A randomized, controlled trial of dietary improvement for adults with major depression (the 'SMILES' trial)

Abstract

Abstract Background The possible therapeutic impact of dietary changes on existing mental illness is largely unknown. Using a randomised controlled trial design, we aimed to investigate the efficacy of a dietary improvement program for the treatment of major depressive episodes. Methods ‘SMILES’ was a 12-week, parallel-group, single blind, randomised controlled trial of an adjunctive dietary intervention in the treatment of moderate to severe depression. The intervention consisted of seven individual nutritional consulting sessions delivered by a clinical dietician. The control condition comprised a social support protocol to the same visit schedule and length. Depression symptomatology was the primary endpoint, assessed using the Montgomery–Åsberg Depression Rating Scale (MADRS) at 12 weeks. Secondary outcomes included remission and change of symptoms, mood and anxiety. Analyses utilised a likelihood-based mixed-effects model repeated measures (MMRM) approach. The robustness of estimates was investigated through sensitivity analyses. Results We assessed 166 individuals for eligibility, of whom 67 were enrolled (diet intervention, n = 33; control, n = 34). Of these, 55 were utilising some form of therapy: 21 were using psychotherapy and pharmacotherapy combined; 9 were using exclusively psychotherapy; and 25 were using only pharmacotherapy. There were 31 in the diet support group and 25 in the social support control group who had complete data at 12 weeks. The dietary support group demonstrated significantly greater improvement between baseline and 12 weeks on the MADRS than the social support control group, t(60.7) = 4.38, p

Full text

R E S E A R C H A R T I C L E Open Access
A randomised controlled trial of dietary
improvement for adults with major
depression (the ‘SMILES’trial)
Felice N. Jacka
1,4,9,10,13*
, Adrienne O’Neil
1,2,13
, Rachelle Opie
5,13
, Catherine Itsiopoulos
5
, Sue Cotton
3
,
Mohammedreza Mohebbi
1
, David Castle
4,11
, Sarah Dash
1,13
, Cathrine Mihalopoulos
7
, Mary Lou Chatterton
7
,
Laima Brazionis
5,6
, Olivia M. Dean
1,4,12,13
, Allison M. Hodge
8
and Michael Berk
1,3,12,13
Abstract
Background: The possible therapeutic impact of dietary changes on existing mental illness is largely unknown.
Using a randomised controlled trial design, we aimed to investigate the efficacy of a dietary improvement program
for the treatment of major depressive episodes.
Methods: ‘SMILES’was a 12-week, parallel-group, single blind, randomised controlled trial of an adjunctive dietary
intervention in the treatment of moderate to severe depression. The intervention consisted of seven individual
nutritional consulting sessions delivered by a clinical dietician. The control condition comprised a social support
protocol to the same visit schedule and length. Depression symptomatology was the primary endpoint, assessed
using the Montgomery–Åsberg Depression Rating Scale (MADRS) at 12 weeks. Secondary outcomes included
remission and change of symptoms, mood and anxiety. Analyses utilised a likelihood-based mixed-effects model
repeated measures (MMRM) approach. The robustness of estimates was investigated through sensitivity analyses.
Results: We assessed 166 individuals for eligibility, of whom 67 were enrolled (diet intervention, n= 33; control,
n= 34). Of these, 55 were utilising some form of therapy: 21 were using psychotherapy and pharmacotherapy
combined; 9 were using exclusively psychotherapy; and 25 were using only pharmacotherapy. There were 31 in
the diet support group and 25 in the social support control group who had complete data at 12 weeks. The dietary
support group demonstrated significantly greater improvement between baseline and 12 weeks on the MADRS
than the social support control group, t(60.7) =4.38, p<0.001, Cohen’sd=–1.16. Remission, defined as a MADRS
score <10, was achieved for 32.3% (n= 10) and 8.0% (n= 2) of the intervention and control groups, respectively
(χ
2
(1) = 4.84, p= 0.028); number needed to treat (NNT) based on remission scores was 4.1 (95% CI of NNT 2.3–27.8).
A sensitivity analysis, testing departures from the missing at random (MAR) assumption for dropouts, indicated that
the impact of the intervention was robust to violations of MAR assumptions.
Conclusions: These results indicate that dietary improvement may provide an efficacious and accessible treatment
strategy for the management of this highly prevalent mental disorder, the benefits of which could extend to the
management of common co-morbidities.
Trial registration: Australia and New Zealand Clinical Trials Register (ANZCTR): ACTRN12612000251820. Registered
on 29 February 2012.
Keywords: Depression, Major depressive disorder, Diet, Nutrition, Randomised controlled trial, Dietetics
* Correspondence: f.jacka@deakin.edu.au
1
IMPACT Strategic Research Centre, Deakin University, Geelong, VIC, Australia
4
Department of Psychiatry, University of Melbourne, Melbourne, VIC, Australia
Full list of author information is available at the end of the article
© The Author(s). 2017 Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0
International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and
reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to
the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver
(http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
Jacka et al. BMC Medicine (2017) 15:23
DOI 10.1186/s12916-017-0791-y
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Background
There is now extensive observational evidence across
countries and age groups supporting the contention that
diet quality is a possible risk or protective factor for
depression [1–5]. Although there are many versions of a
‘healthful diet’in different countries and cultures, the
available evidence from observational studies suggests that
diets higher in plant foods, such as vegetables, fruits,
legumes and whole grains, and lean proteins, including
fish, are associated with a reduced risk for depression,
whilst dietary patterns that include more processed food
and sugary products are associated with an increased risk
of depression [1, 6, 7]. Whilst cognisant of the limitations
of observational data, these associations are usually ob-
served to be independent of socioeconomic status, educa-
tion and other potentially confounding variables and not
necessarily explained by reverse causality (see, e.g. [7–10]).
Recently, a meta-analysis confirmed that adherence to a
‘healthful’dietary pattern, comprising higher intakes of fruit
and vegetables, fish and whole grains, was associated with a
reduced likelihood of depression in adults [1]. Similarly, an-
other meta-analysis reported that higher adherence to a
Mediterranean diet was associated with a 30% reduced risk
for depression, with no evidence for publication bias [11].
The Mediterranean diet is recognised as a healthful dietary
pattern and has been extensively associated with chronic
disease risk reduction [12]. More recently, a systematic re-
view confirmed relationships between unhealthful dietary
patterns, characterised by higher intakes of foods with satu-
rated fat and refined carbohydrates, and processed food
products, and poorer mental health in children and adoles-
cents [2]. Several cohort studies also reported associations
between the quality of women’s diets during pregnancy and
the risk for emotional dysregulation in children [13–15],
with new insights into potential mechanisms of action that
include brain plasticity [16], the gut microbiota [17] and in-
flammatory [18] and oxidative stress [19] pathways.
