High heterogeneity and low reliability in the diagnosis of major depression will impair the development of new drugs

Investigating the association between a history of depression and biomarkers of Alzheimer's disease, cerebrovascular disease, and neurodegeneration in patients with dementia

The association between depression and dementia, particularly Alzheimer's disease (AD) and cerebrovascular disease (CVD), remains an active area of research. This study aimed to investigate the relationship between a history of depression and biomarkers of AD and CVD in patients with dementia in a clinical setting. A total of 126 patients from the University Health Network (UHN) Memory Clinic with comprehensive clinical evaluations, including neuropsychological testing and medical examinations, were included. Lumbar puncture was performed to collect cerebrospinal fluid (CSF) for biomarker analysis, and brain magnetic resonance imaging (MRI) scans were obtained to assess white matter hyperintensity (WMH) burden. The presence of depression was determined through medical records. The study findings did not reveal significant differences between participants with and without a history of depression in terms of AD biomarkers, WMH burden, neurofilament light chain levels, cognitive scores, age of symptom onset, disease duration, or vascular risk scores. Logistic regression analysis did not indicate a meaningful predictive value of these variables for depression status. This clinical study contributes to our understanding regarding the association between depression and AD/CVD biomarkers in patients with cognitive impairment. Further research is needed to elucidate the complex relationship between depression and dementia and to explore the potential mechanisms linking depression, AD, and CVD.

Transforming mental health systems globally: principles and policy recommendations

A burgeoning mental health crisis is emerging globally, regardless of each country's human resources or spending. We argue that effectively responding to this crisis is impeded by the dominant framing of mental ill health through the prism of diagnostic categories, leading to an excessive reliance on interventions that are delivered by specialists; a scarcity of widespread promotive, preventive, and recovery-oriented strategies; and failure to leverage diverse resources within communities. Drawing upon a series of syntheses, we identify five principles to transform current practices; namely, address harmful social environments across the life course, particularly in the early years; ensure that care is not contingent on a categorical diagnosis but aligned with the staging model of mental illness; empower diverse front-line providers to deliver psychosocial interventions; embrace a rights-based approach that seeks to provide alternatives to violence and coercion in care; and centre people with lived experience in all aspects of care. We recommend four policy actions which can transform these principles into reality: a whole of society approach to prevention and care; a redesign of the architecture of care delivery to provide a seamless continuum of care, tailored to the severity of the mental health condition; investing more in what works to enhance the impact and value of the investments; and ensuring accountability through monitoring and acting upon a set of mental health indicators. All these actions are achievable, relying-for the most part-on resources already available to every community and country. What they do require is the acceptance that business as usual will fail and the solutions to transforming mental health-care systems are already present within existing resources.

Reward Behavior Disengagement, a Neuroeconomic Model-Based Objective Measure of Reward Pathology in Depression: Findings from the EMBARC Trial

The probabilistic reward task (PRT) has identified reward learning impairments in those with major depressive disorder (MDD), as well as anhedonia-specific reward learning impairments. However, attempts to validate the anhedonia-specific impairments have produced inconsistent findings. Thus, we seek to determine whether the Reward Behavior Disengagement (RBD), our proposed economic augmentation of PRT, differs between MDD participants and controls, and whether there is a level at which RBD is high enough for depressed participants to be considered objectively disengaged. Data were gathered as part of the Establishing Moderators and Biosignatures of Antidepressant Response in Clinical Care (EMBARC) study, a double-blind, placebo-controlled clinical trial of antidepressant response. Participants included 195 individuals with moderate to severe MDD (Quick Inventory of Depressive Symptomatology (QIDS-SR) score ≥ 15), not in treatment for depression, and with complete PRT data. Healthy controls (n = 40) had no history of psychiatric illness, a QIDS-SR score < 8, and complete PRT data. Participants with MDD were treated with sertraline or placebo for 8 weeks (stage I of the EMBARC trial). RBD was applied to PRT data using discriminant analysis, and classified MDD participants as reward task engaged (n = 137) or reward task disengaged (n = 58), relative to controls. Reward task engaged/disengaged groups were compared on sociodemographic features, reward-behavior, and sertraline/placebo response (Hamilton Depression Rating Scale scores). Reward task disengaged MDD participants responded only to sertraline, whereas those who were reward task engaged responded to sertraline and placebo (F(1293) = 4.33, p = 0.038). Reward task engaged/disengaged groups did not differ otherwise. RBD was predictive of reward impairment in depressed patients and may have clinical utility in identifying patients who will benefit from antidepressants.

