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Development of Proteomic Prediction Models for Transition to Psychotic Disorder in the Clinical High-Risk State and Psychotic Experiences in Adolescence.

ABSTRACT: Importance:Biomarkers that are predictive of outcomes in individuals at risk of psychosis would facilitate individualized prognosis and stratification strategies. Objective:To investigate whether proteomic biomarkers may aid prediction of transition to psychotic disorder in the clinical high-risk (CHR) state and adolescent psychotic experiences (PEs) in the general population. Design, Setting, and Participants:This diagnostic study comprised 2 case-control studies nested within the European Network of National Schizophrenia Networks Studying Gene-Environment Interactions (EU-GEI) and the Avon Longitudinal Study of Parents and Children (ALSPAC). EU-GEI is an international multisite prospective study of participants at CHR referred from local mental health services. ALSPAC is a United Kingdom-based general population birth cohort. Included were EU-GEI participants who met CHR criteria at baseline and ALSPAC participants who did not report PEs at age 12 years. Data were analyzed from September 2018 to April 2020. Main Outcomes and Measures:In EU-GEI, transition status was assessed by the Comprehensive Assessment of At-Risk Mental States or contact with clinical services. In ALSPAC, PEs at age 18 years were assessed using the Psychosis-Like Symptoms Interview. Proteomic data were obtained from mass spectrometry of baseline plasma samples in EU-GEI and plasma samples at age 12 years in ALSPAC. Support vector machine learning algorithms were used to develop predictive models. Results:The EU-GEI subsample (133 participants at CHR (mean [SD] age, 22.6 [4.5] years; 68 [51.1%] male) comprised 49 (36.8%) who developed psychosis and 84 (63.2%) who did not. A model based on baseline clinical and proteomic data demonstrated excellent performance for prediction of transition outcome (area under the receiver operating characteristic curve [AUC], 0.95; positive predictive value [PPV], 75.0%; and negative predictive value [NPV], 98.6%). Functional analysis of differentially expressed proteins implicated the complement and coagulation cascade. A model based on the 10 most predictive proteins accurately predicted transition status in training (AUC, 0.99; PPV, 76.9%; and NPV, 100%) and test (AUC, 0.92; PPV, 81.8%; and NPV, 96.8%) data. The ALSPAC subsample (121 participants from the general population with plasma samples available at age 12 years (61 [50.4%] male) comprised 55 participants (45.5%) with PEs at age 18 years and 61 (50.4%) without PEs at age 18 years. A model using proteomic data at age 12 years predicted PEs at age 18 years, with an AUC of 0.74 (PPV, 67.8%; and NPV, 75.8%). Conclusions and Relevance:In individuals at risk of psychosis, proteomic biomarkers may contribute to individualized prognosis and stratification strategies. These findings implicate early dysregulation of the complement and coagulation cascade in the development of psychosis outcomes.

SUBMITTER: Mongan D

PROVIDER: S-EPMC7450406 | biostudies-literature | 2020 Aug

REPOSITORIES: biostudies-literature

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Development of Proteomic Prediction Models for Transition to Psychotic Disorder in the Clinical High-Risk State and Psychotic Experiences in Adolescence.

Mongan David D Föcking Melanie M Healy Colm C Susai Subash Raj SR Heurich Meike M Wynne Kieran K Nelson Barnaby B McGorry Patrick D PD Amminger G Paul GP Nordentoft Merete M Krebs Marie-Odile MO Riecher-Rössler Anita A Bressan Rodrigo A RA Barrantes-Vidal Neus N Borgwardt Stefan S Ruhrmann Stephan S Sachs Gabriele G Pantelis Christos C van der Gaag Mark M de Haan Lieuwe L Valmaggia Lucia L Pollak Thomas A TA Kempton Matthew J MJ Rutten Bart P F BPF Whelan Robert R Cannon Mary M Zammit Stan S Cagney Gerard G Cotter David R DR McGuire Philip P

JAMA psychiatry 20210101 1

<h4>Importance</h4>Biomarkers that are predictive of outcomes in individuals at risk of psychosis would facilitate individualized prognosis and stratification strategies.<h4>Objective</h4>To investigate whether proteomic biomarkers may aid prediction of transition to psychotic disorder in the clinical high-risk (CHR) state and adolescent psychotic experiences (PEs) in the general population.<h4>Design, setting, and participants</h4>This diagnostic study comprised 2 case-control studies nested wi ...[more]

