Endocannabinoid system alterations in schizophrenia: association with cannabis use and antipsychotic medication.

Determining peripheral modulation of the endocannabinoid system (ECS) may be important for differentiating individuals with schizophrenia. Such differentiation can also be extended to subgroups of individuals, those who use cannabis and antipsychotic medications, particularly those who are treatment resistant. Patients and controls were recruited from the outpatient clinic of the Psychosis Group of the University of São Paulo, Brazil. A final sample of 93 individuals was divided into 3 groups: patients with schizophrenia using clozapine (treatment-resistant) (n = 29), patients with schizophrenia using another antipsychotic (n = 31), and controls (n = 33). By measuring the proteins and metabolites involved in the ECS pathways in the peripheral blood, AEA (anandamide), 2-AG (2-arachidonoyl ethanolamine), and CB2 receptor (peripheral) were quantified. Individuals reporting lifetime cannabis use had lower 2-AG plasma levels (p = 0.011). Regarding the CB2 receptor, the values of patients with schizophrenia and controls were similar, but those of patients using antipsychotics other than clozapine differed (p = 0.022). In generalized linear models to control for confounders, the use of cannabis remained the only factor that significantly influenced 2-AG levels. The relationship for non-clozapine antipsychotics as the only factor related to CB2 changes was marginally significant. We found for the first time that cannabis use and non-clozapine antipsychotic medication are potentially involved in the modulation of the ECS, specifically influencing 2-AG endocannabinoid and CB2 receptor levels. More studies regarding the ECS are needed since it has been increasingly related to the physiopathology of schizophrenia.

Cannabis use influences disorganized symptoms severity but not transition in a cohort of non-help-seeking individuals at-risk for psychosis from São Paulo, Brazil.

BACKGROUND: Cannabis use is associated with an increased risk of developing a psychotic disorder. However, in individuals with at-risk mental states for psychosis (ARMS) this association is not clear, as well as the impact of cannabis use on symptom severity. The objective of this study was to evaluate the association of cannabis use patterns and ARMS risk status, transition to psychotic and psychiatric disorders, and psychopathology. METHOD: A sample of 109 ARMS and 197 control individuals was drawn from the general population. Lifetime, maximum and current amount of cannabis use were assessed with the South Westminster modified questionnaire. Participants were followed-up for a mean of 2.5 years and reassessed for transition to any psychiatric disorder. RESULTS: There were no differences between ARMS and controls regarding lifetime use, current amount of use, or maximum amount of cannabis use. There were also no differences between those who transitioned to a psychiatric disorder and those who did not regarding cannabis use variables. In ARMS individuals, cannabis use was significantly related to disorganization symptoms. CONCLUSION: The results of this study suggest that cannabis plays a role in the psychopathology of ARMS individuals, leading to more severe symptomatology.

Plasma levels of neurotrophin 4/5, NGF and pro-BDNF influence transition to mental disorders in a sample of individuals at ultra-high risk for psychosis.

BACKGROUND: Neurotrophins (NTs) and their precursors (pro-NTs) are polypeptides with important roles in neuronal development, differentiation, growth, survival and plasticity, as well as apoptosis and neuronal death. Imbalance in NT levels were observed in schizophrenia spectrum disorders, but evidence in ultra-high risk for psychosis (UHR) samples is scarce. METHODS: A naturalistic sample of 87 non-help-seeking UHR subjects and 55 healthy controls was drawn from the general population. Blood samples were collected and NT-3, NT-4/5, BDNF, pro-BDNF, NGF, pro-NGF were analyzed through enzyme linked immunosorbent assay (ELISA). Information on cannabis and tobacco use was also collected. Logistic regression models and path analysis were used to control for confounders (tobacco, age, cannabis use). RESULTS: NT-4/5 was significantly decreased, and pro-BDNF was significantly increased in UHR individuals compared to controls. Cannabis use and higher NGF levels were significantly related to transition to psychiatric disorders among UHR subjects. Increased pro-BDNF and decreased NT-4/5 influenced transition by the mediation of perceptual abnormalities. CONCLUSIONS: Our study shows for the first time that NTs are altered in UHR compared to healthy control individuals, and that they can be a predictor of transition to psychiatric illnesses in this population. Future studies should employ larger naturalistic samples to confirm the findings.

