A transdiagnostic prodrome for severe mental disorders: an electronic health record study.

Effective prevention of severe mental disorders (SMD), including non-psychotic unipolar mood disorders (UMD), non-psychotic bipolar mood disorders (BMD), and psychotic disorders (PSY), rely on accurate knowledge of the duration, first presentation, time course and transdiagnosticity of their prodromal stages. Here we present a retrospective, real-world, cohort study using electronic health records, adhering to RECORD guidelines. Natural language processing algorithms were used to extract monthly occurrences of 65 prodromal features (symptoms and substance use), grouped into eight prodromal clusters. The duration, first presentation, and transdiagnosticity of the prodrome were compared between SMD groups with one-way ANOVA, Cohen's f and d. The time course (mean occurrences) of prodromal clusters was compared between SMD groups with linear mixed-effects models. 26,975 individuals diagnosed with ICD-10 SMD were followed up for up to 12 years (UMD = 13,422; BMD = 2506; PSY = 11,047; median[IQR] age 39.8[23.7] years; 55% female; 52% white). The duration of the UMD prodrome (18[36] months) was shorter than BMD (26[35], d = 0.21) and PSY (24[38], d = 0.18). Most individuals presented with multiple first prodromal clusters, with the most common being non-specific ('other'; 88% UMD, 85% BMD, 78% PSY). The only first prodromal cluster that showed a medium-sized difference between the three SMD groups was positive symptoms (f = 0.30). Time course analysis showed an increase in prodromal cluster occurrences approaching SMD onset. Feature occurrence across the prodromal period showed small/negligible differences between SMD groups, suggesting that most features are transdiagnostic, except for positive symptoms (e.g. paranoia, f = 0.40). Taken together, our findings show minimal differences in the duration and first presentation of the SMD prodromes as recorded in secondary mental health care. All the prodromal clusters intensified as individuals approached SMD onset, and all the prodromal features other than positive symptoms are transdiagnostic. These results support proposals to develop transdiagnostic preventive services for affective and psychotic disorders detected in secondary mental healthcare.

Connectome dysfunction in patients at clinical high risk for psychosis and modulation by oxytocin.

Abnormalities in functional brain networks (functional connectome) are increasingly implicated in people at Clinical High Risk for Psychosis (CHR-P). Intranasal oxytocin, a potential novel treatment for the CHR-P state, modulates network topology in healthy individuals. However, its connectomic effects in people at CHR-P remain unknown. Forty-seven men (30 CHR-P and 17 healthy controls) received acute challenges of both intranasal oxytocin 40 IU and placebo in two parallel randomised, double-blind, placebo-controlled cross-over studies which had similar but not identical designs. Multi-echo resting-state fMRI data was acquired at approximately 1 h post-dosing. Using a graph theoretical approach, the effects of group (CHR-P vs healthy control), treatment (oxytocin vs placebo) and respective interactions were tested on graph metrics describing the topology of the functional connectome. Group effects were observed in 12 regions (all pFDR < 0.05) most localised to the frontoparietal network. Treatment effects were found in 7 regions (all pFDR < 0.05) predominantly within the ventral attention network. Our major finding was that many effects of oxytocin on network topology differ across CHR-P and healthy individuals, with significant interaction effects observed in numerous subcortical regions strongly implicated in psychosis onset, such as the thalamus, pallidum and nucleus accumbens, and cortical regions which localised primarily to the default mode network (12 regions, all pFDR < 0.05). Collectively, our findings provide new insights on aberrant functional brain network organisation associated with psychosis risk and demonstrate, for the first time, that oxytocin modulates network topology in brain regions implicated in the pathophysiology of psychosis in a clinical status (CHR-P vs healthy control) specific manner.

Global population attributable fraction of potentially modifiable risk factors for mental disorders: a meta-umbrella systematic review.

