Faculty Bibliography
Searched for:
in-biosketch:true
person:marmac01
Total Results:
343
Identifying Subtypes of PTSD [Meeting Abstract]
ISI:000535308200019
ISSN: 0006-3223
CID: 4560712
Blood Epigenomic Analysis Before and After Deployment in Active Duty Service Members [Meeting Abstract]
ISI:000535308200624
ISSN: 0006-3223
CID: 4560852
Individual Patterns of Abnormality in Resting-State Functional Connectivity Reveal Two Data-Driven PTSD Subgroups
OBJECTIVE/UNASSIGNED:A major challenge in understanding and treating posttraumatic stress disorder (PTSD) is its clinical heterogeneity, which is likely determined by various neurobiological perturbations. This heterogeneity likely also reduces the effectiveness of standard group comparison approaches. The authors tested whether a statistical approach aimed at identifying individual-level neuroimaging abnormalities that are more prevalent in case subjects than in control subjects could reveal new clinically meaningful insights into the heterogeneity of PTSD. METHODS/UNASSIGNED:Resting-state functional MRI data were recorded from 87 unmedicated PTSD case subjects and 105 war zone-exposed healthy control subjects. Abnormalities were modeled using tolerance intervals, which referenced the distribution of healthy control subjects as the "normative population." Out-of-norm functional connectivity values were examined for enrichment in cases and then used in a clustering analysis to identify biologically defined PTSD subgroups based on their abnormality profiles. RESULTS/UNASSIGNED:The authors identified two subgroups among PTSD cases, each with a distinct pattern of functional connectivity abnormalities with respect to healthy control subjects. Subgroups differed clinically on levels of reexperiencing symptoms and improved case-control discriminability and were detectable using independently recorded resting-state EEG data. CONCLUSIONS/UNASSIGNED:The results provide proof of concept for the utility of abnormality-based approaches for studying heterogeneity within clinical populations. Such approaches, applied not only to neuroimaging data, may allow detection of subpopulations with distinct biological signatures so that further clinical and mechanistic investigations can be focused on more biologically homogeneous subgroups.
PMID: 31838870
ISSN: 1535-7228
CID: 4243432
American journal of physiology. Endocrinology & metabolism. 2019:317(5):E879-E898.DOI: 10.1152/ajpendo.00065.2019
Mechanistic inferences on metabolic dysfunction in PTSD from an integrated model and multi-omic analysis: Role of glucocorticoid receptor sensitivity
Post-traumatic stress disorder is associated with neuroendocrine alterations and metabolic abnormalities; however, how metabolism is affected by neuroendocrine disturbances is unclear. The data from combat exposed veterans with PTSD shows increased glycolysis to lactate flux, reduced TCA cycle flux, impaired amino acid and lipid metabolism, insulin resistance, inflammation and hypersensitive HPA-axis. To analyze whether the co-occurrence of multiple metabolic abnormalities are independent, or arises from an underlying regulatory defect, we employed a systems biological approach using an integrated mathematical model and multi-omic analysis. The models for hepatic metabolism, HPA axis, inflammation and regulatory signaling were integrated to perform metabolic control analysis (MCA) with respect to the observations from our data. We combined the metabolomics, neuroendocrine, clinical lab and cytokine data from combat-exposed veterans with and without PTSD to characterize the differences in regulatory effects. MCA revealed mechanistic association of the HPA-axis and inflammation with metabolic dysfunction consistent with PTSD. This was supported by the data using correlational and causal analysis that revealed significant associations between cortisol suppression, hs-CRP, HOMAIR, GGT, hypoxanthine and several metabolites. Causal mediation analysis indicates that the effects of enhanced glucocorticoid receptor sensitivity (GRS) on glycolytic pathway, gluconeogenic and branched chain amino acids, triglycerides and hepatic function are jointly mediated by inflammation, insulin resistance, oxidative stress and energy deficit. Our analysis suggests that the interventions to normalize GRS and inflammation may help to manage features of metabolic dysfunction in PTSD.
PMID: 31322414
ISSN: 1522-1555
CID: 4071462
International meta-analysis of PTSD genome-wide association studies identifies sex- and ancestry-specific genetic risk loci
The risk of posttraumatic stress disorder (PTSD) following trauma is heritable, but robust common variants have yet to be identified. In a multi-ethnic cohort including over 30,000 PTSD cases and 170,000 controls we conduct a genome-wide association study of PTSD. We demonstrate SNP-based heritability estimates of 5-20%, varying by sex. Three genome-wide significant loci are identified, 2 in European and 1 in African-ancestry analyses. Analyses stratified by sex implicate 3 additional loci in men. Along with other novel genes and non-coding RNAs, a Parkinson's disease gene involved in dopamine regulation, PARK2, is associated with PTSD. Finally, we demonstrate that polygenic risk for PTSD is significantly predictive of re-experiencing symptoms in the Million Veteran Program dataset, although specific loci did not replicate. These results demonstrate the role of genetic variation in the biology of risk for PTSD and highlight the necessity of conducting sex-stratified analyses and expanding GWAS beyond European ancestry populations.
