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© 2010 The Royal Australian and New Zealand College of Psychiatrists
Associations between diet quality and
depressed mood in adolescents: results from
the Australian Healthy Neighbourhoods Study
Felice N. Jacka , Peter J. Kremer , Eva R. Leslie , Michael Berk , George C. Patton ,
John W. Toumbourou, Joanne W. Williams
Objective: Adolescence frequently coincides with the onset of psychiatric illness and
depression is commonly observed in adolescents. Recent data suggest a role for diet quality
in adult depression. Given the importance of adequate nutrition for brain development, it is
of interest to examine whether diet quality is also related to depression in adolescents.
Methods: The study examined 7114 adolescents, aged 10–14 years, who participated in
the Australian Healthy Neighbourhoods Study. Healthy and unhealthy diet quality scores
were derived from a dietary questionnaire. The Short Mood and Feelings Questionnaire for
adolescents measured depression. Adjustments were made for age, gender, socioecon-
omic status, parental education, parental work status, family con ict, poor family manage-
ment, dieting behaviours, body mass index, physical activity, and smoking.
Results: Compared to the lowest category of the healthy diet score, the adjusted odds ratios
(95% con dence interval) for symptomatic depression across categories (C) was: C2 0.61
(0.45–0.84); C3 0.58 (0.43–0.79); C4 0.47 (0.35–0.64); and C5 0.55 (0.40–0.77). Com-
pared to the lowest quintile, the adjusted odds ratios (95% con dence interval) for symp-
tomatic depression across increasing quintiles of the unhealthy diet score were: Q2 1.03
(0.87–1.22); Q3 1.22 (1.03–1.44); Q4 1.29 (1.12–1.50); and Q5 1.79 (1.52–2.11).
Conclusions: Our results demonstrate an association between diet quality and adolescent
depression that exists over and above the in uence of socioeconomic, family, and other
potential confounding factors.
Key words: adolescents , depression , diet , nutrition.
Australian and New Zealand Journal of Psychiatry 2010; 44:435–442
Felice N. Jacka , Research Fellow (Correspondence)
The University of Melbourne, Department of Clinical and Biomedical
Sciences: Barwon Health, Geelong, Vic. 3220, Australia. Email: felice@
barwonhealth.org.au
Peter J. Kremer, Lecturer; Eva R. Leslie, Associate Professor
School of Psychology, Deakin University, Geelong, Victoria, Australia
Michael Berk, Professor of Psychiatry
University of Melbourne, Department of Clinical and Biomedical
Sciences: Barwon Health, Orygen and MHRI Research Centre, Mental
Health Research Institute, Melbourne, Victoria, Australia
George Patton, Professor of Adolescent Research; Joanne W. Williams,
Senior Research Fellow
Centre for Adolescent Health, Murdoch Children ’ s Research Institute,
Royal Children’s Hospital, Victoria, Australia
John W. Toumbourou, Professor and Chair in Health Psychology
School of Psychology, Deakin University, Geelong, Victoria, Australia
Received 10 November 2009; accepted 17 December 2009.
Adolescence is a time of rapid physical, psychological,
and social development. Unfortunately, this period fre-
quently coincides with the onset of psychiatric illness;
three-quarters of lifetime psychiatric disorders will fi rst
emerge in adolescence or early adulthood [1]. Diet and
nutrition modulate the pathophysiological factors
underpinning depressive illness, and there are plausible
reasons for examining the potential role of diet in
436 DIET AND DEPRESSION IN ADOLESCENTS
depression [2]. We have recently demonstrated inverse
associations between habitual diet quality and the likeli-
hood of clinically determined depressive and anxiety
disorders in a large, representative sample of Australian
adult women [3], and between the dietary intake of mag-
nesium, which may act as a proxy for a healthy diet, and
depression in a large sample of community-dwelling
Norwegian adults [4].
Two very recent prospective studies have demonstrated
associations between diet quality and incident depression.
