Mechanistic biomarkers provide early and sensitive detection of acetaminophen-induced acute liver injury at first presentation to hospital

Acetaminophen overdose is a common reason for hospital admission and the most frequent cause of hepatotoxicity in the Western world. Early identification would facilitate patient-individualized treatment strategies. We investigated the potential of a panel of novel biomarkers (with enhanced liver expression or linked to the mechanisms of toxicity) to identify patients with acetaminophen-induced acute liver injury (ALI) at first presentation to the hospital when currently used markers are within the normal range. In the first hospital presentation plasma sample from patients (n = 129), we measured microRNA-122 (miR-122; high liver specificity), high mobility group box-1 (HMGB1; marker of necrosis), full-length and caspase-cleaved keratin-18 (K18; markers of necrosis and apoptosis), and glutamate dehydrogenase (GLDH; marker of mitochondrial dysfunction). Receiver operator characteristic curve analysis and positive/negative predictive values were used to compare sensitivity to report liver injury versus alanine transaminase (ALT) and International Normalized Ratio (INR). In all patients, biomarkers at first presentation significantly correlated with peak ALT or INR. In patients presenting with normal ALT or INR, miR-122, HMGB1, and necrosis K18 identified the development of liver injury (n = 15) or not (n = 84) with a high degree of accuracy and significantly outperformed ALT, INR, and plasma acetaminophen concentration for the prediction of subsequent ALI (n = 11) compared with no ALI (n = 52) in patients presenting within 8 hours of overdose. Conclusion: Elevations in plasma miR-122, HMGB1, and necrosis K18 identified subsequent ALI development in patients on admission to the hospital, soon after acetaminophen overdose, and in patients with ALTs in the normal range. The application of such a biomarker panel could improve the speed of clinical decision-making, both in the treatment of ALI and the design/execution of patient-individualized treatment strategies.