ArticleScientific reports2017
Metabolomics biomarkers to predict acamprosate treatment response in alcohol-dependent subjects.
Article in Scientific reports, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers.
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Who cites it
22 citing papers in PubMed.
- Biomarker for craving and acamprosate treatment response in patients with alcohol use disorder: insights from multi-omics.Molecular psychiatry · 2026Article
- Nalmefene and naltrexone reduce alcohol intake via selective efficacy in subpopulations distinguished by behavioral and blood-based biomarkers.Communications medicine · 2026Article
- Leveraging Machine Learning to Advance Alcohol Research: Current Applications, Challenges, and Opportunities.Alcohol research : current reviews · 2026Review
- Plasma metabolic profiles in alcohol use disorder: diagnostic role of arginine and emotional implications of N6-acetyl-lysine and succinic acid.BMC psychiatry · 2025Article
- Genetic liability for anxiety and treatment response to the monoamine stabilizer OSU6162 in alcohol dependence: a retrospective secondary analysis.Pharmacological reports : PR · 2025Article
- Article
- Next-generation biomarkers for alcohol consumption and alcohol use disorder diagnosis, prognosis, and treatment: A critical review.Alcohol, clinical & experimental research · 2025Review
- Molecular mechanisms involved in alcohol craving, IRF3, and endoplasmic reticulum stress: a multi-omics study.Translational psychiatry · 2024Article
- The oxytocin receptor rs2254298 polymorphism and alcohol withdrawal symptoms: a gene-environment interaction in mood disorders.Frontiers in psychiatry · 2023Article
- The Exposome and Nutritional Pharmacology and Toxicology: A New Application for Metabolomics.Exposome · 2023Article
- Genetic variants associated with acamprosate treatment response in alcohol use disorder patients: A multiple omics study.British journal of pharmacology · 2022Article
- Identification of cerebrospinal fluid and serum metabolomic biomarkers in first episode psychosis patients.Translational psychiatry · 2022Article
- RNA biomarkers for alcohol use disorder.Frontiers in molecular neuroscience · 2022Review
- Plasma TNFSF10 levels associated with acamprosate treatment response in patients with alcohol use disorder.Frontiers in pharmacology · 2022Article
- Classical blood biomarkers identify patients with higher risk for relapse 6 months after alcohol withdrawal treatment.European archives of psychiatry and clinical neuroscience · 2021Article
- Combining Metabolomics and Interpretable Machine Learning to Reveal Plasma Metabolic Profiling and Biological Correlates of Alcohol-Dependent Inpatients: What About Tryptophan Metabolism Regulation?Frontiers in molecular biosciences · 2021Article
- Towards early monitoring of chemotherapy-induced drug resistance based on single cell metabolomics: Combining single-probe mass spectrometry with machine learning.Analytica chimica acta · 2019Article
- Prediction of future gastric cancer risk using a machine learning algorithm and comprehensive medical check-up data: A case-control study.Scientific reports · 2019Article
- Statistical Workflow for Feature Selection in Human Metabolomics Data.Metabolites · 2019Review
- Possible Benefit and Validity of Supplements for Alcohol Use Disorder.Alcoholism, clinical and experimental research · 2019Review
Corrections and comments
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Authors and funding
8 authors.
Funding
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.
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