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Assessment of correlations between peripheral interferon γ and interleukin 6, PTSD and
resilience
Dagmar Brueniga,c, Divya Mehtab, Charles P. Morrisa, Bruce Lawfordb, Wendy Harveyc,
Ross McD Youngb, Joanne Voiseya*
a Institute of Health and Biomedical Innovation (IHBI) and School of Biomedical
Sciences
60 Musk Avenue
Queensland University of Technology
Kelvin Grove, Queensland, 4059
Australia
b Institute of Health and Biomedical Innovation (IHBI) and School of Psychological and
Counselling
60 Musk Avenue
Queensland University of Technology
Kelvin Grove, Queensland, 4059
Australia
cGallipoli Medical Research Institute
Greenslopes Private Hospital
Newdegate Street
Greenslopes, Queensland, 4120
Australia
*Corresponding author
Joanne Voisey
School of Biomedical Sciences
Faculty of Health
Institute of Health and Biomedical Innovation (IHBI)
60 Musk Avenue
Queensland University of Technology
Kelvin Grove, Queensland, 4059
Australia
Email: [email protected]
Phone: +61 7 3138 6261
Fax. +61 7 3138 6030
Abstract
Posttraumatic Stress Disorder (PTSD) is a debilitating psychiatric disorder with
decreased general health prognosis and increased mortality. Inflammation has been
hypothesized to be a link between PTSD and the most common co-morbid medical
disorders. However, the relationship between inflammation and PTSD is not clear.
Individual inflammatory markers have shown variable associations with PTSD. This
study investigates the correlations between serum cytokines, PTSD and resilience in a
cohort of Caucasian Vietnam combat veterans (n = 299). After correction for multiple
testing, PTSD severity was correlated with small but significant decreases in interleukin 6
and interferon γ (p = 0.004, p = 0.013, respectively) whereas resilience was correlated
with increased levels of interleukin 6 and interferon γ (p = 0.023; p = 0.007,
respectively). Analyses of sub-symptoms of PTSD revealed that mood and arousal
symptoms showed the most significant effect on interleukin 6 and interferon γ. These
findings are not in agreement with previously reported findings and may point at
resilience mechanisms that affect the immune-system. More research is needed to further
elucidate the mechanisms underlying the relationship between cytokine levels, PTSD
sub-symptoms and trauma outcomes to improve the knowledge base of differences in
trauma response and the biological system.
Keywords: Cytokines, PTSD, mood, resilience, Veterans, inflammation
1. Introduction
Posttraumatic Stress Disorder (PTSD) is a debilitating psychiatric disorder (American
Psychiatric Association, 2013). Cohorts with high risks of trauma exposure are at
particular risk of developing PTSD. For military personnel it is estimated that between 20
and 30% of veterans will develop PTSD (Australian Government, 2014; Dohrenwend et
al., 2006; Hoge et al., 2004). In addition to the severe negative psychological sequelae,
PTSD has also been linked to poorer general health and higher mortality (Boscarino,
2008), especially an increased risk for cardiovascular problems and autoimmune
disorders such as rheumatoid arthritis (Edmondson et al., 2013; Lee et al., 2016; Stein et
al., 2016; Wolf et al., 2016). The molecular mechanisms underlying the psychological
sequelae and medical disorders remain unclear. However, inflammatory pathways have
been hypothesised as a potential link (Leonard and Maes, 2012). For example, interferon
γ is a cytokine that has been shown to affect serotonin through the tryptophan-kynurenine
pathway and is implicated in age-related medical and psychiatric processes (Oxenkrug,
2011). Interleukin 6 is a cytokine that can also cross the blood-brain barrier impacting on
the hypothalamus to regulate body temperature but also influencing sleep and stress
reactions (Rohleder et al., 2012).
Studies investigating inflammatory markers have consistently shown increased
inflammation in PTSD patients (Groer et al., 2015; Lindqvist et al., 2016; Lindqvist et al.,
2014; O'Donovan et al., 2015), and two recent genome-wide association studies found
associations with genes that are relevant in the context of inflammation (Powers et al.,
2016; Stein et al., 2016). A functional polymorphism of the IFNγ gene has previously
been associated with increased infectious disease risk. The same functional
polymorphism has also been associated with health behaviours that are characterised by
social withdrawal and fear of strangers which are hallmarks of anxiety disorders
indicating a potential genetic relationship between infectious diseases and health
behaviours (MacMurray et al., 2014). On an individual inflammation marker level, the
picture is less clear. Case/control studies looking at individual inflammatory markers and
PTSD have shown mixed results (Guo et al., 2012; O'Donovan et al., 2015; von Kanel et
al., 2007), and the evidence is even less clear with depression, one of the most frequent
co-morbidities of PTSD (Dahl et al., 2014; Schmidt et al., 2016). A recent meta-analysis
found increased levels of interleukin 6 (IL6), interleukin 1β (IL1β), tumor necrosis factor
α (TNFα) and interferon γ (IFN γ) in a PTSD cohort as opposed to healthy controls.