Numerous risk factors for mental disorders have been identified. However, we do not know how many disorders we could prevent and to what extent by modifying these risk factors. This study quantifies the Population Attributable Fraction (PAF) of potentially modifiable risk factors for mental disorders. We conducted a PRISMA 2020-compliant (Protocol: https://osf.io/hk2ag ) meta-umbrella systematic review (Web of Science/PubMed/Cochrane Central Register of Reviews/Ovid/PsycINFO, until 05/12/2021) of umbrella reviews reporting associations between potentially modifiable risk factors and ICD/DSM mental disorders, restricted to highly convincing (class I) and convincing (class II) evidence from prospective cohorts. The primary outcome was the global meta-analytical PAF, complemented by sensitivity analyses across different settings, the meta-analytical Generalised Impact Fraction (GIF), and study quality assessment (AMSTAR). Seven umbrella reviews (including 295 meta-analyses and 547 associations) identified 28 class I-II risk associations (23 risk factors; AMSTAR: 45.0% high-, 35.0% medium-, 20.0% low quality). The largest global PAFs not confounded by indication were 37.84% (95% CI = 26.77-48.40%) for childhood adversities and schizophrenia spectrum disorders, 24.76% (95% CI = 13.98-36.49%) for tobacco smoking and opioid use disorders, 17.88% (95% CI = not available) for job strain and depression, 14.60% (95% CI = 9.46-20.52%) for insufficient physical activity and Alzheimer's disease, 13.40% (95% CI = 7.75-20.15%) for childhood sexual abuse and depressive disorders, 12.37% (95% CI = 5.37-25.34%) for clinical high-risk state for psychosis and any non-organic psychotic disorders, 10.00% (95% CI = 5.62-15.95%) for three metabolic factors and depression, 9.73% (95% CI = 4.50-17.30%) for cannabis use and schizophrenia spectrum disorders, and 9.30% (95% CI = 7.36-11.38%) for maternal pre-pregnancy obesity and ADHD. The GIFs confirmed the preventive capacity for these factors. Addressing several potentially modifiable risk factors, particularly childhood adversities, can reduce the global population-level incidence of mental disorders.

Prognostic accuracy and clinical utility of psychometric instruments for individuals at clinical high-risk of psychosis: a systematic review and meta-analysis.

Accurate prognostication of individuals at clinical high-risk for psychosis (CHR-P) is an essential initial step for effective primary indicated prevention. We aimed to summarise the prognostic accuracy and clinical utility of CHR-P assessments for primary indicated psychosis prevention. Web of Knowledge databases were searched until 1st January 2022 for longitudinal studies following-up individuals undergoing a psychometric or diagnostic CHR-P assessment, reporting transition to psychotic disorders in both those who meet CHR-P criteria (CHR-P + ) or not (CHR-P-). Prognostic accuracy meta-analysis was conducted following relevant guidelines. Primary outcome was prognostic accuracy, indexed by area-under-the-curve (AUC), sensitivity and specificity, estimated by the number of true positives, false positives, false negatives and true negatives at the longest available follow-up time. Clinical utility analyses included: likelihood ratios, Fagan's nomogram, and population-level preventive capacity (Population Attributable Fraction, PAF). A total of 22 studies (n = 4 966, 47.5% female, age range 12-40) were included. There were not enough meta-analysable studies on CHR-P diagnostic criteria (DSM-5 Attenuated Psychosis Syndrome) or non-clinical samples. Prognostic accuracy of CHR-P psychometric instruments in clinical samples (individuals referred to CHR-P services or diagnosed with 22q.11.2 deletion syndrome) was excellent: AUC = 0.85 (95% CI: 0.81-0.88) at a mean follow-up time of 34 months. This result was driven by outstanding sensitivity (0.93, 95% CI: 0.87-0.96) and poor specificity (0.58, 95% CI: 0.50-0.66). Being CHR-P + was associated with a small likelihood ratio LR + (2.17, 95% CI: 1.81-2.60) for developing psychosis. Being CHR-P- was associated with a large LR- (0.11, 95%CI: 0.06-0.21) for developing psychosis. Fagan's nomogram indicated a low positive (0.0017%) and negative (0.0001%) post-test risk in non-clinical general population samples. The PAF of the CHR-P state is 10.9% (95% CI: 4.1-25.5%). These findings consolidate the use of psychometric instruments for CHR-P in clinical samples for primary indicated prevention of psychosis. Future research should improve the ability to rule in psychosis risk.

