171784 20260603114408.0 doi 10.1136/bmjopen-2025-106685 sideral 149531 ART-2026-149531 eng Artés, Carmen Designing interventions guided by digital phenotype and pharmacogenetics in Spain for suicidal behaviour based on retrospective data: the multicentre SMARTomicS study protocol 2026 Introduction: Each year, suicide claims approximately 700 000 lives worldwide and generates a significant financial burden. Integrating genomic data, exposomic factors and digital phenotypes can enhance the development of short-term predictive models. Current knowledge and available tools provide the basis for designing personalised treatment strategies that incorporate real-time interventions to prevent suicide attempt recurrence cost-effectively. This study aims to develop a predictive algorithm for suicidal behaviour integrating psychiatric assessments, genetic risk markers, digital phenotypes and exposomic data. Methods and analysis: This protocol describes a retrospective multicentre study that will recruit participants with a clinical history of suicide across 25 hospitals across Spain with a catchment area of 8.6 million people (17.8% of Spain’s population). Our sample target is over 5000 participants, aged over 12 years old, ensuring 93.5% statistical power for genetic analysis. Eligible participants must be over 12 years old. Data collection will include psychiatric assessments, biospecimen collections (DNA, RNA, plasma and serum), Google Takeout data for digital phenotyping, and a standardised set of administrative and clinical data registered for each patient. Genotyping will be performed with the Axiom Spanish array (>750 000 markers), and genome-wide association studies (GWAS) will be performed after genetic imputation in a whole sample of >10 000 individuals (5000 suicide attempters; 5000 controls). Prescription and clinical history will also be retrospectively integrated, and codified data statistics forms will periodically be sent to the Government. Statistical analyses will combine traditional regression models and AI-based algorithms to identify predictive behavioural, genomic profiles, and digital markers of suicidal behaviour. Cost-effectiveness analyses of pharmacogenomic markers for antidepressant response will also be conducted. By successfully implementing this project, we aim to help reduce suicide reattempts and lessen the emotional and economic burden on families and the healthcare system. Access copy available to the general public Unrestricted info:eu-repo/grantAgreement/ES/ISCIII/PI23-00614 info:eu-repo/grantAgreement/ES/ISCIII/PMP24-00026 info:eu-repo/semantics/openAccess by-nc https://creativecommons.org/licenses/by-nc/4.0/deed.es info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion Porras-Segovia, Alejandro Ruiz-Veguilla, Miguel Giner, Lucas Garcia-Campayo, Javier Universidad de Zaragoza (orcid)0000-0002-3797-4218 López del Hoyo, Yolanda Universidad de Zaragoza (orcid)0000-0002-1690-4130 Alejandra-Saiz, Pilar García-Fernández, Ainoa Martínez-Jambrina, Juan José Villa-Díez, Rocío Gili, Margalida Roca, Miquel de Andrés, Fernando Pérez Sola, Víctor Elices, Matilde E. Grande, Iria Cebria, Ana Isabel Irigoyen-Otiñano, Maria Galván-Santiago, Leonardo Aguilar Garcia-Iturrospe, Eduardo Jesús Mesones-Peral, Jesús Mora-Marín, Rafael Sierra, Pîlar Penas-Lledo, Eva LLerena, Adrian Lopez-Castroman, Jorge Cruz, Raquel Arrojo, Manuel Ovejero, Santiago de la Torre-Luque, Alejandro Palao-Tarrero, Ángela Vaquero-Lorenzo, Concepción Toma, Claudio Barrigon, Maria Luisa Artes, Antonio Díaz-Marsá, Marina Ayad-Ahmed, Wala Ibáñez, Ángela Díaz-Sastre, Carmen Salvador-Robert, Mercè Álvarez-García, Raquel Mata-Iturralde, Laura Benavente-López, Sergio Muñoz-Lorenzo, Laura Di Stasio, Ezequiel Fernando Carmona-Camacho, Rodrigo Peñuelas-Calvo, Inmaculada Valencia, Miguel López Peña, Purificación Albarracín-García, Lucía Baca-Garcia, Enrique 4009 730 Universidad de Zaragoza Dpto. Psicología y Sociología Área Psicología Básica 1007 745 Universidad de Zaragoza Dpto. Medicina, Psiqu. y Derm. Area Psiquiatría 16 (2026), e106685 [10 pp.] BMJ Open BMJ Open 2044-6055 672048 http://zaguan.unizar.es/record/171784/files/texto_completo.pdf Versión publicada 3250446 http://zaguan.unizar.es/record/171784/files/texto_completo.jpg?subformat=icon icon Versión publicada oai:zaguan.unizar.es:171784 articulos driver 2026-06-03-11:05:19 ARTICLE