However, heterogeneity of data was high, mostly due to factors such as medication and
major depressive disorder (Passos et al., 2015). Only a small number of studies with
limited participant numbers for IFNγ analysis were recorded (n = 79) and a potential
publication bias for IL 1β was noted (Passos et al., 2015). A replication of the meta-
analysis showed that the potential effect size of IL 6 was probably overestimated
(Nilsonne et al., 2016).
Two replication studies investigating inflammatory markers in large military cohorts
found increased overall levels of inflammation but varying evidence for increased
cytokine levels on an individual marker basis (Lindqvist et al., 2016; Lindqvist et al.,
2014). The authors consistently found elevated IL 6 levels based on PTSD diagnosis but
not in relation to PTSD severity. They did not find a significant association between IFNγ
and PTSD. Increased levels of IFNγ in PTSD were found in an Asian cohort (Guo et al.,
2012) and a very small study cohort of combat veterans (Hammad et al., 2012), but a
study in a much larger military cohort could not replicate these findings (Lindqvist et al.,
2016).
A recent study with a cohort of Nepali child soldiers identified associations with gene
expression profiles of inflammatory genes for trauma-exposed individuals reporting high
levels of resilience that were similar to those of healthy controls (Kohrt et al., 2016). A
study with a female cohort found that women in recovery from PTSD have the same
levels of inflammation as healthy controls, suggesting that improved psychological states
and associated health perceptions contributed to reduced levels of inflammation (Gill et
al., 2013). Another study found that coping factors such as pride and contentment are
associated with decreased levels of IL 6 (Stellar et al., 2015). These findings suggest that
positive beliefs and emotions are associated with reduced inflammation. Resilience is
typically associated with generally positive attitudes and emotions (Bonanno, 2004) and
is worthy of closer investigation regarding the association with inflammatory markers.
Given the mixed research findings into cytokines as markers of PTSD comorbidity and
the potential role of resilience in the relationship between PTSD and inflammation, the
correlation between serum cytokine levels and PTSD, symptom severity and resilience in
a large Vietnam veteran cohort was investigated. PTSD diagnosis was hypothesised to be
associated with increased levels of cytokines. It was further hypothesised that increased
symptom severity would positively correlate with the inflammatory marker and resilience
negatively correlate with cytokine levels.
2. Method
2.1. Participants
A total of 299 male and age-matched participants were recruited through Greenslopes
Private Hospital and the Returned and Services League of Australia by the Gallipoli
Medical Research Foundation. Of these, 159 participants met criteria for PTSD diagnosis
and the remaining 140 participants were assigned to the control group. PTSD diagnosis
was obtained through structured interviews by psychiatrists with substantial clinical
expertise in the assessment and differential diagnosis of PTSD. Inclusion criteria included
deployment to Vietnam during the Vietnam War in the Australian and New Zealand
Defence Force. The mean age of the cohort was 68.82 years (SD = 4.2). Clinical
psychologists performed further assessments using validated psychological measures.
Medical Officers conducted semi-structured interviews to collect a medical history for
each participant. Table 1 shows an overview of the characteristics of the cohort by
diagnostic status.
2.2. Ethics
Each participant gave written informed consent before commencement of data collection.
Ethics approval for the project was obtained from the Human Research Ethics
Committees of the Queensland University of Technology and Greenslopes Private
Hospital. This study was carried out in accordance with The Code of Ethics of the World
Medical Association (Declaration of Helsinki).
2.3. Biomarker analysis
A fasting sample of peripheral blood was taken from participants. Whole blood was
collected in an 8.5 ml serum separator tube (SST). The tubes were left standing upright
for 30 minutes for clotting to occur and then spun at 1500 g for 10 minutes at 20°C. The
serum was aliquoted into micro-tubes with a minimum 0.5 ml each. The tubes were
stored frozen at -80°C. A commercial laboratory, Sullivan Nicolaides Pathology,
Brisbane, tested a range of cytokines in standard multiplex assay (interleukin-1 α,
interleukin-1β, interleukin-6, interleukin-10, tumour necrosis factor α, and interferon γ) in
duplicate using Luminex 100 Milliplex cytokine multiplex bead assay (HCYTOMAG-
60K; assay sensitivity: 0.8 pg/ml; intra-assay CV% = 1.6; inter-assay CV% = 12.0).
Findings relating to Tumour Necrosis Factor Alpha in connection with genetics
hypotheses have previously been published by us (Bruenig et al., 2017) The remaining
cytokines were further analysed for serum level association of PTSD severity and
resilience. Samples (n = 37) that were approaching detectable limit for at least one of the
cytokines assayed on the multiplex were reanalysed. Taken together, 299 data points
remained for subsequent analyses across all cytokines based on averages from the first
run or, if detectable values were observed, from the second run. Data was recoded to 0 if
a reading was below lower detection limit (LDT) yielding the following percentage of
data below lower detection limit (IL1 α = 53.85%, IL 1β = 91.64 %, IL 6 = 85.00%, IL
10 = 66.90%, IFNγ = 43.81%).