Detecting at-risk mental states for psychosis (ARMS) using machine learning ensembles and facial features.

AIMS: Our study aimed to develop a machine learning ensemble to distinguish "at-risk mental states for psychosis" (ARMS) subjects from control individuals from the general population based on facial data extracted from video-recordings. METHODS: 58 non-help-seeking medication-naïve ARMS and 70 healthy subjects were screened from a general population sample. At-risk status was assessed with the Structured Interview for Prodromal Syndromes (SIPS), and "Subject's Overview" section was filmed (5-10 min). Several features were extracted, e.g., eye and mouth aspect ratio, Euler angles, coordinates from 51 facial landmarks. This elicited 649 facial features, which were further selected using Gradient Boosting Machines (AdaBoost combined with Random Forests). Data was split in 70/30 for training, and Monte Carlo cross validation was used. RESULTS: Final model reached 83 % of mean F1-score, and balanced accuracy of 85 %. Mean area under the curve for the receiver operator curve classifier was 93 %. Convergent validity testing showed that two features included in the model were significantly correlated with Avolition (SIPS N2 item) and expression of emotion (SIPS N3 item). CONCLUSION: Our model capitalized on short video-recordings from individuals recruited from the general population, effectively distinguishing between ARMS and controls. Results are encouraging for large-screening purposes in low-resource settings.

Gesticulation in individuals with at risk mental states for psychosis.

Nonverbal communication (NVC) is a complex behavior that involves different modalities that are impaired in the schizophrenia spectrum, including gesticulation. However, there are few studies that evaluate it in individuals with at-risk mental states (ARMS) for psychosis, mostly in developed countries. Given our prior findings of reduced movement during speech seen in Brazilian individuals with ARMS, we now aim to determine if this can be accounted for by reduced gesticulation behavior. Fifty-six medication-naïve ARMS and 64 healthy controls were filmed during speech tasks. The frequency of specifically coded gestures across four categories (and self-stimulatory behaviors) were compared between groups and tested for correlations with prodromal symptoms of the Structured Interview for Prodromal Syndromes (SIPS) and with the variables previously published. ARMS individuals showed a reduction in one gesture category, but it did not survive Bonferroni's correction. Gesture frequency was negatively correlated with prodromal symptoms and positively correlated with the variables of the amount of movement previously analyzed. The lack of significant differences between ARMS and control contradicts literature findings in other cultural context, in which a reduction is usually seen in at-risk individuals. However, gesture frequency might be a visual proxy of prodromal symptoms, and of other movement abnormalities. Results show the importance of analyzing NVC in ARMS and of considering different cultural and sociodemographic contexts in the search for markers of these states.

Inflammatory cytokines and white matter microstructure in the acute phase of first-episode psychosis: A longitudinal study.

OBJECTIVES: Schizophrenia-related psychosis is associated with abnormalities in white matter (WM) microstructure and structural brain dysconnectivity. However, the pathological process underlying such changes is unknown. We sought to investigate the potential association between peripheral cytokine levels and WM microstructure during the acute phase of first-episode psychosis (FEP) in a cohort of drug-naïve patients. METHODS: Twenty-five non-affective FEP patients and 69 healthy controls underwent MRI scanning and blood collection at study entry. After achieving clinical remission, 21 FEP were reassessed; 38 age and biological sex-matched controls also had a second assessment. We measured fractional anisotropy (FA) of selected WM regions-of-interest (ROIs) and plasma levels of four cytokines (IL-6, IL-10, IFN-γ, and TNF-α). RESULTS: At baseline (acute psychosis), the FEP group showed reduced FA relative to controls in half the examined ROIs. Within the FEP group, IL-6 levels were negatively correlated with FA values. Longitudinally, patients showed increments of FA in several ROIs affected at baseline, and such changes were associated with reductions in IL-6 levels. CONCLUSIONS: A state-dependent process involving an interplay between a pro-inflammatory cytokine and brain WM might be associated with the clinical manifestation of FEP. This association suggests a deleterious effect of IL-6 on WM tracts during the acute phase of psychosis.

