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Updated: Sep 13, 2025
Identification and Quantification of Deranged Metabolites in Critically Ill Patients Using NMR-Based Metabolomics
Published on: November 29, 2024
Metabolomic profiling reveals interindividual metabolic variability and its association with
Meng Zhou1,2, Wenxiu Sun3, Yuhan Gao1,2
1Key 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.
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 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).
Conclusions:
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.
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