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

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) fo...

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Detalles Bibliográficos
Autores: Pries, L. K., Lage-Castellanos, A., Delespaul, P., Kenis, G., Luykx, J. J., Lin, B. D., Richards, A. L., Akdede, B., Binbay, T., Altinyazar, V., Yalinçetin, B., Gümüş-Akay, G., Cihan, B., Soygür, H., Ulaş, H., Cankurtaran, E. Ş, Kaymak, S. U., Mihaljevic, M. M., Petrovic, S. A., Mirjanic, T., Bernardo, M., Cabrera, B., Bobes, J., Saiz, P. A., García-Portilla, M. P., Sanjuan, J., Aguilar, E. J., Santos, J. L., Jiménez-López, E., Arrojo Romero, Manuel, Carracedo Álvarez, Ángel, López, G., González-Peñas, J., Parellada, M., Maric, N. P., Atbaşoğlu, C., Ucok, A., Alptekin, K., Saka, M. C., Arango, C., O'Donovan, M., Rutten, B. P. F., van Os, J., Guloksuz, S.
Tipo de recurso: artículo
Fecha de publicación:2019
País:España
Institución:Servizo Galego de Saúde (SERGAS)
Repositorio:RUNA. Repositorio da Consellería de Sanidade e Sergas
OAI Identifier:oai:runa.sergas.gal:20.500.11940/15781
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6737483/pdf/sbz054.pdf
https://www.ncbi.nlm.nih.gov/pubmed/31508804
http://hdl.handle.net/20.500.11940/15781
Access Level:acceso abierto
Palabra clave:Odds Ratio
Adult
Bayes Theorem
Hearing Loss
Child Abuse
Siblings
Area Under Curve
Logistic Models
Schizophrenia
Bullying
Humans
Young Adult
Seasons
ROC Curve
Case-Control Studies
estudios de casos y controles
cociente de probabilidades relativas
curva ROC
hermanos
área bajo la curva
adulto
esquizofrenia
modelos logísticos
acoso
maltrato infantil
pérdida auditiva
estaciones (meteorología)
adulto joven
humanos
teorema de Bayes
FPMX
CHUS
IDIS
Descripción
Sumario: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.

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