Multi-omic biomarker identification and validation for diagnosing warzone-related post-traumatic stress disorder

Prefrontal metabolite alterations in individuals with posttraumatic stress disorder: a 7T magnetic resonance spectroscopy study

Background

Evidence from animal and human studies suggests glutamatergic dysfunction in posttraumatic stress disorder (PTSD). The purpose of this study was to investigate glutamate abnormalities in the dorsolateral prefrontal cortex (DLFPC) of individuals with PTSD using 7T MRS, which has better spectral resolution and signal-to-noise ratio than lower field strengths, thus allowing for better spectral quality and higher sensitivity. We hypothesized that individuals with PTSD would have lower glutamate levels compared to trauma-exposed individuals without PTSD and individuals without trauma exposure. Additionally, we explored potential alterations in other neurometabolites and the relationship between glutamate and psychiatric symptoms.

Methods

Individuals with PTSD (n=27), trauma-exposed individuals without PTSD (n=27), and individuals without trauma exposure (n=26) underwent 7T MRS to measure glutamate and other neurometabolites in the left DLPFC. The severities of PTSD, depression, anxiety, and dissociation symptoms were assessed.

Results

We found that glutamate was lower in the PTSD and trauma-exposed groups compared to the group without trauma exposure. Furthermore, N-acetylaspartate (NAA) was lower and lactate was higher in the PTSD group compared to the group without trauma exposure. Glutamate was negatively correlated with depression symptom severity in the PTSD group. Glutamate was not correlated with PTSD symptom severity.

Conclusion

In this first 7T MRS study of PTSD, we observed altered concentrations of glutamate, NAA, and lactate. Our findings provide evidence for multiple possible pathological processes in individuals with PTSD. High-field MRS offers insight into the neurometabolic alterations associated with PTSD and is a powerful tool to probe trauma- and stress-related neurotransmission and metabolism in vivo.

An exploratory study on lipidomic profiles in a cohort of individuals with posttraumatic stress disorder

Posttraumatic stress disorder (PTSD) can develop after trauma exposure. Some studies report that women develop PTSD at twice the rate of men, despite greater trauma exposure in men. Lipids and their metabolites (lipidome) regulate a myriad of key biological processes and pathways such as membrane integrity, oxidative stress, and neuroinflammation in the brain by maintaining neuronal connectivity and homeostasis. In this study, we analyzed the lipidome of 40 adults with PTSD and 40 trauma-exposed non-PTSD individuals (n = 20/sex/condition; 19-39 years old). Plasma samples were analyzed for lipidomics using Quadrupole Time-of-Flight (QToF) mass spectrometry. Additionally, ~ 90 measures were collected, on sleep, and mental and physical health indices. Poorer sleep quality was associated with greater PTSD severity in both sexes. The lipidomics analysis identified a total of 348 quantifiable known lipid metabolites and 1951 lipid metabolites that are yet unknown; known metabolites were part of 13 lipid subclasses. After adjusting for BMI and sleep quality, in women with PTSD, only one lipid subclass, phosphatidylethanolamine (PE) was altered, whereas, in men with PTSD, 9 out of 13 subclasses were altered compared to non-PTSD women and men, respectively. Severe PTSD was associated with 22% and 5% of altered lipid metabolites in men and women, respectively. Of the changed metabolites, only 0.5% measures (2 PEs and cholesterol) were common between women and men with PTSD. Several sphingomyelins, PEs, ceramides, and triglycerides were increased in men with severe PTSD. The correlations between triglycerides and ceramide metabolites with cholesterol metabolites and systolic blood pressure were dependent upon sex and PTSD status. Alterations in triglycerides and ceramides are linked with cardiac health and metabolic function in humans. Thus, disturbed sleep and higher body mass may have contributed to changes in the lipidome found in PTSD.

Systems biology dissection of PTSD and MDD across brain regions, cell types, and blood

The molecular pathology of stress-related disorders remains elusive. Our brain multiregion, multiomic study of posttraumatic stress disorder (PTSD) and major depressive disorder (MDD) included the central nucleus of the amygdala, hippocampal dentate gyrus, and medial prefrontal cortex (mPFC). Genes and exons within the mPFC carried most disease signals replicated across two independent cohorts. Pathways pointed to immune function, neuronal and synaptic regulation, and stress hormones. Multiomic factor and gene network analyses provided the underlying genomic structure. Single nucleus RNA sequencing in dorsolateral PFC revealed dysregulated (stress-related) signals in neuronal and non-neuronal cell types. Analyses of brain-blood intersections in >50,000 UK Biobank participants were conducted along with fine-mapping of the results of PTSD and MDD genome-wide association studies to distinguish risk from disease processes. Our data suggest shared and distinct molecular pathology in both disorders and propose potential therapeutic targets and biomarkers.

The lipidome of posttraumatic stress disorder

Posttraumatic stress disorder (PTSD) can develop after trauma exposure. Some studies report that women develop PTSD at twice the rate of men, despite greater trauma exposure in men. Lipids and their metabolites (lipidome) regulate a myriad of key biological processes and pathways such as membrane integrity, oxidative stress, and neuroinflammation in the brain by maintaining neuronal connectivity and homeostasis. In this study, we analyzed the lipidome of 40 individuals with PTSD and 40 trauma-exposed non-PTSD individuals. Plasma samples were analyzed for lipidomics using Quadrupole Time-of-Flight (QToF) mass spectrometry. Additionally, ~ 90 measures were collected, on sleep, mental and physical health indices. Sleep quality worsened as PTSD severity increased in both sexes. The lipidomics analysis identified a total of 348 quantifiable known lipid metabolites and 1951 lipid metabolites that are yet unknown; known metabolites were part of 13 classes of lipids. After adjusting for sleep quality, in women with PTSD, only one lipid subclass, phosphatidylethanolamine (PE) was altered, whereas, in men with PTSD, 9 out of 13 subclasses were altered compared to non-PTSD women and men, respectively. Severe PTSD was associated with 22% and 5% of altered lipid metabolites in men and women, respectively. Of the changed metabolites, only 0.5% measures (2 PEs and cholesterol) were common between women and men with PTSD. Several sphingomyelins, PEs, ceramides, and triglycerides were increased in men with severe PTSD. The triglycerides and ceramide metabolites that were most highly increased were correlated with cholesterol metabolites and systolic blood pressure in men but not always in women with PTSD. Alterations in triglycerides and ceramides are linked with cardiac health and metabolic function in humans. Thus, disturbed sleep and higher weight may have contributed to changes in the lipidome found in PTSD.

Blood-based DNA methylation and exposure risk scores predict PTSD with high accuracy in military and civilian cohorts

Background

Incorporating genomic data into risk prediction has become an increasingly useful approach for rapid identification of individuals most at risk for complex disorders such as PTSD. Our goal was to develop and validate Methylation Risk Scores (MRS) using machine learning to distinguish individuals who have PTSD from those who do not.

Methods

Elastic Net was used to develop three risk score models using a discovery dataset (n = 1226; 314 cases, 912 controls) comprised of 5 diverse cohorts with available blood-derived DNA methylation (DNAm) measured on the Illumina Epic BeadChip. The first risk score, exposure and methylation risk score (eMRS) used cumulative and childhood trauma exposure and DNAm variables; the second, methylation-only risk score (MoRS) was based solely on DNAm data; the third, methylation-only risk scores with adjusted exposure variables (MoRSAE) utilized DNAm data adjusted for the two exposure variables. The potential of these risk scores to predict future PTSD based on pre-deployment data was also assessed. External validation of risk scores was conducted in four independent cohorts.

Results

The eMRS model showed the highest accuracy (92%), precision (91%), recall (87%), and f1-score (89%) in classifying PTSD using 3730 features. While still highly accurate, the MoRS (accuracy = 89%) using 3728 features and MoRSAE (accuracy = 84%) using 4150 features showed a decline in classification power. eMRS significantly predicted PTSD in one of the four independent cohorts, the BEAR cohort (beta = 0.6839, p-0.003), but not in the remaining three cohorts. Pre-deployment risk scores from all models (eMRS, beta = 1.92; MoRS, beta = 1.99 and MoRSAE, beta = 1.77) displayed a significant (p < 0.001) predictive power for post-deployment PTSD.

Conclusion

Results, especially those from the eMRS, reinforce earlier findings that methylation and trauma are interconnected and can be leveraged to increase the correct classification of those with vs. without PTSD. Moreover, our models can potentially be a valuable tool in predicting the future risk of developing PTSD. As more data become available, including additional molecular, environmental, and psychosocial factors in these scores may enhance their accuracy in predicting the condition and, relatedly, improve their performance in independent cohorts.

Translational bioinformatics and data science for biomarker discovery in mental health: an analytical review

Translational bioinformatics and data science play a crucial role in biomarker discovery as it enables translational research and helps to bridge the gap between the bench research and the bedside clinical applications. Thanks to newer and faster molecular profiling technologies and reducing costs, there are many opportunities for researchers to explore the molecular and physiological mechanisms of diseases. Biomarker discovery enables researchers to better characterize patients, enables early detection and intervention/prevention and predicts treatment responses. Due to increasing prevalence and rising treatment costs, mental health (MH) disorders have become an important venue for biomarker discovery with the goal of improved patient diagnostics, treatment and care. Exploration of underlying biological mechanisms is the key to the understanding of pathogenesis and pathophysiology of MH disorders. In an effort to better understand the underlying mechanisms of MH disorders, we reviewed the major accomplishments in the MH space from a bioinformatics and data science perspective, summarized existing knowledge derived from molecular and cellular data and described challenges and areas of opportunities in this space.

