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

Quanxian Liu, Chaozhi Wang, Guo Si, Jianqing He

Respiratory research · 2026-03

Abstract

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 ( = 35) and non-ATB-DILI ( = 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&#x2013;30) and was predominantly hepatocellular (54.3%). The pretreatment levels of all five proteins were elevated in ATB-DILI patients (&#x2009;<&#x2009;0.0001). Serum metabolomics revealed 163 differentially abundant metabolites (137&#x2191;/26&#x2193;; OPLS-DA R&#xb2;Y&#x2009;=&#x2009;0.692, Q&#xb2;=0.351), and urine metabolomics revealed 106 (42&#x2191;/64&#x2193;; R&#xb2;Y&#x2009;=&#x2009;0.972, Q&#xb2;=0.364). Beta diversity differed significantly between groups (Adonis&#x2009;=&#x2009;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&#x2009;=&#x2009;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.

SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12931-026-03608-3.