Is there Progress? An Overview of Selecting Biomarker Candidates for Major Depressive Disorder

Upregulation of S100A8 in peripheral blood mononuclear cells from patients with depression treated with SSRIs: a pilot study

BACKGROUND

Major depressive disorder (MDD) affects more than 350 million people worldwide, and there is currently no laboratory test to diagnose it. This pilot study aimed to identify potential biomarkers in peripheral blood mononuclear cells (PBMCs) from MDD patients.

METHODS

We used tandem mass tagging coupled to synchronous precursor selection (mass spectrometry) to obtain the differential proteomic profile from a pool of PBMCs from MDD patients and healthy subjects, and quantitative PCR to assess gene expression of differentially expressed proteins (DEPs) of our interest.

RESULTS

We identified 247 proteins, of which 133 had a fold change ≥ 2.0 compared to healthy volunteers. Using pathway enrichment analysis, we found that some processes, such as platelet degranulation, coagulation, and the inflammatory response, are perturbed in MDD patients. The gene-disease association analysis showed that molecular alterations in PBMCs from MDD patients are associated with cerebral ischemia, vascular disease, thrombosis, acute coronary syndrome, and myocardial ischemia, in addition to other conditions such as inflammation and diabetic retinopathy.

CONCLUSIONS

We confirmed by qRT-PCR that S100A8 is upregulated in PBMCs from MDD patients and thus could be an emerging biomarker of this disorder. This report lays the groundwork for future studies in a broader and more diverse population and contributes to a deeper characterization of MDD.

Identification of mitophagy-related biomarkers and immune infiltration in major depressive disorder

BACKGROUND

Major depressive disorder (MDD) is a life-threatening and debilitating mental health condition. Mitophagy, a form of selective autophagy that eliminates dysfunctional mitochondria, is associated with depression. However, studies on the relationship between mitophagy-related genes (MRGs) and MDD are scarce. This study aimed to identify potential mitophagy-related biomarkers for MDD and characterize the underlying molecular mechanisms.

METHODS

The gene expression profiles of 144 MDD samples and 72 normal controls were retrieved from the Gene Expression Omnibus database, and the MRGs were extracted from the GeneCards database. Consensus clustering was used to determine MDD clusters. Immune cell infiltration was evaluated using CIBERSORT. Functional enrichment analyses were performed to determine the biological significance of mitophagy-related differentially expressed genes (MR-DEGs). Weighted gene co-expression network analysis, along with a network of protein-protein interactions (PPI), was used to identify key modules and hub genes. Based on the least absolute shrinkage and selection operator analysis and univariate Cox regression analysis, a diagnostic model was constructed and evaluated using receiver operating characteristic curves and validated with training data and external validation data. We reclassified MDD into two molecular subtypes according to biomarkers and evaluated their expression levels.

RESULTS

In total, 315 MDD-related MR-DEGs were identified. Functional enrichment analyses revealed that MR-DEGs were mainly enriched in mitophagy-related biological processes and multiple neurodegenerative disease pathways. Two distinct clusters with diverse immune infiltration characteristics were identified in the 144 MDD samples. MATR3, ACTL6A, FUS, BIRC2, and RIPK1 have been identified as potential biomarkers of MDD. All biomarkers showed varying degrees of correlation with immune cells. In addition, two molecular subtypes with distinct mitophagy gene signatures were identified.

CONCLUSIONS

We identified a novel five-MRG gene signature that has excellent diagnostic performance and identified an association between MRGs and the immune microenvironment in MDD.

Biomarkers of intestinal permeability and blood-brain barrier permeability in adolescents with major depressive disorder

BACKGROUND

The etiology in major depressive disorder (MDD) has not been fully understood. Accumulating evidence suggests an association between altered intestinal and blood-brain barrier (BBB) permeability and psychiatric disorders, while its changes in adolescent MDD populations have been received less attention. In this study, our aim was to explore the differences in plasma levels of intestinal and blood-brain barrier permeability markers in adolescents with MDD compared with healthy controls (HCs).

METHODS

We enrolled MDD (n = 50), and HCs (n = 40) with the age of 13-18 years old. The plasma level of zonulin, I-FABP, LPS, and claudin-5 were quantified. The Hamilton Depression Scale 17 items (HAMD-17) and Hamilton Anxiety Scale 14 items (HAMA-14) were used for symptom assessments.

RESULTS

The plasma levels of zonulin, I-FABP, LPS, and claudin-5 in the MDD group were significantly higher than those in the HCs. Plasma I-FABP levels in MDD with moderate to severe anxiety were significantly higher than those in MDD without moderate to severe anxiety and HCs. In addition, these four biomarkers (alone or combined) can be used as diagnostic markers for MDD in adolescents.

LIMITATIONS

The key limitation of this study is the blood measurements at a single time point with a relatively small sample size.

CONCLUSIONS

These findings advance our understanding of the pathophysiology of intestinal barrier injury, bacterial translocation, and blood-brain barrier injury involved in adolescents with MDD.

The first trimester plasma copper-zinc ratio is independently related to pregnancy-specific psychological distress symptoms throughout pregnancy

OBJECTIVES

High plasma copper (Cu) and low zinc (Zn) levels have been associated with depression. However, most studies used low sample sizes and a cross-sectional design, and perinatal data are scarce. We investigated the possible association between pregnancy-specific psychological distress and the plasma CuZn ratio using a prospective design.

METHODS

Pregnancy-specific distress symptoms were assessed at each trimester by means of the Tilburg Pregnancy Distress Scale, negative affect subscale, in 2036 pregnant women. Cu and Zn were assessed at 12 wk of gestation in plasma samples by inductively coupled plasma mass spectrometry. Growth mixture modeling determined trajectories of women's pregnancy-specific negative affect (P-NA) symptoms, which were entered in a multiple logistic regression analysis as dependent variable and the CuZn ratio as independent variable.

RESULTS

Two P-NA symptom classes were found: 1) persistently low (n = 1820) and 2) persistently high (n = 216). A higher CuZn ratio was independently associated with persistently high P-NA symptom scores (odds ratio = 1.52; 95% confidence interval, 1.13-2.04) after adjustment for confounders. A sensitivity analysis was performed excluding all women with high P-NA scores at 12 wk (>1 SD above the mean P-NA score). In the 1719 remaining women, a higher CuZn ratio significantly predicted the development of increasing P-NA symptom scores after adjustment for confounders (odds ratio = 1.40; 95% confidence interval, 1.04-1.95).

CONCLUSIONS

A higher CuZn plasma ratio is an independent determinant of developing pregnancy-specific distress symptoms throughout pregnancy, suggesting that micronutrients could be used as novel biomarkers for psychological distress research of perinatal mood disorders.

Macrophage Migration Inhibitory Factor in Major Depressive Disorder: A Multilevel Pilot Study

Macrophage migration inhibitory factor (MIF) is a controversially discussed inflammatory marker in major depressive disorder (MDD). While some studies show an association of high MIF protein levels with depression, animal models have yielded conflicting results. Thus, it remains elusive as to whether MIF plays an anti- or pro-depressive role. Therefore, we aimed to examine the potential of MIF at the genetic, expression and protein levels as a risk factor and biomarker to diagnose, monitor, or predict the course of MDD. Patients with a current major depressive episode (n = 66 with, and n = 63 without, prior medication) and remitted patients (n = 39) were compared with healthy controls (n = 61). Currently depressed patients provided a second blood sample after three weeks of therapy. Depression severity was assessed by self-evaluation and clinician rating scales. We genotyped for three MIF polymorphisms and analyzed peripheral MIF expression and serum levels. The absence of minor allele homozygous individuals in the large group of 96 female patients compared with 10-16% in female controls suggests a protective effect for MDD, which was not observed in the male group. There were no significant group differences of protein and expression levels, however, both showed predictive potential for the course of depression severity in some subgroups. While MIF protein levels, but not MIF expression, decreased during treatment, they were not associated with changes in depression severity. This project is the first to investigate three biological levels of MIF in depression. The data hint toward a genetic effect in women, but do not provide robust evidence for the utility of MIF as a biomarker for the diagnosis or monitoring of MDD. The observed predictive potential requires further analysis, emphasizing future attention to confounding factors such as sex and premedication.

