Non-invasive separation of alcoholic and non-alcoholic liver disease with predictive modeling.

Abstract

Currently, a major clinical challenge is to distinguish between chronic liver disease caused by metabolic syndrome (non-alcoholic fatty liver disease, NAFLD) from that caused by long term or excessive alcohol consumption (ALD). The etiology of severe liver disease affects treatment options and priorities for liver transplantation and organ allocation. Thus we compared physiologically similar NAFLD and ALD patients to detect biochemical differences for improved separation of these mechanistically overlapping etiologies. In a cohort of 31 NAFLD patients with BMI below 30 and a cohort of ALD patient with (ALDC n = 51) or without cirrhosis (ALDNC n = 51) serum transaminases, cell death markers and (adipo-)cytokines were assessed. Groups were compared with One-way ANOVA and Tukey's correction. Predictive models were built by machine learning techniques. NAFLD, ALDNC or ALDC patients did not differ in demographic parameters. The ratio of alanine aminotransferase/aspartate aminotransferase--common serum parameters for liver damage--was significantly higher in the NAFLD group compared to both ALD groups (each p<0.0001). Adiponectin and tumor necrosis factor(TNF)-alpha were significantly lower in NAFLD than in ALDNC (p<0.05) or ALDC patients (p<0.0001). Significantly higher serum concentrations of cell death markers, hyaluronic acid, adiponectin, and TNF-alpha (each p<0.0001) were found in ALDC compared to ALDNC. Using machine learning techniques we were able to discern NAFLD and ALDNC (up to an AUC of 0.9118±0.0056) or ALDC and ALDNC (up to an AUC of 0.9846±0.0018), respectively. Machine learning techniques relying on ALT/AST ratio, adipokines and cytokines distinguish NAFLD and ALD. In addition, severity of ALD may be non-invasively diagnosed via serum cytokine concentrations.