Assessment of correlations between peripheral interferon γ ... · Assessment of correlations...

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Assessment of correlations between peripheral interferon γ and interleukin 6, PTSD and resilience Dagmar Bruenig a,c , Divya Mehta b , Charles P. Morris a , Bruce Lawford b , Wendy Harvey c , Ross McD Young b , Joanne Voisey a * 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 c Gallipoli 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

Transcript of Assessment of correlations between peripheral interferon γ ... · Assessment of correlations...

Page 1: Assessment of correlations between peripheral interferon γ ... · Assessment of correlations between peripheral interferon γ and interleukin 6, PTSD and resilience Dagmar Brueniga,c,

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

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Kelvin Grove, Queensland, 4059

Australia

Email: [email protected]

Phone: +61 7 3138 6261

Fax. +61 7 3138 6030

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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

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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

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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

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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

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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

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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

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(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

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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

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(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).

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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

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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

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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.

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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

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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).

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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

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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

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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

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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.

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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

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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.

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