The Role of Tryptophan Metabolites in Neuropsychiatric Disorders

In recent decades, neuropsychiatric disorders such as major depressive disorder, schizophrenia, bipolar, etc., have become a global health concern, causing various detrimental influences on patients. Tryptophan is an important amino acid that plays an indisputable role in several physiological processes, including neuronal function and immunity. Tryptophan’s metabolism process in the human body occurs using different pathways, including the kynurenine and serotonin pathways. Furthermore, other biologically active components, such as serotonin, melatonin, and niacin, are by-products of Tryptophan pathways. Current evidence suggests that a functional imbalance in the synthesis of Tryptophan metabolites causes the appearance of pathophysiologic mechanisms that leads to various neuropsychiatric diseases. This review summarizes the pharmacological influences of tryptophan and its metabolites on the development of neuropsychiatric disorders. In addition, tryptophan and its metabolites quantification following the neurotransmitters precursor are highlighted. Eventually, the efficiency of various biomarkers such as inflammatory, protein, electrophysiological, genetic, and proteomic biomarkers in the diagnosis/treatment of neuropsychiatric disorders was discussed to understand the biomarker application in the detection/treatment of various diseases.

Reward-Based Decision-Making Engages Distinct Modes of Cross-Frequency Coupling

Prefrontal cortex exerts control over sensory and motor systems via cross-frequency coupling. However, it is unknown whether these signals play a role in reward-based decision-making and whether such dynamic network configuration is altered in a major depressive episode. We recruited men and women with and without depression to perform a streamlined version of the Expenditure of Effort for Reward Task during recording of electroencephalography. Goal-directed behavior was quantified as willingness to exert physical effort to obtain reward, and reward-evaluation was the degree to which the decision to exert effort was modulated by incentive level. We found that the amplitude of frontal-midline theta oscillations was greatest in participants with the greatest reward-evaluation. Furthermore, coupling between frontal theta phase and parieto-occipital gamma amplitude was positively correlated with reward-evaluation. In addition, goal-directed behavior was positively correlated with coupling between frontal delta phase to motor beta amplitude. Finally, we performed a factor analysis to derive 2 symptom dimensions and found that mood symptoms positively tracked with reward-evaluation and motivation symptoms negatively tracked with goal-directed behavior. Altogether, these results provide evidence that 2 aspects of reward-based decision-making are instantiated by different modes of prefrontal top-down control and are modulated in different symptom dimensions of depression.

Predictors of 4-week antidepressant outcome in patients with first-episode major depressive disorder: An ROC curve analysis

BACKGROUND

Pretreatment characteristics of patients, symptom and function could be associated with antidepressant treatment outcome, but its predictive ability is not adequate. Our study aimed to identify predictors of acute antidepressant efficacy in patients with first-episode Major Depressive Disorder (MDD).

METHODS

187 patients with first-episode MDD were included and assessed clinical symptoms, cognitive function and global functioning using the 17-item Hamilton Depression Inventory (HAMD-17), MATRICS Consensus Cognitive Battery (MCCB) and Global Assessment of Functioning (GAF). Participants received treatment with a SSRI (escitalopram or venlafaxine) for 4 weeks. Logistic regression was used to analyze the association between patients' characteristics, symptom profiles, cognitive performance, and global functioning and the antidepressant outcome at the end of 4 weeks, and ROC curve analysis was performed for predictive accuracy with area under the receiver operating curve (AUC).

RESULTS

Antidepressant improvement, response and remission rate at week 4 was 87.7%, 64.7% and 42.8%, respectively. The combination of pretreatment clinical profiles, speed of processing and global functioning showed moderate discrimination of acute improvement, response and remission with AUCs of 0.863, 0.812 and 0.734, respectively.

LIMITATIONS

The major limitation of the present study is the study did not combine pharmacogenomics from the perspective of antidepressant drug metabolism.

CONCLUSION

Aside from the baseline clinical symptoms, cognitive function and global functioning could be predictors of acute treatment outcome in first episode MDD using escitalopram or venlafaxine. This relatively simple application based on clinical symptoms and function seems to be cost-effective method to identify individuals who are more likely to respond to antidepressant treatment.

Biological Diagnosis of Depression: A Biomarker Panel from Several Nonspecial Indicators Instead of the Specific Biomarker(s)

It is a consensus that the diagnosis efficiency of depression is rather low in clinic. The traditional way of diagnosing depression by symptomatology is flawed. Recent years, a growing body of evidence has underlined the importance of physiological indicators in the diagnosis of depression. However, the diagnosis of depression is difficult to be like some common clinical diseases, which have clear physiological indicators. A single physiological index provides limited information to clinicians and is of little help in the diagnosis of depression. Thus, it is more rational and practical to diagnose depression with a biomarker panel, which covers a few non-specific indicators, such as hormones, cytokines, and neurotrophins. This open review suggested that biomarker panel had a bright future in creating a new model of depression diagnosis or at least providing a reference to the existing depression criteria. The viewpoint is also the future of other psychiatric diagnosis.

Serum NLRP3 Inflammasome and BDNF: Potential Biomarkers Differentiating Reactive and Endogenous Depression

BACKGROUND

The highly heterogeneous pathogenesis of depression and limited response to current antidepressants call for more objective evidence for depression subtypes. Reactive and endogenous depression are two etiologically distinct subtypes associated with different treatment responses. This study aims to explore the potential biomarkers that differentiate reactive and endogenous depressions.

METHODS

The clinical manifestations and biological indicators of 64 unmedicated mild-to-moderate depression patients (32 reactive depression patients and 32 endogenous depression patients) and 21 healthy subjects were observed. The 24-item Hamilton rating scale for depression (HAMD-24) was used to evaluate the severity of depression. Serum levels of depression-related biological indicators were measured by using the enzyme-linked immunosorbent assay.

RESULTS

The NLRP3 level of reactive depression was significantly lower than those of endogenous depression and healthy controls. There was a significant negative correlation between the BDNF level and the HAMD-24 total scores for patients with reactive depression.

CONCLUSION

Our findings suggested the serum NLRP3 and BDNF levels could be potential biomarkers for detecting and evaluating the severity of reactive depression.

Comprehensive analysis of endoplasmic reticulum stress and immune infiltration in major depressive disorder

BACKGROUND

Major depressive disorder (MDD) is a life-threatening, debilitating mental health condition. An important factor in the development of depression is endoplasmic reticulum stress (ERS). However, their roles in MDD have not yet been established. The goal of this study was to examine ERS and its underlying molecular mechanisms in MDD.

METHODS

We used data from two microarray datasets (GSE98793 and GSE39653) and the GeneCards database to examine the reticulum stress-related differentially expressed genes (ERSR-DEGs) associated with MDD. Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), Gene Set Enrichment Analysis (GSEA), and Gene Set Variation Analysis (GSVA) were used to further investigate the function and mechanism of ERS in MDD. Moreover, we constructed protein-protein interaction (PPI) networks to identify hub genes as well as the regulatory network of microRNAs (miRNAs), transcription factors (TFs), and potential drugs related to ERSR-DEGs. CIBERSORT was then used to evaluate the immune activity of MDD samples and conduct a correlation analysis between the hub genes and immune cells.