Although there are data suggesting that some nutri-
tional supplements may be of utility as adjunctive therap-
ies in psychiatric disorders [20], the field of research
focusing on the relationships between overall dietary qual-
ity and mental disorders is new and has thus far been
largely limited to animal studies and observational studies
in humans. Thus, whilst the existing observational data
support a causal relationship between diet quality and de-
pression on the basis of the Bradford Hill criteria [3] and
are supported by extensive experimental data in animals
(see, e.g. [21]), randomised controlled trials are required
to test causal relationships and identify whether or not
dietary change can improve mental health in people with
such conditions. We conducted a systematic review and
identified a number of interventions with a dietary change
component that had examined mental health-related out-
comes [22]. Whilst approximately half of these studies
reported improvements in measures of depression or anx-
iety following the intervention, at the time of the review
no studies fulfilling quality criteria had been conducted in
mental health populations or had been designed to test
the hypothesis that dietary improvement might result in
improvements in mental health. Since then, one study has
been published evaluating the possible impact of a lifestyle
program, comprising both diet and exercise, on mental
health symptoms in patients with depression and/or anx-
iety; this study failed to show any differences in symptom
levels between those in the intervention and those in the
attention control group [23]. On the other hand, post hoc
analysis of a large-scale intervention trial provides prelim-
inary support for dietary improvement as a strategy for
the primary prevention of depression. Individuals at in-
creased risk for cardiovascular events were randomised to
a Mediterranean diet supplemented with either extra-
virgin olive oil or mixed nuts, or a low-fat control diet
[12]. Whilst not statistically powered to assess the effect-
iveness of the intervention for preventing depression,
there was evidence (albeit non-significant) of a reduced
risk for incident depression for those randomised to a
Mediterranean diet with nuts. This protective effect was
statistically significant in those with type 2 diabetes, who
comprised approximately half the sample [24].
Using a randomised controlled trial (RCT) design, we
thus aimed to investigate the efficacy of a dietary program
for the treatment of major depressive episodes. In this
trial, Supporting the Modification of lifestyle In Lowered
Emotional States (SMILES), we hypothesised that struc-
tured dietary support, focusing on improving diet quality
using a modified Mediterranean diet model, would be su-
perior to a social support control condition (befriending)
in reducing the severity of depressive symptomatology.
Methods
Study design
This was a 12-week, parallel-group, single blind RCT of a
dietary intervention in the treatment of moderate to severe
depression (for the protocol see [25]). This trial was regis-
tered in the Australia and New Zealand Clinical Trials
Register (ANZCTR): (ACTRN12612000251820) prior to
commencing recruitment. Participants were recruited from
two sites: Barwon Health in Geelong and St. Vincent’s
Health in Melbourne (Victoria, Australia) over a 3-year
period. Participants were randomised to receive either
dietary support or social support (‘befriending’[26]). Partic-
ipants in both groups completed assessments prior to pro-
gram commencement (baseline), with the primary and
secondary outcomes measured at program completion
(12 weeks, primary endpoint). Approval to conduct the
study was received from Human Research Ethics Commit-
tees of St. Vincent’s and Barwon Health. Written informed
consent was obtained from all participants after they had
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received a complete description of the study. The study’s
protocol was developed in accordance with the Standard
Protocol Items: Recommendations for Interventional Trials
(SPIRIT) guidelines. Reporting of findings pertaining to
primary and secondary outcomes was done in accordance
with the Consolidated Standards of Reporting Trials
(CONSORT) 2010 guidelines and their extension to non-
pharmacologic treatments.
Participants
Inclusion criteria
Eligibility criteria included participants who were at screen-
ing: aged 18 or over and could provide informed consent;
successfully fulfilled the Diagnostic and Statistical Manual
of Mental Disorders (4th ed.; DSM-IV-TR) diagnostic cri-
teria for a major depressive episode (MDE); scored 18 or
over on the Montgomery–Åsberg Depression Rating Scale
(MADRS) [27]; and scored 75 or less, out of a possible
score of 104, on a Dietary Screening Tool (DST) [28]
modified for Australian food products. The DST was
completed to confirm ‘poor’dietary quality, before enrol-
ment. This screening tool was used to reflect usual daily or
weekly intake of specified foods. Broadly defined, partici-
pants had to report a poor (low) intake of dietary fibre, lean
proteins and fruit and vegetables, and a high intake of
sweets, processed meats and salty snacks. If participants
were on antidepressant therapy or undergoing psychother-
apy, they were required to be on the same treatment for at
least 2 weeks prior to randomization. Participants had to
be readily available for a 12-week period and have the
ability to eat foods as prescribed, without religious,
medical, socio-cultural or political factors precluding
participation or adherence to the diet.
Exclusion criteria
Participants were ineligible if they had: (1) a concurrent
diagnosis of bipolar I or II disorder; (2) two or more failed
trials of antidepressant therapy for the current MDE; (3)
known or suspected clinically unstable systemic medical
disorder; (4) pregnancy; (5) commencement of new psy-
chotherapy or pharmacotherapy within the preceding
2 weeks; (6) severe food allergies, intolerances or aver-
sions; (7) current participation in an intervention targeting
diet or exercise; (8) a primary clinical diagnosis of a per-
sonality disorder and/or a current substance use disorder.
Sample recruitment
Community-based recruitment strategies were used to iden-
tify study participants, including flyers in medical waiting
rooms, pharmacies and university campuses; newsletters;
and contact with potential referral sources (e.g. general prac-
titioners, private psychiatrists and local psychiatric inpatient
units). Media interviews and advertisements in social media
(e.g. Twitter, Facebook), Google, local newspapers and radio
stations were also employed as recruitment strategies. Ethics
committee requirements meant that we needed to be expli-
cit regarding our planned intervention, with the advertise-
ments stating: ‘We are trialling the effect of an educational
and counselling program focusing on diet that may help
improve the symptoms of depression’.
Interventions
Dietary support
The dietary intervention comprised personalised dietary
advice and nutritional counselling support, including motiv-
ational interviewing, goal setting and mindful eating, from a
clinical dietician in order to support optimal adherence to
the recommended diet. This comprised the ‘ModiMedDiet’,
developed by RO and CI, which was based on the Australian
Dietary guidelines [29] and the Dietary Guidelines for Adults
in Greece [30] and is concordant with our previous dietary
recommendations for the prevention of depression [31].