Circulating cell-free mitochondrial DNA levels and glucocorticoid sensitivity in a cohort of male veterans with and without combat-related PTSD

Circulating cell-free mitochondrial DNA (ccf-mtDNA) is a biomarker of cellular injury or cellular stress and is a potential novel biomarker of psychological stress and of various brain, somatic, and psychiatric disorders. No studies have yet analyzed ccf-mtDNA levels in post-traumatic stress disorder (PTSD), despite evidence of mitochondrial dysfunction in this condition. In the current study, we compared plasma ccf-mtDNA levels in combat trauma-exposed male veterans with PTSD (n = 111) with those who did not develop PTSD (n = 121) and also investigated the relationship between ccf mt-DNA levels and glucocorticoid sensitivity. In unadjusted analyses, ccf-mtDNA levels did not differ significantly between the PTSD and non-PTSD groups (t = 1.312, p = 0.191, Cohen's d = 0.172). In a sensitivity analysis excluding participants with diabetes and those using antidepressant medication and controlling for age, the PTSD group had lower ccf-mtDNA levels than did the non-PTSD group (F(1, 179) = 5.971, p = 0.016, partial η2 = 0.033). Across the entire sample, ccf-mtDNA levels were negatively correlated with post-dexamethasone adrenocorticotropic hormone (ACTH) decline (r = -0.171, p = 0.020) and cortisol decline (r = -0.149, p = 0.034) (viz., greater ACTH and cortisol suppression was associated with lower ccf-mtDNA levels) both with and without controlling for age, antidepressant status and diabetes status. Ccf-mtDNA levels were also significantly positively associated with IC50-DEX (the concentration of dexamethasone at which 50% of lysozyme activity is inhibited), a measure of lymphocyte glucocorticoid sensitivity, after controlling for age, antidepressant status, and diabetes status (β = 0.142, p = 0.038), suggesting that increased lymphocyte glucocorticoid sensitivity is associated with lower ccf-mtDNA levels. Although no overall group differences were found in unadjusted analyses, excluding subjects with diabetes and those taking antidepressants, which may affect ccf-mtDNA levels, as well as controlling for age, revealed decreased ccf-mtDNA levels in PTSD. In both adjusted and unadjusted analyses, low ccf-mtDNA levels were associated with relatively increased glucocorticoid sensitivity, often reported in PTSD, suggesting a link between mitochondrial and glucocorticoid-related abnormalities in PTSD.

The Psychedelic Future of Post-Traumatic Stress Disorder Treatment

Post-traumatic stress disorder (PTSD) is a mental health condition that can occur following exposure to a traumatic experience. An estimated 12 million U.S. adults are presently affected by this disorder. Current treatments include psychological therapies (e.g., exposure-based interventions) and pharmacological treatments (e.g., selective serotonin reuptake inhibitors (SSRIs)). However, a significant proportion of patients receiving standard-of-care therapies for PTSD remain symptomatic, and new approaches for this and other trauma-related mental health conditions are greatly needed. Psychedelic compounds that alter cognition, perception, and mood are currently being examined for their efficacy in treating PTSD despite their current status as Drug Enforcement Administration (DEA)- scheduled substances. Initial clinical trials have demonstrated the potential value of psychedelicassisted therapy to treat PTSD and other psychiatric disorders. In this comprehensive review, we summarize the state of the science of PTSD clinical care, including current treatments and their shortcomings. We review clinical studies of psychedelic interventions to treat PTSD, trauma-related disorders, and common comorbidities. The classic psychedelics psilocybin, lysergic acid diethylamide (LSD), and N,N-dimethyltryptamine (DMT) and DMT-containing ayahuasca, as well as the entactogen 3,4-methylenedioxymethamphetamine (MDMA) and the dissociative anesthetic ketamine, are reviewed. For each drug, we present the history of use, psychological and somatic effects, pharmacology, and safety profile. The rationale and proposed mechanisms for use in treating PTSD and traumarelated disorders are discussed. This review concludes with an in-depth consideration of future directions for the psychiatric applications of psychedelics to maximize therapeutic benefit and minimize risk in individuals and communities impacted by trauma-related conditions.

Integrated analysis of proteomics, epigenomics and metabolomics data revealed divergent pathway activation patterns in the recent versus chronic post-traumatic stress disorder

Metabolomics, proteomics and DNA methylome assays, when done in tandem from the same blood sample and analyzed together, offer an opportunity to evaluate the molecular basis of post-traumatic stress disorder (PTSD) course and pathogenesis. We performed separate metabolomics, proteomics, and DNA methylome assays on blood samples from two well-characterized cohorts of 159 active duty male participants with relatively recent onset PTSD (<1.5 years) and 300 male veterans with chronic PTSD (>7 years). Analyses of the multi-omics datasets from these two independent cohorts were used to identify convergent and distinct molecular profiles that might constitute potential signatures of severity and progression of PTSD and its comorbid conditions. Molecular signatures indicative of homeostatic processes such as signaling and metabolic pathways involved in cellular remodeling, neurogenesis, molecular safeguards against oxidative stress, metabolism of polyunsaturated fatty acids, regulation of normal immune response, post-transcriptional regulation, cellular maintenance and markers of longevity were significantly activated in the active duty participants with recent PTSD. In contrast, we observed significantly altered multimodal molecular signatures associated with chronic inflammation, neurodegeneration, cardiovascular and metabolic disorders, and cellular attritions in the veterans with chronic PTSD. Activation status of signaling and metabolic pathways at the early and late timepoints of PTSD demonstrated the differential molecular changes related to homeostatic processes at its recent and multi-system syndromes at its chronic phase. Molecular alterations in the recent PTSD seem to indicate some sort of recalibration or compensatory response, possibly directed in mitigating the pathological trajectory of the disorder.

Mental stress objective screening for workers using urinary neurotransmitters

BACKGROUND

Almost 10% of the population develop depression or anxiety disorder during their lifetime. Considering that people who are exposed to high stress are more likely to develop mental disorders, it is important to detect and remove mental stress before depression or anxiety disorder develops. We aimed to develop an objective screening test that quantifies mental stress in workers so that they can recognize and remove it before the disorder develops.

METHODS

We obtained urine specimens from 100 healthy volunteers (49 men and 51 women; age = 48.2 ± 10.8 years) after they received medical checks and answered the Brief Job Stress Questionnaire (BJSQ). Participants were divided into high- and low- stress groups according to their total BJSQ scores. We further analyzed six urinary neurotransmitters (dopamine, serotonin, 5-hydoroxyindoleacetic acid, gamma-aminobutyric acid, homovanillic acid, and vanillylmandelic acid) using liquid chromatography-mass spectrometry to compare their levels between the two groups.

RESULTS

We obtained the concentrations of the six analytes from 100 examinees and revealed that the levels of urinary dopamine (p = 0.0042) and homovanillic acid (p = 0.020) were significantly lower in the high-stress group than those in the low-stress group. No biases were observed between the two groups in 36 laboratory items. The stress index generated from the six neurotransmitter concentrations recognized high-stress group significantly. Moreover, we discovered that the level of each urinary neurotransmitter changed depending on various stress factors, such as dissatisfaction, physical fatigue, stomach and intestine problems, poor appetite, poor working environments, sleep disturbance, isolation, worry, or insecurity.

CONCLUSION

We revealed that urinary neurotransmitters could be a promising indicator to determine underlying mental stress. This study provides clues for scientists to develop a screening test not only for workers but also for patients with depression.

Molecular signatures of post-traumatic stress disorder in war-zone-exposed veteran and active-duty soldiers

Post-traumatic stress disorder (PTSD) is a multisystem syndrome. Integration of systems-level multi-modal datasets can provide a molecular understanding of PTSD. Proteomic, metabolomic, and epigenomic assays are conducted on blood samples of two cohorts of well-characterized PTSD cases and controls: 340 veterans and 180 active-duty soldiers. All participants had been deployed to Iraq and/or Afghanistan and exposed to military-service-related criterion A trauma. Molecular signatures are identified from a discovery cohort of 218 veterans (109/109 PTSD+/-). Identified molecular signatures are tested in 122 separate veterans (62/60 PTSD+/-) and in 180 active-duty soldiers (PTSD+/-). Molecular profiles are computationally integrated with upstream regulators (genetic/methylation/microRNAs) and functional units (mRNAs/proteins/metabolites). Reproducible molecular features of PTSD are identified, including activated inflammation, oxidative stress, metabolic dysregulation, and impaired angiogenesis. These processes may play a role in psychiatric and physical comorbidities, including impaired repair/wound healing mechanisms and cardiovascular, metabolic, and psychiatric diseases.

Prediction of disease-related miRNAs by voting with multiple classifiers

There is strong evidence to support that mutations and dysregulation of miRNAs are associated with a variety of diseases, including cancer. However, the experimental methods used to identify disease-related miRNAs are expensive and time-consuming. Effective computational approaches to identify disease-related miRNAs are in high demand and would aid in the detection of lncRNA biomarkers for disease diagnosis, treatment, and prevention. In this study, we develop an ensemble learning framework to reveal the potential associations between miRNAs and diseases (ELMDA). The ELMDA framework does not rely on the known associations when calculating miRNA and disease similarities and uses multi-classifiers voting to predict disease-related miRNAs. As a result, the average AUC of the ELMDA framework was 0.9229 for the HMDD v2.0 database in a fivefold cross-validation. All potential associations in the HMDD V2.0 database were predicted, and 90% of the top 50 results were verified with the updated HMDD V3.2 database. The ELMDA framework was implemented to investigate gastric neoplasms, prostate neoplasms and colon neoplasms, and 100%, 94%, and 90%, respectively, of the top 50 potential miRNAs were validated by the HMDD V3.2 database. Moreover, the ELMDA framework can predict isolated disease-related miRNAs. In conclusion, ELMDA appears to be a reliable method to uncover disease-associated miRNAs.

