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Glucocorticoid Receptor Pathway Components PredictPosttraumatic Stress Disorder Symptom Development:A Prospective StudyMirjam van Zuiden, Elbert Geuze, Hanneke L.D.M. Willemen, Eric Vermetten, Mirjam Maas,Karima Amarouchi, Annemieke Kavelaars, and Cobi J. Heijnen

Background: Biological correlates of posttraumatic stress disorder (PTSD) have mostly been studied using cross-sectional or posttraumaprospective designs. Therefore, it remains largely unknown whether previously observed biological correlates of PTSD precede traumaexposure. We investigated whether glucocorticoid receptor (GR) pathway components assessed in leukocytes before military deploymentrepresent preexisting vulnerability factors for development of PTSD symptoms.

Methods: Four hundred forty-eight male soldiers were assessed before and 6 months after deployment to a combat zone. Participantswere assigned to the PTSD or comparison group based on Self-Rating Inventory for PTSD scores after deployment. Logistic regressionanalysis was applied to predict development of a high level of PTSD symptoms based on predeployment GR number, messenger (m)RNAexpression of GR target genes FKBP5, GILZ, and SGK1, plasma cortisol, and childhood trauma. We also investigated whether predeployment

R number and FKBP5 mRNA expression were associated with single nucleotide polymorphisms in the GR and FKBP5 genes, either alone orin interaction with childhood trauma.

Results: Several GR pathway components predicted subsequent development of a high level of PTSD symptoms: predeployment high GRnumber, low FKBP5 mRNA expression, and high GILZ mRNA expression were independently associated with increased risk for a high level ofPTSD symptoms. Childhood trauma also independently predicted development of a high level of PTSD symptoms. Additionally, weobserved a significant interaction effect of GR haplotype BclI and childhood trauma on GR number.

Conclusions: Collectively, our results indicate that predeployment GR pathway components are vulnerability factors for subsequent

development of a high level of PTSD symptoms.

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Key Words: Childhood trauma, FKBP5, GILZ, glucocorticoid recep-or, posttraumatic stress disorder, SNP

P osttraumatic stress disorder (PTSD) is a common conse-quence of exposure to trauma, with lifetime prevalence esti-mated at 7% in general populations from the United States

(1) and The Netherlands (2). Biological correlates of PTSD have beenstudied before, but most studies used a cross-sectional or post-trauma prospective design (3). Therefore, it remains to be deter-mined whether biological differences between individuals withand without PTSD are already present before the traumatic eventleading to PTSD. Identification of preexisting vulnerability factorsfor PTSD development would contribute to the identification ofvulnerable individuals working in professions with high risk oftrauma exposure, such as the military and police. This identificationcould eventually lead to improved preventive care.

PTSD is associated with altered functioning of the hypothalam-ic-pituitary-adrenal (HPA) axis (3), although there is ongoing dis-pute with regard to the direction of these alterations. A meta-analysis showed that hypocortisolism is present in specific

From the Laboratory of Neuroimmunology and Developmental Origins ofDisease (MvZ, HLDMW, MM, KA, AK, CJH), University Medical CenterUtrecht Research Centre-Military Mental Health (MvZ, EG, EV), Ministry ofDefence, and Department of Psychiatry (EG, EV), Rudolf Magnus Instituteof Neuroscience, University Medical Center Utrecht, Utrecht, The Neth-erlands.

Address correspondence to Cobi J. Heijnen, Ph.D., Laboratory of Neuroim-munology and Developmental Origins of Disease, University MedicalCenter Utrecht, Room KC03.068.0, P.O. Box 85090, 3508 AB Utrecht, TheNetherlands; E-mail: c.heijnen@umcutrecht.nl.

dReceived Sep 7, 2011; revised Oct 19, 2011; accepted Oct 24, 2011.

0006-3223/$36.00doi:10.1016/j.biopsych.2011.10.026

ubgroups of PTSD patients (4). However, hypercortisolism has alsoeen described in PTSD (5– 8) and may be associated with (fear of)ngoing or repeated traumatization (5,7). Increased negative feed-ack of glucocorticoids (GCs) on the HPA axis is often considered asne of the hallmark biological correlates of PTSD (3,9), but this is notconsistent finding (6,8). It has been reported that increased sensi-

ivity of the HPA axis for negative feedback by GCs can also benduced by adulthood trauma exposure independently of PTSD10 –12). We propose that the mixed results are most likely causedy differences in populations with regard to gender, type and tim-

ng of trauma, presence of comorbid disorders, and time sincerauma.

