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Development of Proteomic Prediction Models for Transition to Psychotic Disorder in the Clinical High-Risk State and Psychotic Experiences in Adolescence

dc.creatorMongan, Davides_ES
dc.creatorFöcking, Melaniees_ES
dc.creatorHealy, Colmes_ES
dc.creatorSusai Raj, Subashes_ES
dc.creatorHeurich, Meikees_ES
dc.creatorWynne, Kieranes_ES
dc.creatorNelson, Barnabyes_ES
dc.creatorMcGorry, Patrick D.es_ES
dc.creatorAmminger, G. Paules_ES
dc.creatorNordentoft, Meretees_ES
dc.creatorKrebs, Marie-Odilees_ES
dc.creatorRiecher-Rössler, Anitaes_ES
dc.creatorBressan, Rodrigo A.es_ES
dc.creatorBarrantes-Vidal, Neuses_ES
dc.creatorBorgwardt, Stefanes_ES
dc.creatorRuhrmann, Stephanes_ES
dc.creatorSachs, Gabrielees_ES
dc.creatorPantelis, Christoses_ES
dc.creatorGaag, Mark van deres_ES
dc.creatorHaan, Lieuwe dees_ES
dc.creatorValmaggia, Luciaes_ES
dc.creatorPollak, Thomas A.es_ES
dc.creatorKempton, Matthew J.es_ES
dc.creatorRutten, Bart P. F.es_ES
dc.creatorWhelan, Robertes_ES
dc.creatorCannon, Maryes_ES
dc.creatorZammit, Stanes_ES
dc.creatorCagney, Gerardes_ES
dc.creatorCotter, David R.es_ES
dc.creatorMcGuire, Philipes_ES
dc.creatorEuropean Network of National Schizophrenia Networks Studying Gene-Environment Interactions (EU-GEI) High Risk Study Groupes_ES
dc.creatorDomínguez-Martínez, Tecelli
dc.date2021
dc.date.accessioned2023-08-16T17:23:29Z
dc.date.available2023-08-16T17:23:29Z
dc.date.issued2021
dc.identifierJC03DIEP20es_ES
dc.identifier.issn2168-622X
dc.identifier.urihttp://repositorio.inprf.gob.mx/handle/123456789/7727
dc.identifier.urihttp://doi.org/10.1001/jamapsychiatry.2020.2459
dc.identifier.urihttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC7450406/
dc.descriptionImportance: Biomarkers that are predictive of outcomes in individuals at risk of psychosis would facilitate individualized prognosis and stratification strategies. Objective: To investigate whether proteomic biomarkers may aid prediction of transition to psychotic disorder in the clinical high-risk (CHR) state and adolescent psychotic experiences (PEs) in the general population. Design, setting, and participants: This diagnostic study comprised 2 case-control studies nested within the European Network of National Schizophrenia Networks Studying Gene-Environment Interactions (EU-GEI) and the Avon Longitudinal Study of Parents and Children (ALSPAC). EU-GEI is an international multisite prospective study of participants at CHR referred from local mental health services. ALSPAC is a United Kingdom-based general population birth cohort. Included were EU-GEI participants who met CHR criteria at baseline and ALSPAC participants who did not report PEs at age 12 years. Data were analyzed from September 2018 to April 2020. Main outcomes and measures: In EU-GEI, transition status was assessed by the Comprehensive Assessment of At-Risk Mental States or contact with clinical services. In ALSPAC, PEs at age 18 years were assessed using the Psychosis-Like Symptoms Interview. Proteomic data were obtained from mass spectrometry of baseline plasma samples in EU-GEI and plasma samples at age 12 years in ALSPAC. Support vector machine learning algorithms were used to develop predictive models. Results: The EU-GEI subsample (133 participants at CHR (mean [SD] age, 22.6 [4.5] years; 68 [51.1%] male) comprised 49 (36.8%) who developed psychosis and 84 (63.2%) who did not. A model based on baseline clinical and proteomic data demonstrated excellent performance for prediction of transition outcome (area under the receiver operating characteristic curve [AUC], 0.95; positive predictive value [PPV], 75.0%; and negative predictive value [NPV], 98.6%). Functional analysis of differentially expressed proteins implicated the complement and coagulation cascade. A model based on the 10 most predictive proteins accurately predicted transition status in training (AUC, 0.99; PPV, 76.9%; and NPV, 100%) and test (AUC, 0.92; PPV, 81.8%; and NPV, 96.8%) data. The ALSPAC subsample (121 participants from the general population with plasma samples available at age 12 years (61 [50.4%] male) comprised 55 participants (45.5%) with PEs at age 18 years and 61 (50.4%) without PEs at age 18 years. A model using proteomic data at age 12 years predicted PEs at age 18 years, with an AUC of 0.74 (PPV, 67.8%; and NPV, 75.8%). Conclusions and relevance: In individuals at risk of psychosis, proteomic biomarkers may contribute to individualized prognosis and stratification strategies. These findings implicate early dysregulation of the complement and coagulation cascade in the development of psychosis outcomes.es_ES
dc.formatPDFes_ES
dc.language.isoenges_ES
dc.publisherAmerican Medical Associationes_ES
dc.relation78(1):77-90
dc.rightsAcceso Cerradoes_ES
dc.titleDevelopment of Proteomic Prediction Models for Transition to Psychotic Disorder in the Clinical High-Risk State and Psychotic Experiences in Adolescencees_ES
dc.typeArtículoes_ES
dc.contributor.affiliationDepartment of Psychiatry, Royal College of Surgeons in Ireland, Dublin, Ireland.
dc.contributor.emaildrcotter@rcsi.ie (David R. Cotter)
dc.relation.jnabreviadoJAMA PSYCHIATRY
dc.relation.journalJAMA Psychiatry
dc.identifier.placeEstados Unidos
dc.date.published2021
dc.identifier.organizacionInstituto Nacional de Psiquiatría Ramón de la Fuente Muñiz
dc.identifier.eissn2168-6238
dc.identifier.doi10.1001/jamapsychiatry.2020.2459.


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