Defining the nuanced nature of redox biology in post-traumatic stress disorder

Post-traumatic stress disorder (PTSD) is a mental health disorder that arises after experiencing or witnessing a traumatic event. Despite affecting around 7% of the population, there are currently no definitive biological signatures or biomarkers used in the diagnosis of PTSD. Thus, the search for clinically relevant and reproducible biomarkers has been a major focus of the field. With significant advances of large-scale multi-omic studies that include genomic, proteomic, and metabolomic data, promising findings have been made, but the field still has fallen short. Amongst the possible biomarkers examined, one area is often overlooked, understudied, or inappropriately investigated: the field of redox biology. Redox molecules are free radical and/or reactive species that are generated as a consequence of the necessity of electron movement for life. These reactive molecules, too, are essential for life, but in excess are denoted as "oxidative stress" and often associated with many diseases. The few studies that have examined redox biology parameters have often utilized outdated and nonspecific methods, as well as have reported confounding results, which has made it difficult to conclude the role for redox in PTSD. Herein, we provide a foundation of how redox biology may underlie diseases like PTSD, critically examine redox studies of PTSD, and provide future directions the field can implement to enhance standardization, reproducibility, and accuracy of redox assessments for the use of diagnosis, prognosis, and therapy of this debilitating mental health disorder.

Heart and brain traumatic stress biomarker analysis with and without machine learning: A scoping review

The enigma of post-traumatic stress disorder (PTSD) is embedded in a complex array of physiological responses to stressful situations that result in disruptions in arousal and cognitions that characterise the psychological disorder. Deciphering these physiological patterns is complex, which has seen the use of machine learning (ML) grow in popularity. However, it is unclear to what extent ML has been used with physiological data, specifically, the electroencephalogram (EEG) and electrocardiogram (ECG) to further understand the physiological responses associated with PTSD. To better understand the use of EEG and ECG biomarkers, with and without ML, a scoping review was undertaken. A total of 124 papers based on adult samples were identified comprising 19 ML studies involving EEG and ECG. A further 21 studies using EEG data, and 84 studies employing ECG meeting all other criteria but not employing ML were included for comparison. Identified studies indicate classical ML methodologies currently dominate EEG and ECG biomarkers research, with derived biomarkers holding clinically relevant diagnostic implications for PTSD. Discussion of the emerging trends, algorithms used and their success is provided, along with areas for future research.

Screening for PTSD and TBI in Veterans using Routine Clinical Laboratory Blood Tests

Post-traumatic stress disorder (PTSD) is a mental disorder diagnosed by clinical interviews, self-report measures and neuropsychological testing. Traumatic brain injury (TBI) can have neuropsychiatric symptoms similar to PTSD. Diagnosing PTSD and TBI is challenging and more so for providers lacking specialized training facing time pressures in primary care and other general medical settings. Diagnosis relies heavily on patient self-report and patients frequently under-report or over-report their symptoms due to stigma or seeking compensation. We aimed to create objective diagnostic screening tests utilizing Clinical Laboratory Improvement Amendments (CLIA) blood tests available in most clinical settings. CLIA blood test results were ascertained in 475 male veterans with and without PTSD and TBI following warzone exposure in Iraq or Afghanistan. Using random forest (RF) methods, four classification models were derived to predict PTSD and TBI status. CLIA features were selected utilizing a stepwise forward variable selection RF procedure. The AUC, accuracy, sensitivity, and specificity were 0.730, 0.706, 0.659, and 0.715, respectively for differentiating PTSD and healthy controls (HC), 0.704, 0.677, 0.671, and 0.681 for TBI vs. HC, 0.739, 0.742, 0.635, and 0.766 for PTSD comorbid with TBI vs HC, and 0.726, 0.723, 0.636, and 0.747 for PTSD vs. TBI. Comorbid alcohol abuse, major depressive disorder, and BMI are not confounders in these RF models. Markers of glucose metabolism and inflammation are among the most significant CLIA features in our models. Routine CLIA blood tests have the potential for discriminating PTSD and TBI cases from healthy controls and from each other. These findings hold promise for the development of accessible and low-cost biomarker tests as screening measures for PTSD and TBI in primary care and specialty settings.

Integrative Multi-omics Analysis of Childhood Aggressive Behavior

This study introduces and illustrates the potential of an integrated multi-omics approach in investigating the underlying biology of complex traits such as childhood aggressive behavior. In 645 twins (cases = 42%), we trained single- and integrative multi-omics models to identify biomarkers for subclinical aggression and investigated the connections among these biomarkers. Our data comprised transmitted and two non-transmitted polygenic scores (PGSs) for 15 traits, 78,772 CpGs, and 90 metabolites. The single-omics models selected 31 PGSs, 1614 CpGs, and 90 metabolites, and the multi-omics model comprised 44 PGSs, 746 CpGs, and 90 metabolites. The predictive accuracy for these models in the test (N = 277, cases = 42%) and independent clinical data (N = 142, cases = 45%) ranged from 43 to 57%. We observed strong connections between DNA methylation, amino acids, and parental non-transmitted PGSs for ADHD, Autism Spectrum Disorder, intelligence, smoking initiation, and self-reported health. Aggression-related omics traits link to known and novel risk factors, including inflammation, carcinogens, and smoking.

Potential peripheral biomarkers associated with the emergence and presence of posttraumatic stress disorder symptomatology: A systematic review

BACKGROUND

Evidence suggests posttraumatic stress disorder (PTSD) involves an interplay between psychological manifestations and biological systems. Biological markers of PTSD could assist in identifying individuals with underlying dysregulation and increased risk; however, accurate and reliable biomarkers are yet to be identified.

METHODS

A systematic review following the PRISMA guidelines was conducted. Databases included EMBASE, MEDLINE, and Cochrane Central. Studies from a comprehensive 2015 review (Schmidt et al., 2015) and English language papers published subsequently (between 2014 and May 2022) were included. Forty-eight studies were eligible.

RESULTS

Alterations in neuroendocrine and immune markers were most commonly associated with PTSD symptoms. Evidence indicates PTSD symptoms are associated with hypothalamic-pituitary-adrenal axis dysfunction as represented by low basal cortisol, a dysregulated immune system, characterized by an elevated pro-inflammatory state, and metabolic dysfunction. However, a considerable number of studies neglected to measure sex or prior trauma, which have the potential to affect the biological outcomes of posttraumatic stress symptoms. Mixed findings are indicative of the complexity and heterogeneity of PTSD and suggest the relationship between allostatic load, biological markers, and PTSD remain largely undefined.

CONCLUSIONS

In addition to prospective research design and long-term follow up, it is imperative future research includes covariates sex, prior trauma, and adverse childhood experiences. Future research should include exploration of biological correlates specific to PTSD symptom domains to determine whether underlying processes differ with symptom expression, in addition to subclinical presentation of posttraumatic stress symptoms, which would allow for greater understanding of biomarkers associated with disorder risk and assist in untangling directionality.

Deriving psychiatric symptom-based biomarkers from multivariate relationships between psychophysiological and biochemical measures

Identification of biomarkers for psychiatric disorders remains very challenging due to substantial symptom heterogeneity and diagnostic comorbidity, limiting the ability to map symptoms to underlying neurobiology. Dimensional symptom clusters, such as anhedonia, hyperarousal, etc., are complex and arise due to interactions of a multitude of complex biological relationships. The primary aim of the current investigation was to use multi-set canonical correlation analysis (mCCA) to derive biomarkers (biochemical, physiological) linked to dimensional symptoms across the anxiety and depressive spectrum. Active-duty service members (N = 2,592) completed standardized depression, anxiety and posttraumatic stress questionnaires and several psychophysiological and biochemical assays. Using this approach, we identified two phenotype associations between distinct physiological and biological phenotypes. One was characterized by symptoms of dysphoric arousal (anhedonia, anxiety, hypervigilance) which was associated with low blood pressure and startle reactivity. This finding is in line with previous studies suggesting blunted physiological reactivity is associated with subpopulations endorsing anxiety with comorbid depressive features. A second phenotype of anxious fatigue (high anxiety and reexperiencing/avoidance symptoms coupled with fatigue) was associated with elevated blood levels of norepinephrine and the inflammatory marker C-reactive protein in conjunction with high blood pressure. This second phenotype may describe populations in which inflammation and high sympathetic outflow might contribute to anxious fatigue. Overall, these findings support the growing consensus that distinct neuropsychiatric symptom patterns are associated with differential physiological and blood-based biological profiles and highlight the potential of mCCA to reveal important psychiatric symptom biomarkers from several psychophysiological and biochemical measures.