Depression as a cardiovascular disorder: central-autonomic network, brain-heart axis, and vagal perspectives of low mood

If depressive symptoms are not caused by the physiological effects of a substance or other medical or neurological conditions, they are generally classified as mental disorders that target the central nervous system. However, recent evidence suggests that peripheral neural dynamics on cardiovascular control play a causal role in regulating and processing emotions. In this perspective, we explore the dynamics of the Central-Autonomic Network (CAN) and related brain-heart interplay (BHI), highlighting their psychophysiological correlates and clinical symptoms of depression. Thus, we suggest that depression may arise from dysregulated cardiac vagal and sympathovagal dynamics that lead to CAN and BHI dysfunctions. Therefore, treatments for depression should target the nervous system as a whole, with particular emphasis on regulating vagal and BHI dynamics.

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.

Assessing the Role of Past Depression in Patients with Mild Cognitive Impairment, with and without Biomarkers for Alzheimer's Disease

Major depressive disorder (MDD) is a risk factor for Alzheimer's disease (AD). Cerebrovascular disease (CVD) is implicated in MDD and AD. Our study compared participants with AD positive and negative cerebrospinal fluid (CSF) biomarkers on neuropsychological performance, remitted MDD status, and CVD burden. Next, we compared AD-CSF biomarkers and white matter hyperintensities (WMH) burden among three groups: mild cognitive impairment (MCI) (n = 12), MCI with remitted MDD (MDD+MCI) (n = 12), and remitted MDD alone (MDD) (n = 7). Few participants (18%) with MCI+MDD exhibited AD(+) biomarkers. Nearly all participants had moderate-severe WMH. WMH may contribute to cognitive impairment or depression in MCI patients with AD(-) biomarkers.

The impact of inconsistent human annotations on AI driven clinical decision making

In supervised learning model development, domain experts are often used to provide the class labels (annotations). Annotation inconsistencies commonly occur when even highly experienced clinical experts annotate the same phenomenon (e.g., medical image, diagnostics, or prognostic status), due to inherent expert bias, judgments, and slips, among other factors. While their existence is relatively well-known, the implications of such inconsistencies are largely understudied in real-world settings, when supervised learning is applied on such 'noisy' labelled data. To shed light on these issues, we conducted extensive experiments and analyses on three real-world Intensive Care Unit (ICU) datasets. Specifically, individual models were built from a common dataset, annotated independently by 11 Glasgow Queen Elizabeth University Hospital ICU consultants, and model performance estimates were compared through internal validation (Fleiss' κ = 0.383 i.e., fair agreement). Further, broad external validation (on both static and time series datasets) of these 11 classifiers was carried out on a HiRID external dataset, where the models' classifications were found to have low pairwise agreements (average Cohen's κ = 0.255 i.e., minimal agreement). Moreover, they tend to disagree more on making discharge decisions (Fleiss' κ = 0.174) than predicting mortality (Fleiss' κ = 0.267). Given these inconsistencies, further analyses were conducted to evaluate the current best practices in obtaining gold-standard models and determining consensus. The results suggest that: (a) there may not always be a "super expert" in acute clinical settings (using internal and external validation model performances as a proxy); and (b) standard consensus seeking (such as majority vote) consistently leads to suboptimal models. Further analysis, however, suggests that assessing annotation learnability and using only 'learnable' annotated datasets for determining consensus achieves optimal models in most cases.