The primary focus was on increasing diet quality by sup-
porting the consumption of the following 12 key food
groups (recommended servings in brackets): whole grains
(5–8 servings per day); vegetables (6 per day); fruit (3 per
day), legumes (3–4 per week); low-fat and unsweetened
dairy foods (2–3 per day); raw and unsalted nuts (1 per day);
fish (at least 2 per week); lean red meats (3–4perweek)
[32], chicken (2–3 per week); eggs (up to 6 per week); and
olive oil (3 tablespoons per day), whilst reducing intake of
‘extras’foods, such as sweets, refined cereals, fried food, fast-
food, processed meats and sugary drinks (no more than 3
per week). Red or white wine consumption beyond 2 stand-
ard drinks per day and all other alcohol (e.g. spirits, beer)
were included within the ‘extras’food group. Individuals
were advised to select red wine preferably and only drink
with meals. The dietary composition of the ModiMedDiet
was as follows: protein 18% of total energy (E); fat 40% of E;
carbohydrates 37% of E; alcohol 2% of E; fibre/other 3% of
E. The diet was designed to be easy to follow, sustainable,
palatable, and satiating. Individuals were advised to consume
the diet ad libitum, as the intervention did not have a
weight loss focus. The method for scoring the ModiMed-
Diet is similar to those used in PREDIMED [33] and the
Framingham Offspring Cohort [34]. It is a criterion-based
diet score that uses pre-defined absolute or normative goals
of consumption for specific food items, independent of the
individual’s characteristics. It was developed based on the
recommended intakes of the 11 food group components
that comprise the ModiMedDiet (as above), and of the score
has a theoretical maximum value of 120.
Participants received seven individual dietary support
sessions of approximately 60 minutes each, delivered by an
Accredited Practising Dietician; the first four sessions oc-
curred weekly and the remaining three sessions occurred
every 2 weeks. At the first session, the dietician conducted
a diet history to assess usual dietary intake. Participants
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were provided with supporting written information specif-
ically designed for the intervention to assist with achieving
dietary adherence. In order to provide examples of serving
sizes and exposure to the recommended foods, participants
were also provided with a food hamper, incorporating the
main components of the diet, along with recipes and meal
plans. Subsequent sessions used motivational interviewing
techniques, and participants were encouraged to set perso-
nalised goals.
Social support
The social support control condition comprised a manua-
lised ‘befriending’protocol [26], using the same visit
schedule and length as the dietary support intervention.
Befriending consists of trained personnel discussing neu-
tral topics of interest to the participant, such as sport,
news or music, or in cases where participants found the
conversation difficult, engaging in alternate activities such
as cards or board games, with the intention of keeping the
participant engaged and positive. This is done without
engaging in techniques specifically used in the major
models of psychotherapy. Research assistants (RAs) in this
trial completed manual-guided training and also partici-
pated in role-playing training exercises to ensure consist-
ent delivery of the protocol. Befriending aims to control
for four factors: time; client expectancy; therapeutic alli-
ance; and therapist factors when compared to the inter-
vention group in an RCT and is often used as a control
condition for clinical trials of psychotherapy [26]. Partici-
pants in the social support control group were provided
with movie tickets as compensation for their time and
participation in the study and were offered participation
in a group dietary counselling session at the conclusion of
the trial.
Assessments and outcomes
Once deemed to be eligible, participants completed a 7-day
food diary and the Cancer Council of Victoria food
frequency questionnaire [35], in the week leading up to
baseline assessment. Participants attended a local pathology
clinic to provide fasting blood samples before undertaking
baseline assessment and randomization.
Baseline and follow-up assessments
Details of baseline and follow-up assessments have been
reported elsewhere [25]. Briefly, primary and secondary
endpoints were as described in the following sections.
Primary outcome The MADRS was used to assess the
change in depressive symptomatology at baseline and at
the primary endpoint of 12 weeks. The MADRS is an
interviewer-rated instrument, comprising 10 items, each
measured on a 6-point scale (scores range from 0–60
with higher scores depicting greater symptom severity).
It has been found to be a robust and psychometrically
sound measure of depressive symptomatology [27].
Secondary outcomes The Hospital Anxiety and Depres-
sion Scale (HADS) [36] was administered as a self-report
questionnaire. The Profile of Mood States (POMS) was
used to assess mood [37], and the Clinical Global Impres-
sion - Improvement (CGI-I) Scale [38] was used to assess
change in symptoms from baseline to endpoint. The
World Health Organization wellbeing scale (WHO-5) [39]
and the Generalized Self-Efficacy Scale [40] were used to
assess wellbeing and self-efficacy, respectively. Clinical
data including height, weight and waist circumference
were also collected and the body mass index (BMI) was
calculated. Participants were also asked the following:
whether they were a current smoker (yes/no); if they had
an existing medical condition (physical or mental); and
the names and doses of any medications they were taking.
Current levels of physical activity were assessed using
International Physical Activity Questionnaire (IPAQ)
scores, which capture Metabolic Equivalent of Task (MET)
minutes per week. A total MET score was calculated for
each participant as a summary of Walking, Moderate and
Vigorous MET scores [41]. Dietary quality was assessed
using the ModiMedDiet score, which was based on con-
sumption of the key food groups (i.e. wholegrains, vegeta-
bles, fruits, legumes, nuts, fish, lean red meats, chicken,
low fat dairy, eggs, olive oil, extras) and will be presented
in more detail, along with the dietary strategy, in a forth-
coming publication. Dietary assessments, using 7-day food
diaries, were administered at baseline and endpoint to both
groups to identify dietary changes and adherence to the
recommended diet; this was done by assessing change in
the ModiMedDiet score, which is based on the consump-
tion of the key food groups. Biomarkers, including plasma
fatty acids, fasting glucose, total and HDL and LDL choles-
terol and triglycerides were also assessed.
Sample size
Our original sample size calculation required 88 people
per group, assuming an attrition of 15%, with 8 predic-
tors. For a one-tailed analysis with type I error or alpha
set at the .05 level, the study would have been powered
at 80% to detect a true difference in rating scale score
between the diet and befriending groups if the effect size
was 0.15 or greater on the MADRS.
Randomization
The randomization sequence was computer generated by an
independent person (OD) using a 2 × 2 block design. The
sequence was saved to a password-protected spreadsheet,
and groups were coded A and B. The randomization alloca-
tion was managed by the trial dieticians or ‘befrienders’,in
order to ensure that the research assistants responsible for
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mental health assessments were blind to participants’group
allocations, and the randomization schedule and coding of
group allocations were not, at any time, accessible to the
research assistants conducting the assessments, or to the
biostatistician (SC). At the conclusion of the baseline ap-
pointment, the dietician/befriender would meet privately
with the participant and inform them of their group alloca-
tion in order to maintain blinding of the research assistants.
