Prospective biomarkers of major depressive disorder: a systematic review and meta-analysis

Ginkgo biloba extract protects against depression-like behavior in mice through regulating gut microbial bile acid metabolism

Depression is a mental disorder with high morbidity, disability and relapse rates. Ginkgo biloba extract (GBE), a traditional Chinese medicine, has a long history of clinical application in the treatment of cerebral and mental disorders, but the key mechanism remains incompletely understood. Here we showed that GEB exerted anti-depressant effect in mice through regulating gut microbial metabolism. GBE protected against unpredictable mild stress (UMS)-induced despair, anxiety-like and social avoidance behavior in mice without sufficient brain distribution. Fecal microbiome transplantation transmitted, while antibiotic cocktail abrogated the protective effect of GBE. Spatiotemporal bacterial profiling and metabolomics assay revealed a potential involvement of Parasutterella excrementihominis and the bile acid metabolite ursodeoxycholic acid (UDCA) in the effect of GBE. UDCA administration induced depression-like behavior in mice. Together, these findings suggest that GBE acts on gut microbiome-modulated bile acid metabolism to alleviate stress-induced depression.

Modulation of resting-state functional connectivity in default mode network is associated with the long-term treatment outcome in major depressive disorder

BACKGROUND

Treatment non-response and recurrence are the main sources of disease burden in major depressive disorder (MDD). However, little is known about its neurobiological mechanism concerning the brain network changes accompanying pharmacotherapy. The present study investigated the changes in the intrinsic brain networks during 6-month antidepressant treatment phase associated with the treatment response and recurrence in MDD.

METHODS

Resting-state functional magnetic resonance imaging was acquired from untreated patients with MDD and healthy controls at baseline. The patients' depressive symptoms were monitored by using the Hamilton Rating Scale for Depression (HAMD). After 6 months of antidepressant treatment, patients were re-scanned and followed up every 6 months over 2 years. Traditional statistical analysis as well as machine learning approaches were conducted to investigate the longitudinal changes in macro-scale resting-state functional network connectivity (rsFNC) strength and micro-scale resting-state functional connectivity (rsFC) associated with long-term treatment outcome in MDD.

RESULTS

Repeated measures of the general linear model demonstrated a significant difference in the default mode network (DMN) rsFNC change before and after the 6-month antidepressant treatment between remitters and non-remitters. The difference in the rsFNC change over the 6-month antidepressant treatment between recurring and stable MDD was also specific to DMN. Machine learning analysis results revealed that only the DMN rsFC change successfully distinguished non-remitters from the remitters at 6 months and recurring from stable MDD during the 2-year follow-up.

CONCLUSION

Our findings demonstrated that the intrinsic DMN connectivity could be a unique and important target for treatment and recurrence prevention in MDD.

Decreased step count prior to the first visit for MDD treatment: a retrospective, observational, longitudinal cohort study of continuously measured walking activity obtained from smartphones

Introduction

Major depressive disorder (MDD) is a common debilitating psychiatric condition and a major cause of productivity loss in workers. Using intermittent, subjective indicators, previous studies have shown that physical activity can predict lower levels of depressive symptoms. However, there is an unmet need for continuous and objective measures to identify MDD development before it results in productivity loss. The aim of this study was to elucidate the association between continuously measured walking activity and the development of MDD.

Methods

This retrospective, observational, longitudinal cohort study used health insurance claims data. Individuals aged 20-74 years were included if they had a record of MDD diagnosis and daily step count data for the 60 days before and after the first recorded MDD-related visit, which was defined as the index date. Multivariate analysis was conducted to compare 7-day moving averages of step counts on each day of the analysis period with the mean step count on the index date. Joinpoint regression analysis was used to determine when the trajectory of the moving step count average changed (inflection point).

Results

In total, 2,143 patients with a mean age of 41.2 (standard deviation [SD]: 10.6) years were included. The majority of patients were men (69.5%) and employed full-time (94.1%). Antidepressants were prescribed for 59.2% of patients. The 7-day moving average step count decreased from 6,310 (SD: 3758) at day -60 to 5,879 (SD: 3183) at the index date (first recorded MDD-related visit), and then increased to 6,062 (SD: 4029) at day +60. Compared with the index date, the 7-day moving average of step counts was significantly higher at days -60 to -1, +23 to +33, and + 42 to +60, and significantly lower at days +2 and + 3. Joinpoint regression analysis of 7-day moving average step counts from day -60 to day 0 identified an inflection point at day -14.

Conclusion

In working-age Japanese people, a formal diagnosis of MDD was preceded by a notable decline in daily step counts by approximately 2 weeks. MDD diagnosis and (presumed) treatment were followed by a gradual increase in daily step counts.

Serum Levels of S100B Protein and Myelin Basic Protein as a Potential Biomarkers of Recurrent Depressive Disorders

Nowadays, nervous tissue damage proteins in serum are considered promising drug targets and biomarkers of Mood Disorders. In a cross-sectional naturalistic study, the S100B, MBP and GFAP levels in the blood serum were compared between two diagnostic groups (patients with Depressive Episode (DE, n = 28) and patients with Recurrent Depressive Disorder (RDD, n = 21)), and healthy controls (n = 25). The diagnostic value of serum markers was assessed by ROC analysis. In the DE group, we did not find changed levels of S100B, MBP and GFAP compared with controls. In the RDD group, we found decreased S100B level (p = 0.011) and increased MBP level (p = 0.015) in comparison to those in healthy controls. Provided ROC analysis indicates that MBP contributes to the development of a DE (AUC = 0.676; 95%Cl 0.525-0.826; p = 0.028), and S100B and MBP have a significant effect on the development of RDD (AUC = 0.732; 95%Cl 0.560-0.903; p = 0.013 and AUC = 0.712; 95%Cl 0.557-0.867; p = 0.015, correspondingly). The study of serum markers of nervous tissue damage in patients with a current DE indicates signs of disintegration of structural and functional relationships, dysfunction of gliotransmission, and impaired secretion of neurospecific proteins. Modified functions of astrocytes and oligodendrocytes are implicated in the pathophysiology of RDD.

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.

Corticosteroids as Selective and Effective Modulators of Glycine Receptors

The mechanism of the negative impact of corticosteroids on the induction and progress of mental illness remains unclear. In this work, we studied the effects of corticosteroids on the activity of neuronal glycine receptors (GlyR) and GABA-A receptors (GABAAR) by measuring the chloride current induced by the application of GABA (2 or 5 μM) to isolated cerebellar Purkinje cells (IGABA) and by the application of glycine (100 μM) to pyramidal neurons of the rat hippocampus (IGly). It was found that corticosterone, 5α-dihydrodeoxycorticosterone, allotetrahydrocorticosterone, cortisol, and 17α,21-dihydroxypregnenolone were able to accelerate the desensitization of the IGly at physiological concentrations (IC50 values varying from 0.39 to 0.72 μM). Next, cortisone, 11-deoxycortisol, 11-deoxycorticosterone, 5β-dihydrodeoxycorticosterone, and tetrahydrocorticosterone accelerated the desensitization of IGly with IC50 values varying from 10.3 to 15.2 μM. Allotetrahydrocorticosterone and tetrahydrocorticosterone potentiated the IGABA albeit with high EC50 values (18-23 μM). The rest of the steroids had no effect on IGABA in the range of concentrations of 1-100 μM. Finally, our study has suggested a structural relationship of the 3β-hydroxyl group/3-oxo group with the selective modulatory activity on GlyRs in contrast to the 3α-hydroxyl group that is pivotal for GABAARs. In summary, our results suggest that increased GlyR desensitization by corticosteroids may contribute to brain dysfunction under chronic stress and identify corticosteroids for further development as selective modulators of GlyRs.

Building an Early Warning System for Depression: Rationale, Objectives, and Methods of the WARN-D Study

Background

Depression is common, debilitating, often chronic, and affects young people disproportionately. Given that only 50% of patients improve under initial treatment, experts agree that prevention is the most effective way to change depression's global disease burden. The biggest barrier to successful prevention is to identify individuals at risk for depression in the near future. To close this gap, this protocol paper introduces the WARN-D study, our effort to build a personalized early warning system for depression.

Method

To develop the system, we follow around 2,000 students over 2 years. Stage 1 comprises an extensive baseline assessment in which we collect a broad set of predictors for depression. Stage 2 lasts 3 months and zooms into participants' daily experiences that may predict depression; we use smartwatches to collect digital phenotype data such as sleep and activity, and we use a smartphone app to query participants about their experiences 4 times a day and once every Sunday. In Stage 3, we follow participants for 21 months, assessing transdiagnostic outcomes (including stress, functional impairment, anxiety, and depression) as well as additional predictors for future depression every 3 months. Collected data will be utilized to build a personalized prediction model for depression onset.