False dogmas in mood disorders research: Towards a nomothetic network approach

The current understanding of major depressive disorder (MDD) and bipolar disorder (BD) is plagued by a cacophony of controversies as evidenced by competing schools to understand MDD/BD. The DSM/ICD taxonomies have cemented their status as the gold standard for diagnosing MDD/BD. The aim of this review is to discuss the false dogmas that reign in current MDD/BD research with respect to the new, data-driven, machine learning method to model psychiatric illness, namely nomothetic network psychiatry (NNP). This review discusses many false dogmas including: MDD/BD are mind-brain disorders that are best conceptualized using a bio-psycho-social model or mind-brain interactions; mood disorders due to medical disease are attributable to psychosocial stress or chemical imbalances; DSM/ICD are the gold standards to make the MDD/BD diagnosis; severity of illness should be measured using rating scales; clinical remission should be defined using threshold values on rating scale scores; existing diagnostic BD boundaries are too restrictive; and mood disorder spectra are the rule. In contrast, our NNP models show that MDD/BD are not mind-brain or psycho-social but systemic medical disorders; the DSM/ICD taxonomies are counterproductive; a shared core, namely the reoccurrence of illness (ROI), underpins the intertwined recurrence of depressive and manic episodes and suicidal behaviors; mood disorders should be ROI-defined; ROI mediates the effects of nitro-oxidative stress pathways and early lifetime trauma on the phenome of mood disorders; severity of illness and treatment response should be delineated using the NNP-derived causome, pathway, ROI and integrated phenome scores; and MDD and BD are the same illness.

Precision Nomothetic Medicine in Depression Research: A New Depression Model, and New Endophenotype Classes and Pathway Phenotypes, and A Digital Self

Machine learning approaches, such as soft independent modeling of class analogy (SIMCA) and pathway analysis, were introduced in depression research in the 1990s (Maes et al.) to construct neuroimmune endophenotype classes. The goal of this paper is to examine the promise of precision psychiatry to use information about a depressed person's own pan-omics, environmental, and lifestyle data, or to tailor preventative measures and medical treatments to endophenotype subgroups of depressed patients in order to achieve the best clinical outcome for each individual. Three steps are emerging in precision medicine: (1) the optimization and refining of classical models and constructing digital twins; (2) the use of precision medicine to construct endophenotype classes and pathway phenotypes, and (3) constructing a digital self of each patient. The root cause of why precision psychiatry cannot develop into true sciences is that there is no correct (cross-validated and reliable) model of clinical depression as a serious medical disorder discriminating it from a normal emotional distress response including sadness, grief and demoralization. Here, we explain how we used (un)supervised machine learning such as partial least squares path analysis, SIMCA and factor analysis to construct (a) a new precision depression model; (b) a new endophenotype class, namely major dysmood disorder (MDMD), which is a nosological class defined by severe symptoms and neuro-oxidative toxicity; and a new pathway phenotype, namely the reoccurrence of illness (ROI) index, which is a latent vector extracted from staging characteristics (number of depression and manic episodes and suicide attempts), and (c) an ideocratic profile with personalized scores based on all MDMD features.

American psychiatry in the new millennium: a critical appraisal

This article casts a critical eye over the development of American psychiatry from 1980 to the present. It notes the rapid decline of psychoanalysis that followed the publication of DSM III; the rising influence of genetics and neuroscience; the re-emphasis on the biology of mental illness; and the collapse of public psychiatry that accompanied deinstitutionalization. It argues that while genetics and neuroscience have made scientific progress, the clinical utility of their findings to date has been very limited. The fifth edition of the DSM was supposed to base itself on this new science but that proved impossible. Diagnosis remains purely phenomenological and controversial. One of the ironies of research on psychiatric genetics is that has failed to find either a Mendelian origin of schizophrenia and depression or to validate the importance of hypothesized candidate genes. Genome-wide association studies have instead uncovered risk factors for major mental illnesses, but these overlap considerably, and the genetic associations are not dispositive. Most of those who carry these genetic variants do not develop mental illness. The status of psychopharmacology since the mid-1950s is scrutinized, as is the influence of the pharmaceutical industry on contemporary psychiatry, and the implications of its recent decision to abandon work in this arena. The paper concludes with an assessment of the crisis that it contends confronts contemporary American psychiatry: its overemphasis on biology; the urgent questions that persist about diagnosis and therapeutics; concerns about the directions of future research; and its inability to reduce the excess mortality that plagues the mentally ill.

