Microbial Signatures of Addiction: a Computational Analysis of GUT Microbiota in Substance Use Disorders

Substance use disorders (SUDs) pose a major global health burden, with current treatments often lacking in personalized assessment. Emerging research links SUDs to gut microbiota changes that influence brain function, drug response, and relapse. This study used computational bioinformatics to analyze 163 gut microbiota samples (16 S rRNA data from NCBI) from individuals with alcohol (AUD, 30 samples), opioid (OUD, 42 samples), and cannabis use disorders (CUD, 43 samples), plus Inflammatory Bowel Disease (IBD, 48 samples) as a dysbiosis reference. Firmicutes and Bacteroidota were dominant, with CUD showing high Bacteroidota and IBD high Firmicutes. PCA and clustering indicated overlapping microbial profiles in SUD and IBD, suggesting shared dysbiosis. AUD samples formed a distinct cluster. Key phyla (Fusobacteria, Campylobacteria, Actinobacteria, Patescibacteria, Bacteroidota, among others) linked to imbalances were identified via effect size analysis and machine learning. These findings highlight the potential of microbiomebased diagnostics and bioinformatics in developing precision therapies for SUDs.