Rare copy number variants in individuals at clinical high risk for psychosis: Enrichment of synaptic/brain-related functional pathways.

Schizophrenia is a complex and chronic neuropsychiatric disorder, with a heritability of around 60-80%. Large (>100 kb) rare (<1%) copy number variants (CNVs) occur more frequently in schizophrenia patients compared to controls. Currently, there are no studies reporting genome-wide CNVs in clinical high risk for psychosis (CHR-P) individuals. The aim of this study was to investigate the role of rare genome-wide CNVs in 84 CHR-P individuals and 124 presumably healthy controls. There were no significant differences in all rare CNV frequencies and sizes between CHR-P individuals and controls. However, brain-related CNVs and brain-related deletions were significantly more frequent in CHR-P individuals than controls. In CHR-P individuals, significant associations were found between brain-related CNV carriers and attenuated positive symptoms syndrome or cognitive disturbances (OR = 3.07, p = .0286). Brain-related CNV carriers experienced significantly higher negative symptoms (p = .0047), higher depressive symptoms (p = .0175), and higher disturbances of self and surroundings (p = .0029) than noncarriers. Furthermore, enrichment analysis of genes was performed in the regions of rare CNVs using three independent methods, which confirmed significant clustering of predefined genes involved in synaptic/brain-related functional pathways in CHR-P individuals. These results suggest that rare CNVs might affect synaptic/brain-related functional pathways in CHR-P individuals.

Clinical high-risk criteria of psychosis in 8-17-year-old community subjects and inpatients not suspected of developing psychosis.

BACKGROUND: In children and adolescents compared to adults, clinical high-risk of psychosis (CHR) criteria and symptoms are more prevalent but less psychosis-predictive and less clinically relevant. Based on high rates of non-converters to psychosis, especially in children and adolescents, it was suggested that CHR criteria were: (1) Pluripotential; (2) A transdiagnostic risk factor; and (3) Simply a severity marker of mental disorders rather than specifically psychosis-predictive. If any of these three alternative explanatory models were true, their prevalence should differ between persons with and without mental disorders, and their severity should be associated with functional impairment as a measure of severity. AIM: To compare the prevalence and severity of CHR criteria/symptoms in children and adolescents of the community and inpatients. METHODS: In the mainly cross-sectional examinations, 8-17-year-old community subjects (n = 233) randomly chosen from the population register of the Swiss Canton Bern, and inpatients (n = 306) with primary diagnosis of attention-deficit/hyperactivity disorder (n = 86), eating disorder (n = 97), anxiety including obsessive-compulsive disorder (n = 94), or autism spectrum disorder (n = 29), not clinically suspected to develop psychosis, were examined for CHR symptoms/criteria. Positive items of the Structured Interview for Psychosis-Risk Syndromes (SIPS) were used to assess the symptomatic ultra-high-risk criteria, and the Schizophrenia Proneness Instrument, Child and Youth version (SPI-CY) was used to assess the 14 basic symptoms relevant to basic symptom criteria. We examined group differences in frequency and severity of CHR symptoms/criteria using χ 2 tests and nonparametric tests with Cramer's V and Rosenthal's r as effect sizes, and their association with functioning using correlation analyses. RESULTS: The 7.3% prevalence rate of CHR criteria in community subjects did not differ significantly from the 9.5% rate in inpatients. Frequency and severity of CHR criteria never differed between the community and the four inpatient groups, while the frequency and severity of CHR symptoms differed only minimally. Group differences were found in only four CHR symptoms: suspiciousness/persecutory ideas of the SIPS [χ 2 (4) = 9.425; P = 0.051, Cramer's V = 0.132; and Z = -4.281, P < 0.001; Rosenthal's r = 0.184], and thought pressure [χ 2 (4) = 11.019; P = 0.026, Cramer's V = 0.143; and Z = -2.639, P = 0.008; Rosenthal's r = 0.114], derealization [χ 2 (4) = 32.380; P < 0.001, Cramer's V = 0.245; and Z = -3.924, P < 0.001; Rosenthal's r = 0.169] and visual perception disturbances [χ 2 (4) = 10.652; P = 0.031, Cramer's V = 0.141; and Z = -2.822, P = 0.005; Rosenthal's r = 0.122] of the SPI-CY. These were consistent with a transdiagnostic risk factor or dimension, i.e., displayed higher frequency and severity in inpatients, in particular in those with eating, anxiety/obsessive-compulsive and autism spectrum disorders. Low functioning, however, was at most weakly related to the severity of CHR criteria/symptoms, with the highest correlation yielded for suspiciousness/persecutory ideas (Kendall's tau = -0.172, P < 0.001). CONCLUSION: The lack of systematic differences between inpatients and community subjects does not support suggestions that CHR criteria/symptoms are pluripotential or transdiagnostic syndromes, or merely markers of symptom severity.

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.

Evaluation of the Combined Treatment Approach "Robin" (Standardized Manual and Smartphone App) for Adolescents at Clinical High Risk for Psychosis.

Introduction: The prevention of schizophrenia and other psychotic disorders has led researchers to focus on early identification of individuals at clinical high risk (CHR) for psychosis and to treat the at-risk symptoms in the pre-psychotic period. Although at-risk symptoms such as attenuated hallucinations or delusions are common in adolescents and associated with a marked reduction in global functioning, the evidence base of effective interventions for adolescents at CHR state and even first-episode psychosis is limited. Thus, the present protocol describes a study design that combines therapy modules for CHR adolescents with a smartphone application supporting the young individuals between the therapy sessions. The treatment approach "Robin" is based on existing therapy strategies for adolescents with first episode of psychosis and the available recommendations for adults with at-risk symptoms. Methods: The evaluation aims firstly to compare the efficacy of Robin in 30 CHR adolescents aged 14-18 to an active control group (treatment as usual) from a previous study. Primary outcome measures will be at-risk symptomatology, comorbid diagnosis, functioning, self-efficacy, and quality of life. For the prospective intervention condition (16 weekly individual sessions + a minimum 4 family sessions), help-seeking adolescents with CHR for psychosis, aged 14-18, will be recruited over 3 years. At-risk and comorbid symptoms, functioning, self-efficacy, and quality of life are monitored at six time points (baseline, during the treatment period; immediately after intervention; and 6, 12, and 24 months later) and compared with the respective measures of the active control group. Discussion: To the best of our knowledge, this is the first controlled trial to test the efficacy of a specific early psychosis treatment in combination with a smartphone application for adolescents at CHR for developing psychosis. The results of the study are expected to add information that may substantially decrease the burden of CHR adolescents and increase their resilience. It may offer age-adapted and targeted strategies to guide clinicians in the treatment of these vulnerable individuals. Furthermore, research in the field of early intervention will be enriched by our findings. Clinical Trial Registration: www.ClinicalTrials.gov, identifier NCT03829527.