Medication optimisation in severe mental illness (MEDIATE): protocol for a realist review

INTRODUCTION

Severe mental illness (SMI) is associated with significant morbidity and mortality. People living with SMI often receive complex medication regimens. Optimising these regimens can be challenging. Non-adherence is common and addressing it requires a collaborative approach to decision making. MEDIATE uses a realist approach with extensive engagement with experts-by-experience to make sense of the complexities and identify potential solutions.Realist research is used to unpack and explain complexity using programme theory/theories that contain causal explanations of outcomes, expressed as context-mechanism-outcome-configurations. The programme theory/theories will enable MEDIATE to address its aim of understanding what works, for whom, in what circumstances, to optimise medication use with people living with SMI.

METHOD AND ANALYSIS

MEDIATE will be conducted over six stages. In stage 1, we will collaborate with our service user/family carer lived experience group (LEG) and practitioner stakeholder group (SG), to determine the focus. In stage 2, we will develop initial programme theories for what needs to be done, by whom, how and why, and in what contexts to optimise medication use. In stage 3, we will develop and run searches to identify secondary data to refine our initial programme theories.Stage 4 involves selection and appraisal: documents will be screened by title, abstract/keywords and full text against inclusion and exclusion criteria. In stage 5, relevant data will extracted, recorded and coded. Data will be analysed using a realist logic with input from the LEG and SG. Finally, in stage 6, refined programme theories will be developed, identifying causal explanations for key outcomes and the strategies required to change contexts to trigger the key mechanisms that produce these outcomes.

ETHICS AND DISSEMINATION

Primary data will not be collected, and therefore, ethical approval is not required. MEDIATE will be disseminated via publications, conferences and form the basis for future grant applications.

PROSPERO REGISTRATION NUMBER

CRD42021280980.

Psychosocial functioning in the balance between autism and psychosis: evidence from three populations

Functional impairment is a core feature of both autism and schizophrenia spectrum disorders. While diagnostically independent, they can co-occur in the same individual at both the trait and diagnostic levels. The effect of such co-occurrence is hypothesized to worsen functional impairment. The diametric model, however, suggests that the disorders are etiologically and phenotypically diametrical, representing the extreme of a unidimensional continuum of cognition and behavior. A central prediction of this model is that functional impairment would be attenuated in individuals with mixed symptom expressions or genetic liability to both disorders. We tested this hypothesis in two clinical populations and one healthy population. In individuals with chronic schizophrenia and in individuals with first episode psychosis we evaluated the combined effect of autistic traits and positive psychotic symptoms on psychosocial functioning. In healthy carriers of alleles of copy number variants (CNVs) that confer risk for both autism and schizophrenia, we also evaluated whether variation in psychosocial functioning depended on the combined risk conferred by each CNV. Relative to individuals with biased symptom/CNV risk profiles, results show that functional impairments are attenuated in individuals with relatively equal levels of positive symptoms and autistic traits-and specifically stereotypic behaviors-, and in carriers of CNVs with relatively equal risks for either disorder. However, the pattern of effects along the "balance axis" varied across the groups, with this attenuation being generally less pronounced in individuals with high-high symptom/risk profile in the schizophrenia and CNV groups, and relatively similar for low-low and high-high individuals in the first episode psychosis group. Lower levels of functional impairments in individuals with "balanced" symptom profile or genetic risks would suggest compensation across mechanisms associated with autism and schizophrenia. CNVs that confer equal risks for both disorders may provide an entry point for investigations into such compensatory mechanisms. The co-assessment of autism and schizophrenia may contribute to personalized prognosis and stratification strategies.

Evidence for Shared Genetic Aetiology Between Schizophrenia, Cardiometabolic, and Inflammation-Related Traits: Genetic Correlation and Colocalization Analyses

BACKGROUND

Schizophrenia commonly co-occurs with cardiometabolic and inflammation-related traits. It is unclear to what extent the comorbidity could be explained by shared genetic aetiology.

METHODS

We used GWAS data to estimate shared genetic aetiology between schizophrenia, cardiometabolic, and inflammation-related traits: fasting insulin (FI), fasting glucose, glycated haemoglobin, glucose tolerance, type 2 diabetes (T2D), lipids, body mass index (BMI), coronary artery disease (CAD), and C-reactive protein (CRP). We examined genome-wide correlation using linkage disequilibrium score regression (LDSC); stratified by minor-allele frequency using genetic covariance analyzer (GNOVA); then refined to locus-level using heritability estimation from summary statistics (ρ-HESS). Regions with local correlation were used in hypothesis prioritization multi-trait colocalization to examine for colocalisation, implying common genetic aetiology.

RESULTS

We found evidence for weak genome-wide negative correlation of schizophrenia with T2D (rg = -0.07; 95% C.I., -0.03,0.12; P = .002) and BMI (rg = -0.09; 95% C.I., -0.06, -0.12; P = 1.83 × 10-5). We found a trend of evidence for positive genetic correlation between schizophrenia and cardiometabolic traits confined to lower-frequency variants. This was underpinned by 85 regions of locus-level correlation with evidence of opposing mechanisms. Ten loci showed strong evidence of colocalization. Four of those (rs6265 (BDNF); rs8192675 (SLC2A2); rs3800229 (FOXO3); rs17514846 (FURIN)) are implicated in brain-derived neurotrophic factor (BDNF)-related pathways.

