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Updated: Aug 6, 2026

Identification and Quantification of Deranged Metabolites in Critically Ill Patients Using NMR-Based Metabolomics
Published on: November 29, 2024
LC-MS-based serum metabolomics reveals distinct metabolic signatures in patients with intracerebral Hemorrhage
Wei Zheng1, Wei Wang1, Xiaohui Lu1
1People's Hospital of Bayingol Mongolian Autonomous Prefecture, Kuerle, China.
Introduction:
Intracerebral hemorrhage (ICH) is a severe neurological disease with high mortality and disability, profoundly affecting patients' neurological function, daily activities, and quality of life. This study aimed to characterize the serum metabolic profile of patients with ICH and identify potential metabolic biomarkers associated with disease pathogenesis.
Methods:
Liquid chromatography-mass spectrometry (LC-MS) was employed to systematically analyze serum metabolite profiles and class distributions in 20 patients with and without ICH. Data quality was evaluated using quality control samples, while orthogonal partial least squares-discriminant analysis (OPLS-DA), differential metabolite analysis, KEGG pathway enrichment, Human Metabolome Database (HMDB), Metabolite Set Enrichment Analysis (MSEA), and receiver operating characteristic (ROC) analyses were performed.
Results:
A total of 3,178 metabolites were identified. In patients with ICH, benzene and substituted derivatives were the most abundant metabolite class (15.63%), followed by organic acids (12.47%), amino acids and their metabolites (12.41%), and heterocyclic compounds (12.07%). Quality assessment demonstrated low variability in control samples (CV < 0.3), and OPLS-DA showed significant separation between the ICH and control groups (p < 0.01). Differential expression analysis revealed increased levels of benzene and substituted derivatives and organic acids, accompanied by decreased amino acids and lipid metabolites. KEGG pathway enrichment indicated significant involvement of linoleic acid, α-linolenic acid, arachidonic acid, retrograde endocannabinoid, choline metabolism in cancer, and glycerophospholipid metabolism. HMDB and MSEA analyses further demonstrated associations between differential metabolites and multiple metabolic diseases and physiological or pathological states. ROC analysis showed excellent diagnostic performance for several metabolites, with Dibutyl phthalate (AUC = 0.980), Octadecanamide (AUC = 0.960), Hypoxanthine (AUC = 0.840), and Lenticin (AUC = 0.810).
Discussion:
These findings demonstrate distinct alterations in the serum metabolomic profile of patients with ICH and provide new insights into the metabolic mechanisms underlying ICH. The identified differential metabolites may serve as promising biomarkers for the diagnosis and investigation of ICH.