RESULTS

In total, 37 ERSR-DEGs and five hub genes were identified (NCF1, MAPK14, CASP1, CYBA, and TNF). Functional enrichment analysis revealed that ERSR-DEGs were predominantly enriched in inflammation-and immunity-related pathways, such as tumor necrosis factor signaling, NF-κB signaling, and Toll-like receptor signaling pathways. Additionally, 179 miRNAs, 25 TFs, and 15 potential drugs were tested for their interactions with the ERSR-DEGs. CIBERSORT found high proportions of Tregs, monocytes, and macrophages M0 in the MDD samples. Among these, hub genes showed a significant correlation with immune cell infiltration in patients with MDD.

CONCLUSIONS

NCF1, MAPK14, CASP1, CYBA, and TNF are potential ERS-related biomarkers for the diagnosis of MDD. Our research has revealed a significant correlation between immune cells and ERS-related genes with MDD. Not only did our study contribute to a better understanding of the regulatory mechanisms of ERS in underlying MDD pathology, but it also established a paradigm for future studies on ERS.

Metabolomic signatures associated with depression and predictors of antidepressant response in humans: A CAN-BIND-1 report

One of the biggest challenges in treating depression is the heterogeneous and qualitative nature of its clinical presentations. This highlights the need to find quantitative molecular markers to tailor existing treatment strategies to the individual's biological system. In this study, high-resolution metabolic phenotyping of urine and plasma samples from the CAN-BIND study collected before treatment with two common pharmacological strategies, escitalopram and aripiprazole, was performed. Here we show that a panel of LDL and HDL subfractions were negatively correlated with depression in males. For treatment response, lower baseline concentrations of apolipoprotein A1 and HDL were predictive of escitalopram response in males, while higher baseline concentrations of apolipoprotein A2, HDL and VLDL subfractions were predictive of aripiprazole response in females. These findings support the potential of metabolomics in precision medicine and the possibility of identifying personalized interventions for depression.

Exposure to particulate matter, prenatal depressive symptoms and HPA axis dysregulation

BACKGROUND

The prevalence of depression during pregnancy is on the rise, affecting women's well-being and their children's health outcomes. Preliminary studies suggest that exposure to air pollution during pregnancy may play a role in development of depressive symptoms. In addition, pollution has been linked to dysregulation of the hypothalamic-pituitary-adrenal (HPA) axis, our brain's primary stress response system. The purpose of this study was to examine the association of air pollution exposure during pregnancy to prenatal depressive symptoms. We also evaluated whether cortisol, the hormonal endpoint of HPA activation, mediated the relationship between exposure to pollution and prenatal depression.

METHODS

Women were recruited in obstetric clinics during their third trimester of pregnancy. They completed the Patient Health Questionnaire-9 to assess depression and provided salivary samples at 4 times during the day for 2 days. Four measures of cortisol were calculated from salivary assays: average cortisol level, cortisol awakening response (CAR), diurnal cortisol slope (DCS), and area under the curve (AUCG). We acquired data on particulate matter with a diameter of 2.5 μm (PM2.5) or less within each woman's residential area from public records of the air quality control district. Structural equation modeling was used to analyze the aims.

RESULTS

Increased prenatal exposure to PM2.5 across pregnancy was associated with more severe depressive symptoms during the 3rd trimester (β = 0.14, p = 0.02). Greater PM2.5 exposure also had significant relationships with both higher cortisol AUCG (β = 15.933, p = 0.005) and average cortisol levels (β = 0.018, p = 0.023) among women. However, no cortisol parameter appeared to mediate the relationship between PM2.5 exposure and depressive symptoms.

CONCLUSIONS

Findings suggest pregnancy may be a critical window of sensitivity to PM2.5 exposure that escalates depression risk and induces activation of the HPA axis, evidenced in greater overall cortisol concentration. Further research is needed to identify mechanisms underlying the effects of particulate matter, especially potential methylation of glucocorticoid or serotonin transporter genes that may elicit changes in both depression and the stress response system. In addition, assessment of depression appears warranted for pregnant women in regions known for high pollution.

Nervonic acid level in cerebrospinal fluid is a candidate biomarker for depressive and manic symptoms: A pilot study

OBJECTIVE

Our previous metabolomics study showed that the plasma nervonic acid levels were higher in patients with major depressive disorder (MDD) than those in healthy controls and patients with bipolar disorder (BD). To examine whether the nervonic acid levels differ in the central nervous system, we investigated the levels in the cerebrospinal fluid (CSF) of patients with MDD, BD, and healthy controls.

METHODS

Nervonic acid levels in CSF were measured by gas chromatography time-of-flight mass spectrometry. The participants included 30 patients with MDD, 30 patients with BD, and 30 healthy controls.

RESULTS

In contrast to our previous study, no significant differences were found in the nervonic acid level in the CSF among the patients with MDD, BD, and the healthy controls. Though no significant state-dependent changes were found among the three groups, we did observe a significant negative correlation between the nervonic acid levels and depressive symptoms in the depressive state of patients with MDD and BD (r = -0.38, p = .046). Further, a significant positive correlation was found between the nervonic acid levels and manic symptoms in the manic state of patients with BD (r = 0.79, p = .031).

CONCLUSION

The nervonic acid levels in the CSF did not differ among the patients with MDD, BD, and the healthy controls; however, a significant negative correlation with depressive symptoms and a positive correlation with manic symptoms was observed. Thus, the nervonic acid levels in the CSF may be a candidate biomarker for mood symptoms.

Benefits of animal models to understand the pathophysiology of depressive disorders

Major depressive disorder (MDD) is a potentially life-threatening mental disorder imposing severe social and economic burden worldwide. Despite the existence of effective antidepressant treatment strategies the exact pathophysiology of the disease is still unknown. Large number of animal models of MDD have been developed over the years, but all of them suffer from significant shortcomings. Despite their limitations these models have been extensively used in academic research and drug development. The aim of this review is to highlight the benefits of animal models of MDD. We focus here on recent experimental data where animal models were used to examine current theories of this complex disease. We argue, that despite their evident imperfections, these models provide invaluable help to understand cellular and molecular mechanisms contributing to the development of MDD. Furthermore, animal models are utilized in research to find clinically useful biomarkers. We discuss recent neuroimaging and microRNA studies since these investigations yielded promising candidates for biomarkers. Finally, we briefly summarize recent progresses in drug development, i.e. the FDA approval of two novel antidepressant drugs: S-ketamine and brexanolone (allopregnanolone). Deeper understanding of the exact molecular and cellular mechanisms of action responsible for the antidepressant efficacy of these rapid acting drugs could aid us to design further compounds with similar effectiveness, but less side effects. Animal studies are likely to provide valuable help in this endeavor.