In the fi rst study of more than 10 000 Spanish adults, a
lower incidence of depression was seen in individuals
adhering to a Mediterranean diet [5]. In the second study,
undertaken in the Whitehall II cohort, a whole food dietary
pattern was associated with reduced odds, and a western
dietary pattern increased odds, of self-reported depression
after fi ve years of follow up [6]. In both of these studies,
the study participants were middle aged. Given the impor-
tance of adequate nutrition for optimal brain development
[7] and function [8], it is of particular interest to examine
whether diet quality is also related to depression in ado-
lescents. Only one previous study, which used parental
ratings of behaviour, has reported on dietary components
and adolescent mental health [9]. This recent study found
an inverse association between the consumption of fruits
and leafy green vegetables and behavioural problems, and
a positive association between a western type dietary pat-
tern (red meat, snack and processed foods) and increased
internalizing and externalizing behaviour [9].
We aimed to replicate and extend this fi nding using a
larger sample of adolescents from a diverse range of socio-
demographic backgrounds, and utilizing a tool specifi cally
designed to measure adolescent depression by self-report.
We hypothesized that a poor quality diet would be associ-
ated with higher rates of depression, and a better quality
diet related to lower rates, in both males and females in
early adolescence. We further hypothesized that the pos-
ited associations would only be partly explained by demo-
graphic, family, lifestyle, eating behaviour, socioeconomic
and other potential confounding factors.
Methods
Participants
The Healthy Neighbourhoods Study was a large observational study
conducted in 231 Australian schools in 2006, and focusing on adoles-
cent health and wellbeing. The participants (n 8255) were year six
and year eight students from 30 communities across three states, strat-
ifi ed by level of socio-economic disadvantage. Socio-economic status
(SES) was determined using Socio-Economic Index For Areas (SEIFA)
scores, based on 2001 Australian census data. The chosen index was
the Index of Relative Socio-economic Advantage/Disadvantage
(IRSAD), which accounts for high and low income, and the type of
occupation from unskilled employment to professional position. A low
score as measured by IRSAD identifi es the most disadvantaged
(quartile 1), and a high score identifi es the most advantaged (quartile
4). Half the communities were from regional areas and the other half
were from urban areas. A random selection of schools in the selected
areas were invited to participate in the survey and just over half (53%)
agreed. There was little difference in the participation rate for the edu-
cation sectors (government, independent and Catholic). Students
required parental consent to participate in the survey. The return rate
of the consent forms varied across schools, however 92% of the parents
who did return the consent forms at year six level gave their permission
for their children to participate, and at year eight, 89% consented.
Those with missing data on any of the key variables (dietary questions,
mental health indicator, or covariates included in analyses) were
excluded from analyses (n 1141, 13.8 %), resulting in a fi nal sample
of 7114 adolescents aged 10–14 years (mean 11.6 years, SD 0.81).
The Royal Children’s Hospital Human Research Ethics Committee
and relevant school and institutional ethics committees in each state
provided ethical approval for the study.
Dietary measures
The Healthy Neighbourhoods Study included a 14-item dietary
questionnaire based on a questionnaire used in the Amherst Health and
Activity Study Adult Survey of Child Health Habits [10] and modifi ed
to include additional questions about the consumption of breakfast,
different types of beverages, and takeaway food. Some items were
modifi ed to Australian equivalents. The questionnaire was designed to
assess dietary patterns associated with positive energy balance and food
behaviours and measures the intake of key foods that are indicative of
less/more healthy food choices.
Based on Dietary Guidelines for Children and Adolescents in
Australia [11], a healthy diet score was constructed from answers to
four dietary items (response options 1–6: none through to fi ve or more
serves per day). Participants were given a point for each of the follow-
ing: breakfast everyday before school; low fat dairy food at least once
per day; at least two serves of fruit per day; and at least four serves of
vegetables per day. The range for the fi nal score was zero (no points)
to four, which was subsequently recoded as categories 1 (low healthy
diet) to 5 (high healthy diet). An unhealthy diet score was derived from
the sum of answers to the following four questions: consumption of
hamburgers, hot dogs or sausages; potato crisps or savoury snacks;
biscuits, doughnuts, cake, pie or chocolate; and sweet drinks such as
soft drinks, cordial, Big M, fl avoured mineral water etc. (response
options 1–6: none through to fi ve or more serves per day); plus a ques-
tion regarding the frequency of takeaway foods (response options 1–6:
less than once per month through to most days). The possible range for
the unhealthy diet score was thus 5–30. The unhealthy diet score was
subsequently categorized into quintiles (Q1 8, Q2 9–10, Q3 11,
Q412–14, Q5 15).
