Characterizing the gut microbiota in adults with bipolar disorder: a pilot study.

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.

The THINC-it Tool for Cognitive Assessment and Measurement in Major Depressive Disorder: Sensitivity to Change.

BACKGROUND: Herein, we sought to determine the sensitivity to change in cognitive function, as measured by the THINC-it tool, in a sample of adults with major depressive disorder (MDD) receiving standardized antidepressant therapy. METHODS: Adults meeting the DSM-5 criteria for MDD with at least moderate depressive symptom severity [i.e., Montgomery Åsberg Depression Rating Scale (MADRS) total score ≥ 20] were treated with open-label vortioxetine (10-20 mg/day, flexibly-dosed) for 8 weeks. The previously validated THINC-it tool was the primary dependent measure. The THINC-it tool was validated against the paper and pencil version of the Digit Symbol Substitution Test (DSST) and the Trails Making Test B (TMTB). RESULTS: After 8 weeks of treatment, adults with MDD exhibited improvement in cognitive function relative to healthy controls (e.g., processing speed) (p = 0.031). A subdomain measure of working memory (i.e., symbol check; SC) exhibited significant improvement at Weeks 2 and 8 in latency (p = 0.032), SC accuracy (p = 0.046), and objective z-score (p = 0.001) independent of depressive symptoms. A linear regression analysis determined that the THINC-it tool measures of processing speed, as well as executive function were significantly associated with changes observed on the pencil and paper version the Digit Symbol Substitution Test (DSST) (p = 0.002) and in Trails Making Test B (TMTB) (p = 0.003), respectively. CONCLUSION: The THINC-it tool demonstrates sensitivity to change in adults with MDD and is highly correlated with improvements on pencil and paper versions of DSST and TMTB. CLINICAL TRIAL REGISTRATION: ClinicalTrials.gov, identifier NCT03053362.

Corrigendum to "Applications of machine learning algorithms to predict therapeutic outcomes in depression: A meta-analysis and systematic review." J Affect Disord. 241 (2018) 519-532.

The authors regret an error in one of the extracted data points in the meta-analysis. The classification accuracy for Serretti et al. (2007) was corrected to 64% (Table 3b). The overall results before and after this correction remain directionally consistent and are summarized below (Figures 2 and 3; Table 2; results subsection 3.6). The authors apologise for any inconvenience caused.

Cognitive impairment in major depressive disorder.

Cognitive dysfunction is a symptomatic domain identified across many mental disorders. Cognitive deficits in individuals with major depressive disorder (MDD) contribute significantly to occupational and functional disability. Notably, cognitive subdomains such as learning and memory, executive functioning, processing speed, and attention and concentration are significantly impaired during, and between, episodes in individuals with MDD. Most antidepressants have not been developed and/or evaluated for their ability to directly and independently ameliorate cognitive deficits. Multiple interacting neurobiological mechanisms (eg, neuroinflammation) are implicated as subserving cognitive deficits in MDD. A testable hypothesis, with preliminary support, posits that improving performance across cognitive domains in individuals with MDD may improve psychosocial function, workplace function, quality of life, and other patient-reported outcomes, independent of effects on core mood symptoms. Herein we aim to (1) provide a rationale for prioritizing cognitive deficits as a therapeutic target, (2) briefly discuss the neurobiological substrates subserving cognitive dysfunction, and (3) provide an update on current and future treatment avenues.

Applications of machine learning algorithms to predict therapeutic outcomes in depression: A meta-analysis and systematic review.

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.

Predicting antidepressant response using early changes in cognition: A systematic review.

BACKGROUND: Despite the widespread use of antidepressants in clinical practice, the current trial-and-error approach to medication selection contributes to treatment failure and underscores the need to identify reliable predictors of antidepressant response. Since changes in measures of cognition have been reported to occur early in treatment and prior to improvements in overall mood symptoms, the present review aims to determine whether early changes in measures of cognition can predict response in individuals with MDD. METHODS: A systematic review of studies evaluating early cognitive change as a predictor of later treatment response in MDD was conducted using PubMed/Medline, Embase and PsychINFO. RESULTS: A total of seven articles were identified. The available evidence suggests the early changes in cognition may predict treatment response in individuals with MDD. This was shown across antidepressant classes (i.e., SSRIs, SNRIs, NRIs, melatonergic antidepressants) and forms of therapy (i.e., pharmacotherapy, rTMS). The results depict an emerging trend towards early changes in facial emotion recognition (i.e., a hot cognitive process) as a predictor of treatment outcome. LIMITATIONS: Our qualitative analysis reflects a very limited number of studies. Moreover, there was significant heterogeneity in the evaluation of cognition across studies. Future research should aim to parse out this heterogeneity by evaluating the relative predictive value of different measures of cognition. CONCLUSION: The identification of reliable early treatment predictors of antidepressant response would be clinically significant, enabling clinicians to more accurately evaluate the efficacy of selected treatment avenues.

