ArticleBMC psychiatry2025
Plasma metabolic profiles in alcohol use disorder: diagnostic role of arginine and emotional implications of N6-acetyl-lysine and succinic acid.
Article in BMC psychiatry, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed.
- Pitolisant Inhibits Alcohol Drinking and Improves Withdrawal Negative Affect Through Lateral Habenula Histaminergic Signaling in Mice.CNS neuroscience & therapeutics · 2026Article
- Chronic alcohol consumption disrupts the gut microbial and metabolic landscapes.Frontiers in microbiology · 2026Article
- Molecular Links Between Metabolism and Mental Health: Integrative Pathways from GDF15-Mediated Stress Signaling to Brain Energy Homeostasis.International journal of molecular sciences · 2025Review
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Authors and funding
12 authors.
Funding
Abstract
backgroundAlcohol Use Disorder (AUD) poses a significant global health burden, yet its metabolic underpinnings remain poorly understood. The negative affective states that emerge during withdrawal drive relapse to alcohol-seeking behavior, highlighting the need for precise diagnostic criteria.
methodsThis exploratory study utilized targeted plasma metabolomics combined with bioinformatics, machine learning, and correlation analysis to identify biomarkers associated with AUD. Plasma samples from 20 AUD patients and 19 healthy controls were analyzed by liquid chromatography-mass spectrometry targeted metabolomics. The depression and anxiety symptoms severity of the participants were assessed using the Patient Health Questionnaire-9 and Hamilton Anxiety Scale, respectively. Orthogonal partial least squares discriminant analysis model and decision tree machine learning model were used to distinguish metabolites specifically associated with AUD. The Pearson correlation method was employed to investigate the relationship between metabolite concentrations and negative affective symptoms severity in AUD group.
results178 differential metabolites across 17 super-classes, with amino acids, peptides, and analogues being the most prevalent. Notably, the cAMP signaling pathway emerged as the most strongly associated with AUD, and machine learning identified arginine as a key metabolite. Importantly, N6-acetyl-lysine showed a strong positive correlation with depression severity, while succinic acid was inversely associated with anxiety levels, suggesting that mitochondrial dysfunction and impaired energy metabolism may underlie negative affect in AUD.
conclusionsThis study provides new insights into metabolic changes in AUD and demonstrates the potential of metabolomic information as diagnostic biomarkers for AUD and treatment targeting.
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