Mental health
Depression was measured utilizing the Short Mood and Feelings
Questionnaire (SMFQ), an instrument designed for use in epidemiological
F. N. JACKA, P. J. KREMER , E. R. LESLIE, M. BERK, G. PATTON, J. W. TOUMBOUROU, J. W. WILLI AMS 437
relationship of interest. Effect modifi cation by age or gender was also
assessed. A separate analysis assessed differences between those
included/not included in the analyses on the basis of missing data. All
analyses were conducted using SPSS (V16) and statistical signifi cance
accepted as p 0.05.
Results
The median SMFQ score was 5 Interquartile range (IQR 2–10).
Neither age nor gender was an effect modifi er. Table 1 presents charac-
teristics of the study participants above and below the symptomatic cut-
off ( 8 symptomatic) for the SMFQ. Comparisons indicated that a
higher proportion of those categorized as symptomatic were female; less
physically active; smoked cigarettes; were in the risk category for both
family confl ict and poor family management; had lower SEIFA scores;
had higher dieting scores; and had a father who was not employed in
full-time work and did not have a tertiary degree.
A higher proportion of those not included in the fi nal analyses on
the basis of missing data were from lower SES backgrounds; had par-
ents who were less likely to be working full-time and with lower edu-
cational levels; and were at risk for family confl ict and poor family
management. They were also slightly younger, and had higher scores
on the Adolescent Dieting Scale. However, there were no differences
observed on total SMFQ scores or in the proportions of those symp-
tomatic on the SMFQ.
Table 2 presents correlations between both healthy and unhealthy
dietary scores and covariates. A higher healthy diet score was associ-
ated with: younger age; more physical activity; less smoking; higher
paternal and maternal education; higher paternal employment status;
higher SES; lower risk of family confl ict and poor family management;
and a higher dieting score. In contrast, an unhealthy diet was associated
with: being male; a lower dieting score; less physical activity; greater
family confl ict and poor family management; smoking; lower parental
employment and education status; a lower BMI; and lower SES. Most
of these correlations were weak, however, and statistically signifi cant
as a function of the large sample size.
Table 3 presents results of the logistic regression analyses, with scores
on the healthy diet score as exposures against symptomatic depression.
Compared to individuals with the lowest score on the healthy diet scale,
being in the category with the highest score (5/5) nearly halved the like-
lihood of being symptomatic. The inverse relationship of healthy diet
score to symptomatic depression was apparent both before and after
adjustments for age, gender, physical activity, father’s work status, SES,
adolescent diet scale, and family confl ict and largely followed a dose-
response curve. Adjustment for the variables of smoking, father’s educa-
tion, mother’s education and work status, poor family management and
BMI, did not attenuate the relationship between diet scores and symp-
tomatic depression, and these variables were not included in the fi nal
models. Figure 1 presents the mean SMFQ scores (and 95% confi dence
intervals) for each category of healthy diet score, and demonstrates that
the pattern of association between dietary scores and SMFQ scores is
concordant with the odds ratios for symptomatic depression.
Table 3 also presents odds ratios for symptomatic depression across
quintiles of unhealthy diet score. In this analysis, quintiles two, three,
four and fi ve are compared to quintile one (lowest) on the unhealthy
diet scale. Results indicated a signifi cant, dose-response relationship
studies of depression for children and adolescents [12]. The SMFQ
comprises 13 items relating to mood states (scale 0–2, yielding total
score ranging from 0 to 26), has high internal consistency (α 0.85),
and correlates with other well-validated measures such as the Children’s
Depression Inventory (CDI) and the Diagnostic Interview Schedule for
Children (DISC) depression scale [12].
