Metabolomic profiling reveals interindividual metabolic variability and its association with cardiovascular-kidney-metabolic syndrome risk

作者：Meng Zhou, Wenxiu Sun, Yuhan Gao, Bei Jiang, Tianwei Sun, Rui‐Hua Xu, Xiujuan Zhang, Qian Wang, Qiuhui Xuan, Shizhan Ma · 发表于：Cardiovascular Diabetology · 年份：2025 · DOI：10.1186/s12933-025-02881-8 · 被引用次数：11 · 研究领域：Metabolomics and Mass Spectrometry Studies、Chronic Kidney Disease and Diabetes、Diet, Metabolism, and Disease

BACKGROUND AND OBJECTIVE: Cardiovascular-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. METHODS: A 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. RESULTS: A 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...