Medical Comorbidities of PTSD October 9, 2017 Charles

Medical Comorbidities of PTSD October 9, 2017 Charles

Medical Comorbidities of PTSD October 9, 2017 Charles R. Marmar, MD Professor and Chair Department of Psychiatry NYU Langone Medical Center History of Posttraumatic Stress Disorder Assyrian warriors afflicted with daytime visual images, nightmares, memory problems and depression Homer: Trojan War Veterans Civil War: Soldiers Heart 19th Century Europe: Railroad Spine WW I: Shell Shock Vietnam War: Vietnam Syndrome 1980 DSM III: PTSD Cardiac Risk Factors in OEF/OIF Veterans Using VA Healthcare: Association with Mental Health Diagnoses Beth Cohen Charles Marmar Li Ren Daniel Bertenthal Karen Seal JAMA. 2009 Aug 5;302(5):489-92.

Prevalence of CVD Risk Factors by Mental Health Status in Iraq and Afghanistan Veterans No MH Dx PTSD Depression N=182,151 N=34,126 N=20,909 PTSD + Depression N=38,441 Tobacco Use 10% 25% 27% 34% Hypertension 8%

13% 16% 18% Dyslipidemia 10% 17% 20% 23% Obesity 6% 11% 14% 15% Diabetes 1.0% 1.4%

2.5% 2.5% P <. 0001 for all comparisons Aging Veterans with PTSD Have an Increased Risk for Developing Dementia Kristine Yaffe, MD Kenneth E Covinsky, MD, MPH Karla Lindquist, MS Eric Vittinghoff, PhD Thomas Neylan, MD Deborah Barnes PhD, Charles R. Marmar, MD Risk of Dementia by PTSD Dx After Excluding At Risk Groups Advancing Biological Markers for PTSD PTSD Systems Biology Consortium Funded by the Department of Defense Award# W81XWH-09-2-0044 (Biomarkers for PTSD- OIF/OEF Males)

Award# W81XWH-14-1-0043 (Biomarkers for PTSD- OIF/OEF Females) Award# W81XWH-11-2-0223 (Validating Biomarkers for PTSD- OIF/OEF Males) May 20 2017 Principal Investigators Charles R. Marmar, MD Marti Jett, PhD Frank Doyle, PhD - Clinical Cores Preclinical and Molecular Cores Bioinformatics Core Partnering Site PIs: Lee Hood, MD PhD; Kerry Ressler MD, PHD; Michael Weiner, MD; Susanne Mueller, MD; Owen Wolkowitz, MD; Rachel Yehuda PhD Funding Source: DoD Why Biomarkers Diagnosis relies on patient self-reports For majority of warfighters and many others stigma results in under reporting of symptoms For a minority exaggeration for compensation Rapid screening before, during and after deployments Prioritizing treatment and disability evaluations Biomarkers will advance personalized medicine Biomarkers will lead to new targets for treatment Guide stratification for determining prognosis Identify co-morbid medical risks Model of PTSD

SAM System: Greater perceived threat leads to greater adrenergic activation during and after exposure HPA Axis: Despite increased CRF expression during acute stress there is a lower cortisol response Lower cortisol response results in reduced braking of adrenergic response Prolonged adrenergic activation results in greater fear conditioning which drives greater avoidance Prolonged adrenergic activation results in greater memory consolidation Renin Angiotensin System: inflammatory responses and cardiovascular health PTSD Neural Circuit Phenotypes Emotion Regulation & Executive Function Amygdala activation to threat modulated by Medial prefrontal cortex Dorsolateral prefrontal cortex Dorsal anterior cingulate Insula PTSD Neural Circuit Phenotypes Threat & Saline Detection Mediated by the amygdala, dorsal Anterior Cingulate (dACC) and insula cortex

