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AIM: The aim of this study was to use a metabonomics approach to identify potential biomarkers of exhaled breath condensate (EBC) for predicting the prognosis of acute-on-chronic liver failure (ACLF). METHODS: Using liquid chromatography mass spectrometry, EBC metabolites of ACLF patients surviving without liver transplantation (n = 57) and those with worse outcomes (n = 45), and controls (n = 15) were profiled from a specialized liver disease center in Beijing. The metabolites were used to identify candidate biomarkers, and the predicted performance of potential biomarkers was tested. RESULTS: Forty-one metabolites, involving glycerophospholipid metabolism, sphingolipid metabolism, arachidonic acid metabolism, and amino acid metabolism, as candidate biomarkers for discriminating the different outcomes of ACLF were selected. A prognostic model was constructed by a panel of four metabolites including phosphatidylinositol [20:4(5Z,8Z,11Z,14Z)/13:0], phosphatidyl ethanolamine (12:0/22:0), L-metanephrine and ethylbenzene, which could predict the worse prognosis in ACLF patients with sensitivity (84.4%) and specificity (89.5%) (area under the receiver operating characteristic curve [AUC] = 0.859, 95% confidence interval [CI] = 0.787-0.931). Compared with Model for End-Stage Liver Disease (MELD) score (AUC = 0.639, 95% CI = 0.526-0.753) and MELD-sodium (MELD-Na) score (AUC = 0.692, 95% CI = 0.582-0.803), EBC-associated metabolite signature model could better predict worse outcomes in patients with ACLF (p < 0.05). Using the MELD-Na score and EBC metabolite signatures, a decision tree model was built for predicting the prognosis of ACLF identified on logistic regression analyses (AUC = 0.906, 95% CI = 0.846-0.965). CONCLUSION: EBC metabolic signatures show promise as potential biomarkers for predicting worse prognosis of ACLF.
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OBJECTIVE: To explore the Chinese medicine (CM) syndrome discipline in patients with antituberculosis drug (ATBD)-induced liver injury to provide the basis of the standard Chinese medicine treatment for the disease with latent variable analysis. METHODS: Epidemiological investigation method was adopted. Two hundred and sixty-one patients with ATBD-induced liver injury were investigated using CM syndrome questionnaire. The syndrome types were determined with exploratory factor analysis (EFA), and the latent variables were analyzed by confirmatory factor analysis (CFA). RESULTS: Totally 26 indexes related to CM syndrome differentiation were obtained from the 261 eligible cases, among them, 5 were as the latent dependent variables, which corresponded to 5 common syndrome types, including dampness encumbering the Spleen (Pi), Liver (Gan)-qi stagnation, Spleen and Stomach (Wei) deficiency, stasis-toxin accumulation, and qi-yin deficiency. CFA indicated that the indexes with loading coefficient [Symbol: see text]0.6 exactly reflected the connotation of its corresponding syndrome type. CONCLUSIONS: Five CM syndrome types are the most common in patients with ATBD-induced liver injury, which relate to their corresponding indexes for differentiation. It is feasible to apply combined EFA and CFA for explaination and measurement of the existence of CM syndrome under specific diseases.