Role of the gut microbiome in three major psychiatric disorders

Major depressive disorder (MDD), bipolar disorder (BD) and schizophrenia-spectrum disorders (SSD) are heterogeneous psychiatric disorders, which place significant burden on patient's well-being and global health. Disruptions in the gut-microbiome may play a role in these psychiatric disorders. This review presents current data on composition of the human gastrointestinal microbiota, and its interaction mechanisms in the gut-brain axis in MDD, BD and SSD. Diversity metrics and microbial relative abundance differed across studies. More studies reported inconsistent findings (n = 7) or no differences (n = 8) than studies who reported lower α-diversity in these psychiatric disorders (n = 5). The most consistent findings across studies were higher relative abundances of the genera Streptococcus, Lactobacillus, and Eggerthella and lower relative abundance of the butyrate producing Faecalibacterium in patients with psychiatric disorders. All three increased genera were associated with higher symptom severity. Confounders, such as medication use and life style have not been accounted for. So far, the results of probiotics trials have been inconsistent. Most traditional and widely used probiotics (consisting of Bifidobacterium spp. and Lactobacillus spp.) are safe, however, they do not correct potential microbiota disbalances in these disorders. Findings on prebiotics and faecal microbiota transplantation (FMT) are too limited to draw definitive conclusions. Disease-specific pro/prebiotic treatment or even FMT could be auspicious interventions for prevention and therapy for psychiatric disorders and should be investigated in future trials

Unhealthy diet in schizophrenia spectrum disorders

PURPOSE OF REVIEW: The high mortality and prevalence of metabolic syndrome in patients with schizophrenia spectrum disorders (SSD) is maintained by poor diet. This narrative review summarizes recent literature to provide a reflection of current eating habits, dietary preferences, and nutritional status of SSD patients. Elucidating these factors provides new insights for potential lifestyle treatment strategies for SSD. RECENT FINDINGS: Only 10.7% of the SSD patients had a healthy dietary pattern, against 23% of the general population. The dietic component of the Keeping the Body in Mind Xtend lifestyle program increased diet quality with 10% for young people with first-episode psychosis, compared to baseline, which was predominantly driven by increased vegetable variety and amounts. SUMMARY: Recent findings render poor dietary habits as potential targets for treatment of SSD patients. Further studies into anti-inflammatory diets and associations with gut-brain biomarkers are warranted. When proven, structured and supervised diet interventions may help SSD patients escape from this entrapment, as only supplementing nutrients or providing dietary advice lacks the impact to significantly reduce the risk of chronic physical illnesses

Anticipating manic and depressive transitions in patients with bipolar disorder using early warning signals

Background In bipolar disorder treatment, accurate episode prediction is paramount but remains difficult. A novel idiographic approach to prediction is to monitor generic early warning signals (EWS), which may manifest in symptom dynamics. EWS could thus form personalized alerts in clinical care. The present study investigated whether EWS can anticipate manic and depressive transitions in individual patients with bipolar disorder. Methods Twenty bipolar type I/II patients (with >= 2 episodes in the previous year) participated in ecological momentary assessment (EMA), completing five questionnaires a day for four months (Mean = 491 observations per person). Transitions were determined by weekly completed questionnaires on depressive (Quick Inventory for Depressive Symptomatology Self-Report) and manic (Altman Self-Rating Mania Scale) symptoms. EWS (rises in autocorrelation at lag-1 and standard deviation) were calculated in moving windows over 17 affective and symptomatic EMA states. Positive and negative predictive values were calculated to determine clinical utility. Results Eleven patients reported 1-2 transitions. The presence of EWS increased the probability of impending depressive and manic transitions from 32-36% to 46-48% (autocorrelation) and 29-41% (standard deviation). However, the absence of EWS could not be taken as a sign that no transition would occur in the near future. The momentary states that indicated nearby transitions most accurately (predictive values: 65-100%) were full of ideas, worry, and agitation. Large individual differences in the utility of EWS were found. Conclusions EWS show theoretical promise in anticipating manic and depressive transitions in bipolar disorder, but the level of false positives and negatives, as well as the heterogeneity within and between individuals and preprocessing methods currently limit clinical utility

Adrift in time:The subjective experience of circadian challenge during COVID-19 amongst people with mood disorders

Social distancing/lockdown policies during the coronavirus (COVID-19) pandemic may alter social rhythms of people through imposition of restrictions on normal daily activities. This may in turn challenge circadian function, particularly in people with mood disorders. Although objective data describing the relationship between circadian disturbances and mood disorders exist, data regarding the subjective experience of circadian challenge is sparse, and its association with mood symptoms is unclear. The present qualitative study was one component of a mixed-methods multi-national project, which took advantage of widespread disruption to daily routines due to Government COVID-related lockdowns during 2020. The Behavior Emotion and Timing during COVID-19 (BEATCOVID) survey study included three open questions generating qualitative data on participants' subjective experience of social disruption due to social distancing/lockdown policies, two of which asked about the barriers and opportunities for stabilizing routines. Responses were coded and analyzed using Thematic Analysis. A total of N = 997 participants responded to at least one of the free-text questions. Four themes were identified: 1) loss of daily timed activities, 2) role of social interaction, 3) altered time perception and 4) disruption to motivation and associated psychological effects. Themes were organized into a provisional heuristic map, generating hypotheses for future research centered on the new concept of 'psychological drift.