Discussion

Overall, WARN-D will function similarly to a weather forecast, with the core difference that one can only seek shelter from a thunderstorm and clean up afterwards, while depression may be successfully prevented before it occurs.

Effects of physical activity in child and adolescent depression and anxiety: role of inflammatory cytokines and stress-related peptide hormones

Depression and anxiety are the most common mental illnesses affecting children and adolescents, significantly harming their well-being. Research has shown that regular physical activity can promote cognitive, emotional, fundamental movement skills, and motor coordination, as a preventative measure for depression while reducing the suicide rate. However, little is known about the potential role of physical activity in adolescent depression and anxiety. The studies reviewed in this paper suggest that exercise can be an effective adjunctive treatment to improve depressive and anxiety symptoms in adolescents, although research on its neurobiological effects remains limited.

Major depressive disorder

Major depressive disorder (MDD) is characterized by persistent depressed mood, loss of interest or pleasure in previously enjoyable activities, recurrent thoughts of death, and physical and cognitive symptoms. People with MDD can have reduced quality of life owing to the disorder itself as well as related medical comorbidities, social factors, and impaired functional outcomes. MDD is a complex disorder that cannot be fully explained by any one single established biological or environmental pathway. Instead, MDD seems to be caused by a combination of genetic, environmental, psychological and biological factors. Treatment for MDD commonly involves pharmacological therapy with antidepressant medications, psychotherapy or a combination of both. In people with severe and/or treatment-resistant MDD, other biological therapies, such as electroconvulsive therapy, may also be offered.

Optimizing Treatment for Major Depressive Disorder in Adolescents: The Impact of Intradermal Acupuncture - A Randomized Controlled Trial Protocol

Background

Major depressive disorder (MDD) exhibits a pronounced occurrence among adolescents, aligning closely with the lifetime prevalence rate of 16.6% observed in adults. It is difficult to treat and prone to recurrence. Acupuncture has shown potential in enhancing treatment effectiveness. Nonetheless, there is a lack of research on the use of intradermal acupuncture (IA) in treating adolescent MDD.

Methods

This study is a double-blind, randomized controlled trial. A cohort of 120 participants will be assigned randomly to three distinct groups, namely a Selective Serotonin Reuptake Inhibitors (SSRIs)-only group, a sham intradermal acupuncture combined with SSRIs (SIA) group, and an active intradermal acupuncture combined with SSRIs (AIA) group. Hamilton Depression Rating Scale will serve as the primary outcome, while Patient Health Questionnaire-9, Self-Rating Depression Scale, Pittsburgh Sleep Quality Index, and Short Form 36 Questionnaire will serve as secondary outcomes in assessing the amelioration of depressive symptoms in patients. These data will be analyzed using SPSS26.0 software.

Results

We will assess the efficacy and safety of IA for MDD using commonly employed clinical psychiatric scales.

Conclusion

The efficacy of IA in treating adolescent MDD may be demonstrated in this study, suggesting its potential for optimizing MDD treatment schemes.

Trial Registration

ClinicalTrials.gov Identifier: NCT05832619 (April 27, 2023).

The connexin hemichannel inhibitor D4 produces rapid antidepressant-like effects in mice

Depression is a common mood disorder characterized by a range of clinical symptoms, including prolonged low mood and diminished interest. Although many clinical and animal studies have provided significant insights into the pathophysiology of depression, current treatment strategies are not sufficient to manage this disorder. It has been suggested that connexin (Cx)-based hemichannels are candidates for depression intervention by modifying the state of neuroinflammation. In this study, we investigated the antidepressant-like effect of a recently discovered selective Cx hemichannel inhibitor, a small organic molecule called D4. We first showed that D4 reduced hemichannel activity following systemic inflammation after LPS injections. Next, we found that D4 treatment prevented LPS-induced inflammatory response and depressive-like behaviors. These behavioral effects were accompanied by reduced astrocytic activation and hemichannel activity in depressive-like mice induced by repeated low-dose LPS challenges. D4 treatment also reverses depressive-like symptoms in mice subjected to chronic restraint stress (CRS). To test whether D4 broadly affected neural activity, we measured c-Fos expression in depression-related brain regions and found a reduction in c-Fos+ cells in different brain regions. D4 significantly normalized CRS-induced hypoactivation in several brain regions, including the hippocampus, entorhinal cortex, and lateral septum. Together, these results indicate that blocking Cx hemichannels using D4 can normalize neuronal activity and reduce depressive-like symptoms in mice by reducing neuroinflammation. Our work provides evidence of the antidepressant-like effect of D4 and supports glial Cx hemichannels as potential therapeutic targets for depression.

Fingernail Cortisol: A Biological Signal of Lifetime Major Depressive Disorder

INTRODUCTION

Elevated levels of the hypothalamic-pituitary-adrenal axis hormone cortisol are a frequently replicated finding in major depressive disorder (MDD). However, the current state of research is inconclusive as to whether hypercortisolism represents a trait- or state-like biological signal of MDD. The aim of the present study was to investigate, for the first time, whether cortisol in fingernails, a highly accessible tissue, could distinguish currently remitted individuals with MDD from healthy controls. A further aim was to identify potential confounders of nail cortisol.

METHODS

A total of N = 100 individuals from the general population were recruited. A structured clinical interview was administered, which resulted in two groups: n = 48 with lifetime MDD and n = 52 healthy controls. All participants answered questions on sociodemographic, lifestyle, and psychosocial characteristics. They also grew their nails for 14 days and cut them for the subsequent determination of cortisol.

RESULTS

The groups differed in their nail cortisol concentrations, such that the individuals with lifetime MDD had significantly higher concentrations than the healthy controls (p = 0.041). Within the group of individuals with lifetime MDD, the number of experienced episodes was significantly correlated with cortisol (p = 0.011). Income emerged as the only significant confounder of cortisol (p = 0.008).

CONCLUSION

Elevated fingernail cortisol appears to be a biological signal of MDD, even in the absence of a current major depressive episode. Its high accessibility and robustness render it a promising methodology for remote research as well as for the integration of biomarkers into clinical research and practice.

The serotonin theory of depression: a systematic umbrella review of the evidence

The serotonin hypothesis of depression is still influential. We aimed to synthesise and evaluate evidence on whether depression is associated with lowered serotonin concentration or activity in a systematic umbrella review of the principal relevant areas of research. PubMed, EMBASE and PsycINFO were searched using terms appropriate to each area of research, from their inception until December 2020. Systematic reviews, meta-analyses and large data-set analyses in the following areas were identified: serotonin and serotonin metabolite, 5-HIAA, concentrations in body fluids; serotonin 5-HT1A receptor binding; serotonin transporter (SERT) levels measured by imaging or at post-mortem; tryptophan depletion studies; SERT gene associations and SERT gene-environment interactions. Studies of depression associated with physical conditions and specific subtypes of depression (e.g. bipolar depression) were excluded. Two independent reviewers extracted the data and assessed the quality of included studies using the AMSTAR-2, an adapted AMSTAR-2, or the STREGA for a large genetic study. The certainty of study results was assessed using a modified version of the GRADE. We did not synthesise results of individual meta-analyses because they included overlapping studies. The review was registered with PROSPERO (CRD42020207203). 17 studies were included: 12 systematic reviews and meta-analyses, 1 collaborative meta-analysis, 1 meta-analysis of large cohort studies, 1 systematic review and narrative synthesis, 1 genetic association study and 1 umbrella review. Quality of reviews was variable with some genetic studies of high quality. Two meta-analyses of overlapping studies examining the serotonin metabolite, 5-HIAA, showed no association with depression (largest n = 1002). One meta-analysis of cohort studies of plasma serotonin showed no relationship with depression, and evidence that lowered serotonin concentration was associated with antidepressant use (n = 1869). Two meta-analyses of overlapping studies examining the 5-HT1A receptor (largest n = 561), and three meta-analyses of overlapping studies examining SERT binding (largest n = 1845) showed weak and inconsistent evidence of reduced binding in some areas, which would be consistent with increased synaptic availability of serotonin in people with depression, if this was the original, causal abnormaly. However, effects of prior antidepressant use were not reliably excluded. One meta-analysis of tryptophan depletion studies found no effect in most healthy volunteers (n = 566), but weak evidence of an effect in those with a family history of depression (n = 75). Another systematic review (n = 342) and a sample of ten subsequent studies (n = 407) found no effect in volunteers. No systematic review of tryptophan depletion studies has been performed since 2007. The two largest and highest quality studies of the SERT gene, one genetic association study (n = 115,257) and one collaborative meta-analysis (n = 43,165), revealed no evidence of an association with depression, or of an interaction between genotype, stress and depression. The main areas of serotonin research provide no consistent evidence of there being an association between serotonin and depression, and no support for the hypothesis that depression is caused by lowered serotonin activity or concentrations. Some evidence was consistent with the possibility that long-term antidepressant use reduces serotonin concentration.