Covariates
SES was categorized using IRSAD scores, described above. Father
and mother’s work status comprised three categories: zero (not work-
ing, retired, or not cohabitating), one (part-time work), and two (full-
time work). Father and mother’s education also comprised three
categories: one (did not complete high school), two (completed high
school), and three (has a university degree). Family environment was
measured using two scales developed for the Communities That Care
youth survey [13]. Poor family management was assessed using nine
questions, including questions such as “would your parents know if you
did not come home on time?” and “the rules in my family are clear”.
Family confl ict was measured with three questions, including “people
in my family have serious arguments” and “people in my family often
insult or yell at each other”. Both scales had a four-point response scale
(NO!, no, yes, YES!), and were subsequently dichotomized to represent
low (mean 3) and high (mean 3) risk. Attitudes to eating were
assessed using the Adolescent Diet Scale [14] and comprised the sum
of eight questions with response options ranging from zero (seldom/
never) to four (almost always). The scale included questions such as
“Do you try to avoid ‘fattening’ foods or foods with sugar in them?”
and “Do you skip meals other than breakfast because you are watching
your weight?”. Physical activity level was based on the question: “Over
a normal week, on how many days were you physically active for a
total of at least 60 minutes per day?” with scores ranging from zero
through to seven (subsequently recoded as 1–8). Smoking was based
on the question “in the last 30 days have you smoked cigarettes” and
comprised the categories zero (no) through to four (10 or more times).
Body mass index (BMI) was calculated, from measurements made at
the time of data collection, as weight/height² (kg/m²).
Statistical analysis
Total SMFQ scores were calculated, and a dichotomized depressive
symptomatology variable was created using the cut-point value
( 8 symptomatic) suggested by Angold et al . [12]. Independent
samples t-tests and Chi-square analyses were used to test for differences
on the covariate measures according to the depressive subgroups. Asso-
ciations between diet scores and other variables were measured using
Pearson’s r or Spearman’s rank correlation coeffi cient (Spearman’s
rho). Those categorized in extreme quintiles/categories of diet score
were also identifi ed.
Logistic regression models were developed to estimate odds ratios
with 95% confi dence intervals using symptomatic depression (no/yes)
as the outcome variable and diet quality scores as the exposures of
interest. Covariates including age, gender, SES, father’s and mother’s
education and work status, physical activity, smoking, BMI, family
confl ict, poor family management, and dieting behaviour, were tested
against depression in univariate analyses, then added sequentially to
the models in order to assess the relative impact of each on the
438 DIET AND DEPRESSION IN ADOLESCENTS
Guidelines for Children and Adolescents in Australia
[11] and recognized as important components of a healthy
diet, and greater consumption of unhealthy and processed
foods, are associated with increased odds for self-
reported symptomatic depression in adolescents. These
relationships remained robust after adjustment for a wide
range of potential confounding factors.
These fi ndings support previous research reporting
associations between diet quality and mental health out-
comes in adolescents [9], and between diet quality and
depression in adults, both cross sectionally [3] and lon-
gitudinally [5,6]. They suggest that both low intakes of
nutrient-dense foods, and/or high intakes of high-energy,
nutrient-poor foods are related to an increase in the like-
lihood of adolescents being depressed. However, it would
seem that an increase in the risk for symptomatic depres-
sion related primarily to those scoring at the extremes of
the dietary ranges, with a signifi cant decrease in the odds
ratio for depression associated with even one healthy
dietary practice, and less difference evident between the
subsequent categories. Only a small number of individu-
als who were low on the healthy diet score were also high
on the unhealthy diet score, indicating that the highest
between unhealthy diet and odds of being symptomatic for depression.
After adjustments for age, gender, physical activity, father’s work
status, SES, adolescent diet scale, and family confl ict, being in the
highest quintile of unhealthy diet score increased the likelihood of
symptomatic depression by nearly 80% when compared with those in
the lowest quintile. Again, adjustments for smoking, father’s educa-
tion, mother’s education and work status, poor family management
and BMI, did not alter these relationships. Figure 2 presents the mean
SMFQ scores (and 95% confi dence intervals) for each quintile of the
unhealthy diet score, concordant with the odds ratios for symptomatic
depression.
