Analysis of factors contributing to diet quality outcomes
Of the 146 participants, 114 had a BMI z-score and were included in the
cluster analysis. Of these, 55 were CCS and 59 were age-matched
controls. The cluster analysis used four factors: child BMI z-score,
ACARFS score, child emotional overeating score and child picky eating
score, and the generated hierarchy was accepted at the three-cluster
level. The allocation of healthy controls and CCS to these three groups
is shown in Table 6, with all three clusters
being a mixture of participants.
The first cluster (normal eating behaviour) was characterised by
children with a healthy BMI z-score, good diet quality and low altered
eating behaviours (picky eating/emotional overeating) and included 15
(27%) CCS and 29 (49%) controls. The second cluster (low BMI z-score,
altered eating behaviour and poor diet quality) had the smallest number
of participants with 8 (15%) CCS and 4 (8%) control. This cluster was
characterised by children with a low (underweight) BMI z-score, poor
diet quality and altered eating behaviour, particularly picky eating.
Cluster 3 (high BMI z-score, altered eating behaviour and poor diet
quality) was characterised by children with a high BMI z-score (trending
to overweight), poor diet quality and altered eating behaviour,
particularly overeating (32 (58%) CCS and 26 (44%) controls (TABLE 6).
Further analysis of the survivor group was undertaken to describe the
treatment received by each child in each group. Children who had
Non-Hodgkin’s lymphoma and Wilms’ tumour were found to have the highest
proportion of CCS in cluster 3, with brain tumour survivors having the
highest proportion of children in cluster 2 (Supplementary Table 1).
Discussion
This study revealed that CCSs consume diets of poorer quality and have
higher rates of picky eating than their age-matched peers in the control
group. It also appears that CCS eating behaviour and diet quality may be
influenced by treatment intensity and the child’s cancer diagnosis.
Both the CCS group and the control group were found to have poor dietary
intake for vegetables, grains, meat protein and dairy, affecting overall
diet quality. These findings are in line with global patterns of dietary
intake in CCSs. Zhang et al (2015) reported young childhood cancer
survivors had poor adherence to the US dietary guidelines and
consequently poor diet quality. This poor diet quality of CCS is of
concern, as CCS are at a higher risk for early mortality and CVD than
their peers (16). There is also a known association
between poor dietary intake and many chronic diseases such as
cardiovascular disease, metabolic syndrome and type 2 diabetes in the
general population(25).
This evidence of poor adherence to dietary guidelines in CCS needs to be
addressed in young cancer survivors. The finding of low vegetable intake
for childhood cancer survivors is of particular concern due to the
importance of folic acid intake and homocysteine
levels(17,26). Decreased folic acid intake through a reduced
intake of green leafy vegetables can result in an increase in
homocysteine levels which are associated with endothelial dysfunction,
increased low-density lipoprotein levels and ultimately atherosclerosis
which can lead to an increased risk of
CVD(17,
26).
This is the first study to demonstrate higher rates of picky eating in
CCS than their peers, especially in CCS diagnosed at a young age.
Published research into the general population has demonstrated the
negative impact of a disrupted learning process for eating behaviour,
which may then lead to the development of eating behaviours such as
picky eating (2-4). The
knowledge that children often receive cancer treatment during this
critical eating development phase disrupting the learning process,
suggests that cancer therapy may have a lasting effect on a child’s
eating behaviour after treatment completion. In the general population
children with picky eating consume less grains, fruits and vegetables
than non-picky children resulting in low levels of vitamin E, vitamin C,
folate and fibre, greatly impacting their diet quality(32,
33). Eating behaviours such as picky
eating in CCS, are also likely to result in limited dietary intake and
poor diet quality. Considering the importance of a healthy diet to
reduce the risk of CVD and MetS(34), education
focusing on eating behaviours needs to be considered.
Our study found several factors that may drive the diet quality of CCS.
Diet quality, picky eating, overeating and BMI z-score were each
represented in the three distinct clusters, suggesting they all may have
a role in body size outcomes after treatment completion. In addition,
comparison of cancer diagnostic classification with cluster group
identification, indicated a distinct difference between the diagnosis of
the child and the cluster they were allocated. This suggests that
diagnosis groups which received single treatment modality regimens, such
as Non-Hodgkin’s lymphoma and Wilms’ tumour, may be more likely to have
a higher BMI z-score, altered eating behaviour and poor diet quality.
These results may indicate an influence of treatment regimen on the four
underlying variables that drive the cluster groups. Diagnosis groups
such as brain tumour survivors that can receive more than one treatment
modality (chemotherapy and radiation) might be more likely to have a
lower BMI z-score, altered eating behaviour and poor diet quality. The
findings are consistent with previous studies that have shown an
increase in energy intake and BMI of Acute Lymphatic Leukaemia survivors
who are considered to have a less intensive treatment regimen than other
diagnostic
categories(35). The
identification of this group through the use of cluster analysis
provides a possible indication of which cancer diagnostic classification
groups might be more likely to develop altered eating behaviour
problems, poor diet quality and altered body size, allowing for targeted
nutritional intervention after cancer treatment.
Limitations
This study was limited by a low response rate from CCS reducing the
generalisability of results and allowing for potential bias within the
small sample. However, low response rates are not unusual in the
childhood cancer study population (36). The small and
heterogeneous nature of the sample may also have limited our ability to
detect significant relationships between certain variables. Secondly,
the reliance on parent-reports of their own and their child’s height and
weight resulted in a high proportion (22%) of missing and possibly
inaccurate data for this variable. In addition, the use of BMI z-scores
to represent nutritional status is not ideal and prone to error in
children with cancer, with BMI being a poor representation of body
composition (37). Finally, the study design involved
parent reported measures for diet and feeding variables. Despite being
validated tools, the potential for reporter bias is high, especially for
diet intake of the child and a parent’s feeding behaviour with the
child. This may have resulted in an over reporting of diet variety and
under-reporting of negative parent feeding behaviours. The use of more
rigorous dietary intake measures such as a weighed food diary might be
required for a more accurate measure of dietary intake in future
studies.
Conclusion
This study found that CCS who have recently completed cancer treatment
have a poor diet quality compared
to age matched controls and have high rates of picky eating. Given that
dietary habits established early in life are known to transpire into
adulthood, and poor eating habits are linked to increased risk of Mets
and CVD, young children who have recently completed cancer treatment
with apparent poor dietary quality and altered eating behaviour are in
critical need of dietary intervention early after cancer treatment. A
tailored intervention that addresses altered eating behaviours, parent
feeding practices and how to manage altered dietary intake due to
treatment side effects is needed to bring about sustainable change for
children early after cancer treatment.