Luciani Lauren L, Miller Leigh M, Zhai Bo, Clarke Karen, Hughes Kramer Kailey, Schratz Lucas J, Balasubramani G K, Dauer Klancie, Nowalk M Patricia, Zimmerman Richard K, Shoemaker Jason E, Alcorn John F
Department of Chemical and Petroleum Engineering, University of Pittsburgh, Pittsburgh, Pennsylvania, USA.
Department of Pediatrics, UPMC Children's Hospital of Pittsburgh, Pittsburgh, Pennsylvania, USA.
Open Forum Infect Dis. 2023 Feb 21;10(3):ofad095. doi: 10.1093/ofid/ofad095. eCollection 2023 Mar.
The ongoing circulation of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) poses a diagnostic challenge because symptoms of coronavirus disease 2019 (COVID-19) are difficult to distinguish from other respiratory diseases. Our goal was to use statistical analyses and machine learning to identify biomarkers that distinguish patients with COVID-19 from patients with influenza.
Cytokine levels were analyzed in plasma and serum samples from patients with influenza and COVID-19, which were collected as part of the Centers for Disease Control and Prevention's Hospitalized Adult Influenza Vaccine Effectiveness Network (inpatient network) and the US Flu Vaccine Effectiveness (outpatient network).
We determined that interleukin (IL)-10 family cytokines are significantly different between COVID-19 and influenza patients. The results suggest that the IL-10 family cytokines are a potential diagnostic biomarker to distinguish COVID-19 and influenza infection, especially for inpatients. We also demonstrate that cytokine combinations, consisting of up to 3 cytokines, can distinguish SARS-CoV-2 and influenza infection with high accuracy in both inpatient (area under the receiver operating characteristics curve [AUC] = 0.84) and outpatient (AUC = 0.81) groups, revealing another potential screening tool for SARS-CoV-2 infection.
This study not only reveals prospective screening tools for COVID-19 infections that are independent of polymerase chain reaction testing or clinical condition, but it also emphasizes potential pathways involved in disease pathogenesis that act as potential targets for future mechanistic studies.
严重急性呼吸综合征冠状病毒2(SARS-CoV-2)的持续传播带来了诊断挑战,因为2019冠状病毒病(COVID-19)的症状难以与其他呼吸道疾病相区分。我们的目标是利用统计分析和机器学习来识别可区分COVID-19患者与流感患者的生物标志物。
对流感患者和COVID-19患者的血浆和血清样本中的细胞因子水平进行分析,这些样本是作为疾病控制与预防中心的住院成人流感疫苗有效性网络(住院患者网络)和美国流感疫苗有效性研究(门诊患者网络)的一部分收集的。
我们确定白细胞介素(IL)-10家族细胞因子在COVID-19患者和流感患者之间存在显著差异。结果表明,IL-10家族细胞因子是区分COVID-19和流感感染的潜在诊断生物标志物,尤其对于住院患者。我们还证明,由多达3种细胞因子组成的细胞因子组合能够在住院患者组(受试者操作特征曲线下面积[AUC]=0.84)和门诊患者组(AUC=0.81)中高精度地区分SARS-CoV-2和流感感染,揭示了另一种用于SARS-CoV-2感染的潜在筛查工具。
本研究不仅揭示了独立于聚合酶链反应检测或临床状况的COVID-19感染的前瞻性筛查工具,还强调了疾病发病机制中涉及的潜在途径,这些途径可作为未来机制研究的潜在靶点。