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dc.contributor.authorPries, L. K.
dc.contributor.authorLage-Castellanos, A.
dc.contributor.authorDelespaul, P.
dc.contributor.authorKenis, G.
dc.contributor.authorLuykx, J. J.
dc.contributor.authorLin, B. D.
dc.contributor.authorRichards, A. L.
dc.contributor.authorAkdede, B.
dc.contributor.authorBinbay, T.
dc.contributor.authorAltinyazar, V.
dc.contributor.authorYalinçetin, B.
dc.contributor.authorGümüş-Akay, G.
dc.contributor.authorCihan, B.
dc.contributor.authorSoygür, H.
dc.contributor.authorUlaş, H.
dc.contributor.authorCankurtaran, E. Ş
dc.contributor.authorKaymak, S. U.
dc.contributor.authorMihaljevic, M. M.
dc.contributor.authorPetrovic, S. A.
dc.contributor.authorMirjanic, T.
dc.contributor.authorBernardo, M.
dc.contributor.authorCabrera, B.
dc.contributor.authorBobes, J.
dc.contributor.authorSaiz, P. A.
dc.contributor.authorGarcía-Portilla, M. P.
dc.contributor.authorSanjuan, J.
dc.contributor.authorAguilar, E. J.
dc.contributor.authorSantos, J. L.
dc.contributor.authorJiménez-López, E.
dc.contributor.authorArrojo Romero, Manuel 
dc.contributor.authorCarracedo Álvarez, Ángel
dc.contributor.authorLópez, G.
dc.contributor.authorGonzález-Peñas, J.
dc.contributor.authorParellada, M.
dc.contributor.authorMaric, N. P.
dc.contributor.authorAtbaşoğlu, C.
dc.contributor.authorUcok, A.
dc.contributor.authorAlptekin, K.
dc.contributor.authorSaka, M. C.
dc.contributor.authorArango, C.
dc.contributor.authorO'Donovan, M.
dc.contributor.authorRutten, B. P. F.
dc.contributor.authorvan Os, J.
dc.contributor.authorGuloksuz, S.
dc.date.accessioned2021-11-30T11:12:36Z
dc.date.available2021-11-30T11:12:36Z
dc.date.issued2019
dc.identifier.issn0586-7614
dc.identifier.otherhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC6737483/pdf/sbz054.pdfes
dc.identifier.otherhttps://www.ncbi.nlm.nih.gov/pubmed/31508804es
dc.identifier.urihttp://hdl.handle.net/20.500.11940/15781
dc.description.abstractExposures 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.en
dc.language.isoenges
dc.rightsAtribución-NoComercial 4.0 Internacional*
dc.rights.urihttp://creativecommons.org/licenses/by-nc/4.0/*
dc.subject.meshOdds Ratio *
dc.subject.meshAdult *
dc.subject.meshBayes Theorem *
dc.subject.meshHearing Loss *
dc.subject.meshChild Abuse *
dc.subject.meshSiblings *
dc.subject.meshArea Under Curve *
dc.subject.meshLogistic Models *
dc.subject.meshSchizophrenia *
dc.subject.meshBullying *
dc.subject.meshHumans *
dc.subject.meshYoung Adult *
dc.subject.meshSeasons *
dc.subject.meshROC Curve *
dc.subject.meshCase-Control Studies *
dc.titleEstimating Exposome Score for Schizophrenia Using Predictive Modeling Approach in Two Independent Samples: The Results From the EUGEI Studyen
dc.typeArtigoes
dc.contributor.authorcorpGenetic Risk and Outcome of Psychosis investigators
dc.authorsophosPries, L. K.
dc.authorsophosLage-Castellanos, A.
dc.authorsophosDelespaul, P.
dc.authorsophosKenis, G.
dc.authorsophosLuykx, J. J.
dc.authorsophosLin, B. D.
dc.authorsophosRichards, A. L.
dc.authorsophosAkdede, B.
dc.authorsophosBinbay, T.
dc.authorsophosAltinyazar, V.
dc.authorsophosYalinçetin, B.
dc.authorsophosGümüş-Akay, G.
dc.authorsophosCihan, B.
dc.authorsophosSoygür, H.
dc.authorsophosUlaş, H.
dc.authorsophosCankurtaran, E. Ş
dc.authorsophosKaymak, S. U.
dc.authorsophosMihaljevic, M. M.
dc.authorsophosPetrovic, S. A.
dc.authorsophosMirjanic, T.
dc.authorsophosBernardo, M.
dc.authorsophosCabrera, B.
dc.authorsophosBobes, J.
dc.authorsophosSaiz, P. A.
dc.authorsophosGarcía-Portilla, M. P.
dc.authorsophosSanjuan, J.
dc.authorsophosAguilar, E. J.
dc.authorsophosSantos, J. L.
dc.authorsophosJiménez-López, E.
dc.authorsophosArrojo, M.
dc.authorsophosCarracedo, A.
dc.authorsophosLópez, G.
dc.authorsophosGonzález-Peñas, J.
dc.authorsophosParellada, M.
dc.authorsophosMaric, N. P.
dc.authorsophosAtbaşoğlu, C.
dc.authorsophosUcok, A.
dc.authorsophosAlptekin, K.
dc.authorsophosSaka, M. C.
dc.authorsophosArango, C.
dc.authorsophosO'Donovan, M.
dc.authorsophosRutten, B. P. F.
dc.authorsophosvan Os, J.
dc.authorsophosGuloksuz, S.
dc.authorsophosGenetic Risk and Outcome of Psychosis, investigators
dc.identifier.doi10.1093/schbul/sbz054
dc.identifier.pmid31508804
dc.identifier.sophos31814
dc.issue.number5es
dc.journal.titleSCHIZOPHRENIA BULLETINes
dc.organizationServizo Galego de Saúde::Dirección Xeral de Asistencia Sanitaria::Fundación Pública Galega de Medicina Xenómicaes
dc.organizationServizo Galego de Saúde::Estrutura de Xestión Integrada (EOXI)::EOXI de Santiago de Compostela - Complexo Hospitalario Universitario de Santiago de Compostela::Psiquiatríaes
dc.organizationServizo Galego de Saúde::Estrutura de Xestión Integrada (EOXI)::Instituto de Investigación Sanitaria de Santiago de Compostela (IDIS)es
dc.page.initial960es
dc.page.final965es
dc.rights.accessRightsopenAccesses
dc.subject.decsestudios de casos y controles *
dc.subject.decscociente de probabilidades relativas *
dc.subject.decscurva ROC *
dc.subject.decshermanos *
dc.subject.decsárea bajo la curva *
dc.subject.decsadulto *
dc.subject.decsesquizofrenia *
dc.subject.decsmodelos logísticos *
dc.subject.decsacoso *
dc.subject.decsmaltrato infantil *
dc.subject.decspérdida auditiva *
dc.subject.decsestaciones (meteorología) *
dc.subject.decsadulto joven *
dc.subject.decshumanos *
dc.subject.decsteorema de Bayes *
dc.subject.keywordFPMXes
dc.subject.keywordCHUSes
dc.subject.keywordIDISes
dc.typefidesArtículo Originales
dc.typesophosArtículo Originales
dc.volume.number45es


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Atribución-NoComercial 4.0 Internacional
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