scholarly journals Exploring the Role and Potential of Probiotics in the Field of Mental Health: Major Depressive Disorder

Nutrients ◽  
2021 ◽  
Vol 13 (5) ◽  
pp. 1728
Author(s):  
Dinyadarshini Johnson ◽  
Sivakumar Thurairajasingam ◽  
Vengadesh Letchumanan ◽  
Kok-Gan Chan ◽  
Learn-Han Lee
Keyword(s):  
Mental Health ◽  
Major Depressive Disorder ◽  
Depressive Disorder ◽  
Epigenetic Mechanisms ◽  
Preclinical Models ◽  
Major Depressive ◽  
Health Domains ◽  
Probiotic Intervention

The field of probiotic has been exponentially expanding over the recent decades with a more therapeutic-centered research. Probiotics mediated microbiota modulation within the microbiota–gut–brain axis (MGBA) have been proven to be beneficial in various health domains through pre-clinical and clinical studies. In the context of mental health, although probiotic research is still in its infancy stage, the promising role and potential of probiotics in various mental disorders demonstrated via in-vivo and in-vitro studies have laid a strong foundation for translating preclinical models to humans. The exploration of the therapeutic role and potential of probiotics in major depressive disorder (MDD) is an extremely noteworthy field of research. The possible etio-pathological mechanisms of depression involving inflammation, neurotransmitters, the hypothalamic–pituitary–adrenal (HPA) axis and epigenetic mechanisms potentially benefit from probiotic intervention. Probiotics, both as an adjunct to antidepressants or a stand-alone intervention, have a beneficial role and potential in mitigating anti-depressive effects, and confers some advantages compared to conventional treatments of depression using anti-depressants.

scholarly journals A review of the antimicrobial side of antidepressants and its putative implications on the gut microbiome

2019 ◽  
Vol 53 (12) ◽  
pp. 1151-1166 ◽  
Author(s):  
Abigail S McGovern ◽  
Adam S Hamlin ◽  
Gal Winter
Keyword(s):  
Major Depressive Disorder ◽  
Gut Microbiota ◽  
Depressive Disorder ◽  
Serotonin Reuptake ◽  
Gastrointestinal Transit ◽  
Antimicrobial Effect ◽  
Serotonin Reuptake Inhibitors ◽  
Major Depressive

Objective: Serotonin reuptake inhibitors are the predominant treatment for major depressive disorder. In recent years, the diversity of the gut microbiota has emerged to play a significant role in the occurrence of major depressive disorder and other mood and anxiety disorders. Importantly, the role of the gut microbiota in the treatment of such disorders remains to be elucidated. Here, we provide a review of the literature regarding the effects of physiologically relevant concentrations of serotonin reuptake inhibitors on the gut microbiota and the implications this might have on their efficacy in the treatment of mood disorders. Methods: First, an estimation of gut serotonin reuptake inhibitor concentrations was computed based on pharmacokinetic and gastrointestinal transit properties of serotonin reuptake inhibitors. Literature regarding the in vivo and in vitro antimicrobial properties of serotonin reuptake inhibitors was gathered, and the estimated gut concentrations were examined in the context of these data. Computer-based investigation revealed putative mechanisms for the antimicrobial effects of serotonin reuptake inhibitors. Results: In vivo evidence using animal models shows an antimicrobial effect of serotonin reuptake inhibitors on the gut microbiota. Examination of the estimated physiological concentrations of serotonin reuptake inhibitors in the gastrointestinal tract collected from in vitro studies suggests that the microbial community of both the small intestine and the colon are exposed to serotonin reuptake inhibitors for at least 4 hours per day at concentrations that are likely to exert an antimicrobial effect. The potential mechanisms of the effect of serotonin reuptake inhibitors on the gut microbiota were postulated to include inhibition of efflux pumps and/or amino acid transporters. Conclusion: This review raises important issues regarding the role that gut microbiota play in the treatment of mood-related behaviours, which holds substantial potential clinical outcomes for patients suffering from major depressive disorder and other mood-related disorders.

scholarly journals A machine learning algorithm to differentiate bipolar disorder from major depressive disorder using an online mental health questionnaire and blood biomarker data

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Jakub Tomasik ◽  
Sung Yeon Sarah Han ◽  
Giles Barton-Owen ◽  
Dan-Mircea Mirea ◽  
Nayra A. Martin-Key ◽  
...  
Keyword(s):  
Mental Health ◽  
Bipolar Disorder ◽  
Major Depressive Disorder ◽  
Depressive Disorder ◽  
Diagnostic Algorithm ◽  
Health Questionnaire ◽  
Major Depressive ◽  
Blood Biomarker ◽  
Mental Health Questionnaire ◽  
Biomarker Data

