PDF(Pubmed)
Abstract：
UNASSIGNED: The global healthcare burden of COVID-19 pandemic has been unprecedented with a high mortality. Metabolomics, a powerful technique, has been increasingly utilized to study the host response to infections and to understand the progression of multi-system disorders such as COVID-19. Analysis of the host metabolites in response to SARS-CoV-2 infection can provide a snapshot of the endogenous metabolic landscape of the host and its role in shaping the interaction with SARS-CoV-2. Disease severity and consequently the clinical outcomes may be associated with a metabolic imbalance related to amino acids, lipids, and energy-generating pathways. Hence, the host metabolome can help predict potential clinical risks and outcomes.
UNASSIGNED: In this prospective study, using a targeted metabolomics approach, we studied the metabolic signature in 154 COVID-19 patients (males=138, age range 48-69 yrs) and related it to disease severity and mortality. Blood plasma concentrations of metabolites were quantified through LC-MS using MxP Quant 500 kit, which has a coverage of 630 metabolites from 26 biochemical classes including distinct classes of lipids and small organic molecules. We then employed Kaplan-Meier survival analysis to investigate the correlation between various metabolic markers, disease severity and patient outcomes.
UNASSIGNED: A comparison of survival outcomes between individuals with high levels of various metabolites (amino acids, tryptophan, kynurenine, serotonin, creatine, SDMA, ADMA, 1-MH and carnitine palmitoyltransferase 1 and 2 enzymes) and those with low levels revealed statistically significant differences in survival outcomes. We further used four key metabolic markers (tryptophan, kynurenine, asymmetric dimethylarginine, and 1-Methylhistidine) to develop a COVID-19 mortality risk model through the application of multiple machine-learning methods.
UNASSIGNED: Metabolomics analysis revealed distinct metabolic signatures among different severity groups, reflecting discernible alterations in amino acid levels and perturbations in tryptophan metabolism. Notably, critical patients exhibited higher levels of short chain acylcarnitines, concomitant with higher concentrations of SDMA, ADMA, and 1-MH in severe cases and non-survivors. Conversely, levels of 3-methylhistidine were lower in this context.
UNASSIGNED: In this prospective study, using a targeted metabolomics approach, we studied the metabolic signature in 154 COVID-19 patients (males=138, age range 48-69 yrs) and related it to disease severity and mortality. Blood plasma concentrations of metabolites were quantified through LC-MS using MxP Quant 500 kit, which has a coverage of 630 metabolites from 26 biochemical classes including distinct classes of lipids and small organic molecules. We then employed Kaplan-Meier survival analysis to investigate the correlation between various metabolic markers, disease severity and patient outcomes.
UNASSIGNED: A comparison of survival outcomes between individuals with high levels of various metabolites (amino acids, tryptophan, kynurenine, serotonin, creatine, SDMA, ADMA, 1-MH and carnitine palmitoyltransferase 1 and 2 enzymes) and those with low levels revealed statistically significant differences in survival outcomes. We further used four key metabolic markers (tryptophan, kynurenine, asymmetric dimethylarginine, and 1-Methylhistidine) to develop a COVID-19 mortality risk model through the application of multiple machine-learning methods.
UNASSIGNED: Metabolomics analysis revealed distinct metabolic signatures among different severity groups, reflecting discernible alterations in amino acid levels and perturbations in tryptophan metabolism. Notably, critical patients exhibited higher levels of short chain acylcarnitines, concomitant with higher concentrations of SDMA, ADMA, and 1-MH in severe cases and non-survivors. Conversely, levels of 3-methylhistidine were lower in this context.
摘要：
COVID-19大流行的全球医疗保健负担是前所未有的，死亡率很高。代谢组学，强大的技术,越来越多地用于研究宿主对感染的反应，并了解多系统疾病如COVID-19的进展。分析响应SARS-CoV-2感染的宿主代谢物可以提供宿主的内源性代谢景观及其在塑造与SARS-CoV-2相互作用中的作用的快照。疾病严重程度和临床结果可能与氨基酸相关的代谢失衡有关。脂质，和能量产生途径。因此，宿主代谢组可以帮助预测潜在的临床风险和结局.
■在这项前瞻性研究中，使用有针对性的代谢组学方法，我们研究了154例COVID-19患者(男性=138例,年龄范围48-69岁)的代谢特征,并将其与疾病严重程度和死亡率相关联.使用MxPQuant500试剂盒通过LC-MS对代谢物的血浆浓度进行定量，它覆盖了来自26个生化类别的630种代谢物，包括不同类别的脂质和小有机分子。然后，我们采用Kaplan-Meier生存分析来研究各种代谢标志物之间的相关性。疾病严重程度和患者预后。
■各种代谢物（氨基酸，色氨酸,犬尿氨酸,血清素,肌酸,SDMA,ADMA,1-MH和肉碱棕榈酰转移酶1和2酶）和低水平的酶在生存结果方面具有统计学上的显着差异。我们进一步使用了四个关键的代谢标志物(色氨酸，犬尿氨酸,不对称二甲基精氨酸，和1-甲基组氨酸），通过应用多种机器学习方法建立COVID-19死亡风险模型。
■代谢组学分析揭示了不同严重程度组之间不同的代谢特征，反映氨基酸水平的明显变化和色氨酸代谢的扰动。值得注意的是,危重患者表现出更高水平的短链酰基肉碱，伴随着更高浓度的SDMA，ADMA,和1-MH在严重病例和非幸存者。相反，在这种情况下,3-甲基组氨酸的水平较低.
■在这项前瞻性研究中，使用有针对性的代谢组学方法，我们研究了154例COVID-19患者(男性=138例,年龄范围48-69岁)的代谢特征,并将其与疾病严重程度和死亡率相关联.使用MxPQuant500试剂盒通过LC-MS对代谢物的血浆浓度进行定量，它覆盖了来自26个生化类别的630种代谢物，包括不同类别的脂质和小有机分子。然后，我们采用Kaplan-Meier生存分析来研究各种代谢标志物之间的相关性。疾病严重程度和患者预后。
■各种代谢物（氨基酸，色氨酸,犬尿氨酸,血清素,肌酸,SDMA,ADMA,1-MH和肉碱棕榈酰转移酶1和2酶）和低水平的酶在生存结果方面具有统计学上的显着差异。我们进一步使用了四个关键的代谢标志物(色氨酸，犬尿氨酸,不对称二甲基精氨酸，和1-甲基组氨酸），通过应用多种机器学习方法建立COVID-19死亡风险模型。
■代谢组学分析揭示了不同严重程度组之间不同的代谢特征，反映氨基酸水平的明显变化和色氨酸代谢的扰动。值得注意的是,危重患者表现出更高水平的短链酰基肉碱，伴随着更高浓度的SDMA，ADMA,和1-MH在严重病例和非幸存者。相反，在这种情况下,3-甲基组氨酸的水平较低.
