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整合 SARS-CoV-2 感染的血浆代谢组学和脂质组学模型与临床严重程度及早期死亡率预测的关系。

Integrative Plasma Metabolic and Lipidomic Modelling of SARS-CoV-2 Infection in Relation to Clinical Severity and Early Mortality Prediction.

机构信息

Australian National Phenome Center, Health Futures Institute, Murdoch University, Harry Perkins Building, Perth, WA 6150, Australia.

Center for Computational and Systems Medicine, Health Futures Institute, Murdoch University, Harry Perkins Building, Perth, WA 6150, Australia.

出版信息

Int J Mol Sci. 2023 Jul 18;24(14):11614. doi: 10.3390/ijms241411614.

Abstract

An integrative multi-modal metabolic phenotyping model was developed to assess the systemic plasma sequelae of SARS-CoV-2 (rRT-PCR positive) induced COVID-19 disease in patients with different respiratory severity levels. Plasma samples from 306 unvaccinated COVID-19 patients were collected in 2020 and classified into four levels of severity ranging from mild symptoms to severe ventilated cases. These samples were investigated using a combination of quantitative Nuclear Magnetic Resonance (NMR) spectroscopy and Mass Spectrometry (MS) platforms to give broad lipoprotein, lipidomic and amino acid, tryptophan-kynurenine pathway, and biogenic amine pathway coverage. All platforms revealed highly significant differences in metabolite patterns between patients and controls ( = 89) that had been collected prior to the COVID-19 pandemic. The total number of significant metabolites increased with severity with 344 out of the 1034 quantitative variables being common to all severity classes. Metabolic signatures showed a continuum of changes across the respiratory severity levels with the most significant and extensive changes being in the most severely affected patients. Even mildly affected respiratory patients showed multiple highly significant abnormal biochemical signatures reflecting serious metabolic deficiencies of the type observed in Post-acute COVID-19 syndrome patients. The most severe respiratory patients had a high mortality (56.1%) and we found that we could predict mortality in this patient sub-group with high accuracy in some cases up to 61 days prior to death, based on a separate metabolic model, which highlighted a different set of metabolites to those defining the basic disease. Specifically, hexosylceramides (HCER 16:0, HCER 20:0, HCER 24:1, HCER 26:0, HCER 26:1) were markedly elevated in the non-surviving patient group (Cliff's delta 0.91-0.95) and two phosphoethanolamines (PE.O 18:0/18:1, Cliff's delta = -0.98 and PE.P 16:0/18:1, Cliff's delta = -0.93) were markedly lower in the non-survivors. These results indicate that patient morbidity to mortality trajectories is determined relatively soon after infection, opening the opportunity to select more intensive therapeutic interventions to these "high risk" patients in the early disease stages.

摘要

我们开发了一种整合的多模态代谢表型模型,以评估 SARS-CoV-2(rRT-PCR 阳性)引起的 COVID-19 疾病在不同呼吸严重程度水平的患者中的系统血浆后遗症。2020 年收集了 306 名未接种疫苗的 COVID-19 患者的血浆样本,并根据从轻度症状到严重通气病例的严重程度分为四个等级。使用定量核磁共振(NMR)光谱和质谱(MS)平台的组合对这些样本进行了研究,以提供广泛的脂蛋白、脂质组学和氨基酸、色氨酸-犬尿氨酸途径和生物胺途径覆盖范围。所有平台都显示,与 COVID-19 大流行前收集的 89 名对照患者相比,患者的代谢物模式存在高度显著差异。随着严重程度的增加,显著代谢物的总数也随之增加,1034 个定量变量中有 344 个在所有严重程度类别中都存在。代谢特征显示出在呼吸严重程度水平上的连续变化,最严重的影响患者发生的变化最大、最广泛。即使是轻度受呼吸影响的患者也表现出多种高度显著的异常生化特征,反映了在急性 COVID-19 综合征患者中观察到的严重代谢缺陷。最严重的呼吸患者的死亡率很高(56.1%),我们发现,我们可以在某些情况下,在死亡前 61 天,通过一个单独的代谢模型,以高精度预测这个患者亚组的死亡率,该模型突出了一组与定义基本疾病的代谢物不同的代谢物。具体来说,半乳糖脑苷脂(HCER 16:0、HCER 20:0、HCER 24:1、HCER 26:0、HCER 26:1)在非存活患者组中显著升高(Cliff 的 delta 0.91-0.95),而两种磷酸乙醇胺(PE.O 18:0/18:1,Cliff 的 delta = -0.98 和 PE.P 16:0/18:1,Cliff 的 delta = -0.93)在非存活者中显著降低。这些结果表明,患者的发病率到死亡率轨迹是在感染后相对较早的时候确定的,为在疾病早期阶段向这些“高风险”患者选择更强化的治疗干预提供了机会。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/bc28/10380980/f994f1c6c122/ijms-24-11614-g001.jpg

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