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Despite ample experimental data indicating a role of inflammatory mediators in the behavioral and neurobiological manifestations elicited by exposure to physical and psychologic stressors, causative associations between systemic low-grade inflammation and central nervous system inflammatory processes in posttraumatic stress disorder (PTSD) patients remain largely conceptual. As in other stress-related disorders, pro-inflammatory activity may play an equivocal role in PTSD pathophysiology, one that renders indiscriminate employment of anti-inflammatory agents of questionable relevance. In fact, as several pieces of preclinical and clinical research convergingly suggest, timely and targeted potentiation rather than inhibition of inflammatory responses may actually be beneficial in patients who are characterized by suppressed microglia function in the face of systemic low-grade inflammation. The deleterious impact of chronic stress-associated inflammation on the systemic level may, thus, need to be held in context with the - often not readily apparent - adaptive payoffs of low-grade inflammation at the tissue level.

BackgroundThe Collaborative Outcome study on Health and Functioning during Infection Times (COH-FIT; www.coh-fit.com) is an anonymous and global online survey measuring health and functioning during the COVID-19 pandemic. The aim of this study was to test concurrently the validity of COH-FIT items and the internal validity of the co-primary outcome, a composite psychopathology "P-score".MethodsThe COH-FIT survey has been translated into 30 languages (two blind forward-translations, consensus, one independent English back-translation, final harmonization). To measure mental health, 1-4 items ("COH-FIT items") were extracted from validated questionnaires (e.g. Patient Health Questionnaire 9). COH-FIT items measured anxiety, depressive, post-traumatic, obsessive-compulsive, bipolar and psychotic symptoms, as well as stress, sleep and concentration. COH-FIT Items which correlated r ≥ 0.5 with validated companion questionnaires, were initially retained. A P-score factor structure was then identified from these items using exploratory factor analysis (EFA) and confirmatory factor analyses (CFA) on data split into training and validation sets. Consistency of results across languages, gender and age was assessed.ResultsFrom >150,000 adult responses by May 6th, 2022, a subset of 22,456 completed both COH-FIT items and validated questionnaires. Concurrent validity was consistently demonstrated across different languages for COH-FIT items. CFA confirmed EFA results of five first-order factors (anxiety, depression, post-traumatic, psychotic, psychophysiologic symptoms) and revealed a single second-order factor P-score, with high internal reliability (ω = 0.95). Factor structure was consistent across age and sex.ConclusionsCOH-FIT is a valid instrument to globally measure mental health during infection times. The P-score is a valid measure of multidimensional mental health.

Abstract Schizophrenia (SCZ) is a chronic, severe, and complex psychiatric disorder that affects all aspects of personal functioning. While SCZ has a very strong biological component, there are still no objective diagnostic tests. Lately, special attention has been given to epigenetic biomarkers in SCZ. In this study, we introduce a three-step, automated machine learning (AutoML)-based, data-driven, biomarker discovery pipeline approach, using genome-wide DNA methylation datasets and laboratory validation, to deliver a highly performing, blood-based epigenetic biosignature of diagnostic clinical value in SCZ. Publicly available blood methylomes from SCZ patients and healthy individuals were analyzed via AutoML, to identify SCZ-specific biomarkers. The methylation of the identified genes was then analyzed by targeted qMSP assays in blood gDNA of 30 first-episode drug-naïve SCZ patients and 30 healthy controls (CTRL). Finally, AutoML was used to produce an optimized disease-specific biosignature based on patient methylation data combined with demographics. AutoML identified a SCZ-specific set of novel gene methylation biomarkers including IGF2BP1, CENPI, and PSME4. Functional analysis investigated correlations with SCZ pathology. Methylation levels of IGF2BP1 and PSME4, but not CENPI were found to differ, IGF2BP1 being higher and PSME4 lower in the SCZ group as compared to the CTRL group. Additional AutoML classification analysis of our experimental patient data led to a five-feature biosignature including all three genes, as well as age and sex, that discriminated SCZ patients from healthy individuals [AUC 0.755 (0.636, 0.862) and average precision 0.758 (0.690, 0.825)]. In conclusion, this three-step pipeline enabled the discovery of three novel genes and an epigenetic biosignature bearing potential value as promising SCZ blood-based diagnostics.

