Investigates metabolic and microbiome changes in methamphetamine users with alcohol and tobacco use, suggesting new intervention targets.
Background: The oral microbiome comprises the microbial communities inhabiting the oral cavity, whereas the oral metabolome reflects the small molecules generated by host and microbial metabolic activity. These systems may provide insight into substance-related physiological disruption, including altered inflammation, immune signaling, and host – microbial interactions. Although the individual effects of methamphetamine, alcohol, and smoking have been explored, their combined impact on these systems remains largely unexplored.Objectives: To investigate the metabolic and microbiome alterations associated with chronic methamphetamine use in individuals with alcohol and tobacco use.Methods: High-throughput metabolomic and microbiome datasets from methamphetamine users (Males:168, Females: 50), stratified by self-reported tobacco smoking and alcohol use, were analyzed using integrative bioinformatics approaches, including multivariate and pathway enrichment analyses, to identify dysregulated metabolic pathways and microbial alterations across defined subgroups.Results: The study revealed significant upregulation in metabolites like prostaglandin E2 (log-2-fold-change: 2.63, Cohen’s D: |~0.881|, p-val: 7.1 × 10−10) and glutamylisoleucine (log-2-fold-change: 1.42, Cohen’s D: |~0.88|, p-val: 2.5 × 10−2). Microbes such as Bacteroides (log-2-fold-change: −4.91, Cohen’s D: |~1.95|, p-val: 1.3 × 10−4) and Brachymonas (log-2-fold-change: −2.47, Cohen’s D: |~1.09|, p-val: 5.8 × 10−3) were significantly downregulated. This suggests that long-term concurrent methamphetamine use, alcohol consumption, and smoking are associated with alterations in microbial and metabolic pathways related to oxidative stress, glutathione metabolism, and neuroactive signaling.Conclusions: The oral microbiome and metabolomic profiles may serve as accessible indicators of substance-related biological disruption. They may also help identify clinically relevant targets for monitoring risk, guiding personalized interventions, and developing informed strategies to support recovery.
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Tandon et al. (2026) studied this question.
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