CONCLUSIONS

LDSC may lead to downwardly-biased genetic correlation estimates between schizophrenia, cardiometabolic, and inflammation-related traits. Common genetic aetiology for these traits could be confined to lower-frequency common variants and involve opposing mechanisms. Genes related to BDNF and glucose transport amongst others may partly explain the comorbidity between schizophrenia and cardiometabolic disorders.

Structure and stability of symptoms in first episode psychosis: a longitudinal network approach

Early psychosis is characterised by heterogeneity in illness trajectories, where outcomes remain poor for many. Understanding psychosis symptoms and their relation to illness outcomes, from a novel network perspective, may help to delineate psychopathology within early psychosis and identify pivotal targets for intervention. Using network modelling in first episode psychosis (FEP), this study aimed to identify: (a) key central and bridge symptoms most influential in symptom networks, and (b) examine the structure and stability of the networks at baseline and 12-month follow-up. Data on 1027 participants with FEP were taken from the National EDEN longitudinal study and used to create regularised partial correlation networks using the 'EBICglasso' algorithm for positive, negative, and depressive symptoms at baseline and at 12-months. Centrality and bridge estimations were computed using a permutation-based network comparison test. Depression featured as a central symptom in both the baseline and 12-month networks. Conceptual disorganisation, stereotyped thinking, along with hallucinations and suspiciousness featured as key bridge symptoms across the networks. The network comparison test revealed that the strength and bridge centralities did not differ significantly between the two networks (C = 0.096153; p = 0.22297). However, the network structure and connectedness differed significantly from baseline to follow-up (M = 0.16405, p = <0.0001; S = 0.74536, p = 0.02), with several associations between psychosis and depressive items differing significantly by 12 months. Depressive symptoms, in addition to symptoms of thought disturbance (e.g. conceptual disorganisation and stereotyped thinking), may be examples of important, under-recognized treatment targets in early psychosis, which may have the potential to lead to global symptom improvements and better recovery.

T regulatory cells as a potential therapeutic target in psychosis? Current challenges and future perspectives

Many studies have reported that patients with psychosis, even before drug treatment, have mildly raised levels of blood cytokines relative to healthy controls. In contrast, there is a remarkable scarcity of studies investigating the cellular basis of immune function and cytokine changes in psychosis. The few flow-cytometry studies have been limited to counting the proportion of the major classes of monocyte and lymphocytes without distinguishing their pro- and anti-inflammatory subsets. Moreover, most of the investigations are cross-sectional and conducted with patients on long-term medication. These features make it difficult to eliminate confounding of illness-related changes by lifestyle factors, disease duration, and long exposure to antipsychotics. This article focuses on regulatory T cells (Tregs), cornerstone immune cells that regulate innate and adaptive immune forces and neuro-immune interactions between astrocytes and microglia. Tregs are also implicated in cardio-metabolic disorders that are common comorbidities of psychosis. We have recently proposed that Tregs are hypofunctional ('h-Tregs') in psychosis driven by our clinical findings and other independent research. Our h-Treg-glial imbalance hypothesis offers a new account for the co-occurrence of systemic immune dysregulation and mechanisms of psychosis development. This article extends our recent review, the h-Treg hypothesis, to cover new discoveries on Treg-based therapies from pre-clinical findings and their clinical implications. We provide a detailed characterisation of Treg studies in psychosis, identifying important methodological limitations and perspectives for scientific innovation. The outcomes presented in this article reaffirms our proposed h-Treg state in psychosis and reveals emerging preclinical research suggesting the potential benefit of Treg-enhancing therapies. There is a clear need for longitudinal studies conducted with drug-naïve or minimally treated patients using more sophisticated techniques of flow-cytometry, CyTOF expression markers, and in vitro co-culture assays to formally test the suppressive capacity of Tregs. Investment in Treg research offers major potential benefits in targeting emerging immunomodulatory treatment modalities on person-specific immune dysregulations.

Toward Generalizable and Transdiagnostic Tools for Psychosis Prediction: An Independent Validation and Improvement of the NAPLS-2 Risk Calculator in the Multisite PRONIA Cohort

BACKGROUND

Transition to psychosis is among the most adverse outcomes of clinical high-risk (CHR) syndromes encompassing ultra-high risk (UHR) and basic symptom states. Clinical risk calculators may facilitate an early and individualized interception of psychosis, but their real-world implementation requires thorough validation across diverse risk populations, including young patients with depressive syndromes.

METHODS

We validated the previously described NAPLS-2 (North American Prodrome Longitudinal Study 2) calculator in 334 patients (26 with transition to psychosis) with CHR or recent-onset depression (ROD) drawn from the multisite European PRONIA (Personalised Prognostic Tools for Early Psychosis Management) study. Patients were categorized into three risk enrichment levels, ranging from UHR, over CHR, to a broad-risk population comprising patients with CHR or ROD (CHR|ROD). We assessed how risk enrichment and different predictive algorithms influenced prognostic performance using reciprocal external validation.

RESULTS

After calibration, the NAPLS-2 model predicted psychosis with a balanced accuracy (BAC) (sensitivity, specificity) of 68% (73%, 63%) in the PRONIA-UHR cohort, 67% (74%, 60%) in the CHR cohort, and 70% (73%, 66%) in patients with CHR|ROD. Multiple model derivation in PRONIA-CHR|ROD and validation in NAPLS-2-UHR patients confirmed that broader risk definitions produced more accurate risk calculators (CHR|ROD-based vs. UHR-based performance: 67% [68%, 66%] vs. 58% [61%, 56%]). Support vector machines were superior in CHR|ROD (BAC = 71%), while ridge logistic regression and support vector machines performed similarly in CHR (BAC = 67%) and UHR cohorts (BAC = 65%). Attenuated psychotic symptoms predicted psychosis across risk levels, while younger age and reduced processing speed became increasingly relevant for broader risk cohorts.

