Plasma Metabolome Alterations Discriminate between COVID-19 and Non-COVID-19 Pneumonia

被引:7
|
作者
More, Tushar H. [1 ]
Mozafari, Bahareh [2 ]
Maertens, Andre [1 ]
Herr, Christian [2 ]
Lepper, Philipp M. [2 ]
Danziger, Guy [2 ]
Volk, Thomas [3 ]
Hoersch, Sabrina [3 ]
Krawczyk, Marcin [4 ]
Guenther, Katharina [2 ]
Hiller, Karsten [1 ]
Bals, Robert [2 ,5 ]
机构
[1] Tech Univ Carolo Wilhelmina Braunschweig, Braunschweig Integrated Ctr Syst Biol BRICS, Dept Bioinformat & Biochem, D-38106 Braunschweig, Germany
[2] Saarland Univ, Dept Internal Med Pulmonol Allergol & Crit Care M, D-66421 Homburg, Germany
[3] Saarland Univ, Dept Anesthesiol Intens Care Med & Pain Therapy, D-66421 Homburg, Germany
[4] Saarland Univ, Dept Internal Med Gastroenterol 2, D-66421 Homburg, Germany
[5] Helmholtz Ctr Infect Res HZI, Helmholtz Inst Pharmaceut Res Saarland HIPS, Saarland Univ Campus, D-66123 Saarbrucken, Germany
关键词
COVID-19; non-COVID-19; pneumonia; metabolomics; metabolic profiling; multivariate statistics; machine learning; plasma; mass spectrometry; community-acquired pneumonia; system biology; GLUTATHIONE; MORTALITY; SEVERITY; TARGETS;
D O I
10.3390/metabo12111058
中图分类号
Q5 [生物化学]; Q7 [分子生物学];
学科分类号
071010 ; 081704 ;
摘要
Pneumonia is a common cause of morbidity and mortality and is most often caused by bacterial pathogens. COVID-19 is characterized by lung infection with potential progressive organ failure. The systemic consequences of both disease on the systemic blood metabolome are not fully understood. The aim of this study was to compare the blood metabolome of both diseases and we hypothesize that plasma metabolomics may help to identify the systemic effects of these diseases. Therefore, we profiled the plasma metabolome of 43 cases of COVID-19 pneumonia, 23 cases of non-COVID-19 pneumonia, and 26 controls using a non-targeted approach. Metabolic alterations differentiating the three groups were detected, with specific metabolic changes distinguishing the two types of pneumonia groups. A comparison of venous and arterial blood plasma samples from the same subjects revealed the distinct metabolic effects of pulmonary pneumonia. In addition, a machine learning signature of four metabolites was predictive of the disease outcome of COVID-19 subjects with an area under the curve (AUC) of 86 +/- 10%. Overall, the results of this study uncover systemic metabolic changes that could be linked to the etiology of COVID-19 pneumonia and non-COVID-19 pneumonia.
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页数:17
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