GABA-A receptor differences in schizophrenia: a positron emission tomography study using [C-11]Ro154513

A loss of GABA signaling is a prevailing hypothesis for the pathogenesis of schizophrenia. Preclinical studies indicate that blockade of the α5 subtype of the GABA receptor (α5-GABAARs) leads to behavioral phenotypes associated with schizophrenia, and postmortem evidence indicates lower hippocampal α5-GABAARs protein and mRNA levels in schizophrenia. However, it is unclear if α5-GABAARs are altered in vivo or related to symptoms. We investigated α5-GABAARs availability in antipsychotic-free schizophrenia patients and antipsychotic-medicated schizophrenia patients using [11C]Ro15-4513 PET imaging in a cross-sectional, case–control study design. Thirty-one schizophrenia patients (n = 10 antipsychotic free) and twenty-nine matched healthy controls underwent a [11C]Ro15-4513 PET scan and MRI. The α5 subtype GABA-A receptor availability was indexed using [11C]Ro15-4513 PET imaging. Dynamic PET data were analyzed using the two-tissue compartment model with an arterial plasma input function and total volume of distribution (VT) as the outcome measure. Symptom severity was assessed using the PANSS scale. There was significantly lower [11C]Ro15-4513 VT in the hippocampus of antipsychotic-free patients, but not in medicated patients (p = 0.64), relative to healthy controls (p < 0.05; effect size = 1.4). There was also a significant positive correlation between [11C]Ro15-4513 VT and total PANSS score in antipsychotic-free patients (r = 0.72; p = 0.044). The results suggest that antipsychotic-free patients with schizophrenia have lower α5-GABAARs levels in the hippocampus, consistent with the hypothesis that GABA hypofunction underlies the pathophysiology of the disorder

Multiple imputation for estimating hazard ratios and predictive abilities in case-cohort surveys

<p>Abstract</p> <p>Background</p> <p>The weighted estimators generally used for analyzing case-cohort studies are not fully efficient and naive estimates of the predictive ability of a model from case-cohort data depend on the subcohort size. However, case-cohort studies represent a special type of incomplete data, and methods for analyzing incomplete data should be appropriate, in particular multiple imputation (MI).</p> <p>Methods</p> <p>We performed simulations to validate the MI approach for estimating hazard ratios and the predictive ability of a model or of an additional variable in case-cohort surveys. As an illustration, we analyzed a case-cohort survey from the Three-City study to estimate the predictive ability of D-dimer plasma concentration on coronary heart disease (CHD) and on vascular dementia (VaD) risks.</p> <p>Results</p> <p>When the imputation model of the phase-2 variable was correctly specified, MI estimates of hazard ratios and predictive abilities were similar to those obtained with full data. When the imputation model was misspecified, MI could provide biased estimates of hazard ratios and predictive abilities. In the Three-City case-cohort study, elevated D-dimer levels increased the risk of VaD (hazard ratio for two consecutive tertiles = 1.69, 95%CI: 1.63-1.74). However, D-dimer levels did not improve the predictive ability of the model.</p> <p>Conclusions</p> <p>MI is a simple approach for analyzing case-cohort data and provides an easy evaluation of the predictive ability of a model or of an additional variable.</p

In Vivo Availability of Cannabinoid 1 Receptor Levels in Patients with First-Episode Psychosis

Importance: Experimental and epidemiological studies implicate the cannabinoid 1 receptor (CB1R) in the pathophysiology of psychosis. However, whether CB1R levels are altered in the early stages of psychosis and whether they are linked to cognitive function or symptom severity remain unknown.Objective: To investigate CB1R availability in first-episode psychosis (FEP) without the confounds of illness chronicity or the use of illicit substances or antipsychotics.Design, Setting, and Participants: This cross-sectional, case-control study of 2 independent samples included participants receiving psychiatric early intervention services at 2 independent centers in Turku, Finland (study 1) and London, United Kingdom (study 2). Study 1 consisted of 18 volunteers, including 7 patients with affective or nonaffective psychoses taking antipsychotic medication and 11 matched controls; study 2, 40 volunteers, including 20 antipsychotic-naive or antipsychotic-free patients with schizophrenia or schizoaffective disorder and 20 matched controls. Data were collected from January 5, 2015, through September 26, 2018, and analyzed from June 20, 2016, through February 12, 2019.Main Outcomes and Measures: The availability of CB1R was indexed using the distribution volume (VT, in milliliters per cubic centimeter) of 2 CB1R-selective positron emission tomography radiotracers: fluoride 18–labeled FMPEP-d2 (study 1) and carbon 11–labeled MePPEP (study 2). Cognitive function was measured using the Wechsler Digit Symbol Coding Test. Symptom severity was measured using the Brief Psychiatric Rating Scale for study 1 and the Positive and Negative Syndrome Scale for study 2.Results: A total of 58 male individuals were included in the analyses (mean [SD] age of controls, 27.16 [5.93] years; mean [SD] age of patients, 26.96 [4.55] years). In study 1, 7 male patients with FEP (mean [SD] age, 26.80 [5.40] years) were compared with 11 matched controls (mean [SD] age, 27.18 [5.86] years); in study 2, 20 male patients with FEP (mean [SD] age, 27.00 [5.06] years) were compared with 20 matched controls (mean [SD] age, 27.15 [6.12] years). In study 1, a significant main effect of group on [18F]FMPEP-d2 VT was found in the anterior cingulate cortex (ACC) (t16 = −4.48; P Conclusions and Relevance: The availability of CB1R was lower in antipsychotic-treated and untreated cohorts relative to matched controls. Exploratory analyses indicated that greater reductions in CB1R levels were associated with greater symptom severity and poorer cognitive functioning in male patients. These findings suggest that CB1R may be a potential target for the treatment of psychotic disorders.</p