Abstract The main goal driving this work is to develop computer-aided classification models relying on clinical data to identify coronary artery disease (CAD) instances with high accuracy while incorporating the expert’s opinion as input, making it a "man-in-the-loop" approach. CAD is traditionally diagnosed in a definite manner by Invasive Coronary Angiography (ICA). A dataset was created using biometric and clinical data from 571 patients (21 total features, 43% ICA-confirmed CAD instances) along with the expert’s diagnostic yield. Five machine learning classification algorithms were applied to the dataset. For the selection of the best feature set for each algorithm, three different parameter selection algorithms were used. Each ML model’s performance was evaluated using common metrics, and the best resulting feature set for each is presented. A stratified ten-fold validation was used for the performance evaluation. This procedure was run both using the assessments of experts/doctors as input and without them. The significance of this paper lies in its innovative approach of incorporating the expert's opinion as input in the classification process, making it a "man-in-the-loop" approach. This approach not only increases the accuracy of the models but also provides an added layer of explainability and transparency, allowing for greater trust and confidence in the results. Maximum achievable accuracy, sensitivity, and specificity are 83.02%, 90.32%, and 85.49% when using the expert's diagnosis as input, compared to 78.29%, 76.61%, and 86.07% without the expert's diagnosis. The results of this study demonstrate the potential for this approach to improve the diagnosis of CAD and highlight the importance of considering the role of human expertise in the development of computer-aided classification models.

Increasing evidence supports a role for the vaginal microbiome (VM) in the severity of HPV infection and its potential link to cervical intraepithelial neoplasia. However, a lot remains unclear regarding the precise role of certain bacteria in the context of HPV positivity and persistence of infection. Here, using next generation sequencing (NGS), we comprehensively profiled the VM in a series of 877 women who tested positive for at least one high risk HPV (hrHPV) type with the COBAS® 4,800 assay, after self-collection of a cervico-vaginal sample. Starting from gDNA, we PCR amplified the V3–V4 region of the bacterial 16S rRNA gene and applied a paired-end NGS protocol (Illumina). We report significant differences in the abundance of certain bacteria compared among different HPV-types, more particularly concerning species assigned to Lacticaseibacillus, Megasphaera and Sneathia genera. Especially for Lacticaseibacillus, we observed significant depletion in the case of HPV16, HPV18 versus hrHPVother. Overall, our results suggest that the presence or absence of specific cervicovaginal microbial genera may be linked to the observed severity in hrHPV infection, particularly in the case of HPV16, 18 types.

Primary hyperthyroidism (PHPT) is a common endocrine disorder characterized by hypercalcemia and elevated parathyroid hormone (PTH) levels. The most common cause is a single parathyroid adenoma, though the rest of the cases are due to multiglandular disease [double adenoma/hyperplasia]. The main focus driving this work is to develop a computer-aided classification model relying on clinical data to classify PHPT instances and, at the same time, offer explainability for the classification process. A highly imbalanced dataset was created using biometric and clinical data from 134 patients (six total features, 20.2% multiglandular instances). The features used by the current study are age, sex, max diameter index, number of deficiencies, Wisconsin index, and the reference variable indicating the type of PHPT. State-of-the-art machine learning (ML) classification algorithms were used in order to create trained prediction models and give predicted classifications based on all features/indexes. Of the ML models considered (Support Vector Machines, CatBoost, LightGBM, and AdaBoost), LightGBM was able to procure the best performing prediction model. Given the highly imbalanced nature of the particular dataset, oversampling was opted for, so as to increase prediction robustness for both classes. The ML model’s performance was then evaluated using common metrics and stratified ten-fold validation. The significance of this work is rooted in two axes: firstly, in the incorporation of oversampling to smooth out the highly imbalanced dataset and offer good prediction accuracy for both classes, and secondly, in offering an explainability aspect to an otherwise black-box ML prediction model. The maximum achievable accuracy for adenoma is 86.9% and for multigland disease 81.5%. Summarizing the above, this study demonstrates the potential for an ML approach to improve the diagnosis of PHPT and also highlights the importance of explainable artificial intelligence (AI).