Machine learning and big data analytics in bipolar disorder:A position paper from the International Society for Bipolar Disorders Big Data Task Force

Objectives The International Society for Bipolar Disorders Big Data Task Force assembled leading researchers in the field of bipolar disorder (BD), machine learning, and big data with extensive experience to evaluate the rationale of machine learning and big data analytics strategies for BD. Method A task force was convened to examine and integrate findings from the scientific literature related to machine learning and big data based studies to clarify terminology and to describe challenges and potential applications in the field of BD. We also systematically searched PubMed, Embase, and Web of Science for articles published up to January 2019 that used machine learning in BD. Results The results suggested that big data analytics has the potential to provide risk calculators to aid in treatment decisions and predict clinical prognosis, including suicidality, for individual patients. This approach can advance diagnosis by enabling discovery of more relevant data-driven phenotypes, as well as by predicting transition to the disorder in high-risk unaffected subjects. We also discuss the most frequent challenges that big data analytics applications can face, such as heterogeneity, lack of external validation and replication of some studies, cost and non-stationary distribution of the data, and lack of appropriate funding. Conclusion Machine learning-based studies, including atheoretical data-driven big data approaches, provide an opportunity to more accurately detect those who are at risk, parse-relevant phenotypes as well as inform treatment selection and prognosis. However, several methodological challenges need to be addressed in order to translate research findings to clinical settings.Peer reviewe

Monocyte mitochondrial dysfunction, inflammaging, and inflammatory pyroptosis in major depression

BACKGROUND: The macrophage theory of depression states that macrophages play an important role in Major Depressive Disorder (MDD). METHODS: MDD patients (N = 140) and healthy controls (N = 120) participated in a cross-sectional study investigating the expression of apoptosis/growth and lipid/cholesterol pathway genes (BAX, BCL10, EGR1, EGR2, HB-EGF, NR1H3, ABCA1, ABCG1, MVK, CD163, HMOX1) in monocytes (macrophage/microglia precursors). Gene expressions were correlated to a set of previously determined and reported inflammation-regulating genes and analyzed with respect to various clinical parameters. RESULTS: MDD monocytes showed an overexpression of the apoptosis/growth/cholesterol and the TNF genes forming an inter-correlating gene cluster (cluster 3) separate from the previously described inflammation-related gene clusters (containing IL1 and IL6). While upregulation of monocyte gene cluster 3 was a hallmark of monocytes of all MDD patients, upregulation of the inflammation-related clusters was confirmed to be found only in the monocytes of patients with childhood adversity. The latter group also showed a downregulation of the cholesterol metabolism gene MVK, which is known to play an important role in trained immunity and proneness to inflammation. CONCLUSIONS: The upregulation of cluster 3 genes in monocytes of all MDD patients suggests a premature aging of the cells, i.e. mitochondrial apoptotic dysfunction and TNF "inflammaging", as a general feature of MDD. The overexpression of the IL-1/IL-6 containing inflammation clusters and the downregulation of MVK in monocytes of patients with childhood adversity indicates a shift in this condition to a more severe inflammation form (pyroptosis) of the cells, additional to the signs of premature aging and inflammaging

What we learn about bipolar disorder from large-scale neuroimaging:Findings and future directions from the ENIGMA Bipolar Disorder Working Group

MRI-derived brain measures offer a link between genes, the environment and behavior and have been widely studied in bipolar disorder (BD). However, many neuroimaging studies of BD have been underpowered, leading to varied results and uncertainty regarding effects. The Enhancing Neuro Imaging Genetics through Meta-Analysis (ENIGMA) Bipolar Disorder Working Group was formed in 2012 to empower discoveries, generate consensus findings and inform future hypothesis-driven studies of BD. Through this effort, over 150 researchers from 20 countries and 55 institutions pool data and resources to produce the largest neuroimaging studies of BD ever conducted. The ENIGMA Bipolar Disorder Working Group applies standardized processing and analysis techniques to empower large-scale meta- and mega-analyses of multimodal brain MRI and improve the replicability of studies relating brain variation to clinical and genetic data. Initial BD Working Group studies reveal widespread patterns of lower cortical thickness, subcortical volume and disrupted white matter integrity associated with BD. Findings also include mapping brain alterations of common medications like lithium, symptom patterns and clinical risk profiles and have provided further insights into the pathophysiological mechanisms of BD. Here we discuss key findings from the BD working group, its ongoing projects and future directions for large-scale, collaborative studies of mental illness

In vivo hippocampal subfield volumes in bipolar disorder—A mega-analysis from The Enhancing Neuro Imaging Genetics through Meta-Analysis Bipolar Disorder Working Group

The hippocampus consists of anatomically and functionally distinct subfields that may be differentially involved in the pathophysiology of bipolar disorder (BD). Here we, the Enhancing NeuroImaging Genetics through Meta‐Analysis Bipolar Disorder workinggroup, study hippocampal subfield volumetry in BD. T1‐weighted magnetic resonance imaging scans from 4,698 individuals (BD = 1,472, healthy controls [HC] = 3,226) from 23 sites worldwide were processed with FreeSurfer. We used linear mixed‐effects models and mega‐analysis to investigate differences in hippocampal subfield volumes between BD and HC, followed by analyses of clinical characteristics and medication use. BD showed significantly smaller volumes of the whole hippocampus (Cohen's d = −0.20), cornu ammonis (CA)1 (d = −0.18), CA2/3 (d = −0.11), CA4 (d = −0.19), molecular layer (d = −0.21), granule cell layer of dentate gyrus (d = −0.21), hippocampal tail (d = −0.10), subiculum (d = −0.15), presubiculum (d = −0.18), and hippocampal amygdala transition area (d = −0.17) compared to HC. Lithium users did not show volume differences compared to HC, while non‐users did. Antipsychotics or antiepileptic use was associated with smaller volumes. In this largest study of hippocampal subfields in BD to date, we show widespread reductions in nine of 12 subfields studied. The associations were modulated by medication use and specifically the lack of differences between lithium users and HC supports a possible protective role of lithium in BD