Associations of body mass index and waist circumference with: energy intake and percentage energy from macronutrients, in a cohort of australian children
Associations of body mass index and waist circumference with: energy intake and percentage energy from macronutrients, in a cohort of australian children
Sarah A Elliott 2
Helen Truby 0
Amanda Lee 1
Catherine Harper 4
Rebecca A Abbott 3
Peter SW Davies 2
0 Monash University, Southern Clinical School, Department of Nutrition and Dietetics , Clayton VIC 3168 , Australia
1 Nutrition and Physical Activity Health Promotion Branch, Queensland Health , Brisbane, QLD 4001 , Australia
2 The University of Queensland, Children's Nutrition Research Centre, School of Medicine, Royal Children's Hospital , Brisbane , Australia
3 School of Human Movement Studies, University of Queensland , Brisbane, QLD, 4069 , Australia
4 Planning and Development Unit, Population Health Queensland, Queensland Health , Brisbane, QLD 4001 , Australia
Background: It is evident from previous research that the role of dietary composition in relation to the development of childhood obesity remains inconclusive. Several studies investigating the relationship between body mass index (BMI), waist circumference (WC) and/or skin fold measurements with energy intake have suggested that the macronutrient composition of the diet (protein, carbohydrate, fat) may play an important contributing role to obesity in childhood as it does in adults. This study investigated the possible relationship between BMI and WC with energy intake and percentage energy intake from macronutrients in Australian children and adolescents. Methods: Height, weight and WC measurements, along with 24 h food and drink records (FDR) intake data were collected from 2460 boys and girls aged 5-17 years living in the state of Queensland, Australia. Results: Statistically significant, yet weak correlations between BMI z-score and WC with total energy intake were observed in grades 1, 5 and 10, with only 55% of subjects having a physiologically plausible 24 hr FDR. Using Pearson correlations to examine the relationship between BMI and WC with energy intake and percentage macronutrient intake, no significant correlations were observed between BMI z-score or WC and percentage energy intake from protein, carbohydrate or fat. One way ANOVAs showed that although those with a higher BMI z-score or WC consumed significantly more energy than their lean counterparts. Conclusion: No evidence of an association between percentage macronutrient intake and BMI or WC was found. Evidently, more robust longitudinal studies are needed to elucidate the relationship linking obesity and dietary intake.
BMI; waist circumference; macronutrient intake; energy intake; children; obesity
Several studies investigating the relationship between
body mass index (BMI), waist circumference (WC) and/
or skin fold measurements with energy intake have
suggested that the macronutrient composition of the diet
(protein, carbohydrate, fat) may play an important
contributing role to obesity in childhood as it does in adults
[1-4]. A report by the World Health Organisation in
2000 revealed that worldwide over 1.5 billion adults are
overweight, with approximately 4 million of these adults
being classified as clinically obese . According to
Rodriguez et al. (2006) the proportion of children who
are overweight or obese ranges between 15-25% in
many Western societies . Equally alarming, however,
is the dramatic increase in the prevalence of childhood
obesity which, in Australia, has more than doubled
within the last two decades [7,8].
The Australia Health and Fitness Survey conducted in
1985, reported that 10.7% of boys and 11.8% of girls aged
seven to fifteen years were overweight or obese .
Margery et al. (2001) reanalysed data from this study and the
1995 Australian National Nutrition Survey  using
Body Mass Index (BMI) cut-off points established by the
International Obesity Task Force , and concluded that
the prevalence of overweight and obese children aged 7 to
fifteen years had increased to 20% for boys and 21.5% for
Since 1995, in Australia, there have been no nationally
representative studies into the habitual nutritional
intakes of children and the population of children who
are overweight and/or obese. Three individual states
(New South Wales, Western Australia, and Queensland)
however, have recently conducted state based surveys
[13,14] illustrating that the number of overweight and
obese children had increased in recent years.
The aetiology of obesity, like most biological
functions, is multi-factorial but is believed to arise due to
an imbalance in the generalised energy balance
equation [15,16]. However, some studies, which have
investigated energy intake in children, have failed to show
that fatter children have higher reported energy
intakes, which may have been due to underreporting of
food intake in obese children/adolescents [17,18].