A leaky umbrella has little value: evidence clearly indicates the serotonin system is implicated in depression

A recent “umbrella” review examined various biomarkers relating to the serotonin system, and concluded there was no consistent evidence implicating serotonin in the pathophysiology of depression. We present reasons for why this conclusion is overstated, including methodological weaknesses in the review process, selective reporting of data, over-simplification, and errors in the interpretation of neuropsychopharmacological findings. We use the examples of tryptophan depletion and serotonergic molecular imaging, the two research areas most relevant to the investigation of serotonin, to illustrate this.

The pharmacological bases for repurposing statins in depression: a review of mechanistic studies

Statins are commonly prescribed medications widely investigated for their potential actions on the brain and mental health. Pre-clinical and clinical evidence suggests that statins may play a role in the treatment of depressive disorders, but only the latter has been systematically assessed. Thus, the physiopathological mechanisms underlying statins' putative antidepressant or depressogenic effects have not been established. This review aims to gather available evidence from mechanistic studies to strengthen the pharmacological basis for repurposing statins in depression. We used a broad, well-validated search strategy over three major databases (Pubmed/MEDLINE, Embase, PsychINFO) to retrieve any mechanistic study investigating statins' effects on depression. The systematic search yielded 8068 records, which were narrowed down to 77 relevant papers. The selected studies (some dealing with more than one bodily system) described several neuropsychopharmacological (44 studies), endocrine-metabolic (17 studies), cardiovascular (6 studies) and immunological (15 studies) mechanisms potentially contributing to the effects of statins on mood. Numerous articles highlighted the beneficial effect of statins on depression, particularly through positive actions on serotonergic neurotransmission, neurogenesis and neuroplasticity, hypothalamic-pituitary axis regulation and modulation of inflammation. The role of other mechanisms, especially the association between statins, lipid metabolism and worsening of depressive symptoms, appears more controversial. Overall, most mechanistic evidence supports an antidepressant activity for statins, likely mediated by a variety of intertwined processes involving several bodily systems. Further research in this area can benefit from measuring relevant biomarkers to inform the selection of patients most likely to respond to statins' antidepressant effects while also improving our understanding of the physiopathological basis of depression.

Depression among Patients with an Implanted Left Ventricular Assist Device: Uncovering Pathophysiological Mechanisms and Implications for Patient Care

Depression is a common and devastating mental illness associated with increased morbidity and mortality, partially due to elevated rates of suicidal attempts and death. Select patients with end-stage heart failure on a waiting-list for a donor heart undergo left ventricular assist device (LVAD) implantation. The LVAD provides a circulatory flow of oxygenated blood to the body, mimicking heart functionality by operating on a mechanical technique. LVAD improves functional capacity and survivability among patients with end-stage heart failure. However, accumulating data suggests that LVAD recipients suffer from an increased incidence of depression and suicide attempts. There is scarce knowledge regarding the pathological mechanism and appropriate treatment approach for depressed LVAD patients. This article summarizes the current evidence on the association between LVAD implantation and occurrence of depression, suggesting possible pathological mechanisms underlying the device-associated depression and reviewing the current treatment strategies. The summarized data underscores the need for a rigorous pre-(LVAD)-implantation psychiatric evaluation, continued post-implantation mental health assessment, and administration of antidepressant treatment as necessary.

Multi-modal MRI measures reveal sensory abnormalities in major depressive disorder patients: A surface-based study

BACKGROUND

Multi-modal magnetic resonance imaging (MRI) measures are supposed to be able to capture different brain neurobiological aspects of major depressive disorder (MDD). A fusion analysis of structural and functional modalities may better reveal the disease biomarker specific to the MDD disease.

METHODS

We recruited 30 MDD patients and 30 matched healthy controls (HC). For each subject, we acquired high-resolution brain structural images and resting-state fMRI (rs-fMRI) data using a 3 T MRI scanner. We first extracted the brain morphometric measures, including the cortical volume (CV), cortical thickness (CT), and surface area (SA), for each subject from the structural images, and then detected the structural clusters showing significant between-group differences in each measure using the surface-based morphology (SBM) analysis. By taking the identified structural clusters as seeds, we performed seed-based functional connectivity (FC) analyses to determine the regions with abnormal FC in the patients. Based on a logistic regression model, we performed a classification analysis by selecting these structural and functional cluster-wise measures as features to distinguish the MDD patients from the HC.

RESULTS

The MDD patients showed significantly lower CV in a cluster involving the right superior temporal gyrus (STG) and middle temporal gyrus (MTG), and lower SA in three clusters involving the bilateral STG, temporal pole gyrus, and entorhinal cortex, and the left inferior temporal gyrus, and fusiform gyrus, than the controls. No significant difference in CT was detected between the two groups. By taking the above-detected clusters as seeds to perform the seed-based FC analysis, we found that the MDD patients showed significantly lower FC between STG/MTG (CV's cluster) and two clusters located in the bilateral visual cortices than the controls. The logistic regression model based on the structural and functional features reached a classification accuracy of 86.7% (p < 0.001) between MDD and controls.

CONCLUSION

The present study showed sensory abnormalities in MDD patients using the multi-modal MRI analysis. This finding may act as a disease biomarker distinguishing MDD patients from healthy individuals.

Ubrogepant and rimegepant: signal detection using spontaneous reports of adverse events from the Food and Drug Administration Adverse Event Reporting System

BACKGROUND

In this study, we fill this gap in knowledge by updating the safety profile of ubrogepant and rimegepant via disproportionality analysis in the Food and Drug Administration (FDA) Adverse Event Reporting System (FAERS), a US-based database registering spontaneous reports.

RESEARCH DESIGN AND METHODS

ASCII files of quarterly extraction of FAERS data were downloaded from the FDA website up to the 3rd quarter (Q3) of 2021 (last accessed 03/02/2022). Disproportionality analysis was done using the Reporting Odds Ratio (ROR) as a disproportionality measure. RORs of all AEs related to ubrogepant and rimegepant in FAERS were calculated in comparison with those related to erenumab. Drug-event pairs with a frequency ≤ 2, were removed according to European Medicine Agency (EMA)'s procedures.

RESULTS

In total, 2010 and 3691 individual case safety reports (ICSRs) recorded in FAERS reported ubrogepant and rimegepant, respectively, as suspect drugs. Ten disproportionality signals for ubrogepant and 25 disproportionality signals for rimegepant were identified; these were mostly related to psychiatric, neurological, gastrointestinal, skin, vascular, and infectious type of adverse events.

CONCLUSIONS

New safety aspects related to the treatment of ubrogepant and rimegepant using disproportionality analysis from spontaneous reporting databases were identified. Further studies are needed to confirm these findings.

Novel Lipids to Regulate Obesity and Brain Function: Comparing Available Evidence and Insights from QSAR In Silico Models

Lipid molecules, such as policosanol, ergosterol, sphingomyelin, omega 3 rich phosphatidylcholine, α-tocopherol, and sodium butyrate, have emerged as novel additions to the portfolio of bioactive lipids. In this state-of-the-art review, we discuss these lipids, and their activity against obesity and mental or neurological disorders, with a focus on their proposed cellular targets and the ways in which they produce their beneficial effects. Furthermore, this available information is compared with that provided by in silico Absorption, Distribution, Metabolism, Excretion, and Toxicity (ADMET) models in order to understand the usefulness of these tools for the discovery of new bioactive compounds. Accordingly, it was possible to highlight how these lipids interact with various cellular targets related to the molecule transportation and absorption (e.g., α-tocopherol transfer protein for α-Tocopherol, ATP-binding cassette ABC transporters or Apolipoprotein E for sphingomyelins and phospholipids) or other processes, such as the regulation of gene expression (involving Sterol Regulatory Element-Binding Proteins for ergosterol or Peroxisome Proliferator-Activated Receptors in the case of policosanol) and inflammation (the regulation of interleukins by sodium butyrate). When comparing the literature with in silico Quantitative Structure-Activity Relationship (QSAR) models, it was observed that although they are useful for selecting bioactive molecules when compared in batch, the information they provide does not coincide when assessed individually. Our review highlights the importance of considering a broad range of lipids as potential bioactives and the need for accurate prediction of ADMET parameters in the discovery of new biomolecules. The information presented here provides a useful resource for researchers interested in developing new strategies for the treatment of obesity and mental or neurological disorders.