Application of Lipidomics in Psychiatry: Plasma-Based Potential Biomarkers in Schizophrenia and Bipolar Disorder.

In this study, we obtained a lipidomic profile of plasma samples from drug-naïve patients with schizophrenia (SZ) and bipolar disorder (BD) in comparison to healthy controls. The sample cohort consisted of 30 BD and 30 SZ patients and 30 control individuals. An untargeted lipidomics strategy using liquid chromatography coupled with high-resolution mass spectrometry was employed to obtain the lipid profiles. Data were preprocessed, then univariate (t-test) and multivariate (principal component analysis and orthogonal partial least squares discriminant analysis) statistical tools were applied to select differential lipids, which were putatively identified. Afterward, multivariate receiver operating characteristic tests were performed, and metabolic pathway networks were constructed, considering the differential lipids. Our results demonstrate alterations in distinct lipid pathways, especially in glycerophospholipids, sphingolipids and glycerolipids, between SZ and BD patients. The results obtained in this study may serve as a basis for differential diagnosis, which is crucial for effective treatment and improving the quality of life of patients with psychotic disorders.

A dopamine receptor D2 genetic polymorphism associated with transition to mental disorders in a cohort of individuals with at-risk mental state for psychosis.

OBJECTIVES: To test the association of 45 single nucleotide polymorphisms (SNPs) with transition to psychiatric disorders in a cohort of individuals at ultrahigh risk (UHR) mental state for psychosis. METHODS: Through general population screening, 88 non-help-seeking UHR subjects and 130 healthy control individuals were genotyped for 45 SNPs related to psychosis. They were followed for a mean of 2.5 years, and conversion to psychotic and to general psychiatric disorders was assessed. Genotype frequencies between controls, converters, and non-converters were analyzed. RESULTS: There were no differences in sociodemographics between controls and UHR. Also, UHR converters and non-converters had no differences in their baseline symptoms scores. The dopamine receptor D2 gene (DRD2) SNP rs6277 was significantly more common among UHR who transitioned to psychosis (p < 0.001) and to UHR who transitioned to any psychiatric disorders (p = 0.001) when compared to UHR who did not transition. The rs6277 T allele was related to psychiatric morbidity in a dose-response fashion, being significantly more frequent in UHR converters than UHR non-converters and control subjects (p = 0.003). CONCLUSION: Our findings suggest that rs6277 could potentially constitute a genetic marker of transition to psychiatric disorders in subjects with at-risk mental states, warranting further investigation in larger samples.

BDNF blood levels after electroconvulsive therapy in patients with mood disorders: An updated systematic review and meta-analysis.

OBJECTIVES: Studies have suggested Brain-Derived Neurotrophic Factors (BDNF) increase after electroconvulsive therapy (ECT) although they were methodologically limited and enrolled small sample sizes. We aimed at updating a systematic review and meta-analysis to explore BDNF changes after ECT for the treatment of depression. METHODS: PubMed, PsycInfo, Embase and Global health were searched (March, 2021). Clinical trials that measured BDNF in the blood before and after ECT in adults (≥ 18 years old) with depression (major depressive disorder or bipolar disorder) were eligible. Data were pooled through random-effects meta-analyses. RESULTS: Twenty-eight studies involving 778 participants were included. Meta-analysis showed a significant increase in BDNF levels after ECT (Hedges' g = 0.28; 95% CI: 0.10, 0.46) while there was evidence of significant heterogeneity (I2 = 67.64%) but not publication bias/small-study effect. Subgroup analyses and meta-regressions were underpowered to detect significant differences. Meta-analysis of depression severity scores demonstrated a considerable larger treatment effect in reducing depressive symptoms after ECT (Hedge's g = -3.72 95% CI: -4.23, -3.21). CONCLUSION: This updated review showed that BDNF blood levels increased after ECT treatment. However, there was still evidence of substantial heterogeneity and there were limited sample sizes to investigate factors driving the variability of effects across studies. Importantly, the increase in BDNF levels was substantially smaller than the observed in depressive symptomatology, which could be indicative that the former was independent than the latter. Additional studies with larger sample sizes are currently required.

