Background Schizophrenia risk is associated with both genetic and environmental risk factors. Furthermore, cognitive abnormalities are established core characteristics of schizophrenia. We aim to assess whether a classification approach encompassing risk factors, cognition and their associations can discriminate patients (SCZ) from healthy controls (HC). We hypothesized that cognition would demonstrate greater HC-SCZ classification accuracy, and that combined gene-environment stratification would improve the discrimination performance of cognition. Methods GWAS-based genetic, environmental, and neurocognitive classifiers were trained to separate 337 HC from 103 SCZ using support vector classification and repeated nested cross-validation. We validated classifiers on independent datasets using within-diagnostic (SCZ) and cross-diagnostic (Clinically Isolated Syndrome for Multiple Sclerosis – CIS, another condition with cognitive abnormalities) approaches. Then, we tested whether gene-and-environment multivariate stratification modulated the discrimination performance of the cognitive classifier in iterative subsamples. Results The cognitive classifier discriminated SCZ from HC with a Balanced Accuracy (BAC) of 88.7%, followed by environmental (BAC= 65.1%) and genetic classifiers (BAC=55.5%). Similar classification performance was measured in the within-diagnosis validation sample (HC-SCZ BACs, cognition=70.5%; environment=65.8%; genetics=49.9%). The cognitive classifier was relatively specific to schizophrenia (HC-CIS BAC=56.7%). Combined gene-environment stratification allowed cognitive features to classify HC from SCZ with 89.4% BAC. Conclusion Consistent with cognitive deficits being core features of the phenotype of SCZ, our results suggest that cognitive features alone bear the greatest amount of information for SCZ classification. Consistent with their being risk factors, gene-environment stratification modulates HC-SCZ classification performance of cognition, perhaps providing another target for refining early identification and intervention strategies.

A pattern of cognitive deficits stratified for genetic and environmental risk reliably classifies patients with schizophrenia from healthy control subjects

Linda A. Antonucci
Investigation
;
Giulio Pergola
Conceptualization
;
Raffaella Romano
Data Curation
;
Barbara Gelao
Data Curation
;
Silvia Torretta
Data Curation
;
Antonio Rampino
Data Curation
;
Maria Trojano
Resources
;
Giuseppe Blasi
Conceptualization
;
Alessandro Bertolino
Supervision
2020

Abstract

Background Schizophrenia risk is associated with both genetic and environmental risk factors. Furthermore, cognitive abnormalities are established core characteristics of schizophrenia. We aim to assess whether a classification approach encompassing risk factors, cognition and their associations can discriminate patients (SCZ) from healthy controls (HC). We hypothesized that cognition would demonstrate greater HC-SCZ classification accuracy, and that combined gene-environment stratification would improve the discrimination performance of cognition. Methods GWAS-based genetic, environmental, and neurocognitive classifiers were trained to separate 337 HC from 103 SCZ using support vector classification and repeated nested cross-validation. We validated classifiers on independent datasets using within-diagnostic (SCZ) and cross-diagnostic (Clinically Isolated Syndrome for Multiple Sclerosis – CIS, another condition with cognitive abnormalities) approaches. Then, we tested whether gene-and-environment multivariate stratification modulated the discrimination performance of the cognitive classifier in iterative subsamples. Results The cognitive classifier discriminated SCZ from HC with a Balanced Accuracy (BAC) of 88.7%, followed by environmental (BAC= 65.1%) and genetic classifiers (BAC=55.5%). Similar classification performance was measured in the within-diagnosis validation sample (HC-SCZ BACs, cognition=70.5%; environment=65.8%; genetics=49.9%). The cognitive classifier was relatively specific to schizophrenia (HC-CIS BAC=56.7%). Combined gene-environment stratification allowed cognitive features to classify HC from SCZ with 89.4% BAC. Conclusion Consistent with cognitive deficits being core features of the phenotype of SCZ, our results suggest that cognitive features alone bear the greatest amount of information for SCZ classification. Consistent with their being risk factors, gene-environment stratification modulates HC-SCZ classification performance of cognition, perhaps providing another target for refining early identification and intervention strategies.
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11586/251655
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