Non-pharmacological treatment-related changes of molecular biomarkers in major depressive disorder: A systematic review and meta-analysis

Background

Major depressive disorder (MDD) is a serious mood disorder and leading cause of disability. Despite treatment advances, approximately 30% of individuals with MDD do not achieve adequate clinical response. Better understanding the biological mechanism(s) underlying clinical response to specific psychopharmacological interventions may help fine tune treatments in order to further modulate their underlying mechanisms of action. However, little is known regarding the effect of non-pharmacological treatments (NPTs) on candidate molecular biomarker levels in MDD. This review aims to identify molecular biomarkers that may elucidate NPT response for MDD.

Methods

We performed a systematic review and a multilevel linear mixed-effects meta-analyses, and a meta-regression. Searches were performed in PubMed, Scopus, and PsycINFO in October 2020 and July 2021.

Results

From 1387 retrieved articles, 17 and six studies were included in the systematic review and meta-analyses, respectively. Although there was little consensus associating molecular biomarker levels with symptomology and/or treatment response, brain metabolites accessed via molecular biomarker-focused neuroimaging techniques may provide promising information on whether an individual with MDD would respond positively to NPTs. Furthermore, non-invasive brain stimulation interventions significantly increased the expression of neurotrophic factors (NTFs) compared to sham/placebo, regardless of add-on pharmacological treatment.

Conclusions

NTFs are candidate biomarkers to fine-tune NIBS for MDD treatment.

Frequency specificity of aberrant triple networks in major depressive disorder: a resting-state effective connectivity study

Major depressive disorder (MDD) has been associated with aberrant effective connectivity (EC) among the default mode network (DMN), salience network (SN), and central executive network (CEN)-collectively referred to as triple networks. However, prior research has predominantly concentrated on broad frequency bands (0.01-0.08 Hz or 0.01-0.15 Hz), ignoring the influence of distinct rhythms on triple network causal dynamics. In the present study, we aim to investigate EC alterations within the triple networks across various frequency bands in patients with MDD. Utilizing a data-driven frequency decomposition approach and a multivariate Granger causality analysis, we characterized frequency-specific EC patterns of triple networks in 49 MDD patients and 54 healthy controls. A support vector machine classifier was subsequently employed to assess the discriminative capacity of the frequency-specific EC features. Our findings revealed that, compared to controls, patients exhibited not only enhanced mean EC within the CEN in the conventional frequency band (0.01-0.08 Hz), but also decreased mean EC from the SN to the DMN in a higher frequency band (0.12-0.18 Hz), and increased mean EC from the CEN to the SN in a sub-frequency band (0.04-0.08 Hz); the latter was significantly correlated with disease severity. Moreover, optimal classification performance for distinguishing patients from controls was attained by combining EC features across all three frequency bands, with the area under the curve (AUC) value of 0.8831 and the corresponding accuracy, sensitivity, and specificity of 89.97%, 92.63%, and 87.32%, respectively. These insights into EC changes within the triple networks across multiple frequency bands offer valuable perspectives on the neurobiological basis of MDD and could aid in developing frequency-specific EC features as potential biomarkers for disease diagnosis.

Amygdala activity and amygdala-hippocampus connectivity: Metabolic diseases, dementia, and neuropsychiatric issues

With rapid aging of the population worldwide, the number of people with dementia is dramatically increasing. Some studies have emphasized that metabolic syndrome, which includes obesity and diabetes, leads to increased risks of dementia and cognitive decline. Factors such as insulin resistance, hyperglycemia, high blood pressure, dyslipidemia, and central obesity in metabolic syndrome are associated with synaptic failure, neuroinflammation, and imbalanced neurotransmitter levels, leading to the progression of dementia. Due to the positive correlation between diabetes and dementia, some studies have called it "type 3 diabetes". Recently, the number of patients with cognitive decline due to metabolic imbalances has considerably increased. In addition, recent studies have reported that neuropsychiatric issues such as anxiety, depressive behavior, and impaired attention are common factors in patients with metabolic disease and those with dementia. In the central nervous system (CNS), the amygdala is a central region that regulates emotional memory, mood disorders, anxiety, attention, and cognitive function. The connectivity of the amygdala with other brain regions, such as the hippocampus, and the activity of the amygdala contribute to diverse neuropathological and neuropsychiatric issues. Thus, this review summarizes the significant consequences of the critical roles of amygdala connectivity in both metabolic syndromes and dementia. Further studies on amygdala function in metabolic imbalance-related dementia are needed to treat neuropsychiatric problems in patients with this type of dementia.

Active learning-based systematic reviewing using switching classification models: the case of the onset, maintenance, and relapse of depressive disorders

Introduction

This study examines the performance of active learning-aided systematic reviews using a deep learning-based model compared to traditional machine learning approaches, and explores the potential benefits of model-switching strategies.

Methods

Comprising four parts, the study: 1) analyzes the performance and stability of active learning-aided systematic review; 2) implements a convolutional neural network classifier; 3) compares classifier and feature extractor performance; and 4) investigates the impact of model-switching strategies on review performance.

Results

Lighter models perform well in early simulation stages, while other models show increased performance in later stages. Model-switching strategies generally improve performance compared to using the default classification model alone.

Discussion

The study's findings support the use of model-switching strategies in active learning-based systematic review workflows. It is advised to begin the review with a light model, such as Naïve Bayes or logistic regression, and switch to a heavier classification model based on a heuristic rule when needed.

Frequency-resolved connectome alterations in major depressive disorder: A multisite resting fMRI study

BACKGROUND

Functional connectome studies have revealed widespread connectivity alterations in major depressive disorder (MDD). However, the low frequency bandpass filtering (0.01-0.08 Hz or 0.01-0.1 Hz) in most studies have impeded our understanding on whether and how these alterations are affected by frequency of interest.

METHODS

Here, we performed frequency-resolved (0.01-0.06 Hz, 0.06-0.16 Hz and 0.16-0.24 Hz) connectome analyses using a large-sample resting-state functional MRI dataset of 1002 MDD patients and 924 healthy controls from seven independent centers.

RESULTS

We reported significant frequency-dependent connectome alterations in MDD in left inferior parietal, inferior temporal, precentral, and fusiform cortices and bilateral precuneus. These frequency-dependent connectome alterations are mainly derived by abnormalities of medium- and long-distance connections and are brain network-dependent. Moreover, the connectome alteration of left precuneus in high frequency band (0.16-0.24 Hz) is significantly associated with illness duration.

LIMITATIONS

Multisite harmonization model only removed linear site effects. Neurobiological underpinning of alterations in higher frequency (0.16-0.24 Hz) should be further examined by combining fMRI data with respiration, heartbeat and blood flow recordings in future studies.

CONCLUSIONS

These results highlight the frequency-dependency of connectome alterations in MDD and the benefit of examining connectome alteration in MDD under a wider frequency band.

An update on pharmacotherapy for recurrent depression in 2022

INTRODUCTION

Major depressive disorder remains a major challenge due to its biopsychosocial burden with increased morbidity and mortality. Despite successful treatment options for the acute episode, recurrence rates are high, on average four times in a life span.

AREAS COVERED

Both pharmacological as non-pharmacological evidence-based therapeutic options to prevent and treat recurrent depression are discussed.

EXPERT OPINION

Although some risk factors for recurrence are well known, better evidence is needed. Antidepressant medication should be continued after acute treatment at its full therapeutic dose for longer periods, at least 1 year. There are no clear differences between classes of antidepressant medication when treatment is focused on preventing relapse. Bupropion is the only antidepressant with a proven efficacy to prevent recurrence in seasonal affective disorder. Recent findings conclude maintenance subanesthetic ketamine and esketamine treatment can be effective in sustaining antidepressant effect following remission. Furthermore, the pharmacological approach must be integrated with lifestyle interventions, especially aerobic exercise. Finally, combining pharma- and psychotherapy seems to improve outcome. Network and complexity sciences will help to decrease the high recurrence rates of MDD by developing more integrative and personalized approaches.

Systematic reviews and meta-analyses on major depressive disorder: a bibliometric perspective

Background

There is a vast amount of evidence-based medicine research on the major depressive disorder (MDD) available in the literature, however, no studies on the overall performance, productivity and impact of such research have been published to date. This study explored and mapped the research outputs of MDD-related systematic reviews and meta-analyses (SR/MA) from a bibliometric perspective.

Methods

Relevant data were retrieved with search terms on MDD, systematic review and meta-analysis.

