Gut microbiome and adaptive immunity in schizophrenia.

Over the past few years, immunopathogenesis has emerged as one of the most compelling aetiopathological models of schizophrenia (SCZ), suggesting a chronic, immune-based, low-grade inflammatory background of this devastating disorder.1,2 Mounting evidence points towards a prominent role of the adaptive immune system in SCZ, suggesting alterations in defense mechanisms, such as altered T-cell function and a shift towards B-cell immunity.3 Immune cells have the ability to infiltrate the brain and mediate a neuroimmune cross-talk through activation of microglia, production of proinflammatory cytokines and reactive oxygen species, leading to neuroinflammation, as mediator of neuroprogressive and neurodegenerative changes in SCZ.4 Antipsychotic drugs, commonly used to treat SCZ, are also known to affect the adaptive immune system, interfering with the differentiation and function of immune cells, towards their normalization in response to treatment. Adaptive immunity is principally founded on T-cell and B-cell populations, but also includes the host microbiome. The gastrointestinal microbiota is a complex ecosystem with a great organism diversity and refined genomic structure that resides in the intestinal tract and has a central position in human health and disease.5 Neuroimmune dysregulation, relying of the highly sensitive and fine-tuned equilibrium between microbiome and adaptive immunity, can tip the scales towards neuroinflammation and disruption of higher-order brain networks.4,6,7 During the last decade, the human microbiome and the microbiota-gut-brain (MGB)-axis have become a novel epicentre in mental health research as a potentially vital new determinant in the field of neuroimmunoregulation, brain development, emotions, cognition and behaviour.7 The MGB-axis represents a bidirectional, key communication pathway between the immune system and the brain, thus partly also mediating the regulation of cognitive and emotional processing. An imbalanced human microbiome might greatly influence proper neuroimmune reactions and neurodevelopment with long-lasting effects and could thus play a pivotal role in the susceptibility and aetiology of psychiatric illness. Recent research offers first evidence that patients with SCZ show marked disturbances of gut bacterial taxa composition with a decreased microbiome diversity index, party associated with specific SCZ phenotype, symptom severity and treatment response.8,9 As the elegant education of the adaptive immune compartment depends on the colonization niche, antigen type and metabolic property of different gut microbes, T-cell differentiation as well as a continuous diversification of B-cell repertoire is expressed through microbiome-related, antigen-specific receptors that define a unique clonotype.10 However, there is only sparse evidence on the precise role of the microbiome on the programming of T- and B-cells in the underlying neurobiological pathways of SCZ and even less findings on the association of molecular T- and B-cell receptor repertoire signature and microbiome clonal landscape with specific phenotypical features of the disease.11 The latest conceptual advances in immunology urge an integrative re-evaluation of previous immunological findings in SCZ through modern approaches. High-throughput, next-generation sequencing (NGS) represents a powerful singlecell transcriptomic tool to profile the whole clonal landscape of T and B cells and human microbiome. NGS thus offers a unique opportunity for in-depth characterization of cellular and molecular signatures of adaptive immune receptor repertoires and microbiome taxonomy in SCZ and investigation of their intersection as a relevant pathway of disease progression and phenotype differentiation. SCZ patients are likely to show a diverging host-microbiome immune homeostasis with disease-specific clonotypes of adaptive immune receptor repertoires associated with altered microbiome taxonomy and molecular signature differences, which, in turn, may be related to distinct symptomatic phenotypes and neurocognitive patterns. Such sophisticated immuno-bioinformatic analyses may transform our understanding of SCZ by identification of novel neuroimmune pathways, offering us clinically accessible symptomatic and diagnostic biomarkers important for personalized medicine implications.12 An increased understanding and better characterization of immuno-phenotypes in SCZ will better guide the development of novel immune-based treatments in this severe disease and pave the way for possible prevention options through implementation of antibody engineering, vaccine design, and cellular immunotherapy.

[Treatment of depression in coronary heart disease]. / Behandlung der Depression bei koronarer Herzerkrankung.

Depression and coronary heart disease (CHD) are leading causes of disability and show a high comorbidity. Furthermore, depression is an independent risk factor for an unfavorable course and increased mortality in patients with CHD. In contrast, successful treatment of depression can reduce the risk of cardiac events. Currently, there are several treatment options for the management of depression in CHD, including self-management strategies, psychotherapy, pharmacotherapy and collaborative care models. This article provides an overview of the epidemiology of depression in CHD, the mechanisms of association and the current state of evidence with respect to the different treatment options.

Association between cortisol awakening response and memory function in major depression.

BACKGROUND: While impaired memory and altered cortisol secretion are characteristic features of major depression, much less is known regarding the impact of antidepressant medication. We examined whether the cortisol awakening response (CAR) is increased in depressed patients with and without medication compared with healthy controls (HC) and whether CAR is associated with memory function in each group. METHOD: We examined 21 patients with major depression without medication, 20 depressed patients on antidepressant treatment, and 41 age-, sex- and education-matched healthy subjects. We tested verbal (Auditory Verbal Learning Task) and visuospatial (Rey figure) memory and measured CAR on two consecutive days. RESULTS: Patient groups did not differ in severity of depression. We found a significant effect of group (p = 0.03) for CAR. Unmedicated patients exhibited a greater CAR compared with medicated patients (p = 0.04) with no differences between patient groups and HC. We found a significant effect of group for verbal (p = 0.03) and non-verbal memory (p = 0.04). Unmedicated patients performed worse compared with medicated patients and HC in both memory domains. Medicated patients and HC did not differ. Regression analyses revealed a negative association between CAR and memory function in depressed patients, but not in HC. CONCLUSIONS: While in unmedicated depressed patients the magnitude of CAR is associated with impaired memory, medicated patients showed a smaller CAR and unimpaired cognitive function compared with HC. Our findings are compatible with the idea that antidepressants reduce CAR and partially restore memory function even if depressive psychopathology is still present.

Are ratings on the positive and negative syndrome scale for schizophrenia biased by personality traits?

OBJECTIVE: The aim of this study was to estimate the potential bias by personality traits for ratings on the Positive and Negative Syndrome Scale (PANSS). METHODS: Personality dimensions (five factor model), personality traits (SCID-II) and PANSS scores were assessed prospectively in 45 patients with schizophrenia spectrum disorders (SSD). RESULTS: Borderline (r=0.34; p=0.021), avoidant (r=0.66; p<0.001) and depressive (r=0.51; p<0.001) personality traits were significantly correlated with the PANSS total score. There were significant correlations for all PANSS subscores with the exemption of PANSS positive. In multivariate analyses, the final models for PANSS total score and PANSS depressive explained a total of 45.3% and 54.3% of the variance. Avoidant traits could lead to a difference of 13.1 (95% CI: 5.6-20.7) points regarding PANSS total score, depressive traits could cause differences of 4.8 points (95% CI: 2.2-7.3) for PANSS depressive subscore. CONCLUSION: Although PANSS positive subscore and PANSS excited component are relatively robust against bias by personality traits, PANSS total score and the remaining subscores are affected to a clinically relevant degree. Outcome studies in SSD patients should control for personality traits.