Modulated by regulatory control mechanisms involving the hippocampus and medial and lateral prefrontal cortex regions PTSD Neural Circuit Phenotypes Contextual Processing Garfinkel, Abelson, King, Sripada, Wang, Gaines and Liberzon J. Neuroscience, 2014 Diminished capacity to use safety context to modulate fear expression Diminished capacity to use danger signals adaptively Deficits in context updating Hippocampal dependent process with insula and PFC PTSD Neural Circuit Phenotypes Fear Learning

Orchestrated by central nucleus of amygdala Outputs to sympathetic and parasympathetic systems including cardiovascular and respiratory reactions, and activation of the hypothalamicpituitary-adrenal (HPA) axis Behavioral responses include defensive fight, flight, startle and freezing behaviors, and changes in information processing Modulated by baso-lateral amygdala, hippocampus, insula and prefrontal structures Stratification of Cohorts to Account for PTSD Clinical Heterogeneity Males and females Children and adults African, Asian and European ancestry Civilian and military Single event and repetitive trauma Acute stress disorder and chronic PTSD Stratification of Cohorts to Account for PTSD Clinical Heterogeneity With and without psychiatric co-morbidities (depression, TBI, alcohol and drugs) With and without medical co-morbidities (metabolic syndrome, disorders affecting CNS function)

With and without prominent dissociation Taking or not taking psychotropics and other medications effecting CNS function Neurocognitively impaired and unimpaired PTSD Phenotyping Responding to Challenge Animal models informing intermediate endophenotypes Advanced bioinformatics for human diagnostic phenotyping systems biology fluid/circuitry markers Clinical phenotypes intrusion, avoidance, arousal In animals avoidance and arousal are readily modelled Biomarker discovery for primary clinical phenotypes Stratification for heterogeneity - subtypes Biomarkers for human intermediary endophenotypes Human genetics/epigenetics, pluripotent stem cells, brain/ olfactory neuron biopsy, circuit/molecular imaging and brain banks bridge animal and clinical discoveries Biomarker Cores Neurogenetics Core Emory & Harvard (Ressler) Administrative , Neurocog, and Clinical Cores NYU (Marmar, Abu Amara and Newman)

Metabolism & Cell Aging UCSF (Wolkowitz) Multi-Omics Core Integrative Systems Biology (Jett/Hammamieh) and Institute for Systems Biology (Hood) Neuroimaging Core NYU (Sodickson) Neuroimaging is acquired for all participants UCSF (Weiner/Mueller) Bioinformatics Core Harvard University (Doyle) Neuroendocrine and Clinical Core Mt. Sinai; Bronx VA (Yehuda, Flory, Makotkine) Blood is acquired and processed for

all participants Study Cohort Cross Sectional: OIF/OEF/OND Veterans Initial Award to study 100 PTSD+/100 PTSD OIF/OEF males Discovery/Training subjects completing blood draw= 83 cases and 83 controls; Second Award to study Validation subjects- OIF/OEF males Validation/Test subjects completing blood draw= New: 29 cases and 40 controls; Recalls: 30 cases and 29 controls; Third Award to study biomarkers in OIF/OEF females 20 cases and 21 controls completing blood draw Participant Inclusion/Exclusion Inclusion Criteria: Male Age range: 20-60 Veterans of OEF/ OIF Recruited at NYU and MSSM/Bronx VA Exclusion Criteria: Current alcohol/ substance dependence History of psychosis, bipolar or OCD Trauma within past 3 months Suicidality/ homicidality PTSD(+): War zone PTSD

Neurologic or systemic illness symptoms > 3 months affecting the brain CAPS > 40 Anemia Not stable for at least 2 months PTSD (-): Experienced war on current medication zone trauma but no lifetime PTSD Moderate or severe TBI LOC > 10 minutes symptoms CAPS < 20 Study Flow Visit 3 Pre-Screening Process Military Demographics Medical, Psychiatric, and Substance Use History 30m Visit 1 2nd Blood Draw At-Home 24-hr. Urine Collection for Dexamethasone Suppression Test 30m