Influence of cannabis use on incidence of psychosis in people at clinical high risk.

AIMS: Evidence for case-control studies suggests that cannabis use is a risk factor for the development of psychosis. However, there have been limited prospective studies and the direction of this association remains controversial. The primary aim of the present study was to examine the association between cannabis use and the incidence of psychotic disorders in people at clinical high risk of psychosis. Secondary aims were to assess associations between cannabis use and the persistence of psychotic symptoms, and with functional outcome. METHODS: Current and previous cannabis use were assessed in individuals at clinical high risk of psychosis (n = 334) and healthy controls (n = 67), using a modified version of the Cannabis Experience Questionnaire. Participants were assessed at baseline and followed up for 2 years. Transition to psychosis and persistence of psychotic symptoms were assessed using the Comprehensive Assessment of At-Risk Mental States criteria. Level of functioning at follow up was assessed using the Global Assessment of Functioning disability scale. RESULTS: During follow up, 16.2% of the clinical high-risk sample developed psychosis. Of those who did not become psychotic, 51.4% had persistent symptoms and 48.6% were in remission. There was no significant association between any measure of cannabis use at baseline and either transition to psychosis, the persistence of symptoms, or functional outcome. CONCLUSIONS: These findings contrast with epidemiological data that suggest that cannabis use increases the risk of psychotic disorder.

Prefrontal cortical activation associated with prospective memory while walking around a real-world street environment.

Rostral PFC (area 10) activation is common during prospective memory (PM) tasks. But it is not clear what mental processes these activations index. Three candidate explanations from cognitive neuroscience theory are: (i) monitoring of the environment; (ii) spontaneous intention retrieval; (iii) a combination of the two. These explanations make different predictions about the temporal and spatial patterns of activation that would be seen in rostral PFC in naturalistic settings. Accordingly, we plotted functional events in PFC using portable fNIRS while people were carrying out a PM task outside the lab and responding to cues when they were encountered, to decide between these explanations. Nineteen people were asked to walk around a street in London, U.K. and perform various tasks while also remembering to respond to prospective memory (PM) cues when they detected them. The prospective memory cues could be either social (involving greeting a person) or non-social (interacting with a parking meter) in nature. There were also a number of contrast conditions which allowed us to determine activation specifically related to the prospective memory components of the tasks. We found that maintaining both social and non-social intentions was associated with widespread activation within medial and right hemisphere rostral prefrontal cortex (BA 10), in agreement with numerous previous lab-based fMRI studies of prospective memory. In addition, increased activation was found within lateral prefrontal cortex (BA 45 and 46) when people were maintaining a social intention compared to a non-social one. The data were then subjected to a GLM-based method for automatic identification of functional events (AIDE), and the position of the participants at the time of the activation events were located on a map of the physical space. The results showed that the spatial and temporal distribution of these events was not random, but aggregated around areas in which the participants appeared to retrieve their future intentions (i.e., where they saw intentional cues), as well as where they executed them. Functional events were detected most frequently in BA 10 during the PM conditions compared to other regions and tasks. Mobile fNIRS can be used to measure higher cognitive functions of the prefrontal cortex in "real world" situations outside the laboratory in freely ambulant individuals. The addition of a "brain-first" approach to the data permits the experimenter to determine not only when haemodynamic changes occur, but also where the participant was when it happened. This can be extremely valuable when trying to link brain and cognition.

Prevalence and incidence of psychotic disorders in 22q11.2 deletion syndrome: a meta-analysis.

