Comparative effects of 18 antipsychotics on metabolic function in patients with schizophrenia, predictors of metabolic dysregulation, and association with psychopathology: a systematic review and network meta-analysis.

BACKGROUND Antipsychotic treatment is associated with metabolic disturbance. However, the degree to which metabolic alterations occur in treatment with different antipsychotics is unclear. Predictors of metabolic dysregulation are poorly understood and the association between metabolic change and change in psychopathology is uncertain. We aimed to compare and rank antipsychotics on the basis of their metabolic side-effects, identify physiological and demographic predictors of antipsychotic-induced metabolic dysregulation, and investigate the relationship between change in psychotic symptoms and change in metabolic parameters with antipsychotic treatment. METHODS We searched MEDLINE, EMBASE, and PsycINFO from inception until June 30, 2019. We included blinded, randomised controlled trials comparing 18 antipsychotics and placebo in acute treatment of schizophrenia. We did frequentist random-effects network meta-analyses to investigate treatment-induced changes in body weight, BMI, total cholesterol, LDL cholesterol, HDL cholesterol, triglyceride, and glucose concentrations. We did meta-regressions to examine relationships between metabolic change and age, sex, ethnicity, baseline weight, and baseline metabolic parameter level. We examined the association between metabolic change and psychopathology change by estimating the correlation between symptom severity change and metabolic parameter change. FINDINGS Of 6532 citations, we included 100 randomised controlled trials, including 25 952 patients. Median treatment duration was 6 weeks (IQR 6-8). Mean differences for weight gain compared with placebo ranged from -0·23 kg (95% CI -0·83 to 0·36) for haloperidol to 3·01 kg (1·78 to 4·24) for clozapine; for BMI from -0·25 kg/m2 (-0·68 to 0·17) for haloperidol to 1·07 kg/m2 (0·90 to 1·25) for olanzapine; for total-cholesterol from -0·09 mmol/L (-0·24 to 0·07) for cariprazine to 0·56 mmol/L (0·26-0·86) for clozapine; for LDL cholesterol from -0·13 mmol/L (-0.21 to -0·05) for cariprazine to 0·20 mmol/L (0·14 to 0·26) for olanzapine; for HDL cholesterol from 0·05 mmol/L (0·00 to 0·10) for brexpiprazole to -0·10 mmol/L (-0·33 to 0·14) for amisulpride; for triglycerides from -0·01 mmol/L (-0·10 to 0·08) for brexpiprazole to 0·98 mmol/L (0·48 to 1·49) for clozapine; for glucose from -0·29 mmol/L (-0·55 to -0·03) for lurasidone to 1·05 mmol/L (0·41 to 1·70) for clozapine. Greater increases in glucose were predicted by higher baseline weight (p=0·0015) and male sex (p=0·0082). Non-white ethnicity was associated with greater increases in total cholesterol (p=0·040) compared with white ethnicity. Improvements in symptom severity were associated with increases in weight (r=0·36, p=0·0021), BMI (r=0·84, p<0·0001), total-cholesterol (r=0·31, p=0·047), and LDL cholesterol (r=0·42, p=0·013), and decreases in HDL cholesterol (r=-0·35, p=0·035). INTERPRETATION Marked differences exist between antipsychotics in terms of metabolic side-effects, with olanzapine and clozapine exhibiting the worst profiles and aripiprazole, brexpiprazole, cariprazine, lurasidone, and ziprasidone the most benign profiles. Increased baseline weight, male sex, and non-white ethnicity are predictors of susceptibility to antipsychotic-induced metabolic change, and improvements in psychopathology are associated with metabolic disturbance. Treatment guidelines should be updated to reflect our findings. However, the choice of antipsychotic should be made on an individual basis, considering the clinical circumstances and preferences of patients, carers, and clinicians. FUNDING UK Medical Research Council, Wellcome Trust, National Institute for Health Research Oxford Health Biomedical Research Centre

An automatic analysis framework for FDOPA PET neuroimaging

In this study we evaluate the performance of a fully automated analytical framework for FDOPA PET neuroimaging data, and its sensitivity to demographic and experimental variables and processing parameters. An instance of XNAT imaging platform was used to store the King's College London institutional brain FDOPA PET imaging archive, alongside individual demographics and clinical information. By re-engineering the historical Matlab-based scripts for FDOPA PET analysis, a fully automated analysis pipeline for imaging processing and data quantification was implemented in Python and integrated in XNAT. The final data repository includes 892 FDOPA PET scans organized from 23 different studies. We found good reproducibility of the data analysis by the automated pipeline (in the striatum for the Kicer: for the controls ICC = 0.71, for the psychotic patients ICC = 0.88). From the demographic and experimental variables assessed, gender was found to most influence striatal dopamine synthesis capacity (F = 10.7, p < 0.001), with women showing greater dopamine synthesis capacity than men. Our automated analysis pipeline represents a valid resourse for standardised and robust quantification of dopamine synthesis capacity using FDOPA PET data. Combining information from different neuroimaging studies has allowed us to test it comprehensively and to validate its replicability and reproducibility performances on a large sample size

Magnitude and variability of structural brain abnormalities in neuropsychiatric disease:Protocol for a network meta-analysis of MRI studies

INTRODUCTION Structural MRI is the most frequently used method to investigate brain volume alterations in neuropsychiatric disease. Previous meta-analyses have typically focused on a single diagnosis, thereby precluding transdiagnostic comparisons. METHODS AND ANALYSIS We will include all structural MRI studies of adults that report brain volumes for participants from at least two of the following diagnostic groups: healthy controls, schizophrenia, schizoaffective disorder, delusional disorder, psychotic depression, clinical high risk for psychosis, schizotypal personality disorder, psychosis unspecified, bipolar disorder, autism spectrum disorder, major depressive disorder, attention deficit hyperactivity disorder, obsessive compulsive disorder, post-traumatic stress disorder, emotionally unstable personality disorder, 22q11 deletion syndrome, generalised anxiety disorder, social anxiety disorder, panic disorder, mixed anxiety and depression. Network meta-analysis will be used to synthesise eligible studies. The primary analysis will examine standardised mean difference in average volume, a secondary analysis will examine differences in variability of volumes. DISCUSSION This network meta-analysis will provide a transdiagnostic integration of structural neuroimaging studies, providing researchers with a valuable summary of a large literature. PROSPERO REGISTRATION NUMBER CRD42020221143

Shared and separate patterns in brain morphometry across transdiagnostic dimensions

Determining similarities and differences in brain structure across psychiatric disorders is important to determine if psychiatric taxonomy is reflected in distinct brain structural changes. As previous neuroimaging meta-analyses have typically focused on a single disorder, precluding transdiagnostic comparisons, we aimed to quantify patterns of similarity and differences between psychiatric disorders in terms of regional brain volumes. Here we show, in network and pairwise meta-analyses of 498 studies (51,227 individuals, 17 psychiatric disorders and 17 brain regions), that psychiatric disorders show both distinct and overlapping patterns of brain volume gain and loss. A principal components analysis demonstrated that the first principal component could account for 48% of variance and corresponded to a pattern of increased basal ganglia and decreased hippocampal and amygdala volumes. This component loaded most strongly for disorders on the psychosis spectrum, and most weakly for affective disorders. Our findings illustrated that, while similar volumetric alterations are frequently shared between disorders, neuroanatomical patterns also appear related to clinically meaningful categories. (PROSPERO Registration: CRD42020221143.