24-hr Visit 4 Informed Consent Visit 2 1h 8 Am Fasting Blood Draw Physical Examination Initial Paperwork Anthropomorphic Body Measurements DD214 collected CLIA-Certified Laboratory Testing Research Samples (whole blood, plasma, serum, buffy coat, RNA, DNA) 30m Baseline Clinical Interview Eligibility Study Groupings Deep Phenotype Data 3-5h Digitally recorded. May be completed in person or by phone Dex Pill

for Dexamethasone Suppression Test 11:00pm Neurocognitive Testing Clinician Administered Assessment (WAIS, FAS) Computerized Tasks 2.5h Self Report Standardized Mental Health & Wellbeing Questionnaires 1.5h May be completed at home MRI Structural + Resting State Imaging 1.5h Systems Biology Biomarkers for PTSD Phone Pre-Screen Completed (N=1,990) Complete (N=699) Baseline Diagnostic Clinical Interview (N=805) Partial Complete (N=106)

Eligible (Male & Female) Not Eligible does not meet study criteria (N=448) (N=357) PTSD+ (N= 168; with blood N=132) Femal Male (N=144; with blood e N=112) Discovery Sample (N=83; with blood N=83) Validation PTSD+ (N=24; with blood N=20) Re-evaluation (N=35/35; with blood N=30) New (N=37/35; with blood N=29) PTSD- (N=189; with blood N=143) Male (N=161; with blood N=122)

Discovery Sample (N=83; with blood N=82) Female (N=28; with blood N=21) Validation PTSDRe-evaluation (N=33/35; with blood N=29) New (N=53/50; with blood N=40) Discovery/Training and Validation/Test of Biomarkers for PTSD Plan Plan Discovery Discovery Phase Phase N N == 166 166 83 83 PTSD+/83 PTSD+/83 PTSDPTSD- Validation Validation Phase Phase N

N == 69 69 29 29 PTSD+/40 PTSD+/40 PTSDPTSD- Validation Validation on on Independent Independent Cohorts: Cohorts: Military Military Civilians Civilians SCID Results Eligible Male Sample (N=188) PTSD Positive (N=90) PTSD Negative (N=99) P Value Lifetime MDD

76 (85.4%) 26 (26.3%) <.0001 Current MDD 50 (56.2%) 3 (3.0%) <.0001 Absent 34 (38.2%) 70 (70.7%) <.0001 Dependence 27 (30.3%) 12 (12.1%) <.0001 Abuse

28 (31.5%) 17 (17.2%) <.0001 Major Depressive Disorder Alcohol Use Disorder Participant Demographics (All Males) PTSD Positive PTSD Positive PTSD Negative PTSD Negative Discovery/ Training sample Validation/Test sample Discovery/Training sample Validation/Test sample

(N=83) (N=37) (N=83) (N=53) 32.98 (7.73) 35.60 (9.37) 32.47 (8.00) 33.77 (8.77) 2 (2.41%) 1 (2.86%) 2 (2.41%) 1 (1.89%) H.S. Diploma or GED 31 (37.35%) 14 (40%) 15 (18.07%)

13 (24.53%) 2 yrs. College A.A. Degree 25 (30.12%) 11 (31.43%) 22 (26.51%) 12 (22.64%) 4 yrs. College Bachelor's Degree 22 (26.51%) 5 (14.29%) 31 (37.35%) 18 (33.96%) 3 (3.61%) 12 (14.46%) 0 (0%) 4 (11.43%) 0 (0%) 1 (1.2%)

9 (16.98%) 0 (0%) 38 (45.78%) 14 (40%) 26 (31.33%) 11 (20.75%) Non-Hispanic African American 21 (25.3%) 7 (20%) 20 (24.1%) 8 (15.09%) Non-Hispanic Caucasian 20 (24.1%) 11 (31.43%) 28 (33.73%) 27 (50.94%)