Modern views of machine learning for precision psychiatry

In light of the National Institute of Mental Health (NIMH)'s Research Domain Criteria (RDoC), the advent of functional neuroimaging, novel technologies and methods provide new opportunities to develop precise and personalized prognosis and diagnosis of mental disorders. Machine learning (ML) and artificial intelligence (AI) technologies are playing an increasingly critical role in the new era of precision psychiatry. Combining ML/AI with neuromodulation technologies can potentially provide explainable solutions in clinical practice and effective therapeutic treatment. Advanced wearable and mobile technologies also call for the new role of ML/AI for digital phenotyping in mobile mental health. In this review, we provide a comprehensive review of ML methodologies and applications by combining neuroimaging, neuromodulation, and advanced mobile technologies in psychiatry practice. We further review the role of ML in molecular phenotyping and cross-species biomarker identification in precision psychiatry. We also discuss explainable AI (XAI) and neuromodulation in a closed human-in-the-loop manner and highlight the ML potential in multi-media information extraction and multi-modal data fusion. Finally, we discuss conceptual and practical challenges in precision psychiatry and highlight ML opportunities in future research.

Exploring the pathogenesis linking traumatic brain injury and epilepsy via bioinformatic analyses

Traumatic brain injury (TBI) is a serious disease that could increase the risk of epilepsy. The purpose of this article is to explore the common molecular mechanism in TBI and epilepsy with the aim of providing a theoretical basis for the prevention and treatment of post-traumatic epilepsy (PTE). Two datasets of TBI and epilepsy in the Gene Expression Omnibus (GEO) database were downloaded. Functional enrichment analysis, protein-protein interaction (PPI) network construction, and hub gene identification were performed based on the cross-talk genes of aforementioned two diseases. Another dataset was used to validate these hub genes. Moreover, the abundance of infiltrating immune cells was evaluated through Immune Cell Abundance Identifier (ImmuCellAI). The common microRNAs (miRNAs) between TBI and epilepsy were acquired via the Human microRNA Disease Database (HMDD). The overlapped genes in cross-talk genes and target genes predicted through the TargetScan were obtained to construct the common miRNAs-mRNAs network. A total of 106 cross-talk genes were screened out, including 37 upregulated and 69 downregulated genes. Through the enrichment analyses, we showed that the terms about cytokine and immunity were enriched many times, particularly interferon gamma signaling pathway. Four critical hub genes were screened out for co-expression analysis. The miRNA-mRNA network revealed that three miRNAs may affect the shared interferon-induced genes, which might have essential roles in PTE. Our study showed the potential role of interferon gamma signaling pathway in pathogenesis of PTE, which may provide a promising target for future therapeutic interventions.

Resting-state EEG functional connectivity predicts post-traumatic stress disorder subtypes in veterans

Objective. Post-traumatic stress disorder (PTSD) is highly heterogeneous, and identification of quantifiable biomarkers that could pave the way for targeted treatment remains a challenge. Most previous electroencephalography (EEG) studies on PTSD have been limited to specific handpicked features, and their findings have been highly variable and inconsistent. Therefore, to disentangle the role of promising EEG biomarkers, we developed a machine learning framework to investigate a wide range of commonly used EEG biomarkers in order to identify which features or combinations of features are capable of characterizing PTSD and potential subtypes.Approach. We recorded 5 min of eyes-closed and 5 min of eyes-open resting-state EEG from 202 combat-exposed veterans (53% with probable PTSD and 47% combat-exposed controls). Multiple spectral, temporal, and connectivity features were computed and logistic regression, random forest, and support vector machines with feature selection methods were employed to classify PTSD. To obtain robust results, we performed repeated two-layer cross-validation to test on an entirely unseen test set.Main results. Our classifiers obtained a balanced test accuracy of up to 62.9% for predicting PTSD patients. In addition, we identified two subtypes within PTSD: one where EEG patterns were similar to those of the combat-exposed controls, and another that were characterized by increased global functional connectivity. Our classifier obtained a balanced test accuracy of 79.4% when classifying this PTSD subtype from controls, a clear improvement compared to predicting the whole PTSD group. Interestingly, alpha connectivity in the dorsal and ventral attention network was particularly important for the prediction, and these connections were positively correlated with arousal symptom scores, a central symptom cluster of PTSD.Significance. Taken together, the novel framework presented here demonstrates how unsupervised subtyping can delineate heterogeneity and improve machine learning prediction of PTSD, and may pave the way for better identification of quantifiable biomarkers.

The Genetic Basis for the Increased Prevalence of Metabolic Syndrome among Post-Traumatic Stress Disorder Patients

Post-traumatic stress disorder (PTSD) is a highly debilitating psychiatric disorder that can be triggered by exposure to extreme trauma. Even if PTSD is primarily a psychiatric condition, it is also characterized by adverse somatic comorbidities. One illness commonly co-occurring with PTSD is Metabolic syndrome (MetS), which is defined by a set of health risk/resilience factors including obesity, elevated blood pressure, lower high-density lipoprotein cholesterol, higher low-density lipoprotein cholesterol, higher triglycerides, higher fasting blood glucose and insulin resistance. Here, phenotypic association between PTSD and components of MetS are tested on a military veteran cohort comprising chronic PTSD presentation (n = 310, 47% cases, 83% male). Consistent with previous observations, we found significant phenotypic correlation between the various components of MetS and PTSD severity scores. To examine if this observed symptom correlations stem from a shared genetic background, we conducted genetic correlation analysis using summary statistics data from large-scale genetic studies. Our results show robust positive genetic correlation between PTSD and MetS (r_g[SE] = 0.33 [0.056], p = 4.74E-09), and obesity-related components of MetS (r_g = 0.25, SE = 0.05, p = 6.4E-08). Prioritizing genomic regions with larger local genetic correlation implicate three significant loci. Overall, these findings show significant genetic overlap between PTSD and MetS, which may in part account for the markedly increased occurrence of MetS among PTSD patients.

Predictive models for small-for-gestational-age births in women exposed to pesticides before pregnancy based on multiple machine learning algorithms

Background

The association between prenatal pesticide exposures and a higher incidence of small-for-gestational-age (SGA) births has been reported. No prediction model has been developed for SGA neonates in pregnant women exposed to pesticides prior to pregnancy.

Methods

A retrospective cohort study was conducted using information from the National Free Preconception Health Examination Project between 2010 and 2012. A development set (n = 606) and a validation set (n = 151) of the dataset were split at random. Traditional logistic regression (LR) method and six machine learning classifiers were used to develop prediction models for SGA neonates. The Shapley Additive Explanation (SHAP) model was applied to determine the most influential variables that contributed to the outcome of the prediction.

Results

757 neonates in total were analyzed. SGA occurred in 12.9% (n = 98) of cases overall. With an area under the receiver-operating-characteristic curve (AUC) of 0.855 [95% confidence interval (CI): 0.752–0.959], the model based on category boosting (CatBoost) algorithm obtained the best performance in the validation set. With the exception of the LR model (AUC: 0.691, 95% CI: 0.554–0.828), all models had good AUCs. Using recursive feature elimination (RFE) approach to perform the feature selection, we included 15 variables in the final model based on CatBoost classifier, achieving the AUC of 0.811 (95% CI: 0.675–0.947).

Conclusions

Machine learning algorithms can develop satisfactory tools for SGA prediction in mothers exposed to pesticides prior to pregnancy, which might become a tool to predict SGA neonates in the high-risk population.

Abnormal intestinal milieu in posttraumatic stress disorder is not impacted by treatment that improves symptoms

Posttraumatic stress disorder (PTSD) is a psychiatric disorder, resulting from exposure to traumatic events. Current recommended first-line interventions for the treatment of PTSD include evidence-based psychotherapies, such as cognitive processing therapy (CPT). Psychotherapies are effective for reducing PTSD symptoms, but approximately two-thirds of veterans continue to meet diagnostic criteria for PTSD after treatment, suggesting there is an incomplete understanding of what factors sustain PTSD. The intestine can influence the brain and this study evaluated intestinal readouts in subjects with PTSD. Serum samples from controls without PTSD (n = 40) from the Duke INTRuST Program were compared with serum samples from veterans with PTSD (n = 40) recruited from the Road Home Program at Rush University Medical Center. Assessments included microbial metabolites, intestinal barrier, and intestinal epithelial cell function. In addition, intestinal readouts were assessed in subjects with PTSD before and after a 3-wk CPT-based intensive treatment program (ITP) to understand if treatment impacts the intestine. Compared with controls, veterans with PTSD had a proinflammatory intestinal environment including lower levels of microbiota-derived metabolites, such as acetic, lactic, and succinic acid, intestinal barrier dysfunction [lipopolysaccharide (LPS) and LPS-binding protein], an increase in HMGB1, and a concurrent increase in the number of intestinal epithelial cell-derived extracellular vesicles. The ITP improved PTSD symptoms but no changes in intestinal outcomes were noted. This study confirms the intestine is abnormal in subjects with PTSD and suggests that effective treatment of PTSD does not alter intestinal readouts. Targeting beneficial changes in the intestine may be an approach to enhance existing PTSD treatments.NEW & NOTEWORTHY This study confirms an abnormal intestinal environment is present in subjects with PTSD. This study adds to what is already known by examining the intestinal barrier and evaluating the relationship between intestinal readouts and PTSD symptoms and is the first to report the impact of PTSD treatment (which improves symptoms) on intestinal readouts. This study suggests that targeting the intestine as an adjunct approach could improve the treatment of PTSD.