PMID: 32857162

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Project description:ImportanceUrbanicity is a well-established risk factor for clinical (eg, schizophrenia) and subclinical (eg, hearing voices and paranoia) expressions of psychosis. To our knowledge, no studies have examined the association of air pollution with adolescent psychotic experiences, despite air pollution being a major environmental problem in cities.ObjectivesTo examine the association between exposure to air pollution and adolescent psychotic experiences and test whether exposure mediates the association between urban residency and adolescent psychotic experiences.Design, setting, and participantsThe Environmental-Risk Longitudinal Twin Study is a population-based cohort study of 2232 children born during the period from January 1, 1994, through December 4, 1995, in England and Wales and followed up from birth through 18 years of age. The cohort represents the geographic and socioeconomic composition of UK households. Of the original cohort, 2066 (92.6%) participated in assessments at 18 years of age, of whom 2063 (99.9%) provided data on psychotic experiences. Generation of the pollution data was completed on October 4, 2017, and data were analyzed from May 4 to November 21, 2018.ExposuresHigh-resolution annualized estimates of exposure to 4 air pollutants-nitrogen dioxide (NO2), nitrogen oxides (NOx), and particulate matter with aerodynamic diameters of less than 2.5 (PM2.5) and less than 10 μm (PM10)-were modeled for 2012 and linked to the home addresses of the sample plus 2 commonly visited locations when the participants were 18 years old.Main outcomes and measuresAt 18 years of age, participants were privately interviewed regarding adolescent psychotic experiences. Urbanicity was estimated using 2011 census data.ResultsAmong the 2063 participants who provided data on psychotic experiences, sex was evenly distributed (52.5% female). Six hundred twenty-three participants (30.2%) had at least 1 psychotic experience from 12 to 18 years of age. Psychotic experiences were significantly more common among adolescents with the highest (top quartile) level of annual exposure to NO2 (odds ratio [OR], 1.71; 95% CI, 1.28-2.28), NOx (OR, 1.72; 95% CI, 1.30-2.29), and PM2.5 (OR, 1.45; 95% CI, 1.11-1.90). Together NO2 and NOx statistically explained 60% of the association between urbanicity and adolescent psychotic experiences. No evidence of confounding by family socioeconomic status, family psychiatric history, maternal psychosis, childhood psychotic symptoms, adolescent smoking and substance dependence, or neighborhood socioeconomic status, crime, and social conditions occurred.Conclusions and relevanceIn this study, air pollution exposure-particularly NO2 and NOx-was associated with increased odds of adolescent psychotic experiences, which partly explained the association between urban residency and adolescent psychotic experiences. Biological (eg, neuroinflammation) and psychosocial (eg, stress) mechanisms are plausible.

Project description:IMPORTANCE:The onset of psychosis is usually preceded by psychotic experiences (PE). Little is known about the etiology of PE and whether the degree of genetic and environmental influences varies across different levels of severity. A recognized challenge is to identify individuals at high risk of developing psychotic disorders prior to disease onset. OBJECTIVES:To investigate the degree of genetic and environmental influences on specific PE, assessed dimensionally, in adolescents in the community and in those who have many, frequent experiences (defined using quantitative cutoffs). We also assessed the degree of overlap in etiological influences between specific PE. DESIGN, SETTING, AND PARTICIPANTS:Structural equation model-fitting, including univariate and bivariate twin models, liability threshold models, DeFries-Fulker extremes analysis, and the Cherny method, was used to analyze a representative community sample of 5059 adolescent twin pairs (mean [SD] age, 16.31 [0.68] years) from England and Wales. MAIN OUTCOMES AND MEASURES:Psychotic experiences assessed as quantitative traits (self-rated paranoia, hallucinations, cognitive disorganization, grandiosity, and anhedonia, as well as parent-rated negative symptoms). RESULTS:Genetic influences were apparent for all PE (15%-59%), with modest shared environment for hallucinations and negative symptoms (17%-24%) and significant nonshared environment (49%-64%) for the self-rated scales and 17% for parent-rated negative symptoms. Three empirical approaches converged to suggest that the etiology in extreme-scoring groups (most extreme scoring: 5%, 10%, and 15%) did not differ significantly from that of the whole distribution. There was no linear change in heritability across the distribution of PE, with the exception of a modest increase in heritability for increasing severity of parent-rated negative symptoms. Of the PE that showed covariation, this appeared to be due to shared genetic influences (bivariate heritabilities, 0.54-0.71). CONCLUSIONS AND RELEVANCE:These findings are consistent with the concept of a psychosis continuum, suggesting that the same genetic and environmental factors influence both extreme, frequent PE and milder, less frequent manifestations in adolescents. Individual PE in adolescence, assessed quantitatively, have lower heritability estimates and higher estimates of nonshared environment than those for the liability to schizophrenia. Heritability varies by type of PE, being highest for paranoia and parent-rated negative symptoms and lowest for hallucinations.