The regulation of the immune system by GCs in PTSD has alsoeen studied. An increased sensitivity of immune cells to regulationy GCs has repeatedly been described for PTSD (13,14), althoughecreased GC-sensitivity of immune cells has also been observed

15). GCs are important regulators of the immune system by inhib-ting cell proliferation, regulating cytokine production and stimu-ating apoptosis (16). The actions of GCs are mediated by glucocor-icoid receptors (GR) and mineralocorticoid receptors (MR).C-regulation of the immune system, especially under stressfulonditions, is predominantly mediated via GR (17). The number ofR may contribute to the level of GR signalling, and there is evi-ence that the relative expression of various GR subtypes contrib-tes to GR binding capacity and functional effects of GCs (18,19).everal cross-sectional studies have indicated an increased GRumber in peripheral blood mononuclear cells (PBMCs) of individ-als with PTSD (20,21). FK506 binding protein 5 (FKBP5) is a targetene of GR that is upregulated by activation of the receptor. More-ver, FKBP5 functions as a co-chaperone molecule of the GR and

owers the affinity of GR which reduces GC binding, leading to

ecreased GR signalling capacity (22). It has been shown that FKBP5

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messenger (m)RNA expression is decreased in PTSD (23, 24). Inaddition, FKBP5 mRNA expression immediately posttrauma pre-dicted subsequent PTSD status (25). We recently described that theGR number in PBMCs was higher before military deployment in 34soldiers with a high level of PTSD symptoms after deploymentcompared with a sample of 34 matched control subjects without ahigh level of PTSD symptoms after deployment (26).

The working model for this study was that military personnelwho develop a high level of PTSD symptoms in response to deploy-ment have a dysregulation at various levels of the GR pathwaybefore deployment. In the first part of this study, we investigatedwhich components of the GR pathway contribute to prediction ofthe development of PTSD symptoms in response to deployment.For this purpose, we investigated whether predeployment mRNAexpression levels of genes directly regulated by the GR (i.e., geneswith a glucocorticoid response element) (27), predicted the pres-ence of a high level of PTSD symptoms 6 months after deployment.We selected three GR target genes: glucocorticoid-induced leucinezipper (GILZ), a mediator of the anti-inflammatory and immunosup-pressive effects of GCs (28); serum/glucocorticoid regulated kinase1 (SGK1), which is involved in modulating apoptosis (29); and FKBP5.Furthermore, we aimed to confirm our previous finding on thepredictive value of the predeployment GR number for develop-ment of a high level of PTSD symptoms within this large sample of448 male soldiers. We also investigated whether predeploymentplasma cortisol, as an important outcome of the HPA axis, prede-ployment predicted a high level of PTSD symptoms. In addition,because childhood trauma is a well-known risk factor for adult PTSD(30), we also investigated whether childhood trauma predicted ahigh level of PTSD symptoms.

GR number, GILZ mRNA expression, and FKBP5 mRNA expres-sion turned out to independently predict development of PTSDsymptoms. Various single nucleotide polymorphisms (SNPs) in theGR and FKBP5 gene have been described. In the second part of thestudy, we investigated whether SNPs in these genes were related tothe number of GR and the level of FKBP5 mRNA expression in oursample. In addition, we also investigated whether interactions be-tween childhood trauma and SNPs in GR and FKBP5 genes wererelated to GR number and to FKBP5 mRNA levels. We selected fiveGR SNPs that are associated with sensitivity of PBMCs and the HPAaxis for regulation by GCs and with cortisol and corticotropin re-sponses to stress in Caucasians (31,32). In addition, one of theseSNPs (BclI) has previously been found to be associated with in-creased risk for development of PTSD (33). Furthermore, we se-lected two FKBP5 SNPs associated with peri- and posttraumaticdissociation within a population of Caucasian children (34). TheseSNPs have also been shown to be associated with increased risk forPTSD development in African American samples with high levels ofchildhood trauma (35,36).