Inflamed brain: Targeting immune changes and inflammation for treatment of depression

Although there are a number of clinically effective treatments for depression, many patients exhibit treatment resistance. Recent clinical and preclinical studies reveal that peripheral and brain immune changes and inflammation are involved in the pathophysiology of depression. This 'Inflamed Brain' research provides critical clues for understanding of disease pathophysiology and many candidate molecules that are potentially useful for identifying novel drug targets for the treatment of depression. In this review, we will present clinical evidence on the role of inflammation in the pathophysiology of depression. We will also summarize current clinical trials which test drugs targeting inflammation for the treatment of patients with depression. Furthermore, we will briefly provide preclinical evidence demonstrating altered immune system function and inflammation in stress-induced animal models and will discuss the future potential of inflammation-related drug targets. Collectively, inflammatory signatures identified in clinical and preclinical studies may allow us to stratify depressive patients based on biotypes, contributing to the development of novel mechanism-based interventions that target specific patient populations.

Towards a new model and classification of mood disorders based on risk resilience, neuro-affective toxicity, staging, and phenome features using the nomothetic network psychiatry approach

Current diagnoses of mood disorders are not cross validated. The aim of the current paper is to explain how machine learning techniques can be used to a) construct a model which ensembles risk/resilience (R/R), adverse outcome pathways (AOPs), staging, and the phenome of mood disorders, and b) disclose new classes based on these feature sets. This study was conducted using data of 67 healthy controls and 105 mood disordered patients. The R/R ratio, assessed as a combination of the paraoxonase 1 (PON1) gene, PON1 enzymatic activity, and early life time trauma (ELT), predicted the high-density lipoprotein cholesterol - paraoxonase 1 complex (HDL-PON1), reactive oxygen and nitrogen species (RONS), nitro-oxidative stress toxicity (NOSTOX), staging (number of depression and hypomanic episodes and suicidal attempts), and phenome (the Hamilton Depression and Anxiety scores and the Clinical Global Impression; current suicidal ideation; quality of life and disability measurements) scores. Partial Least Squares pathway analysis showed that 44.2% of the variance in the phenome was explained by ELT, RONS/NOSTOX, and staging scores. Cluster analysis conducted on all those feature sets discovered two distinct patient clusters, namely 69.5% of the patients were allocated to a class with high R/R, RONS/NOSTOX, staging, and phenome scores, and 30.5% to a class with increased staging and phenome scores. This classification cut across the bipolar (BP1/BP2) and major depression disorder classification and was more distinctive than the latter classifications. We constructed a nomothetic network model which reunited all features of mood disorders into a mechanistically transdiagnostic model.

How to construct neuroscience-informed psychiatric classification? Towards nomothetic networks psychiatry

Psychiatry remains in a permanent state of crisis, which fragmented psychiatry from the field of medicine. The crisis in psychiatry is evidenced by the many different competing approaches to psychiatric illness including psychodynamic, biological, molecular, pan-omics, precision, cognitive and phenomenological psychiatry, folk psychology, mind-brain dualism, descriptive psychopathology, and postpsychiatry. The current "gold standard" Diagnostic and Statistical Manual of Mental Disorders/International Classification of Diseases taxonomies of mood disorders and schizophrenia are unreliable and preclude to employ a deductive reasoning approach. Therefore, it is not surprising that mood disorders and schizophrenia research was unable to revise the conventional classifications and did not provide more adequate therapeutic approaches. The aim of this paper is to explain the new nomothetic network psychiatry (NNP) approach, which uses machine learning methods to build data-driven causal models of mental illness by assembling risk-resilience, adverse outcome pathways (AOP), cognitome, brainome, staging, symptomatome, and phenomenome latent scores in a causal model. The latter may be trained, tested and validated with Partial Least Squares analysis. This approach not only allows to compute pathway-phenotypes or biosignatures, but also to construct reliable and replicable nomothetic networks, which are, therefore, generalizable as disease models. After integrating the validated feature vectors into a well-fitting nomothetic network, clustering analysis may be applied on the latent variable scores of the R/R, AOP, cognitome, brainome, and phenome latent vectors. This pattern recognition method may expose new (transdiagnostic) classes of patients which if cross-validated in independent samples may constitute new (transdiagnostic) nosological categories.

