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

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 p...

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Bibliographic Details
Published in:Scientific reports Vol. 7; no. 1; pp. 2496 - 8
Main Authors: Hinton, David J., Vázquez, Marely Santiago, Geske, Jennifer R., Hitschfeld, Mario J., Ho, Ada M. C., Karpyak, Victor M., Biernacka, Joanna M., Choi, Doo-Sup
Format: Journal Article
Language:English
Published: London Nature Publishing Group UK 31.05.2017
Nature Publishing Group
Nature Portfolio
Subjects:
692/308/53/2423
692/53/2423
Acamprosate - administration & dosage
Adult
Alcohol Drinking - blood
Alcohol Drinking - drug therapy
Alcohol Drinking - epidemiology
Alcohol Drinking - pathology
Alcohol use
Alcoholic beverages
Alcoholism - blood
Alcoholism - drug therapy
Alcoholism - epidemiology
Alcoholism - pathology
Alcohols
Biomarkers
Biomarkers - blood
Female
Humanities and Social Sciences
Humans
Male
Metabolites
Metabolomics
Middle Aged
multidisciplinary
Precision Medicine
Prediction models
Science
Science (multidisciplinary)
Treatment Outcome
ISSN:2045-2322, 2045-2322
Online Access:Get full text
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Summary: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.
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ISSN:2045-2322
2045-2322
DOI:10.1038/s41598-017-02442-4