2.4. Scales
Clinician-Administered PTSD Scale for DSM 5 (CAPS-5): Clinical psychologists
assessed severity of PTSD with the Clinician Administered PTSD Scale for DSM 5
(CAPS-5) (Weathers et al., 2014). Higher scores reflect increased PTSD severity.
The Connor-Davidson Resilience Scale (CD RISC) measures resilience via a range of
self-reported behaviours and beliefs thought to be successful in dealing with adverse
situations (Connor and Davidson, 2003). The scale has sound psychometric properties
(Bezdjian et al., 2016). Higher scores indicate higher resilience. Cronbach’s Alpha was
high: α = 0.92.
The Mini International Neuropsychiatric Interview DSM IV (MINI), an instrument
designed to assess Axis 1 disorders with high validity and reliability (Sheehan et al.,
1998), was used to assess common psychological comorbidities.
2.5. Co-variates:
Based on previous literature (Lindqvist et al., 2016), we assessed the following co-
variates for the cytokines through a Tweedie Model.
Age: Age was assessed through self-report.
Body-Mass-Index (BMI): BMI was assessed during the medical assessment through a
Medical Officer.
Medication: Medication intake was coded based on the World Health Organisation’s
Collaborating Centre for Drug Statistics Methodology
(http://www.whocc.no/atc_ddd_index/) top level coding categories. The approach of a
cumulative score was chosen as all medications from the prescribed list of drugs were
deemed to potentially influence inflammation in this cohort. Medication was scored by
number of prescribed medication per category. That means that a participant would, for
example, score 2 points in Category A if he was taking two different medications from
the corresponding Anatomical Therapeutic Chemical (ATC) category. A cumulative
score was calculated per participant with higher scores reflecting higher intake of
medication types.
Depression: The Depression Anxiety Stress Scale 21 (DASS-21) is a self-report scale
measuring three different constructs of psychological states: stress, depression and
anxiety (Lovibond and Lovibond, 1995). The depression subscale was used to control for
a potential influence of depression on cytokine levels in this study. Increased symptoms
of depression are represented by higher subscale scores. Cronbach’s Alpha was high: α =
0.95.
Alcohol: Alcohol history was dichotomised into high-intake vs low-intake lifetime
history. The classification was based on qualitative data provided by participants and
reconciled with AUDIT risk scores (Alcohol Use Disorder Identification Test; (Bohn et
al., 1995)).
Smoking: Number of years smoked as reported by participants informed this potential
covariate.
2.6. Statistical Analyses:
All statistics were performed using SPSS 23(2015). Tweedie Model with Log Link was
used to observe any potential influence of covariates on cytokine levels and to account
for the zero inflation for each cytokine. All subsequent analyses were performed non-
parametrically to account for the non-normal distribution of the data. To control for
multiple testing, a False Discovery Rate (FDR) and Bonferroni adjustment was applied
using R (https://www.r-project.org/).
3. Results
3.1. Demographics
A total of 299 participants yielded usable cytokine data and were used for analysis (cases:
n = 159; controls: n = 140). Table 1 shows the demographic descriptors of the
participants included in the study. As would be expected, the PTSD group had higher
rates of co-morbid psychological disorders, such as MDD, agoraphobia and suicide risk
(p = 4.190E-04, 8.000E-0, 52.000E-06 respectively. The PTSD group had significantly
higher numbers of participants taking psychotropic medications, such as antidepressants
and anti-anxiety medications, than the control group (p = 5.498E-19).
Independent sample t-tests revealed that mean scores for PTSD severity were
significantly different between the groups, with higher mean scores in the PTSD group
than the controls (p = 2.637E-36; PTSD: M = 15.64; SD = 9.79; No PTSD: M = 2.52; SD
= 3.72) and with the resilience scale showing opposing results as would be expected (p =
1.332E-11; PTSD: M = 68.28; SD = 15.37; No PTSD: M = 70.12; SD = 11.08).
Table 1: Demographics and clinical summary
PTSD
(159)
No PTSD
(140)
p-value
Age M (SD) 68.47 (4.16) 69.23 (4.13) 0.113
Marital Status (current) 0.494
Married (current) 116 108
Divorced/Separated (current) 9 8
Psychotropic Medication Yes: 94
No: 51
Yes: 15
No: 112
5.498E-19
Education level 0.005
Less than year 10 26 8
Year 10 29 23
Vocational 32 20
Year 11 or 12 34 33
University 37 56
Comorbidities1
Major depression 21 2 4.190E-04
Suicide risk 31 2 2.000E-06
Agoraphobia 33 6 8.000E-05
Social phobia 8 0 0.017
Alcohol dependence 22 6 0.019
Alcohol abuse 4 1 0.029
Generalised anxiety disorder 12 3 0.092
Auto-Immune Disorders 0.806
Rheumatoid Arthritis 3 5
Psoriasis 2 0
Other 7 5
Note. M = mean; SD = standard deviation; 1all comorbidity counts as per Mini
International Neuropsychiatric Interview (MINI) for DSM IV (Sheehan et al., 1998).