Abnormal levels of mitochondrial proteins in plasma neuronal extracellular vesicles in major depressive disorder

To characterize neuronal mitochondrial abnormalities in major depressive disorder (MDD), functional mitochondrial proteins (MPs) extracted from enriched plasma neuron-derived extracellular vesicles (NDEVs) of MDD participants (n = 20) were quantified before and after eight weeks of treatment with a selective serotonin reuptake inhibitor (SSRI). Pretreatment baseline NDEV levels of the transcriptional type 2 nuclear respiratory factor (NRF2) which controls mitochondrial biogenesis and many anti-oxidant gene responses, regulators of diverse neuronal mitochondrial functions cyclophilin D (CYPD) and mitofusin-2 (MFN2), leucine zipper EF-hand containing transmembrane 1 protein (LETM1) component of a calcium channel/calcium channel enhancer, mitochondrial tethering proteins syntaphilin (SNPH) and myosin VI (MY06), inner membrane electron transport complexes I (subunit 6) and III (subunit 10), the penultimate enzyme of nicotinamide adenine dinucleotide (NAD) generation nicotinamide mononucleotide adenylytransferase 2 (NMNAT2), and neuronal mitochondrial metabolic regulatory and protective factors humanin and mitochondrial open-reading frame of the 12S rRNA-c (MOTS-c) all were significantly lower than those of NDEVs from matched controls (n = 10), whereas those of pro-neurodegenerative NADase Sterile Alpha and TIR motif-containing protein 1 (SARM1) were higher. The baseline NDEV levels of transcription factor A mitochondrial (TFAM) and the transcriptional master-regulator of mitochondrial biogenesis PPAR γ coactivator-1α (PGC-1α) showed no differences between MDD participants and controls. Several of these potential biomarker proteins showed substantially different changes in untreated MDD than those we reported in untreated first-episode psychosis. NDEV levels of MPs of all functional classes, except complex I-6, NRF2 and PGC-1α were normalized in MDD participants who responded to SSRI therapy (n = 10) but not in those who failed to respond (n = 10) by psychiatric evaluation. If larger studies validate NDEV MP abnormalities, they may become useful biomarkers and identify new drug targets.

BJPsych Open fifth anniversary editorial: history, accomplishments, trajectory and passion

BJPsych Open has come of age. This editorial celebrates the journal's fifth anniversary by reviewing the history of BJPsych Open, what we have accomplished, where we strive to go (our planned trajectory) and the passion of being an Editor-in-Chief.

Early childhood and adolescent risk factors for psychotic depression in a general population birth cohort sample

BACKGROUND AND PURPOSE

In the group of severe mental disorders, psychotic depression (PD) is essentially under-researched. Knowledge about the risk factors is scarce and this applies especially to early risk factors. Our aim was to study early childhood and adolescent risk factors of PD in a representative birth cohort sample with a follow-up of up to 50 years.

METHODS

The study was carried out using the Northern Finland Birth Cohort 1966 (NFBC 1966). We used non-psychotic depression (NPD) (n = 746), schizophrenia (SZ) (n = 195), psychotic bipolar disorder (PBD) (n = 27), other psychoses (PNOS) (n = 136) and healthy controls (HC) (n = 8200) as comparison groups for PD (n = 58). We analysed several potential early risk factors from time of birth until the age of 16 years.

RESULTS

The main finding was that parents' psychiatric illness [HR 3.59 (1.84-7.04)] was a risk factor and a high sports grade in school was a protective factor [HR 0.29 (0.11-0.73)] for PD also after adjusting for covariates in the multivariate Cox regression model. Parental psychotic illness was an especially strong risk factor for PD. The PD subjects had a parent with psychiatric illness significantly more often (p < 0.05) than NPD subjects. Differences between PD and other disorder groups were otherwise small.

CONCLUSIONS

A low sports grade in school may be a risk factor for PD. Psychiatric illnesses, especially psychoses, are common in the parents of PD subjects. A surprisingly low number of statistically significant risk factors may have resulted from the size of the PD sample and the underlying heterogeneity of the etiology of PD.