Statistical techniques to reduce reporting bias which
exclude illogical or invalid (either from over or
underreporting) energy intake data, have been developed by
Goldberg et al. (1991) and further refined by McCrory
et al. (2002) [19,20].
Studies investigating the relationship between energy
intake and adiposity in children and adolescents have
produced confounding results. While some studies have
found an inverse relationship, others have found no
association between the two variables. As a result of the
discrepancies between studies and the inconsistency of
results, researchers have started to explore the possible
relationship between adiposity and diet composition
(percentage energy intake from macronutrients). Several
cross sectional studies have reported a positive
relationship between fat intake and the degree of adiposity in
children [3,18,21] while others have not [12,22]. There
are only few published studies, however, which have
specifically addressed fat, protein and carbohydrate
intake in relation to BMI and WC in children [23,24].
From these studies it is evident that the role of dietary
composition in relation to the development of childhood
obesity remains inconclusive and requires more
The aim of the present study was to explore the
possible relationships of BMI and WC in children and
adolescents and relate these parameters to gross daily
energy intake and the percentage energy intake derived
from macronutrients. As under-reporting of energy
intake is common, particularly in overweight
populations [17,25,26] the effect of mis-reporting on these
associations was addressed at the outset by applying the
McCrory cut-off  which eliminated both under and
over reporters of energy intake.
The Healthy Kids Queensland Survey took place
throughout Queensland, Australia from April to September 2006.
A random sample of 112 schools from all primary and
secondary schools taken from government and
non-government sectors were invited to take part. A random cluster
design was used and the data were weighted to ensure
equal probability of inclusion of all children in the target
population. Further, to maximise the statistical power of
the survey, three key age groups at critical times in growth
and development were chosen: the first year of
compulsory schooling, i.e. about 5 years of age (Year 1), just prior
to puberty, i.e. about 9-11 years of age (Year 5) and about
14 to 16 years of age, (the last year of compulsory
schooling), (Year 10).
The survey aimed to recruit children representative of
Queensland children and, to this end, 59 schools in
urban areas and 53 from rural areas were chosen
randomly. Of these, 72 agreed to take part. The sample
represented a mix of 39 schools in urban areas and 33
schools in rural areas. The definition of an urban school
was that the school was based in a location with an
Accessibility-Remoteness Index of Australia Plus (ARIA
+) category of 1 and was deemed highly accessible
(AIHW, 2004). A rural school was defined as a school
location with an ARIA+ category of 2-4 that was deemed
accessible through to remote. The only exclusion criteria
were schools with fewer than 25 students, special schools
and schools that were classified as very remote
according to ARIA+. A total of 3691 children and adolescents
from years 1, 5 and 10 participated in the survey. The
Healthy Kids Queensland 2006 survey was conducted
according to the guidelines laid down in the Declaration
of Helsinki and all procedures involving human subjects
were approved by The University of Queensland Ethics
Committee, and the Education Queensland Ethics
Committee. Informed written consent was obtained from
both the participating subject and their parent/guardian
before the study commenced.
Height and weight were measured and data were
collected as described by Davies et al., 200. Height was
measured twice to the last completed millimetre and the
mean of the two measurements recorded. If the two
measurements differed by more than 5 mm, height was
recorded again until two measurements which did not
differ by more than 5 mm were recorded. Body weight
was measured once, to the nearest 0.1 kg using digital
scales (Tanita HD316, Tokyo, Japan). WC was measured
to the nearest 0.1 cm in two places: around the
umbilicus, and half way between the last rib and iliac crest. The
mean of these two measures were included in the
analysis. BMI was calculated using height and weight, and
converted into age and gender specific BMI z-scores using
LMS reference parameters provided by the Centres for
Disease Control (CDC Growth Charts, Accessed 9 March
2007) to provide a continuous variable, appropriate for
comparing overweight and obesity among children .
However, raw WC data split into age and sex bands were
used instead of z-scores as previous studies have shown
that there is a relationship between age and WC .
