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.

Parsing neurobiological heterogeneity of the clinical high-risk state for psychosis: A pseudo-continuous arterial spin labelling study.

Introduction: The impact of the clinical high-risk for psychosis (CHR-P) construct is dependent on accurately predicting outcomes. Individuals with brief limited intermittent psychotic symptoms (BLIPS) have higher risk of developing a first episode of psychosis (FEP) compared to individuals with attenuated psychotic symptoms (APS). Supplementing subgroup stratification with information from candidate biomarkers based on neurobiological parameters, such as resting-state, regional cerebral blood flow (rCBF), may help refine risk estimates. Based on previous evidence, we hypothesized that individuals with BLIPS would exhibit increased rCBF compared to APS in key regions linked to dopaminergic pathways. Methods: Data from four studies were combined using ComBat (to account for between-study differences) to analyse rCBF in 150 age- and sex-matched subjects (n = 30 healthy controls [HCs], n = 80 APS, n = 20 BLIPS and n = 20 FEP). Global gray matter (GM) rCBF was examined in addition to region-of-interest (ROI) analyses in bilateral/left/right frontal cortex, hippocampus and striatum. Group differences were assessed using general linear models: (i) alone; (ii) with global GM rCBF as a covariate; (iii) with global GM rCBF and smoking status as covariates. Significance was set at p < 0.05. Results: Whole-brain voxel-wise analyses and Bayesian ROI analyses were also conducted. No significant group differences were found in global [F(3,143) = 1,41, p = 0.24], bilateral frontal cortex [F(3,143) = 1.01, p = 0.39], hippocampus [F(3,143) = 0.63, p = 0.60] or striatum [F(3,143) = 0.52, p = 0.57] rCBF. Similar null findings were observed in lateralized ROIs (p > 0.05). All results were robust to addition of covariates (p > 0.05). No significant clusters were identified in whole-brain voxel-wise analyses (p > 0.05FWE). Weak-to-moderate evidence was found for an absence of rCBF differences between APS and BLIPS in Bayesian ROI analyses. Conclusion: On this evidence, APS and BLIPS are unlikely to be neurobiologically distinct. Due to this and the weak-to-moderate evidence for the null hypothesis, future research should investigate larger samples of APS and BLIPS through collaboration across large-scale international consortia.

Impact of mental disorders on clinical outcomes of physical diseases: an umbrella review assessing population attributable fraction and generalized impact fraction.

Empirical evidence indicates a significant bidirectional association between mental disorders and physical diseases, but the prospective impact of men-tal disorders on clinical outcomes of physical diseases has not been comprehensively outlined. In this PRISMA- and COSMOS-E-compliant umbrella review, we searched PubMed, PsycINFO, Embase, and Joanna Briggs Institute Database of Systematic Reviews and Implementation Reports, up to March 15, 2022, to identify systematic reviews with meta-analysis that examined the prospective association between any mental disorder and clinical outcomes of physical diseases. Primary outcomes were disease-specific mortality and all-cause mortality. Secondary outcomes were disease-specific incidence, functioning and/or disability, symptom severity, quality of life, recurrence or progression, major cardiac events, and treatment-related outcomes. Additional inclusion criteria were further applied to primary studies. Random effect models were employed, along with I2 statistic, 95% prediction intervals, small-study effects test, excess significance bias test, and risk of bias (ROBIS) assessment. Associations were classified into five credibility classes of evidence (I to IV and non-significant) according to established criteria, complemented by sensitivity and subgroup analyses to examine the robustness of the main analysis. Statistical analysis was performed using a new package for conducting umbrella reviews (https://metaumbrella.org). Population attributable fraction (PAF) and generalized impact fraction (GIF) were then calculated for class I-III associations. Forty-seven systematic reviews with meta-analysis, encompassing 251 non-overlapping primary studies and reporting 74 associations, were included (68% were at low risk of bias at the ROBIS assessment). Altogether, 43 primary outcomes (disease-specific mortality: n=17; all-cause mortality: n=26) and 31 secondary outcomes were investigated. Although 72% of associations were statistically significant (p<0.05), only two showed convincing (class I) evidence: that between depressive disorders and all-cause mortality in patients with heart failure (hazard ratio, HR=1.44, 95% CI: 1.26-1.65), and that between schizophrenia and cardiovascular mortality in patients with cardiovascular diseases (risk ratio, RR=1.54, 95% CI: 1.36-1.75). Six associations showed highly suggestive (class II) evidence: those between depressive disorders and all-cause mortality in patients with diabetes mellitus (HR=2.84, 95% CI: 2.00-4.03) and with kidney failure (HR=1.41, 95% CI: 1.31-1.51); that between depressive disorders and major cardiac events in patients with myocardial infarction (odds ratio, OR=1.52, 95% CI: 1.36-1.70); that between depressive disorders and dementia in patients with diabetes mellitus (HR=2.11, 95% CI: 1.77-2.52); that between alcohol use disorder and decompensated liver cirrhosis in patients with hepatitis C (RR=3.15, 95% CI: 2.87-3.46); and that between schizophrenia and cancer mortality in patients with cancer (standardized mean ratio, SMR=1.74, 95% CI: 1.41-2.15). Sensitivity/subgroup analyses confirmed these results. The largest PAFs were 30.56% (95% CI: 27.67-33.49) for alcohol use disorder and decompensated liver cirrhosis in patients with hepatitis C, 26.81% (95% CI: 16.61-37.67) for depressive disorders and all-cause mortality in patients with diabetes mellitus, 13.68% (95% CI: 9.87-17.58) for depressive disorders and major cardiac events in patients with myocardial infarction, 11.99% (95% CI: 8.29-15.84) for schizophrenia and cardiovascular mortality in patients with cardiovascular diseases, and 11.59% (95% CI: 9.09-14.14) for depressive disorders and all-cause mortality in patients with kidney failure. The GIFs confirmed the preventive capacity of these associations. This umbrella review demonstrates that mental disorders increase the risk of a poor clinical outcome in several physical diseases. Prevention targeting mental disorders - particularly alcohol use disorders, depressive disorders, and schizophrenia - can reduce the incidence of adverse clinical outcomes in people with physical diseases. These findings can inform clinical practice and trans-speciality preventive approaches cutting across psychiatric and somatic medicine.