PMID: 31594949
ISSN: 2041-1723
CID: 4130622
Factors associated with high functioning despite distress in post-9/11 veterans
OBJECTIVE:This study aimed to identify modifiable factors associated with perceived functioning among veterans with high symptoms of posttraumatic stress disorder (PTSD). METHOD/METHODS:Two hundred fifty-one post-9/11 veterans completed a survey of psychosocial symptoms and functioning; a subset participated in a follow-up survey (n = 109). Latent profile analysis (LPA) at baseline identified groups that differed by level of functioning (high/low). Items utilized in the LPA analysis were derived from the World Health Organization Quality of Life-Bref self-report measure. Veterans with high PTSD symptoms in both groups were compared and logistic regression was utilized to predict group membership. RESULTS:Veterans with high functioning/high symptoms (n = 45) had significantly lower alcohol use and sleep problems, and higher postdeployment social support, posttraumatic growth, and optimism than veterans with low functioning/high symptoms (n = 100). Fewer sleep difficulties and higher postdeployment social support and optimism were associated with membership in the high functioning/high symptom group. CONCLUSIONS:These findings support the importance of identifying factors that can facilitate higher social, occupational, and general functional capacity for those with high levels of PTSD symptomatology. (PsycINFO Database Record (c) 2019 APA, all rights reserved).
PMID: 30985153
ISSN: 1939-1544
CID: 3808262
Distinct Profiles of Cell-Free MicroRNAs in Plasma of Veterans with Post-Traumatic Stress Disorder
Dysregulation of circulating microRNAs (miRNAs) in body fluids has been reported in psychiatric disorders such as schizophrenia, bipolar disorder, major depressive disorder, and post-traumatic stress disorder (PTSD). Recent studies of various diseases showed that extracellular vesicles (EV) in body fluids can provide different spectra of circulating miRNAs and disease-associated signatures from whole fluid or EV-depleted fraction. However, the association of miRNAs in EVs to PTSD has not been studied. In this study, we performed a comprehensive profiling of miRNAs in whole plasma, extracellular vesicles (EV) and EV-depleted plasma (EVD) samples collected from combat veterans with PTSD and matched controls by utilizing a next-generation sequencing (NGS) platform. In total, 520 circulating miRNAs were quantified from 24 male Iraq and Afghanistan combat veterans with (n = 12) and without (n = 12) PTSD. The overall miRNA profiles in whole plasma, EV and EVD fractions were different and miRNAs affected by PTSD were also distinct in each sample type. The concentration changes of miR-203a-3p in EV and miR-339-5p in EVD were confirmed in an independent validation cohort that consisted of 20 veterans (10 with and 10 without PTSD) using qPCR. The target genes of these two miRNAs were involved in signaling pathways and comorbid conditions associated with PTSD (e.g., neurotransmitter systems such as dopaminergic and serotonergic signaling, inflammatory response, and cardiovascular diseases). Our findings suggest that PTSD may have different impacts on miRNAs encapsulated in vesicles and outside of vesicles. Further studies using larger samples are needed to evaluate the utility of these miRNAs as diagnostic biomarkers for PTSD.
PMCID:6678393
PMID: 31277223
ISSN: 2077-0383
CID: 4064362
Speech-based markers for posttraumatic stress disorder in US veterans
BACKGROUND:The diagnosis of posttraumatic stress disorder (PTSD) is usually based on clinical interviews or self-report measures. Both approaches are subject to under- and over-reporting of symptoms. An objective test is lacking. We have developed a classifier of PTSD based on objective speech-marker features that discriminate PTSD cases from controls. METHODS:Speech samples were obtained from warzone-exposed veterans, 52 cases with PTSD and 77 controls, assessed with the Clinician-Administered PTSD Scale. Individuals with major depressive disorder (MDD) were excluded. Audio recordings of clinical interviews were used to obtain 40,526 speech features which were input to a random forest (RF) algorithm. RESULTS:The selected RF used 18 speech features and the receiver operating characteristic curve had an area under the curve (AUC) of 0.954. At a probability of PTSD cut point of 0.423, Youden's index was 0.787, and overall correct classification rate was 89.1%. The probability of PTSD was higher for markers that indicated slower, more monotonous speech, less change in tonality, and less activation. Depression symptoms, alcohol use disorder, and TBI did not meet statistical tests to be considered confounders. CONCLUSIONS:This study demonstrates that a speech-based algorithm can objectively differentiate PTSD cases from controls. The RF classifier had a high AUC. Further validation in an independent sample and appraisal of the classifier to identify those with MDD only compared with those with PTSD comorbid with MDD is required.