Assessing the links between childhood trauma, C-reactive protein and response to antidepressant treatment in patients with affective disorders

Adverse Childhood Experiences (ACE) are a well-known risk-factor for depression. Additionally, (high-sensitive) C-reactive Protein (hsCRP) is elevated in subgroups of depressed patients and high following ACE. In this context the literature considers hsCRP and ACE to be associated with treatment resistant depression. With the data being heterogenous, this study aimed to explore the associations of ACE, hsCRP levels and response to antidepressant treatment in uni- and bipolar depression. N = 76 patients diagnosed with uni- or bipolar depression and N = 53 healthy controls were included. Treatment was over 6 weeks in an inpatient psychiatric setting within an observatory study design. Depressive symptoms were assessed by the Montgomery-Asberg Depression Rating Scale (MADRS), ACE were assessed by the Childhood Trauma Questionnaire (CTQ); the body-mass-index (BMI) and hsCRP were measured. HsCRP levels did not differ between the study population and the healthy controls. While the depressive symptoms decreased, the hsCRP levels increased. Sexual abuse was associated with significant higher and emotional abuse with lower levels of hsCRP after 6 weeks. The baseline hsCRP levels and the ACE subgroups did not show significant associations with the treatment response in unipolar depressed patients. The long-lasting effects of specific forms of ACE may have relevant impact on inflammation, supporting hsCRP to be a suitable biomarker. With ACE and hsCRP not showing any significant associations with treatment response in the unipolar depressed subgroup, a more differentiate research concerning biomarkers and treatment regimens is needed when talking about treatment response.

Inflammation Biomarkers in Psychiatry

Introduction:

There has long been a need for diagnostic, theragnostic, and prognostic biomarkers for psychiatric disorders. Biomarkers help in reducing ambiguity and arbitrariness and increase objectivity. In this context, many candidates for hormonal, immunological, serological, and neuroimaging markers have been proposed, but none of these marker candidates alone nor a biomarker panel has been approved for any disease. The fact that almost all psychiatric disorders are heterogeneous makes this process challenging. However, strong biomarker candidates have been identified, especially in light of the large number of clinical and preclinical studies conducted within the last five years.

Objective:

The aim of this article was to compile and discuss the current information on immune biomarkers in major psychiatric disorders, such as schizophrenia, depression, bipolar disorder, and anxiety disorders.

Methods:

In this study, respected scientific databases were searched using key terms related to the subject, and the related literature was examined in detail.

Results:

There are many relationships between psychiatric disorders and immune system parameters. Evidence also suggests that neuroinflammation is involved in the etiopathogenesis of psychiatric disorders. Markers, such as proinflammatory cytokines, tumor necrosis factor alpha, and C-reactive protein have been associated with psychiatric disorders in numerous studies.

Conclusions:

The neuroinflammation hypothesis has an important place in the etiopathogenesis of psychiatric disorders. Uncertainty remains as to whether neuroinflammation is a cause or consequence of psychiatric disorders. Some researchers have indicated that intestinal microbiota composition disorders and dysbiosis are sources of neuroinflammation. Immune marker studies are of great importance in terms of eliminating this uncertainty and overcoming diagnostic and treatment difficulties in the clinic. In this review, biomarker studies on psychiatric disorders were examined from the viewpoint of the immune system and discussed in light of the current studies.

Kynurenic acid is a potential overlapped biomarker between diagnosis and treatment response for depression from metabolome analysis

Since optimal treatment at an early stage leads to remission of symptoms and recovery of function, putative biomarkers leading to early diagnosis and prediction of therapeutic responses are desired. The current study aimed to use a metabolomic approach to extract metabolites involved in both the diagnosis of major depressive disorder (MDD) and the prediction of therapeutic response for escitalopram. We compared plasma metabolites of MDD patients (n = 88) with those in healthy participants (n = 88) and found significant differences in the concentrations of 20 metabolites. We measured the Hamilton Rating Scale for Depression (HRSD) on 62 patients who completed approximately six-week treatment with escitalopram before and after treatment and found that kynurenic acid and kynurenine were significantly and negatively associated with HRSD reduction. Only one metabolite, kynurenic acid, was detected among 73 metabolites for overlapped biomarkers. Kynurenic acid was lower in MDD, and lower levels showed a better therapeutic response to escitalopram. Kynurenic acid is a metabolite in the kynurenine pathway that has been widely accepted as being a major mechanism in MDD. Overlapping biomarkers that facilitate diagnosis and prediction of the treatment response may help to improve disease classification and reduce the exposure of patients to less effective treatments in MDD.

Effect of Xiaoyaosan on Colon Morphology and Intestinal Permeability in Rats With Chronic Unpredictable Mild Stress

Purpose

In our present study, a rat depression model induced by 6 weeks of chronic unpredictable mild stress (CUMS) was established, and we investigated how Xiaoyaosan affects the intestinal permeability of depressed rats and alterations in tight-junction proteins (TJs) involved in this process.

Methods

The rat depression model was established using CUMS for 6 consecutive weeks. A total of 40 healthy male Sprague-Dawley rats were randomly sorted into four groups: the control group, CUMS group, Xiaoyaosan group, and fluoxetine group. All groups, excluding the control group, were subjected to the 6-week CUMS program to generate the depression model. Body weight, food intake, and behaviors were observed during the modeling period. Histopathological alterations of colon tissue were evaluated by hematoxylin-eosin staining (H&E), and mucus-containing goblet cells were detected by periodic acid-Schiff (PAS) staining. The ultrastructural morphology of colonic mucosa was observed by transmission electron microscopy. Furthermore, immunohistochemistry (IHC) and quantitative reverse transcription polymerase chain reaction (qRT-PCR) were used to determine the expression of TJs. The concentrations of 5-hydroxytryptamine (5-HT) in the hypothalamus and colon were also assessed using enzyme-linked immunosorbent assay (ELISA).

Results

Treatment of depressed rats with Xiaoyaosan alleviated depression-like behaviors as demonstrated by increases in the total distance traveled, the number of entries into the central area in the open field test, the duration spent in the central area, and sucrose preference. Xiaoyaosan treatment also increased body weight gain and food intake in depressed rats. Moreover, Xiaoyaosan treatment effectively improved the colonic pathological and ultrastructural changes, upregulated the expression of ZO-1, occludin, and claudin-1 in the colon, and increased 5-HT levels in the hypothalamus and colonic mucosa.

Conclusions

Xiaoyaosan treatment attenuates depression-like behaviors caused by CUMS and ameliorates CUMS-induced abnormal intestinal permeability, which may be associated with the expression of TJs. These results suggest that Xiaoyaosan exerts an antidepressant effect that may be related to an improvement of intestinal barrier function via the brain-gut axis.

Homocysteine as a peripheral biomarker in bipolar disorder: A meta-analysis

Background:

Bipolar disorder (BD) is a psychiatric disorder with an uncertain aetiology. Recently, special attention has been given to homocysteine (Hcy), as it has been suggested that alterations in 1-carbon metabolism might be implicated in diverse psychiatric disorders. However, there is uncertainty regarding possible alterations in peripheral Hcy levels in BD.

Methods:

This study comprises a meta-analysis comparing serum and plasma Hcy levels in persons with BD and healthy controls. We conducted a systematic search for all eligible English and non-English peer-reviewed articles.

Results:

Nine cross-sectional studies were included in the meta-analyses, providing data on 1547 participants. Random-effects meta-analysis showed that serum and plasma levels of Hcy were increased in subjects with BD in either mania or euthymia when compared to healthy controls, with a large effect size in the mania group (g= 0.98, 95% CI: 0.8–1.17,P< 0.001,n= 495) and a small effect in the euthymia group (g= 0.3, 95% CI: 0.11–0.48,P= 0.002,n= 1052).

Conclusions:

Our meta-analysis provides evidence that Hcy levels are elevated in persons with BD during mania and euthymia. Peripheral Hcy could be considered as a potential biomarker in BD, both of trait (since it is increased in euthymia), and also of state (since its increase is more accentuated in mania). Longitudinal studies are needed to clarify the relationship between bipolar disorder and Hcy, as well as the usefulness of peripheral Hcy as both a trait and state biomarker in BD.