Fronto-Limbic Connectivity as a Predictor of Improvement in Nonsuicidal Self-Injury in Adolescents Following Psychotherapy

Objectives: Key neurobiological factors contribute to vulnerability to nonsuicidal self-injury (NSSI) among adolescents and how they respond to treatment targeted to reduce such behaviors. This study aims to examine differences in intrinsic functional connectivity between adolescents with NSSI and healthy controls (HCs) and to identify baseline connectivity markers that predict improvements in NSSI after psychotherapy. Methods: Adolescents aged 12-17 (n = 24) with repetitive NSSI along with demographically similar HCs (n = 16) underwent resting-state functional MRI scanning after which patients received up to 4 months of psychological treatment. A seed-based approach was used to examine baseline between-group differences in intrinsic functional connectivity of the amygdala and the medial prefrontal cortex (mPFC). Further analyses examined the associations between intrinsic functional connectivity at baseline and improvement in NSSI after psychological treatment. Results: Compared with HCs, adolescents with NSSI showed significantly reduced connectivity between the amygdala and the anterior cingulate cortex, subcallosal cortex, and paracingulate gyrus, as well as between the amygdala and a cluster encompassing the right planum temporale and right insula. Adolescents with NSSI, compared with HCs, also showed reduced connectivity between the mPFC and two clusters: one located in the precentral and postcentral gyri and another in the left insula. After treatment, 50% of patients reported fewer NSSI episodes compared to baseline, which was considered as improvement. Stronger negative amygdala-prefrontal connectivity was associated with greater posttreatment improvement in NSSI. Conclusions: Adolescents with NSSI may have aberrant amygdala and mPFC connectivity compared with HCs. Furthermore, stronger baseline negative amygdala-prefrontal connectivity may predict greater improvement in NSSI after psychological intervention. Given that no prior study has used resting-state functional connectivity to predict response to psychological treatment in adolescents with NSSI, replication of these findings is needed.

NV-5138 as a fast-acting antidepressant via direct activation of mTORC1 signaling

Growing evidence implicates altered mTORC1 signaling cascades in the pathophysiology of depression, suggesting that direct modulation of mTORC1 signaling may offer novel therapeutic potential. In this issue of the JCI, Kato and colleagues reported that administration of NV-5138, a recently developed synthetic leucine analog, has a rapid and sustained antidepressant action in rat models via activation of mTORC1 signaling. The investigators also found that the antidepressant effect of NV-5138 is mediated by upregulation of brain-derived neurotrophic factor (BDNF) signaling and that NV-5138 treatment produces rapid synaptic responses in the medial prefrontal cortex. These findings highlight the direct activation of mTORC1 signaling as a potential pharmacological intervention for the treatment of depression.

Altered mRNA expressions for N-methyl-D-aspartate receptor-related genes in WBC of patients with major depressive disorder

OBJECTIVE

Major depressive disorder (MDD) is a complex mental disorder. The lack of well-established biomarkers hinders its diagnosis, treatment, and new-drug development. N-methyl-D-aspartate receptor (NMDAR) dysfunction has been implicated in the pathogenesis of MDD. This study examined whether expressions of the NMDAR-related genes are characteristic of MDD.

METHODS

Expressions of NMDAR-related genes including SRR, SHMT2, PSAT1, GCAT, GAD1, SLC1A4, NRG1 and COMT in peripheral WBCs of 110 patients with MDD (25 drug-naïve, 21 drug-free, and 64 medicated patients) and 125 healthy individuals were measured using quantitative PCR.

RESULTS

The mRNA expression levels of SRR, PSAT1, GCAT, GAD1, NRG1 and COMT were significantly different among the four groups (all p < 0.05). For drug-naïve patients, the ΔΔCT values of SRR, PSAT1, GCAT, GAD1, and NRG1 mRNA expressions were significantly different from those in healthy individuals (all p < 0.05). The ROC analysis of the ΔΔCT values of the target genes for differentiating drug-naïve patients from healthy controls showed an excellent sensitivity (0.960) and modest specificity (0.640) (AUC = 0.889). Drug-free and medicated patients obtained less favorable AUC values while compared to healthy controls. The results for the age- and sex-matched cohort were similar to those of the unmatched cohort.

CONCLUSIONS

This is the first study demonstrating that the peripheral mRNA expression levels of NMDAR-related genes may be altered in patients with MDD, especially drug-naïve individuals. The finding supports the NMDAR hypothesis of depression. Whether mRNA expresssion of NMDAR-related genes could serve as a potential biomarker of MDD deserves further investigations.

