A Pattern of Cognitive Deficits Stratified for Genetic and Environmental Risk Reliably Classifies Patients With Schizophrenia From Healthy Control Subjects

Linda A. Antonucci, Giulio Pergola, Alessandro Pigoni, Dominic Dwyer, Lana Kambeitz-Ilankovic, Nora Penzel, Raffaella Romano, Barbara Gelao, Silvia Torretta, Antonio Rampino, Maria Trojano, Grazia Caforio, Peter Falkai, Giuseppe Blasi, Nikolaos Koutsouleris, Alessandro Bertolino

Research output: Contribution to journalArticle

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 with schizophrenia (SCZs) from healthy control subjects (HCs). 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: Genome-wide association study–based genetic, environmental, and neurocognitive classifiers were trained to separate 337 HCs from 103 SCZs 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, another condition with cognitive abnormalities) approaches. Then, we tested whether gene–environment multivariate stratification modulated the discrimination performance of the cognitive classifier in iterative subsamples. Results: The cognitive classifier discriminated SCZs from HCs with a balanced accuracy (BAC) of 88.7%, followed by environmental (BAC = 65.1%) and genetic (BAC = 55.5%) classifiers. 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–clinically isolated syndrome for multiple sclerosis BAC = 56.7%). Combined gene–environment stratification allowed cognitive features to classify HCs from SCZs with 89.4% BAC. Conclusions: Consistent with cognitive deficits being core features of the phenotype of SCZs, our results suggest that cognitive features alone bear the greatest amount of information for classification of SCZs. Consistent with genes and environment being risk factors, gene–environment stratification modulates HC-SCZ classification performance of cognition, perhaps providing another target for refining early identification and intervention strategies.

Original languageEnglish
Pages (from-to)697-707
Number of pages11
JournalBiological Psychiatry
Volume87
Issue number8
DOIs
Publication statusPublished - Apr 15 2020

Keywords

  • Cognition
  • Disease markers
  • Gene–environment
  • Machine learning
  • Predictive psychiatry
  • Schizophrenia

ASJC Scopus subject areas

  • Biological Psychiatry

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    Antonucci, L. A., Pergola, G., Pigoni, A., Dwyer, D., Kambeitz-Ilankovic, L., Penzel, N., Romano, R., Gelao, B., Torretta, S., Rampino, A., Trojano, M., Caforio, G., Falkai, P., Blasi, G., Koutsouleris, N., & Bertolino, A. (2020). A Pattern of Cognitive Deficits Stratified for Genetic and Environmental Risk Reliably Classifies Patients With Schizophrenia From Healthy Control Subjects. Biological Psychiatry, 87(8), 697-707. https://doi.org/10.1016/j.biopsych.2019.11.007