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Background <p>Alcohol 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.</p> Methods <p>This 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.</p> Results <p>178 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.</p> Conclusions <p>This study provides new insights into metabolic changes in AUD and demonstrates the potential of metabolomic information as diagnostic biomarkers for AUD and treatment targeting.</p>

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Plasma metabolic profiles in alcohol use disorder: diagnostic role of arginine and emotional implications of N6-acetyl-lysine and succinic acid

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年6月2日

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  • Guoxin Cao,
  • Bingqing Chen,
  • Yu Sun,
  • Jiansheng Qiao,
  • Tianhao Liu,
  • Jiawei Hou,
  • Xiaojiao Han,
  • Ying Tang,
  • Yixin Fu,
  • Jiang-Hong Ye,
  • Qingfeng Shen,
  • Rao Fu

摘要

Background

Alcohol 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.

Methods

This 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.

Results

178 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.

Conclusions

This study provides new insights into metabolic changes in AUD and demonstrates the potential of metabolomic information as diagnostic biomarkers for AUD and treatment targeting.