PMID: 31006959
ISSN: 1520-6394
CID: 3821282
Polygenic risk associated with post-traumatic stress disorder onset and severity
Post-traumatic stress disorder (PTSD) is a psychiatric illness with a highly polygenic architecture without large effect-size common single-nucleotide polymorphisms (SNPs). Thus, to capture a substantial portion of the genetic contribution, effects from many variants need to be aggregated. We investigated various aspects of one such approach that has been successfully applied to many traits, polygenic risk score (PRS) for PTSD. Theoretical analyses indicate the potential prediction ability of PRS. We used the latest summary statistics from the largest published genome-wide association study (GWAS) conducted by Psychiatric Genomics Consortium for PTSD (PGC-PTSD). We found that the PRS constructed for a cohort comprising veterans of recent wars (n = 244) explains a considerable proportion of PTSD onset (Nagelkerke R2 = 4.68%, P = 0.003) and severity (R2 = 4.35%, P = 0.0008) variances. However, the performance on an African ancestry sub-cohort was minimal. A PRS constructed with schizophrenia GWAS also explained a significant fraction of PTSD diagnosis variance (Nagelkerke R2 = 2.96%, P = 0.0175), confirming previously reported genetic correlation between the two psychiatric ailments. Overall, these findings demonstrate the important role polygenic analyses of PTSD will play in risk prediction models as well as in elucidating the biology of the disorder.
PMID: 31175274
ISSN: 2158-3188
CID: 3923622
Microstate Features Predict Severity of PTSD and Depression Symptoms [Meeting Abstract]
Background: Electroencephalography (EEG) signals consist of topographically defined, stable, and short-lived microstates (Poulsen et al., 2018). Microstates underlie functionally defined networks described in the fMRI literature and play an important role in human cognition. Differences in duration, frequency of occurrence, and transition probabilities of microstates have been found between clinical populations and healthy controls (e.g., Abell et al., 2013).
Method(s): With 504 subjects, the present study is one of the largest investigations of EEG microstates in the literature, and the largest that investigates EEG microstates in clinical populations. The current study includes participants in each of the following populations: Posttraumatic Stress Disorder, Traumatic Brain Injury, Major Depression, and healthy controls. Resting eyes closed EEG recordings were analyzed using the microstate MATLAB toolbox (Poulsen et al., 2018) and custom scripts. Seven discrete microstate classes were identified using a hold-out sample of 40 healthy controls, and the resulting microstate maps were back-fitted to the other participants. Microstate statistics were extracted for each participant and entered into regression analyses with PTSD severity and depression severity as dependent variables.
Result(s): The occurrence, duration, and transition probabilities of multiple microstates significantly predicted PTSD severity (multiple R2 =.19, p =.01), but only certain microstate transition probabilities predicted depression severity (multiple R2 =.18, p =.10). The neural generators of these microstates were determined to provide further insight into these findings.
Conclusion(s): This and future studies can provide promising targets for interventions in an effort to reduce symptom burden in these clinical populations. Supported By: Cohen Veterans Bioscience Keywords: EEG Microstate Analysis, PT
Method(s): With 504 subjects, the present study is one of the largest investigations of EEG microstates in the literature, and the largest that investigates EEG microstates in clinical populations. The current study includes participants in each of the following populations: Posttraumatic Stress Disorder, Traumatic Brain Injury, Major Depression, and healthy controls. Resting eyes closed EEG recordings were analyzed using the microstate MATLAB toolbox (Poulsen et al., 2018) and custom scripts. Seven discrete microstate classes were identified using a hold-out sample of 40 healthy controls, and the resulting microstate maps were back-fitted to the other participants. Microstate statistics were extracted for each participant and entered into regression analyses with PTSD severity and depression severity as dependent variables.
Result(s): The occurrence, duration, and transition probabilities of multiple microstates significantly predicted PTSD severity (multiple R2 =.19, p =.01), but only certain microstate transition probabilities predicted depression severity (multiple R2 =.18, p =.10). The neural generators of these microstates were determined to provide further insight into these findings.
Conclusion(s): This and future studies can provide promising targets for interventions in an effort to reduce symptom burden in these clinical populations. Supported By: Cohen Veterans Bioscience Keywords: EEG Microstate Analysis, PT
EMBASE:2001857639
ISSN: 1873-2402
CID: 4131852