Results

A total of 4,870 papers with 365,402 citations published from 1983 to 2022 were included in the analysis. The publication output has grown steadily over time with the most publications originating from the USA (1,020; 20.94%), the UK (516; 10.60%) and China (448; 9.20%). The research collaborations between countries were most frequent between the USA and UK (266; 5.46%). Journal of Affective Disorders (379; 7.78%) was the most productive journal, while Cuijpers P was the most productive author (121; 2.48%), and University of Toronto (569; 11.78%) was the most productive institution. The top 10 most cited articles on MDD-related SR/MA had citations ranging from 1,806 to 3,448. The high-frequency keywords were mainly clustered into four themes, including psychiatric comorbidities, clinical trials, treatment, and brain stimulation in MDD.

Conclusion

The rapid increase in the number of SR/MA of MDD in recent years highlights the importance of this research field. Psychiatric comorbidities, clinical interventions, and treatment of MDD have been identified as hot topics, while biological mechanisms in MDD are likely to be an emerging research priority.

Screening for Major Depressive Disorder Using a Wearable Ultra-Short-Term HRV Monitor and Signal Quality Indices

To encourage potential major depressive disorder (MDD) patients to attend diagnostic sessions, we developed a novel MDD screening system based on sleep-induced autonomic nervous responses. The proposed method only requires a wristwatch device to be worn for 24 h. We evaluated heart rate variability (HRV) via wrist photoplethysmography (PPG). However, previous studies have indicated that HRV measurements obtained using wearable devices are susceptible to motion artifacts. We propose a novel method to improve screening accuracy by removing unreliable HRV data (identified on the basis of signal quality indices (SQIs) obtained by PPG sensors). The proposed algorithm enables real-time calculation of signal quality indices in the frequency domain (SQI-FD). A clinical study conducted at Maynds Tower Mental Clinic enrolled 40 MDD patients (mean age, 37.5 ± 8.8 years) diagnosed on the basis of the Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition, and 29 healthy volunteers (mean age, 31.9 ± 13.0 years). Acceleration data were used to identify sleep states, and a linear classification model was trained and tested using HRV and pulse rate data. Ten-fold cross-validation showed a sensitivity of 87.3% (80.3% without SQI-FD data) and specificity of 84.0% (73.3% without SQI-FD data). Thus, SQI-FD drastically improved sensitivity and specificity.

Optimizing multi-domain hematologic biomarkers and clinical features for the differential diagnosis of unipolar depression and bipolar depression

There is a lack of objective features for the differential diagnosis of unipolar and bipolar depression, especially those that are readily available in practical settings. We investigated whether clinical features of disease course, biomarkers from complete blood count, and blood biochemical markers could accurately classify unipolar and bipolar depression using machine learning methods. This retrospective study included 1160 eligible patients (918 with unipolar depression and 242 with bipolar depression). Patient data were randomly split into training (85%) and open test (15%) sets 1000 times, and the average performance was reported. XGBoost achieved the optimal open-test performance using selected biomarkers and clinical features-AUC 0.889, sensitivity 0.831, specificity 0.839, and accuracy 0.863. The importance of features for differential diagnosis was measured using SHapley Additive exPlanations (SHAP) values. The most informative features include (1) clinical features of disease duration and age of onset, (2) biochemical markers of albumin, low density lipoprotein (LDL), and potassium, and (3) complete blood count-derived biomarkers of white blood cell count (WBC), platelet-to-lymphocyte ratio (PLR), and monocytes (MONO). Overall, onset features and hematologic biomarkers appear to be reliable information that can be readily obtained in clinical settings to facilitate the differential diagnosis of unipolar and bipolar depression.

Epigenetic clock analysis reveals increased plasma cystatin C levels based on DNA methylation in major depressive disorder

Major depressive disorder (MDD) is a common mental illness and a major public health concern worldwide. Depression is associated with epigenetic changes that regulate gene expression, and analyzing these changes may help elucidate the pathophysiology of MDD. Genome-wide DNA methylation (DNAm) profiles can function as 'epigenetic clocks' that can help estimate biological aging. Here, we assessed biological aging in patients with MDD using various DNAm-based indicators of epigenetic aging. We used a publicly available dataset containing data obtained from the whole blood samples of MDD patients (n = 489) and controls (n = 210). We analyzed five epigenetic clocks (HorvathAge, HannumAge, SkinBloodAge, PhenoAge, and GrimAge) and DNAm-based telomere length (DNAmTL). We also investigated seven DNAm-based age-predictive plasma proteins (including cystatin C) and smoking status, which are components of GrimAge. Following adjustment for confounding factors such as age and sex, patients with MDD showed no significant difference in epigenetic clocks and DNAmTL. However, DNAm-based plasma cystatin C levels were significantly higher in patients with MDD than controls. Our findings revealed specific DNAm changes predicting plasma cystatin C levels in MDD. These findings may help elucidate the pathophysiology of MDD, leading to the development of new biomarkers and medications.

Air pollution associated with hospital visits for mental and behavioral disorders in Northeast China

Background

Related studies have found that air pollution is an important factor affecting mental and behavioral disorders. Thus, we performed this time-series study to evaluate the relationship between short-term exposure to ambient air pollutants and visits to hospital by patients with mental and behavioral disorders in northeastern China.

Methods

We used quasi-Poisson regression models and generalized additive models to probe the links between air pollution and mental and behavioral disorders. The possible influences were also explored stratified by season, age and gender.

Results

We found that sulfur dioxide (SO2) had a cumulative effect on mental and behavioral disorders at lag04-lag07 and had the greatest effect at lag07 [Relative risk (RR) = 1.068, 95%CI = 1.021-1.117]. Particulate matter of size 2.5 μm (PM2.5) and SO2 had a cumulative effect on depression and both had the largest effect at lag07 (RR = 1.021, 95%CI = 1.002-1.041; RR = 1.103, 95%CI = 1.032-1.178); SO2 also had a cumulative effect on anxiety disorders, with the largest effect at lag06 (RR = 1.058, 95%CI = 1.009-1.110). In the stratified analysis, people are more susceptible in the cold season compared to the warm season and females and the 18-60-year age group are more sensitive to air pollutants. It is suggested to strengthen management and preventive measures to decrease air pollution exposure.

Conclusion

This study found an association between increased concentrations of air pollutants and increased outpatient visits for mental and behavioral disorders. We recommend that preventive and protective measures should be strengthened in an effort to reduce exposure to air pollution in order to maintain physical and mental health.

Classification of major depressive disorder using an attention-guided unified deep convolutional neural network and individual structural covariance network

Major depressive disorder (MDD) is the second leading cause of disability worldwide. Currently, the structural magnetic resonance imaging-based MDD diagnosis models mainly utilize local grayscale information or morphological characteristics in a single site with small samples. Emerging evidence has demonstrated that different brain structures in different circuits have distinct developmental timing, but mature coordinately within the same functional circuit. Thus, establishing an attention-guided unified classification framework with deep learning and individual structural covariance networks in a large multisite dataset could facilitate developing an accurate diagnosis strategy. Our results showed that attention-guided classification could improve the classification accuracy from primary 75.1% to ultimate 76.54%. Furthermore, the discriminative features of regional covariance connectivities and local structural characteristics were found to be mainly located in prefrontal cortex, insula, superior temporal cortex, and cingulate cortex, which have been widely reported to be closely associated with depression. Our study demonstrated that our attention-guided unified deep learning framework may be an effective tool for MDD diagnosis. The identified covariance connectivities and structural features may serve as biomarkers for MDD.

Pre-Surgery Inflammatory and Angiogenesis Biomarkers as Predictors of 12-Month Cancer-Related Distress: Results from the ColoCare Study

BACKGROUND

Patients with colorectal cancer commonly suffer from complex psychological distress. Elevated distress may be linked to systemic biomarkers. We investigated associations of biomarkers of inflammation and angiogenesis with cancer-related distress (CTXD) score.

METHODS

N = 315 patients (stage I-IV) from 2 centers of the ColoCare Study were included: Huntsman Cancer Institute and University of Heidelberg. Biomarkers (e.g., IL6, VEGF-A, VEGF-D) were measured in serum collected pre-surgery and 12 months thereafter. The CTXD overall score and 4 subscales were collected 12 months after surgery and dichotomized to investigate biomarkers as predictors of distress 12 months after surgery; adjusted for age, sex, body mass index, tumor stage, center, and baseline levels of biomarkers.

RESULTS

Doubling of IL6 predicted future increased risk of overall distress [odds ratio (OR), 1.20; 95% confidence interval (CI), 1.02-1.41; P = 0.03]. VEGF-A-predicted future increased risk of high family strain (VEGF-A: OR, 1.21; 95% CI, 1.01-1.44; P = 0.04) and VEGF-D was associated with medical and financial demands (OR, 1.34; 95% CI, 1.01-1.74; P = 0.03).

