Machine learning of plasma metabolome identifies biomarker panels for metabolic syndrome: findings from the China Suboptimal Health Cohort
Open accessAbstract
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.
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Traditional medical diagnostics rely heavily on blood sampling, which is invasive and poses discomfort and risks to patients.
“Traditional medical diagnostics rely heavily on blood sampling to assess metabolite levels and enzyme activities, which, while accurate, is invasive and poses discomfort and risks to patients [5, 6].”
From Machine learning with the body roundness index and associated indicators: a new approach to predicting metabolic syndrome (2025) · cites this paper as [6]
The Materials and methods section (Measurements and sample collection; page unavailable) says blood was collected by venipuncture for plasma metabolomics, supporting the blood-sampling and invasive-procedure aspects. It does not say that traditional diagnostics rely heavily on blood sampling or that sampling causes patient discomfort or risks; it also does not address enzyme-activity testing. (LLM, conf=0.84)
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- Authors
- Wang, H., Wang, Y., Li, X., Deng, X., Kong, Y., Wang, W., & Zhou, Y.
- Affiliations
- Capital Medical University (CN)
- Edith Cowan University (AU)
- Shanghai Jiao Tong University (CN)
- Corresponding author
- Zhou, Yong
- Venue
- Cardiovascular Diabetology
- Published
- 2022-12-23
- Volume
- 21
- Issue
- 1
- Pages
- 288-288
- ISSN
- 1475-2840 (ISSN-L)
- PMC
- PMC9789589
- OpenAlex
- W4312179103
- Cited by
- 34
- References
- 42
- Citations by year
- 2026: 6 · 2025: 16 · 2024: 4 · 2023: 8
- FWCI
- 2.37
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- Cardiovascular Diabetology (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Australasian Journal of Paramedicine (submittedVersion, cc-by)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- National Natural Science Foundation of China (81973112, 92049302)
- National Key Research and Development Program of China (2021YFC2500500)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Metabolomics and Mass Spectrometry Studies
- Health Sciences › Medicine › Endocrinology, Diabetes and Metabolism › Diabetes, Cardiovascular Risks, and Lipoproteins
- Health Sciences › Medicine › Endocrinology, Diabetes and Metabolism › Diet, Metabolism, and Disease
- Keywords
- metabolic syndrome, plasma metabolome, metabolic biomarkers, biomarker panel, arginine metabolism, proline metabolism, glutathione metabolism, amino acid metabolism, metabolic pathway enrichment, diagnostic model, cardiometabolic disease
- MeSH terms
- Machine Learning, Diabetes Mellitus, Type 2, Humans, Biomarkers, Metabolic Syndrome, Metabolomics, Metabolome
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