Only a subset of all comorbidities is shown. Rare comorbidities with no current
information or both groups = 0 were excluded from the table. Only autoimmune disorders
are shown for physical conditions.
3.2. Co-variates:
IL 1β had very few values above lower detection limit (n = 25), hence the variable was
dichotomised for analyses. None of the analyses yielded significant results. All of these
analyses were considered for correction for multiple testing. All other cytokines were
tested for a range of potential covariates per Tweedie Model with Log Link. Less than
10% of the participants were diagnosed with comorbid MDD. The data from the
depression subscale of the DASS was hence used to control for depressive symptoms.
Depression showed a significant association with IFNγ levels and IL 6 levels (p = 0.005,
respectively). Medication showed a significant association with IL 10 (p = 0.015). All
subsequent analyses were performed using the fitted residuals for these cytokines. There
were no influential co-variates for IL 1α and the raw data was subsequently used.
Table 2 shows the p-values for the co-variates across the different cytokines.
Table 2:
Co-variate association between cytokines (p-values)
Depression Age Medication BMI Smoking Alcohol
IL 1α 0.264 0.727 0.787 0.316 0.173 0.098
IL 6 0.005 0.421 0.407 0.764 0.978 0.251
IL 10 0.169 0.681 0.015 0.696 0.704 0.158
IFNγ 0.005 0.526 0.742 0.504 0.124 0.137
Note: IL1α = interleukin1 α, IL6 = interleukin 6, IL10 = interleukin 10, IFNγ = interferon
γ; BMI – Body Mass Index
3.3. Assessment of cytokines with PTSD and resilience:
Group differences: To test for differences in serum cytokine levels across the groups,
Mann-Whitney U Tests were applied. IL 1α and IFNγ levels were not significantly
different between the groups (p = 0.704, p = 129, respectively). A marginal group
difference was observed for IL 6 (p =0.045; PTSD: M = 0.720; SD = 16.207; No PTSD:
M = -0.855, SD = 8.009). IL 10 levels showed significant group differences (p =0.018;
PTSD: M = -1.708; SD = 22.495; No PTSD: M = 1.933, SD = 25.052). For both
cytokines, mean levels were significantly lower in the PTSD group than in the control
group. After adjusting for multiple testing, none of these findings remained significant in
either the FDR or the Bonferroni approach.
PTSD symptom severity: Spearman’s rho correlation (2-tailed) revealed a trend-line
negative correlation between PTSD severity and IL 6 (r = -0.177; r2 =0.031; p = 0.004),
IL 10 (marginal; r = -0.126; r2 =0.016; p = 0.042) and IFNγ (r = -0.153; r2 =0.023; p =
0.013) and PTSD severity. IL1α had no significant correlation with PTSD severity (p =
0.219). After correcting for multiple testing, our findings for IL 6 and IFNγ remained
significant (FDR 5 %). When applying Bonferroni adjustment, none of the findings
remained significant.
Sub-scale analyses: Given the trend-line finding for PTSD severity and IL 6, IL 10, and
IFNγ levels, we pursued sub-symptom analyses to elucidate the potential role of different
sub-symptom criteria.. Criterion D (negative cognitions and mood) showed small but
significant negative correlations with all three cytokines (p = 0.001 (IL 6); p = 0.019 (IL
10); p = 0.003 (IFNγ). Criterion C (avoidance) was significantly negatively correlated
with IFNγ (p = 0.026). Criteria B (intrusions; marginally) and E (arousal; marginally)
were significantly negatively correlated with IL 6 (p = 0.050; p = 0.010; respectively).
After correction for multiple testing, the findings for IL 6 for criterion D (FDR 5%;
Bonferroni) and E (FDR 5% only), and the finding for IFNγ and criterion D and C
(FDR5% only) remained significant. Table 3 shows the correlations of sub-symptoms and
p-values per cytokine.
Table 2: Spearman’s rho correlations between sub-symptoms of PTSD and IL 6, IL 10
and IFNγ.
Criterion B
(intrusions)
Criterion C
(avoidance)
Criterion D
(mood)
Criterion E
(arousal)
IL 6 --0.121 -0.100 -0.196 -0.160
p (2-tailed) 0.050 0.105 0.001 0.010
IL 10 -0.088 -0.083 -0.145 -0.097
p (2-tailed) 0.157 0.183 0.019 0.119
IFNγ -0.082 -0.138 -0.183 -0.109
p (2-tailed) 0.183 0.026 0.003 0.078
Note: IL6 = interleukin 6, IL10 = interleukin 10, IFNγ = interferon γ
Resilience: The correlation between the cytokines and resilience was tested through
Spearman’s rho test. We observed significant but small positive correlations for IL 6 (r =
0.138; p = 0.023) and IFNγ (r = 0.162; p = 0.007). These findings withstood correction
for multiple testing with FDR 5% but not Bonferroni adjustment. IL 1α and IL 10 did not
show a significant correlation with resilience (p = 0.461; p = 0.114, respectively).