22q11.2 deletion syndrome (22q.11.2DS) might be one of the strongest genetic risk factors for psychosis, but robust estimates of prevalence and incidence of psychotic disorders in this condition are not available. To address this gap, we performed a multistep systematic PRISMA/MOOSE-compliant literature search of articles reporting prevalence (primary outcome) or incidence (secondary outcome) of psychotic disorders in 22q11.2DS samples (protocol: https://osf.io/w6hpg) using random-effects meta-analysis, subgroup analyses and meta-regressions. The pooled prevalence of psychotic disorders was 11.50% (95%CI:9.40-14.00%), largely schizophrenia (9.70%, 95%CI:6.50-14.20). Prevalence was significantly higher in samples with a mean age over 18 years, with both psychiatric and non-psychiatric comorbidities and recruited from healthcare services (compared to the community). Mean age was also significantly positively associated with prevalence in meta-regressions (p < 0.01). The pooled incidence of psychotic disorders was 10.60% (95%CI:6.60%-16.70%) at a mean follow-up time of 59.27 ± 40.55 months; meta-regressions were not significant. To our knowledge, this is the first comprehensive systematic review and meta-analysis of the prevalence and incidence of psychotic disorders in 22q11.2DS individuals. It demonstrates that around one in ten individuals with 22q11.2DS displays comorbid psychotic disorders, and around one in ten will develop psychosis in the following five years, indicating that preventive approaches should be implemented systematically in 22q11.2DS.

The effects of acute hyperglycaemia on sports and exercise performance in type 1 diabetes: A systematic review and meta-analysis.

OBJECTIVES: People with type 1 diabetes (T1D) are advised by health care professionals to target mild hyperglycaemia before and during exercise, to reduce the risk of hypoglycaemia. This review aimed to summarise the available evidence on the effects of acute hyperglycaemia on sports and exercise performance in T1D. DESIGN: Systematic review and meta-analysis. METHODS: Medline, EMBASE, CENTRAL, and Web of Science were searched until 29th May 2023 for studies investigating the effects of acute hyperglycaemia on any sports or exercise performance outcome in T1D. Random-effects meta-analysis was performed using standardised mean differences (SMD) when more than one study reported data for similar outcomes. Certainty of evidence for each outcome was assessed using GRADE. RESULTS: Seven studies were included in the review, comprising data from 119 people with T1D. Meta-analysis provided moderate-certainty evidence that acute hyperglycaemia does not significantly affect aerobic exercise performance (SMD -0.17; 95 % CI -0.59, 0.26; p = 0.44). There is low- or very-low certainty evidence that acute hyperglycaemia has no effect on anaerobic (two outcomes), neuromuscular (seven outcomes) or neurocognitive performance (three outcomes), except impaired isometric knee extension strength. One study provided low-certainty evidence that the performance effects of hyperglycaemia may depend on circulating insulin levels. CONCLUSIONS: Acute hyperglycaemia before or during exercise appears unlikely to affect aerobic performance to an extent that is relevant to most people with T1D, based on limited evidence. Future research in this field should focus on anaerobic, neuromuscular and neurocognitive performance, and examine the relevance of circulating insulin levels.

Dynamic and Transdiagnostic Risk Calculator Based on Natural Language Processing for the Prediction of Psychosis in Secondary Mental Health Care: Development and Internal-External Validation Cohort Study.

BACKGROUND: Automatic transdiagnostic risk calculators can improve the detection of individuals at risk of psychosis. However, they rely on assessment at a single point in time and can be refined with dynamic modeling techniques that account for changes in risk over time. METHODS: We included 158,139 patients (5007 events) who received a first index diagnosis of a nonorganic and nonpsychotic mental disorder within electronic health records from the South London and Maudsley National Health Service Foundation Trust between January 1, 2008, and October 8, 2021. A dynamic Cox landmark model was developed to estimate the 2-year risk of developing psychosis according to the TRIPOD (Transparent Reporting of a multivariate prediction model for Individual Prognosis or Diagnosis) statement. The dynamic model included 24 predictors extracted at 9 landmark points (baseline, 0, 6, 12, 24, 30, 36, 42, and 48 months): 3 demographic, 1 clinical, and 20 natural language processing-based symptom and substance use predictors. Performance was compared with a static Cox regression model with all predictors assessed at baseline only and indexed via discrimination (C-index), calibration (calibration plots), and potential clinical utility (decision curves) in internal-external validation. RESULTS: The dynamic model improved discrimination performance from baseline compared with the static model (dynamic: C-index = 0.9; static: C-index = 0.87) and the final landmark point (dynamic: C-index = 0.79; static: C-index = 0.76). The dynamic model was also significantly better calibrated (calibration slope = 0.97-1.1) than the static model at later landmark points (≥24 months). Net benefit was higher for the dynamic than for the static model at later landmark points (≥24 months). CONCLUSIONS: These findings suggest that dynamic prediction models can improve the detection of individuals at risk for psychosis in secondary mental health care settings.

