More Tushar H, Mozafari Bahareh, Märtens Andre, Herr Christian, Lepper Philipp M, Danziger Guy, Volk Thomas, Hoersch Sabrina, Krawczyk Marcin, Guenther Katharina, Hiller Karsten, Bals Robert
Department of Bioinformatics and Biochemistry, Braunschweig Integrated Centre of Systems Biology (BRICS), Technische Universität Braunschweig, 38106 Braunschweig, Germany.
Department of Internal Medicine V-Pulmonology, Allergology and Critical Care Medicine, Saarland University, 66421 Homburg, Germany.
Metabolites. 2022 Nov 2;12(11):1058. doi: 10.3390/metabo12111058.
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.
肺炎是发病和死亡的常见原因,最常由细菌病原体引起。新型冠状病毒肺炎(COVID-19)的特征是肺部感染并可能伴有进行性器官衰竭。这两种疾病对全身血液代谢组的系统性影响尚未完全了解。本研究的目的是比较这两种疾病的血液代谢组,我们假设血浆代谢组学可能有助于识别这些疾病的系统性影响。因此,我们采用非靶向方法对43例COVID-19肺炎患者、23例非COVID-19肺炎患者和26名对照者的血浆代谢组进行了分析。检测到区分这三组的代谢改变,有特定的代谢变化区分了两种肺炎组。对同一受试者的静脉血和动脉血浆样本进行比较,揭示了肺炎的不同代谢影响。此外,四种代谢物的机器学习特征对COVID-19患者的疾病转归具有预测性,曲线下面积(AUC)为86±10%。总体而言,本研究结果揭示了可能与COVID-19肺炎和非COVID-19肺炎病因相关的系统性代谢变化。