Central and Peripheral Immune Dysregulation in Posttraumatic Stress Disorder: Convergent Multi-Omics Evidence

Posttraumatic stress disorder (PTSD) is a chronic and multifactorial disorder with a prevalence ranging between 6–10% in the general population and ~35% in individuals with high lifetime trauma exposure. Growing evidence indicates that the immune system may contribute to the etiology of PTSD, suggesting the inflammatory dysregulation as a hallmark feature of PTSD. However, the potential interplay between the central and peripheral immune system, as well as the biological mechanisms underlying this dysregulation remain poorly understood. The activation of the HPA axis after trauma exposure and the subsequent activation of the inflammatory system mediated by glucocorticoids is the most common mechanism that orchestrates an exacerbated immunological response in PTSD. Recent high-throughput analyses in peripheral and brain tissue from both humans with and animal models of PTSD have found that changes in gene regulation via epigenetic alterations may participate in the impaired inflammatory signaling in PTSD. The goal of this review is to assess the role of the inflammatory system in PTSD across tissue and species, with a particular focus on the genomics, transcriptomics, epigenomics, and proteomics domains. We conducted an integrative multi-omics approach identifying TNF (Tumor Necrosis Factor) signaling, interleukins, chemokines, Toll-like receptors and glucocorticoids among the common dysregulated pathways in both central and peripheral immune systems in PTSD and propose potential novel drug targets for PTSD treatment.

AI-Based Prediction and Prevention of Psychological and Behavioral Changes in Ex-COVID-19 Patients

The COVID-19 pandemic has adverse consequences on human psychology and behavior long after initial recovery from the virus. These COVID-19 health sequelae, if undetected and left untreated, may lead to more enduring mental health problems, and put vulnerable individuals at risk of developing more serious psychopathologies. Therefore, an early distinction of such vulnerable individuals from those who are more resilient is important to undertake timely preventive interventions. The main aim of this article is to present a comprehensive multimodal conceptual approach for addressing these potential psychological and behavioral mental health changes using state-of-the-art tools and means of artificial intelligence (AI). Mental health COVID-19 recovery programs at post-COVID clinics based on AI prediction and prevention strategies may significantly improve the global mental health of ex-COVID-19 patients. Most COVID-19 recovery programs currently involve specialists such as pulmonologists, cardiologists, and neurologists, but there is a lack of psychiatrist care. The focus of this article is on new tools which can enhance the current limited psychiatrist resources and capabilities in coping with the upcoming challenges related to widespread mental health disorders. Patients affected by COVID-19 are more vulnerable to psychological and behavioral changes than non-COVID populations and therefore they deserve careful clinical psychological screening in post-COVID clinics. However, despite significant advances in research, the pace of progress in prevention of psychiatric disorders in these patients is still insufficient. Current approaches for the diagnosis of psychiatric disorders largely rely on clinical rating scales, as well as self-rating questionnaires that are inadequate for comprehensive assessment of ex-COVID-19 patients' susceptibility to mental health deterioration. These limitations can presumably be overcome by applying state-of-the-art AI-based tools in diagnosis, prevention, and treatment of psychiatric disorders in acute phase of disease to prevent more chronic psychiatric consequences.

Association of acute psychosocial stress with oxidative stress: Evidence from serum analysis

Growing evidence implicates an association between psychosocial stress and oxidative stress (OxSt) although there are not yet reliable biomarkers to study this association. We used a Trier Social Stress Test (TSST) and compared the response of a healthy control group (HC; N=10) against the response of a schizophrenia group (SCZ; N=10) that is expected to have higher levels of OxSt. Because our previous study showed inconsistent changes in conventional molecular markers for stress responses in the neuroendocrine and immune systems, we analyzed the same serum samples using a separate reducing capacity assay that provides a more global measurement of OxSt. This assay uses the moderately strong oxidizing agent iridium (Ir) to probe a sample's reducing capacity. Specifically, we characterized OxSt by this Ir-reducing capacity assay (Ir-RCA) using two measurement modalities (optical and electrochemical) and we tuned this assay by imposing an input voltage sequence that generates multiple output metrics for data-driven analysis. We defined five OxSt metrics (one optical and four electrochemical metrics) and showed: (i) internal consistency among each metric in the measurements of all 40 samples (baseline and post TSST for N=20); (ii) all five metrics were consistent with expectations of higher levels of OxSt for the SCZ group (three individual metrics showed statistically significant differences); and (iii) all five metrics showed higher levels of OxSt Post-TSST (one metric showed statistically significant difference). Using multivariant analysis, we showed that combinations of OxSt metrics could discern statistically significant increases in OxSt for both the SCZ and HC groups 90 min after the imposed acute psychosocial stress.

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Ir-reducing capacity assay (Ir-RCA) provides a robust global measure of oxidative stress in serum.

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The multiple oxidative stress (OxSt) output metrics of this Ir-RCA are useful for data-driven analysis.

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The combination of OxSt metrics can discern significant increases in OxStwithin 90 mins of an imposed psychosocial stress.

Cross-Platform Evaluation of Commercially Targeted and Untargeted Metabolomics Approaches to Optimize the Investigation of Psychiatric Disease

Metabolomics methods often encounter trade-offs between quantification accuracy and coverage, with truly comprehensive coverage only attainable through a multitude of complementary assays. Due to the lack of standardization and the variety of metabolomics assays, it is difficult to integrate datasets across studies or assays. To inform metabolomics platform selection, with a focus on posttraumatic stress disorder (PTSD), we review platform use and sample sizes in psychiatric metabolomics studies and then evaluate five prominent metabolomics platforms for coverage and performance, including intra-/inter-assay precision, accuracy, and linearity. We found performance was variable between metabolite classes, but comparable across targeted and untargeted approaches. Within all platforms, precision and accuracy were highly variable across classes, ranging from 0.9-63.2% (coefficient of variation) and 0.6-99.1% for accuracy to reference plasma. Several classes had high inter-assay variance, potentially impeding dissociation of a biological signal, including glycerophospholipids, organooxygen compounds, and fatty acids. Coverage was platform-specific and ranged from 16-70% of PTSD-associated metabolites. Non-overlapping coverage is challenging; however, benefits of applying multiple metabolomics technologies must be weighed against cost, biospecimen availability, platform-specific normative levels, and challenges in merging datasets. Our findings and open-access cross-platform dataset can inform platform selection and dataset integration based on platform-specific coverage breadth/overlap and metabolite-specific performance.

Pre-deployment risk factors for PTSD in active-duty personnel deployed to Afghanistan: a machine-learning approach for analyzing multivariate predictors

Active-duty Army personnel can be exposed to traumatic warzone events and are at increased risk for developing post-traumatic stress disorder (PTSD) compared with the general population. PTSD is associated with high individual and societal costs, but identification of predictive markers to determine deployment readiness and risk mitigation strategies is not well understood. This prospective longitudinal naturalistic cohort study-the Fort Campbell Cohort study-examined the value of using a large multidimensional dataset collected from soldiers prior to deployment to Afghanistan for predicting post-deployment PTSD status. The dataset consisted of polygenic, epigenetic, metabolomic, endocrine, inflammatory and routine clinical lab markers, computerized neurocognitive testing, and symptom self-reports. The analysis was computed on active-duty Army personnel (N = 473) of the 101st Airborne at Fort Campbell, Kentucky. Machine-learning models predicted provisional PTSD diagnosis 90-180 days post deployment (random forest: AUC = 0.78, 95% CI = 0.67-0.89, sensitivity = 0.78, specificity = 0.71; SVM: AUC = 0.88, 95% CI = 0.78-0.98, sensitivity = 0.89, specificity = 0.79) and longitudinal PTSD symptom trajectories identified with latent growth mixture modeling (random forest: AUC = 0.85, 95% CI = 0.75-0.96, sensitivity = 0.88, specificity = 0.69; SVM: AUC = 0.87, 95% CI = 0.79-0.96, sensitivity = 0.80, specificity = 0.85). Among the highest-ranked predictive features were pre-deployment sleep quality, anxiety, depression, sustained attention, and cognitive flexibility. Blood-based biomarkers including metabolites, epigenomic, immune, inflammatory, and liver function markers complemented the most important predictors. The clinical prediction of post-deployment symptom trajectories and provisional PTSD diagnosis based on pre-deployment data achieved high discriminatory power. The predictive models may be used to determine deployment readiness and to determine novel pre-deployment interventions to mitigate the risk for deployment-related PTSD.

Serum brain-derived neurotrophic factor remains elevated after long term follow-up of combat veterans with chronic post-traumatic stress disorder

Attempts to correlate blood levels of brain-derived neurotrophic factor (BDNF) with post-traumatic stress disorder (PTSD) have provided conflicting results. Some studies found a positive association between BDNF and PTSD diagnosis and symptom severity, while others found the association to be negative. The present study investigated whether serum levels of BDNF are different cross-sectionally between combat trauma-exposed veterans with and without PTSD, as well as whether longitudinal changes in serum BDNF differ as a function of PTSD diagnosis over time. We analyzed data of 270 combat trauma-exposed veterans (230 males, 40 females, average age: 33.29 ± 8.28 years) and found that, at the initial cross-sectional assessment (T0), which averaged 6 years after the initial exposure to combat trauma (SD=2.83 years), the PTSD positive group had significantly higher serum BDNF levels than the PTSD negative controls [31.03 vs. 26.95 ng/mL, t(268) = 3.921, p < 0.001]. This difference remained significant after excluding individuals with comorbid major depressive disorder, antidepressant users and controlling for age, gender, race, BMI, and time since trauma. Fifty-nine of the male veterans who participated at the first timepoint (T0) were re-assessed at follow-up evaluation (T1), approximately 3 years (SD=0.88 years) after T0. A one-way ANOVA comparing PTSD positive, "subthreshold PTSD" and control groups revealed that serum BDNF remained significantly higher in the PTSD positive group than the control group at T1 [30.05 vs 24.66 ng/mL, F(2, 56)= 3.420, p = 0.040]. Serum BDNF levels did not correlate with PTSD symptom severity at either time point within the PTSD group [r(128) = 0.062, p = 0.481 and r(28) = 0.157, p = 0.407]. Serum BDNF did not significantly change over time within subjects [t(56) = 1.269, p = 0.210] nor did the change of serum BDNF from T0 to T1 correlate with change in PTSD symptom severity within those who were diagnosed with PTSD at T0 [r(27) = -0.250, p = 0.192]. Our longitudinal data are the first to be reported in combat PTSD and suggest that higher serum BDNF levels may be a stable biological characteristic of chronic combat PTSD independent of symptom severity.

