TY - JOUR
T1 - An opportunity for primary prevention research in psychotic disorders
AU - Gershon, Elliot S.
AU - Lee, S. Hong
AU - Zhou, Xuan
AU - Sweeney, John A.
AU - Tamminga, Carol
AU - Pearlson, Godfrey A.
AU - Clementz, Brett A.
AU - Keshavan, Matcheri S.
AU - Alliey-Rodriguez, Ney
AU - Hudgens-Haney, Matthew
AU - Keedy, Sarah K.
AU - Glahn, David C.
AU - Asif, Huma
AU - Lencer, Rebekka
AU - Hill, S. Kristian
N1 - Funding Information:
Dr. Rebecca Shafee, of Harvard Medical School and the Broad Institute of MIT and Harvard, kindly supplied Polygenic Risk Scores for Schizophrenia for the data in this paper. John I. Nurnberger, Jr. of Indiana University offered comments and advice on a draft of this paper. Grant support: US National Institute of Mental Health: E.S.G. MH103368, B.A.C. MH103366, M.S.K. MH078113, G.D.P. MH077945, C.A.T. MH077851. US National Institute of Mental Health: E.S.G. MH103368, B.A.C. MH103366, M.S.K. MH078113, G.D.P. MH077945, C.A.T. MH077851. Dr Sweeney consults to VeraSci.
Publisher Copyright:
© 2021 Elsevier B.V.
PY - 2022/5
Y1 - 2022/5
N2 - An opportunity has opened for research into primary prevention of psychotic disorders, based on progress in endophenotypes, genetics, and genomics. Primary prevention requires reliable prediction of susceptibility before any symptoms are present. We studied a battery of measures where published data supports abnormalities of these measurements prior to appearance of initial psychosis symptoms. These neurobiological and behavioral measurements included cognition, eye movement tracking, Event Related Potentials, and polygenic risk scores. They generated an acceptably precise separation of healthy controls from outpatients with a psychotic disorder. Methods: The Bipolar and Schizophrenia Network on Intermediate Phenotypes (B-SNIP) measured this battery in an ancestry-diverse series of consecutively recruited adult outpatients with a psychotic disorder and healthy controls. Participants include all genders, 16 to 50 years of age, 261 with psychotic disorders (Schizophrenia (SZ) 109, Bipolar with psychosis (BPP) 92, Schizoaffective disorder (SAD) 60), 110 healthy controls. Logistic Regression, and an extension of the Linear Mixed Model to include analysis of pairwise interactions between measures (Environmental kernel Relationship Matrices (ERM)) with multiple iterations, were performed to predict case-control status. Each regression analysis was validated with four-fold cross-validation. Results and conclusions: Sensitivity, specificity, and Area Under the Curve of Receiver Operating Characteristic of 85%, 62%, and 86%, respectively, were obtained for both analytic methods. These prediction metrics demonstrate a promising diagnostic distinction based on premorbid risk variables. There were also statistically significant pairwise interactions between measures in the ERM model. The strong prediction metrics of both types of analytic model provide proof-of-principle for biologically-based laboratory tests as a first step toward primary prevention studies. Prospective studies of adolescents at elevated risk, vs. healthy adolescent controls, would be a next step toward development of primary prevention strategies.
AB - An opportunity has opened for research into primary prevention of psychotic disorders, based on progress in endophenotypes, genetics, and genomics. Primary prevention requires reliable prediction of susceptibility before any symptoms are present. We studied a battery of measures where published data supports abnormalities of these measurements prior to appearance of initial psychosis symptoms. These neurobiological and behavioral measurements included cognition, eye movement tracking, Event Related Potentials, and polygenic risk scores. They generated an acceptably precise separation of healthy controls from outpatients with a psychotic disorder. Methods: The Bipolar and Schizophrenia Network on Intermediate Phenotypes (B-SNIP) measured this battery in an ancestry-diverse series of consecutively recruited adult outpatients with a psychotic disorder and healthy controls. Participants include all genders, 16 to 50 years of age, 261 with psychotic disorders (Schizophrenia (SZ) 109, Bipolar with psychosis (BPP) 92, Schizoaffective disorder (SAD) 60), 110 healthy controls. Logistic Regression, and an extension of the Linear Mixed Model to include analysis of pairwise interactions between measures (Environmental kernel Relationship Matrices (ERM)) with multiple iterations, were performed to predict case-control status. Each regression analysis was validated with four-fold cross-validation. Results and conclusions: Sensitivity, specificity, and Area Under the Curve of Receiver Operating Characteristic of 85%, 62%, and 86%, respectively, were obtained for both analytic methods. These prediction metrics demonstrate a promising diagnostic distinction based on premorbid risk variables. There were also statistically significant pairwise interactions between measures in the ERM model. The strong prediction metrics of both types of analytic model provide proof-of-principle for biologically-based laboratory tests as a first step toward primary prevention studies. Prospective studies of adolescents at elevated risk, vs. healthy adolescent controls, would be a next step toward development of primary prevention strategies.
KW - Cognition
KW - Diagnostic tests
KW - Event-related potentials
KW - Eye movements
KW - Polygenic risk score
KW - Prediction metrics
KW - Prevention
KW - Psychosis
UR - http://www.scopus.com/inward/record.url?scp=85111531118&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85111531118&partnerID=8YFLogxK
U2 - 10.1016/j.schres.2021.07.001
DO - 10.1016/j.schres.2021.07.001
M3 - Article
C2 - 34315649
AN - SCOPUS:85111531118
SN - 0920-9964
VL - 243
SP - 433
EP - 439
JO - Schizophrenia Research
JF - Schizophrenia Research
ER -