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新冠病毒肺炎死亡率和严重程度的代谢预测因子:生存分析。

Metabolic predictors of COVID-19 mortality and severity: a survival analysis.

机构信息

College of Medicine, Qatar University (QU) Health, Qatar University, Doha, Qatar.

Biomedical Research Center (BRC), Qatar University, Doha, Qatar.

出版信息

Front Immunol. 2024 May 10;15:1353903. doi: 10.3389/fimmu.2024.1353903. eCollection 2024.

Abstract

INTRODUCTION

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.

METHODS

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.

RESULTS

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.

CONCLUSIONS

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

简介

COVID-19 大流行给全球医疗保健带来了前所未有的负担,死亡率很高。代谢组学是一种强大的技术,已越来越多地用于研究宿主对感染的反应,并了解 COVID-19 等多系统疾病的进展。分析宿主对 SARS-CoV-2 感染的代谢物可以提供宿主内源性代谢景观的快照及其在塑造与 SARS-CoV-2 相互作用中的作用。疾病严重程度以及因此的临床结局可能与与氨基酸、脂质和产生能量的途径相关的代谢失衡有关。因此,宿主代谢组学可以帮助预测潜在的临床风险和结局。

方法

在这项前瞻性研究中,我们使用靶向代谢组学方法研究了 154 例 COVID-19 患者(男性 138 例,年龄 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。通过 LC-MS 使用 MxP Quant 500 试剂盒定量测定代谢物的血浆浓度,该试剂盒涵盖了来自 26 个生化类别的 630 种代谢物,包括不同类别的脂质和小分子有机化合物。然后,我们采用 Kaplan-Meier 生存分析来研究各种代谢标志物、疾病严重程度和患者结局之间的相关性。

结果

比较高浓度各种代谢物(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)和低浓度个体的生存结果,结果显示生存结果存在统计学差异。我们进一步使用四个关键代谢标志物(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来开发 COVID-19 死亡率风险模型。

结论

代谢组学分析揭示了不同严重程度组之间的不同代谢特征,反映了氨基酸水平的明显变化和色氨酸代谢的干扰。值得注意的是,危重症患者表现出较高水平的短链酰基肉碱,同时严重病例和非幸存者中 SDMA、ADMA 和 1-MH 的浓度较高。相反,在这种情况下 3-甲基组氨酸的水平较低。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2e00/11127595/dd423be8a00f/fimmu-15-1353903-g001.jpg

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