Physical Health in Clinical High Risk for Psychosis Individuals: A Cross-Sectional Study.

BACKGROUND: The clinical high risk for psychosis (CHR-P) phase represents an opportunity for prevention and early intervention in young adults, which also could focus on improving physical health trajectories. METHODS: We conducted a RECORD-compliant clinical register-based cohort study. The primary outcome was to describe the physical health of assessed CHR-P individuals, obtained via Electronic Health Records at the South London and Maudsley (SLaM) NHS Foundation Trust, UK (January 2013-October 2020). RESULTS: The final database included 194 CHR-P subjects (46% female). Mean age was 23.70 ± 5.12 years. Percentage of tobacco smokers was 41% (significantly higher than in the age-matched general population [24%]). We found that 49% of subjects who consumed alcohol had an AUDIT-C (Alcohol Use Disorder Identification Test) score above 5 (hazardous drinking), with an average score of 4.94 (significantly higher than in the general population [2.75]). Investigating diet revealed low fiber intake in most subjects and high saturated fat intake in 10% of the individuals. We found that 47% of CHR-P subjects met the UK recommended physical activity guidelines (significantly lower than in the general population [66%]). Physical parameters (e.g., weight, heart rate, blood pressure) were not significantly different from the general population. CONCLUSIONS: This evidence corroborates the need for monitoring physical health parameters in CHR-P subjects, to implement tailored interventions that target daily habits.

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.

Universal and Selective Interventions to Prevent Poor Mental Health Outcomes in Young People: Systematic Review and Meta-analysis.

BACKGROUND: Much is not known about the efficacy of interventions to prevent poor mental health outcomes in young people by targeting either the general population (universal prevention) or asymptomatic individuals with high risk of developing a mental disorder (selective prevention). METHODS: We conducted a PRISMA/MOOSE-compliant systematic review and meta-analysis of Web of Science to identify studies comparing post-test efficacy (effect size [ES]; Hedges' g) of universal or selective interventions for poor mental health outcomes versus control groups, in samples with mean age <35 years (PROSPERO: CRD42018102143). Measurements included random-effects models, I2 statistics, publication bias, meta-regression, sensitivity analyses, quality assessments, number needed to treat, and population impact number. RESULTS: 295 articles (447,206 individuals; mean age = 15.4) appraising 17 poor mental health outcomes were included. Compared to control conditions, universal and selective interventions improved (in descending magnitude order) interpersonal violence, general psychological distress, alcohol use, anxiety features, affective symptoms, other emotional and behavioral problems, consequences of alcohol use, posttraumatic stress disorder features, conduct problems, tobacco use, externalizing behaviors, attention-deficit/hyperactivity disorder features, and cannabis use, but not eating-related problems, impaired functioning, internalizing behavior, or sleep-related problems. Psychoeducation had the highest effect size for ADHD features, affective symptoms, and interpersonal violence. Psychotherapy had the highest effect size for anxiety features. CONCLUSION: Universal and selective preventive interventions for young individuals are feasible and can improve poor mental health outcomes.

Transdiagnostic Risk Calculator for the Automatic Detection of Individuals at Risk and the Prediction of Psychosis: Second Replication in an Independent National Health Service Trust.

BACKGROUND: The benefits of indicated primary prevention among individuals at Clinical High Risk for Psychosis (CHR-P) are limited by the difficulty in detecting these individuals. To overcome this problem, a transdiagnostic, clinically based, individualized risk calculator has recently been developed and subjected to a first external validation in 2 different catchment areas of the South London and Maudsley (SLaM) NHS Trust. METHODS: Second external validation of real world, real-time electronic clinical register-based cohort study. All individuals who received a first ICD-10 index diagnosis of nonorganic and nonpsychotic mental disorder within the Camden and Islington (C&I) NHS Trust between 2009 and 2016 were included. The model previously validated included age, gender, ethnicity, age by gender, and ICD-10 index diagnosis to predict the development of any ICD-10 nonorganic psychosis. The model's performance was measured using Harrell's C-index. RESULTS: This study included a total of 13702 patients with an average age of 40 (range 16-99), 52% were female, and most were of white ethnicity (64%). There were no CHR-P or child/adolescent services in the C&I Trust. The C&I and SLaM Trust samples also differed significantly in terms of age, gender, ethnicity, and distribution of index diagnosis. Despite these significant differences, the original model retained an acceptable predictive performance (Harrell's C of 0.73), which is comparable to that of CHR-P tools currently recommended for clinical use. CONCLUSIONS: This risk calculator may pragmatically support an improved transdiagnostic detection of at-risk individuals and psychosis prediction even in NHS Trusts in the United Kingdom where CHR-P services are not provided.