4. Discussion
This study systematically investigated the role of individual cytokines with a large and
well controlled group of participants with comparable trauma exposure across patients
and controls. After correcting for multiple testing, group differences between IL 6, IFNγ
and PTSD and controls were observed. A role of IL 6 in PTSD has been implied before
(Lindqvist et al., 2016; Lindqvist et al., 2014; Passos et al., 2015).Increased inflammatory
markers (Lindqvist et al., 2016; Lindqvist et al., 2014) and general ill-health (Edmondson
et al., 2013; Lee et al., 2016; Stein et al., 2016; Wolf et al., 2016) have been observed in
PTSD patients, however in our studies lower levels of cytokines were observed in the
PTSD group than the controls. Studies investigating other inflammatory markers, such as
C-reactive protein and serum amyloid A have previously reported negative correlations
between inflammation and PTSD (Sondergaard et al., 2004).
In contrast to Guo et al (2012) and Hammad et al. (2009) (Guo et al., 2012; Hammad et
al., 2012) we did not observe group differences for IFNγ. This is in line with two other
studies employing larger cohorts than the previous two studies that also did not find
group differences for IFNγ (Hoge et al., 2009; Lindqvist et al., 2016). However, a recent
meta-analysis with a sample size closer to our present study did find a significant effect
(Passos et al., 2015). Similarly, this study did not find a significant difference in cytokine
levels for IL 6 whereas other studies have been able to establish such an effect (Passos et
al., 2015).
For symptom severity score, the correlations with the cytokines showed small effects that
remained significant after multiple testing for IL6 and IFNγ, only. Other studies did not
find a significant correlation between a summative inflammatory score and PTSD
severity (Lindqvist et al., 2016). The cohort sizes and average age between this study and
the previously published study were substantially different in that this study had a much
older and larger cohort which may have potentially contributed to the difference in
findings. We further pursued sub-symptom testing for severity scores to uncover potential
differential drivers of cytokine levels. Our analyses revealed the IL 6 was driven by
arousal and mood symptoms and IFNγ by mood and intrusion symptoms. This is of
interest as these findings may indicate differential influences of sub-systems on
inflammatory markers. It may be important in the future to examine symptom clusters of
PTSD with inflammatory markers to better understand the relationship between
psychological distress and inflammation to identify specific therapeutic targets.(Del
Grande da Silva et al., 2016; Ragen et al., 2015)
Both, IL 6 and IFNγ also showed significant correlations with resilience that withstood
multiple testing. A previous study showed positive affect such as contentment and pride
to be associated with lower levels of Interleukin 6 (Stellar et al., 2015). Another study
identified self-efficacy as a crucial resilience factor for normalised mRNA expression
(Kohrt et al., 2016).(Stellar et al., 2015).
Taken together, our findings replicate the association of inflammation in PTSD, but with
lower levels of inflammation found with increased PTSD severity. Our study design
included an ageing cohort that was well enough to participate in a comprehensive
research study. This implies relatively high levels of functioning which might account for
the small effect sizes we observed in all analyses and might also account for the
differences in findings with some of the literature. Increased resilience may impact
negatively on the stress system reflected by the extra effort that has to be applied to
maintain a more normalised level of functioning (Schoenfeld et al., 2017). Research has
shown that there is a wide range of post-trauma outcomes that can influence a person’s
life after a life-shattering experience (Tedeschi and Calhoun, 2004). These evaluations
occur in the face of PTSD symptoms with research showing that moderate levels of
PTSD correlate to personal growth (Shakespeare-Finch and Lurie-Beck, 2014). While we
did not measure personal growth in our participants, the moderate mean of PTSD
symptom severity in our cohort may hint at mechanisms of survival and potential
reconstruction of meaning in life (growth) that may be at play. More research is needed to
further disentangle the likely complex relationship of PTSD and positive trauma
outcomes (Sondergaard et al., 2004).
There are limitations to our study that should be noted. All effect sizes in our study are
small. While they reached significance the utility of these findings need to be interpreted
with caution. Generally, inflammation within PTSD has been associated with low-grade
levels. Statistical overestimation of the relationship with individual inflammatory levels
and PTSD has previously been suggested (Nilsonne et al., 2016). Inflammatory markers
are highly variable and it is hence possible that despite all efforts to control for co-
variates, the variance between studies may stem from sources that are difficult to control
(Nilsonne et al., 2016). .