The objective of this study was to investigate the influence of tannins and gallic acid on the salivary lubrication behavior. Furthermore, the effects of pH and mannoproteins in combination with gallic acid on the lubrication of saliva were studied. The addition of gallic acid and tannins were found to increase friction caused by the removal of the saliva film. Tannins resulted in higher friction compared to gallic acid. Lowering pH increased friction of gallic acid mixtures with saliva, due to stronger interactions between gallic acid and saliva. The increased friction caused by gallic acid was inhibited by the addition of mannoproteins due to the hydrogen bond interactions between gallic acid and mannoproteins, thereby decreasing the complex formation between gallic acid and salivary proteins. A correlation of 0.96 was found between the hydrodynamic diameter of the aggregate and the delta friction suggesting that the formation of aggregates determined the lubrication behavior.

In recent times, coronary artery disease (CAD) prediction and diagnosis have been the subject of many Medical decision support systems (MDSS) that make use of machine learning (ML) and deep learning (DL) algorithms. The common ground of most of these applications is that they function as black boxes. They reach a conclusion/diagnosis using multiple features as input; however, the user is oftentimes oblivious to the prediction process and the feature weights leading to the eventual prediction. The primary objective of this study is to enhance the transparency and comprehensibility of a black-box prediction model designed for CAD. The dataset employed in this research comprises biometric and clinical information obtained from 571 patients, encompassing 21 different features. Among the instances, 43% of cases of CAD were confirmed through invasive coronary angiography (ICA). Furthermore, a prediction model utilizing the aforementioned dataset and the CatBoost algorithm is analyzed to highlight its prediction making process and the significance of each input datum. State-of-the-art explainability mechanics are employed to highlight the significance of each feature, and common patterns and differences with the medical bibliography are then discussed. Moreover, the findings are compared with common risk factors for CAD, to offer an evaluation of the prediction process from the medical expert’s point of view. By depicting how the algorithm weights the information contained in features, we shed light on the black-box mechanics of ML prediction models; by analyzing the findings, we explore their validity in accordance with the medical literature on the matter.

The autonomic nervous system (ANS) is responsible for the precise regulation of tissue functions and organs and, thus, is crucial for optimal stress reactivity, adaptive responses and health in basic and challenged states (survival). The fine-tuning of central ANS activity relies on the internal central autonomic regulation system of the central autonomic network (CAN), while the peripheral activity relies mainly on the two main and interdependent peripheral ANS tracts, the sympathetic nervous system (SNS) and the parasympathetic nervous system (PNS). In disease, autonomic imbalance is associated with decreased dynamic adaptability and increased morbidity and mortality. Acute or prolonged autonomic dysregulation, as observed in stress-related disorders, affects CAN core centers, thereby altering downstream peripheral ANS function. One of the best established and most widely used non-invasive methods for the quantitative assessment of ANS activity is the computerized analysis of heart rate variability (HRV). HRV, which is determined by different methods from those used to determine the fluctuation of instantaneous heart rate (HR), has been used in many studies as a powerful index of autonomic (re)activity and an indicator of cardiac risk and ageing. Psychiatric patients regularly show altered autonomic function with increased HR, reduced HRV and blunted diurnal/circadian changes compared to the healthy state. The aim of this article is to provide basic knowledge on ANS function and (re)activity assessment and, thus, to support a much broader use of HRV as a valid, transdiagnostic and fully translational dynamic biomarker of stress system sensitivity and vulnerability to stress-related disorders in neuroscience research and clinical psychiatric practice. In particular, we review the functional levels of central and peripheral ANS control, the main neurobiophysiologic theoretical models (e.g., polyvagal theory, neurovisceral integration model), the precise autonomic influence on cardiac function and the definition and main aspects of HRV and its different measures (i.e., time, frequency and nonlinear domains). We also provide recommendations for the proper use of electrocardiogram recordings for HRV assessment in clinical and research settings and highlight pathophysiological, clinical and research implications for a better functional understanding of the neural and molecular mechanisms underlying healthy and malfunctioning brain–heart interactions in individual stress reactivity and psychiatric disorders.