4 (4.82%) 3 (8.57%) 9 (10.84%) 7 (13.21%) Age in Yrs [Mean(SD)] Education Level [Frequency(%)] Up to 12th grade Masters Degree PhD Degree Race/Ethnicity [Frequency(%)] Hispanic Non-Hispanic Other Harvard University, McLean Hospital Biomarker Approaches to Understanding the Genomic Architecture of PTSD Guia Guffanti, PhD Kerry Ressler, MD, PhD Neurogenetics Core Significance 1. Genetic account for 30% - 40% of the risk for PTSD 2. Elucidation of specific genetic variants that are associated with PTSD can provide useful information: a) Predictive SNP profiles for resilience or pathology

b) Identification of novel biological correlates of PTSD and related phenotypes 3. Genetic variants may serve as proxies for phenotypes that are difficult or expensive to obtain The Genome-wide Landscape for PTSD Study Ancestry SNP Gene Sample Size P-value Logue et al. (2012) European American, African American rs8042149 RORA (protein-coding) Discovery: N = 491 EA

Replication: N = 600 AA 2.5 x 10-8 Xie et al. (2013) European American, African American rs6812849 TLL1 (protein-coding) Discovery: N = 1,838 EA N = 3,380 AA Replication: N = 1,578 EA, N = 744 AA 3.1 x 10-9 Guffanti et al. (2013) European American, African American

rs10170218 LINC01090 (noncoding RNA) Discovery: N = 413 AA Replication: N = 2,541 EA 5.09 x 10-8 Nievergelt et al. (2014) European American, African American, Latin American, East Asian rs6482463 PRTFDC1 (protein-coding) Discovery: N = 3,494 (multi) Replication: N = 491 EA

2.04 x 10-9 Almli et al. (2015) European American, African American, Latin American, East Asian rs717947 BC036345 (noncoding RNA) Discovery: N = 147 (multi) Replication: N = 2,006 AA 1.28 x 10-8 Stein et al. (2016) European American, African American rs11085374 rs159572

ZNF626 ANKRD55 (protein-coding) N=5,049 EA N=1,312 AA 4.59 108 2.34 108 Genetic Approaches to PTSD: Early Data from Systems Biology Cohort rs717947 SNP peak on Chromosome 4 additive P-value=1.28x10-08 (N = 147) Almli et al., AJMG, 2015 Genetic Approaches to PTSD: Systems Biology Cohort Descriptive and Replication Graphs Biomarkers_148 Current CAPS total 60 50 N=123 F(1, 123)=32, p<1x10-7 Grady Replication PTSD diagnosis

Biomarkers_148 PCL score 60 N=122 F(1, 122)=12.5, p<1x10-5 45% 50 40% 40 40 35% 30 30 20 20 10 10

0 0 GG AG, AA Chromosome 4 SNP N=2270 F(1, 2270)=11.1, p<0.001 30% 25% 20% GG AG, AA Chromosome 4 SNP GG AA, AG Chromosome 4 SNP The Chromosome 4 SNP risk allele is also associated with decreased medial and dorsolateral activation to fearful faces. Almli et al., AJMG, 2015 Psychiatric Genomics Consortium-Posttraumatic Stress Disorder Largest GWAS of PTSD (N=20070) yields genetic overlap with schizophrenia and sex differences in heritability Study design for phase 1 PGC-PTSD

PGC-PTSD GWAS plot in AA, EA and AA+EA Duncan et al. Molecular Psychiatry 2017 Psychiatric Genomics Consortium-Posttraumatic Stress Disorder Largest GWAS of PTSD (N=20070) yields genetic overlap with schizophrenia and sex differences in heritability Polygenic risk score SNP-chip heritability Duncan et al. Molecular Psychiatry 2017 Pituitary adenylate cyclase-activating peptide receptor (PACAP1) gene associated with PTSD in veterans PACAP regulates glucocorticoid receptor sensivity SNP rs2267735 in PACAP receptor gene is associated with PTSD in female civilians Estrogens regulate PACAP R1 sensitivity Associated with CAPS PTSD symptom severity in this cohort of male Iraq and Afghanistan veterans Male brain production of estrogens by circulating testosterone may regulate PACAP FK506 binding protein = FKBP5 A chaperone protein critically involved in Glucocorticoid Receptor (GR) feedback sensitivity

