Abstract
Objectives
This study aimed to characterize serum lipid metabolism and identify potential biomarkers for compensated cirrhosis (CC) predicting and decompensated cirrhosis (DC) discrimination using targeted quantitative lipidomics and machine learning approaches.
Methods
Serum samples from a cohort of 120 participants was analyzed, including 90 cirrhosis patients (45 CC patients and 45 DC patients) and 30 healthy individuals. Lipid metabolic profiling was performed using targeted LC-MS/MS. Two machine learning methods, least absolute shrinkage and selection operator (LASSO), and random forest (RF) were applied to screen for candidate metabolite biomarkers.
Results
The metabolic profiling analysis showed a significant disruption in patients with CC and DC. Compared to the CC group, the DC group exhibited a significant upregulation in the abundance of glycochenodeoxycholic acid (GCDCA), glyco-ursodeoxycholic acid (GUDCA), glycocholic acid (GCA), phosphatidylethanolamine (PE), N-acyl-lyso-phosphatidylethanolamine (LNAPE), and triglycerides (TG), and a significant downregulation in the abundance of ceramides (Cer) and lysophosphatidylcholines (LPC). Machine learning identified 11 lipid metabolites (abbreviated as BMP11) as potential CC biomarkers with excellent prediction performance, with an AUC of 0.944, accuracy of 94.7 %, precision of 95.6 %, and recall of 95.6 %. For DC discrimination, eight lipids (abbreviated as BMP8) were identified, demonstrating strong efficacy, with an AUC of 0.968, accuracy of 92.2 %, precision of 88.0 %, and recall of 97.8 %.
Conclusions
This study unveiled distinct lipidomic profiles in CC and DC patients and established robust lipid-based models for CC predicting and DC discrimination.
Funding source: National Natural Science Foundation of China
Award Identifier / Grant number: 82172338
Award Identifier / Grant number: 82202596
Award Identifier / Grant number: 82372316
Acknowledgments
The authors thank all the participants in this study for their cooperation in sample preparation.
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Research ethics: The study was approved by the Ethics Committee of the First Affiliated Hospital of Fujian Medical University (MTCA, ECFAH of FMU [2021] 047).
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Informed consent: Written informed consent was obtained from each patient.
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Author contributions: Yongbin Zeng, Ya Fu and Li Zhang: Data analysis, Manuscript writing; Zhiyi Zheng and Jingyi Su: Data collection; Yongbin Zeng, Yongjun Zeng and Qishui Ou: Project development, Manuscript editing; Kun Lin and Huanhuan Huang: Samples preparation; Tianbin Chen and Can Liu: Laboratory testing.
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Competing interests: The authors declare that they have no conflicts of interest regarding the publication of this article.
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Research funding: The study is supported by grants from National Natural Science Foundation of China (82172338, 82202596, 82372316).
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Data availability: The raw data can be obtained on request from the corresponding author.
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Supplementary Material
This article contains supplementary material (https://doi.org/10.1515/cclm-2023-0798).
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