Blinding
Although full blinding of participants to condition in this
study was not possible, several strategies were employed
to reduce the risk of bias. First, participants were provided
with only partial information on the study hypothesis; the
social support control condition was termed ‘befriending’
and research assistants emphasised the link between social
support and mental health as an outcome of interest; and
participants in both the intervention and the social
support control group were provided with standardised
care, with all participants attending appointments in the
same location and with the same format, as well as similar
duration and frequency. All communication between
participants and research staff during the period of inter-
vention (i.e. scheduling concerns, questions regarding
intervention) was done directly between participants and
their respective ‘clinician’. Participants were clearly
instructed only to contact the dietician/befriender person-
ally and to avoid contact with the research assistant, and
voice messages were checked daily by the dietician/
befriender to avoid unintended contact or information on
participants’allocation. Research assistants did not have
direct contact with participants for the duration of the
intervention. Final assessments were organised by the
dietician or befriender, and research assistants remained
blind to condition for the final assessment of outcomes.
Prior to assessment, participants were reminded not to re-
veal the group to which they had been assigned. Statistical
analyses were conducted by an external statistician (SC),
who was blind to group allocation prior to analysis.
Data analyses
The analyses were conducted in accordance with the
International Conference on Harmonization E9 statistical
principles. Independent samples ttests and chi-square (χ
2
)
analyses were used to compare participants who com-
pleted and did not complete the 12 weeks of the trial.
Intention-to-treat (ITT) analyses were adopted. The pri-
mary efficacy analysis was based on between-group differ-
ences in average change from baseline to 12 weeks for the
primary outcome measure (MADRS); these analyses were
conducted using planned comparisons within a restricted
maximum likelihood (REML)-based mixed-effects model,
repeated measures (MMRM) approach. Within the
MMRM, treatment and assessment occasion and the
interaction between treatment group and assessment occa-
sion were included as fixed factors. The MMRM approach
is the preferred method of dealing with clinical trial data in
psychiatry [42]. The benefits of these MMRM methods are
that all available participant data are included in the model
[42].ByplanningtouseMMRM,wemadetheapriorias-
sumption that missing data were missing at random
(MAR); however, we tested these assumptions in sensitivity
analyses (as below). The Toeplitiz covariance structure was
used to model the relations between observations on differ-
ent occasions. Planned comparisons using MMRM were
also conducted to examine group differences in mean
change on the secondary outcome measures from baseline
to 12 weeks. Cohen’sdas a measure of effect size was
calculated based on observed data. Supplementary sensitiv-
ity analyses with the MMRM models were conducted, con-
trolling for relevant confounding variables such as gender,
education, physical activity, baseline BMI and baseline
ModiMedDiet score. All tests of treatment effects were
conducted using an alpha level of 0.05 and reporting 95%
confidence intervals. Pearson’s product-moment correla-
tions were calculated to determine whether changes in
MADRS scores correlated to changes in biomarkers.
Analysis of covariance (ANCOVA) was implemented to
evaluate interactions between group allocation and change
adherence to ModiMedDiet on MADRS scores at 12 weeks,
adjusting for MADRS at baseline. Whilst acknowledging
the increased potential for type 1 errors, given that reported
comparisons for all primary and secondary outcomes were
pre-planned comparisons that were determined a priori
and documented in the trial protocol, we did not make ad-
justments for multiple comparisons.
Sensitivity analyses
We compared demographic, health measures, current
treatment, diet quality and psychological measures at
baseline between participants with complete follow-up
and those with missing data at follow-up, using the chi-
squared test for categorical data and ttests for continu-
ous measures. To test departures from missing at ran-
dom (MAR), a weighted sensitivity analysis using the
Selection Model Approach was applied to the main out-
come findings [43, 44]. Briefly, once data had been im-
puted under MAR (n= 5), parameter estimates from
each imputed dataset were reweighted to allow for the
data to be missing not at random (MNAR). The chosen
constant values used to add to the imputed missing data
to account for MNAR were multiplications of standard
error (i.e. 1.6) for main outcome comparison under
MAR assumptions. To evaluate the robustness of our
findings, different degrees of departure from the MAR
assuming plausible values ranging from 10*SE to –8*SE
were considered.
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Results
We assessed 166 individuals for eligibility. Of these, 99
were excluded. We thus randomised 67 individuals with
MDD to the trial (intervention, n= 33; social support
control, n= 34). Figure 1 presents a CONSORT flow
chart. Baseline characteristics of all enrolled participants
are presented in Table 1. The dietary group had signifi-
cantly lower scores on the dietary screening tool and the
ModiMedDiet score than the social support control
group at baseline, primarily due to lower intakes of fruit
and higher intakes of extras. Otherwise, groups were
well matched on characteristics.
Completer analysis
Fifty-six individuals (83.6%) completed the assessment at
the 12-week endpoint. There were significantly more
completers in the dietary support group (93.9%, n= 31)
than the social support control group (73.5%, n=25), χ
2
(1) = 5.08, p= 0.024. Those who did not complete the
intervention were significantly more likely to have post-
secondary education (81.8%, n= 9) than those who
completed (45.5%, n= 25), χ
2
(1) = 4.85, p= 0.028; this
relationship was observed for the social support control
group, χ
2
(1) = 6.92, p= 0.009 and not in the dietary
support group, χ
2
(1) = 0.01, p= 0.965.
Primary outcome: depressive symptomatology
The dietary support group demonstrated significantly
greater improvement in MADRS scores between baseline
and 12 weeks than the social support control group,
t(60.7) =4.38, p<.001 (Fig. 2). The effect size for this
difference was a Cohen’sdof –1.16 (95% CI –1.73, –0.59)
and represented an estimated average between group
difference, in terms of change from baseline to 12 weeks,
of 7.1 points on the MADRS (SE = 1.6). The MMRM was
rerun, adjusting for variables such as sex, education, phys-
ical activity, baseline BMI and baseline ModiMedDiet
score; the significant between-group difference in change
from baseline to 12 weeks remained, t(58.7) =4.40, p<
0.001.
Results from sensitivity analyses accounting for missing
data under the NMAR assumption are presented in Fig. 3.