P23 GR GR HSP90 cortisol FKBP5 dynein FKBP5 FKBP4 GR GR AAAAA HSP90 FKBP4 Ultrashort negative feedback on GR sensitivity FKBP5 and PTSD FKBP5 chaperone protein is critically involved in the feedback regulation of GR sensitivity Cortisol induces FKBP5 expression reducing GR binding affinity in healthy controls; may increase GR binding affinity in PTSD

FKBP5 polymorphisms associated with peritraumatic dissociation in medically injured children (Koenen et al., Mol Psychiatry, 10, 10581059, 2005) with relevance for DSM-V FKBP5 polymorphisms x severity of childhood abuse predicts adult PTSD symptom severity (Binder et al., JAMA, 299, 1291-305, 2008) FKBP5 polymorphisms explain enhanced negative feedback sensitivity to dexamethasone Increased FKBP5 mRNA expression levels immediately after exposure predict PTSD at 4 months (Begman et al., Mol Psych, 10, 500-513, 2005) Multidimensional Network-Clusters to Elucidate PTSD Biology: An Epigenomic Study of OEF/OIF Cohort US Army Center for Environmental Health Research Rasha Hammamieh, PhD Integrative Systems Biology [email protected] UNCLASSIFIED Rasha Hammamieh, PhD 301.619.2338 Challenge to Identify PTSD-Specific Etiology PTSD symptoms and its molecular underpinnings are closely aligned/ significantly overlapped with many co-morbidities and PTSD-linked somatic complications Metabolic dysfunction Social

withdrawal Sleep apnea Cognitive disorder Anxiety PTSD Anger Fear Cardiovasc ular disease Obesity Immature aging Addiction Drug abuse Diabetes Depression Hence, it is a challenge to mine PTSD-specific information [email protected] UNCLASSIFIED

Rasha Hammamieh, PhD 301.619.2338 DNA Methylation Assay DNA from Paxgene tube Array based DNA methylation assay Agilent CpG Island Chip Size (CpG) ~450K Coverage Island Region Annotation Promoter, Inside, Divergent promoter human reference sequence Hg18 [email protected] UNCLASSIFIED Rasha Hammamieh, PhD 301.619.2338

GR Gene Methylation at 1F Exon Promoter Region 80 PTSD+ PTSD- 70 % Methylated Sites 60 50 40 30 20 10 0 Group: F(1,101)=5.969; p=.023 Race/Ethnicity: ns Interaction: ns BMI: ns Volcano Diagram of Differentially Methylated Probes Cutoff: 1.5 Fold change and p <0.05 Red dots represents the CpG probes in the promoter regions

[email protected] UNCLASSIFIED Rasha Hammamieh, PhD 301.619.2338 Network linked to PTSD-associated somatic complications Hypermethylated Hypormethylated [email protected] UNCLASSIFIED Rasha Hammamieh, PhD 301.619.2338 PTSD Associated with the Cardio-Metabolic Syndrome Between-Group Differences Fasting Glucose Insulin HOMA-IR Cholesterol Triglycerides BMI Weight Pulse METABOLIC SYNDROME TOTAL SCORE*

N (-/ +) 51/ 51 51/ 51 51/51 51/51 51/51 51/ 51 51/ 51 51/50 51/51 PTSD (-) PTSD (+) Mean + SD 79 + 11.5 12.16 + 10.44 2.65 + 3.41 171.2 + 26.5 107.7+ 110.4 28.3 + 4.2 190.4 + 32.2 64 + 11 -0.84 + 3.12 Mean + SD