AbstractThe vast personal and economic burden of mood disorders is largely caused by their under- and misdiagnosis, which is associated with ineffective treatment and worsening of outcomes. Here, we aimed to develop a diagnostic algorithm, based on an online questionnaire and blood biomarker data, to reduce the misdiagnosis of bipolar disorder (BD) as major depressive disorder (MDD). Individuals with depressive symptoms (Patient Health Questionnaire-9 score ≥5) aged 18–45 years were recruited online. After completing a purpose-built online mental health questionnaire, eligible participants provided dried blood spot samples for biomarker analysis and underwent the World Health Organization World Mental Health Composite International Diagnostic Interview via telephone, to establish their mental health diagnosis. Extreme Gradient Boosting and nested cross-validation were used to train and validate diagnostic models differentiating BD from MDD in participants who self-reported a current MDD diagnosis. Mean test area under the receiver operating characteristic curve (AUROC) for separating participants with BD diagnosed as MDD (N = 126) from those with correct MDD diagnosis (N = 187) was 0.92 (95% CI: 0.86–0.97). Core predictors included elevated mood, grandiosity, talkativeness, recklessness and risky behaviour. Additional validation in participants with no previous mood disorder diagnosis showed AUROCs of 0.89 (0.86–0.91) and 0.90 (0.87–0.91) for separating newly diagnosed BD (N = 98) from MDD (N = 112) and subclinical low mood (N = 120), respectively. Validation in participants with a previous diagnosis of BD (N = 45) demonstrated sensitivity of 0.86 (0.57–0.96). The diagnostic algorithm accurately identified patients with BD in various clinical scenarios, and could help expedite accurate clinical diagnosis and treatment of BD.

Intravenous ketamine for rapid treatment of major depressive disorder in the general medical hospital

2021 ◽  
Vol 14 (10) ◽  
pp. e239587
Author(s):  
Siobhan Helen Gee ◽  
Camille Wratten ◽  
Ruth Cairns ◽  
Alastair Santhouse ◽  
David Taylor
Keyword(s):  
Mental Health ◽  
Major Depressive Disorder ◽  
Depressive Disorder ◽  
Oral Treatment ◽  
Medical Problem ◽  
Major Depressive ◽  
Medical Settings ◽  
Mental Health Settings ◽  
Intravenous Ketamine ◽  
Rapid Treatment

Major depressive disorder (MDD) is common in general medical settings, and can usually be treated with conventional oral antidepressants. For some patients, however, oral treatment is refused or not possible, and the untreated symptoms can have a significant impact on the treatment of the acute medical problem. Use of intravenous ketamine has been widely reported in mental health settings for the treatment of MDD. We describe use of intravenous ketamine in a general medical hospital for the treatment of MDD in an 83-year-old male patient who refused food, fluid and medical investigations following a stroke.

scholarly journals Convergent molecular, cellular, and cortical neuroimaging signatures of major depressive disorder

2020 ◽  
Vol 117 (40) ◽  
pp. 25138-25149
Author(s):  
Kevin M. Anderson ◽  
Meghan A. Collins ◽  
Ru Kong ◽  
Kacey Fang ◽  
Jingwei Li ◽  
...  
Keyword(s):  
Major Depressive Disorder ◽  
Negative Affect ◽  
Depressive Disorder ◽  
Single Cell ◽  
In Vivo Imaging ◽  
Ex Vivo ◽  
Integrated Analysis ◽  
Down Regulation ◽  
Major Depressive

Major depressive disorder emerges from the complex interactions of biological systems that span genes and molecules through cells, networks, and behavior. Establishing how neurobiological processes coalesce to contribute to depression requires a multiscale approach, encompassing measures of brain structure and function as well as genetic and cell-specific transcriptional data. Here, we examine anatomical (cortical thickness) and functional (functional variability, global brain connectivity) correlates of depression and negative affect across three population-imaging datasets: UK Biobank, Brain Genomics Superstruct Project, and Enhancing NeuroImaging through Meta Analysis (ENIGMA; combined n ≥ 23,723). Integrative analyses incorporate measures of cortical gene expression, postmortem patient transcriptional data, depression genome-wide association study (GWAS), and single-cell gene transcription. Neuroimaging correlates of depression and negative affect were consistent across three independent datasets. Linking ex vivo gene down-regulation with in vivo neuroimaging, we find that transcriptional correlates of depression imaging phenotypes track gene down-regulation in postmortem cortical samples of patients with depression. Integrated analysis of single-cell and Allen Human Brain Atlas expression data reveal somatostatin interneurons and astrocytes to be consistent cell associates of depression, through both in vivo imaging and ex vivo cortical gene dysregulation. Providing converging evidence for these observations, GWAS-derived polygenic risk for depression was enriched for genes expressed in interneurons, but not glia. Underscoring the translational potential of multiscale approaches, the transcriptional correlates of depression-linked brain function and structure were enriched for disorder-relevant molecular pathways. These findings bridge levels to connect specific genes, cell classes, and biological pathways to in vivo imaging correlates of depression.

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