24 hr Food and Drink Record
Food and drink records over a 24-hour period (24 hr
FDR) were completed by all Grade 5 and 10 children
and 25% of Grade 1 children. A comprehensive 24 hr
FDRs methodology is detailed in the Healthy Kids
Queensland Survey 2006 Summary Report . In
summary, children were shown how to complete a 24 hr
FDR in a classroom presentation. They were asked to
record information on the day/date the record was
completed, start (time they woke-up) and finish (bedtime)
times, detailed descriptions of all foods and beverages
consumed during the period they were awake, recipes,
as well as the amounts eaten. They were provided with
a standard set of measuring cups and spoons and a
ruler to assist with quantification of foods and beverages
consumed. Parents, of children in Year 1 were asked to
assist the children in completing the records if
necessary. All 24 hr FDRs were reviewed by the research
team with each individual student (and parents of the
youngest children), to ensure sufficient detail and
accuracy of records and measurement were provided for the
analysis of dietary intake.
The 24 hr FDRs were analysed using FoodWorks
Professional Edition (2005 Version 4.00) Foodworks is a
nutrition analysis software program that uses the
AUSNUT database (produced by Food Standards Australia
New Zealand). For energy intake analysis, Foodworks
output presented data on total energy intake as well as
energy from protein; carbohydrates and fat (saturated,
A team of coders completed the 24 hr FDR data entry.
Two team members were responsible for researching,
developing coding instructions, and checking queries for
quality control purposes and to ensure consistency
between all the coders. All entered data were then
assessed twice by two different coders to eliminate
errors that would affect total energy intake calculations.
Identification of mis-reporters of energy intake
Basal Metabolic Rate (BMR) was predicted using age
and gender specific equations, taking into account
measured height and weight . Total Energy Expenditure
(TEE) was estimated from BMR multiplied by a physical
activity level, assumed to be 1.5 (PAL = 1.5) . A
ratio of reported energy intake to predicted TEE was
calculated for each child as suggested by McCrory et al.
(2002) . The plausible range of these ratios was
calculated using data from Huang et al. (2004) . The
equation used takes into account the coefficient of
variation in energy intake reporting, the coefficient of
variation in predicting total energy expenditure using
published equations and the coefficient of variation in
total energy expenditure as measured by doubly labelled
water. Only those who returned physiologically plausible
energy intake records have been used in this analysis.
A detailed description of the sampling method has been
reported in the HKQ Summary report. All data arising
from the survey was weighted to ensure the equal
probability of inclusions of all children in the target
population. Results are reported as mean standard deviation.
Associations between dietary intake variables and BMI
z-score and WC were examined by Pearsons
productmoment correlation coefficients. To see if there was any
association between energy intake and macronutrient
intake with BMI and WC, subjects were divided into
quartiles based on their BMI z-score and WC in
centimetres. Mean energy intakes and percentage energy
from various macronutrients were ascertained for each
quartile. Dunnets post hoc tests were performed to
examine which quartiles were significantly different to
Regression analysis was used to determine the extent
to which diet composition, age and gender were
associated with BMI and WC. Statistical analyses were
performed using the SPSS for Windows statistical package
(Version 17.0; SPPS Inc, Chicago, IL.).
Data were collected throughout Queensland from April
to September 2006. The HKQ survey aimed to recruit
children across Queensland, and to this end, 59 schools
in urban areas and 53 from rural areas were chosen
randomly. Of these, 72 agreed to take part - a response rate
of 65%. The sample represented a mix of 39 schools in
urban areas and 33 schools in rural areas. Of the 3691
children who participated in the survey 2460 subjects
had both complete 24HFDR and anthropometric data
(Height, Weight, and WC). The participation rates in
this aspect of the survey were 85%, 94% and 92% in
grades 1, 5, and 10, respectively.
After screening out implausible energy intake
records using the McCrory approach, 1352 records
(55%) remained (Table 1). The mean and standard
deviation for energy intake and percentage energy intake
from various macronutrients for both sexes in the 3
Table 1 Subject characteristics, energy intake and percentage macronutrient intake in plausible energy reporters (n =
Age (years) Height (cm) Weight (kg)
Values are means standard deviation. WC; waist circumference. Fat, Carbohydrate and Protein are expressed as % of energy intake.
