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Glutamatergic dysfunction is implicated in schizophrenia pathoaetiology, but this may vary in extent between patients. It is unclear whether inter-individual variability in glutamate is greater in schizophrenia than the general population. We conducted meta-analyses to assess (1) variability of glutamate measures in patients relative to controls (log coefficient of variation ratio: CVR); (2) standardised mean differences (SMD) using Hedges g; (3) modal distribution of individual-level glutamate data (Hartigan’s unimodality dip test). MEDLINE and EMBASE databases were searched from inception to September 2022 for proton magnetic resonance spectroscopy (1H-MRS) studies reporting glutamate, glutamine or Glx in schizophrenia. 123 studies reporting on 8256 patients and 7532 controls were included. Compared with controls, patients demonstrated greater variability in glutamatergic metabolites in the medial frontal cortex (MFC, glutamate: CVR = 0.15, p < 0.001; glutamine: CVR = 0.15, p = 0.003; Glx: CVR = 0.11, p = 0.002), dorsolateral prefrontal cortex (glutamine: CVR = 0.14, p = 0.05; Glx: CVR = 0.25, p < 0.001) and thalamus (glutamate: CVR = 0.16, p = 0.008; Glx: CVR = 0.19, p = 0.008). Studies in younger, more symptomatic patients were associated with greater variability in the basal ganglia (BG glutamate with age: z = −0.03, p = 0.003, symptoms: z = 0.007, p = 0.02) and temporal lobe (glutamate with age: z = −0.03, p = 0.02), while studies with older, more symptomatic patients associated with greater variability in MFC (glutamate with age: z = 0.01, p = 0.02, glutamine with symptoms: z = 0.01, p = 0.02). For individual patient data, most studies showed a unimodal distribution of glutamatergic metabolites. Meta-analysis of mean differences found lower MFC glutamate (g = −0.15, p = 0.03), higher thalamic glutamine (g = 0.53, p < 0.001) and higher BG Glx in patients relative to controls (g = 0.28, p < 0.001). Proportion of males was negatively associated with MFC glutamate (z = −0.02, p < 0.001) and frontal white matter Glx (z = −0.03, p = 0.02) in patients relative to controls. Patient PANSS total score was positively associated with glutamate SMD in BG (z = 0.01, p = 0.01) and temporal lobe (z = 0.05, p = 0.008). Further research into the mechanisms underlying greater glutamatergic metabolite variability in schizophrenia and their clinical consequences may inform the identification of patient subgroups for future treatment strategies.

Background Within outpatient mental health services there exists an important awareness of the difficulties in engaging and maintaining contact with patients, as well as the understanding of the negative effects of disengagement, including worse patient outcomes and increased healthcare burden. Despite the importance of engagement on service delivery and recovery outcomes, few studies have examined rates and predictors of engagement in the early phase psychosis population. Although better than community care, it has been reported that an average of 30% of patients disengage from specialized early intervention services for psychosis (EIS). We examined rates of disengagement to a 5 year EIS for psychosis, including potential individual risk factors for disengagement at entry to service. Methods This cross-sectional cohort study examined engagement to services to a single EIS site from November 2006 to November 2016. Disengagement was determined retrospectively on review of medical records, defined as not attending to clinic services despite repeated attempts by clinicians/clinic for a three month time frame. Gender, age at clinic entry, ethnicity, Positive and Negative Syndrome Scale (PANSS), Drug Attitude Inventory (DAI-30), General Assessment of Function (GAF), Social and Occupational Functioning Assessment Scale (SOFAS), WHO-ASSIST version 3.0, and the Psychological General Well Being (PGWB)scale at entry to service were examined between groups. . Descriptive statistical and survival analyses for time to disengagement were conducted on the patient data set. Results 331 patient records were complete (with above scales) from entry to service to discharge or loss to follow-up. Patients were found to fall into 3 categories with regard to patterns of engagement. The first category we named “engagers” as they remained committed to their care throughout the program and comprised 50% of the sample. The second group were labeled the disengagers (20% of the group) and these were individuals who disengaged at some point in the program and did not return, in contrast to “intermittent engagers“ who comprised 30% of the sample. Intermittent engagers were patients who at some point during their care would meet criteria for disengagement but would re-engage later (still within the 5 years from entry to EIS) and complete the program. Absolute disengagement by the disengager group was predominantly prior to 12 months of treatment (78% of the group) with a survival analysis showing a median time to absolute disengagement of 8 months. The 3 groups though defined based on their engagement status, did not significantly differ in age, gender and ethnicity. Additionally, the clinician reported scores GAF and SOFAS did not differ between the groups. Patterns of substance use differed between the groups. There was a trend toward higher tobacco use in the two groups showing disengagement. Cannabis use did not differ significantly between groups but the pattern of use was highest in the disengagers followed by the engagers and then intermittent engagers. Alcohol use was significantly different between the groups with 81% of the disengagers having problem levels of alcohol use (WHO ASSIST v. 3.0 score above 4), however, there was no correlation between alcohol score and time to disengagement. Discussion Our retrospective study found a surprisingly large portion of the patient population will wax and wane in their commitment to health services but ultimately maintain attendance to complete the program, suggesting that patients should not be discharged early from EIS for psychosis. Substance use patterns and functional measures may identify patients who are at risk of early disengagement from EIS.

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