Longitudinal genome-wide methylation study of PTSD treatment using prolonged exposure and hydrocortisone

Epigenetic changes are currently invoked as explanations for both the chronicity and tenacity of post-traumatic stress disorder (PTSD), a heterogeneous condition showing varying, sometimes idiosyncratic responses to treatment. This study evaluated epigenetic markers in the context of a randomized clinical trial of PTSD patients undergoing prolonged-exposure psychotherapy with and without a hydrocortisone augmentation prior to each session. The purpose of the longitudinal epigenome-wide analyses was to identify predictors of recovery (from pretreatment data) or markers associated with symptom change (based on differences between pre- and post-therapy epigenetic changes). The results of these analyses identified the CREB-BDNF signaling pathway, previously linked to startle reaction and fear learning and memory processes, as a convergent marker predicting both symptom change and severity. Several previous-reported resilience markers were also identified (FKBP5, NR3C1, SDK1, and MAD1L1) to associate with PTSD recovery in this study. Especially, the methylation levels of FKBP5 in the gene body region decreased significantly as CAPS score decreased in responders, while no changes occurred in nonresponders. These biomarkers may have future utility in understanding clinical recovery in PTSD and potential applications in predicting treatment effects.

Socio-demographic and trauma-related predictors of PTSD within 8 weeks of a motor vehicle collision in the AURORA study

This is the initial report of results from the AURORA multisite longitudinal study of adverse post-traumatic neuropsychiatric sequelae (APNS) among participants seeking emergency department (ED) treatment in the aftermath of a traumatic life experience. We focus on n = 666 participants presenting to EDs following a motor vehicle collision (MVC) and examine associations of participant socio-demographic and participant-reported MVC characteristics with 8-week posttraumatic stress disorder (PTSD) adjusting for pre-MVC PTSD and mediated by peritraumatic symptoms and 2-week acute stress disorder (ASD). Peritraumatic Symptoms, ASD, and PTSD were assessed with self-report scales. Eight-week PTSD prevalence was relatively high (42.0%) and positively associated with participant sex (female), low socioeconomic status (education and income), and several self-report indicators of MVC severity. Most of these associations were entirely mediated by peritraumatic symptoms and, to a lesser degree, ASD, suggesting that the first 2 weeks after trauma may be a uniquely important time period for intervening to prevent and reduce risk of PTSD. This observation, coupled with substantial variation in the relative strength of mediating pathways across predictors, raises the possibility of diverse and potentially complex underlying biological and psychological processes that remain to be elucidated with more in-depth analyses of the rich and evolving AURORA data.

A review on post-traumatic stress disorder (PTSD): "Symptoms, Therapies and Recent Case Studies"

Post-traumatic stress disorder (PTSD), previously known as battle fatigue syndrome or shell shock, is a severe mental disturbance condition that is normally triggered by the experience of some frightening/scary events or trauma where a person undergoes some serious physical or mental harm or threatened. PTSD is a long-life effect of the continuous occurrence of traumatic conditions which, leading the production of feelings of helplessness, intense fear, and horror in the person. There are various examples of events that can cause PTSD, such as physical, mental, or sexual assault at home or working place by others, unexpected death of a loved one, an accidental event, war, or some kind of natural disaster. Treatment of PTSD includes the removal or reduction of these emotional feelings or symptoms with the aim to improve the daily life functioning of a person. Problems which are needed to be considered in case of PTSD like ongoing trauma, abusive or bad relationships. Various drugs which are used for the treatment of PTSD include selective serotonin reuptake inhibitors (SSRIs) (citalopram, fluvoxamine, fluoxetine, etc.); tricyclic antidepressants (amitriptyline and isocarboxazid); mood stabilizers (Divalproex and lamotrigine); atypical antipsychotics (aripiprazole and quetiapine), etc. In this review, we have covered the different risk factors, case studies related to various treatment options with different age group peoples in PTSD and their effects on them. We have also covered the symptoms and associated disorders which can play a key role in the development of PTSD.

Utilization of machine learning for identifying symptom severity military-related PTSD subtypes and their biological correlates

We sought to find clinical subtypes of posttraumatic stress disorder (PTSD) in veterans 6-10 years post-trauma exposure based on current symptom assessments and to examine whether blood biomarkers could differentiate them. Samples were males deployed to Iraq and Afghanistan studied by the PTSD Systems Biology Consortium: a discovery sample of 74 PTSD cases and 71 healthy controls (HC), and a validation sample of 26 PTSD cases and 36 HC. A machine learning method, random forests (RF), in conjunction with a clustering method, partitioning around medoids, were used to identify subtypes derived from 16 self-report and clinician assessment scales, including the clinician-administered PTSD scale for DSM-IV (CAPS). Two subtypes were identified, designated S1 and S2, differing on mean current CAPS total scores: S2 = 75.6 (sd 14.6) and S1 = 54.3 (sd 6.6). S2 had greater symptom severity scores than both S1 and HC on all scale items. The mean first principal component score derived from clinical summary scales was three times higher in S2 than in S1. Distinct RFs were grown to classify S1 and S2 vs. HCs and vs. each other on multi-omic blood markers feature classes of current medical comorbidities, neurocognitive functioning, demographics, pre-military trauma, and psychiatric history. Among these classes, in each RF intergroup comparison of S1, S2, and HC, multi-omic biomarkers yielded the highest AUC-ROCs (0.819-0.922); other classes added little to further discrimination of the subtypes. Among the top five biomarkers in each of these RFs were methylation, micro RNA, and lactate markers, suggesting their biological role in symptom severity.

Prior sleep problems and adverse post-traumatic neuropsychiatric sequelae of motor vehicle collision in the AURORA study

STUDY OBJECTIVES

Many patients in Emergency Departments (EDs) after motor vehicle collisions (MVCs) develop post-traumatic stress disorder (PTSD) or major depressive episode (MDE). This report from the AURORA study focuses on associations of pre-MVC sleep problems with these outcomes 8 weeks after MVC mediated through peritraumatic distress and dissociation and 2-week outcomes.

METHODS

A total of 666 AURORA patients completed self-report assessments in the ED and at 2 and 8 weeks after MVC. Peritraumatic distress, peritraumatic dissociation, and pre-MVC sleep characteristics (insomnia, nightmares, daytime sleepiness, and sleep duration in the 30 days before the MVC, trait sleep stress reactivity) were assessed retrospectively in the ED. The survey assessed acute stress disorder (ASD) and MDE at 2 weeks and at 8 weeks assessed PTSD and MDE (past 30 days). Control variables included demographics, MVC characteristics, and retrospective reports about PTSD and MDE in the 30 days before the MVC.

RESULTS

Prevalence estimates were 41.0% for 2-week ASD, 42.0% for 8-week PTSD, 30.5% for 2-week MDE, and 27.2% for 8-week MDE. Pre-MVC nightmares and sleep stress reactivity predicted 8-week PTSD (mediated through 2-week ASD) and MDE (mediated through the transition between 2-week and 8-week MDE). Pre-MVC insomnia predicted 8-week PTSD (mediated through 2-week ASD). Estimates of population attributable risk suggest that blocking effects of sleep disturbance might reduce prevalence of 8-week PTSD and MDE by as much as one-third.

CONCLUSIONS

Targeting disturbed sleep in the immediate aftermath of MVC might be one effective way of reducing MVC-related PTSD and MDE.

The impact of psychopathology, social adversity and stress-relevant DNA methylation on prospective risk for post-traumatic stress: A machine learning approach

BACKGROUND

A range of factors have been identified that contribute to greater incidence, severity, and prolonged course of post-traumatic stress disorder (PTSD), including: comorbid and/or prior psychopathology; social adversity such as low socioeconomic position, perceived discrimination, and isolation; and biological factors such as genomic variation at glucocorticoid receptor regulatory network (GRRN) genes. This complex etiology and clinical course make identification of people at higher risk of PTSD challenging. Here we leverage machine learning (ML) approaches to identify a core set of factors that may together predispose persons to PTSD.

METHODS

We used multiple ML approaches to assess the relationship among DNA methylation (DNAm) at GRRN genes, prior psychopathology, social adversity, and prospective risk for PTS severity (PTSS).

RESULTS

ML models predicted prospective risk of PTSS with high accuracy. The Gradient Boost approach was the top-performing model with mean absolute error of 0.135, mean square error of 0.047, root mean square error of 0.217, and R2 of 95.29%. Prior PTSS ranked highest in predicting the prospective risk of PTSS, accounting for >88% of the prediction. The top ranked GRRN CpG site was cg05616442, in AKT1, and the top ranked social adversity feature was loneliness.

CONCLUSION

Multiple factors including prior PTSS, social adversity, and DNAm play a role in predicting prospective risk of PTSS. ML models identified factors accounting for increased PTSS risk with high accuracy, which may help to target risk factors that reduce the likelihood or course of PTSD, potentially pointing to approaches that can lead to early intervention.

LIMITATION

One of the limitations of this study is small sample size.

