Multivariate metabotyping of plasma predicts survival in patients with decompensated cirrhosis

Abstract

Background & aims: Predicting survival in decompensated cirrhosis (DC) is important in decision making for liver transplantation and resource allocation. We investigated whether high-resolution metabolic profiling can determine a metabolic phenotype associated with 90-day survival.

Methods: Two hundred and forty-eight subjects underwent plasma metabotyping by (1)H nuclear magnetic resonance (NMR) spectroscopy and reversed-phase ultra-performance liquid chromatography coupled to time-of-flight mass spectrometry (UPLC-TOF-MS; DC: 80-derivation set, 101-validation; stable cirrhosis (CLD) 20 and 47 healthy controls (HC)).

Results: (1)H NMR metabotyping accurately discriminated between surviving and non-surviving patients with DC. The NMR plasma profiles of non-survivors were attributed to reduced phosphatidylcholines and lipid resonances, with increased lactate, tyrosine, methionine and phenylalanine signal intensities. This was confirmed on external validation (area under the receiver operating curve [AUROC]=0.96 (95% CI 0.90-1.00, sensitivity 98%, specificity 89%). UPLC-TOF-MS confirmed that lysophosphatidylcholines and phosphatidylcholines [LPC/PC] were downregulated in non-survivors (UPLC-TOF-MS profiles AUROC of 0.94 (95% CI 0.89-0.98, sensitivity 100%, specificity 85% [positive ion detection])). LPC concentrations negatively correlated with circulating markers of cell death (M30 and M65) levels in DC. Histological examination of liver tissue from DC patients confirmed increased hepatocyte cell death compared to controls. Cross liver sampling at time of liver transplantation demonstrated that hepatic endothelial beds are a source of increased circulating total cytokeratin-18 in DC.

Conclusion: Plasma metabotyping accurately predicts mortality in DC. LPC and amino acid dysregulation is associated with increased mortality and severity of disease reflecting hepatocyte cell death.

Keywords: Acute on chronic liver failure; Metabolic profiling; Metabolomics; Metabonomics; Outcome prediction; Personalised medicine.