Zhou Meng, Sun Wenxiu, Gao Yuhan, Jiang Bei, Sun Tianwei, Xu Rui, Zhang Xiujuan, Wang Qian, Xuan Qiuhui, Ma Shizhan
Key Laboratory of Endocrine Glucose and Lipids Metabolism and Brain Aging, Ministry of Education, Department of Endocrinology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, Shandong, China.
Shandong Institute of Endocrine and Metabolic Disease, Jinan, Shandong, China.
Cardiovasc Diabetol. 2025 Aug 1;24(1):315. doi: 10.1186/s12933-025-02881-8.
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.
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.
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 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).
Untargeted 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.
心血管-肾脏-代谢（CKM）综合征反映了肥胖、胰岛素抵抗、2型糖尿病、慢性肾脏病和心血管疾病之间相互关联的病理生理学。传统的CKM分期通常仅在器官出现严重功能障碍后才检测到风险，可能会忽略早期代谢异质性。本研究旨在利用血浆代谢组学来识别与CKM严重程度相关的代谢亚型，并探索高危个体的早期生物标志物。
进行了一项横断面研究，纳入163名成年人，其中86名根据美国心脏协会（AHA）提出的标准临床分期为CKM 0-3期。血浆样本采用液相色谱-质谱联用（LC-MS）进行非靶向代谢组学和脂质组学分析。通过无监督聚类确定代谢亚型，并通过随机森林分析进行验证。使用正交偏最小二乘判别分析（OPLS-DA）和逻辑回归分类器评估组间差异。
共鉴定出390种代谢物，分为9个超类和30个子类。出现了三个不同的代谢簇：簇1（富含甘油磷脂）、簇2（以脂肪酰基为主）和簇3（富含糖脂）。在个体差异代谢物水平上，簇1表现出总体较低的代谢状态，簇2表现出中等代谢特征，簇3表现出高代谢状态。高危CKM个体主要归为簇3（p < 0.001）。在每个簇内，OPLS-DA基于脂质谱有效地区分了高危和低危个体，突出了甘油三酯、脂肪酸、磷脂酰胆碱、鞘脂和酰基肉碱作为关键判别指标。CKM患者3期内的二次聚类显示出显著的代谢异质性。一组20种代谢物对CKM 3期个体具有较高的诊断性能（AUC = 0.875）。
非靶向血浆代谢组学分析揭示了与CKM严重程度相对应的不同代谢亚型，并揭示了高危组内明显的异质性。关键代谢物特征可能会加强早期风险分层，并支持超越传统CKM分期的更个性化管理策略。