A dopamine receptor D2 genetic polymorphism associated with transition to mental disorders in a cohort of individuals with at-risk mental state for psychosis

Objectives: To test the association of 45 single nucleotide polymorphisms (SNPs) with transition to psychiatric disorders in a cohort of individuals at ultrahigh risk (UHR) mental state for psychosis. Methods: Through general population screening, 88 non-help-seeking UHR subjects and 130 healthy control individuals were genotyped for 45 SNPs related to psychosis. They were followed for a mean of 2.5 years, and conversion to psychotic and to general psychiatric disorders was assessed. Genotype frequencies between controls, converters, and non-converters were analyzed. Results: There were no differences in sociodemographics between controls and UHR. Also, UHR converters and non-converters had no differences in their baseline symptoms scores. The dopamine receptor D2 gene (DRD2) SNP rs6277 was significantly more common among UHR who transitioned to psychosis (p < 0.001) and to UHR who transitioned to any psychiatric disorders (p = 0.001) when compared to UHR who did not transition. The rs6277 T allele was related to psychiatric morbidity in a dose-response fashion, being significantly more frequent in UHR converters than UHR non-converters and control subjects (p = 0.003). Conclusion: Our findings suggest that rs6277 could potentially constitute a genetic marker of transition to psychiatric disorders in subjects with at-risk mental states, warranting further investigation in larger samples.

Treatment of depression in the elderly with repetitive transcranial magnetic stimulation using theta-burst stimulation: Study protocol for a randomized, double-blind, controlled trial.

Introduction: Transcranial magnetic stimulation (TMS) is a consolidated procedure for the treatment of depression, with several meta-analyses demonstrating its efficacy. Theta-burst stimulation (TBS) is a modification of TMS with similar efficacy and shorter session duration. The geriatric population has many comorbidities and a high prevalence of depression, but few clinical trials are conducted specifically for this age group. TBS could be an option in this population, offering the advantages of few side effects and no pharmacological interactions. Therefore, our aim is to investigate the efficacy of TBS in geriatric depression. Clinical trial registration: [https://clinicaltrials.gov/ct2/], identifier [NCT04842929].

Motion energy analysis during speech tasks in medication-naïve individuals with at-risk mental states for psychosis.

Movement abnormalities are commonly observed in schizophrenia and at-risk mental states (ARMS) for psychosis. They are usually detected with clinical interviews, such that automated analysis would enhance assessment. Our aim was to use motion energy analysis (MEA) to assess movement during free-speech videos in ARMS and control individuals, and to investigate associations between movement metrics and negative and positive symptoms. Thirty-two medication-naïve ARMS and forty-six healthy control individuals were filmed during speech tasks. Footages were analyzed using MEA software, which assesses movement by differences in pixels frame-by-frame. Two regions of interest were defined-head and torso-and mean amplitude, frequency, and coefficient of variability of movements for them were obtained. These metrics were correlated with the Structured Interview for Prodromal Syndromes (SIPS) symptoms, and with the risk of conversion to psychosis-inferred with the SIPS risk calculator. ARMS individuals had significantly lower mean amplitude of head movement and higher coefficients of movement variability for both head and torso, compared to controls. Higher coefficient of variability was related to higher risk of conversion. Negative correlations were seen between frequency of movement and most SIPS negative symptoms. All positive symptoms were correlated with at least one movement variable. Movement abnormalities could be automatically detected in medication-naïve ARMS subjects by means of a motion energy analysis software. Significant associations of movement metrics with symptoms were found, supporting the importance of movement analysis in ARMS. This could be a potentially important tool for early diagnosis, intervention, and outcome prediction.