CONCLUSIONS

Clinical-neurocognitive machine learning models operating in young patients with affective and CHR syndromes facilitate a more precise and generalizable prediction of psychosis. Future studies should investigate their therapeutic utility in large-scale clinical trials.

Detailed clinical phenotyping and generalisability in prognostic models of functioning in at-risk populations

Personalised prediction of functional outcomes is a promising approach for targeted early intervention in psychiatry. However, generalisability and resource efficiency of such prognostic models represent challenges. In the PRONIA study (German Clinical Trials Register: DRKS00005042), we demonstrate excellent generalisability of prognostic models in individuals at clinical high-risk for psychosis or with recent-onset depression, and substantial contributions of detailed clinical phenotyping, particularly to the prediction of role functioning. These results indicate that it is possible that functioning prediction models based only on clinical data could be effectively applied in diverse healthcare settings, so that neuroimaging data may not be needed at early assessment stages.

Associations of immunological proteins/traits with schizophrenia, major depression and bipolar disorder: A bi-directional two-sample mendelian randomization study

BACKGROUND

Schizophrenia, bipolar disorder and depression are associated with inflammation. However, it is unclear whether associations of immunological proteins/traits with these disorders are likely to be causal, or could be explained by reverse causality/residual confounding.

METHODS

We used bi-directional two-sample Mendelian randomization (MR) and multi-variable MR (MVMR) analysis to examine evidence of causality, specificity and direction of association of 20 immunological proteins/traits (pro-inflammatory cytokines: interleukin (IL)-6, tumour necrosis factor (TNF)-α, IL-12, IL-16, IL-17, IL-18; anti-inflammatory cytokines: IL-1 receptor antagonist (RA), IL-10, IL-13; chemokines: IL-8, monocyte chemo-attractant protein-1 (MCP-1); lymphoid growth-factors: soluble (s) IL-2Rα, IL-4, IL-7, IL-9; myeloid growth-factor: IL-5; acute phase protein: C-Reactive Protein (CRP); immune cells: neutrophils, lymphocytes; neurotrophic factor: brain derived neurotrophic factor (BDNF)) with schizophrenia, major depression and bipolar disorder.

RESULTS

Genetically-predicted IL-6 was associated with increased risk of schizophrenia in univariable MR (OR = 1.24; 95% C.I., 1.05-1.47) and with major depression in MVMR (OR = 1.08; 95% C.I., 1.03-1.12). These results survived Bonferroni-correction. Genetically-predicted sIL-2Rα (OR = 1.07; 95% C.I., 1.01-1.12) and IL-9 (OR = 1.06; 95% C.I., 1.01-1.11) were associated with increased schizophrenia risk. Genetically-predicted BDNF (OR = 0.97; 95% C.I., 0.94-1.00) and MCP-1 (OR = 0.96; 95% C.I., 0.91-0.99) were associated with reduced schizophrenia risk. However, these findings did not survive correction for multiple testing. The CRP-schizophrenia association attenuated completely after taking into account IL-6 and sIL-2Rα in MVMR (OR = 1.02; 95% C.I., 0.81-1.28). No significant associations were observed for bipolar disorder. Evidence from bidirectional MR did not support reverse causality.

CONCLUSIONS

We report evidence in support of potential causal associations of several immunological proteins/traits with schizophrenia, and of IL-6 with depression. Some of the findings did not survive correction for multiple testing and so replication in larger samples is required. Experimental studies are also required to further examine causality, mechanisms, and treatment potential for these immunological proteins/pathways for schizophrenia and depression.

Novel Gyrification Networks Reveal Links with Psychiatric Risk Factors in Early Illness

Adult gyrification provides a window into coordinated early neurodevelopment when disruptions predispose individuals to psychiatric illness. We hypothesized that the echoes of such disruptions should be observed within structural gyrification networks in early psychiatric illness that would demonstrate associations with developmentally relevant variables rather than specific psychiatric symptoms. We employed a new data-driven method (Orthogonal Projective Non-Negative Matrix Factorization) to delineate novel gyrification-based networks of structural covariance in 308 healthy controls. Gyrification within the networks was then compared to 713 patients with recent onset psychosis or depression, and at clinical high-risk. Associations with diagnosis, symptoms, cognition, and functioning were investigated using linear models. Results demonstrated 18 novel gyrification networks in controls as verified by internal and external validation. Gyrification was reduced in patients in temporal-insular, lateral occipital, and lateral fronto-parietal networks (pFDR < 0.01) and was not moderated by illness group. Higher gyrification was associated with better cognitive performance and lifetime role functioning, but not with symptoms. The findings demonstrated that gyrification can be parsed into novel brain networks that highlight generalized illness effects linked to developmental vulnerability. When combined, our study widens the window into the etiology of psychiatric risk and its expression in adulthood.

Depression and suicidal ideation in schizophrenia spectrum disorder: a cross-sectional study from a lower middle-income country

OBJECTIVES

Depression has long been considered a significant feature of schizophrenia and is associated with more frequent psychotic episodes, increased service utilisation, substance misuse, poor quality of life and completed suicide. However, there is a distinct lack of literature on this comorbidity from low- and middle-income countries or non-western cultural backgrounds.