1Significantly different to female.
year groups are shown in Table 1. There was a
difference in energy intake between the two sexes in all age
groups, with males having a significantly higher energy
intake than females. Macronutrient distribution on
average was protein 17%, carbohydrate 51%, and fat 32% of
BMI z-score was significantly but modestly correlated
with total energy intake across all age groups (Table 2).
However, no correlation was found between BMI
z-score and percentage energy intake from protein,
carbohydrate or fat. Likewise, there was a significant
correlation between WC and energy intake across all age
groups (Table 2).
When subjects were grouped based on quartiles of
BMI z-score, a one way ANOVA showed that those
with a higher BMI z-score consumed significantly more
energy than their lean counterparts, however, percentage
of protein, carbohydrate and fat intake were similar
across the groups (Table 3). When grouping subjects
Table 2 Pearson product-moment correlation coefficients
between BMI z-score and WC with dietary intake
based on quartiles of WC, there was no significant
difference between quartiles in any of the age groups for
any macronutrient (Table 4). For year 1 children there
was no significant difference in total energy intake
across the quartiles of WC, however in years 5 and 10,
there was a significant increase in total energy intake in
relation to WC (Table 4).
Using multiple regression techniques, with BMI
zscore as the independent variable, and percentage
energy from protein, carbohydrate and fat as dependent
variables (all entered together), neither the percentage
carbohydrate, protein nor fat intake had a significant
impact on predicting BMI z-score. Moreover, when
using percentage energy from protein, carbohydrate and
fat intake to predict WC in a multiple regression, none
of the macronutrients had a significant affect.
Numerous studies in adults have found that diet
composition may play an important role in the development of
adiposity [2,4]. Researchers are now investigating the
relationship between diet composition and adiposity in
children [3,33]. Several studies investigating the
relationship between BMI, waist circumference (WC) and/or
skin fold measurements with energy intake have
suggested that the macronutrient composition of the diet
(protein, carbohydrate, fat) may play an important
contributing role to obesity in childhood [33-35] as it does
in adults [1-4]. However conflicting results have been
found . This study aimed to explore the possible
relationship between BMI and WC with energy intake
and percentage energy intake from macronutrients in
children and adolescents, using a more robust technique
by eliminating potential food reporting errors.
Studies using BMI as a parameter for overweight and
obesity such as Rocandio et al., (2001) and Hassapidou
et al., (2006) found that overweight adolescences of both
sexes reported lower energy intake than non- overweight
Table 3 Energy intake and percentage energy from macronutrients based on quartiles of BMI z-score
1 Significantly different to quartile 3.
2 Significantly different to quartile 4. (p < 0.05) ANOVA.
subjects [36,37]. However, our findings, where we have
accounted for mis-reporters, demonstrate a weak but
significant positive correlation between BMI z-score and
energy intake. When examining the relationship between
BMI z-score and energy intake based on quartiles, there
was a significant difference amongst the groups,
suggesting that our population of overweight children and
adolescents consume more energy than their leaner
counterparts. Similar findings were seen when using WC
to assess the degree of overweight and obesity. Once
again a weak but significant positive correlation between
WC and total energy intake across all age bands was
demonstrated. When comparing groups based on
quartiles of WC there was significant difference between the
groups in year five and ten. Yet, no significant differences
between the groups were found in younger children (year
Energy intake from carbohydrate has been shown
in adults to be inversely associated with body fat
[22,36,38]. Studies by Hassapidou et al. (2006) and
Ortega et al. (1995) found that overweight and obese
adolescents consumed fewer carbohydrates than lean
subjects [23,37]. Again, once under-reporters are
omitted, the results from this study differ in that there
was no association between carbohydrate intake and
either BMI z-score or WC. Again, there was no
significant difference between the groups when comparing
percentage carbohydrate intake based on quartiles of
BMI z-score and WC. Similar to studies by Rocandio et
al. (2001) and Maffeis et al. (1996) in which no
difference in protein intake was found between overweight
and non overweight children and adolescents, our
findings show that percentage protein intake did not differ
between quartiles of BMI z-scores or WC [18,36].
