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Convergent evidence demonstrates that immune dysfunction (e.g. chronic low-grade inflammatory activation) plays an important role in the development and progression of mood disorders. The Janus kinase/signal transducers and activators of transcription (JAK/STAT) signaling pathway is a pleiotropic cellular cascade that transduces numerous signals, including signals from the release of cytokines and growth factors. The JAK/STAT signaling pathway is involved in mediating several functions of the central nervous system, including neurogenesis, synaptic plasticity, gliogenesis, and microglial activation, all of which have been implicated in the pathophysiology of mood disorders. In addition, the antidepressant actions of current treatments have been shown to be mediated by JAK/STAT-dependent mechanisms. To date, two JAK inhibitors (JAKinibs) have been approved by the U.S. Food and Drug Administration and are primarily indicated for the treatment of inflammatory conditions such as rheumatoid arthritis. Indirect evidence from studies in populations with inflammatory conditions indicates that JAKinibs significantly improve measures of mood and quality of life. There is also direct evidence from studies in populations with depressive disorders, suggesting that JAK/STAT pathways may be involved in the pathophysiology of depression and that the inhibition of specific JAK/STAT pathways (i.e. via JAKinibs) may be a promising novel treatment for depressive disorders.

Background: Convergent evidence implicates gut microbiota in human health and disease. Hitherto, relatively few studies have evaluated the gut microbiota profile in individuals with bipolar disorder (BD) relative to healthy controls (HC). Methods: Fecal samples were collected from subjects (aged 18–65) meeting DSM-5-defined criteria for BD and age- and sex-matched HC without current or past history of mental or major medical disorders. Samples were sequenced using Illumina sequencing and association of specific taxa and co-occurrence of taxa with sample groups including the effect of diet was assessed using cluster analysis and analysis of communities of microorganisms (ANCOM). Nutritional composition was evaluated using the Dietary Questionnaire for Epidemiological Studies (DQES v2) Food Frequency Questionnaire. Results: Forty-six subjects were enrolled (n=23 BD, n=23 HC). Cluster analyses did not identify any significant differences between BD and HC (p=0.38). Lower microbiota diversity was observed among BD subjects relative to HC (p=0.04). A greater abundance of a Clostridiaceae OTU was observed among BD subjects when compared to HC and of Collinsella among BD-II subjects relative to BD-I. Cluster analysis revealed that neither diagnosis (p=0.38) nor diet (p=0.43) had a significant effect on overall gut microbiota composition. Limitations: This study has a small sample size and insufficient control for some potential moderating factors (e.g. psychotropic medication and smoking). Conclusion: This study suggests that individuals with BD may have a distinct gut microbiota profile compared to healthy controls, with a greater abundance of Clostridiaceae and Collinsella. These findings need to be replicated in future studies with larger sample sizes. [ABSTRACT FROM AUTHOR]

Convergent evidence demonstrates that immune dysfunction (e.g. chronic low-grade inflammatory activation) plays an important role in the development and progression of mood disorders. The Janus kinase/signal transducers and activators of transcription (JAK/STAT) signaling pathway is a pleiotropic cellular cascade that transduces numerous signals, including signals from the release of cytokines and growth factors. The JAK/STAT signaling pathway is involved in mediating several functions of the central nervous system, including neurogenesis, synaptic plasticity, gliogenesis, and microglial activation, all of which have been implicated in the pathophysiology of mood disorders. In addition, the antidepressant actions of current treatments have been shown to be mediated by JAK/STAT-dependent mechanisms. To date, two JAK inhibitors (JAKinibs) have been approved by the U.S. Food and Drug Administration and are primarily indicated for the treatment of inflammatory conditions such as rheumatoid arthritis. Indirect evidence from studies in populations with inflammatory conditions indicates that JAKinibs significantly improve measures of mood and quality of life. There is also direct evidence from studies in populations with depressive disorders, suggesting that JAK/STAT pathways may be involved in the pathophysiology of depression and that the inhibition of specific JAK/STAT pathways (i.e. via JAKinibs) may be a promising novel treatment for depressive disorders. [ABSTRACT FROM AUTHOR]

Learning objectives: After participating in this activity, learners should be better able to: • Characterize cognitive dysfunction in patients with major depressive disorder. • Evaluate approaches to treating cognitive dysfunction in patients with major depressive disorder. Cognitive dysfunction is a core psychopathological domain in major depressive disorder (MDD) and is no longer considered to be a pseudo-specific phenomenon. Cognitive dysfunction in MDD is a principal determinant of patient-reported outcomes, which, hitherto, have been insufficiently targeted with existing multimodal treatments for MDD. The neural structures and substructures subserving cognitive function in MDD overlap with, yet are discrete from, those subserving emotion processing and affect regulation. Several modifiable factors influence the presence and extent of cognitive dysfunction in MDD, including clinical features (e.g., episode frequency and illness duration), comorbidity (e.g., obesity and diabetes), and iatrogenic artefact. Screening and measurement tools that comport with the clinical ecosystem are available to detect and measure cognitive function in MDD. Notwithstanding the availability of select antidepressants capable of exerting procognitive effects, most have not been sufficiently studied or rigorously evaluated. Promising pharmacological avenues, as well as psychosocial, behavioral, chronotherapeutic, and complementary alternative approaches, are currently being investigated. [ABSTRACT FROM AUTHOR]