Finally, an analysis of extreme quintiles/categories of diet scores
indicated that individuals with a low healthy diet score were not the
same participants as those with high scores on the unhealthy diet; of
those in the lowest two categories for a healthy diet, only 515 (7.2%)
of these participants fell also into the top two quintiles for an unhealthy
diet. Of those in the top two categories for a healthy diet, however,
1557 (21.9%) were also in the lowest two quintiles on the unhealthy
diet score.
Discussion
In this study we report that both a lower adherence
to the consumption of foods promoted by the Dietary
Table 1. Characteristics of study sample (n 7114): Short Mood and Feelings Questionnaire (SMFQ)
non-symptomatic versus SMFQ symptomatic. Results presented as mean ( SD), median (IQR), or N (%)
SMFQ Non-symptomatic
(n 4748)
SMFQ Symptomatic
(n 2366) p-value
Age 11.6 (0.78) 11.6 (0.84) 0.15
Gender
Male 2384 (50.2) 1011 (42.7) 0.001
Female 2364 (49.8) 1355 (57.3)
Father’s employment:
Unemployed/retired/not
cohabitating
502 (10.6) 351 (14.8) 0.001
Part time 616 (13.0) 345 (14.6)
Full time 3630 (76.5) 345 (70.6)
SES (IRSAD score)
1 1288 (27.1) 690 (29.2) 0.001
2 1130 (23.8) 604 (25.5)
3 1071 (22.6) 577 (24.4)
4 1259 (26.5) 495 (20.9)
Dieting Behaviour 11 (9–11) 13 (10–16) 0.001
Physical Activity
( 60mins-days per week) 6 (4–7) 5 (3–7) 0.001
Smoking (times past 30 days)
None 4618 (97.3) 2192 (92.6) 0.001
1–3 113 (2.4) 135 (5.7)
3 17 (0.4) 39 (1.6)
Poor family management
High 83 (1.7) 131 (5.5) 0.001
Low 4665 (98.3) 2234 (94.5)
Family con ict
High 1009 (21.3) 1179 (49.8) 0.001
Low 3739 (78.7) 1187 (51.2)
F. N. JACKA, P. J. KREMER , E. R. LESLIE, M. BERK, G. PATTON, J. W. TOUMBOUROU, J. W. WILLI AMS 439
In interpreting these results, the characteristics of this
study should be considered. A particular problem in the
study of diet and disease is the potential for associations
to arise because of confounding by factors in the indi-
vidual’s background and social context. We were able to
assess SES at the household (parent’s work and educa-
tional status) and the area level (IRSAD scores), as well
as self-reported health behaviours (physical activity and
smoking). Moreover, important aspects of family envi-
ronment (confl ict and poor family management) that are
likely to have an impact on both diet and mental health
in adolescents were included as potential confounders.
The demonstrated associations between diet and depres-
sion status were robust after adjustments for these fac-
tors, however it is not possible to rule out residual
confounding as an explanation for these fi ndings.
Due to the cross-sectional design of this study, the
direction of the relationship between diet and depression
cannot be determined. Appetite changes are a common
feature of major depressive disorder in adults [15] and
dietary choices may be infl uenced by mental health
status. Adolescents are often concerned with physical
appearance, body weight and shape [16], and food choices
may be associated with unhealthy weight control behav-
iours, which may also be related to depression [17]. In
this study, however, we were able to assess adolescents’
attitudes to food and dieting and exclude unhealthy diet-
ing behaviours as a confounder in the relationship
between diet quality and depression. Moreover, previous
longitudinal research [5,6] does not support the reverse
causality hypothesis.
The questions that were used to assess healthy diet qual-
ity did not include several other accepted components of a
healthy diet (such as wholegrains, olive oils, and fi sh),
while the unhealthy diet questions may not have captured
all aspects of unhealthy food consumption (e.g. white
bread, sweet spreads, fatty and processed meats). Nor did
we have information regarding the composition of break-
fasts consumed. There is likely to have also been some
degree of social desirability bias in the answers to questions
regarding the frequency of fruit and vegetable consump-
tion, as guidelines regarding the adequate intake of fruit
and vegetables are likely to be familiar to many students
through educational programs directed at this age group.
