Schizophrenia is a serious mental illness and one of the top 15 leading causes of disability worldwide. The disorder is characterized by alterations to a person’s thoughts, feelings, and behaviors, which can include a loss of contact with reality known as psychosis. These symptoms typically emerge in adolescence or early adulthood and can be persistent and disabling when left untreated, interfering with a person’s ability to engage in typical school, work, and social activities. In addition, individuals with schizophrenia often experience a delay between diagnosis and the start of treatment — ranging from 1 to 3 years. Delaying the start of treatment is often associated with poorer response and significantly worse long-term outcomes. Detection and intervention before psychosis develops, when individuals are at clinical high risk (CHR) for psychosis, could attenuate, postpone, or even prevent the transition to psychosis, and improve individuals’ clinical and functional outcomes.
Although research has developed clinical and biological measures that can identify individuals who are at increased risk for developing psychosis, these findings have not yet translated into targeted interventions. AMP SCZ aims to develop measures that further define early stages of risk and predict the likelihood of progression to psychosis and other outcomes. Such tools will enable clinical trials to test new pharmacologic interventions that may prevent the onset of psychosis.
NIMH and AMP SCZ
NIMH is currently supporting three research projects as part of the AMP SCZ initiative:
Trajectories and Predictors in the Clinical High Risk for Psychosis Population: Australian Network of Clinics and International Partners (CHR-Aus)
Barnaby Nelson, Ph.D., head of ultra-high risk for psychosis research at the Center for Youth Mental Health at the University of Melbourne and at Orygen, Melbourne, Australia, and Patrick McGorry, M.D., Ph.D., head of the Center for Youth Mental Health at the University of Melbourne and executive director of Orygen, are leading a multisite project focused on developing models that can predict a wide range of clinical outcomes in CHR individuals. As part of this project, Nelson and colleagues will collect a diverse set of biomarkers along with clinical data to develop CHR trajectory-prediction tools that can be used to facilitate the selection of CHR individuals to enroll in clinical trials and monitor disease progression and outcomes.
ProNET: Psychosis-Risk Outcomes Network
Scott Woods. M.D., professor of psychiatry at Yale University, and co-principal investigators Carrie Bearden, Ph.D., professor of psychiatry and biobehavioral sciences and psychology at the University of California, Los Angeles, and John Kane, M.D., professor and chair of psychiatry at the Donald and Barbara Zucker School of Medicine at Hofstra/Northwell, are leading a multisite project, including 26 international sites, mapping a wide range of biomarkers (including brain structure and function, psychopathology and cognition, genetics, behavior, and natural language and speech) onto a set of CHR trajectories and outcomes. Woods and colleagues will then test whether data-driven variation in these biomarkers can be used to predict individual clinical trajectories.
Psychosis Risk Evaluation, Data Integration and Computational Technologies (PREDICT): Data Processing, Analysis, and Coordination Center
Martha Shenton, Ph.D., professor of psychiatry and radiology at Harvard Medical School and Brigham and Women's Hospital, and Rene Kahn, M.D., Ph.D., chair of the Department of Psychiatry and Behavioral Health at Icahn School of Medicine at Mount Sinai, are leading a project creating a data processing, analysis, and coordination center that will integrate and analyze CHR biomarker and clinical data generated by the two multisite research networks (listed above) as well as key existing CHR-related datasets. Using these data, the researchers plan to develop algorithms that can identify biomarkers predictive of CHR outcomes — biomarkers that can then be used to identify clinically useful subtypes of CHR.
All of the data generated by the research networks will be archived and publicly available in the AMP SCZ Data Repository within the NIMH Data Archive.
AMP SCZ marks the first AMP initiative focused on a neuropsychiatric disorder and the fifth AMP initiative overall. Ongoing AMP projects are focused on improving the productivity of therapeutic development for Parkinson’s disease (PD), Alzheimer’s disease (AD), type 2 diabetes (T2D), and the autoimmune disorders rheumatoid arthritis and systemic lupus erythematosus (RA/Lupus).
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