A high number of analyses were performed to identify correlations between individual
markers, PTSD and resilience. We accounted for this by applying methodologies that
counter-act Type 1 errors. Other studies have taken different approaches to avoiding type
1 error rates, however these approaches come at the cost of granularity on an individual
marker level (Lindqvist et al., 2016). We measured PTSD and resilience as trauma
outcomes, however a wider range of potential outcomes has been suggested
(Shakespeare-Finch and Enders, 2008; Tedeschi and Calhoun, 2004). The relationship
between trauma response and mental and medical well-being may be more complex than
our dichotomous and cross-sectional approach was able to assess.
Because of the high variability of cytokine elevation in individuals, it would be preferable
in the future to apply longitudinal measures that include several times points of cytokine
measurements for the determination of a relationship between PTSD, resilience and
cytokines. Differences in assay performance and limitations of assay reliabilities make
comparability of results across studies difficult. A more unified approach with standard
protocols would enhance the area of research significantly. Lastly, comorbidity of PTSD
with other disorders such as MDD is often problematic due to overlaps of phenotypical
symptoms and common molecular pathways. However, we were able to assess a range of
potentially confounding parameters, including depressive symptoms through a
comprehensive data set. Given that our cohort was an all-male war veteran cohort limits
the generalisability of the data.
5. Conclusion
This study systematically investigated the role of individual cytokines in a well-screened
and large cohort of Vietnam veterans. A marginal correlation between IL 6, IFNγ and
PTSD was established, likely driven by sub-symptoms of PTSD. Replication studies in
equally large and well-screened cohorts are recommended with a particular emphasis on
symptom clusters within PTSD and their correlation with inflammation. Research into the
contribution of more positive trauma outcomes, such as resilience, adaptive coping and
posttraumatic growth on inflammation may help in improving our understanding of the
complex relationship between trauma, trauma response and well-being.
Conflict of Interest
None
Author Contributions
Dagmar Bruenig and Joanne Voisey substantially contributed to the study design,
statistical analyses, writing and critical editing of the manuscript. Charles P. Morris
substantially contributed to the study design, writing and critical editing of the
manuscript. Divya Mehta substantially contributed to the statistical analyses and critical
editing of the manuscript. Bruce Lawford and Ross McD Young substantially contributed
to the study design and critical editing of the manuscript. Wendy Harvey substantially
contributed to the study design, ethics submission and data collection. All authors
reviewed and approved the final version of the manuscript for publication.
Role of Funding:
The PTSD Initiative (or ‘This study’) was funded by the Queensland Branch of the
Returned & Services League of Australia (RSL QLD). Financial support was also
provided by the Institute of Health and Biomedical Innovation and the School of
Biomedical Sciences, Queensland University of Technology, Australia.
Acknowledgements
The first author would like to thank the Gallipoli Medical Research Foundation for their
generous provision of a scholarship to DB, and Miriam Dwyer and Dr Sarah McLeay for
their project management support. The authors would also like to acknowledge Dr
Madeline Romaniuk for psychological input, Dr John Gibson and the team at the Keith
Payne Unit, and the staff and investigators at Greenslopes Private Hospital for their
valuable contribution to the study. All authors would like to extend their gratitude to the
participants of our study for their generous provision of data and time. The Gallipoli
Medical Research Foundation wishes to thank the RSL QLD for their generous donation,
and Sullivan Nicolaides Pathology and Queensland X-Ray for their in-kind support.
References
IBM SPSS Statistics for Windows, 2015. 23.0. ed. IBM Corp. , Armonk, NY.
American Psychiatric Association, A.P.A., 2013. Diagnostic and Statistical Manual of
Mental Disorders, Fifth Edition (DSM-5**). American Psychiatric Publishing.
Australian Government, D.o.V.A., 2014. Vietnam Veterans Family Study (VVFS).
Bezdjian, S., Schneider, K.G., Burchett, D., Baker, M.T., Garb, H.N., 2016. Resilience in
the United States Air Force: Psychometric properties of the Connor-Davidson
Resilience Scale (CD-RISC). Psychol. Assess.
Bohn, M.J., Babor, T.F., Kranzler, H.R., 1995. The Alcohol Use Disorders Identification
Test (AUDIT): validation of a screening instrument for use in medical settings. J.
Stud. Alcohol 56 (4), 423-432.
Bonanno, G.A., 2004. Loss, trauma, and human resilience: Have we underestimated the
human capacity to thrive after extremely aversive events? Am. Psychol. 59 (1),
20-28.
Boscarino, J.A., 2008. A prospective study of ptsd and early-age heart disease mortality
among vietnam veterans: Implications for surveillance and prevention.
Psychosom. Med. 70 (6), 668-676.
Bruenig, D., Mehta, D., Morris, C.P., Harvey, W., Lawford, B., Young, R.M., et al.,
2017. Genetic and serum biomarker evidence for a relationship between TNFα
and PTSD in Vietnam war combat veterans. Compr. Psychiatry 74, 125-133.