Decision tree-based classification as a support to diagnosis in the Alzheimer's disease continuum using cerebrospinal fluid biomarkers: insights from automated analysis

Objective: Cerebrospinal fluid (CSF) biomarkers add accuracy to the diagnostic workup of cognitive impairment by illustrating Alzheimer's disease (AD) pathology. However, there are no universally accepted cutoff values for the interpretation of AD biomarkers. The aim of this study is to determine the viability of a decision-tree method to analyse CSF biomarkers of AD as a support for clinical diagnosis. Methods: A decision-tree method (automated classification analysis) was applied to concentrations of AD biomarkers in CSF as a support for clinical diagnosis in older adults with or without cognitive impairment in a Brazilian cohort. In brief, 272 older adults (68 with AD, 122 with mild cognitive impairment [MCI], and 82 healthy controls) were assessed for CSF concentrations of Aβ1-42, total-tau, and phosphorylated-tau using multiplexed Luminex assays; biomarker values were used to generate decision-tree algorithms (classification and regression tree) in the R statistical software environment. Results: The best decision tree model had an accuracy of 74.65% to differentiate the three groups. Cluster analysis supported the combination of CSF biomarkers to differentiate AD and MCI vs. controls, suggesting the best cutoff values for each clinical condition. Conclusion: Automated analyses of AD biomarkers provide valuable information to support the clinical diagnosis of MCI and AD in research settings.

Decision tree-based classification as a support to diagnosis in the Alzheimer's disease continuum using cerebrospinal fluid biomarkers: insights from automated analysis.

OBJECTIVE: Cerebrospinal fluid (CSF) biomarkers add accuracy to the diagnostic workup of cognitive impairment by illustrating Alzheimer's disease (AD) pathology. However, there are no universally accepted cutoff values for the interpretation of AD biomarkers. The aim of this study is to determine the viability of a decision-tree method to analyse CSF biomarkers of AD as a support for clinical diagnosis. METHODS: A decision-tree method (automated classification analysis) was applied to concentrations of AD biomarkers in CSF as a support for clinical diagnosis in older adults with or without cognitive impairment in a Brazilian cohort. In brief, 272 older adults (68 with AD, 122 with mild cognitive impairment [MCI], and 82 healthy controls) were assessed for CSF concentrations of Aß1-42, total-tau, and phosphorylated-tau using multiplexed Luminex assays; biomarker values were used to generate decision-tree algorithms (classification and regression tree) in the R statistical software environment. RESULTS: The best decision tree model had an accuracy of 74.65% to differentiate the three groups. Cluster analysis supported the combination of CSF biomarkers to differentiate AD and MCI vs. controls, suggesting the best cutoff values for each clinical condition. CONCLUSION: Automated analyses of AD biomarkers provide valuable information to support the clinical diagnosis of MCI and AD in research settings.

Lithium increases cortical and subcortical volumes in subjects with bipolar disorder.

Bipolar disorder (BD) is a highly variable and burdensome disease for patients and caregivers. A BD diagnosis almost triples the likelihood of developing dementia as the disease progresses. Neurocognitive reserve appears to be one of the most important influences on lifelong functional outcomes and quality of life in BD. Though several prior studies have assessed the effects of lithium on regional gray and white matter volumes in this population, representative cohorts are typically middle-aged, have a more severe pathology, and are not as commonly assessed in the depressive phase (which represents the majority of most patients' lifespans outside of remission). Here we have shown that positive adaptations with lithium can be observed throughout the brain after only six weeks of monotherapy at low-therapeutic serum levels. Importantly, these results remove some confounders seen in prior studies (patients were treatment free at time of enrollment and mostly treatment naïve). This cohort also includes underrepresented demographics in the literature (young adult patients, mostly bipolar II, and exclusively in the depressed phase). These findings bolster the extensive body of evidence in support of long-term lithium therapy in BD, furthering the possibility of its expanded use to wider demographics.

The role of lithium treatment on comorbid anxiety symptoms in patients with bipolar depression.