CONCLUSIONS

This is the first study to show that systemic biomarkers are significantly associated with future CTXD score. Distress was not measured at baseline; we cannot rule out ongoing associations of inflammation and distress throughout treatment versus a direct effect of inflammation on distress. Nonetheless, these data add to evidence that biobehavioral processes interact and that systemic biomarkers are associated with cancer-related distress one year after surgery.

IMPACT

Exercise and diet interventions that lower systemic cytokine levels may impact longer-term CTXD score and improve quality of life of patients with colorectal cancer.

Transcranial Sonography as a Diagnostic Tool for Depressive Disorders

BACKGROUND

Transcranial sonography (TCS) is an available and noninvasive neuroimaging method that has been found to reduce the echogenicity of the brainstem raphe (BR) in patients with depression. Applying the criteria of the Diagnostic and Statistical Manual of Mental Disorders-IV (DSM-IV), we performed a meta-analysis of the diagnostic accuracy of TCS.

METHODS

A systematic search was conducted in PubMed, EMBASE, The Cochrane Library, and Web of Science. The databases were searched from inception to December 2021. The quality of the included literature was assessed using the QUADAS-2. Heterogeneity analysis was performed. A summary receiver operating characteristic (SROC) curve was generated to evaluate the diagnostic accuracy of TCS.

RESULTS

We included 12 studies with 809 patients. The pooled sensitivity was 0.66 (95% confidence interval [CI]: 0.61-0.71), and the specificity was 0.84 (95% CI: 0.80-0.87). The combined positive likelihood ratio (LR) was 3.84 (95% CI: 2.68-5.51), the negative LR was 0.41 (95% CI: 0.29-0.57), and the diagnostic odds ratio (DOR) was 11.45 (95% CI: 5.57-23.02). The area under the curve (AUC) of the plotted SROC curve was 0.86 (95% CI: 0.83-0.89). The meta-regression and subgroup analyses found no source of heterogeneity.

CONCLUSION

TCS has high potential and efficacy in diagnosing depression and may be a reasonable test to perform clinically for the assessment of depression.

Altered fronto-central theta-gamma coupling in major depressive disorder during auditory steady-state responses

OBJECTIVE

Neural oscillations during sensory and cognitive events interact at different frequencies. However, such evidence in major depressive disorder (MDD) remains scarce. We explored the possible abnormal neural oscillations in MDD by analyzing theta-phase/gamma-amplitude coupling (TGC).

METHODS

Resting-state and auditory steady-state response (ASSR) electroencephalography recordings were obtained from 35 first-episode MDD and 35 healthy controls (HCs). TGC during rest, ASSR stimulation, and ASSR baseline between and within groups were analyzed to evaluate MDD alterations. Receiver operating characteristic (ROC), TGC comparison between MDD severity subgroups (mild, moderate, major), and correlations were investigated to determine the potential use of altered TGC for identifying MDD.

RESULTS

In MDD, left fronto-central TGC decreased during stimulation, while right fronto-central TGC increased during baseline. The area under ROC curve for altered TGC was 0.863. Furthermore, during stimulation, moderate and major MDD groups exhibited significantly lower TGC than mild group, and fronto-central TGC was negatively correlated with depression scale scores.

CONCLUSIONS

Our results provided the first evidence for an abnormal TGC response of fronto-central regions in MDD during an ASSR task. Importantly, altered TGC may be promising biomarkers of MDD.

SIGNIFICANCE

Our findings enhance the understanding of physiological mechanisms underlying MDD and aid in its clinical diagnosis.

Multiple examinations indicated associations between abnormal regional homogeneity and cognitive dysfunction in major depressive disorder

Background

This study aimed to investigate the relationships between regional neural activity and multiple related indicators in patients with major depressive disorder (MDD).

Methods

Forty-two patients and 42 healthy controls (HCs) were enrolled. Pearson/Spearman correlation analyses were applied to examine the associations between abnormal regional homogeneity (ReHo) and different indicators in the patients.

Results

Compared with HCs, patients with MDD had increased ReHo in the left inferior temporal gyrus (ITG) and decreased ReHo values in the left putamen, anterior cingulate cortex (ACC), and precentral gyrus. The ReHo of the left putamen was positively correlated with the PR interval, Repeatable Battery for the Assessment of Neuropsychological Status 4A, and Discriminant analysis (D), and negatively correlated with Ae (block) and Ae (total) in the patients. The ReHo value of the left ACC was positively correlated with the severity of depression, Stroop Color Word Test of C - 2B + 100 in reaction time, and negatively correlated with Ce (Missay) and Perseverative Responses in the patients. The ReHo of the left ITG was positively correlated with the Neuroticism scores and negatively correlated with the Lie scores in the patients.

Conclusion

These results suggested that the decreased ReHo of the salience network might be the underpinning of cognitive impairments in patients with MDD.

Intergenerational impact of childhood trauma on hair cortisol concentrations in mothers and their young infants

Background

Alterations in stress regulation and function of the hypothalamic-pituitary-adrenal (HPA) axis during infancy may be a risk factor for the development of psychopathology later in life. Maternal childhood trauma, depression, anxiety and stressful life events are individually associated with HPA axis dysregulation. Less is known about their interdependent influence on maternal and infant stress regulation in at risk populations. In a sample of mothers with a history of depressive-, and/or anxiety disorders and their infants we explored if a history of maternal childhood trauma, current depressive and anxiety symptomatology, and recent life events were associated with maternal and infant long-term cortisol levels three months postpartum.

Methods

Data were available of 89 mothers and 49 infants. All mothers fulfilled criteria for a lifetime depressive or anxiety disorder. Diagnosis was established with a diagnostic interview. Current depressive symptomatology was assessed with the Edinburgh Postnatal Depression Scale (EPDS), current anxiety with the State-Trait Anxiety Inventory (STAI), maternal childhood trauma with the Childhood Trauma Questionnaire (CTQ) and recent life events with the Everyday Problem Checklist (EPC). Maternal and infant hair cortisol concentrations (HCC) were quantified with liquid chromatography with tandem mass spectrometry (LC-MS/MS) three months after birth. Total scores of the CTQ and subscales, EPDS, STAI, and EPC were regressed on maternal and infant HCC using regression analyses. Differences in HCC regarding trauma history were tested with t-tests. Potential confounders were identified and adjusted for.

Results

In regression analyses, a positive curvilinear relationship was found between CTQ total score and maternal HCC (n = 83, B = 0.076, SE 0.033, p = .021), but not for current depression (n = 88, B = -0.001, SE 0.011, p = .931), current anxiety (n = 88, B = 0.002, SE 0.004, p = .650) or recent life events (n = 89, B = 0.018, SE 0.032, p = .568). Analyses were adjusted for confounders. A negative linear relationship was found between maternal CTQ score and infant HCC (n = 49, β = -0.264, B = -0.006, SE 0.003, p = .052), but not for current maternal depression (n = 45, β = -0.182, B = -0.011, SE 0.008, p = .164), current maternal anxiety (n = 45, β = -0.209, B = -0.005, SE 0.003, p = .113) or recent life events (n = 46, β = -0.128, B = -0.022, SE 0.023, p = .325). Analyses were adjusted for relevant infant hair characteristics. Specifically, maternal emotional and physical neglect were related to HCC in both mothers and infants.

Conclusions

Results suggest that maternal childhood trauma is more prominent in altering maternal and infant long-term cortisol levels than perinatal depressive and anxiety symptomatology or recent life stressors in mothers with a history of depressive and/or anxiety disorders, and their infants. As infants of mothers with psychopathology are at increased risk for later psychiatric disease, future studies should investigate the interplay of possible risk factors for transgenerational transmission, intra-uterine programming of the HPA axis, including (epi-)genetic phenomena, of the HPA axis, and the influence of parenting impairment.

Electroencephalography for predicting antidepressant treatment success: A systematic review and meta-analysis

BACKGROUND

Patients suffering from major depressive disorder (MDD) regularly experience non-response to treatment for their depressive episode. Personalized clinical decision making could shorten depressive episodes and reduce patient suffering. Although no clinical tools are currently available, machine learning analysis of electroencephalography (EEG) shows promise in treatment response prediction.

METHODS

With a systematic review and meta-analysis, we evaluated the accuracy of EEG for individual patient response prediction. Importantly, we included only prediction studies that used cross-validation. We used a bivariate model to calculate prediction success, as expressed by area-under the curve, sensitivity and specificity. Furthermore, we analyzed prediction success for separate antidepressant interventions.