The Translational Future of Stress Neurobiology and Psychosis Vulnerability: A Review of the Evidence.

Psychosocial stress is a well-established risk factor for psychosis, yet the neurobiological mechanisms underlying this relationship have yet to be fully elucidated. Much of the research in this field has investigated hypothalamic-pituitary-adrenal (HPA) axis function and immuno-inflammatory processes among individuals with established psychotic disorders. However, as such studies are limited in their ability to provide knowledge that can be used to develop preventative interventions, it is important to shift the focus to individuals with increased vulnerability for psychosis (i.e., high-risk groups). In the present article, we provide an overview of the current methods for identifying individuals at high-risk for psychosis and review the psychosocial stressors that have been most consistently associated with psychosis risk. We then describe a network of interacting physiological systems that are hypothesised to mediate the relationship between psychosocial stress and the manifestation of psychotic illness and critically review evidence that abnormalities within these systems characterise highrisk populations. We found that studies of high-risk groups have yielded highly variable findings, likely due to (i) the heterogeneity both within and across high-risk samples, (ii) the diversity of psychosocial stressors implicated in psychosis, and (iii) that most studies examine single markers of isolated neurobiological systems. We propose that to move the field forward, we require well-designed, largescale translational studies that integrate multi-domain, putative stress-related biomarkers to determine their prognostic value in high-risk samples. We advocate that such investigations are highly warranted, given that psychosocial stress is undoubtedly a relevant risk factor for psychotic disorders.

Exploring the relationship between frequent cannabis use, belief updating under uncertainty and psychotic-like symptoms.

Background: Cannabis users present an important group for investigating putative mechanisms underlying psychosis, as cannabis-use is associated with an increased risk of psychosis. Recent work suggests that alterations in belief-updating under uncertainty underlie psychosis. We therefore compared belief updating under uncertainty between cannabis and non-cannabis users. Methods: 49 regular cannabis users and 52 controls completed the Space Game, via an online platform used for behavioral testing. In the task, participants were asked to predict the location of the stimulus based on previous information, under different uncertainty conditions. Mixed effects models were used to identify significant predictors of mean score, confidence, performance error and learning rate. Results: Both groups showed decreased confidence in high noise conditions, and increased belief updating in more volatile conditions, suggesting that they could infer the degree and sources of uncertainty. There were no significant effects of group on any of the performance indices. However, within the cannabis group, frequent users showed worse performance than less frequent users. Conclusion: Belief updating under uncertainty is not affected by cannabis use status but could be impaired in those who use cannabis more frequently. This finding could show a similarity between frequent cannabis use and psychosis risk, as predictors for abnormal belief-updating.

Understanding psychosis complexity through a syndemic framework: A systematic review.

Psychotic conditions pose significant challenges due to their complex aetiology and impact on individuals and communities. Syndemic theory offers a promising framework to understand the interconnectedness of various health and social problems in the context of psychosis. This systematic review aims to examine existing literature on testing whether psychosis is better understood as a component of a syndemic. We conducted a systematic search of 7 databases, resulting in the inclusion of five original articles. Findings from these studies indicate a syndemic characterized by the coexistence of various health and social conditions, are associated with a greater risk of psychosis, adverse health outcomes, and disparities, especially among ethnic minorities and deprived populations. This review underscores the compelling need for a new paradigm and datasets that can investigate how psychosis emerges in the context of a syndemic, ultimately guiding more effective preventive and care interventions as well as policies to improve the health of marginalised communities living in precarity.

Physical Health and Transition to Psychosis in People at Clinical High Risk.