Connor, K.M., Davidson, J.R.T., 2003. Development of a new resilience scale: The
Connor‐Davidson Resilience Scale (CD‐RISC). Depress. Anxiety 18 (2), 76-82.
Dahl, J., Ormstad, H., Aass, H.C.D., Malt, U.F., Bendz, L.T., Sandvik, L., et al., 2014.
The plasma levels of various cytokines are increased during ongoing depression
and are reduced to normal levels after recovery. Psychoneuroendocrinology 45
(Apr 6), 77-86.
Del Grande da Silva, G., Wiener, C.D., Barbosa, L.P., Gonçalves Araujo, J.M., Molina,
M.L., San Martin, P., et al., 2016. Pro-inflammatory cytokines and psychotherapy
in depression: Results from a randomized clinical trial. J. Psychiatr. Res. 75, 57-
64.
Dohrenwend, B.P., Turner, J.B., Turse, N.A., Adams, B.G., Koenen, K.C., Marshall, R.,
2006. The psychological risks of Vietnam for U.S. veterans: a revisit with new
data and methods. Science 313 (5789), 979-982.
Edmondson, D., Kronish, I.M., Shaffer, J.A., Falzon, L., Burg, M.M., 2013.
Posttraumatic stress disorder and risk for coronary heart disease: A meta-analytic
review. Am. Heart J. 166 (5), 806-814.
Groer, M.W., Kane, B., Williams, S.N., Duffy, A., 2015. Relationship of PTSD
symptoms with combat exposure, stress, and inflammation in American soldiers.
Biol. Res. Nurs. 17 (3), 303-310.
Guo, M., Liu, T., Guo, J.C., Jiang, X.L., Chen, F., Gao, Y.S., 2012. Study on serum
cytokine levels in posttraumatic stress disorder patients. Asian Pac. J. Trop. Med.
5 (4), 323-325.
Hammad, S.M., Truman, J.-P., Al Gadban, M.M., Smith, K.J., Twal, W.O., Hamner,
M.B., 2012. Altered blood sphingolipidomics and elevated plasma inflammatory
cytokines in combat veterans with post-traumatic stress disorders. Neurobiol.
Lipids 10, 2.
Hoge, C.W., Castro, C.A., Messer, S.C., McGurk, D., Cotting, D.I., Koffman, R.L., 2004.
Combat duty in Iraq and Afghanistan, mental health problems, and barriers to
care. N. Engl. J. Med. 351 (1), 13-22.
Hoge, E.A., Brandstetter, K., Moshier, S., Pollack, M.H., Wong, K.K., Simon, N.M.,
2009. Broad spectrum of cytokine abnormalities in panic disorder and
posttraumatic stress disorder. Depress. Anxiety 26 (5), 447-455.
Kohrt, B.A., Worthman, C.M., Adhikari, R.P., Luitel, N.P., Arevalo, J.M.G., Ma, J., et
al., 2016. Psychological resilience and the gene regulatory impact of
posttraumatic stress in Nepali child soldiers. Proc. Natl. Acad. Sci. U. S. A. 113
(29), 8156-8161.
Lee, Y.C., Agnew-Blais, J., Malspeis, S., Keyes, K., Costenbader, K., Kubzansky, L.D.,
et al., 2016. Post-traumatic stress disorder and risk for incident rheumatoid
arthritis. Arthritis Care Res (Hoboken) 68 (3), 292-298.
Leonard, B., Maes, M., 2012. Mechanistic explanations how cell-mediated immune
activation, inflammation and oxidative and nitrosative stress pathways and their
sequels and concomitants play a role in the pathophysiology of unipolar
depression. Neurosci. Biobehav. Rev. 36 (2), 764-785.
Lindqvist, D., Dhabhar, F.S., Mellon, S.H., Yehuda, R., Grenon, S.M., Flory, J.D., et al.,
2016. Increased pro-inflammatory milieu in combat related PTSD - A new cohort
replication study. Brain. Behav. Immun.
Lindqvist, D., Wolkowitz, O.M., Mellon, S., Yehuda, R., Flory, J.D., Henn-Haase, C., et
al., 2014. Proinflammatory milieu in combat-related PTSD is independent of
depression and early life stress. Brain. Behav. Immun. 42, 81-88.
Lovibond, S.H., Lovibond, P.F., 1995. The structure of negative emotional states:
Comparison of the Depression Anxiety Stress Scales (DASS) with the Beck
Depression and Anxiety Inventories. Behav. Res. Ther. 33 (3), 335-343.
MacMurray, J., Comings, D.E., Napolioni, V., 2014. The gene-immune-behavioral
pathway: Gamma-interferon (IFN-γ) simultaneously coordinates susceptibility to
infectious disease and harm avoidance behaviors. Brain, Behav., Immun. 35, 169-
175.
Nilsonne, G., Hilgard, J., Lekander, M., Arnberg, F.K., Stressforskningsinstitutet,
Stockholms, u., et al., 2016. Post-traumatic stress disorder and interleukin 6.