An investigation of cortical thickness and antidepressant response in major depressive disorder: A CAN-BIND study report

Major depressive disorder (MDD) is considered a highly heterogeneous clinical and neurobiological mental disorder. We employed a novel layered treatment design to investigate whether cortical thickness features at baseline differentiated treatment responders from non-responders after 8 and 16 weeks of a standardized sequential antidepressant treatment. Secondary analyses examined baseline differences between MDD and controls as a replication analysis and longitudinal changes in thickness after 8 weeks of escitalopram treatment. 181 MDD and 95 healthy comparison (HC) participants were studied. After 8 weeks of escitalopram treatment (10-20 mg/d, flexible dosage), responders (>50% decrease in Montgomery-Åsberg Depression Scale score) were continued on escitalopram; non-responders received adjunctive aripiprazole (2-10 mg/d, flexible dosage). MDD participants were classified into subgroups according to their response profiles at weeks 8 and 16. Baseline group differences in cortical thickness were analyzed with FreeSurfer between HC and MDD groups as well as between response groups. Two-stage longitudinal processing was used to investigate 8-week escitalopram treatment-related changes in cortical thickness. Compared to HC, the MDD group exhibited thinner cortex in the left rostral middle frontal cortex [MNI(X,Y,Z=-29,9,54.5,-7.7); CWP=0.0002]. No baseline differences in cortical thickness were observed between responders and non-responders based on week-8 or week-16 response profile. No changes in cortical thickness was observed after 8 weeks of escitalopram monotherapy. In a two-step 16-week sequential clinical trial we found that baseline cortical thickness does not appear to be associated to clinical response to pharmacotherapy at 8 or 16 weeks.

The Missing Link: How Exosomes and miRNAs can Help in Bridging Psychiatry and Molecular Biology in the Context of Depression, Bipolar Disorder and Schizophrenia

MicroRNAs (miRNAs) only recently have been recognized as promising molecules for both fundamental and clinical neuroscience. We provide a literature review of miRNA biomarker studies in three most prominent psychiatric disorders (depression, bipolar disorder and schizophrenia) with the particular focus on depression due to its social and healthcare importance. Our search resulted in 191 unique miRNAs across 35 human studies measuring miRNA levels in blood, serum or plasma. 30 miRNAs replicated in more than one study. Most miRNAs targeted neuroplasticity and neurodevelopment pathways. Various limitations do not allow us to make firm conclusions on clinical potential of studied miRNAs. Based on our results we discuss the rationale for future research investigations of exosomal mechanisms to overcome methodological caveats both in studying etiology and pathogenesis, and providing an objective back-up for clinical decisions.

Brain IGFBP-5 modifies the relation of depressive symptoms to decline in cognition in older persons

BACKGROUND

Brain proteins, including Insulin-like Growth Factor Binding Protein 5 (IGFBP-5), have been associated with cognitive dysfunction in aging. Mechanisms linking depression with cognition are poorly understood. We hypothesize that the association of depressive symptoms with cognition is mediated or modified by brain proteins.

METHODS

IGFBP-5, HSPB2, AK4, ITPK1 and PLXNB1 were measured in dorsolateral prefrontal cortex in 1057 deceased participants, who underwent annual assessments of depressive symptoms and cognition for a mean of 8.9 years. The average number of depressive symptoms per year before a dementia diagnosis was calculated for each person.

RESULTS

A one standard deviation above the mean IGFBP-5 was associated with a 14% higher odds of having more depressive symptoms (p < 0.031). Higher IGFBP-5 was associated with faster decline in global cognition (p < 0.001) and five cognitive domains (p < 0.008), controlling for depressive symptoms. IGFBP-5 moderated the association of depressive symptoms with decline in global cognition (p = 0.045). IGFBP-5 mediated ten percent or less of the total effect of depressive symptoms on decline in global cognition and the cognitive domains (p > 0.070).

LIMITATIONS

Participants were volunteers and self-selection bias limits the generalizability of our findings. In addition, we used self-reported data on depressive symptoms. However, we also used data on depression medications as sensitivity analyses to confirm findings.

CONCLUSIONS

In old age, brain IGFBP-5 is associated with depressive symptoms and cognition. The association of depressive symptoms with cognitive decline is conditional on IGFBP-5.

Prioritization and comprehensive analysis of genes related to major depressive disorder

Background

Major depressive disorder (MDD) is a serious mental health problem in modern society, which is difficult to identify and diagnose in the early stages. Despite strong evidence supporting the heritability of MDD, progresses in large‐scale and individual genetic studies remain preliminary.

Methods

In this study, a multi‐data source‐based prioritization (MDSP) method was proposed, and an appropriate threshold was determined for the optimization of depression‐related genes (DEPgenes). Analyses on Gene Ontology biological processes, KEGG pathway and the specific pathway crosstalk network were further proposed.

Results

A total of 143 DEPgenes were identified and the MDD‐specific network was constructed for the pathogenesis investigation and therapeutic methods development of MDD. Comparing with existing research strategies, the genetic optimization and analysis results were confirmed to be reliable. Finally, the pathway enrichment and crosstalk analyses revealed two unique pathway interaction modules that were significantly enriched with MDD genes. The related core pathways of neuroactive ligand‐receptor interaction and dopaminergic synapse supported the neuropathology hypothesis of MDD. And the pathways of serotonergic synapse and morphine addiction indicated the mechanism of drug addiction caused by serotonin used in the treatment.

Conclusions

This work provided a reference for the study of MDD, although future validation by extensive experimentation is still required.

White matter abnormalities in depression: A categorical and phenotypic diffusion MRI study

Mood depressive disorder is one of the most disabling chronic diseases with a high rate of everyday life disability that affects 350 million people around the world. Recent advances in neuroimaging have reported widespread structural abnormalities, suggesting a dysfunctional frontal-limbic circuit involved in the pathophysiological mechanisms of depression. However, a variety of different white matter regions has been implicated and is sought to suffer from lack of reproducibility of such categorical-based biomarkers. These inconsistent results might be attributed to various factors: actual categorical definition of depression as well as clinical phenotype variability. In this study, we 1/ examined WM changes in a large cohort (114 patients) compared to a healthy control group and 2/ sought to identify specific WM alterations in relation to specific depressive phenotypes such as anhedonia (i.e. lack of pleasure), anxiety and psychomotor retardation –three core symptoms involved in depression. Consistent with previous studies, reduced white matter was observed in the genu of the corpus callosum extending to the inferior fasciculus and posterior thalamic radiation, confirming a frontal-limbic circuit abnormality. Our analysis also reported other patterns of increased fractional anisotropy and axial diffusivity as well as decreased apparent diffusion coefficient and radial diffusivity in the splenium of the corpus callosum and posterior limb of the internal capsule. Moreover, a positive correlation between FA and anhedonia was found in the superior longitudinal fasciculus as well as a negative correlation in the cingulum. Then, the analysis of the anxiety and diffusion metric revealed that increased anxiety was associated with greater FA values in genu and splenium of corpus callosum, anterior corona radiata and posterior thalamic radiation. Finally, the motor retardation analysis showed a correlation between increased Widlöcher depressive retardation scale scores and reduced FA in the body and genu of the corpus callosum, fornix, and superior striatum. Through this twofold approach (categorical and phenotypic), this study has underlined the need to move forward to a symptom-based research area of biomarkers, which help to understand the pathophysiology of mood depressive disorders and to stratify precise phenotypes of depression with targeted therapeutic strategies.

