Linda A Antonucci1, Giulio Pergola2, Alessandro Pigoni3, Dominic Dwyer4, Lana Kambeitz-Ilankovic4, Nora Penzel4, Raffaella Romano5, Barbara Gelao5, Silvia Torretta5, Antonio Rampino6, Maria Trojano6, Grazia Caforio6, Peter Falkai4, Giuseppe Blasi6, Nikolaos Koutsouleris4, Alessandro Bertolino7. 1. Department of Psychiatry and Psychotherapy, Ludwig-Maximilian-University, Munich, Germany; Department of Basic Medical Sciences, Neuroscience and Sense Organs, University of Bari Aldo Moro, Bari, Italy; Department of Education, Psychology and Communication, University of Bari Aldo Moro, Bari, Italy. Electronic address: linda.antonucci@med.uni-muenchen.de. 2. Department of Basic Medical Sciences, Neuroscience and Sense Organs, University of Bari Aldo Moro, Bari, Italy; Lieber Institute for Brain Development, Johns Hopkins Medical Campus, Baltimore, Maryland. 3. Department of Psychiatry and Psychotherapy, Ludwig-Maximilian-University, Munich, Germany; Department of Neurosciences and Mental Health, Fondazione IRCCS Ca' Granda Ospedale Maggiore Policlinico, University of Milan, Milan, Italy. 4. Department of Psychiatry and Psychotherapy, Ludwig-Maximilian-University, Munich, Germany. 5. Department of Basic Medical Sciences, Neuroscience and Sense Organs, University of Bari Aldo Moro, Bari, Italy. 6. Department of Basic Medical Sciences, Neuroscience and Sense Organs, University of Bari Aldo Moro, Bari, Italy; Bari University Hospital, Bari, Italy. 7. Department of Basic Medical Sciences, Neuroscience and Sense Organs, University of Bari Aldo Moro, Bari, Italy; Bari University Hospital, Bari, Italy. Electronic address: alessandro.bertolino@uniba.it.
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.
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.
Authors: Nora Penzel; Linda A Antonucci; Linda T Betz; Rachele Sanfelici; Johanna Weiske; Oliver Pogarell; Paul Cumming; Boris B Quednow; Oliver Howes; Peter Falkai; Rachel Upthegrove; Alessandro Bertolino; Stefan Borgwardt; Paolo Brambilla; Rebekka Lencer; Eva Meisenzahl; Marlene Rosen; Theresa Haidl; Lana Kambeitz-Ilankovic; Stephan Ruhrmann; Raimo R K Salokangas; Christos Pantelis; Stephen J Wood; Nikolaos Koutsouleris; Joseph Kambeitz Journal: Neuropsychopharmacology Date: 2021-03-03 Impact factor: 7.853
Authors: Linda A Antonucci; Alessandro Taurino; Domenico Laera; Paolo Taurisano; Jolanda Losole; Sara Lutricuso; Chiara Abbatantuono; Mariateresa Giglio; Maria Fara De Caro; Giustino Varrassi; Filomena Puntillo Journal: Pain Ther Date: 2020-09-03
Authors: Linda A Antonucci; Alessandra Raio; Giulio Pergola; Barbara Gelao; Marco Papalino; Antonio Rampino; Ileana Andriola; Giuseppe Blasi; Alessandro Bertolino Journal: BMC Psychol Date: 2021-03-23
Authors: Clarissa Ferrari; Giovanni de Girolamo; Laura Iozzino; Philip D Harvey; Nicola Canessa; Pawel Gosek; Janusz Heitzman; Ambra Macis; Marco Picchioni; Hans Joachim Salize; Johannes Wancata; Marlene Koch Journal: Transl Psychiatry Date: 2021-12-08 Impact factor: 6.222
Authors: Nora Penzel; Rachele Sanfelici; Linda A Antonucci; Linda T Betz; Dominic Dwyer; Anne Ruef; Kang Ik K Cho; Paul Cumming; Oliver Pogarell; Oliver Howes; Peter Falkai; Rachel Upthegrove; Stefan Borgwardt; Paolo Brambilla; Rebekka Lencer; Eva Meisenzahl; Frauke Schultze-Lutter; Marlene Rosen; Theresa Lichtenstein; Lana Kambeitz-Ilankovic; Stephan Ruhrmann; Raimo K R Salokangas; Christos Pantelis; Stephen J Wood; Boris B Quednow; Giulio Pergola; Alessandro Bertolino; Nikolaos Koutsouleris; Joseph Kambeitz Journal: Schizophrenia (Heidelb) Date: 2022-03-09
Authors: Antonio Rampino; Rosa M Falcone; Arianna Giannuzzi; Rita Masellis; Linda A Antonucci; Silvia Torretta Journal: Clin Pract Epidemiol Ment Health Date: 2021-05-24