Moreover, bias may have also resulted from differential
dietary reporting by those with depressive symptoms. The
survey items used may be less accurate than more in-depth
measures of dietary intake, such as 24-hour dietary recall
or food frequency questionnaires. However, given that
such measures may be unreliable in adolescents [18], sim-
pler dietary questionnaires such as we have used, are likely
to afford suffi cient information to adequately rank indi-
viduals in terms of their diet quality [19].
odds for depression according to the different measures
of dietary intake were not seen in the same individuals.
It appears that adolescents with a low healthy diet score
do not necessarily replace their healthy foods (such as
fruits, vegetables and low fat dairy), with less healthy
options (such as sweets, savoury snacks and takeaway
foods), but may instead consume a limited range of other
foods, such as bread, cereals, and meat. Conversely, a
larger number of individuals scored both high on the
healthy diet score and low on the unhealthy diet score,
indicating that those eating a healthier diet are less likely
to additionally eat unhealthy and processed foods on a
regular basis.
Table 2. Correlations (Pearson’s r or Spearman’s
Rho) between diet scores and covariates
Healthy diet
score
R or RS
Unhealthy
diet score
R or RS
Age –0.03∗–0.01
Gender
(Male 1, Female 2) 0.02 –0.08∗∗
Dieting scale 0.11∗∗ –0.04∗
Physical activity 0.22∗∗ –0.04∗
Risk poor family management –0.06∗∗ 0.05∗∗
Risk family con ict –0.09∗∗ 0.14∗∗
Smoking –0.05∗∗ 0.07∗∗
Father’s employment 0.06∗∗ –0.07∗∗
Father education 0.09∗∗ –0.11∗∗
Mother’s employment 0.01 –0.02
Mother’s education 0.10∗∗ –0.11∗∗
SES 0.03∗–0.09∗∗
BMI –0.02 –0.05∗∗
Signi cance: ∗ p 0.01, ∗∗p 0.001.
Figure 1. Mean total Short Mood and Feelings
Questionnaire score (95% CI) for each category
of healthy diet score.
440 DIET AND DEPRESSION IN ADOLESCENTS
While residual or unmeasured confounding may explain
these results, several biological mechanisms could also
explain the demonstrated relationship between adolescent
diet and depression. A poor quality diet may infl uence
depressive status via two pathways: both by increasing
the risk of nutrient defi ciencies, which are associated with
depression [20,21], as well as the duration [22] and sever-
ity [23] of depressive illness, and by having a direct det-
rimental impact on biological systems that underpin the
pathogenesis of depression [2]. For example, experimen-
tal data indicate that a diet high in saturated fat activates
the stress response system and reduces the ability of nor-
mal feedback mechanisms to return the stress system to
homeostasis [24,25]. High fat and high sugar diets also
promote obesity and insulin resistance, which in turn con-
tribute to systemic infl ammation [26]. Dietary factors
such as a high glycemic load diet [27] and a western type
dietary pattern [28] are also associated with increased
systemic infl ammation. Pro-infl ammatory cytokines are
thought to directly mediate many of the behavioural, neu-
roendocrine, and neurochemical changes seen in depres-
sive illnesses [29].
Nutritional exposures during periods of rapid growth
may also impact on the risk for depression via a detri-
mental impact on brain development and plasticity, and
by modifying gene expression. Adequate nutrition in
childhood appears to be essential for the optimal devel-
opment of the human brain [30], and brain derived neu-
rotrophic factor (BDNF) may be of particular interest as
it plays a central role in neurogenesis and is also regarded
as a crucial factor in depressive illness [31]. In animal
models, a high-fat, refi ned sugar diet reduces levels of
BDNF within 3 weeks, with a resulting impact on func-
tioning that is independent of nutritional defi cits, obesity
or insulin resistance [32]. Moreover, increasing evidence
There were observed differences between those
included in the analyses and those not included on the
basis of missing data; those not included were from
more disadvantaged backgrounds and had higher dieting
scores. Regardless, there were no differences between
the groups on the outcome measure, and, as adolescents
from disadvantaged backgrounds were more likely to
have poorer quality diets, any bias is likely to tend
towards under- rather than over-estimation of the
reported associations. The Healthy Neighbourhoods
Study sample cannot be considered as representative of
the population, as it was selected to equally represent
the quartiles of SES and rural/urban location. As such,
these results may not be generalizable to the population.