Lancet Psychiatry 3 (3), 200-201.
O'Donovan, A., Chao, L.L., Paulson, J., Samuelson, K.W., Shigenaga, J.K., Grunfeld, C.,
et al., 2015. Altered inflammatory activity associated with reduced hippocampal
volume and more severe posttraumatic stress symptoms in Gulf War veterans.
Psychoneuroendocrinology 51, 557-566.
Oxenkrug, G.F., 2011. Interferon-gamma-inducible kynurenines/pteridines inflammation
cascade: implications for aging and aging-associated psychiatric and medical
disorders. J. Neural Transm. 118 (1), 75-85.
Passos, I.C., Vasconcelos-Moreno, M.P., Costa, L.G., Kunz, M., Brietzke, E., Quevedo,
J., et al., 2015. Inflammatory markers in post-traumatic stress disorder: a
systematic review, meta-analysis, and meta-regression. The Lancet Psychiatry 2
(11), 1002-1012.
Powers, A., Almli, L., Smith, A., Lori, A., Leveille, J., Ressler, K.J., et al., 2016. A
genome-wide association study of emotion dysregulation: Evidence for
interleukin 2 receptor alpha. J. Psychiatr. Res. 83, 195-202.
Ragen, B.J., Seidel, J., Chollak, C., Pietrzak, R.H., Neumeister, A., 2015. Investigational
drugs under development for the treatment of PTSD. Expert Opin. Investig. Drugs
24 (5), 659-672.
Rohleder, N., Aringer, M., Boentert, M., 2012. Role of interleukin-6 in stress, sleep, and
fatigue. Ann. N. Y. Acad. Sci. 1261 (1), 88-96.
Schmidt, F.M., Schröder, T., Kirkby, K.C., Sander, C., Suslow, T., Holdt, L.M., et al.,
2016. Pro- and anti-inflammatory cytokines, but not CRP, are inversely correlated
with severity and symptoms of major depression. Psychiatry Res. 239, 85-91.
Schoenfeld, P., Preusser, F., Margraf, J., 2017. Costs and benefits of self-efficacy:
Differences of the stress response and clinical implications. Neurosci. Biobehav.
Rev. 75, 40-52.
Shakespeare-Finch, J., Enders, T., 2008. Corroborating evidence of posttraumatic growth.
J. Trauma. Stress 21 (4), 421-424.
Shakespeare-Finch, J., Lurie-Beck, J., 2014. A meta-analytic clarification of the
relationship between posttraumatic growth and symptoms of posttraumatic
distress disorder. J. Anxiety Disord. 28 (2), 223-229.
Sheehan, D.V., Lecrubier, Y., Sheehan, K.H., Amorim, P., Janavs, J., Weiller, E., et al.,
1998. The Mini-International Neuropsychiatric Interview (M.I.N.I.): the
development and validation of a structured diagnostic psychiatric interview for
DSM-IV and ICD-10. J. Clin. Psychiatry 59 Suppl 20, 22-33;quiz 34-57.
Sondergaard, H.P., Hansson, L.O., Theorell, T., 2004. The inflammatory markers C-
reactive protein and serum amyloid A in refugees with and without posttraumatic
stress disorder. Clin. Chim. Acta 342 (1-2), 93-98.
Stein, M.B., Chen, C.Y., Ursano, R.J., Cai, T., Gelernter, J., Heeringa, S.G., et al., 2016.
Genome-wide association studies of posttraumatic stress disorder in 2 cohorts of
US Army soldiers. JAMA Psychiatry 73 (7), 695-704.
Stellar, J.E., John-Henderson, N., Anderson, C.L., Gordon, A.M., McNeil, G.D., Keltner,
D., 2015. Positive affect and markers of inflammation: discrete positive emotions
predict lower levels of inflammatory cytokines. Emotion 15 (2), 129-133.
Tedeschi, R.G., Calhoun, L.G., 2004. Posttraumatic Growth: Conceptual foundations and
empirical evidence. Psychol. Inq. 15 (1), 1-18.
von Kanel, R., Hepp, U., Kraemer, B., Traber, R., Keel, M., Mica, L., et al., 2007.
Evidence for low-grade systemic proinflammatory activity in patients with
posttraumatic stress disorder. J. Psychiatr. Res. 41 (9), 744-752.
Weathers, F.W., Marx, B.P., Friedman, M.J., Schnurr, P.P., 2014. Posttraumatic stress
disorder in DSM-5: New criteria, new measures, and implications for assessment.
Psychol. Inj. Law 7 (2), 93-107.
Wolf, E.J., Bovin, M.J., Green, J.D., Mitchell, K.S., Stoop, T.B., Barretto, K.M., et al.,
2016. Longitudinal associations between post-traumatic stress disorder and
metabolic syndrome severity. Psychol. Med. 46 (10), 2215-2226.