This survey assessed psychiatry residents'/early-career psychiatrists' attitudes towards the utility of therapeutic drug monitoring (TDM) of antipsychotics. A previously developed questionnaire on attitudes on TDM utility during antipsychotic treatment was cross-sectionally disseminated by national coordinators between 01/01/2022–31/12/2023. The frequency of using TDM for antipsychotics other than clozapine was the main outcome in a linear regression analysis, including sex, clinical setting, caseload, and factors generated by an exploratory factor analysis. Comparisons between residents and early-career psychiatrists, respondents working in in- and outpatient settings, and low-/middle- and high–income countries were performed. Altogether, 1,237 respondents completed the survey, with 37.9% having never used TDM for antipsychotics. Seven factors explained 41% of response variance; six of them were associated with frequency of TDM use (p < 0.05). Items with highest loadings for factors included clinical benefits of TDM (factors A and E: 0.7), negative expectations for beliefs of patients towards TDM (factor B: 0.6–0.7), weak TDM scientific evidence (factor C: 0.8), and TDM availability (factor D: −0.8). Respondents from low-/middle-income countries were less likely to frequently/almost always use TDM compared to high-income countries (9.4% vs. 21.5%, p < 0.001). TDM use for antipsychotics was poor and associated with limited knowledge and insufficient availability. [ABSTRACT FROM AUTHOR]

The mission of the human stress system is the maintenance of homeostasis in the presence of real or perceived, acute or chronic stressors. The hypothalamic–pituitary–adrenal (HPA) axis and the autonomic nervous system (ANS) are the stress system-related neuroendocrine pathways. There is abundant evidence that children and adolescents with autism spectrum disorder (ASD) may exhibit atypical function within the HPA axis and the ANS both at the resting state and during the presence of social and/or non-social stressors. The aim of this review is to provide an up-to-date summary of the findings regarding stress system alterations in children and adolescents with ASD. We focus on the variations of stress hormones circadian rhythms, specifically cortisol and alpha-amylase (i.e., a surrogate index of epinephrine/norepinephrine secretion), and on the alterations of stress system responsivity to different stressors. Also, we present imaging and immunological findings that have been associated with stress system dysregulation in children and adolescents with ASD. Finally, we review the pivotal role of HPA axis-ANS coordination, the developmental trajectory of the stress system in ASD, and the possible role of early life stress in the dysregulation of the stress system demonstrated in children and adolescents with ASD. This synthesis will hopefully provide researchers with a foundation for an integrated approach to future research into stress system variations in children and adolescents with ASD.

Immune dysregulation is implicated in the pathophysiology of both bipolar and major depressive disorder, while immune cell ratios (IRCs) have recently been proposed as clinically applicable immune biomarkers. We investigated IRCs differences in affective disorders and their association with current mood episodes and clinical features. This retrospective cohort study analyzed neutrophil–lymphocyte (NLR), monocyte–lymphocyte (MLR), and platelet–lymphocyte (PLR) ratios upon admission in 135 affective disorder in-patients with mania (MA, n = 36), bipolar depression (BiD, n = 38), and unipolar depression (MDD, n = 61). Demographic, clinical, and immune data were extracted from medical records. Monocyte count was significantly higher in BiD compared to MDD (p < 0.001). Multivariable regression models suggested higher NLR in MA compared to MDD (p = 0.039), higher MLR in both MA and BiD compared to MDD (p < 0.001 and p = 0.004 respectively), while we found neither group differences in PLR nor an effect of type and duration of hospitalization, current psychotic, or suicidal features and psychiatric history on IRCs. Here, we show that IRCs are elevated in bipolar disorder versus MDD and affected by mood episode, while MLR could be especially valuable in the differential diagnosis between bipolar and unipolar depression. IRCs represent inexpensive, routinely accessible and clinically applicable biomarkers with diagnostic validity in affective disorders that could be easily implemented as illness activity indicators, to better follow the course of illness and eventually predict relapse or treatment response and, thus, guide therapeutic targeting.

