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...
| Autores: | , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , |
|---|---|
| Tipo de recurso: | artículo |
| Estado: | Versión publicada |
| Fecha de publicación: | 2019 |
| País: | España |
| Institución: | INCLIVA |
| Repositorio: | r-INCLIVA. Repositorio Institucional de Producción Científica de INCLIVA |
| OAI Identifier: | oai:incliva.fundanetsuite.com:p15803 |
| Acceso en línea: | https://incliva.portalinvestigacion.com/publicaciones/15803 |
| Access Level: | acceso abierto |
| Palabra clave: | schizophrenia psychosis predictive modeling machine learning risk score environment childhood trauma cannabis winter birth hearing impairment |
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Estimating Exposome Score for Schizophrenia Using Predictive Modeling Approach in Two Independent Samples: The Results From the EUGEI StudyPries, LKLage-Castellanos, ADelespaul, PKenis, GLuykx, JJLin, BDRichards, ALAkdede, BBinbay, TAltinyazar, VYalincetin, BGumus-Akay, GCihan, BSoygur, HUlas, HCankurtaran, ESKaymak, SUMihaljevic, MMPetrovic, SAMirjanic, TBernardo, MCabrera, BBobes, JSaiz, PAGarcia-Portilla, MPSanjuan, JAguilar, EJSantos, JLJimenez-Lopez, EArrojo, MCarracedo, ALopez, GGonzalez-Penas, JParellada, MMaric, NPAtbasoglu, CUcok, AAlptekin, KSaka, MCArango, CO'Donovan, MRutten, BPFvan Os, JGuloksuz, SAlizadeh, BZvan Amelsvoort, TBruggeman, RCahnm, Wde Haan, Lvan Winkel, RGenetic Risk Outcome Psychosis Grpschizophreniapsychosispredictive modelingmachine learningrisk scoreenvironmentchildhood traumacannabiswinter birthhearing impairmentExposures 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 R-2 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.OXFORD UNIV PRESS2019info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttps://incliva.portalinvestigacion.com/publicaciones/15803SCHIZOPHRENIA BULLETINISSN: 05867614ISSNe: 17451701reponame:r-INCLIVA. Repositorio Institucional de Producción Científica de INCLIVAinstname:INCLIVAInglésinfo:eu-repo/semantics/openAccessoai:incliva.fundanetsuite.com:p158032026-06-07T16:35:31Z |
| dc.title.none.fl_str_mv |
Estimating Exposome Score for Schizophrenia Using Predictive Modeling Approach in Two Independent Samples: The Results From the EUGEI Study |
| title |
Estimating Exposome Score for Schizophrenia Using Predictive Modeling Approach in Two Independent Samples: The Results From the EUGEI Study |
| spellingShingle |
Estimating Exposome Score for Schizophrenia Using Predictive Modeling Approach in Two Independent Samples: The Results From the EUGEI Study Pries, LK schizophrenia psychosis predictive modeling machine learning risk score environment childhood trauma cannabis winter birth hearing impairment |
| title_short |
Estimating Exposome Score for Schizophrenia Using Predictive Modeling Approach in Two Independent Samples: The Results From the EUGEI Study |
| title_full |
Estimating Exposome Score for Schizophrenia Using Predictive Modeling Approach in Two Independent Samples: The Results From the EUGEI Study |
| title_fullStr |
Estimating Exposome Score for Schizophrenia Using Predictive Modeling Approach in Two Independent Samples: The Results From the EUGEI Study |
| title_full_unstemmed |
Estimating Exposome Score for Schizophrenia Using Predictive Modeling Approach in Two Independent Samples: The Results From the EUGEI Study |
| title_sort |
Estimating Exposome Score for Schizophrenia Using Predictive Modeling Approach in Two Independent Samples: The Results From the EUGEI Study |
| dc.creator.none.fl_str_mv |
Pries, LK Lage-Castellanos, A Delespaul, P Kenis, G Luykx, JJ Lin, BD Richards, AL Akdede, B Binbay, T Altinyazar, V Yalincetin, B Gumus-Akay, G Cihan, B Soygur, H Ulas, H Cankurtaran, ES Kaymak, SU Mihaljevic, MM Petrovic, SA Mirjanic, T Bernardo, M Cabrera, B Bobes, J Saiz, PA Garcia-Portilla, MP Sanjuan, J Aguilar, EJ Santos, JL Jimenez-Lopez, E Arrojo, M Carracedo, A Lopez, G Gonzalez-Penas, J Parellada, M Maric, NP Atbasoglu, C Ucok, A Alptekin, K Saka, MC Arango, C O'Donovan, M Rutten, BPF van Os, J Guloksuz, S Alizadeh, BZ van Amelsvoort, T Bruggeman, R Cahnm, W de Haan, L van Winkel, R Genetic Risk Outcome Psychosis Grp |
