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Schizophr Bull. 2019 Sep 11;45(5):960-965. doi: 10.1093/schbul/sbz054.

Estimating Exposome Score for Schizophrenia Using Predictive Modeling Approach in Two Independent Samples: The Results From the EUGEI Study.

Author information

1
Department of Psychiatry and Neuropsychology, School for Mental Health and Neuroscience, Maastricht University Medical Center, Maastricht, The Netherlands.
2
Department of Cognitive Neuroscience, Faculty of Psychology and Neuroscience, Maastricht University, Maastricht, The Netherlands.
3
Department of NeuroInformatics, Cuban Center for Neuroscience, Havana, Cuba.
4
Department of Psychiatry, UMC Utrecht Brain Center, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands.
5
Department of Translational Neuroscience, UMC Utrecht Brain Center, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands.
6
GGNet Mental Health, Apeldoorn, The Netherlands.
7
MRC Centre for Neuropsychiatric Genetics and Genomics, Division of Psychological Medicine and Clinical Neurosciences, School of Medicine, Cardiff University, Cardiff, UK.
8
Department of Psychiatry, Dokuz Eylul University School of Medicine, Izmir, Turkey.
9
Department of Psychiatry, Faculty of Medicine, Adnan Menderes University, Aydin, Turkey.
10
Department of Neuroscience, Health Sciences Institute, Dokuz Eylul University, Izmir, Turkey.
11
Ankara University Brain Research Center, Ankara, Turkey.
12
Department of Psychology, Middle East Technical University, Ankara, Turkey.
13
Turkish Federation of Schizophrenia Associations, Ankara, Turkey.
14
Dokuz Eylül University, Medical School, Psychiatry Department (Discharged from by statutory decree No:701 at 8th July of 2018 because of signing "Peace Petition").
15
Güven Çayyolu Healthcare Campus, Ankara, Turkey.
16
Atatürk Research and Training Hospital Psychiatry Clinic, Ankara, Turkey.
17
Faculty of Medicine, University of Belgrade, Belgrade, Serbia.
18
Clinic for Psychiatry CCS, Belgrade, Serbia.
19
Special Hospital for Psychiatric Disorders Kovin, Kovin, Serbia.
20
Barcelona Clinic Schizophrenia Unit, Neuroscience Institute, Hospital Clinic of Barcelona, University of Barcelona, Barcelona, Spain.
21
Institut d'Investigacions Biomèdiques August Pi I Sunyer, Barcelona, Spain.
22
Biomedical Research Networking Centre in Mental Health (CIBERSAM), Spain.
23
Department of Psychiatry, School of Medicine, University of Oviedo, Oviedo, Spain.
24
Instituto de Investigación Sanitaria del Principado de Asturias, Oviedo, Spain.
25
Mental Health Services of Principado de Asturias, Oviedo, Spain.
26
Department of Psychiatry, Hospital Clínico Universitario de Valencia, School of Medicine, Universidad de Valencia, Valencia, Spain.
27
Department of Psychiatry, Hospital Virgen de la Luz, Cuenca, Spain.
28
Universidad de Castilla-La Mancha, Health and Social Research Center, Cuenca, Spain.
29
Department of Psychiatry, Instituto de Investigación Sanitaria, Complejo Hospitalario Universitario de Santiago de Compostela, Santiago de Compostela, Spain.
30
Fundación Publica Galega de Medicina Xenómica, Universidad de Santiago de Compostela, Santiago de Compostela, Spain.
31
Department of Child and Adolescent Psychiatry, Hospital General Universitario Gregorio Marañón, IiSGM, School of Medicine, Universidad Complutense, Madrid, Spain.
32
Department of Psychiatry, School of Medicine, Ankara University, Ankara, Turkey.
33
Department of Psychiatry, Faculty of Medicine, Istanbul University, Istanbul, Turkey.
34
Department of Psychosis Studies, King's College London, Institute of Psychiatry, London, UK.
35
Department of Psychiatry, Yale School of Medicine, New Haven, CT.

Abstract

Exposures constitute a dense network of the environment: exposome. Here, we argue for embracing the exposome paradigm to investigate the sum of nongenetic "risk" and show how predictive modeling approaches can be used to construct an exposome score (ES; an aggregated score of exposures) for schizophrenia. The training dataset consisted of patients with schizophrenia and controls, whereas the independent validation dataset consisted of patients, their unaffected siblings, and controls. Binary exposures were cannabis use, hearing impairment, winter birth, bullying, and emotional, physical, and sexual abuse along with physical and emotional neglect. We applied logistic regression (LR), Gaussian Naive Bayes (GNB), the least absolute shrinkage and selection operator (LASSO), and Ridge penalized classification models to the training dataset. ESs, the sum of weighted exposures based on coefficients from each model, were calculated in the validation dataset. In addition, we estimated ES based on meta-analyses and a simple sum score of exposures. Accuracy, sensitivity, specificity, area under the receiver operating characteristic, and Nagelkerke's R2 were compared. The ESMeta-analyses performed the worst, whereas the sum score and the ESGNB were worse than the ESLR that performed similar to the ESLASSO and ESRIDGE. The ESLR distinguished patients from controls (odds ratio [OR] = 1.94, P < .001), patients from siblings (OR = 1.58, P < .001), and siblings from controls (OR = 1.21, P = .001). An increase in ESLR was associated with a gradient increase of schizophrenia risk. In reference to the remaining fractions, the ESLR at top 30%, 20%, and 10% of the control distribution yielded ORs of 3.72, 3.74, and 4.77, respectively. Our findings demonstrate that predictive modeling approaches can be harnessed to evaluate the exposome.

KEYWORDS:

cannabis; childhood trauma; environment; hearing impairment; machine learning; predictive modeling; psychosis; risk score; schizophrenia; winter birth

PMID:
31508804
PMCID:
PMC6737483
[Available on 2020-09-11]
DOI:
10.1093/schbul/sbz054

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