代谢组学
葡萄糖醛酸盐
尿
代谢物
医学
代谢组
糖酵解
喹啉酸盐
新陈代谢
内科学
生物
生物化学
生物信息学
氨基酸
喹啉酸
色氨酸
作者
Gerard Baiges-Gaya,Simona Iftimie,Helena Castañé,Elisabet Rodríguez-Tomàs,Andrea Jiménez,Ana F. López-Azcona,Antoni Castro,Jordi Camps,Jorge Joven
出处
期刊:Biomolecules
[MDPI AG]
日期:2023-01-12
卷期号:13 (1): 163-163
摘要
Viral infections cause metabolic dysregulation in the infected organism. The present study used metabolomics techniques and machine learning algorithms to retrospectively analyze the alterations of a broad panel of metabolites in the serum and urine of a cohort of 126 patients hospitalized with COVID-19. Results were compared with those of 50 healthy subjects and 45 COVID-19-negative patients but with bacterial infectious diseases. Metabolites were analyzed by gas chromatography coupled to quadrupole time-of-flight mass spectrometry. The main metabolites altered in the sera of COVID-19 patients were those of pentose glucuronate interconversion, ascorbate and fructose metabolism, nucleotide sugars, and nucleotide and amino acid metabolism. Alterations in serum maltose, mannonic acid, xylitol, or glyceric acid metabolites segregated positive patients from the control group with high diagnostic accuracy, while succinic acid segregated positive patients from those with other disparate infectious diseases. Increased lauric acid concentrations were associated with the severity of infection and death. Urine analyses could not discriminate between groups. Targeted metabolomics and machine learning algorithms facilitated the exploration of the metabolic alterations underlying COVID-19 infection, and to identify the potential biomarkers for the diagnosis and prognosis of the disease.
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