METHODS

This is a cross-sectional analysis of baseline data from a large randomised controlled trial, examining the prevalence of depression and suicidal ideation in patients with schizophrenia spectrum disorder. A total of 298 participants were recruited from inpatient and outpatient psychiatric units in Karachi, Pakistan. Participants completed the Calgary Depression Scale for Schizophrenia (CDSS), Positive and Negative Syndrome Scale (PANSS), Euro Qol (EQ-5D) and Social Functioning Scale (SFS).

RESULTS

Data indicate that 36% of participants in the study were depressed and 18% endorsed suicidal ideation. Depression was associated with higher positive symptom scores and reduced quality of life, but no significant difference in negative symptoms and social functioning.

CONCLUSIONS

Depression and suicidal ideation are prevalent in Pakistani patients diagnosed with schizophrenia spectrum disorder. Evaluation of depressive symptoms in this group may help identify individuals at higher risk of completed suicide, allowing for targeted interventions to improve outcomes.Key pointsTo our knowledge, this is the first study describing the prevalence of depression and suicidal ideation in individuals with schizophrenia from Pakistan.Our data indicate that 36% of individuals with schizophrenia in our sample were depressed and 18% endorsed suicidal ideation.Depression in schizophrenia was associated with poorer quality of life and higher positive symptom burden.This study adds to the scarce literature from low- and middle-income countries where the burden of mental illness is great and where the majority of suicide deaths occur.Addressing social inequality, food insecurity, high rates of unemployment and low levels of literacy in these settings may have a profound effect on population mental health and suicide risk.

Oxidative Stress and the Pathophysiology and Symptom Profile of Schizophrenia Spectrum Disorders

Schizophrenia is associated with increased levels of oxidative stress, as reflected by an increase in the concentrations of damaging reactive species and a reduction in anti-oxidant defences to combat them. Evidence has suggested that whilst not the likely primary cause of schizophrenia, increased oxidative stress may contribute to declining course and poor outcomes associated with schizophrenia. Here we discuss how oxidative stress may be implicated in the aetiology of schizophrenia and examine how current understanding relates associations with symptoms, potentially via lipid peroxidation induced neuronal damage. We argue that oxidative stress may be a good target for future pharmacotherapy in schizophrenia and suggest a multi-step model of illness progression with oxidative stress involved at each stage.

Development and external validation of the Psychosis Metabolic Risk Calculator (PsyMetRiC): a cardiometabolic risk prediction algorithm for young people with psychosis

BACKGROUND

Young people with psychosis are at high risk of developing cardiometabolic disorders; however, there is no suitable cardiometabolic risk prediction algorithm for this group. We aimed to develop and externally validate a cardiometabolic risk prediction algorithm for young people with psychosis.

METHODS

We developed the Psychosis Metabolic Risk Calculator (PsyMetRiC) to predict up to 6-year risk of incident metabolic syndrome in young people (aged 16-35 years) with psychosis from commonly recorded information at baseline. We developed two PsyMetRiC versions using the forced entry method: a full model (including age, sex, ethnicity, body-mass index, smoking status, prescription of a metabolically active antipsychotic medication, HDL concentration, and triglyceride concentration) and a partial model excluding biochemical results. PsyMetRiC was developed using data from two UK psychosis early intervention services (Jan 1, 2013, to Nov 4, 2020) and externally validated in another UK early intervention service (Jan 1, 2012, to June 3, 2020). A sensitivity analysis was done in UK birth cohort participants (aged 18 years) who were at risk of developing psychosis. Algorithm performance was assessed primarily via discrimination (C statistic) and calibration (calibration plots). We did a decision curve analysis and produced an online data-visualisation app.

FINDINGS

651 patients were included in the development samples, 510 in the validation sample, and 505 in the sensitivity analysis sample. PsyMetRiC performed well at internal (full model: C 0·80, 95% CI 0·74-0·86; partial model: 0·79, 0·73-0·84) and external validation (full model: 0·75, 0·69-0·80; and partial model: 0·74, 0·67-0·79). Calibration of the full model was good, but there was evidence of slight miscalibration of the partial model. At a cutoff score of 0·18, in the full model PsyMetRiC improved net benefit by 7·95% (sensitivity 75%, 95% CI 66-82; specificity 74%, 71-78), equivalent to detecting an additional 47% of metabolic syndrome cases.

INTERPRETATION

We have developed an age-appropriate algorithm to predict the risk of incident metabolic syndrome, a precursor of cardiometabolic morbidity and mortality, in young people with psychosis. PsyMetRiC has the potential to become a valuable resource for early intervention service clinicians and could enable personalised, informed health-care decisions regarding choice of antipsychotic medication and lifestyle interventions.

FUNDING

National Institute for Health Research and Wellcome Trust.

Co-occurrence of autistic and psychotic traits: implications for depression, self-harm and suicidality

BACKGROUND

There is increasing interest in the clinical and aetiological overlap between autism spectrum disorders and schizophrenia spectrum disorders, reported to co-occur at both diagnostic and trait levels. Individually, sub-clinical autistic and psychotic traits are associated with poor clinical outcomes, including increased depressive symptomatology, self-harming behaviour and suicidality. However, the implications when both traits co-occur remain poorly understood. The study aimed to (1) examine the relationship between autistic and psychotic traits and (2) determine if their co-occurrence increases depressive symptomatology, self-harm and suicidality.

METHODS

Cross-sectional data from a self-selecting (online and poster advertising) sample of the adult UK population (n = 653) were collected using an online survey. Validated self-report measures were used to assess sub-clinical autistic and psychotic traits, depressive symptomatology, self-harming behaviour and suicidality. Correlation and regression analyses were performed.