Probiotics for the treatment of depressive symptoms: An anti-inflammatory mechanism?

During the past decade, there has been renewed interest in the relationship between brain-based disorders, the gut microbiota, and the possible beneficial effects of probiotics. Emerging evidence suggests that modifying the composition of the gut microbiota via probiotic supplementation may be a viable adjuvant treatment option for individuals with major depressive disorder (MDD). Convergent evidence indicates that persistent low-grade inflammatory activation is associated with the diagnosis of MDD as well as the severity of depressive symptoms and probability of treatment response. The objectives of this review are to (1) evaluate the evidence supporting an anti-inflammatory effect of probiotics and (2) describe immune system modulation as a potential mechanism for the therapeutic effects of probiotics in populations with MDD. A narrative review of studies investigating the effects of probiotics on systemic inflammation was conducted. Studies were identified using PubMed/Medline, Google Scholar, and clinicaltrials.gov (from inception to November 2017) using the following search terms (and/or variants): probiotic, inflammation, gut microbiota, and depression. The available evidence suggests that probiotics should be considered a promising adjuvant treatment to reduce the inflammatory activation commonly found in MDD. Several controversial points remain to be addressed including the role of leaky gut, the role of stress exposure, and the role of blood-brain-barrier permeability. Taken together, the results of this review suggest that probiotics may be a potentially beneficial, but insufficiently studied, antidepressant treatment intervention.

Predictors of Response to Ketamine in Treatment Resistant Major Depressive Disorder and Bipolar Disorder.

OBJECTIVES: Extant evidence indicates that ketamine exerts rapid antidepressant effects in treatment-resistant depressive (TRD) symptoms as a part of major depressive disorder (MDD) and bipolar disorder (BD). The identification of depressed sub-populations that are more likely to benefit from ketamine treatment remains a priority. In keeping with this view, the present narrative review aims to identify the pretreatment predictors of response to ketamine in TRD as part of MDD and BD. METHOD: Electronic search engines PubMed/MEDLINE, ClinicalTrials.gov, and Scopus were searched for relevant articles from inception to January 2018. The search term ketamine was cross-referenced with the terms depression, major depressive disorder, bipolar disorder, predictors, and response and/or remission. RESULTS: Multiple baseline pretreatment predictors of response were identified, including clinical (i.e., Body Mass Index (BMI), history of suicide, family history of alcohol use disorder), peripheral biochemistry (i.e., adiponectin levels, vitamin B12 levels), polysomnography (abnormalities in delta sleep ratio), neurochemistry (i.e., glutamine/glutamate ratio), neuroimaging (i.e., anterior cingulate cortex activity), genetic variation (i.e., Val66Met BDNF allele), and cognitive functioning (i.e., processing speed). High BMI and a positive family history of alcohol use disorder were the most replicated predictors. CONCLUSIONS: A pheno-biotype of depression more, or less likely, to benefit with ketamine treatment is far from complete. Notwithstanding, metabolic-inflammatory alterations are emerging as possible pretreatment response predictors of depressive symptom improvement, most notably being cognitive impairment. Sophisticated data-driven computational methods that are iterative and agnostic are more likely to provide actionable baseline pretreatment predictive information.

Sex differences in the mediators of functional disability in Major Depressive Disorder.

The aim of this study was to investigate sex differences in discrete domains of psychopathology as mediators of functional disability among individuals with Major Depressive Disorder (MDD). Adults (ages 18-65) with moderate-to-severe MDD (n = 100) and age-, sex-, and education-matched healthy controls (HC; n = 100) participated in a clinical trial validating the THINC-integrated tool, a newly developed cognitive assessment tool for patients with MDD. Variables assessed as possible mediators included depression symptom severity, anxiety symptoms, sleep disturbance, perceived cognitive deficits, and objective cognitive performance. Functional disability was assessed using the total score on the Sheehan Disability Scale. Separate mediation analyses were conducted for men and women. No significant differences were detected between men and women on the assessed domains of psychopathology or functional disability (ps > 0.05). However, the mediation analyses demonstrated different patterns with respect to determinants of functional disability in MDD between men and women. Functional disability was mediated by anxiety (95% CI: -3.17, -0.28) and sleep disturbance (95% CI: -0.69, -0.05) among men and by depressive symptom severity (95% CI: -7.82, -0.32) among women. These preliminary results instantiate the need to dimensionalize psychopathology in MDD. Our results at least in part support the hypothesis that, consistent with the sex differences in the prevalence and illness presentation of MDD, determinants of functional outcomes also differ between men and women, underscoring the need to consider sex differences in order to improve functional outcomes in the treatment of MDD.