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Mood depressive disorder is one of the most disabling chronic disease.

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Past studies of diffusion analysis had found inconsistent results.

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We analyzed white matter integrity in a large cohort of depressed patients.

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We conducted both categorical and dimensional approaches.

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In the future, these biomarkers could help to develop new therapeutic strategies.

Brain and behavioral correlates of insulin resistance in youth with depression and obesity

Depression, together with insulin resistance, is increasingly prevalent among youth. These conditions have traditionally been compartmentalized, but recent evidence suggests that a shared brain motivational network underlies their co-occurrence. We posit that, in the context of depressive symptoms, insulin resistance is associated with aberrant structure and functional connectivity in the Anterior Cingulate Cortex (ACC) and hippocampus. This motivational neural circuit underlies dysfunctional behavioral responses and increased sensitivity to rewarding aspects of ingesting high calorie food that lead to disinhibition of eating even when satiated. To investigate this shared mechanism, we evaluated a sample of forty-two depressed and overweight (BMI > 85th%) youth aged 9 to 17. Using ACC and hippocampus structural and seed-based regions of interest, we investigated associations between insulin resistance, depression, structure (ACC thickness, and ACC and hippocampal area), and resting-state functional connectivity (RSFC). We predicted that aberrant associations among these neural and behavioral characteristics would be stronger in insulin resistant compared to insulin sensitive youth. We found that youth with greater insulin resistance had higher levels of anhedonia and more food seeking behaviors, reduced hippocampal and ACC volumes, and greater levels of ACC and hippocampal dysconnectivity to fronto-limbic reward networks at rest. For youth with high levels of insulin resistance, thinner ACC and smaller hippocampal volumes were associated with more severe depressive symptoms, whereas the opposite was true for youth with low levels of insulin resistance. The ACC-hippocampal motivational network that subserves depression and insulin resistance separately, may represent a critical neural interaction that link these syndromes together.

A systematic meta-review of predictors of antidepressant treatment outcome in major depressive disorder

INTRODUCTION

The heterogeneity of symptoms and complex etiology of depression pose a significant challenge to the personalization of treatment. Meanwhile, the current application of generic treatment approaches to patients with vastly differing biological and clinical profiles is far from optimal. Here, we conduct a meta-review to identify predictors of response to antidepressant therapy in order to select robust input features for machine learning models of treatment response. These machine learning models will allow us to learn associations between patient features and treatment response which have predictive value at the individual patient level; this learning can be optimized by selecting high-quality input features for the model. While current research is difficult to directly apply to the clinic, machine learning models built using knowledge gleaned from current research may become useful clinical tools.

METHODS

The EMBASE and MEDLINE/PubMed online databases were searched from January 1996 to August 2017, using a combination of MeSH terms and keywords to identify relevant literature reviews. We identified a total of 1909 articles, wherein 199 articles met our inclusion criteria.

RESULTS

An array of genetic, immune, endocrine, neuroimaging, sociodemographic, and symptom-based predictors of treatment response were extracted, varying widely in clinical utility.

LIMITATIONS

Due to heterogeneous sample sizes, effect sizes, publication biases, and methodological disparities across reviews, we could not accurately assess the strength and directionality of every predictor.

CONCLUSION

Notwithstanding our cautious interpretation of the results, we have identified a multitude of predictors that can be used to formulate a priori hypotheses regarding the input features for a computational model. We highlight the importance of large-scale research initiatives and clinically accessible biomarkers, as well as the need for replication studies of current findings. In addition, we provide recommendations for future improvement and standardization of research efforts in this field.

Treatment-resistant depression and peripheral C-reactive protein

BACKGROUND

C-reactive protein (CRP) is a candidate biomarker for major depressive disorder (MDD), but it is unclear how peripheral CRP levels relate to the heterogeneous clinical phenotypes of the disorder.AimTo explore CRP in MDD and its phenotypic associations.

METHOD

We recruited 102 treatment-resistant patients with MDD currently experiencing depression, 48 treatment-responsive patients with MDD not currently experiencing depression, 48 patients with depression who were not receiving medication and 54 healthy volunteers. High-sensitivity CRP in peripheral venous blood, body mass index (BMI) and questionnaire assessments of depression, anxiety and childhood trauma were measured. Group differences in CRP were estimated, and partial least squares (PLS) analysis explored the relationships between CRP and specific clinical phenotypes.

RESULTS

Compared with healthy volunteers, BMI-corrected CRP was significantly elevated in the treatment-resistant group (P = 0.007; Cohen's d = 0.47); but not significantly so in the treatment-responsive (d = 0.29) and untreated (d = 0.18) groups. PLS yielded an optimal two-factor solution that accounted for 34.7% of variation in clinical measures and for 36.0% of variation in CRP. Clinical phenotypes most strongly associated with CRP and heavily weighted on the first PLS component were vegetative depressive symptoms, BMI, state anxiety and feeling unloved as a child or wishing for a different childhood.

CONCLUSIONS

CRP was elevated in patients with MDD, and more so in treatment-resistant patients. Other phenotypes associated with elevated CRP included childhood adversity and specific depressive and anxious symptoms. We suggest that patients with MDD stratified for proinflammatory biomarkers, like CRP, have a distinctive clinical profile that might be responsive to second-line treatment with anti-inflammatory drugs.Declaration of interestS.R.C. consults for Cambridge Cognition and Shire; and his input in this project was funded by a Wellcome Trust Clinical Fellowship (110049/Z/15/Z). E.T.B. is employed half time by the University of Cambridge and half time by GlaxoSmithKline; he holds stock in GlaxoSmithKline. In the past 3 years, P.J.C. has served on an advisory board for Lundbeck. N.A.H. consults for GlaxoSmithKline. P.d.B., D.N.C.J. and W.C.D. are employees of Janssen Research &amp; Development, LLC., of Johnson &amp; Johnson, and hold stock in Johnson &amp; Johnson. The other authors report no financial disclosures or potential conflicts of interest.

Interleukin, tumor necrosis factor-α and C-reactive protein profiles in melancholic and non-melancholic depression: A systematic review

OBJECTIVE

The current diagnostic criteria for major depressive disorder (MDD) do not allow prediction of prognosis and therapeutic response. A possible strategy to improve this situation is the identification of depression subtypes on the bases of biomarkers reflecting underlying pathological processes such as neuro-inflammation.

METHODS

The PubMed/Medline database was searched until Apr 25th, 2017. In the initial search 1018 articles were retrieved, which were subsequently screened and only selected when the inclusion and exclusion criteria were fulfilled.

RESULTS

Eight eligible studies were found. Overall, serum interleukin-6 and 1β values were increased in the melancholic MDD subtype compared to controls and the non-melancholic MDD subtype. C-reactive protein was increased in non-melancholic MDD in 2 out of 4 studies, while there was no difference for tumor necrosis factor-α and interleukin-2 and 10.

CONCLUSION

Given the paucity of eligible studies the tentative conclusion must be drawn that peripheral inflammation markers have limited added value thus far to distinguish between melancholic and non-melancholic depression. To allow for a more definitive conclusion, further research is warranted using a broader panel of inflammatory markers in MDD subtypes, preferably based on a general consensus regarding diagnostic criteria and subtype definitions.

Circulating microRNAs as biomarkers for depression: Many candidates, few finalists

BACKGROUND

Recent research has highlighted the potential of microRNAs to serve as physiological indicators of disease process among clinically depressed patients.