Methods and Materials

General ProcedureMilitary personnel of the Dutch Armed Forces assigned to a

4-month deployment to Afghanistan were included on a voluntarybasis after oral and written informed consent. Their duties duringdeployment included combat patrols, clearing or searching build-ings, participation in demining operations, and transportationacross enemy territory. Participants were exposed to typical com-bat-zone stressors including enemy fire, armed combat, and com-bat casualties. We included participants from 11 sequential rota-tions deployed from 2005 to 2009. Several weeks before

deployment and approximately 6 months after deployment, partic- C

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pants filled out questionnaires, and a blood sample was drawn. Thetudy was approved by the Institutional Review Board of the Uni-ersity Medical Center Utrecht.

articipantsBecause more than 90% of the total participant population was

ale, we included only males in the current study. Four hundredorty-eight male participants completed the assessments beforend 6 months after deployment (for procedure, see van Zuiden et al.26]). The sample had a predominantly Caucasian background�95%). Participants were assigned to the PTSD group (n � 35)

hen their score on the Self-Rating Inventory for PTSD (SRIP) (37,38)as above cutoff (�38) 6 months after deployment and their SRIP

core before deployment was below cutoff. This cutoff equals theean plus 2 SD, and corresponds with the 95th percentile of SRIP

cores before deployment within a population of 704 soldiers fromhe Dutch Armed Forces (mean [SD]: 26.91 [5.34]). All remainingarticipants with SRIP scores below cutoff level both before and 6onths after deployment were included in the comparison group

n � 413). Thirty-four participants of the PTSD group and 34 partic-pants of the comparison group were included in our previoustudy (26). Before deployment, medication use was very limitedlocal use of corticosteroids, n � 5; antihypertensives, n � 3; antide-ressants, n � 2; antihistamines, n � 11; and cholesterol-loweringedications, n � 5) and did not differ between groups (p � 1.000).

nalysis of GR pathway-components was performed by investiga-ors blind to the PTSD status of the participants.

easuresQuestionnaires. PTSD symptoms over the previous 4 weeks

ere assessed with the 22-item SRIP (37,38). The SRIP has goodoncurrent validity with other PTSD measures such as the Cliniciandministered PTSD Scale and the Mississippi Scale for PTSD. Thealidity of our cutoff score as representing a high level of PTSDymptoms is supported by van Zelst et al. (39), who tested theensitivity and specificity of various cutoffs on the SRIP for a diag-osis of PTSD according to the DSM-IV. A cutoff in our range pro-ided the highest sensitivity and specificity for a PTSD diagnosis.

Levels of depressive symptoms, anxiety symptoms and sleepisturbances were assessed using subscales of the Dutch version of

he 90-item Symptom Checklist (SCL-90) (40). This questionnaireas good reliability and is frequently used within research andlinical settings. The validity of the depression subscale as a screen-

ng instrument for depression has been shown in various patientamples (41– 43).

Exposure to potential traumatic experiences during childhood wasssessed using the Dutch version of the 27-item self-report version ofhe Early Trauma Inventory (44). Exposure to potentially traumaticeployment stressors was assessed with a 13-item checklist (26).

Dexamethasone Binding. For determination of the capacityf PBMCs to bind GCs, a validated whole-cell single-point bindingssay was used as described previously (45). This method provides aeliable estimate of Bmax as determined using a classical bindingssay with 3-200 nmol/L 3H-dexamethasone (r2 � .92) (45). Briefly,BMCs were isolated from whole blood using Ficoll-Paque (Phar-acia, Uppsala, Sweden) and 107 cells were frozen in dimethylsulf-

xide. After thawing and 60-min equilibration in culture medium,ells were washed twice, resuspended in assay buffer (RPMI-1640ith 5% fetal calf serum) and incubated in duplicate with 100mol/L 3H-dexamethasone (Amersham, Buckinghamshire, Unitedingdom) in the presence or absence of excess unlabeled dexa-ethasone (Sigma-Aldrich, Steinheim, Germany) for 1 hour at 37°C.

ell-bound radioactivity was quantified by liquid scintillation anal-

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ysis. We refer to the results of the binding assay as GR number,because the GR/MR ratio in human PBMCs is approximately 10:1and the ratio of dexamethasone binding affinity is 4:1 (17).