Purpose of reviewThe human circadian system creates and maintains cellular and systemic rhythmicity essential to homeostasis. Loss of circadian rhythmicity fundamentally affects the neuroendocrine, immune and autonomic system, similar to chronic stress and, thus, may play a central role in the development of stress-related disorders. This article focuses on the role of circadian misalignment in the pathophysiology of posttraumatic stress disorder (PTSD).Recent findingsSleep disruption is a core feature of PTSD supporting the important supraordinate pathophysiological role of circadian system in PTSD. Furthermore, direct and indirect human and animal PTSD research suggests circadian system linked neuroendocrine, immune, metabolic and autonomic dysregulation with blunted diurnal rhythms, specific sleep pattern pathologies and cognitive deficits, as well as endocannabinoid and neuropeptide Y system alterations and altered circadian gene expression, linking circadian misalignment to PTSD pathophysiology.SummaryPTSD development is associated with chronodisruption findings. Evaluation and treatment of sleep and circadian disruption should be the first steps in PTSD management. State-of-the-art methods of circadian rhythm assessment should be applied to bridge the gap between clinical significance and limited understanding of the relationship between traumatic stress, sleep and circadian system.

ImportancePosttraumatic stress disorder (PTSD) has been associated in cross-sectional studies with peripheral inflammation. It is not known whether this observed association is the result of PTSD predisposing to inflammation (as sometimes postulated) or to inflammation predisposing to PTSD.ObjectiveTo determine whether plasma concentration of the inflammatory marker C-reactive protein (CRP) helps predict PTSD symptoms.Design, setting, and participantsThe Marine Resiliency Study, a prospective study of approximately 2600 war zone-deployed Marines, evaluated PTSD symptoms and various physiological and psychological parameters before deployment and at approximately 3 and 6 months following a 7-month deployment. Participants were recruited from 4 all-male infantry battalions imminently deploying to a war zone. Participation was requested of 2978 individuals; 2610 people (87.6%) consented and 2555 (85.8%) were included in the present analysis. Postdeployment data on combat-related trauma were included for 2208 participants (86.4% of the 2555 included) and on PTSD symptoms at 3 and 6 months after deployment for 1861 (72.8%) and 1617 (63.3%) participants, respectively.Main outcomes and measuresSeverity of PTSD symptoms 3 months after deployment assessed by the Clinician-Administered PTSD Scale (CAPS).ResultsWe determined the effects of baseline plasma CRP concentration on postdeployment CAPS using zero-inflated negative binomial regression (ZINBR), a procedure designed for distributions, such as CAPS in this study, that have an excess of zeroes in addition to being positively skewed. Adjusting for the baseline CAPS score, trauma exposure, and other relevant covariates, we found baseline plasma CRP concentration to be a highly significant overall predictor of postdeployment CAPS scores (P = .002): each 10-fold increment in CRP concentration was associated with an odds ratio of nonzero outcome (presence vs absence of any PTSD symptoms) of 1.51 (95% CI, 1.15-1.97; P = .003) and a fold increase in outcome with a nonzero value (extent of symptoms when present) of 1.06 (95% CI, 0.99-1.14; P = .09).ConclusionsAND RELEVANCE A marker of peripheral inflammation, plasma CRP may be prospectively associated with PTSD symptom emergence, suggesting that inflammation may predispose to PTSD.