RESULTS

A positive correlation between sub-clinical autistic and positive psychotic traits was confirmed (r_s = 0.509, p < 0.001). Overall, autistic traits and psychotic traits were, independently, significant predictors of depression, self-harm and suicidality. Intriguingly, however, depression was associated with a negative interaction between the autistic domain attention to detail and psychotic traits.

CONCLUSIONS

This study supports previous findings that sub-clinical autistic and psychotic traits are largely independently associated with depression, self-harm and suicidality, and is novel in finding that their combined presence has no additional effect on depression, self-harm or suicidality. These findings highlight the importance of considering both autistic and psychotic traits and their symptom domains in research and when developing population-based depression prevention and intervention strategies.

Longitudinal Trends in Childhood Insulin Levels and Body Mass Index and Associations With Risks of Psychosis and Depression in Young Adults

IMPORTANCE

Cardiometabolic disorders often occur concomitantly with psychosis and depression, contribute to high mortality rates, and are detectable from the onset of the psychiatric disorders. However, it is unclear whether longitudinal trends in cardiometabolic traits from childhood are associated with risks for adult psychosis and depression.

OBJECTIVE

To examine whether specific developmental trajectories of fasting insulin (FI) levels and body mass index (BMI) from early childhood were longitudinally associated with psychosis and depression in young adults.

DESIGN, SETTING, AND PARTICIPANTS

A cohort study from the Avon Longitudinal Study of Parents and Children, a prospective study including a population-representative British cohort of 14 975 individuals, was conducted using data from participants aged 1 to 24 years. Body mass index and FI level data were used for growth mixture modeling to delineate developmental trajectories, and associations with psychosis and depression were assessed. The study was conducted between July 15, 2019, and March 24, 2020.

EXPOSURES

Fasting insulin levels were measured at 9, 15, 18, and 24 years, and BMI was measured at 1, 2, 3, 4, 7, 9, 10, 11, 12, 15, 18, and 24 years. Data on sex, race/ethnicity, paternal social class, childhood emotional and behavioral problems, and cumulative scores of sleep problems, average calorie intake, physical activity, smoking, and alcohol and substance use in childhood and adolescence were examined as potential confounders.

MAIN OUTCOMES AND MEASURES

Psychosis risk (definite psychotic experiences, psychotic disorder, at-risk mental state status, and negative symptom score) depression risk (measured using the computerized Clinical Interview Schedule-Revised) were assessed at 24 years.

RESULTS

From data available on 5790 participants (3132 [54.1%] female) for FI levels and data available on 10 463 participants (5336 [51.0%] female) for BMI, 3 distinct trajectories for FI levels and 5 distinct trajectories for BMI were noted, all of which were differentiated by mid-childhood. The persistently high FI level trajectory was associated with a psychosis at-risk mental state (adjusted odds ratio [aOR], 5.01; 95% CI, 1.76-13.19) and psychotic disorder (aOR, 3.22; 95% CI, 1.29-8.02) but not depression (aOR, 1.38; 95% CI, 0.75-2.54). A puberty-onset major increase in BMI was associated with depression (aOR, 4.46; 95% CI, 2.38-9.87) but not psychosis (aOR, 1.98; 95% CI, 0.56-7.79).

CONCLUSIONS AND RELEVANCE

The cardiometabolic comorbidity of psychosis and depression may have distinct, disorder-specific early-life origins. Disrupted insulin sensitivity could be a shared risk factor for comorbid cardiometabolic disorders and psychosis. A puberty-onset major increase in BMI could be a risk factor or risk indicator for adult depression. These markers may represent targets for prevention and treatment of cardiometabolic disorders in individuals with psychosis and depression.

Longitudinal Associations Between Cognitive Deficits in Childhood and Psychopathological Symptoms in Adolescence and Young Adulthood

IMPORTANCE

Cognitive deficits are core features of mental disorders and are important in predicting long-term prognosis. However, it is still unknown whether individual patterns of cognitive deficits predate specific mental disorders.

OBJECTIVE

To investigate the specificity of the associations of attention, working memory, and inhibition in childhood with borderline personality disorder (BPD), psychosis, depression, and hypomania in adolescence and young adulthood.

DESIGN, SETTING, AND PARTICIPANTS

This cohort study obtained data from the Avon Longitudinal Study of Parents and Children in the United Kingdom. All pregnant women resident in Avon, United Kingdom, with an expected date of delivery from April 1, 1991, and December 31, 1992, were eligible. Data analysis was conducted from April 1 to September 30, 2020. The sample initially comprised 13 988 participants who were alive at 1 year of age. For this study, data were available for 6333 individuals reporting on any psychopathological measure at ages 11 to 12 years, 4903 individuals at ages 17 to 18 years, and 2963 individuals at 22 to 23 years.

EXPOSURES

Sustained attention, selective attention, and attentional control were assessed with the Test of Everyday Attention for Children at age 8 years, and working memory and inhibition were assessed at age 10 years with the Counting Span Task and the stop-signal paradigm, respectively.

MAIN OUTCOMES AND MEASURES

Symptoms of BPD were assessed at ages 11 to 12 years, psychotic experiences and depression were examined at ages 17 to 18 years, and hypomania was examined at ages 22 to 23 years.