GR Target Gene mRNA Expression. We investigated prede-loyment mRNA expression of GR target genes (FKBP5, GILZ, andGK1). Total RNA was isolated from PBMCs with Trizol (Invitrogen,arlsbad, California). One �g of total RNA was used to synthesizeDNA with SuperScript Reverse Transcriptase (Invitrogen). Real-ime polymerase chain reactions were performed with an iQ5 Real-ime PCR Detection System (Bio-Rad, Hercules, California) (see vanuiden et al. [26] for primer sequences). Data were normalized forAPDH and �-actin expression.

GR and FKBP5 SNPs. DNA was extracted from whole bloodamples by using the Puregene DNA purification kit (Qiagen, Valen-ia, California). Five common polymorphisms of the GR gene (SNPsth111l [rs10052957], ER22/23EK [rs6189/90], N363S [rs6195], BclI

[rs41423247], and A3669G 9� [rs6198]) and two common polymor-phisms of the FKBP5 gene (rs3800373, rs1360780) were selected(31,34). SNPs were genotyped using Taqman Assay-by-design (Ap-plied Biosystems, Nieuwerkerk aan den IJssel, The Netherlands).Assays were performed according to the manufacturer’s instruc-tions. The genotypes were analyzed using an ABI 7900HT instru-ment (Applied Biosystems).

Cortisol. A venous blood sample was collected between 8 and1:30 AM in ethylenediamine tetraacetate vacutainers. Plasma wasollected after centrifugation and stored at �80°C. Cortisol levelsere measured using electrochemiluminescence (ECL) immunoas-

ay on the Modular E170 (Roche Diagnostics, Mannheim, Germany).ower detection limit: 3 nmol/L. Interassay variation: �3%. Refer-nce values (7–10 AM): 170 –540 nmol/L.

ata AnalysisAnalyses were performed using SPSS 15.0. p � .05 (two-tailed)

as considered significant. Variables were tested for normality and10log-transformed when necessary. Nontransformed values are re-ported in figures and tables. Because of technical problems, missingvalues were present for a number of participants (GR number: 4;mRNA expression: 42; cortisol: 16; GR SNPs: 7, FKBP5 SNPs: 5). Outli-ers were removed if their values exceeded SD � 3.29 from the mean

Table 1. Participant Characteristics and Pre- and Postdeployment Question

PTSD Group (n � 35)

Predeployment Postde

PTSD (SRIP) Total Score 28.23 (4.19) 44.2SCL-90 Depression Score 19.76 (4.43) 23.9SCL-90 Anxiety Score 11.74 (1.87) 13.4SCL-90 Sleep Disturbances Score 4.34 (1.68) 6.1No. of Deployment Stressors

Experienced 5.9Age During Deployment 27.29 (9.95)No. of Previous Deployments .68 (1.15)Early Trauma Inventory, No. of

Experiences4.45 (3.11)

BMI Before Deployment 25.39 (3.62)Smoking Before Deployment

(Yes) 20 (58.8%)Alcohol/Week Before

DeploymentNo alcohol 4 (11.8%)1–20 units/week 27 (79.4%)�20/week 3 (8.8%)

BMI, body mass index; PTSD, posttraumatic stress disorder; SCL-90, Symptom

FKBP5: 4; GILZ: 3; SGK1: 3; cortisol: 2). Removal of outliers did notlter our results. In case of missing values, participants were deleted

istwise from the analyses for which the values were missing.Group differences were tested with t tests for continuous paramet-

ic variables, chi-square tests for categorical variables and repeatedeasures analysis of variance for variables measured longitudinally.eviations from Hardy-Weinberg equilibrium in genotype data weressessed with chi-square tests. Linkage disequilibrium among theNPs was estimated with D= using HaploView (46). Haplotypes weressigned using PHASE, which uses a Bayesian estimation method toeconstruct haplotypes from population genotype data (47). Only hap-otypes with a frequency �1% were included in the analyses. Haplo-ypes could be inferred with 95% or greater certainty for both alleles in5% of participants for GR haplotypes and in 89% of participants forKBP5 haplotypes.