Abstract Background HPV DNA Array is an E1-targeting PCR genotyping test, with capability of distinguishing 18 high-risk (16, 18, 26, 31, 33, 35, 39, 45, 51, 52, 53, 56, 58, 59, 66, 68, 73, 82) and 11 low-risk HPV types (6, 11, 40, 42, 44, 54, 67, 69, 70, 85, 97). HPV DNA Array uses multiplex PCR for E1-gene sequence amplification. The amplicons are detected and genotyped by reverse hybridization to immobilized DNA probes spotted as triplets in single 96 well-plate wells and read by AID ELISPOT reader. Methods Aim of the study was to evaluate the clinical performance of the assay against internationally accepted and FDA approved Cobas 4800 HPV test (Roche Diagnostics). Study population comprised of 500 cervical samples. Results HPV DNA Array demonstrated a very high sensitivity of 100% for CIN2+ and 100% for CIN3+ detection, same as Cobas 4800. HPV DNA Array showed greater sensitivity for CIN2+ detection than cytology (100% vs. 13.6%). The agreement to Cobas 4800 for HPV detection, irrespective of type, was 81.4% with κ = 0.613. The agreement for HPV 16 was 92.8% (κ = 0.929), and for HPV 18 54.2% (κ = 0.681). Conclusion HPV DNA Array demonstrated good clinical performance for detection of high-grade lesions, and may be considered for usage in a screening setting.

Major depressive disorder is a leading cause of disability worldwide and a major contributor to the overall global burden of disease. While there are several options for antidepressant treatment, only about 40–60% of patients respond to initial monotherapy, while 30–40% of patients may even show resistance to treatment. This article offers a narrative review of those studies evaluating the predictive properties of various blood-based baseline biomarkers regarding treatment responses to the pharmacological, stimulation, or behavioral treatment of patients with treatment-resistant depression (TRD). Our results show that overall, there is only a very limited number of studies assessing baseline peripheral biomarkers regarding treatment response in TRD. Although there is some evidence for the predictive significance of particular biomarkers (e.g., IL-6, CRP, BDNF), the majority of the results are either single-study reports or studies with conflicting results. This may contribute to the wide variety of treatment protocols and different TRD definition criteria, the small number of patients included, and the existence of different biological phenotypes of the disorder used within the various studies. Taken together, there does not yet appear to be any specific baseline peripheral biomarker with sufficient discriminative predictive validity that can be used in the routine clinical practice of TRD. The discovery of new biomarkers and the better clinical characterization of known biomarkers could support the better classification and staging of TRD, the development of personalized treatment algorithms with higher rates of remission and fewer side effects, and the development of new precision drugs for specific subgroups of patients.

Introduction There is a considerable association between major depressive disorder (MDD) and cardiovascular disease, most possibly relying on abnormalities in the autonomic nervous system (ANS)-related cardiac reactivity, although the exact underlying pathophysiological pathway is unclear. Objectives This study tends to shed some additional light on this background by investigating ANS reactivity in MDD with respect to previous depression history through an objective stress challenge paradigm. Methods The study assessed the effects of an overnight hypothalamus-pituitary-adrenal (HPA) axis stimulation with metyrapone (MET) on baseline ANS activity through linear and non-linear heart rate variability (HRV) measures in the morning of two continuous days in a group of 14 physically healthy, antidepressant-free patients with clinical, non-psychotic MDD, to investigate differences in autonomic reactivity with respect to prior MDD history. Results The main findings of this study include statistically significant time x group interactions with respect to several HRV measures, suggesting substantial differences on autonomic reactivity between patients with and without depression history. Hereby, recurrent-episode MDD patients showed lower vagal activity, while first-episode MDD patients increased PNS activity after HPA axis stimulation. Conclusions These findings indicate that HPA axis stimulation in MDD patients leads to inverse vagal response according to MDD history. We suggest that chronic stress system overactivation, as found in MDD, might lead to a progressive inversion of the original stress response through HPA axis and ANS divergence over the course of a recurrent illness. HRV could, thus, represent a significant biomarker in MDD with temporal sensitivity.