RESULTS

Among 5315 individuals included in the statistical analysis, 2551 (48.0%) were male and 2764 (52.0) were female. Higher sustained attention at 8 years was associated with decreased risk of BPD symptoms at ages 11 to 12 years (adjusted odds ratio [aOR], 0.964; 95% CI, 0.933-0.996; P = .03), better performance on inhibition at age 10 years with decreased risk of psychotic experiences at ages 17 to 18 years (aOR, 0.938; 95% CI, 0.890-0.989; P = .02), higher sustained attention at age 8 years with decreased risk of depressive symptoms at ages 17 to 18 years (aOR, 0.969; 95% CI 0.938-0.9997; P = .048), and better performance in working memory at age 10 years with decreased risk of hypomania symptoms at ages 22 to 23 years (aOR, 0.694; 95% CI, 0.529-0.911; P = .008). After controlling for potential psychopathological overlay, all the associations remained, except for working memory and hypomania. Higher sustained attention at age 8 years was associated with decreased risk of BPD symptoms at ages 11 to 12 years (β = -0.05; P < .001) and of depression at ages 17 to 18 years (β = -0.03; P = .04), and better performance in inhibition at age 10 years was associated with decreased risk of psychotic experiences at ages 17 to 18 years (β = -0.03; P = .04).

CONCLUSIONS AND RELEVANCE

These findings suggest that specific cognitive deficits in childhood are distinctively associated with different psychopathological symptoms in young people. Furthermore, these results suggest the potential of early cognitive interventions in childhood as a way of modifying or attenuating risk for subsequent psychopathological symptoms.

Reduced prefrontal activity and suicidal behaviour in early schizophrenia

Introduction

Approximately, 15- 26% of patients with first-episode psychosis, including schizophrenia, are likely to have attempted suicide by their first treatment contact. Studies of suicidal behavior outside of schizophrenia have indicated grey matter volume loss in the prefrontal and orbitofrontal cortex, and aberrant brain activity in relation to emotional recognition and dysfunction.

Objectives

This study aimed to investigate the functional neural correlates of suicidal behavior in early schizophrenia.

Methods

fMRI faces task was conducted (fearful face versus neutral face) in 8 participants with first-episode schizophrenia together with standardised scales including PANSS and SBQ-R. fMRI activation was compared using a two-sample t-test in participants with low and high suicidal behavior. Extent threshold is 0 voxels and significance level p<0.001 (FWE corrected). Processing of images was carried out using SPM12 and Matlab.

Results

8 participants were recruited; 5 males and 3 females, mean age of 26.5. Results suggest that participants with higher suicidal behaviour showed reduced activation on the anterior-cingulate gyrus and medial frontal gyrus, which are parts of PFC, (p= .005). There was also a significant difference in task response accuracy, where, participants with high suicidal behaviour made more accurate responses compared to low group (t (3) = 3.65, p = .035).

Conclusions

This is an exploratory study, investigated the differences in brain activity in patients with schizophrenia who are at risk of completed suicide and, therefore might provide new insights into the underlying mechanisms. Further work should address how PFC activity changes with risk over time and its potential utility as a biomarker in suicide.

The potential shared role of inflammation in insulin resistance and schizophrenia: A bidirectional two-sample mendelian randomization study

BACKGROUND

Insulin resistance predisposes to cardiometabolic disorders, which are commonly comorbid with schizophrenia and are key contributors to the significant excess mortality in schizophrenia. Mechanisms for the comorbidity remain unclear, but observational studies have implicated inflammation in both schizophrenia and cardiometabolic disorders separately. We aimed to examine whether there is genetic evidence that insulin resistance and 7 related cardiometabolic traits may be causally associated with schizophrenia, and whether evidence supports inflammation as a common mechanism for cardiometabolic disorders and schizophrenia.

METHODS AND FINDINGS

We used summary data from genome-wide association studies of mostly European adults from large consortia (Meta-Analyses of Glucose and Insulin-related traits Consortium (MAGIC) featuring up to 108,557 participants; Diabetes Genetics Replication And Meta-analysis (DIAGRAM) featuring up to 435,387 participants; Global Lipids Genetics Consortium (GLGC) featuring up to 173,082 participants; Genetic Investigation of Anthropometric Traits (GIANT) featuring up to 339,224 participants; Psychiatric Genomics Consortium (PGC) featuring up to 105,318 participants; and Cohorts for Heart and Aging Research in Genomic Epidemiology (CHARGE) consortium featuring up to 204,402 participants). We conducted two-sample uni- and multivariable mendelian randomization (MR) analysis to test whether (i) 10 cardiometabolic traits (fasting insulin, high-density lipoprotein and triglycerides representing an insulin resistance phenotype, and 7 related cardiometabolic traits: low-density lipoprotein, fasting plasma glucose, glycated haemoglobin, leptin, body mass index, glucose tolerance, and type 2 diabetes) could be causally associated with schizophrenia; and (ii) inflammation could be a shared mechanism for these phenotypes. We conducted a detailed set of sensitivity analyses to test the assumptions for a valid MR analysis. We did not find statistically significant evidence in support of a causal relationship between cardiometabolic traits and schizophrenia, or vice versa. However, we report that a genetically predicted inflammation-related insulin resistance phenotype (raised fasting insulin (raised fasting insulin (Wald ratio OR = 2.95, 95% C.I, 1.38-6.34, Holm-Bonferroni corrected p-value (p) = 0.035) and lower high-density lipoprotein (Wald ratio OR = 0.55, 95% C.I., 0.36-0.84; p = 0.035)) was associated with schizophrenia. Evidence for these associations attenuated to the null in multivariable MR analyses after adjusting for C-reactive protein, an archetypal inflammatory marker: (fasting insulin Wald ratio OR = 1.02, 95% C.I, 0.37-2.78, p = 0.975), high-density lipoprotein (Wald ratio OR = 1.00, 95% C.I., 0.85-1.16; p = 0.849), suggesting that the associations could be fully explained by inflammation. One potential limitation of the study is that the full range of gene products from the genetic variants we used as proxies for the exposures is unknown, and so we are unable to comment on potential biological mechanisms of association other than inflammation, which may also be relevant.

