Discovery of serum biomarkers predicting development of a subsequent depressive episode in social anxiety disorder

The Current State and Validity of Digital Assessment Tools for Psychiatry: Systematic Review

BACKGROUND

Given the role digital technologies are likely to play in the future of mental health care, there is a need for a comprehensive appraisal of the current state and validity (ie, screening or diagnostic accuracy) of digital mental health assessments.

OBJECTIVE

The aim of this review is to explore the current state and validity of question-and-answer-based digital tools for diagnosing and screening psychiatric conditions in adults.

METHODS

This systematic review was based on the Population, Intervention, Comparison, and Outcome framework and was carried out in accordance with the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines. MEDLINE, Embase, Cochrane Library, ASSIA, Web of Science Core Collection, CINAHL, and PsycINFO were systematically searched for articles published between 2005 and 2021. A descriptive evaluation of the study characteristics and digital solutions and a quantitative appraisal of the screening or diagnostic accuracy of the included tools were conducted. Risk of bias and applicability were assessed using the revised tool for the Quality Assessment of Diagnostic Accuracy Studies 2.

RESULTS

A total of 28 studies met the inclusion criteria, with the most frequently evaluated conditions encompassing generalized anxiety disorder, major depressive disorder, and any depressive disorder. Most of the studies used digitized versions of existing pen-and-paper questionnaires, with findings revealing poor to excellent screening or diagnostic accuracy (sensitivity=0.32-1.00, specificity=0.37-1.00, area under the receiver operating characteristic curve=0.57-0.98) and a high risk of bias for most of the included studies.

CONCLUSIONS

The field of digital mental health tools is in its early stages, and high-quality evidence is lacking.

INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID)

RR2-10.2196/25382.

Alterations in brain synaptic proteins and mRNAs in mood disorders: a systematic review and meta-analysis of postmortem brain studies

The pathophysiological mechanisms underlying bipolar (BD) and major depressive disorders (MDD) are multifactorial but likely involve synaptic dysfunction and dysregulation. There are multiple synaptic proteins but three synaptic proteins, namely SNAP-25, PSD-95, and synaptophysin, have been widely studied for their role in synaptic function in human brain postmortem studies in BD and MDD. These studies have yielded contradictory results, possibly due to the small sample size and sourcing material from different cortical regions of the brain. We performed a systematic review and meta-analysis to understand the role of these three synaptic proteins and other synaptic proteins, messenger RNA (mRNA) and their regional localizations in BD and MDD. A systematic literature search was conducted and the review is reported in accordance with the MOOSE Guidelines. Meta-analysis was performed to compare synaptic marker levels between BD/MDD groups and controls separately. 1811 papers were identified in the literature search and screened against the preset inclusion and exclusion criteria. A total of 72 studies were screened in the full text, of which 47 were identified as eligible to be included in the systematic review. 24 of these 47 papers were included in the meta-analysis. The meta-analysis indicated that SNAP-25 protein levels were significantly lower in BD. On average, PSD-95 mRNA levels were lower in BD, and protein levels of SNAP-25, PSD-95, and syntaxin were lower in MDD. Localization analysis showed decreased levels of PSD-95 protein in the frontal cortex. We found specific alterations in synaptic proteins and RNAs in both BD and MDD. The review was prospectively registered online in PROSPERO international prospective register of systematic reviews, registration no. CRD42020196932.

Relationships Between Circulating Tenascin-C Levels and Gonadal Hormones in Male Patients with Depressive Disorder: A Retrospective, Cross-Sectional Study

OBJECTIVE

Tenascin-C (TNC) is an extracellular matrix glycoprotein closely associated with the progression of psychiatric disorders. The present study was performed to investigate the possible association between serum gonadal hormones and TNC levels in male patients with depressive disorder.

MATERIALS AND METHODS

We measured serum TNC levels by enzyme-linked immunosorbent assay. In addition, we investigated the influence of testosterone (T) and estradiol (E2) on TNC levels in primary neuronal cultures.

RESULTS

Patients with depression had lower levels of T, free tri-iodothyronine (FT3), thyroid-stimulating hormone (TSH), and the T/E2 ratio than healthy control patients. Levels of TNC and high-sensitivity C-reactive protein were significantly higher in patients than in healthy volunteers. Serum TNC concentrations were negatively associated with levels of E2 and T and with the T/E2 ratio. Levels of TNC, TSH, and FT3 and the T/E2 ratio were predictors of depression. Among men with depression, TNC was negatively associated with T levels and with the T/E2 ratio. Incubating pheochromocytoma 12 cells with the combination of T and E2 greatly decreased TNC levels in the culture medium.

CONCLUSION

Increased TNC levels may predict imbalance between T and E2 in patients with depression, and gonadal hormones may modulate TNC expression in vivo.

Toward an Extended Definition of Major Depressive Disorder Symptomatology: Digital Assessment and Cross-validation Study

Background

Diagnosing major depressive disorder (MDD) is challenging, with diagnostic manuals failing to capture the wide range of clinical symptoms that are endorsed by individuals with this condition.