The predictive value of GR number, mRNA expression levels,lasma cortisol levels and childhood trauma for the presence of aigh level of PTSD symptoms was investigated with logistic regres-ion analysis. By standardizing the continuous variables, we wereble to compare the predictive value of the variables. We subse-uently controlled for possibly confounding effects of deploymenttressors, age, number of previous deployments, predeploymentTSD and depression questionnaire scores, and predeploymentody mass index, smoking, alcohol, and medication use. All vari-bles in the model were forced into entry.

Additionally, we performed a median split on the number ofraumatic childhood experiences (low childhood trauma: �2 re-orted events, high childhood trauma: �3 reported events). We

ncluded childhood trauma, dichotomous haplotype carrier status,nd an interaction-term between haplotype carrier status andhildhood trauma in linear regression analyses to predict prede-loyment GR number and FKBP5 mRNA expression. Bonferroni cor-

ection was applied.

esults

articipant CharacteristicssWe included 448 male soldiers, of whom 35 reported a high level

f PTSD symptoms after return from deployment (Table 1). Partici-

Scores of the PTSD Symptoms Group and Comparison Group

Comparison Group (n � 413)

p Valueent Predeployment Postdeployment

1) 25.86 (3.63) 26.05 (4.03)6) 17.50 (2.22) 17.67 (3.04)7) 10.76 (1.36) 10.69 (1.46)5) 3.84 (1.37) 3.84 (1.48)

4) 5.02 (2.60) .05529.07 (8.98) .265

1.00 (1.28) .0982.96 (2.65) .001

24.84 (2.69) .324

169 (40.9%) .042

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344 (84.9%)24 (5.9%)

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Checklist 90; SRIP, Self-Rating Inventory for PTSD.

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pants in the PTSD group reported more PTSD symptoms than thecomparison group before and after deployment [F (1,446): 227.245,p � .001]. More importantly, the PTSD group reported a strongincrease in PTSD symptoms in response to deployment, while PTSDsymptoms in the comparison group did not increase [F (1,446):281.715, p � .001; Table 1]. The self-reported longitudinal course ofdepressive symptoms, general anxiety, and sleep disturbances fol-lowed the same pattern [depression: group: F (1,444):102.098, p �.001, interaction: F (1,444):53.769, p � .001; anxiety: group: F (1,442):64.005, p � .001, interaction: F (1,442):31.757, p � .001; sleep prob-ems: group: F (1,444):45.013, p � .001, interaction: F (1,444):35.619,� .001]. Furthermore, participants in the PTSD group had experi-

nced more potentially traumatic experiences during childhoodt (444) � �3.217, p � .001]. In addition, a higher percentage of

participants in the PTSD group smoked before deployment (p �.042). The two groups did not differ in age, body mass index, andalcohol use before deployment.

Prospective AnalysesPredictive Value of GR Number, GR Target Genes, Plasma

Cortisol, and Childhood Trauma for PTSD Symptoms AfterDeployment. We included GR number and mRNA expression ofGR target genes in PBMCs, plasma cortisol and childhood traumameasured before deployment in the logistic regression to predictthe presence of a high level of PTSD symptoms after deployment(Table 2). A high predeployment number of GR in PBMCs was inde-pendently associated with increased risk for a high level of PTSDsymptoms after deployment (W � 13.631 p � .001); the odds for ahigh level of PTSD symptoms increased 2.6-fold with each SD in-crease in GR number. Furthermore, high predeployment GILZmRNA expression was independently associated with increasedrisk for a high level of PTSD symptoms after deployment (W �25.616, p � .001); the odds increased fivefold with each SD increasein GILZ mRNA expression. Additionally, low predeployment FKBP5mRNA expression was independently associated with increasedrisk for a high level of PTSD symptoms after deployment (W �25.584, p � .001): the odds decreased 14.5-fold with each SD in-crease in FKBP5 mRNA expression. Furthermore, childhood traumawas independently associated with increased risk for a high level ofPTSD symptoms (W � 4.748, p � .029): the odds increased 1.8-foldwith each SD increase in the number of reported childhood trau-matic experiences. SGK1 mRNA expression and plasma cortisol did

Table 2. Predictive Value of Predeployment GlucocortiExpression Levels in PBMCs, Plasma Cortisol, and ChildhLevel of PTSD Symptoms 6 Months After Deployment A

