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 trea… Show more
“…However, even in these cases peripheral changes in metabolite concentrations can serve as important biomarkers. For example, there is preliminary evidence that serum metabolite concentrations could be used to predict acamprosate treatment outcomes although the mechanisms of action are based on the central neurotransmitter affinity (Hinton et al, 2017;Nam et al, 2015).…”
Our aim was to analyze metabolite profile changes in serum associated with moderate-to-heavy consumption of alcohol in young adults and to evaluate whether these changes are connected to reduced brain gray matter volumes. These study population consisted of young adults with a 10-year history of moderate-to-heavy alcohol consumption (n ¼ 35) and light-drinking controls (n ¼ 27). We used the targeted liquid chromatography mass spectrometry method to measure concentrations of metabolites in serum, and 3.0 T magnetic resonance imaging to assess brain gray matter volumes. Alterations in amino acid and energy metabolism were observed in the moderate-to-heavy drinking young adults when compared to the controls. After correction for multiple testing, the group of moderate-to-heavy drinking young adults had increased serum concentrations of 1-methylhistamine (p ¼ 0.001, d ¼ 0.82) when compared to the controls. Furthermore, concentrations of 1-methylhistamine (r ¼ À0.48, p ¼ 0.004) and creatine (r ¼ À0.52, p ¼ 0.001) were negatively correlated with the brain gray matter volumes in the females. Overall, our results show association between moderate-to-heavy use of alcohol and altered metabolite profile in young adults as well as suggesting that some of these changes could be associated with the reduced brain gray matter volume.
“…However, even in these cases peripheral changes in metabolite concentrations can serve as important biomarkers. For example, there is preliminary evidence that serum metabolite concentrations could be used to predict acamprosate treatment outcomes although the mechanisms of action are based on the central neurotransmitter affinity (Hinton et al, 2017;Nam et al, 2015).…”
Our aim was to analyze metabolite profile changes in serum associated with moderate-to-heavy consumption of alcohol in young adults and to evaluate whether these changes are connected to reduced brain gray matter volumes. These study population consisted of young adults with a 10-year history of moderate-to-heavy alcohol consumption (n ¼ 35) and light-drinking controls (n ¼ 27). We used the targeted liquid chromatography mass spectrometry method to measure concentrations of metabolites in serum, and 3.0 T magnetic resonance imaging to assess brain gray matter volumes. Alterations in amino acid and energy metabolism were observed in the moderate-to-heavy drinking young adults when compared to the controls. After correction for multiple testing, the group of moderate-to-heavy drinking young adults had increased serum concentrations of 1-methylhistamine (p ¼ 0.001, d ¼ 0.82) when compared to the controls. Furthermore, concentrations of 1-methylhistamine (r ¼ À0.48, p ¼ 0.004) and creatine (r ¼ À0.52, p ¼ 0.001) were negatively correlated with the brain gray matter volumes in the females. Overall, our results show association between moderate-to-heavy use of alcohol and altered metabolite profile in young adults as well as suggesting that some of these changes could be associated with the reduced brain gray matter volume.
“…Glmnet, especially LASSO, has also been used in various metabolomics studies. For instance, this method has been used to predict the acamprosate treatment response in alcohol-dependent subjects based on metabolomics profile and clinical data 26 , and the age and sex of participants of the Karlsruhe Metabolomics and Nutrition (KarMeN) study based on their metabolomic profile 27 . To the best of our knowledge, this is the first report using elastic net on a time-series data for metabolomics analysis.…”
In this paper, the stability of the plasma metabolome at −20 °C for up to 30 days was evaluated using liquid chromatography-high resolution mass spectrometric metabolomics analysis. To follow the time-series deterioration of the plasma metabolome, the use of an elastic net regularized regression model for the prediction of storage time at −20 °C based on the plasma metabolomic profile, and the selection and ranking of metabolites with high temporal changes was demonstrated using the glmnet package in R. Out of 1229 (positive mode) and 1483 (negative mode) metabolite features, the elastic net model extracted 32 metabolites of interest in both positive and negative modes. L-gamma-glutamyl-L-(iso)leucine (tentative identification) was found to have the highest time-dependent change and significantly increased proportionally to the storage time of plasma at −20 °C (R2 = 0.6378 [positive mode], R2 = 0.7893 [negative mode], p-value < 0.00001). Based on the temporal profiles of the extracted metabolites by the model, results show only minimal deterioration of the plasma metabolome at −20 °C up to 1 month. However, majority of the changes appeared at around 12–15 days of storage. This allows scientists to better plan logistics and storage strategies for samples obtained from low-resource settings, where −80 °C storage is not guaranteed.
“…“In the case of acamprosate, one study (Nam et al, 2015) suggests that elevated baseline serum glutamate levels are associated with a positive response to acamprosate and might serve as a biomarker predicting efficacy,” he said. “Hinton et al (2017) observed that levels of six metabolites (glutamate, ammonia, 1‐methylhistidine, taurine, aspartate and threonine) differed between responders and nonresponders to acamprosate.” “Hopefully, signatures comprised of various biomarkers will one day optimize the pharmacological treatment of AUD and other disorders, but we are not there yet.” George Koob, Ph.D.…”
Section: Alcoholmentioning
confidence: 99%
“…Researchers also associated a response to acamprosate with single‐nucleotide polymorphisms (SNPs) in genes that code for neurotransmitter receptors, including a GABAA receptor subunit gene and an NMDA glutamate receptor subunit gene (Hinton et al, 2017). “Recently, Ho et al (2020) reported an association between three SNPs in a gene related to serotonin levels, TSPAN5, and longer abstinence in acamprosate‐treated patients,” he said.…”
It's no secret that addiction has a genetic component. But researchers have been focusing on ways to actually test people for propensity for addiction, for which medications might work best for them, and more. In the science of “prescription medicine,” the National Institutes of Health is working on what is sometimes called personalized medicine, sometimes called precision medicine and sometimes called personalized precision medicine in the field of addiction. This began in general medicine but has moved to include mental and substance use disorders.
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