METHODS

In a comprehensive literature search through PubMed, we identified 23 articles comparing circulating (blood, plasma, or serum) microRNA expression levels in depressed versus healthy human subjects. Six studies examining circulatory microRNA expression through animal models of depression were also identified through the search and details of each study were outlined. A meta-analytic evaluation of these studies was not considered feasible, given the absence of concordance in the literature to date.

RESULTS

A total of 178 specific microRNA candidates were identified in the human studies as significantly expressed among depressed samples. Ninety-seven of these microRNAs were upregulated, 75 were downregulated, and 6 showed mixed expression in depressed samples. Few microRNAs were consistently expressed across studies; the most consistent evidence was for microRNA-132, with replication in 4 different studies. Among animal studies, 2 studies investigated microRNA-16 through distinct stress-induced depression models.

LIMITATIONS

Structural variations in microRNA sequences, methodological inconsistencies in technology used among studies to measure microRNA expression levels, differences in the clinical severity and forms of depression among subjects, and the overall paucity of studies make it difficult to ascertain any robust, preliminary targets deserving of biomarker potential.

CONCLUSIONS

Ongoing research needs to address this high rate of non-replication as well as the methodological and reporting challenges of microRNA experimentation in order to determine valid effect sizes for the more proliferative candidates associated with depression.

Suggested Biomarkers for Major Depressive Disorder

Currently, the diagnosis of major depressive disorder (MDD) mainly relies on clinical examination and subjective evaluation of depressive symptoms. There is no non-invasive, quantitative test available today for the diagnosis of MDD. In MDD, exploration of biomarkers will be helpful in diagnosing the disorder as well as in choosing a treatment, and predicting the treatment response. In this article, it is aimed to review the findings of suggested biomarkers such as growth factors, cytokines and other inflammatory markers, oxidative stress markers, endocrine markers, energy balance hormones, genetic and epigenetic features, and neuroimaging in MDD and to evaluate how these findings contribute to the pathophysiology of MDD, the prediction of treatment response, severity of the disorder, and identification of subtypes. Among these, the findings related to the brain-derived neurotrophic factor, the hypothalamo-pituitary-adrenal axis, cytokines, and neuroimaging may be strong candidates for being biomarkers MDD, and may provide critical information in understanding biological etiology of depression. Although the findings are not sufficient yet, we think that the results of epigenetic studies will also provide very important contributions to the biomarker research in MDD. The availability of biomarkers in MDD will be an advancement that will facilitate the diagnosis of the disorder, treatment choices in the early stages, and prediction of the course of the disorder.

Will imaging individual raphe nuclei in males with major depressive disorder enhance diagnostic sensitivity and specificity?

BACKGROUND

Positron emission tomography (PET) studies in major depressive disorder (MDD) have reported higher serotonin 1A (5-HT_1A ) autoreceptor binding in the raphe. In males, the difference is so large that it can potentially be used as the first biological marker for MDD. However, the raphe includes several nuclei, which project to different regions of the brain and spinal cord and may be differentially involved in disease. We aimed to identify 5-HT_1A differences in individual raphe nuclei using PET in order to determine whether use of subnuclei would provide greater sensitivity and specificity of diagnosing MDD.

METHODS

We identified individual nuclei using a hybrid set-level technique on an average [¹¹ C]-WAY100635 PET image derived from 52 healthy volunteers (HV). We delineated three nuclei: dorsal raphe nucleus (DRN), median raphe nucleus (MRN), and raphe magnus (RMg). An atlas image of these nuclei was created and nonlinearly warped to each subject (through an associated MRI) in a separate sample of 41 males (25 HV, 16 MDD) who underwent [¹¹ C]-WAY100635 PET.

RESULTS

5-HT_1A binding was elevated in DRN in MDD (P < .01), and was not different in the RMg and MRN between groups. Receiver operating characteristic (ROC) curves showed that combining DRN and MRN produces highest sensitivity (94%) and specificity (84%) to identify MDD.

CONCLUSION

In agreement with postmortem studies, we found higher 5-HT_1A autoreceptor binding in MDD selectively in the DRN. 5-HT_1A autoreceptor binding in the combined DRN and MRN is a better biomarker for MDD than in the raphe as a whole.

Chemokines in depression in health and in inflammatory illness: a systematic review and meta-analysis

Inflammatory illness is associated with depression. Preclinical work has shown that chemokines are linked with peripheral-central crosstalk and may be important in mediating depressive behaviours. We sought to establish what evidence exists that differences in blood or cerebrospinal fluid chemokine concentration discriminate between individuals with depression and those without. Following PRISMA guidelines, we systematically searched Embase, PsycINFO and Medline databases. We included participants with physical illness for subgroup analysis, and excluded participants with comorbid psychiatric diagnoses. Seventy-three studies met the inclusion criteria for the meta-analysis. Individuals with depression had higher levels of blood CXCL4 and CXCL7 and lower levels of blood CCL4. Sensitivity analysis of studies with only physically healthy participants identified higher blood levels of CCL2, CCL3, CCL11, CXCL7 and CXCL8 and lower blood levels of CCL4. All other chemokines examined did not reveal significant differences (blood CCL5, CCL7, CXCL9, CXCL10 and cerebrospinal fluid CXCL8 and CXCL10). Analysis of the clinical utility of the effect size of plasma CXCL8 in healthy individuals found a negative predictive value 93.5%, given the population prevalence of depression of 10%. Overall, our meta-analysis finds evidence linking abnormalities of blood chemokines with depression in humans. Furthermore, we have demonstrated the possibility of classifying individuals with depression based on their inflammatory biomarker profile. Future research should explore putative mechanisms underlying this association, attempt to replicate existing findings in larger populations and aim to develop new diagnostic and therapeutic strategies.

The Perspectives Associated With the Computer-Based Diagnostic Method of Depressive Disorder

Depressive disorder (DD) shortens a healthy and productive human life, has significant public health costs is and associated with high suicide rates. In depression sadness and emotional misery manifest in facial expressions, as psychomotor slowing, lack of energy, high tension, and attenuated sensory perception. Loss of appetite, changes to the taste of food, and the loss of pleasure in eating are important criteria in the diagnosis of DD. We hypothesized that a patient's facial expressions and emotional responses to different tastes can be used as the diagnostic moderators for the development of a new contactless, computer-based method for diagnosis of DD. The confirmation of this hypothesis can shed a new perspective on early contactless, computer-based psychiatric diagnostic strategies and early identification of DD symptoms, as DD is an important issue in public mental health. The benefits of this method are evidence from several perspectives (I) patients can use a self-rating instrument to assess DD symptoms; this may act as an incentive to seek professional help; (II) family and community can use an instrument for early recognition of DD symptoms and suicidal tendencies, making it possible to encourage the individual to seek professional health care; (III) general practitioners have a reliable instrument for preliminary diagnosis of DD in primary care, thus saving the time and resources; (IV) public health benefits include early diagnosis and treatment of DD and better outcomes, reductions in disability-adjusted life years and the global burden of the disease. It is nevertheless important to recognize the limitations and risks of contactless diagnosis of DD. As it is a self-assessment method it is not possible to rule out false positives and false negatives. However, this method might be used for early diagnosis of DD symptoms. Also, it should be mentioned that further evaluation and an experts opinion about this method is needed. The clinical diagnosis of DD should continue to be made by healthcare professionals. Finally, this method may perspectively predict DD at an early stage and may ensure a higher quality of the patients' primary care in the public health system.