Automatic detection of major depressive disorder using electrodermal activity

Major depressive disorder (MDD) is a common psychiatric disorder and the leading cause of disability worldwide. However, current methods used to diagnose depression mainly rely on clinical interviews and self-reported scales of depressive symptoms, which lack objectivity and efficiency. To address this challenge, we present a machine learning approach to screen for MDD using electrodermal activity (EDA). Participants included 30 patients with MDD and 37 healthy controls. Their EDA was measured during five experimental phases consisted of baseline, mental arithmetic task, recovery from the stress task, relaxation task, and recovery from the relaxation task, which elicited multiple alterations in autonomic activity. Selected EDA features were extracted from each phase, and differential EDA features between two distinct phases were evaluated. By using these features as input data and performing feature selection with SVM-RFE, 74% accuracy, 74% sensitivity, and 71% specificity could be achieved by our decision tree classifier. The most relevant features selected by SVM-RFE included differential EDA features and features from the stress and relaxation tasks. These findings suggest that automatic detection of depression based on EDA features is feasible and that monitoring changes in physiological signal when a subject is experiencing autonomic arousal and recovery may enhance discrimination power.

Application value of selected serum indicators in the differential diagnosis of geriatric depression and transient depressive state

BACKGROUND

Transient depressive state (TDS) is a transient, negative emotional state caused by certain events or situations. Because of the similarity in depressive symptoms between depression and TDS that arise within 2 weeks of their onset, it is difficult to distinguish TDS from depression. The aims of the present study were to investigate the application value of selected serum indicators in the differential diagnosis of geriatric depression and TDS in the early stage and to provide evidence for treatment.

PATIENTS AND METHODS

In this study, a total of 274 elderly patients were divided into the depression group (n=144) and the TDS group (n=130). All participants' serum samples were collected, and 9 selected serum indicators were analyzed. Afterward, 90 patients with depression and 90 patients with TDS were used to build the diagnostic model. A binary logistic regression analysis was used to establish regression models, and the area under the receiver operating characteristic (ROC) curve was drawn. Finally, another 54 patients with depression and 40 patients with TDS were used to validate our model.

RESULTS

For the 9 screening serum indicators, the 3 serum indicators selected to build the regression model were BDNF (P=0.001), IL-1β (P<0.001), and cortisol (P<0.001). The regression equation was Y = 1/[1 + e^{-(-16.258 - 0.018 (BDNF) + 0.256 (IL-1β) + 0.093 (Cortisol))}], and the ROC curve of combined detection was 0.926. The diagnostic rate of the logistic model was 89.36%.

CONCLUSION

The logistic regression model and ROC curves based on serum levels of BDNF, IL-1β, and cortisol could distinguish depression from TDS in early stage, which could provide assistance to the differential diagnosis of geriatric depression and TDS.

Altered Resting-State Brain Activities in Drug-Naïve Major Depressive Disorder Assessed by fMRI: Associations With Somatic Symptoms Defined by Yin-Yang Theory of the Traditional Chinese Medicine

Identification of biological markers for defining subtypes of major depressive disorder (MDD) is critical for better understanding MDD pathophysiology and finding effective treatment intervention. The "Yin and Yang" theory is a fundamental concept of traditional Chinese Medicine (TCM). The theory differentiates MDD patients into two subtypes, Yin and Yang, based on their somatic symptoms, which had empirically been used for the delivery of effective treatment in East Asia. Nonetheless, neural processes underlying Yin and Yang types in MDD are poorly understood. In this study, we aim to provide physiological evidence using functional magnetic resonance imaging (fMRI) to identify altered resting-state brain activity associated with Yin and Yang types in drug-naïve MDD patients. The Yin type and Yang type MDD patients showed increased amplitude of low-frequency fluctuation (ALFF) in different cortical brain areas in the parietal, temporal, and frontal lobe, compared to matched healthy controls. Differential ALFF is also observed in several cortical areas in frontal lobe and insula between Yin and Yang type group. Of note, although ALFF is increased in the inferior parietal lobe in both Yin and Yang type group, inferior parietal lobe-centered functional connectivity (FC) is increased in Yang type, but is decreased in Ying type, compared with matched healthy controls. These results suggest that differential resting-state brain activity and functional connectivity in Yin and Yang types may contribute to biological measures for better stratification of heterogeneous MDD patients.