BACKGROUND: Comorbid anxiety is pervasive and carries an immense psychosocial burden for patients with bipolar disorder. Despite this, trials reporting anxiety-related outcomes in this population are uncommon, particularly with regards to monotherapies. METHODS: Patients (n = 31) with both bipolar I or II disorder in current depressive episodes were enrolled in a six-week, open-label, single-center trial assessing the efficacy of lithium monotherapy in treating symptoms depression and comorbid anxiety. Patients were mostly medication-free and lithium-naïve at baseline. RESULTS: Significant improvements in depression (HAMD) and anxiety (HAM-A) were observed at the six-week endpoint, with remission and response rates greater than 50%. There was a positive correlation between endpoint HAM-A scores and HAM-D scores, r = 0.80, (p < 0.01). Improvements were realized at low serum lithium concentrations (0.49 ± 0.20 mEq/L). LIMITATIONS: Lack of placebo control and small sample size warrants validation in larger randomized studies. CONCLUSIONS: Taken in the context of prior evidence, lithium may have an important role in treating comorbid anxiety in bipolar disorder, both as adjunct and monotherapy. Lower doses of lithium may provide equivalent efficacy and enhance tolerability and compliance.

Plasmatic endocannabinoids are decreased in subjects with ultra-high risk of psychosis.

The onset of frank psychosis is usually preceded by a prodromal phase characterized by attenuated psychotic symptoms. Currently, research on schizophrenia prodromal phase (ultra-high risk for psychosis [UHR]) has focused on the risk of developing psychosis, on the transition to full blown psychosis and on its prediction. Neurobiological differences between UHR individuals who fully recover (remitters) versus those who show persistent/progressive prodromal symptoms (nonremitters) have been little explored. The endocannabinoid system constitutes a neuromodulatory system that plays a major role in brain development, synaptic plasticity, emotional behaviours and cognition. It comprises two cannabinoid receptors (CB1/CB2), two endocannabinoid ligands, arachidonylethanolamide (AEA) and 2-arachidonoylglycerol (2AG) along with their inactivation enzymes. Despite much evidence that the endocannabinoid system is imbalanced during psychosis, very little is known about it in UHR. Therefore, we aimed to quantify the plasma endocannabinoid levels in UHR and healthy controls (HC) and verify if these metabolites could differentiate between remitters and nonremitters. Circulating concentrations of AEA (p = .003) and 2AG (p < .001) were lower in UHR when compared with HC, with no difference between remitters and nonremitters. Regarding clinical evolution, it was observed that out of 91 UHRs initially considered, 16 had psychiatric complaints (3 years of follow-up). Considering those subjects, there were weak correlations between clinical parameters and plasma concentrations of endocannabinoids. Our results suggest that the endocannabinoids are imbalanced before frank psychosis and that changes can be seen in plasma of UHR individuals. These molecules proved to be potential biomarkers to identify individuals in the prodromal phase of psychosis.

Use of a Bayesian Network Model to predict psychiatric illness in individuals with 'at risk mental states' from a general population cohort.

The 'at risk mental state' (ARMS) paradigm has been introduced in psychiatry to study prodromal phases of schizophrenia. With time it was seen that the ARMS state can also precede mental disorders other than schizophrenia, such as depression and anxiety. However, several problems hamper the paradigm's use in preventative medicine, such as varying transition rates across studies, the use of non-naturalistic samples, and the multifactorial nature of psychiatric disorders. To strengthen ARMS predictive power, there is a need for a holistic model incorporating-in an unbiased fashion-the small-effect factors that cause mental disorders. Bayesian networks, a probabilistic graphical model, was used in a populational cohort of 83 ARMS individuals to predict conversion to psychiatric illness. Nine predictors-including state, trait, biological and environmental factors-were inputted. Dopamine receptor 2 polymorphism, high private religiosity, and childhood trauma remained in the final model, which reached an 85.51% (SD = 0.1190) accuracy level in predicting conversion. This is the first time a robust model was produced with Bayesian networks to predict psychiatric illness among at risk individuals from the general population. This could be an important tool to strengthen predictive measures in psychiatry which should be replicated in larger samples to provide the model further learning.