Translational Identification of Transcriptional Signatures of Major Depression and Antidepressant Response

Major depressive disorder (MDD) is a highly prevalent mental illness whose therapy management remains uncertain, with more than 20% of patients who do not achieve response to antidepressants. Therefore, identification of reliable biomarkers to predict response to treatment will greatly improve MDD patient medical care. Due to the inaccessibility and lack of brain tissues from living MDD patients to study depression, researches using animal models have been useful in improving sensitivity and specificity of identifying biomarkers. In the current study, we used the unpredictable chronic mild stress (UCMS) model and correlated stress-induced depressive-like behavior (n = 8 unstressed vs. 8 stressed mice) as well as the fluoxetine-induced recovery (n = 8 stressed and fluoxetine-treated mice vs. 8 unstressed and fluoxetine-treated mice) with transcriptional signatures obtained by genome-wide microarray profiling from whole blood, dentate gyrus (DG), and the anterior cingulate cortex (ACC). Hierarchical clustering and rank-rank hypergeometric overlap (RRHO) procedures allowed us to identify gene transcripts with variations that correlate with behavioral profiles. As a translational validation, some of those transcripts were assayed by RT-qPCR with blood samples from 10 severe major depressive episode (MDE) patients and 10 healthy controls over the course of 30 weeks and four visits. Repeated-measures ANOVAs revealed candidate trait biomarkers (ARHGEF1, CMAS, IGHMBP2, PABPN1 and TBC1D10C), whereas univariate linear regression analyses uncovered candidates state biomarkers (CENPO, FUS and NUBP1), as well as prediction biomarkers predictive of antidepressant response (CENPO, NUBP1). These data suggest that such a translational approach may offer new leads for clinically valid panels of biomarkers for MDD.

Smartphone-Based Monitoring of Objective and Subjective Data in Affective Disorders: Where Are We and Where Are We Going? Systematic Review

Background

Electronic mental health interventions for mood disorders have increased rapidly over the past decade, most recently in the form of various systems and apps that are delivered via smartphones.

Objective

We aim to provide an overview of studies on smartphone-based systems that combine subjective ratings with objectively measured data for longitudinal monitoring of patients with affective disorders. Specifically, we aim to examine current knowledge on: (1) the feasibility of, and adherence to, such systems; (2) the association of monitored data with mood status; and (3) the effects of monitoring on clinical outcomes.

Methods

We systematically searched PubMed, Web of Science, PsycINFO, and the Cochrane Central Register of Controlled Trials for relevant articles published in the last ten years (2007-2017) by applying Boolean search operators with an iterative combination of search terms, which was conducted in February 2017. Additional articles were identified via pearling, author correspondence, selected reference lists, and trial protocols.

Results

A total of 3463 unique records were identified. Twenty-nine studies met the inclusion criteria and were included in the review. The majority of articles represented feasibility studies (n=27); two articles reported results from one randomized controlled trial (RCT). In total, six different self-monitoring systems for affective disorders that used subjective mood ratings and objective measurements were included. These objective parameters included physiological data (heart rate variability), behavioral data (phone usage, physical activity, voice features), and context/environmental information (light exposure and location). The included articles contained results regarding feasibility of such systems in affective disorders, showed reasonable accuracy in predicting mood status and mood fluctuations based on the objectively monitored data, and reported observations about the impact of monitoring on clinical state and adherence of patients to the system usage.

Conclusions

The included observational studies and RCT substantiate the value of smartphone-based approaches for gathering long-term objective data (aside from self-ratings to monitor clinical symptoms) to predict changes in clinical states, and to investigate causal inferences about state changes in patients with affective disorders. Although promising, a much larger evidence-base is necessary to fully assess the potential and the risks of these approaches. Methodological limitations of the available studies (eg, small sample sizes, variations in the number of observations or monitoring duration, lack of RCT, and heterogeneity of methods) restrict the interpretability of the results. However, a number of study protocols stated ambitions to expand and intensify research in this emerging and promising field.

The influence of the rs6295 gene polymorphism on serotonin-1A receptor distribution investigated with PET in patients with major depression applying machine learning

Major depressive disorder (MDD) is the most common neuropsychiatric disease and despite extensive research, its genetic substrate is still not sufficiently understood. The common polymorphism rs6295 of the serotonin-1A receptor gene (HTR1A) is affecting the transcriptional regulation of the 5-HT_1A receptor and has been closely linked to MDD. Here, we used positron emission tomography (PET) exploiting advances in data mining and statistics by using machine learning in 62 healthy subjects and 19 patients with MDD, which were scanned with PET using the radioligand [carbonyl-¹¹C]WAY-100635. All the subjects were genotyped for rs6295 and genotype was grouped in GG vs C allele carriers. Mixed model was applied in a ROI-based (region of interest) approach. ROI binding potential (BP_ND) was divided by dorsal raphe BP_ND as a specific measure to highlight rs6295 effects (BP_Div). Mixed model produced an interaction effect of ROI and genotype in the patients' group but no effects in healthy controls. Differences of BP_Div was demonstrated in seven ROIs; parahippocampus, hippocampus, fusiform gyrus, gyrus rectus, supplementary motor area, inferior frontal occipital gyrus and lingual gyrus. For classification of genotype, 'RandomForest' and Support Vector Machines were used, however, no model with sufficient predictive capability could be computed. Our results are in line with preclinical data, mouse model knockout studies as well as previous clinical analyses, demonstrating the two-pronged effect of the G allele on 5-HT_1A BP_ND for, we believe, the first time. Future endeavors should address epigenetic effects and allosteric heteroreceptor complexes. Replication in larger samples of MDD patients is necessary to substantiate our findings.

Repeated systemic administration of the cinnamon essential oil possesses anti-anxiety and anti-depressant activities in mice

OBJECTIVES

The present study aimed to evaluate the putative antidepressant and anti-anxiety effects of the cinnamon essential oil when administered acute (for 3 doses) and sub-acute (for 14 days) to mice.

MATERIALS AND METHODS

In an acute experimental study, forced swim test (FST) was conducted to evaluate the antidepressant-like behavior of animals treated with the intraperitoneal (IP) essential oil of cinnamon in triple doses (0.5, 1, and 2 mg/kg). In a sub-acute study (14 days in 24-hr intervals) antidepressant-like effects of essential oil (0.5, 1, and 2 mg/kg) with the same route were assessed in FST and tail suspension test (TST). Anti-anxiety and motor activities were evaluated using elevated plus-maze (EPM) and open field tests, respectively. Determination of different constituents within the sample oil was via gas chromatography-mass spectrometry (GC-MS) analysis.

RESULTS

Repetitive administration of cinnamon essential oil (0.5, 1, 2 mg/kg) during 14 days significantly decreased the time of immobility in both FST and TST as compared to the control group. Mice treated with oil at the dose of 2 mg/kg spent a longer time and had more entries into the open arms of EPM as compared with the vehicle-treated ones. According to GC-MS analysis, 46 chemical compounds were identified in the studied cinnamon essential oil with the main constituent being trans-cinnamaldehyde (87.32%).

CONCLUSION

Cinnamon essential oil might be used as an adjunctive therapy in improving symptoms of depressive and anxiety disorders. However, dose-response effects need further evaluation. Trans-cinnamaldehyde might be responsible for the beneficial effect observed.

Why is Diagnosing MDD Challenging?

Depression is highly prevalent and one of the major contributors to disability worldwide. However, one of the findings from the DSM-5 field trials was that inter-rater reliability for diagnosing major depressive disorder was very poor. Why is diagnosing MDD so challenging? This article attempts to explain why undefined pathogenesis and complicated phenotypes complicate the diagnosis of MDD. However, further biomarker and translational research is still necessary to help clinicians screen and diagnose depression in the future rather than relying solely on current subjective diagnostic criteria.