Objective

This study aims to provide evidence for an extended definition of MDD symptomatology.

Methods

Symptom data were collected via a digital assessment developed for a delta study. Random forest classification with nested cross-validation was used to distinguish between individuals with MDD and those with subthreshold symptomatology of the disorder using disorder-specific symptoms and transdiagnostic symptoms. The diagnostic performance of the Patient Health Questionnaire–9 was also examined.

Results

A depression-specific model demonstrated good predictive performance when distinguishing between individuals with MDD (n=64) and those with subthreshold depression (n=140) (area under the receiver operating characteristic curve=0.89; sensitivity=82.4%; specificity=81.3%; accuracy=81.6%). The inclusion of transdiagnostic symptoms of psychopathology, including symptoms of depression, generalized anxiety disorder, insomnia, emotional instability, and panic disorder, significantly improved the model performance (area under the receiver operating characteristic curve=0.95; sensitivity=86.5%; specificity=90.8%; accuracy=89.5%). The Patient Health Questionnaire–9 was excellent at identifying MDD but overdiagnosed the condition (sensitivity=92.2%; specificity=54.3%; accuracy=66.2%).

Conclusions

Our findings are in line with the notion that current diagnostic practices may present an overly narrow conception of mental health. Furthermore, our study provides proof-of-concept support for the clinical utility of a digital assessment to inform clinical decision-making in the evaluation of MDD.

Predictors of social anxiety disorder with major depressive episodes among Japanese university students

Background

Social anxiety disorder (SAD) develops in the early teens and is a common disorder among university students. Understanding the predictive factors of SAD comorbid with major depressive episode (MDE) is important for student mental health care. The aim of this study was to identify the personality traits that predict SAD with MDE by analyzing longitudinal data of Japanese university students.

Methods

In this retrospective study, Japanese university students who visited the health care center of Hokkaido University for the first time were divided into the following four groups: “Control” (n = 43), “MDE” (n = 16), “SAD” (n = 28), and “SAD with MDE” (n = 61) based on the Patient Health Questionnaire-9 (PHQ-9), the Liebowitz Social Anxiety Scale, and core anxiety symptoms for SAD in the Mini International Neuropsychiatric Interview during screening. Predictors for SAD with MDE were identified by a four-group comparison of the Temperament and Character Inventory and PHQ-9 data previously obtained at the enrollment using analysis of variance and post-hoc tests.

Results

Upon comparing the four groups using analysis of variance, there were significant differences in the baseline PHQ-9 summary score, Harm-Avoidance (HA), and Self-Directedness (SD). According to results of the post-hoc test, all three showed a significant difference between the “Control” and “SAD with MDE.” Furthermore, there was a significant difference in HA scores between “SAD” and “Control.” In all the groups, the mean time from enrollment to the first visit to the center was >2 years.

Conclusion

A higher HA score at baseline is a predictor of SAD with or without MDE. Higher PHQ-9 summary and lower SD scores at baseline are predictive factors of SAD with MDE.

Exposure-induced changes of plasma metabolome and gene expression in patients with panic disorder

BACKGROUND

Anxiety disorders including panic disorder (PD) are the most prevalent psychiatric diseases leading to high disability and burden in the general population. Acute panic attacks are distinctive for PD but also frequent in other anxiety disorders. The neurobiology or specific molecular changes leading to and present during panic attacks are insufficiently known so far.

METHODS

In the present pilot study, we investigated dynamic metabolomic and gene expression changes in peripheral blood of patients with PD (n = 25) during two exposure-induced acute panic attacks.

RESULTS

The results show that the metabolite glyoxylate was dynamically regulated in peripheral blood. Additionally, glyoxylate levels were associated with basal anxiety levels and showed gender-related differences at baseline. As glyoxylate is part of the degradation circuit of cholecystokinin, this suggests that this neuropeptide might be directly involved in exposure-induced panic attacks. Only gene expression changes of very small magnitude were observed in this experimental setting.

CONCLUSIONS

From this first metabolome and gene expression study in exposure-induced acute panic attacks in PD we conclude that metabolites can potentially serve as dynamic markers for different anxiety states. However, these findings have to be replicated in cohorts with greater sample sizes.

Major depressive disorder is associated with changes in a cluster of serum and urine biomarkers

Major Depressive Disorder (MDD) is a heterogeneous disorder with a considerable symptomatic overlap with other psychiatric and somatic disorders. This study aims at providing evidence for association of a set of serum and urine biomarkers with MDD. We analyzed urine and serum samples of 40 MDD patients and 47 age- and sex-matched controls using 40 potential MDD biomarkers (21 serum biomarkers and 19 urine biomarkers). All participants were of Caucasian origin. We developed an algorithm to combine the heterogeneity at biomarker level. This method enabled the identification of correlating biomarkers based on differences in variation and distribution between groups, combined the outcome of the selected biomarkers, and calculated depression probability scores (the "bio depression score"). Phenotype permutation analysis showed a significant discrimination between MDD and euthymic (control) subjects for biomarkers in urine (P < .001), in serum (P = .02) and in the combined serum plus urine result (P < .001). Based on this algorithm, a combination of 8 urine biomarkers and 9 serum biomarkers were identified to correlate with MDD, enabling an area under the curve (AUC) of 0.955 in a Receiver Operating Characteristic (ROC) analysis. Selection of either urine biomarkers or serum biomarkers resulted in AUC values of 0.907 and 0.853, respectively. Internal cross-validation (5-fold) confirmed the association of this set of biomarkers with MDD.