RESULTS

15 studies with 12 individual patient samples and a total of 479 patients were included. Research methods varied considerably between studies. Meta-analysis of results from this heterogeneous set of studies resulted in an area under the curve of 0.91, a sensitivity of 83 % (95 % CI 74-89 %), and a specificity of 86 % (95 % CI 81-90 %). Classification performance did not significantly differ between treatments. Although studies were all internally validated, no externally validated studies have been reported. We found substantial risk of bias caused by methodological shortcomings such as non-independent feature selection, though performance of non-biased studies was comparable.

LIMITATIONS

Sample sizes were relatively small and no study used external validation, increasing the risk of overestimation of accuracy.

CONCLUSIONS

Electroencephalography can predict the response to antidepressant treatment with high accuracy. However, future studies with more rigorous validation are needed to produce a clinical tool to guide interventions in MDD.

PROSPERO REGISTRATION NUMBER

CRD42021268169.

EEG-based spatio-temporal relation signatures for the diagnosis of depression and schizophrenia

The diagnosis of psychiatric disorders is currently based on a clinical and psychiatric examination (intake). Ancillary tests are used minimally or only to exclude other disorders. Here, we demonstrate a novel mathematical approach based on the field of p-adic numbers and using electroencephalograms (EEGs) to identify and differentiate patients with schizophrenia and depression from healthy controls. This novel approach examines spatio-temporal relations of single EEG electrode signals and characterizes the topological structure of these relations in the individual patient. Our results indicate that the relational topological structures, characterized by either the personal universal dendrographic hologram (DH) signature (PUDHS) or personal block DH signature (PBDHS), form a unique range for each group of patients, with impressive correspondence to the clinical condition. This newly developed approach results in an individual patient signature calculated from the spatio-temporal relations of EEG electrodes signals and might help the clinician with a new objective tool for the diagnosis of a multitude of psychiatric disorders.

Incorporating multi-stage diagnosis status to mine associations between genetic risk variants and the multi-modality phenotype network in major depressive disorder

Depression (major depressive disorder, MDD) is a common and serious medical illness. Globally, it is estimated that 5% of adults suffer from depression. Recently, imaging genetics receives growing attention and become a powerful strategy for discoverying the associations between genetic variants (e.g., single-nucleotide polymorphisms, SNPs) and multi-modality brain imaging data. However, most of the existing MDD imaging genetic research studies conducted by clinicians usually utilize simple statistical analysis methods and only consider single-modality brain imaging, which are limited in the deeper discovery of the mechanistic understanding of MDD. It is therefore imperative to utilize a powerful and efficient technology to fully explore associations between genetic variants and multi-modality brain imaging. In this study, we developed a novel imaging genetic association framework to mine the multi-modality phenotype network between genetic risk variants and multi-stage diagnosis status. Specifically, the multi-modality phenotype network consists of voxel node features and connectivity edge features from structural magnetic resonance imaging (sMRI) and resting-state functional magnetic resonance imaging (rs-fMRI). Thereafter, an association model based on multi-task learning strategy was adopted to fully explore the relationship between the MDD risk SNP and the multi-modality phenotype network. The multi-stage diagnosis status was introduced to further mine the relation among the multiple modalities of different subjects. A multi-modality brain imaging data and genotype data were collected by us from two hospitals. The experimental results not only demonstrate the effectiveness of our proposed method but also identify some consistent and stable brain regions of interest (ROIs) biomarkers from the node and edge features of multi-modality phenotype network. Moreover, four new and potential risk SNPs associated with MDD were discovered.

The association between maternal symptoms of depression and hair glucocorticoids in infants across the perinatal period

BACKGROUND

Maternal symptoms of depression constitute an early adversity for infants that is considered to exert its effects via the maternal-placental-fetal neuroendocrine axis. Previous research implicates associations between maternal prenatal symptoms of depression and infants' glucocorticoid (GC) levels shortly after birth. To date, associations have not been investigated in the early postnatal period. The current study aimed to investigate the influence of maternal perinatal symptoms of depression on infants' neonatal and postnatal hair GCs providing a retrospective reflection of integrated cortisol secretion in the intrauterine and early postnatal period, respectively.

METHODS

As part of a prospective cohort study, hair samples of infants were taken up to two weeks after delivery (N = 152) and again eight weeks after delivery (N = 165). Liquid chromatography-tandem mass spectrometry was used to determine hair cortisol and cortisone in scalp-near 2-cm hair segments. Maternal symptoms of depression were assessed during pregnancy and eight weeks postnatally based on the Edinburgh Postnatal Depression Scale.

RESULTS

Higher maternal prenatal symptoms of depression showed a significant association with higher infants' neonatal hair cortisol, when controlling for confounding variables (i.e., gestational age, mode of delivery, parity, storage time, pregnancy complications). A non-significant trend for this effect was found for the hair cortisol-to-cortisone ratio while no effect occurred for hair cortisone. No association of maternal postnatal symptoms of depression with infants' postnatal hair GCs was observed. Further exploratory analyses revealed no relationship between a change of maternal prenatal to postnatal symptoms of depression with the change from infants' neonatal to postnatal hair GC levels or postnatal hair GCs.

CONCLUSION

Our results suggest that maternal prenatal symptoms of depression are associated with dysregulated infants' hair cortisol levels mainly incorporated in the intrauterine period which, in turn, might contribute to increased susceptibility for later diseases. However, no relationship was observed in infants' hair samples additionally reflecting hair GCs of the early postnatal period. Future studies should consider research on associations between maternal symptoms of depression and infants' hair GCs also later in life and take into account additional risk factors with potential impacts on GC secretion during early infancy.

Targeting the Arginine Vasopressin V1b Receptor System and Stress Response in Depression and Other Neuropsychiatric Disorders

A healthy stress response is critical for good mental and overall health and promotes neuronal growth and adaptation, but the intricately balanced biological mechanisms that facilitate a stress response can also result in predisposition to disease when that equilibrium is disrupted. The hypothalamic-pituitary-adrenal (HPA) axis neuroendocrine system plays a critical role in the body's response and adaptation to stress, and vasopressinergic regulation of the HPA axis is critical to maintaining system responsiveness during chronic stress. However, exposure to repeated or excessive physical or emotional stress or trauma can shift the body's stress response equilibrium to a "new normal" underpinned by enduring changes in HPA axis function. Exposure to early life stress due to adverse childhood experiences can also lead to lasting neurobiological changes, including in HPA axis function. HPA axis impairment in patients with depression is considered among the most reliable findings in biological psychiatry, and chronic stress has been shown to play a major role in the pathogenesis and onset of depression and other neuropsychiatric disorders. Modulating HPA axis activity, for example via targeted antagonism of the vasopressin V_1b receptor, is a promising approach for patients with depression and other neuropsychiatric disorders associated with HPA axis impairment. Despite favorable preclinical indications in animal models, demonstration of clinical efficacy for the treatment of depressive disorders by targeting HPA axis dysfunction has been challenging, possibly due to the heterogeneity and syndromal nature of depressive disorders. Measures of HPA axis function, such as elevated cortisol levels, may be useful biomarkers for identifying patients who may benefit from treatments that modulate HPA axis activity. Utilizing clinical biomarkers to identify subsets of patients with impaired HPA axis function who may benefit is a promising next step in fine-tuning HPA axis activity via targeted antagonism of the V_1b receptor.

Effects of Flavonoid-Rich Orange Juice Intervention on Major Depressive Disorder in Young Adults: A Randomized Controlled Trial

Many individuals are suffering from depression, and various improvements are being proposed. This study was conducted on young people diagnosed with depression and aimed to assess the effects of flavonoid-rich orange juice on the major depressive disorder (MDD) using a randomized controlled trial. In all, 40 young men and women with MDD aged 18−29 years were randomly assigned to a flavonoid-rich orange juice group (FR group) and a flavonoid-low orange cordial group (FL group). The subjects drank the corresponding juice three times a day (190 mL per bottle) for 8 weeks. The blood BDNF, zonulin, and claudin-5 levels significantly increased (p < 0.0001, p < 0.01, and p < 0.05, respectively) in the FR group, and the fatty acid binding protein 2 (FABP2) level was significantly decreased (p < 0.0001) in the FR group after the juice intervention. The FABP2, LPS, and valeric acid levels were negatively correlated with the abundance of Butyricicoccus pullicaecorum, which was higher in the FR group. Orange juice intake improved depressive symptoms in young adults with MDD in the FR group. This B. pullicaecorum can be a potential biomarker for clinical improvement in young adults with MDD patients.

PM2.5 exposure associated with prenatal anxiety and depression in pregnant women

BACKGROUND

Associations of air pollution with anxiety and depression were found in previous studies. However, whether air pollution exposure during pregnancy contributes to prenatal anxiety and depression or not is under-investigated. In this study, we aimed to analyze associations between fine particulate matter (PM_2.5) exposure with anxiety and depression during pregnancy and to explore the critical window of PM_2.5 exposure.

