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Targeted quantitative lipidomic uncovers lipid biomarkers for predicting the presence of compensated cirrhosis and discriminating decompensated cirrhosis from compensated cirrhosis

  • Yongbin Zeng , Li Zhang , Zhiyi Zheng , Jingyi Su , Ya Fu , Tianbin Chen , Kun Lin , Can Liu , Huanhuan Huang , Qishui Ou EMAIL logo and Yongjun Zeng EMAIL logo
Published/Copyright: November 6, 2023
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Clinical Chemistry and Laboratory Medicine (CCLM)
From the journal Clinical Chemistry and Laboratory Medicine (CCLM) Volume 62 Issue 3

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.

Keywords: cirrhosis; decompensated; lipids; machine learning; biomarkers
Corresponding authors: Qishui Ou, Department of Laboratory Medicine, Gene Diagnosis Research Center, the First Affiliated Hospital, Fujian Medical University, Fuzhou 350005, P.R. China; Department of Laboratory Medicine, National Regional Medical Center, Binhai Campus of the First Affiliated Hospital, Fujian Medical University, Fuzhou 350212, P.R. China; Fujian Key Laboratory of Laboratory Medicine, The First Affiliated Hospital, Fujian Medical University, Fuzhou 350005, P.R. China; and Fujian Clinical Research Center for Laboratory Medicine of Immunology, The First Affiliated Hospital, Fujian Medical University, Fuzhou 350005, P.R. China, E-mail: ; and Yongjun Zeng, Department of Cardiology, The First Affiliated Hospital, Fujian Medical University, Fuzhou 350005, P.R. China, E-mail:
Yongbin Zeng and Li Zhang contributed equally to this work.

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.

  1. 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).

  2. Informed consent: Written informed consent was obtained from each patient.

  3. 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.

  4. Competing interests: The authors declare that they have no conflicts of interest regarding the publication of this article.

  5. Research funding: The study is supported by grants from National Natural Science Foundation of China (82172338, 82202596, 82372316).

  6. 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).

Received: 2023-07-26
Accepted: 2023-09-23
Published Online: 2023-11-06
Published in Print: 2024-02-26

© 2023 Walter de Gruyter GmbH, Berlin/Boston

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https://doi.org/10.1515/cclm-2023-0798
Keywords for this article
cirrhosis; decompensated; lipids; machine learning; biomarkers

Articles in the same Issue

  1. Frontmatter
  2. Editorial
  3. Unraveling the mystery of blood groups and COVID-19
  4. Reviews
  5. Serum hepcidin levels in chronic liver disease: a systematic review and meta-analysis
  6. Platelet distribution width (PDW) as a significant correlate of COVID-19 infection severity and mortality
  7. Mini Reviews
  8. ABO blood group-related mechanism of infection of SARS-CoV-2: an overview of systematic reviews
  9. Opinion Paper
  10. Personalized laboratory medicine in the digital health era: recent developments and future challenges
  11. Guidelines and Recommendations
  12. Algorithm of differential diagnosis of anemia involving laboratory medicine specialists to advance diagnostic excellence
  13. General Clinical Chemistry and Laboratory Medicine
  14. Rescaling creatinine makes GFR estimation equations generally applicable across populations – validation results for the Lund-Malmö equation in a French cohort of sub-Saharan ancestry
  15. Periodic verification of results’ comparability between several analyzers: experience in the application of the EP31-A-IR guideline
  16. Machine learning to optimize cerebrospinal fluid dilution for analysis of MRZH reaction
  17. Diagnostic performance of automated red cell parameters in predicting bone marrow iron stores
  18. Reliability of hemoglobin A2 value as measured by the Premier Resolution system for screening of β-thalassemia carriers
  19. Amino acid sequence homology of monoclonal serum free light chain dimers and tissue deposited light chains in AL amyloidosis: a pilot study
  20. Development of high-performance point-of-care aqueous VEGF detection system and proof-of-concept validation in RVO patients
  21. Detection rate of IGF-1 variants and their implication to protein binding: study of over 240,000 patients
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  30. Bioactive adrenomedullin (bio-ADM) is associated with endothelial dysfunction in infants and children with complex congenital heart disease undergoing open-heart surgery on cardiopulmonary bypass
  31. Infectious Diseases
  32. Monocyte distribution width as an early predictor of short-term outcome in adult patients with sepsis
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