Publication
Development of Proteomic Prediction Models for Transition to Psychotic Disorder in the Clinical High-Risk State and Psychotic Experiences in Adolescence
Publisher:
American Medical Association (AMA)
Date:
2021
DOI:
10.1001/JAMAPSYCHIATRY.2020.2459
Abstract: Biomarkers that are predictive of outcomes in in iduals at risk of psychosis would facilitate in idualized prognosis and stratification strategies. 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. 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. 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 s les in EU-GEI and plasma s les at age 12 years in ALSPAC. Support vector machine learning algorithms were used to develop predictive models. The EU-GEI subs le (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 subs le (121 participants from the general population with plasma s les 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%). In in iduals at risk of psychosis, proteomic biomarkers may contribute to in idualized prognosis and stratification strategies. These findings implicate early dysregulation of the complement and coagulation cascade in the development of psychosis outcomes.