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通过免疫分析确定的长期新冠的显著特征。

Distinguishing features of Long COVID identified through immune profiling.

作者信息

Klein Jon, Wood Jamie, Jaycox Jillian, Lu Peiwen, Dhodapkar Rahul M, Gehlhausen Jeff R, Tabachnikova Alexandra, Tabacof Laura, Malik Amyn A, Kamath Kathy, Greene Kerrie, Monteiro Valter Silva, Peña-Hernandez Mario, Mao Tianyang, Bhattacharjee Bornali, Takahashi Takehiro, Lucas Carolina, Silva Julio, Mccarthy Dayna, Breyman Erica, Tosto-Mancuso Jenna, Dai Yile, Perotti Emily, Akduman Koray, Tzeng Tiffany J, Xu Lan, Yildirim Inci, Krumholz Harlan M, Shon John, Medzhitov Ruslan, Omer Saad B, van Dijk David, Ring Aaron M, Putrino David, Iwasaki Akiko

出版信息

medRxiv. 2022 Aug 10:2022.08.09.22278592. doi: 10.1101/2022.08.09.22278592.

Abstract

SARS-CoV-2 infection can result in the development of a constellation of persistent sequelae following acute disease called post-acute sequelae of COVID-19 (PASC) or Long COVID . Individuals diagnosed with Long COVID frequently report unremitting fatigue, post-exertional malaise, and a variety of cognitive and autonomic dysfunctions ; however, the basic biological mechanisms responsible for these debilitating symptoms are unclear. Here, 215 individuals were included in an exploratory, cross-sectional study to perform multi-dimensional immune phenotyping in conjunction with machine learning methods to identify key immunological features distinguishing Long COVID. Marked differences were noted in specific circulating myeloid and lymphocyte populations relative to matched control groups, as well as evidence of elevated humoral responses directed against SARS-CoV-2 among participants with Long COVID. Further, unexpected increases were observed in antibody responses directed against non-SARS-CoV-2 viral pathogens, particularly Epstein-Barr virus. Analysis of circulating immune mediators and various hormones also revealed pronounced differences, with levels of cortisol being uniformly lower among participants with Long COVID relative to matched control groups. Integration of immune phenotyping data into unbiased machine learning models identified significant distinguishing features critical in accurate classification of Long COVID, with decreased levels of cortisol being the most significant individual predictor. These findings will help guide additional studies into the pathobiology of Long COVID and may aid in the future development of objective biomarkers for Long COVID.

摘要

严重急性呼吸综合征冠状病毒2(SARS-CoV-2)感染可导致急性疾病后出现一系列持续的后遗症,称为冠状病毒病的急性后遗症(PASC)或长新冠。被诊断为长新冠的个体经常报告持续疲劳、运动后不适以及各种认知和自主神经功能障碍;然而,导致这些使人衰弱症状的基本生物学机制尚不清楚。在此,215名个体被纳入一项探索性横断面研究,以结合机器学习方法进行多维度免疫表型分析,以识别区分长新冠的关键免疫学特征。相对于匹配的对照组,在特定的循环髓系和淋巴细胞群体中发现了显著差异,并且在长新冠参与者中存在针对SARS-CoV-2的体液反应升高的证据。此外,观察到针对非SARS-CoV-2病毒病原体,特别是爱泼斯坦-巴尔病毒的抗体反应意外增加。对循环免疫介质和各种激素的分析也显示出明显差异,长新冠参与者的皮质醇水平相对于匹配的对照组一致较低。将免疫表型数据整合到无偏机器学习模型中,确定了对准确分类长新冠至关重要的显著区分特征,皮质醇水平降低是最显著的个体预测指标。这些发现将有助于指导对长新冠病理生物学的进一步研究,并可能有助于未来开发长新冠的客观生物标志物。

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