Two NMAR scenarios were investigated in the sensitivity
analyses: (1) dropouts in the intervention group had worse
MADRS outcome at 12 weeks, and (2) dropouts in the con-
trol group had better MADRS outcomes. As Fig. 3 shows,
Fig. 1 CONSORT flow chart
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Table 1 Baseline characteristics of all those randomised to the dietary support (DS) and social support (SS) groups
Total
(n= 67)
DS
(n= 33)
SS
(n= 34)
Demographic
Gender % female % (n) 71.6 (48) 81.8 (27) 61.8 (21)
Age M (SD) 40.3 (13.1) 37.5 (10.7) 43.1 (14.6)
Post-secondary school education % (n) 51.5 (34) 51.5 (17) 51.5 (17)
Household income
Above $80,000 per annum % (n) 23.1 (15) 25.0 (8) 21.2 (7)
Health measures
BMI M (SD) 29.5 (8.0) 30.0 (9.3) 29.0 (6.5)
Current smoker % (n) 14.1 (9) 10.0 (3) 17.6 (6)
Comorbid disorder % (n) 71.2 (47) 75.0 (24) 67.6 (23)
Number of comorbid disorders M (SD) 1.5 (1.4) 1.6 (1.4) 1.5 (1.4)
Physical activity (IPAQ score) M (SD) 2336 (2585) 2146 (2565) 2509 (2629)
Current treatments
Psychopharmacotherapy % (n) 68.7 (46) 75.8 (25) 61.8 (21)
Psychological therapy % (n) 44.8 (30) 39.4 (13) 50.0 (17)
Diet quality
Screen of diet quality M (SD) 51.2 (11.0) 48.5 (10.3) 53.9 (11.3)
ModiMedDiet (0-120) M (SD) 41.5 (14.3) 36.2 (12.8) 47.3 (13.7)
Psychological measures
MADRS (0-60) M (SD) 25.4 (4.6) 26.1 (4.9) 24.7 (4.2)
HADS –(D) (0-21) M (SD) 9.6 (3.7) 10.0 (4.0) 9.2 (3.4)
HADS –(A) (0-21) M (SD) 11.7 (3.0) 12.1 (3.1) 11.2 (2.8)
BMI body mass index, MADRS Montgomery-Åsberg Depression Rating Scale, HADS Hospital Anxiety and Depression Scale
0
5
10
15
20
25
30
Diet intervention Social support
Group
Baseline
MADRS
3 month
Estimated mean (±SE) for
Fig. 2 MADRS scores for dietary support and social support control groups at baseline and endpoint. Effect size: Cohen’sd=–1.16 (95% CI –1.73, –0.59).
Baseline data n= 67; 12 week data n=56
Jacka et al. BMC Medicine (2017) 15:23 Page 7 of 13
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findings were insensitive to assumption 1, even when as-
suming outcomes as large as 10*SE (an increase of 16 in
MADRS score compared to imputation under the MAR as-
sumption). Findings were also robust under assumption 2,
and only a large departure from the MAR assumption (i.e.
8*SE = 12.8 reduction on MADRS) made the observed
intervention effect non-significant.
Secondary outcomes
At 12 weeks, 32.3% (n= 10) of the dietary support group
and 8.0% (n= 2) of the social support control group
achieved remission criteria of a score less than 10 on the
MADRS; this between-group difference was significant,
χ
2
(1) = 4.84, p= 0.028. Based on these remission data,
the number needed to treat (NNT) is 4.1 (95% CI of
NNT 2.3–27.8).
Concordant with the findings for the MADRS, the diet-
ary support group demonstrated significantly greater im-
provement from baseline to 12 weeks than the social
support control group on the Hospital Anxiety and
Depression Scale (HADS)-depression subscale, t(55.1) =
2.20, p= 0.032 (Table 2). Similar findings were obtained
with the HADS-anxiety subscale, t(59.0) = 2.19, p=0.033.
These significant differences remained after controlling
for sex, education, physical activity, baseline BMI and
baseline ModiMedDiet scores. Cohen’sdfor HADS-
depression was –0.632 (95% CI –1.186, –0.078), and for
HADS-anxiety it was –0.594 (95% CI –1.147, –0.042).
On the CGI-I at 12 weeks, the dietary support group
had significantly lower average scores (M= 2.1, SD = 1.3)
than the social support control group (M= 3.0, SD =
1.3), t(50) = –2.58, p= 0.013. Based on these figures, the
dietary support group on average had ’much improved’
scores, whereas the social support control group had
’minimally improved’scores on the CGI-I.
On the POMS total mood disturbance score, as well as
the subscale scores (subscales not reported) there were
no significant differences between the groups. Similarly,
there were no significant differences between groups
with respect to self-efficacy or wellbeing.
At intervention cessation, the dietary support group had
significant improvements in the consumption of the
following food groups: whole grain cereals (mean increase
1.21 (SD 1.77) servings/day); fruit (0.46 (0.71) servings/
day); dairy (0.52 (0.72) servings/day); olive oil (0.42 (0.49)
servings/day); pulses (1.40 (2.39) servings/week); and fish
(1.12 (2.65) servings/week). With respect to the consump-
tion of unhealthful food items, intake of extras substan-
tially declined (mean decrease 21.76 (SD 16.01) servings/
week) in the dietary support group. Conversely, there were
no significant changes observed in the social support
control group for any of the key food groups. These find-
ings were confirmed by analysis of the ModiMedDiet
scores: the dietary support group showed significantly
greater improvement from baseline to 12 weeks on Modi-
MedDiet scores than controls, t(55.6) = –4.78, p<0.001;
the differences remained after controlling for sex, educa-
tion, physical activity, baseline BMI and baseline Modi-
MedDiet score. Cohen’sdfor the ModiMedDiet was 1.36
(95% CI 0.74–1.98). There were no significant differences
between groups with respect to BMI or physical activity.
Data on change in psychopharmacological medications
over the 12 weeks were available for 53 individuals. One
person in each of the dietary support and social support
groups started taking psychopharmacological medications
over the 12 weeks. There were two patients in the social
support group who ceased their medications. There were
too few participants to undertake inferential statistics.
Changes in biomarkers are also detailed in Table 2. The
only significant difference between the two groups was with
respect to change in total polyunsaturated fatty acids; the
social support group showed a significant drop in polyunsa-
turates over the 12 weeks, t(54.9) = –2.41, p= 0.019.
