Circulating Extracellular Vesicles Carrying Sphingolipid Cargo for the Diagnosis and Dynamic Risk Profiling of Alcoholic Hepatitis.

ABSTRACT

BACKGROUND AND AIMS: Alcoholic hepatitis (AH) is diagnosed by clinical criteria, although several objective scores facilitate risk stratification. Extracellular vesicles (EVs) have emerged as biomarkers for many diseases and are also implicated in the pathogenesis of AH. Therefore, we investigated whether plasma EV concentration and sphingolipid cargo could serve as diagnostic biomarkers for AH and inform prognosis to permit dynamic risk profiling of AH subjects. APPROACH AND RESULTS: EVs were isolated and quantified from plasma samples from healthy controls, heavy drinkers, and subjects with end-stage liver disease (ESLD) attributed to cholestatic liver diseases and nonalcoholic steatohepatitis, decompensated alcohol-associated cirrhosis (AC), and AH. Sphingolipids were quantified by tandem mass spectroscopy. The median plasma EV concentration was significantly higher in AH subjects (5.38 × 1011 /mL) compared to healthy controls (4.38 × 1010 /mL; P < 0.0001), heavy drinkers (1.28 × 1011 /mL; P < 0.0001), ESLD (5.35 × 1010 /mL; P < 0.0001), and decompensated AC (9.2 × 1010 /mL; P < 0.0001) disease controls. Among AH subjects, EV concentration correlated with Model for End-Stage Liver Disease score. When EV counts were dichotomized at the median, survival probability for AH subjects at 90 days was 63.0% in the high-EV group and 90.0% in the low-EV group (log-rank P value = 0.015). Interestingly, EV sphingolipid cargo was significantly enriched in AH when compared to healthy controls, heavy drinkers, ESLD, and decompensated AC (P = 0.0001). Multiple sphingolipids demonstrated good diagnostic and prognostic performance as biomarkers for AH. CONCLUSIONS: Circulating EV concentration and sphingolipid cargo signature can be used in the diagnosis and differentiation of AH from heavy drinkers, decompensated AC, and other etiologies of ESLD and predict 90-day survival permitting dynamic risk profiling.