Kenny Walter is an editor with HCPLive. Prior to joining MJH Life Sciences in 2019, he worked as a digital reporter covering nanotechnology, life sciences, material science and more with R&D Magazine. He graduated with a degree in journalism from Temple University in 2008 and began his career as a local reporter for a chain of weekly newspapers based on the Jersey shore. When not working, he enjoys going to the beach and enjoying the shore in the summer and watching North Carolina Tar Heel basketball in the winter.
A new model using proteomic data from individuals at age 12 could help predict psychotic experiences at age 18.
Predictive biomarkers in individuals at risk of psychosis could facilitate individualized prognosis and stratification strategies.
A team, led by David Mongan, MB BCh, BAO, Department of Psychiatry, Royal College of Surgeons in Ireland, investigated whether proteomic biomarkers could aid prediction of transition to psychotic disorders in the clinical high-risk (CHR) state and adolescent psychotic experiences in the general population.
The diagnostic study was comprised of a pair of case-control studies nested within the European Network of National Schizophrenia Networks Studying Gene-Environment Interactions (EU-GEI) and the Avon Longitudinal al Study of Parents and Children (ALSPAC). EU-GEI is an international multisite prospective study comprised of participants at CHR referred from local mental health services.
The ALSPAC trial is a UK-based general population birth cohort. Included in the analysis were EU-GEI participants who met clinical high risk criteria at baseline and ALSPAC participants who did not report psychotic experiences at age 12.
The investigators sought main outcomes in the EU-GEI study of the transition status assessed by the Comprehensive Assessment of At-Risk Mental States or contact with clinical services.
For the ALSPAC study, the investigators sought main outcomes of psychotic experiences at age 18, assessed using the Psychosis-Like Symptoms Interview. They also obtained proteomic data from mass spectrometry of baseline plasma samples in EU-GEI and plasma samples at age 12 in ALSPAC.
Finally, they developed predicative models using support vector machine learning algorithms.
In the EU-GEI subsample, the investigators examined 133 individuals with a mean age of 22.6 years old. This sample included 49 (36.8%) individuals who developed psychosis and 84 (63.2%) participants who did not.
The model based on baseline clinical proteomic data demonstrated excellent performance for prediction of transition outcome (area under the receive operating characteristic curve [AUC], 0.95; positive predictive value [PPV], 75.0%; negative predictive value [NPV], 100%).
In the 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.
In the ALSPAC subsample, the investigators examined 121 patients from the general population with plasma sample available at age 12, 55 (45.5%) of which suffered from psychotic experiences at age 18 and 61 (50.4%) of which who did not.
A model using proteomic data at age 12 predicted physical experiences at age 18 (AUC, 0.74; PPV, 67.8%; NPV, 75.8%).
“In individuals at risk of psychosis, proteomic biomarkers may contribute to individualized prognosis and stratification strategies,” the authors wrote. “These findings implicate early dysregulation of the complement and coagulation cascade in the development of psychosis outcomes.”
The study, “Development of Proteomic Prediction Models for Transition to Psychotic Disorder in the Clinical High-Risk State and Psychotic Experiences in Adolescence,” was published online in JAMA Psychiatry.