Changes in MADRS did not correlate with any of the
changes in biomarkers; all correlations were less than 0.2
and were not significant at the p< .050 level. Finally, change
in dietary quality, measured using 12 week ModiMedDiet
score differences from baseline scores, was associated with
change in depression scores in the intervention group: the
interaction between group allocation and change in
Fig. 3 Weighted sensitivity analyses using the Selection Model Approach for MADRS scores, accounting for missing data under the non-missing
at random (NMAR) assumption
Jacka et al. BMC Medicine (2017) 15:23 Page 8 of 13
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ModiMedDiet scores after adjusting for baseline MADRS
scores was statistically significant, F(2) = 9.6, p< 0.001. The
correlation was only significant in the intervention group
(p< 0.001); the unstandardised beta coefficient was –0.22
(95% CI –0.32, –0.12), indicating a 2.2 score improvement
in MADRS with every 10% increase in dietary adherence.
Discussion
These results provide preliminary RCT evidence for
dietary improvement as an efficacious treatment strategy
for treating major depressive episodes. We report
significant reductions in depression symptoms as a result
of this intervention, with an overall effect size of –1.16.
These effects appear to be independent of any changes in
BMI, self-efficacy, smoking rates and/or physical activity.
Concordant with our primary outcome, significant im-
provements were also observed on self-reported depres-
sive and anxiety symptoms and on the Clinical Global
Impressions Improvement scale. Whilst other mood
(POMS) and wellbeing (WHO-5) scores did not differ
between groups, changes were in the expected direction
and were likely affected by lack of statistical power.
Table 2 Mean (±standard error) estimates derived from mixed model repeated measures (MMRM, unadjusted estimates) comparing
differences between the dietary support (DS) and social support (SS) groups in terms of changes from baseline to primary endpoint
of 12 weeks
Characteristic DS SS Between-group differences
in change from baseline to 12 weeks
Baseline 12 weeks Baseline 12 weeks
M (SE) M (SE) M (SE) M (SE) M (SE) 95% CI [LCI, UCI] t
a
df p
Symptoms
MADRS (0-60) 26.1 (1.0) 14.8 (1.1) 24.7 (1.0) 20.5 (1.2) 7.1 (1.6) 3.9, 10.4 4.38 60.7 <.001
HADS –(D) (0-21) 10.0 (0.6) 5.3 (0.7) 9.2 (0.6) 6.8 (0.7) 2.3 (1.1) 0.2, 4.4 2.20 55.1 .032
HADS –(A) (0-21) 12.1 (0.6) 8.4 (0.6) 11.2 (0.6) 9.5 (0.7) 2.0 (0.9) 0.2, 3.9 2.19 59.0 .033
Mood
POMS (-32–200) 54.5 (6.0) 30.3 (6.5) 40.9 (5.8) 32.1 (6.7) 15.4 (9.9) -4.6, 35.3 1.56 43.5 .127
Self-efficacy
GSE (10-40) 24.6 (1.0) 28.1 (1.0) 25.7 (1.0) 26.7 (1.1) -2.4 (1.6) -5.7, 0.9 -1.44 59.0 .156
Wellbeing
WHO-5 (0-25) 6.9 (0.8) 12.3 (0.9) 6.7 (0.8) 10.3 (1.0) -1.8 (1.7) -5.2, 1.6 -1.04 62.0 .304
Anthropometry
BMI 30.0 (1.4) 29.9 (1.4) 29.0 (1.4) 29.1 (1.4) 0.15 (0.27) -0.4, 0.7 0.56 47.0 .579
Physical activity
(IPAQ scores)
2307.7 (664.8) 2251.3 (645.9) 2509.4 (636.3) 2892.2 (636.3) 439.3 (1097.6) -1757.2, 2635 0.40 58.9 .690
Diet
ModiMedDiet (0-120) 36.2 (2.5) 55.1 (2.8) 47.3 (2.6) 45.4 (2.9) -20.7 (4.3) -20.7, -12.1 -4.78 55.62 <.001
Biomarkers
Glucose (mmol/L) 4.5 (0.5) 4.7 (5.5) 5.4 (0.5) 5.5 (0.6) -0.1 (4.3) -0.9, 0.8 -0.10 48.5 .919
Cholesterol (mmol/L) 4.9 (0.2) 4.8 (0.2) 5.5 (0.2) 5.2 (0.2) -0.2 (0.2) -0.5, 0.2 -1.08 47.7 .287
Triglycerides (mmol/L) 1.3 (0.2) 1.2 (0.2) 1.4 (0.2) 1.5 (0.2) 0.2 (0.2) -0.3, 0.7 0.88 53.4 .386
HDL cholesterol (mmol/L) -1.5 (0.1) 1.5 (0.1) 1.4 (0.1) 1.3 (0.1) -0.1 (0.1) -0.2, 0.1 -1.11 47.7 .272
LDL cholesterol (mmol/L) 2.9 (0.2) 2.8 (0.1) 3.4 (0.2) 3.2 (0.2) -0.1 (0.2) -0.4, 0.3 -0.48 47.1 .636
Total saturates (% FA) 35.7 (0.5) 36.6 (0.5) 35.6 (0.5) 38.1 (0.6) 1.6 (1.0) -0.5, 3.6 1.54 62.1 .128
Total monounsaturates
(% FA)
23.2 (0.4) 22.2 (0.5) 22.8 (0.4) 23.2 (0.6) 1.4 (0.8) -0.3, 3.1 1.69 55.4 .096
Total polyunsaturates
(% FA)
41.1 (0.7) 41.3 (0.8) 41.6 (0.7) 38.6 (0.9) -3.1 (1.3) -5.7, 0.5 -2.41 54.9 .019
n6 (% PUFA) 14.6 (0.4) 14.5 (0.4) 14.3 (0.4) 12.8 (0.5) -1.4 (0.8) -3.0, 0.3 -1.69 62.1 .096
n3 (% PUFA) 6.9 (0.3) 6.8 (0.3) 6.5 (0.3) 7.2 (0.4) 0.9 (0.6) -0.3, 2.1 1.57 57.8 .123
MADRS Montgomery-Åsberg Depression Rating Scale, HADS Hospital Anxiety and Depression Scale, POMS Profile of Mood States, GSE Generalized Self-Efficacy
scale, WHO-5 WHO (Five) wellbeing index, BMI body mass index, FA fatty acids, PUFA polyunsaturated fatty acids
a
Derived from planned comparison within mixed model repeated measures (MMRM) using all available data (baseline data n= 67, 12 week data n= 56) and based
on unadjusted estimates
Jacka et al. BMC Medicine (2017) 15:23 Page 9 of 13
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Critically, substantial improvements on the ModiMedDiet
score were evident in the dietary support group but not in
the social support control group, and these changes corre-
lated with changes in MADRS scores.