Proteomic Studies of Psychiatric Disorders

Many diseases result from programming effects in utero. This chapter describes recent advances in proteomic studies which have improved our understanding of the underlying pathophysiological pathways in the major psychiatric disorders, resulting in the development of potential novel biomarker tests. Such tests should be based on measurement of blood-based proteins given the ease of accessibility of this medium and the known connections between the periphery and the central nervous system. Most importantly, emerging biomarker tests should be developed on lab-on-a-chip and other handheld devices to enable point-of-care use. This should help to identify individuals with psychiatric disorders much sooner than ever before, which will allow more rapid treatment options for the best possible patient outcomes.

Parallel changes in serum proteins and diffusion tensor imaging in methamphetamine-associated psychosis

Methamphetamine-associated psychosis (MAP) involves widespread neurocognitive and molecular deficits, however accurate diagnosis remains challenging. Integrating relationships between biological markers, brain imaging and clinical parameters may provide an improved mechanistic understanding of MAP, that could in turn drive the development of better diagnostics and treatment approaches. We applied selected reaction monitoring (SRM)-based proteomics, profiling 43 proteins in serum previously implicated in the etiology of major psychiatric disorders, and integrated these data with diffusion tensor imaging (DTI) and psychometric measurements from patients diagnosed with MAP (N = 12), methamphetamine dependence without psychosis (MA; N = 14) and healthy controls (N = 16). Protein analysis identified changes in APOC2 and APOH, which differed significantly in MAP compared to MA and controls. DTI analysis indicated widespread increases in mean diffusivity and radial diffusivity delineating extensive loss of white matter integrity and axon demyelination in MAP. Upon integration, several co-linear relationships between serum proteins and DTI measures reported in healthy controls were disrupted in MA and MAP groups; these involved areas of the brain critical for memory and social emotional processing. These findings suggest that serum proteomics and DTI are sensitive measures for detecting pathophysiological changes in MAP and describe a potential diagnostic fingerprint of the disorder.

Making Sense of Blood-Based Proteomics and Metabolomics in Psychiatric Research

This manuscript describes the basics of proteomic and metabolic profiling of blood serum and plasma from patients with psychiatric disorders. It will also explain the rationale behind the use of these bodily fluids, due to the need for user-friendly and rapid tests in clinics with simple sampling procedures. It has become evident over the last 15 years or so that psychiatric disorders are whole-body diseases and the bloodstream is a means of molecular transport that therefore provides a conduit for two-way communication with the brain. Here we also describe some of the basic biomarker findings from studies of serum or plasma from patients with psychiatric disorders like schizophrenia, major depression, and bipolar disorder. Finally, we will discuss potential future advancements in this area, which include the development of hand-held devices containing miniature proteomic and metabolic assays which can be used for facilitating diagnoses in a point-of-care setting and yield results in less than 15 minutes from a single drop of blood.

Serum biomarkers predictive of depressive episodes in panic disorder

Panic disorder with or without comorbid agoraphobia (PD/PDA) has been linked to an increased risk to develop subsequent depressive episodes, yet the underlying pathophysiology of these disorders remains poorly understood. We aimed to identify a biomarker panel predictive for the development of a depressive disorder (major depressive disorder and/or dysthymia) within a 2-year-follow-up period. Blood serum concentrations of 165 analytes were evaluated in 120 PD/PDA patients without depressive disorder baseline diagnosis (6-month-recency) in the Netherlands Study of Depression and Anxiety (NESDA). We assessed the predictive performance of serum biomarkers, clinical, and self-report variables using receiver operating characteristics curves (ROC) and the area under the ROC curve (AUC). False-discovery-rate corrected logistic regression model selection of serum analytes and covariates identified an optimal predictive panel comprised of tetranectin and creatine kinase MB along with patient gender and scores from the Inventory of Depressive Symptomatology (IDS) rating scale. Combined, an AUC of 0.87 was reached for identifying the PD/PDA patients who developed a depressive disorder within 2 years (n = 44). The addition of biomarkers represented a significant (p = 0.010) improvement over using gender and IDS alone as predictors (AUC = 0.78). For the first time, we report on a combination of biological serum markers, clinical variables and self-report inventories that can detect PD/PDA patients at increased risk of developing subsequent depressive disorders with good predictive performance in a naturalistic cohort design. After an independent validation our proposed biomarkers could prove useful in the detection of at-risk PD/PDA patients, allowing for early therapeutic interventions and improving clinical outcome.