The gut microbiota as a potential biomarker for methamphetamine use disorder: evidence from two independent datasets

Abstract

BackgroundMethamphetamine use disorder (MUD) poses a considerable public health threat, and its identification remains challenging due to the subjective nature of the current diagnostic system that relies on self-reported symptoms. Recent studies have suggested that MUD patients may have gut dysbiosis and that gut microbes may be involved in the pathological process of MUD. We aimed to examine gut dysbiosis among MUD patients and generate a machine-learning model utilizing gut microbiota features to facilitate the identification of MUD patients.MethodFecal samples from 78 MUD patients and 50 sex- and age-matched healthy controls (HCs) were analyzed by 16S rDNA sequencing to identify gut microbial characteristics that could help differentiate MUD patients from HCs. Based on these microbial features, we developed a machine learning model to help identify MUD patients. We also used public data to verify the model; these data were downloaded from a published study conducted in Wuhan, China (with 16 MUD patients and 14 HCs). Furthermore, we explored the gut microbial features of MUD patients within the first three months of withdrawal to identify the withdrawal period of MUD patients based on microbial features.ResultsMUD patients exhibited significant gut dysbiosis, including decreased richness and evenness and changes in the abundance of certain microbes, such as Proteobacteria and Firmicutes. Based on the gut microbiota features of MUD patients, we developed a machine learning model that demonstrated exceptional performance with an AUROC of 0.906 for identifying MUD patients. Additionally, when tested using an external and cross-regional dataset, the model achieved an AUROC of 0.830. Moreover, MUD patients within the first three months of withdrawal exhibited specific gut microbiota features, such as the significant enrichment of Actinobacteria. The machine learning model had an AUROC of 0.930 for identifying the withdrawal period of MUD patients.ConclusionIn conclusion, the gut microbiota is a promising biomarker for identifying MUD and thus represents a potential approach to improving the identification of MUD patients. Future longitudinal studies are needed to validate these findings.

Artificial intelligence-driven microbiome data analysis for estimation of postmortem interval and crime location

“…Deep learning models, such as CNNs, allow researchers to identify specific biomarkers from microbial data. This could include microbial communities or gene expression patterns linked to specific toxin exposures (Liu et al, 2023). Microorganism that is stably expressed in different postmortem organs can be used as a biomarker (Javan et al, 2016b), and further, a series of dynamically changing biomarkers can be combined to accurately predict PMI.…”

Associations between impulsivity and fecal microbiota in individuals abstaining from methamphetamine

“…Both compositional and structural alterations of gut microbiota were observed in MA abusers, 26,27 and the alterations were related to psychiatric symptoms and cognitive dysfunctions 27 . Additionally, intestinal microbiota was also suggested to potentially differentiate MA abusers from healthy group 28 . In animal studies, MA exposure was shown to induce gut microbiota dysbiosis, intestinal barrier impairment, neuroinflammation elevation, and behavioral abnormalities 29,30 .…”

“… 27 Additionally, intestinal microbiota was also suggested to potentially differentiate MA abusers from healthy group. 28 In animal studies, MA exposure was shown to induce gut microbiota dysbiosis, intestinal barrier impairment, neuroinflammation elevation, and behavioral abnormalities. 29 , 30 However, the neurotoxic effects induced by MA could be ameliorated by optimizing gut homeostasis.…”