The results of this trial suggest that improving one’sdiet
according to current recommendations targeting depres-
sion [31] may be a useful and accessible strategy for ad-
dressing depression in both the general population and in
clinical settings. Whilst there are many data to suggest
that eating a more healthful diet is more expensive than a
less healthful diet [45], our detailed modelling of the costs
of 20 of the SMILES participants’baseline diets compared
to the costs of the diet we advocated showed that our
strategy can be affordable [46]. Indeed, we estimated that
participants spent an average of AU$138 per week on food
and beverages for personal consumption at baseline,
whilst the costs per person per week for the diet we rec-
ommended was AU$112 per week, with both estimations
based on mid-range product costs [46].
A pertinent observation was that improvements in de-
pressive symptoms were independent of weight change.
These findings were expected, as the diet intervention was
ad libitum and did not have a weight loss focus, but
provide further support for the beneficial role of dietary
improvement per se. The extensive observational evidence
linking diet quality to mental health has repeatedly shown
that the observed relationships exist independently of
various measures of body composition.
Although dietary changes were not reflected in the
traditional cardiovascular disease biomarkers, the protective
effects of healthful dietary patterns are often independent
of these risk factors [47]. There are many other biological
pathways by which dietary improvement may influence
depressive illness; previous discussions have centered on in-
flammatory [18] and oxidative stress [19] pathways, as well
as brain plasticity [16] and the new evidence base focused
on the gut microbiota [17]. Each of these pathways is sug-
gested to play a role in depression and is also influenced by
diet quality. Moreover, behavioural changes associated with
food (cooking/shopping/meal patterns) are an expected
outcome of a nutrition intervention, and these changes in
activity may also have had a therapeutic benefit.
Strengths and limitations
There are methodological features of our study that
must be considered. Firstly, there is the issue of expect-
ation bias due to the fact that we needed to be explicit
in our advertising regarding the nature of the interven-
tion and to the inability to blind the participants to their
intervention group; this may have biased the results and
also resulted in differential dropout rates. Moreover, in
regard to our randomisation process, a block size of four,
whilst recommended for small sample sizes to avoid im-
balances in allocation, may have been insufficient to
support allocation concealment. As discussed above, to
mitigate these issues significant effort was made to mask
our hypothesis from the participants, and emphasis was
placed on the potential benefit of social support to men-
tal health. Clearly, our results must also be considered in
light of the small sample size. Failure to reach our
planned sample size increases the possibility that our
sample was not representative and limited our ability to
conduct subgroup analyses. It may also have inflated the
effect size we observed. However, our original power cal-
culations were based on a very small effect size; argu-
ably, this would not have been clinically significant.
There were differential completion rates in each group:
94% versus 73.5% in the dietary and social support
groups, respectively. This suggests that the mechanisms
underpinning missingness may be different between the
two groups; however, results from comprehensive sensi-
tivity analyses testing alternatives to the MAR assump-
tion revealed that, whilst under the NMAR assumptions
observed intervention effects moved towards the null,
our findings remained robust against departures from
the MAR assumption. A larger sample size and assess-
ments at more than two time points would have
afforded more sophisticated statistical modelling; this
should be a key focus of future replication studies.
Importantly, the high completion rates in the interven-
tion group point to the acceptability of the dietary inter-
vention to the participants. The fact that the dietary
intervention group was able to make significant improve-
ments to their diet quality suggests that dietary improve-
ment is achievable for those with clinical depression
despitethefatigueandlackofmotivation that are promin-
ent symptoms of this disorder. On the other hand, the
challenges we had with recruiting this clinical population,
likely due to the aforementioned symptoms and the re-
quirement to attend the study centre on several occasions,
points to the need to utilise different methods for deliver-
ing the intervention that do not require attendance with
the dietician in person, such as telephone or Skype. Finally,
given that we recruited participants on the basis of existing
‘poor’quality diet, this may limit the generalisability of our
findings to the wider population of individuals with depres-
sion. However, evidence suggests that our study sample
was not necessarily a special subgroup; the recent 2014–
2015 Australian Health Survey tells us that only 5.6% of
Australian adults had an adequate intake of vegetables and
fruits. In this study, only 15 out of 166 people screened
were excluded on the basis of a pre-existing ‘good’diet,
suggesting that —concordant with the wider population
—poor diet is the norm in those with depressive illness.
Implications
Recent updates to clinical guidelines for the treatment of
mood disorders in Australia have, in recognition of the
Jacka et al. BMC Medicine (2017) 15:23 Page 10 of 13
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emerging and established data regarding the importance
of health behaviours (diet, exercise, sleep and smoking)
to mood disorders, made explicit recommendations re-
garding the need to address these behaviours as a first
step in the treatment of patients [48]. The results of this
RCT offer further support for the need to focus on ad-
dressing poor diet in clinical practice and provide some
guidance regarding the strategies that may be used to
support this imperative. They suggest the new possibility
of adding clinical dieticians to multidisciplinary mental
health teams and making dietician support available to
those experiencing depressive symptoms in primary and
other care settings. Clearly, successfully improving diet
quality in patients will also benefit the physical illnesses
that are so commonly comorbid with depression and
which are both a cause and consequence of depression.
Upskilling dieticians to best deliver this program to this
patient population may also be required.
Conclusions
In summary, this is the first RCT to explicitly seek to an-
swer the question: If I improve my diet, will my mental
health improve? Whilst emphasising the preliminary na-
ture of this study and the imperative for replication in
studies with larger sample sizes, the results of our study
suggest that dietary improvement guided by a clinical
dietician may provide an efficacious treatment strategy
for the management of this highly prevalent mental
disorder. Future work in this new field of nutritional
psychiatry research should focus on replication, ensuring
larger samples and more sophisticated study designs, in
order to confirm effects and afford sensitivity analyses to
identify predictors of treatment response. The scaling up
of interventions and identification of the pathways that
mediate the impact of dietary improvement on depres-
sive illness are also key imperatives. Clinicians should
also consider promoting the benefits of dietary improve-
ment and facilitating access to dietetics support for their
patients with depression.
Acknowledgements
We would like to acknowledge the contributions of Josephine Pizzinga,
Melanie Ashton, Siobahn Housden, Laura Nicholls, Thea Valkidis, Thaise
Mondin, Meghan Hockey, Olivia Young and Clare Daley —all of whom
contributed their time and efforts to the running of this RCT. Finally, we offer
our profound thanks to the participants, who contributed their time to this
study.
