TY - JOUR
T1 - Estimating Exposome Score for Schizophrenia Using Predictive Modeling Approach in Two Independent Samples
T2 - The Results From the EUGEI Study
AU - Genetic Risk and OUtcome of Psychosis (GROUP) Investigators
AU - Pries, Lotta-Katrin
AU - Lage-Castellanos, Agustin
AU - Delespaul, Philippe
AU - Kenis, Gunter
AU - Luykx, Jurjen J
AU - Lin, Bochao D
AU - Richards, Alexander L
AU - Akdede, Berna
AU - Binbay, Tolga
AU - Altinyazar, Vesile
AU - Yalinçetin, Berna
AU - Gümüş-Akay, Güvem
AU - Cihan, Burçin
AU - Soygür, Haldun
AU - Ulaş, Halis
AU - Cankurtaran, Eylem Şahin
AU - Kaymak, Semra Ulusoy
AU - Mihaljevic, Marina M
AU - Petrovic, Sanja Andric
AU - Mirjanic, Tijana
AU - Bernardo, Miguel
AU - Cabrera, Bibiana
AU - Bobes, Julio
AU - Saiz, Pilar A
AU - García-Portilla, María Paz
AU - Sanjuan, Julio
AU - Aguilar, Eduardo J
AU - Santos, José Luis
AU - Jiménez-López, Estela
AU - Arrojo, Manuel
AU - Carracedo, Angel
AU - López, Gonzalo
AU - González-Peñas, Javier
AU - Parellada, Mara
AU - Maric, Nadja P
AU - Atbaşoğlu, Cem
AU - Ucok, Alp
AU - Alptekin, Köksal
AU - Saka, Meram Can
AU - Arango, Celso
AU - O'Donovan, Michael
AU - Rutten, Bart P F
AU - van Os, Jim
AU - Guloksuz, Sinan
AU - Study group members AMC, null
AU - de Haan, Lieuwe
N1 - © The Author(s) 2019. Published by Oxford University Press on behalf of the Maryland Psychiatric Research Center. All rights reserved. For permissions, please email: journals.permissions@oup.com.
PY - 2019/9/11
Y1 - 2019/9/11
N2 - 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.
AB - 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.
U2 - https://doi.org/10.1093/schbul/sbz054
DO - https://doi.org/10.1093/schbul/sbz054
M3 - Article
C2 - 31508804
SN - 0586-7614
VL - 45
SP - 960
EP - 965
JO - Schizophrenia Bulletin
JF - Schizophrenia Bulletin
IS - 5
ER -