METHODS

This study was based on the Shanghai Maternal-Child Pairs Cohort (Shanghai MCPC). We used a gap-filling random forest model to estimate PM_2.5 exposure concentration during pregnancy of each participant. The Self-Rating Anxiety Scale (SAS) and the Center for Epidemiological Survey-Depression Scale (CES-D) were used to quantify the anxiety and depression levels in late pregnancy. Covariate information was obtained from medical records and questionnaires. We performed generalized linear regression and logistic regression models to assess the association and the critical window.

RESULTS

Totally 3731 pregnant women were included, with the age of 28.85 ± 3.97 years old. Anxiety and depression rates were 10.8 % and 11.5 % respectively, according to the cut-off value of SAS and CES-D. Generalized linear regression results showed that the increase of PM_2.5 concentration in three stages (gestational 0-13 weeks, 0-26 weeks, 0-36 weeks) was related to the increase of scale score. The PM_2.5 concentration in 0-13 weeks could increase the risk of anxiety and depression by approximately 23 % and 25 %, respectively. And the gestational weeks 4th-13th were the suspicious critical window of PM_2.5 exposure.

CONCLUSION

The increased risk of anxiety or depression was related to PM_2.5 exposure during pregnancy, especially early pregnancy.

The gut microbiota and depressive symptoms across ethnic groups

The gut microbiome is thought to play a role in depressive disorders, which makes it an attractive target for interventions. Both the microbiome and depressive symptom levels vary substantially across ethnic groups. Thus, any intervention for depression targeting the microbiome requires understanding of microbiome-depression associations across ethnicities. Analysing data from the HELIUS cohort, we characterize the gut microbiota and its associations with depressive symptoms in 6 ethnic groups (Dutch, South-Asian Surinamese, African Surinamese, Ghanaian, Turkish, Moroccan; N = 3211), living in the same urban area. Diversity of the gut microbiota, both within (α-diversity) and between individuals (β-diversity), predicts depressive symptom levels, taking into account demographic, behavioural, and medical differences. These associations do not differ between ethnic groups. Further, β-diversity explains 29%-18% of the ethnic differences in depressive symptoms. Bacterial genera associated with depressive symptoms belong to mulitple families, prominently including the families Christensenellaceae, Lachnospiraceae, and Ruminococcaceae. In summary, the results show that the gut microbiota are linked to depressive symptom levels and that this association generalizes across ethnic groups. Moreover, the results suggest that ethnic differences in the gut microbiota may partly explain parallel disparities in depression.

Individual differences in latent trait cortisol (LTC): Implications for the onset and course of future depressive symptoms

Research suggests that various indicators of hypothalamic pituitary adrenal (HPA) axis activity prospectively predict depression, but few studies have evaluated whether trait indicators of HPA axis activity are related to depression. Further, no prior study has examined links between trait cortisol and psychopathology using a trait indicator that captures HPA axis activity over multiple time points. Here we examined whether we could construct an across-wave latent trait cortisol (LTC) factor using cortisol samples collected over 13 weeks, and whether the across-wave LTC prospectively predicted new depressive symptom onsets and symptom duration. Emerging adults (n = 85; M age = 19.37 years) provided salivary cortisol samples four times a day (waking, 30 min and 45 min post-waking and bedtime) over three 3-day measurement waves separated by 6 weeks. Diagnostic interviews at 3 timepoints (baseline, 1- and 2.5 years post-baseline) assessed lifetime and current depressive symptoms. Results indicated that the across-wave LTC predicted new onsets of depressive symptoms and longer symptom duration. Follow-up tests revealed that the link between the across-wave LTC and new onsets was not significant after adjusting for past depressive symptoms. These findings suggest that an indicator of individual differences in HPA axis regulation has implications for depressive symptom onsets and course.

A kynurenine pathway enzyme aminocarboxymuconate-semialdehyde decarboxylase may be involved in treatment-resistant depression, and baseline inflammation status of patients predicts treatment response: a pilot study

The kynurenine pathway (KP) and inflammation are substantial in depression pathogenesis. Although there is a crosstalk between the KP, inflammation, and neurotrophic factors, few studies examine these topics together. Novel medications may be developed by clarifying dysregulations related to inflammation, KP, and neurotrophic factors in treatment-resistant depression (TRD). We aimed to evaluate the serum levels of KP metabolites, proinflammatory biomarkers, and brain-derived neurotrophic factor (BDNF) in healthy controls (HC) and the patients with TRD whose followed up with three different treatments. Moreover, the effect of electroconvulsive therapy (ECT) and repetitive transcranial magnetic stimulation (rTMS) on biomarkers was investigated. Study groups comprised a total of 30 unipolar TRD patients consisting of three separate patient groups (ECT = 8, rTMS = 10, pharmacotherapy = 12), and 9 HC. The decision to administer only pharmacotherapy or ECT/rTMS besides pharmacotherapy was given independently of this research by psychiatrists. Blood samples and symptom scores were obtained three times for patients. At baseline, quinolinic acid (QUIN) was higher in the patients with TRD compared to HC, whereas picolinic acid (PIC), PIC/QUIN, and PIC/3-hydroxykynurenine were lower. Baseline interleukin-6 (IL-6), and high-sensitivity C-reactive protein (hsCRP) were higher in nonresponders and non-remitters. ECT had an acute effect on cytokines. In the rTMS group, tumor necrosis factor-α (TNF-α) decreased in time. PIC, QUIN, and aminocarboxymuconate-semialdehyde decarboxylase (ACMSD) enzyme may play a role in TRD pathogenesis, and have diagnostic potential. rTMS and ECT have modulatory effects on low-grade inflammation seen in TRD. Baseline inflammation severity is predictive in terms of response and remission in depression.

Glucocorticoids unmask silent non-coding genetic risk variants for common diseases

Understanding the function of non-coding genomic sequence variants represents a challenge for biomedicine. Many diseases are products of gene-by-environment interactions with complex mechanisms. This study addresses these themes by mechanistic characterization of non-coding variants that influence gene expression only after drug or hormone exposure. Using glucocorticoid signaling as a model system, we integrated genomic, transcriptomic, and epigenomic approaches to unravel mechanisms by which variant function could be revealed by hormones or drugs. Specifically, we identified cis-regulatory elements and 3D interactions underlying ligand-dependent associations between variants and gene expression. One-quarter of the glucocorticoid-modulated variants that we identified had already been associated with clinical phenotypes. However, their affected genes were 'unmasked' only after glucocorticoid exposure and often with function relevant to the disease phenotypes. These diseases involved glucocorticoids as risk factors or therapeutic agents and included autoimmunity, metabolic and mood disorders, osteoporosis and cancer. For example, we identified a novel breast cancer risk gene, MAST4, with expression that was repressed by glucocorticoids in cells carrying the risk genotype, repression that correlated with MAST4 expression in breast cancer and treatment outcomes. These observations provide a mechanistic framework for understanding non-coding genetic variant-chemical environment interactions and their role in disease risk and drug response.

A novel set of volatile urinary biomarkers for late-life major depressive and anxiety disorders upon the progression of frailty: a pilot study

Mood and anxiety disorders are frequent in the elderly and increase the risk of frailty. This study aimed to identify novel biomarkers of major depressive disorder (MDD) and anxiety in the elderly. We examined 639 participants in the community-dwelling Otassha Study (518 individuals considered healthy control, 77 with depression, anxiety, etc.), mean age 75 years, 58.4% of female. After exclusion criteria, we analyzed VOCs from 18 individuals (9 healthy control, 9 of MDD/agoraphobia case). Urinary volatile and semi-volatile organic compounds (VOCs) were profiled using solid-phase microextraction and gas chromatography-mass spectrometry. Six urinary VOCs differed in the absolute area of the base peak between participants with MDD and/or agoraphobia and controls. High area under the receiver-operating characteristic curve (AUC) values were found for phenethyl isothiocyanate (AUC: 0.86, p = 0.009), hexanoic acid (AUC: 0.85, p = 0.012), texanol (AUC: 0.99, p = 0.0005), and texanol isomer (AUC: 0.89, p = 0.005). The combined indices of dimethyl sulfone, phenethyl isothiocyanate, and hexanoic acid, and texanol and texanol isomer showed AUCs of 0.91 (p = 0.003) and 0.99 (p = 0.0005) and correlated with the GRID-HAMD and the Kihon Checklist (CL score), respectively. These VOCs may be valuable biomarkers for evaluating MDD and/or agoraphobia in the elderly.