B SE W

GR No. in PBMCs .965 .261 1FKBP5 mRNA

Expression –2.680 .530 2GILZ mRNA

Expression 1.605 .317 2SGK1 mRNA

Expression �.378 .315Plasma Cortisol Levels �.062 .272Childhood Traumatic

Experiences .587 .269

All variables are standardized, mean (SD) � 0(1).CI, confidence interval; GR, glucocorticoid receptor

blood mononuclear cells.

not significantly predict PTSD symptom status. F

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The predictive value of the GR pathway components remainedignificant after controlling for deployment stressors, age, numberf previous deployments, predeployment PTSD and depressionuestionnaire scores, and predeployment BMI, smoking, alcohol,nd medication use. Childhood trauma no longer significantly pre-icted the development of a high level of PTSD symptoms afterontrolling for these variables.

ross-Sectional AnalysesGR and FKBP5 Genotypes. We determined carrier status for

ve common GR SNPs and two common FKBP5 SNPs. SNPs in theILZ gene have not been identified yet and were therefore not

nvestigated. All SNPs, except for N363S, were in Hardy-Weinbergquilibrium. Linkage disequilibrium (LD) between the GR SNPs in-icated high LD between a substantial proportion of SNPs. Addi-

ionally there was high LD between the two FKBP5 SNPs. Haplotypenalysis indicated the presence of six GR haplotypes (Figure 1) andhree FKBP5 haplotypes (Figure 2) with a frequency of .01 or greater.

Predictive Value of Genotypes and Childhood Trauma forR Number and FKBP5 mRNA Expression. We investigated theredictive value of SNP haplotype carrier status, alone and in inter-ction with childhood trauma, for predeployment GR number andKBP5 mRNA expression. This approach was selected because ourTSD group was not large enough to reliably predict PTSD grouptatus based on the SNPs. Main effects of GR haplotype carriertatus did not significantly predict predeployment GR number. Inddition, main effects of childhood trauma did not significantlyredict predeployment GR number after applying Bonferroni cor-

ection. We observed a significant interaction effect between child-ood trauma and GR BclI haplotype carrier status: individualsith high childhood trauma who carried the BclI haplotype had

n increased predeployment GR number (Figure 3, Table 3).ithin the group BclI carriers with high childhood trauma, the

ercentage of individuals with PTSD symptomatology was3.9%. Of the BclI noncarriers with high childhood trauma, 9.4%ad PTSD symptoms after deployment. In contrast, only 4.8% of

he BclI carriers, and 5.6% of the BclI noncarriers with low child-ood trauma had PTSD symptoms after deployment. However,

his difference between groups in the percentage of individualsith a high levels of PTSD symptoms did not reach statistical

ignificance [�2(3): 6.122, p � .124].FKBP5 mRNA expression was not significantly associated with

eceptor Number in PBMCs, GR Target Gene mRNAraumatic Experiences for the Development of a High388 Male Soldiers

df p Value OR 95% CI

1 �.001 2.625 1.573–4.382

1 �.001 .069 .024–.194

1 �.001 4.978 2.674–9.268

1 .230 .685 .369–1.2711 .819 .940 .552–1.601

1 .029 1.798 1.061–3.048

odds ratio; mRNA, messenger RNA; PBMCs, peripheral

coid Rood Tmong

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5.616

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between FKBP5 haplotype carrier status and childhood trauma (all pvalues �.05).

Discussion

This study reveals that multiple GR pathway components mea-sured before deployment are vulnerability factors for developmentof a high level of PTSD symptoms in response to military deploy-ment. We identified three independent predictors of a high level ofPTSD symptoms in the GR pathway: low FKBP5 mRNA expression,high GILZ mRNA expression, and high GR number, measured inPBMCs obtained from a group of 448 male soldiers before theirdeployment to a combat-zone in Afghanistan.

We show that high levels of GILZ and low levels of FKBP5 mRNAexpression before deployment were independently associatedwith increased risk for a high level of PTSD symptoms after deploy-ment. Within a small, carefully matched subgroup of the populationstudied here, we recently reported a higher number of predeploy-ment GR in the PTSD group compared to the matched controlsubjects (26). Our current results validate this initial observationwithin a larger, heterogeneous group. High GR number, high GILZmRNA expression and low FKBP5 mRNA expression in PBMCs sug-

est elevated signaling in the peripheral GR pathway in individualsulnerable for development of PTSD symptomatology. It remains toe determined whether the observed vulnerability factors mediate

Figure 1. Schematic overview of the glucocorticoid receptor single nucleotihe glucocorticoid receptor (NR3C1) gene is indicated. Estimated allele frequhan 1%.