91 +16.2 19.18 + 16.96 4.66* + 4.75 175.4 + 35.3 121.2 + 62.3 29.9 + 5.1 206.1 + 39.6 71 + 12 0.84 + 3.15 Statistic1 t = 3.92 F = 3.16 F = 4.54 F = 0.05 F =1.71 t = 1.95 t = 2.20 F = 9.24 T = 2.70 p 0.001 0.08 0.04 NS 0.19 0.06 0.03 0.003 0.008

1 Independent t-tests used unless covariates (age and/or BMI) applied, in which case ANCOVA used. Raw data are presented in Table, but data were transformed to achieve normal distributions before analysis, when required. As an exploratory study, significance values are not corrected for multiple comparisons, but to limit Type I errors, subsequent analyses use the Metabolic Syndrome Total Score. * METABOLIC SYNDROME TOTAL SCORE= Sum of standardized z-scores of: HOMA-IR, BMI, Diastolic BP, LDL and Pulse. HOMA-IR= Homeostatic Model Assessment- Insulin Resistance. *HOMA-IR values >3.80 identify Insulin Resistance with high sensitivity. May 2013, APA Pro-Inflammatory Cytokines are Elevated in PTSD Cytokine (pg/ ml) PTSD (-) PTSD (+) (N=51) (N=51) IFN- g 0.58 0.76 (0.45-0.69) (0.42-1.42)

TNF- a IL-1 b IL-6 IL-10 hs CRP Total ProInflam. Score* 2.98 3.69 (2.52-3.51) (2.48-4.49) 0.08 0.10 (0.05-0.13) (0.05-0.18) 0.51 0.79 (0.44-0.76) (0.60-1.12) (1.26-1.87)

(1.25-2.32) 1.00 1.33 (0.40-1.80) (0.50-3.95) 1.53 -1.32 (-2.54 - -0.05) 1.56 0.83 (-1.24 - -3.56) t- test p 2.04 0.001 1.93

0.058 0.93 0.354 2.92 0.004 0.89 0.373 1.95 0.054 3.58 0.001 *Total Pro-Inflammatory Score= Sum of standardized z-scores of: IL6, IL1b, TNFa, IFNg and CRP. Values= Medians + Inter-Quartile range. T tests are based on Ln. (Extreme values excluded if distribution not normalized by Ln- transformation). As an exploratory study, significance values are not corrected for multiple comparisons. (N=102) May 2013, APA

Natural Killer Cell Senescence in PTSD (Fluorescence-Activated Cell Sorting) %NK Cell i CD16-CD56+ (Ln) PTSD (-) PTSD (+) (N=39) (N=37) 1.97 + 0.63 1.73 + 0.49 1.93 (p < 0.06) 1.83 + 0.72 2.14 + 0.69 2.02 (p < 0.05) Bright h CD16+CD56- (Ln) Dim

t (p) CD16-CD56+ (bright) NK cells tend to be decreased in PTSD CD16+CD56- (dim) NK cells are significantly increased in PTSD, conducive to a pro-inflammatory state and suggesting NK cell aging May 2013, APA Increased circulating blood cell counts in combat-related PTSD: Associations with inflammation and PTSD severity Daniel Lindqvist, MD, PhD, Ass. Professor Lund University Owen Wolkowitz, MD, Professor UCSF Synthia Mellon, PhD, Professor UCSF Charles Marmar, MD, Professor NYU Rachel Yehuda, PhD, Professor, Icahn School of Medicine and the Systems Biology of PTSD Consortium (last slide) May 20, 2017 Background A new concept in PTSD is that it is a disease affecting the whole body, and not just the mind PTSD affects different physiological systems Cardiovascular disease Metabolic (diabetes, metabolic syndrome, obesity)

