Machine learning of plasma metabolome identifies biomarker panels for metabolic syndrome: findings from the China Suboptimal Health Cohort
Abstract Background Metabolic syndrome (MetS) has been proposed as a clinically identifiable high-risk state for the prediction and prevention of cardiovascular diseases and type 2 diabetes mellitus. As a promising “omics” technology, metabolomics provides an innovative strategy to gain a deeper und...
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| Format: | Article |
| Language: | English |
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BMC
2022-12-01
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| Series: | Cardiovascular Diabetology |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s12933-022-01716-0 |
| _version_ | 1828087253125038080 |
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| author | Hao Wang Youxin Wang Xingang Li Xuan Deng Yuanyuan Kong Wei Wang Yong Zhou |
| author_facet | Hao Wang Youxin Wang Xingang Li Xuan Deng Yuanyuan Kong Wei Wang Yong Zhou |
| author_sort | Hao Wang |
| collection | DOAJ |
| description | Abstract Background Metabolic syndrome (MetS) has been proposed as a clinically identifiable high-risk state for the prediction and prevention of cardiovascular diseases and type 2 diabetes mellitus. As a promising “omics” technology, metabolomics provides an innovative strategy to gain a deeper understanding of the pathophysiology of MetS. The study aimed to systematically investigate the metabolic alterations in MetS and identify biomarker panels for the identification of MetS using machine learning methods. Methods Nuclear magnetic resonance-based untargeted metabolomics analysis was performed on 1011 plasma samples (205 MetS patients and 806 healthy controls). Univariate and multivariate analyses were applied to identify metabolic biomarkers for MetS. Metabolic pathway enrichment analysis was performed to reveal the disturbed metabolic pathways related to MetS. Four machine learning algorithms, including support vector machine (SVM), random forest (RF), k-nearest neighbor (KNN), and logistic regression were used to build diagnostic models for MetS. Results Thirteen significantly differential metabolites were identified and pathway enrichment revealed that arginine, proline, and glutathione metabolism are disturbed metabolic pathways related to MetS. The protein-metabolite-disease interaction network identified 38 proteins and 23 diseases are associated with 10 MetS-related metabolites. The areas under the receiver operating characteristic curve of the SVM, RF, KNN, and logistic regression models based on metabolic biomarkers were 0.887, 0.993, 0.914, and 0.755, respectively. Conclusions The plasma metabolome provides a promising resource of biomarkers for the predictive diagnosis and targeted prevention of MetS. Alterations in amino acid metabolism play significant roles in the pathophysiology of MetS. The biomarker panels and metabolic pathways could be used as preventive targets in dealing with cardiometabolic diseases related to MetS. |
| first_indexed | 2024-04-11T05:09:29Z |
| format | Article |
| id | doaj.art-3becbe30d9264fb4a47db1734f741e74 |
| institution | Directory Open Access Journal |
| issn | 1475-2840 |
| language | English |
| last_indexed | 2024-04-11T05:09:29Z |
| publishDate | 2022-12-01 |
| publisher | BMC |
| record_format | Article |
| series | Cardiovascular Diabetology |
| spelling | doaj.art-3becbe30d9264fb4a47db1734f741e742022-12-25T12:04:29ZengBMCCardiovascular Diabetology1475-28402022-12-0121111210.1186/s12933-022-01716-0Machine learning of plasma metabolome identifies biomarker panels for metabolic syndrome: findings from the China Suboptimal Health CohortHao Wang0Youxin Wang1Xingang Li2Xuan Deng3Yuanyuan Kong4Wei Wang5Yong Zhou6Department of Clinical Epidemiology and Evidence-Based Medicine, Beijing Clinical Research Institute, Beijing Friendship Hospital, Capital Medical UniversityBeijing Key Laboratory of Clinical Epidemiology, School of Public Health, Capital Medical UniversityCenter for Precision Medicine, School of Medical and Health Sciences, Edith Cowan UniversityClinical Research Institute, Shanghai General Hospital, Shanghai Jiao Tong University School of MedicineDepartment of Clinical Epidemiology and Evidence-Based Medicine, Beijing Clinical Research Institute, Beijing Friendship Hospital, Capital Medical UniversityBeijing Key Laboratory of Clinical Epidemiology, School of Public Health, Capital Medical UniversityClinical Research Institute, Shanghai General Hospital, Shanghai Jiao Tong University School of MedicineAbstract Background Metabolic syndrome (MetS) has been proposed as a clinically identifiable high-risk state for the prediction and prevention of cardiovascular diseases and type 2 diabetes mellitus. As a promising “omics” technology, metabolomics provides an innovative strategy to gain a deeper understanding of the pathophysiology of MetS. The study aimed to systematically investigate the metabolic alterations in MetS and identify biomarker panels for the identification of MetS using machine learning methods. Methods Nuclear magnetic resonance-based untargeted metabolomics analysis was performed on 1011 plasma samples (205 MetS patients and 806 healthy controls). Univariate and multivariate analyses were applied to identify metabolic biomarkers for MetS. Metabolic pathway enrichment analysis was performed to reveal the disturbed metabolic pathways related to MetS. Four machine learning algorithms, including support vector machine (SVM), random forest (RF), k-nearest neighbor (KNN), and logistic regression were used to build diagnostic models for MetS. Results Thirteen significantly differential metabolites were identified and pathway enrichment revealed that arginine, proline, and glutathione metabolism are disturbed metabolic pathways related to MetS. The protein-metabolite-disease interaction network identified 38 proteins and 23 diseases are associated with 10 MetS-related metabolites. The areas under the receiver operating characteristic curve of the SVM, RF, KNN, and logistic regression models based on metabolic biomarkers were 0.887, 0.993, 0.914, and 0.755, respectively. Conclusions The plasma metabolome provides a promising resource of biomarkers for the predictive diagnosis and targeted prevention of MetS. Alterations in amino acid metabolism play significant roles in the pathophysiology of MetS. The biomarker panels and metabolic pathways could be used as preventive targets in dealing with cardiometabolic diseases related to MetS.https://doi.org/10.1186/s12933-022-01716-0Metabolic syndromeMachine learningMetabolomicsBiomarkersDiagnostic modelsAmino acid metabolism |
| spellingShingle | Hao Wang Youxin Wang Xingang Li Xuan Deng Yuanyuan Kong Wei Wang Yong Zhou Machine learning of plasma metabolome identifies biomarker panels for metabolic syndrome: findings from the China Suboptimal Health Cohort Cardiovascular Diabetology Metabolic syndrome Machine learning Metabolomics Biomarkers Diagnostic models Amino acid metabolism |
| title | Machine learning of plasma metabolome identifies biomarker panels for metabolic syndrome: findings from the China Suboptimal Health Cohort |
| title_full | Machine learning of plasma metabolome identifies biomarker panels for metabolic syndrome: findings from the China Suboptimal Health Cohort |
| title_fullStr | Machine learning of plasma metabolome identifies biomarker panels for metabolic syndrome: findings from the China Suboptimal Health Cohort |
| title_full_unstemmed | Machine learning of plasma metabolome identifies biomarker panels for metabolic syndrome: findings from the China Suboptimal Health Cohort |
| title_short | Machine learning of plasma metabolome identifies biomarker panels for metabolic syndrome: findings from the China Suboptimal Health Cohort |
| title_sort | machine learning of plasma metabolome identifies biomarker panels for metabolic syndrome findings from the china suboptimal health cohort |
| topic | Metabolic syndrome Machine learning Metabolomics Biomarkers Diagnostic models Amino acid metabolism |
| url | https://doi.org/10.1186/s12933-022-01716-0 |
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