CONCLUSIONS

Our findings support a role for inflammation as a common cause for insulin resistance and schizophrenia, which may at least partly explain why the traits commonly co-occur in clinical practice. Inflammation and immune pathways may represent novel therapeutic targets for the prevention or treatment of schizophrenia and comorbid insulin resistance. Future work is needed to understand how inflammation may contribute to the risk of schizophrenia and insulin resistance.

Gender equality in academic publishing: action from the BJPsych

Women in academic publishing and academic psychiatry face many challenges of gender inequality, including significant pay differentials, poor visibility in senior positions and a male-dominated hierarchical system. We discuss this problem and outline how the BJPsych plans to tackle these issues it in its own publishing.

A multivariate neuromonitoring approach to neuroplasticity-based computerized cognitive training in recent onset psychosis

Two decades of studies suggest that computerized cognitive training (CCT) has an effect on cognitive improvement and the restoration of brain activity. Nevertheless, individual response to CCT remains heterogenous, and the predictive potential of neuroimaging in gauging response to CCT remains unknown. We employed multivariate pattern analysis (MVPA) on whole-brain resting-state functional connectivity (rsFC) to (neuro)monitor clinical outcome defined as psychosis-likeness change after 10-hours of CCT in recent onset psychosis (ROP) patients. Additionally, we investigated if sensory processing (SP) change during CCT is associated with individual psychosis-likeness change and cognitive gains after CCT. 26 ROP patients were divided into maintainers and improvers based on their SP change during CCT. A support vector machine (SVM) classifier separating 56 healthy controls (HC) from 35 ROP patients using rsFC (balanced accuracy of 65.5%, P < 0.01) was built in an independent sample to create a naturalistic model representing the HC-ROP hyperplane. This model was out-of-sample cross-validated in the ROP patients from the CCT trial to assess associations between rsFC pattern change, cognitive gains and SP during CCT. Patients with intact SP threshold at baseline showed improved attention despite psychosis status on the SVM hyperplane at follow-up (p < 0.05). Contrarily, the attentional gains occurred in the ROP patients who showed impaired SP at baseline only if rsfMRI diagnosis status shifted to the healthy-like side of the SVM continuum. Our results reveal the utility of MVPA for elucidating treatment response neuromarkers based on rsFC-SP change and pave the road to more personalized interventions.

Towards clinical application of prediction models for transition to psychosis: A systematic review and external validation study in the PRONIA sample

A multitude of prediction models for a first psychotic episode in individuals at clinical high-risk (CHR) for psychosis have been proposed, but only rarely validated. We identified transition models based on clinical and neuropsychological data through a registered systematic literature search and evaluated their external validity in 173 CHRs from the Personalised Prognostic Tools for Early Psychosis Management (PRONIA) study. Discrimination performance was assessed with the area under the receiver operating characteristic curve (AUC), and compared to the prediction of clinical raters. External discrimination performance varied considerably across the 22 identified models (AUC 0.40-0.76), with two models showing good discrimination performance. None of the tested models significantly outperformed clinical raters (AUC = 0.75). Combining predictions of clinical raters and the best model descriptively improved discrimination performance (AUC = 0.84). Results show that personalized prediction of transition in CHR is potentially feasible on a global scale. For implementation in clinical practice, further rounds of external validation, impact studies, and development of an ethical framework is necessary.

Heterogeneity and Classification of Recent Onset Psychosis and Depression: A Multimodal Machine Learning Approach

Diagnostic heterogeneity within and across psychotic and affective disorders challenges accurate treatment selection, particularly in the early stages. Delineation of shared and distinct illness features at the phenotypic and brain levels may inform the development of more precise differential diagnostic tools. We aimed to identify prototypes of depression and psychosis to investigate their heterogeneity, with common, comorbid transdiagnostic symptoms. Analyzing clinical/neurocognitive and grey matter volume (GMV) data from the PRONIA database, we generated prototypic models of recent-onset depression (ROD) vs. recent-onset psychosis (ROP) by training support-vector machines to separate patients with ROD from patients with ROP, who were selected for absent comorbid features (pure groups). Then, models were applied to patients with comorbidity, ie, ROP with depressive symptoms (ROP+D) and ROD participants with sub-threshold psychosis-like features (ROD+P), to measure their positions within the affective-psychotic continuum. All models were independently validated in a replication sample. Comorbid patients were positioned between pure groups, with ROP+D patients being more frequently classified as ROD compared to pure ROP patients (clinical/neurocognitive model: χ2 = 14.874; P < .001; GMV model: χ2 = 4.933; P = .026). ROD+P patient classification did not differ from ROD (clinical/neurocognitive model: χ2 = 1.956; P = 0.162; GMV model: χ2 = 0.005; P = .943). Clinical/neurocognitive and neuroanatomical models demonstrated separability of prototypic depression from psychosis. The shift of comorbid patients toward the depression prototype, observed at the clinical and biological levels, suggests that psychosis with affective comorbidity aligns more strongly to depressive rather than psychotic disease processes. Future studies should assess how these quantitative measures of comorbidity predict outcomes and individual responses to stratified therapeutic interventions.

