Lipidomics for the Prediction of Progressive Liver Disease in Patients with Alcohol Use Disorder
Bei Gao,
Suling Zeng,
Luca Maccioni,
Xiaochun Shi,
Aaron Armando,
Oswald Quehenberger,
Xinlian Zhang,
Peter Stärkel,
Bernd Schnabl
Affiliations
Bei Gao
School of Marine Sciences, Nanjing University of Information Science and Technology, Nanjing 210044, China
Suling Zeng
Department of Medicine, University of California San Diego, La Jolla, CA 92093, USA
Luca Maccioni
Laboratory of Hepato-Gastroenterology, Institute of Experimental and Clinical Research, Université Catholique de Louvain, 1200 Brussels, Belgium
Xiaochun Shi
School of Environmental Science and Engineering, Nanjing University of Information Science and Technology, Nanjing 210044, China
Aaron Armando
Department of Medicine, University of California San Diego, La Jolla, CA 92093, USA
Oswald Quehenberger
Department of Medicine, University of California San Diego, La Jolla, CA 92093, USA
Xinlian Zhang
Division of Biostatistics and Bioinformatics, Department of Family Medicine and Public Health, University of California San Diego, La Jolla, CA 92093, USA
Peter Stärkel
Laboratory of Hepato-Gastroenterology, Institute of Experimental and Clinical Research, Université Catholique de Louvain, 1200 Brussels, Belgium
Bernd Schnabl
Department of Medicine, University of California San Diego, La Jolla, CA 92093, USA
Alcohol-related liver disease is a public health care burden globally. Only 10–20% of patients with alcohol use disorder have progressive liver disease. This study aimed to identify lipid biomarkers for the early identification of progressive alcohol-related liver disease, which is a key step for early intervention. We performed untargeted lipidomics analysis in serum and fecal samples for a cohort of 49 subjects, including 17 non-alcoholic controls, 16 patients with non-progressive alcohol-related liver disease, and 16 patients with progressive alcohol-related liver disease. The serum and fecal lipidome profiles in the two patient groups were different from that in the controls. Nine lipid biomarkers were identified that were significantly different between patients with progressive liver disease and patients with non-progressive liver disease in both serum and fecal samples. We further built a random forest model to predict progressive alcohol-related liver disease using nine lipid biomarkers. Fecal lipids performed better (Area Under the Curve, AUC = 0.90) than serum lipids (AUC = 0.79). The lipid biomarkers identified are promising candidates for the early identification of progressive alcohol-related liver disease.
