Metabolomics biomarkers to predict acamprosate treatment response in alcohol-dependent subjects

Abstract Precision medicine for alcohol use disorder (AUD) allows optimal treatment of the right patient with the right drug at the right time. Here, we generated multivariable models incorporating clinical information and serum metabolite levels to predict acamprosate treatment response. The sample...

Descripción completa

Guardado en:

Detalles Bibliográficos
Autores principales:	David J. Hinton, Marely Santiago Vázquez, Jennifer R. Geske, Mario J. Hitschfeld, Ada M. C. Ho, Victor M. Karpyak, Joanna M. Biernacka, Doo-Sup Choi
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2017
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/d17a8ba0a08e49ca9953eacbb578a883
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

id	oai:doaj.org-article:d17a8ba0a08e49ca9953eacbb578a883
record_format	dspace
spelling	oai:doaj.org-article:d17a8ba0a08e49ca9953eacbb578a8832021-12-02T12:32:44ZMetabolomics biomarkers to predict acamprosate treatment response in alcohol-dependent subjects10.1038/s41598-017-02442-42045-2322https://doaj.org/article/d17a8ba0a08e49ca9953eacbb578a8832017-05-01T00:00:00Zhttps://doi.org/10.1038/s41598-017-02442-4https://doaj.org/toc/2045-2322Abstract Precision medicine for alcohol use disorder (AUD) allows optimal treatment of the right patient with the right drug at the right time. Here, we generated multivariable models incorporating clinical information and serum metabolite levels to predict acamprosate treatment response. The sample of 120 patients was randomly split into a training set (n = 80) and test set (n = 40) five independent times. Treatment response was defined as complete abstinence (no alcohol consumption during 3 months of acamprosate treatment) while nonresponse was defined as any alcohol consumption during this period. In each of the five training sets, we built a predictive model using a least absolute shrinkage and section operator (LASSO) penalized selection method and then evaluated the predictive performance of each model in the corresponding test set. The models predicted acamprosate treatment response with a mean sensitivity and specificity in the test sets of 0.83 and 0.31, respectively, suggesting our model performed well at predicting responders, but not non-responders (i.e. many non-responders were predicted to respond). Studies with larger sample sizes and additional biomarkers will expand the clinical utility of predictive algorithms for pharmaceutical response in AUD.David J. HintonMarely Santiago VázquezJennifer R. GeskeMario J. HitschfeldAda M. C. HoVictor M. KarpyakJoanna M. BiernackaDoo-Sup ChoiNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 7, Iss 1, Pp 1-8 (2017)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q David J. Hinton Marely Santiago Vázquez Jennifer R. Geske Mario J. Hitschfeld Ada M. C. Ho Victor M. Karpyak Joanna M. Biernacka Doo-Sup Choi Metabolomics biomarkers to predict acamprosate treatment response in alcohol-dependent subjects
description	Abstract Precision medicine for alcohol use disorder (AUD) allows optimal treatment of the right patient with the right drug at the right time. Here, we generated multivariable models incorporating clinical information and serum metabolite levels to predict acamprosate treatment response. The sample of 120 patients was randomly split into a training set (n = 80) and test set (n = 40) five independent times. Treatment response was defined as complete abstinence (no alcohol consumption during 3 months of acamprosate treatment) while nonresponse was defined as any alcohol consumption during this period. In each of the five training sets, we built a predictive model using a least absolute shrinkage and section operator (LASSO) penalized selection method and then evaluated the predictive performance of each model in the corresponding test set. The models predicted acamprosate treatment response with a mean sensitivity and specificity in the test sets of 0.83 and 0.31, respectively, suggesting our model performed well at predicting responders, but not non-responders (i.e. many non-responders were predicted to respond). Studies with larger sample sizes and additional biomarkers will expand the clinical utility of predictive algorithms for pharmaceutical response in AUD.
format	article
author	David J. Hinton Marely Santiago Vázquez Jennifer R. Geske Mario J. Hitschfeld Ada M. C. Ho Victor M. Karpyak Joanna M. Biernacka Doo-Sup Choi
author_facet	David J. Hinton Marely Santiago Vázquez Jennifer R. Geske Mario J. Hitschfeld Ada M. C. Ho Victor M. Karpyak Joanna M. Biernacka Doo-Sup Choi
author_sort	David J. Hinton
title	Metabolomics biomarkers to predict acamprosate treatment response in alcohol-dependent subjects
title_short	Metabolomics biomarkers to predict acamprosate treatment response in alcohol-dependent subjects
title_full	Metabolomics biomarkers to predict acamprosate treatment response in alcohol-dependent subjects
title_fullStr	Metabolomics biomarkers to predict acamprosate treatment response in alcohol-dependent subjects
title_full_unstemmed	Metabolomics biomarkers to predict acamprosate treatment response in alcohol-dependent subjects
title_sort	metabolomics biomarkers to predict acamprosate treatment response in alcohol-dependent subjects
publisher	Nature Portfolio
publishDate	2017
url	https://doaj.org/article/d17a8ba0a08e49ca9953eacbb578a883
work_keys_str_mv	AT davidjhinton metabolomicsbiomarkerstopredictacamprosatetreatmentresponseinalcoholdependentsubjects AT marelysantiagovazquez metabolomicsbiomarkerstopredictacamprosatetreatmentresponseinalcoholdependentsubjects AT jenniferrgeske metabolomicsbiomarkerstopredictacamprosatetreatmentresponseinalcoholdependentsubjects AT mariojhitschfeld metabolomicsbiomarkerstopredictacamprosatetreatmentresponseinalcoholdependentsubjects AT adamcho metabolomicsbiomarkerstopredictacamprosatetreatmentresponseinalcoholdependentsubjects AT victormkarpyak metabolomicsbiomarkerstopredictacamprosatetreatmentresponseinalcoholdependentsubjects AT joannambiernacka metabolomicsbiomarkerstopredictacamprosatetreatmentresponseinalcoholdependentsubjects AT doosupchoi metabolomicsbiomarkerstopredictacamprosatetreatmentresponseinalcoholdependentsubjects
_version_	1718394007750443008

Metabolomics biomarkers to predict acamprosate treatment response in alcohol-dependent subjects

Ejemplares similares