Putative Blood Somatic Mutations in Post-Traumatic Stress Disorder-Symptomatic Soldiers: High Impact of Cytoskeletal and Inflammatory Proteins

Background: We recently discovered autism/intellectual disability somatic mutations in postmortem brains, presenting higher frequency in Alzheimer’s disease subjects, compared with the controls. We further revealed high impact cytoskeletal gene mutations, coupled with potential cytoskeleton-targeted repair mechanisms. Objective: The current study was aimed at further discerning if somatic mutations in brain diseases are presented only in the most affected tissue (the brain), or if blood samples phenocopy the brain, toward potential diagnostics. Methods: Variant calling analyses on an RNA-seq database including peripheral blood samples from 85 soldiers (58 controls and 27 with symptoms of post-traumatic stress disorder, PTSD) was performed. Results: High (e.g., protein truncating) as well as moderate impact (e.g., single amino acid change) germline and putative somatic mutations in thousands of genes were found. Further crossing the mutated genes with autism, intellectual disability, cytoskeleton, inflammation, and DNA repair databases, identified the highest number of cytoskeletal-mutated genes (187 high and 442 moderate impact). Most of the mutated genes were shared and only when crossed with the inflammation database, more putative high impact mutated genes specific to the PTSD-symptom cohorts versus the controls (14 versus 13) were revealed, highlighting tumor necrosis factor specifically in the PTSD-symptom cohorts. Conclusion: With microtubules and neuro-immune interactions playing essential roles in brain neuroprotection and Alzheimer-related neurodegeneration, the current mutation discoveries contribute to mechanistic understanding of PTSD and brain protection, as well as provide future diagnostics toward personalized military deployment strategies and drug design.

The opportunities and challenges of machine learning in the acute care setting for precision prevention of posttraumatic stress sequelae

Personalized medicine is among the most exciting innovations in recent clinical research, offering the opportunity for tailored screening and management at the individual level. Biomarker-enriched clinical trials have shown increased efficiency and informativeness in cancer research due to the selective exclusion of patients unlikely to benefit. In acute stress situations, clinically significant decisions are often made in time-sensitive manners and providers may be pressed to make decisions based on abbreviated clinical assessments. Up to 30% of trauma survivors admitted to the Emergency Department (ED) will develop long-lasting posttraumatic stress psychopathologies. The long-term impact of those survivors with posttraumatic stress sequelae are significant, impacting both long-term psychological and physiological recovery. An accurate prognostic model of who will develop posttraumatic stress symptoms does not exist yet. Additionally, no scalable and cost-effective method that can be easily integrated into routine care exists, even though especially the acute care setting provides a critical window of opportunity for prevention in the so-called golden hours when preventive measures are most effective. In this review, we aim to discuss emerging machine learning (ML) applications that are promising for precisely risk stratification and targeted treatments in the acute care setting. The aim of this narrative review is to present examples of digital health innovations and to discuss the potential of these new approaches for treatment selection and prevention of posttraumatic sequelae in the acute care setting. The application of artificial intelligence-based solutions have already had great success in other areas and are rapidly approaching the field of psychological care as well. New ways of algorithm-based risk predicting, and the use of digital phenotypes provide a high potential for predicting future risk of PTSD in acute care settings and to go new steps in precision psychiatry.

Epigenetic biotypes of post-traumatic stress disorder in war-zone exposed veteran and active duty males

Post-traumatic stress disorder (PTSD) is a heterogeneous condition evidenced by the absence of objective physiological measurements applicable to all who meet the criteria for the disorder as well as divergent responses to treatments. This study capitalized on biological diversity observed within the PTSD group observed following epigenome-wide analysis of a well-characterized Discovery cohort (N = 166) consisting of 83 male combat exposed veterans with PTSD, and 83 combat veterans without PTSD in order to identify patterns that might distinguish subtypes. Computational analysis of DNA methylation (DNAm) profiles identified two PTSD biotypes within the PTSD+ group, G1 and G2, associated with 34 clinical features that are associated with PTSD and PTSD comorbidities. The G2 biotype was associated with an increased PTSD risk and had higher polygenic risk scores and a greater methylation compared to the G1 biotype and healthy controls. The findings were validated at a 3-year follow-up (N = 59) of the same individuals as well as in two independent, veteran cohorts (N = 54 and N = 38), and an active duty cohort (N = 133). In some cases, for example Dopamine-PKA-CREB and GABA-PKC-CREB signaling pathways, the biotypes were oppositely dysregulated, suggesting that the biotypes were not simply a function of a dimensional relationship with symptom severity, but may represent distinct biological risk profiles underpinning PTSD. The identification of two novel distinct epigenetic biotypes for PTSD may have future utility in understanding biological and clinical heterogeneity in PTSD and potential applications in risk assessment for active duty military personnel under non-clinician-administered settings, and improvement of PTSD diagnostic markers.

[A Review of Research Progress in the Pathophysiological Mechanism of Stress-related Mental Disorders]

Stress can improve an individual's ability to adapt to environmental changes. However, excessive stress can induce stress-related mental disorders, including anxiety disorder, depression disorder and post-traumatic stress disorder (PTSD). Stress can regulate the level of hormones and immune inflammation in the body through the brain network, neural circuits, hypothalamic-pituitary-adrenal axis and the sympathetic nervous system, thereby causing the occurrence of mental disorders. In addition, stress can mediate the occurrence of mental disorders by regulating molecular changes in the level of genes, transcription, protein and metabolism, etc. Studies have shown that the brain-gut axis also plays an important role in the pathogenesis of stress-related mental disorders. However, the pathophysiological mechanism of stress-related mental disorders remains unclear. Besides, studies have also shown that the onset of stress-related mental disorders is closely associated with the individual's physiological and psychological qualities,which has a cross-talk with other mental and physical diseases as well. Therefore, it is important to study individual premorbid diathesis clinical, and to conduct clinical medical, basic medical, and psychological studies of the different stages of the disease, so as to obtain further understanding of the pathogenesis of stress-related mental disorders.

The Greater Houston Area Bipolar Registry-Clinical and Neurobiological Trajectories of Children and Adolescents With Bipolar Disorders and High-Risk Unaffected Offspring

The aims of this article are to discuss the rationale, design, and procedures of the Greater Houston Area Bipolar Registry (HBR), which aims at contributing to the effort involved in the investigation of neurobiological mechanisms underlying bipolar disorder (BD) as well as to identify clinical and neurobiological markers able to predict BD clinical course. The article will also briefly discuss examples of other initiatives that have made fundamental contributions to the field. This will be a longitudinal study with participants aged 6-17 at the time of enrollment. Participants will be required to meet diagnostic criteria for BD, or to be offspring of a parent with BD. We will also enroll healthy controls. Besides clinical information, which includes neurocognitive performance, participants will be asked to provide blood and saliva samples as well as to perform neuroimaging exams at baseline and follow-ups. Several studies point to the existence of genetic, inflammatory, and brain imaging alterations between individuals at higher genetic risk for BD compared with healthy controls. Longitudinal designs have shown high conversion rates to BD among high-risk offspring, with attempts to identify clinical predictors of disease onset, as well as clarifying the burden associated with environmental stressors. The HBR will help in the worldwide effort investigating the clinical course and neurobiological mechanisms of affected and high-risk children and adolescents with BD.

Challenges of post-traumatic stress disorder (PTSD) in Iraq: biochemical network and methodologies. A brief review

Post-traumatic stress disorder (PTSD) is a multifaceted syndrome due to its complex pathophysiology. Signals of illness include alterations in genes, proteins, cells, tissues, and organism-level physiological modifications. Specificity of sensitivity to PTSD suggests that response to trauma depend on gender and type of adverse event being experienced. Individuals diagnosed with PTSD represent a heterogeneous group, as evidenced by differences in symptoms, course, and response to treatment. It is clear that the biochemical mechanisms involved in PTSD need to be elucidated to identify specific biomarkers. A brief review of the recent literature in Pubmed was made to explore the major biochemical mechanisms involved in PTSD and the methodologies applied in the assessment of the disease. PTSD shows pre-exposure vulnerability factors in addition to trauma-induced alterations. The disease was found to be associated with dysfunctions of the hypothalamic-pituitary-adrenal axis (HPA) and hypothalamus-pituitary-thyroid axis. Sympathetic nervous system (SNS) activity play a role in PTSD by releasing norepinephrine and epinephrine. Cortisol release from the adrenal cortex amplifies the SNS response. Cortisol levels in PTSD patients, especially women, are later reduced by a negative feedback mechanism which contributes to neuroendocrine alterations and promotes structural changes in the brain leading to PTSD. Gender differences in normal HPA responsiveness may be due to an increased vulnerability in women to PTSD. Serotonin and dopamine levels were found to be abnormal in the presence of PTSD. Mechanisms such as the induction of neuroinflammation and alterations of mitochondrial energy processing were also associated with PTSD.

Virtual Reality-Based Treatment for Military Members and Veterans With Combat-Related Posttraumatic Stress Disorder: Protocol for a Multimodular Motion-Assisted Memory Desensitization and Reconsolidation Randomized Controlled Trial

Background

Military members are at elevated risk of operational stress injuries, including posttraumatic stress disorder (PTSD) and moral injury. Although psychotherapy can reduce symptoms, some military members may experience treatment-resistant PTSD. Multimodular motion-assisted memory desensitization and reconsolidation (3MDR) has been introduced as a virtual reality (VR) intervention for military members with PTSD related to military service. The 3MDR intervention incorporates exposure therapy, psychotherapy, eye movement desensitization and reconsolidation, VR, supportive counselling, and treadmill walking.