While the majority of studies have demonstrated a
positive association between adiposity and dietary fat
our results conflict with these findings [3,22,33,39]. An
inverse relationship was observed between fat intake and
Table 4 Energy intakes and % energy from macronutrients based on quartiles of waist circumference in year 1, 5 and
Total Energy (Kj/day)
1 Significant difference between quartiles of energy intake- significantly different to quartiles 3 and 4.
BMI z-score and similar results were also found when
using WC as a reference for adiposity, however these
correlations were not significant. When subjects were
split into quartiles based on BMI z-score and WC, there
was no significant difference between the groups, which
support the results found by Hassapiduo et al. in 2006,
in which no significant difference in percentage fat
intake was found in overweight and non overweight
Using multiple regressions we found that the
macronutrient composition of the diet had no significant
impact on the ability to predict BMI z-score or WC.
Researchers have suggested that the reason why a
clear relationship between total energy intake and
percentage macronutrient intake and adiposity has not
been seen is due to the under reporting of foods. This
study applied the McCrory cut off  and those with
an implausible energy intake were excluded from the
analysis, thus applying a more robust method of self
reported energy intake. The results suggest that the lack
of association between BMI z-score and WC with
macronutrient intake was not a direct result of
underreporting and that total energy intake is more influential
than the macronutrient composition of the diet in the
development of childhood obesity.
However, perhaps one of the reasons of conflicting
findings from various studies is the use of BMI itself as
a measure of adiposity. While a simple, convenient
assessment, the accuracy and use of the BMI as an
indicator of body fatness in children is questionable .
Riley et al. 2000, suggested that BMI cut -offs are non
specific, thus tending to identify non-obese stocky
children as obese . On the other hand, there is also
a concern that BMI cut-offs fail to identify the obese
child . We attempted to partly overcome this by also
exploring WC as a measure of obesity; however our
correlations with WC and dietary components were very
similar to those with BMI, and added nothing further to
the study. It may be that WC should have been adjusted
for body size. Studies investigating the relationship
between dietary intake and the development of
childhood obesity have used various screening tools to group
children based on their level of adiposity, and as a
result, the difference in methodologies between studies
may account for conflicting findings.
Similarly, the use of different assessment tools to assess
dietary intake in children and adolescents may explain
some of the variability in previous studies assessing
dietary intake and adiposity. In the current study, dietary
intakes and food habits were assess by a 24 h food and
drink record, similar to the 2003 Physical Activity and
Nutrition Levels in Western Australian Children and
Adolescents Report, which was adapted from the 1995
National Nutrition Survey. In this survey a 24 hour
dietary record was used, a common approach in large
population based studies. Whilst the 24 hour dietary
record may not always represent habitual intake we have
attempted to limit the error by screening the intake data
via the method described by McCrory et al. .
In light of the escalating prevalence rates of obesity and
chronic disease, governments and health organisations
are endeavouring to develop and implement appropriate
public health strategies to prevent and manage obesity,
and promote nutrition and physical activity. This study
presents vital information on the energy intake, diet
composition, and adiposity of Queensland children and
adolescents, which may prove useful in refining policies
and practice. Several studies have failed to demonstrate
that overweight and obese children have a higher energy
intake than healthy children. This was not the case here.
We showed that total daily energy intake was higher in
the overweight and obese groups. It has also been
suggested that diets high in fat and low in carbohydrate
may cause an accumulation of excess body fat even
when total energy intake is not in excess. However, in
this current study there was no evidence of such a
relationship. As this was a cross -sectional study designed
to provide a snap shot of dietary intake and
anthropometry, no valid assumptions into the cause/effect
relationship of obesity and dietary intake can be made and
more longitudinal studies to elucidate this relationship
are urgently needed, including those that use more
rigorous techniques to measure body composition.
Queensland Health commissioned and funded the Healthy Kids Queensland
Survey. A Steering Committee including representation from Queensland
Health, Education Queensland, Independent Schools Queensland, the
Queensland Catholic Education Commission and the Queensland
Department of Local Government Sport and Recreation provided advice,
guidance and support regarding the survey. The authors would like to
acknowledge the research officers that collected the data on behalf of the
Healthy Kids Queensland research team and the families and schools who
kindly agreed to participate in the State survey.
SE wrote the manuscript, RA and AL critically reviewed and commented on
manuscript. PSWD, HT and CH provided analytical support and commented
on manuscript. All authors have read and approved the final manuscript.
The authors declare that they have no competing interests.
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