Integrated multiomics profiling predicts anti-tuberculosis drug-induced liver injury.

BACKGROUND: Anti-tuberculosis drug-induced liver injury (ATB-DILI) severely compromises tuberculosis (TB) treatment. We aimed to identify pretreatment predictors via integrated proteomic, metabolomic, and gut microbiome profiling. METHODS: This prospective multicenter study enrolled 72 adults who were receiving HRZ therapy. Serum, urine, and stool samples were collected before pretreatment. Serum proteins (RBP-4, CHGA, CPB2, ANT3, APOD) were quantified via ELISA. Nontargeted metabolomics (LC-MS) was used to analyze serum/urine, and 16 S rRNA sequencing was used to characterize the fecal microbiota. Liver injury (ALT/TBIL ≥ 2×ULN; RUCAM ≥ 3) was monitored biweekly/monthly. The ATB-DILI (n = 35) and non-ATB-DILI (n = 37) groups were compared statistically. A random forest model was used to integrate significant features (100-fold cross-validation). RESULTS: ATB-DILI developed at a median of 29 days (IQR:14–30) and was predominantly hepatocellular (54.3%). The pretreatment levels of all five proteins were elevated in ATB-DILI patients (p < 0.0001). Serum metabolomics revealed 163 differentially abundant metabolites (137↑/26↓; OPLS-DA R²Y = 0.692, Q²=0.351), and urine metabolomics revealed 106 (42↑/64↓; R²Y = 0.972, Q²=0.364). Beta diversity differed significantly between groups (Adonis P = 0.004), with Catenibacterium/Lactococcus enriched in ATB-DILI. Strong correlations linked the microbiota, metabolites, and liver enzymes. The integrated multiomics model (serum/urine metabolites, microbiome, proteins) achieved superior prediction (AUC = 0.880), outperforming single-platform models (serum metabolites:0.859; urine:0.803; microbiome:0.691; proteins:0.671). CONCLUSION: Pretreatment alterations in serum proteins, host metabolism, and the gut microbiota predict ATB-DILI risk. An integrated multiomics model enables early intervention for personalized TB therapy.