Multimodal Machine Learning Workflows for Prediction of Psychosis in Patients With Clinical High-Risk Syndromes and Recent-Onset Depression

IMPORTANCE

Diverse models have been developed to predict psychosis in patients with clinical high-risk (CHR) states. Whether prediction can be improved by efficiently combining clinical and biological models and by broadening the risk spectrum to young patients with depressive syndromes remains unclear.

OBJECTIVES

To evaluate whether psychosis transition can be predicted in patients with CHR or recent-onset depression (ROD) using multimodal machine learning that optimally integrates clinical and neurocognitive data, structural magnetic resonance imaging (sMRI), and polygenic risk scores (PRS) for schizophrenia; to assess models' geographic generalizability; to test and integrate clinicians' predictions; and to maximize clinical utility by building a sequential prognostic system.

DESIGN, SETTING, AND PARTICIPANTS

This multisite, longitudinal prognostic study performed in 7 academic early recognition services in 5 European countries followed up patients with CHR syndromes or ROD and healthy volunteers. The referred sample of 167 patients with CHR syndromes and 167 with ROD was recruited from February 1, 2014, to May 31, 2017, of whom 26 (23 with CHR syndromes and 3 with ROD) developed psychosis. Patients with 18-month follow-up (n = 246) were used for model training and leave-one-site-out cross-validation. The remaining 88 patients with nontransition served as the validation of model specificity. Three hundred thirty-four healthy volunteers provided a normative sample for prognostic signature evaluation. Three independent Swiss projects contributed a further 45 cases with psychosis transition and 600 with nontransition for the external validation of clinical-neurocognitive, sMRI-based, and combined models. Data were analyzed from January 1, 2019, to March 31, 2020.

MAIN OUTCOMES AND MEASURES

Accuracy and generalizability of prognostic systems.

RESULTS

A total of 668 individuals (334 patients and 334 controls) were included in the analysis (mean [SD] age, 25.1 [5.8] years; 354 [53.0%] female and 314 [47.0%] male). Clinicians attained a balanced accuracy of 73.2% by effectively ruling out (specificity, 84.9%) but ineffectively ruling in (sensitivity, 61.5%) psychosis transition. In contrast, algorithms showed high sensitivity (76.0%-88.0%) but low specificity (53.5%-66.8%). A cybernetic risk calculator combining all algorithmic and human components predicted psychosis with a balanced accuracy of 85.5% (sensitivity, 84.6%; specificity, 86.4%). In comparison, an optimal prognostic workflow produced a balanced accuracy of 85.9% (sensitivity, 84.6%; specificity, 87.3%) at a much lower diagnostic burden by sequentially integrating clinical-neurocognitive, expert-based, PRS-based, and sMRI-based risk estimates as needed for the given patient. Findings were supported by good external validation results.

CONCLUSIONS AND RELEVANCE

These findings suggest that psychosis transition can be predicted in a broader risk spectrum by sequentially integrating algorithms' and clinicians' risk estimates. For clinical translation, the proposed workflow should undergo large-scale international validation.

Development and Validation of a Nonremission Risk Prediction Model in First-Episode Psychosis: An Analysis of 2 Longitudinal Studies

Psychosis is a major mental illness with first onset in young adults. The prognosis is poor in around half of the people affected, and difficult to predict. The few tools available to predict prognosis have major weaknesses which limit their use in clinical practice. We aimed to develop and validate a risk prediction model of symptom nonremission in first-episode psychosis. Our development cohort consisted of 1027 patients with first-episode psychosis recruited between 2005 and 2010 from 14 early intervention services across the National Health Service in England. Our validation cohort consisted of 399 patients with first-episode psychosis recruited between 2006 and 2009 from a further 11 English early intervention services. The one-year nonremission rate was 52% and 54% in the development and validation cohorts, respectively. Multivariable logistic regression was used to develop a risk prediction model for nonremission, which was externally validated. The prediction model showed good discrimination C-statistic of 0.73 (0.71, 0.75) and adequate calibration with intercept alpha of 0.12 (0.02, 0.22) and slope beta of 0.98 (0.85, 1.11). Our model improved the net-benefit by 15% at a risk threshold of 50% compared to the strategy of treating all, equivalent to 15 more detected nonremitted first-episode psychosis individuals per 100 without incorrectly classifying remitted cases. Once prospectively validated, our first episode psychosis prediction model could help identify patients at increased risk of nonremission at initial clinical contact.

Research priorities for neuroimmunology: identifying the key research questions to be addressed by 2030

Neuroimmunology in the broadest sense is the study of interactions between the nervous and the immune systems. These interactions play important roles in health from supporting neural development, homeostasis and plasticity to modifying behaviour. Neuroimmunology is increasingly recognised as a field with the potential to deliver a significant positive impact on human health and treatment for neurological and psychiatric disorders. Yet, translation to the clinic is hindered by fundamental knowledge gaps on the underlying mechanisms of action or the optimal timing of an intervention, and a lack of appropriate tools to visualise and modulate both systems. Here we propose ten key disease-agnostic research questions that, if addressed, could lead to significant progress within neuroimmunology in the short to medium term. We also discuss four cross-cutting themes to be considered when addressing each question: i) bi-directionality of neuroimmune interactions; ii) the biological context in which the questions are addressed (e.g. health vs disease vs across the lifespan); iii) tools and technologies required to fully answer the questions; and iv) translation into the clinic. We acknowledge that these ten questions cannot represent the full breadth of gaps in our understanding; rather they focus on areas which, if addressed, may have the most broad and immediate impacts. By defining these neuroimmunology priorities, we hope to unite existing and future research teams, who can make meaningful progress through a collaborative and cross-disciplinary effort.