BACKGROUND: The clinical high risk for psychosis (CHR-P) construct represents an opportunity for prevention and early intervention in young adults, but the relationship between risk for psychosis and physical health in these patients remains unclear. METHODS: We conducted a RECORD-compliant clinical register-based cohort study, selecting the long-term cumulative risk of developing a persistent psychotic disorder as the primary outcome. We investigated associations between primary outcome and physical health data with Electronic Health Records at the South London and Maudsley (SLaM) NHS Trust, UK (January 2013-October 2020). We performed survival analyses using Kaplan-Meier curves, log-rank tests, and Cox proportional hazard models. RESULTS: The database included 137 CHR-P subjects; 21 CHR-P developed psychosis during follow-up, and the cumulative incidence of psychosis risk was 4.9% at 1 year and 56.3% at 7 years. Log-rank tests suggested that psychosis risk might change between different levels of nicotine and alcohol dependence. Kaplan-Meier curve analyses indicated that non-hazardous drinkers may have a lower psychosis risk than non-drinkers. In the Cox proportional hazard model, nicotine dependence presented a hazard ratio of 1.34 (95% CI: 1.1-1.64) (p = 0.01), indicating a 34% increase in psychosis risk for every additional point on the Fagerström Test for Nicotine Dependence. CONCLUSIONS: Our findings suggest that a comprehensive assessment of tobacco and alcohol use, diet, and physical activity in CHR-P subjects is key to understanding how physical health contributes to psychosis risk.

Cannabidiol does not attenuate acute delta-9-tetrahydrocannabinol-induced attentional bias in healthy volunteers: A randomised, double-blind, cross-over study.

AIMS: To test how attentional bias and explicit liking are influenced by delta-9-tetrahydrocannabinol (THC) and whether these effects are moderated by cannabidiol (CBD). DESIGN: Double-blind, randomised, within-subjects cross-over study. SETTING: NIHR Wellcome Trust Clinical Research Facility at King's College Hospital, London, United Kingdom. PARTICIPANTS/CASES: Forty-six infrequent cannabis users (cannabis use <1 per week). INTERVENTION(S): Across four sessions, participants inhaled vaporised cannabis containing 10 mg of THC and either 0 mg (0:1 CBD:THC), 10 mg (1:1), 20 mg (2:1) or 30 mg (3:1) of CBD, administered in a randomised order and counter-balanced across participants (a total of 24 order groups). MEASUREMENTS: Participants completed two tasks: (1) Attentional Bias (AB), comparing reaction times toward visual probes presented behind 28 target stimuli (cannabis/food) compared with probes behind corresponding non-target (neutral) stimuli. Participants responding more quickly to probes behind target than non-target stimuli would indicate greater attentional bias to cannabis/food; (2) Picture Rating (PR), where all AB stimuli were rated on a 7-point pleasantness scale, measuring explicit liking. FINDINGS: During the AB task, participants were more biased toward cannabis stimuli in the 0:1 condition compared with baseline (mean difference = 12.2, 95% confidence intervals [CIs] = 1.20-23.3, d = 0.41, P = 0.03). No other significant AB or PR differences were found between cannabis and food stimuli between baseline and 0:1 condition (P > 0.05). No significant CBD effect was found on AB or PR task performance at any dose (P > 0.05). There was additionally no cumulative effect of THC exposure on AB or PR outcomes (P > 0.05). CONCLUSIONS: A double-blind, randomised, cross-over study among infrequent cannabis users found that inhaled delta-9-tetrahydrocannabinol increased attentional bias toward cannabis in the absence of explicit liking, a marker of liability toward cannabis use disorder. At the concentrations normally found in legal and illegal cannabis, cannabidiol had no influence on this effect.

Using Electronic Health Records to Facilitate Precision Psychiatry.

The use of clinical prediction models to produce individualized risk estimates can facilitate the implementation of precision psychiatry. As a source of data from large, clinically representative patient samples, electronic health records (EHRs) provide a platform to develop and validate clinical prediction models, as well as potentially implement them in routine clinical care. The current review describes promising use cases for the application of precision psychiatry to EHR data and considers their performance in terms of discrimination (ability to separate individuals with and without the outcome) and calibration (extent to which predicted risk estimates correspond to observed outcomes), as well as their potential clinical utility (weighing benefits and costs associated with the model compared to different approaches across different assumptions of the number needed to test). We review 4 externally validated clinical prediction models designed to predict psychosis onset, psychotic relapse, cardiometabolic morbidity, and suicide risk. We then discuss the prospects for clinically implementing these models and the potential added value of integrating data from evidence syntheses, standardized psychometric assessments, and biological data into EHRs. Clinical prediction models can utilize routinely collected EHR data in an innovative way, representing a unique opportunity to inform real-world clinical decision making. Combining data from other sources (e.g., meta-analyses) or enhancing EHR data with information from research studies (clinical and biomarker data) may enhance our abilities to improve the performance of clinical prediction models.