However, the inclusion of adolescents from a wide
range of socio-demographic backgrounds, the stringent
measures employed to address issues of confounding,
and the use of a valid and reliable measure of self-
reported depression [12] are all important strengths in
this study. Finally, the large sample size allowed for
the effects of multiple interacting variables of small
effect to be determined.
Figure 2. Mean total Short Mood and Feelings
Questionnaire score (95% CI) for each quintile of
unhealthy diet score.
Table 3. Odds ratios (95% confi dence intervals) for
symptomatic depression across categories/quintiles
of healthy and unhealthy diet scores (n 7114)
Healthy diet score
(Categories 1 – 5)
SMFQ symptomatic
(n 2366) OR (95%CI)
Unadjusted
C1 (n 209)
C2 (n 1138) 0.54 (0.40–0.72)
C3 (n 2255) 0.48 (0.36–0.63)
C4 (n 2545) 0.36 (0.27–0.48)
C5 (n 967) 0.44 (0.33–0.60)
Adjusted
C2 0.61 (0.45–0.84)
C3 0.58 (0.43–0.79)
C4 0.47 (0.35–0.64)
C5 0.55 (0.40–0.77)
Unhealthy diet score (Quintiles)
Unadjusted
Q1 (n 2123)
Q2 (n 1129) 1.01 (0.86–1.18)
Q3 (n 1086) 1.26 (1.08–1.48)
Q4 (n 1633) 1.32 (1.15–1.52)
Q5 (n 1143) 2.10 (1.81–2.44)
Adjusted
a
Q2 1.03 (0.87–1.22)
Q3 1.22 (1.03–1.44)
Q4 1.29 (1.12–1.50)
Q5 1.79 (1.52–2.11)
a Adjusted for age, gender, physical activity, father’s work
status, socio-economic status, adolescent diet scale, and
family con ict.
F. N. JACKA, P. J. KREMER , E. R. LESLIE, M. BERK, G. PATTON, J. W. TOUMBOUROU, J. W. WILLI AMS 441
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Conclusion
We have demonstrated cross-sectional relationships
between the quality of adolescents’ diets and their depres-
sion status. These relationships were not fully explained
by age, gender, SES, family environment, problematic
dieting behaviours, body habitus, or lifestyle behaviours
other than diet, however reverse causality and/or residual
or unmeasured confounding cannot be ruled out as expla-
nations for these fi ndings. Nevertheless, these results are
concordant with recent studies in adults [3,5,6], and sup-
port the hypothesis that poor quality diets play a role in
increasing the risk for adolescent depression. If further
longitudinal studies confi rm these associations, the
potential exists for the development of an evidence-based
strategy for the primary prevention of adolescent depres-
sion, based in part on dietary modifi cation. Such modifi -
cations would align with other current public health
strategies aimed at reducing the impact of child and ado-
lescent obesity [36], and hold the promise of reducing
the burden of depression in the community.
Acknowledgements
An NHMRC Project Grant #334304 funded the Healthy
Neighbourhoods Study. Associate Professor Leslie is
supported by an NHMRC Public Health Fellowship
#301261. Professor Toumbourou is supported by a
VicHealth Senior Research Fellowship. Dr Jacka was the
recipient of postgraduate scholarship funding from the
“Australian Rotary Health” and is supported by NHMRC
project grant #454356.
The funding providers played no role in the design or
conduct of the study; collection, management, analysis,
and interpretation of the data; or in the preparation,
review, or approval of the manuscript.
Dr Jacka had full access to all the data in the study and
takes responsibility for the integrity of the data and the
accuracy of the data analysis.
Declaration of interest: The authors report no confl icts
of interest. The authors alone are responsible for the
content and writing of the paper.
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