Mortality among Norwegian doctors 1960-2000

<p>Abstract</p> <p>Background</p> <p>To study the mortality pattern of Norwegian doctors, people in human service occupations, other graduates and the general population during the period 1960-2000 by decade, gender and age. The total number of deaths in the study population was 1 583 559.</p> <p>Methods</p> <p>Census data from 1960, 1970, 1980 and 1990 relating to education were linked to data on 14 main causes of death from Statistics Norway, followed up for two five-year periods after census, and analyzed as stratified incidence-rate data. Mortality rate ratios were computed as combined Mantel-Haenzel estimates for each sex, adjusting for both age and period when appropriate.</p> <p>Results</p> <p>The doctors had a lower mortality rate than the general population for all causes of death except suicide. The mortality rate ratios for other graduates and human service occupations were 0.7-0.8 compared with the general population. However, doctors have a higher mortality than other graduates. The lowest estimates of mortality for doctors were for endocrine, nutritional and metabolic diseases, diseases in the urogenital tract or genitalia, digestive diseases and sudden death, for which the numbers were nearly half of those for the general population. The differences in mortality between doctors and the general population increased during the periods.</p> <p>Conclusions</p> <p>Between 1960 and 2000 mortality for doctors converged towards the mortality for other university graduates and for people in human service occupations. However, there was a parallel increase in the gap between these groups and the rest of the population. The slightly higher mortality for doctors compared with mortality for other university graduates may be explained by the higher suicide rate for doctors.</p

Merging transcriptomics and metabolomics - advances in breast cancer profiling

Background Combining gene expression microarrays and high resolution magic angle spinning magnetic resonance spectroscopy (HR MAS MRS) of the same tissue samples enables comparison of the transcriptional and metabolic profiles of breast cancer. The aim of this study was to explore the potential of combining these two different types of information. Methods Breast cancer tissue from 46 patients was analyzed by HR MAS MRS followed by gene expression microarrays. Two strategies were used to combine the gene expression and metabolic data; first using multivariate analyses to identify different groups based on gene expression and metabolic data; second correlating levels of specific metabolites to transcripts to suggest new hypotheses of connections between metabolite levels and the underlying biological processes. A parallel study was designed to address experimental issues of combining microarrays and HR MAS MRS. Results In the first strategy, using the microarray data and previously reported molecular classification methods, the majority of samples were classified as luminal A. Three subgroups of luminal A tumors were identified based on hierarchical clustering of the HR MAS MR spectra. The samples in one of the subgroups, designated A2, showed significantly lower glucose and higher alanine levels than the other luminal A samples, suggesting a higher glycolytic activity in these tumors. This group was also enriched for genes annotated with Gene Ontology (GO) terms related to cell cycle and DNA repair. In the second strategy, the correlations between concentrations of myo-inositol, glycine, taurine, glycerophosphocholine, phosphocholine, choline and creatine and all transcripts in the filtered microarray data were investigated. GO-terms related to the extracellular matrix were enriched among the genes that correlated the most to myo-inositol and taurine, while cell cycle related GO-terms were enriched for the genes that correlated the most to choline. Additionally, a subset of transcripts was identified to have slightly altered expression after HR MAS MRS and was therefore removed from all other analyses. Conclusions Combining transcriptional and metabolic data from the same breast carcinoma sample is feasible and may contribute to a more refined subclassification of breast cancers as well as reveal relations between metabolic and transcriptional levels. See Commentary: http://www.biomedcentral.com/1741-7015/8/7

Variability and magnitude of brain glutamate levels in schizophrenia: a meta and mega-analysis.

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\u27s 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 \u3c 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 \u3c 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 \u3c 0.001) and higher BG Glx in patients relative to controls (g = 0.28, p \u3c 0.001). Proportion of males was negatively associated with MFC glutamate (z = -0.02, p \u3c 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

Variability and magnitude of brain glutamate levels in schizophrenia: a meta and mega-analysis

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 &lt; 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 &lt; 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 &lt; 0.001) and higher BG Glx in patients relative to controls (g = 0.28, p &lt; 0.001). Proportion of males was negatively associated with MFC glutamate (z = −0.02, p &lt; 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