Explorative study of serum biomarkers of liver failure after liver resection

• Published in: Scientific reports 2020-06, Vol.10 (1), p.9960-9960, Article 9960
• Main Authors: Yoon, Kyung Chul, Kwon, Hyung Do, Jo, Hye-Sung, Choi, Yoon Young, Seok, Jin-I, Kang, Yujin, Lee, Do Yup, Kim, Dong-Sik
• Format: Article
• Language: English
• Subjects: 692/4020/4021; 692/4020/4021/288; Animals; Biochemical markers; Biomarkers; Biomarkers - blood; Biomarkers - metabolism; Female; Health risks; Hepatectomy; Hepatectomy - adverse effects; Hepatectomy - mortality; Humanities and Social Sciences; Liver; Liver - metabolism; Liver - surgery; Liver failure; Liver Failure - blood; Liver Failure - metabolism; Liver Failure - mortality; Liver Function Tests - methods; Malic acid; Metabolites; Methionine; Mortality; Mortality risk; multidisciplinary; Prediction models; Regression analysis; Risk Factors; Science; Science (multidisciplinary); Sensitivity and Specificity; Surgery; Swine; Tryptophan; γ-Aminobutyric acid
• ISSN: 2045-2322; 2045-2322
• DOI: 10.1038/s41598-020-66947-1

Conventional biochemical markers have limited usefulness in the prediction of early liver dysfunction. We, therefore, tried to find more useful liver failure biomarkers after liver resection that are highly sensitive to internal and external challenges in the biological system with a focus on liver metabolites. Twenty pigs were divided into the following 3 groups: sham operation group (n = 6), 70% hepatectomy group (n = 7) as a safety margin of resection model, and 90% hepatectomy group (n = 7) as a liver failure model. Blood sampling was performed preoperatively and at 1, 6, 14, 30, 38, and 48 hours after surgery, and 129 primary metabolites were profiled. Orthogonal projection to latent structures-discriminant analysis revealed that, unlike in the 70% hepatectomy and sham operation groups, central carbon metabolism was the most significant factor in the 90% hepatectomy group. Binary logistic regression analysis was used to develop a predictive model for mortality risk following hepatectomy. The recommended variables were malic acid, methionine, tryptophan, glucose, and γ-aminobutyric acid. Area under the curve of the linear combination of five metabolites was 0.993 (95% confidence interval: 0.927–1.000, sensitivity: 100.0, specificity: 94.87). We proposed robust biomarker panels that can accurately predict mortality risk associated with hepatectomy.