ArticleCardiovascular diabetology2025
Metabolomic profiling reveals interindividual metabolic variability and its association with cardiovascular-kidney-metabolic syndrome risk.
Article in Cardiovascular diabetology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers, 1 of them a synthesis that pooled it.
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Who cites it
13 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Metabolomics and metabolites in cancer diagnosis and treatment.Molecular biomedicine · 2025Pooled it
- A machine learning-derived aging index for risk stratification and mortality prediction in cardiovascular-kidney-metabolic syndrome: A retrospective cohort study.PLoS medicine · 2026Article
- Metabolomic Profiling Delineates Stage-Associated Metabolic Remodelling in Cardiovascular-Kidney-Metabolic Syndrome.Diabetes/metabolism research and reviews · 2026Article
- Observational
- Review
- Across Clinical Profiles of Cardiorenal-Metabolic (CKM) Syndrome: A Phenotype-Driven Therapeutic Approach.Biomedicines · 2026Review
- Accurate and spatially stable quantification of hepatic steatosis using ultrasound-derived fat fraction in metabolic dysfunction-associated steatotic liver disease (MASLD): a prospective magnetic resonance imaging-proton density fat fraction (MRI-PDFF) study.Quantitative imaging in medicine and surgery · 2026Article
- Emerging multidimensional biomarker system for cardiovascular-kidney-metabolic syndrome: from multi-omics integration to clinical artificial intelligence.Cardiovascular diabetology · 2026Review
- Spatial metabolomics: A new tool for unravelling the metabolic disorders and heterogeneity in diabetic kidney disease (Review).International journal of molecular medicine · 2026Review
- Ultra-Processed Foods and the Cardiovascular-Kidney-Metabolic Continuum: Integrating Epidemiological, Multi-Omics, and Translational Evidence.Nutrients · 2026Review
- Current Appraisal and Gaps in Knowledge in Cardio-Kidney Metabolic Syndrome Definition.International journal of molecular sciences · 2026Review
- Effects of Balanced Dietary Patterns and/or Integrated Exercise on Serum 1,5-Anhydroglucitol and CVD Risk Factors in Individuals with Prediabetes.Life (Basel, Switzerland) · 2026Article
- Quantifying Metabolic Syndrome Severity: Methodological Evolution, Clinical Validation, and Translational Perspectives.Diabetes, metabolic syndrome and obesity : targets and therapy · 2026Review
Corrections and comments
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Authors and funding
10 authors.
Funding
Abstract
BACKGROUND AND
objectiveCardiovascular-Kidney-Metabolic (CKM) syndrome reflects the interrelated pathophysiology of obesity, insulin resistance, type 2 diabetes, chronic kidney disease, and cardiovascular disease. Conventional CKM staging often detects risk only after substantial organ dysfunction and may overlook early metabolic heterogeneity. This study aimed to employ plasma metabolomics to identify metabolic subtypes linked to CKM severity and explore early biomarkers for high-risk individuals.
methodsA cross-sectional study was conducted involving 163 adults, which included 86 individuals clinically staged as CKM 0-3 according to the criteria proposed by the American Heart Association (AHA). Plasma samples underwent untargeted metabolomic and lipidomic profiling using liquid chromatography-mass spectrometry (LC-MS). Unsupervised clustering identified metabolic subtypes, with validation via random forest analysis. Group differences were assessed using orthogonal partial least squares-discriminant analysis (OPLS-DA) and logistic regression classifiers.
resultsA total of 390 metabolites, categorized into 9 superclasses and 30 subclasses, were identified. Three distinct metabolic clusters emerged: Cluster 1 (glycerophospholipid-enriched), Cluster 2 (fatty acyl-dominant), and Cluster 3 (glycolipid-enriched). At the individual differential metabolite level, Cluster 1 exhibited a generally low metabolic status, Cluster 2 demonstrated an intermediate metabolic profile, and Cluster 3 showed a high metabolic status. High-risk CKM individuals were predominantly assigned to Cluster 3 (p < 0.001). Within each cluster, OPLS-DA effectively differentiated high- and low-risk individuals based on lipid profiles, highlighting triglycerides, fatty acids, phosphatidylcholines, sphingolipids, and acylcarnitines as key discriminators. Secondary clustering among stage 3 of CKM patients revealed substantial metabolic heterogeneity. A panel of 20 metabolites achieved high diagnostic performance for stage 3 of CKM individual (AUC = 0.875).
conclusionsUntargeted plasma metabolomic profiling reveals distinct metabolic subtypes corresponding to CKM severity and uncovers marked heterogeneity within the high-risk group. Key metabolite signatures may enhance early risk stratification and support more personalized management strategies beyond conventional CKM staging.
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