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.

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.

Cognitive Patterns and Conversion in a Representative Sample of Individuals at Risk for Psychosis.

ABSTRACT: Clinical high-risk (CHR) individuals belong to a heterogeneous group, of which only a few will cross the threshold for a clinical diagnosis. Cognitive disturbances are present in CHR subjects and may be indicative of transition. Our study aims to identify such deficits in a representative CHR for psychosis sample. Our sample comprised 92 CHR individuals and 54 controls from a representative cohort of the general population. They were followed up for a mean of 2.5 years, with 15 individuals converting to schizophrenia or other Diagnostic and Statistical Manual of Mental Disorders, 5th Edition diagnoses. Neurocognitive assessment was performed with the University of Pennsylvania Computerized Neuropsychological Testing, and CHR status was assessed with the Structured Interview for Prodromal Syndromes (SIPS). Baseline scores were entered in a latent profile analysis model. Our study brought forward a four-class model on cognitive performance. One class displayed better performance, whereas the other three performed worse, all compared with controls. The class with lower executive function also had the highest score on disorganized communication (SIPS P5 = 1.36, p < 0.05), although unrelated to conversion. Among the low performers, the class significantly related to conversion (p = 0.023) had the highest score in decreased expression of emotion (SIPS N3 = 0.85, p < 0.05). Our study brings new and relevant data on non-help-seeking CHR individuals and the relationship between cognitive patterns and conversion. We have highlighted a specific cognitive signature, associated with negative symptoms, which represents a stable trait with presumed lower conversion to a psychiatric illness.