Objective

The objective of this study is to investigate whether 3MDR reduces PTSD symptoms among military members with combat-related treatment-resistant PTSD (TR-PTSD); examine the technology acceptance and usability of the Computer Assisted Rehabilitation ENvironment (CAREN) and 3MDR interventions by Canadian Armed Forces service members (CAF-SMs), veterans, 3MDR clinicians, and operators; and evaluate the impact on clinicians and operators of delivering 3MDR.

Methods

This is a mixed-methods waitlist controlled crossover design randomized controlled trial. Participants include both CAF-SMs and veterans (N=40) aged 18-60 years with combat-related TR-PTSD (unsuccessful experience of at least 2 evidence-based trauma treatments). Participants will also include clinicians and operators (N=12) who have been trained in 3MDR and subsequently utilized this intervention with patients. CAF-SMs and veterans will receive 6 weekly 90-minute 3MDR sessions. Quantitative and qualitative data will be collected at baseline and at 1, 3, and 6 months postintervention. Quantitative data collection will include multiomic biomarkers (ie, blood and salivary proteomic and genomic profiles of neuroendocrine, immune-inflammatory mediators, and microRNA), eye tracking, electroencephalography, and physiological data. Data from outcome measures will capture self-reported symptoms of PTSD, moral injury, resilience, and technology acceptance and usability. Qualitative data will be collected from audiovisual recordings of 3MDR sessions and semistructured interviews. Data analysis will include univariate and multivariate approaches, and thematic analysis of treatment sessions and interviews. Machine learning analysis will be included to develop models for the prediction of diagnosis, symptom severity, and treatment outcomes.

Results

This study commenced in April 2019 and is planned to conclude in April 2021. Study results will guide the further evolution and utilization of 3MDR for military members with TR-PTSD and will have utility in treating other trauma-affected populations.

Conclusions

The goal of this study is to utilize qualitative and quantitative primary and secondary outcomes to provide evidence for the effectiveness and feasibility of 3MDR for treating CAF-SMs and veterans with combat-related TR-PTSD. The results will inform a full-scale clinical trial and stimulate development and adaptation of the protocol to mobile VR apps in supervised clinical settings. This study will add to knowledge of the clinical effectiveness of 3MDR, and provide the first comprehensive analysis of biomarkers, technology acceptance and usability, moral injury, resilience, and the experience of clinicians and operators delivering 3MDR.

Trial Registration

ISRCTN Registry 11264368; http://www.isrctn.com/ISRCTN11264368.

International Registered Report Identifier (IRRID)

DERR1-10.2196/20620

Novel Pharmacological Targets for Combat PTSD-Metabolism, Inflammation, The Gut Microbiome, and Mitochondrial Dysfunction

INTRODUCTION

Current pharmacological treatments of post-traumatic stress disorder (PTSD) have limited efficacy. Although the diagnosis is based on psychopathological criteria, it is frequently accompanied by somatic comorbidities and perhaps "accelerated biological aging," suggesting widespread physical concomitants. Such physiological comorbidities may affect core PTSD symptoms but are rarely the focus of therapeutic trials.

METHODS

To elucidate the potential involvement of metabolism, inflammation, and mitochondrial function in PTSD, we integrate findings and mechanistic models from the DOD-sponsored "Systems Biology of PTSD Study" with previous data on these topics.

RESULTS

Data implicate inter-linked dysregulations in metabolism, inflammation, mitochondrial function, and perhaps the gut microbiome in PTSD. Several inadequately tested targets of pharmacological intervention are proposed, including insulin sensitizers, lipid regulators, anti-inflammatories, and mitochondrial biogenesis modulators.

CONCLUSIONS

Systemic pathologies that are intricately involved in brain functioning and behavior may not only contribute to somatic comorbidities in PTSD, but may represent novel targets for treating core psychiatric symptoms.

Feasibility and Efficacy of a Resiliency Intervention for the Prevention of Chronic Emotional Distress Among Survivor-Caregiver Dyads Admitted to the Neuroscience Intensive Care Unit: A Randomized Clinical Trial

IMPORTANCE

To our knowledge, there are no evidence-based interventions to prevent chronic emotional distress (ie, depression, anxiety, and posttraumatic stress [PTS]) in critical care survivors and their informal caregivers.

OBJECTIVE

To determine the feasibility and preliminary effect of the novel dyadic resiliency intervention Recovering Together (RT) on reducing symptoms of depression, anxiety, and PTS among hospitalized patients and their informal caregivers.

DESIGN, SETTING, AND PARTICIPANTS

This single-blind, pilot randomized clinical trial of RT vs an educational control was conducted among 58 dyads in which either the survivor or caregiver endorsed clinically significant symptoms of depression, anxiety, or PTS. The study was conducted in the neuroscience intensive care unit at Massachusetts General Hospital. Data were collected from September 2019 to March 2020.

INTERVENTIONS

Both RT and control programs had 6 sessions (2 at bedside and 4 via live video after discharge), and both survivor and caregiver participated together.

MAIN OUTCOMES AND MEASURES

The primary outcomes were feasibility of recruitment and intervention delivery, credibility, and satisfaction. The secondary outcomes included depression and anxiety (measured by the Hospital Depression and Anxiety Scale), PTS (measured by the PTSD Checklist-Civilian Version), and intervention targets (ie, mindfulness, measured by the Cognitive and Affective Mindfulness Scale-Revised; coping, measured by the Measure of Current Status-Part A; and dyadic interpersonal interactions, measured by the Dyadic Relationship Scale). Main outcomes and targets were assessed at baseline, 6 weeks, and 12 weeks.

RESULTS

The 58 dyads were randomized to RT (29 dyads [50.0%]; survivors: mean [SD] age, 49.3 [16.7] years; 9 [31.0%] women; caregivers: mean [SD] age, 52.4 [14.3] years; 22 [75.9%] women) or control (29 dyads [50.0%]; survivors: mean [SD] age, 50.3 [16.4] years; 12 [41.3%] women; caregivers, mean [SD] age, 52.1 [14.9], 17 [58.6%] women). Feasibility (recruitment [76%], randomization [100%], and data collection [83%-100%]), adherence (86%), fidelity (100%; κ = 0.98), satisfaction (RT: 57 of 58 [98%] with scores >6; control: 58 of 58 [100%] with scores >6), credibility (RT: 47 of 58 [81%] with scores >6; control: 46 of 58 [80%] with scores >6), and expectancy (RT: 49 of 58 [85%] with scores >13.5; 51 of 58 [87%] with scores >13.5) exceeded benchmarks set a priori. Participation in RT was associated with statistically and clinically significant improvement between baseline and postintervention in symptoms of depression (among survivors: -4.0 vs -0.6; difference, -3.4; 95% CI, -5.6 to -1.3; P = .002; among caregivers: -3.8 vs 0.6; difference, -4.5; 95% CI, -6.7 to -2.3; P < .001), anxiety (among survivors: -6.0 vs 0.3; difference, -6.3; 95% CI, -8.8 to -3.8; P < .001; among caregivers: -5.0 vs -0.9; difference, -4.1; 95% CI, -6.7 to -1.5, P = .002), and PTS (among survivors: -11.3 vs 1.0; difference, -12.3; 95% CI, -18.1 to -6.5, P < .001; among caregivers, -11.4 vs 5.0; difference, -16.4, 95% CI, -21.8 to -10.9; P < .001). Improvements sustained through the 12-week follow-up visit. We also observed RT-dependent improvement in dyadic interpersonal interactions for survivors (0.2 vs -0.2; difference, 0.4; 95% CI, 0.0 to 0.8; P = .04).

CONCLUSIONS AND RELEVANCE

In this pilot randomized clinical trial, RT was feasible and potentially efficacious in preventing chronic emotional distress in dyads of survivors of the neuroscience intensive care unit and their informal caregivers.

TRIAL REGISTRATION

ClinicalTrials.gov Identifier: NCT03694678.

Current methods for stress marker detection in saliva

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Introduction of relevant biomarkers in stress conditions.

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Reference ranges of biomarkers in normal conditions.

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Saliva as easy-accessible specimen.

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Review of analytical methods for biomarker determination in saliva.

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Possibilities for design of point-of-care devices.

Stress and stress-related diseases are leading to drastic consequences in private and professional life. Therefore, the need for stress prevention strategies is of personal and economic interest. Especially during the recent period related to covid-19 outbreak and lock-down, an ongoing discussion of increasing stress etiology is reported. Biomarker analysis may help to assist diagnosis and classification of stress-related diseases and therefore support therapeutical decisions. Due to its non-invasive sampling, the analysis of saliva has become highly attractive compared to the detection methods in other specimen. This review article summarizes the status of research, innovative approaches, and trends. Scientific literature published since 2011 was excerpted with concentration on the detection of up to seven promising marker substances. Most often reported cortisol represents the currently best evaluated stress marker, while norepinephrine (noradrenaline) or its metabolite 3-methoxy-4-hydroxyphenylglycol is also a quite commonly considered stress marker. Other complementary stress marker candidates are testosterone, dehydroepiandrosterone (DHEA) and its sulfonated analogue DHEA-S, alpha-amylase, secretory immunoglobulin A, and chromogranin A. Several working groups are researching in the field of stress marker detection to develop reliable, fast, and affordable methods. Analytical methods reported mainly focused on immunological and electrochemical as well as chromatographic methods hyphenated to mass spectrometric detection to yield the required detection limits.