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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.

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posted on 16.03.2021, 16:40 authored by David MonganDavid Mongan, Melanie FockingMelanie Focking, Colm HealyColm Healy, Subash Raj SusaiSubash Raj Susai, Meike Heurich, Kieran Wynne, Barnaby Nelson, Patrick D McGorry, G Paul Amminger, Merete Nordentoft, Marie-Odile Krebs, Anita Riecher-Rössler, Rodrigo A Bressan, Neus Barrantes-Vidal, Stefan Borgwardt, Stephan Ruhrmann, Gabriele Sachs, Christos Pantelis, Mark Van Der Gaag, Lieuwe De Haan, Lucia Valmaggia, Thomas A Pollak, Matthew J Kempton, Bart P F Rutten, Robert Whelan, Mary CannonMary Cannon, Stan Zammit, Gerard Cagney, David CotterDavid Cotter, Philip McGuire, European Network of National Schizophrenia Networks Studying Gene-Environment Interactions (EU-GEI) High Risk Study Group

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.

Funding

Framework 7 Grant (HEALTH-F2-2010-241909) for the European Network of National Schizophrenia Networks Studying Gene-Environment Interactions (EU-GEI) study

Health Research Board Ireland through a Clinician Scientist Award

Medical Research Council Fellowship (grant MR/J008915/1)

Ministerio de Ciencia, Innovación e Universidades (grant PSI2017-87512-C2-1-R)

Generalitat de Catalunya (grant 2017SGR1612 and Catalan Institution for Research and Advanced Studies [ICREA] Academia award)

The UK Medical Research Council and the Wellcome Trust (grant 102215/2/13/2)

University of Bristol provide core support for the Avon Longitudinal Study of Parents and Children (ALSPAC)

Medical Research Council (grant G0701503/85179)

Bristol National Institute for Health Research Biomedical Research Centre

Wellcome Trust and Health Research Board Ireland (grant 203930/B/16/Z)

Health Service Executive National Doctors Training and Planning, and the Health and Social Care Research and Development Division, Northern Ireland.

History

Comments

The original article is available at https://jamanetwork.com

Published Citation

Mongan D, Föcking M, Healy C, Susai SR, Heurich M, Wynne K, Nelson B, McGorry PD, Amminger GP, Nordentoft M, Krebs MO, Riecher-Rössler A, Bressan RA, Barrantes-Vidal N, Borgwardt S, Ruhrmann S, Sachs G, Pantelis C, van der Gaag M, de Haan L, Valmaggia L, Pollak TA, Kempton MJ, Rutten BPF, Whelan R, Cannon M, Zammit S, Cagney G, Cotter DR, McGuire P; European Network of National Schizophrenia Networks Studying Gene-Environment Interactions (EU-GEI) High Risk Study Group. Development of proteomic prediction models for transition to psychotic disorder in the clinical high-risk state and psychotic experiences in adolescence. JAMA Psychiatry. 2021;78(1):77-90.

Publication Date

26 August 2020

PubMed ID

32857162

Department/Unit

  • Beaumont Hospital
  • Psychiatry

Research Area

  • Neurological and Psychiatric Disorders
  • Population Health and Health Services
  • Immunity, Infection and Inflammation

Publisher

American Medical Association (AMA)

Version

  • Published Version (Version of Record)