Biomarkers for Psychosis: Are We There Yet? Umbrella Review of 1478 Biomarkers.

Background and Hypothesis: This umbrella review aims to comprehensively synthesize the evidence of association between peripheral, electrophysiological, neuroimaging, neuropathological, and other biomarkers and diagnosis of psychotic disorders. Study Design: We selected systematic reviews and meta-analyses of observational studies on diagnostic biomarkers for psychotic disorders, published until February 1, 2018. Data extraction was conducted according to the Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) guidelines. Evidence of association between biomarkers and psychotic disorders was classified as convincing, highly suggestive, suggestive, weak, or non-significant, using a standardized classification. Quality analyses used the Assessment of Multiple Systematic Reviews (AMSTAR) tool. Study Results: The umbrella review included 110 meta-analyses or systematic reviews corresponding to 3892 individual studies, 1478 biomarkers, and 392 210 participants. No factor showed a convincing level of evidence. Highly suggestive evidence was observed for transglutaminase autoantibodies levels (odds ratio [OR] = 7.32; 95% CI: 3.36, 15.94), mismatch negativity in auditory event-related potentials (standardized mean difference [SMD] = 0.73; 95% CI: 0.5, 0.96), P300 component latency (SMD = -0.6; 95% CI: -0.83, -0.38), ventricle-brain ratio (SMD = 0.61; 95% CI: 0.5, 0.71), and minor physical anomalies (SMD = 0.99; 95% CI: 0.64, 1.34). Suggestive evidence was observed for folate, malondialdehyde, brain-derived neurotrophic factor, homocysteine, P50 sensory gating (P50 S2/S1 ratio), frontal N-acetyl-aspartate, and high-frequency heart rate variability. Among the remaining biomarkers, weak evidence was found for 626 and a non-significant association for 833 factors. Conclusions: While several biomarkers present highly suggestive or suggestive evidence of association with psychotic disorders, methodological biases, and underpowered studies call for future higher-quality research.

Exploring causal mechanisms of psychosis risk.

Robust epidemiological evidence of risk and protective factors for psychosis is essential to inform preventive interventions. Previous evidence syntheses have classified these risk and protective factors according to their strength of association with psychosis. In this critical review we appraise the distinct and overlapping mechanisms of 25 key environmental risk factors for psychosis, and link these to mechanistic pathways that may contribute to neurochemical alterations hypothesised to underlie psychotic symptoms. We then discuss the implications of our findings for future research, specifically considering interactions between factors, exploring universal and subgroup-specific factors, improving understanding of temporality and risk dynamics, standardising operationalisation and measurement of risk and protective factors, and developing preventive interventions targeting risk and protective factors.

Duration of Untreated Psychosis and Outcomes in First-Episode Psychosis: Systematic Review and Meta-analysis of Early Detection and Intervention Strategies.

