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https://hdl.handle.net/10495/23361
Título : | ADGRL3 (LPHN3) Variants Predict Substance Use Disorder |
Autor : | Arcos Burgos, Oscar Mauricio Vélez Valbuena, Jorge Iván Martínez, F. Ribasés, Marta Ramos Quiroga, Josep A. Sánchez Mora, Cristina Richarte, Vanesa Roncero, Carlos Cormand, Bru Fernández Castillo, Noelia Casas, Miguel Lopera Restrepo, Francisco Javier Pineda Salazar, David Antonio Palacio Ortiz, Juan David Acosta López, Johan Cervantes Henríquez, Martha Lucía Sánchez Rojas, Manuel Puentes Rozo, Pedro Molina, Brooke Boden, Margaret T. Wallis, Deeann Lidbury, Brett Newman, Saul Easteal, Simon Swanson, James Patel, Hardip Volkow, Nora Acosta, María T. Castellanos, Francisco X. De León, Jose Mastronardi, Claudio Alberto Muenke, Maximilian |
metadata.dc.subject.*: | Public Health Salud Pública Health Policy Política de Salud Psychiatry Psiquiatría Neurosciences Neurociencias Translational Medical Research Investigación en Medicina Traslacional |
Fecha de publicación : | 2019 |
Editorial : | Nature Pub. Grupo |
Citación : | Arcos Burgos M., Vélez JI., Martinez AF. et al. ADGRL3 (LPHN3) variants predict substance use disorder. Transl Psychiatry 9, 42 (2019). https://doi.org/10.1038/s41398-019-0396-7 |
Resumen : | ABSTRACT: Genetic factors are strongly implicated in the susceptibility to develop externalizing syndromes such as attentiondeficit/hyperactivity disorder (ADHD), oppositional defiant disorder, conduct disorder, and substance use disorder (SUD). Variants in the ADGRL3 (LPHN3) gene predispose to ADHD and predict ADHD severity, disruptive behaviors comorbidity, long-term outcome, and response to treatment. In this study, we investigated whether variants within ADGRL3 are associated with SUD, a disorder that is frequently comorbid with ADHD. Using family-based, case-control, and longitudinal samples from disparate regions of the world (n = 2698), recruited either for clinical, genetic epidemiological or pharmacogenomic studies of ADHD, we assembled recursive-partitioning frameworks (classification tree analyses) with clinical, demographic, and ADGRL3 genetic information to predict SUD susceptibility. Our results indicate that SUD can be efficiently and robustly predicted in ADHD participants. The genetic models used remained highly efficient in predicting SUD in a large sample of individuals with severe SUD from a psychiatric institution that were not ascertained on the basis of ADHD diagnosis, thus identifying ADGRL3 as a risk gene for SUD. Recursive-partitioning analyses revealed that rs4860437 was the predominant predictive variant. This new methodological approach offers novel insights into higher order predictive interactions and offers a unique opportunity for translational application in the clinical assessment of patients at high risk for SUD. |
metadata.dc.identifier.eissn: | 2158-3188 |
metadata.dc.identifier.doi: | 10.1038/s41398-019-0396-7 |
Aparece en las colecciones: | Artículos de Revista en Ciencias Médicas |
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Fichero | Descripción | Tamaño | Formato | |
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ArcosMauricio_2019_ADGRL3LPHN3SubstanceDisorder.pdf | Artículo de investigación | 2.35 MB | Adobe PDF | Visualizar/Abrir |
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