Machine learning-based plasma metabolomics for improved cirrhosis risk stratification
Abstract Background Cirrhosis is a leading cause of mortality in patients with chronic liver disease (CLD). The rapid development of metabolomic technologies has enabled the capture of metabolic changes related to the progression of cirrhosis. Methods This study used proton nuclear magnetic resonanc...
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BMC
2025-02-01
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| Series: | BMC Gastroenterology |
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| Online Access: | https://doi.org/10.1186/s12876-025-03655-y |
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| author | Jingru Song Ziwei Gao Liqun Lai Jie Zhang Binbin Liu Yi Sang Siqi Chen Jiachen Qi Yujun Zhang Huang Kai Wei Ye |
| author_facet | Jingru Song Ziwei Gao Liqun Lai Jie Zhang Binbin Liu Yi Sang Siqi Chen Jiachen Qi Yujun Zhang Huang Kai Wei Ye |
| author_sort | Jingru Song |
| collection | DOAJ |
| description | Abstract Background Cirrhosis is a leading cause of mortality in patients with chronic liver disease (CLD). The rapid development of metabolomic technologies has enabled the capture of metabolic changes related to the progression of cirrhosis. Methods This study used proton nuclear magnetic resonance (1 H-NMR) serum metabolomics data from the UK Biobank (UKB) and employed elastic net-regularized Cox proportional hazards models to explore the role of metabolomics in cirrhosis risk stratification in patients with CLD. Metabolomic data were integrated with aspartate aminotransferase to platelet ratio index (APRI) and fibrosis-4 score (FIB-4) to construct predictive models for cirrhosis risk. The model performance was assessed in both the derivation and validation cohorts. Results A total of 2,738 eligible patients were included in the analysis. Several metabolites showed an independent association with cirrhosis events (68 out of 168 metabolites after adjustment for age and sex, and 21 out of 168 metabolites after full adjustment). The integration of metabolomics with FIB-4 improved the predictive performance compared to FIB-4 alone (Harrell’s C: 0.717 vs. 0.696, ΔC = 0.021, 95% confidence interval [CI] 0.014–0.028, Net Reclassification Improvement [NRI]: 0.504 [0.488–0.520]). Similarly, the combination of metabolomics with APRI also improved predictive performance compared to APRI alone (Harrell’s C: 0.747 vs. 0.718, ΔC = 0.029, 95% CI 0.022–0.035, NRI: 0.378 [0.366–0.389]). Key metabolites, including branched-chain amino acids (BCAAs), lipids, and markers of oxidative stress, were identified as significant predictors. Pathway enrichment analysis revealed that disruptions in lipid and amino acid metabolism play a central role in the progression of cirrhosis. Conclusion 1 H-NMR serum metabolomics significantly improves the prediction of cirrhosis risk in patients with CLD. The APRI + Metabolomics model demonstrated strong discriminatory power, with key metabolites involved in fatty acid and amino acid metabolism, providing a promising tool for the early screening of cirrhosis risk. |
| format | Article |
| id | doaj-art-f406dfc9d68745eca6fec9194572a775 |
| institution | Kabale University |
| issn | 1471-230X |
| language | English |
| publishDate | 2025-02-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Gastroenterology |
| spelling | doaj-art-f406dfc9d68745eca6fec9194572a7752025-02-09T12:39:33ZengBMCBMC Gastroenterology1471-230X2025-02-0125111510.1186/s12876-025-03655-yMachine learning-based plasma metabolomics for improved cirrhosis risk stratificationJingru Song0Ziwei Gao1Liqun Lai2Jie Zhang3Binbin Liu4Yi Sang5Siqi Chen6Jiachen Qi7Yujun Zhang8Huang Kai9Wei Ye10Department of Gastroenterology, Hangzhou TCM Hospital Affiliated to Zhejiang Chinese Medical UniversityHangzhou School of Clinical Medicine, Zhejiang Chinese Medical UniversityDepartment of Gastroenterology, Hangzhou TCM Hospital Affiliated to Zhejiang Chinese Medical UniversityDepartment of Gastroenterology, Hangzhou TCM Hospital