Figure 2. Schematic overview of the FKBP5 single nucleotide polymorphisms (SNindicated. Estimated allele frequencies of the haplotypes are depicted at the righ

he repeatedly observed increased GC-sensitivity of the immuneystem in PTSD (48,49).

Interestingly, a recent study showed that a higher cortisol awak-ning response before trauma exposure predicted peritraumaticissociation (50), a risk factor for subsequent development of PTSD

51). In our study, morning plasma cortisol levels before deploy-ent did not predict PTSD symptomatology after deployment.oreover, the predictive effect of GR, FKBP5, and GILZ for a high

evel of PTSD symptoms was independent of plasma cortisol levels.hese observations fit with data in the literature showing that theortisol awakening response before trauma exposure was not di-ectly associated with subsequent PTSD symptoms (52,53).

Experiencing traumatic events during childhood is one of theost consistently observed risk factors for adult PTSD (30). As ex-

ected, our PTSD group on average reported a higher number ofhildhood traumatic experiences. Childhood trauma significantlyontributed to the prediction of a high level of PTSD symptoms

ndependently of the contribution of the identified predictors in theR pathway.

The peripheral GR pathway is often considered to be an ac-essible model for GR signalling in the brain. However, it has noteen studied extensively whether regulatory mechanisms ofentral and peripheral GR signaling are similar. Rodent studiesave shown that neuronal and lymphoid cytosolic GRs are simi-

lymorphisms (SNPs) and associated haplotypes. Localization of the SNPs ines of the haplotypes are depicted at the right when expected to be greater

de po

Ps) and associated haplotypes. Localization of the SNPs in the FKBP5 gene ist when expected to be greater than 1%.

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314 BIOL PSYCHIATRY 2012;71:309–316 M. van Zuiden et al.

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lar in GC-affinity and specificity (54). In addition, cytosolic GRs inthe brain and peripheral immune tissues are both downregu-lated after chronic corticosterone administration following ad-renalectomy (55). We speculate that the observed peripheral GRpathway components may be paralleled in the brain and that animbalance within the GR signaling cascade in the brain may beinvolved in the pathophysiology of PTSD. In individuals withPTSD, central GC-sensitivity has been investigated by assessingthe effects of GC administration on learning and memory (56),and on glucose metabolic rate of brain regions with fluorode-oxyglucose positron emission tomography studies (57). Overallresults suggest increased GC-sensitivity in the brain of PTSDpatients. We hypothesize that this increased GC-sensitivity may

Figure 3. Significant interaction effect of glucocorticoid receptor (GR) genehaplotype BclI carrier status and childhood trauma on predeployment GRnumber. BclI noncarriers with low childhood trauma n � 83, BclI carriers withlow childhood trauma n � 128, BclI noncarriers with high childhood trauman � 126, BclI carriers with high childhood trauma n � 79. The interactioneffect depicted is corrected for main effects of BclI haplotype carrier statusand childhood trauma.

Table 3. Predictive Value of Glucocorticoid Receptor (GHaplotype Carrier Status by Childhood Trauma for Prede

Haplotype CarrierStatus

Beta p

GR Most CommonHaplotype –.006 .931

GR BclI �.097 .155GR tth111l BclI .034 .625GR tth111l A3669G 9� �.008 .906GR N363S .054 .409GR tth111l ER22/23EK

A3669G 9� .031 .647

Haplotype carrier status was included as a dichotocarried. See Figure 1 for a schematic overview of the obse

trauma was included as dichotomous variable based on a metrauma. Bonferroni correction was applied: �: .05/6 � .0083 fo