Endocrine Immune disorders A better understanding of PTSD may aid in the identification of novel biomarkers as well as optimized prevention and treatment Inflammation: PTSD is Associated with Increased Inflammatory Cytokines Sum of Z scores of IL1-b, IL6, TNF-a, IFN-g, CRP Total pro-inflammatory score 10 PTSD+ n=77 PTSD- n=81 5 0 -5 p<0.0001 Cohen's d=0.68 -10 PTSD - PTSD +

Significant even after adjusting for age, BMI, MDD diagnosis, NSAIDs (p<0.0001) Lindqvist et al. Brain Behav Immun. 2014; Lindqvist et al. Brain Behav Immun. 2017 This has been confirmed in a recent meta-analysis by Passos et al. (2015, Lancet Psychiatry) Inflammation in PTSD vulnerability marker or cause of trauma/stress? Inflammation was not significantly correlated with symptom severity or degree of combat exposure Lindqvist et al. Brain Behav Immun. 2014 and Lindqvist et al. Brain Behav Immun. 2017 In a prospective study on US Marines (n=2555), pre-deployment CRP was highly predictive of PTSD symptom severity post-deployment, after adjusting baseline symptom severity, and trauma exposure (p=0.002) Eraly et al. JAMA Psychiatry, 2014 The largest GWAS to date on combat-related PTSD, found that the ANKRD55 gene was associated with PTSD. This gene is linked to autoimmune and inflammatory conditions. Stein et al. JAMA Psychiatry, 2016 Circulating blood cells in PTSD Blood cell type Biological role

Higher counts= Role in PTSD Platelets Coagulation Inflammation Plateletleukocyteendothelial interactions Increases risk for fatal heart disease (Thaulow et al., 1991) One small-scale study showed higher platelet activation in PTSD vs controls (Vidovic et al., 2011) White blood cells Immune response Platelet activation & adhesion Thrombus formation Risk for CVD,

HbA1c, diabetes (Jiang et al., 2014; Libby et al., 2010; Pfister et al., 2012)) Higher WBC in PTSD vs controls (Boscarino & Chang, 1999; Vidovic et al., 2011) Red blood cells Thrombosis and CVD mortality (Braekkan et al., 2010; Skretteberg et al., 2010 Not known Oxygen delivery Inflammatory and pro-atherogenic effects when oxidized Chronic stress promotes hematopoiesis Heidt et al., 2014, Nat. Med. Figure from Hanna & Hedrick, 2014, Nat. Med.

The dual role of platelets Platelets are not only hemostatic players but should also be considered as inflammatory cells Platelets may promote inflammation via Interaction with endothelial cells and leukocytes The release of inflammatory mediators Cross-talk between platelets endothelium - leukocytes From Stokes & Granger, Platelets: A critical link between inflammation and microvascular dysfunction, Journal of Physiology Circulating blood cells in PTSD Smoking, physical inactivity and increased BMI has been associated with PTSD and higher blood cell counts Poor health behaviors could mediate the relationship between PTSD and increased blood cell counts ? PTSD Poor health behaviors ? Blood cell

counts Increased WBC, RBC and platelet count in PTSD 15 500 7 400 6 5 300 RBC WBC Platelet count 10 5 200 4 100 p=0.004 Cohen's d=0.42

0 PTSD - n=81 PTSD + n=82 p=0.003 Cohen's d=0.50 p=0.028 Cohen's d=0.29 0 PTSD - n=81 PTSD + n=82 3 PTSD - n=81 PTSD +

n=82 There were small but significant mediation effects of BMI on the relationship between PTSD and all of the assessed blood cell counts, and smoking partially mediated the relationship between PTSD and high WBC. The between-group differences in WBC and RBC remained significant after controlling for smoking and BMI, with the exception of platelet count, which differed between groups at the trend level. Lindqvist et al, in review Correlations between cytokines and blood cell counts in PTSD Total pro-inflammatory score correlated significantly with all blood cell counts (p<0.01) The correlation between total pro-inflammatory score and platelet count was significant only in the PTSD group and platelet count was associated with severity of PTSD symptoms 10 300 5 0 -5 -10 100 200