| author |
Pries, LK |
| author_facet |
Pries, LK Lage-Castellanos, A Delespaul, P Kenis, G Luykx, JJ Lin, BD Richards, AL Akdede, B Binbay, T Altinyazar, V Yalincetin, B Gumus-Akay, G Cihan, B Soygur, H Ulas, H Cankurtaran, ES Kaymak, SU Mihaljevic, MM Petrovic, SA Mirjanic, T Bernardo, M Cabrera, B Bobes, J Saiz, PA Garcia-Portilla, MP Sanjuan, J Aguilar, EJ Santos, JL Jimenez-Lopez, E Arrojo, M Carracedo, A Lopez, G Gonzalez-Penas, J Parellada, M Maric, NP Atbasoglu, C Ucok, A Alptekin, K Saka, MC Arango, C O'Donovan, M Rutten, BPF van Os, J Guloksuz, S Alizadeh, BZ van Amelsvoort, T Bruggeman, R Cahnm, W de Haan, L van Winkel, R Genetic Risk Outcome Psychosis Grp |
| author_role |
author |
| author2 |
Lage-Castellanos, A Delespaul, P Kenis, G Luykx, JJ Lin, BD Richards, AL Akdede, B Binbay, T Altinyazar, V Yalincetin, B Gumus-Akay, G Cihan, B Soygur, H Ulas, H Cankurtaran, ES Kaymak, SU Mihaljevic, MM Petrovic, SA Mirjanic, T Bernardo, M Cabrera, B Bobes, J Saiz, PA Garcia-Portilla, MP Sanjuan, J Aguilar, EJ Santos, JL Jimenez-Lopez, E Arrojo, M Carracedo, A Lopez, G Gonzalez-Penas, J Parellada, M Maric, NP Atbasoglu, C Ucok, A Alptekin, K Saka, MC Arango, C O'Donovan, M Rutten, BPF van Os, J Guloksuz, S Alizadeh, BZ van Amelsvoort, T Bruggeman, R Cahnm, W de Haan, L van Winkel, R Genetic Risk Outcome Psychosis Grp |
| author2_role |
author author author author author author author author author author author author author author author author author author author author author author author author author author author author author author author author author author author author author author author author author author author author author author author author author author |
| dc.subject.none.fl_str_mv |
schizophrenia psychosis predictive modeling machine learning risk score environment childhood trauma cannabis winter birth hearing impairment |
| topic |
schizophrenia psychosis predictive modeling machine learning risk score environment childhood trauma cannabis winter birth hearing impairment |
| description |
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 R-2 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. |
| publishDate |
2019 |
| dc.date.none.fl_str_mv |
2019 |
| dc.type.none.fl_str_mv |
info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion |
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article |
| status_str |
publishedVersion |
| dc.identifier.none.fl_str_mv |
https://incliva.portalinvestigacion.com/publicaciones/15803 |
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https://incliva.portalinvestigacion.com/publicaciones/15803 |
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Inglés |
| language_invalid_str_mv |
Inglés |
| dc.rights.none.fl_str_mv |
info:eu-repo/semantics/openAccess |
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openAccess |
| dc.publisher.none.fl_str_mv |
OXFORD UNIV PRESS |
| publisher.none.fl_str_mv |
OXFORD UNIV PRESS |
| dc.source.none.fl_str_mv |
SCHIZOPHRENIA BULLETIN ISSN: 05867614 ISSNe: 17451701 reponame:r-INCLIVA. Repositorio Institucional de Producción Científica de INCLIVA instname:INCLIVA |
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INCLIVA |
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r-INCLIVA. Repositorio Institucional de Producción Científica de INCLIVA |
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r-INCLIVA. Repositorio Institucional de Producción Científica de INCLIVA |
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1869419423567183872 |
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15,812429 |