16S rRNA gene sequencing and machine learning reveal correlation between drug abuse and human host gut microbiota

Abstract

Over the past few years, there has been increasing evidence highlighting the strong connection between gut microbiota and overall well-being of the host. This has led to a renewed emphasis on studying and addressing substance use disorder from the perspective of brain-gut axis. Previous studies have suggested that alcohol, food, and cigarette addictions are strongly linked to gut microbiota and faecal microbiota transplantation or the use of probiotics achieved significant efficacy. Unfortunately, little is known about the relationship between drug abuse and gut microbiota. This paper aims to reveal the potential correlation between gut microbiota and drug abuse and to develop an accurate identification model for drug-related faeces samples by machine learning. Faecal samples were collected from 476 participants from three regions in China (Shanghai, Yunnan, and Shandong). Their gut microbiota information was obtained using 16S rRNA gene sequencing, and a substance use disorder identification model was developed by machine learning. Analysis revealed a lower diversity and a more homogeneous gut microbiota community structure among participants with substance use disorder. Bacteroides, Prevotella_9, Faecalibacterium, and Blautia were identified as important biomarkers associated with substance use disorder. The function prediction analysis revealed that the citrate and reductive citrate cycles were significantly upregulated in the substance use disorder group, while the shikimate pathway was downregulated. In addition, the machine learning model could distinguish faecal samples between substance users and nonsubstance users with an AUC = 0.9, indicating its potential use in predicting and screening individuals with substance use disorder within the community in the future.