Project description:Accumulating evidence suggests individuals with psychotic disorder show abnormalities in metabolic and inflammatory processes. Recently, several studies have employed blood-based predictors in models predicting transition to psychotic disorder in risk-enriched populations. A systematic review of the performance and methodology of prognostic models using blood-based biomarkers in the prediction of psychotic disorder from risk-enriched populations is warranted. Databases (PubMed, EMBASE and PsycINFO) were searched for eligible texts from 1998 to 15/05/2023, which detailed model development or validation studies. The checklist for Critical Appraisal and Data Extraction for Systematic Reviews of Prediction Modelling Studies (CHARMS) was used to guide data extraction from eligible texts and the Prediction Model Risk of Bias Assessment Tool (PROBAST) was used to assess the risk of bias and applicability of the studies. A narrative synthesis of the included studies was performed. Seventeen eligible studies were identified: 16 eligible model development studies and one eligible model validation study. A wide range of biomarkers were assessed, including nucleic acids, proteins, metabolites, and lipids. The range of C-index (area under the curve) estimates reported for the models was 0.67-1.00. No studies assessed model calibration. According to PROBAST criteria, all studies were at high risk of bias in the analysis domain. While a wide range of potentially predictive biomarkers were identified in the included studies, most studies did not account for overfitting in model performance estimates, no studies assessed calibration, and all models were at high risk of bias according to PROBAST criteria. External validation of the models is needed to provide more accurate estimates of their performance. Future studies which follow the latest available methodological and reporting guidelines and adopt strategies to accommodate required sample sizes for model development or validation will clarify the value of including blood-based biomarkers in models predicting psychosis.

Project description:ImportanceThere is concern about potentially causal effects of tobacco use on psychosis, but epidemiological studies have been less robust in attempts to minimize effects of confounding than studies of cannabis use have been.ObjectivesTo examine the association of patterns of cigarette and cannabis use with preceding and subsequent psychotic experiences, and to compare effects of confounding across these patterns.Design, setting, and participantsThis cohort study used data from the Avon Longitudinal Study of Parents and Children, which initially consisted of 14 062 children. Data were collected periodically from September 6, 1990, with collection ongoing, and analyzed from August 8, 2016, through June 14, 2017. Cigarette and cannabis use data were summarized using longitudinal latent class analysis to identify longitudinal classes of substance use. Associations between classes and psychotic experiences at age 18 years were assessed.ExposuresDepending on the analysis model, exposures were longitudinal classes of substance use or psychotic experiences at age 12 years.Main outcomes and measuresLogistic regression was used to examine the associations between substance use longitudinal classes and subsequent onset of psychotic experiences.ResultsLongitudinal classes were derived using 5300 participants (56.1% female) who had at least 3 measures of cigarette and cannabis use from ages 14 to 19 years. Prior to adjusting for a range of potential confounders, there was strong evdience that early-onset cigarette-only use (4.3%), early-onset cannabis use (3.2%), and late-onset cannabis use (11.9%) (but not later-onset cigarette-only use [14.8%]) latent classes were associated with increased psychotic experiences compared with nonusers (65.9%) (omnibus P < .001). After adjusting for confounders, the association for early-onset cigarette-only use attenuated substantially (unadjusted odds ratio [OR], 3.03; 95% CI, 1.13-8.14; adjusted OR, 1.78; 95% CI, 0.54-5.88), whereas those for early-onset cannabis use (adjusted OR, 3.70; 95% CI, 1.66-8.25) and late-onset cannabis use (adjusted OR, 2.97; 95% CI, 1.63-5.40) remained consistent.Conclusions and relevanceIn this study, our findings indicate that while individuals who use cannabis or cigarettes during adolescence have an increased risk of subsequent psychotic experiences, epidemiological evidence is substantively more robust for cannabis use than it is for tobacco use.

Dataset Information

Development of Proteomic Prediction Models for Transition to Psychotic Disorder in the Clinical High-Risk State and Psychotic Experiences in Adolescence.

Publications

Development of Proteomic Prediction Models for Transition to Psychotic Disorder in the Clinical High-Risk State and Psychotic Experiences in Adolescence.

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