Ecological momentary assessment (EMA) for mental disorders, using application-based (app) technology capable of passive and ambient data collection, has been insufficiently evaluated and validated with rigorous, adequately-powered, high-quality studies. Herein, we sought to validate the mind.me application for the assessment of depressive symptoms in adults. Adults (ages 18–65) who self-identified as having clinically significant depressive symptoms [i.e. Patient Health Questionnaire 9 (PHQ-9) ≥ 5] utilized the mind.me app—a mobile phone technology that collects data passively and continuously, and is capable of integrating broad multimodal data [e.g., location variance (e.g. GPS), behavioural (e.g. social network activity), and communication data (e.g. SMS texting, phone calls)]. The primary outcome was predictive accuracy (i.e. convergent validity with depressive symptom measurement, as captured by the PHQ-9). 200 subjects were enrolled in the study (mean age 46 ± 12.71). The average PHQ-9 score was 12.8 ± 6.9. The predictive accuracy of the mind.me app was 0.91 ± 0.06. The sensitivity was 0.98 and the specificity was 0.93. The mind.me app was rated by 200 users as highly usable and informative to their illness. The mind.me app exhibits robust predictive accuracy in detecting depressive symptoms in adults with clinically relevant depressive symptoms. The mind.me app more specifically demonstrates convergence with the PHQ-9. [ABSTRACT FROM AUTHOR]

Background: No previous study has comprehensively reviewed the application of machine learning algorithms in mood disorders populations. Herein, we qualitatively and quantitatively evaluate previous studies of machine learning-devised models that predict therapeutic outcomes in mood disorders populations.Methods: We searched Ovid MEDLINE/PubMed from inception to February 8, 2018 for relevant studies that included adults with bipolar or unipolar depression; assessed therapeutic outcomes with a pharmacological, neuromodulatory, or manual-based psychotherapeutic intervention for depression; applied a machine learning algorithm; and reported predictors of therapeutic response. A random-effects meta-analysis of proportions and meta-regression analyses were conducted.Results: We identified 639 records: 75 full-text publications were assessed for eligibility; 26 studies (n=17,499) and 20 studies (n=6325) were included in qualitative and quantitative review, respectively. Classification algorithms were able to predict therapeutic outcomes with an overall accuracy of 0.82 (95% confidence interval [CI] of [0.77, 0.87]). Pooled estimates of classification accuracy were significantly greater (p < 0.01) in models informed by multiple data types (e.g., composite of phenomenological patient features and neuroimaging or peripheral gene expression data; pooled proportion [95% CI] = 0.93[0.86, 0.97]) when compared to models with lower-dimension data types (pooledproportion=0.68[0.62,0.74]to0.85[0.81,0.88]).Limitations: Most studies were retrospective; differences in machine learning algorithms and their implementation (e.g., cross-validation, hyperparameter tuning); cannot infer importance of individual variables fed into learning algorithm.Conclusions: Machine learning algorithms provide a powerful conceptual and analytic framework capable of integrating multiple data types and sources. An integrative approach may more effectively model neurobiological components as functional modules of pathophysiology embedded within the complex, social dynamics that influence the phenomenology of mental disorders. [ABSTRACT FROM AUTHOR]

Objectives: To explore differences in adipokine levels (i.e., leptin and adiponectin levels) between adults with Major Depressive Disorder (MDD) and healthy controls (HC), and to discuss the possible role of adipokine regulation in the development and progression of MDD.Methods: A systematic review and meta-analysis were conducted based on the preferred reporting items for systematic reviews and meta-analyses (PRISMA) guidelines. A systematic search was conducted for all English and Chinese peer-reviewed articles from inception to November 2017. A random effects model was used to calculate the standardized mean difference (SMD) of leptin and/or adiponectin levels in subjects diagnosed with MDD versus HC within a 95% confidence interval (CI).Results: Thirty-three studies were included in this meta-analysis. In total, 4,372 (52.3%) subjects with MDD and 3,984 (47.7%) HC were compared. We identified significant lower adiponectin levels in MDD compared to HC with a small effect size (ES) (SMD = -0.25; 95% CI: -0.48, -0.02; P < 0.001). However, no significant difference was observed in leptin levels between MDD subjects and HC (SMD = 0.13; 95% CI: -0.06, 0.31; P = 0.170). The heterogeneity in the results of our meta-analysis could not be completely explained by dividing subjects into subgroups. Results from subgroup analyses suggested that studies involving samples with BMI ≥ 25 had lower adiponectin levels in subjects with MDD compared to HC, and older age samples (i.e., age ≥ 40) with BMI ≥ 25 had both higher leptin levels and lower adiponectin levels in MDD subjects as compared to HC.Limitations: The heterogeneity of included studies, small sample sizes, and potential publication bias were significant limitations.Conclusions: The current systematic review and meta-analysis indicated that lower adiponectin levels may be associated with MDD. Moreover, the results suggest that males expressing lower adiponectin and leptin levels have an increased likelihood of developing MDD. Future studies should aim to investigate the manifestation of depressive phenotypes in older, obese populations with altered metabolic profiles resulting from adipokine dysregulation. The review has been registered with PROSPERO (registration number CRD42018082733). [ABSTRACT FROM AUTHOR]