort of pre-school children we are unable
therefore to con®rm the recent ®ndings (Gazzaniga &
Burns, 1993; Rolland-Cachera et al, 1995; Oretega et al,
1995), in much smaller samples of older children, that diet
composition effects body size. This may be due to the age
of the children studied here or possibly other factors such as
energy intake and levels of habitual physical activity might
have a more important bearing on the size, BMI and body
composition of pre-school children.
AcknowledgementsÐThis study was a part of the National Diet and
Nutrition Survey of Children Aged 1.5±4.5 y, which was funded jointly
by the Ministry of Agriculture, Fisheries and Food and the Department of
Health and conducted by the Social Survey Division of the Of®ce of
Population Censuses and Surveys in conjunction with the MRC Dunn
Nutrition Unit.
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