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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]

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]