Introduction Major depressive disorder (MDD) constitutes the leading cause of disability worldwide. Although efficacious antidepressant pharmacotherapies exist for MDD, only about 40-60% of the patients respond to initial treatment. However, there is still a lack of robustly established and applicable biomarkers for antidepressant response in everyday clinical practice. Objectives This study targets the assessment of the vasopressin (AVP) surrogate marker Copeptin (CoP), as a potential peripheral hypothalamic-level biomarker of antidepressant treatment response in MDD. Methods We measured baseline and dynamic levels of plasma CoP along with plasma ACTH and cortisol (CORT) in drug-naive outpatients with MDD before and after overnight manipulation of the hypothalamic-pituitary-adrenal (HPA) axis [i.e., stimulation (metyrapone) and suppression (dexamethasone)] on three consecutive days and their association with treatment response to 4 weeks of escitalopram treatment. Results Our findings suggest significantly higher baseline and post-metyrapone plasma CoP levels in future non-responders, a statistically significant invert association between baseline CoP levels and probability of treatment response and a potential baseline plasma CoP cut-off level of above 2.9 pmol/L for future non-response screening. Baseline and dynamic plasma ACTH and CORT levels showed no association with treatment response. Conclusions This pilot study provide first evidence in humans that CoP may represent a novel, clinically easily applicable, endocrine biomarker of antidepressant response, based on a single-measurement, cut-off level. These findings, underline the role of the vasopressinergic system in the pathophysiology of MDD and may represent a significant new tool in the clinical and biological phenotyping of MDD enhancing individual-tailored therapies.

The human circadian system creates and maintains cellular and systemic rhythmicity essential for the temporal organization of physiological processes promoting homeostasis and environmental adaptation. Sleep disruption and loss of circadian rhythmicity fundamentally affects master homeostasic regulating systems at the crossroads of peripheral and central susceptibility pathways, similar to acute or chronic stress and, thus, may play a central role in the development of stress-related disorders. Direct and indirect human and animal PTSD research accordingly suggests circadian-system-linked sleep, neuroendocrine, immune, metabolic and autonomic dysregulation, linking circadian misalignment to PTSD pathophysiology. Additionally, there is evidence that sleep and circadian disruption may represent a vital pre-existing risk factor in the prediction of PTSD development, while sleep-related symptoms are among the most prominent in trauma-associated disorders. These facts may represent a need for a shift towards a more chronobiological understanding of traumatic sequel and could support better prevention, evaluation and treatment of sleep and circadian disruption as first steps in PTSD management. In this special issue, we highlight and review recent advances from human sleep and chronobiological research that enhances our understanding of the development and maintenance of trauma-related disorders.

Abstract Background Posttraumatic stress disorder (PTSD) is one of the most commonly observed stress-related conditions following combat exposure and its effective prevention is a high health-care priority. Reports of peritraumatic reactions have been shown to be highly associated with PTSD among combat exposed service members. However, existing instruments measuring peritraumatic symptoms were not specifically developed to assess combat-related peritraumatic stress and each demonstrates a different peritraumatic focus. We therefore developed the Peritraumatic Behavior Questionnaire (PBQ), a new military-specific rating scale focused upon the wide range of symptoms suggestive of combat-related peritraumatic distress in actively deployed Service Members. This study describes the development of the PBQ and reports on the psychometric properties of its self-rated version (PBQ-SR). Methods 688 Marine infantry service members were retrospectively assessed by the PBQ-SR within the scope of the Marine Resiliency Study after their deployment to war zone. Participants have been additionally assessed by a variety of questionnaires, as well as clinical interviews both pre and post-deployment. Results The PBQ-SR demonstrated satisfactory internal consistency, convergent and discriminant validity, as well as high correlation with trait dissociation prior to deployment. Component analysis suggested a latent bi-dimensional structure separating a peritraumatic emotional distress and physical awareness factor. The PBQ-SR total score showed high correlation to general anxiety, depression, poorer general health and posttraumatic symptoms after deployment and remained a significant predictor of PTSD severity, after controlling for those measures. The suggested screening cut-off score of 12 points demonstrated satisfactory predictive power. Conclusions This study confirms the ability of the PBQ-SR to unify the underlying peritraumatic symptom dimensions and reliably assess combat-related peritraumatic reaction as a general construct. The PBQ-SR demonstrated promise as a potential standard screening measure in military clinical practice, while It’s predictive power should be established in prospective studies.

