Liu Y, Xiao Y, Ai LF, Zhang JJ, Zhang JD, Qi ZQ, Dong L, Wang YD. Serum metabolomic characteristics and their predictive value for ninety-day prognosis in patients with acute-on-chronic liver failure. World J Gastroenterol 2025; 31(30): 110401 [DOI: 10.3748/wjg.v31.i30.110401]
Corresponding Author of This Article
Ya-Dong Wang, Department of Infectious Diseases, The Hebei Medical University Third Hospital, No. 275 Zhongshan West Road, Qiaoxi District, Shijiazhuang 050000, Hebei Province, China. wangyadong@hebmu.edu.cn
Research Domain of This Article
Gastroenterology & Hepatology
Article-Type of This Article
Case Control Study
Open-Access Policy of This Article
This article is an open-access article which was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/4.0/
World J Gastroenterol. Aug 14, 2025; 31(30): 110401 Published online Aug 14, 2025. doi: 10.3748/wjg.v31.i30.110401
Serum metabolomic characteristics and their predictive value for ninety-day prognosis in patients with acute-on-chronic liver failure
Yan Liu, Ying Xiao, Lian-Feng Ai, Jing-Jing Zhang, Jian-Dong Zhang, Ze-Qiang Qi, Lei Dong, Ya-Dong Wang
Yan Liu, Jing-Jing Zhang, Jian-Dong Zhang, Department of Clinical Laboratory, The Hebei Medical University Third Hospital, Shijiazhuang 050051, Hebei Province, China
Ying Xiao, Ze-Qiang Qi, Ya-Dong Wang, Department of Infectious Diseases, The Hebei Medical University Third Hospital, Shijiazhuang 050000, Hebei Province, China
Lian-Feng Ai, Technology Center of Shijiazhuang Customs, Shijiazhuang 050000, Hebei Province, China
Lei Dong, Key Laboratory of Molecular Medicine and Biological Diagnosis and Treatment, Aerospace Center Hospital, School of Life Science, Beijing Institute of Technology, Beijing 100000, China
Co-first authors: Yan Liu and Ying Xiao.
Author contributions: Liu Y and Xiao Y conceived the idea for the study and were responsible for metabolomics testing, data extraction, statistical analysis, and manuscript drafting, they contributed equally to this article, they are the co-first authors of this manuscript; Ai LF supervised the study and revised the manuscript for important intellectual content; Zhang JJ and Zhang JD assisted in collecting clinical data and serum specimens; Qi ZQ assisted with data analysis and statistical application; Dong L and Wang YD performed methodological assessment and revised the manuscript for important intellectual content; and all authors have read and approve the final manuscript.
Supported by the Hebei Natural Science Foundation, No. H2023206042; and Medical Science Research Project of Hebei, No. 20230670.
Institutional review board statement: This study was approved by the Medical Ethics Committee of the Hebei Medical University Third Hospital, approval No. W2023-043-1.
Informed consent statement: All procedures complied with the Declaration of Helsinki. Informed consent was obtained and documented from all participants before enrollment.
Conflict-of-interest statement: All the authors report no relevant conflicts of interest for this article.
STROBE statement: The authors have read the STROBE Statement-checklist of items, and the manuscript was prepared and revised according to the STROBE Statement-checklist of items.
Data sharing statement: The datasets used and/or analyzed during the current study are available from the first author on reasonable request (Yan Liu, Department of Clinical Laboratory, The Hebei Medical University Third Hospital, Shijiazhuang 050051, China. E-mail: 396758927@qq.com).
Open Access: This article is an open-access article that was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution NonCommercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: https://creativecommons.org/Licenses/by-nc/4.0/
Corresponding author: Ya-Dong Wang, Department of Infectious Diseases, The Hebei Medical University Third Hospital, No. 275 Zhongshan West Road, Qiaoxi District, Shijiazhuang 050000, Hebei Province, China. wangyadong@hebmu.edu.cn
Received: June 7, 2025 Revised: June 26, 2025 Accepted: July 21, 2025 Published online: August 14, 2025 Processing time: 62 Days and 22.3 Hours
Abstract
BACKGROUND
Acute-on-chronic liver failure (ACLF) is characterized by severe metabolic disturbances; however, the specific metabolomic features and their predictive value on 90-day prognosis remain unclear.
AIM
To identify serum metabolomic changes in patients with ACLF with different prognoses to support clinical prediction of outcomes and treatment decisions.
METHODS
This non-interventional, observational case-control study enrolled 58 patients with ACLF. Fasting venous blood samples were analyzed using targeted metabolomics. Univariate and multivariate statistical analyses identified differential metabolites among 18 amino acids, 11 fatty acids, 5 gut microbiota-related metabolites, and 4 bile acid metabolites. Binary logistic regression identified independent mortality risk factors, visualized via forest plots and receiver operating characteristic curves.
RESULTS
Significant differences (P < 0.05) were observed between the death and survival groups in baseline age, model for end-stage liver disease score, model for end-stage liver disease with sodium, neutrophil-to-lymphocyte ratio (NLR), total bilirubin, serum creatinine, blood urea nitrogen, and platelet count. Metabolites, including L-carnitine, creatinine, alanine, arginine (Arg), proline, choline, and oleic acid, also showed statistically significant differences between the groups. Multivariate analysis identified age, NLR, and Arg as independent risk factors for 90-day mortality in patients with ACLF. The predictive model, age-NLR-Arg = -15.481 + 0.135 × age + 0.156 × NLR + 0.203 × Arg, with a cutoff of 0.759, achieved an area under the receiver operating characteristic curve of 0.945 with sensitivity of 84.0% and specificity of 87.9%.
CONCLUSION
The age-NLR-Arg model demonstrates a strong predictive value for 90-day mortality risk in patients with ACLF.
Core Tip: Acute-on-chronic liver failure (ACLF) is a rapidly progressing condition with high mortality and limited treatment options. Traditional prognostic models fail to capture its dynamic metabolic disturbances. This study identifies seven key metabolites linked to 90-day ACLF prognosis, with Arginine as an independent risk factor. Age- neutrophil-to-lymphocyte ratio-arginine model expressed perfect predictive efficiency for 90-day prognosis of patients with ACLF. In addition, artificial liver blood purification system treatment modulated alanine and L-carnitine, reducing inflammation and promoting liver regeneration. These findings highlight the potential of metabolomics to enhance ACLF prognosis, offering a more precise approach for clinical assessment and management.