BACKGROUND: The role of duration of untreated psychosis (DUP) as an early detection and intervention target to improve outcomes for individuals with first-episode psychosis is unknown. STUDY DESIGN: PRISMA/MOOSE-compliant systematic review to identify studies until February 1, 2023, with an intervention and a control group, reporting DUP in both groups. Random effects meta-analysis to evaluate (1) differences in DUP in early detection/intervention services vs the control group, (2) the efficacy of early detection strategies regarding eight real-world outcomes at baseline (service entry), and (3) the efficacy of early intervention strategies on ten real-world outcomes at follow-up. We conducted quality assessment, heterogeneity, publication bias, and meta-regression analyses (PROSPERO: CRD42020163640). STUDY RESULTS: From 6229 citations, 33 intervention studies were retrieved. The intervention group achieved a small DUP reduction (Hedges' g = 0.168, 95% CI = 0.055-0.283) vs the control group. The early detection group had better functioning levels (g = 0.281, 95% CI = 0.073-0.488) at baseline. Both groups did not differ regarding total psychopathology, admission rates, quality of life, positive/negative/depressive symptoms, and employment rates (P > .05). Early interventions improved quality of life (g = 0.600, 95% CI = 0.408-0.791), employment rates (g = 0.427, 95% CI = 0.135-0.718), negative symptoms (g = 0.417, 95% CI = 0.153-0.682), relapse rates (g = 0.364, 95% CI = 0.117-0.612), admissions rates (g = 0.335, 95% CI = 0.198-0.468), total psychopathology (g = 0.298, 95% CI = 0.014-0.582), depressive symptoms (g = 0.268, 95% CI = 0.008-0.528), and functioning (g = 0.180, 95% CI = 0.065-0.295) at follow-up but not positive symptoms or remission (P > .05). CONCLUSIONS: Comparing interventions targeting DUP and control groups, the impact of early detection strategies on DUP and other correlates is limited. However, the impact of early intervention was significant regarding relevant outcomes, underscoring the importance of supporting early intervention services worldwide.

Why we need to pursue both universal and targeted prevention to reduce the incidence of affective and psychotic disorders: Systematic review and meta-analysis.

The effectiveness of universal preventive approaches in reducing the incidence of affective/psychotic disorders is unclear. We therefore aimed to synthesise the available evidence from randomised controlled trials. For studies reporting change in prevalence, we simulated all possible scenarios for the proportion of individuals with the disorder at baseline and at follow-up to exclude them. We then combined these data with studies directly measuring incidence and conducted random effects meta-analysis with relative risk (RR) to estimate the incidence in the intervention group compared to the control group. Eighteen studies (k=21 samples) were included investigating the universal prevention of depression in 66,625 individuals. No studies were available investigating universal prevention on the incidence of bipolar/psychotic disorders. 63 % of simulated scenarios showed a significant preventive effect on reducing the incidence of depression (k=9 - 19, RR=0.75-0.94, 95 %CIs=0.55-0.87,0.93-1.15, p=0.007-0.246) but did not survive sensitivity analyses. There is some limited evidence for the effectiveness of universal interventions for reducing the incidence of depression but not for bipolar/psychotic disorders.

Associations of remote mental healthcare with clinical outcomes: a natural language processing enriched electronic health record data study protocol.

INTRODUCTION: People often experience significant difficulties in receiving mental healthcare due to insufficient resources, stigma and lack of access to care. Remote care technology has the potential to overcome these barriers by reducing travel time and increasing frequency of contact with patients. However, the safe delivery of remote mental healthcare requires evidence on which aspects of care are suitable for remote delivery and which are better served by in-person care. We aim to investigate clinical and demographic associations with remote mental healthcare in a large electronic health record (EHR) dataset and the degree to which remote care is associated with differences in clinical outcomes using natural language processing (NLP) derived EHR data. METHODS AND ANALYSIS: Deidentified EHR data, derived from the South London and Maudsley (SLaM) National Health Service Foundation Trust Biomedical Research Centre (BRC) Case Register, will be extracted using the Clinical Record Interactive Search tool for all patients receiving mental healthcare between 1 January 2019 and 31 March 2022. First, data on a retrospective, longitudinal cohort of around 80 000 patients will be analysed using descriptive statistics to investigate clinical and demographic associations with remote mental healthcare and multivariable Cox regression to compare clinical outcomes of remote versus in-person assessments. Second, NLP models that have been previously developed to extract mental health symptom data will be applied to around 5 million documents to analyse the variation in content of remote versus in-person assessments. ETHICS AND DISSEMINATION: The SLaM BRC Case Register and Clinical Record Interactive Search (CRIS) tool have received ethical approval as a deidentified dataset (including NLP-derived data from unstructured free text documents) for secondary mental health research from Oxfordshire REC C (Ref: 18/SC/0372). The study has received approval from the SLaM CRIS Oversight Committee. Study findings will be disseminated through peer-reviewed, open access journal articles and service user and carer advisory groups.