Authors’contributions
FNJ conceived the study and developed the protocol, led the RCT and has
been primarily responsible for drafting the manuscript. AON acted as trial
coordinator in the first year of the RCT, developed and applied for ethical
approvals, played a primary role in overseeing the RCT and has made a
substantial contribution to the drafting of the manuscript. RO and CI were
responsible for the development of the ModiMedDiet and the associated
score, and RO played a primary role in delivering the dietary intervention. CI
(and AON) developed the bloods protocol for the RCT. Both RO and CI have
made a substantial contribution to the data interpretation and the drafting
of the manuscript. As the team statistician, SC played the primary role in
data analysis, as well as data interpretation and the drafting of the
manuscript. MM undertook sensitivity and adherence analyses at the review
stage and contributed to the final manuscript. SD has made a substantial
contribution to the running of the study, the data collation, cleaning and
analysis and the drafting of the manuscript. CM, MLC, LB, OMD and AMH
have all made an important contribution to the original design of the study
protocol, data interpretation and the drafting of the manuscript. DC and MB
were the consultant psychiatrists on the study and made an important
contribution to the design of the study protocol, the running of the study,
the interpretation of the data and the drafting of the manuscript. All authors
read and approved the final manuscript.
Competing interests
This study was supported by a grant from the National Health and Medical
Research Council of Australia (NHMRC) (#1021347). Woolworths Limited
provided sponsorship in the form of food vouchers for participants. Village
cinemas donated cinema vouchers and Carman’s Fine Foods donated muesli
bars for participants. A grant from Meat and Livestock Australia (2013)
funded biochemistry data collected and analysed as part of the SMILES trial.
These sponsors had no role in the design, analysis or preparation of the
manuscript for publication.
Felice N Jacka has received Grant/Research support from the Brain and
Behaviour Research Institute, the National Health and Medical Research
Council (NHMRC), Australian Rotary Health, the Geelong Medical Research
Foundation, the Ian Potter Foundation, Eli Lilly, Meat and Livestock Australia,
Woolworths Limited and The University of Melbourne and has received
speakers honoraria from Sanofi-Synthelabo, Janssen Cilag, Servier, Pfizer,
Health Ed, Network Nutrition, Angelini Farmaceutica, Metagenics and Eli Lilly.
She is supported by an NHMRC Career Development Fellowship (2)
(#1108125).
Adrienne O’Neil has received funding from Meat and Livestock Australia and
is supported by an NHMRC ECR Fellowship (#1052865).
Catherine Itsiopoulos has received funding from the NHMRC, the University
of Melbourne, Deakin University, La Trobe University, Meat and Livestock
Board, Australian Society for Enteral and Parenteral Nutrition, Harokopio
University in Athens, Commonwealth Department of Education, Employment
and Workplace relations, Diabetes Australia and SWISSE Wellness P/L. She
has received speaker honoraria from Astra Zeneca, Boehringer Ingelheim and
Dairy Australia.
Cathrine Mihalopoulos is supported by an NHMRC Early Career Development
Fellowship (#1035887). She has received funding support from the NHMRC,
Cancer Council, Mental Illness Research Fund, Medibank Private Health
Research Fund, Macquarie University and Beyond Blue.
David Castle has received grant monies for research from Eli Lilly, Janssen
Cilag, Roche, Allergen, Bristol-Myers Squibb, Pfizer, Lundbeck, Astra Zeneca
and Hospira and travel support and honoraria for talks and consultancy
from Eli Lilly, Bristol-Myers Squibb, Astra Zeneca, Lundbeck, Janssen Cilag,
Pfizer, Organon, Sanofi-Aventis, Wyeth, Hospira and Servier. He is a current
Advisory Board Member for Lu AA21004; Lundbeck; Varenicline: Pfizer;
Asenapine: Lundbeck; Aripiprazole LAI: Lundbeck; Lisdexamfetamine: Shire;
Lurasidone: Servier. He has no stocks or shares in any pharmaceutical
company.
Olivia Dean has received grant support from the Brain and Behaviour
Foundation, Marion and EH Flack Trust, Simons Autism Foundation,
Australian Rotary Health, Stanley Medical Research Institute, Deakin
University, Eli Lilly, NHMRC, Australasian Society for Bipolar and Depressive
Disorders and Servier. She has also received in-kind support from BioMedica
Nutracuticals, NutritionCare and Bioceuticals.
Michael Berk has received Grant/Research Support from the NIH, Cooperative
Research Centre, Simons Autism Foundation, Cancer Council of Victoria,
Stanley Medical Research Foundation, MBF, NHMRC, Beyond Blue, Rotary
Health, Geelong Medical Research Foundation, Bristol Myers Squibb, Eli Lilly,
Glaxo SmithKline, Meat and Livestock Board, Organon, Novartis, Mayne
Pharma, Servier and Woolworths. He has been a speaker for Astra Zeneca,
Bristol Myers Squibb, Eli Lilly, Glaxo SmithKline, Janssen Cilag, Lundbeck,
Merck, Pfizer, Sanofi Synthelabo, Servier, Solvay and Wyeth, and served as a
consultant to Allergan, Astra Zeneca, Bioadvantex, Bionomics, Collaborative
Medicinal Development, Eli Lilly, Glaxo SmithKline, Janssen Cilag, Lundbeck
Merck, Pfizer and Servier. He is supported by an NHMRC Senior Principal
Research Fellowship (#1059660).
The other authors have no relevant financial disclosures to declare.
Jacka et al. BMC Medicine (2017) 15:23 Page 11 of 13
Content courtesy of Springer Nature, terms of use apply. Rights reserved.

Author details
1
IMPACT Strategic Research Centre, Deakin University, Geelong, VIC, Australia.
2
School of Population Health, The University of Melbourne, Melbourne, VIC,
Australia.
3
Orygen, The National Centre of Excellence in Youth Mental Health,
Parkville, VIC, Australia.
4
Department of Psychiatry, University of Melbourne,
Melbourne, VIC, Australia.
5
School of Allied Health, La Trobe University,
Melbourne, VIC, Australia.
6
Department of Medicine, The University of
Melbourne, Melbourne, VIC, Australia.
7
Centre for Population Health Research,
Deakin University, Geelong, VIC, Australia.
8
Cancer Epidemiology and
Intelligence Division, Cancer Council Victoria, Carlton, VIC, Australia.
9
Centre
for Adolescent Health, Murdoch Childrens Research Institute, Melbourne, VIC,
Australia.
10
Black Dog Institute, Randwick, NSW, Australia.
11
St Vincents
Hospital, Fitzroy, VIC, Australia.
12
The Florey Institute of Neuroscience and
Mental Health, Parkville, VIC, Australia.
13
Food & Mood Centre, Deakin
University, IMPACT SRC, School of Medicine, PO Box 281, Geelong 3220,
Victoria, Australia.
Received: 31 August 2016 Accepted: 11 January 2017
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