Model-Based Economic Evaluation of Treatments for Depression: A Systematic Literature Review

BACKGROUND

An increasing number of model-based studies that evaluate the cost effectiveness of treatments for depression are being published. These studies have different characteristics and use different simulation methods.

OBJECTIVE

We aimed to systematically review model-based studies evaluating the cost effectiveness of treatments for depression and examine which modelling technique is most appropriate for simulating the natural course of depression.

METHODS

The literature search was conducted in the databases PubMed, EMBASE and PsycInfo between 1 January 2002 and 1 October 2016. Studies were eligible if they used a health economic model with quality-adjusted life-years or disability-adjusted life-years as an outcome measure. Data related to various methodological characteristics were extracted from the included studies. The available modelling techniques were evaluated based on 11 predefined criteria.

RESULTS

This methodological review included 41 model-based studies, of which 21 used decision trees (DTs), 15 used cohort-based state-transition Markov models (CMMs), two used individual-based state-transition models (ISMs), and three used discrete-event simulation (DES) models. Just over half of the studies (54%) evaluated antidepressants compared with a control condition. The data sources, time horizons, cycle lengths, perspectives adopted and number of health states/events all varied widely between the included studies. DTs scored positively in four of the 11 criteria, CMMs in five, ISMs in six, and DES models in seven.

CONCLUSION

There were substantial methodological differences between the studies. Since the individual history of each patient is important for the prognosis of depression, DES and ISM simulation methods may be more appropriate than the others for a pragmatic representation of the course of depression. However, direct comparisons between the available modelling techniques are necessary to yield firm conclusions.

Analysis of genome-wide association data highlights candidates for drug repositioning in psychiatry

Knowledge of psychiatric disease genetics has advanced rapidly during the past decade with the advent of genome-wide association studies (GWAS). However, less progress has been made in harnessing these data to reveal new therapies. Here we propose a framework for drug repositioning by comparing transcriptomes imputed from GWAS data with drug-induced gene expression profiles from the Connectivity Map database and apply this approach to seven psychiatric disorders. We found a number of repositioning candidates, many supported by preclinical or clinical evidence. Repositioning candidates for a number of disorders were also significantly enriched for known psychiatric medications or therapies considered in clinical trials. For example, candidates for schizophrenia were enriched for antipsychotics, while those for bipolar disorder were enriched for both antipsychotics and antidepressants. These findings provide support for the usefulness of GWAS data in guiding drug discovery.

Mood Instability and Irritability as Core Symptoms of Major Depression: An Exploration Using Rasch Analysis

BACKGROUND

Mood instability (MI) and irritability are related to depression but are not considered core symptoms. Instruments typically code clusters of symptoms that are used to define syndromic depression, but the place of MI and irritability has been under-investigated. Whether they are core symptoms can be examined using Rasch analysis.

METHOD

We used the UK Psychiatric Morbidity Survey 2000 data (n = 8,338) to determine whether the nine ICD/DSM symptoms, plus MI and irritability, constitute a valid depression scale. Rasch analysis was used, a method concerned with ensuring that items constitute a robust scale and tests whether the count of symptoms reflects an underlying interval-level measure. Two random samples of 500 were drawn, serving as calibration and validation samples. As part of the analysis, we examined whether the candidate symptoms were unidimensional, followed a Guttman pattern, were locally independent, invariant with respect to age and sex, and reliably distinguished different levels of depression severity.

RESULTS

A subset of five symptoms (sad, no interest, sleep, cognition, suicidal ideas) together with mood instability and irritability satisfactorily fits the Rasch model. However, these seven symptoms do not separate clinically depressed persons from the rest of the population with adequate reliability (Cronbach α = 0.58; Person Separation Index = 0.35), but could serve as a basis for scale development. Likewise, the original nine DSM depression symptoms failed to achieve satisfactory reliability (Cronbach α = 0.67; Person Separation Index = 0.51).

LIMITATIONS

The time frame over which symptoms were experienced varied, and some required recall over the last year. Symptoms other than those examined here might also be core depression symptoms.

CONCLUSION

Mood instability and irritability are candidate core symptoms of the depressive syndrome and should be part of its clinical assessment.