LDH and LV218 as biomarkers for diagnosing microbiologically positive tuberculous pleural effusions

BACKGROUND/OBJECTIVES: Tuberculous pleuritis (TP), a common manifestation of Mycobacterium tuberculosis infection, poses challenges in differentiating microbiologically positive (PEMP-MT) from negative (PEMN-MT) pleural effusions due to the limited sensitivity of traditional diagnostic methods. METHODS: Proteomics analysis using iTRAQ, non-targeted metabolomics, parallel reaction monitoring (PRM), and machine learning were employed to diagnose PEMN-MT or PEMP-MT. A validation cohort of 63 PEMN-MT and 28 PEMP-MT patients underwent ELISA experiments. Receiver operating characteristic (ROC) curves evaluated the predictive value of LDH and LV218 individually and in combination. RESULTS: Differentially expressed proteins (DEPs) and metabolites (DEMs) were identified using bioinformatics tools and pathway enrichment analyses. A machine learning model utilizing six biomarkers (LV218, F13A, RET4, LV321, TBA1C, and LDH) demonstrated excellent diagnostic performance with an AUROC of 0.987 and an AUPR of 0.974, distinguishing PEMP-MT from PEMN-MT. ROC curve analysis showed that both LDH and LV218, alone and in combination, provided strong predictive value for distinguishing the two groups. CONCLUSION: LDH and LV218 are promising biomarkers for differentiating microbiologically positive and negative pleural effusions in tuberculous pleuritis. These biomarkers, particularly when combined, could improve diagnostic accuracy and clinical management.