300 Lindqvist et al, in review 200 100 r=0.32, p=0.004 Platelet count PTSD subjects (n=82) Platelet count Total pro-inflammatory score PTSD subjects (n=82) 400 r=0.30, p=0.007 60 80 100

CAPS total lifetime 120 Platelet count correlates negatively with GABR in PTSD subjects but not in controls Global Arginine Bioavailability Ratio (GABR) is an indicator of capacity for nitric oxide synthesis. GABR is decreased in PTSD and inversely related to symptom severity (see Bersani et al., 2016, Brain, Behavior and Immunity) PTSD subjects (n=81) Control subjects (n=81) 300 Platelet count Platelet count 300 200 100 0.0 0.4 0.6 0.8

GABR 100 rho=-0.25, p=0.03 0.2 Lindqvist et al, unpublished data 200 1.0 0.0 rho=-0.08, p=0.48 0.2 0.4 0.6 GABR 0.8 1.0 Mechanisms underlying increased blood cell counts in PTSD

1. 2. 3. Poor health behaviors may mediate these relationships and increase risk for somatic illness Acute and chronic stress can lead to to alterations and redistributions of WBCs (Dhabhar et al., 2012) Stress activates upstream hematopoietic stem cells, potentially via activation of the sympathetic nervous system and the action of noradrenaline (Heidt et al., 2014) In our study, perceived stress during the last month did not correlate significantly with any of the blood cell counts (all p>0.11) Also, the controls in our study had all been exposed to combatrelated stress Summary 1. WBC, RBC, and platelets are increased in PTSD 2. Poor health behaviors explain a small part of these relationships 3. Degree of acute and unspecific stress was not significantly associated with blood cell counts 4. All blood cell counts were associated with increased inflammation 5. Platelet count was associated with symptom severity 6. Interaction between these biological systems may contribute to the increased risk for cardiovascular disease seen in PTSD Co-Investigators

Charles Marmar Lab, NYU Rachel Yehuda Lab, Bronx VA, MSSM Jett/ Hammamieh Labs; Ruoting Yang (USACEHR, US Dept of Defense) F. Saverio Bersani (Sapienza Universit di Roma, Rome, Italy) Josine Verhoeven (VU Univ., Amsterdam, Holland) Elissa Epel (UCSF) Victor Reus (UCSF) Jue Lin (UCSF) Elizabeth Blackburn (UCSF) Firdaus Dhabhar (Miller School of Medicine, University of Miami) Elizabeth Sinclair, Jeff Milush and Marlene Grenon (UCSF) Michelle Coy, Christina Hough (UCSF) Jill James (U. Arkanas) Kirstin Aschbacher (UCSF) FUNDING: DOD W81XWH-11-2-0223 (PI: Marmar); DOD W81XWH-10-1-0021 (PI: Wolkowitz) Acknowledgments

PTSD Systems Biology Consortium Leadership Dr. Charles Marmar, New York University (NYU) Dr. Marti Jett, Chief Scientist, Systems Biology Enterprise US Army Medical Command (MEDCOM), USACEHR Dr. Frank J. Doyle III, Harvard University Dr. Rachel Yehuda, Mount Sinai School of Medicine (MSSM) and Bronx VA Dr. Owen Wolkowitz, University of California at San Francisco (UCSF) Dr. Kerry Ressler, Harvard University Dr. Ron Hoover, MOMRP Psychological Health Deputy Portfolio Manager Co-investigators & Study Team

Jennifer Newman, PhD Duna Abu-Amara, MPH Janine Flory, PhD Rasha Hammamieh, PhD Sindy Mellon, PhD Anna Suessbrick, PhD Emily Purchia, MPH Afia Genfi, BA Rohini Bagrodia, MA Meng Qian, PhD Meng Li, MS Guia Guffanti, PhD Lynn Almli, PhD Iouri Makotkine, MD

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