Plasma SARS-CoV-2 RNA elimination and RAGE kinetics distinguish COVID-19 severity

IF 4.6 2区医学 Q2 IMMUNOLOGY

Clinical & Translational Immunology Pub Date : 2023-11-23 DOI:10.1002/cti2.1468

Xiaoyan Deng, Pierre Gantner, Julia Forestell, Amélie Pagliuzza, Elsa Brunet-Ratnasingham, Madeleine Durand, Daniel E Kaufmann, Nicolas Chomont, Morgan Craig

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引用次数: 0

Abstract

Objectives

Identifying biomarkers causing differential SARS-CoV-2 infection kinetics associated with severe COVID-19 is fundamental for effective diagnostics and therapeutic planning.

Methods

In this work, we applied mathematical modelling to investigate the relationships between patient characteristics, plasma SARS-CoV-2 RNA dynamics and COVID-19 severity. Using a straightforward mathematical model of within-host viral kinetics, we estimated key model parameters from serial plasma viral RNA (vRNA) samples from 256 hospitalised COVID-19⁺ patients.

Results

Our model predicted that clearance rates distinguish key differences in plasma vRNA kinetics and severe COVID-19. Moreover, our analyses revealed a strong correlation between plasma vRNA kinetics and plasma receptor for advanced glycation end products (RAGE) concentrations (a plasma biomarker of lung damage), collected in parallel to plasma vRNA from patients in our cohort, suggesting that RAGE can substitute for viral plasma shedding dynamics to prospectively classify seriously ill patients.

Conclusion

Overall, our study identifies factors of COVID-19 severity, supports interventions to accelerate viral clearance and underlines the importance of mathematical modelling to better understand COVID-19.

Abstract Image

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血浆SARS-CoV-2 RNA消除和RAGE动力学可区分COVID-19严重程度

确定与重症COVID-19相关的引起差异SARS-CoV-2感染动力学的生物标志物是有效诊断和治疗计划的基础。方法应用数学模型研究患者特征、血浆SARS-CoV-2 RNA动力学与COVID-19严重程度之间的关系。利用宿主内病毒动力学的简单数学模型，我们从256名住院的COVID-19+患者的一系列血浆病毒RNA (vRNA)样本中估计了关键模型参数。我们的模型预测清除率区分血浆vRNA动力学和严重COVID-19的关键差异。此外，我们的分析显示血浆vRNA动力学与血浆晚期糖基化终产物受体(RAGE)浓度(肺损伤的血浆生物标志物)之间存在很强的相关性，该浓度与我们队列患者的血浆vRNA同时收集，这表明RAGE可以替代病毒血浆脱落动力学，对重症患者进行前瞻性分类。总体而言，我们的研究确定了COVID-19严重程度的因素，支持加快病毒清除的干预措施，并强调了数学建模对更好地了解COVID-19的重要性。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Clinical & Translational Immunology Medicine-Immunology and Allergy

CiteScore

12.00

自引率

1.70%

发文量

审稿时长

13 weeks

期刊介绍： Clinical & Translational Immunology is an open access, fully peer-reviewed journal devoted to publishing cutting-edge advances in biomedical research for scientists and physicians. The Journal covers fields including cancer biology, cardiovascular research, gene therapy, immunology, vaccine development and disease pathogenesis and therapy at the earliest phases of investigation.