Analytical methods : advancing methods and applications · 2026
Metabolomics-based study of potential biomarkers and metabolic pathways in sepsis-associated acute kidney injury.
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Sun Xinyi, Li Fang, Yang Gaoyin, Wu Yuxin, Wang Jing, Yang Junhong, Li Jiatong, Wang Huanqin, Wang Shu
Аннотация
UNLABELLED: One of the most frequent consequences of sepsis is sepsis-associated acute kidney injury (SA-AKI), which increases hospitalization and mortality risk. A qualitative and quantitative analysis of the metabolic components in SA-AKI patients versus those with sepsis without acute kidney injury (SA-NK) and normal controls (NC) was conducted to determine differentially expressed metabolites and investigate the biomarkers and pathophysiological mechanisms of SA-AKI.
METHODS: Serum samples from SA-AKI patients, SA-NK patients, and NCs were randomly selected for mass spectrometry scanning in positive ion mode. The metabolites of the three sample sets were qualitatively and quantitatively analyzed. Multivariate statistical techniques, including principal component analysis, orthogonal partial least squares discriminant analysis, and cluster analysis, were subsequently utilized to investigate the variations and traits of the metabolites in the three groups of samples.
RESULTS: Hierarchical clustering analysis revealed that 29 metabolites, such as urocanic acid, acetyl-L-carnitine, creatine, and tyramine, were significantly upregulated in SA-AKI patients. Furthermore, phenylalanine, tyrosine, and tryptophan biosynthetic metabolism was significantly enriched in SA-AKI patients.
CONCLUSION: Targeted validation confirmed phenylethanolamine and tyramine as potential biomarkers for SA-AKI, and creatine for sepsis without kidney injury, offering new insights into diagnosis and pathophysiology.
METHODS: Serum samples from SA-AKI patients, SA-NK patients, and NCs were randomly selected for mass spectrometry scanning in positive ion mode. The metabolites of the three sample sets were qualitatively and quantitatively analyzed. Multivariate statistical techniques, including principal component analysis, orthogonal partial least squares discriminant analysis, and cluster analysis, were subsequently utilized to investigate the variations and traits of the metabolites in the three groups of samples.
RESULTS: Hierarchical clustering analysis revealed that 29 metabolites, such as urocanic acid, acetyl-L-carnitine, creatine, and tyramine, were significantly upregulated in SA-AKI patients. Furthermore, phenylalanine, tyrosine, and tryptophan biosynthetic metabolism was significantly enriched in SA-AKI patients.
CONCLUSION: Targeted validation confirmed phenylethanolamine and tyramine as potential biomarkers for SA-AKI, and creatine for sepsis without kidney injury, offering new insights into diagnosis and pathophysiology.