Background: Humans have an evolutionary need for a well-preserved internal ‘clock’, adjusted to the 24-hour rotation period of our planet. This intrinsic circadian timing system enables the temporal organization of numerous physiologic processes, from gene expression to behaviour. The human circadian system is tightly and bidirectionally interconnected to the human stress system, as both systems regulate each other’s activity along the anticipated diurnal challenges. The understanding of the temporal relationship between stressors and stress responses is critical in the molecular pathophysiology of stress-and trauma-related diseases, such as posttraumatic stress disorder (PTSD). Objectives/Methods: In this narrative review, we present the functional components of the stress and circadian system and their multilevel interactions and discuss how traumatic stress can affect the harmonious interplay between the two systems. Results: Circadian dysregulation after trauma exposure (posttraumatic chronodisruption) may represent a core feature of trauma-related disorders mediating enduring neurobiological correlates of traumatic stress through a loss of the temporal order at different organizational levels. Posttraumatic chronodisruption may, thus, affect fundamental properties of neuroendocrine, immune and autonomic systems, leading to a breakdown of biobehavioral adaptive mechanisms with increased stress sensitivity and vulnerability. Given that many traumatic events occur in the late evening or night hours, we also describe how the time of day of trauma exposure can differentially affect the stress system and, finally, discuss potential chronotherapeutic interventions. Conclusion: Understanding the stress-related mechanisms susceptible to chronodisruption and their role in PTSD could deliver new insights into stress pathophysiology, provide better psychochronobiological treatment alternatives and enhance preventive strategies in stress-exposed populations.

BackgroundMajor depressive disorder (MDD) constitutes the leading cause of disability worldwide. Although efficacious antidepressant pharmacotherapies exist for MDD, only about 40–60% of the patients respond to initial treatment. However, there is still a lack of robustly established and applicable biomarkers for antidepressant response in everyday clinical practice.ObjectiveThis study targets the assessment of the vasopressin (AVP) surrogate marker Copeptin (CoP), as a potential peripheral hypothalamic-level biomarker of antidepressant treatment response in MDD.MethodsWe measured baseline and dynamic levels of plasma CoP along with plasma ACTH and cortisol (CORT) in drug-naive outpatients with MDD before and after overnight manipulation of the hypothalamic-pituitary-adrenal (HPA) axis [i.e., stimulation (metyrapone) and suppression (dexamethasone)] on three consecutive days and their association with treatment response to 4 weeks of escitalopram treatment.ResultsOur findings suggest significantly higher baseline and post-metyrapone plasma CoP levels in future non-responders, a statistically significant invert association between baseline CoP levels and probability of treatment response and a potential baseline plasma CoP cut-off level of above 2.9 pmol/L for future non-response screening. Baseline and dynamic plasma ACTH and CORT levels showed no association with treatment response.ConclusionsThis pilot study provide first evidence in humans that CoP may represent a novel, clinically easily applicable, endocrine biomarker of antidepressant response, based on a single-measurement, cut-off level. These findings, underline the role of the vasopressinergic system in the pathophysiology of MDD and may represent a significant new tool in the clinical and biological phenotyping of MDD enhancing individual-tailored therapies.