Affiliated to Zhejiang Chinese Medical UniversityDepartment of Gastroenterology, Hangzhou TCM Hospital Affiliated to Zhejiang Chinese Medical UniversityDepartment of Gastroenterology, Hangzhou TCM Hospital Affiliated to Zhejiang Chinese Medical UniversityHangzhou School of Clinical Medicine, Zhejiang Chinese Medical UniversityHangzhou School of Clinical Medicine, Zhejiang Chinese Medical UniversityHangzhou School of Clinical Medicine, Zhejiang Chinese Medical UniversityDepartment of cardiovascular surgery, Sun Yat-sen Memorial Hospital, Sun Yat-Sen UniversityDepartment of Gastroenterology, Hangzhou TCM Hospital Affiliated to Zhejiang Chinese Medical UniversityAbstract Background Cirrhosis is a leading cause of mortality in patients with chronic liver disease (CLD). The rapid development of metabolomic technologies has enabled the capture of metabolic changes related to the progression of cirrhosis. Methods This study used proton nuclear magnetic resonance (1 H-NMR) serum metabolomics data from the UK Biobank (UKB) and employed elastic net-regularized Cox proportional hazards models to explore the role of metabolomics in cirrhosis risk stratification in patients with CLD. Metabolomic data were integrated with aspartate aminotransferase to platelet ratio index (APRI) and fibrosis-4 score (FIB-4) to construct predictive models for cirrhosis risk. The model performance was assessed in both the derivation and validation cohorts. Results A total of 2,738 eligible patients were included in the analysis. Several metabolites showed an independent association with cirrhosis events (68 out of 168 metabolites after adjustment for age and sex, and 21 out of 168 metabolites after full adjustment). The integration of metabolomics with FIB-4 improved the predictive performance compared to FIB-4 alone (Harrell’s C: 0.717 vs. 0.696, ΔC = 0.021, 95% confidence interval [CI] 0.014–0.028, Net Reclassification Improvement [NRI]: 0.504 [0.488–0.520]). Similarly, the combination of metabolomics with APRI also improved predictive performance compared to APRI alone (Harrell’s C: 0.747 vs. 0.718, ΔC = 0.029, 95% CI 0.022–0.035, NRI: 0.378 [0.366–0.389]). Key metabolites, including branched-chain amino acids (BCAAs), lipids, and markers of oxidative stress, were identified as significant predictors. Pathway enrichment analysis revealed that disruptions in lipid and amino acid metabolism play a central role in the progression of cirrhosis. Conclusion 1 H-NMR serum metabolomics significantly improves the prediction of cirrhosis risk in patients with CLD. The APRI + Metabolomics model demonstrated strong discriminatory power, with key metabolites involved in fatty acid and amino acid metabolism, providing a promising tool for the early screening of cirrhosis risk.https://doi.org/10.1186/s12876-025-03655-yCirrhosisChronic liver diseaseMetabolomicsRisk stratificationElastic net regularization |
| spellingShingle | Jingru Song Ziwei Gao Liqun Lai Jie Zhang Binbin Liu Yi Sang Siqi Chen Jiachen Qi Yujun Zhang Huang Kai Wei Ye Machine learning-based plasma metabolomics for improved cirrhosis risk stratification BMC Gastroenterology Cirrhosis Chronic liver disease Metabolomics Risk stratification Elastic net regularization |
| title | Machine learning-based plasma metabolomics for improved cirrhosis risk stratification |
| title_full | Machine learning-based plasma metabolomics for improved cirrhosis risk stratification |
| title_fullStr | Machine learning-based plasma metabolomics for improved cirrhosis risk stratification |
| title_full_unstemmed | Machine learning-based plasma metabolomics for improved cirrhosis risk stratification |
| title_short | Machine learning-based plasma metabolomics for improved cirrhosis risk stratification |
| title_sort | machine learning based plasma metabolomics for improved cirrhosis risk stratification |
| topic | Cirrhosis Chronic liver disease Metabolomics Risk stratification Elastic net regularization |
| url | https://doi.org/10.1186/s12876-025-03655-y |
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