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.