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e a preexisting characteristic in individuals vulnerable for PTSDevelopment. To elucidate further GR-mediated abnormalities

n the brain, positron emission tomography neuroreceptor map-ing of GRs would be useful. Unfortunately, a suitable GR radio-

racer is not yet available (58).We sought to identify causal factors associated with the prede-

loyment high GR number and low FKBP5 mRNA expression predic-ive for subsequent PTSD symptom development. Therefore, wenvestigated whether GR number and FKBP5 mRNA expression

ere associated with five common GR SNPs (tth111l, ER22/23EK,363S, BclI, and A3669G 9�) and two common FKBP5 SNPs

rs3800373, rs1360780), either alone or in interaction with child-ood trauma. We did not observe an association between the hap-

otypes of the selected SNPs and GR number or FKBP5 mRNA ex-ression, indicating that these SNPs are not the major determinantsf GR number and FKBP5 mRNA expression. However, we did ob-erve a significant interaction effect between haplotype carrier sta-us and childhood trauma on GR number: high levels of GR beforeeployment were present in individuals with the minor allele of GRNP BclI who had also experienced a high number of childhoodraumatic events. These results indicate that these individuals maye at greater risk for developing PTSD symptoms after a traumaticvent in adulthood.

The two FKBP5 SNPs analyzed in our study (rs3800373,s1360780), were associated with higher peri- and posttraumaticissociation in Caucasian children with an acute medical injury (34),hich are risk factors for subsequent PTSD development (51). Ad-itionally, a significant interaction effect between these SNPs andhildhood trauma on PTSD risk was previously established in Afri-an American samples (35,36). However, we did not observe a sig-ificant association between FKBP5 SNPs and childhood trauma onKBP5 mRNA expression in our predominantly Caucasian sample.ehta et al. (59) recently described a reversal of the association

etween FKBP5 SNP rs9296158 and FKBP5 mRNA expression inndividuals with PTSD. Within healthy individuals, the FKBP5 SNP

as associated with increased FKBP5 mRNA expression; in PTSDatients, this SNP was associated with decreased FKBP5 mRNA ex-ression. It is possible that a similar mechanism is operative in ourample. However, because of our limited number of participantsith a high level of PTSD symptoms, we were not able to perform

hese analyses with sufficient statistical power.A limitation of the current study is that participants in the PTSD

roup were not diagnosed with PTSD according to the DSM-IVriteria. However, the validity of our questionnaire and the cutoff

plotype Carrier Status, Childhood Trauma, andment GR Number Among 412 Male Soldiers

Childhood TraumaHaplotype

Childhood Trauma

Beta p Beta p

.001 .991 –.002 .984�.141 .024 .284 <.001

.040 .486 �.103 .175

.005 .925 �.014 .856

.016 .748 �.093 .159

.012 .812 �.065 .350

variable: one or two copies carried versus no copieshaplotypes and estimated allele frequencies. Childhood

R) Haploy

mousrved

dian split: low childhood trauma versus high childhoodr significance.

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we used for identifying high levels of PTSD symptoms score issupported by van Zelst et al. (39), who showed that the cutoff weused is sensitive and specific for a DSM-IV PTSD diagnosis.

In conclusion, our results demonstrate that GR pathway compo-nents FKBP5 mRNA expression, GILZ mRNA expression and GR num-ber measured in PBMCs before deployment, are vulnerability fac-tors for development of a high level of PTSD symptoms in responseto deployment to a combat-zone. One of these vulnerability factorsis influenced by a gene– environment interaction: a high GR num-ber may develop as a consequence of the presence of GR haplotypeBclI combined with a high number of childhood traumatic events.Future research should aim at elucidating the causal relation be-tween the observed vulnerability factors and subsequent develop-ment of PTSD symptoms.

This study was funded by a grant from the Dutch Ministry of De-fence, which had no further role in study design; in the collection,analysis, and interpretation of data; in writing the report; or in thedecision to submit the paper for publication. The authors are greatlyindebted to Colonel C. IJzerman and the commanders and troops fortheir time and effort. We thank Kim Kroezen, Anne Muilwijk, LieutenantMaurits Baatenburg de Jong, Jessie Smulders and Sergeant Major Mar-tijn Derks for organizing the data acquisition for the study. We alsothank Linda Schild, Zabi Mohklis, Marijke Tersteeg-Kamperman, EstherRudolph, and Jitske Zijlstra for excellent technical assistance in theframework of the PRISMO project.

All authors reported no biomedical financial interests or potentialconflicts of interest.

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